kalman

说明：  C#高级编程(中文第七版)是一本非常具有实用性的书籍。(C # Advanced Programming (Chinese 7th Edition))

sar图像去噪的几种新的方法，使用matlab实现智能预测控制算法，利用matlab写成的窄带噪声发生。( Several new methods sar image denoising, Use matlab intelligent predictive control algorithm, Using matlab written narrowband noise occurs.)

说明：  代码源自论文“Coding Prony's method in MATLAB and applying it to biomedical signal filtering”，附测试例程(Code comes from the paper "coding prony's method in MATLAB and applying it to biomedical signal filtering", with test routine)

说明：  c primer plus 中文版课本中代码(Codes in Chinese C Primer Plus)

C++应用教程原码,里面包含该书中有十三章内容的代码,详细具体(C++ Application Guide the original code, which contains the book has 13 chapters of the code, detailed and specific)

说明：  Unreal.Engine.4.AI.Programming.Essentials.2016 虚幻四AI编程设计。(Unreal.Engine.4.AI.Programming.Essentials.2016)

断裂的K文件,用于lsdyna，裂纹扩展，前处理用ansys或者hypermesh.(The broken K file is used for LSDYNA, crack propagation, pretreatment with ANSYS or hypermesh.)

时间序列数据分析中的梅林变换工具，包含特征值与特征向量的提取、训练样本以及最后的识别，基于matlab GUI界面设计。( Time series data analysis Mellin transform tool, Contains the eigenvalue and eigenvector extraction, the training sample, and the final recognition, Based on matlab GUI interface design.)

毕设内容，高光谱图像基本处理，遗传算法无功优化，小波包分析提取振动信号中的特征频率。( Complete set content, basic hyperspectral image processing, Genetic algorithm based reactive power optimization, Wavelet packet analysis to extract vibration signal characteristic frequency.)

这本书在稀疏的多尺度图像和信号处理提出了艺术状态，包括线性多尺度变换，如小波，脊波和曲波变换、非线性、多尺度变换基于中值和数学形态学算子。最近的稀疏性和形态多样性的概念描述和利用各种问题，如去噪，反问题正规化，稀疏信号分解，盲源分离，压缩感知。 这本书的理论和实践研究相结合的领域，如天文学、生物学、物理学、数字媒体应用和取证。最后一章探讨了信号处理中的一个范式转换，表明以前的信息取样和提取的限制可以用非常重要的方法加以克服。 MATLAB和IDL代码伴随这些方法和应用程序重现。 实验并说明了在相关网站上可下载的研究的推理和方法。(This book presents the state of the art in sparse and multiscale image and signal processing, covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Recent concepts of sparsity and morphological diversity are described and exploited for various problems such as denoising, inverse problem regularization, sparse signal decomposition, blind source separation, and compressed sensing. This book weds theory and practice in examining applications in areas such as astronomy, biology, physics, digital media, and forensics. A final chapter explores a paradigm shift in signal processing, showing that previous limits to information sampling and extraction can be overcome in very significant ways.)