application_fec.m

简单的PID——PID串级控制matlab仿真(Simple PID- PID cascade control matlab simulation)

说明：  继续上次传的简单遗传算法，本次加入啦测试部分。(Continue to the last mass of the simple genetic algorithm, to join you in this part of the test.)

说明：  声学 边界元程序 matlab(boundary element acoustics Fundamentals and coputer codes)

说明：  PPC上面类似Matlab的语言环境，希望有pda的同学们喜欢(PPC above language similar to Matlab environment, and hope to have the students like pda)

时频分析基本理论 时频分析工具箱 时频分析的应用实例(Time-Frequency Analysis)

Eigenfunctions of integrable planar billiards are studied — in particular, the number of nodal domains, ν of the eigenfunctions with Dirichlet boundary conditions are considered. The billiards for which the time-independent Schrö dinger equation (Helmholtz equation) is separable admit trivial expressions for the number of domains. Here, we discover that for all separable and nonseparable integrable billiards, ν satisfies certain difference equations. This has been possible because the eigenfunctions can be classified in families labelled by the same value ofm mod kn, given a particular k, for a set of quantum numbers, m, n. Further, we observe that the patterns in a family are similar and the algebraic representation of the geometrical nodal patterns is found. Instances of this representation are explained in detail to understand the beauty of the patterns. This paper therefore presents a mathematical connection

This document describes the feature by feature the use of Wavelet Methods for Time Series Analysis.

It generates a 2dbreast phantom

The program contains a rectangular window

用于信号特征提取、信号消噪，快速扩展随机生成树算法，最小二乘回归分析算法，包括广义互相关函数GCC时延估计，信号处理中的旋转不变子空间法，包括最小二乘法、SVM、神经网络、1_k近邻法,包含特征值与特征向量的提取、训练样本以及最后的识别，针对EMD方法的不足。( For feature extraction, signal de-noising, Rapid expansion of random spanning tree algorithm, Least-squares regression analysis algorithm, Including the generalized cross-correlation function GCC time delay estimation, Signal Processing ESPRIT method, Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Contains the eigenvalue and eigenvector extraction, the training sample, and the final recognition, For lack of EMD.)