Discrete autocorrelation functionsAcoustic signalsStatistical significancePaired testsHypothesis testingFor purposes of signal analysis, a wide spectrum of methods has appeared in the mathematical statistics. With regards to a random behavior of considered signals, the methods are based on the probability ...
An Autocorrelation Function is defined as a time domain measure that shows how the correlation between different values of a signal changes as their separation varies. It helps in understanding the memory of a stochastic process without providing information about its frequency content. ...
The relationship between the areas of the two peaks will determine which of these is the case. It is also possible to determine the amount of dispersive wave generated by the pulse if the signal-to-noise ratio is high enough to examine the pulses on a logarithmic scale [45]. View chapter...
Code Issues Pull requests The simulation of stationary time-series (discrete-time random process) with a specific autocorrelation function (ACF) and continuous probability distribution. python time-series simulation wss autocorrelation random-process autocovariance Updated Feb 4, 2024 Python morrow...
Use the discrete Fourier transform (DFT) to obtain the least-squares fit to the sine wave at 100 Hz. The least-squares estimate of the amplitude is 2 /Ntimes the DFT coefficient corresponding to 100 Hz, whereNis the length of the signal. The real part is the amplitude of a cosine at...
Then to have the correlation, The correlation sequence can be derived from the PSD by use of the inverse discrete-time Fourier transform: so I did like this: corr=abs(ifft(Gxx)); but the result it is not correct. How can I obtain the correlation directrly from the Psd?
If this autocorrelation is not accounted for, spuriously high fMRI signal at one time point can be prolonged to the subsequent time points, which increases the likelihood of obtaining false positives in task studies3. As a result, parts of the brain might erroneously appear active during an ...
This MATLAB function returns the autocorrelation coefficients, r, of the output of the discrete-time prediction error filter from the lattice-form reflection coefficients k and initial zero-lag autocorrelation r0.
What characteristics of a random process make it ergodic? To get some better insight toward answering this question, consider a discrete time process, X[n], where each random variable in the sequence is IID and consider forming the time average: (8.18)〈x[n]〉=limm→∞1m∑n=1mX[n]....
New discrete time blind deconvolution methods are proposed for nonminimum phase linear channels driven by cyclo-stationary inputs. The methods rely exclusi... Hatzinakos,D. - 《IEEE Transactions on Signal Processing》 被引量: 49发表: 1994年 Deconvolution of dynamic mechanical networks Time-resolved...