Matlabhanshudaquan

a matlab program which depicts the transmission , reception of ofdm signal

SFSDP is a Matlab package for solving sensor network localization problems. The package contains four functions, SFSDP.m, SFSDPplus.m, generateProblem.m, test SFSDP.m, and some numerical examples.

matlab在图像处理中的应用介绍，还不错哦(matlab image processing application in the introduction, not bad oh)

emd中处理端点的方法，对于端点的效果很好。(emd endpoint processing method for endpoint works well.)

LSL最小二乘方格型算法的自适应滤波程序及其应用(LSL least-squares grid-based algorithm for adaptive filtering procedures and its application)

这是基于matlab的关于模糊控制pid的源代码，对于学习pid控制器的很有用(This is based on the fuzzy control pid matlab source code for learning useful pid controller)

This file is about vector control of PMSM. DTC - Direct torque control.

现代通信系统(MATLAB版)(第二版)刘树棠译 本书系Brooks/Cole出版公司（Thomson Learning出版集团的下属子公司）2000年推出的BookWare系列丛书（BookWare Companion SeriseTM）之一种。该书提供了利用MATLAB的普及型学生版本在计算机上解决“现代通信系统”这门课程中涉及的几乎所有方面的问题的分析思路、方法、MATLAB脚本文件和处理结果的范例以及供学生自主学习研讨的习题。全书内容分为9章，分别是：信号与线性系统；随机过程；模拟调制；模拟/数字转换；基带数字传输；带限信道的数字传输；载波调制的数字传输；信道容量和编码；扩频通信系统。 (This book Brooks/Cole Publishing (Thomson Learning Publishing Group, a subsidiary) in 2000 launched BookWare Series (BookWare Companion SeriseTM) of a kind. The book provides the advantage of the popularity-based student version of MATLAB on the computer to solve the " modern communications systems," This course covered almost all aspects of the analysis of ideas, methods, MATLAB script files and processing the results of examples, as well as independent study for students seminar to answer. The book consists of nine chapters, namely: Signal and Linear System stochastic process analog modulation analog/digital conversion base-band digital transmission band-limited channel digital transmission carrier modulation digital transmission channel capacity and coding spread-spectrum communication systems.)

模型是结构体类型的线性投影Y = linproj(X, model) 其中W.model为线性投影矩阵， Y = model.W *X + model.b b为偏差值( Description: Y = linproj(X, model) linearly projects data in X such that Y = model.W *X+ model.b out_data = linproj(in_data, model) projects in_data.X out_data.X = model.W *in_data.X+ model.b out_data.y = in_data.y Input: model [struct] linear projection: .W [dim x ncomp] Projection matrix. .b [ncomp x 1] Bias.)

最小二乘拟合，包括3次，9次，15次曲线拟合(Least-squares fit, including three times, nine times, 15 times Curve Fitting)