EasyCar: Location-Aware Assistance System Based on Smartphone in Automotive Aftermarket

Cloud databases provide facilities for large scale data storage and retrieval of distributed data. However, the current access control techniques provided in database systems for maintaining security are not sufficient to secure the private data stored in public cloud databases. In this paper, a new secured data storage algorithm for effective maintenance of confidential data is proposed. To perform storage and retrieval operations of data in the cloud data storage effectively, map reduce algorithms are developed in this work which performs data reduction and fast processing. In order to consider the temporal nature of documents to be retrieved, we propose a new algorithm called Temporal Secured Cloud Map Reduced Algorithm which integrates temporal constraints with map reduce algorithms and also the chaining Hill Cipher encryption algorithms which is proposed newly in this work. The main advantages of the proposed algorithm is that they reduce the processing time and maintains security effectively. The experimental results obtained from this work depict that the proposed model is optimizing cost and it ensures data security.

     

红外焦平面阵列非均匀性多点实时压缩校正研究

非均匀性校正是提高红外图像质量的主要方法之一.针对传统的多点校正方法运算量大、实时性能差等缺点,提出多点实时压缩校正新方法,该方法将校正曲线相同或相似的不同探测单元归为一类,并将归类后的校正曲线的映射表存储在查找表中,校正过程中仅对查找表中的数据进行实时读取.实验结果表明:该算法计算量小、校正速度快,更加适合硬件实时实现.     

Neighbor caching in multi-hop wireless ad hoc networks

We propose a neighbor caching strategy to overcome the overhead of multi-hop wireless communications. Neighbor caching makes a node able to expand its caching storage instantaneously by storing its data in the storage of idle neighbors. We also present the ranking based prediction that selects the most appropriate neighbor which data can be stored in. The ranking based prediction is an adaptive algorithm that adjusts the frequency of neighbor caching and makes neighbor caching flexible according to the idleness of nodes.     

基于云计算的海量数据存储模型

针对目前网络产生的数据越来越多以及随之而来的海量数据的存储问题,在云计算技术上,依据Hadoop及虚拟化技术,提出了基于云计算的海量数据存储模型。将医院信息化的海量数据部署在Hadoop平台上,根据云计算的核心算法MapReduce来处理数据,最后将数据存储在虚拟资源池中。通过实际的应用,该模型可以很好地克服现行的存储方式存在的不足,解决海量数据在存储当中存在的问题,并且能够很好地提高存储的效率。     

利用空闲空间抗两个磁盘失效的RAID5阵列算法

传统的RAID5阵列存储算法只能保证一个磁盘失效的时候重建数据,本文设计了一种新的RAID5阵列算法,包括储存算法和重建算法,使得在磁盘在使用少于一半空间的情况下,即使有两块磁盘失效,也可以重建数据,即抗2个磁盘失效失效,而当使用大于一半空间的时候,恢复使用传统算法进行存储重建。比较于传统算法,新的算法使RAID5阵列的可容忍磁盘失效数量提高50％,可容忍失效的己存储数据块量提高25％,磁盘空间利用率提高25％。     

Dynamic image data compression in spatial and temporal domains:theory and algorithm

Advanced medical imaging requires storage of large quantities of digitized clinical data. These data must be stored in such a way that their retrieval does not impair the clinician's ability to make a diagnosis. We propose a theory and algorithm for near lossless dynamic image data compression. Taking advantage of domain-specific knowledge related to medical imaging, medical practice and the dynamic imaging modality, a compression ratio greater than 80:1 is achieved. The high compression ratios are achieved by the proposed algorithm through three stages: (1) addressing temporal redundancies in the data through application of image optimal sampling, (2) addressing spatial redundancies in the data through cluster analysis, and (3) efficient coding of image data using standard still-image compression techniques. To illustrate the practicality of the algorithm, a simulated positron emission tomography (PET) study using the fluoro-deoxy-glucose (FDG) tracer is presented. Realistic dynamic image data are generated by virtual scanning of a simulated brain phantom as a real PET scanner. These data are processed using the conventional and proposed algorithms as well as the techniques for storage and analysis. The resulting parametric images obtained from the conventional and proposed approaches are subsequently compared to evaluate the proposed compression algorithm. The storage space for dynamic image data reduced by more than 95%, without loss in diagnostic quality. Therefore, the proposed theory and algorithm are expected to be very useful in medical image database management and telecommunication     

基于混合ACA的缺陷钢轨高效电磁建模

提出了一种基于矩量法(MoM)结合多层快速多极子(MLFMA)和自适应交叉近似(ACA)算法计算目标电磁特性的算法,该算法实现了对电大尺寸复合目标散射计算的加速和内存的降低。对于目标自作用的近场区域,多层快速多极子加速矩量法中的矩阵矢量乘运算,降低了计算的存储和复杂度;对于远场区域,根据阻抗矩阵的低秩特性,采用ACA对其压缩,加速矩阵的填充。矩阵填充按照树形结构划分的单元块间的相互作用依次进行存储,对每一块与块之间的求解采用ACA算法,对矩阵做压缩处理。提出的基于ACA的混合算法能够对2个目标耦合作用的阻抗矩阵进行压缩,缩短矩阵的填充时间并降低内存需求,同时也能够减少迭代求解过程中矩阵向量的计算时间,从而极大缩短电磁散射计算的总时间。数值仿真实验表明该算法比传统方法计算更高效,且计算精确度保持一致。     

基于FPGA和NAND Flash的存储器ECC设计与实现

针对以NAND Flash为存储介质的高速大容量固态存储器,在存储功能实现的过程中可能出现的错“位”现象,在存储器的核心控制芯片,即Xilinx公司Virtex-4系列FPGA XC4VLX80中,设计和实现了用于对存储数据进行纠错的ECC算法模块。在数据存入和读出过程中,分别对其进行ECC编码,通过对两次生成的校验码比较,对发生错误的数据位进行定位和纠正,纠错能力为1 bit/4 kB。ECC算法具有纠错能力强、占用资源少、运行速度快等优点。该设计已应用于某星载存储系统中,为存储系统的可靠性提供了保证。     

Secure K-Nearest neighbor queries in two-tiered mobile wireless sensor networks

Tiered Mobile Wireless Sensor Network(TMWSN) is a new paradigm introduced by mobile edge computing. Now it has received wide attention because of its high scalability, robustness, deployment flexibility, and it has a wide range of application scenarios. In TMWSNs, the storage nodes are the key nodes of the network and are more easily captured and utilized by attackers. Once the storage nodes are captured by the attackers, the data stored on them will be exposed. Moreover, the query process and results will not be trusted any more. This paper mainly studies the secure KNN query technology in TMWSNs, and we propose a secure KNN query algorithm named the Basic Algorithm For Secure KNN Query(BAFSKQ) first, which can protect privacy and verify the integrity of query results. However, this algorithm has a large communication overhead in most cases. In order to solve this problem, we propose an improved algorithm named the Secure KNN Query Algorithm Based on MR-Tree(SEKQAM). The MR-Trees are used to find the K-nearest locations and help to generate a verification set to process the verification of query results. It can be proved that our algorithms can effectively guarantee the privacy of the data stored on the storage nodes and the integrity of the query results. Our experimental results also show that after introducing the MR-Trees in KNN queries on TMWSNs, the communication overhead has an effective reduction compared to BAFSKQ.     

High Utility Itemsets Mining Based on Divide-and-Conquer Strategy

High utility itemsets mining has become a hot research topic in association rules mining. But many algorithms directly mine datasets, and there is a problem on dense datasets, that is, too many itemsets stored in each transaction. In the process of mining association rules, it takes a lot of storage space and affects the running efficiency of the algorithm. In the existing algorithms, there is a lack of efficient itemset mining algorithms for dense datasets. Aiming at this problem, a high utility itemsets mining algorithm based on divide-and-conquer strategy is proposed. Using the improved silhouette coefficient to select the best K-means cluster number, the datasets are divided into many smaller subclasses. Then, the association rules mining is performed by Boolean matrix compression operation on each subclass, and iteratively merge them to get the final mining results. We also analyze the time complexity of our method and Apriori algorithm. Finally, experimental results on several well-known real world datasets are conducted to show that the improved algorithm performs faster and consumes less memory on dense datasets, which can effectively improve the computational efficiency of the algorithm.

     

基于物联网技术的计算机网络数据分类存储系统的设计

为提高计算机网络数据的存储效率,文中设计了一种基于物联网技术的计算机网络数据分类存储系统。在该设计中,物联网技术架构为多层结构,分别为数据感知层、信息交互层和数据存储层。该系统可以利用数据感知层来感知、采集数据,通过信息交互层实现计算机网络数据的传输与交互,结合分簇算法并对采集到的计算机网络数据进行分类处理,将分类后的数据传输至存储层,同时将计算机网络存储于固定的数据库内,实现计算机网络数据的分类存储。实验表明,该系统存储响应时间较短,整体存储效率较高,具有较强的实用性。     

Distributed data storage solution under sink failures in wireless sensor networks

In challenging environment, sensory data must be stored inside the network in case of sink failures, we need to redistribute overflowing data items from the depleted storage source nodes to sensor nodes with available storage space and residual energy. We design a distributed energy efficient data storage algorithm named distributed data preservation with priority (D²P²). This algorithm takes both data redistribution costs and data retrieval costs into account and combines these two problems into a single problem. D²P² can effectively realize data redistribution by using cooperative communication among sensor nodes. In order to solve the redistribution contention problem, we introduce the concept of data priority, which can avoid contention consultations between source nodes and reduce energy consumption. Finally, we verify the performance of the proposed algorithm by both theory and simulations. We demonstrate that D²P²'s performance is close to the optimal centralized algorithm in terms of energy consumption and shows superiority in terms of data preservation time.     

存储网络扩展研究

采用光传输和FCIP网络存储扩展技术，当灾难发生时保护当地存储网络中的数据的安全。使得本地的存储网络能够扩展至另一城市或国家的远地站点，从而同一份数据可以同步或异步的同时存储在本地和远程。当一地发生灾难时数据在另一地继续存在，从而保护了数据的安全。     

基于压缩感知的室内多目标无线定位算法

针对现存无线传感器网络定位算法中需要采集、存储和处理大量数据导致运算量较大与能耗过高的问题,提出了一种改进的基于贝叶斯压缩感知的多目标定位算法.该算法利用锚节点对监控区域的划分,结合贝叶斯压缩感知理论将多目标定位问题转换为稀疏信号重构的问题.针对传统观测矩阵难以实现的缺陷,该算法中改进观测矩阵的设计可实现且与稀疏变换基相关性较低,进而使得算法的重构性能较高,从而降低了定位的误差.仿真结果表明,与现有的一些方法相比,所提算法在保证较低的计算复杂度的情况下更加充分地利用了网络节点,有效提高了定位精度,同时具有较强的鲁棒性.     

基于随机滤波的探地雷达成像方法

探地雷达是一种超宽带雷达系统,若按传统的奈奎斯特采样,雷达回波信号需要大量空间存储。压缩感知可以实现利用少量的测量值对稀疏信号进行重构,其中最为关键的是测量矩阵和重构算法的选择。本文将压缩感知应用于探地雷达成像,并利用随机滤波的思想选择测量矩阵,可以有效减少测量矩阵中非零值的个数。利用正交匹配追踪算法对信号进行重构,算法简单,降低了数据的存储量和运算复杂度,该算法同样可以对时间和空间上同时压缩的数据进行成像。最后,本文给出基于时间连续信号的GPR接收机一种CS实现方案。仿真结果表明,本文提出的成像方法可以以少量数据精确地对信号进行重构,并且运算量少。      

基于在线Music算法的DOA估计

基于模式识别领域中的CCIPCA算法,该文给出了一种低运算量的在线Music算法。它无需估计协方差矩阵和对其进行特征值分解,信号子空间的估计与快拍数据的接收是同时进行的,而且只需存储当前的快拍数据,因此大大降低了存储量及运算量的要求;并针对上述算法在小快拍情况下性能较差的缺点,利用数据复用的方法有效提高了其估计性能。最后,计算机仿真验证了该文方法的有效性。     

大容量弹载数据记录器的设计与实现

提出了一种大容量弹载数据记录器的设计方案,该方案主要完成3路高速图像数据的接收,每个通道的数据带宽为每秒150Mbyte/s,存储容量为128GByte。设计选用Xilinx公司的FPGA作为主控制器,完成对高速数据的接收,缓存和存储。接收单元采用FPGA内部集成的高速串行收发器RocketIO GTP,单个链路的数据接收速率为3.125Gbps;缓存单元采用两片DDR2 SDRAM芯片对接收到的高速数据进行乒乓缓存;存储单元采用32片NAND FLASH构成存储阵列,对缓存后的数据进行存储。同时,该记录器能够对存储的数据进行事后读取并进行分析。     

一种基于ORAM的数据可恢复性证明与访问模式的隐藏

随着云计算的发展与应用,越来越多的客户选择云存储作为存储媒质,因此,数据的完整性和私密性成为客户关心的主要问题。基于无关RAM模型机提出一种新的结构,将客户文件分割成大小相等的数据块,每个数据块在云存储中有两个备份,且随机地存储在不同的文件中,以保证数据的完整性。利用同态散列算法验证数据的可持有性,通过无关RAM隐藏客户对服务器的访问模式,敌手无法从客户的数据访问模式中获取有用的信息,从而实现了数据的私密性。     

多通道测试记录仪

本文介绍了一种用于飞行器从发射到着地全弹道姿态测试数字记录装置,它由传感器、模拟适配调理电路、AD变换器、存储器、控制器、接口电路、电池等组成。该装置采用静态存储器完成发射前初始姿态数据的负延时循环记录,启动飞行后,NOR闪存作为主存储器记录发射前静态存储器数据和发射后飞行姿态数据。本装置实现了对飞行体弹头飞行全过程状态参数的存储测试,具有小体积,微功耗,抗高冲击等特点。经过实弹实验,成功的经受住了着地2．8万g的重力加速度的过载,完整记录了弹飞行的姿态数据。     

Guest Editorial Data Communication Techniques for Storage Channels and Networks

Since the inception of direct access magnetic storage about 55 years ago, data storage has both benefited from and given rise to extraordinary progress in many technological areas, including materials science, tribology, servo control and actuation, and signal processing and coding. The number of data bits that can be stored in a unit area ? the areal recording density ? has increased by eight orders of magnitude for harddisk magnetic storage, with compound annual growth rates at times exceeding 100%. Moreover, the cost of this form of storage has dropped by about seven orders of magnitude. For this reason, data storage has been one of the main enablers of the information technology revolution. According to a recent estimation by the technology analysis firm IDC, the amount of data created worldwide has now started to exceed the capacity of storage that is physically available. This so-called digital universe is forecasted to grow explosively and reach more than 10²¹ bytes (1 ZB) in 2011.