感知器的demo，批处理模式识别作业type.typically有用

主要完成了在虹膜识别中的虹膜定位功能，采用了动态轮廓模型（ACTIVE CONTOUR MODEL）-一个学生的毕业设计，支持开源-Mainly completed in the iris recognition function of the iris location, using a dynamic contour model (ACTIVE CONTOUR MODEL)- a student

A*算法实现迷宫寻路功能，用启发式函数的编写以及各类启发式函数效果的比较。

海量数据中包含一个或者多个主题，可以通过面向主题模型的算法求解。lda是其中一个典型代表。然后，这些主题模型本身又包含丰富的结构，如，层次型结构。这里给出的是一种进行层次型主题发现的算法。

Unix下的复数运算，实现了复数的加减、开方运算-Unix complex operation, realized the plural number addition and subtraction, the root has operated

这是一个用Visual C++和OpenCV制作的程序。

最大覆盖问题：给定n个整数a , a , ,an 1 2  组成的序列。如果对于i £ k £ j ，有k | j | a £ a ，则称j a 覆 盖序列区间i i j a , a , , a +1  ，相应的覆盖区间长度为j-i+1。-Maximum coverage problem: Given n integers a, a,, an 1 2

This file implements a pid controller used to simulator cruise control in a car The input is a throtle value between 0 - 100 ( read on P1 ) The output is the car s speed ( P2 - P0 ) -This file implements a pid controller used to simulator cruise control in a car The input is a throtle value between 0- 100 (read on P1) The output is the car s speed (P2- P0)

这是灰色预测代码,一般灰色预测的参数需要应用到矩阵的运算,本代码通过对其变形,使其运算简单化.-This is a gray code forecast, Gray forecast general parameters applied to the needs of the matrix computation, the code through its deformation, it Operational simplistic.

用一观测器从t=1秒开始对一个运动目标的距离进行连续地跟踪测量，假设观测的间隔为一秒钟，雷达到运动目标之间的距离为S(t)（1） 统计特性的初值为 （2）观测误差是与和均不相关的白噪声序列，并且有 （3）观测数据存放在附加的文件中（单位：m）。 要求：分析上述对象，建立系统模型，构造卡尔曼滤波器，编程计算，求： （1） 距离S(t)的最佳估计及估计误差， （2） 距离S(t-5)的最佳平滑及估计误差， （3） 距离S(t+5)的最佳预测及估计误差， （4） 对结果进行分析讨论。 -By one observer from the t = 1 PST on a moving target tracking for distance measurement, assuming that the observation interval is one second, the radar that the distance between the moving target for the S (t) (1) the statistical characteristics of the initial condition (2) observational error is not associated with white noise sequence, and (3) observational data stored in the attached document (unit: m). Requirements: Analysis of the above-mentioned object, the establishment of the system model, constructed Kalman filter, programming terms, seeking: (1) distance from S (t) the best estimate and the estimation error, (2) distance from S (t-5) the most good smoothing and estimation error, (3) distance from S (t+ 5) t

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