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2dlda face recgnition...............matlab

Matlab教程第六章案例，时间序列分析与计量(Matlab Guide Chapter cases, time series analysis and measurement)

使用MATLAB实现FIR滤波器设计，内含详细代码和原理(FIR filter design using MATLAB implementation, containing detailed codes and principles of)

CW频率步进信号的时域和频率的包络波形的仿真(CW frequency step signal time domain and frequency envelope waveform simulation)

/*A student is a part of institute, the type of student varies on the basis of their department, year of registration, course –UG or PG, status regular, a student of alumni. Based on these details, a student performs various functions in an institute like: registering for a course, attending seminars, viewing and editing details, etc.*/

hua fft可以同时计算频谱和功率谱，同时生成图像(hua fft can calculate the spectrum and power spectrum, while generating image)

PCA是一种掌握事物主要矛盾的统计分析方法，它可以从多元事物中解析出主要影响因素，揭示事物的本质，简化复杂的问题。本程序实现了基于PCA特征脸的人脸识别。(PCA is a major contradiction to grasp things, statistical analysis, it can parse out things from multiple main factors, revealing the essence of things, simplifying complex issues. This program implements the features of face recognition based on PCA.)

说明：  压缩包里包含了无约束优化问题常用的几种求解方法的源程序：变量轮换法（variable_rotation.m）、最速下降法（steepest_descent.m）、修正牛顿法（modified_newton.m）、共轭梯度法（conjugate_gradient.m）。另外，coefficient_matrix.m为目标函数系数获得矩阵，minval.m为最小值计算函数，gradient.m为梯度计算函数(Compression bag contains unconstrained optimization problems of several commonly used method of source: variable rotation Act (variable_rotation.m), steepest descent method (steepest_descent.m), as amended Newton (modified_newton.m), conjugate gradient method (conjugate_gradient.m). In addition, coefficient_matrix.m as the objective function coefficients obtained matrix, minval.m function for the minimum calculation, gradient.m for gradient calculation function)

线性调频脉冲压缩的Matlab程序,滤波求和方式实现宽带波束形成，部分实现了追踪测速迭代松弛算法。( LFM pulse compression of the Matlab program, Filtering summation way broadband beamforming, Partially achieved tracking speed iterative relaxation algorithm.)

利用KALMAN函数对运动物体进行跟踪，有较好的效果 (make use of KALMAN function to track moving objects, which have a good result)