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小波分析理论及MATLAB7实现例程,第15章小波工具箱的其他功能(Wavelet analysis theory and realize MATLAB7 routines, Chapter 15 Other features of wavelet toolbox)

用径向基函数实现的信道自适应均衡，一个比较简单的例程(Radial Basis Functions Channel adaptive equalization, a relatively simple routines)

Example of complete coder.

霍夫变换的令一种算法，对于直线检测效果很好(Hough transform to make an algorithm that works well for line detection)

空间谱估计music 谱估计方法,估计信号参数(Spatial spectrum estimation music spectrum estimation methods, estimates signal parameters)

AP算法源码代 运行可以画出图 AP算法源码代 运行可以画出图(AP algorithm source code to run on behalf of AP algorithm source code can be drawn diagram can be drawn map on behalf of running)

设计一个四用户的扩频通信系统，扩频增益为N=16，采用Walsh码进行直接序列扩频，BPSK调制，无线信道为AWGN信道。在接收端进行解扩，解调，判决，通过仿真得到其中一个用户的误比特率与信噪比的仿真结果。(Designed a four-user spread spectrum communication system, spreading gain for N = 16, using Walsh code Direct Sequence Spread Spectrum, BPSK modulation, AWGN channel for the wireless channel. At the receiving end for despreading, demodulation, judgments, through the simulation to be one of a user' s bit error rate and SNR of the simulation results.)

压缩图像，本代码主要介绍压缩图形的主要步骤，非常简单有很明了，非常适合阅读(The compressed image, the code describes the main steps of compressed images are very clear, very simple, very suitable for reading)

使用高斯模型对威斯康辛州大学医学院长期乳腺癌数据进行了贝叶斯模式识别。识别率为95以上，可以作为模式识别的重要案例。(Gaussian model using the University of Wisconsin School of Medicine conducted a long-term breast cancer data Bayesian pattern recognition. Recognition rate is above 95 can be regarded as an important case of pattern recognition.)

用Cohen--Coon整定公式来求P,PI,PID的个参数，其中Gc是校正器的传递函数,kp为比例系数， Ti为积分时间常数，Td为微分时间常数，输入参量vars为带迟滞--惯性环节模型的KT τ 输入参量vars为带迟滞--惯性环节的K T ι，已知参数k=vars（1）；T=vars（2）；ι=vars（3）。(With Cohen- Coon tuning formula to seek P, PI, PID' s parameters, which Gc is the corrector transfer function, kp the proportional coefficient, Ti the integral time constant, Td for the differential time constant, input parameters vars as with hysteresis- inertia model KT τ as input parameters vars with hysteresis- inertia of KT ι, the known parameters k = vars (1) T = vars (2) ι = vars (3).)