medfilt1 1D median filtering y = medfilt1(x) y = medfilt1(x, n) y = medfilt1(x, n, dim) y = medfitl1(__, nanflag, padding) 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. x: int | double Input signal. n: positive integer, scalar Filter order. Defaults to 3.The order of the median filter. Must be less than (length of the signal) where signals are 1D vectors along the dimension of x to be filtered dim: positive integer scalar Dimension to filter along. Defaults to first non-singleton dimension of x nanflag: 'includenan' (default) | 'omitnan' NaN condition. * includenan: Filtering such that the median of any segment containing a NaN is also a NaN. * omitnan: Filtering with NaNs omitted in each segment. If a segment contains all NaNs, the result is NaN y: int | double The filtered signal. y has the same size as x //Generate a sinusoidal signal sampled for 1 second at 100 Hz. Add a higher-frequency sinusoid to simulate noise. fs = 100; t = 0:1/fs:1; x = sin(2*%pi*t*3)+0.25*sin(2*%pi*t*40); //Use a 10th-order median filter to smooth the signal. Plot the result. y = medfilt1(x,10); plot(t,x,t,y) legend('Original','Filtered'); y = round(y*10000)/10000; y = y' | hampel | median | sgolayfilt Ayush Baid