高速信号采集存储及传输系统的设计与实现

为解决高速数据采集系统中的数据缓存和传输速度瓶颈,设计并实现了一种基于光纤通道协议和DDR2 SODIMM存储的高速数据传输、存储系统。利用Stratix Ⅳ GX系列FPGA和QuartusⅡ中自带的DDR2 IP核以及高速收发器IP核,实现了PCI9056的本地接口、DDR2控制器、光纤通道协议和高速串行数据的转换发送,最终实现了数据的高速存储和传输。     

基于光源调制技术的金标试纸条定量分析仪器

基于光电检测原理,研发了一套可用于金标免疫试纸条定量检测的分析仪器。采用绿光发光二极管(LED)作为发射光源,以光电二极管接收试纸条反射光,并通过步进电机带动检测系统扫描试纸条待检区域。系统采用光源信号的调制与接收信号的解调技术,以消除环境干扰噪声。实验结果表明,该仪器检测结果准确,与真实值之间误差<1%;稳定性高,变异系数CV<2%。     

基于PSoC的飞艇艇内温度采集系统设计

介绍基于PSoC、nRF24L01射频芯片和DS18B20数字温度传感器的智能无线温度采集系统在飞艇上的设计,并对系统实际运行的结果数据进行测试分析,实现温度的测量与数据传输。重点描述了系统采集端软硬件的设计与实现,阐述了软件与硬件的设计要求。本系统功耗低、结构简单、通信效率高,可实现飞艇范围内全方位的温度测量。     

基于ARM和FPGA的高扩展性超声检测模块设计与实现

介绍了一种以ARM和FPGA联合作为中央控制处理单元的4路超声探伤模块。给出了其整体结构方案,阐述了以4路超声模拟信号为一组的多路超声探伤模块硬件扩展的设计思路和实现方案,讨论了FPGA对高速LVDS数据的采集、处理、时序同步功能的实现,ARM与FPGA之间总线接口的实现,ARM嵌入式系统功能以及网络通信功能的实现。实际应用表明,该功能模块能达到预期的设计要求,并能方便地实现硬件扩展。     

基于Cortex-A8的移动在线水质监测平台的研究

为实现大面积水域的实时监测,提出一种低成本、高实时性的移动在线水质监测平台。该平台采用Cortex-A8为核心处理器,通过GPS模块实时监控平台位置,通过GPRS技术实现移动平台与监控中心的数据通信及控制,为保证平台能长时间在水面检测,系统配置了太阳能电池板。阐述了运动模块的改进和图像的远程传输。实践证明,该系统在江河中运行良好,数据通信流畅,有一定的经济价值。     

Ring-zone-based with cluster-heads＇ hybrid communication routing algorithm for heterogeneous WSNs

簇结构是减少无线传感网能耗的基础,针对经典LEACH协议簇头能耗不均衡的缺点,提出了一种适用于异构传感网中的基于圆环域的簇头混合通信的路由算法LEACH-RMC.该算法通过在监测区划分圆环域确定簇头的等级,簇头采用单、多跳结合通信方式均衡了簇头的能耗.网络中采用固定簇头,节省了普通节点的硬件花费.实验表明,新的算法明显延长了网络的生存期,均衡了簇头的负载.     

用于高速列车移动网络的资源分配实时算法

误码率高、频繁中断的高速列车移动场景宜采用多宿主容迟网络。但其资源分配计算复杂,实时性较差。基于效用差分法设计初始化过程资源分配算法,以保证过程中每一步的效用单调不减,算法时间复杂度降至O(n)。     

Alternative mixed integer linear programming models for identifying the most efficient decision making unit in data envelopment analysis

A mixed integer linear model for selecting the best decision making unit (DMU) in data envelopment analysis (DEA) has recently been proposed by Foroughi [Foroughi, A. A. (2011a). A new mixed integer linear model for selecting the best decision making units in data envelopment analysis. Computers and Industrial Engineering, 60(4), 550–554], which involves many unnecessary constraints and requires specifying an assurance region (AR) for input weights and output weights, respectively. Its selection of the best DMU is easy to be affected by outliers and may sometimes be incorrect. To avoid these drawbacks, this paper proposes three alternative mixed integer linear programming (MILP) models for identifying the most efficient DMU under different returns to scales, which contain only essential constraints and decision variables and are much simpler and more succinct than Foroughi’s. The proposed alternative MILP models can make full use of input and output information without the need of specifying any assurance regions for input and output weights to avoid zero weights, can make correct selections without being affected by outliers, and are of significant importance to the decision makers whose concerns are not DMU ranking, but the correct selection of the most efficient DMU. The potential applications of the proposed alternative MILP models and their effectiveness are illustrated with four numerical examples.     

Multi-level delegations with trust management in access control systems

Delegation is a mechanism that allows one agent to act on another’s privilege. It is important that the privileges should be delegated to a person who is trustworthy. In this paper, we propose a multi-level delegation model with trust management in access control systems. We organize the delegation tasks into three levels, Low, Medium, and High, according to the sensitivity of the information contained in the delegation tasks. It motivates us that the more sensitive the delegated task is, the more trustworthy the delegatee should be. In order to assess how trustworthy a delegatee is, we devise trust evaluation techniques to describe a delegatee’s trust history and also predict the future trend of trust. In our proposed delegation model, a delegatee with a higher trust level could be assigned with a higher level delegation task. Extensive experiments show that our proposed multi-level delegation model is effective in accurately predicting trust and avoiding sensitive information disclosure.     

Probabilistic skylines on uncertain data: model and bounding-pruning-refining methods

Uncertain data are inherent in some important applications. Although a considerable amount of research has been dedicated to modeling uncertain data and answering some types of queries on uncertain data, how to conduct advanced analysis on uncertain data remains an open problem at large. In this paper, we tackle the problem of skyline analysis on uncertain data. We propose a novel probabilistic skyline model where an uncertain object may take a probability to be in the skyline, and a p-skyline contains all objects whose skyline probabilities are at least p (0 < p ≤ 1). Computing probabilistic skylines on large uncertain data sets is challenging. We develop a bounding-pruning-refining framework and three algorithms systematically. The bottom-up algorithm computes the skyline probabilities of some selected instances of uncertain objects, and uses those instances to prune other instances and uncertain objects effectively. The top-down algorithm recursively partitions the instances of uncertain objects into subsets, and prunes subsets and objects aggressively. Combining the advantages of the bottom-up algorithm and the top-down algorithm, we develop a hybrid algorithm to further improve the performance. Our experimental results on both the real NBA player data set and the benchmark synthetic data sets show that probabilistic skylines are interesting and useful, and our algorithms are efficient on large data sets.