Digital_Image_Correlation_2012

This Matlab script file explainns the qpsk scheme in awgn channel.

人脸检测的程序，效果不错！内附用法说明，调用一张图片，将现实出人脸的边缘。(code for facedetection，the performance is nice！ usage: I=double(imread c:Datagirl1.jpg )) detect_face(I) The function will display the bounding box if a face is found. )

BCH(15,5,7)a communication system consists of binary source, 3 error-correcting BCH code of length 15 over GF(24) (i.e, BCH (15,5,7 code), the primitive polynomial is p(x)=1+x+x4), BPSK modulator, AWGN channel, coherent demodulator, decoder (either BMA or EA could be utilized to solve the error locator polynomial) and sink. Simulate and draw its error performance with and without BCH coding, together with the theoretical no-coding curve, i.e. two BER-EbNo curves.(a communication system consists of binary source, 3 error-correcting BCH code of length 15 over GF(24) (i.e, BCH (15,5,7 code), the primitive polynomial is p(x)=1+x+x4), BPSK modulator, AWGN channel, coherent demodulator, decoder (either BMA or EA could be utilized to solve the error locator polynomial) and sink. Simulate and draw its error performance with and without BCH coding, together with the theoretical no-coding curve, i.e. two BER-EbNo curves. )

V-Detector algorithm with tree-based structures

发射线性调频信号，假定三个点目标，模拟信号碰到目标返回后的回波。(Transmit chirp signal, assuming three points target, the target return echo analog signal after the encounter.)

RTW方面的文献,对vc和matlab混合编程有很好的借鉴意义(RTW literature)

一个MATLAB版编的 神经网络程序 (MATLAB version of a series of the neural network programming)

matlab基础教程，适用于matlab初学者(matlab Essentials)

基于matlab实现的循环谱估计算法，可以对具有循环平稳特点的信号进行参数估计，如直扩信号和周期调制信号。(based on the cycle of Matlab achieve spectral estimation algorithm, be right circle with a smooth characteristics of the signal to estimate the parameters, such as DS signal and modulation signal cycle.)

This paper presents a new approach to image segmentation using Pillar K-means algorithm. This segmentation method includes a new mechanism for grouping the elements of high resolution images in order to improve accuracy and reduce the computation time. The system uses K-means for image segmentation optimized by the algorithm after Pillar. The Pillar algorithm considers the placement of pillars should be located as far from each other to resist the pressure distribution of a roof, as same as the number of centroids between the data distribution. This algorithm is able to optimize the K-means clustering for image segmentation in the aspects of accuracy and computation time. This algorithm distributes all initial centroids according to the maximum cumulative distance metric. This paper evaluates the proposed approach for image segmentation by comparing with K-means clustering algorithm and Gaussian mixture model and the participation of RGB, HSV, HSL and CIELAB color spaces.