MOSGA4

信号与系统软件实验工具箱，本系统包括七个实验，分别是：信号的时域基本运算、连续信号的卷积与连续时间系统的时域分析、离散信号的卷积与离散时间系统的时域分析、信号的频域分析、连续信号的采样与恢复、系统的频域分析、信号的幅度调制与解调。为了加强学生的计算机编程能力和应用能力，所有实验均提供设计性实验内容，让学生参与编程。本系统既可作为教师教学的实验演示，又可作为学生动手实验的实验系统。(Signals and Systems Software experimental toolbox, the system includes seven experiments, namely: basic operations signals in time domain, the convolution of continuous signals and continuous-time system time domain analysis, convolution of discrete signals and discrete-time systems when domain analysis, frequency domain signal analysis, continuous signal sampling and recovery, system frequency domain analysis, signal amplitude modulation and demodulation. In order to strengthen the student s computer programming skills and ability to provide all the experiments were designed experiment content, allowing students to participate in programming. This system can be used as experimental demonstration of teaching, but also as a student hands-on experiments the experimental system.)

matlab的教学案例分析，详细介绍了matlab的一些功能(matlab teaching case studies, detailing some of the features matlab)

卡尔曼滤波应用 通过卡尔曼滤波跟踪移动中的目标(Kalman filtering applications through the Kalman filter tracking moving target)

Steffensen迭代法求非线性方程的根，和Steffensen加速迭代法求非线性方程的根做对比的例程，有详细中文注释。(Steffensen iteration method for the nonlinear equations of the root, and Steffensen iteration method for the nonlinear equations to accelerate root to do comparison routine, detailed notes in Chinese.)

technique d accès multiple CDMA (Code Division Multiple Access)avec une HD (Hard Decision)

Digital Signal and Image Processing using MATLAB

MATLAB BS option prcing

TEST SAMPLES TO ECG IN MATLAB

可逆跳跃马尔科夫蒙特卡洛贝叶斯模型选择，主要用于神经网络(Reversible Jump MCMC Bayesian Model Selection This demo demonstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar-xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.)

The performance coefficient depends on both the pitch angle (β) and the tip speed ratio (λ). The tip speed ratio is calculated by using blade tip speed and wind speed upstream of the rotor. The relationship between performance coeffi cient (c_p), pitch angle (β) and tip speed ratio (λ) is established by the c_p- λ approximation (3) for different blade pitch angle.