Digital signal processing

Lms Matlab Code

Least mean squares (LMS) algorithms are a class of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean squares of the error signal (difference between the desired and the actual signal). It is a stochastic gradient descent method in that the filter is only adapted based on the error at the current time. It was invented in 1960 by Stanford University professor Bernard Widrow and his first Ph.D. student, Ted Hoff.

Adaptive line enhancemment application in matlab

The following Matlab project contains the source code and Matlab examples used for adaptive line enhancemment application. %function [ALEstruct]=ale(f,fs,munoise,sigmanise,mulms,ncoef,dur) %function to perform adaptive line enhancement using LMS algorithm and an adaptive FIR filter %ALE concept enhances a single tone signal (frequency f) affected by white noise (mu,sigma) %REQUIRES FILTER DESIGN TOOLBOX, SIGNAL PROCESSING TOOLBOX %INPUTS %f: frequency of sinusoid %fs: sampling signal of sinusoid (At least 2f) %munoise, sigma: mean and variance of white noise

Fundamental frequency tracking through comb (notch) iir filtering in matlab

The following Matlab project contains the source code and Matlab examples used for fundamental frequency tracking through comb (notch) iir filtering. Implements the algorithm described in IEEE Signal Processing Magazine (11/2009) by Tan and Jiang for tracking fundamental frequency of a harmonic complex based on IIR comb (notch) filtering and the LMS algorithm.

Lms time delay simulink

The following Matlab project contains the source code and Matlab examples used for lms time delay simulink. This Simulink Application Simulates and LMS adaptive filter when the input x(i)=0.7x(i-1)+w(i) , where w(i) is white noise N(0,1.5) , mean= 0 ,and variance =1.5 and d(i)=x(i-2)

Low Pass Filter Matlab Code

A low-pass filter is a filter that passes low-frequency signals and attenuates (reduces the amplitude of) signals with frequencies higher than the cutoff frequency. The actual amount of attenuation for each frequency varies depending on specific filter design. It is sometimes called a high-cut filter, or treble cut filter in audio applications. A low-pass filter is the opposite of a high-pass filter. A band-pass filter is a combination of a low-pass and a high-pass.

Impulse response invariant discretization of fractional second order filter. in matlab

The following Matlab project contains the source code and Matlab examples used for impulse response invariant discretization of fractional second order filter.. irid_fsof function is prepared to compute a discrete-time finite dimensional (z) transfer function to approximate a continuous-time fractional second order low-pass filter (LPF) [1/(s^2 + a*s + b)]^r, where "s" is the Laplace transform variable; "r" is a real number in the range of (0,1); a and b are the time constant of LPF [1/(s^2 + a*s + b)]^r, where a, b >= 0.

Optimal sub-nyquist nonuniform sampling and reconstruction for multiband signals in matlab

The following Matlab project contains the source code and Matlab examples used for optimal sub-nyquist nonuniform sampling and reconstruction for multiband signals . simulation of the following paper: Venkataramani, R.; Bresler, Y., "Optimal sub-Nyquist nonuniform sampling and reconstruction for multiband signals," Signal Processing, IEEE Transactions on , vol.49, no.10, pp.2301,2313, Oct. 2001

Impulse response invariant discretization of distributed order low pass filter in matlab

The following Matlab project contains the source code and Matlab examples used for impulse response invariant discretization of distributed order low pass filter.  irid_dolpf function is prepared to compute a discrete-time finite dimensional (z) transfer function to approximate a distributed order integrator ((c^r)/(b-a))*int(1/(s+c)^r,r,a,b), where "s" is the Laplace transform variable.

Image compression in matlab

The following Matlab project contains the source code and Matlab examples used for image compression.  OBJECTIVE   To implement curvelet transform for the compression of images and to achieve higher compression ratio than the other existing compression algorithms like JPEG2000,SPIHT   WAVELET based algorithms can only reproduce points and straight lines   Theoretically curvelets can store curved edges using fewer coefficients.

2 d dct idct for jpeg compression in matlab

The following Matlab project contains the source code and Matlab examples used for 2 d dct idct for jpeg compression. to understand the Algorithm go to matlab help in page dct2 and idct2 to get the mathematical expression for M = N = 8, we can calculate the most of hard values and save it as LUTs to speed up the execution now compare our special 8X8 functions with the internal general functions use this code: A = int32(100*rand(8,8)); tic;for i = 1 : 1000 IDCT_8X8(DCT_8X8(A));end;toc; tic;for i = 1 : 1000 idct2(dct2(A));end;toc;

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