资源列表
Linux
- Linux基础教程大作业第04 讲_X_Window系统第9讲 Linux网络服务与配置第二讲 Shell编程-The Linux Essentials jobs (04) _X_Window system (9) Linux Network services and configuration Lecture Shell Programming
博创UP-NET2410开发板的使用
- 博创UP-NET2410开发板的使用，适合于嵌入式开发初学者，对开发平台的认识和使用。
HighspeedECCalgorithmcoprocessor
- 本课题设计的高速 ECC 算法协处理器用于满足大型认证服务器的高数据吞吐 量的需求。其中，ECC 算法芯片的吞吐率是解决服务器高数据吞吐量的关键。本 课题的主要研究任务是高速 ECC 算法协处理器的算法改进以及硬件实现。 -According to the requirement of the high data throughput of authorization service, ECC public-key cryptosystems based high-speed E
H
- 全速率语音突发的的发送端总程序，其中包括预编码，卷积编码，交织，调制等步骤的matlab程序，内付详细说明文档-Matlab program, which pay full rate speech sudden sender program, including pre-coding, convolutional coding, interleaving, modulation step detailed documentation
Basics_signal_processing
- Basics of Signal Processing. A good review!
client1
- client and servver ggo back n
Mutual-information_CS
- 基于互信息的分布式压缩感知。本文详细的介绍了分布式压缩感知的原理及应用，将其与互信息联系起来。-Based on mutual information distributed compressed sensing . The detailed introduction to the theory and application of distributed compressed sensing linked with mutual information .
PLC-communication
- PLC通讯方面资料，包括以太网、DP通信等方式，非常实用-PLC communications information, including Ethernet, DP communication, very practical
Block-DCT-transform
- 分块DCT变换，基于压缩感知信号的压缩。详细阐述了信号基于DCT分块的变换算法。为后面的信号压缩和恢复做铺垫。-Block DCT transform based on compressed sensing signal compression. Elaborates the signal based on the sub-block of DCT transform algorithm . For the following signal compression and pave the wa
Compressed-sensing-Profile
- 压缩传感图像。介绍了压缩感知在图像恢复处理方面的应用。-Compressed sensing image . Compressed sensing image restoration processing .
Short-duration-power_CS
- 根据压缩传感(Compressed Sensing，cs)N论，首次提出了短时电能质量扰动信号的压缩采样方法，该方法突破了奈奎斯特采样频率的限制，实现了低于奈奎斯特采样频率的低速率采样。文中对比分析了CS理论与传统采样理论，研究了cS短时电能质量信号压缩采样的实现方法，包括：测量矩阵的构建、稀疏基的选取和电能质量信号快速贝叶斯匹配追踪重构算法(FBMP)-Compressed sensing ( Compressed Sensing , cs ) N theory , first propose
Image-reconstruction_CS
- 合稀疏贝叶斯学习(SBL)和可压缩传感理论(CS)，给出一种在噪声测量条件下重建可压缩图像的方法。该方法将cS理论中图像重建过程看作一个线性回归问题，而待重建的图像是该回归模型巾的未知权值参数；利用sBL方法对权值赋予确定的先验条件概率分布用以限制模型的复杂度，并引入超参数- Hop sparse Bayesian learning ( SBL ) and compressible sensing theory ( CS ) , give a compressible image recon