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Abstract—Numerous methods have been developed for holistic face recognition with impressive performance. However, few studies have tackled how to recognize an arbitrary patch of a face image. Partial faces frequently appear in unconstrained scenarios, with images captured by surveillance cameras or handheld devices (e.g., mobile phones) in particular.

运用matlab语言基于极大似然估计法对非负二项进行拟合的小程序(Matlab language use based on maximum likelihood estimation method for two non-negative small-fitting procedures)

主要对扩展卡尔曼滤波（EKF）、无迹卡尔曼滤波（UKF）及改进无迹卡尔曼滤波（MAUKF）算法进行研究，研究了三种算法的基本原理和各自的特点。其中扩展卡尔曼滤波器是将卡尔曼滤波器局部线性化，其算法简单，计算量小，适用于弱非线性、高斯环境。无迹卡尔曼滤波器是用一系列确定样本来逼近状态的后验概率密度。改进无迹卡尔曼滤波算法在UKF的基础上引入衰减因子。(Improved Kalman filtering algorithm)

关于多基地主动声纳中的自动跟踪探测方面的文章来源IEEE(Adaptive Track Detection for Multi-Static Active Sonar Systems)

m序列的matlab实现，m序列的matlab实现(Implementation of m sequences)

介绍了matlab程序在信号处理中的应用和其中使用的程序(the introduction of matlab using in SP)

8FSK的调制信号的产生、理论误码率及实际误码率统计分析(8FSK modulation signal s theoretical and actual BER analysis)

针对二维矩形条带装箱问题提出了一种启发式布局算法，即底部左齐择优匹配算法（lowest—level left align bestfit，简称LLABF）．LLABF算法遵循最佳匹配优先原则，该原则综合考虑完全匹配优先、宽度匹配优先、高度匹配优先、组合宽度匹配优先及可装入优先等启发式规则．与BL（bottom-left），IBL（improved-bottom-left）与BLF（bottom—left-fill）等启发算法不同的是，LLABF能够在矩形装入过程中自动选择与可装区域匹配的下一个待装矩形．计算结果表明，LLABF结合遗传算法（genetie algorithm,简称GA）解决二维条带装箱问题更加有效．(A heuristic placement algorithm for two-dimensional rectangular strip packing problem is proposed, which is called bottom-level left align best fit algorithm (LLABF). Different from BL (bottom-left), IBL (improved-bottom-left) and BLF (bottom-left-fill) heuristic algorithms, LLABF can automatically select the next rectangle to be loaded that matches the mountable region during the rectangular loading process. Genetie algorithm (GA) is more effective to solve the two-dimensional strip packing problem.)

这些文件夹里面包含着卷积码、调制、加高斯白噪声的程序、多径衰落径道的程序。。对那些初学者是很有帮助的。。。(The folder contains a convolutional coding, modulation, plus Gaussian white noise process, multi-path channel fading process. . Is very helpful for those beginners. . .)

MATLAB simulinkLMS算法仿真(Matlab simulink LMS algorithm)