VMware

The objective of this paper is to provide a road map to understanding and use of real-time design patterns. To meet this objective, we provide a brief overview of the origins of design patterns, then discuss a wide variety of specific real-time design patterns, and finally provide our own classification of patterns, which is derived from a systematic approach to real-time system design and real -time software architectures.

我编写的svm程序用于分类，用matlab实现(I write the SVM program for classification, matlab)

在Matlab环境下完成线性外插和内插数值算法，包含多维数据插值计算。( interpne: Interpolates and extrapolates using n-linear interpolation (tensor product linear) )

Single objective Particle Swarm Optimization. Matlab Code

说明：  该算法是结合减法聚类与FCM聚类算法融合的远代码。算法通过融合这两种算法，从而可以直接在线确定聚类数目。(The algorithm is combined with Subtractive Clustering with FCM Clustering Algorithm far integration code. Algorithm through the integration of these two algorithms, which can be directly online to determine the number of clustering.)

该程序基于运动区域和运动区域外截矩形进行运动目标检测 (Based on motion region and exercise outside cutting a rectangular moving target detection )

统计信号处理：非高斯信号处理及其应用，对非高斯信号的处理及应用作出了详细的讨论。(Statistical Signal Processing: non-Gaussian signal processing and its applications, processing and application of non-Gaussian signal given a detailed discussion.)

计算成组滑动的滑窗检测器的最佳门限已知: 击中数N = 128, 组宽n = 8，PF = 10¡ 6，PD = 90 假设以下条件: (a) 信号不起伏 (b) 忽略天线波束调制 1 (c) 计算PD时忽略目标区以外噪声的影响 组门限(第二门限)为K=8 滑窗的门限(第三门限)为L=16 (滑窗中包含的组数为16) 求解: 最佳的KOPT 和LOPT，对这题做了具体仿真实现。需要的朋友，一定知道很有用。(Optimal threshold calculated as group slidable window detector is known: hit number N = 128, the group width n = 8, PF = 106, PD = 90 assuming the following conditions: (a) signal is not undulation ( b) ignore the antenna beam modulation 1 (c) the calculation of PD ignored outside the target area noise of impact group door limit (the second door limit) for K = 8 sliding window of the door limit (third door limit) for L = 16 (sliding window contains group 16) to solve: the best of the KOPT the LOPT, these questions do specific simulation to achieve. A friend in need, we must know to be useful.)

Arduino ECG sheild (sparkfun SHIELD-EKG-EMG_Rev_B builtin)

在计算机上产生一组实验数据，首先产生一段零均值白噪声数据u（n）,令功率为 ，让u(n)通过一个三阶FIR： 得到y(n). .y(n)上加三个实正弦信号f1’=0.1,f2’=0.25,f3’=0.26调整 和正弦信号幅度信噪比大致为10dB,50dB,50dB. (1) 令N=256,描绘xn波形； (2)得出真实功率谱密度 . (3) 利用此实验数据Pisarenko谐波分解法估计该实验数据的正弦频率及幅度。 (On the computer to generate a set of experimental data, first generate a zero mean white noise data u (n), so that power, let u (n) through a third-order FIR: get y (n).. Y (n) added three real sinusoidal signal f1 ' = 0.1, f2' = 0.25, f3 ' = 0.26 and sine signal amplitude adjustment signal to noise ratio is approximately 10dB, 50dB, 50dB. (1) that N = 256, depicted xn waveform (2) the true power spectral density obtained. (3) using this experimental data, Pisarenko harmonic decomposition method to estimate the experimental data of the sinusoidal frequency and amplitude.)