signal
Signal processing tools, including filtering, windowing and display functions.
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Signals
Signal Measurement
Correlation and Convolution
Filtering
Filter Analysis
Filter Conversion
IIR Filter Design
FIR Filter Design
Transforms
Power Spectrum Analysis
Window Functions
System Identification
Sample Rate Change
Pulse Metrics
Utility
Buffer a signal into a data frame.
Evaluate a chirp signal at time T.
Compute the Complex Morlet wavelet.
Compute the dirichlet function.
Generate a Gaussian modulated sinusoidal pulse sampled at times T.
Return the gaussian monopulse.
Compute the Mexican hat wavelet.
Compute the Meyer wavelet auxiliary function.
Compute the Morlet wavelet.
Generate the signal y=sum(func(t+d,...)) for each d.
Generate a rectangular pulse over the interval [-W/2,W/2), sampled at times T.
Generates a sawtooth wave of period ‘2 * pi’ with limits ‘+1/-1’ for the elements of T.
Compute the Complex Shannon wavelet.
Shift data IN to permute the dimension DIM to the first column.
Evaluate a train of sigmoid functions at T.
Generate a spectrogram for the signal X.
Generate a square wave of period 2 pi with limits +1/-1.
Generate a triangular pulse over the interval [-W/2,W/2), sampled at times T.
Invert the operation of uencode.
Quantize the entries of the array IN using 2^N quantization levels.
Reverse what is done by shiftdata.
Creates a signal that oscillates at a frequency determined by input X with a sampling frequency FS.
Finds peaks on DATA.
Compute the difference between the maximum and minimum values in the vector X.
Compute the ratio of the largest absolute value to the root-mean-square (RMS) value of the vector X.
Compute the root-mean-square (RMS) of the vector X.
Compute the root-sum-of-squares (RSS) of the vector X.
Compute the modulo-N circular convolution.
If A is a column vector and X is a column vector of length N, then
1-D or 2-D convolution.
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))].
Forward and reverse filter the signal.
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
Apply a one dimensional median filter with a window size of N to the data X, which must be real, double and full.
Calculate moving RMS value of the signal in X.
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.
Compute the 2-norm of a digital filter defined by the numerator coefficients, B, and the denominator coefficients, A.
Returns the filter order N for a filter defined by the numerator coefficients, B, and the denominator coefficients, A.
Compute the s-plane frequency response of the IIR filter B(s)/A(s) as H = polyval(B,j*W)./polyval(A,j*W).
Plot the amplitude and phase of the vector H.
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).
Compute the group delay of a filter.
Generate impulse-response characteristics of the filter.
Determine whether a digital filter is allpass.
Determine whether a digital filter is maximum phase (maximum energy-delay).
Determine whether a digital filter is minimum phase.
Returns a logical output equal to TRUE, if the filter is stable.
Compute the phase response of digital filter defined either by its coefficients (B and A are the numerator and denominator coefficients respectively) or by its second-order sections representation,...
Plot the poles and zeros on a complex plane.
b = polystab(a)
Compute the partial fraction expansion (PFE) of filter H(z) = B(z)/A(z).
Compute the partial fraction expansion of filter H(z) = B(z)/A(z).
Convert series second-order sections to state-space.
Convert series second-order sections to transfer function.
Convert series second-order sections to zeros, poles, and gains (pole residues).
Conversion from state-space to transfer function representation.
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.
Convert 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.
Return bessel analog filter prototype.
Generate a Bessel filter.
Transform a s-plane filter specification into a z-plane specification.
Design lowpass analog Butterworth filter.
Generate a Butterworth filter.
Compute the minimum filter order of a Butterworth filter with the desired response characteristics.
Returns the value of the nth-order Chebyshev polynomial calculated at the point x.
Design lowpass analog Chebyshev type I filter.
Compute the minimum filter order of a Chebyshev type I filter with the desired response characteristics.
Design lowpass analog Chebyshev type II filter.
Compute the minimum filter order of a Chebyshev type II filter with the desired response characteristics.
Generate a Chebyshev type I filter with RP dB of passband ripple.
Generate a Chebyshev type II filter with RS dB of stopband attenuation.
Generate an elliptic or Cauer filter with RP dB of passband ripple and RS dB of stopband attenuation.
Design lowpass analog elliptic filter.
Compute the minimum filter order of an elliptic filter with the desired response characteristics.
IIR Low Pass Filter to Multiband Filter Transformation
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.
Designs a linear-phase FIR filter according to given specifications and the ‘minimax’ criterion.
Estimate the filter-order needed for ‘firpm’ to design a type-I or type-II linear-phase FIR filter according to the given specifications.
Analog prototype for Cauer filter.
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’.
Transform band edges of a generic lowpass filter (cutoff at W=1) represented in splane zero-pole-gain form.
Constrained L2 bandpass FIR filter design.
Produce an order N FIR filter with the given frequency cutoff W, returning the N+1 filter coefficients in B.
Produce an order N FIR filter with arbitrary frequency response M over frequency bands F, returning the N+1 filter coefficients in B.
FIR filter design using least squares method.
Return the parameters needed to produce a filter of the desired specification from a Kaiser window.
Computes a finite impulse response (FIR) filter for use with a quasi-perfect reconstruction polyphase-network filter bank.
Parks-McClellan optimal FIR filter design.
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.
Reorder the elements of the vector X in bit-reversed order.
Return the complex cepstrum of the vector X.
Sort the numbers Z into complex-conjugate-valued and real-valued elements.
Chirp z-transform.
Compute the discrete cosine transform of X.
Compute the 2-D discrete cosine transform of matrix X.
Return the DCT transformation matrix of size N-by-N.
Compute the N-by-N Fourier transformation matrix.
Reorder the elements of the vector X in digit-reversed order.
Computes the type I discrete sine transform of X.
Discrete wavelet transform (1D).
Calculate the Fast Hartley Transform of real input D.
Compute the Walsh-Hadamard transform of X using the Fast Walsh-Hadamard Transform (FWHT) algorithm.
Analytic extension of real valued signal.
Compute the inverse discrete cosine transform of X.
Compute the inverse 2-D discrete cosine transform of matrix X.
Computes the inverse type I discrete sine transform of Y.
Calculate the inverse Fast Hartley Transform of real input D.
Compute the inverse Walsh-Hadamard transform of X using the Fast Walsh-Hadamard Transform (FWHT) algorithm.
Return the cepstrum of the signal X.
Calculate the power spectrum of the autoregressive model
Estimate (mean square) coherence of signals "x" and "y".
Estimate cross power spectrum of data X and Y by the Welch (1967) periodogram/FFT method.
Estimate cross power spectrum of data "x" and "y" by the Welch (1967) periodogram/FFT method.
Convert decibels (dB) to power.
Estimate (mean square) coherence of signals X and Y.
Calculate Burg maximum-entropy power spectral density.
Convert power to decibels (dB).
Estimate power spectral density of data "x" by the Welch (1967) periodogram/FFT method.
Calculates a Yule-Walker autoregressive (all-pole) model of the data "x" and computes the power spectrum of the model.
Estimate transfer function of system with input "x" and output "y".
Estimate transfer function of system with input X and output Y.
Plot the power spectrum of the given ARMA model.
Return the filter coefficients of a modified Bartlett-Hann window of length M.
Return the filter coefficients of a Blackman-Harris window of length M.
Return the filter coefficients of a Blackman-Nuttall window of length M.
Return the filter coefficients of a Bohman window of length M.
Return the filter coefficients of a rectangular window of length M.
Return the filter coefficients of a Dolph-Chebyshev window of length M.
Return the filter coefficients of a Flat Top window of length M.
Return a Gaussian convolution window of length M.
Return the filter coefficients of a Gaussian window of length M.
Return the filter coefficients of a Hanning window of length M.
Return the filter coefficients of a Kaiser window of length M.
Return the filter coefficients of a Blackman-Harris window defined by Nuttall of length M.
Return the filter coefficients of a Parzen window of length M.
Return the filter coefficients of a rectangular window of length M.
Return the filter coefficients of a triangular window of length M.
Return the filter coefficients of a Tukey window (also known as the cosine-tapered window) of length M.
Return the coefficients of an Ultraspherical window of length M.
Return the filter coefficients of a Welch window of length M.
Create an M-point window from the function F.
Calculate coefficients of an autoregressive (AR) model of complex data X using the whitening lattice-filter method of Burg (1968).
Fit an AR (P)-model with Yule-Walker estimates.
Fit filter B(z)/A(z) or B(s)/A(s) to complex frequency response at frequency points F.
Fit filter B(s)/A(s)to the complex frequency response H at frequency points F.
Fit filter B(z)/A(z)to the complex frequency response H at frequency points F.
Use the Durbin-Levinson algorithm to solve: toeplitz(acf(1:p)) * x = -acf(2:p+1).
Determines the forward linear predictor by minimizing the prediction error in the least squares sense.
Create a vectorized function based on data samples using interpolation.
Downsample the signal X by a reduction factor of Q.
Downsample the signal, selecting every Nth element.
Upsample the signal x by a factor of q, using an order 2*q*n+1 FIR filter.
Change the sample rate of X by a factor of P/Q.
Upsample, FIR filtering, and downsample.
Upsample the signal, inserting N-1 zeros between every element.
Estimate state-level for bilevel waveform A using histogram method
Buffer a signal into a data frame.
Calculate boundary indexes of clusters of 1’s.
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.
Calculate the primitive of a function.
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.
Upsamples a vector interleaving given values or copies of the vector elements.
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.
Package: signal