signal
Signal processing tools, including filtering, windowing and display functions.
Select category:
Signals
Filtering
Filter analysis
Filter conversion
IIR Filter design
FIR filter design
Transforms
Power spectrum analysis
Window functions
System identification
Sample rate change
Utility
Compute the dirichlet function.
Return the Gaussian modulated sinusoidal pulse.
Return the gaussian monopulse.
usage: y=pulstran(t,d,'func',.
usage: y = tripuls(t, w, skew)
usage: y = rectpuls(t, w)
Generates a sawtooth wave of period `2 * pi' with limits `+1/-1' for the elements of T.
Generate a square wave of period 2 pi with limits +1/-1.
usage: y = chirp(t [, f0 [, t1 [, f1 [, form [, phase]]]]])
usage: [S [, f [, t]]] = specgram(x [, n [, Fs [, window [, overlap]]]])
Buffer a signal into a data frame.
Compute the Mexican hat wavelet.
Compute the Meyer wavelet auxiliary function.
Compute the Morlet wavelet.
Compute the Complex Shannon wavelet.
Compute the Complex Morlet wavelet.
Evaluates a train of sigmoid functions at T.
usage: y = filtfilt(b, a, x)
Set initial condition vector for filter function The vector zf has the same values that would be obtained from function filter given past inputs x and outputs y
y = sgolayfilt (x, p, n [, m [, ts]]) Smooth the data in x with a Savitsky-Golay smoothing filter of polynomial order p and length n, n odd, n > p.
Second order section IIR filtering of X.
y = medfilt1(x [, n])
Calculates moving RMS value of the signal in X.
Usage: H = freqs(B,A,W);
Plot the amplitude and phase of the vector H.
Compute the group delay of a filter.
usage: [x, t] = impz(b [, a, n, fs])
usage: zplane(b [, a]) or zplane(z [, p])
Compute peak full-width at half maximum (FWHM) or at another level of peak maximum for vector or matrix data y, optionally sampled as y(x).
If A is a column vector and X is a column vector of length N, then
Compute the partial fraction expansion of filter H(z) = B(z)/A(z).
Compute the partial fraction expansion (PFE) of filter H(z) = B(z)/A(z).
Convert series second-order sections to direct form H(z) = B(z)/A(z).
Convert series second-order sections to zeros, poles, and gains (pole residues).
Conversion from transfer function to state-space.
Converts a state space representation to a set of poles and zeros; K is a gain associated with the zeros.
Convert direct-form filter coefficients to series second-order sections.
Conversion from transfer function to state-space.
Converts transfer functions to poles-and-zero representations.
Convert filter poles and zeros to second-order sections.
Conversion from zero / pole to state space.
Converts zeros / poles to a transfer function.
b = polystab(a)
Generate a bessel filter.
Design lowpass analog Butterworth filter.
Generate a butterworth filter.
Usage: cheb (n, x)
Design lowpass analog Chebyshev type I filter.
Design lowpass analog Chebyshev type II filter.
Generate an Chebyshev type I filter with Rp dB of pass band ripple.
Generate an Chebyshev type II filter with Rs dB of stop band attenuation.
Design lowpass analog elliptic filter.
N-ellip 0.
usage: [Zz, Zp, Zg] = ncauer(Rp, Rs, n)
Compute butterworth filter order and cutoff for the desired response characteristics.
Compute chebyshev type I filter order and cutoff for the desired response characteristics.
Compute chebyshev type II filter order and cutoff for the desired response characteristics.
usage: [n,wp] = ellipord(wp,ws, rp,rs)
Return bessel analog filter prototype.
usage: [Sz, Sp, Sg] = sftrans(Sz, Sp, Sg, W, stop)
usage: [Zz, Zp, Zg] = bilinear(Sz, Sp, Sg, T) [Zb, Za] = bilinear(Sb, Sa, T)
Converts analog filter with coefficients B and A to digital, conserving impulse response.
Converts digital filter with coefficients B and A to analog, conserving impulse response.
IIR Low Pass Filter to Multiband Filter Transformation
Return coefficients for an IIR notch-filter with one or more filter frequencies and according (very narrow) bandwidths to be used with `filter' or `filtfilt'.
usage: b = fir1(n, w [, type] [, window] [, noscale])
usage: b = fir2(n, f, m [, grid_n [, ramp_n]] [, window])
b = firls(N, F, A); b = firls(N, F, A, W);
usage: [n, Wn, beta, ftype] = kaiserord(f, m, dev [, fs])
b = remez(n, f, a [, w] [, ftype] [, griddensity]) Parks-McClellan optimal FIR filter design.
F = sgolay (p, n [, m [, ts]]) Computes the filter coefficients for all Savitzsky-Golay smoothing filters of order p for length n (odd).
Usage: qp_kaiser (nb, at, linear)
Constrained L2 bandpass FIR filter design.
usage y=czt(x, m, w, a)
T = dctmtx (n) Return the DCT transformation matrix of size n x n.
y = dct2 (x) Computes the 2-D discrete cosine transform of matrix x
y = idct2 (x) Computes the inverse 2-D discrete cosine transform of matrix x
y = dct (x, n) Computes the discrete cosine transform of x.
y = dct (x, n) Computes the inverse discrete cosine transform of x.
Computes the type I discrete sine transform of X.
Computes the inverse type I discrete sine transform of Y.
If N is a scalar, produces a N-by-N matrix D such that the Fourier transform of a column vector of length N is given by `dftmtx(N) * x' and the inverse Fourier transform is given by `inv(dftmtx(N)) *
Analytic extension of real valued signal
usage: [y, xm] = rceps(x) Produce the cepstrum of the signal x, and if desired, the minimum phase reconstruction of the signal x.
usage: cceps (x [, correct])
Split the vector z into its complex (ZC) and real (ZR) elements, eliminating one of each complex-conjugate pair.
Reorder x in the bit reversed order
Comupte de discrete wavelet transform of x with one level.
The function fht calculates Fast Hartley Transform where D is the real input vector (matrix), and M is the real-transform vector.
The function ifht calculates Fast Hartley Transform where D is the real input vector (matrix), and M is the real-transform vector.
Compute the Walsh-Hadamard transform of X using the Fast Walsh-Hadamard Transform (FWHT) algorithm.
Compute the inverse Walsh-Hadamard transform of X using the Fast Walsh-Hadamard Transform (FWHT) algorithm.
USAGE: [spectra,freq] = pwelch(x,window,overlap,Nfft,Fs, range,plot_type,detrend,sloppy) Estimate power spectral density of data "x" by the Welch (1967) period
Usage: [Pxx,freq] = tfe(x,y,Nfft,Fs,window,overlap,range,plot_type,detrend)
Usage: [Pxx,freq]=tfestimate(x,y,window,overlap,Nfft,Fs,range)
Usage: [Pxx,freq] = cohere(x,y,Nfft,Fs,window,overlap,range,plot_type,detrend)
Usage: [Pxx,freq] = csd(x,y,Nfft,Fs,window,overlap,range,plot_type,detrend)
Usage: [psd,f_out] = ar_psd(a,v,freq,Fs,range,method,plot_type)
Usage: [Pxx,freq] = cpsd(x,y,window,overlap,Nfft,Fs,range)
Usage: [Pxx,freq]=mscohere(x,y,window,overlap,Nfft,Fs,range)
usage: [psd,f_out] = pburg(x,poles,freq,Fs,range,method,plot_type,criterion)
usage: [psd,f_out] = pyulear(x,poles,freq,Fs,range,method,plot_type)
Estimates the cross-correlation.
Compute the 2D cross-correlation of matrices A and B.
Compute covariance at various lags [=correlation(x-mean(x),y-mean(y))].
usage: [P, w] = __power (b, a, [, nfft [, Fs]] [, range] [, units]) Plot the power spectrum of the given ARMA model.
Create a N-point windowing from the function F.
Compute the modified Bartlett-Hann window of lenght L.
Compute the Blackman-Harris window.
Compute the Blackman-Nuttall window.
Compute the Bohman window of lenght L.
usage: w = boxcar (n)
Usage: chebwin (L, at)
flattopwin(L, [periodic|symmetric])
w = hann(n) see hanning
usage: kaiser (L, beta)
Compute the Blackman-Harris window defined by Nuttall of length L.
usage: w = triang (L)
usage: w = gaussian(n, a)
usage: w = gausswin(L, a)
Return the filter coefficients of a Tukey window (also known as the cosine-tapered window) of length L.
Return the filter coefficients of a rectangle window of length L.
Returns a row vector containing a Welch window, given by W(n)=1-(n/N-1)^2, n=[0,1, .
Compute the Parzen window of lenght L.
[a,v,k] = arburg(x,poles,criterion)
usage: [a, v, k] = aryule (x, p) fits an AR (p)-model with Yule-Walker estimates.
usage: [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)
Usage: [B,A] = invfreqs(H,F,nB,nA) [B,A] = invfreqs(H,F,nB,nA,W) [B,A] = invfreqs(H,F,nB,nA,W,iter,tol,'trace')
usage: [B,A] = invfreqz(H,F,nB,nA) [B,A] = invfreqz(H,F,nB,nA,W) [B,A] = invfreqz(H,F,nB,nA,W,iter,tol,'trace')
usage: [a, v, ref] = levinson (acf [, p])
usage: y = decimate(x, q [, n] [, ftype])
usage: y = interp(x, q [, n [, Wc]])
Downsample the signal, selecting every nth element.
Upsample the signal, inserting n-1 zeros between every element.
Change the sample rate of X by a factor of P/Q.
Upsample, FIR filtering and downsample.
Creates a vectorized function based on data samples using interpolation.
Buffer a signal into a data frame.
Finds peaks on DATA.
Shift the series X by a (possibly fractional) number of samples D.
Compute the generalized Marcum Q function of order M with noncentrality parameter A and argument B.
Extract the elements of x of size l from the center, the right or the left.
Reverse the order of the element of the vector x.
Estimates the points at which a given waveform y=y(x) crosses the x-axis using linear interpolation.
Usage: xt = sampled2continuous( xn , T, t ) Calculate the x(t) reconstructed from samples x[n] sampled at a rate 1/T samples per unit time.
Implements a multisignal Schmitt trigger with levels LVL.
Calculate boundary indexes of clusters of 1's.
Package: signal
