Chapter9

这是偏微分方程在图像处理中的应用一书的配套光盘，非常好的东东哦(This is a partial differential equations in image processing applications supporting CD-ROM book, very good stuff oh)

斐波纳契数列在现代物理、准晶体结构、化学等领域都有直接的应用，为此，美国数学会从1960年代起出版了《斐波纳契数列》季刊，专门刊载这方面的研究成果。 (Fibonacci series in modern physics, quasi-crystalline structure, chemical and other fields have direct application, for which the American Mathematical Society published since the 1960s, " Fibonacci series" quarterly, is devoted to research in this area .)

tutorial to mathcad 15

这个MATLAB例程是系统误码率变化仿真(The MATLAB routine changes in the system simulation of bit error rate)

电信课程设计题目要求图片以及实现上的程序代码 实验结果图片(Telecommunications curriculum subject requirements of the picture of the picture and the realization of the program code on the experimental results)

Driver For ACR120U Smart Card Reader ACR120U proprietary driver package for win98/ME, 2000, XP, 2003/2008, Vista and Windows 7 (x86 & x64)

为sinudoidal模型的AIC阶估计.AIC信息准则即Akaike information criterion，是衡量统计模型拟合优良性的一种标准，又由于它为日本统计学家赤池弘次创立和发展的，因此又称赤池信息量准则。它建立在熵的概念基础上，可以权衡所估计模型的复杂度和此模型拟合数据的优良性。 在一般的情况下，AIC可以表示为： AIC=2k-2ln(L) 其中：k是参数的数量，L是似然函数。 假设条件是模型的误差服从独立正态分布。 让n为观察数，RSS为剩余平方和，那么AIC变为： AIC=2k+nln(RSS/n) 增加自由参数的数目提高了拟合的优良性，AIC鼓励数据拟合的优良性但是尽量避免出现过度拟合(Overfitting)的情况。所以优先考虑的模型应是AIC值最小的那一个。赤池信息准则的方法是寻找可以最好地解释数据但包含最少自由参数的模型。(AIC order estimation for sinudoidal model)

附件中的pdf文件是韩国作者08年在Automatica上发表的文章。本代码给出了文章中定理1的线性矩阵不等式的求解程序，通过给定不同的a来求解最大的b,进而得到界定时滞系统的最小的椭圆。(Attached pdf file of 2008 in South Korea articles published in Automatica. This code is given in Theorem 1 article LMI solver through a given different to solve the biggest b, then get the minimum of delay systems defined ellipse.)

Compressive sampling is an emerging technique that promises to effectively recover a sparse signal from far fewer measurements than its dimension. The compressive sampling theory assures almost an exact recovery of a sparse signal if the signal is sensed randomly where the number of the measurements taken is proportional to the sparsity level and a log factor of the signal dimension. Encouraged by this emerging technique, this thesis briefly reviews the application of Compressive sampling in speech processing. It comprises the basic study of two necessary condition of compressive sensing theory: sparsity and incoherence. In this thesis, various sparsity domain and sensing matrix for speech signal and different pairs that satisfy incoherence condition has been compiled.

LLVM: A Compilation Framework forLifelong Program Analysis & Transformation. 2004 International Symposium on Code Generation and Optimization (CGO’04)