LZ78Encode

AM_DSB-SC_Modulation Techniques

Contourlet变换思想，选用拉普拉斯塔式滤波器结构对图像多分辨率分解来捕捉奇异点。(Contourlet transform thinking, the choice of Laplace tower filter structure for multi-resolution images to capture the singularity exploded.)

Input: X, training data, dim*num Y, training data labels, num*class para.alpha, para.beta, regularization parameters para.rd, reduced dimensionality Web Image Annotation via Subspace-Sparsity Collaborated Feature Selection. Zhigang Ma, Feiping Nie, Yi Yang, Jasper Uijlings, and Nicu Sebe. IEEE Transactions on Multimedia (TMM) 14(4):1021-1030, 2012.

数字信号处理基于matlab 文件内容 /conv(卷积部分) dupconv 重叠保留法 freqcirconv 频域圆周卷积 freqconv 频域卷积 timecirconv 时域圆周卷积 timeconv 时域卷积 /fft(频域分析部分) d2fft 基2FFT dft DFT最简化程序 dtft DTFT演示 /fir(fir滤波器设计) fir 得到fir滤波器hn getwindow 得到fir滤波器窗函数wn idealfilter 理想滤波器(低通,高通,带通)hdn idealhd 理想低通滤波器 /fir gui(fir滤波器GUI设计) gui_top 总图 gui_fir fir滤波器GUI gui_sigout 选择信号GUI /power caculate(功率谱分析) directpower 直接谱分析 indirectpower 间接谱分析(估计谱分析) noise 噪声谱分析 /sigout(常见信号产生) sigout 离散信号产生(Digital signal processing matlab based on the content of the document/conv (convolution part) dupconv law of reservations freqcirconv overlapping frequency domain circular convolution freqconv frequency domain convolution timecirconv time-domain circular convolution timeconv time-domain convolution/fft (frequency domain analysis part) d2fft base 2FFT dft DFT the most simplified procedure dtft DTFT demo/fir (fir filter design) fir be fir filter hn getwindow be fir filter window function wn idealfilter ideal filter (low pass, high pass, band-pass) hdn idealhd ideal low-pass Filter/fir gui (fir filter GUI design) gui_top general layout gui_fir fir filter selection GUI gui_sigout signal GUI/power caculate (power spectrum analysis) directpower direct spectral analysis indirectpower indirect spectral analysis (spectral analysis estimated) noise noise spectrum analysis/sigout (common signals) sigout discrete signals)

多天线技术MIMO的vblast编码，实现信道编码(MIMO vblast encoder)

OFDM channel estimation

自己编的，数字信号处理实验的源代码，实现维纳滤波和预测功能(Own series, digital filter design using Wiener filtering methods.)

利用夹角余弦距离进行样本数据分类。实现步骤主要分为以下两部分：a、待测样品X与训练集里每个样品Xi的距离采用夹角余弦距离公式计算。b、循环计算待测样品和训练集中各已知样品之间的距离，找出距离待测样品最近的已知样品，该已知样品的类别就是待测样品的类别。(Using the sample data classification Angle cosine distance.Implementation steps are divided into the following two parts: a, sample under test with each of the samples in the training set X Xi distance using the included Angle cosine distance formula.B, loop calculation under test samples and the distance between the known samples training focus, find out the known closest to the sample under test samples, the samples of known category is the category of the sample under test.)

调试所用Matlab版本是6.5，gatbx-toolbox是调试过程中使用的工具箱，有些热心的研友已经提供了该工具箱，之所以再贴一遍，是因为6.5版本下调试该工具箱会出现一些错误提示，在该版本中已经进行了改正，使用该工具箱只需要将压缩包下的gatbx和gatbxTest_fns加入到Matlab搜索路径即可，另外gatbx-origin是Sheffield大学提供的原始版本的工具箱，未做任何改动，两者可做比较之用 3.gatbx-example是算例调试源码和各算例的调试说明，大家可以对照原书进行比较 4.窃以为gatbx工具箱较之Matlab7新版下的gads工具箱更能让人熟悉和了解遗传算法的原理和计算过程，虽然在可视化方面不及gads 5.学习遗传算法如果首先用熟相关工具箱的话，能够先培养信心，然后更有兴趣去专研和了解具体的算法，如果就用现有的工具箱就能解决问题的话，不了解其详细的算法也未尝不可，毕竟不是每个研友都能向see_moonlight那样开发自己的工具箱吧, ( The debugging uses the Matlab edition is 6.5, gatbx-toolbox debugs the toolbox which in the process uses, somewhat warm-hearted ground the friend already to provide this toolbox, the reason that pasted again, was because under 6.5 editions debugged this toolbox to be able to present some wrong prompts, had already carried on the correction in this edition, will use this toolbox only to need to compress package of under gatbx and gatbx Test_fns joins to the Matlab search path then, moreover gatbx-origin was the primitive edition toolbox which the Sheffield university provided, has not made any modification, both might make the comparison are the example debugging sound code and various examples debugging explained with 3.gatbx-example, everybody might compare the original bookCarries on comparison 4. in my opinion under the gatbx toolbox s to be able to let the human compared with Matlab7 update gads toolbox be familiar with the understanding genetic algorithm principle and)

SOQPSK调制，根升余弦滤波器源代码，用于初学者的使用和学习(SOQPSK modulation,root-raise cosine filter to use and study for a new user)