dcmstart

对于中断技术有非常详细的讲解，带图片完整版(Technology for the interruption of a very detailed explanation, with pictures full version)

It is great model of one part of engine diesel model. This particular file contains model of intake manifold.

模糊粗糙课程中某些问题的MATLAB算法(it is MATLAB)

Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of speech production, for recovering compressive sensed speech. Since the linear transform is signal dependent and unknown, unlike the standard CS formulation, a codebook of transfer functions is proposed in a matching pursuit (MP) framework for CS recovery. It is found that MP is efficient and effective to recover CS encoded speech as well as jointly estimate the linear model. Moderate number of CS measurements and low order sparsity estimate will result in MP converge to the same linear transform as direct VQ of the LP vector derived the original signal. There is also high positive correlation between signal domain approximation and CS measurement domain approximation for a large variety of speech spectra.

说明：  蚁群算法 matlab程序，已运行，很好用奥(Ant colony algorithm is already running and matlab, very good)

基于matlab边界元的经典算法。(Classical algorithm matlab Boundary Element. Vertical ground electrode radius r = 0.05m, length l = 0.5m vertical grounding on potential V0 = 30V, the Boundary Element Method radially outwardly eight-point potential.)

Simulation of digital M-QAM modulation schemes over an AWGN channel

用simulink中的SimMechanics模块搭建两关节机械臂模型，并用S-function函数编写控制程序，采用的是计算力矩法跟踪参考信号。(SimMechanics module structures in simulink model of two-joint robot arm, and write control program, a function of the S-function is computed torque method tracks the reference signal.)

双边滤波的matlab源码，可用于灰度或彩色图像，很经典。(Bilateral filtering matlab source code, can be used for grayscale or color images, very classic.)

This function computes autocorrelation with a biased scaling.