original_101_0.5

Name: gmac = global minimization of the active contour model ％ Description: see paper "Fast Global Minimization of the Active Contour/Snake Model" in JMIV07 ％ Author: Xavier Bresson (xbresson@math.ucla.edu) ％ Lastest version: 07-09-21(Name: gmac = global minimization of the active contour model Description: see paper Fast Global Minimization of the Active Contour/Snake Model in JMIV07 Author: Xavier Bresson (xbresson@math.ucla.edu) Lastest version: 07-- 09-21)

说明：  关于改进的DLDA的算法，结合了人脸识别(DLDA on the improvement of the algorithm, a combination of face recognition)

通过对RSSI测距模型进行分析， 并提出一种针对室内环境的参数修正方案。 通过 自行研发的传感器节点，上进行验证分析，实验表明，采用环境参数修正方案后， 明显提高了测距的精度。(We studied andanalyzed RSSI—based distance estimated model, and provide aparameter calibration method which in specific to indoor environmental applications．Experience on our prototype node)

The package includes the MATLAB code of the multi-instance learning algorithm miFV, which is an efficient and scalable MIL algorithm. miFV maps the original MIL bags into a new feature vector representation, which can obtain bag-level information, and meanwhile lead to excellent performances even with linear classifiers. In consequence, thanks to the low computational cost in the mapping step and the scalability of linear classifiers, miFV can handle large scale MIL data efficiently and effectively. A Readme file and some data files are included in the package.

brain connectivity toolbox,生成随机网络(brain connectivity )

求两个序列的互相关函数，或一个序列的自相关函数；并显示了不同功率噪声下的效果。(Sequences for the two cross-correlation function, or a sequence of auto-correlation function and display a different power under the effect of noise.)

matlab2007a版本的使用教程和范例，包括book文件夹和m文件夹。(matlab2007a version of the use of tutorials and examples, including folders and m book folder.)

matlabcode for statics information bla bla

电路的混沌描述与混沌计算，分岔与混沌的演化图(Chaos Chaos circuit description and calculation of Bifurcation and Chaos evolution diagram)

统一离散多尺度优化方法。 Unified Discrete Multiscale Optimization 相关的文章：http://arxiv.org/abs/1204.4867(Unified Discrete Multiscale Optimization )