QPSK-OQPSK

rls算法,实现含噪声信号的消噪，其噪声为高斯白噪声加入，绘制了滤波前后的波形图(rls algorithm to achieve denoising noisy signals, the noise is Gaussian white noise is added, drawing the waveforms before and after filtering)

PCA和KPCA综合处理，用于对TE数据的故障监控，有很好的效果(PCA and KPCA integrated approach for fault monitoring of TE data, have a good effect)

说明：  duffing方程相关程序，方程求解，时间历程绘制，相图及庞加莱映射图(duffing, Poincare,phase diagram)

说明：  传感器阵列波束优化设计与应用书籍第6章的matlab程序(Chapter 6 matlab program of book Sensor array beam optimization design and application)

说明：  本程序是基于S变换的短时电能质量扰动检测方法实现的，用于分析电压暂降的幅值、相位跳变、持续时间和谐波含量进行分析(This procedure is based on the S transform short-term power quality disturbance detection method implemented for the analysis of voltage sag magnitude, phase transition, duration, and harmonic content analysis)

matlab 降维工具箱，最新版本。包含各类线性及非线性降维代码，lle,lpp,mvu,isomap,npe等皆在其中。(DRTOOL, by itself, creates a new DRTOOL or raises the existing singleton*. H = DRTOOL returns the handle to a new DRTOOL or the handle to the existing singleton*. DRTOOL( CALLBACK ,hObject,eventData,handles,...) calls the local function named CALLBACK in DRTOOL.M with the given input arguments. DRTOOL( Property , Value ,...) creates a new DRTOOL or raises the existing singleton*. Starting from the left, property value pairs are applied to the GUI before drtool_OpeningFunction gets called. An unrecognized property name or invalid value makes property application stop. All inputs are passed to drtool_OpeningFcn via varargin. *See GUI Options on GUIDE s Tools menu. Choose "GUI allows only one instance to run (singleton)".)

归纳了基于WLAN和WSN的异构网络协同定位算法研究中的关键问题指出了如何根据终端位置环境同时搜索WLAN和无线传感器网络以及改进后的网络融合定位算法( The key problems of heterogeneous network based on WLAN and WSN in the study of collaborative localization algorithm were summarized. According to terminal location environment, the searching of WLAN and WSN at the same time and the improved network fusion localization algorithm were pointed out)

说明：  一种新型自适应信号时频处理方法，特别适用于非线性非平稳信号的分析处理。(A new adaptive signal time-frequency processing method is proposed, which is especially suitable for the analysis and processing of non-linear and non-stationary signals.)

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.)

The above file show the Independent componets analysis used in face recognisation