metodo_gauss

一个运用模拟退火算法求解TSP问题的小例子程序(A simulated annealing algorithm for TSP small example program)

采用matlab来检测肤色区域，其他区域置为灰色，一个很简单的程序，基于RGB颜色空间(skin color detection)

本程序可以进行有限元模型修正和结构损伤识别，优点是基本解决了测量信息有限的问题。(This program can perform finite element model correction and structural damage identification. The advantage is that the problem of limited measurement information is basically solved.)

四阶龙格库塔法解微分方程，并与ode45，精确解比较(Fourth-order Runge-Kutta method for solving differential equations with ode45 exact solution)

已实现了MATLAB下的音频AGC，效果还可以，做起来才发觉比较简单，关键是要有高效的算法。如果能在实现AGC的同时做到噪音消除和回音消除，则效果更佳。(MATLAB has been achieved under the audio AGC, the effect can be done only to find relatively simple, the key is to have efficient algorithms. If you can do while in the realization of the AGC noise cancellation and echo cancellation, the better.)

强主动声纳信号干扰被动声探测的仿真分析.被动声探测是探测和跟踪水中噪声目标的重要手段,但强主动声纳信号的出现将对被动声探测产生很强的干扰作用,影响被动声纳对噪声目标的检测或跟踪。本文通过计算机仿真,分析被动声纳的积分时间、强主动声纳信号的强度、脉冲宽度和发射周期四个因素对强主动声纳信号干扰被动声探测程度的影响。(Simulation analysis of passive acoustic detection of a strong active sonar signal interference passive acoustic detection is an important means to detect and track water noise goals, but the emergence of strong active sonar signal will be passive acoustic detection generate strong interference effect, the impact of passive acoustic satisfied noise target detection or tracking. This paper through computer simulation, and analysis of the four factors of the integration time of the passive sonar, a strong active sonar signal intensity, pulse width, and launch cycle impact of passive acoustic detection of a strong active sonar signal interference.)

examples of rna in matlab. PCA also.

分类工具包，含有多个分类算法的matlab源程序，适用于类别数据的分类和可视化(The Classification toolbox for MATLAB is a collection of MATLAB modules for calculating classification (supervised pattern recognition) multivariate models: Discriminant Analysis, Partial Least Square Discriminant Analysis (PLSDA), Classification trees (CART), K-Nearest Neighbors (kNN) and Soft Independent Modeling of Class Analogy (SIMCA). This is the version 3.1 of the Classification toolbox for MATLAB Classification toolbox for MATLAB has been released by Milano Chemometrics and QSAR research Group. Visit our website at www.disat.unimib.it/chm )

The BCJR algorithm is an algorithm for maximum a posteriori decoding of error correcting codes defined on trellises (principally convolutional codes). The algorithm is named after its inventors: Bahl, Cocke, Jelinek and Raviv.[1] This algorithm is critical to modern iteratively-decoded error-correcting codes including turbo codes and low-density parity-check codes.

《Multivariable feedback control analysis and design () (2nd Edof electronic source code》一书的电子源代码(Multivariable feedback control analysis and design () (2nd Edof electronic source code)