SA

水声通信面临的最困难的问题是多途干扰,其中自多途引起接收信号的幅度衰落,互多途引起接收信号的码间干扰,再加上海洋环境的高噪声背景、低的载波频率、极为有限的带宽以及传输条件的时间—空间—频率变化特性,使得通信过程十分困难。此外，水声通信试验的成本高，难度大，也为水声通信研究带来挑战。 本软件对水声信道的仿真研究很有帮助，可以产生声线图，冲激响应，声速图等仿真文件，结合Matlab画图工具绘制图像，对水声信道的模拟仿真很有帮助。(The most difficult problem faced by underwater acoustic communication is multi-path interference, which since multipath fading caused by the magnitude of the received signal, and mutual inter-symbol interference caused by multipath signal reception, plus the marine environment of high background noise, low carrier frequency , very limited bandwidth and transmission conditions of time- space- frequency characteristic such that the communication process is very difficult. In addition, the high cost of underwater acoustic communication experiment, difficult, and challenges for the study of underwater acoustic communication. This software is useful for simulation study of underwater acoustic channel can produce sound chart, impulse response, sound velocity diagrams simulation files, combined with Matlab drawing tools to draw the image of underwater acoustic channel simulation helpful.)

sar图像去噪的几种新的方法，到达过程是的泊松过程，ML法能够很好的估计信号的信噪比。( Several new methods sar image denoising, Arrival process is a Poisson process, ML estimation method can be a good signal to noise ratio.)

联合信道功率选择穷搜算法 给定信道功率策略数和节点数目下，遍历得出最优(joint channel and power selection exhaustive search)

说明：  数学建模中的图论知识来研究最短路径的问题，利用Dijkstra算法matlab实现(Graph theory knowledge in mathematical modeling is used to study the shortest path problem, which is realized by using Dijkstra algorithm matlab.)

基音端点检测，可用于语音识别，语音识别，和语音编码中。(speech point detection)

基于Hopfield神经网络对符号进行识别。 根据符号点阵创建并训练网络，使网络达到稳定状态，用随机带噪声的符号测试训练的网络。 (Hopfield neural network based recognition of symbols. Lattice under the symbol creation and training network, the network reaches a steady state, the symbol of random noise test with the training network.)

有关matlab7如何运用的介绍，详细，充分(For matlab7 how to use the introduction of a detailed, fully)

different classifier for matlab

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说明：  自适应滤波器解决非平稳的过程，因为实际信号的统计特性可能是非平稳的或者是未知的。(Adaptive filter solves the non-stationary process, because the statistical characteristics of the actual signal may be non-stationary or unknown.)