hilbert2

Line detection in gray scale using hough transform(Line detection in gray scale using hough tr ansform)

implement basic routines in Matlab and Octave (currently using Matlab R13) for simulating adaptive beamforming and filtering for antenna arrays.

BCH(15,11,1)+交织编解码matlab代码(BCH (15,11,1) with interleaver matlab code )

轮轨冲击信号的小波分析matlab程序,程序中H5V20zrail为轮轨冲击信号输入数据。(Wheel-rail impact of wavelet analysis matlab program, the program H5V20zrail impact signal for the wheel-rail input data.)

在matlab软件中，用A星算法进行栅格法路径规划，且路径不固定(In matlab software, using the grid method A star path planning algorithm, and the path is not fixed)

一堆数字信号处理的小程序,打个包发上来,上数字信号处理课写的,包括复倒谱、傅立叶变换、各种滤波器等~(heap of digital signal processing procedures for small, one packet onto fat, digital signal processing course was made, including the complex cepstrum, Fourier transform, various filter ~)

一个查找表的程序，使用了matlab和C语言混合编程实现，提高查找效率(A look-up table of procedures, the use of matlab and C mixed-language programming, improve search efficiency)

独立成份分析（ＩＣＡ）以及ｗｉｎｎｅｒ滤波 Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S + N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...( Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S+ N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...)

Matlab写的fisher 分类器,很简单适合初学者，体验机器学习的过程(Fisher Linear Discriminant Analysis in Matlab。It fit the people who learning the Machine Learning at first time.)

hi my name is bhavesh pithva from gujarat i am a gtu student i upload a best papers for asics