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一个修改后的PCA进行人脸识别的Matlab代码~~~~~~~!!!(A revised PCA for Face Recognition Matlab code ~~~~~~~!!!)

The objective of this project is to investigate the possibility of applying a dynamic channel allocation to cMCN. The proposed method is a multihop dynamic channel assignment (MDCA) scheme that works by assigning channels based on interference information in the surrounding cells. A channel searching strategy, sequential channel searching (SCS) is developed for the MDCA scheme. This strategy is responsible for the channel updation which includes channel assignment for a new request and channel release after the completion of the communication process. Through computer simulation, we show that the proposed MDCA scheme using the SCS strategy improves the system capacity significantly.

关于地震以及测井方面matlab代码 很实用(Seismic logging matlab code)

SRM ,Dynamic model of a swithce reluctanc emotor.

Matlab - Neural network RBF for function approximation.

This is a technician handbook for fiber optics

用matlab编辑音乐，模拟萧的声音演奏天空之城(Use matlab to edit music)

Matlab 实现求复杂网络的度分布，并返回度，以及平均度(Matlab seeking to achieve the degree distribution of complex networks)

Power system stabilizers (PSS) are now routinely used in the industry to damp out power system oscillations. In this paper, real-coded genetic algorithm (RCGA) optimization technique is applied to design robust power system stabilizer for both singlemachine infinite-bus (SMIB) and multi-machine power system. The design problem of the proposed controller is formulated as an optimization problem and RCGA is employed to search for optimal controller parameters. By minimizing the time-domain based objective function, in which the deviation in the oscillatory rotor speed of the generator is involved stability performance of the system is improved. The non-linear simulation results are presented under wide range of operating conditions disturbances at different locations as well as for various fault clearing sequences to show the effectiveness and robustness of the proposed controller and their ability to provide efficient damping of low frequency oscillations.

A Comprehensive Sparse Learning Package(Descriptions: Six sparse algorithms are implemented in this package. They are 1) SVM 2) L1-norm SVM a) Linear Programming SVM b) SC-SVM 3) L0-norm SVM 4) RVM (Relevance Vector Machine) 5) SPC (Sparse Probit Classifier))