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.     

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.     

Rolling-horizon scheduling for energy constrained distributed real-time embedded systems

Energy-efficient scheduling approaches are critical to battery driven real-time embedded systems. Traditional energy-aware scheduling schemes are mainly based on the individual task scheduling. Consequently, the scheduling space for each task is small, and the schedulability and energy saving are very limited, especially when the system is heavily loaded. To remedy this problem, we propose a novel rolling-horizon (RH) strategy that can be applied to any scheduling algorithm to improve schedulability. In addition, we develop a new energy-efficient adaptive scheduling algorithm (EASA) that can adaptively adjust supply voltages according to the system workload for energy efficiency. Both the RH strategy and EASA algorithm are combined to form our scheduling approach, RH-EASA. Experimental results show that in comparison with some typical traditional scheduling schemes, RH-EASA can achieve significant energy savings while meeting most task deadlines (namely, high schedulability) for distributed real-time embedded systems with dynamic workloads.     

3D navigation and monitoring for spinal milling operation based on registration between multiplanar fluoroscopy and CT images

Milling operations in spinal surgery demand much experience and skill for the surgeon to perform the procedure safely. A 3D navigation method is introduced aiming at providing a monitoring system with enhanced safety and minimal intraoperative interaction. An automatic registration method is presented to establish the 3D-3D transformation between the preoperative CT images and a common reference system in the surgical space, and an intensity-based similarity metric adapted for the multi-planar configuration is introduced in the registration procedure. A critical region is defined for real-time monitoring in order to prevent penetration of the lamina and avoid violation of nerve structures. The contour of the spinal canal is reconstructed as the critical region, and different levels of warning limits are defined. During the milling procedure, the position of the surgical instrument relative to the critical region is provided with augmented display and audio warnings. Timely alarm is provided for surgeons to prevent surgical failure when the mill approaches the critical region. Our validation experiment shows that real-time 3D navigation and monitoring is advantageous for improving the safety of the milling operation.     

An Efficient Algorithm for Haplotype Inference on?Pedigrees with a Small Number of Recombinants

Combinatorial (or rule-based) methods for inferring haplotypes from genotypes on a pedigree have been studied extensively in the recent literature. These methods generally try to reconstruct the haplotypes of each individual so that the total number of recombinants is minimized in the pedigree. The problem is NP-hard, although it is known that the number of recombinants in a practical dataset is usually very small. In this paper, we consider the question of how to efficiently infer haplotypes on a large pedigree when the number of recombinants is bounded by a small constant, i.e. the so called k-recombinant haplotype configuration (k-RHC) problem. We introduce a simple probabilistic model for k-RHC where the prior haplotype probability of a founder and the haplotype transmission probability from a parent to a child are all assumed to follow the uniform distribution and k random recombination events are assumed to have taken place uniformly and independently in the pedigree. We present an O(mnlog? ^k+1 n) time algorithm for k-RHC on tree pedigrees without mating loops, where m is the number of loci and n is the size of the input pedigree, and prove that when 90log?n<m<n ³, the algorithm can correctly find a feasible haplotype configuration that obeys the Mendelian law of inheritance and requires no more than k recombinants with probability $1 -O(k^{2}\frac{\log^{2}n}{mn}+\frac{1}{n^{2}})$ . The algorithm is efficient when k is of a moderate value and could thus be used to infer haplotypes from genotypes on large tree pedigrees efficiently in practice. We have implemented the algorithm as a C++ program named Tree-k-RHC. The implementation incorporates several ideas for dealing with missing data and data with a large number of recombinants effectively. Our experimental results on both simulated and real datasets show that Tree-k-RHC can reconstruct haplotypes with a high accuracy and is much faster than the best combinatorial method in the literature.