Regularization-toolbox

MATLAB 调用IGES文件的工具箱 利用工具箱函数 可直接对IGES文件进行处理(MATLAB toolbox called IGES file using toolbox functions can be processed directly on the IGES file)

基于simulink的双馈异步风力发电机的仿真模型(Simulink simulation model of doubly-fed induction wind turbines based)

matlab 中关于RTDX的应用，含有例程，适合初学者学习(matlab on RTDX application contains routines, suitable for beginners to learn)

说明：  基于MATLAB的图像处理与分析，对于学习图像处理方面有很大的帮助，希望大家喜欢(MATLAB based image processing and analysis, image processing, for learning is very helpful, hope you like)

里面包含了步态识别的整一套流程的代码实现，包括从视频流里面提取图片帧，背景建模，提取运动目标（运动检测），形态学处理（膨胀与腐蚀，连通性检测），归一化大小，步态能量图的构建，主成分分析（PCA）降维,线性判别分析（LDA）分类等等功能的代码实现。均通过测试。(Contains the gait recognition of the whole process of a set of code, including the extraction and picture frames from the video stream, and background modeling, extract the moving object (motion detection), morphology processing (expansion and corrosion, connectivity), normalized size, gait energy diagram construction, principal component analysis (PCA) dimensionality reduction and linear discriminant analysis (LDA) classification code implementation and so on functions. All pass the test.)

内点优化法，stanford大学The Systems Optimization Laboratory 开发(Interior point,stanford university The Systems Optimization Laboratory)

说明：  his work proposes two novel optimization algorithms called Salp Swarm Algorithm (SSA) and Multiobjective Salp Swarm Algorithm (MSSA) for solving optimization problems with single and multiple objectives. The main inspiration of SSA and MSSA is the swarming behaviour of salps when navigating and foraging in oceans. These two algorithms are tested on several mathematical optimization functions to observe and confirm their effective behaviours in finding the optimal solutions for optimization problems.

there is a matlab cod for Autonomous system, that describe to compute trajectory and point of the system, and plotting it.

time response of a transfer function

说明：  两种不同的假设： H1 ： 0 xn A fn wn ( ) cos(2 ) ( ) = ++ π θ n=1,2,…,N，f0 为规一化频率 H0 ： xn wn () () = n=1,2,…,N 其中 w[n]是均值为 0，方差为 2 σ n 的高斯白噪声，A 已知，样本间相互 独立，信号与噪声相互独立; 相位θ 是随机变量，它服从均匀分布 1 0 2 ( ) 20 p θ π θ π ?? ≤ ≤ = ??? 其它 1）改变输入信噪比（改变 A 或噪声方差均可），给定虚警概率，画出 输入信噪比与检测概率之间的理论曲线。(注意：理论检测曲线与样本 数有关) 2）改变样本数，用 Monte-Carlo 实验方法得出 PF＝0.001 时输入信噪比 与检测概率之间关系曲线（至少三条），并得出结论。 3）改变 M-C 实验次数，样本数不变，用 Monte-Carlo 实验方法得出 PF ＝0.001 时输入信噪比与检测概率之间关系曲线（至少三条），并得出 结论。(Draw the theoretical curve between input SNR and detection probability)