Target tracking is a typical and important application of wireless sensor networks (WSNs). Existing target tracking protocols focus mainly on energy efficiency, and little effort has been put into network management and real-time data routing, which are also very important issues for target tracking. In this paper, we propose a scalable cluster-based target tracking framework, namely the hierarchical prediction strategy (HPS), for energy-efficient and real-time target tracking in large-scale WSNs. HPS organizes sensor nodes into clusters by using suitable clustering protocols which are beneficial for network management and data routing. As a target moves in the network, cluster heads predict the target trajectory using Kalman filter and selectively activate the next round of sensors in advance to keep on tracking the target. The estimated locations of the target are routed to the base station via the backbone composed of the cluster heads. A soft handoff algorithm is proposed in HPS to guarantee smooth tracking of the target when the target moves from one cluster to another. Under the framework of HPS, we design and implement an energy-efficient target tracking system, HierTrack, which consists of 36 sensor motes, a sink node, and a base station. Both simulation and experimental results show the efficiency of our system.

Text categorization is a significant technique to manage the surging text data on the Internet. The k-nearest neighbors (kNN) algorithm is an effective, but not efficient, classification model for text categorization. In this paper, we propose an effective strategy to accelerate the standard kNN, based on a simple principle: usually, near points in space are also near when they are projected into a direction, which means that distant points in the projection direction are also distant in the original space. Using the proposed strategy, most of the irrelevant points can be removed when searching for the k-nearest neighbors of a query point, which greatly decreases the computation cost. Experimental results show that the proposed strategy greatly improves the time performance of the standard kNN, with little degradation in accuracy. Specifically, it is superior in applications that have large and high-dimensional datasets.

We describe the design of a multiple maneuvering targets tracking algorithm under the framework of Gaussian mixture probability hypothesis density (PHD) filter. First, a variation of the generalized pseudo-Bayesian estimator of first order (VGPB1) is designed to adapt to the Gaussian mixture PHD filter for jump Markov system models (JMS-PHD). The probability of each kinematic model, which is used in the JMS-PHD filter, is updated with VGPB1. The weighted sum of state, associated covariance, and weights for Gaussian components are then calculated. Pruning and merging techniques are also adopted in this algorithm to increase efficiency. Performance of the proposed algorithm is compared with that of the JMS-PHD filter. Monte-Carlo simulation results demonstrate that the optimal subpattern assignment (OSPA) distances of the proposed algorithm are lower than those of the JMS-PHD filter for maneuvering targets tracking.

One of the important characteristics of pulsar radiation is polarization. It is considered not only as a probe for recognizing the structure of a magnetic field, but also as a lighthouse for estimating spacecraft attitude via orientation information between the pulsar and the detector. Although polarization of a pulsar has been studied for decades, until recently applications to determination of spacecraft attitude have been seldom reported. This paper deals with analysis of the feasibility of applying polarization information to attitude estimation. The stability factor (SFR) and observation fluctuation factor (OFR) are introduced to analyze the stability of a pulsar’s polarized position angle. Based on European Pulsar Network (EPN) data, several simulated instances are used to demonstrate that the accuracy requirement of attitude determination can be met via polarization measurement. The SFR of a pulsar is evaluated using simulated polarization data, and the OFR is used to analyze the relationship between fluctuation extent and observation time. Simulation results show that the polarized measurement of candidate pulsars PSR B0470-28 and PSR B2319+60 reaches the specification for attitude determination.

Real-time Ethernet (RTE) control systems with critical real-time requirements are called fast real-time (FRT) systems. To improve the real-time performance of Ethernet for plant automation (EPA), we propose an EPA-FRT scheme. The minimum macrocycle of EPA networks is reduced by redefining the EPA network frame format, and the synchronization process is modified to acquire higher accuracy. A multi-segmented topology with a scheduling scheme is introduced to increase effective bandwidth utilization and reduce protocol overheads, and thus to shorten the communication cycle significantly. Performance analysis and practical tests on a prototype system show the effectiveness of the proposed scheme, which achieves the best performance at small periodic payload in large scale systems.

Context-based adaptive binary arithmetic coding (CABAC) is the major entropy-coding algorithm employed in H.264/AVC. In this paper, we present a new VLSI architecture design for an H.264/AVC CABAC decoder, which optimizes both decode decision and decode bypass engines for high throughput, and improves context model allocation for efficient external memory access. Based on the fact that the most possible symbol (MPS) branch is much simpler than the least possible symbol (LPS) branch, a newly organized decode decision engine consisting of two serially concatenated MPS branches and one LPS branch is proposed to achieve better parallelism at lower timing path cost. A look-ahead context index (ctxIdx) calculation mechanism is designed to provide the context model for the second MPS branch. A head-zero detector is proposed to improve the performance of the decode bypass engine according to UEGk encoding features. In addition, to lower the frequency of memory access, we reorganize the context models in external memory and use three circular buffers to cache the context models, neighboring information, and bit stream, respectively. A pre-fetching mechanism with a prediction scheme is adopted to load the corresponding content to a circular buffer to hide external memory latency. Experimental results show that our design can operate at 250 MHz with a 20.71k gate count in SMIC18 silicon technology, and that it achieves an average data decoding rate of 1.5 bins/cycle.

Modeling the generation of a wind farm and its effect on power system reliability is a challenging task, largely due to the random behavior of the output power. In this paper, we propose a new probabilistic model for assessing the reliability of wind farms in a power system at hierarchical level II (HLII), using a Monte Carlo simulation. The proposed model shows the effect of correlation between wind and load on reliability calculation. It can also be used for identifying the priority of various points of the network for installing new wind farms, to promote the reliability of the whole system. A simple grid at hierarchical level I (HLI) and a network in the north-eastern region of Iran are studied. Simulation results showed that the correlation between wind and load significantly affects the reliability.