kuandaizaoshengganrao

实习的时候写的拉东变换重建和维娜滤波的代码，Matlab做的

matlab数据结构 算法工具箱, 包括堆栈 队列 2叉树 红黑树 等等(Data Structures & Algorithms Toolbox ==================================== The Data Structures & Algorithms Toolbox provides advanced data structures and algorithms for the Matlab(r) 5 environment. It implements functions to create and destroy singly and doubly linked list, stack, queue, binary tree and red-black (balanced) binary tree. Data elements can be inserted, deleted or searched for. A graphical representation of trees can be shown. All algorithms are clearly written as m-files, which allows the toolbox to be used as a teaching aid in educational data structures courses. This is the first implementation available of advanced datatypes, common in other languages, for Matlab.)

说明：  利用winpcap编写的，直接从链路层收取数据帧的小程序，希望能有点用处。(use winpcap prepared directly from the data link layer frame collection of small procedures in the hope that this will usefulness.)

认知无线电频谱感知能量检测算法，噪声不确定(Cognitive radio spectrum sensing energy detection algorithm, noise uncertainty)

频率切片小波变换程序（FTWT），matlab的m文件。从一种新的角度出发，通过自由选择频率切片函数、引进新尺度参数，在频率域实现小波变换，该变换能够很好地刻画信号各成分之间的相对能量关系。此外，频率切片小波变换的时频窗中心频率就是观测频率，而无需进行尺度换算。(Frequency slice wavelet transform procedures (FTWT), matlab m file. Starting from a new angle, through freely chosen frequency slice function, the introduction of a new scale parameter, the wavelet transform in the frequency domain, the transformation can portray the relative energy relations between the components of the signal. In addition, the frequency slice wavelet transform frequency is the observation of the time-frequency window center frequency without scale translation.)

Finite element method introduction in matlab

最小均方误差（MMSE）的算法，包含位置式PID算法、积分分离式PID，是小学期课程设计的题目，仿真效率很高的，用于信号特征提取、信号消噪，大学数值分析算法，处理信号的时频分析，合成孔径雷达（SAR)目标成像仿真。( Minimum mean square error (MMSE) algorithm, It contains positional PID algorithm, integral separate PID, Is the topic of the elementary school stage curriculum design, High simulation efficiency, For feature extraction, signal de-noising, University of numerical analysis algorithms, When processing a signal frequency analysis, Synthetic Aperture Radar (SAR) imaging simulation target.)

高斯分布表面反射特性，应用于激光雷达等领域。(it is used for lidar)

MATLAB环境下的基于HMM模型的语音识别系统(MATLAB environment model based on HMM Speech Recognition System)

efficient LBP Local binary patterns (LBP) is a type of feature used for classification in computer vision. LBP is the particular case of the Texture Spectrum model proposed in 1990.[1][2] LBP was first described in 1994.[3][4] It has since been found to be a powerful feature for texture classification it has further been determined that when LBP is combined with the Histogram of oriented gradients (HOG) descriptor, it improves the detection performance considerably on some dataset