energy

在一无人机机身安装共形阵，将机身视为一的规则圆柱体，对此共形阵进行波束扫描 (At one UAV airframe installation conformal array, will be regarded as one of the rules of airframe cylinder, for which conformal array for beam scanning)

针对mean shift 跟踪方法中存在的光照变化不稳定问题， 提出了基于梯度特征与彩色 特征相融合的mean shift 跟踪方法。首先分别提取目标的梯度特征和彩色特征，利用多尺度的相似度 计算方法进行特征的匹配，然后通过最大化相似度对目标进行跟踪。通过物体和人体等运动目标的跟 踪，验证了改进的跟踪算法在光照变化情况下的鲁棒性优于原有的算法，显著降低了跟踪位置误差。(The instability of the light changes in the mean shift tracking method, the mean shift tracking method based on gradient features and color characteristics of fusion. First extract gradient features and color features of the target, the similarity calculation method using multi-scale feature matching, and then maximize the similarity of the target track. Through the tracking of objects and people moving target, verify the robustness of the improved tracking algorithm in the case of illumination changes is better than the original algorithm significantly reduces the tracking position error.)

可以向图片嵌入水印，来保护图片的版权信息，使用MATLAB开发(Be able to image embedded watermark to protect copyright of the image information, the use of MATLAB development)

这是一个基本的数模题目的套路，分析数模聚类问题，例子是乳腺癌的诊断模型，过程为标准化数据，逐步回归，分析主要因素，聚类方法有距离法和神经网络法，均附有源码和解释说明(This is an essential topic of routine digital-analog, digital-analog cluster analysis problem example is the breast cancer diagnostic model, the process for the standardized data, stepwise regression, analysis of major factors, clustering methods from the method and neural network are with source code and explanations)

The Bayesian Committee Machine (BCM) is an approximation method for large-scale Gaussian process regression. - The code is for Matlab Version 1.0, November 2005(The Bayesian Committee Machine (BCM) is an approximation method for large-scale Gaussia n regression process.- The code is for Matlab Ve rsion 1.0 EDN 2005)

很好的关于风力发电的资料，希望对大家有所帮组。(good material)

the key parameters of Ant System

MVAcltbern illustrates the (univariate) Central Limit Theorem (CLT). n*1000 sets of n-dimensional Bernoulli samples are generated and used to approximate the distribution of t = sqrt(n)*(mean(x)-mu)/sigma -> N(0,1). The estimated density (blue) of t is shown together with the standard normal (red).

target tracking using kalman

this mi test m-file for matlab program