dtgh

针对当前存在的大量数值型数据，将连续型数据离散化，有利于计算机直接处理变量。(In view of the existence of a large number of numerical data, the discretization of continuous data is conducive to deal directly with the variable computer.)

omnet++ implementation of wsn.

Field 2 是丹麦理工大学（Technical University of Demark）的J.A.延森（Jorgen Arendt Jenson）开发的一款超声模拟软件，该软件基于线性声场理论， 其最基本概念是空间冲击响应，最早由Tupholme和Stepannishen等提出，空间冲击响应是一个和何置相关的函数，它与换能器上的激励信号卷积，可以得到发射波到焦点的任意一点的声场。换能器接收同波信号相当与发射的逆过程，将空间的反射点作为发射源，与接收换能器的空间冲击响应卷积，从而得到回波信号。(Field 2 is the Technical University of Denmark (Technical University of Demark) is an ultrasound simulation software JA Jensen (Jorgen Arendt Jenson) developed the software based on the linear sound field theory, the basic concept is the spatial impulse response, first by Tupholme and Stepannishen and other proposed space impulse response is a home-related functions and what its convolution with the excitation signal transducer, you can get any of the transmitted wave to the focal point of the sound field. Wave transducer receives a signal corresponding with the inverse process of transmission, the reflector point in space as a transmission source, and the receiving transducer spatial convolution of the impulse response, thereby obtaining the echo signal.)

Using matlab bit plane decomposition. It contained 2 versions. One is using functions. One is using matlab codes.

基于小波变换的语音信号分解、重构去噪，希望对大家有所帮助。(The voice signal decomposition and reconstruction based on wavelet transform denoising.I hope to be helpful.)

内容为哈尔滨工业大学MATLAB讲义，为ppt格式，方便大家学习交流。(Contents of Harbin Institute of Technology MATLAB lectures, in order to ppt format, to facilitate them to learn from the exchange.)

在MATLAB中，进行FDTD仿真，二维电磁波传播。(In MATlab, FDTD Simulation for Electromagnetic Wave.)

TEXT BOOK TO LEARN PROGRAMMING IN C

In this paper, an attractive approach for teaching genetic algorithm (GA) is pre-sented. This approach is based primarily on using MATLAB in implementing the genetic operators: crossover, mutation and selection. A detailed illustrative example is presented to demonstrate that GA is capable of finding global or near-global optimum solutions of multi-modal functions. An application of GA in designing a robust controller for uncertain control systems is also given to show its potential in designing engineering intelligent systems

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