New-WinRAR-ZIP-archive

上电后,在BRAKE信号无效,且保护逻辑无输出的情况下? 电机按DIRECTION信号的方向转动? 电流传感放大器采样电机电枢电流,经13脚的RC网络转换为电压值,与D/A转换输出的预定阀值相比较。如果当前的电流使转换的电压值小于阀值?(Power after the BRAKE signal is invalid, and the protection logic no circumstances ? electrical output signals according to the direction of rotation DIRECTION ? current sensing amplifiers motor armature current sample by 13 feet of RC network is converted to voltage value, and D/A conversion output compared to the target threshold. If the current of current to voltage conversion value is less than threshold ?)

matlab例程，非常好用，便以移植,bianyuxuxi(susan Algorithm is very easy to use, then to transplant)

Lab 2 – Synthesizable MATLAB This lab exercise will explore the effects that different MATLAB coding styles have on hardware. The lab has two parts, each of which begins with a short introduction. This lab exercise is based on the simple MATLAB FIR filter model shown below:(Lab 2- Synthesizable MATLABThis lab exercise will explore the effects that different MATLAB coding styles have on hardware. The lab has two parts, each of which begins with a short introduction. This lab exercise is based on the simple MATLAB FIR filter model shown below:)

一种快速求得最短路径的经典算法，可以在运筹规划或在统筹安排中使用(Express obtained a classic shortest path algorithm, can be planned or in operation in co-ordinating arrangements for the use of)

说明：  MATLAB仿真IS-95开环闭环功率控制 (MATLAB simulation IS-95 open-loop power control loop)

对一段声音进行高通低通和带通滤波 ，观察其波形，滤波频率可选择(A sound of high-pass low-pass and band-pass filter, and observe the waveform, the filter frequency selectable)

对频率、幅值可变的正弦信号进行dq变换的Matlab仿真(Variable-amplitude variable- frequency sinusoidal signals dq transformation)

by using matpower we can solve power system load flows

一阶扩展卡尔曼滤波，二阶扩展卡尔曼滤波，和Rauch - tu - Striebel平滑器(Perform Extended Kalman Filter prediction step. Extended Kalman Filter measurement update step. Extended Rauch-Tung-Striebel smoother algorithm. Calculate "smoothed" sequence from given Kalman filter output sequence by conditioning all steps to all measurements.)

Hidden_Markov_model_for_automatic_speech_recognition This code implements in C++ a basic left-right hidden Markov model and corresponding Baum-Welch (ML) training algorithm. It is meant as an example of the HMM algorithms described by L.Rabiner (1) and others. Serious students are directed to the sources listed below for a theoretical description of the algorithm. KF Lee (2) offers an especially good tutorial of how to build a speech recognition system using hidden Markov models.