16qam

精通matlab6.5版_数值计算本章将花较大的篇幅讨论若干常见数值计算问题：线性分析、一元和多元函数分析、微积分、数据分析、以及常微分方程（初值和边值问题）求解等。但与一般数值计算教科书不同，本章的讨论重点是：如何利用现有的世界顶级数值计算资源 MATLAB。至于数学描述，本章将遵循“最低限度自封闭”的原则处理，以最简明的方式阐述理论数学、数值数学和 MATLAB计算指令之间的内在联系及区别。(matlab simulation)

信号与系统基础练习，演示了数字信号处理中常用的傅里叶变换，分析时域和频域的信息(Signals and systems based practice, demonstration of digital signal processing used in the Fourier transform of the time domain and frequency domain information analysis)

通过邻接矩阵，计算网络的聚类系数。聚类系数是复杂网络中一个重要参量。 (Through the adjacency matrix, computing networks, clustering coefficient. Clustering coefficient is a complex network, an important parameter.)

Digital Communication Intel Engineering

手机定位中基于TDOA技术的多种算法的性能比较仿真(TDOA mobile location technology based on performance comparison of a variety of simulation algorithms)

这是周建兴编写的<<matlab从入门到精通》第三章的所有源代码，都可以运行的出来，和课本上的一样，(This is Zhoujian Xing written < <matlab从入门到精通》第三章的所有源代码，都可以运行的出来，和课本上的一样，)

维纳滤波语音增强算法:语音增强算法的维纳滤波器，通过scalart 1996提出的实现。使用一个决策指导的方法跟踪的先验信噪比。请注意，该信号的第一0.25sec用于模型的噪声，即，它是假定的演讲之后，0.25sec开始(Wiener Filter for Noisy Speech Enhancement, implemented as proposed by Scalart 1996. Uses a decision directed method to track the a priori SNR. Note that the first 0.25sec of the signal is used to model the noise, that is, it is assumed that speech starts after that 0.25sec)

reglement_concours_Tss2015 -

In this paper we present the usefulness of the algorithm DCT (Discrete cosine Transform) blocks applied level image compression JPEG (Joint Photographic Experts Goup). It has been shown that this algorithm is the most efficient in the case of compression because it has very good properties for compaction energy related values. As a basic principle we eliminate high frequencies and keep the low and medium without visibly affecting image. DCT in JPEG compression algorithm is encountered in practical applications such as face detection, skin color, facial recognition, iris, fingerprints

基于一种新的变换模型的步态识别算法matlab程序，运用了主成分分析进行降维。(Transformation model based on a new gait recognition algorithm matlab procedures, the use of principal component analysis for dimensionality reduction.)