基于过完备字典稀疏表示的多通道脑电信号压缩感知联合重构

该文基于多通道脑电信号时空特性构建非正交变换过完备字典,准确稀疏表示蕴含时空相关性信息的多通道脑电信号,提高基于时空稀疏贝叶斯学习模型的多通道脑电信号压缩感知联合重构算法性能。实验选用eegmmidb脑电数据库的多通道脑电信号验证所提算法有效性。结果表明,基于过完备字典稀疏表示的多通道脑电信号,能够为多通道脑电信号压缩感知重构算法提供更多的时空相关性信息,比传统多通道脑电信号压缩感知重构算法所得的信噪比值提高近12 dB,重构时间减少0.75 s,显著提高多通道脑电信号联合重构性能。     

基于稀疏贝叶斯学习的低空测角算法

为解决米波雷达低空测角的精度问题,该文结合稀疏贝叶斯学习方法,利用相邻快拍稀疏结构的相似性,将多观测向量模型通过Kronecker积变换成具有块稀疏结构的单观测向量模型,同时通过矩阵变换解决了贝叶斯准则在复数域中的应用。通过稀疏贝叶斯学习的不断迭代恢复出了信号在感知矩阵下的系数矩阵,得到了信源的角度信息。仿真实验验证了该方法相对于广义MUSIC和M-FOCUSS算法具有更好的性能,并且分析了快拍数变化对算法性能的影响。     

基于压缩感知的加速前向后向匹配追踪算法

前向后向匹配追踪(FBP)算法作为一个新颖的两阶段贪婪逼近算法,因为较高的重构精度和不需要稀疏度作为先验信息的特点,受到了人们的广泛关注。然而,FBP算法必须运行更多的时间才能得到更高的精度。鉴于此,该文提出加速前向后向匹配追踪(AFBP)算法。该算法利用每次迭代中候选支撑集的信息,实现对已删除原子的再次加入,以此减少算法迭代次数。通过不同非零项分布的稀疏信号和稀疏图像的仿真结果表明,相对于FBP算法,该文提出的方案在不降低重构精度的同时,大幅降低了算法运行时间。     

Parallel GMRES solver for fast analysis of large linear dynamic systems on GPU platforms

In this paper, we propose an efficient parallel dynamic linear solver, called GPU-GMRES, for transient analysis of large linear dynamic systems such as large power grid networks. The new method is based on the preconditioned generalized minimum residual (GMRES) iterative method implemented on heterogeneous CPU–GPU platforms. The new solver is very robust and can be applied to power grids with different structures as well as for general analysis problems for large linear dynamic systems with asymmetric matrices. The proposed GPU-GMRES solver adopts the very general and robust incomplete LU based preconditioner. We show that by properly selecting the right amount of fill-ins in the incomplete LU factors, a good trade-off between GPU efficiency and convergence rate can be achieved for the best overall performance. Such tunable feature can make this algorithm very adaptive to different problems. GPU-GMRES solver properly partitions the major computing tasks in GMRES solver to minimize the data traffic between CPU and GPUs to enhance performance of the proposed method. Furthermore, we propose a new fast parallel sparse matrix–vector (SpMV) multiplication algorithm to further accelerate the GPU-GMRES solver. The new algorithm, called segSpMV, can enjoy full coalesced memory access compared to existing approaches. To further improve the scalability and efficiency, segSpMV method is further extended to multi-GPU platforms, which leads to more scalable and faster multi-GPU GMRES solver. Experimental results on the set of the published IBM benchmark circuits and mesh-structured power grid networks show that the GPU-GMRES solver can deliver order of magnitudes speedup over the direct LU solver, UMFPACK. The resulting multi-GPU-GMRES can also deliver 3–12× speedup over the CPU implementation of the same GMRES method on transient analysis.     

Robust visual tracking via CAMShift and structural local sparse appearance model

This paper addresses issues in visual tracking where videos contain object intersections, pose changes, occlusions, illumination changes, motion blur, and similar color distributed background. We apply the structural local sparse representation method to analyze the background region around the target. After that, we reduce the probability of prominent features in the background and add new information to the target model. In addition, a weighted search method is proposed to search the best candidate target region. To a certain extent, the weighted search method solves the local optimization problem. The proposed scheme, designed to track single human through complex scenarios from videos, has been tested on some video sequences. Several existing tracking methods are applied to the same videos and the corresponding results are compared. Experimental results show that the proposed tracking scheme demonstrates a very promising performance in terms of robustness to occlusions, appearance changes, and similar color distributed background.     

基于相关向量机的高光谱影像混合像元分解

 提出了一种利用相关向量机后验概率进行高光谱影像混合像元分解的方法.基于支持向量机后验概率输出的高光谱影像混合像元分解方法中,类别后验概率需要通过带参数的S形函数近似,而且模型需要通过交叉验证获取较好的规则化系数.相关向量机是在贝叶斯框架下提出的更加稀疏的学习机器,它没有规则化系数,核函数不需要满足Mercer条件.本文从分析支持向量机用于高光谱影像混合像元分解存在的不足出发,介绍了稀疏贝叶斯分类模型和模型参数推断,采用了快速序列稀疏贝叶斯学习算法.通过PHI影像的混合像元分解实验分析,表明了基于相关向量机的高光谱影像混合像元分解方法的优势.     

视频目标跟踪算法综述

介绍了视频目标跟踪算法及其研究进展,包括基于对比度分析的目标跟踪算法、基于匹配的目标跟踪算法和基于运动检测的目标跟踪算法.重点分析了目标跟踪中特征匹配、贝叶斯滤波、概率图模型和核方法的主要内容及最新进展.此外,还介绍了多特征跟踪、利用上下文信息的目标跟踪和多目标跟踪算法及其进展.     

混合算法在稀布阵设计中的应用

提出把混合优化算法IGA-EDSPSO(Improved Genetic Algorithm-Extremum Disturbed Simple Particle Swarm Optimization)和基于RWG基函数的MoM-PO混合算法用于最小阵元间距约束的稀布阵列设计中.通过调整阵元间距来优化辐射阵因子,改善了阵列天线的辐射性能.与以前的方法相比,该方法在处理大型阵列时,降低了对计算机资源的需求,有效地实现了考虑互耦情况下副瓣电平的抑制,真实地考虑了天线的整个结构对于方向图的影响,对低副瓣电平阵列天线的设计有一定的指导意义和参考价值.     

基于变分贝叶斯估计的相机抖动模糊图像的盲复原算法

在曝光过程中由于相机抖动而导致的运动模糊,是一种常见的图像降质现象。该文提出了一种基于变分贝叶斯估计和自然图像梯度统计特性的盲复原算法,用于恢复相机抖动模糊图像,同时针对图像复原过程中出现的振铃效应,设计了一种基于分区域检测和Fuzzy滤波器的去振铃效应方法。实验结果表明,该文提出的盲复原算法能够有效地去除图像中因相机抖动而产生的模糊,而且在保持图像边缘和细节的同时,可以较好地降低振铃效应对图像复原质量的影响。     

逆合成孔径成像激光雷达低信噪比稀疏多孔径成像方法研究

受目标非合作特性的影响,逆合成孔径成像激光雷达(ISAIL)回波存在缺失;同时受大气衰减和自然背景光等因素的影响,ISAIL回波信号信噪比较低,因此,常规的稀疏多孔径成像方法不再适用。针对上述问题,该文提出了一种结合压缩感知(CS)和权矩阵的稀疏多孔径成像方法。首先,通过基于CS的稀疏多孔径成像方法对原始数据处理,得到目标像的支撑域;然后,据此建立权矩阵,优化采用CS重构时的代价函数,对稀疏多孔径ISAIL原始数据进行成像处理,利用不完整的回波信号获得高分辨目标像。此算法具有较好的抗噪能力。采用室内ISAIL系统实测数据验证了算法的有效性。