MPEGAudio

The objective of this project was to learn how to create high dynamic range (HDR) images from a sequence of exposures. A single exposure is limited in its dynamic range by the bit-depth of its sensors, which is usualy 8 bits per channel. This leads to saturation where regions of the image are washed out or completely black. By combining the information from multiple exposures with different shutter speeds, a larger dynamic range can be obtained and the saturated regions can be restored.

海杂波的matlab仿真，希望对大家有用(Matlab simulation of sea clutter, in the hope that useful)

简单的用labview结合mathlab的混合编程的频谱分析实验文件包含常见的方波、三角波和正弦波的分析。(Simple example of combination of mixed programming with labview frequency spectrum analysis of the experimental mathlab)

说明：  主要是用于提取超声导波的时域特征，例如偏度参数、峰度系数、形状系数、离散系数等等(Time Domain Characteristics for Extracting Ultrasound Guided Waves)

Regularized OMP by Vershyn and Kneedell

一本非常有价值的MAtlab编程参考书，值得一看。(A very valuable reference book MAtlab programming, worth a visit.)

正弦信号的频谱图形，短小的编码程序，用matlab实现。可实现简单频谱图(Sinusoidal signal spectrum graphics, short of coding procedures, using matlab realize. Can realize a simple frequency spectrum)

试者工作特征曲线 （receiver operating characteristic curve，简称ROC曲线），又称为感受性曲线（sensitivity curve）。得此名的原因在于曲线上各点反映着相同的感受性，它们都是对同一信号刺激的反应，只不过是在几种不同的判定标准下所得的结果而已。接受者操作特性曲线就是以虚报概率为横轴，击中概率为纵轴所组成的坐标图，和被试在特定刺激条件下由于采用不同的判断标准得出的不同结果画出的曲线。(PLOTROC Plot receiver operating characteristic. Syntax plotroc(targets,outputs) plotroc(targets1,outputs1, name1 ,targets,outputs2, name2 , ...) Description PLOTROC(TARGETS,OUTPUTS) plots the receiver operating characteristic for each output class. The more each curve hugs the left and top edges of the plot, the better the classification. PLOTROC(TARGETS1,OUTPUTS2, name1 ,...) generates multiple plots. )

This is the BER plot for QPSK..

说明：  遗传算法是计算数学中用于解决最佳化的搜索算法，是进化算法的一种。进化算法最初是借鉴了进化生物学中的一些现象而发展起来的，这些现象包括遗传、突变、自然选择以及杂交等。遗传算法通常实现方式为一种计算机模拟。对于一个最优化问题，一定数量的候选解（称为个体）的抽象表示（称为染色体）的种群向更好的解进化。传统上，解用二进制表示（即0和1的串），但也可以用其他表示方法。进化从完全随机个体的种群开始，之后一代一代发生。在每一代中，整个种群的适应度被评价，从当前种群中随机地选择多个个体（基于它们的适应度），通过自然选择和突变产生新的生命种群，该种群在算法的下一次迭代中成为当前种群。(Genetic algorithm (GA) is a search algorithm used to solve optimization in computational mathematics. It is one of evolutionary algorithms. Evolutionary algorithms were originally developed by borrowing phenomena in evolutionary biology, such as heredity, mutation, natural selection and hybridization. Genetic algorithms are usually implemented as a computer simulation. For an optimization problem, an abstract representation of a certain number of candidate solutions (called individuals) (called chromosomes) evolves towards a better solution. Traditionally, solutions are represented in binary (that is, strings of zeros and ones), but other representations are possible. Evolution begins with a population of completely random individuals, and it happens from generation to generation.)