VoiceBasedBiometricSystem

说明：  经典无网格教程的随书算法的例程，属于第7章例程(Classical meshless algorithm tutorial routines with books belonging to Chapter 7 Routine)

说明：  本程序是用matlab开发的扩展Kalman Filter(this procedure is used Matlab development of the extended Kalman Filter)

This is a MATLAB Simulation file in which Predictive current control(PCC) modulation method for a Full Bridge (2-legs) inverter has been done.The load is a single Phase R_L.

可一步做到计算电力系统的节点有功矩阵的程序。(Can step do calculation of power system node meritorious matrix program. )

求系统的时域性能指标的函数，给定系统的数学模型，即可通过该函数计算系统的超调量、峰值时间、调整时间和上升时间，经测试非常好用。(system for the time-domain performance of the function, given the mathematical model, This function can be calculated by the overshoot, the peak time, adjusting time and rise time, the test is very handy.)

2进制振幅键控的源代码，运行后得到正常结果！(2 binary amplitude shift keying of the source code, get the normal result of running!)

对读入的两幅图像进行逐个像元比较，取其中最大或最小值形成新图像矩阵，输出。 已形成界面。可通过界面进行上述操作。(To read the two images pixel-by-comparison, the maximum or minimum value from which to form a new image matrix, the output. Interface has formed. Through the interface to carry out such operations.)

self adapt algorithm using matlab

本书介绍射频微波系统设计，也包括其他阻抗匹配、天线等高频技术。(This book is about systems that operate at radio frequencies (RF) (including microwaves) where high-frequency techniques, such as impedance matching, are important.)

隐马尔科夫模型是关于时序的概率模型，描述由一个隐藏的马尔科夫链随机生成不可观测的状态随机序列，再由各个状态生成一个观测而产生观测序列的过程。隐藏的马尔科夫链随机生成的状态的序列，称为状态序列；每个状态生成一个观测，而由此产生的观测的随机序列，称为观测序列。马尔科夫链由初始概率分布、状态转移概率分布以及观测概率分布确定(The hidden Markov model is a probabilistic model for time series. It describes the process of randomly generating unobservable state random sequences from a hidden Markov chain, and then generating an observation by each state to produce an observation sequence. A sequence of randomly generated states of hidden Markov chains, called a sequence of states; each state produces an observation, and the resulting random sequence of observations is called an observation sequence. Markov chain is determined by initial probability distribution, state transition probability distribution and observation probability distribution)