huffman_encode.tar

基于matlab的cdma系统，扩频通信(The cdma matlab-based systems, spread spectrum communication)

对语音进行的时频分析,语音识别技术作为信息社会朝着智能化和自动化方向发展的关键技术之一，具有重要的研究意义和实用价值。经过近五十年的艰苦探索和研究，语音识别技术研究获得了极大的发展，其中有些比较成熟的技术已经逐步应用于日常生活中。但总体来说，语音识别在研究和实用化方面的难度还比较大。本文概括介绍了语音识别技术的全貌，所采用的关键技术、具体应用以及当前所面临的困难,并对单个语音样本在MATLAB上进行了仿真分析。(right voice for the time-frequency analysis Speech recognition technology as the information society towards automation and intelligent direction of one of the key technologies. The study has important significance and practical value. After nearly 50 years of painstaking study and research, speech recognition technology is a great development, Some of the more mature technologies have gradually applied to daily life. But on the whole, the speech recognition research and the practical difficulty is relatively large. This paper introduces voice recognition technology landscape and the use of key technologies, specific application and the current difficulties faced by, as well as samples in a single voice on the MATLAB simulation analysis.)

用C语言编写MATLAB 函数， 主要用于DSP芯片， 后期可参考转化为汇编， 提高速度(Use the programming language C to write the MATLAB function, bwlabel )

本源程序是基于matlab实现了音乐信号处理中的恒Q变换(Constant Q Transformation)并得到频谱矩阵和语谱图(This Code is based on the matlab, being executed for the musical signal processing by using the CQT(Constant Q Transformation)and finally get the frequecy Information)

基于802.11a协议的OFDM程序 matlab，很有用哦(OFDM-based 802.11a protocol procedures matlab, oh, very useful)

形态滤波，各种算法。可修改后实现各种组合。(morphological filtering, algorithms. Can be modified to achieve various combinations.)

matlab 椭圆拟合，直接使用可以，无需修改(the matlab ellipse fitting, direct use can, without modification)

L1-SVD与OMP算法在重构稀疏信号上的比较(L1-SVD and OMP for sparse signal reconstruction)

该为matlab详尽学习资料，第七章“矩阵分析”(The details for the matlab learning materials, Chapter 7, " Matrix Analysis" )

Robust Non-negative Dictionary Learning for Visual Tracking The provided codes could be either embedded into the benchmark framework of paper Online Object Tracking: A Benchmark (CVPR2013) (You can find details here: http://visual-tracking.net/) or run on individual sequence. To run the benchmark, just put the entire folder into the /trackers folder in the benchmark code base, and modify the configTrackers.m in util folder. DLT gets an AUC of 0.436, which ranks 5th among 26 in the benchmark by 19/03/2014. We don t tune parameters for single sequence in this case, all the parameters are stored in trackparam_DLT.m. To run on individual video, you need to modify the dataPath and title in run_individual.m. If you run MATLAB version after 2012, and have a CUDA compatible GPU installed, you may enjoy the fast computation speed by GPU, just set useGPU to true in trackparam_DLT.m and run_individual.m!