Multi-channel SAR imaging based on distributed compressive sensing

The rapid development of compressive sensing(CS)shows that it is possible to recover a sparse signal from very limited measurements.Synthetic aperture radar(SAR)imaging based on CS can reconstruct the target scene with a reduced number of collected samples by solving an optimization problem.For multi-channel SAR imaging based on CS,each channel requires sufficient samples for separate imaging and the total number of samples could still be large.We propose an imaging algorithm based on distributed compressive sensing(DCS)that reconstructs scenes jointly under multiple channels.Multi-channel SAR imaging based on DCS not only exploits the sparsity of the target scene,but also exploits the correlation among channels.It requires significantly fewer samples than multi-channel SAR imaging based on CS.If multiple channels offer different sampling rates,DCS joint processing can reconstruct target scenes with a much more flexible allocation of the number of measurements offered by each channel than that used in separate CS processing.     

Sparse synthetic aperture radar imaging with optimized azimuthal aperture

To counter the problem of acquiring and processing huge amounts of data for synthetic aperture radar(SAR)using traditional sampling techniques,a method for sparse SAR imaging with an optimized azimuthal aperture is presented.The equivalence of an azimuthal match filter and synthetic array beamforming is shown so that optimization of the azimuthal sparse aperture can be converted to optimization of synthetic array beamforming.The azimuthal sparse aperture,which is composed of a middle aperture and symmetrical bilateral apertures,can be obtained by optimization algorithms(density weighting and simulated annealing algorithms,respectively).Furthermore,sparse imaging of spectrum analysis SAR based on the optimized sparse aperture is achieved by padding zeros at null samplings and using a non-uniform Taylor window.Compared with traditional sampling,this method has the advantages of reducing the amount of sampling and alleviating the computational burden with acceptable image quality.Unlike periodic sparse sampling,the proposed method exhibits no image ghosts.The results obtained from airborne measurements demonstrate the effectiveness and superiority of the proposed method.     

PERFORMANCE ANALYSIS OF TASK CLUSTERING HEURISTICS FOR SCHEDULING STATIC DAGS ON MULTIPROCESSOR SYSTEM*

CASS is a task management system that provides facilities for automatic grain-size optimization and task scheduling of parallel programs on distributed memory parallel architectures. The heart of CASS is a clustering module that partitions the tasks of a program into clusters that match the granularity of the target machine. This paper describes the clustering algorithms used by CASS and compares them with the best known algorithms reported in the literature, namely the PY algorithm (for clustering with task duplication) and the DSC algorithm (for clustering with no task duplication). It is shown that the clustering algorithms used by CASS outperform both the PY and DSC algorithms in terms of both speed and solution quality.     

几个常见分布式文件系统特征分析和性能对比

近年来随着云计算市场规模不断增长,作为云计算平台基础设施的云存储系统也随之显得越来越重要。数以万计的互联网应用已经运行于云计算环境,同时大量不同的应用也即将从传统运行环境转移到云计算平台。不同的互联网应用的存储需求可能不一样。例如:应用中涉及的单个文件大小,文件数量,IO访问模式,读写比率等,都对底层存储系统提出了不同的要求。这说明在云计算环境中,单个文件系统可能无法满足全部应用的存储需求,本文尝试通过在单一云计算平台中部署多个不同分布式文件系统来优化存储系统的总体性能。为了优化混合式文件系统的性能,首先需要分析不同文件系统的性能特征。本文通过量化方法分析了云计算环境下几个常用的分布式文件系统,这些文件系统分别是ceph,moosefs,glusterfs和hdfs。实验结果显示:即使针对同一文件的相同读写操作,不同分布式文件系统之间的性能也差异显著,当单个文件的大小小于256MB时,moosefs的平均写性能比其它几个文件系统高22.3%;当单个文件大小大于256KB时,glusterfs的平均读性能比其它几个文件系统高21.0%。这些结果为设计和实现一个基于以上几个分布式文件系统的混合式文件系统提供了基础。     

时分复用光纤光栅传感阵列中DFB激光器的高精度温控设计

针对时分复用光纤光栅(TDM-FBG)传感阵列中使用的分布反馈(DFB)激光器高波长稳定度的要求,设计了一个高精度的DFB温控方案,包括温度测量与设定、热敏电阻线性化、PID补偿回路、H桥驱动、温度电压采集与显示等。使用DFB内置的热敏电阻和半导体制冷器(TEC)对温控电路进行了测试,其精度在180s内达到±0.04℃,温控后激光器温度变化引起的应变测量误差为±3.4με左右,满足TDM-FBG传感阵列的要求。     

小孔内径测量的传感器精度分析

针对小孔的结构特点,介绍了电容传感器探针测量小孔孔径的原理;分析了测量时电容传感器由于参数的线性化、电容探针与小孔的中心线偏心带来的测量误差;并针对以上因素影响,采取了自动对中机构实现测量过程中的最小化偏心,通过标定得到了孔径值与测量值之间的对应关系。实验结果表明,系统的重复性精度可达到0.186μm,稳定性能达到6 h内漂移不超过0.375μm,该指标完全可以满足实际测量要求。     

涡流阵列传感器半解析模型的直接FFT构建方法

针对分布式激励源涡流阵列传感器半解析模型的构建问题,提出了传感器半解析模型的直接FFT构建方法。该方法首先通过虚拟采样和FFT得到传感器导线界面处线电流密度的傅里叶变换系数,然后根据层状介质空间中场量傅里叶变换系数传递关系,经反FFT得到场量的空间分布。建模过程和数值计算结果表明此方法不仅简化了半解析模型的建立过程,而且直接将激励源导线宽度考虑在建模过程之中,提高了建模精度;建立了该传感器的应用物理模型,数值仿真结果表明该传感器对其周围介质空间的电磁属性较为敏感,可望将该类型的传感器运用于金属结构的在线监测之中。     

分布式信源编码在视频编码中的应用研究

分布式视频编码对编码器的功耗、复杂度、存储能力和传输能力都有较高的要求,而分布式信源编码理论正好适应了这一需求。为了使人们对这一技术有所了解,首先阐述了分布式视频编码的基本原理与其应用研究现状。并针对多视点分布式视频编码的Wyner—Ziv编解码器设计、虚拟“相关信道”建模、辅助边信息生成等几项关键技术存在的问题及最新研究进展进行分析和实验,最后总结了分布式信源编码理论在多视点视频编码的发展与研究方向。     

基于分布式信息流控制的MapReduce隐私安全加强方案研究

随着信息技术的发展,分布式系统被广泛应用于金融、医疗等领域。其中以基于MapReduce的数据挖掘为代表的应用对隐私的保护往往有很高的要求。本文提出一个基于分布式信息流控制的MapReduce框架,用以保证MapReduce中信息的私密性和完整性,同时给出系统原型的实现。