A low light natural image statistical model for joint contrast enhancement and denoising

We study the problem of joint low light image contrast enhancement and denoising using a statistical approach. The low light natural image in the band pass domain is modeled by statistically relating a Gaussian scale mixture model for the pristine image, to the low light image, through a detail loss coefficient and Gaussian noise. The detail loss coefficient is statistically described using a posterior distribution with respect to its estimate based on a prior contrast enhancement algorithm. We then design our low light enhancement and denoising (LLEAD) method by computing the minimum mean squared error estimate of the pristine image band pass coefficients. We create the Indian Institute of Science low light image dataset of well-lit and low light image pairs to learn the model parameters and evaluate our enhancement method. We show through extensive experiments on multiple datasets that our method helps better enhance the contrast while simultaneously controlling the noise when compared to other state of the art joint contrast enhancement and denoising methods.     

Semiconductor Yield Analysis and Multi-Chip Package(MCP) Dic Pairing Optimization using Machine Learning

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Protel 在有线电视网络规划和管理中的应用

利用Protel可方便地绘制有线电视工程设计方案图纸和网络图纸，并可对网络使用的材料和空余端口进行统计，生成的图纸和报表结构清晰、兼容性强，给出了使用Protel对有线电视网络进行规划和管理的具体方法和技术要点。     

基于蚂蚁算法的分布式QoS路由选择算法

本文基于蚂蚁的自组织能力描述了一种新的分布动态路由选择方法,针对当前通信网中存在的服务质量问题提出了QoS保证的分布式路由选择算法（DQRA）。DRQA能以并行的方式解决网络中呼叫的时延和带宽分配问题,避免了NP－完全问题,算法简单,易于实现,能有效地解决大型网络的负载平衡问题,本文选择了OPNET仿真环境对网络进行实时仿真,通过和最小代价算法的比较,进一步表明了算法的有效性。     

Salient edge and region aware image retargeting

The purpose of image retargeting is to automatically adapt a given image to fit the size of various displays without introducing severe visual distortions. The seam carving method can effectively achieve this task and it needs to define image importance to detect the salient context of images. In this paper we present a new image importance map and a new seam criterion for image retargeting. We first decompose an image into a cartoon and a texture part. The higher order statistics (HOS) on the cartoon part provide reliable salient edges. We construct a salient object window and a distance dependent weight to modify the HOS. The weighted HOS effectively protects salient objects from distortion by seam carving. We also propose a new seam criterion which tends to spread seam uniformly in nonsallient regions and helps to preserve large scale geometric structures. We call our method salient edge and region aware image retargeting (SERAR). We evaluate our method visually, and compare the results with related methods. Our method performs well in retargeting images with cluttered backgrounds and in preserving large scale structures.     

支持向量机训练算法及其应用

支持向量机是在统计学习理论基础上提出的一种新的机器学习方法。由于其出．色尊学习性能，该技术已成为机器学习界的研究热点，并成功地应用在模式识别、时间序列预测、苛线性系统辨识、控制及其它方面。本文简要介绍了支持向量机训练算法及其应用，并且讨论了未来的发展方向。     

多光子J-C模型的研究

本文推广二能级原子J-C模型到多光子过程。求得在存在偏调情况下,单模相干场对二能级原子激发时的光子数几率分布函数,由此研究了原子受激辐射性质。     

观测通道相关时MIMO雷达的检测性能

针对MIMO雷达的各观测通道相关情况下单目标回波检测问题,通过对回波通道系数的协方差矩阵特征分解,利用特征函数与概率密度函数间的特性,得到通道相关时MIMO雷达检测方法。通过数值仿真表明：观测通道的相关性会降低MIMO空间分集能力,在高信噪比时,MIMO雷达的检测性能随着观测通道相关性的增强而恶化。而在低信噪比时,观测通道的相关性能够改善检测性能。     

一种基于路集矩阵的系统可靠度算法

利用路集矩阵与布尔运算计算系统可靠度是一种新的尝试,这种方法原理简单,易编程实现,然而人们对此方法的研究仅仅停留在实现层面,而在算法效率的提高方面研究较少。该文从网络的最小路集矩阵出发,从提高内存利用率,减少冗余运算人手,在一定程度上提高了算法的效率,并通过实例对改进后算法的有效性做了验证。