DMC

计算多重分形非趋势波动分析matlab程序,完整的基于HMM的语音识别系统，构成不同频率的调制信号。( Calculation multifractal detrended fluctuation analysis matlab program, Complete HMM-based speech recognition system, Constituting the modulated signals of different frequencies.)

小波分析与应用例程在matlaB中的实现，压缩包里面包含讲义和源代码。(Wavelet analysis routines in MATLAB to achieve compression package which contains the lecture notes and source code.)

关于Matlab PACE workshop 的详细介绍资料，可以供大家参考(Matlab PACE Workshop of presentation materials for your reference)

能量效率相关通信文档，MIMO技术相关，能效定义(An information-theoretic look at MIMO energy-effi cient communications- valuetools-belmega-lasaulce_final)

MATLAB中WVD变换，可将时域数据转换为时频域能量密度(WVD Transform)

matpower 中文说明书，对于学习MATLAB编写潮流程序和最优潮流的同学非常有用(matpower Chinese Manual, prepared for the trend of learning MATLAB and Optimal Power Flow program is very useful for students)

his is a simple method for silence removal and segmentation of audio streams that contain speech. The method is based in two simple audio features (signal energy and spectral centroid). As long as the feature sequences are extracted, as thresholding approach is applied on those sequence, in order to detect the speech segment

粒子群算法优化函数求解极值的问题。通过该程序我们可以看到粒子群优化算法的优化过程。(Particle Swarm Optimization for Solving Extremum problems. Through this program we can see that PSO optimization process.)

这是一个经典的形变模型实施,在一个单一的文件用简单的可以理解的代码。 功能包括两部分一个简单的弱分类器和一个促进部分: 弱分类器试图找到最佳阈值的数据维数对数据进行分离成两个阶级1和1 要求的进一步提高分类器部分迭代,每一步是变化分类权重miss-classified例子。这造成了一连串的“弱分类器”,表现得像一个“强大分类器” (This a classic AdaBoost implementation, in one single file with easy understandable code. The function consist of two parts a simple weak classifier and a boosting part: The weak classifier tries to find the best threshold in one of the data dimensions to separate the data into two classes-1 and 1 The boosting part calls the classifier iteratively, after every classification step it changes the weights of miss-classified examples. This creates a cascade of "weak classifiers" which behaves like a "strong classifier" . )

ofdm implementation using 16-Qam