matlab小波分析程序

lms算法 滤波器 解释及仿真 ，基于LMS算法自适应滤波器析(lms filter interpretation and simulation algorithm, based on the analysis of LMS adaptive filter algorithm)

书中全面系统地介绍了优化方法的基础理论和优化工具箱v2.2函数的功能、语法和工程实际应用。 (The book introduces a comprehensive and systematic optimization approach is based on theory and optimization toolbox v2.2 of functionality, syntax and engineering applications.)

复杂场景中的红色交通标志牌检测与识别，结合颜色与圆形的标志牌定位，然后提取特征，最后用模板匹配的方法实现识别，内附图片和程序说明，对初学者有帮助(Red detection and recognition of traffic signs in complex scenes, the combination of colors and circular signs positioning, and feature extraction, the last with template matching methods to achieve recognition, included pictures and a description of the procedures for beginners)

estimate shape of a noisy image from the shading information

用谱方法计算流体力学一些流动现象的整体稳定性,多机电力系统仿真及其潮流计算，使用混沌与分形分析的例程，数据包传送源码程序，计算时间和二维直方图,基于负熵最大的独立分量分析。

commande vectorielle

用matlab写的steger算法，用于结构光条提取(Steger algorithm written in MATLAB for structural light strip extraction)

EMD分解源码，可用于信号的分解，去噪以及细节分析。是最新的2007的版本。

Iterative Solutions is committed to the mission of developing accurate and efficient software for the simulation of modern communication systems. All software is licensed as open source software under the Lesser GPL. The code is designed to run in matlab, though the compute-intensive portions are written in C and Java. A wide variety of error control codes are supported, including turbo and LDPC codes, and an emphasis is placed on nonbinary modulation formats such as QAM and nonbinary continuous-phase FSK. The software goes beyond the usual error rate analysis by including tools for determining the Shannon capacity of arbitrary modulation formats.

The basic steps of the edge-based text extraction algorithm are given below 1. Create a Gaussian pyramid by convolving the input image with a Gaussian kernel and successively down-sample each direction by half. (Levels: 4) 2. Create directional kernels to detect edges at 0, 45, 90 and 135 orientations. 3. Convolve each image in the Gaussian pyramid with each orientation filter. 4. Combine the results of step 3 to create the Feature Map. 5. Dilate the resultant image using a sufficiently large structuring element (7x7 [1]) to cluster candidate text regions together. 6. Create final output image with text in white pixels against a plain black background.