modelcs_v1.1

This is for multiuser detection in telecommunication

reciver of qutature mirror filter

Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999.[1] Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving. The algorithm is patented in the US the owner is the University of British Columbia.

说明：  DC machine simulation with matlab simulink and parameters of machine are not defined

Implements Long Staff Schwartz methodolgy.

一个基于matlab的多机器人仿真平台，多智能体仿真(A Robot Simulator)

基于Matlab产生1024QAM程序，然后导入到任意波形发生器AWG中，最后由示波器进行采样离线分析的程序。可以结合硬件学习这整个过程。(Matlab based programs produce 1024QAM program, and then imported into the arbitrary waveform generator AWG, the last off-line analysis by the sampling oscilloscope. You can learn this whole process with hardware.)

SOM-BASED METHODS FOR NOVELTY DETECTION Global Normality Profile Approach (GNP) Local Normality Profile Approach (LNP) First Global Then Local Normality Profiles Approach (FGTL)

对从金属棒材中提取的超声缺陷信号经过自适应滤波以后的极坐标显示图(polar graph of ultrasonic defect signal)

利用相关的瞬时值通过在工作过程中的逐步调整参数逼近信号的统计特性，实现最优滤波。由此，我们得到一种最常用的算法——最小均方算法，简称LMS算法。(By using the instantaneous value of the correlation operation of the progressive approximation of the statistical properties of the signal to adjust the parameters to achieve optimal filtering. Thus, we get one of the most commonly used algorithms- the minimum mean square algorithm, called LMS algorithm.)