# signal processing

# Adds a small amount of noise to an array. ideal for signal processing or chaos theory. in matlab

# Tool for processing the podaac level2a products in matlab

# Introduction to simulink for signal processing

# Efficient kernel smoothing regression using kd tree in matlab

# A numerical tour of signal processing in matlab

# Covolution function in matlab

# Movable, stretchable zoom box in parent axes controls axes limits in all children axes. in matlab

# The complex optimal step size for tensor decomp. in matlab

# Window function for signal processing in matlab

# Bregman cookbook in matlab

# Signal processing with matlab webinar

# Ber curve for bpsk in gaussian environment in matlab

# Efficient k nearest neighbor search using jit in matlab

# Analog modulation technique fm in matlab

# Object Tracking Matlab Code

Object Tracking is hot topic in signal processing.

# Background Subtraction Matlab Code

Background subtraction, also known as Foreground Detection, is a technique in the fields of image processing and computer vision wherein an image's foreground is extracted for further processing (object recognition etc.).

# Tools for data analysis in optics, acoustics, signal processing in matlab

# Median Filter Matlab Code

median filter is a nonlinear digital filtering technique, often used to remove noise. The main idea of the median filter is to run through the signal entry by entry, replacing each entry with the median of neighboring entries.

# Wiener Filter Matlab Code

Wiener filter is a filter used to produce an estimate of a desired or target random process by linear time-invariant filtering an observed noisy process, assuming known stationary signal and noise spectra, and additive noise. The Wiener filter minimizes the mean square error between the estimated random process and the desired process.

# K Means Clustering Matlab Code

k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.

# Fft Matlab Code

A fast Fourier transform (FFT) is an algorithm to compute the discrete Fourier transform (DFT) and its inverse. Fourier analysis converts time (or space) to frequency and vice versa; an FFT rapidly computes such transformations by factorizing the DFT matrix into a product of sparse (mostly zero) factors.

Radix-2 FFT