topology

基于Matlab开发环境的FIR数字低通滤波器设计的源代码。(Based on the development environment of Matlab FIR digital low-pass filter design source code.)

判断数据是否符合概率分布；内含多种概率分布(Determine whether the probability distribution of the data)

关于AIS与雷达信息融合方面的文章，其中包括航迹融合和模糊融合算法方面的知识，可为写这方面论文或者研究这方面的人员提供一些新的思路想法。(About AIS and radar data fusion aspects of the article, including track fusion algorithm and fuzzy aspects of the integration of knowledge can provide ideas for some new ideas to write papers or research in this area in this area staff.)

三维时域有限差分matlab程序，带完美匹配层(Three-dimensional finite-difference time-domain matlab program, with perfectly matched layer)

这是模式识别中最小风险Bayes分类器的设计方案。在参考例程的情况下，自行完善了在一定先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。 全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。 调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从而得到概率密度函数。 调用最小风险贝叶斯分类器决策子函数时，根据先验概率，再根据自行给出的5*5的决策表，通过比较概率大小判断一个体重身高二维向量代表的人是男是女，放入决策数组中。 主函数第一步打开“MAIL.TXT”和“FEMALE.TXT”文件，并调用最大似然估计求取概率密度子函数，对分类器进行训练。第二步打开“test2.txt”，调用最小风险贝叶斯分类器决策子函数，然后再将数组中逐一与已知性别的数据比较，就可以得到在一定先验概率条件下，决策表中不同决策的错误率的统计。 (This is a pattern recognition classifier minimum risk Bayes design .In reference to the case of routine , self- improvement in a certain a priori probability conditions, male , female and total error rate error rate statistics , into which each array . All programs from the main function , maximum likelihood estimation subroutine strike probability density , the minimum error rate Bayesian classifier composed of decision-making three subfunctions . Strike called maximum likelihood estimate probability density subroutine , the first step to obtain the sample data , stored as a matrix the second step of the matrix, each row sum , and divided by the total number of samples N, be the average vector The third step is the application of the formula ( 3-43 ) using matrix and loop control statements , obtain the covariance matrix fourth step through the variance-covariance matrix and correlation coefficient obtained , resulting in the probability density function . Bayesian classifier )

Finite impulse response filter

Transformaç ã o Linear

The receiver performance is quantified by depicting the receiver operating char- acteristics (ROC) curves. This curves serve as a tool to and study the performance of a sensing scheme. ROC graphs are employed to show trade-of vs between detection probability and false alarm rates, (i.e. PD versus PFA), thus allowing the determination of an optimal threshold. To illustrate the performance of energy detectors, we generate complementary ROC curves (the average probability of miss detection $ P_{md} 1 - P_d$ versus the false alarm probability $P_f$ for different receiver structures and in different fading environments.Fig. 1 illustrates the complementary ROC curves for a single channel reception (no diversity) over AWGN channels. This figure depicts the complementary ROC curves for energy detectors.

Locating moving target systems

用 matlab 实 现 信 号 的 相 关 变 换(Associated with matlab for signal transformation )