From 6011540ce239922c0723ff2f1534e915b420cd1f Mon Sep 17 00:00:00 2001
From: Sunil Shetye
Date: Tue, 17 Jul 2018 18:02:33 +0530
Subject: remove autogenerated files
remove execute permission from scilab files
update macros/names
fix build process by building macros before help
---
.gitignore | 5 +
.travis.yml | 3 -
builder.sce | 2 +-
demos/demo.sce | 0
demos/df_filtering.mat | Bin
help/en_US/master_help.xml | 581 -------
help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS | Bin 18298 -> 0 bytes
.../scilab_en_US_help/JavaHelpSearch/DOCS.TAB | Bin 2282 -> 0 bytes
.../en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS | Bin 991 -> 0 bytes
.../scilab_en_US_help/JavaHelpSearch/POSITIONS | Bin 83632 -> 0 bytes
help/en_US/scilab_en_US_help/JavaHelpSearch/SCHEMA | 2 -
help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP | Bin 32768 -> 0 bytes
help/en_US/scilab_en_US_help/ScilabCaution.png | Bin 603 -> 0 bytes
help/en_US/scilab_en_US_help/ScilabEdit.png | Bin 574 -> 0 bytes
help/en_US/scilab_en_US_help/ScilabExecute.png | Bin 660 -> 0 bytes
help/en_US/scilab_en_US_help/ScilabImportant.png | Bin 717 -> 0 bytes
help/en_US/scilab_en_US_help/ScilabNote.png | Bin 863 -> 0 bytes
help/en_US/scilab_en_US_help/ScilabTip.png | Bin 863 -> 0 bytes
help/en_US/scilab_en_US_help/ScilabWarning.png | Bin 603 -> 0 bytes
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delete mode 100644 macros/stft.bin
delete mode 100644 macros/stmcb.bin
delete mode 100644 macros/strips.bin
delete mode 100644 macros/subspaceMethodsInputParser.bin
delete mode 100644 macros/synthesis.bin
delete mode 100644 macros/tf2sos.bin
delete mode 100644 macros/tf2zp.bin
delete mode 100644 macros/tf2zpk.bin
delete mode 100644 macros/tfe.bin
delete mode 100644 macros/tfestimate.bin
delete mode 100644 macros/transpose.bin
delete mode 100644 macros/trial_iirlp2mb.bin
delete mode 100644 macros/triang.bin
delete mode 100644 macros/tripuls.bin
delete mode 100644 macros/truth.bin
delete mode 100644 macros/tukeywin.bin
delete mode 100644 macros/udecode.bin
delete mode 100644 macros/uencode.bin
delete mode 100644 macros/ultrwin.bin
delete mode 100644 macros/unshiftdata.bin
delete mode 100644 macros/unwrap2.bin
delete mode 100644 macros/upfirdn.bin
delete mode 100644 macros/upsample.bin
delete mode 100644 macros/upsamplefill.bin
delete mode 100644 macros/var.bin
delete mode 100644 macros/vco.bin
delete mode 100644 macros/wconv.bin
delete mode 100644 macros/welchwin.bin
delete mode 100644 macros/window.bin
delete mode 100644 macros/wkeep.bin
delete mode 100644 macros/wrev.bin
delete mode 100644 macros/xcorr1.bin
delete mode 100644 macros/xcorr2.bin
delete mode 100644 macros/xcov1.bin
delete mode 100644 macros/yulewalker.bin
delete mode 100644 macros/zerocrossing.bin
delete mode 100644 macros/zp2sos.bin
delete mode 100644 macros/zp2ss.bin
delete mode 100644 macros/zp2tf.bin
delete mode 100644 macros/zplane.bin
diff --git a/.gitignore b/.gitignore
index b25c15b..a14e583 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,6 @@
*~
+/help/en_US/master_help.xml
+/help/en_US/scilab_en_US_help/
+/jar/scilab_en_US_help.jar
+/macros/*.bin
+/macros/lib
diff --git a/.travis.yml b/.travis.yml
index 6561d90..f26e505 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -2,9 +2,6 @@ language: scilab
before_install:
- sudo apt-get install scilab
- - sudo apt-get install octave
- - wget http://scilab.in/scilab_toolbox/download_codes/FOSSEE_Scilab_Octave_Interface_Toolbox.tar.gz
- - tar -xf FOSSEE_Scilab_Octave_Interface_Toolbox.tar.gz
script:
- scilab -nw -f test.sce
diff --git a/builder.sce b/builder.sce
index d67b226..5abb459 100644
--- a/builder.sce
+++ b/builder.sce
@@ -30,9 +30,9 @@ TOOLBOX_TITLE = "FOSSEE Signal Processing Toolbox";
toolbox_dir = get_absolute_file_path("builder.sce");
//tbx_builder_gateway(toolbox_dir);
+tbx_builder_macros(toolbox_dir);
tbx_builder_help(toolbox_dir);
tbx_build_loader(TOOLBOX_NAME, toolbox_dir);
tbx_build_cleaner(TOOLBOX_NAME, toolbox_dir);
-tbx_builder_macros(toolbox_dir);
clear toolbox_dir TOOLBOX_NAME TOOLBOX_TITLE;
diff --git a/demos/demo.sce b/demos/demo.sce
old mode 100755
new mode 100644
diff --git a/demos/df_filtering.mat b/demos/df_filtering.mat
old mode 100755
new mode 100644
diff --git a/help/en_US/master_help.xml b/help/en_US/master_help.xml
deleted file mode 100644
index 7d4f85e..0000000
--- a/help/en_US/master_help.xml
+++ /dev/null
@@ -1,581 +0,0 @@
-
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-]>
-
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- FOSSEE Signal Processing Toolbox
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-&a06be1cb23c87c32ae0e55bbb6fad91b7;
-&a9e92cabf7783cb93d5fbb0993146cf9b;
-&a8d0ef54b40a688cbb701f95fd96d6f7b;
-&a1f8c28d4c4be970d488c7e7152d32269;
-&a6106a98e095d64701dcbf930d2a5b49c;
-&a318410c8b2b23e37cc91ca4041c8ca78;
-&ae97c52d48dc2e5b25fafd2c1146bed2d;
-&a106daf6eb25c55a692efac0f5baa624b;
-&a79da6ff7526979c02da0d435f741b4b8;
-&ac407bf41aafc36d3e11e8f85ea4017d8;
-&ad865ea1e8e5ab7d8f37ca06ccb1441cf;
-&a0311468189a1adc24ae3457fc793bd09;
-&a76fee6ade07e8c5cd4ec6d1cb65613a9;
-&a05d7250643cd83876af4c50ddf6b5771;
-&a8b6ff9e8f230bae22c9bc065923dcf15;
-&a6ae70c3f8100258d4d32723037975837;
-&a68b00096efd598686bb99b3ef4a7dcf6;
-&a240f2d47d40d81a581e1ff44b26fd84e;
-&ac17fc50654689bcff45fa0d8930e8016;
-&a9238c6cac4daa3d6ce80cd781225e523;
-&ab522c4caa0887c0a13ce55b9643bbfc2;
-&a74d6f4cdc0fd908b9a7ffeeee8de2bbc;
-&a31b7175454b6367813d3b89b7eb3a662;
-&a225974736af7deedfc2e21cbfc97a16b;
-&a28a69191b7ab75bcc9b5820f9d80b904;
-&a6eff9513929596d29f1e0de73164933d;
-&a7c20a7d32f8827d8fefde2e5e2c1561b;
-&a74e687942ce0b706254992bafb481610;
-&a564cb867b1caf69cefa1e1b7c8c3cfbf;
-&affeadc702da3b9c5b09c64844e943b4c;
-&acddf8ecd9d9848c114a949f31756db22;
-&af1f99d76b051c09a299f1e8614f3b4ed;
-&a779926c3cc1ad1b29337ebfb6f6e8da1;
-&ae37d3ccee6da029cacee4697d75c7cd7;
-&a859d279efe1eb8f3fd90e3cf4a9f971f;
-&ab0c4f3dbe6ba9b831161c475d4e6cae5;
-&ac70ab2143cf0a93d6dbc931c3f77953a;
-&a235ab910c295d843987825384238b3b2;
-
-
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- ac2poly
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > ac2polyac2poly
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Convert autocorrelation sequence to polynomial of prediction filter
Calling Sequence
-
a = ac2poly ( r )
-[ a , e ] = ac2poly ( r ) Parameters
-
r:
- Autocorrelation sequence to be represented with an FIR linear prediction filter
a:
- Output polynomial representing the linear prediction filter e/(a(1) + a(2)z + a(3)z^2 .. a(N)z^N-1)
e:
- Output scaling for the lienar prediction filter
Description
-
Function ac2poly() finds the best fit polynomial for FIR linear prediction filter a, corresponding to the autocorrelation sequence r. a is the same length as r, and is normalized with the first element. So a(1) = 1.
-
Author
-Parthe Pandit
-
Bibliography
-
Kay, Steven M. Modern Spectral Estimation. Englewood Cliffs, NJ: Prentice-Hall, 1988.
ac2rc
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-
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > ac2rcac2rc
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Convert autocorrelation sequence to reflection coefficients.
Calling Sequence
-
k = ac2rc ( R )
-[ k , R0 ] = ac2rc ( R ) Parameters
-
R:
- The input autocorrelation sequence. If r is a matrix, each column of r is treated as a separate signal.
k:
- Returns the reflection coefficients
R0:
- the zero lag autocorrelation, R0, based on the autocorrelation sequence, R.
Examples
-
X = [ 7 6 5 8 3 6 8 7 5 2 4 7 4 3 2 5 4 9 5 3 5 7 3 9 4 1 2 0 5 4 8 6 4 6 5 3 ] ;
-[ k , R0 ] = ac2rc ( X )
-or t=[2 5 6 ; 8 6 5 ; 8 9 4 ]
-[ k , R0 ] = ac2rc ( t )
-
-Author
-Jitendra Singh
arParEst
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-
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-
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > arParEstar_psd
-
-
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-
-
-
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > ar_psdar_psd
-
Calculate the power spectrum of the autoregressive model
Calling Sequence
-
[ PSD , F_OUT ]= ar_psd ( A , V )
-[ PSD , F_OUT ]= ar_psd ( A , V , FREQ )
-[ PSD , F_OUT ]= ar_psd ( A , V , FREQ , FS )
-[ PSD , F_OUT ]= ar_psd (..., RANGE )
-[ PSD , F_OUT ]= ar_psd (..., METHOD )
-[ PSD , F_OUT ]= ar_psd (..., PLOTTYPE ) Parameters
-
A:
- List of M=(order+1) autoregressive model coefficients. The first element of "ar_coeffs" is the zero-lag coefficient, which always has a value of 1.
V:
- Square of the moving-average coefficient of the AR model.
FREQ:
- Frequencies at which power spectral density is calculated, or a scalar indicating the number of uniformly distributed frequency values at which spectral density is calculated. (default = 256)
FS:
- Sampling frequency (Hertz) (default=1)
Range:
- 'half', 'onesided' : frequency range of the spectrum is from zero up to but not including sample_f/2. Power from negative frequencies is added to the positive side of the spectrum.'whole', 'twosided' : frequency range of the spectrum is-sample_f/2 to sample_f/2, with negative frequencies stored in "wrap around" order after the positive frequencies; e.g. frequencies for a 10-point 'twosided' spectrum are 0 0.1 0.2 0.3 0.4 0.5 -0.4 -0.3 -0.2 -0.1 'shift', 'centerdc' : same as 'whole' but with the first half of the spectrum swapped with second half to put the zero-frequency value in the middle. (See "help fftshift". If "freq" is vector, 'shift' is ignored. If model coefficients "ar_coeffs" are real, the default range is 'half', otherwise default range is 'whole'.
Method:
- 'fft': use FFT to calculate power spectrum. 'poly': calculate power spectrum as a polynomial of 1/z N.B. this argument is ignored if the "freq" argument is a vector. The default is 'poly' unless the "freq" argument is an integer power of 2.
Plot type:
- 'plot', 'semilogx', 'semilogy', 'loglog', 'squared' or 'db':specifies the type of plot. The default is 'plot', which means linear-linear axes. 'squared' is the same as 'plot'. 'dB' plots "10*log10(psd)". This argument is ignored and a spectrum is not plotted if the caller requires a returned value.
PSD:
- estimate of power-spectral density
F_OUT:
- frequency values
Description
-
If the FREQ argument is a vector (of frequencies) the spectrum is calculated using the polynomial method and the METHOD argument is ignored. For scalar FREQ, an integer power of 2, or METHOD = "FFT", causes the spectrum to be calculated by FFT. Otherwise, the spectrum is calculated as a polynomial. It may be computationally more efficient to use the FFT method if length of the model is not much smaller than the number of frequency values. The spectrum is scaled so that spectral energy (area under spectrum) is the same as the time-domain energy (mean square of the signal).
Examples
-
[ a , b ] = ar_psd ( [ 1 , 2 , 3 ] , 2 )
arburg
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-
-
-
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-
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > arburgarburg
-
This function calculates coefficients of an autoregressive (AR) model of complex data.
Calling Sequence
-
a = arburg ( x , poles )
-a = arburg ( x , poles , criterion )
-[ a , v ] = arburg (...)
-[ a , v , k ] = arburg (...) Parameters
-
x:
- vector of real or complex numbers, of length > 2
poles:
- positive integer value < length(x) - 2
criterion:
- string value, takes in "AKICc", "KIC", "AICc", "AIC" and "FPE", default it not using a model-selection criterion
a, v, k:
- Output variables
Description
-
This is an Octave function.
-
This function calculates coefficients of an autoregressive (AR) model of complex data x using the whitening lattice-filter method of Burg.
-
The first argument is the data sampled. The second argument is the number of poles in the model (or limit in case a criterion is supplied).
-The third parameter takes in the criterion to limit the number of poles. The acceptable values are "AIC", "AKICc", "KIC", "AICc" which are based on information theory.
-Output variable a is a list of P+1 autoregression coefficients.
-Output variable v is the mean square of residual noise from the whitening operation of the Burg lattice filter.
-Output variable k corresponds to the reflection coefficients defining the lattice-filter embodiment of the model.
Examples
-
arburg ( [ 1 , 2 , 3 , 4 , 5 ] , 2 )
-ans =
-1.00000 - 1.86391 0.95710
arch_fit
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-
-
-
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > arch_fitarch_fit
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This functions fits an ARCH regression model to the time series Y using the scoring algorithm in Engle's original ARCH paper.
Calling Sequence
-
[ A , B ] = arch_fit ( Y , X , P , ITER , GAMMA , A0 , B0 ) Description
-
Fit an ARCH regression model to the time series Y using the scoring algorithm in Engle's original ARCH paper.
-
The model is
-
y(t) = b(1) * x(t,1) + ... + b(k) * x(t,k) + e(t),
-h(t) = a(1) + a(2) * e(t-1)^2 + ... + a(p+1) * e(t-p)^2
-
in which e(t) is N(0, h(t)), given a time-series vector Y up to time t-1 and a matrix of (ordinary) regressors X up to t. The order of the regression of the residual variance is specified by P.
-
If invoked as 'arch_fit (Y, K, P)' with a positive integer K, fit an ARCH(K, P) process, i.e., do the above with the t-th row of X given by
-
[1, y(t-1), ..., y(t-k)]
-
Optionally, one can specify the number of iterations ITER, the updating factor GAMMA, and initial values a0 and b0 for the scoring algorithm.
arch_rnd
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-
-
-
-
-
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > arch_rndarch_rnd
-
Simulate an ARCH sequence of length t with AR coefficients b and CH coefficients a.
Parameters
-
a:
- CH coefficients
b:
- AR coefficients
t:
- Length of ARCH sequence
Description
-
This is an Octave function.
-It Simulates an ARCH sequence of length t with AR coefficients b and CH coefficients a.
-The result y(t) follows the model
-
y(t) = b(1) + b(2) * y(t-1) + … + b(lb) * y(t-lb+1) + e(t),
-where e(t), given y up to time t-1, is N(0, h(t)), with
-
h(t) = a(1) + a(2) * e(t-1)^2 + … + a(la) * e(t-la+1)^2
Examples
-
a = [ 1 2 3 4 5 ] ;
-b = [ 7 8 9 10 ] ;
-arch_rnd ( a , b , t )
-ans =
-
-6.1037e+00
-5.7294e+01
-5.7390e+02
-6.3063e+03
-6.8695e+04
arch_test
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-
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > arch_testarch_test
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perform a Lagrange Multiplier (LM) test of thenull hypothesis of no conditional heteroscedascity against the alternative of CH(P)
Calling Sequence
-
arch_test ( Y , X , P )
-PVAL = arch_test ( Y , X , P )
-[ PVAL , LM ]= arch_test ( Y , X , P ) Parameters
-
P:
- Degrees of freedom
PVAL:
- PVAL is the p-value (1 minus the CDF of this distribution at LM) of the test
Description
-
perform a Lagrange Multiplier (LM) test of thenull hypothesis of no conditional heteroscedascity against the alternative of CH(P).
-
I.e., the model is
-
y(t) = b(1) * x(t,1) + ... + b(k) * x(t,k) + e(t),
-
given Y up to t-1 and X up to t, e(t) is N(0, h(t)) with
-
h(t) = v + a(1) * e(t-1)^2 + ... + a(p) *e(t-p)^2, and the null is a(1) == ... == a(p) == 0.
-
If the second argument is a scalar integer, k,perform the sametest in a linear autoregression model of orderk, i.e., with
-
[1, y(t-1), ..., y(t-K)] as the t-th row of X.
-
Under the null, LM approximatel has a chisquare distribution with P degrees of freedom and PVAL is the p-value (1 minus the CDF of this distribution at LM) of the test.
-
If no output argument is given, the p-value is displayed.
arcov
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > arcovarcov
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Autoregressive all-pole model parameters — covariance method
Calling Sequence
-
a = arcov ( x , p )
-[ a , e ] = arcov ( x , p ) Parameters
-
a:
- contains normalized estimates of the AR system parameters, A(z), in descending powers of z.
e:
- variance estimate of the white noise input to the AR model
x:
- is the input signal
p:
- is the order of the auto regressive model
arma_rnd
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-
-
-
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > arma_rndarma_rnd
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Return a simulation of the ARMA model.
Calling Sequence
-
arma_rnd ( a , b , v , t , n )
-arma_rnd ( a , b , v , t ) Parameters
-
a:
- vector
b:
- vector
v:
- Variance
t:
- Length of output vector
n:
- Number of dummy x(i) used for initialization
Description
-
This is an Octave function.
-The ARMA model is defined by
-
x(n) = a(1) * x(n-1) + … + a(k) * x(n-k)
-+ e(n) + b(1) * e(n-1) + … + b(l) * e(n-l)
-in which k is the length of vector a, l is the length of vector b and e is Gaussian white noise with variance v. The function returns a vector of length t.
-
The optional parameter n gives the number of dummy x(i) used for initialization, i.e., a sequence of length t+n is generated and x(n+1:t+n) is returned. If n is omitted, n = 100 is used.
Examples
-
a = [ 1 2 3 4 5 ] ;
-b = [ 7 ; 8 ; 9 ; 10 ; 11 ] ;
-v = 10 ;
-t = 5 ;
-n = 100 ;
-arma_rnd ( a , b , v , t , n )
-ans =
-
-- 1.6176e+05
-- 4.1663e+05
-- 1.0732e+06
-- 2.7648e+06
-- 7.1221e+06
armcov
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > armcovaryule
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > aryulearyule
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This function fits an AR (p)-model with Yule-Walker estimates.
Calling Sequence
-
a = aryule ( x , p )
-[ a , v ] = aryule ( x , p )
-[ a , v , k ] = aryule ( x , p ) Parameters
-
x:
- vector of real or complex numbers, length > 2
p:
- positive integer value < length(x) - 1
a, v, k:
- Output variables
Description
-
This is an Octave function.
-
This function fits an AR (p)-model with Yule-Walker estimates.
-The first argument is the data vector which is to be estimated.
-Output variable a gives the AR coefficients, v gives the variance of the white noise and k gives the reflection coefficients to be used in the lattice filter.
Examples
-
aryule ( [ 1 , 2 , 3 , 4 , 5 ] , 2 )
-ans =
-1. - 0.8140351 0.1192982
autoreg_matrix
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-
-
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > autoreg_matrixautoreg_matrix
-
Given a time series (vector) Y, return a matrix with ones in the first column and the first K lagged values of Y in the other columns.
Parameters
-
Y:
- Vector
K:
- Scalar or Vector
Description
-
Given a time series (vector) Y, return a matrix with ones in the first column and the first K lagged values of Y in the other columns.
-
In other words, for T > K, '[1, Y(T-1), ..., Y(T-K)]' is the t-th row of the result.
-
The resulting matrix may be used as a regressor matrix in autoregressions.
Examples
-
autoreg_matrix ( [ 1 , 2 , 3 ] , 2 )
-ans =
-1. 0. 0.
-1. 1. 0.
-1. 2. 1.
barthannwin
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > barthannwinbarthannwin
-
This function returns the filter coefficients of a modified Bartlett-Hann window.
Parameters
-
m:
- positive integer value
y:
- output variable, vector of real numbers
Description
-
This is an Octave function.
-This function returns the filter coefficients of a modified Bartlett Hann window of length m supplied as input, to the output vector y.
Examples
-
barthannwin ( 4 )
-ans =
-0.
-0.73
-0.73
-0.
bartlett
-
-
-
-
-
-
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > bartlettbartlett
-
Generates a Bartlett window
Description
-
w=bartlett(L) returns an L-point Bartlett window in a column vector w
-Example
-w=bartlett(4)
-w =
-
0.
-0.6666667
-0.6666667
-0.
-Author
-Ankur Mallick
-References
-[1] Oppenheim, Alan V., Ronald W. Schafer, and John R. Buck. Discrete-Time Signal Processing. Upper Saddle River, NJ: Prentice Hall, 1999.
besselap
-
-
-
-
-
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > besselapbesselap
-
Return bessel analog filter prototype.
Calling Sequence
-
[ zero , pole , gain ] = besselap ( n )
-[ zero , pole ] = besselap ( n )
-zero = besselap ( n ) Parameters
-
n:
- Filter Order
zero:
- Zeros
pole:
- Poles
gain:
- Gain
Description
-
This is an Octave function.
-It Return bessel analog filter prototype of nth order.
Examples
-
[ zero , pole , gain ] = besselap ( 5 )
-zero = [ ] ( 0 x0 )
-pole =
-
-- 0.59058 + 0.90721 i
-- 0.59058 - 0.90721 i
-- 0.92644 + 0.00000 i
-- 0.85155 + 0.44272 i
-- 0.85155 - 0.44272 i
-
-gain = 1
besself
-
-
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-
-
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > besselfbesself
-
This function generates a Bessel filter.
Calling Sequence
-
[ a , b ] = besself ( n , w )
-[ a , b ] = besself ( n , w , "high ")
-[ a , b , c ] = besself (…)
-[ a , b , c , d ] = besself (…)
-[…] = besself (…, "z ") Parameters
-
n:
- positive integer value
w:
- positive real value
Description
-
This is an Octave function.
-This function generates a Bessel filter. The default is a Laplace space (s) filter.
-The third parameter takes in high or low, the default value being low. The cutoff is pi*Wc radians.
-[z,p,g] = besself(...) returns filter as zero-pole-gain rather than coefficients of the numerator and denominator polynomials.
-[...] = besself(...,’z’) returns a discrete space (Z) filter. w must be less than 1.
-[a,b,c,d] = besself(...) returns state-space matrices.
Examples
-
[ a , b ] = besself ( 2 , 3 , " low " )
-a = 9.0000
-b =
-1.0000 5.1962 9.0000
bilinear
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > bilinearbilinear
-
Transform a s-plane filter specification into a z-plane specification
Calling Sequence
-
[ ZB , ZA ] = bilinear ( SB , SA , T )
-[ ZB , ZA ] = bilinear ( SZ , SP , SG , T )
-[ ZZ , ZP , ZG ] = bilinear (...) Description
-
Transform a s-plane filter specification into a z-plane specification. Filters can be specified in either zero-pole-gain or transfer function form. The input form does not have to match the output form. 1/T is the sampling frequency represented in the z plane.
-
Note: this differs from the bilinear function in the signal processing toolbox, which uses 1/T rather than T.
-
Theory: Given a piecewise flat filter design, you can transform it from the s-plane to the z-plane while maintaining the band edges by means of the bilinear transform. This maps the left hand side of the s-plane into the interior of the unit circle. The mapping is highly non-linear, so you must design your filter with band edges in the s-plane positioned at 2/T tan(w*T/2) so that they will be positioned at w after the bilinear transform is complete.
Examples
-
[ ZB , ZA ] = bilinear ( [ 1 ] , [ 2 , 3 ] , 3 )
bitrevorder
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > bitrevorderParameters
-
x:
- Vector of real or complex values
Description
-
This is an Octave function.
-This function returns the input data after reversing the bits of the indices and reordering the elements of the input array.
Examples
-
1. [ y ] = bitrevorder ( [ i , 1 , 3 , 6 i ] )
-y = [ 0 + 1 i 3 + 0 i 1 + 0 i 0 + 6 i ]
-2. [ y , i ] = bitrevorder ( [ ' a ' , ' b ' , ' c ' , ' d ' ] )
-y = acbd
-i = [ 1 3 2 4 ]
blackman
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > blackmanblackman
-
Generates a Blackman window
Calling Sequence
-
w = blackman ( N )
-w = blackman ( N , sflag ) Description
-
w=blackman(N) returns an N-point symmetric Blackman window in a column vector w
-w=blackman(N,sflag)
-Returns an N point Blackman window using the type of sampling specified by sflag
-sflag can be either 'symmetric' (default) or 'periodic' (used in spectral analysis)
-Example
-w=blackman(4)
-w =
-
- 1.388D-17
-0.63
-0.63
-- 1.388D-17
-Author
-Ankur Mallick
-References
-[1] Oppenheim, Alan V., Ronald W. Schafer, and John R. Buck. Discrete-Time Signal Processing. Upper Saddle River, NJ: Prentice Hall, 1999.
blackmanharris
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > blackmanharrisblackmanharris
-
This function returns the filter coefficients of a Blackman-Harris window.
Calling Sequence
-
w = blackmanharris ( m )
-w = blackmanharris ( m , opt ) Parameters
-
m:
- positive integer value
opt:
- string value, takes "periodic" or "symmetric"
w:
- output variable, vector of real numbers
Description
-
This is an Octave function.
-This function returns the filter coefficients of a Blackman-Harris window of length m supplied as input, to the output vector w.
-The second parameter can take the values "periodic" or "symmetric", depending on which the corresponding form of window is returned. The default is symmetric.
Examples
-
blackmanharris ( 5 , " periodic " )
-ans =
-0.00006
-0.1030115
-0.7938335
-0.7938335
-0.1030115
blackmannuttall
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > blackmannuttallblackmannuttall
-
This function returns the filter coefficients of a Blackman-Nuttall window.
Calling Sequence
-
w = blackmannuttall ( m )
-w = blackmannuttall ( m , opt ) Parameters
-
m:
- positive integer value
opt:
- string value, takes "periodic" or "symmetric"
w:
- output variable, vector of real numbers
Examples
-
blackmannuttall ( 5 , " symmetric " )
-ans =
-0.0003628
-0.2269824
-1.
-0.2269824
-0.0003628
bohmanwin
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > bohmanwinbohmanwin
-
This function returns the filter coefficients of a Bohman window.
Parameters
-
m:
- positive integer value
y:
- output variable, vector of real numbers
Description
-
This is an Octave function.
-This function returns the filter coefficients of a Bohman window of length m supplied as input, to the output vector y.
Examples
-
bohmanwin ( 4 )
-ans =
-0.
-0.6089978
-0.6089978
-0.
boxcar
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > boxcarboxcar
-
This function returns the filter coefficients of a rectangular window.
Parameters
-
m:
- positive integer value
y:
- output variable, vector of real numbers
Description
-
This is an Octave function.
-This function returns the filter coefficients of a rectangular window of length m supplied as input, to the output vector y.
Examples
-
boxcar ( 6 )
-ans =
-1.
-1.
-1.
-1.
-1.
-1.
buffer
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > bufferbuffer
-
This function buffers the given data into a matrix of signal frames
Calling Sequence
-
[ y ] = buffer ( x , n )
-[ y ] = buffer ( x , n , p )
-[ y ] = buffer ( x , n , p )
-[ y , z , opt ] = buffer (...) Parameters
-
x:
- Data to be buffered
n:
- Positive integer equal to number of rows in the produced data buffer
p:
- Integer less than n, default value 0
opt:
- In case of overlap, it can be a vector of length p or the string "nodelay", In case of underlap, it is an integer between 0 and p
Description
-
This function buffers the given data into a matrix of signal frames
Examples
-
buffer ( 1 , 3 , 2 )
-ans =
-0 0
-0 1
-1 0
-This function being called from Octave
buttap
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > buttapbuttap
-
Design a lowpass analog Butterworth filter.
Calling Sequence
-
z = buttap ( n )
-[ z , p ] = buttap ( n )
-[ z , p , g ] = buttap ( n ) Parameters
-
n:
- Filter Order
z:
- Zeros
p:
- Poles
g:
- Gain
Description
-
This is an Octave function.
-It designs a lowpass analog Butterworth filter of nth order.
Examples
-
[ z , p , g ] = buttap ( 5 )
-z = [ ] ( 0 x0 )
-p =
-
-- 0.30902 + 0.95106 i - 0.80902 + 0.58779 i - 1.00000 + 0.00000 i - 0.80902 - 0.58779 i - 0.30902 - 0.95106 i
-
-g = 1
butter
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > butterbutter
-
This function generates a Butterworth filter.
Calling Sequence
-
[ a , b ] = butter ( n , w )
-[ a , b ] = butter ( n , w , "high ")
-[ a , b ] = butter ( n , [ wl , wh ])
-[ b , a ] = butter ( n , [ wl , wh ], "stop ")
-[ a , b , c ] = butter (…)
-[ a , b , c , d ] = butter (…)
-[…] = butter (…, "s ") Parameters
-
n:
- positive integer value
w:
- positive real value, w in the range [0,1]
Description
-
This is an Octave function.
-This function generates a Butterworth filter. Default is a discrete space (Z) filter.
-The third parameter takes in low or high, default value is low. The cutoff is pi*Wc radians.
-[b,a] = butter(n, [Wl, Wh]) indicates a band pass filter with edges pi*Wl and pi*Wh radians.
-[b,a] = butter(n, [Wl, Wh], ’stop’) indicates a band reject filter with edges pi*Wl and pi*Wh radians.
-[z,p,g] = butter(...) returns filter as zero-pole-gain rather than coefficients of the numerator and denominator polynomials.
-[...] = butter(...,’s’) returns a Laplace space filter, w can be larger than 1.
-[a,b,c,d] = butter(...) returns state-space matrices.
Examples
-
[ a , b ] = butter ( 3 , 0.7 )
-a =
-0.37445 1.12336 1.12336 0.37445
-b =
-1.00000 1.16192 0.69594 0.13776
buttord
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > buttordbuttord
-
/This function computes the minimum filter order of a Butterworth filter with the desired response characteristics.
Calling Sequence
-
n = buttord ( Wp , Ws , Rp , Rs )
-[ n , Wc ] = buttord ( Wp , Ws , Rp , Rs ) Parameters
-
Wp:
- scalar or vector of length 2
Ws:
- scalar or vector of length 2, elements must be in the range [0,1]
Rp:
- real or complex value
Rs:
- real or complex value
Description
-
This is an Octave function.
-This function computes the minimum filter order of a Butterworth filter with the desired response characteristics.
-The filter frequency band edges are specified by the passband frequency wp and stopband frequency ws.
-Frequencies are normalized to the Nyquist frequency in the range [0,1].
-Rp is measured in decibels and is the allowable passband ripple, and Rs is also in decibels and is the minimum attenuation in the stop band.
-If ws>wp, the filter is a low pass filter. If wp>ws, the filter is a high pass filter.
-If wp and ws are vectors of length 2, then the passband interval is defined by wp the stopband interval is defined by ws.
-If wp is contained within the lower and upper limits of ws, the filter is a band-pass filter. If ws is contained within the lower and upper limits of wp the filter is a band-stop or band-reject filter.
Examples
-
Wp = 40 / 500
-Ws = 150 / 500
-[ n , Wn ] = buttord ( Wp , Ws , 3 , 60 )
-n = 5
-Wn = 0.080038
cceps
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > ccepscceps
-
Return the complex cepstrum of the vector x
Calling Sequence
-
cceps ( x )
-cceps ( x , correct ) Parameters
-
x:
- vector.
correct:
- if 1, a correction method is applied.
Description
-
This function return the complex cepstrum of the vector x. If the optional argument correct has the value 1, a correction method is applied. The default is not to do this.
Examples
-
cceps ( [ 1 , 2 , 3 ] , 1 )
-ans =
-1.92565
-0.96346
-- 1.09735
cconv
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > cconvcconv
-
[nargout,nargin]=argn();
cell2sos
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > cell2soscell2sos
-
Converts a cell array to a second order section matrix
Description
-
s=cell2sos(c) converts a a cell array c = { {B1},{A1}, {B2},{A2}, ... {BL},{AL}}
-to an L-by-6 second-order-section matrix s given by:
-s = [B1 A1
-B2 A2
-...
-BL AL]
-numerator vector Bi and denominator vector Ai contains the coefficients of a
-linear or quadratic polynomial. If the polynomial is linear, the coefficients
-zero-padded on the right.
-[s,g]=cell2sos(c) estimates the gain from the leading term of the cell array
-c={ {[g1,g2]},{B1},{A1}, {B2},{A2}, ... {BL},{AL}} to give g=g1/g2 as the gain
-Example
-c=cell(1,5);
-
c(1,1).entries=[2, 1];
-
c(1,2).entries=rand(1,3);
-
c(1,3).entries=rand(1,3);
-
c(1,4).entries=rand(1,3);
-
c(1,5).entries=rand(1,3);
-
c =
-column 1 to 3
-
![2,1] [0.2113249,0.7560439,0.0002211] [0.3303271,0.6653811,0.6283918] !
-
column 4 to 5
-
![0.8497452,0.6857310,0.8782165] [0.0683740,0.5608486,0.6623569] !
-[s,g]=cell2sos(c);
-s =
-
column 1 to 5
-
0.2113249 0.7560439 0.0002211 0.3303271 0.6653811
-0.8497452 0.6857310 0.8782165 0.0683740 0.5608486
-
column 6
-
0.6283918
-0.6623569
-
g =
-
2.
-Author
-Ankur Mallick
cheb
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > chebcheb
-
Calculates the nth-order Chebyshev polynomial at the point x.
Parameters
-
n:
- Filter order
x:
- Point at which the Chebyshev polynomial is calculater.
Description
-
This is an Octave function.
-Equation for Chebyshev polynomial is
-/ cos(n acos(x), |x| <= 1
-Tn(x) = |
-\ cosh(n acosh(x), |x| > 1
-
x can also be a vector. In that case the output will also be a vector of same size as x.
Examples
-
x = [ 1 2 3 4 ]
-cheb ( 10 , x )
-ans =
-
-1.0000e+00 2.6209e+05 2.2620e+07 4.5747e+08
cheb1ap
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > cheb1apcheb1ap
-
This function designs a lowpass analog Chebyshev type I filter.
Calling Sequence
-
[ z , p , g ] = cheb1ap ( n , Rp )
-[ z , p ] = cheb1ap ( n , Rp )
-p = cheb1ap ( n , Rp ) Parameters
-
n:
- Filter Order
Rp:
- Peak-to-peak passband ripple
z:
- Zeros
p:
- Poles
g:
- Gain
Description
-
This is an Octave function.
-It designs a lowpass analog Chebyshev type I filter of nth order and with a Peak-to-peak passband ripple of Rp.
Examples
-
[ z , p , g ] = cheb1ap ( 10 , 20 )
-z = [ ] ( 0 x0 )
-p =
-
-Columns 1 through 6 :
-
-- 0.00157 - 0.98774 i - 0.00456 - 0.89105 i - 0.00709 - 0.70714 i - 0.00894 - 0.45401 i - 0.00991 - 0.15644 i - 0.00991 + 0.15644 i
-
-Columns 7 through 10 :
-
-- 0.00894 + 0.45401 i - 0.00709 + 0.70714 i - 0.00456 + 0.89105 i - 0.00157 + 0.98774 i
-
-g = 1.9630e-04 - 6.3527e-22 i
cheb1ord
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > cheb1ordcheb1ord
-
This function computes the minimum filter order of a Chebyshev type I filter with the desired response characteristics.
Calling Sequence
-
n = cheb1ord ( Wp , Ws , Rp , Rs )
-[ n , Wc ] = cheb1ord ( Wp , Ws , Rp , Rs ) Parameters
-
Wp:
- scalar or vector of length 2, all elements must be in the range [0,1]
Ws:
- scalar or vector of length 2, all elements must be in the range [0,1]
Rp:
- real value
Rs:
- real value
Description
-
This is an Octave function.
-This function computes the minimum filter order of a Chebyshev type I filter with the desired response characteristics.
-Stopband frequency ws and passband frequency wp specify the the filter frequency band edges.
-Frequencies are normalized to the Nyquist frequency in the range [0,1].
-Rp is measured in decibels and is the allowable passband ripple and Rs is also measured in decibels and is the minimum attenuation in the stop band.
-If ws>wp then the filter is a low pass filter. If wp>ws, then the filter is a high pass filter.
-If wp and ws are vectors of length 2, then the passband interval is defined by wp and the stopband interval is defined by ws.
-If wp is contained within the lower and upper limits of ws, the filter is a band-pass filter. If ws is contained within the lower and upper limits of wp, the filter is a band-stop or band-reject filter.
Examples
-
cheb1ord ( 0.1 , 0.2 , - 0.3 , 4 )
-ans = 2
cheb2ap
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > cheb2apcheb2ap
-
This function designs a lowpass analog Chebyshev type II filter.
Calling Sequence
-
[ z , p , g ] = cheb2ap ( n , Rs )
-[ z , p ] = cheb2ap ( n , Rs )
-p = cheb2ap ( n , Rs ) Parameters
-
n:
- Filter Order
Rs:
- Stopband attenuation
z:
- Zeros
p:
- Poles
g:
- Gain
Description
-
This is an Octave function.
-This function designs a lowpass analog Chebyshev type II filter of nth order and with a stopband attenuation of Rs.
cheb2ord
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > cheb2ordcheb2ord
-
This function computes the minimum filter order of a Chebyshev type II filter with the desired response characteristics.
Calling Sequence
-
n = cheb2ord ( Wp , Ws , Rp , Rs )
-[ n , Wc ] = cheb2ord ( Wp , Ws , Rp , Rs ) Parameters
-
Wp:
- scalar or vector of length 2, all elements must be in the range [0,1]
Ws:
- scalar or vector of length 2, all elements must be in the range [0,1]
Rp:
- real value
Rs:
- real value
Description
-
This is an Octave function.
-This function computes the minimum filter order of a Chebyshev type II filter with the desired response characteristics.
-Stopband frequency ws and passband frequency wp specify the the filter frequency band edges.
-Frequencies are normalized to the Nyquist frequency in the range [0,1].
-Rp is measured in decibels and is the allowable passband ripple and Rs is also measured in decibels and is the minimum attenuation in the stop band.
-If ws>wp then the filter is a low pass filter. If wp>ws, then the filter is a high pass filter.
-If wp and ws are vectors of length 2, then the passband interval is defined by wp and the stopband interval is defined by ws.
-If wp is contained within the lower and upper limits of ws, the filter is a band-pass filter. If ws is contained within the lower and upper limits of wp, the filter is a band-stop or band-reject filter.
Examples
-
cheb2ord ( [ 0.25 , 0.3 ] , [ 0.2 , 0.8 ] , 0.3 , 0.4 )
-ans = 1
chebwin
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > chebwinchebwin
-
This function returns the filter coefficients of a Dolph-Chebyshev window.
Calling Sequence
-
w = chebwin ( m )
-w = chebwin ( m , at ) Parameters
-
m:
- positive integer value
at:
- real scalar value
w:
- output variable, vector of real numbers
Description
-
This is an Octave function.
-This function returns the filter coefficients of a Dolph-Chebyshev window of length m supplied as input, to the output vector w.
-The second parameter is the stop band attenuation of the Fourier transform in dB. The default value is 100 dB.
Examples
-
chebwin ( 7 )
-ans =
-0.0565041
-0.3166085
-0.7601208
-1.
-0.7601208
-0.3166085
-0.0565041
cheby1
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > cheby1cheby1
-
This function generates a Chebyshev type I filter with rp dB of passband ripple.
Calling Sequence
-
[ a , b ] = cheby1 ( n , rp , w )
-[ a , b ] = cheby1 ( n , rp , w , "high ")
-[ a , b ] = cheby1 ( n , rp , [ wl , wh ])
-[ a , b ] = cheby1 ( n , rp , [ wl , wh ], "stop ")
-[ a , b , c ] = cheby1 (…)
-[ a , b , c , d ] = cheby1 (…)
-[…] = cheby1 (…, "s ") Parameters
-
n:
- positive integer value
rp:
- non negative scalar value
w:
- vector, all elements must be in the range [0,1]
Description
-
This is an Octave function.
-This function generates a Chebyshev type I filter with rp dB of passband ripple.
-The fourth parameter takes in high or low, default value is low. The cutoff is pi*Wc radians.
-[b, a] = cheby1(n, Rp, [Wl, Wh]) indicates a band pass filter with edges pi*Wl and pi*Wh radians.
-[b, a] = cheby1(n, Rp, [Wl, Wh], ’stop’) indicates a band reject filter with edges pi*Wl and pi*Wh radians.
-[z, p, g] = cheby1(...) returns filter as zero-pole-gain rather than coefficients of the numerator and denominator polynomials.
-[...] = cheby1(...,’s’) returns a Laplace space filter, w can be larger than 1.
-[a,b,c,d] = cheby1(...) returns state-space matrices.
Examples
-
[ a , b , c ] = cheby1 ( 2 , 6 , 0.7 , " high " )
-a =
-1 1
-b =
-- 0.62915 + 0.55372 i - 0.62915 - 0.55372 i
-c = 0.055649
cheby2
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > cheby2cheby2
-
This function generates a Chebyshev type II filter with rs dB of stopband attenuation.
Calling Sequence
-
[ a , b ] = cheby2 ( n , rs , wc )
-[ a , b ] = cheby2 ( n , rs , wc , "high ")
-[ a , b ] = cheby2 ( n , rs , [ wl , wh ])
-[ a , b ] = cheby2 ( n , rs , [ wl , wh ], "stop ")
-[ a , b , c ] = cheby2 (…)
-[ a , b , c , d ] = cheby2 (…)
-[…] = cheby2 (…, "s ") Parameters
-
n:
- positive integer value
rp:
- non negative scalar value
w:
- vector, all elements must be in the range [0,1]
Description
-
This is an Octave function.
-This function generates a Chebyshev type II filter with rs dB of stopband attenuation.
-The fourth parameter takes in high or low, default value is low. The cutoff is pi*Wc radians.
-[b, a] = cheby2(n, Rp, [Wl, Wh]) indicates a band pass filter with edges pi*Wl and pi*Wh radians.
-[b, a] = cheby2(n, Rp, [Wl, Wh], ’stop’) indicates a band reject filter with edges pi*Wl and pi*Wh radians.
-[z, p, g] = cheby2(...) returns filter as zero-pole-gain rather than coefficients of the numerator and denominator polynomials.
-[...] = cheby2(...,’s’) returns a Laplace space filter, w can be larger than 1.
-[a,b,c,d] = cheby2(...) returns state-space matrices.
Examples
-
[ a , b , c ] = cheby2 ( 2 , 5 , 0.7 , " high " )
-a =
-- 0.31645 - 0.94861 i - 0.31645 + 0.94861 i
-b =
-- 0.39388 + 0.53138 i - 0.39388 - 0.53138 i
-c = 0.47528
check
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > checkchirp
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > chirpchirp
-
This function evaluates a chirp signal at time t.
Calling Sequence
-
y = chirp ( t )
-y = chirp ( t , f0 )
-y = chirp ( t , f0 , t1 )
-y = chirp ( t , f0 , t1 , f1 )
-y = chirp ( t , f0 , t1 , f1 , frm )
-y = chirp ( t , f0 , t1 , f1 , frm , phse ) Parameters
-
t:
- vector
f0:
-
t1:
-
f1:
-
frm:
- string value, takes in "linear", "quadratic", "logarithmic"
phse:
-
Description
-
This is an Octave function.
-This function evaluates a chirp signal at time t. A chirp signal is a frequency swept cosine wave.
-The first argument is a vector of times to evaluate the chirp signal, second argument is the frequency at t=0, third argument is time t1 and fourth argument is frequency at t1.
-The fifth argument is the form which takes in values "linear", "quadratic" and "logarithmic", the sixth argument gives the phase shift at t=0.
Examples
-
chirp ( [ 4 , 3 , 2 , 1 ] , 4 , 5 , 0.9 )
-ans =
-column 1 to 3
-0.9685832 0.2486899 0.0627905
-column 4
-- 0.3681246
cl2bp
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > cl2bpcl2bp
-
Constrained L2 bandpass FIR filter design.
Calling Sequence
-
h = cl2bp ( m , w1 , w2 , up , lo , gridsize )
-h = cl2bp ( m , w1 , w2 , up , lo ) Parameters
-
m:
- degree of cosine polynomial, i.e. the number of output coefficients will be m*2+1
w1 and w2:
- bandpass filter cutoffs in the range 0 <= w1 < w2 <= pi, where pi is the Nyquist frequency
up:
- vector of 3 upper bounds for [stopband1, passband, stopband2]
lo:
- vector of 3 lower bounds for [stopband1, passband, stopband2]
gridsize:
- search grid size; larger values may improve accuracy, but greatly increase calculation time.
Description
-
This is an Octave function.
-Constrained L2 bandpass FIR filter design. Compared to remez, it offers implicit specification of transition bands, a higher likelihood of convergence, and an error criterion combining features of both L2 and Chebyshev approaches.
Examples
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h = cl2bp ( 5 , 0.3 * pi , 0.6 * pi , [ 0.02 , 1.02 , 0.02 ] , [ - 0.02 , 0.98 , - 0.02 ] , 2 ^ 11 )
-h =
-
-0.038311
-0.082289
-- 0.086163
-- 0.226006
-0.047851
-0.307434
-0.047851
-- 0.226006
-- 0.086163
-0.082289
-0.038311
clustersegment
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > clustersegmentclustersegment
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This function calculates boundary indexes of clusters of 1’s.
Parameters
-
s:
- scalar, vector or matrix of real numbers (clusters of 1s)
c:
- output variable, cell array of size 1 by N, where N is the number of rows in s
Description
-
This is an Octave function.
-This function calculates boundary indexes of clusters of 1’s.
-This function calculates the initial and end indices of the sequences of 1's present in the input argument.
-The output variable c is a cell array of size 1 by N, where N is the number of rows in s and each element has two rows indicating the initial index and end index of the cluster of 1's respectively. The indexing starts from 1.
Examples
-
y = clustersegment ( [ 0 , 1 , 0 , 0 , 1 , 1 ] )
-y =
-2. 5.
-2. 6.
cmorwavf
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > cmorwavfcohere
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > coherecohere
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Estimate (mean square) coherence of signals "x" and "y"
Calling Sequence
-
[ Pxx , freqs ] = cohere ( x , y , Nfft , Fs , win , overlap , ran , plot_type , detrends ) Parameters
-
x:
- [non-empty vector] system-input time-series data
y:
- [non-empty vector] system-output time-series data
win:
- [real vector] of window-function values between 0 and 1; the data segment has the same length as the window. Default window shape is Hamming. [integer scalar] length of each data segment. The default value is window=sqrt(length(x)) rounded up to the nearest integer power of 2; see 'sloppy' argument.
overlap:
- [real scalar] segment overlap expressed as a multiple of window or segment length. 0 <= overlap < 1, The default is overlap=0.5 .
Nfft:
- [integer scalar] Length of FFT. The default is the length of the "window" vector or has the same value as the scalar "window" argument. If Nfft is larger than the segment length, "seg_len", the data segment is padded with "Nfft-seg_len" zeros. The default is no padding. Nfft values smaller than the length of the data segment (or window) are ignored silently.
Fs:
- [real scalar] sampling frequency (Hertz); default=1.0
range:
- 'half', 'onesided' : frequency range of the spectrum is zero up to but not including Fs/2. Power from negative frequencies is added to the positive side of the spectrum, but not at zero or Nyquist (Fs/2) frequencies. This keeps power equal in time and spectral domains. See reference [2]. 'whole', 'twosided' : frequency range of the spectrum is-Fs/2 to Fs/2, with negative frequenciesstored in "wrap around" order after the positivefrequencies; e.g. frequencies for a 10-point 'twosided'spectrum are 0 0.1 0.2 0.3 0.4 0.5 -0.4 -0.3 -0.2 -0.1 'shift', 'centerdc' : same as 'whole' but with the first half of the spectrum swapped with second half to put the zero-frequency value in the middle. (See "help fftshift". If data (x and y) are real, the default range is 'half', otherwise default range is 'whole'.
plot_type:
- 'plot', 'semilogx', 'semilogy', 'loglog', 'squared' or 'db': specifies the type of plot. The default is 'plot', which means linear-linear axes. 'squared' is the same as 'plot'. 'dB' plots "10*log10(psd)". This argument is ignored and a spectrum is not plotted if the caller requires a returned value.
detrends:
- 'no-strip', 'none' -- do NOT remove mean value from the data'short', 'mean' -- remove the mean value of each segment from each segment of the data. 'linear',-- remove linear trend from each segment of the data.'long-mean'-- remove the mean value from the data before splitting it into segments. This is the default.
Description
-
Estimate (mean square) coherence of signals "x" and "y".
-
Use the Welch (1967) periodogram/FFT method.
convmtx
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > convmtxcorrmtx
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > corrmtxcorrmtx
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Generate data matrix for autocorrelation matrix estimation
Calling Sequence
-
X = corrmtx ( x , m )
-[ X , R ] = corrmtx ( x , m )
-X = corrmtx ( x , m , s )
-[ X , R ] = corrmtx ( x , m , s ) Parameters
-
x:
- input vector of size N for which correlation matrix of size m is to be calculated
m:
- size of correlation matrix to be computed. Positive integer strictly smaller than the length of the input x
X:
- data matrix as specified according to the input 'method'
s:
- method for type of output matrix X
'autocorrelation':
- (default) X is the (n + m)-by-(m + 1) rectangular Toeplitz matrix that generates an autocorrelation estimate for the leng th-n data vector x, derived using prewindowed and postwindowed data, based on an mth-order prediction error model.
'prewindowed':
- X is the n-by-(m + 1) rectangular Toeplitz matrix that generates an autocorrelation estimate for the length-n data vector x, derived using prewindowed data, based on an mth-order prediction error model.
'postwindowed':
- X is the n-by-(m + 1) rectangular Toeplitz matrix that generates an autocorrelation estimate for the length-n data vector x , derived using postwindowed data, based on an mth-order prediction error model.
'covariance':
- X is the (n – m)-by-(m + 1) rectangular Toeplitz matrix that generates an autocorrelation estimate for the length-n data vect or x, derived using nonwindowed data, based on an mth-order prediction error model.
'modified':
- X is the 2(n – m)-by-(m + 1) modified rectangular Toeplitz matrix that generates an autocorrelation estimate for the length-n d ata vector x, derived using forward and backward prediction error estimates, based on an mth-order prediction error model.
R:
- (m + 1)-by-(m + 1) autocorrelation matrix estimate calculated as X'*X
Description
-
Consider the generic matrix X below
-_ _
-| x(1) ..........0 |
-| : . : |
-| : . : |
-| x(m+1).......x(1)|
-| : . : |
-| : . : |
-X = | x(n-m).....x(m+1)|
-| : . : |
-| : . : |
-| x(n).......x(n-m)|
-| : . : |
-| : . : |
-|_0 ..........x(n)_|
---
-For different inputs of string s the output would vary ass described below
-'autocorrelation' — (default) X = X, above.
-'prewindowed' — X is the n-by-(m + 1) submatrix of X whose first row is [x(1) … 0] and whose last row is [x(n) … x(n – m)]
-'postwindowed' — X is the n-by-(m + 1) submatrix of X whose first row is [x(m + 1) … x(1)] and whose last row is [0 … x(n)]
-'covariance' — X is the (n – m)-by-(m + 1) submatrix of X whose first row is [x(m + 1) … x(1)] and whose last row is [x(n) … x(n – m)]
-'modified' — X is the 2(n – m)-by-(m + 1) matrix X_mod shown below
-_ _
-| x(m+1) ......x(1)|
-| : . : |
-| : . : |
-| x(n-m).....x(m+1)|
-| : . : |
-| : . : |
-| x(n).......x(n-m)|
-X_mod= | x*(1).....x*(m+1)|
-| : . : |
-| : . : |
-| x*(m+1)...x*(n-m)|
-| : . : |
-| : . : |
-|_x*(n-m) ...x*(n)_|
-
cplxreal
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > cplxrealcplxreal
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Function to divide vector z into complex and real elements, removing the one of each complex conjugate pair.
Calling Sequence
-
[ zc , zr ] = cplxreal ( z , thresh )
-[ zc , zr ] = cplxreal ( z )
-zc = cplxreal ( z , thresh )
-zc = cplxreal ( z ) Parameters
-
z:
- vector of complex numbers.
thresh:
- tolerance for comparisons.
zc:
- vector containing the elements of z that have positive imaginary parts.
zr:
- vector containing the elements of z that are real.
Description
-
This is an Octave function.
-Every complex element of z is expected to have a complex-conjugate elsewhere in z. From the pair of complex-conjugates, the one with the negative imaginary part is removed.
-If the magnitude of the imaginary part of an element is less than the thresh, it is declared as real.
Examples
-
[ zc , zr ] = cplxreal ( [ 1 2 3 + i 4 3 - i 5 ] )
-zc = 3 + 1 i
-zr =
-1 2 4 5
cpsd
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > cpsdcpsd
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This function estimates cross power spectrum of data x and y by the Welch (1967) periodogram/FFT method.
Calling Sequence
-
[ PXX , FREQ ] = cpsd ( X , Y )
-[...] = cpsd ( X , Y , WINDOW )
-[...] = cpsd ( X , Y , WINDOW , OVERLAP )
-[...] = cpsd ( X , Y , WINDOW , OVERLAP , NFFT )
-[...] = cpsd ( X , Y , WINDOW , OVERLAP , NFFT , FS )
-[...] = cpsd ( X , Y , WINDOW , OVERLAP , NFFT , FS , RANGE )
-cpsd (...) Parameters
-
X, Y:
- Matrix or integer
Description
-
Estimate cross power spectrum of data X and Y by the Welch (1967) periodogram/FFT method.
Examples
-
[ a , b ] = cpsd ( [ 1 , 2 , 3 ] , [ 4 , 5 , 6 ] )
-ans =
-b =
-0.
-0.25
-0.5
-a =
-2.7804939
-4.4785583 + 1.0743784 i
-0.7729851
cummax
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > cummaxcummax
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Cumulative maximum
Calling Sequence
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M = cummax ( A )
-returns the cumulative maximum of the arguments of A . The dimension
-of M is same as the dimension of A . If A is a 2 D matrix , the operation
-is performed along the columns . For a hypermatrix , the operation is
-performed along the first non - zero dimension
-M = cummax ( A , dim )
-The operation is performed along the dimension specified by dim
-M = cummax ( _ , direction )
-direction specifies as the direction of operation Examples
-
1 ) Cumulative maximum values in a vector
-v = [ 8 9 1 10 6 1 3 6 10 10 ]
-M = cummax ( v )
-
-Expected output : [ 8 8 1 1 1 1 1 1 1 1 ]
cummin
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > cummincummin
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Cumulative minimum
Calling Sequence
-
M = cummin ( A )
-returns the cumulative minimum of the arguments of A . The dimension
-of M is same as the dimension of A . If A is a 2 D matrix , the operation
-is performed along the columns . For a hypermatrix , the operation is
-performed along the first non - zero dimension
-M = cummin ( A , dim )
-The operation is performed along the dimension specified by dim
-M = cummin ( _ , direction )
-direction specifies as the direction of operation Examples
-
1 ) Cumulative minimum values in a vector
-v = [ 8 9 1 10 6 1 3 6 10 10 ]
-M = cummin ( v )
-
-Expected output : [ 8 8 1 1 1 1 1 1 1 1 ]
czt
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > cztCalling Sequence
-
czt ( x )
-czt ( x , m )
-czt ( x , m , w )
-czt ( x , m , w , a ) Parameters
-
x:
- Input scalar or vector
m:
- Total Number of steps
w:
- ratio between points in each step
a:
- point in the complex plane
Description
-
This is an Octave function.
-Chirp z-transform. Compute the frequency response starting at a and stepping by w for m steps. a is a point in the complex plane, and w is the ratio between points in each step (i.e., radius increases exponentially, and angle increases linearly).
Examples
-
m = 32 ; ## number of points desired
-w = exp ( - j * 2 * pi * ( f2 - f1 ) / ( ( m - 1 ) * Fs ) ) ; ## freq . step of f2 - f1 / m
-a = exp ( j * 2 * pi * f1 / Fs ) ; ## starting at frequency f1
-y = czt ( x , m , w , a ) ;
db
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > dbdb2pow
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > db2powParameters
-
ydb :
- scalar or vector or matrix or N-D array
Examples
-
ydb = 20
-y = mag2pow ( ydb )
Authors
-
Ishita Bedi
- Modified to handle char i/p by Debdeep Dey dctmtx
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > dctmtxParameters
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n:
- Real scalar integer greater than or equal to 1
Description
-
This is an Octave function
-dctmtx(n) returns a Discrete cosine transform matrix of order n-by-n. It is useful for jpeg image compression. D*A is the DCT of the columns of A and D'*A is the inverse DCT of the columns of A (when A is n-by-n).
Examples
-
1. dctmtx ( 2 )
-ans = [ 0.70711 0.70711 ; 0.70711 - 0.70711 ]
-2. dctmtx ( 3 )
-ans = [ 5.7735e-01 5.7735e-01 5.7735e-01 ;
-7.0711e-01 4.9996e-17 - 7.0711e-01 ;
-4.0825e-01 - 8.1650e-01 4.0825e-01 ]
decimate
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > decimatedetrend1
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > detrend1detrend1
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This function removes the best fit of a polynomial of order P from the data X
Parameters
-
X:
- Input vecor or matrix.
P:
- The order of polnomial
Description
-
If X is a vector, 'detrend1(X, P)' removes the best fit of apolynomial of order P from the data X.If X is a matrix, 'detrend1(X, P)' does the same for each column in X.
-
The second argument P is optional. If it is not specified, a value of 1 is assumed. This corresponds to removing a linear trend.
-The order of the polynomial can also be given as a string, in which case P must be either "constant" (corresponds to 'P=0') or "linear"(corresponds to 'P=1')
dftmtx
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > dftmtxParameters
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n:
- Real positive scalar number
Description
-
This is an Octave function
-This fuction gives a complex matrix of values whose product with a vector produces the discrete Fourier transform. This can also be achieved by directly using the fft function i.e. y=fft(x) is same as y=A*x where A=dftmtx(n).
Examples
-
1. dftmtx ( 3 )
-ans = 1.00000 + 0.00000 i 1.00000 + 0.00000 i 1.00000 + 0.00000 i
-1.00000 + 0.00000 i - 0.50000 - 0.86603 i - 0.50000 + 0.86603 i
-1.00000 - 0.00000 i - 0.50000 + 0.86603 i - 0.50000 - 0.86603 i
diffpara
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > diffparadiffpara
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Return the estimator D for the differencing parameter of an integrated time series
Calling Sequence
-
[ D , DD ] = diffpara ( X )
-[ D , DD ] = diffpara ( X , A )
-[ D , DD ] = diffpara ( X , A , B ) Parameters
-
X:
- Input scalar or vector.
DD:
- The estimators for all frequencies in the intervals described above.
D:
- The mean of DD
Description
-
Return the estimator D for the differencing parameter of an integrated time series.
-
The frequencies from [2*pi*a/t, 2*pi*b/T] are used for the estimation. If B is omitted, the interval [2*pi/T, 2*pi*a/T] is used. If both B and A are omitted then a = 0.5 * sqrt (T) and b = 1.5 * sqrt (T) is used, where T is the sample size. If X is a matrix, the differencing parameter of each column is estimated.
-
The estimators for all frequencies in the intervals described above is returned in DD.
-
The value of D is simply the mean of DD.
diric
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > diricParameters
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x:
- Real valued vector or matrix
n:
- Real positive integer or complex integer
Description
-
This is an Octave function
-y=diric(x,n) returns the dirichlet function values of parameter x.
Examples
-
1. diric ( [ 1 2 3 ] , 3 )
-ans = 0.6935349 0.0559021 - 0.3266617
-2. diric ( 1 , 2 )
-ans = 0.8775826
downsample
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > downsampledownsample
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This function downsamples the signal by selecting every nth element.
Calling Sequence
-
y = downsample ( x , n )
-y = downsample ( x , n , phase ) Parameters
-
x:
- scalar, vector or matrix of real or complex numbers
n:
- real number or vector
phase:
- integer value, 0 <= phase <= (n - 1), default value 0, or logical
Description
-
This is an Octave function.
-This function downsamples the signal by selecting every nth element supplied as parameter 2. If x is a matrix, the function downsamples every column.
-If the phase is specified, every nth element is selected starting from the sample phase. The default phase is 0.
Examples
-
downsample ( [ 1 , 2 , 4 ] , 2 )
-ans =
-1. 4.
dst1
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > dst1dst1
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Computes the type I discrete sine transform of x
Parameters
-
x:
- real or complex valued vector
Description
-
This is an Octave function.
-Computes the type I discrete sine transform of x. If n is given, then x is padded or trimmed to length n before computing the transform. If x is a matrix, compute the transform along the columns of the the matrix.
durbinlevinson
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > durbinlevinsondurbinlevinson
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Perform one step of the Durbin-Levinson algorithm..
Calling Sequence
-
durbinlevinson ( C );
-durbinlevinson ( C , OLDPHI );
-durbinlevinson ( C , OLDPHI , OLDV ); Parameters
-
C:
- The vector C specifies the autocovariances '[gamma_0, ..., gamma_t]' from lag 0 to T.
OLDPHI:
- It specifies the coefficients based on C(T-1).
OLDV:
- It specifies the corresponding error.
Description
-
This is an Octave function.
-Perform one step of the Durbin-Levinson.
-If OLDPHI and OLDV are omitted, all steps from 1 to T of the algorithm are performed.
dutycycle
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > dutycycledwt
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > dwtdwt
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Discrete wavelet transform (1D)
Calling Sequence
-
[ U , V ] = dwt ( X , WNAME )
-[ U , V ] = dwt ( X , HP , GP )
-[ U , V ] = dwt ( X , HP , GP ,...) Parameters
-
Inputs:
-
X:
- Signal Vector.
WNAME:
- Wavelet name.
HP:
- Coefficients of low-pass decomposition FIR filter.
GP:
- Coefficients of high-pass decomposition FIR filter.
Outputs:
-
U:
- Signal vector of average, approximation.
V:
- Signal vector of difference, detail.
Description
-
This function calculates the discrete wavelet transform (1D).
ellip
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > ellipellip
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This function generates an elliptic or Cauer filter with rp dB of passband ripple and rs dB of stopband attenuation.
Calling Sequence
-
[ a , b ] = ellip ( n , rp , rs , wp )
-[ a , b ] = ellip ( n , rp , rs , wp , "high ")
-[ a , b ] = ellip ( n , rp , rs , [ wl , wh ])
-[ a , b ] = ellip ( n , rp , rs , [ wl , wh ], "stop ")
-[ a , b , c ] = ellip (…)
-[ a , b , c , d ] = ellip (…)
-[…] = ellip (…, "s ") Parameters
-
n:
- positive integer value
rp:
- non negative scalar value
rs:
- non negative scalar value
w:
- scalar or vector, all elements should be in the range [0,1]
Description
-
This is an Octave function.
-This function generates an elliptic or Cauer filter with rp dB of passband ripple and rs dB of stopband attenuation.
-[b, a] = ellip(n, Rp, Rs, Wp) indicates low pass filter with order n, Rp decibels of ripple in the passband and a stopband Rs decibels down and cutoff of pi*Wp radians. If the fifth argument is high, then the filter is a high pass filter.
-[b, a] = ellip(n, Rp, Rs, [Wl, Wh]) indictaes band pass filter with band pass edges pi*Wl and pi*Wh. If the fifth argument is stop, the filter is a band reject filter.
-[z, p, g] = ellip(...) returns filter as zero-pole-gain.
-[...] = ellip(...,’s’) returns a Laplace space filter, w can be larger than 1.
-[a, b, c, d] = ellip(...) returns state-space matrices.
Examples
-
[ a , b ] = ellip ( 2 , 0.5 , 0.7 , [ 0.3 , 0.4 ] )
-a =
-0.88532 - 1.58410 2.40380 - 1.58410 0.88532
-b =
-1.00000 - 1.78065 2.68703 - 1.75725 0.97454
ellipap
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > ellipapellipap
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Designs a lowpass analog elliptic filter.
Calling Sequence
-
[ z , p , g ] = ellipap ( n , Rp , Rs )
-[ z , p ] = ellipap ( n , Rp , Rs )
-z = ellipap ( n , Rp , Rs ) Parameters
-
n:
- Filter Order
Rp:
- Peak-to-peak passband ripple
Rs:
- Stopband attenuation
Description
-
This is an Octave function.
-It designs a lowpass analog elliptic filter of nth order, with a Peak-to-peak passband ripple of Rp and a stopband attenuation of Rs.
Examples
-
[ z , p , g ] = ellipap ( 5 , 10 , 10 )
-z =
-
-0.0000 + 2.5546 i 0.0000 + 1.6835 i - 0.0000 - 2.5546 i - 0.0000 - 1.6835 i
-
-p =
-
-- 0.05243 + 0.63524 i - 0.01633 + 0.96289 i - 0.05243 - 0.63524 i - 0.01633 - 0.96289 i - 0.07369 + 0.00000 i
-
-g = 0.0015012
ellipord
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > ellipordellipord
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This function computes the minimum filter order of an elliptic filter with the desired response characteristics.
Calling Sequence
-
[ n ] = ellipord ( Wp , Ws , Rp , Rs )
-[ n , Wp ] = ellipord ( Wp , Ws , Rp , Rs ) Parameters
-
Wp:
- scalar or vector of length 2, all elements must be in the range [0,1]
Ws:
- scalar or vector of length 2, all elements must be in the range [0,1]
Rp:
- real or complex value
Rs:
- real or complex value
Description
-
This is an Octave function.
-This function computes the minimum filter order of an elliptic filter with the desired response characteristics.
-Stopband frequency ws and passband frequency wp specify the the filter frequency band edges.
-Frequencies are normalized to the Nyquist frequency in the range [0,1].
-Rp is measured in decibels and is the allowable passband ripple and Rs is also measured in decibels and is the minimum attenuation in the stop band.
-If ws>wp then the filter is a low pass filter. If wp>ws, then the filter is a high pass filter.
-If wp and ws are vectors of length 2, then the passband interval is defined by wp and the stopband interval is defined by ws.
-If wp is contained within the lower and upper limits of ws, the filter is a band-pass filter. If ws is contained within the lower and upper limits of wp, the filter is a band-stop or band-reject filter.
Examples
-
[ a , b ] = ellipord ( 0.2 , 0.5 , 0.7 , 0.4 )
-a = 1
-b = 0.20000
enbw
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > enbweqtflength
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > eqtflengtheqtflength
-
Modifies the input vector to give output vectors of the same length
Calling Sequence
-
[ b , a ] = eqtflength ( b , a )
-[ b , a , N , M ] = eqtflength ( b , a ) falltime
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > falltimeCalling Sequence
-
r = falltime ( x )
-r = falltime ( x , t )
-r = falltime ( x , Fs )
-r = falltime ( x , t , 'PercentReferenceLevels ', N )
-r = falltime ( x , t , 'Tolerance ', M )
-r = falltime ( x , t , 'StateLevels ', O )
-[ r lowercrossvalue uppercrossvalue lowerreference upperreference ]= falltime ( x )
-[ r lowercrossvalue uppercrossvalue lowerreference upperreference ]= falltime ( x , Fs )
-[ r lowercrossvalue uppercrossvalue lowerreference upperreference ]= falltime ( x , t )
-[ r lowercrossvalue uppercrossvalue lowerreference upperreference ]= falltime ( x , t , 'PercentReferenceLevels ', N )
-[ r lowercrossvalue uppercrossvalue lowerreference upperreference ]= falltime ( x , t , 'Tolerance ', M )
-[ r lowercrossvalue uppercrossvalue lowerreference upperreference ]= falltime ( x , t , 'StateLevels ', O )
-[ r lowercrossvalue uppercrossvalue lowerreference upperreference ]= falltime ( x , t , 'StateLevels ', O , 'fig ', on or off ) Parameters
-
x:
- real vector.
Fs:
- specifies the sample rate, Fs, as a positive scalar, where the first sample instant corresponds to a time of zero.
t:
- defiene instant sample time t as vector with same length of x, or specifies the sample rate, t, as a positive scalar.
PercentReferenceLevels:
- specify the percentreferenceleves as a percentage, default value of N is [10 90].
Tolerance:
- define the tolerance value as real scaler value, where default value of M is 2.0.
StateLevels:
- define the lower and upper state levels as two element real vector.
fig:
- specify the logical input value to display figure as one of 'on' or 'off', where the default input in 'off'.
f:
- return fall time of negative-going bilevel waveform transitions X.
lowercrossvalue:
- return the lowerc cross value.
uppercrossvalue:
- return the upper cross value.
lowerreference:
- return lower reference value corrosponding to lower percent reference value.
upperreference:
- return upper reference value corrosponding to upper percent reference value.
fft
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > fftfft
-
Calculates the discrete Fourier transform of a matrix using Fast Fourier Transform algorithm.
Calling Sequence
-
fft ( x , n , dim )
-fft ( x , n )
-fft ( x ) Parameters
-
x:
- input matrix
n:
- Specifies the number of elements of x to be used
dim:
- Specifies the dimention of the matrix along which the FFT is performed
Description
-
This is an Octave function.
-The FFT is calculated along the first non-singleton dimension of the array. Thus, FFT is computed for each column of x.
-
n is an integer specifying the number of elements of x to use. If n is larger than dimention along. which the FFT is calculated, then x is resized and padded with zeros.
-Similarly, if n is smaller, then x is truncated.
-
dim is an integer specifying the dimension of the matrix along which the FFT is performed.
Examples
-
x = [ 1 2 3 ; 4 5 6 ; 7 8 9 ]
-n = 3
-dim = 2
-fft ( x , n , dim )
-ans =
-
-6.0000 + 0.0000 i - 1.5000 + 0.8660 i - 1.5000 - 0.8660 i
-15.0000 + 0.0000 i - 1.5000 + 0.8660 i - 1.5000 - 0.8660 i
-24.0000 + 0.0000 i - 1.5000 + 0.8660 i - 1.5000 - 0.8660 i
fft1
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-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > fft1fft1
-
Calculates the discrete Fourier transform of a matrix using Fast Fourier Transform algorithm.
Calling Sequence
-
fft ( x , n , dim )
-fft ( x , n )
-fft ( x ) Parameters
-
x:
- input matrix
n:
- Specifies the number of elements of x to be used
dim:
- Specifies the dimention of the matrix along which the FFT is performed
Description
-
This is an Octave function.
-The FFT is calculated along the first non-singleton dimension of the array. Thus, FFT is computed for each column of x.
-
n is an integer specifying the number of elements of x to use. If n is larger than dimention along. which the FFT is calculated, then x is resized and padded with zeros.
-Similarly, if n is smaller, then x is truncated.
-
dim is an integer specifying the dimension of the matrix along which the FFT is performed.
Examples
-
x = [ 1 2 3 ; 4 5 6 ; 7 8 9 ]
-n = 3
-dim = 2
-fft1 ( x , n , dim )
-ans =
-
-6.0000 + 0.0000 i - 1.5000 + 0.8660 i - 1.5000 - 0.8660 i
-15.0000 + 0.0000 i - 1.5000 + 0.8660 i - 1.5000 - 0.8660 i
-24.0000 + 0.0000 i - 1.5000 + 0.8660 i - 1.5000 - 0.8660 i
fft2
-
-
-
-
-
-
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > fft2fft2
-
Calculates the two-dimensional discrete Fourier transform of A using a Fast Fourier Transform algorithm.
Parameters
-
A:
- input matrix
m:
- number of rows of A to be used
n:
- number of columns of A to be used
Description
-
This is an Octave function.
-It performs two-dimentional FFT on the matrix A. m and n may be used specify the number of rows and columns of A to use. If either of these is larger than the size of A, A is resized and padded with zeros.
-If A is a multi-dimensional matrix, each two-dimensional sub-matrix of A is treated separately.
Examples
-
x = [ 1 2 3 ; 4 5 6 ; 7 8 9 ]
-m = 4
-n = 4
-fft2 ( A , m , n )
-ans =
-
-45 + 0 i - 6 - 15 i 15 + 0 i - 6 + 15 i
-- 18 - 15 i - 5 + 8 i - 6 - 5 i 5 - 4 i
-15 + 0 i - 2 - 5 i 5 + 0 i - 2 + 5 i
-- 18 + 15 i 5 + 4 i - 6 + 5 i - 5 - 8 i
fft21
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-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > fft21fft21
-
Calculates the two-dimensional discrete Fourier transform of A using a Fast Fourier Transform algorithm.
Parameters
-
A:
- input matrix
m:
- number of rows of A to be used
n:
- number of columns of A to be used
Description
-
This is an Octave function.
-It performs two-dimentional FFT on the matrix A. m and n may be used specify the number of rows and columns of A to use. If either of these is larger than the size of A, A is resized and padded with zeros.
-If A is a multi-dimensional matrix, each two-dimensional sub-matrix of A is treated separately.
Examples
-
x = [ 1 2 3 ; 4 5 6 ; 7 8 9 ]
-m = 4
-n = 4
-fft21 ( A , m , n )
-ans =
-
-45 + 0 i - 6 - 15 i 15 + 0 i - 6 + 15 i
-- 18 - 15 i - 5 + 8 i - 6 - 5 i 5 - 4 i
-15 + 0 i - 2 - 5 i 5 + 0 i - 2 + 5 i
-- 18 + 15 i 5 + 4 i - 6 + 5 i - 5 - 8 i
fftconv
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > fftconvfftconv
-
Convolve two vectors using the FFT for computation.
Calling Sequence
-
Y = fftconv ( X , Y )
-Y = fftconv ( X , Y , N ) Parameters
-
X, Y:
- Vectors
Description
-
Convolve two vectors using the FFT for computation. 'c' = fftconv (X, Y)' returns a vector of length equal to 'length(X) + length (Y) - 1'. If X and Y are the coefficient vectors of two polynomials, the returned value is the coefficient vector of the product polynomial.
Examples
-
fftconv ( [ 1 , 2 , 3 ] , [ 3 , 4 , 5 ] )
-ans =
-3. 10. 22. 22. 15.
fftfilt
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-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > fftfiltfftfilt
-
Performs FFT-based FIR filtering using overlap-add method
Parameters
-
x:
- real|complex numbers - vector|matrix
b:
- real|complex numbers - vector|matrix
n:
- positive integer
Description
-
y = fftfilt(b,x) filters the data in vector x with the filter described
-by coefficient vector b.
-y = fftfilt(b,x,n) uses n to determine the length of the FFT.
-
Examples
-
1 ) Filtering a sine wave
-x = sin ( 1 : 2000 ) ;
-b = [ 1 1 / 2 ] ;
-y = fftfilt ( b , x ) ;
-2 ) Multiple filters ( 1 , 1 / 3 ) and ( 1 / 4 , 1 / 5 ) ;
-x = sin ( 1 : 2000 ) ;
-b = [ 1 1 / 4 ; 1 / 3 1 / 5 ] ;
-y = fftfilt ( b , x ) ;
fftn
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-
-
-
-
-
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > fftnfftn
-
This function computes the N-dimensional discrete Fourier transform of A using a Fast Fourier Transform (FFT) algorithm.
Calling Sequence
-
Y = fftn ( A )
-Y = fftn ( A , size ) Description
-
This function computes the N-dimensional discrete Fourier transform of A using a Fast Fourier Transform (FFT) algorithm. The optional vector argument SIZE may be used specify the dimensions of the array to be used. If an element of SIZE is smaller than the corresponding dimension of A, then the dimension of A is truncated prior to performing the FFT. Otherwise, if an element of SIZE is larger than the corresponding dimension then A is resized and padded with zeros.
Examples
-
fftn ( [ 2 , 3 , 4 ] )
-ans =
-9. - 1.5 + 0.8660254 i - 1.5 - 0.8660254 i
fftshift1
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-
-
-
-
-
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > fftshift1fftshift1
-
Perform a shift of the vector X, for use with the 'fft1' and 'ifft1' functions, in order the move the frequency 0 to the center of the vector or matrix.
Calling Sequence
-
fftshift1 ( X )
-fftshift1 ( X , DIM ) Parameters
-
X:
- It is a vector of N elements corresponding to time samples
DIM:
- The optional DIM argument can be used to limit the dimension along which the permutation occurs
Description
-
This is an Octave function.
-Perform a shift of the vector X, for use with the 'fft1' and 'ifft1' functions, in order the move the frequency 0 to the center of the vector or matrix.
-
If X is a vector of N elements corresponding to N time samples spaced by dt, then 'fftshift1 (fft1 (X))' corresponds to frequencies
-
f = [ -(ceil((N-1)/2):-1:1)*df 0 (1:floor((N-1)/2))*df ]
-
where df = 1 / dt.
-
If X is a matrix, the same holds for rows and columns. If X is an array, then the same holds along each dimension.
-
The optional DIM argument can be used to limit the dimension along
fftw1
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-
-
-
-
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-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > fftw1fftw1
-
Manage FFTW wisdom data.
Calling Sequence
-
method = fftw ( "planner ")
-fftw ( "planner ", method )
-wisdom = fftw ( "dwisdom ")
-fftw ( "dwisdom ", wisdom )
-fftw ( "threads ", nthreads )
-nthreads = fftw ( "threads ") Description
-
This is an Octave function.
-Wisdom data can be used to significantly accelerate the calculation of the FFTs, but implies an initial cost in its calculation. When the FFTW libraries are initialized, they read a system wide wisdom
-file (typically in /etc/fftw/wisdom), allowing wisdom to be shared between applications other than Octave. Alternatively, the fftw function can be used to import wisdom. For example,
-
wisdom = fftw ("dwisdom")
-will save the existing wisdom used by Octave to the string wisdom. This string can then be saved to a file and restored using the save and load commands respectively. This existing wisdom can be re
-imported as follows
-
fftw ("dwisdom", wisdom)
-If wisdom is an empty string, then the wisdom used is cleared.
-
During the calculation of Fourier transforms further wisdom is generated. The fashion in which this wisdom is generated is also controlled by the fftw function. There are five different manners in which
-the wisdom can be treated:
-
"estimate"
-Specifies that no run-time measurement of the optimal means of calculating a particular is performed, and a simple heuristic is used to pick a (probably sub-optimal) plan. The advantage of this method
-is that there is little or no overhead in the generation of the plan, which is appropriate for a Fourier transform that will be calculated once.
-
"measure"
-In this case a range of algorithms to perform the transform is considered and the best is selected based on their execution time.
-
"patient"
-Similar to "measure", but a wider range of algorithms is considered.
-
"exhaustive"
-Like "measure", but all possible algorithms that may be used to treat the transform are considered.
-
"hybrid"
-As run-time measurement of the algorithm can be expensive, this is a compromise where "measure" is used for transforms up to the size of 8192 and beyond that the "estimate" method is used.
-
The default method is "estimate". The current method can be queried with
-
method = fftw ("planner")
-or set by using
-
fftw ("planner", method)
fht
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > fhtfht
-
The Function calculates the Fast Hartley Transform of real input.
Calling Sequence
-
M = fht ( D )
-M = fht ( D , N )
-M = fht ( D , N , DIM ) Description
-
This function calculates the Fast Hartley transform of real input D. If D is a matrix, the Hartley transform is calculated along the columns by default.
Examples
-
fht ( 1 : 4 )
-ans =
-10 - 4 - 2 0
-This function being called from Octave .
filter1
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-
-
-
-
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > filter1filter1
-
Apply a 1-D digital filter to the data X.
Calling Sequence
-
Y = filter1 ( B , A , X )
-[ Y , SF ] = filter1 ( B , A , X , SI )
-[ Y , SF ] = filter1 ( B , A , X , [], DIM )
-[ Y , SF ] = filter1 ( B , A , X , SI , DIM ) Parameters
-
B:
- Matrix or Integer
A:
- Matrix or Integer
X:
- Matrix or Integer
Description
-
'filter' returns the solution to the following linear, time-invariant difference equation:
-
N M
-
SUM a(k+1) y(n-k) = SUM b(k+1) x(n-k) for 1<=n<=length(x)
-
k=0 k=0
-
where N=length(a)-1 and M=length(b)-1. The result is calculated over the first non-singleton dimension of X or over DIM if supplied.
-
An equivalent form of the equation is:
-
N M
-
y(n) = - SUM c(k+1) y(n-k) + SUM d(k+1) x(n-k) for 1<=n<=length(x)
-
k=1 k=0
-
where c = a/a(1) and d = b/a(1).
Examples
-
filter ( [ 1 , 2 , 3 ] , [ 3 , 4 , 5 ] , [ 5 , 6 , 7 ] )
-ans =
-1.6666667 3.1111111 4.4074074
filter2
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-
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-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > filter2filter2
-
Apply the 2-D FIR filter B to X.
Calling Sequence
-
Y = filter2 ( B , X )
-Y = filter2 ( B , X , SHAPE ) Parameters
-
B, X:
- Matrix
SHAPE:
-
'full':
- pad X with zeros on all sides before filtering.
'same':
- unpadded X (default)
'valid':
- trim X after filtering so edge effects are no included.
Description
-
This function applies the 2-D FIR filter B to X. If the argument SHAPE is specified, return an array of the desired shape.
Examples
-
filter2 ( [ 1 , 3 ] , [ 4 , 5 ] )
-ans =
-19. 5.
filternorm
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > filternormfilternorm
-
Calculates the L-2 norm or L-infinity norm of a digital filter
Calling Sequence
-
L = filternorm ( b , a )
-L = filternorm ( b , a , pnorm )
-L = filternorm ( b , a ,2, tol ) Parameters
-
b:
- The filter numerator coefficients.
a:
- The filter denominator coefficients.
pnorm:
- The L-norm to be calculated. The values accepted are 2 (L2 norm) or %inf (L-infinity norm). Default value is 2.
tol:
- The tolerance of the L-2 norm to be calculated. If tol not specified, it defaults to 10^(-8). tol must be a positive scalar
Examples
-
-b = [ - 3 2 ] ;
-a = [ 1 - 0.5 ] ;
-L = filternorm ( b , a , 2 , 10d-10 ) ;
filtfilt
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > filtfiltParameters
-
b:
- Real or complex valued vector or matrix
a:
- Real or complex valued vector or matrix
x:
- Real or complex valued vector or matrix
Description
-
This is an Octave function
-In theory, it forwards and reverse filters the signal and corrects phase distortion upto an extent by a one-pass filter but squares the magnitude response in the process. Practically though, the correction isn't perfect and magnitude response, particularly the stop band is distorted.
Examples
-
1. [ a , b ] = filtfilt ( 1 , 2 i , [ i - 4 0 ] )
-a = [ 0.00000 - 0.25000 i 1.00000 + 0.00000 i 0.00000 + 0.00000 i ]
filtic
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > filticCalling Sequence
-
zf = filtic ( b , a , y )
-zf = filtic ( b , a , y , x ) Parameters
-
b:
- vector of real or complex numbers
a:
- vector of real or complex numbers
y:
- vector of real or complex numbers
x:
- vector of real or complex numbers
Description
-
This function finds the initial conditions for the delays in the transposed direct-form II filter implementation.
-The vectors b and a represent the numerator and denominator coefficients of the filter's transfer function.
Examples
-
filtic ( [ i , 1 , - i , 5 ] , [ 1 , 2 , 3 i ] , [ 0.8 i , 7 , 9 ] )
-ans =
-0.00000 - 22.60000 i
-2.40000 + 0.00000 i
-0.00000 + 0.00000 i
-This function being called from Octave
filtord
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > filtordfiltord
-
and denominator coefficients, a.
findpeaks
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > findpeaksfindpeaks
-
This function find peaks on DATA.
Calling Sequence
-
[ PKS , LOC , EXTRA ] = findpeaks ( DATA )
-[ PKS , LOC , EXTRA ] = findpeaks (..., PROPERTY , VALUE )
-[ PKS , LOC , EXTRA ] = findpeaks (..., "DoubleSided ") Description
-
Peaks of a positive array of data are defined as local maxima. For double-sided data, they are maxima of the positive part and minima of the negative part. DATA is expected to be a single column vector.
-
The function returns the value of DATA at the peaks in PKS. The index indicating their position is returned in LOC.
-
The third output argument is a structure with additional information:
-
"parabol":
-A structure containing the parabola fitted to each returned peak. The structure has two fields, "x" and "pp". The field "pp" contains the coefficients of the 2nd degree polynomial and "x" the extrema of the intercal here it was fitted.
-
"height":
-The estimated height of the returned peaks (in units of DATA).
-
"baseline":
-The height at which the roots of the returned peaks were calculated (in units of DATA).
-
"roots":
-The abscissa values (in index units) at which the parabola fitted to each of the returned peaks crosses the "baseline" value. The width of the peak is calculated by 'diff(roots)'.
-
This function accepts property-value pair given in the list below:
-
"MinPeakHeight":
-Minimum peak height (positive scalar). Only peaks that exceed this value will be returned. For data taking positive and negative values use the option "DoubleSided". Default value '2*std (abs (detrend (data,0)))'.
-
"MinPeakDistance":
-Minimum separation between (positive integer). Peaks separated by less than this distance are considered a single peak. This distance is also used to fit a second order polynomial to the peaks to estimate their width, therefore it acts as a smoothing parameter. Default value 4.
-
"MinPeakWidth":
-Minimum width of peaks (positive integer). The width of the peaks is estimated using a parabola fitted to the neighborhood of each peak. The neighborhood size is equal to the value of "MinPeakDistance". The width is evaluated at the half height of the peak with baseline at "MinPeakHeight". Default value 2.
-
"DoubleSided":
-Tells the function that data takes positive and negative values. The base-line for the peaks is taken as the mean value of the function. This is equivalent as passing the absolute value of the data after removing the mean.
fir1
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > fir1fir1
-
Produce an order N FIR filter with the given frequency cutoff, returning the N+1 filter coefficients in B.
Calling Sequence
-
B = fir1 ( N , W )
-B = fir1 ( N , W , TYPE )
-B = fir1 ( N , W , TYPE , WINDOW )
-B = fir1 ( N , W , TYPE , WINDOW , NOSCALE ) Parameters
-
N:
- Integer
W:
- Integer or Vector
Description
-
Produce an order N FIR filter with the given frequency cutoff W, returning the N+1 filter coefficients in B. If W is a scalar, it specifies the frequency cutoff for a lowpass or highpass filter. If W is a two-element vector, the two values specify the edges of a bandpass or bandstop filter. If W is an N-element vector, each value specifies a band edge of a multiband pass/stop filter.
-
The filter TYPE can be specified with one of the following strings: "low", "high", "stop", "pass", "bandpass", "DC-0", or "DC-1". The default is "low" is W is a scalar, "pass" if W is a pair, or "DC-0" if W is a vector with more than 2 elements.
-
An optional shaping WINDOW can be given as a vector with length N+1. If not specified, a Hamming window of length N+1 is used.
-
With the option "noscale", the filter coefficients are not normalized. The default is to normalize the filter such that the magnitude response of the center of the first passband is 1.
Examples
-
fir1 ( 5 , 0.4 )
-ans =
-9.2762e-05 9.5482e-02 4.0443e-01 4.0443e-01 9.5482e-02 9.2762e-05
fir2
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > fir2fir2
-
Produce an order N FIR filter with arbitrary frequency response M over frequency bands F, returning the N+1 filter coefficients in B.
Calling Sequence
-
B = fir2 ( N , F , M )
-B = fir2 ( N , F , M , GRID_N )
-B = fir1 ( N , F , M , GRID_N , RAMP_N )
-B = fir1 ( N , F , M , GRID_N , RAMP_N , WINDOW ) Parameters
-
N:
- Integer
F, M:
- Vector
Description
-
Produce an order N FIR filter with arbitrary frequency response M over frequency bands F, returning the N+1 filter coefficients in B. The vector F specifies the frequency band edges of the filter response and M specifies the magnitude response at each frequency.
-
The vector F must be nondecreasing over the range [0,1], and the first and last elements must be 0 and 1, respectively. A discontinuous jump in the frequency response can be specified by duplicating a band edge in F with different values in M.
-
The resolution over which the frequency response is evaluated can be controlled with the GRID_N argument. The default is 512 or the next larger power of 2 greater than the filter length.
-
The band transition width for discontinuities can be controlled with the RAMP_N argument. The default is GRID_N/25. Larger values will result in wider band transitions but better stopband rejection.
-
An optional shaping WINDOW can be given as a vector with length N+1. If not specified, a Hamming window of length N+1 is used.
Examples
-
fir2 ( 10 , [ 0 , 0.5 , 1 ] , [ 1 , 2 , 3 ] )
-ans =
-- 0.00130 0.00000 - 0.01792 0.00000 - 0.36968 2.00000 - 0.36968 0.00000 - 0.01792 0.00000 - 0.00130
firpmord
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > firpmordfirpmord
-
Parks-McClennan optimal FIR filter order estimation
Parameters
-
f:
- double - positive - vector
a:
- double - positive - vector
dev:
- double - positive - vector
n:
- int - scalar
fo:
- double - positive - vector
ao:
- double - positive - vector
w:
- double - vector
Examples
-
[ 1 ] A low - pass filter
-f = [ 1500 2000 ] ;
-a = [ 1 0 ] ;
-dev = [ 0.01 0.1 ] ;
-fs = 8000 ;
-[ n , fo , ao , w ] = firpmord ( f , a , dev , fs ) ;
-
-[ 2 ] A bandstop filter
-f = [ 1000 1800 2400 3000 ] ;
-a = [ 1 0 0.5 ] ;
-dev = [ 0.01 0.1 0.03 ] ;
-fs = 8000 ;
-[ n , fo , ao , w ] = firpmord ( f , a , dev , fs ) ;
-
-References
-[ 1 ] Rabiner , Lawrence R . , and Bernard Gold. "Theory and application of
-digital signal processing . " Englewood Cliffs, NJ, Prentice-Hall, Inc.,
-1975. 777 p . 156 - 7 ( 1975 ) .
-[ 2 ] Rabiner , Lawrence R . , and Otto Herrmann. "The predictability of certain
-optimum finite - impulse - response digital filters . " Circuit Theory,
-IEEE Transactions on 20.4 ( 1973 ) : 401 - 408.
firtype
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > firtypefirtype
-
if (type(b)~=1) then
flattopwin
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > flattopwinflattopwin
-
This function returns the filter coefficients of a Flat Top window.
Calling Sequence
-
w = flattopwin ( m )
-w = flattopwin ( m , opt ) Parameters
-
m:
- positive integer value
opt:
- string value, takes in "periodic" or "symmetric"
w:
- output variable, vector of real numbers
Description
-
This is an Octave function.
-This function returns the filter coefficients of a Flat Top window of length m supplied as input, to the output vector w.
-The second parameter can take the values "periodic" or "symmetric", depending on which the corresponding form of window is returned. The default is symmetric.
-This window has low pass-band ripple but a high bandwidth.
Examples
-
flattopwin ( 8 , " periodic " )
-ans =
-0.0009051
-- 0.0264124
-- 0.0555580
-0.4435496
-1.
-0.4435496
-- 0.0555580
-- 0.0264124
fracshift
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > fracshiftfracshift
-
This function shifts the series x supplied as input argument by a number of samples d.
Calling Sequence
-
y = fracshift ( x , d )
-y = fracshift ( x , d , h )
-[ y , h ] = fracshift (...) Parameters
-
x:
-
d:
-
h:
-
Description
-
This is an Octave function.
-This function shifts the series x supplied as input argument by a number of samples d.
-The third parameter is the interpolator, which is designed with a Kaiser-windowed sinecard by default, if not supplied.
Examples
-
fracshift ( [ 1 , 2 , 3 ] , 5 )
-ans =
-1. 2. 3.
fractdiff
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > fractdifffractdiff
-
Compute the fractional differences (1-L)^d x where L denotes the lag-operator and d is greater than -1.
Description
-
This is an Octave function.
-Compute the fractional differences (1-L)^d x where L denotes the lag-operator and d is greater than -1.
freqs
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > freqsfreqs
-
Compute the s-plane frequency response of the IIR filter.
Parameters
-
b:
- vector containing the coefficients of the numerator of the filter.
a:
- vector containing the coefficients of the denominator of the filter.
w:
- vector containing frequencies
Description
-
This is an Octave function.
-It computes the s-plane frequency response of the IIR filter B(s)/A(s) as H = polyval(B,j*W)./polyval(A,j*W).
-If called with no output argument, a plot of magnitude and phase are displayed.
Examples
-
B = [ 1 2 ] ;
-A = [ 1 1 ] ;
-w = linspace ( 0 , 4 , 128 ) ;
-freqs ( B , A , w ) ;
freqz
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > freqzfreqz
-
This function returns the complex frequency response H of the rational IIR filter whose numerator and denominator coefficients are B and A, respectively.
Calling Sequence
-
[ H , W ] = freqz ( B , A , N , "whole ")
-[ H , W ] = freqz ( B )
-[ H , W ] = freqz ( B , A )
-[ H , W ] = freqz ( B , A , N )
-H = freqz ( B , A , W )
-[ H , W ] = freqz (..., FS )
-freqz (...) Parameters
-
B, A, N:
- Integer or Vector
Description
-
Return the complex frequency response H of the rational IIR filter whose numerator and denominator coefficients are B and A, respectively.
-
The response is evaluated at N angular frequencies between 0 and 2*pi.
-
The output value W is a vector of the frequencies.
-
If A is omitted, the denominator is assumed to be 1 (this corresponds to a simple FIR filter).
-
If N is omitted, a value of 512 is assumed. For fastest computation, N should factor into a small number of small primes.
-
If the fourth argument, "whole", is omitted the response is evaluated at frequencies between 0 and pi.
-
'freqz (B, A, W)'
-
Evaluate the response at the specific frequencies in the vector W. The values for W are measured in radians.
-
'[...] = freqz (..., FS)'
-
Return frequencies in Hz instead of radians assuming a sampling rate FS. If you are evaluating the response at specific frequencies W, those frequencies should be requested in Hz rather than radians.
-
'freqz (...)'
-
Plot the magnitude and phase response of H rather than returning them.
Examples
-
H = freqz ( [ 1 , 2 , 3 ] , [ 4 , 3 ] , [ 1 , 2 , 5 ] )
-ans =
-0.4164716 - 0.5976772 i - 0.4107690 - 0.2430335 i 0.1761948 + 0.6273032 i
fwhm
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > fwhmfwhm
-
This function computes peak full width at half minimum or at another level of peak minimum for vector or matrix data y supplied as input.
Calling Sequence
-
f = fwhm ( y )
-f = fwhm ( x , y )
-f = fwhm (…, "zero ")
-f = fwhm (…, "min ")
-f = fwhm (…, "alevel ", level )
-f = fwhm (…, "rlevel ", level ) Parameters
-
y:
- vector or matrix
fwhmjlt
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > fwhmjltfwht
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > fwhtfwht
-
Compute the Walsh-Hadamard transform of x using the Fast Walsh-Hadamard Transform (FWHT) algorithm
Calling Sequence
-
fwht ( x )
-fwht ( x , n )
-fwht ( x , n , order ) Parameters
-
x:
- real or complex valued scalar or vector
n:
- x is truncated or extended to have length n
order:
- Specification of order in which coefficients should be arranged
Description
-
Compute the Walsh-Hadamard transform of x using the Fast Walsh-Hadamard Transform (FWHT) algorithm. If the input is a matrix, the FWHT is calculated along the columns of x.
-
The number of elements of x must be a power of 2; if not, the input will be extended and filled with zeros. If a second argument is given, the input is truncated or extended to have length n.
-
The third argument specifies the order in which the returned Walsh-Hadamard transform coefficients should be arranged. The order may be any of the following strings:
-
"sequency"
-The coefficients are returned in sequency order. This is the default if order is not given.
-
"hadamard"
-The coefficients are returned in Hadamard order.
-
"dyadic"
-The coefficients are returned in Gray code order.
gauspuls
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > gauspulsCalling Sequence
-
[ y ]= gauspuls ( t , fc , bw )
-[ y ]= gauspuls ( t , fc )
-[ y ]= gauspuls ( t ) Parameters
-
t:
- Real or complex valued vector or matrix
fc:
- Real non negative number or complex number
bw:
- Real positive number or complex number
Description
-
This is an Octave function
-This function returns a Gaussian RF pulse of unity amplitude at the times indicated in array t.
Examples
-
1. gauspuls ( 1 , 2 , 3 )
-ans = 1.427D-56
-2. gauspuls ( [ 1 2 3 ] , 1 , 1 )
-ans = 0.0281016 0.0000006 1.093D-14
gaussdesign
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > gaussdesigngaussdesign
-
GAUSSDESIGN designs a Gaussian pulse-shaping filter which is a low pass FIR
gaussian
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > gaussiangaussian
-
This function returns a Gaussian convolution window.
Calling Sequence
-
w = gaussian ( m )
-w = gaussian ( m , a ) Parameters
-
m:
- positive integer value
a:
-
w:
- output variable, vector of real numbers
Description
-
This is an Octave function.
-This function returns a Gaussian convolution window of length m supplied as input, to the output vector w.
-The second parameter is the width measured in sample rate/number of samples and should be f for time domain and 1/f for frequency domain. The width is inversely proportional to a.
Examples
-
gaussian ( 5 , 6 )
-ans =
-5.380D-32
-1.523D-08
-1.
-1.523D-08
-5.380D-32
gausswin
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > gausswingausswin
-
This function returns the filter coefficients of a Gaussian window.
Calling Sequence
-
w = gausswin ( m )
-w = gausswin ( m , a ) Parameters
-
m:
- positive integer value
a:
-
w:
- output variable, vector of real numbers
Description
-
This is an Octave function.
-This function returns the filter coefficients of a Gaussian window of length m supplied as input, to the output vector w.
-The second parameter is the width measured in sample rate/number of samples and should be f for time domain and 1/f for frequency domain. The width is inversely proportional to a.
Examples
-
gausswin ( 3 )
-ans =
-0.2493522
-1.
-0.2493522
gmonopuls
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > gmonopulsParameters
-
t:
- Real or complex valued vector or matrix
fc:
- Real non-negative value or complex value or a vector or matrix with not all real values negative.
Description
-
This is an Octave function
-This function returns samples of the Gaussian monopulse of amplitude unity.
Examples
-
1. gmonopuls ( [ 1 2 3 ] , 0.1 )
-ans = 0.85036 0.94070 0.52591
-2. gmonopuls ( [ 1 2 3 ] )
-ans = 0 0 0
goertzel
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > goertzelgoertzel
-
Computes DFT using the second order Goertzel Algorithm
Calling Sequence
-
Y = goertzel ( X , INDVEC , DIM ) Description
-
goertzel(X,INDVEC)
-Computes the DFT of X at indices INDVEC using the second order algorithm along
-the first non-singleton dimension. Elements of INDVEC must be positive integers
-less than the length of the first non-singleton dimension. If INDVEC is empty
-the DFT is computed at all indices along the first non-singleton dimension
-goertzel(X,INDVEC,DIM)
-Implements the algorithm along dimension DIM
-In general goertzel is slower than fft when computing the DFT for all indices
-along a particular dimension. However it is computationally more efficient when
-the DFT at only a subset of indices is desired
-Example
-x=rand(1,5)
-x =
-
0.6283918 0.8497452 0.6857310 0.8782165 0.0683740
-y=goertzel(x,2);
-y =
-
- 0.3531539 - 0.6299881i
-Author
-Ankur Mallick
-References
-Goertzel, G. (January 1958), "An Algorithm for the Evaluation of Finite Trigonometric Series", American Mathematical Monthly 65 (1): 34–35, doi:10.2307/2310304
grpdelay
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > grpdelaygrpdelay
-
This function computes the group delay of a filter.
Calling Sequence
-
gd = grpdelay ( b )
-gd = grpdelay ( b , a )
-gd = grpdelay ( b , a , nfft )
-gd = grpdelay ( b , a , nfft , whole )
-gd = grpdelay ( b , a , nfft , whole , Fs )
-[ gd , w ] = grpdelay (...) hamming
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > hamminghamming
-
Return the filter coefficients of a Hamming window of length M
Calling Sequence
-
hamming ( M )
-hamming ( M , "periodic ")
-hamming ( M , "symmetric ") Parameters
-
M:
- real scalar, which will be the length of hamming window
Description
-
Return the filter coefficients of a Hamming window of length M.
-If the optional argument "periodic" is given, the periodic form of the window is returned. This is equivalent to the window of length M+1 with the last coefficient removed. The optional argument "symmetric" is equivalent to not specifying a second argument.
hann
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > hannhann
-
This function returns the filter coefficients of a Hanning window.
Calling Sequence
-
w = hann ( m )
-w = hann ( m , "symmteric ")
-w = hann ( m , "periodic ") Parameters
-
m:
- positive integer value
opt:
- string value, takes in "periodic" or "symmetric"
w:
- output variable, vector of real numbers
Description
-
This is an Octave function.
-This function returns the filter coefficients of a Hanning window of length m supplied as input, to the output vector w.
-The second parameter can take the values "periodic" or "symmetric", depending on which the corresponding form of window is returned. The default is symmetric.
Examples
-
hann ( 6 , " symmetric " )
-ans =
-0.
-0.3454915
-0.9045085
-0.9045085
-0.3454915
-0.
hanning
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > hanninghanning
-
Return the filter coefficients of a Hanning window of length M
Calling Sequence
-
hanning ( M )
-hanning ( M , "periodic ")
-hanning ( M , "symmetric ") Parameters
-
M:
- real scalar, which will be the length of hanning window
Description
-
Return the filter coefficients of a Hanning window of length M.
-If the optional argument "periodic" is given, the periodic form of the window is returned. This is equivalent to the window of length M+1 with the last coefficient removed. The optional argument "symmetric" is equivalent to not specifying a second argument.
helperHarmonicDistortionAmplifier
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > helperHarmonicDistortionAmplifierhelperHarmonicDistortionAmplifier
-
helperHarmonicDistortionADC Helper function for HarmonicDistortionExample.m
hilbert1
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > hilbert1hilbert1
-
Analytic extension of real valued signal.
Calling Sequence
-
h = hilbert1 ( f )
-h = hilbert1 ( f , N )
-h = hilbert1 ( f , N , dim ) Parameters
-
f:
- real or complex valued scalar or vector
N:
- The result will have length N
dim :
- It analyses the input in this dimension
Description
-
h = hilbert1 (f) computes the extension of the real valued signal f to an analytic signal. If f is a matrix, the transformation is applied to each column. For N-D arrays, the transformation is applied to the first non-singleton dimension.
-
real (h) contains the original signal f. imag (h) contains the Hilbert transform of f.
-
hilbert1 (f, N) does the same using a length N Hilbert transform. The result will also have length N.
-
hilbert1 (f, [], dim) or hilbert1 (f, N, dim) does the same along dimension dim.
Examples
-
## notice that the imaginary signal is phase - shifted 90 degrees
-t = linspace ( 0 , 10 , 256 ) ;
-z = hilbert1 ( sin ( 2 * pi * 0.5 * t ) ) ;
-grid on ; plot ( t , real ( z ) , ' ;real; ' , t , imag ( z ) , ' ;imag; ' ) ;
hurst
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > hursthurst
-
Estimate the Hurst parameter of sample X via the rescaled r statistic.
Calling Sequence
-
hurst ( X )
-variable = hurst ( X ) Parameters
-
X:
- X is a matrix, the parameter of sample X via the rescaled r statistic
Description
-
This is an Octave function.
-This function estimates the Hurst parameter of sample X via the rescaled rstatistic.
icceps
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > iccepsicceps
-
ICCEPS computes the inverse cepstrum of a real-valued input. This spectrum
idct1
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > idct1idct1
-
Compute the inverse discrete cosine transform of input.
Calling Sequence
-
Y = idct1 ( X )
-Y = idct1 ( X , N ) Parameters
-
X:
- Matrix or integer
N:
- If N is given, then X is padded or trimmed to length N before computing the transform.
Description
-
This function computes the inverse discrete cosine transform of input X. If N is given, then X is padded or trimmed to length N before computing the transform. If X is a matrix, compute the transform along the columns of the the matrix. The transform is faster if X is real-valued and even length.
Examples
-
idct1 ( [ 1 , 3 , 6 ] )
-ans =
-5.1481604 - 4.3216292 0.9055197
idct2
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > idct2idct2
-
This function computes the inverse 2-D discrete cosine transform of input matrix.
Calling Sequence
-
Y = idct2 ( X )
-Y = idct2 ( X , M , N )
-Y = idct2 ( X , [ M , N ]) Parameters
-
X:
- Matrix or integer
M, N:
- If specified Matrix X is padded with M rows and N columns.
Description
-
This function computes the inverse 2-D discrete cosine transform of matrix X. If M and N are specified, the input is either padded or truncated to have M rows and N columns.
Examples
-
idct2 ( 3 , 4 , 6 )
-ans =
-2.811261 0.612372 - 0.525856 0.250601 0.612372 - 0.086516
idst1
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > idst1idst1
-
This function computes the inverse type I discrete sine transform.
Calling Sequence
-
Y = idst ( X )
-Y = idst ( X , N ) Parameters
-
X:
- Matrix or integer
N:
- If N is given, then X is padded or trimmed to length N before computing the transform.
Description
-
This function computes the inverse type I discrete sine transform of Y. If N is given, then Y is padded or trimmed to length N before computing the transform. If Y is a matrix, compute the transform along the columns of the the matrix.
Examples
-
idst ( [ 1 , 3 , 6 ] )
-ans =
-3.97487 - 2.50000 0.97487
ifft
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > ifftifft
-
Calculates the inverse discrete Fourier transform of a matrix using Fast Fourier Transform algorithm.
Calling Sequence
-
ifft ( x , n , dim )
-ifft ( x , n )
-ifft ( x ) Parameters
-
x:
- input matrix
n:
- Specifies the number of elements of x to be used
dim:
- Specifies the dimention of the matrix along which the inverse FFT is performed
Description
-
This is an Octave function.
Description
-
This is an Octave function.
-The inverse FFT is calculated along the first non-singleton dimension of the array. Thus, inverse FFT is computed for each column of x.
-
n is an integer specifying the number of elements of x to use. If n is larger than dimention along. which the inverse FFT is calculated, then x is resized and padded with zeros.
-Similarly, if n is smaller, then x is truncated.
-
dim is an integer specifying the dimension of the matrix along which the inverse FFT is performed.
Examples
-
x = [ 1 2 3 ; 4 5 6 ; 7 8 9 ]
-n = 3
-dim = 2
-ifft ( x , n , dim )
-ans =
-
-2.00000 + 0.00000 i - 0.50000 - 0.28868 i - 0.50000 + 0.28868 i
-5.00000 + 0.00000 i - 0.50000 - 0.28868 i - 0.50000 + 0.28868 i
-8.00000 + 0.00000 i - 0.50000 - 0.28868 i - 0.50000 + 0.28868 i
ifft1
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > ifft1ifft1
-
Calculates the inverse discrete Fourier transform of a matrix using Fast Fourier Transform algorithm.
Calling Sequence
-
ifft ( x , n , dim )
-ifft ( x , n )
-ifft ( x ) Parameters
-
x:
- input matrix
n:
- Specifies the number of elements of x to be used
dim:
- Specifies the dimention of the matrix along which the inverse FFT is performed
Description
-
This is an Octave function.
Description
-
This is an Octave function.
-The inverse FFT is calculated along the first non-singleton dimension of the array. Thus, inverse FFT is computed for each column of x.
-
n is an integer specifying the number of elements of x to use. If n is larger than dimention along. which the inverse FFT is calculated, then x is resized and padded with zeros.
-Similarly, if n is smaller, then x is truncated.
-
dim is an integer specifying the dimension of the matrix along which the inverse FFT is performed.
Examples
-
x = [ 1 2 3 ; 4 5 6 ; 7 8 9 ]
-n = 3
-dim = 2
-ifft1 ( x , n , dim )
-ans =
-
-2.00000 + 0.00000 i - 0.50000 - 0.28868 i - 0.50000 + 0.28868 i
-5.00000 + 0.00000 i - 0.50000 - 0.28868 i - 0.50000 + 0.28868 i
-8.00000 + 0.00000 i - 0.50000 - 0.28868 i - 0.50000 + 0.28868 i
ifft2
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > ifft2ifft2
-
Calculates the inverse two-dimensional discrete Fourier transform of A using a Fast Fourier Transform algorithm.
Calling Sequence
-
ifft2 ( A , m , n )
-ifft2 ( A ) Parameters
-
A:
- input matrix
m:
- number of rows of A to be used
n:
- number of columns of A to be used
Description
-
This is an Octave function.
-It performs inverse two-dimensional FFT on the matrix A. m and n may be used specify the number of rows and columns of A to use. If either of these is larger than the size of A, A is resized and padded with zeros.
-If A is a multi-dimensional matrix, each two-dimensional sub-matrix of A is treated separately.
Examples
-
x = [ 1 2 3 ; 4 5 6 ; 7 8 9 ]
-m = 4
-n = 4
-ifft2 ( A , m , n )
-ans =
-
-2.81250 + 0.00000 i - 0.37500 + 0.93750 i 0.93750 + 0.00000 i - 0.37500 - 0.93750 i
-- 1.12500 + 0.93750 i - 0.31250 - 0.50000 i - 0.37500 + 0.31250 i 0.31250 + 0.25000 i
-0.93750 + 0.00000 i - 0.12500 + 0.31250 i 0.31250 + 0.00000 i - 0.12500 - 0.31250 i
-- 1.12500 - 0.93750 i 0.31250 - 0.25000 i - 0.37500 - 0.31250 i - 0.31250 + 0.50000 i
ifftn
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > ifftnifftn
-
Compute the inverse N-dimensional discrete Fourier transform of A using a Fast Fourier Transform (FFT) algorithm.
Calling Sequence
-
Y = ifftn ( A )
-Y = ifftn ( A , size ) Description
-
Compute the inverse N-dimensional discrete Fourier transform of A using a Fast Fourier Transform (FFT) algorithm. The optional vector argument SIZE may be used specify the dimensions of the array to be used. If an element of SIZE is smaller than the corresponding dimension of A, then the dimension of A is truncated prior to performing the inverse FFT. Otherwise, if an element of SIZE is larger than the corresponding dimension then A is resized and padded with zeros.
Examples
-
ifftn ( [ 2 , 3 , 4 ] )
-ans =
-3. - 0.5 - 0.2886751 i - 0.5 + 0.2886751 i
ifftshift1
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > ifftshift1ifftshift1
-
Undo the action of the 'fftshift1' function.
Calling Sequence
-
ifftshift1 ( X )
-ifftshift1 ( X , DIM ) Parameters
-
X:
- It is a vector of N elements corresponding to time samples
DIM:
- The optional DIM argument can be used to limit the dimension along which the permutation occurs
Description
-
This is an Octave function.
-Undo the action of the 'fftshift1' function.
-
For even length X, 'fftshift1' is its own inverse, but odd lengths differ slightly.
ifht
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > ifhtifht
-
Calculate the inverse Fast Hartley Transform of real input D
Calling Sequence
-
m = ifht ( d )
-m = ifht ( d , n )
-m = ifht ( d , n , dim ) Parameters
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d:
- real or complex valued scalar or vector
n:
- Similar to the options of FFT function
dim:
- Similar to the options of FFT function
Description
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Calculate the inverse Fast Hartley Transform of real input d. If d is a matrix, the inverse Hartley transform is calculated along the columns by default. The options n and dim are similar to the options of FFT function.
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The forward and inverse Hartley transforms are the same (except for a scale factor of 1/N for the inverse hartley transform), but implemented using different functions.
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The definition of the forward hartley transform for vector d, m[K] = 1/N \sum_{i=0}^{N-1} d[i]*(cos[K*2*pi*i/N] + sin[K*2*pi*i/N]), for 0 <= K < N. m[K] = 1/N \sum_{i=0}^{N-1} d[i]*CAS[K*i], for 0 <= K < N.
Examples
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ifht ( 1 : 4 )
-ifht ( 1 : 4 , 2 )
ifwht
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > ifwhtifwht
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Compute the inverse Walsh-Hadamard transform of x using the Fast Walsh-Hadamard Transform (FWHT) algorithm
Calling Sequence
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ifwht ( x )
-ifwht ( x , n )
-ifwht ( x , n , order ) Parameters
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x:
- real or complex valued scalar or vector
n:
- Input is truncated or extended to have a length of n
order:
- Specifies the order in which the returned inverse Walsh-Hadamard transform
Description
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Compute the inverse Walsh-Hadamard transform of x using the Fast Walsh-Hadamard Transform (FWHT) algorithm. If the input is a matrix, the inverse FWHT is calculated along the columns of x.
-The number of elements of x must be a power of 2; if not, the input will be extended and filled with zeros. If a second argument is given, the input is truncated or extended to have length n.
-The third argument specifies the order in which the returned inverse Walsh-Hadamard transform coefficients should be arranged. The order may be any of the following strings:
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"sequency"
-The coefficients are returned in sequency order. This is the default if order is not given.
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"hadamard"
-The coefficients are returned in Hadamard order.
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"dyadic"
-The coefficients are returned in Gray code order.
iirlp2mb
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > iirlp2mbiirlp2mb
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This function does IIR Low Pass Filter to Multiband Filter Transformation.
Calling Sequence
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[ Num , Den , AllpassNum , AllpassDen ] = iirlp2mb ( B , A , Wo , Wt )
-[ Num , Den , AllpassNum , AllpassDen ] = iirlp2mb ( B , A , Wo , Wt , Pass ) Parameters
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B:
- real or complex value
A:
- real or complex value
Wo:
- scalar or vector
Wt:
- scalar or vector, elements must be monotonically increasing and >= 0 and <= 1.
Description
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This is an Octave function.
-This function does IIR Low Pass Filter to Multiband Filter Transformation.
-The first two parameters give the numerator and denominator of the prototype low pass filter.
-The third parameter is the normalized angular frequency/pi to be transformed.
-The fourth parameter is the normalized angular frequency/pi target vector.
-The first two output variables are the numerator and denominator of the transformed filter.
-The third and fourth output variables are the numerator and denominator of the allpass transform.
-The fifth parameter can have values pass or stop, default value is pass.
Examples
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iirlp2mb ( 5 , 9 , 0.3 , 0.4 )
-ans = 0.55556
impinvar
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > impinvarimpinvar
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This function converts analog filter with coefficients b and a to digital, conserving impulse response.
Calling Sequence
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[ b , a ] = impinvar ( b , a )
-[ b , a ] = impinvar ( b , a , fs )
-[ b , a ] = impinvar ( b , a , fs , tol ) Parameters
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b:
- real or complex valued scalar or vector
a:
- real or complex valued scalar or vector, order should be greater than b
fs:
- real or complex value, default value 1Hz
tol:
- real or complex value, default value 0.0001
Description
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This is an Octave function.
-This function converts analog filter with coefficients b and a to digital, conserving impulse response.
-This function does the inverse of impinvar.
Examples
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b = 0.0081000
-a = [ 2.0000000 , 0.56435378 , 0.4572792 , 0.00705544 , 0.091000 ]
-[ ay , by ] = impinvar ( b , a , 10 )
-ay =
-0.0000e+00 7.5293e-08 2.9902e-07 7.4238e-08
-by =
-1.00000 - 3.96992 5.91203 - 3.91428 0.97218
impz
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > impzCalling Sequence
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x_r = impz ( b )
-x_r = impz ( b , a )
-x_r = impz ( b , a , n )
-x_r = impz ( b , a , n , fs )
-[ x_r , t_r ] = impz ( b , a , n , fs ) impzlength
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > impzlengthimpzlength
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Impulse response length
Calling Sequence
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len = impzlength ( b , a , tol )
-returns the impulse response length for the causal discrete - time filter
-with the transfer function coefficients for numerator and denominator in
-a and b respectively . For stable IIR filters , len is the effective length
-impulse response length , i . e . the length after which the response is
-essentially zero
-len = impzlength ( sos )
-returns the impulse response length of the filter specified by second
-order sections matrix . sos is a Kx6 matrix , where K is the number of
-sections . Each row of the sos matrix corresponds to a second order
-biquad filter
-len = impzlength ( __ , tol )
-specifies a tolerance for estimating the effective impulse response
-length in case of an IIR filter . By default , tol is 5 e -5. Increasing tol
-leads to shorter len and vice - versa Examples
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1 ) Low pass IIR filter with pole at 0.9
-b = 1 ;
-a = [ 1 - 0.9 ] ;
-len = impzlength ( b , a ) ;
FOSSEE Signal Processing Toolbox
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-FOSSEE Signal Processing Toolbox
-ac2poly — Convert autocorrelation sequence to polynomial of prediction filter ac2rc — Convert autocorrelation sequence to reflection coefficients. arParEst — ar_psd — Calculate the power spectrum of the autoregressive model arburg — This function calculates coefficients of an autoregressive (AR) model of complex data. arch_fit — This functions fits an ARCH regression model to the time series Y using the scoring algorithm in Engle's original ARCH paper. arch_rnd — Simulate an ARCH sequence of length t with AR coefficients b and CH coefficients a. arch_test — perform a Lagrange Multiplier (LM) test of thenull hypothesis of no conditional heteroscedascity against the alternative of CH(P) arcov — Autoregressive all-pole model parameters — covariance method arma_rnd — Return a simulation of the ARMA model. armcov — aryule — This function fits an AR (p)-model with Yule-Walker estimates. autoreg_matrix — Given a time series (vector) Y, return a matrix with ones in the first column and the first K lagged values of Y in the other columns. barthannwin — This function returns the filter coefficients of a modified Bartlett-Hann window. bartlett — Generates a Bartlett window besselap — Return bessel analog filter prototype. besself — This function generates a Bessel filter. bilinear — Transform a s-plane filter specification into a z-plane specification bitrevorder — blackman — Generates a Blackman window blackmanharris — This function returns the filter coefficients of a Blackman-Harris window. blackmannuttall — This function returns the filter coefficients of a Blackman-Nuttall window. bohmanwin — This function returns the filter coefficients of a Bohman window. boxcar — This function returns the filter coefficients of a rectangular window. buffer — This function buffers the given data into a matrix of signal frames buttap — Design a lowpass analog Butterworth filter. butter — This function generates a Butterworth filter. buttord — /This function computes the minimum filter order of a Butterworth filter with the desired response characteristics. cceps — Return the complex cepstrum of the vector x cconv — [nargout,nargin]=argn(); cell2sos — Converts a cell array to a second order section matrix cheb — Calculates the nth-order Chebyshev polynomial at the point x. cheb1ap — This function designs a lowpass analog Chebyshev type I filter. cheb1ord — This function computes the minimum filter order of a Chebyshev type I filter with the desired response characteristics. cheb2ap — This function designs a lowpass analog Chebyshev type II filter. cheb2ord — This function computes the minimum filter order of a Chebyshev type II filter with the desired response characteristics. chebwin — This function returns the filter coefficients of a Dolph-Chebyshev window. cheby1 — This function generates a Chebyshev type I filter with rp dB of passband ripple. cheby2 — This function generates a Chebyshev type II filter with rs dB of stopband attenuation. check — funcprot(0); chirp — This function evaluates a chirp signal at time t. cl2bp — Constrained L2 bandpass FIR filter design. clustersegment — This function calculates boundary indexes of clusters of 1’s. cmorwavf — funcprot(0); cohere — Estimate (mean square) coherence of signals "x" and "y" convmtx — n=double(n); corrmtx — Generate data matrix for autocorrelation matrix estimation cplxreal — Function to divide vector z into complex and real elements, removing the one of each complex conjugate pair. cpsd — This function estimates cross power spectrum of data x and y by the Welch (1967) periodogram/FFT method. cummax — Cumulative maximum cummin — Cumulative minimum czt — Chirp Z Transform db — db2pow — dctmtx — decimate — rhs = argn(2) detrend1 — This function removes the best fit of a polynomial of order P from the data X dftmtx — diffpara — Return the estimator D for the differencing parameter of an integrated time series diric — downsample — This function downsamples the signal by selecting every nth element. dst1 — Computes the type I discrete sine transform of x durbinlevinson — Perform one step of the Durbin-Levinson algorithm.. dutycycle — dwt — Discrete wavelet transform (1D) ellip — This function generates an elliptic or Cauer filter with rp dB of passband ripple and rs dB of stopband attenuation. ellipap — Designs a lowpass analog elliptic filter. ellipord — This function computes the minimum filter order of an elliptic filter with the desired response characteristics. enbw — eqtflength — Modifies the input vector to give output vectors of the same length falltime — fft — Calculates the discrete Fourier transform of a matrix using Fast Fourier Transform algorithm. fft1 — Calculates the discrete Fourier transform of a matrix using Fast Fourier Transform algorithm. fft2 — Calculates the two-dimensional discrete Fourier transform of A using a Fast Fourier Transform algorithm. fft21 — Calculates the two-dimensional discrete Fourier transform of A using a Fast Fourier Transform algorithm. fftconv — Convolve two vectors using the FFT for computation. fftfilt — Performs FFT-based FIR filtering using overlap-add method fftn — This function computes the N-dimensional discrete Fourier transform of A using a Fast Fourier Transform (FFT) algorithm. fftshift1 — Perform a shift of the vector X, for use with the 'fft1' and 'ifft1' functions, in order the move the frequency 0 to the center of the vector or matrix. fftw1 — Manage FFTW wisdom data. fht — The Function calculates the Fast Hartley Transform of real input. filter1 — Apply a 1-D digital filter to the data X. filter2 — Apply the 2-D FIR filter B to X. filternorm — Calculates the L-2 norm or L-infinity norm of a digital filter filtfilt — filtic — filtord — and denominator coefficients, a. findpeaks — This function find peaks on DATA. fir1 — Produce an order N FIR filter with the given frequency cutoff, returning the N+1 filter coefficients in B. fir2 — Produce an order N FIR filter with arbitrary frequency response M over frequency bands F, returning the N+1 filter coefficients in B. firpmord — Parks-McClennan optimal FIR filter order estimation firtype — if (type(b)~=1) then flattopwin — This function returns the filter coefficients of a Flat Top window. fracshift — This function shifts the series x supplied as input argument by a number of samples d. fractdiff — Compute the fractional differences (1-L)^d x where L denotes the lag-operator and d is greater than -1. freqs — Compute the s-plane frequency response of the IIR filter. freqz — This function returns the complex frequency response H of the rational IIR filter whose numerator and denominator coefficients are B and A, respectively. fwhm — This function computes peak full width at half minimum or at another level of peak minimum for vector or matrix data y supplied as input. fwhmjlt — rhs = argn(2) fwht — Compute the Walsh-Hadamard transform of x using the Fast Walsh-Hadamard Transform (FWHT) algorithm gauspuls — gaussdesign — GAUSSDESIGN designs a Gaussian pulse-shaping filter which is a low pass FIR gaussian — This function returns a Gaussian convolution window. gausswin — This function returns the filter coefficients of a Gaussian window. gmonopuls — goertzel — Computes DFT using the second order Goertzel Algorithm grpdelay — This function computes the group delay of a filter. hamming — Return the filter coefficients of a Hamming window of length M hann — This function returns the filter coefficients of a Hanning window. hanning — Return the filter coefficients of a Hanning window of length M helperHarmonicDistortionAmplifier — helperHarmonicDistortionADC Helper function for HarmonicDistortionExample.m hilbert1 — Analytic extension of real valued signal. hurst — Estimate the Hurst parameter of sample X via the rescaled r statistic. icceps — ICCEPS computes the inverse cepstrum of a real-valued input. This spectrum idct1 — Compute the inverse discrete cosine transform of input. idct2 — This function computes the inverse 2-D discrete cosine transform of input matrix. idst1 — This function computes the inverse type I discrete sine transform. ifft — Calculates the inverse discrete Fourier transform of a matrix using Fast Fourier Transform algorithm. ifft1 — Calculates the inverse discrete Fourier transform of a matrix using Fast Fourier Transform algorithm. ifft2 — Calculates the inverse two-dimensional discrete Fourier transform of A using a Fast Fourier Transform algorithm. ifftn — Compute the inverse N-dimensional discrete Fourier transform of A using a Fast Fourier Transform (FFT) algorithm. ifftshift1 — Undo the action of the 'fftshift1' function. ifht — Calculate the inverse Fast Hartley Transform of real input D ifwht — Compute the inverse Walsh-Hadamard transform of x using the Fast Walsh-Hadamard Transform (FWHT) algorithm iirlp2mb — This function does IIR Low Pass Filter to Multiband Filter Transformation. impinvar — This function converts analog filter with coefficients b and a to digital, conserving impulse response. impz — impzlength — Impulse response length interp — function y = interp(x, q, n, Wc) intfilt — invfreq — Calculates inverse frequency vectors invfreqs — Fit filter B(s)/A(s)to the complex frequency response H at frequency points F. A and B are real polynomial coefficients of order nA and nB. invfreqz — Fit filter B(z)/A(z)to the complex frequency response H at frequency points F. A and B are real polynomial coefficients of order nA and nB. invimpinvar — This function converts digital filter with coefficients b and a to analog, conserving impulse response. is2rc — Convert inverse sine parameters to reflection coefficients isallpass — [nargout,nargin]=argn(); isfir — [nargout,nargin]=argn(); islinphase — [nargout,nargin]=argn(); ismaxphase — [nargout,nargin]=argn(); isminphase — [nargout,nargin]=argn(); isstable — SOS matrix corresponds to [bi(1) bi(2) bi(3) ai(1) ai(2) ai(3)]. kaiser — This function returns the filter coefficients of a Kaiser window. kaiserord — Return the parameters needed to produce a filter of the desired specification from a Kaiser window. lar2rc — latc2tf — Convert lattice filter parameters to transfer function coefficients latcfilt — function [f,g,zo]=latcfilt(k,x,v,zi,dim) latcfilt1 — function [f,g,zo]=latcfilt1(k,v,x,zi) levdown — levin — [ar,sigma2,rc]=lev(r) levinson — lpc — Linear prediction filter coefficients lsf2poly — lsf2poly function convert line spectral frequencies to prediction polynomial. mag2db — funcprot(0); marcumq — This function computes the generalized Marcum Q function of order m with noncentrality parameter a and argument b. medfilt1 — 1D median filtering mexihat — meyeraux — midcross — modulate — Modulates signal according to the modulation method morlet — movingrms — mscohere — It estimate (mean square) coherence of signals x and y. musicBase — Implements the core of the MUSIC algorithm ncauer — Analog prototype for Cauer filter. nnls — Non Negative Least Squares (nnls) for Ex=f with the constraint x>=0 nuttallwin — This function returns the filter coefficients of a Blackman-Harris window. parzenwin — This function returns the filter coefficients of a Parzen window. pburg — Calculate Burg maximum-entropy power spectral density. pchip — pchips — peak2peak — funcprot(0); peak2rms — This function calculates the ratio of peak magnitude to the Root Mean Square(RMS) value. pei_tseng_notch — peig — Psuedospectrum using the eigenvector method. periodogram — Return the periodogram (Power Spectral Density) of X phaseInputParseAs_ab — fs=0; phaseInputParseAs_sos — fs=0; phasedelay — cas variable is 2 if sos form is involved and 1 if direct rational form is given phasez — cas variable is 2 if sos form is involved and 1 if direct rational form is given pmusic — Psuedospectrum using MUSIC algorithm poly2ac — Convert prediction polynomial to autocorrelation sequence. poly2lsf — poly2rc — polyscale — errcheck1 polystab — This function stabilizes the polynomial transfer function. polyval — pow2db — rhs = argn(2) primitive — This function calculates the primitive of a given function supplied as input. prony — pulseperiod — pulsesep — pulsewidth — pulstran — This function generates the signal y = sum(func(t+d,...)) for each d. pwelch — Estimate power spectral density of data "x" by the Welch (1967) periodogram/FFT method. pyulear — qp_kaiser — Computes a finite impulse response (FIR) filter for use with a quasi-perfect reconstruction polyphase-network filter bank. rc2ac — rc2is — Convert reflection coefficients to inverse sine parameters rc2lar — rc2poly — rceps — Produce the cepstrum of the signal x, and if desired, the minimum phase reconstruction of the signal x. rcosdesign — RCOSDESIGN computes the raised cosine FIR filter rectpuls — rectwin — This function returns the filter coefficients of a rectangular window. remez1 — Parks-McClellan optimal FIR filter design resample — This function resamples in the input sequence x supplied by a factor of p/q. residued — residuez — risetime — rlevinson — rms — convert i/p values to their ascii values if they are of type char rooteig — Frequencies and power of sinusoids using eigenvector algorithm rootmusic — Frequencies and power of sinusoids using the root MUSIC algorithm rssq — This function calculates the square root of the sum of values of input vector IN. sampled2continuous — This function calculates the output reconstructed from the samples n supplied as input, at a rate of 1/s samples per unit time. sawtooth — schtrig — This function implements a multisignal Schmitt triggers with lev levels supplied as input. schurrc — narginchk(1,1,argn(2)); seqperiod — Calculates the period of a sequence sftrans — Transform band edges of a generic lowpass filter (cutoff at W=1) represented in splane zero-pole-gain form. sgolay — This function computes the filter coefficients for all Savitzsky-Golay smoothing filters. sgolayfilt — shanwavf — shiftdata — Shifts data by rearranging dimensions sigmoid_train — Evaluate a train of sigmoid functions at T. sinetone — Return a sinetone of the input sinewave — Return an M-element vector with I-th element given by 'sin(2* pi *(I+D-1)/N).' slewrate — sos2cell — Converts a second order section matrix to a cell array sos2ss — [nargout,nargin]=argn(); sos2tf — This function converts series second-order sections to direct H(z) = B(z)/A(z) form. sos2zp — This function converts series second-order sections to zeros, poles, and gains (pole residues). sosbreak — function for breaking a polynomial in second order polynomials (and an extra linear) sosfilt — specgram — spectral_adf — Return the spectral density estimator given a vector of autocovariances C, window name WIN, and bandwidth, B. spectral_xdf — Return the spectral density estimator given a data vector X, window name WIN, and bandwidth, B. spencer — Return Spencer's 15 point moving average of each column of X. ss2sos — not taking if, order and scale as input since they do not seem useful statelevels — stft — Compute the short-time Fourier transform of the vector X stmcb — function [b,a] = stmcb( x, u_in, q, p, niter, a_in ) strips — Plots vector or matrix in strips subspaceMethodsInputParser — Input parser to be used by pmusic and peig synthesis — Compute a signal from its short-time Fourier transform tf2sos — This function converts direct-form filter coefficients to series second-order sections. tf2zp — [z,p,k]= tf2zp(b,a); tf2zpk — form tfe — Estimate transfer function of system with input "x" and output "y". Use the Welch (1967) periodogram/FFT method. tfestimate — transpose — funcprot(0); trial_iirlp2mb — B = varargin(1) triang — This function returns the filter coefficients of a triangular window. tripuls — truth — y = %t tukeywin — This function returns the filter coefficients of a Tukey window. udecode — Decodes the input uniformly quantized values uencode — Performs uniform quantization of the input into 2^n levels ultrwin — This function returns the coefficients of an Ultraspherical window. unshiftdata — Inverts the effect of shiftdata unwrap2 — Unwrap radian phases by adding or subtracting multiples of 2*pi. upfirdn — This function upsamples the input data, applies the FIR filter and then downsamples it. upsample — This function upsamples the signal, inserting n-1 zeros between every element. upsamplefill — This function upsamples a vector interleaving given values or copies of the vector elements. var — vco — Voltage Controlled Oscillator wconv — Performs 1D or 2D convolution. welchwin — This function returns the filter coefficients of a Welch window. window — This function creates an m-point window from the function f given as input. wkeep — wrev — xcorr1 — Estimates the cross-correlation. xcorr2 — xcov1 — Compute covariance at various lags [=correlation(x-mean(x),y-mean(y))]. yulewalker — Fit an AR (p)-model with Yule-Walker estimates given a vector C of autocovariances '[gamma_0, ..., gamma_p]'. zerocrossing — This function estimates the points at which a given waveform crosses the x-axis. zp2sos — This function converts filter poles and zeros to second-order sections. zp2ss — Converts zeros / poles to state space. zp2tf — Converts zeros / poles to a transfer function. zplane — funcprot(0); interp
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > interpinterp
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function y = interp(x, q, n, Wc)
Calling Sequence
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y = interp ( x , q )
-y = interp ( x , q , n )
-y = interp ( x , q , n , Wc ) Parameters
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x:
- scalar or vector of complex or real numbers
q:
- positive integer value, or logical
n:
- positive integer, default value 4
Wc:
- non decreasing vector or scalar, starting from 0 uptill 1, default value 0.5
Description
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This is an Octave function.
-This function upsamples the signal x by a factor of q, using an order 2*q*n+1 FIR filter.
-The second argument q must be an integer. The default values of the third and fourth arguments (n, Wc) are 4 and 0.5 respectively.
Examples
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interp ( 1 , 2 )
-ans =
-0.4792743 0.3626016
intfilt
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > intfiltinvfreq
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > invfreqinvfreq
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Calculates inverse frequency vectors
Calling Sequence
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[ B , A ] = invfreq ( H , F , nB , nA )
-[ B , A ] = invfreq ( H , F , nB , nA , W )
-[ B , A ] = invfreq ( H , F , nB , nA , W ,[],[], plane )
-[ B , A ] = invfreq ( H , F , nB , nA , W , iter , tol , plane ) Parameters
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H:
- desired complex frequency response,It is assumed that A and B are real polynomials, hence H is one-sided.
F:
- vector of frequency samples in radians
nA:
- order of denominator polynomial A
nB:
- order of numerator polynomial B
Description
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Fit filter B(z)/A(z) or B(s)/A(s) to complex frequency response at frequency points F. A and B are real polynomial coefficients of order nA and nB respectively. Optionally, the fit-errors can be weighted vs frequency according to the weights W. Also, the transform plane can be specified as either 's' for continuous time or 'z' for discrete time. 'z' is chosen by default. Eventually, Steiglitz-McBride iterations will be specified by iter and tol.
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Examples
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[ B , A ] = butter ( 12 , 1 / 4 ) ;
-[ H , w ] = freqz ( B , A , 128 ) ;
-[ Bh , Ah ] = invfreq ( H , F , 4 , 4 ) ;
-Hh = freqz ( Bh , Ah ) ;
-disp ( sprintf ( ' ||frequency response error|| = %f ' , norm ( H - Hh ) ) ) ;
invfreqs
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > invfreqsinvfreqs
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Fit filter B(s)/A(s)to the complex frequency response H at frequency points F. A and B are real polynomial coefficients of order nA and nB.
Calling Sequence
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[ B , A , C ] = invfreqs ( H , F , nB , nA , W , iter , tol , trace )
-[ B , A , C ] = invfreqs ( H , F , nB , nA , W )
-[ B , A , C ] = invfreqs ( H , F , nB , nA ) Parameters
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H:
- desired complex frequency response.
F:
- frequency (must be same length as H).
nB:
- order of the numerator polynomial B.
nA:
- order of the denominator polynomial A.
W:
- vector of weights (must be same length as F).
Description
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This is an Octave function.
-Fit filter B(s)/A(s)to the complex frequency response H at frequency points F. A and B are real polynomial coefficients of order nA and nB.
-Optionally, the fit-errors can be weighted vs frequency according to the weights W.
-Note: all the guts are in invfreq.m
Examples
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B = [ 1 / 2 1 ] ;
-A = [ 1 1 ] ;
-w = linspace ( 0 , 4 , 128 ) ;
-H = freqs ( B , A , w ) ;
-[ Bh , Ah , C ] = invfreqs ( H , w , 1 , 1 ) ;
-Bh =
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-0.50000 1.00000
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-Ah =
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-1.0000 1.0000
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-C = - 3.0964e-15
invfreqz
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > invfreqzinvfreqz
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Fit filter B(z)/A(z)to the complex frequency response H at frequency points F. A and B are real polynomial coefficients of order nA and nB.
Calling Sequence
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[ B , A , C ] = invfreqz ( H , F , nB , nA , W , iter , tol , trace )
-[ B , A , C ] = invfreqz ( H , F , nB , nA , W )
-[ B , A , C ] = invfreqz ( H , F , nB , nA ) Parameters
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H:
- desired complex frequency response.
F:
- frequency (must be same length as H).
nB:
- order of the numerator polynomial B.
nA:
- order of the denominator polynomial A.
W:
- vector of weights (must be same length as F).
Description
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This is an Octave function.
-Fit filter B(z)/A(z)to the complex frequency response H at frequency points F. A and B are real polynomial coefficients of order nA and nB.
-Optionally, the fit-errors can be weighted vs frequency according to the weights W.
-Note: all the guts are in invfreq.m
Examples
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[ B , A ] = butter ( 4 , 1 / 4 ) ;
-[ H , F ] = freqz ( B , A ) ;
-[ Bh , Ah , C ] = invfreq ( H , F , 4 , 4 )
-Bh =
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-0.010209 0.040838 0.061257 0.040838 0.010209
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-Ah =
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-1.00000 - 1.96843 1.73586 - 0.72447 0.12039
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-C = - 7.7065e-15
invimpinvar
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > invimpinvarinvimpinvar
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This function converts digital filter with coefficients b and a to analog, conserving impulse response.
Calling Sequence
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[ b , a ] = impinvar ( b , a )
-[ b , a ] = impinvar ( b , a , fs )
-[ b , a ] = impinvar ( b , a , fs , tol ) Parameters
-
b:
- real or complex valued scalar or vector
a:
- real or complex valued scalar or vector, order should be greater than b
fs:
- real or complex value, default value 1Hz
tol:
- real or complex value, default value 0.0001
Description
-
This is an Octave function.
-This function converts digital filter with coefficients b and a to analog, conserving impulse response.
-This function does the inverse of impinvar.
Examples
-
b = 0.0081000
-a = [ 2.0000000 , 0.56435378 , 0.4572792 , 0.00705544 , 0.091000 ]
-[ ay , by ] = invimpinvar ( b , a , 10 )
-ay =
-- 1.6940e-16 4.6223e+00 - 4.5210e+00 7.2880e+02
-by =
-Columns 1 through 4 :
-1.0000e+00 3.0900e+01 9.6532e+02 1.2232e+04
-Column 5 :
-1.1038e+05
is2rc
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > is2rcis2rc
-
Convert inverse sine parameters to reflection coefficients
Parameters
-
isin:
- input inverse sine parameters. Needs to be an array real numbers
k:
- output reflection coefficients corresponding to the reflection coefficients in input
Description
-
This function returns a vector of reflection coefficients from a vector of inverse sine parameters
-output array has k(i) = sin(pi/2*isin(i))
-
Example
-k = [0.3090 0.9801 0.0031 0.0082 -0.0082];
-isin = rc2is(k) //Gives inverse sine parameters
-k_dash = is2rc(isin)
-
Bibliography
-
J.R. Deller, J.G. Proakis, J.H.L. Hansen, "Discrete-Time Processing of Speech Signals", Prentice Hall, Section 7.4.5
isallpass
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > isallpassisallpass
-
[nargout,nargin]=argn();
isfir
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > isfirisfir
-
[nargout,nargin]=argn();
islinphase
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > islinphaseislinphase
-
[nargout,nargin]=argn();
ismaxphase
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > ismaxphaseismaxphase
-
[nargout,nargin]=argn();
isminphase
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > isminphaseisminphase
-
[nargout,nargin]=argn();
isstable
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > isstableisstable
-
SOS matrix corresponds to [bi(1) bi(2) bi(3) ai(1) ai(2) ai(3)].
-
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-
-
-
-FOSSEE Signal Processing Toolbox
-
-top
-
-
-TOC
-Table Of Contents
-javax.help.TOCView
-jhelptoc.xml
-
-
-Index
-Index
-javax.help.IndexView
-jhelpidx.xml
-
-
-Search
-Search
-javax.help.SearchView
-JavaHelpSearch
-
-
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diff --git a/help/en_US/scilab_en_US_help/jhelptoc.xml b/help/en_US/scilab_en_US_help/jhelptoc.xml
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-
- kaiser
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > kaiserkaiser
-
This function returns the filter coefficients of a Kaiser window.
Calling Sequence
-
w = kaiser ( m )
-w = kaiser ( m , beta ) Parameters
-
m:
- positive integer value
beta:
- real scalar value
w:
- output variable, vector of real numbers
Description
-
This is an Octave function.
-This function returns the filter coefficients of a Kaiser window of length m supplied as input, to the output vector w.
-The second parameter gives the stop band attenuation of the Fourier transform of the window on derivation.
Examples
-
kaiser ( 6 , 0.2 )
-ans =
-0.9900745
-0.9964211
-0.9996020
-0.9996020
-0.9964211
-0.9900745
kaiserord
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > kaiserordkaiserord
-
Return the parameters needed to produce a filter of the desired specification from a Kaiser window.
Calling Sequence
-
[ n , Wn , beta , ftype ] = kaiserord ( f , m , dev , fs )
-[…] = kaiserord ( f , m , dev , fs )
-[…] = kaiserord ( f , m , dev ) Parameters
-
f:
- Pairs of frequency band edges.
m:
- Magnitude response for each band.
dev:
- Deviation of the filter.
fs:
- Sampling rate.
Description
-
This is an Octave function.
-The vector f contains pairs of frequency band edges in the range [0,1]. The vector m specifies the magnitude response for each band. The values of m must be zero for all stop bands and must have the
-same magnitude for all pass bands. The deviation of the filter dev can be specified as a scalar or a vector of the same length as m. The optional sampling rate fs can be used to indicate that f is in
-Hz in the range [0,fs/2].
-
The returned value n is the required order of the filter (the length of the filter minus 1). The vector Wn contains the band edges of the filter suitable for passing to fir1. The value beta is the
-parameter of the Kaiser window of length n+1 to shape the filter. The string ftype contains the type of filter to specify to fir1.
-
The Kaiser window parameters n and beta are computed from the relation between ripple (A=-20*log10(dev)) and transition width (dw in radians) discovered empirically by Kaiser:
-
-
/ 0.1102(A-8.7) A > 50
-beta = | 0.5842(A-21)^0.4 + 0.07886(A-21) 21 <= A <= 50
-\ 0.0 A < 21
-
n = (A-8)/(2.285 dw)
Examples
-
[ n , w , beta , ftype ] = kaiserord ( [ 1000 , 1200 ] , [ 1 , 0 ] , [ 0.05 , 0.05 ] , 11025 )
-n = 1
-w = 1100
-beta = 1.5099
-ftype = low
lar2rc
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > lar2rcParameters
-
g:
- define log area ratios.
k:
- returns the reflection coefficients.
Examples
-
X = [ 7 6 5 8 3 6 8 7 5 2 4 7 4 3 2 5 4 9 5 3 5 7 3 9 4 1 2 0 5 4 8 6 4 6 5 3 ] ;
-k = lar2rc ( X )
-or t=[2 5 6 ; 8 6 5 ; 8 9 4 ]
-k = lar2rc ( t )
latc2tf
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > latc2tflatc2tf
-
Convert lattice filter parameters to transfer function coefficients
Description
-
[num,den] = latc2tf(k,v)
-Finds the transfer function of the IIR filter from the lattice
-coefficients k and ladder coefficients v.
-[num,den] = latc2tf(k,'iiroption')
-Finds the transfer function of the allpass or allpole (specified by
-the iiroption flag) IIR filter.
-num = latc2tf(k,'firoption')
-Finds the transfer function of the FIR filter from the lattice
-coefficients k. The firoption flag specifies the type of the FIR
-filter (can be 'min, 'max', or 'FIR')
-
Parameters:
-k - double - vector
-Lattice coefficients
-Lattice coefficients for FIR/IIR filter. Can be real or complex.
-v - double - vector
-Ladder coefficients
-Ladder coefficients for IIR filters. Can be real or complex.
-iiroption - string flag - 'allpole', or 'allpass'
-Specification of the type if IIR filter
-firoption - string flag - 'min', 'max', or 'FIR' (default)
-Speficication of the type of FIR filter
-
Examples
-
1 ) FIR filter
-k1 = [ 1 / 2 1 / 2 1 / 4 ] ;
-[ num1 , den1 ] = latc2tf ( k1 ) ;
See also
-
| tf2latc References J.G. Proakis, D.G. Manolakis, Digital Signal Processing, ed., Prentice Hall, N.J., 1996, Chapter 7. S. K. Mitra, Digital Signal Processing, A Computer Approach, McGraw-Hill, N.Y., 1998, Chapter 6. latcfilt
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > latcfiltlatcfilt
-
function [f,g,zo]=latcfilt(k,x,v,zi,dim)
latcfilt1
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > latcfilt1latcfilt1
-
function [f,g,zo]=latcfilt1(k,v,x,zi)
levdown
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > levdownlevin
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > levinlevin
-
[ar,sigma2,rc]=lev(r)
levinson
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > levinsonlpc
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > lpclpc
-
Linear prediction filter coefficients
Calling Sequence
-
[ a , g ] = lpc ( x )
-[ a , g ] = lpc ( x , p ) Description
-
[a,g] = lpc(x,p)
-Determines the coefficients of a pth order forward linear predictor
-filter by minimizing the squared error. If p is unspecified, a
-default value of length(x)-1 is used.
-
Parameters
-
x:
- double
p:
- int, natural number, scalar
a:
- double
g:
- double
Examples
-
1 )
-noise = randn ( 20000 , 1 ) ;
-x = filter ( 1 , [ 1 1 / 5 1 / 3 1 / 4 ] , noise ) ;
-x = x ( 15904 : 20000 ) ;
-[ a , g ] = lpc ( x , 3 ) ;
-
-References
-[ 1 ] Hayes , Monson H . Statistical digital signal processing and modeling.
-John Wiley & Sons , 2009 , pg . 220
lsf2poly
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > lsf2polylsf2poly
-
lsf2poly function convert line spectral frequencies to prediction polynomial.
mag2db
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > mag2dbmarcumq
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > marcumqmarcumq
-
This function computes the generalized Marcum Q function of order m with noncentrality parameter a and argument b.
Calling Sequence
-
q = marcumq ( a , b )
-q = marcumq ( a , b , m )
-q = marcumq ( a , b , m , tol ) Parameters
-
a:
-
b:
-
m:
- default value 1
tol:
- default value eps
Description
-
This is an Octave function.
-This function computes the generalized Marcum Q function of order m with noncentrality parameter a and argument b.
-The third argument m is the order, which by default is 1.
-The fourth argument tol is the tolerance, which by default is eps.
-If input arguments are vectors which correspond in size and degree, the output is a table of values.
-This function calculates Marcum’s Q function using the infinite Bessel series, which is truncated when the relative error is less than the specified tolerance.
Examples
-
marcumq ( [ 1 , 2 , 3 ] , 4 )
-ans =
-0.0028895 0.0341348 0.1965122
medfilt1
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > medfilt1medfilt1
-
1D median filtering
Description
-
y = medfilt1(x)
-Applies a 3rd order 1-dimensional median filter to input x along the
-first non-zero dimension. The function appropriately pads the signal
-with zeros at the endings. For a segment, a median is calculated as
-the middle value (average of two middle values) for odd number
-number (even number) of data points.
-y = medfilt1(x,n)
-Applies a nth order 1-dimensional median filter.
-y = medfilt1(x,n,dim)
-Applies the median filter along the n-th dimension
-y = medfilt1(__, nanflag, padding)
-nanflag specifies how NaN values are treated. padding specifies the
-type of filtering to be performed at the signal edges.
-
Parameters
-
x:
- int | double
n:
- positive integer scalar
dim:
- positive integer scalar
nanflag:
- 'includenan' (default) | 'omitnan'
* includenan:
- Filtering such that the median of any segment
* omitnan:
- Filtering with NaNs omitted in each segment. If a segment
y:
- int | double
Examples
-
1 ) Noise supression using median filtering
-fs = 1e3 ;
-t = 1 : 1 / fs : 1 ;
-s = sin ( 2 * %pi * 2 * t ) + cos ( 2 * %pi * 5 * t ) ;
-
-x = s + 0.1 * randn ( size ( s ) ) ;
-y = medfilt1 ( x ) ;
mexihat
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > mexihatParameters
-
lb:
- Real or complex valued vector or matrix
ub:
- Real or complex valued vector or matrix
n:
- Real strictly positive scalar number
Description
-
This is an Octave function
-This function returns values of the Mexican hat wavelet in the specified interval at all the sample points.
Examples
-
1. [ a , b ] = mexihat ( 1 , 2 , 3 )
-a = [ 0.00000 - 0.35197 - 0.35214 ]
-b = [ 1.0000 1.5000 2.0000 ]
-2. [ a , b ] = mexihat ( [ 1 2 3 ] , 1 , 1 )
-a = [ 0 ; 0 ; 0 ]
-b = [ 1 ; 1 ; 1 ]
meyeraux
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > meyerauxParameters
-
x:
- Real or complex valued vector or matrix
Description
-
This is an Octave function.
-This function returns values of the auxiliary function used for Meyer wavelet generation.
Examples
-
1. meyeraux ( [ 1 2 3 ] )
-ans = [ 1 - 208 - 10287 ]
-2. meyeraux ( [ 1 2 3 ; 4 5 6 ] )
-ans = [ 1 - 208 - 10287 ; - 118016 - 709375 - 2940624 ]
midcross
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > midcrossmodulate
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > modulatemodulate
-
Modulates signal according to the modulation method
Calling Sequence
-
y = modulate ( x , fc , fs , method , opt )
-[ y , t ]= modulate ( x , fc , fs , method , opt ) Description
-
[y,t]=modulate(x,fc,fs,method,opt)
-Returns the modulated vector y and the time vector 't'
-Modulation is performed according to the following table
-METHOD MODULATION SCHEME
-'am', Amplitude modulation, double side-band, suppressed carrier
-'amdsb-sc' opt not used. This is the default method.
-'amdsb-tc' Amplitude modulation, double side-band, transmitted carrier
-opt is a scalar subtracted from x before multiplying x
-with the carrier wave. It defaults to min(min(x)) so that
-the input signal after offset is always non-negative
-'amssb' Amplitude modulation, single side-band
-OPT not used.
-'fm' Frequency modulation
-opt is the constant of frequency modulation.
-opt = (fc/fs)*2*pi/max(max(abs(x))) by default
-'pm' Phase modulation
-OPT is the constant of phase modulation.
-opt = pi/max(max(abs(x))) by default
-Phase lies between -pi and +pi
-'pwm' Pulse width modulation
-opt='left' corresponds to left justified pulses.
-opt='centered' correspondes to centered pulses.
-The default value of opt is 'left'.
-'ppm' Pulse position modulation
-opt is a scalar between 0 and 1 which specifies the pulse
-width in fractions of the carrier period with default value 0.1.
-'qam' Quadrature amplitude modulation
-opt is a matrix of the same size as X which is modulated in
-quadrature with x.
-
If x is a matrix, its columns are modulated.
-Example
-y =
-
1. 1. 0. 0.
-Author
-Ankur Mallick
morlet
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > morletParameters
-
lb:
- Real or complex valued vector or matrix
ub:
- Real or complex valued vector or matrix
n:
- Real strictly positive scalar number
Description
-
This is an Octave function
-This function returns values of the Morlet wavelet in the specified interval for all the sample points.
Examples
-
1. [ a , b ] = morlet ( 1 , 2 , 3 )
-a = [ 0.17205 0.11254 - 0.11356 ]
-b = [ 1.0000 1.5000 2.0000 ]
-2. [ a , b ] = morlet ( [ 1 2 3 ] , [ 1 2 3 ] , 1 )
-a = [ 0.1720498 ; - 0.1135560 ; - 0.0084394 ]
-b = [ 1 ; 2 ; 3 ]
movingrms
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > movingrmsCalling Sequence
-
[ rmsx , w ]= movingrms ( x , w , rc , Fs =1) Parameters
-
x:
- Real or complex valued vector or matrix
w:
- Real or complex scalar value
rc:
- Real or complex scalar value
Fs:
- Real or complex scalar value
Description
-
This is an Octave function.
-The signal is convoluted against a sigmoid window of width w and risetime rc with the units of these parameters relative to the value of the sampling frequency given in Fs (Default value=1).
Examples
-
1. [ a , b ] = movingrms ( [ 4.4 94 1 ; - 2 5 i 5 ] , 1 , - 2 )
-a = 0.91237 17.71929 0.96254
-0.91237 17.71929 0.96254
-b = 0.18877
-0.18877
-2. [ a , b ] = movingrms ( [ 4.4 94 1 ; - 2 5 i 5 ] , 1 , - 2 , 2 )
-a = 4.8332 93.8669 5.0990
-4.8332 93.8669 5.0990
-b = 1
-1
mscohere
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > mscoheremscohere
-
It estimate (mean square) coherence of signals x and y.
Calling Sequence
-
[ Pxx , freq ] = mscohere ( x , y )
-[ Pxx , freq ] = mscohere ( x , y , window )
-[ Pxx , freq ] = mscohere ( x , y , window , overlap )
-[ Pxx , freq ] = mscohere ( x , y , window , overlap , Nfft )
-[ Pxx , freq ] = mscohere ( x , y , window , overlap , Nfft , Fs )
-[ Pxx , freq ] = mscohere ( x , y , window , overlap , Nfft , Fs , range )
-mscohere (...) Description
-
This function estimate (mean square) coherence of signals x and y.
Examples
-
[ Pxx , freq ] = mscohere ( 4 , 5 )
-ans =
-PXX =
-Nan
-1
-FREQ =
-0
-0.5
musicBase
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > musicBasemusicBase
-
Implements the core of the MUSIC algorithm
ncauer
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > ncauerncauer
-
Analog prototype for Cauer filter.
Calling Sequence
-
[ Zz , Zp , Zg ] = ncauer ( Rp , Rs , n )
-[ Zz , Zp ] = ncauer ( Rp , Rs , n )
-Zz = ncauer ( Rp , Rs , n ) Parameters
-
n:
- Filter Order
Rp:
- Peak-to-peak passband ripple
Rs:
- Stopband attenuation
Description
-
This is an Octave function.
-It designs an analog prototype for Cauer filter of nth order, with a Peak-to-peak passband ripple of Rp and a stopband attenuation of Rs.
Examples
-
n = 5 ;
-Rp = 5 ;
-Rs = 5 ;
-[ Zz , Zp , Zg ] = ncauer ( Rp , Rs , n )
-Zz =
-
-0.0000 + 2.5546 i 0.0000 + 1.6835 i - 0.0000 - 2.5546 i - 0.0000 - 1.6835 i
-
-Zp =
-
-- 0.10199 + 0.64039 i - 0.03168 + 0.96777 i - 0.10199 - 0.64039 i - 0.03168 - 0.96777 i - 0.14368 + 0.00000 i
-
-Zg = 0.0030628
nnls
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > nnlsnnls
-
Non Negative Least Squares (nnls) for Ex=f with the constraint x>=0
nuttallwin
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > nuttallwinnuttallwin
-
This function returns the filter coefficients of a Blackman-Harris window.
Calling Sequence
-
w = nuttallwin ( m )
-w = nuttallwin ( m , opt ) Parameters
-
m:
- positive integer value
opt:
- string value, takes in "periodic" or "symmetric"
w:
- output variable, vector of real numbers
Description
-
This is an Octave function.
-This function returns the filter coefficients of a Blackman-Harris window defined by Nuttall of length m supplied as input, to the output vector w.
-The second parameter can take the values "periodic" or "symmetric", depending on which the corresponding form of window is returned. The default is symmetric.
Examples
-
nuttallwin ( 2 , " periodic " )
-ans =
-- 2.429D-17
-1.
parzenwin
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > parzenwinparzenwin
-
This function returns the filter coefficients of a Parzen window.
Parameters
-
m:
- positive integer value
y:
- output variable, vector of real numbers
Description
-
This is an Octave function.
-This function returns the filter coefficients of a Parzen window of length m supplied as input, to the output vector y.
Examples
-
parzenwin ( 3 )
-ans =
-0.0740741
-1.
-0.0740741
pburg
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > pburgpburg
-
Calculate Burg maximum-entropy power spectral density.
Calling Sequence
-
[ psd , f_out ] = pburg ( x , poles , freq , Fs , range , method , plot_type , criterion )
-All but the first two arguments are optional and may be empty . Parameters
-
x:
- [vector] sampled data
poles:
- [integer scalar] required number of poles of the AR model
freq:
- [real vector] frequencies at which power spectral density is calculated [integer scalar] number of uniformly distributed frequency values at which spectral density is calculated. [default=256]
Fs:
- [real scalar] sampling frequency (Hertz) [default=1]
range:
- 'half', 'onesided' : frequency range of the spectrum is from zero up to but not including sample_f/2. Power from negative frequencies is added to the positive side of the spectrum. 'whole', 'twosided' : frequency range of the spectrum is -sample_f/2 to sample_f/2, with negative frequencies stored in "wrap around" order after the positive frequencies; e.g. frequencies for a 10-point 'twosided' spectrum are 0 0.1 0.2 0.3 0.4 0.5 -0.4 -0.3 -0.2 -0.1 'shift', 'centerdc' : same as 'whole' but with the first half of the spectrum swapped with second half to put the zero-frequency value in the middle. (See "help fftshift". If "freq" is vector, 'shift' is ignored. If model coefficients "ar_coeffs" are real, the default range is 'half', otherwise default range is 'whole'.
method:
- 'fft': use FFT to calculate power spectral density. 'poly': calculate spectral density as a polynomial of 1/z N.B. this argument is ignored if the "freq" argument is a vector. The default is 'poly' unless the "freq" argument is an integer power of 2.
plot_type:
- 'plot', 'semilogx', 'semilogy', 'loglog', 'squared' or 'db' specifies the type of plot. The default is 'plot', which means linear-linear axes. 'squared' is the same as 'plot'. 'dB' plots "10*log10(psd)". This argument is ignored and a spectrum is not plotted if the caller requires a returned value.
criterion:
- [optional string arg] model-selection criterion. Limits the number of poles so that spurious poles are not added when the whitened data has no more information in it (see Kay & Marple, 1981). Recognized values are 'AKICc' -- approximate corrected Kullback information criterion (recommended), 'KIC' -- Kullback information criterion 'AICc' -- corrected Akaike information criterion 'AIC' -- Akaike information criterion 'FPE' -- final prediction error" criterion The default is to NOT use a model-selection criterion.
Description
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This function is being called from Octave
-This function is a wrapper for arburg and ar_psd.
-The functions "arburg" and "ar_psd" do all the work.
-See "help arburg" and "help ar_psd" for further details.
Examples
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a = [ 1.0 - 1.6216505 1.1102795 - 0.4621741 0.2075552 - 0.018756746 ] ;
-[ psd , f_out ] = pburg ( a , 2 ) ;
pchip
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > pchippchips
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > pchipspeak2peak
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > peak2peakpeak2rms
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > peak2rmspeak2rms
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This function calculates the ratio of peak magnitude to the Root Mean Square(RMS) value.
Calling Sequence
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OUT = peak2rms ( IN )
-OUT = peak2rms ( IN , orientation ) Parameters
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in:
- Vector or Matrix of real or complex elements.
orientation:
- A string with possible values "r", "c" or "m" giving the dimension along which the peak2rms value is to be calculated.
out:
- A scalar with real value when input is a vector.When input is a matrix, out is the peak magnitude to RMS value along the orientation specified or the default one when not specified.
Description
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For vector as input, the output is the ratio of peak value to the RMS value. The RMS value can be calculated by taking the square root of mean value of the squared sum of the elements.
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When a matrix is given as input the output is peak to RMS ratio in the orientation specified.
-The orientation can be given as string with values "r","c" or "m".
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peak2rms(in, 1) calculates the values of ratio of peak to RMS of columns of matrix. The output in this case is a row vector with peak2rms value of each column of in.
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peak2rms(in, 2) calculates the values of ratio of peak to RMS of rows of matrix, where the output would be a column vector having peak2rms value of each row of in.
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The default orientation is chosen to be the index of first dimension of input greater than 1.Hence peak2rms(in) is equivalent to peak2rms(in, "m").
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For an N dimensional array the orientation is the index of first non singleton dimension of the array.
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If the elements of matrix are complex the absolute values are considered in the calculation of RMS value.
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Examples
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To calculate peak2rms of a vector :
-IN = [ 6 19 10 25 ]
-OUT = peak2rms ( IN )
-The output is OUT =
-1.4638501
-1.3887301
-1.119186
Examples
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To calculate peak2rms of rows of matrix :
-IN = [ 1 3 5 ; 2 4 6 ; 7 8 9 ]
-OUT = peak2rms ( IN , 2 )
-The output is
-OUT = 1.3719887
Examples
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To calculate peak magnitude to RMS value of sinusoid :
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-t = 0 : 0.6 : 9
-IN = cos ( 6 * %pi * t ) ;
-OUT = peak2rms ( IN )
-The output is
-OUT = 1.3719887
Bibliography
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Matlab help document.
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Modified to accept char i/p
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MOdified function to match MATLAB input arguments
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Now for calculating the values of ratio of peak to RMS of columns of matrix use peak2rms(in,1)
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And for calculates the values of ratio of peak to RMS of rows of matrix. use peak2rms(in,2)
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Updated help comments accordingly
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MOdifications done by by Debdeep Dey
pei_tseng_notch
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > pei_tseng_notchCalling Sequence
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[ b , a ] = pei_tseng_notch ( frequencies , bandwidths )
-b = pei_tseng_notch ( frequencies , bandwidths ) Parameters
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frequencies:
- filter frequencies
bandwidths:
- bandwidths to be used with filter
Description
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This is an Octave function.
-It return coefficients for an IIR notch-filter with one or more filter frequencies and according bandwidths. The filter is based on a all pass filter that performs phasereversal at filter frequencies.
-This leads to removal of those frequencies of the original and phase-distorted signal.
Examples
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sf = 800 ; sf2 = sf / 2 ;
-data = [ [ 1 ; zeros ( sf - 1 , 1 ) ] , sinetone ( 49 , sf , 1 , 1 ) , sinetone ( 50 , sf , 1 , 1 ) , sinetone ( 51 , sf , 1 , 1 ) ] ;
-[ b , a ] = pei_tseng_notch ( 50 / sf2 , 2 / sf2 )
-b =
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-0.99213 - 1.83322 0.99213
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-a =
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-1.00000 - 1.83322 0.98426
peig
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > peigpeig
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Psuedospectrum using the eigenvector method.
Calling Sequence
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[ S , w ] = peig ( x , p )
-[ S , w ] = peig ( x , p , w )
-[ S , w ] = peig ( x , p , nfft )
-[ S , w ] = peig ( x , p , nfft , fs )
-[ S , w ] = peig ( x , p , f , fs )
-[ S , f ] = peig (..., 'corr ')
-[ S , f ] = peig ( x , p , nfft , fs , nwin , noverlap )
-[...] = peig (..., freqrange )
-[..., v , e ] = peig (...)
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-Parameters :
-x - int | double - vector | matrix
-Input signal . In case of a matrix , each row of x represents a
-seperate observation of the signal . If 'corr ' flag is specified ,
-then x is the correlation matrix .
-If w is not specified in the input , it is determined by the
-algorithm . If x is real valued , then range of w is [0, pi ].
-Otherwise , the range of w is [0, 2 pi )
-p - int | double - scalar | vector
-p (1) is the dimension of the signal subspace
-p (2), if specified , represents a threshold that is multiplied by
-the smallest estimated eigenvalue of the signal 's correlation matrix .
-w - int | double - vector
-w is the vector of normalized frequencies over which the
-pseuspectrogram is to be computed .
-nfft - int - scalar ( Default = 256)
-Length of the fft used to compute pseudospectrum . The length of S
-( and hence w / f ) depends on the type of values in x and nfft .
-If x is real , length of s is ( nfft / 2 + 1) { Range of w = [0, pi ]} if
-nfft is even and ( nfft +1) / 2 { Range of w = [0, pi )} otherwise .
-If x is complex , length of s is nfft .
-fs - int | double - scalar ( Default = 1)
-Sampling rate . Used to convert the normalized frequencies ( w ) to
-actual values ( f ) and vice - versa .
-nwin - int | double - scalar ( int only )| vector ( Default = 2* p (1))
-If nwin is scalar , it is the length of the rectangular window .
-Otherwise , the vector input is considered as the window coefficients .
-Not used if 'corr ' flag present .
-If x is a vector , windowing not done in nwin in scalar . If x is a
-matrix ,
-noverlap - int - scalar ( Default = nwin -1)
-number of points by which successive windows overlap . noverlap not
-used if x is a matrix
-freqrange - string
-The range of frequencies over which the pseudospetrogram is
-computed . Three possible values - 'onesided ', 'twosided ', 'centered '
-'corr ' flag
-Presence indicates that the primary input x is actually a
-correlation matrix
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-Examples :
-TODO : Authors
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Ayush Baid
- References
- [1] Petre Stoica and Randolph Moses, Introduction To Spectral
- Analysis, Prentice-Hall, 1997, pg. 15
- [2] S. J. Orfanidis, Optimum Signal Processing. An Introduction.
- 2nd Ed., Macmillan, 1988. periodogram
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > periodogramperiodogram
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Return the periodogram (Power Spectral Density) of X
Calling Sequence
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[ PXX , W ] = periodogram ( X )
-[ PXX , W ] = periodogram ( X , WIN )
-[ PXX , W ] = periodogram ( X , WIN , NFFT )
-[ PXX , W ] = periodogram ( X , WIN , NFFT , FS )
-[ PXX , W ] = periodogram (..., "RANGE ") Parameters
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X:
- data vector. If X is real-valued a one-sided spectrum is estimated. If X is complex-valued, or "RANGE" specifies "twosided", the full spectrum is estimated.
WIN:
- window weight data. If window is empty or unspecified a default rectangular window is used. Otherwise, the window is applied to the signal ('X .* WIN') before computing th periodogram. The window data must be a vector of the same length as X.
NFFT:
- number of frequency bins. The default is 256 or the next higher power of 2 greater than the length of X ('max (256,2.^nextpow2 (length (x)))'). If NFFT is greater than the length of the input then X will be zero-padded to the length of NFFT.
FS:
- sampling rate. The default is 1.
RANGE:
- range of spectrum. "onesided" computes spectrum from [0..nfft/2+1]."twosided" computes spectrum from [0..nfft-1].
Description
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The optional second output W are the normalized angular frequencies. For a one-sided calculation W is in the range [0, pi]. If NFFT is even and [0, pi) if NFFT is odd. Similarly, for a two-sided calculation W is in the range [0, 2*pi] or [0, 2*pi)depending on NFFT.
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If a sampling frequency is specified, FS, then the output frequencies F will be in the range [0, FS/2] or [0, FS/2) for one-sided calculations. For two-sided calculations the range will be [0, FS).
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When called with no outputs the periodogram is immediately plotted in the current figure window.
phaseInputParseAs_ab
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > phaseInputParseAs_abphaseInputParseAs_ab
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fs=0;
phaseInputParseAs_sos
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > phaseInputParseAs_sosphaseInputParseAs_sos
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fs=0;
phasedelay
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > phasedelayphasedelay
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cas variable is 2 if sos form is involved and 1 if direct rational form is given
phasez
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > phasezphasez
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cas variable is 2 if sos form is involved and 1 if direct rational form is given
pmusic
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > pmusicpmusic
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Psuedospectrum using MUSIC algorithm
Calling Sequence
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[ S , w ] = pmusic ( x , p )
-[ S , w ] = pmusic ( x , p , w )
-[ S , w ] = pmusic ( x , p , nfft )
-[ S , w ] = pmusic ( x , p , nfft , fs )
-[ S , w ] = pmusic ( x , p , f , fs )
-[ S , f ] = pmusic (..., 'corr ')
-[ S , f ] = pmusic ( x , p , nfft , fs , nwin , noverlap )
-[...] = pmusic (..., freqrange )
-[..., v , e ] = pmusic (...)
-
-Parameters :
-x - int | double - vector | matrix
-Input signal . In case of a matrix , each row of x represents a
-seperate observation of the signal . If 'corr ' flag is specified ,
-then x is the correlation matrix .
-If w is not specified in the input , it is determined by the
-algorithm . If x is real valued , then range of w is [0, pi ].
-Otherwise , the range of w is [0, 2 pi )
-p - int | double - scalar | vector
-p (1) is the dimension of the signal subspace
-p (2), if specified , represents a threshold that is multiplied by
-the smallest estimated eigenvalue of the signal 's correlation matrix .
-w - int | double - vector
-w is the vector of normalized frequencies over which the
-pseuspectrogram is to be computed .
-nfft - int - scalar ( Default = 256)
-Length of the fft used to compute pseudospectrum . The length of S
-( and hence w / f ) depends on the type of values in x and nfft .
-If x is real , length of s is ( nfft / 2 + 1) { Range of w = [0, pi ]} if
-nfft is even and ( nfft +1) / 2 { Range of w = [0, pi )} otherwise .
-If x is complex , length of s is nfft .
-fs - int | double - scalar ( Default = 1)
-Sampling rate . Used to convert the normalized frequencies ( w ) to
-actual values ( f ) and vice - versa .
-nwin - int | double - scalar ( int only )| vector ( Default = 2* p (1))
-If nwin is scalar , it is the length of the rectangular window .
-Otherwise , the vector input is considered as the window coefficients .
-Not used if 'corr ' flag present .
-If x is a vector , windowing not done in nwin in scalar . If x is a
-matrix ,
-noverlap - int - scalar ( Default = nwin -1)
-number of points by which successive windows overlap . noverlap not
-used if x is a matrix
-freqrange - string
-The range of frequencies over which the pseudospetrogram is
-computed . Three possible values - 'onesided ', 'twosided ', 'centered '
-'corr ' flag
-Presence indicates that the primary input x is actually a
-correlation matrix
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-Examples :
-TODO : Authors
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Ayush Baid
- References
- [1] Petre Stoica and Randolph Moses, Introduction To Spectral
- Analysis, Prentice-Hall, 1997, pg. 15
- [2] S. J. Orfanidis, Optimum Signal Processing. An Introduction.
- 2nd Ed., Macmillan, 1988. poly2ac
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > poly2acpoly2ac
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Convert prediction polynomial to autocorrelation sequence.
Parameters
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a:
- input prediction polynomial with 1st element 1 (if not, poly2ac normalizes it to 1 before proceeding).
efinal:
- input prediction error
r:
- output autocorrelation sequence
Description
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This function obtains the underlying autocorrelation sequence that would best fit a linear prediction filter described by the
-denominator polynomial and the numerator scaling. The filter is H(z) = efinal/(a(1) + a(2) x z a(3) x z^2 ... a(n) x z^n-1)
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Examples
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a = [ 1.0000 0.4288 0.76 0.0404 - 0.02 ] ;
-efinal = 0.2 ;
-r = poly2ac ( a , efinal )
Bibliography
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S. Kay, Modern Spectral Estimation, Prentice Hall, N.J., 1987, Chapter 6.
poly2lsf
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > poly2lsfpoly2rc
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > poly2rcCalling Sequence
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kr = poly2rc ( a )
-[ kr , R0 ] = rc2poly ( a , efinal ) polyscale
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > polyscalepolystab
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > polystabpolystab
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This function stabilizes the polynomial transfer function.
Description
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This is an Octave function.
-This function stabilizes the polynomial transfer function by replacing all roots outside the unit circle with their reflection inside the unit circle.
Examples
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polystab ( [ 1 , 3 , 5 ] )
-ans =
-1. 0.6 0.2
polyval
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > polyvalpow2db
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > pow2dbprimitive
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > primitiveprimitive
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This function calculates the primitive of a given function supplied as input.
Calling Sequence
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y = primitive ( f , t )
-y = primitive ( f , t , x ) Description
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This is an Octave function.
-This function calculates the primitive of a given function supplied as input.
-The second parameter t is a vector at which the output is evaluated (at the points t). This vector should be ascending and ordered.
-The function approximates the primitive (indefinite integral) of the univariate function handle f with constant of integration x.
Examples
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primitive ( [ 1 , 4 , 5 ] , 3 , 9 )
-ans =
-9.
prony
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > pronypulseperiod
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > pulseperiodCalling Sequence
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p = pulseperiod ( x )
-p = pulseperiod ( x , Fs )
-p = pulseperiod ( x , t )
-p = pulseperiod ( x , t , 'Polarity ', pol )
-p = pulseperiod ( x , t , 'MidPercentReferenceLevel ', N )
-p = pulseperiod ( x , t , 'Tolerance ', M )
-p = pulseperiod ( x , t , 'StateLevels ', O ) pulsesep
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > pulsesepCalling Sequence
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s = pulsesep ( x )
-s = pulsesep ( x , Fs )
-s = pulsesep ( x , t )
-s = pulsesep ( x , t , 'Polarity ', pol )
-s = pulsesep ( x , t , 'MidPercentReferenceLevel ', N )
-s = pulsesep ( x , t , 'Tolerance ', M )
-s = pulsesep ( x , t , 'StateLevels ', [ O 1]) pulsewidth
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > pulsewidthCalling Sequence
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w = pulsewidth ( x )
-w = pulsewidth ( x , Fs )
-w = pulsewidth ( x , t )
-w = pulsewidth ( x , t , 'Polarity ', pol )
-w = pulsewidth ( x , t , 'MidPercentReferenceLevel ', N )
-w = pulsewidth ( x , t , 'Tolerance ', M )
-w = pulsewidth ( x , t , 'StateLevels ', O ) pulstran
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > pulstranpulstran
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This function generates the signal y = sum(func(t+d,...)) for each d.
Calling Sequence
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y = pulstran ( t , d , func , ...)
-y = pulstran ( t , d , p )
-y = pulstran ( t , d , p , fs )
-y = pulstran ( t , d , p , Ffs , meth ) Parameters
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t:
-
d:
- vector or matrix
p:
-
fs:
- default value 1Hz
func:
- function which accepts vector (of times)
Description
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This is an Octave function.
-This function generates the signal y = sum(func(t+d,...)) for each d. If d is a matrix of two columns, the first column is the delay d and the second column is the amplitude a, and y = sum(a*func(t+d)) for each d, a. Here, func is a function which accepts a vector of times.
-If a pulse shape sampled at frequency Fs (default 1 Hz) is supplied instead of a function name, an interpolated version of the pulse is added at each delay d.
Examples
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pulstran ( [ 0.5 , 9 , 8 , 7 ] , [ 4 , 6 ] , [ - 7 , 0.5 ] )
-ans =
-0. 0. 0. 0.5
pwelch
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > pwelchpwelch
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Estimate power spectral density of data "x" by the Welch (1967) periodogram/FFT method.
Calling Sequence
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[ spectra , freq ] = pwelch ( x , window , overlap , Nfft , Fs , range , plot_type , detrend , sloppy )
-[ spectra , freq ] = pwelch ( x , y , window , overlap , Nfft , Fs , range , plot_type , detrend , sloppy , results )
-[ spectra , Pxx_ci , freq ] = pwelch ( x , window , overlap , Nfft , Fs , conf , range , plot_type , detrend , sloppy )
-[ spectra , Pxx_ci , freq ] = pwelch ( x , y , window , overlap , Nfft , Fs , conf , range , plot_type , detrend , sloppy , results ) Parameters
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x:
- [non-empty vector] system-input time-series data
y:
- [non-empty vector] system-output time-series data
window:
- [real vector] of window-function values between 0 and 1; the data segment has the same length as the window. Default window shape is Hamming. [integer scalar] length of each data segment. The default value is window=sqrt(length(x)) rounded up to the nearest integer power of 2; see 'sloppy' argument.
overlap:
- [real scalar] segment overlap expressed as a multiple of window or segment length. 0 <= overlap < 1, The default is overlap=0.5 .
Nfft:
- [integer scalar] Length of FFT. The default is the length of the "window" vector or has the same value as the scalar "window" argument. If Nfft is larger than the segment length, "seg_len", the data segment is padded with "Nfft-seg_len" zeros. The default is no padding. Nfft values smaller than the length of the data segment (or window) are ignored silently.
Fs:
- [real scalar] sampling frequency (Hertz); default=1.0
conf:
- [real scalar] confidence level between 0 and 1. Confidence intervals of the spectral density are estimated from scatter in the periodograms and are returned as Pxx_ci. Pxx_ci(:,1) is the lower bound of the confidence interval and Pxx_ci(:,2) is the upper bound. If there are three return values, or conf is an empty matrix, confidence intervals are calculated for conf=0.95 . If conf is zero or is not given, confidence intervals are not calculated. Confidence intervals can be obtained only for the power spectral density of x; nothing else.
range:
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'half', 'onesided' :
- frequency range of the spectrum is zero up to but not including Fs/2. Power from negative frequencies is added to the positive side of the spectrum, but not at zero or Nyquist (Fs/2) frequencies. This keeps power equal in time and spectral domains. See reference [2].
'whole', 'twosided' :
- frequency range of the spectrum is -Fs/2 to Fs/2, with negative frequencies stored in "wrap around" order after the positive frequencies; e.g. frequencies for a 10-point 'twosided' spectrum are 0 0.1 0.2 0.3 0.4 0.5 -0.4 -0.3 -0.2 -0.1
'shift', 'centerdc' :
- same as 'whole' but with the first half of the spectrum swapped with second half to put the zero-frequency value in the middle. (See "help fftshift". If data (x and y) are real, the default range is 'half', otherwise default range is 'whole'.
plot_type:
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'plot', 'semilogx', 'semilogy', 'loglog', 'squared' or 'db':
- specifies the type of plot. The default is 'plot', which means linear-linear axes. 'squared' is the same as 'plot'. 'dB' plots "10*log10(psd)". This argument is ignored and a spectrum is not plotted if the caller requires a returned value.
detrend:
- 'no-strip', 'none' -- do NOT remove mean value from the data 'short', 'mean' -- remove the mean value of each segment from each segment of the data.
sloppy:
- FFT length is rounded up to the nearest integer power of 2 by zero padding. FFT length is adjusted after addition of padding by explicit Nfft argument. The default is to use exactly the FFT and window.
Description
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Estimate power spectral density of data "x" by the Welch (1967) periodogram/FFT method. The data is divided into segments. If "window" is a vector, each segment has the same length as "window" and is multiplied by "window" before (optional) zero-padding and calculation of its periodogram. If "window" is a scalar, each segment has a length of "window" and a Hamming window is used. The spectral density is the mean of the periodograms, scaled so that area under the spectrum is the same as the mean square of the data. This equivalence is supposed to be exact, but in practice there is a mismatch of up to 0.5% when comparing area under a periodogram with the mean square of the data.
pyulear
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > pyulearCalling Sequence
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[ psd , f_out ] = pyulear ( x , poles , freq , Fs , range , method , plot_type )
-All but the first two arguments are optional and may be empty . Parameters
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x:
- [vector] sampled data
poles:
- [integer scalar] required number of poles of the AR model
freq:
- [real vector] frequencies at which power spectral density is calculated [integer scalar] number of uniformly distributed frequency values at which spectral density is calculated. [default=256]
Fs:
- [real scalar] sampling frequency (Hertz) [default=1]
range:
- 'half', 'onesided' : frequency range of the spectrum is from zero up to but not including sample_f/2. Power from negative frequencies is added to the positive side of the spectrum. 'whole', 'twosided' : frequency range of the spectrum is -sample_f/2 to sample_f/2, with negative frequencies stored in "wrap around" order after the positive frequencies; e.g. frequencies for a 10-point 'twosided' spectrum are 0 0.1 0.2 0.3 0.4 0.5 -0.4 -0.3 -0.2 -0.1 'shift', 'centerdc' : same as 'whole' but with the first half of the spectrum swapped with second half to put the zero-frequency value in the middle. (See "help fftshift". If "freq" is vector, 'shift' is ignored. If model coefficients "ar_coeffs" are real, the default range is 'half', otherwise default range is 'whole'.
method:
- 'fft': use FFT to calculate power spectral density. 'poly': calculate spectral density as a polynomial of 1/z N.B. this argument is ignored if the "freq" argument is a vector. The default is 'poly' unless the "freq" argument is an integer power of 2.
plot_type:
- 'plot', 'semilogx', 'semilogy', 'loglog', 'squared' or 'db' specifies the type of plot. The default is 'plot', which means linear-linear axes. 'squared' is the same as 'plot'. 'dB' plots "10*log10(psd)". This argument is ignored and a spectrum is not plotted if the caller requires a returned value.
qp_kaiser
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > qp_kaiserqp_kaiser
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Computes a finite impulse response (FIR) filter for use with a quasi-perfect reconstruction polyphase-network filter bank.
Calling Sequence
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qp_kaiser ( nb , at , linear )
-qp_kaiser ( nb , at ) Parameters
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nb:
- Number of bands
at:
- Attenuation
linear:
- When not zero, minimum-phase calculation is omitted.
Description
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This is an Octave function.
-This version utilizes a Kaiser window to shape the frequency response of the designed filter. Tha number nb of bands and the desired attenuation at in the stop-band are given as parameters.
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The Kaiser window is multiplied by the ideal impulse response h(n)=a.sinc(a.n) and converted to its minimum-phase version by means of a Hilbert transform.
Examples
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qp_kaiser ( 5 , 5 , 1 )
-ans =
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-0.11591 0.25606 0.25606 0.25606 0.11591
rc2ac
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > rc2acrc2is
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > rc2isrc2is
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Convert reflection coefficients to inverse sine parameters
Parameters
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k:
- input reflection coefficients. Needs to be an array of real numbers between -1 and 1
isin:
- inverse sine parameters corresponding to the reflection coefficients in input
Description
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This function returns the inverse sine parameters corresponding to the input reflection coefficients K.
-output array has isin(i) = 2/pi*asin(k(i))
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Example
-k = [0.3090 0.9801 0.0031 0.0082 -0.0082];
-isin = rc2is(k) //Gives inverse sine parameters
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Bibliography
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J.R. Deller, J.G. Proakis, J.H.L. Hansen, "Discrete-Time Processing of Speech Signals", Prentice Hall, Section 7.4.5
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modified function to handle char i/p and also changed error statements to match those of MATLAB by Debdeep Dey
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convert char i/p to their respective ascii values
rc2lar
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > rc2larrc2poly
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > rc2polyCalling Sequence
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a = rc2poly ( kr )
-[ a , efinal ] = rc2poly ( kr , R0 ) rceps
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > rcepsrceps
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Produce the cepstrum of the signal x, and if desired, the minimum phase reconstruction of the signal x.
Parameters
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x:
- real or complex vector input
Examples
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f0 = 70 ; Fs = 10000 ; # 100 Hz fundamental , 10 kHz sampling rate
-a = poly ( 0.985 * exp ( 1 i * pi * [ 0.1 , - 0.1 , 0.3 , - 0.3 ] ) ) ; # two formants
-s = 0.005 * randn ( 1024 , 1 ) ; # Noise excitation signal
-s ( 1 : Fs / f0 : length ( s ) ) = 1 ; # Impulse glottal wave
-x = filter ( 1 , a , s ) ; # Speech signal in x
-[ y , xm ] = rceps ( x .* hanning ( 1024 ) ) ; # cepstrum and min phase reconstruction
rcosdesign
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > rcosdesignrcosdesign
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RCOSDESIGN computes the raised cosine FIR filter
rectpuls
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > rectpulsCalling Sequence
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[ y ]= rectpuls ( t )
-[ y ]= rectpuls ( t , w ) Parameters
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t:
- Real or complex valued vector or matrix
w:
- Real or complex valued vector or matrix
Description
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This is an Octave function
-y = rectpuls(t) returns a continuous, aperiodic, unity-height rectangular pulse depending upon input t, centered about t=0 and having default width of 1.
-y = rectpuls(t,w) generates a rectangle of width w.
Examples
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1. rectpuls ( [ 10 100 1000 13 839 ] , 27 )
-ans = 1 0 0 1 0
-2. rectpuls ( [ 1000 1000 100 100 ] )
-ans = 0 0 0 0
rectwin
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > rectwinrectwin
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This function returns the filter coefficients of a rectangular window.
Parameters
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m:
- positive integer value
y:
- output variable, vector of real numbers
Description
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This is an Octave function.
-This function returns the filter coefficients of a rectangular window of length m supplied as input, to the output vector y.
Examples
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rectwin ( 3 )
-ans =
-1.
-1.
-1.
remez1
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > remez1remez1
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Parks-McClellan optimal FIR filter design
Calling Sequence
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b = remez1 ( n , f , a )
-b = remez1 ( n , f , a , w )
-b = remez1 ( n , f , a , w , ftype )
-b = remez1 ( n , f , a , w , ftype , griddensity ) Parameters
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n:
- gives the number of taps in the returned filter
f:
- gives frequency at the band edges [b1 e1 b2 e2 b3 e3 …]
a:
- gives amplitude at the band edges [a(b1) a(e1) a(b2) a(e2) …]
w:
- gives weighting applied to each band
ftype:
- is "bandpass", "hilbert" or "differentiator"
griddensity:
- determines how accurately the filter will be constructed. The minimum value is 16, but higher numbers are slower to compute.
Description
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Frequency is in the range (0, 1), with 1 being the Nyquist frequency.
resample
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > resampleresample
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This function resamples in the input sequence x supplied by a factor of p/q.
Calling Sequence
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y = resample ( x , p , q )
-y = resample ( x , p , q , h )
-[ y , h ] = resample (...) Parameters
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x:
- scalar, vector or matrix of real or complex numbers
p:
- positive integer value
q:
- positive integer value
h:
- scalar, vector or matrix of real or complex numbers
Description
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This is an Octave function.
-This function resamples in the input sequence x supplied by a factor of p/q. If x is a matrix, then every column is resampled.hange the sample rate of x by a factor of p/q.
-This is performed using a polyphase algorithm. The impulse response h, given as parameter 4, of the antialiasing filter is either specified or designed with a Kaiser-windowed sinecard.
Examples
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resample ( 1 , 2 , 3 )
-ans = 0.66667
residued
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > residuedParameters
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b:
- Real or complex valued vector or matrix
a:
- Real or complex valued vector or matrix
Description
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This is an Octave function.
-Similar to the "residuez" function. The difference being in the function "residuez", the IIR part (poles p and residues r) is driven in parallel with the FIR part(f) whereas in the function "residued", the IIR part is driven by the output of the FIR part. In signal modeling applications, this structure can be more accurate.
Examples
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1. [ a , b , c , d ] = residued ( [ 1 i ; 3 - 4 ] , [ 1 2 ; 3 4 ] )
-a = [ 0.19405 - 1.31377 i ; 0.08329 + 0.99163 i ; - 0.27734 + 0.32215 i ]
-b = [ - 0.10184 - 1.19167 i ; - 0.10184 + 1.19167 i ; - 2.79632 - 0.00000 i ]
-c = 1
-d = [ 1 ; 1 ; 1 ]
residuez
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > residuezParameters
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b:
- Real or complex valued vector or matrix
a:
- Real or complex valued vector or matrix
Description
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This is an Octave function
-It compute the PFE of filter H(z)= B(z)/A(z) where inputs b and a are vectors specifying the digital filter.
Examples
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1. [ a , b , c , d ] = residuez ( [ i 2 i 3 i ; - 4 1 4 i ] , [ 1 2 3 ] )
-a = [ 0.6262 - 1.4412 i ; - 0.4039 + 1.4658 i ]
-b = [ - 1.0000 - 1.4142 i ; - 1.0000 + 1.4142 i ]
-c = [ - 0.22222 - 0.97531 i 0.33333 + 0.51852 i 0.00000 - 0.11111 i ; 0.00000 - 1.33333 i ]
-d = 1
risetime
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > risetimerlevinson
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > rlevinsonCalling Sequence
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a = rlevinson ( a , efinal )
-[ a , U ] = rlevinson ( a , efinal )
-[ a , U , kr ] = rlevinson ( a , efinal )
-[ a , U , kr , e ] = rlevinson ( a , efinal ) rms
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > rmsrms
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convert i/p values to their ascii values if they are of type char
rooteig
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > rooteigrooteig
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Frequencies and power of sinusoids using eigenvector algorithm
Calling Sequence
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w = rooteig ( x , p )
-[ w , pow ] = rooteig ( x , p )
-[ f , pow ] = rooteig (..., fs )
-[ w , pow ] = rooteig (..., 'corr ') Parameters
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Examples:
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1) 3 complex exponentials:
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n=0:
- 99;
rootmusic
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > rootmusicrootmusic
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Frequencies and power of sinusoids using the root MUSIC algorithm
Calling Sequence
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w = rootmusic ( x , p )
-[ w , pow ] = rootmusic ( x , p )
-[ f , pow ] = rootmusc (..., fs )
-[ w , pow ] = rootmusic (..., 'corr ') Parameters
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Examples:
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1) 3 complex exponentials:
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n=0:
- 99;
rssq
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > rssqrssq
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This function calculates the square root of the sum of values of input vector IN.
Calling Sequence
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OUT = rssq ( IN )
-OUT = rssq ( IN , orientation ) Parameters
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in:
- Vector or Matrix of real or complex elements.
orientation:
- A string with possible values "r", "c" or "m" or numericals such as '1' or '2',giving the dimension along which the rssq value is to be calculated.
out:
- A scalar with real value when input is a vector.When input is a matrix, out is the root sum squared value along the orientation specified or the default one when not specified.
Description
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For vector as input, the output is real valued scalar containing the rssq value. The rssq value can be calculated by taking the square root of the squared sum of the elements.
-If the input IN is a matrix, the output of function is rssq value of each column stored in a row vector OUT.
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When the elements of IN are COMPLEX, the absolute value of the element is used to calculate the output.
-When the orientation is not specified for N dimensional array, it is taken as the index of the first dimension of IN that is greater than 1 and calculation is done along that orientation.
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When the orientation is specified the output is calculated along that dimension.
-The orientation can be specified as 1 for rssq value of columns of matrix IN or as r.
-For rssq value of rows of matrix orientation should be 2 or c.
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Examples
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To calculate rssq of a vector :
-IN = [ 2 4 6 ]
-OUT = rssq ( IN )
-The output is 7.4833148
Examples
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To calculate rssq of rows of matrix :
-IN = [ 1 3 5 ; 2 4 6 ; 7 8 9 ]
-OUT = rssq ( IN , 2 )
-The output should be OUT =
-5.9160798
-7.4833148
-13.928388
Examples
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To calculate rssq of a columns of complex matrix:
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-IN = [ 5 + %i * 3 2 + %i * 4 ; 3 + %i * 6 1 + %i * 2 ]
-OUT = rssq ( IN , 1 )
-The output should be OUT = 8.8881944 5.
Bibliography
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Matlab help document.
sampled2continuous
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > sampled2continuoussampled2continuous
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This function calculates the output reconstructed from the samples n supplied as input, at a rate of 1/s samples per unit time.
Calling Sequence
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x = sampled2continuous ( n , s , t ) Parameters
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n:
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s:
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t:
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Description
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This is an Octave function.
-This function calculates the output reconstructed from the samples n supplied as input, at a rate of 1/s samples per unit time.
-The third parameter t is all the instants where output x is needed from intput n and this time is relative to x(0).
Examples
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sampled2continuous ( [ 1 , 2 , 3 ] , 5 , 6 )
-ans =
-2.4166806
sawtooth
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > sawtoothCalling Sequence
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[ y ]= sawtooth ( t )
-[ y ]= sawtooth ( t , width ) Parameters
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t:
- Real valued vector or matrix
width:
- Real number between 0 and 1
Description
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This is an Octave function
-This function returns a sawtooth wave with period 2*pi with +1/-1 as the maximum and minimum values for elements of t. If width is specified, it determines where the maximum is in the interval [0,2*pi].
Examples
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1. sawtooth ( [ 1 2 3 4 5 ] , 0.5 )
-ans = [ - 0.36338 0.27324 0.90986 0.45352 - 0.18310 ]
-2. sawtooth ( [ 1 2 ; 4 5 ] )
-ans = [ - 0.68169 - 0.36338 ; 0.27324 0.59155 ]
schtrig
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > schtrigschtrig
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This function implements a multisignal Schmitt triggers with lev levels supplied as input.
Calling Sequence
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v = schtrig ( x , lev )
-v = schtrig ( x , lev , rs ) Parameters
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x:
- vector or matrix of real numbers
lev:
- real number
rs:
- default value 1
Description
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This is an Octave function.
-This function implements a multisignal Schmitt triggers with lev levels supplied as input.
-The argument 1 is a matrix (or a vector) and this trigger works along its first dimension.
Examples
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schtrig ( [ 0.2 , - 3 , 5 ] , - 4 )
-ans =
-0. 0. 1.
schurrc
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > schurrcschurrc
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narginchk(1,1,argn(2));
FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing ToolboxFOSSEE Signal Processing Toolbox
-ac2poly — Convert autocorrelation sequence to polynomial of prediction filter ac2rc — Convert autocorrelation sequence to reflection coefficients. arParEst — ar_psd — Calculate the power spectrum of the autoregressive model arburg — This function calculates coefficients of an autoregressive (AR) model of complex data. arch_fit — This functions fits an ARCH regression model to the time series Y using the scoring algorithm in Engle's original ARCH paper. arch_rnd — Simulate an ARCH sequence of length t with AR coefficients b and CH coefficients a. arch_test — perform a Lagrange Multiplier (LM) test of thenull hypothesis of no conditional heteroscedascity against the alternative of CH(P) arcov — Autoregressive all-pole model parameters — covariance method arma_rnd — Return a simulation of the ARMA model. armcov — aryule — This function fits an AR (p)-model with Yule-Walker estimates. autoreg_matrix — Given a time series (vector) Y, return a matrix with ones in the first column and the first K lagged values of Y in the other columns. barthannwin — This function returns the filter coefficients of a modified Bartlett-Hann window. bartlett — Generates a Bartlett window besselap — Return bessel analog filter prototype. besself — This function generates a Bessel filter. bilinear — Transform a s-plane filter specification into a z-plane specification bitrevorder — blackman — Generates a Blackman window blackmanharris — This function returns the filter coefficients of a Blackman-Harris window. blackmannuttall — This function returns the filter coefficients of a Blackman-Nuttall window. bohmanwin — This function returns the filter coefficients of a Bohman window. boxcar — This function returns the filter coefficients of a rectangular window. buffer — This function buffers the given data into a matrix of signal frames buttap — Design a lowpass analog Butterworth filter. butter — This function generates a Butterworth filter. buttord — /This function computes the minimum filter order of a Butterworth filter with the desired response characteristics. cceps — Return the complex cepstrum of the vector x cconv — [nargout,nargin]=argn(); cell2sos — Converts a cell array to a second order section matrix cheb — Calculates the nth-order Chebyshev polynomial at the point x. cheb1ap — This function designs a lowpass analog Chebyshev type I filter. cheb1ord — This function computes the minimum filter order of a Chebyshev type I filter with the desired response characteristics. cheb2ap — This function designs a lowpass analog Chebyshev type II filter. cheb2ord — This function computes the minimum filter order of a Chebyshev type II filter with the desired response characteristics. chebwin — This function returns the filter coefficients of a Dolph-Chebyshev window. cheby1 — This function generates a Chebyshev type I filter with rp dB of passband ripple. cheby2 — This function generates a Chebyshev type II filter with rs dB of stopband attenuation. check — funcprot(0); chirp — This function evaluates a chirp signal at time t. cl2bp — Constrained L2 bandpass FIR filter design. clustersegment — This function calculates boundary indexes of clusters of 1’s. cmorwavf — funcprot(0); cohere — Estimate (mean square) coherence of signals "x" and "y" convmtx — n=double(n); corrmtx — Generate data matrix for autocorrelation matrix estimation cplxreal — Function to divide vector z into complex and real elements, removing the one of each complex conjugate pair. cpsd — This function estimates cross power spectrum of data x and y by the Welch (1967) periodogram/FFT method. cummax — Cumulative maximum cummin — Cumulative minimum czt — Chirp Z Transform db — db2pow — dctmtx — decimate — rhs = argn(2) detrend1 — This function removes the best fit of a polynomial of order P from the data X dftmtx — diffpara — Return the estimator D for the differencing parameter of an integrated time series diric — downsample — This function downsamples the signal by selecting every nth element. dst1 — Computes the type I discrete sine transform of x durbinlevinson — Perform one step of the Durbin-Levinson algorithm.. dutycycle — dwt — Discrete wavelet transform (1D) ellip — This function generates an elliptic or Cauer filter with rp dB of passband ripple and rs dB of stopband attenuation. ellipap — Designs a lowpass analog elliptic filter. ellipord — This function computes the minimum filter order of an elliptic filter with the desired response characteristics. enbw — eqtflength — Modifies the input vector to give output vectors of the same length falltime — fft — Calculates the discrete Fourier transform of a matrix using Fast Fourier Transform algorithm. fft1 — Calculates the discrete Fourier transform of a matrix using Fast Fourier Transform algorithm. fft2 — Calculates the two-dimensional discrete Fourier transform of A using a Fast Fourier Transform algorithm. fft21 — Calculates the two-dimensional discrete Fourier transform of A using a Fast Fourier Transform algorithm. fftconv — Convolve two vectors using the FFT for computation. fftfilt — Performs FFT-based FIR filtering using overlap-add method fftn — This function computes the N-dimensional discrete Fourier transform of A using a Fast Fourier Transform (FFT) algorithm. fftshift1 — Perform a shift of the vector X, for use with the 'fft1' and 'ifft1' functions, in order the move the frequency 0 to the center of the vector or matrix. fftw1 — Manage FFTW wisdom data. fht — The Function calculates the Fast Hartley Transform of real input. filter1 — Apply a 1-D digital filter to the data X. filter2 — Apply the 2-D FIR filter B to X. filternorm — Calculates the L-2 norm or L-infinity norm of a digital filter filtfilt — filtic — filtord — and denominator coefficients, a. findpeaks — This function find peaks on DATA. fir1 — Produce an order N FIR filter with the given frequency cutoff, returning the N+1 filter coefficients in B. fir2 — Produce an order N FIR filter with arbitrary frequency response M over frequency bands F, returning the N+1 filter coefficients in B. firpmord — Parks-McClennan optimal FIR filter order estimation firtype — if (type(b)~=1) then flattopwin — This function returns the filter coefficients of a Flat Top window. fracshift — This function shifts the series x supplied as input argument by a number of samples d. fractdiff — Compute the fractional differences (1-L)^d x where L denotes the lag-operator and d is greater than -1. freqs — Compute the s-plane frequency response of the IIR filter. freqz — This function returns the complex frequency response H of the rational IIR filter whose numerator and denominator coefficients are B and A, respectively. fwhm — This function computes peak full width at half minimum or at another level of peak minimum for vector or matrix data y supplied as input. fwhmjlt — rhs = argn(2) fwht — Compute the Walsh-Hadamard transform of x using the Fast Walsh-Hadamard Transform (FWHT) algorithm gauspuls — gaussdesign — GAUSSDESIGN designs a Gaussian pulse-shaping filter which is a low pass FIR gaussian — This function returns a Gaussian convolution window. gausswin — This function returns the filter coefficients of a Gaussian window. gmonopuls — goertzel — Computes DFT using the second order Goertzel Algorithm grpdelay — This function computes the group delay of a filter. hamming — Return the filter coefficients of a Hamming window of length M hann — This function returns the filter coefficients of a Hanning window. hanning — Return the filter coefficients of a Hanning window of length M helperHarmonicDistortionAmplifier — helperHarmonicDistortionADC Helper function for HarmonicDistortionExample.m hilbert1 — Analytic extension of real valued signal. hurst — Estimate the Hurst parameter of sample X via the rescaled r statistic. icceps — ICCEPS computes the inverse cepstrum of a real-valued input. This spectrum idct1 — Compute the inverse discrete cosine transform of input. idct2 — This function computes the inverse 2-D discrete cosine transform of input matrix. idst1 — This function computes the inverse type I discrete sine transform. ifft — Calculates the inverse discrete Fourier transform of a matrix using Fast Fourier Transform algorithm. ifft1 — Calculates the inverse discrete Fourier transform of a matrix using Fast Fourier Transform algorithm. ifft2 — Calculates the inverse two-dimensional discrete Fourier transform of A using a Fast Fourier Transform algorithm. ifftn — Compute the inverse N-dimensional discrete Fourier transform of A using a Fast Fourier Transform (FFT) algorithm. ifftshift1 — Undo the action of the 'fftshift1' function. ifht — Calculate the inverse Fast Hartley Transform of real input D ifwht — Compute the inverse Walsh-Hadamard transform of x using the Fast Walsh-Hadamard Transform (FWHT) algorithm iirlp2mb — This function does IIR Low Pass Filter to Multiband Filter Transformation. impinvar — This function converts analog filter with coefficients b and a to digital, conserving impulse response. impz — impzlength — Impulse response length interp — function y = interp(x, q, n, Wc) intfilt — invfreq — Calculates inverse frequency vectors invfreqs — Fit filter B(s)/A(s)to the complex frequency response H at frequency points F. A and B are real polynomial coefficients of order nA and nB. invfreqz — Fit filter B(z)/A(z)to the complex frequency response H at frequency points F. A and B are real polynomial coefficients of order nA and nB. invimpinvar — This function converts digital filter with coefficients b and a to analog, conserving impulse response. is2rc — Convert inverse sine parameters to reflection coefficients isallpass — [nargout,nargin]=argn(); isfir — [nargout,nargin]=argn(); islinphase — [nargout,nargin]=argn(); ismaxphase — [nargout,nargin]=argn(); isminphase — [nargout,nargin]=argn(); isstable — SOS matrix corresponds to [bi(1) bi(2) bi(3) ai(1) ai(2) ai(3)]. kaiser — This function returns the filter coefficients of a Kaiser window. kaiserord — Return the parameters needed to produce a filter of the desired specification from a Kaiser window. lar2rc — latc2tf — Convert lattice filter parameters to transfer function coefficients latcfilt — function [f,g,zo]=latcfilt(k,x,v,zi,dim) latcfilt1 — function [f,g,zo]=latcfilt1(k,v,x,zi) levdown — levin — [ar,sigma2,rc]=lev(r) levinson — lpc — Linear prediction filter coefficients lsf2poly — lsf2poly function convert line spectral frequencies to prediction polynomial. mag2db — funcprot(0); marcumq — This function computes the generalized Marcum Q function of order m with noncentrality parameter a and argument b. medfilt1 — 1D median filtering mexihat — meyeraux — midcross — modulate — Modulates signal according to the modulation method morlet — movingrms — mscohere — It estimate (mean square) coherence of signals x and y. musicBase — Implements the core of the MUSIC algorithm ncauer — Analog prototype for Cauer filter. nnls — Non Negative Least Squares (nnls) for Ex=f with the constraint x>=0 nuttallwin — This function returns the filter coefficients of a Blackman-Harris window. parzenwin — This function returns the filter coefficients of a Parzen window. pburg — Calculate Burg maximum-entropy power spectral density. pchip — pchips — peak2peak — funcprot(0); peak2rms — This function calculates the ratio of peak magnitude to the Root Mean Square(RMS) value. pei_tseng_notch — peig — Psuedospectrum using the eigenvector method. periodogram — Return the periodogram (Power Spectral Density) of X phaseInputParseAs_ab — fs=0; phaseInputParseAs_sos — fs=0; phasedelay — cas variable is 2 if sos form is involved and 1 if direct rational form is given phasez — cas variable is 2 if sos form is involved and 1 if direct rational form is given pmusic — Psuedospectrum using MUSIC algorithm poly2ac — Convert prediction polynomial to autocorrelation sequence. poly2lsf — poly2rc — polyscale — errcheck1 polystab — This function stabilizes the polynomial transfer function. polyval — pow2db — rhs = argn(2) primitive — This function calculates the primitive of a given function supplied as input. prony — pulseperiod — pulsesep — pulsewidth — pulstran — This function generates the signal y = sum(func(t+d,...)) for each d. pwelch — Estimate power spectral density of data "x" by the Welch (1967) periodogram/FFT method. pyulear — qp_kaiser — Computes a finite impulse response (FIR) filter for use with a quasi-perfect reconstruction polyphase-network filter bank. rc2ac — rc2is — Convert reflection coefficients to inverse sine parameters rc2lar — rc2poly — rceps — Produce the cepstrum of the signal x, and if desired, the minimum phase reconstruction of the signal x. rcosdesign — RCOSDESIGN computes the raised cosine FIR filter rectpuls — rectwin — This function returns the filter coefficients of a rectangular window. remez1 — Parks-McClellan optimal FIR filter design resample — This function resamples in the input sequence x supplied by a factor of p/q. residued — residuez — risetime — rlevinson — rms — convert i/p values to their ascii values if they are of type char rooteig — Frequencies and power of sinusoids using eigenvector algorithm rootmusic — Frequencies and power of sinusoids using the root MUSIC algorithm rssq — This function calculates the square root of the sum of values of input vector IN. sampled2continuous — This function calculates the output reconstructed from the samples n supplied as input, at a rate of 1/s samples per unit time. sawtooth — schtrig — This function implements a multisignal Schmitt triggers with lev levels supplied as input. schurrc — narginchk(1,1,argn(2)); seqperiod — Calculates the period of a sequence sftrans — Transform band edges of a generic lowpass filter (cutoff at W=1) represented in splane zero-pole-gain form. sgolay — This function computes the filter coefficients for all Savitzsky-Golay smoothing filters. sgolayfilt — shanwavf — shiftdata — Shifts data by rearranging dimensions sigmoid_train — Evaluate a train of sigmoid functions at T. sinetone — Return a sinetone of the input sinewave — Return an M-element vector with I-th element given by 'sin(2* pi *(I+D-1)/N).' slewrate — sos2cell — Converts a second order section matrix to a cell array sos2ss — [nargout,nargin]=argn(); sos2tf — This function converts series second-order sections to direct H(z) = B(z)/A(z) form. sos2zp — This function converts series second-order sections to zeros, poles, and gains (pole residues). sosbreak — function for breaking a polynomial in second order polynomials (and an extra linear) sosfilt — specgram — spectral_adf — Return the spectral density estimator given a vector of autocovariances C, window name WIN, and bandwidth, B. spectral_xdf — Return the spectral density estimator given a data vector X, window name WIN, and bandwidth, B. spencer — Return Spencer's 15 point moving average of each column of X. ss2sos — not taking if, order and scale as input since they do not seem useful statelevels — stft — Compute the short-time Fourier transform of the vector X stmcb — function [b,a] = stmcb( x, u_in, q, p, niter, a_in ) strips — Plots vector or matrix in strips subspaceMethodsInputParser — Input parser to be used by pmusic and peig synthesis — Compute a signal from its short-time Fourier transform tf2sos — This function converts direct-form filter coefficients to series second-order sections. tf2zp — [z,p,k]= tf2zp(b,a); tf2zpk — form tfe — Estimate transfer function of system with input "x" and output "y". Use the Welch (1967) periodogram/FFT method. tfestimate — transpose — funcprot(0); trial_iirlp2mb — B = varargin(1) triang — This function returns the filter coefficients of a triangular window. tripuls — truth — y = %t tukeywin — This function returns the filter coefficients of a Tukey window. udecode — Decodes the input uniformly quantized values uencode — Performs uniform quantization of the input into 2^n levels ultrwin — This function returns the coefficients of an Ultraspherical window. unshiftdata — Inverts the effect of shiftdata unwrap2 — Unwrap radian phases by adding or subtracting multiples of 2*pi. upfirdn — This function upsamples the input data, applies the FIR filter and then downsamples it. upsample — This function upsamples the signal, inserting n-1 zeros between every element. upsamplefill — This function upsamples a vector interleaving given values or copies of the vector elements. var — vco — Voltage Controlled Oscillator wconv — Performs 1D or 2D convolution. welchwin — This function returns the filter coefficients of a Welch window. window — This function creates an m-point window from the function f given as input. wkeep — wrev — xcorr1 — Estimates the cross-correlation. xcorr2 — xcov1 — Compute covariance at various lags [=correlation(x-mean(x),y-mean(y))]. yulewalker — Fit an AR (p)-model with Yule-Walker estimates given a vector C of autocovariances '[gamma_0, ..., gamma_p]'. zerocrossing — This function estimates the points at which a given waveform crosses the x-axis. zp2sos — This function converts filter poles and zeros to second-order sections. zp2ss — Converts zeros / poles to state space. zp2tf — Converts zeros / poles to a transfer function. zplane — funcprot(0); FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing ToolboxFOSSEE Signal Processing Toolbox
-ac2poly — Convert autocorrelation sequence to polynomial of prediction filter ac2rc — Convert autocorrelation sequence to reflection coefficients. arParEst — ar_psd — Calculate the power spectrum of the autoregressive model arburg — This function calculates coefficients of an autoregressive (AR) model of complex data. arch_fit — This functions fits an ARCH regression model to the time series Y using the scoring algorithm in Engle's original ARCH paper. arch_rnd — Simulate an ARCH sequence of length t with AR coefficients b and CH coefficients a. arch_test — perform a Lagrange Multiplier (LM) test of thenull hypothesis of no conditional heteroscedascity against the alternative of CH(P) arcov — Autoregressive all-pole model parameters — covariance method arma_rnd — Return a simulation of the ARMA model. armcov — aryule — This function fits an AR (p)-model with Yule-Walker estimates. autoreg_matrix — Given a time series (vector) Y, return a matrix with ones in the first column and the first K lagged values of Y in the other columns. barthannwin — This function returns the filter coefficients of a modified Bartlett-Hann window. bartlett — Generates a Bartlett window besselap — Return bessel analog filter prototype. besself — This function generates a Bessel filter. bilinear — Transform a s-plane filter specification into a z-plane specification bitrevorder — blackman — Generates a Blackman window blackmanharris — This function returns the filter coefficients of a Blackman-Harris window. blackmannuttall — This function returns the filter coefficients of a Blackman-Nuttall window. bohmanwin — This function returns the filter coefficients of a Bohman window. boxcar — This function returns the filter coefficients of a rectangular window. buffer — This function buffers the given data into a matrix of signal frames buttap — Design a lowpass analog Butterworth filter. butter — This function generates a Butterworth filter. buttord — /This function computes the minimum filter order of a Butterworth filter with the desired response characteristics. cceps — Return the complex cepstrum of the vector x cconv — [nargout,nargin]=argn(); cell2sos — Converts a cell array to a second order section matrix cheb — Calculates the nth-order Chebyshev polynomial at the point x. cheb1ap — This function designs a lowpass analog Chebyshev type I filter. cheb1ord — This function computes the minimum filter order of a Chebyshev type I filter with the desired response characteristics. cheb2ap — This function designs a lowpass analog Chebyshev type II filter. cheb2ord — This function computes the minimum filter order of a Chebyshev type II filter with the desired response characteristics. chebwin — This function returns the filter coefficients of a Dolph-Chebyshev window. cheby1 — This function generates a Chebyshev type I filter with rp dB of passband ripple. cheby2 — This function generates a Chebyshev type II filter with rs dB of stopband attenuation. check — funcprot(0); chirp — This function evaluates a chirp signal at time t. cl2bp — Constrained L2 bandpass FIR filter design. clustersegment — This function calculates boundary indexes of clusters of 1’s. cmorwavf — funcprot(0); cohere — Estimate (mean square) coherence of signals "x" and "y" convmtx — n=double(n); corrmtx — Generate data matrix for autocorrelation matrix estimation cplxreal — Function to divide vector z into complex and real elements, removing the one of each complex conjugate pair. cpsd — This function estimates cross power spectrum of data x and y by the Welch (1967) periodogram/FFT method. cummax — Cumulative maximum cummin — Cumulative minimum czt — Chirp Z Transform db — db2pow — dctmtx — decimate — rhs = argn(2) detrend1 — This function removes the best fit of a polynomial of order P from the data X dftmtx — diffpara — Return the estimator D for the differencing parameter of an integrated time series diric — downsample — This function downsamples the signal by selecting every nth element. dst1 — Computes the type I discrete sine transform of x durbinlevinson — Perform one step of the Durbin-Levinson algorithm.. dutycycle — dwt — Discrete wavelet transform (1D) ellip — This function generates an elliptic or Cauer filter with rp dB of passband ripple and rs dB of stopband attenuation. ellipap — Designs a lowpass analog elliptic filter. ellipord — This function computes the minimum filter order of an elliptic filter with the desired response characteristics. enbw — eqtflength — Modifies the input vector to give output vectors of the same length falltime — fft — Calculates the discrete Fourier transform of a matrix using Fast Fourier Transform algorithm. fft1 — Calculates the discrete Fourier transform of a matrix using Fast Fourier Transform algorithm. fft2 — Calculates the two-dimensional discrete Fourier transform of A using a Fast Fourier Transform algorithm. fft21 — Calculates the two-dimensional discrete Fourier transform of A using a Fast Fourier Transform algorithm. fftconv — Convolve two vectors using the FFT for computation. fftfilt — Performs FFT-based FIR filtering using overlap-add method fftn — This function computes the N-dimensional discrete Fourier transform of A using a Fast Fourier Transform (FFT) algorithm. fftshift1 — Perform a shift of the vector X, for use with the 'fft1' and 'ifft1' functions, in order the move the frequency 0 to the center of the vector or matrix. fftw1 — Manage FFTW wisdom data. fht — The Function calculates the Fast Hartley Transform of real input. filter1 — Apply a 1-D digital filter to the data X. filter2 — Apply the 2-D FIR filter B to X. filternorm — Calculates the L-2 norm or L-infinity norm of a digital filter filtfilt — filtic — filtord — and denominator coefficients, a. findpeaks — This function find peaks on DATA. fir1 — Produce an order N FIR filter with the given frequency cutoff, returning the N+1 filter coefficients in B. fir2 — Produce an order N FIR filter with arbitrary frequency response M over frequency bands F, returning the N+1 filter coefficients in B. firpmord — Parks-McClennan optimal FIR filter order estimation firtype — if (type(b)~=1) then flattopwin — This function returns the filter coefficients of a Flat Top window. fracshift — This function shifts the series x supplied as input argument by a number of samples d. fractdiff — Compute the fractional differences (1-L)^d x where L denotes the lag-operator and d is greater than -1. freqs — Compute the s-plane frequency response of the IIR filter. freqz — This function returns the complex frequency response H of the rational IIR filter whose numerator and denominator coefficients are B and A, respectively. fwhm — This function computes peak full width at half minimum or at another level of peak minimum for vector or matrix data y supplied as input. fwhmjlt — rhs = argn(2) fwht — Compute the Walsh-Hadamard transform of x using the Fast Walsh-Hadamard Transform (FWHT) algorithm gauspuls — gaussdesign — GAUSSDESIGN designs a Gaussian pulse-shaping filter which is a low pass FIR gaussian — This function returns a Gaussian convolution window. gausswin — This function returns the filter coefficients of a Gaussian window. gmonopuls — goertzel — Computes DFT using the second order Goertzel Algorithm grpdelay — This function computes the group delay of a filter. hamming — Return the filter coefficients of a Hamming window of length M hann — This function returns the filter coefficients of a Hanning window. hanning — Return the filter coefficients of a Hanning window of length M helperHarmonicDistortionAmplifier — helperHarmonicDistortionADC Helper function for HarmonicDistortionExample.m hilbert1 — Analytic extension of real valued signal. hurst — Estimate the Hurst parameter of sample X via the rescaled r statistic. icceps — ICCEPS computes the inverse cepstrum of a real-valued input. This spectrum idct1 — Compute the inverse discrete cosine transform of input. idct2 — This function computes the inverse 2-D discrete cosine transform of input matrix. idst1 — This function computes the inverse type I discrete sine transform. ifft — Calculates the inverse discrete Fourier transform of a matrix using Fast Fourier Transform algorithm. ifft1 — Calculates the inverse discrete Fourier transform of a matrix using Fast Fourier Transform algorithm. ifft2 — Calculates the inverse two-dimensional discrete Fourier transform of A using a Fast Fourier Transform algorithm. ifftn — Compute the inverse N-dimensional discrete Fourier transform of A using a Fast Fourier Transform (FFT) algorithm. ifftshift1 — Undo the action of the 'fftshift1' function. ifht — Calculate the inverse Fast Hartley Transform of real input D ifwht — Compute the inverse Walsh-Hadamard transform of x using the Fast Walsh-Hadamard Transform (FWHT) algorithm iirlp2mb — This function does IIR Low Pass Filter to Multiband Filter Transformation. impinvar — This function converts analog filter with coefficients b and a to digital, conserving impulse response. impz — impzlength — Impulse response length interp — function y = interp(x, q, n, Wc) intfilt — invfreq — Calculates inverse frequency vectors invfreqs — Fit filter B(s)/A(s)to the complex frequency response H at frequency points F. A and B are real polynomial coefficients of order nA and nB. invfreqz — Fit filter B(z)/A(z)to the complex frequency response H at frequency points F. A and B are real polynomial coefficients of order nA and nB. invimpinvar — This function converts digital filter with coefficients b and a to analog, conserving impulse response. is2rc — Convert inverse sine parameters to reflection coefficients isallpass — [nargout,nargin]=argn(); isfir — [nargout,nargin]=argn(); islinphase — [nargout,nargin]=argn(); ismaxphase — [nargout,nargin]=argn(); isminphase — [nargout,nargin]=argn(); isstable — SOS matrix corresponds to [bi(1) bi(2) bi(3) ai(1) ai(2) ai(3)]. kaiser — This function returns the filter coefficients of a Kaiser window. kaiserord — Return the parameters needed to produce a filter of the desired specification from a Kaiser window. lar2rc — latc2tf — Convert lattice filter parameters to transfer function coefficients latcfilt — function [f,g,zo]=latcfilt(k,x,v,zi,dim) latcfilt1 — function [f,g,zo]=latcfilt1(k,v,x,zi) levdown — levin — [ar,sigma2,rc]=lev(r) levinson — lpc — Linear prediction filter coefficients lsf2poly — lsf2poly function convert line spectral frequencies to prediction polynomial. mag2db — funcprot(0); marcumq — This function computes the generalized Marcum Q function of order m with noncentrality parameter a and argument b. medfilt1 — 1D median filtering mexihat — meyeraux — midcross — modulate — Modulates signal according to the modulation method morlet — movingrms — mscohere — It estimate (mean square) coherence of signals x and y. musicBase — Implements the core of the MUSIC algorithm ncauer — Analog prototype for Cauer filter. nnls — Non Negative Least Squares (nnls) for Ex=f with the constraint x>=0 nuttallwin — This function returns the filter coefficients of a Blackman-Harris window. parzenwin — This function returns the filter coefficients of a Parzen window. pburg — Calculate Burg maximum-entropy power spectral density. pchip — pchips — peak2peak — funcprot(0); peak2rms — This function calculates the ratio of peak magnitude to the Root Mean Square(RMS) value. pei_tseng_notch — peig — Psuedospectrum using the eigenvector method. periodogram — Return the periodogram (Power Spectral Density) of X phaseInputParseAs_ab — fs=0; phaseInputParseAs_sos — fs=0; phasedelay — cas variable is 2 if sos form is involved and 1 if direct rational form is given phasez — cas variable is 2 if sos form is involved and 1 if direct rational form is given pmusic — Psuedospectrum using MUSIC algorithm poly2ac — Convert prediction polynomial to autocorrelation sequence. poly2lsf — poly2rc — polyscale — errcheck1 polystab — This function stabilizes the polynomial transfer function. polyval — pow2db — rhs = argn(2) primitive — This function calculates the primitive of a given function supplied as input. prony — pulseperiod — pulsesep — pulsewidth — pulstran — This function generates the signal y = sum(func(t+d,...)) for each d. pwelch — Estimate power spectral density of data "x" by the Welch (1967) periodogram/FFT method. pyulear — qp_kaiser — Computes a finite impulse response (FIR) filter for use with a quasi-perfect reconstruction polyphase-network filter bank. rc2ac — rc2is — Convert reflection coefficients to inverse sine parameters rc2lar — rc2poly — rceps — Produce the cepstrum of the signal x, and if desired, the minimum phase reconstruction of the signal x. rcosdesign — RCOSDESIGN computes the raised cosine FIR filter rectpuls — rectwin — This function returns the filter coefficients of a rectangular window. remez1 — Parks-McClellan optimal FIR filter design resample — This function resamples in the input sequence x supplied by a factor of p/q. residued — residuez — risetime — rlevinson — rms — convert i/p values to their ascii values if they are of type char rooteig — Frequencies and power of sinusoids using eigenvector algorithm rootmusic — Frequencies and power of sinusoids using the root MUSIC algorithm rssq — This function calculates the square root of the sum of values of input vector IN. sampled2continuous — This function calculates the output reconstructed from the samples n supplied as input, at a rate of 1/s samples per unit time. sawtooth — schtrig — This function implements a multisignal Schmitt triggers with lev levels supplied as input. schurrc — narginchk(1,1,argn(2)); seqperiod — Calculates the period of a sequence sftrans — Transform band edges of a generic lowpass filter (cutoff at W=1) represented in splane zero-pole-gain form. sgolay — This function computes the filter coefficients for all Savitzsky-Golay smoothing filters. sgolayfilt — shanwavf — shiftdata — Shifts data by rearranging dimensions sigmoid_train — Evaluate a train of sigmoid functions at T. sinetone — Return a sinetone of the input sinewave — Return an M-element vector with I-th element given by 'sin(2* pi *(I+D-1)/N).' slewrate — sos2cell — Converts a second order section matrix to a cell array sos2ss — [nargout,nargin]=argn(); sos2tf — This function converts series second-order sections to direct H(z) = B(z)/A(z) form. sos2zp — This function converts series second-order sections to zeros, poles, and gains (pole residues). sosbreak — function for breaking a polynomial in second order polynomials (and an extra linear) sosfilt — specgram — spectral_adf — Return the spectral density estimator given a vector of autocovariances C, window name WIN, and bandwidth, B. spectral_xdf — Return the spectral density estimator given a data vector X, window name WIN, and bandwidth, B. spencer — Return Spencer's 15 point moving average of each column of X. ss2sos — not taking if, order and scale as input since they do not seem useful statelevels — stft — Compute the short-time Fourier transform of the vector X stmcb — function [b,a] = stmcb( x, u_in, q, p, niter, a_in ) strips — Plots vector or matrix in strips subspaceMethodsInputParser — Input parser to be used by pmusic and peig synthesis — Compute a signal from its short-time Fourier transform tf2sos — This function converts direct-form filter coefficients to series second-order sections. tf2zp — [z,p,k]= tf2zp(b,a); tf2zpk — form tfe — Estimate transfer function of system with input "x" and output "y". Use the Welch (1967) periodogram/FFT method. tfestimate — transpose — funcprot(0); trial_iirlp2mb — B = varargin(1) triang — This function returns the filter coefficients of a triangular window. tripuls — truth — y = %t tukeywin — This function returns the filter coefficients of a Tukey window. udecode — Decodes the input uniformly quantized values uencode — Performs uniform quantization of the input into 2^n levels ultrwin — This function returns the coefficients of an Ultraspherical window. unshiftdata — Inverts the effect of shiftdata unwrap2 — Unwrap radian phases by adding or subtracting multiples of 2*pi. upfirdn — This function upsamples the input data, applies the FIR filter and then downsamples it. upsample — This function upsamples the signal, inserting n-1 zeros between every element. upsamplefill — This function upsamples a vector interleaving given values or copies of the vector elements. var — vco — Voltage Controlled Oscillator wconv — Performs 1D or 2D convolution. welchwin — This function returns the filter coefficients of a Welch window. window — This function creates an m-point window from the function f given as input. wkeep — wrev — xcorr1 — Estimates the cross-correlation. xcorr2 — xcov1 — Compute covariance at various lags [=correlation(x-mean(x),y-mean(y))]. yulewalker — Fit an AR (p)-model with Yule-Walker estimates given a vector C of autocovariances '[gamma_0, ..., gamma_p]'. zerocrossing — This function estimates the points at which a given waveform crosses the x-axis. zp2sos — This function converts filter poles and zeros to second-order sections. zp2ss — Converts zeros / poles to state space. zp2tf — Converts zeros / poles to a transfer function. zplane — funcprot(0); seqperiod
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > seqperiodseqperiod
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Calculates the period of a sequence
Parameters
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x:
- A vector matrix or n-dimensional array
Description
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[p,num]=seqperiod(x)
-Returns an integer p such that x(1:p) is the smallest subsequence that repeats in x
-The number of times the subsequence repeats is returned in num (may not be an integer)
-Repetitions may be incomplete at the end of the sequence but no breaks are permitted between repetitions
-If there is no subsequence that repeats in x then p=length(x)
-If x is a matrix or n-dimesnional array, the function operates along the first non-singleton dimension of x
Examples
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x = [ 4 0 1 5 ;
-1 1 2 5 ;
-2 0 3 5 ;
-3 1 1 5 ] ;
-p = seqperiod ( x )
-p =
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-4. 2. 3. 1.
-A = zeros ( 4 , 1 , 4 ) ;
-A ( : , 1 , : ) = x ;
-p1 = seqperiod ( A ) ;
-p1 =
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-( : , : , 1 )
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-4.
-( : , : , 2 )
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-2.
-( : , : , 3 )
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-3.
-( : , : , 4 )
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-1.
sftrans
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > sftranssftrans
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Transform band edges of a generic lowpass filter (cutoff at W=1) represented in splane zero-pole-gain form.
Calling Sequence
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[ Sz , Sp , Sg ] = sftrans ( Sz , Sp , Sg , W , stop )
-[ Sz , Sp ] = sftrans ( Sz , Sp , Sg , W , stop )
-[ Sz ] = sftrans ( Sz , Sp , Sg , W , stop ) Parameters
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Sz:
- Zeros.
Sp:
- Poles.
Sg:
- Gain.
W:
- Edge of target filter.
stop:
- True for high pass and band stop filters or false for low pass and band pass filters.
Description
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This is an Octave function.
-Theory: Given a low pass filter represented by poles and zeros in the splane, you can convert it to a low pass, high pass, band pass or band stop by transforming each of the poles and zeros
-individually. The following table summarizes the transformation:
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Transform Zero at x Pole at x
----------------- ------------------------- ------------------------
-Low Pass zero: Fc x/C pole: Fc x/C
-S -> C S/Fc gain: C/Fc gain: Fc/C
----------------- ------------------------- ------------------------
-High Pass zero: Fc C/x pole: Fc C/x
-S -> C Fc/S pole: 0 zero: 0
-gain: -x gain: -1/x
----------------- ------------------------- ------------------------
-Band Pass zero: b +- sqrt(b^2-FhFl) pole: b +- sqrt(b^2-FhFl)
-S^2+FhFl pole: 0 zero: 0
-S -> C -------- gain: C/(Fh-Fl) gain: (Fh-Fl)/C
-S(Fh-Fl) b=x/C (Fh-Fl)/2 b=x/C (Fh-Fl)/2
----------------- ------------------------- ------------------------
-Band Stop zero: b +- sqrt(b^2-FhFl) pole: b +- sqrt(b^2-FhFl)
-S(Fh-Fl) pole: +-sqrt(-FhFl) zero: +-sqrt(-FhFl)
-S -> C -------- gain: -x gain: -1/x
-S^2+FhFl b=C/x (Fh-Fl)/2 b=C/x (Fh-Fl)/2
----------------- ------------------------- ------------------------
-Bilinear zero: (2+xT)/(2-xT) pole: (2+xT)/(2-xT)
-2 z-1 pole: -1 zero: -1
-S -> - --- gain: (2-xT)/T gain: (2-xT)/T
-T z+1
----------------- ------------------------- ------------------------
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where C is the cutoff frequency of the initial lowpass filter, Fc is the edge of the target low/high pass filter and [Fl,Fh] are the edges of the target band pass/stop filter. With abundant tedious
-algebra, you can derive the above formulae yourself by substituting the transform for S into H(S)=S-x for a zero at x or H(S)=1/(S-x) for a pole at x, and converting the result into the form:
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H(S)=g prod(S-Xi)/prod(S-Xj)
Examples
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[ Sz , Sp , Sg ] = sftrans ( 5 , 10 , 15 , 20 , 30 )
-Sz = 4
-Sp = 2
-Sg = 7.5000
sgolay
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > sgolaysgolay
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This function computes the filter coefficients for all Savitzsky-Golay smoothing filters.
Calling Sequence
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F = sgolay ( p , n )
-F = sgolay ( p , n , m )
-F = sgolay ( p , n , m , ts ) Parameters
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p:
- polynomial
n:
- odd integer value, larger than polynomial p
m:
- positive integer less than 2^31 or logical
ts:
- real or complex value
Description
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This is an Octave function.
-This function computes the filter coefficients for all Savitzsky-Golay smoothing filters of order p for length n (odd).
-m can be used in order to get directly the mth derivative; ts is a scaling factor.
Examples
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y = sgolay ( 1 , 3 , 0 )
-y =
-0.83333 0.33333 - 0.16667
-0.33333 0.33333 0.33333
-- 0.16667 0.33333 0.83333
sgolayfilt
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > sgolayfiltCalling Sequence
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y = sgolayfilt ( x )
-y = sgolayfilt ( x , p )
-y = sgolayfilt ( x , p , n )
-y = sgolayfilt ( x , p , n , m )
-y = sgolayfilt ( x , p , n , m , ts ) Parameters
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x:
- vector or matrix of real or complex numbers
p:
- polynomial order, real number less than n, default value 3
n:
- integer, odd number greater than p
m:
- vector of real positive valued numbers, length n
ts:
- real number, default value 1
Description
-
This function applies a Savitzky-Golay FIR smoothing filter to the data given in the vector x; if x is a matrix, this function operates
-on each column.
-The polynomial order p should be real, less than the size of the frame given by n.
-m is a weighting vector with default value identity matrix.
-ts is the dimenstion along which the filter operates. If not specified, the function operates along the first non singleton dimension.
Examples
-
sgolayfilt ( [ 1 ; 2 ; i ; 4 ; 7 ] , 0.3 , 3 , 0 , 0 )
-ans =
-1.0000 + 0.3333 i
-1.0000 + 0.3333 i
-2.0000 + 0.3333 i
-3.6667 + 0.3333 i
-3.6667 + 0.3333 i
-This function being called from Octave
shanwavf
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > shanwavfCalling Sequence
-
[ psi , x ]= shanwavf ( lb , ub , n , fb , fc ) Parameters
-
lb:
- Real or complex valued vector or matrix
ub:
- Real or complex valued vector or matrix
n:
- Real valued integer strictly positive
fb:
- Real or complex valued vector or matrix, strictly positive value for scalar input
fc:
- Real or complex valued vector or matrix, strictly positive value for scalar input
Description
-
This is an Octave function
-This function implements the complex Shannon wavelet function and returns the value obtained. The complex Shannon wavelet is defined by a bandwidth parameter FB, a wavelet center frequency FC on an N point regular grid in the interval [LB,UB].
Examples
-
1. [ a , b ] = shanwavf ( 2 , 8 , 3 , 1 , 6 )
-a = [ - 3.8982e-17 + 1.1457e-31 i 3.8982e-17 - 8.4040e-31 i - 3.8982e-17 + 4.5829e-31 i ]
-b = [ 2 5 8 ]
-2. [ a , b ] = shanwavf ( 1 , 2 , 1 , [ 2 , 2 ; i , 2 ] , [ - 1 , 2 ; - i , i ] )
-a = [ - 5.5128e-17 - 2.7005e-32 i - 5.5128e-17 + 5.4010e-32 i ;
-8.6404e+06 + 8.6404e+06 i - 1.9225e-22 - 0.0000e+00 i ]
-b = 2
shiftdata
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > shiftdatashiftdata
-
Shifts data by rearranging dimensions
Description
-
[y,perm,nshifts]=shiftdata(x,dim)
-Shifts the entries along dimension dim in x to the first column and returns the permutation vector in perm
-[y,perm,nshifts]=shiftdata(x)
-Shifts the entries along dimension dim in x to the first column and returns the number of shifts in nshifts
Examples
-
-x = testmatrix ( ' magi ' , 3 )
-x =
-
-8. 1. 6.
-3. 5. 7.
-4. 9. 2.
-[ y , perm , nshifts ] = shiftdata ( x , 2 )
-nshifts =
-
-[ ]
-perm =
-
-2. 1.
-y =
-
-8. 3. 4.
-1. 5. 9.
-6. 7. 2.
-
-x = 1 : 5
-x =
-
-1. 2. 3. 4. 5.
-[ y , perm , nshifts ] = shiftdata ( x )
-nshifts =
-
-1.
-perm =
-
-[ ]
-y =
-
-1.
-2.
-3.
-4.
-5.
sigmoid_train
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > sigmoid_trainsigmoid_train
-
Evaluate a train of sigmoid functions at T.
Calling Sequence
-
y = sigmoid_train ( t , ranges , rc ) Parameters
-
t:
- integer
ranges:
- matrix
Description
-
The number and duration of each sigmoid is determined from RANGES. Each row of RANGES represents a real interval, e.g. if sigmoid 'i' starts at 't=0.1' and ends at 't=0.5', then 'RANGES(i,:) = [0.1 0.5]'. The input RC is an array that defines the rising and falling time constants of each sigmoid. Its size must equal the size of RANGES.
Examples
-
sigmoid_train ( 0.1 , [ 1 : 3 ] , 4 )
-ans =
-0.27375
sinetone
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > sinetonesinetone
-
Return a sinetone of the input
Calling Sequence
-
y = sinetone ( FREQ )
-y = sinetone ( FREQ , RATE )
-y = sinetone ( FREQ , RATE , SEC )
-y = sinetone ( FREQ , RATE , SEC , AMPL ) Parameters
-
FREQ:
- frequency of sinetone
RATE:
- Sampling rate
SEC:
- Length in seconds
AMPL:
- Amplitude
Description
-
Return a sinetone of frequency FREQ with a length of SEC seconds atsampling rate RATE and with amplitude AMPL.The arguments FREQ and AMPL may be vectors of common size.The defaults are RATE = 8000, SEC = 1, and AMPL = 64.
sinewave
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > sinewavesinewave
-
Return an M-element vector with I-th element given by 'sin(2* pi *(I+D-1)/N).'
Calling Sequence
-
y = sinewave ( M )
-y = sinewave ( M , N )
-y = sinewave ( M , N , D ) Parameters
-
M:
- Input vector
N:
- The default value for N is M
D:
- The default value for D is 0
AMPL:
- Amplitude
Description
-
Return an M-element vector with I-th element given by 'sin(2* pi *(I+D-1)/N).'
slewrate
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > slewratesos2cell
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > sos2cellsos2cell
-
Converts a second order section matrix to a cell array
Description
-
c=sos2cell(s) converts an L-by-6 second-order-section matrix s given by:
-s = [B1 A1
-B2 A2
-...
-BL AL]
-to a cell array c = { {B1},{A1}, {B2},{A2}, ... {BL},{AL}} where each
-numerator vector Bi and denominator vector Ai contains the coefficients of a
-linear or quadratic polynomial. If the polynomial is linear, the coefficients
-zero-padded on the right
-c=sos2cell(s,g) adds a leading gain term to the start of the cell array as:
-c={ {[g,1]},{B1},{A1}, {B2},{A2}, ... {BL},{AL}}
-Example
-s=rand(2,6)
-s =
-
-
column 1 to 5
-
0.0437334 0.2639556 0.2806498 0.7783129 0.1121355
-0.4818509 0.4148104 0.1280058 0.2119030 0.6856896
-
column 6
-
0.1531217
-0.6970851
-
sos2cell(s,2)
-ans =
-
-
-
column 1 to 3
-
![2,1] [0.0437334,0.2639556,0.2806498] [0.7783129,0.1121355,0.1531217] !
-
column 4 to 5
-
![0.4818509,0.4148104,0.1280058] [0.2119030,0.6856896,0.6970851] !
-Author
-Ankur Mallick
sos2ss
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > sos2sssos2ss
-
[nargout,nargin]=argn();
sos2tf
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > sos2tfsos2tf
-
This function converts series second-order sections to direct H(z) = B(z)/A(z) form.
Calling Sequence
-
[ B ] = sos2tf ( sos )
-[ B ] = sos2tf ( sos , g )
-[ B , A ] = sos2tf (...) Parameters
-
sos:
- matrix of real or complex numbers
g:
- real or complex value, default value is 1
Description
-
This is an Octave function.
-This function converts series second-order sections to direct H(z) = B(z)/A(z) form.
-The input is the sos matrix and the second parameter is the overall gain, default value of which is 1.
-The output is a vector.
Examples
-
[ a , b ] = sos2tf ( [ 1 , 2 , 3 , 4 , 5 , 6 ] )
-a =
-1 2 3
-b =
-4 5 6
sos2zp
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > sos2zpsos2zp
-
This function converts series second-order sections to zeros, poles, and gains (pole residues).
Calling Sequence
-
z = sos2zp ( sos )
-z = sos2zp ( sos , g )
-[ z , p ] = sos2zp (...)
-[ z , p , k ] = sos2zp (...) Parameters
-
sos:
- matrix of real or complex numbers
g:
- real or complex value, default value is 1
z:
- column vector
p:
- column vector
Description
-
This is an Octave function.
-This function converts series second-order sections to zeros, poles, and gains (pole residues).
-The input is the sos matrix and the second parameter is the overall gain, default value of which is 1.
-The outputs are z, p, k. z and p are column vectors containing zeros and poles respectively, and k is the overall gain.
Examples
-
[ a , b , c ] = sos2zp ( [ 1 , 2 , 3 , 4 , 5 , 6 ] )
-a =
-- 1.0000 + 1.4142 i
-- 1.0000 - 1.4142 i
-b =
-- 0.6250 + 1.0533 i
-- 0.6250 - 1.0533 i
-c = 1
sosbreak
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > sosbreaksosbreak
-
function for breaking a polynomial in second order polynomials (and an extra linear)
sosfilt
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > sosfiltParameters
-
sos:
- Real or complex valued Lx6 vector or matrix
x:
- Real or complex valued vector or matrix
Description
-
This is an Octave function
-Second order section digital filter sos is applied to the input vector and the output vector obtained is of the same length.
Examples
-
1. sosfilt ( [ 1 2 3 4 5 6 ] , [ - 1 10 i ; 1 2 ] )
-ans = [ - 0.25000 0.00000 ; 0.06250 0.50000 ]
-2. sosfilt ( [ 32 28 84 47 2 29 ] , - 1 )
-ans = - 0.68085
specgram
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > specgramspectral_adf
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > spectral_adfspectral_adf
-
Return the spectral density estimator given a vector of autocovariances C, window name WIN, and bandwidth, B.
Calling Sequence
-
spectral_adf ( C )
-spectral_adf ( C , WIN )
-spectral_adf ( C , WIN , B ) Parameters
-
C:
- Autocovariances
WIN:
- Window names
B:
- Bandwidth
Description
-
Return the spectral density estimator given a vector ofautocovariances C, window name WIN, and bandwidth, B.
-The window name, e.g., "triangle" or "rectangle" is used to search for a function called 'WIN_lw'.
-If WIN is omitted, the triangle window is used.
-If B is omitted, '1 / sqrt (length (C))' is used.
spectral_xdf
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > spectral_xdfspectral_xdf
-
Return the spectral density estimator given a data vector X, window name WIN, and bandwidth, B.
Calling Sequence
-
spectral_xdf ( X )
-spectral_xdf ( X , WIN )
-spectral_xdf ( X , WIN , B ) Parameters
-
X:
- Data Vector
WIN:
- Window names
B:
- Bandwidth
Description
-
Return the spectral density estimator given a data vector X, window name WIN, and bandwidth, B.
-The window name, e.g., "triangle" or "rectangle" is used to search for a function called 'WIN_lw'.
-If WIN is omitted, the triangle window is used.
-If B is omitted, '1 / sqrt (length (X))' is used.
spencer
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > spencerspencer
-
Return Spencer's 15 point moving average of each column of X.
Parameters
-
X:
- Real scalar or vector
Description
-
Return Spencer's 15 point moving average of each column of X.
ss2sos
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > ss2sosss2sos
-
not taking if, order and scale as input since they do not seem useful
statelevels
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > statelevelsCalling Sequence
-
levels = statelevels ( x , nbins , method , bounds )
-[ levels histogram ]= statelevels ( x , nbins , method , bounds )
-[ levels histogram bins ]= statelevels ( x , nbins , method , bounds )
-[ levels histogram bins ]= statelevels ( x , nbins , method , bounds , 'fig ', On or Off ) Parameters
-
x:
- real vector
nbins:
- number of histogram bins to use in the histogram as a positive scalar, where the default value is 100
method:
- method to estimate the statelevels using specified METHOD as one of 'mean' or 'mode', where the default value is 'mode'
bounds:
- specify the lower and upper bound for the histogram as a two-element row vector
fig:
- specify the logical input value to display figure as one of 'on' or 'off', where the default input in 'off'.
levels:
- return lower and upper level values
histogram:
- return histogram values
bins:
- return binlevels values
Examples
-
x = [ 1.2 , 5 , 10 , - 20 , 12 ]
-nbins = 10
-method = ' mode '
-bounds = [ 1 10 ]
-levels = statelevels ( x , nbins , method , bounds )
stft
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > stftstft
-
Compute the short-time Fourier transform of the vector X
Calling Sequence
-
Y = stft ( X )
-Y = stft ( X , WIN_SIZE )
-Y = stft ( X , WIN_SIZE , INC )
-Y = stft ( X , WIN_SIZE , INC , NUM_COEF )
-Y = stft ( X , WIN_SIZE , INC , NUM_COEF , WIN_TYPE )
-[ Y , C ] = stft ( X )
-[ Y , C ] = stft ( X , WIN_SIZE )
-[ Y , C ] = stft ( X , WIN_SIZE , INC )
-[ Y , C ] = stft ( X , WIN_SIZE , INC , NUM_COEF )
-[ Y , C ] = stft ( X , WIN_SIZE , INC , NUM_COEF , WIN_TYPE ) Parameters
-
X:
- Real scalar or vector
WIN_SIZE:
- Size of the window used
INC:
- Increment
WIN_TYPE:
- Type of window
Description
-
Compute the short-time Fourier transform of the vector X with NUM_COEF coefficients by applying a window of WIN_SIZE data points and an increment of INC points.
-
Before computing the Fourier transform, one of the following windows is applied:
-
"hanning" -> win_type = 1
-
"hamming" -> win_type = 2
-
"rectangle" -> win_type = 3
-
The window names can be passed as strings or by the WIN_TYPE number.
-
The following defaults are used for unspecified arguments:WIN_SIZE= 80, INC = 24, NUM_COEF = 64, and WIN_TYPE = 1.
-
Y = stft (X, ...)' returns the absolute values of the Fourier coefficients according to the NUM_COEF positive frequencies.
-
'[Y, C] = stft (x, ...)' returns the entire STFT-matrix Y and a 3-element vector C containing the window size, increment, and window type, which is needed by the 'synthesis' function.
stmcb
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > stmcbstmcb
-
function [b,a] = stmcb( x, u_in, q, p, niter, a_in )
strips
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > stripsstrips
-
Plots vector or matrix in strips
Calling Sequence
-
strips ( x );
-strips ( x , sd );
-strips ( x , sd , fs );
-strips ( x , sd , fs , scale ); Description
-
strips(x)
-Plots a vector x in horizontal strips of length 250
-If x is a matrix, it plots each column of x on a separate strip with the leftmost
-column as the topmost strip
-strips(x,sd)
-Plots x in strips of length sd samples each
-strips(x,sd,fs)
-Plots x in strips of duration sd seconds with sampling frequency fs (in Hz)
-strips(x,sd,fs,scale)
-Plots x in strips as above, and scales the vertical axis by scale
-If x is a matrix, strips uses a column vector of all the elements of x for the strip plot
-If x has complex entries, only the real part of those entries are considered
-Author
-Ankur Mallick
subspaceMethodsInputParser
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > subspaceMethodsInputParsersubspaceMethodsInputParser
-
Input parser to be used by pmusic and peig
synthesis
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > synthesissynthesis
-
Compute a signal from its short-time Fourier transform
Parameters
-
Y:
- Shirt-time fourier transform
C:
- 3-element vector C specifying window size, increment, window type.
Description
-
Compute a signal from its short-time Fourier transform Y and a 3-element vector C specifying window size, increment, and window type.
-The values Y and C can be derived by
-[Y, C] = stft (X , ...)
tf2sos
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > tf2sostf2sos
-
This function converts direct-form filter coefficients to series second-order sections.
Calling Sequence
-
[ sos ] = tf2sos ( b , a )
-[ sos , g ] = tf2sos ( b , a ) Parameters
-
b:
- matrix of real numbers
a:
- matrix of real numbers
Description
-
This is an Octave function.
-This function converts direct-form filter coefficients to series second-order sections.
-The input parameters b and a are vectors specifying the digital filter H(z) = B(z)/A(z).
-The output is the sos matrix and the overall gain.
-If there is only one output argument, the overall filter gain is applied to the first second-order section in the sos matrix.
Examples
-
tf2sos ( [ 1 , 2 , 3 , 4 , 5 , 6 ] , 2 )
-ans =
-0.50000 0.80579 1.07239 1.00000 0.00000 0.00000
-1.00000 - 1.10337 1.87524 1.00000 0.00000 0.00000
-1.00000 1.49180 - 0.00000 1.00000 0.00000 0.00000
tf2zp
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > tf2zptf2zp
-
[z,p,k]= tf2zp(b,a);
tf2zpk
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > tf2zpktfe
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > tfetfe
-
Estimate transfer function of system with input "x" and output "y". Use the Welch (1967) periodogram/FFT method.
Calling Sequence
-
[ Pxx , freq ] = tfe ( x , y , Nfft , Fs , window , overlap , range , plot_type , detrend ) Parameters
-
x:
- [non-empty vector] system-input time-series data
y:
- [non-empty vector] system-output time-series data
win:
- [real vector] of window-function values between 0 and 1; the data segment has the same length as the window. Default window shape is Hamming. [integer scalar] length of each data segment. The default value is window=sqrt(length(x)) rounded up to the nearest integer power of 2; see 'sloppy' argument.
overlap:
- [real scalar] segment overlap expressed as a multiple of window or segment length. 0 <= overlap < 1, The default is overlap=0.5 .
Nfft:
- [integer scalar] Length of FFT. The default is the length of the "window" vector or has the same value as the scalar "window" argument. If Nfft is larger than the segment length, "seg_len", the data segment is padded with "Nfft-seg_len" zeros. The default is no padding. Nfft values smaller than the length of the data segment (or window) are ignored silently.
Fs:
- [real scalar] sampling frequency (Hertz); default=1.0
range:
- 'half', 'onesided' : frequency range of the spectrum is zero up to but not including Fs/2. Power from negative frequencies is added to the positive side of the spectrum, but not at zero or Nyquist (Fs/2) frequencies. This keeps power equal in time and spectral domains. See reference [2]. 'whole', 'twosided' : frequency range of the spectrum is-Fs/2 to Fs/2, with negative frequenciesstored in "wrap around" order after the positivefrequencies; e.g. frequencies for a 10-point 'twosided'spectrum are 0 0.1 0.2 0.3 0.4 0.5 -0.4 -0.3 -0.2 -0.1 'shift', 'centerdc' : same as 'whole' but with the first half of the spectrum swapped with second half to put the zero-frequency value in the middle. (See "help fftshift". If data (x and y) are real, the default range is 'half', otherwise default range is 'whole'.
plot_type:
- 'plot', 'semilogx', 'semilogy', 'loglog', 'squared' or 'db': specifies the type of plot. The default is 'plot', which means linear-linear axes. 'squared' is the same as 'plot'. 'dB' plots "10*log10(psd)". This argument is ignored and a spectrum is not plotted if the caller requires a returned value.
detrends:
- 'no-strip', 'none' -- do NOT remove mean value from the data'short', 'mean' -- remove the mean value of each segment from each segment of the data. 'linear',-- remove linear trend from each segment of the data.'long-mean'-- remove the mean value from the data before splitting it into segments. This is the default.
Description
-
Estimate transfer function of system with input "x" and output "y". Use the Welch (1967) periodogram/FFT method.
tfestimate
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > tfestimateCalling Sequence
-
tfestimate ( x , y , window , overlap , Nfft , Fs , range )
-[ Pxx , freq ] = tfestimate (…) Parameters
-
x:
- Input.
y:
- Output.
window:
- [real vector] of window-function values between 0 and 1; the data segment has the same length as the window. Default window shape is Hamming. [integer scalar] length of each data segment. The default value is window=sqrt(length(x)) rounded up to the nearest integer power of 2; see 'sloppy' argument.
overlap:
- [real scalar] segment overlap expressed as a multiple of window or segment length. 0 <= overlap < 1, The default is overlap=0.5 .
Nfft:
- [integer scalar] Length of FFT. The default is the length of the "window" vector or has the same value as the scalar "window" argument. If Nfft is larger than the segment length, "seg_len", the data segment is padded with "Nfft-seg_len" zeros. The default is no padding. Nfft values smaller than the length of the data segment (or window) are ignored silently.
Fs:
- [real scalar] sampling frequency (Hertz); default=1.0
range:
- 'half', 'onesided' : frequency range of the spectrum is zero up to but not including Fs/2. Power from negative frequencies is added to the positive side of the spectrum, but not at zero or Nyquist (Fs/2) frequencies. This keeps power equal in time and spectral domains. See reference [2]. 'whole', 'twosided' : frequency range of the spectrum is -Fs/2 to Fs/2, with negative frequencies stored in "wrap around" order after the positive frequencies; e.g. frequencies for a 10-point 'twosided' spectrum are 0 0.1 0.2 0.3 0.4 0.5 -0.4 -0.3 -0.2 -0.1 'shift', 'centerdc' : same as 'whole' but with the first half of the spectrum swapped with second half to put the zero-frequency value in the middle. (See "help fftshift". If data (x and y) are real, the default range is 'half', otherwise default range is 'whole'.
Description
-
This function is being called from Octave.
-Estimate transfer function of system with input x and output y. Use the Welch (1967) periodogram/FFT method.
Examples
-
[ Pxx , freq ] = tfestimate ( [ 1 2 3 ] , [ 4 5 6 ] )
-Pxx =
-
-1.7500 + 0.0000 i
-1.5947 + 0.3826 i
-1.2824 + 0.0000 i
-
-freq =
-
-0.00000
-0.25000
-0.50000
transpose
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > transposetrial_iirlp2mb
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > trial_iirlp2mbtrial_iirlp2mb
-
B = varargin(1)
triang
-
-
-
-
-
-
-
- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > triangtriang
-
This function returns the filter coefficients of a triangular window.
Parameters
-
m:
- positive integer value
y:
- output variable, vector of real numbers
Description
-
This is an Octave function.
-This function returns the filter coefficients of a triangular window of length m supplied as input, to the output vector y.
Examples
-
triang ( 5 )
-ans =
-0.3333333
-0.6666667
-1.
-0.6666667
-0.3333333
tripuls
-
-
-
-
-
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > tripulsCalling Sequence
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[ y ] = tripuls ( t )
-[ y ] = tripuls ( t , w )
-[ y ] = tripuls ( t , w , skew ) Parameters
-
t:
- vector of real or complex numbers
w:
- real or complex number
skew:
- real number, -1 <= s <= 1
Description
-
This function generates a triangular pulse which is sampled at times t over the interval [-w/2,w/2]. The value of skew lies between -1
-and 1.
-The value of skew represents the relative placement of the peak in the given width.
Examples
-
tripuls ( [ 0 , .5 , .6 , 1 ] , 0.9 )
-ans =
-1 0 0 0
-This function being called from Octave
truth
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > truthtukeywin
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > tukeywintukeywin
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This function returns the filter coefficients of a Tukey window.
Calling Sequence
-
w = tukeywin ( m )
-w = tukeywin ( m , r ) Parameters
-
m:
- positive integer
r:
- positive real number, between 0 and 1
Description
-
This is an Octave function.
-This function returns the filter coefficients of a Tukey window of length m supplied as input, to the output vector w.
-The second parameter r defines the ratio between the constant and cosine section and its value has to be between 0 and 1, with default value 0.5.
Examples
-
tukeywin ( 5 , 2 )
-ans =
-0.
-0.5
-1.
-0.5
-0.
udecode
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > udecodeudecode
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Decodes the input uniformly quantized values
Calling Sequence
-
y = uencode ( u , n , v , 'saturatemode ') Description
-
Uniformly decodes the input vector or n-dimensional array of integers u with peak values +/- v
-If u has only positive values, the range of integers is assumed to be [0,2^n-1]
-If u has positive and negative values the range of integers is assumed to be [-2^(n-1),2^(n-1)-1]
-If v is not specified, its default value is 1
-If saturatemode='wrap' the output is wrapped using modulo arithmetic if overflow occurs
-If saturatemode='saturate' the output is saturated if overflow accors
-Example
-u = int8([-1 1 2 -5]);
-ysat = udecode(u,3)
-ysat =
-
- 0.25 0.25 0.5 - 1.
-Author
-Ankur Mallick
-[1] International Telecommunication Union. General Aspects of Digital Transmission Systems: Vocabulary of Digital Transmission and Multiplexing, and Pulse Code Modulation (PCM) Terms. ITU-T Recommendation G.701. March, 1993.
uencode
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > uencodeuencode
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Performs uniform quantization of the input into 2^n levels
Calling Sequence
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y = uencode ( u , n , v , 'signflag ') Description
-
Uniformly quantizes the input vector or n-dimensional array u into 2^n levels in the interval [-v,v]
-If v is not specified, its default value is 1
-'signflag' is a string that determines the nature of the quantization
-If signflag='unsigned' then y contains unsigned integers in the range [0,2^n-1] corresponding to the 2^n levels
-If signflag='unsigned' then y contains signed integers in the range [-2^(n-1),2^(n-1)-1] corresponding to the 2^n levels
-The size of the integers in y in bits(8,16, or 32) depends on the value of n
-If the input lies beyond +/- v it is saturated
-Example
-y=uencode(-1:0.5:1,3)
-y =
-
0 2 4 6 7
-Author
-Ankur Mallick
-References
-[1] International Telecommunication Union. General Aspects of Digital Transmission Systems: Vocabulary of Digital Transmission and Multiplexing, and Pulse Code Modulation (PCM) Terms. ITU-T Recommendation G.701. March, 1993.
ultrwin
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > ultrwinultrwin
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This function returns the coefficients of an Ultraspherical window.
Calling Sequence
-
w = ultrwin ( m , mu , par )
-w = ultrwin ( m , mu , par , key )
-w = ultrwin ( m , mu , par , key , norm )
-[ w , xmu ] = ultrwin (...) Parameters
-
m:
- positive integer value
mu:
-
par:
-
key:
-
norm:
-
Description
-
This is an Octave function.
-This function returns the coefficients of an Ultraspherical window of length m supplied as input, to the output vector w.
-The second parameter controls the ratio between side lobe to side lobe of the window's Fourier transform.
-The third parameter controls the ratio between main lobe width to side lobe. The default value is beta.
-The value of xmu is also returned for given beta, att or latt.
Examples
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ultrwin ( 3 , - 0.4 , 0.5 )
-ans =
-- 1.
-1.
-- 1.
unshiftdata
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > unshiftdataunshiftdata
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Inverts the effect of shiftdata
Description
-
y=unshiftdata(x,perm,nshifts)
-Applies the permutation perm or number of shifts nshifts on x to invert shiftdata
Examples
-
x = testmatrix ( ' magi ' , 3 )
-x =
-
-8. 1. 6.
-3. 5. 7.
-4. 9. 2.
-[ y , perm , nshifts ] = shiftdata ( x , 2 )
-nshifts =
-
-[ ]
-perm =
-
-2. 1.
-y =
-
-8. 3. 4.
-1. 5. 9.
-6. 7. 2.
-z = unshiftdata ( y , perm , nshifts )
-z =
-
-8. 1. 6.
-3. 5. 7.
-4. 9. 2.
-
-x = 1 : 5
-x =
-
-1. 2. 3. 4. 5.
-[ y , perm , nshifts ] = shiftdata ( x )
-nshifts =
-
-1.
-perm =
-
-[ ]
-y =
-
-1.
-2.
-3.
-4.
-5.
-
-z =
-
-1. 2. 3. 4. 5.
unwrap2
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > unwrap2unwrap2
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Unwrap radian phases by adding or subtracting multiples of 2*pi.
Calling Sequence
-
B = unwrap ( X )
-B = unwrap ( X , TOL )
-B = unwrap ( X , TOL , DIM ) Description
-
This function unwraps radian phases by adding or subtracting multiples of 2*pi as appropriate to remove jumps greater than TOL.
-
TOL defaults to pi.
-
Unwrap will work along the dimension DIM. If DIM is unspecified it defaults to the first non-singleton dimension.
Examples
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unwrap2 ( [ 1 , 2 , 3 ] )
-ans =
-1. 2. 3.
upfirdn
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > upfirdnupfirdn
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This function upsamples the input data, applies the FIR filter and then downsamples it.
Parameters
-
x:
-
h:
-
p:
-
q:
-
Description
-
This is an Octave function.
-This function upsamples the input data in the matrix by a factor of n. Then the upsampled data is FIR filtered. After this, the resultant is downsampled.
Examples
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upfirdn ( [ 1 , 2 , 3 ] , 2 , 3 , 5 )
-ans =
-
-2. 0.
upsample
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > upsampleupsample
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This function upsamples the signal, inserting n-1 zeros between every element.
Calling Sequence
-
y = upsample ( x , n )
-y = upsample ( x , n , phase ) Parameters
-
x:
- scalar, vector or matrix of real or complex numbers
n:
- real number or vector
phase:
- integer value, 0 <= phase <= (n - 1 ), default value 0, or logical
Description
-
This is an Octave function.
-This function upsamples the signal, inserting n-1 zeros between every element. If x is a matrix, every column is upsampled.
-The phase determines the position of the inserted sample in the block of zeros. The default value is 0.
Examples
-
upsample ( 4 , 5 , 2 )
-ans =
-0. 0. 4. 0. 0.
upsamplefill
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > upsamplefillupsamplefill
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This function upsamples a vector interleaving given values or copies of the vector elements.
Calling Sequence
-
y = upsamplefill ( x , w )
-y = upsamplefill ( x , w , cpy ) Parameters
-
x:
- scalar, vector or matrix of real or complex numbers
w:
- scalar or vector of real or complex values
cpy:
- can take in "true" or "false", default is false
Description
-
This is an Octave function.
-This function upsamples a vector interleaving given values or copies of the vector elements.
-The second argument has the values in the vector w that are placed in between the elements of x.
-The third argument, if true, means that w should be scalar and that each value in x repeated w times.
Examples
-
upsamplefill ( [ 0.4 , 0.5 ] , 7 )
-ans =
-0.4 7. 0.5 7.
var
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > varCalling Sequence
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y = var ( x )
-y = var ( x , w )
-y = var ( x , w , dim ) Parameters
-
x:
- a vector or matrix.
w:
- weight vector W of length X, or may take the value of 0 and 1. The default value is 0. Consider only non-negative values.
dim:
- consider the variance along the dimension of X. 1 for clumun wise variamce and 2 for row wise variance.
y:
- returns the variance of the values in X.
vco
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > vcovco
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Voltage Controlled Oscillator
Calling Sequence
-
y = vco ( x , fc , fs )
-y = vco ( x ,[ fmin fmax ], fs ) Description
-
y=vco(x,fc,fs)
-Creates a frequency modulated cosine wave y whose frequency varies as the magnitude of x
-x lies in [-1,1]. x=-1 corresponds to a frequency of 0, x=0 corresponds to a frequency of fc
-and x=1 corresponds to a frequency of 2fc.
-y=vco(x,[fmin fmax],fs)
-Scales the frequency range so that x=-1 corresponds to a frequency of fmin and
-x=1 corresponds to a frequency of fmax
-If x is a matrix the same operation is performed on the columns on x
-Size of y is the same as the size of x
-Example
-x=rand()
-x =
-
0.2113249
-y=vco(x,2000,8000)
-y =
-
0.9454092
-Author
-Ankur Mallick
wconv
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > wconvwconv
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Performs 1D or 2D convolution.
Calling Sequence
-
y = wconv ( type , x , f )
-y = wconv ( type , x , f , shape ) Parameters
-
type:
- convolution type.
x:
- Signal vector or matrix.
f:
- FIR filter coefficients.
shape:
- Shape.
Description
-
This is an Octave function.
-It performs 1D or 2D convolution between the signal x and the filter coefficients f.
Examples
-
a = [ 1 2 3 4 5 ]
-b = [ 7 8 9 10 ]
-wconv ( 1 , a , b )
-ans =
-7 22 46 80 114 106 85 50
welchwin
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > welchwinwelchwin
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This function returns the filter coefficients of a Welch window.
Calling Sequence
-
w = welchwin ( m )
-w = welchwin ( m , opt ) Parameters
-
m:
- positive integer value
opt:
- string value, takes "periodic" or "symmetric"
w:
- output variable, vector of real numbers
Description
-
This is an Octave function.
-This function returns the filter coefficients of a Welch window of length m supplied as input, to the output vector w.
-The second parameter can take the values "periodic" or "symmetric", depending on which the corresponding form of window is returned. The default is symmetric.
-For symmetric, the length should be an integer>2. For periodic, the length should be an integer>1.
Examples
-
welchwin ( 4 , " symmetric " )
-ans =
-0.
-0.8888889
-0.8888889
-0.
window
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > windowwindow
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This function creates an m-point window from the function f given as input.
Calling Sequence
-
w = window ( f , m )
-w = window ( f , m , opts ) Parameters
-
f:
- string value
m:
- positive integer value
opts:
- string value, takes in "periodic" or "symmetric"
w:
- output variable, vector of real numbers
Description
-
This is an Octave function.
-This function creates an m-point window from the function f given as input, in the output vector w.
-f can take any valid function as a string, for example "blackmanharris".
Examples
-
window ( " hanning " , 5 )
-ans =
-0.
-0.5
-1.
-0.5
-0.
wkeep
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > wkeepCalling Sequence
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[ y ]= wkeep ( x , l )
-[ y ]= wkeep ( x , l , opt ) Parameters
-
x:
- Real, complex or string type input vector or matrix
l:
- Length of matrix required
opt:
- Character input to determine which side to extract from
Description
-
This is an Octave function
-[y]=wkeep(x,l) extracts a vector of length l from the centre of input vector x.
-[y]=wkeep(x,l,opt) extracts vector based on opt which could be 'l','r' or 'c' (left, right or central).
Examples
-
1. [ y ] = wkeep ( [ 1 2 3 ; 4 5 6 ] , [ 2 2 ] )
-y = 1 2
-2. [ y ] = wkeep ( [ 1 2 3 4 5 6 ] , 3 , ' r ' )
-y = 4 5 6
wrev
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > wrevParameters
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x:
- Input vector of string, real or complex values
Description
-
This is an Octave function.
-This function reverses the order of elements of the input vector x.
Examples
-
1. wrev ( [ 1 2 3 ] )
-ans = 3 2 1
-2. wrev ( [ ' a ' , ' b ' , ' c ' ] )
-ans = cba
xcorr1
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > xcorr1xcorr1
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Estimates the cross-correlation.
Calling Sequence
-
[ R , lag ] = xcorr1 ( X , Y , maxlag , scale )
-[ R , lag ] = xcorr1 ( X , Y , maxlag )
-[ R , lag ] = xcorr1 ( X , Y ) Parameters
-
X:
- [non-empty; real or complex; vector or matrix] data.
Y:
- [real or complex vector] data.
maxlag:
- [integer scalar] maximum correlation lag If omitted, the default value is N-1, where N is the greater of the lengths of X and Y or, if X is a matrix, the number of rows in X.
scale:
- [character string] specifies the type of scaling applied to the correlation vector (or matrix). is one of:
Description
-
This is an Octave function.
-Estimate the cross correlation R_xy(k) of vector arguments X and Y or, if Y is omitted, estimate autocorrelation R_xx(k) of vector X, for a range of lags k specified by argument "maxlag". If X is a
-matrix, each column of X is correlated with itself and every other column.
-
The cross-correlation estimate between vectors "x" and "y" (of length N) for lag "k" is given by
-
N
-R_xy(k) = sum x_{i+k} conj(y_i),
-i=1
-
where data not provided (for example x(-1), y(N+1)) is zero. Note the definition of cross-correlation given above. To compute a cross-correlation consistent with the field of statistics, see xcov.
Examples
-
[ R , lag ] = xcorr1 ( [ 5 5 ] , [ 2 2 ] , 2 , ' biased ' )
-
-R =
-
-0 5 10 5 0
-
-lag =
-
-- 2 - 1 0 1 2
xcorr2
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > xcorr2Calling Sequence
-
c = xcorr2 ( a )
-c = xcorr2 ( a , b )
-c = xcorr2 ( a , b , biasflag ) xcov1
-
-
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > xcov1xcov1
-
Compute covariance at various lags [=correlation(x-mean(x),y-mean(y))].
Calling Sequence
-
[ R , lag ] = xcov ( X )
-... = xcov ( X , Y )
-... = xcov (..., maxlag )
-... = xcov (..., scale ) Parameters
-
X:
- Input vector
Y:
- if specified, compute cross-covariance between X and Y, otherwise compute autocovariance of X.
maxlag:
- is specified, use lag range [-maxlag:maxlag], otherwise use range [-n+1:n-1].
scale:
-
'biased':
- for covariance=raw/N,
'unbiased':
- for covariance=raw/(N-|lag|),
'coeff':
- for covariance=raw/(covariance at lag 0),
'none':
- for covariance=raw
'none':
- is the default.
Description
-
Compute covariance at various lags [=correlation(x-mean(x),y-mean(y))]. Returns the covariance for each lag in the range, plus an optional vector of lags.
yulewalker
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > yulewalkeryulewalker
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Fit an AR (p)-model with Yule-Walker estimates given a vector C of autocovariances '[gamma_0, ..., gamma_p]'.
Calling Sequence
-
A = yulewalker ( C )
-[ A , V ]= yulewalker ( C ) Parameters
-
C:
- Autocovariances
Description
-
Fit an AR (p)-model with Yule-Walker estimates given a vector C of autocovariances '[gamma_0, ..., gamma_p]'.
-Returns the AR coefficients, A, and the variance of white noise, V.
zerocrossing
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > zerocrossingzerocrossing
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This function estimates the points at which a given waveform crosses the x-axis.
Parameters
-
w:
-
y:
-
x:
-
Description
-
This is an Octave function.
-This function estimates the points at which a given waveform y = y(w) crosses the x-axis. It uses linear interpolation.
zp2sos
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > zp2soszp2sos
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This function converts filter poles and zeros to second-order sections.
Calling Sequence
-
[ sos ] = zp2sos ( z )
-[ sos ] = zp2sos ( z , p )
-[ sos ] = zp2sos ( z , p , k )
-[ sos , g ] = zp2sos (...) Parameters
-
z:
- column vector
p:
- column vector
k:
- real or complex value, default value is 1
Description
-
This is an Octave function.
-This function converts filter poles and zeros to second-order sections.
-The first and second parameters are column vectors containing zeros and poles. The third parameter is the overall filter gain, the default value of which is 1.
-The output is the sos matrix and the overall gain.
-If there is only one output argument, the overall filter gain is applied to the first second-order section in the sos matrix.
Examples
-
zp2sos ( [ 1 , 2 , 3 ] , 2 , 6 )
-ans =
-6 - 18 12 1 - 2 0
-1 - 3 0 1 0 0
zp2ss
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > zp2sszp2ss
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Converts zeros / poles to state space.
Calling Sequence
-
[ a , b , c , d ] = zp2ss ( z , p , k )
-[ a , b , c ] = zp2ss ( z , p , k )
-[ a , b ] = zp2ss ( z , p , k )
-a = zp2ss ( z , p , k ) Parameters
-
z:
- Zeros
p:
- Poles
k:
- Leading coefficient
a:
- State space parameter
a:
- State space parameter
b:
- State space parameter
c:
- State space parameter
d:
- State space parameter
Description
-
This is an Octave function.
-It converts zeros / poles to state space.
Examples
-
z = [ 1 2 3 ]
-p = [ 4 5 6 ]
-k = 5
-[ a , b , c , d ] = zp2ss ( z , p , k )
-a =
-
-- 0.00000 0.00000 - 1.20000
-- 10.00000 0.00000 - 7.40000
-0.00000 10.00000 15.00000
-
-b =
-
-- 5.7000
-- 31.5000
-45.0000
-
-c =
-
-0.00000 0.00000 1.00000
-
-d = 5
zp2tf
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > zp2tfzp2tf
-
Converts zeros / poles to a transfer function.
Calling Sequence
-
[ num , den ] = zp2tf ( z , p , k )
-num = zp2tf ( z , p , k ) Parameters
-
z:
- Zeros
p:
- Poles
k:
- Leading coefficient
Num:
- Numerator of the transfer function
den:
- Denomenator of the transfer function
Description
-
This is an Octave function.
-It converts zeros / poles to a transfer function.
Examples
-
z = [ 1 2 3 ]
-p = [ 4 5 6 ]
-k = 5
-[ num , den ] = zp2tf ( z , p , k )
-num =
-
-5 - 30 55 - 30
-
-den =
-
-1 - 15 74 - 120
zplane
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- FOSSEE Signal Processing Toolbox >> FOSSEE Signal Processing Toolbox > zplane 
