dec2bin22

求解长相关指数程序，简单，可以比较一下(Solving long-range dependence index procedure, simple, can compare)

RNNSIM v.2 package contains a number of m-files for training and evaluation of the random neural network. All functions have been thoroughly tested. After downloading the separate files or the zipped file, make sure that they are stored or extracted in the directory /rnnsimv2 Below an overview of the files contained in this directory along with a brief description of what they do. The on-line help facility explains how to call the different functions. You simply write help <function-name> in the MATLAB command window. Along with the m-files in this directory you will find a manual for the simulator in PDF format (rnnsimv2.pdf). Start by printing this out and read the release notes. Two simple demonstration programs are given to illustrate how most of the functions work.

matlab program for papr reduction using companding technique

语音识别是近年来发展非常迅速的一项计算机智能技术，广泛应用在语音控制、身份识别等多个领域。本次项目主要研究语音识别的预处理过程和特征参数的提取环节。通过对原始语音信号进行预加重和分帧、加窗，滤除低频干扰，提升对语音识别有用的部分，消除了部分噪音和失真。预处理之后进行信号的特征提取，主要选取了短时平均过零率和MFCC两个特征参数，应用matlab软件绘制波形图并提取特征参数矩阵，为之后的语音信号的识别打下了基础。(Speech recognition is a computer intelligent technology which has developed rapidly in recent years. It is widely used in speech control, identity recognition and other fields. This project mainly studies the pretreatment of speech recognition and the extraction of feature parameters. Through pre-emphasis, framing and windowing of the original speech signal, the low frequency interference is filtered out, the useful part of speech recognition is enhanced, and some noise and distortion are eliminated. After pretreatment, feature extraction of signal is carried out. Two feature parameters, short-term average zero-crossing rate and MFCC, are selected. Waveform diagram is drawn by MATLAB software and feature parameter matrix is extracted, which lays a foundation for speech signal recognition.)

Decryption algorithm of caeser which generate all possible combinations

Abstract—Several algorithms have been developed for tracking formant frequency trajectories of speech signals, however most of these algorithms are either not robust in real-life noise environments or are not suitable for real-time implementation. The algorithm presented in this paper obtains formant frequency estimates voiced segments of continuous speech by using a time-varying adaptive filterbank to track individual formant frequencies. The formant tracker incorporates an adaptive voicing detector and a gender detector for formant extraction continuous speech, for both male and female speakers. The algorithm has a low signal delay and provides smooth and accurate estimates for the first four formant frequencies at moderate and high signal-to-noise ratios. Thorough testing of the algorithm has shown that it is robust over a wide range of signal-to-noise ratios for various types of background noises.

Matlab LAWS能量滤波函数 主要用于纹理的分析(Matlab LAWS energy filtering function is mainly used for texture analysis)

指数平滑法是对时间序列进行预测常用的方法之一,其理论基础是趋势外延，即未来一定时期内事物在数量上的演变特征不脱离过去的发展趋势,预测值是以前所有观测值的加权和,且对不同的数据给予不同的权数,越近期数据给予越大的权数,越远期的数据给予越小的权数。(Exponential smoothing method is commonly used to predict the time series one of the ways, the theoretical basis of the trend of the extension, that is, a certain period of time what the next evolution in the number of characteristics are not out of the development trend of the past to predict the value of all previous observations is the weighted and, on different data and different weights given to more recent data to give greater weights, the more long-term data to give the smaller weights.)

Based on Sparse Representation

说明：  通信原理课本中的一些实例代码，方便大家使用(Communication principles in the textbook of some examples of code, easy to use)