MATLAB7_0

复杂的卡尔曼滤波例子,跟踪物体穿越空中大气层 (Hybrid extended Kalman filter example.Track a body falling through the atmosphere.)

基于MATLAB信号处理工具箱的数字滤波器设计与仿真(Digital filter design and simulation based on MATLAB Signal Processing Toolbox)

介绍了matlab程序在信号处理中的应用和其中使用的程序(the introduction of matlab using in SP)

MATLAB code for trajectory smoothing and curvature calculation.

说明：  调用matlab详细程序 调用matlab详细程序 (Detailed procedures to call matlab to call matlab detailed procedures)

哈希函数的matlab代码，此函数不可逆，具有加密功能。(Hash function matlab code, this function is not reversible, with encryption features.)

使用matlab实现基于AM解调的广播声音接收，并通过GUI界面实现。(Using matlab based on AM demodulation receiving radio voice, and through the GUI interface implementation.)

MATLAB CODE FUNCTION CASCADE DETECT FACE AND RECOGNITION

Gauss Seidel equation solving for power system analysis

GloptiPoly 3: moments, optimization and semidefinite programming. Gloptipoly 3 is intended to solve, or at least approximate, the Generalized Problem of Moments (GPM), an infinite-dimensional optimization problem which can be viewed as an extension of the classical problem of moments [8]. From a theoretical viewpoint, the GPM has developments and impact in various areas of mathematics such as algebra, Fourier analysis, functional analysis, operator theory, probability and statistics, to cite a few. In addition, and despite a rather simple and short formulation, the GPM has a large number of important applications in various fields such as optimization, probability, finance, control, signal processing, chemistry, cristallography, tomography, etc. For an account of various methodologies as well as some of potential applications, the interested reader is referred to [1, 2] and the nice collection of papers [5].(GloptiPoly 3: moments, optimization andsemidefinite programming.Gloptipoly 3 is intended to solve, or at least approximate, the Generalized Problem ofMoments (GPM), an infinite-dimensional optimization problem which can be viewed asan extension of the classical problem of moments [8] . From a theoretical viewpoint, theGPM has developments and impact in various areas of mathematics such as algebra, Fourier analysis, functional analysis, operator theory, probability and statistics, to citea few. In addition, and despite a rather simple and short formulation, the GPM has alarge number of important applications in various fields such as optimization, probability, finance, control, signal processing, chemistry, cristallography, tomography, etc. For anaccount of various methodologies as well as some of potential applications, the interestedreader is referred to [ 1, 2] and the nice collection of papers [5].)