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The Ozone Mapping and Profiler Suite Limb Profiler (OMPS/LP) ozone product requires the determination of cloud height for each event to establish the lower boundary of the profile for the retrieval algorithm. We have created a revised cloud detection algorithm for LP measurements that uses the spectral dependence of the vertical gradient in radiance between two wavelengths in the visible and near-IR spectral regions. This approach provides better discrimination between clouds and aerosols than results obtained using a single wavelength. Observed LP cloud height values show good agreement with coincident Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements. [ABSTRACT FROM AUTHOR]

How to facilitate collaborative development between the enterprise and the environment under the dual constraints of resources and the environment is the focus of today's green supply chain management system research. Through the performance evaluation of the green supply chain, we can understand the operation of the whole supply chain and its shortcomings, provide a basis for improving related processes, and have important practical significance for improving the competitiveness and protection of its products. First of all, by summarising and analysing the research status of sustainable supply chain management in different countries, the research idea and overall background of this paper are proposed. It discusses the theory of sustainable supply chain management and the performance evaluation system and calculation types of sustainable supply chain management. Finally, the relative weight of each index is determined based on the sustainability calculation method, and then the decentralisation degree of the index is constructed. During this period, the fuzzy comprehensive evaluation method is used to evaluate the performance of sustainable supply chain, conduct case analysis and summary, and evaluate the performance of green supply chain component in economic, social, environmental and other aspects. In this paper, representative companies are selected as examples to evaluate their green supply chain management performance, and the evaluation algorithm is studied based on sustainable calculation method. The results show that a reasonable and effective evaluation of the enterprise performance of green supply chain management and a sustainable algorithm study can effectively identify potential problems in the operation of the company and improve the overall operation of the company at this stage. [ABSTRACT FROM AUTHOR]

This paper demonstrates the feasibility of implementing real-time state estimators for active distribution networks in field-programmable gate arrays (FPGAs) by presenting an operational prototype. The prototype is based on a linear state estimator that uses synchrophasor measurements from phasor measurement units. The underlying algorithm is the sequential discrete Kalman filter (SDKF), an equivalent formulation of the DKF for the case of uncorrelated measurement noise. In this regard, this paper formally proves the equivalence of SDKF and the DKF, and highlights the suitability of the SDKF for an FPGA implementation by means of a computational complexity analysis. The developed prototype is validated using a case study adapted from the IEEE 34-node distribution test feeder. [ABSTRACT FROM PUBLISHER]

Research on mobile robots began in the late 1960s. Mobile robots are a typical autonomous intelligent system and a hot spot in the high-tech field. They are the intersection of multiple technical disciplines such as computer artificial intelligence, robotics, control theory and electronic technology. The product not only has potentially very attractive application value and commercial value, but the research on it is also a challenge to intelligent technology. The development of mobile robots provides excellent research for various intelligent technologies and solutions. This dissertation aims to study the research of multi-sensor information fusion and intelligent optimization methods and the methods of applying them to mobile robot related technologies, and in-depth study of the construction of mobile robot maps from the perspective of multi-sensor information fusion. And, in order to achieve this function, combined with autonomous exploration and other related theories and algorithms, combined with the Robot Operating System (ROS). This paper proposes the area equalization method, equalization method, fuzzy neural network and other methods to promote the realization of related technologies. At the same time, this paper conducts simulation research based on the SLAM comprehensive experiment of the JNPF-4WD square mobile robot. On this basis, the high precision and high reliability of robot positioning are further realized. The experimental results in this paper show that the maximum error of the X-axis and Y-axis, FastSLAM algorithm is smaller than EKF algorithm, and the improved FASTSALM algorithm error is further reduced compared with the original FastSLAM algorithm, the value is less than 0.1. [ABSTRACT FROM AUTHOR]

The pseudospectral analytical time-domain (PSATD) particle-in-cell algorithm solves the vacuum Maxwell's equations exactly, has no Courant time-step limit (as conventionally defined), and offers substantial flexibility in plasma and particle beam simulations. It is, however, not free of the usual numerical instabilities, including the numerical Cherenkov instability, when applied to relativistic beam simulations. This paper presents several approaches that, when combined with digital filtering, almost completely eliminate the numerical Cherenkov instability. This paper also investigates the numerical stability of the PSATD algorithm at low beam energies, drawing in many cases on the pioneering work of Prof. Ned Birdsall, in whose memory this paper is dedicated. [ABSTRACT FROM PUBLISHER]

Access point (APs) selection approaches have been used in location fingerprinting systems to improve positioning accuracy and to reduce computational overhead. Although the interference between APs is unavoidable due to the overlapped channel, traditional methods treat APs individually by assuming independence among them. This paper proposes a novel group discriminant (GD)-based AP selection approach for improving location fingerprinting, in which the dependence between APs is considered. The proposed GD approach focuses on measuring the positioning capabilities of each group of APs instead of ranking APs based on their individual importance. It utilizes the risk function from support vector machines (SVMs) to estimate the GD value by maximizing the margin between reference locations. Moreover, this paper proposes a faster version, namely, recursive feature elimination (RFE-GD), to find a suboptimal solution of GD efficiently. This paper applies the proposed algorithms to realistic wireless local area networks (WLANs). Experimental results from two different test beds demonstrate that GD and RFE-GD outperform traditional AP selection schemes, reducing the mean localization error by 40.58%–41.13%. The experiments based on different fingerprinting approaches also confirm the advantages of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

In a recent paper we presented a beam-based backpropagation and correlation imaging scheme for targets in a homogeneous medium. This beam-based approach is extended here for MUSIC-imaging algorithms. In the beam approach, the fields of the physical arrays of point-sources and point receivers are expanded using special sets of collimated beam-sources and beam-receivers. This converts the physical scattering data into a beam-domain data, describing the scattering amplitudes seen (synthetically) by receiver beams due to excitation by source beams. The image is then formed by applying the MUSIC algorithm directly in the beam domain. We derive a closed form expression for the data transformation to the beam domain and then derive multi-experiments MUSIC-imaging algorithms that accommodate the measurement noise. The beam approach enables local imaging of any given sub-domain of interest by retaining only the subset of beams that pass through that domain, thus reducing the overall computation complexity relative to the conventional Green function approach, and filtering out of data and noise that arrive from non-relevant directions. Numerical investigations reveal that the beam approach also provides a better resolution under low S/N conditions. [ABSTRACT FROM PUBLISHER]

As more and more applications produce streaming data, clustering data streams has become an important technique for data and knowledge engineering. A typical approach is to summarize the data stream in real-time with an online process into a large number of so called micro-clusters. Micro-clusters represent local density estimates by aggregating the information of many data points in a defined area. On demand, a (modified) conventional clustering algorithm is used in a second offline step to recluster the micro-clusters into larger final clusters. For reclustering, the centers of the micro-clusters are used as pseudo points with the density estimates used as their weights. However, information about density in the area between micro-clusters is not preserved in the online process and reclustering is based on possibly inaccurate assumptions about the distribution of data within and between micro-clusters (e.g., uniform or Gaussian). This paper describes DBSTREAM, the first micro-cluster-based online clustering component that explicitly captures the density between micro-clusters via a shared density graph. The density information in this graph is then exploited for reclustering based on actual density between adjacent micro-clusters. We discuss the space and time complexity of maintaining the shared density graph. Experiments on a wide range of synthetic and real data sets highlight that using shared density improves clustering quality over other popular data stream clustering methods which require the creation of a larger number of smaller micro-clusters to achieve comparable results. [ABSTRACT FROM PUBLISHER]

Dynamic grain state estimation (DGSE) algorithms for 2-D magnetic recording (TDMR) employ probabilistic message-passing algorithms that jointly estimate magnetic grain configurations and coded data bits, in order to iteratively assist channel decoding. At high densities (e.g., between 1 and 3 magnetic grains per coded bit), occasionally, a bit will not be written on any grain, and hence will effectively be overwritten (or erased) by bits on surrounding grains. DGSE enables the detection of overwritten bits so that their log-likelihood ratios are assigned small magnitudes, effectively making them erasures, which are easily filled in by linear channel codes. Past papers employing Bahl-Cocke-Jelinek-Raviv-based detectors on a simple four-rectangular-grain model have shown that the DGSE is surprisingly resilient to mismatch between the detector’s assumed grain model and the actual model generating the data. This paper presents a novel DGSE–TDMR detector based on the generalized belief propagation (GBP) algorithm. The new detector employs a random discretized-nuclei Voronoi grain model. Simulation results show that the GBP-based TDMR turbo-detector accurately detects the overwritten bits and that it achieves low decoded bit error rates at densities as high as 0.4966 user bits per grain. [ABSTRACT FROM AUTHOR]

The paper deals with parallelization of computing similarity measures between large vectors. Such computations are important components within many applications and consequently are of high importance. Rather than focusing on optimization of the algorithm itself, assuming specific measures, the paper assumes a general scheme for finding similarity measures for all pairs of vectors and investigates optimizations for scalability in a hybrid Intel Xeon/Xeon Phi system. Hybrid systems including multicore CPUs and many-core compute devices such as Intel Xeon Phi allow parallelization of such computations using vectorization but require proper load balancing and optimization techniques. The proposed implementation uses C/OpenMP with the offload mode to Xeon Phi cards. Several results are presented: execution times for various partitioning parameters such as batch sizes of vectors being compared, impact of dynamic adjustment of batch size, overlapping computations and communication. Execution times for comparison of all pairs of vectors are presented as well as those for which similarity measures account for a predefined threshold. The latter makes load balancing more difficult and is used as a benchmark for the proposed optimizations. Results are presented for the native mode on an Intel Xeon Phi, CPU only and the CPU $$+$$ offload mode for a hybrid system with 2 Intel Xeons with 20 physical cores and 40 logical processors and 2 Intel Xeon Phis with a total of 120 physical cores and 480 logical processors. [ABSTRACT FROM AUTHOR]

The Economic Load Dispatch (ELD) problem is an essential aspect of power system planning and operational scheduling. Different techniques and algorithms have been recommended to solve it, aiming to minimize the cost of power generation with satisfying the load requirements. In this paper, a new algorithm called Electric Fish Optimization (EFO) is used to solve the ELD problem by considering the line losses, ramp rate limits, maximum and minimum capacities of the generators and prohibited operating zones (POZ). The algorithm has been utilized in test systems consisting of 6 and 15 units and its outcomes have been compared to those from previous research studies. The proposed algorithm has been shown to achieve minimum cost, indicating its superiority and effectiveness in addressing power system planning challenges. It is evident that the presented algorithm offers a valuable solution for optimizing ELD problems. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including research on algorithm for parallel and distribution system, empirical installation of linear algebra for auto-tuning and identification of parameters of metabolic network.

Recently, it has been shown that the stability of the least mean fourth (LMF) algorithm depends on the nonstationarity of the plant. The present paper investigates the possibility of overcoming this problem by normalization of the weight vector update term. A rigorous mean-square stability analysis is provided for a recent normalized LMF algorithm, which is normalized by a term that is second order in the estimation error and fourth order in the regressor. The analysis is done for a Markov plant with a stationary white input with even probability density function and a stationary zero-mean white noise. It is proved that the mean-square deviation (MSD) of the algorithm is bounded for all finite values of the input variance, noise variance, initial MSD, and mean-square plant parameter increment. Analytical results are supported by simulations. [ABSTRACT FROM PUBLISHER]

In this paper, efficient deployment algorithms are proposed for a mobile sensor network to improve the coverage area. The proposed algorithms find the target position of each sensor iteratively, based on the existing coverage holes in the network. The multiplicatively weighted Voronoi (MW-Voronoi) diagram is used to discover the coverage holes corresponding to different sensors with different sensing ranges. Three sensor deployment algorithms are provided, which tend to either move sensors out of densely packed areas or place them in proper positions with respect to the boundaries of the MW-Voronoi regions. Under the proposed procedures, the sensors move in such a way that the coverage holes in the target field are reduced. Simulations confirm the effectiveness of the deployment algorithms proposed in this paper. [ABSTRACT FROM PUBLISHER]

Copyright of Revista Internacional de Pensamiento Político is the property of Revista Internacional de Pensamiento Politico - Universidad Pablo de Olavide and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents the system and algorithm designs of a real-time automated integrated circuit (IC) marking inspection system based on the embedded platform. Specifically, the system-level design and the integration of hardware and software components are illustrated in this paper. Furthermore, in order to enhance the accuracy of IC inspection, this paper presents a novel algorithm which integrates the classic template matching approach with an efficient angle estimation method, so that the rotation and location of the IC chip can be identified precisely. Formal outline of the proposed algorithm is given in this paper. Moreover, aiming at reducing the computation time, the algorithmic optimizations based on the multi-core embedded processor and the single instruction, multiple data architecture are presented. The experiment results show that, compared to the conventional IC marking inspection algorithm, the proposed system and algorithm greatly improve the efficiency and accuracy of image processing on the embedded system. In particular, when the size of the target image is $640\times480$ pixels and size of the template image is 80 $\times $ 100, the average inspection time is 31 ms, which is an approximately $20{\times }$ improvement from the conventional inspection scheme. [ABSTRACT FROM AUTHOR]

Conventional power system security analysis based on multiple case studies cannot predict all operational states and hence neither guarantees an adequate security margin nor an economical operation for the power system. Based on the trend analysis method which is used in the field of economics, this paper introduces the concept and a methodology for “trend security analysis” of power systems. This method utilizes the load profile forecast and the contingency occurrence probability and determines the system security trend in the subsequent time window. Based on a recursive algorithm which is developed by utilizing the higher order derivatives of power flow equations, this paper also presents a method for fast determination of the trend variations of the system states and security indices. Three IEEE test systems are used to demonstrate the applications of the proposed concepts, evaluate their performance and verify their accuracy. [ABSTRACT FROM PUBLISHER]

This paper describes a simple approach for model-based template matching, which is robust to undergo rotation and scaling variations. An efficient image warping scheme spiral aggregation image (SAI), which has been utilized in this paper, provides a method for generating projection profiles for matching. In addition, it determines the rotation angle and is invariant to scale changes. The proposed spiral projection algorithm (SPA) for template matching enables the simultaneous representation for each value of projection profile, obtained through SAI, and provides structural and statistical information on the template. The experimental evaluation shows that the proposed SPA achieves very attractive results for template matching in the industrial automation application. [ABSTRACT FROM PUBLISHER]

In spaceborne synthetic aperture radar, the sliding spotlight mode can acquire high resolution and large azimuth scene size simultaneously. However, when the resolution is very high and the azimuth scene size is large, the traditional hyperbolic range model (HRM) is inaccurate and the variation of the equivalent velocity in azimuth dimension cannot be ignored. Thus, the traditional imaging algorithms based on HRM are no longer available. For this problem, this paper proposes an equivalent acceleration range model, which can precisely take into account the spaceborne curved orbit. Then, velocity scaling algorithm based on this new range model is proposed to meet the needs of very high resolution and large azimuth scene size. The results of the simulation validate the effectiveness of the new range model and the imaging algorithm. [ABSTRACT FROM PUBLISHER]

This paper presents a novel approach for time-varying parameter adaptive throttle and brake control for vehicle speed tracking. A control algorithm has been developed based on a linearized longitudinal vehicle model with characteristic lumped parameters. The lumped parameters are slowly time varying, except when a vehicle experiences gear shift. Combined parameter adaptation and throttle/brake control algorithm have been developed. The performance of the proposed control algorithm has been evaluated via simulations and vehicle tests. Since the proposed control algorithm has been designed using a generic form of the vehicle model, it can be implemented for different classes of vehicles with no information about the vehicle powertrain and the brake system. It has been shown from both simulations and vehicle tests that the vehicle speed tracking performance is robust to external disturbance. [ABSTRACT FROM PUBLISHER]

Smart scheduling can be used to reduce infrastructure-to-vehicle energy costs in delay-tolerant vehicular networks. In this paper, we show that, by combining this with vehicle-to-vehicle (V2V) forwarding, downlink (DL) traffic schedules can be generated, whose energy costs are lower than that in the single-hop case. This is accomplished by having the roadside units (RSUs) dynamically forward packets through vehicles, which are in energy-favorable locations. This paper considers both constant bit rate (CBR) and variable bit rate (VBR) air-interface options. We first derive offline schedulers for the DL RSU energy usage when V2V forwarding is added to RSU-to-vehicle communication. Both in-channel and off-channel forwarding cases are considered. The CBR and VBR cases are obtained using integer linear programming (ILP) and time-expanded graph (TEG) formulations, respectively. These schedulers provide lower bounds on energy performance and are used for comparisons with a variety of proposed online scheduling algorithms. The first algorithm is based on a greedy local optimization (GLOA). A version of this algorithm, which uses a minimum-cost flow graph (MCFG) scheduler, is also introduced. A more sophisticated algorithm is then proposed, which is based on a finite-window group optimization (FWGO). Results from various experiments show that the proposed algorithms can generate traffic schedules with much improved DL energy requirements compared with the case where V2V packet forwarding is not used. The performance improvements are particularly strong when under heavy loading conditions and when the variation in vehicle communication requirements or vehicle speed is high. Results that compare the proposed algorithms with conventional nonenergy-aware schedulers are also presented. [ABSTRACT FROM PUBLISHER]

Online social networks have become increasingly popular. One of the interesting applications is the friend locator, in which the application server informs a user through a mobile device if some of his/her listed friends are close enough in terms of geographical locations. However, in such services, it is challenging to protect the privacy of the individual users. Previous solutions for the friend locator do not guarantee a high level of privacy and do not maintain efficiency. In this paper, we propose a dual-resolution system structure to guarantee both strong privacy and efficiency. Additionally, we use the polygon decomposition method to achieve both accuracy and flexibility. To be more specific, in the coarse resolution level, each regular mobile user uploads his/her encrypted coarse location information to a central server periodically for comparisons. If a regular mobile user is found to be in the same grid block as an active mobile user, then the friend locator procedure with a higher resolution level will be conducted. Finally, through numerical analysis and simulations, we show that the proposed system design and algorithm can achieve high privacy, efficiency, accuracy, and flexibility. [ABSTRACT FROM PUBLISHER]

The paper is concerned with distributed learning in large-scale games. The well-known fictitious play (FP) algorithm is addressed, which, despite theoretical convergence results, might be impractical to implement in large-scale settings due to intense computation and communication requirements. An adaptation of the FP algorithm, designated as the empirical centroid fictitious play (ECFP), is presented. In ECFP players respond to the centroid of all players’ actions rather than track and respond to the individual actions of every player. Convergence of the ECFP algorithm in terms of average empirical frequency (a notion made precise in the paper) to a subset of the Nash equilibria is proven under the assumption that the game is a potential game with permutation invariant potential function. A more general formulation of ECFP is then given (which subsumes FP as a special case) and convergence results are given for the class of potential games. Furthermore, a distributed formulation of the ECFP algorithm is presented, in which, players endowed with a (possibly sparse) preassigned communication graph, engage in local, non-strategic information exchange to eventually agree on a common equilibrium. Convergence results are proven for the distributed ECFP algorithm. [ABSTRACT FROM PUBLISHER]

In this paper we address the problem of finding the most probable state of a discrete Markov random field (MRF), also known as the MRF energy minimization problem. The task is known to be NP-hard in general and its practical importance motivates numerous approximate algorithms. We propose a submodular relaxation approach (SMR) based on a Lagrangian relaxation of the initial problem. Unlike the dual decomposition approach of Komodakis et al. [29] SMR does not decompose the graph structure of the initial problem but constructs a submodular energy that is minimized within the Lagrangian relaxation. Our approach is applicable to both pairwise and high-order MRFs and allows to take into account global potentials of certain types. We study theoretical properties of the proposed approach and evaluate it experimentally. [ABSTRACT FROM PUBLISHER]

The development of advanced techniques for process monitoring and fault diagnosis using both model-based and data-driven approaches has led to many practical applications. One issue that has not been considered in such applications is the ability to deal with key performance indicators (KPIs) that are only sporadically measured and with significant time delay. Therefore, in this paper, the data-driven design of diagnostic-observer-based process monitoring schemes is extended to include the ability to detect changes given infrequently measured KPIs. The extended diagnostic observer is shown to be stable and hence able to converge to the true value. The proposed method is tested using both Monte Carlo simulations and the Tennessee-Eastman problem. It is shown that although time delay and sampling time increase the detection delay, the overall effect can be mitigated by using a soft sensor. Furthermore, it is shown that the results are not strongly dependent on the sampling time, but do depend on the time delay. Therefore, the proposed soft-sensor-based monitoring scheme can efficiently detect faults even in the absence of direct process information. [ABSTRACT FROM PUBLISHER]

The improvement of high-through-put gene profiling based microarrays technology has provided monitoring the expression value of thousands of genes simultaneously. Detailed examination of changes in expression levels of genes can help physicians to have efficient diagnosing, classification of tumors and cancer's types as well as effective treatments. Finding genes that can classify the group of cancers correctly based on hybrid optimization algorithms is the main purpose of this paper. In this paper, a hybrid particle swarm optimization and genetic algorithm method are used for gene selection and also artificial neural network (ANN) is adopted as the classifier. In this work, we have improved the ability of the algorithm for the classification problem by finding small group of biomarkers and also best parameters of the classifier. The proposed approach is tested on three benchmark gene expression data sets: Blood (acute myeloid leukemia, acute lymphoblastic leukemia), colon and breast datasets. We used 10-fold cross-validation to achieve accuracy and also decision tree algorithm to find the relation between the biomarkers for biological point of view. To test the ability of the trained ANN models to categorize the cancers, we analyzed additional blinded samples that were not previously used for the training procedure. Experimental results show that the proposed method can reduce the dimension of the data set and confirm the most informative gene subset and improve classification accuracy with best parameters based on datasets. [ABSTRACT FROM AUTHOR]

The traditional Self-Organize Map (SOM) method is used for the arrangement of seabed nodes in this paper. If the distance between the nodes and the events is long, these nodes cannot be victory nodes and they will be abandoned, because they cannot move to the direction of events, and as a result they are not being fully utilized and are destroying the balance of energy consumption in the network. Aiming at this problem, this paper proposes an improved self-organize map algorithm with the introduction of the probability-selection mechanism in Gibbs sampling to select victory nodes, thus optimizing the selection strategy for victory nodes. The simulation results show that the Improved Self-Organize Map (ISOM) algorithm can balance the energy consumption in the network and prolong the network lifetime. Compared with the traditional self-organize map algorithm, the adopting of the improved self-organize map algorithm can make the event driven coverage rate increase about 3%. [ABSTRACT FROM AUTHOR]

A wirelength-driven placer without considering routability could introduce irresolvable routing-congested placements. Therefore, it is desirable to develop an effective routability-driven placer for modern mixed-size designs employing hierarchical methodologies for faster turnaround time. In this paper, we propose a novel routability-driven analytical placement algorithm for hierarchical mixed-size circuit designs. This paper presents a novel design hierarchy identification technique to effectively identify design hierarchies and guide placement for better wirelength and routability. The proposed algorithm optimizes routability from four major aspects: 1) narrow channel handling; 2) pin density; 3) routing overflow optimization; and 4) net congestion optimization. Routability-driven legalization and detailed placement are also proposed to further optimize routing congestion. Compared with the participating teams for the 2012 ICCAD Design Hierarchy Aware Routability-driven Placement Contest, our placer can achieve the best quality (both the average overflow and wirelength) and the best overall score (by additionally considering running time). [ABSTRACT FROM PUBLISHER]

An autonomous car is a self-driving vehicle that has the capability to perceive the surrounding environment and navigate itself without human intervention. For autonomous driving, complex autonomous driving algorithms, including perception, localization, planning, and control, are required with many heterogeneous sensors, actuators, and computers. To manage the complexity of the driving algorithms and the heterogeneity of the system components, this paper applies distributed system architecture to the autonomous driving system, and proposes a development process and a system platform for the distributed system of an autonomous car. The development process provides the guidelines to design and develop the distributed system of an autonomous vehicle. For the heterogeneous computing system of the distributed system, a system platform is presented, which provides a common development environment by minimizing the dependence between the software and the computing hardware. A time-triggered network protocol, FlexRay, is applied as the main network of the software platform to improve the network bandwidth, fault tolerance, and system performance. Part II of this paper will provide the evaluation of the development process and system platform by using an autonomous car, which has the ability to drive in an urban area. [ABSTRACT FROM PUBLISHER]

This paper describes a modification to a standard migration algorithm for land mine detection with a ground-penetrating radar (GPR) system. High directivity from the antenna requires a significantly large aperture in relation to the operating wavelength, but at the frequencies of operation of GPR, this would result in a large and impractical antenna. For operator convenience, most GPR antennas are small and exhibit low directivity and a wide beamwidth. This causes the GPR image to bear little resemblance to the actual target scattering centers. Migration algorithms attempt to reduce this effect by focusing the scattered energy from the source reflector and consequentially improve the target detection rate. However, problems occur due to the varying operational conditions, which result in the migration algorithm requiring vastly different calibration parameters. In order to combat this effect, this migration scheme stacks multiple versions of the same migrated data with different velocity values, whereas some other migration schemes only use a single velocity value. [ABSTRACT FROM PUBLISHER]

Polar codes are the first error-correcting codes to provably achieve channel capacity, asymptotically in code length, with an explicit construction. However, under successive-cancellation decoding, polar codes require very long code lengths to compete with existing modern codes. Nonetheless, the successive cancellation algorithm enables very-low-complexity implementations in hardware, due to the regular structure exhibited by polar codes. In this paper, we present an improved architecture for successive-cancellation decoding of polar codes, making use of a novel semi-parallel, encoder-based partial-sum computation module. We also provide quantization results for realistic code length N=2^15, and explore various optimization techniques such as a chained processing element and a variable quantization scheme. This design is shown to scale to code lengths of up to N=2^21, enabled by its low logic use, low register use and simple datapaths, limited almost exclusively by the amount of available SRAM. It also supports an overlapped loading of frames, allowing full-throughput decoding with a single set of input buffers. [ABSTRACT FROM PUBLISHER]

This paper presents an efficient method for extracting a yield-aware Pareto front between two competing metrics of an analog circuit block, with the purpose of performing hierarchical, system-level optimization using the component-level Pareto fronts as meta-models. The proposed method consists of three steps: finding a set of Pareto-optimal design points by tracing them on a discrete grid, estimating the yield distribution of each optimal design point using a control-variate technique, and constructing a yield-aware Pareto front by interpolation. The proposed algorithm is demonstrated on a problem of finding the optimal power allocation among the components composing a clock recovery path to minimize the final clock jitter. The algorithm can estimate the Pareto front of each circuit block within a 2% error, expressing the minimum achievable jitter with 99% yield for different power budgets, while requiring only \(600~\sim ~1100\) Monte-Carlo simulation samples in total. [ABSTRACT FROM PUBLISHER]

In this paper, the target patrolling problem was considered, in which a set of mobile data collectors, known as data mules (DMs), must efficiently patrol a given set of targets. Because the time interval (or visiting interval) between consecutive visits to each target reflects the degree to which that target is monitored, the goal of this paper was to balance the visiting interval of each target. This paper first presents the basic target points patrolling algorithm, which enables an efficient patrolling route to be constructed for numerous DMs, such that the visiting intervals of all target points are stable. For scenarios containing weighted target points, a weighted target points patrolling (W-TPP) algorithm is presented, which ensures that targets with higher weights have higher data collection frequencies. The energy constraint of each DM was also considered, and this paper presents a W-TPP with recharge (RW-TPP) algorithm, which treats the energy recharge station as a weighted target and arranges for DMs to visit the recharge station before running out of energy. The performance results demonstrated that the proposed algorithms outperformed existing approaches in average visiting frequency, DM movement distance, average quality of monitoring satisfaction rate, and efficiency index. [ABSTRACT FROM PUBLISHER]

In this paper, a real-time vehicle behavior analysis system is presented, which can be used in traffic jams and under complex weather conditions. In recent years, many works based on background estimation and foreground extraction for traffic event detection have been reported. In these studies, the vehicle images need to be accurately segmented, although uneven illumination, shadows, and vehicle overlapping are difficult to handle. The main contribution of this paper is to make a point tracking system for vehicle behavior analysis without a difficult image segmentation procedure. In the proposed system, feature points are extracted using an improved Moravec algorithm. A specially designed template is used to track the feature points through the image sequences. Then, trajectories of feature points can be obtained, whereas unqualified track trajectories are removed using decision rules. Finally, the vehicle behavior analysis algorithms are applied on the track trajectories for traffic event detection. The proposed system has been used widely by Chinese highway management departments. The application performances show that the newly developed system and its algorithms are robust enough for vehicle behavior analysis under complex weather conditions. [ABSTRACT FROM PUBLISHER]

Legacy IEEE 802.11 does not efficiently support multicast transmissions. To cope with the increasing demand for multicast, which is mainly required to deliver multimedia traffic, the IEEE 802.11aa Task Group has recently standardized new mechanisms for allowing efficient and robust transmission of multicast flows in wireless local area networks (WLAN). However, the standard allows the use of different mechanisms for this purpose, and leaves open the choice of which one to use for a given scenario. In this paper, we present an analytical model for evaluating the performance of the mechanisms included in the 802.11aa standard and then compare their performance. Our analysis shows that there is no absolute winner out of these mechanisms and performance strongly depends on the scenario. Building on our model, we then propose a novel algorithm that selects the best multicast mechanism to use as a function of the scenario conditions. Our results are validated by extensive simulations. [ABSTRACT FROM PUBLISHER]

The design of efficient and distributed scheduling algorithms is essential to garner the full potential of cognitive radio networks (CRNs). In this paper, we propose a distributed orthogonal frequency-division multiplexing (OFDM)-based scheduling algorithm, which is called the collision-queue-regulated algorithm, that aims to limit the collision rate to a level imposed by primary users of a CRN. Via a novel equivalent queue system analysis, we prove that the proposed algorithm can achieve at least a constant fraction of the capacity region in the many-channel regime, and our numerical studies indicate that the proposed distributed collision-queue-regulated algorithm achieves a throughput very close to that achievable by a centralized throughput-optimal back-pressure-based scheduling algorithm. [ABSTRACT FROM PUBLISHER]

Untethered vehicular roadside infrastructure will eventually be deployed using various energy sustainable designs. In the solar-powered case, cost-effective nodes must be provisioned with a combination of a solar panel and a battery, which is sufficient to prevent future node outage. During the network design phase, a bandwidth usage profile (BUP) is assumed, and historical solar insolation traces are used to determine the minimum-cost provisioning that is required for each network node. In practical systems, however, there are usually restrictions in the way that the nodes can be positioned, and this results in a time-varying and node-dependent attenuation of the available solar energy. Unfortunately, conventional resource provisioning methods cannot take this into account; therefore, the deployed system may be unnecessarily expensive. In this paper, the resource provisioning problem is considered from this point of view. We first review conventional resource provisioning mechanisms and give an example that shows the value of introducing positional solar insolation awareness. A provisioning algorithm is then introduced that takes known positional variations into consideration when performing the energy provisioning. A variety of results are then presented, which show that reductions in total network provisioning cost can be obtained using the proposed methodology, compared with conventional algorithms. The proposed algorithm also performs very well compared with a linear programming formulation, which gives lower bounds on the node resource provisioning cost assignments. [ABSTRACT FROM PUBLISHER]

This paper is concerned with an online tethered coverage (TC), in which a mobile robot of size D is attached to a fixed point S by a cable of finite length L. Starting at S, the robot has to cover an unknown planar environment that contains obstacles and return to S with the cable fully retracted. The paper first establishes an optimal offline TC methodology, then introduces the TC algorithm that performs an online TC using position and local obstacle detection sensors. The performance of the TC algorithm is measured by its competitiveness, determined by measuring its total online path length l relative to the optimal offline solution l\rm{opt}. The paper establishes that the TC algorithm has a competitive performance of l \!\le \! 2L\over D l\rm{opt}. The paper additionally establishes a lower bound of l \!\ge \! \log (L\over D) l\rm{opt} over a generic family of TC algorithms of which the TC algorithm is a special case. Execution example and experiments with a tethered recoiling mechanism illustrate the usefulness of the TC algorithm. [ABSTRACT FROM PUBLISHER]

This paper discusses the design and usage of a Simulink-based phasor measurement unit (PMU) simulator package. The simulator and PMU data is available upon request from the first author. The motivation for developing the package is to provide a flexible environment for teaching phasor measurement and frequency estimation to advanced undergraduate and graduate students, and providing a knowledge base to students for research. This software allows for the exploration of the algorithms and phenomena involved in the phasor estimation process. The package is built on the Simulink platform and is able to process simulated signals as well as measured point-on-wave data. The popularity and relevance of the proposed package is evaluated using formal questioners. [ABSTRACT FROM PUBLISHER]

This paper considers base station cooperation (BSC) strategies for the uplink of a multiuser multicell high-frequency reuse scenario where distributed iterative detection (DID) schemes with soft/hard interference cancelation (IC) algorithms are studied. The conventional distributed detection scheme exchanges soft-symbol estimates with all cooperating BSs. Since a large amount of information needs to be shared via the backhaul, the exchange of hard bit information is preferred; however, performance degradation is experienced. In this paper, we consider a reduced message passing (RMP) technique in which each BS generates a detection list with the probabilities for the desired symbol that are sorted according to their values in a decreasing order. The network then selects the best detection candidates from the lists and conveys the index of the constellation symbols (instead of double-precision values) among the cooperating cells. The proposed DID-RMP achieves intercell interference (ICI) suppression with low backhaul traffic overhead compared with the conventional soft bit exchange and outperforms previously reported hard/soft information exchange algorithms. [ABSTRACT FROM PUBLISHER]

Sparse representation has been widely used in image classification. Sparsity-based algorithms are, however, known to be time consuming. Meanwhile, recent work has shown that it is the collaborative representation (CR) rather than the sparsity constraint that determines the performance of the algorithm. We therefore propose a nonlocal joint CR classification method with a locally adaptive dictionary (NJCRC-LAD) for hyperspectral image (HSI) classification. This paper focuses on the working mechanism of CR and builds the joint collaboration model (JCM). The joint-signal matrix is constructed with the nonlocal pixels of the test pixel. A subdictionary is utilized, which is adaptive to the nonlocal signal matrix instead of the entire dictionary. The proposed NJCRC-LAD method is tested on three HSIs, and the experimental results suggest that the proposed algorithm outperforms the corresponding sparsity-based algorithms and the classical support vector machine hyperspectral classifier. [ABSTRACT FROM PUBLISHER]

This paper presents a new graphical processing technique for fast computation of PO surface integral. In contrast with the original graphical processing approach introduced by the authors in 1993, the new one combines a novel shadowing computation algorithm together with the conventional facet-based Gordon's formula, instead of the pixel-based Asvestas' approximation. The resulting hybrid approach needs more CPU power for very complex radar targets, but is free from the pixel discretization noise inherent to graphical processing. It has the same accuracy as conventional Physical Optics computation, but shadowed facets detection is more than 10 times faster than with the most efficient alternative algorithms of \cal O(N\log N) computational cost. [ABSTRACT FROM PUBLISHER]

Rapid and accurate timing synchronization is a critical task in ultrawideband (UWB) systems. Yang and Giannakis introduced a promising algorithm, i.e., data-aided timing with dirty templates (TDT), which is known for low complexity and relaxed operation conditions in the presence of unknown time hopping and multipath channels. In this paper, we will explore the optimality of TDT. We develop a maximum-likelihood (ML) timing algorithm and obtain its optimum training sequence. It is shown that the optimum training sequence of the ML timing estimator coincides with that of the TDT algorithm. In addition, we prove that the ML algorithm can be simplified using this training sequence and that the simplified ML (SML) is equivalent to TDT. [ABSTRACT FROM PUBLISHER]

The Warp code (and its framework of associated tools) was initially developed for particle-in-cell simulations of space-charge-dominated ion beams in accelerators, for heavy-ion-driven inertial fusion energy, and related experiments. It has found a broad range of applications, including nonneutral plasmas in traps, stray electron clouds in accelerators, laser-based acceleration, and the focusing of ion beams produced when short-pulse lasers irradiate foil targets. We summarize novel methods used in Warp, including: time-stepping conducive to diagnosis and particle injection; an interactive Python-Fortran-C structure that enables scripted and interactive user steering of runs; a variety of geometries (3-D $x$ , $y$ , $z$ ; 2-D $r$ , $z$ ; 2-D $x$ , $y$ ); electrostatic and electromagnetic field solvers; a cut-cell representation for internal boundaries; the use of warped coordinates for bent beam lines; adaptive mesh refinement, including a capability for time-dependent space-charge-limited flow from curved surfaces; models for accelerator lattice elements (magnetic or electrostatic quadrupole lenses, accelerating gaps, etc.) at user-selectable levels of detail; models for particle interactions with gas and walls; moment/envelope models that support sophisticated particle loading; a drift-Lorentz mover for rapid tracking through regions of strong and weak magnetic field; a Lorentz-boosted frame formulation with a Lorentz-invariant modification of the Boris mover; an electromagnetic solver with tunable dispersion and stride-based digital filtering; and a pseudospectral electromagnetic solver. Warp has proven useful for a wide range of applications, described very briefly herein. It is available as an open-source code under a BSD license. This paper describes material presented during the Prof. Charles K. (Ned) Birdsall Memorial Session of the 2013 IEEE Pulsed Power and Plasma Science Conference. In addition to our overview of the computational methods used in Warp, we summarize a few aspects of Ned's contributions to plasma simulation and to the careers of those he mentored. [ABSTRACT FROM PUBLISHER]

Environmental factors, such as weather, trees, and animals, are major causes of power outages in electric utility distribution systems. Of these factors, wind and lightning have the most significant impacts. The objective of this paper is to investigate models to estimate wind and lighting related outages. Such estimation models hold the potential for lowering operational costs and reducing customer downtime. This paper proposes an ensemble learning approach based on a boosting algorithm, AdaBoost^+, for estimation of weather-caused power outages. Effectiveness of the model is evaluated using actual data, which comprised of weather data and recorded outages for four cities of different sizes in Kansas. The proposed ensemble model is compared with previously presented regression, neural network, and mixture of experts models. The results clearly show that AdaBoost^+ estimates outages with greater accuracy than the other models for all four data sets. [ABSTRACT FROM PUBLISHER]

This paper presents the solution algorithm and test results of the market-splitting approach, proposed in the first part of this two-paper series, for the clearing of the forthcoming Internal Electricity Market. The market-splitting approach is implemented in a Europe-wide level, indicatively defining three power pools and 22 power exchanges, comprising 42 bidding areas in total, with each local/national market respecting the standard market regulatory framework of power pools and power exchanges (PXs). The performance and computational requirements of this implementation is presented; the model is tested in terms of problem solvability to its limits, considering a day-ahead market clearing time threshold equal to one hour. The results demonstrate that a viable solution to the clearing of the Internal Electricity Market can be attained with the current state-of-the-art of computers' processing power. Further discussion is made concerning the viability of the proposed approach and other harmonization issues that should be considered for successful market integration. [ABSTRACT FROM PUBLISHER]

Automatic test pattern generation (ATPG) is one of the first applications that motivated the development of modern Boolean satisfiability (SAT). It is now widely accepted that ATPG is easy for current state-of-the-art SAT solvers. Nevertheless, as with any NP-hard problem, for large complex industrial circuits, some faults may be difficult to detect or prove undetectable. Recent work on SAT-based ATPG has been motivated by industrial designs with ever increasing size, for which more efficient ATPG models are essential. Moreover, ATPG models and algorithms find applications in a number of other settings, which further motivate the development of more efficient SAT-based ATPG solutions. Interestingly, despite the interest in more efficient ATPG approaches, the core SAT-based ATPG model has remained essentially unchanged since it was first proposed in the 1980s. This paper describes an alternative model for SAT-based ATPG. The proposed model is fundamentally different from previous SAT-based ATPG models in that the number of used variables is significantly reduced. This paper proposes extensions and optimizations to the basic model, and integrates known techniques for further improving performance. This paper extends the new model, proposes optimizations to it, and integrates known techniques for further improving performance. To achieve an unbiased evaluation, this paper also reimplements previous models and comprehensively compares them with the proposed models. Experimental results, obtained on a wide range of publicly available benchmarks for ATPG, demonstrate that the basic model and extended models allow significant performance improvements over other well-established models. [ABSTRACT FROM PUBLISHER]