ASUS_P8H61M_bios1102

说明：  C# 语言实现指纹一对一的录入以及一对一多对一验证等等(C# Language implements fingerprint one-to-one input and one-to-one, one-to-many, one-to-one verification, etc.)

数理统计软件SPSS使用说明，该软件实现功能包括了基本上常用的概率统计方法。(SPSS statistical software for use, the software features include a basically realize the probability of statistical methods commonly used.)

利用剖面上的异常值计算二度异常向上延拓，matlab，实用(By using the abnormal values on the section, the abnormal upward continuation of two degrees is calculated, and MATLAB is practical)

该程序可以实现平面波与球面波的干涉图案。(Interference pattern between plane wave and spherical wave can be realized.)

扫描版的Energy Function Analysis For Power System Stability 数据部分，此书目前很难获得，资料珍贵。(Scan version of Energy Function Analysis For Power System Stability data section, the book is currently difficult to obtain valuable information.)

该算法用于彩色图像的预处理，为后续图像识别做准备(Preprocessing of color image)

说明：  采用最大似然估计算法进行波达角的估计，同时计算估计均方误差。(Use maximum likelihood method to get DOA estimation and calculate the mean square error.)

PIC16F676 based solar charger

说明：  字典学习与快速正交匹配追踪。正交匹配追求 （OMP） 在过去十年中已被证明是图像和信号处理领域的重要算法，用于估计字典学习中的稀疏表示。多年来，加快 OMP 算法的精度的努力一直通过通用 OMP （g-OMP） 和快速 OMP （f-OMP） 等变体进行。所有这些算法都递归地求解了"S"样本数中每个信号样本的OMP。该算法，快速 OMP （r-OMP），运行"N"原子的循环，同时估计所有样本，并在自N<<S以来的真实场景中，建议的方法将OMP的速度加快几个数量级。(Orthogonal Matching Pursuit (OMP) has proven itself to be a significant algorithm in image and signal processing domain in the last decade to estimate sparse representations in dictionary learning. Over the years, efforts to speed up the OMP algorithm for the same accuracy has been through variants like generalized OMP (g-OMP) and fast OMP (f-OMP). All of these algorithms solve OMP recursively for each signal sample among 'S' number of samples. This algorithm, rapid OMP (r-OMP), runs the loop for 'N' atoms, simultaneously estimating for all samples, and, in a real scene since N<<S, the proposed approach speeds up OMP by several orders of magnitude.)

LABVIEW程序语言中属性节点相关内容的使用实例-LABVIEW programming language attribute nodes using the example of relevant content