Projeto_MS

说明：  最优化理论 认知无线电 功率控制方面的资料，写得很详细。( Probability-based optimization of inter-sensing duration and power control in cognitive radio )

说明：  对OFDM（正交频分复用）的调制和解调过程的仿真(On OFDM (Orthogonal Frequency Division Multiplexing) modulation and demodulation process simulation)

遗传算法求解测试函数,进行了改进,提高了算法效率 (genetic algorithm )

BCH(15,5,7)a communication system consists of binary source, 3 error-correcting BCH code of length 15 over GF(24) (i.e, BCH (15,5,7 code), the primitive polynomial is p(x)=1+x+x4), BPSK modulator, AWGN channel, coherent demodulator, decoder (either BMA or EA could be utilized to solve the error locator polynomial) and sink. Simulate and draw its error performance with and without BCH coding, together with the theoretical no-coding curve, i.e. two BER-EbNo curves.(a communication system consists of binary source, 3 error-correcting BCH code of length 15 over GF(24) (i.e, BCH (15,5,7 code), the primitive polynomial is p(x)=1+x+x4), BPSK modulator, AWGN channel, coherent demodulator, decoder (either BMA or EA could be utilized to solve the error locator polynomial) and sink. Simulate and draw its error performance with and without BCH coding, together with the theoretical no-coding curve, i.e. two BER-EbNo curves. )

波束形成算法中一种变换形式的极坐标算法（与传统的极坐标算法不同）(Polardb is a modified version of the regular polar function.)

用C语言编写的IEEE34节点系统画图例程，用于matlab(the graghing programme of IEEE34 node system written in C, for matlab )

Comme les autres régimes d’états liés présentés précédemment, l’état de quatre impulsions est très sensible à l’énergie de saturation et au gain petit signal. Quand l’énergie de saturation (respectivement le gain petit signal) varie dans la gamme de quelques pJ

LDPC的分层NMS算法的matlab仿真程序，包含编码程序、译码程序以及画误码率图程序(NMS layered LDPC algorithm matlab simulation program, including coding procedures, as well as painting BER decoding process diagram program)

Probability and Stochastics蒙特卡洛算法等(Probability and Stochastics)

支持向量机(SVM)用于分类的算法实现 function [D, a_star] = SVM(train_features, train_targets, params, region)(Support Vector Machine (SVM) algorithm for classification in function [D, a_star] = SVM (train_features, train_targets, params, region))