CAD-sim-control-system-MATLAB

ar模型 阶数和参数估计的matlab程序

说明：  matlab下的kalman实现，内附详细的解释，有助于初学者学习，望高手免进(matlab kalman under implementation, containing a detailed explanation to help beginners learn to avoid looking into the master)

说明：  该代码是多视图聚类的统一图学习框架算法代码，运用Matlab编写，有利于多视图聚类的学习(A Unified Graph Learning Framework for Multi-view Clustering)

作为算法实现的应用,对光子晶体光波导的电磁耦合效应进行了数值仿真研究,结果证实:波导耦合区域内不同半径比介质柱所导致的结构变化将造成耦合长度的改变,且其耦合关系曲线具有平稳区与迅变区两类不同特性的变化范围区间。(As the algorithm' s application, on the photonic crystal waveguide for electromagnetic coupling numerical simulation, results show: waveguide coupling region different than the dielectric cylinder radius of the structural changes caused by the coupling length will cause a change, and its coupling curve has a smooth area with the fast-changing area characteristics of two different range interval.)

包含DVB-T标准中的内交织，内编码，卷积交织，和基4FFT 1024。所有程序为MATLAB实现，特别推荐FFT旋转因子求法。(Includes DVB-T standard within intertwined, with coding, Convolutional Interleaver, and base 4FFT 1024. MATLAB realization of all the procedures, in particular, recommended rotating factor FFT method.)

伪彩色变换，采用MATLAB制作。效果清晰，明显。(Pseudo-color transformation, making use of MATLAB. Effects clearly, obviously.)

模数转换的数字锁相环，代码中有详细的说明(digital phase lock loop)

基于遗传非常简单实用。算法的多目标优化方案, 用matlab程序编写,(Multi-objective genetic algorithm-based optimization scheme is very simple and practical. Programming with matlab)

本代码为最佳滤波器的研究，关于维纳滤波器的设计实验(This code is the best filter research, design experiments on the Wiener filter)

A. Fusion at the Feature Extraction Level The data obtained from each sensor is used to compute a feature vector. As the features extracted from one biometric trait are independent of those extracted from the other, it is reasonable to concatenate the two vectors into a single new vector. The primary benefit of feature level fusion is the detection of correlated feature values generated by different feature extraction algorithms and, in the process, identifying a salient set of features that can improve recognition accuracy [14]. The new vector has a higher dimension and represents the identity of the person in a different hyperspace. Eliciting this feature set typically requires the use of dimensionality reduction/selection methods and, therefore, feature level fusion assumes the availability of a large number of training data.