3freedomnon-linemodel

visual c++ matlab图像处理与识别实用案例精选，帮助我们学习(visual c++ matlab image processing and recognition of practical case selected to help us learn)

　　　近20年来，阵列信号处理已成为信号处理的一个重要分支，本次实验就是通过MUSIC（Multiple Signal Classification）算法对混叠有高斯白噪声的双信源信号进行频谱分析。(Space there is more than one letter (No.) source, it is often necessary to separate these signals in order to track or detect the spatial signal we are interested in, inhibition of those who are considered interference signal. To this end, the need to use the antenna array to receive multiple spatial signal analysis and processing of the the spatial signal received by the antenna array known as array signal processing. The past 20 years, array signal processing has become an important branch of signal processing, this experiment is the MUSIC (Multiple Signal Classification) algorithm aliasing Gaussian white noise dual source signal spectrum analysis.)

This PPT is described Real-time Optimization for Distributed Model Predictive Control.

MATLAB实现快速正交变换，与传统的FFT算法相比拥有更高的分辨率，但是运算时间较大， 在目标检测、频率分析、降噪方面都有着广泛的应用(MATLAB to achieved FOS_convert, compared with the traditional FFT algorithm has a higher resolution, but the computation time is large, the target detection, frequency analysis, noise reduction have a wide range of applications)

pso source code jornal very nice

Finally, probability analysis, based on the chosen database, is performed and judgment whether the person is recognized is pronounced. Two different modeling techniques can be suggested for purposes of person identification or verification. The approach based on Hidden Markov Speaker Models (HMMs) with the mixtures of Gaussian distribution is text-dependent one. For this technology a speaker is described by a set of HMMs, constructing the set of speech groups spoken by that person. The speech groups may be phonemes or words [7]. The text-independent technology is rested upon Gaussian Mixture Speaker Models. For this technology each speaker is defined by only one model describing all utterances of that person. Identification in the range of both approaches is executed by evaluation of maximum a posteriori probability [7].

给定特征函数，求解正交基,利用了SVM分解，快速有效兼容性好(Given the characteristic function, solving orthogonal basis)

在有限区域内分布的稀疏不均的、具有一定分布结构的海量数据集的高效、高精度聚 类问题是一个尚未完全圆满解决的难题。针对Affinity Propagation 聚类算法(AP)的不足之处， 提出了两个改进型的聚类算法(In a limited area of uneven distribution of sparse, has certain distribution structure of the mass datasets of high efficiency, high precision together Problem is a perfectly solved problem has not yet completely. For Affinity Propagation clustering algorithm (AP) of the deficiencies, Put forward the two improved version of the clustering algorithm)

用于进行MNF变换，mnf用于对高维数据进行降维，特征提取(For MNF transformation, MNF is used for dimensionality reduction and feature extraction of high-dimensional data.)

说明：  利用AR模型实现功率谱估计的方法，包括直接求解，L-D递推，自相关法和Burg算法。(AR model power spectrum estimation methods, including direct solving LD recursive autocorrelation method and Burg algorithm.)