Synchronization

说明：  文章：利用三次样条函数绘制光电管的I-U特性曲线，有matlab代码。(Article: the use of cubic spline function mapping phototube of IU characteristic curve, there is matlab code.)

RTDX与Matlab实现基于DSP的FIR滤波器设计.pdf(RTDX and Matlab for DSP-based FIR filter design. Pdf)

多元数据分析的主分量分析投影，进行逐步线性回归，未来线路预测,分析误差。( Principal component analysis of multivariate data analysis projection, Stepwise linear regression, Future line prediction, error analysis.)

Reads TDMS files into Matlab. See TDMS_exampleFunctionCalls.m to get started. Advantages: - supports reading v2 files - doesn t require the NI DLL, thus it doesn t require 32bit windows - supports interleaved data - allows only reading names & properties to get a quick feel for what is in the file - Since streaming files can get large (many GB), allows only retrieving subsets of data via keep or ignore flags Doesn t yet support: - big endian files Doesn t support: - files with DAQmx data - files with unsupported Matlab data types (extended float, etc) I haven t written very many wrappers to change the output data into more traditional formats. I d appreciate any submissions of wrappers that people make (like my TDMS_dataToGroupChanStruct_v1 & v2). Thanks! If the function doesn t work for you please let me know.

Gantry detector tilt correction

pca matlab 人脸识别，靠，怎么上传个文件还这么烦，我倒！我晕！(face recognition pca)

说明：  LDPC的编码和译码程序 采用vc及matlab(LDPC coding and decoding process, and matlab using vc)

椭圆滤波器，fp=2400hz,fs=5000hz,rp=3dB,rs=25dB,求其3dB时的各项参数！(Elliptic filter, fp = 2400hz, fs = 5000hz, rp = 3dB, rs = 25dB, for its 3dB at the time of the parameters!)

SFN MFN BER with Matlab

(1)针对在线计算量大这一缺陷，将预测控制中的柔化输出信号的思想推广到柔化输入信号，使得约束条件被简化为仅对当前控制量的约束，可以直接计算得出；同时该方法避免了求逆矩阵，大大减小了计算量，并能够保证控制算法的可行性和良好的控制性能。 (2)针对传统算法中设计参数整定困难这一缺点，应用基于BP神经网络变参数设计的广义预测控制算法，实现了对控制量柔化参数的在线调整。 (3)利用带有遗忘因子的最小二乘法对系统辨识。本文通过仿真发现该方法对于Hénon混沌系统并不完全适用，可考虑利用其他优化系统辨识的方法对本方法进行改进，以期达到更好的辨识效果。 (4)针对系统稳定性分析复杂，本文在控制增量前加入前馈因子，保证所选的Lyapunov函数使闭环系统满足Lyapunov稳定判据，由此证明闭环系统稳定。 (1. To solve the problem of GPC huge computation, algorithm with input increment constraints is presented in which the concept of output softness was used to soften the input increments．As a result, the constraints are simplified to be the only one constraint on the current control increment which can be computed directly. At the same time, it needn’t computing the inverse matrix and thus reduces large computation. Moreover, it guarantees the feasibility of the algorithm and has good control performance． 2. To overcome the difficulty in the choice of tuning parameters in traditional GPC, a GPC algorithm with variable parameter design based on BP neural network. is presented，in which the input softness parameters are tuned on line. 3. In this paper, we Identify system by using the least square method with forgetting factor. However, after system simulation, we realize that this method doesn’t fit the Hénon chaotic system perfectly. So we recommend modify this method by other Optimizati)