资源列表
adaptive-lms
- 有源自适应噪声的最常用算法，该算法是基于滤波器的输出信号与期望响应之间的误差的均方值为最小。-Active adaptive noise most commonly used algorithm, which is based on the error between the filter output signal and the desired response minimum mean-square value.
adaptive-rms
- 有源自适应噪声的最常用算法之一，该算法是另一个基于最小二乘准则的精确方法，它具有快速收敛和稳定的滤波器特性。-Active adaptive noise, one of the most commonly used algorithm, the algorithm is another accurate method of least squares criterion, it has fast convergence and stability of the filter characteri
computer-vision
- 计算机视觉方向在国际上比较热门的研究点及现状。文章均是排名前25的热门文章。-Direction is more popular in the international research in computer vision point and the status quo. The articles are top 25 popular articles.
Analysis-report
- 对通信行业的现状进行分析以及对通信行业未来3到5年的发展的展望。-Analysis of the current situation of the communications industry as well as the outlook for the next 3-5 years, the development of the communication industry.
modulation-scheme
- 室内可将光通信系统的构建及其关键技术的研究，包括光源布局，信道分析，调制解调等关键技术-Indoor optical communication system and its key technologies, including the light source layout, channel analysis, key technologies modem
wlrc
- 网络蠕虫传播的检测和分类,IDS 的相关资料，自己用的-Detection and classification of network worm propagation, IDS use
5-files
- It contains 4 matlab codes for ZF,MMSE,ML equalizer for MIMO systems and 1 document based on linear detector for MIMO system
Image-Retrievals
- 基于图像内容的多维特征检索技术，主要利用图像的形状特征提取-Retrieval technique based on multi-dimensional characteristics of the image content, the main use of the shape of the image feature extraction
CONVEX-HULL-OF-A-SIMPLE-POLYLINE
- 一种快速计算图形凸包的算法，算法比较简单，实现起来也很容易，有兴趣的可以参阅一下。-A fast algorithm to calculate the graphics convex hull algorithm is relatively simple, and is also very easy to implement, are interested can see what.
China-Trust-Industry
- 中国信托业发展报告（2012），最全的行业分析-China Trust Industry Development Report (2012)
standard-terms-of-breast-cancer
- 乳腺癌标准影像术语,包括良性、恶性及各自的特性和病灶的定位-terminology standard terms of breast cancer
01._THE_MULTI-DIMENSIONAL_ENSEMBLE_EMPIRICAL_MODE
- A multi-dimensional ensemble empirical mode decomposition (MEEMD) for multi dimensional data (such as images or solid with variable density) is proposed here. The decomposition is based on the applications of ensemble empirical mode decomposition