KALMAN

互补滤波算法解析，利用加速度计和陀螺仪采集的数据，通过互补滤波算法实现对姿态的解算。(Complementary filtering algorithm analysis, the use of accelerometers and gyroscopes data collected through the complementary filter algorithm for solving attitude.)

一个C和C++的函授参考手册，希望能够成为您编程的好帮手。(A C and C++ Correspondence reference manual, I hope to become a good helper of your programming.)

说明：  逆变器的simulink仿真，采用dq解耦的空间矢量控制模式(Inverter simulink simulation, using dq space vector control mode decoupling)

魔力宝贝私服可以辅助软件，功能强大，破解免费版。(Free Cracking Software Green Plug-in-Free)

一种改进的变步长LMS自适应算法，论文不错！-An improved variable step-size LMS adaptive algorithm, thesis is true!

说明：  克拉美罗界限，任何无偏估计量的方差确定一个下限。(Cramero bound, a lower bound for the variance of any unbiased estimator.)

椭圆拟合，用于在jpg格式的图片上寻找椭圆，并进行拟合-Ellipse fitting to the picture in jpg format to find oval, and fitting

此文档为visual c++平台实现俄罗斯方块的部分参考代码。已经通过调试通过。(This document is visual c++ platform Tetris part reference code. Has passed through debugging.)

4GBOX 是基于运营商网络的无线数据通讯模块，支持 TCP/IP 协议，配合 XD 或 XC 系列 PLC 广泛应用于自动化系统，实现自动化系统远程无线监控。主要监控方式有短信、XCPPro 或 XD/E 系列 PLC 编辑工具软件、网站、APP 等。旨在为设备制造、生产加工、城市建设、仓储物流、智慧农业等行业提供更加方便快捷、可靠易用的数字化、智能化服务。(4GBOX is a wireless data communication module based on the operator network. It supports TCP/IP protocol. It is widely used in automation system with XD or XC series PLC, and realizes remote wireless monitoring of automation system. The main monitoring methods are SMS, XCPPro or XD/E series PLC editing tool software, web site, APP and so on. It aims to provide more convenient, fast, reliable, and convenient digital and intelligent services for equipment manufacturing, production and processing, urban construction, warehousing and logistics, intelligent agriculture and other industries.)

说明：  字典学习与快速正交匹配追踪。正交匹配追求 （OMP） 在过去十年中已被证明是图像和信号处理领域的重要算法，用于估计字典学习中的稀疏表示。多年来，加快 OMP 算法的精度的努力一直通过通用 OMP （g-OMP） 和快速 OMP （f-OMP） 等变体进行。所有这些算法都递归地求解了"S"样本数中每个信号样本的OMP。该算法，快速 OMP （r-OMP），运行"N"原子的循环，同时估计所有样本，并在自N<<S以来的真实场景中，建议的方法将OMP的速度加快几个数量级。(Orthogonal Matching Pursuit (OMP) has proven itself to be a significant algorithm in image and signal processing domain in the last decade to estimate sparse representations in dictionary learning. Over the years, efforts to speed up the OMP algorithm for the same accuracy has been through variants like generalized OMP (g-OMP) and fast OMP (f-OMP). All of these algorithms solve OMP recursively for each signal sample among 'S' number of samples. This algorithm, rapid OMP (r-OMP), runs the loop for 'N' atoms, simultaneously estimating for all samples, and, in a real scene since N<<S, the proposed approach speeds up OMP by several orders of magnitude.)