Search

The quality of an ultrasonic array image depends on accurate information about its acoustic properties. Inaccurate acoustic properties can cause image degradation such as blurring, mislocation of reflectors, and the introduction of artifacts. In this paper, for the specific case of an inhomogeneous and anisotropic austenitic steel weld, Monte Carlo Markov Chain (MCMC) inversion is used to estimate unknown acoustic properties from array data. The approach uses active beacons that transmit ultrasound through the anisotropic weld; the ultrasound is then captured by a receiving array. A forward model of the ultrasonic array data is then optimized with respect to the experimental data using an MCMC inversion. The result of this process is the extraction of a material property map that describes the anisotropy distribution within the weld region. These extracted material properties are then used within an imaging algorithm?the total focusing method in this paper?to produce autofocused images. This MCMC inversion approach is first applied to simulated data to test the convergence, robustness, and accuracy of the method and its implementation. The extracted weld map is used to show improved imaging of defects within the weld, relative to an image formed assuming a constant velocity. Finally, the MCMC inversion approach is used on experimental data from a 110-mm-thick steel plate containing an austenitic weld. Here the extracted weld map is used to show that defect location errors of greater than 5 mm are reduced to around 2 mm when the extracted weld map is used [ABSTRACT FROM PUBLISHER]

This paper addresses the protection of a high voltage direct current (HVDC) transmission system, utilizing the modular multilevel converter (MMC) topology in addition to incorporating a hybrid transmission corridor (transmission line including overhead line and cable sections). A solution is proposed for identifying the section within which a dc fault is located for the purpose of maintaining power delivery. A detailed model of the MMC-HVDC system is simulated using PSCAD and an in depth fault analysis is performed. A characteristic signal is discovered and then implemented into a novel solution. The end result is a fault section identification protection algorithm, implementing protective relay coordination to protect the system from false circuit breaker reclose as well as enabling fast system restart for nonpermanent faults. This restart protection algorithm is implemented without the use of a communications channel between converter stations, introducing novelty and quick restart response. [ABSTRACT FROM PUBLISHER]

For an orthogonal frequency-division multiplexing (OFDM) system over a doubly selective channel, a large number of pilot subcarriers are needed to estimate the numerous channel parameters, resulting in low-spectral efficiency. In this paper, by exploiting temporal correlation of practical wireless channels, we propose a highly efficient structured distributed compressive sensing-based joint multisymbol channel estimation scheme. Specifically, by using the complex exponential basis expansion model (CE-BEM) and exploiting the sparsity in the delay domain within multiple OFDM symbols, we turn to estimate jointly sparse CE-BEM coefficient vectors rather than numerous channel taps. Then a sparse pilot pattern within multiple OFDM symbols is designed to obtain an ICI-free structure and transform the channel estimation problem into a joint-block-sparse model. Next, a novel block-based simultaneous orthogonal matching pursuit algorithm is proposed to jointly recover coefficient vectors accurately. Finally, to reduce the CE-BEM modeling error, we carry out smoothing treatments of already estimated channel taps via piecewise linear approximation. Simulation results demonstrate that the proposed channel estimation scheme can achieve higher estimation accuracy than conventional schemes, although with a smaller number of pilot subcarriers. [ABSTRACT FROM AUTHOR]

Shamir's secret sharing scheme is an effective way to distribute secret to a group of shareholders. The security of the unprotected sharing scheme, however, can be easily broken by cheaters or attackers who maliciously feed incorrect shares during the secret recovery stage or inject faults into hardware computing the secret. In this paper, we propose cheater detection and identification schemes based on robust and algebraic manipulation detection (AMD) codes and m-disjunct matrices (superimposed codes). We present the constructions of codes for cheater detection and identification and describe how the cheater identification problem can be related to the classic group testing algorithms based on m-disjunct matrices. Simulation and synthesis results show that the proposed architecture can improve the security level significantly even under strong cheating attack models with reasonable area and timing overheads. [ABSTRACT FROM PUBLISHER]

A main problem in autonomous vehicles in general, and in unmanned aerial vehicles (UAVs) in particular, is the determination of the attitude angles. A novel method to estimate these angles using off-the-shelf components is presented. This paper introduces an attitude heading reference system (AHRS) based on the unscented Kalman filter (UKF) using the three-axis attitude determination (TRIAD) algorithm as the observation model. The performance of the method is assessed through simulations and compared to an AHRS based on the extended Kalman filter (EKF). The paper presents field experiment results using a real fixed-wing UAV. The results show good real-time performance with low computational cost in a microcontroller. [ABSTRACT FROM PUBLISHER]

In substations with an inner bridge connection, the two main transformers are generally connected in parallel. The differential protection of the transformer, under normal operating conditions, may maloperate when the other transformer is switching on through the bridge breaker after scheduled maintenance. This paper analyzes the factors that may lead to the maloperation of transformer differential protection and concludes that local transient saturation of the current transformer (CT) caused by the decaying dc component in the magnetizing inrush is the main cause of the maloperation of the transformer differential protection. By detecting the time difference between the instant of sudden changes occurring in the transformer wye-side current and the instant of the differential current increasing, a new algorithm based on the time differential method is proposed in this paper, which can prevent the maloperation of transformer differential protection in substations with an inner bridge connection. On the basis of detailed analysis and simulation work, the new algorithm is verified to be applicable under the conditions of sympathetic inrush and CT saturation. [ABSTRACT FROM AUTHOR]

In this paper, the noise reduction property of type-2 fuzzy logic (FL) systems (FLSs) (T2FLSs) that use a novel type-2 fuzzy membership function is studied. The proposed type-2 membership function has certain values on both ends of the support and the kernel and some uncertain values for the other values of the support. The parameter tuning rules of a T2FLS that uses such a membership function are derived using the gradient descend learning algorithm. There exist a number of papers in the literature that claim that the performance of T2FLSs is better than type-1 FLSs under noisy conditions, and the claim is tried to be justified by simulation studies only for some specific systems. In this paper, a simpler T2FLS is considered with the novel membership function proposed in which the effect of input noise in the rule base is shown numerically in a general way. The proposed type-2 fuzzy neuro structure is tested on different input–output data sets, and it is shown that the T2FLS with the proposed novel membership function has better noise reduction property when compared to the type-1 counterparts. [ABSTRACT FROM AUTHOR]

This paper introduces a new noniterative fault-location algorithm for long transmission lines compensated by a series flexible ac transmission systems (FACTS) device. In the proposed algorithm, synchronous voltage and current samples from both ends of the transmission line are used and the distributed parameter line model in the time domain is applied. Due to the difficulties in the modeling of the series FACTS devices during a fault, the presented method does not use the model of the series devices. In this paper, the fault-location problem is converted to an optimization one in which the location and resistance of the fault are the decision variables. The proposed method includes three stages. Two fault locations are obtained, corresponding to the first two stages. In stage three, the obtained results from stages 1 and 2 are compared, and the correct fault location is selected. The proposed algorithm is a noniterative one and it is suitable for transmission lines compensated by any series FACTS device which could have the overvoltage protection and can be in any operating mode. The performance of the proposed algorithm is evaluated under different structural and fault conditions. The simulation results confirm the accuracy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The selection of optimum parameters in electromagnetic design usually requires optimization of multimodal, nonlinear functions. This leads to extensive calculations which pose a huge inconvenience in the design process. This paper proposes a novel algorithm for dealing efficiently with this issue. The proposed algorithm interprets the problem as an unexplored terrain for climbing. Through the use of contour line concept coupled with Kriging, the algorithm finds out all the peaks in the problem domain with as few function calls as possible. The efficiency of the proposed algorithm is demonstrated by application to conventional test functions. In this paper, the simulation results show that skewing does not necessarily reduce the cogging torque but may cause it to increase for certain pole-arc to pole-pitch ratio. The developed algorithm is applied to the magnet shape optimization of an axial flux permanent magnet synchronous machine and the cogging torque was reduced to 79.8% of the initial one. [ABSTRACT FROM AUTHOR]

Electrical impedance tomography (EIT) is an imaging modality of determining imaging the interior conductivity distribution within an object. In the paper, generalized back projection algorithm (GBPA) is proposed for EIT image reconstruction. The algorithm is based on node back projection algorithm (NBPA), and extends projection range to arbitrary part of the object. To evaluate the proposed method, GBPA is first applied to image reconstruction using a simulated cylinder model. Compared with conventional back projection algorithm (BPA) and NBPA, the images reconstructed by GBPA are more accurate in terms of several metrics used in the paper. In addition, we present image reconstruction results using a 128-channel EIT system to demonstrate the performance of GBPA in real experiments. [ABSTRACT FROM PUBLISHER]

This paper considers the problem of state estimation in autonomous navigation from a theoretical perspective. In particular, the investigation concerns problems where the information provided by the sensor data is not sufficient to carry out the state estimation (i.e., the state is not observable). For these systems, the concept of continuous symmetry is introduced. Detection of the continuous symmetries of a given system has a very practical importance. It allows the detection of an observable state whose components are nonlinear functions of the original nonobservable state. So far, this theoretical and very general concept has been applied to deal with two distinct fundamental estimation problems in the framework of mobile robotics. The former is in the framework of self-calibration, and the latter is in the framework of the fusion of the data provided by inertial sensors and vision sensors. For reasons of length, only the former is discussed. In particular, the theoretical machinery is used to address a specific calibration problem. The solution constrains the robot to move along specific trajectories in order to be able to apply the calibration algorithm. This paper provides two distinct contributions. The first is the introduction of this concept of continuous symmetry. The second is the introduction of a simple and efficient strategy to extrinsically calibrate a bearing sensor (e.g., a vision sensor) mounted on a vehicle and, simultaneously, estimate the parameters describing the systematic error of its odometry system. Many accurate simulations and real experiments show the robustness, the efficiency, and the accuracy of the proposed strategy. [ABSTRACT FROM PUBLISHER]

This paper deals with subspace-based target detection in hyperspectral images. Specifically, it focuses on a general detection scheme where, first, background is suppressed through orthogonal-subspace projection and then target detection is accomplished. An adequate estimation of the background subspace is essential to a successful outcome. The background subspace has been typically estimated globally. However, global approaches may be ineffective for small-target-detection applications since they tend to overestimate the background interference affecting a given target. This may result in a low target residual energy after background suppression that is detrimental to detection performance. In this paper, we propose a novel and fully automatic algorithm for local background-subspace estimation (LBSE). Local background has typically a lower inherent complexity than that of global background. By estimating the background subspace over a local neighborhood of the test pixel, the resulting background-subspace dimension is expected to be low, thus resulting in a higher target residual energy after suppression which benefits the detection performance. Specifically, the proposed LBSE acts on a per-pixel basis, thus adaptively tailoring the estimated basis to the local complexity of background. Both simulated and real hyperspectral data are employed to investigate the detection-performance improvements offered by LBSE with respect to both global and local methodologies previously presented. [ABSTRACT FROM AUTHOR]

This paper presents an online procedure for the automatic search of the maximum-torque-per-ampere operating region for a synchronous reluctance motor. The algorithm is based on a signal-injection method with a random-based perturbation pattern applied to a common direct-torque-controlled drive. Among motor parameters, only the stator resistance is required to perform the automatic procedure. Simulations and experimental results are presented in the paper, demonstrating the benefits of the proposed algorithm. The solution is easily extended to any ac drive. [ABSTRACT FROM PUBLISHER]

This paper presents a dynamic predictive-optimization framework of a nonlinear temporal process. Data-mining (DM) and evolutionary strategy algorithms are integrated in the framework for solving the optimization model. DM algorithms learn dynamic equations from the process data. An evolutionary strategy algorithm is then applied to solve the optimization problem guided by the knowledge extracted by the DM algorithm. The concept presented in this paper is illustrated with the data from a power plant, where the goal is to maximize the boiler efficiency and minimize the limestone consumption. This multiobjective optimization problem can be either transformed into a single-objective optimization problem through preference aggregation approaches or into a Pareto-optimal optimization problem. The computational results have shown the effectiveness of the proposed optimization framework. [ABSTRACT FROM AUTHOR]

This paper provides an integrated algorithm to identify a cutset for a large power system for the application of a slow coherency based controlled islanding scheme. Controlled islanding is employed as a corrective measure of last resort to prevent cascading outages caused by large disturbances. The large scale power system is represented as a graph and a simplification algorithm is used to reduce the complexity of the system. Generators belonging to the same slowly coherent group are collapsed into a dummy node, and a graph partition library is used to split the graph into a given number of parts. Some extra islands formed by the partition library are merged into their adjacent large islands and the original cutset of the actual power system is recovered from the highly simplified graph. A software package was developed to test the efficiency of the algorithm, and dynamic simulations were run on the WECC system to verify the effectiveness of the cutset obtained. The WECC system has more than 15 000 buses and 2300 generators. Detailed steps to develop an islanding strategy for a specified contingency for a large system are described in this paper. [ABSTRACT FROM AUTHOR]

In this paper, a mathematical model for the beacon-enabled mode of the IEEE 802.15.4 medium-access control (MAC) protocol is provided. A personal area network (PAN) composed of multiple nodes, which transmit data to a PAN coordinator through direct links or multiple hops, is considered. The application is query based: Upon reception of the beacon transmitted by the PAN coordinator, each node tries to transmit its packet using the superframe structure defined by the IEEE 802.15.4 protocol. Those nodes that do not succeed in accessing the channel discard the packet; at the next superframe, a new packet is generated. The aim of the paper is to develop a flexible mathematical tool able to study beacon-enabled 802.15.4 networks organized in different topologies. Both the contention access period (CAP) and the contention-free period defined by the standard are considered. The slotted carrier-sense multiple access with collision avoidance (CSMA/CA) algorithm used in the CAP portion of the superframe is analytically modeled. The model describes the probability of packet successful reception and access delay statistics. Moreover, both star and tree-based topologies are dealt with; a suitable comparison between these topologies is provided. The model is a useful tool for the design of MAC parameters and to select the better topology. The mathematical model is validated through simulation results. The model differs from those previously published by other authors in the literature as it precisely follows the MAC procedure defined by the standard in the context of the application scenario described. [ABSTRACT FROM AUTHOR]

This paper presents a novel intelligent run-to-run control strategy for chemical-mechanical polishing (CMP) processes. With the help of the recursive least squares identification method for model building, a real-coded genetic algorithm is applied to adaptively adjust the discount coefficients for double exponentially weighted moving average (EWMA) controller. The online intelligent scheme can effectively prevent the CMP processes from reaching unstable condition and can thus achieve high control performance. To demonstrate the effectiveness and applicability of the proposed intelligent run-to-run control strategy, two typical case studies are worked out in this paper. Extensive simulation comparisons with traditional double EWMA run-to-run control were performed. The simulation results show that the proposed intelligent run-to-run control is able to achieve better control performance than conventional schemes, especially for a process that has nonlinearities, process noise, and extra large metrology delays. [ABSTRACT FROM AUTHOR]

This paper introduces Bayes risk (expected loss) as a criterion for linear equalization. Since the probability of error is equal to the Bayes risk (BR) for a particular binary loss function, this work is a natural generalization of previous works on minimum probability of error (PE) equalizers. Adaptive equalization algorithms are developed that minimize the BR. Like the minimum PE equalizers, the BR algorithms have low computational complexity which is comparable to that of the LMS algorithm. The advantage of the BR criterion is that the loss function can be specified in a manner that accelerates adaptive equalizer convergence relative to the minimum PE adaptive algorithm as illustrated in simulation examples. Besides introducing a new criterion, this paper provides another independent contribution to the field of PE minimizing equalization. While most prior works focus on M-ary QAM type modulations with rectangular decision regions, this paper uses upper bounds on the probabilities of certain events to yield tractable mathematics that apply to two-dimensional constellations with arbitrarily shaped decision regions. The resulting adaptive algorithm use the full information available in the phase of the error signal, whereas previous algorithms use a quantized version of this error phase. [ABSTRACT FROM AUTHOR]

This paper deals with an omega-K algorithm for focusing bistatic synthetic aperture radar (SAR) data. The key of the proposed algorithm is the derivation of a frequency mapping function by using Neo's method, of series reversion. The registration of the focused image onto the ground plane is discussed in detail, which is based on a conclusion that the targets whose instantaneous Doppler frequencies at zero azimuth time are the same as that of the reference target at zero azimuth time will all be focused at the reference target's azimuth position in the focused image. The range invariance region size, which the proposed algorithm can process, is also determined in this paper. The proposed algorithm can handle extreme bistatic configurations with wide apertures and large squint angles. The effectiveness of the proposed algorithm is confirmed by simulation results. Although developed for the azimuth-invariant case, the proposed algorithm can be readily extended to the azimuth-variant case, as long as we divide the SAR data into blocks in the azimuth direction so that each block of SAR data can be assumed quasi-stationary in the azimuth direction. [ABSTRACT FROM AUTHOR]

Abstract-Sensor position uncertainty is known to degrade significantly the source localization accuracy. This paper investigates the use of a single calibration emitter, whose position is known to the sensor array, to reduce the loss in localization accuracy due to sensor position errors that are random. Using a Gaussian noise model, we first derive the Cramér-Rao lower bound (CRLB) for a time difference of arrival (TDOA)-based source location estimate with the use of a calibration source. The differential calibration technique that is commonly used in Global Positioning System through the use of a calibration source to mitigate the inaccuracy in satellite ephemeris data is analyzed. The analysis indicates that differential calibration in most cases cannot reach the CRLB accuracy. The paper then proceeds to propose an algebraic closed-form solution for the source location estimate using both TDOA measurements from the unknown and the calibration source. The proposed algorithm is shown analytically, under high signal-to-noise ratio (SNR) and small sensor position noise, or under moderate level of SNR and sensor position noise together with distant unknown and calibration sources, to reach the CRLB accuracy. Simulations are used to corroborate and support the theoretical development. [ABSTRACT FROM AUTHOR]

The penetration level of wind energy is continuously growing, and it is especially relevant in European countries such as Denmark, Germany, and Spain. For this reason, grid codes in different countries have been recently revised, or are now under revision in order to integrate this energy in the network taking into account the security of supply. This paper is related to reactive compensation, which is one aspect usually included in these codes. On the other hand, a great number of installed wind parks are formed by fixed speed wind turbines equipped with induction generators. The typical scheme for reactive compensation in this kind of wind parks is based on capacitor banks locally controlled in each machine. This configuration makes very difficult to follow the requirements of the new grid codes. To overcome this problem, a configuration with a central controller that coordinates the actuation over all the capacitor steps in the wind park is proposed in this paper. A central controller algorithm that is based on a dynamic programming is presented and evaluated by means of simulation. At this time, the proposed scheme has been installed at the Sotavento Experimental Wind Park (Spain) and it is currently being tested. [ABSTRACT FROM AUTHOR]

The paper presents an admission approach for IPTV service providers that is designed to minimize QoS violations whilst effectively utilizing available bandwidth. Central to the approach is an empirical method of estimating the effective bandwidth required to satisfy QoS targets for admitted traffic flows. The paper describes this method and specifies two admission control algorithms based on the use of effective bandwidth estimates. The first algorithm employs a simple evaluation of whether there is sufficient bandwidth available to ensure, with an appropriate degree of confidence, that QoS targets will not be violated if a requested flow is admitted. The second algorithm utilizes information relating to the cost, duration and request frequency of specific IPTV content to prioritize higher revenue flows within the admission control process. Results of a simulation study (employing real traffic traces of long-lived flows) indicate that the proposed algorithms ensure that an adequate, but not overly generous, amount of bandwidth is allocated to ensure that QoS targets for accepted flows are met. Furthermore, they demonstrate the potential advantage of using content specific information in the admission control process to maximize generated revenue. [ABSTRACT FROM AUTHOR]

This paper considers localization of a source or a sensor from distance measurements. We argue that linear algorithms proposed for this purpose are susceptible to poor noise performance. Instead given a set of sensors/anchors of known positions and measured distances of the source/sensor to be localized from them we propose a potentially nonconvex weighted cost function whose global minimum estimates the location of the source/sensor one seeks. The contribution of this paper is to provide nontrivial ellipsoidal and polytopic regions surrounding these sensors/anchors of known positions, such that if the object to be localized is in this region, localization occurs by globally exponentially convergent gradient descent in the noise free case. Exponential convergence in the noise free case represents practical convergence as it ensures graceful performance degradation in the presence of noise. These results guide the deployment of sensors/anchors so that small subsets can be made responsible for practical localization in geographical areas determined by our approach. [ABSTRACT FROM AUTHOR]

The frequency scaling algorithm (FSA) was proposed to process the synthetic aperture radar (SAR) data acquired via the dechirp-on-receive approach. Some aspects of improving the FSA are investigated in this paper, based on which an extended FSA (EFSA) is presented. The general purpose of the EFSA is to reduce the effect of range spectrum shift of the intermediate processing results, which occurs during the scaling operation in the FSA, so as to achieve a more effective utilization of the processed bandwidth. The EFSA is implemented through time shifting the scaling and the inverse scaling functions used in the FSA and also the adjustment of the scaling factor. The derivation of the EFSA is detailed in this paper. Point target simulation in squinted imaging geometry indicates that the presented algorithm is more suitable for large-squint applications. [ABSTRACT FROM AUTHOR]

It is anticipated that a large amount of multicast traffic needs to be supported in future communication networks. The network coding technique proposed recently is promising for establishing multicast connections with a significantly lower bandwidth requirement than that of traditional Steiner-tree-based multicast connections. How to design multicast network topologies with the consideration of efficiently supporting multicast by the network coding technique becomes an important issue now. It is notable, however, that the conventional algorithms for network topology design are mainly unicast-oriented, and they cannot be adopted directly for the efficient topology design of network-coding-based multicast networks by simply treating each multicast as multiple unicasts. In this paper, we consider for the first time the novel topology design problem of network-coding-based multicast networks. Based on the characteristics of multicast and network coding, we first formulate this problem as a mixed-integer nonlinear programming problem, which is NP-hard, and then propose two heuristic algorithms for it. The effectiveness of our heuristics is verified through simulation and comparison with the exhaustive search method. We demonstrate in this paper that, in the topology design of multicast networks, adopting the network coding technique to support multicast transmissions can significantly reduce the overall topology cost as compared to conventional unicast-oriented design and the Steiner-tree-based design. [ABSTRACT FROM AUTHOR]

In this paper, closed-form expressions for the uncoded but error probability of closed-loop transmit diversity (CLTD) algorithms with two transmit and one receive antennas and noisy channel state information (CSI) in time-varying Rayleigh fading channels are derived. Two CLTD algorithms considered are the phase-amplitude CLTD (PA-CLTD), where the transmit antennas may transmit with different signal energy, and the phase-only CLTD (PO-CLTD), where the transmit antennas must transmit with the same signal energy. In addition to the uncoded bit error probability, this paper also derives the pairwise error probability when finite-depth interleaved convolutional codes are used with CLTD algorithms. However, due to the complexity of the coded system, the pairwise error probabilities are not in the closed-form expressions. The results have shown that PA-CLTD performs slightly better than PO-CLTD although PA-CLTD requires significantly more feedback information and that, when the Doppler spread is large, the performance of CLTD algorithms may degrade significantly. Moreover, a comparison between PA-CLTD, the Alamouti space-time code and the SISO system indicates that PA-CLTD outperforms the other two systems when the Doppler spread is small and the pilot SNR is large. Finally, the analytical results are compared with results from Monte Carlo simulation and the comparison shows that the analytical results match well with the simulation results. [ABSTRACT FROM AUTHOR]

This paper addresses the robot and landmark localization problem from bearing-only data in three views, simultaneously to the robust association of this data. The localization algorithm is based on the 1-D trifocal tensor, which relates linearly the observed data and the robot localization parameters. The aim of this work is to bring this useful geometric construction from computer vision closer to robotic applications. One contribution is the evaluation of two linear approaches of estimating the 1-D tensor: the commonly used approach that needs seven bearing- only correspondences and another one that uses only five correspondences plus two calibration constraints. The results in this paper show that the inclusion of these constraints provides a simpler and faster solution and better estimation of robot and landmark locations in the presence of noise. Moreover, a new method that makes use of scene planes and requires only four correspondences is presented. This proposal improves the performance of the two previously mentioned methods in typical man-made scenarios with dominant planes, while it gives similar results in other cases. The three methods are evaluated with simulation tests as well as with experiments that perform automatic real data matching in conventional and omnidirectional images. The results show sufficient accuracy and stability to be used in robotic tasks such as navigation, global localization or initialization of simultaneous localization and mapping (SLAM) algorithms. [ABSTRACT FROM AUTHOR]

This paper studies a family of distributions constructed from multivariate gamma distributions to model the statistical properties of multisensor synthetic aperture radar (SAR) images. These distributions referred to as multisensor multivariate gamma distributions (MuMGDs) are potentially interesting for detecting changes in SAR images acquired by different sensors having different numbers of looks. The first part of this paper compares different estimators for the parameters of MuMGDs. These estimators are based on the maximum likelihood principle, the method of inference function for margins, and the method of moments. The second part of the paper studies change detection algorithms based on the estimated correlation coefficient of MuMGDs. Simulation results conducted on synthetic and real data illustrate the performance of these change detectors. [ABSTRACT FROM AUTHOR]

In this paper, we present two methods, direct simulation (DS), and conditional expectation estimation (CEE), for estimating the unreliability, transient unavailability, steady unavailability, mean time to failure (MTTF), mean time between failure (MTBF), and their parameter sensitivities of consecutive-k-out-of-n: F repairable systems with (k - 1)-step Markov dependence. After an expression of the likelihood ratio estimator of parameter sensitivity is introduced, the direct estimators, and conditional expectation estimators are derived. The analytical results in a study by Lam & Ng were used to verify the algorithms presented in this paper. Numerical examples of highly reliable linear, and circular C (4, 50: F), and C (4,100: F) systems were illustrated to compare the algorithm efficiencies of DS, and CEE and CEE was found to be remarkably more efficient than DS. [ABSTRACT FROM AUTHOR]

There are numerous research challenges that need to be addressed until a wide deployment of vehicular ad hoc networks (VANETs) becomes possible. One of the critical issues consists of the design of scalable routing algorithms that are robust to frequent path disruptions caused by vehicles' mobility. This paper argues the use of information on vehicles' movement information (e.g., position, direction, speed, and digital mapping of roads) to predict a possible link-breakage event prior to its occurrence. Vehicles are grouped according to their velocity vectors. This kind of grouping ensures that vehicles, belonging to the same group, are more likely to establish stable single and multihop paths as they are moving together. Setting up routes that involve only vehicles from the same group guarantees a high level of stable communication in VANETs. The scheme presented in this paper also reduces the overall traffic in highly mobile VANET networks. The frequency of flood requests is reduced by elongating the link duration of the selected paths. To prevent broadcast storms that may be intrigued during path discovery operation, another scheme is also introduced. The basic concept behind the proposed scheme is to broadcast only specific and well-defined packets, referred to as "best packets" in this paper. The performance of the scheme is evaluated through computer simulations. Simulation results indicate the benefits of the proposed routing strategy in terms of increasing link duration, reducing the number of link-breakage events and increasing the end-to-end throughput. [ABSTRACT FROM AUTHOR]

Truthful task scheduling mechanisms are designed to cope with the selfishness of the participating agents. They assume that the agents are selfish; each agent's goal is to maximize its own profit. However, this is not always the case; an agent may want to cause losses to the other agents besides maximizing its profit. Such an agent is said to be an antisocial agent. An antisocial agent will try to gain as much profit as possible relative to the other agents. This paper presents an antisocial strategy which can be used by the antisocial agents to inflict losses on the other agents participating in a task scheduling mechanism on related machines. This paper also studies, by simulation, the effect of different parameters, such as the degree of antisociality on the relative losses that can be inflicted on the participating agents. [ABSTRACT FROM AUTHOR]

In this paper, a flux search controller is proposed to increase the efficiency of a direct torque-controlled induction motor in full load range. The value of the flux reference is determined through a minimization algorithm. The amplitude of stator current is used as the objective function since it is shown that samples of the stator current have better statistical properties than input power. In addition, the stator current has more sensitivity to the flux variation than input power. These two properties allow implementing an adaptive algorithm to determine the proper flux step without waste of time. The minimum allowable value of the flux step has been determined based on this process to prevent the divergence problem. This adaptive algorithm set a large flux step for transient state to speed up the convergence process and a small flux step for steady state to minimize the flux ripple. Simulation and experimental results presented in this paper confirm the fast dynamic of the proposed method. [ABSTRACT FROM AUTHOR]

This paper addresses the accurate time-domain simulation of closed-loop switched circuits. It is known that for such systems, conventional analog simulation can be critical due to the presence of strongly nonlinear switching devices. In order to overcome the drawback, in this paper, an in-depth investigation of the weakness points of analog simulation is first presented. On the basis of the achieved understanding, a novel accurate and efficient simulation technique that combines conventional analog simulation with a suited event-driven management of internally controlled commutations is presented. The proposed method employs a novel algorithm for locating commutation time instants and a regionwise evaluation of critical switching devices. The method is general and can be easily inserted into analog simulator tools. [ABSTRACT FROM AUTHOR]

Developing Takagi-Sugeno fuzzy models by evolutionary algorithms mainly requires three factors: an encoding scheme, an evaluation method, and appropriate evolutionary operations. At the same time, these three factors should be designed so that they can consider three important aspects of fuzzy modeling: modeling accuracy, compactness, and interpretability. This paper proposes a new evolutionary algorithm that fulfills such requirements and solves fuzzy modeling problems. Two major ideas proposed in this paper lie in a new encoding scheme and a new fitness function, respectively. The proposed encoding scheme consists of three chromosomes, one of which uses unique chained possibilistic representation of rule structure. The proposed encoding scheme can achieve simultaneous optimization of parameters of antecedent membership functions and rule structures with the new fitness function developed in this paper. The proposed fitness function consists of five functions that consider three evaluation criteria in fuzzy modeling problems. The proposed fitness function guides evolutionary search direction so that the proposed algorithm can find more accurate compact fuzzy models with interpretable antecedent membership functions. Several evolutionary operators that are appropriate for the proposed encoding scheme are carefully designed. Simulation results on three modeling problems show, that the proposed encoding scheme and the proposed fitness functions are effective in finding accurate, compact, and interpretable Takagi-Sugeno fuzzy models. From the simulation results, it is shown that the proposed algorithm can successfully find fuzzy models that approximate the given unknown function accurately with a compact number of fuzzy rules and membership functions. At the same time, the fuzzy models use interpretable antecedent membership functions, which are helpful in understanding the underlying behavior of the obtained fuzzy models. [ABSTRACT FROM AUTHOR]

In order to resolve the problem of track-to-track association in a distributed multisensor situation, this paper presents independent and dependent sequential track correlation algorithms based on Singer's and Bar-Shalom's algorithms. Based on sequential track correlation algorithm, the restricted and attenuation memory track correlation algorithms and sequential classic assignment rules are proposed. In this paper, these algorithms are described in detail. Then, the track correlation mass and multivalency processing methods are discussed as well. Finally, simulations are designed to compare the correlation performance of these algorithms with that of Singer's and Bar-Shalom's algorithms. The simulation results show that the performance of these algorithms proposed here is much better than that of the classical methods under the environments of dense targets, interfering, noise, track cross, and so on. Under the above situations, their correct correlation ratio is improved about 69 percent over the classical methods. [ABSTRACT FROM AUTHOR]

Channel order estimation is a critical step in most blind single-input multiple-output (SIMO) channel identification/equalization algorithms. Several methods for estimating either the true channel order or its most significant part (the so-called effective channel order) have been recently proposed, but a solution able to work in practical scenarios (low or moderate signal-to-noise ratios (SNR5) and channels with small leading and/or trailing coefficients) has not been found yet. In this paper, a new criterion for effective channel order detection of SIMO channels is presented. The method is based on the fact that the cost function typically used in blind identification algorithms decreases monotonically with the estimated channel order, whereas for blind equalization algorithms, the cost function increases monotonically. In this paper, it is shown that a straightforward combination of both cost functions attains its minimum at the correct channel order even for moderate SNRS. The proposed method is able to work with small data sets, colored signals, and channels with small head and tail taps, which is a common problem in communication applications. The improvement of the proposed criterion over a number of existing algorithms is demonstrated through simulations. [ABSTRACT FROM AUTHOR]

This paper presents a simple yet efficient dynamic-programming (DP) shortest path algorithm for real-time collision-free robot-path planning applicable to situations in which targets and barriers are permitted to move. The algorithm works in real time and requires no prior knowledge of target or barrier movements. In the case that the barriers are stationary, this paper proves that this algorithm always results in the robot catching the target, provided it moves at a greater speed than the target, and the dynamic-system update frequency is sufficiently large. Like most robot-path-planning approaches, the environment is represented by a topologically organized map. Each grid point on the map has only local connections to its neighboring grid points from which it receives information in real time. The information stored at each point is a current estimate of the distance to the nearest target and the neighbor from which this distance was determined. Updating the distance estimate at each grid point is done using only the information gathered from the point's neighbors, that is, each point can be considered an independent processor, and the order in which grid points are updated is not determined based on global knowledge of the current distances at each point or the previous history of each point. The robot path is determined in real time completely from the information at the robot's current grid-point location. The computational effort to update each point is minimal, allowing for rapid propagation of the distance information outward along the grid from the target locations. In the static situation, where both the targets and the barriers do not move, this algorithm is a DP solution to the shortest path problem, but is restricted by lack of global knowledge. In this case, this paper proves that the dynamic system converges in a small number of iterations to a state where the minimal distance to a target is recorded at each grid point and shows that this robot-path-planning algorithm can be made to always choose an optimal path. The effectiveness of this algorithm is demonstrated through a number of simulations. [ABSTRACT FROM AUTHOR]

This paper proposes a framework for the incorporation of formal methods in the design flow of digital signal processing (DSP) systems in a rigorous way. In the proposed approach, DSP descriptions were modeled and verified at different abstraction levels using higher order logic based on the higher order logic (HOL) theorem prover. This framework enables the formal verification of DSP designs that in the past could only be done partially using conventional simulation techniques. To this end, a shallow embedding of DSP descriptions in HOL at the floating-point (FP), fixed-point (FXP), behavioral, register transfer level (RTL), and netlist gate levels is provided. The paper made use of existing formalization of FP theory in HOL and a parallel one developed for FXP arithmetic. The high ability of abstraction in HOL allows a seamless hierarchical verification encompassing the whole DSP design path, starting from top-level FP and FXP algorithmic descriptions down to RTL, and gate level implementations. The paper illustrates the new verification framework on the fast Fourier transform (FFT) algorithm as a case study. [ABSTRACT FROM AUTHOR]

This paper concerns the optimization and performance analysis of an automatic control algorithm for managing power output of large multielement array hyperthermia applicators. Simulation and corresponding measurement of controller performance in a solid tissue equivalent phantom model is utilized for analysis of controller response to dynamically varying thermal load conditions that simulate clinical treatments. The analysis leads to an optimum controller which demonstrates the ability to achieve a uniform and stable temperature profile over a large surface area regardless of surrounding thermal load. This paper presents several advancements to the performance of a previously published control routine, including: 1) simplified simulation techniques for thorough characterization of controller performance; 2) an optimization procedure leading to an improved hybrid control algorithm for maintaining optimal performance during periods of both "rising" and "steady-state" temperature; 3) performance analysis of a control algorithm tailored for large area hyperthermia treatments with a mulitelement array applicator. The optimized hybrid controller is applied to the conformal microwave array (CMA) hyperthermia system previously developed for heating large area surface disease such as diffuse chestwall recurrence of breast carcinoma, and shown to produce stable, uniform temperatures under the multielement array applicator for all thermal load conditions. [ABSTRACT FROM AUTHOR]

A novel radial basis function neural network for discriminant analysis is presented in this paper. In contrast to many other researches, this work focuses on the exploitation of the weight structure of radial basis function neural networks using the Bayesian method. It is expected that the performance of a radial basis function neural network with a well-explored weight structure can be improved. As the weight structure of a radial basis function neural network is commonly unknown, the Bayesian method is, therefore, used in this paper to study this a priori structure. Two weight structures are investigated in this study, i.e., a single-Gaussian structure and a two-Gaussian structure. An expectation-maximization learning algorithm is used to estimate the weights. The simulation results showed that the proposed radial basis function neural network with a weight structure of two Gaussians outperformed the other algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a new method of motion analysis of articulated objects from feature point correspondences over monocular perspective images without imposing any constraints on motion. An articulated object is modeled as a kinematic chain consisting of joints and links, and its 3D joint positions are estimated within a scale factor using the connection relationship of two links over two or three images. Then, twists and exponential maps are employed to represent the motion of each link, including the general motion of the base link and the rotation of other links around their joints. Finally, constraints from image point correspondences, which are similar to that of the essential matrix in rigid motion, are developed to estimate the motion. In the algorithm, the characteristic of articulated motion, i.e., motion correlation among links, is applied to decrease the complexity of the problem and improve the robustness. A point pattern matching algorithm for articulated objects is also discussed in this paper. Simulations and experiments on real images show the correctness and efficiency of the algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates joint power control and routing policies for general multihop wireless networks when all the transmitting nodes are subject to a long-term average power constraint. The main contribution of this paper is to propose online power and rate control algorithms and prove that these policies stabilize the entire queuing network whenever the packet arrival rates at each node are in the corresponding region of achievable rates. The online policies are time varying and based on the queue size at each node and the instantaneous channel conditions. The theoretical results are supported by simulations for the illustrative cases of both a multiple-access channel and a relay channel. [ABSTRACT FROM AUTHOR]

Techniques to determine subarrays when processing elements of VLSI arrays become faulty have been investigated extensively. These tend to identify the largest subarray that is possible without concentrating on the power efficiency of the resulting subarray. In this paper, we propose new techniques, based on heuristic strategy and dynamic programming, to minimize the interconnect length in an attempt to reduce power dissipation without performance penalty. Our algorithms show that notable improvements in the reduction of the number of long interconnects could be realized in linear time and without sacrificing the size of the subarray. Our evaluations show that, for a VLSI array of size 256 x 256, the number of long interconnects in the subarray can be reduced by up to 95 percent for clustered faults and up to 50 percent and 73 percent for a random fault with density of 10 percent and 0.1 percent, respectively, when compared with the most efficient implementation cited in the literature. The interconnect power saving for a VLSI array of size 512 · 512 is by up to 11 percent for a random fault. We have also shown that interconnect power savings of up to 14 percent are possible for the cases investigated. Simulations based on several random and clustered fault scenarios clearly reveal the superiority of the proposed techniques for power efficient realizations. In addition, the lower bound of the performance has been proposed to demonstrate that the proposed algorithms are nearly optimal for the cases considered in this paper. [ABSTRACT FROM AUTHOR]

This paper discusses underdetermined (i.e., with more sources than sensors) blind source separation (BSS) using a two-stage sparse representation approach. The first challenging task of this approach is to estimate precisely the unknown mixing matrix. In this paper, an algorithm for estimating the mixing matrix that can be viewed as an extension of the DUET and the TIFROM methods is first developed. Standard clustering algorithms (e.g., K-means method) also can be used for estimating the mixing matrix if the sources are sufficiently sparse. Compared with the DUET, the TIFROM methods, and standard clustering algorithms, with the authors' proposed method, a broader class of problems can be solved, because the required key condition on sparsity of the sources can be considerably relaxed. The second task of the two-stage approach is to estimate the source matrix using a standard linear programming algorithm. Another main contribution of the work described in this paper is the development of a recoverability analysis. After extending the results in [7], a necessary and sufficient condition for recoverability of a source vector is obtained. Based on this condition and various types of source sparsity, several probability inequalities and probability estimates for the recoverability issue are established. Finally, simulation results that illustrate the effectiveness of the theoretical results are presented. [ABSTRACT FROM AUTHOR]

High-rate low-density parity-check (LDPC) codes are the focus of intense research in magnetic recording because, when decoded by the iterative sum-product algorithm, they show decoding performance close to the Shannon capacity. However, cycles, especially short cycles, are harmful to LDPC codes. The paper describes the partition-and-shift LDPC (PS-LDPC) codes, a new class of regular, structured LDPC codes that can be designed with large girth and arbitrary large minimum distance. Large girth leads to more efficient iterative decoding and codes with better error-floor properties than random LDPC codes. PS-LDPC codes can be designed for any desired column weight and with flexible code rates. The paper details the girth and distance properties of the codes and their systematic construction and presents analytical and simulation performance results that show that, in the high signal-to-noise ratio region, PS-LDPC codes out- perform random codes, alleviating the error floor phenomenon. [ABSTRACT FROM AUTHOR]

In this paper, a method for approximating a multi- input multi-output (MIMO) transfer function by a causal finite-im- pulse response (FIR) paraunitary (PU) system in a weighted least- squares sense is presented. Using a complete parameterization of FIR PU systems in terms of Householder-like building blocks, an iterative algorithm is proposed that is greedy in the sense that the observed mean-squared error at each iteration is guaranteed to not increase. For certain design problems in which there is a phase- type ambiguity in the desired response, which is formally defined in the paper, a phase feedback modification is proposed in which the phase of the FIR approximant is fed back to the desired response. With this modification in effect, it is shown that the resulting iterative algorithm not only still remains greedy, but also offers a better magnitude-type fit to the desired response. Simulation results show the usefulness and versatility of the proposed algorithm with respect to the design of principal component filter bank (PCFB)-like filter banks and the FIR PU interpolation problem. Concerning the PCFB design problem, it is shown that as the McMillan degree of the FIR PU approximant increases, the resulting filter bank behaves more and more like the infinite-order PCFB, consistent with intuition. In particular, this PCFB-like behavior is shown in terms of filter response shape, multiresolution, coding gain, noise reduction with zeroth-order Wiener filtering in the subbands, and power minimization for discrete multitone (DMT)-type transmul- tiplexers. [ABSTRACT FROM AUTHOR]

Interpolation or mapping of data from a given real array to data from a virtual array of more suitable geometry is well known in array signal processing. This operation allows arrays of any geometry to be used with fast direction-of-arrival (DOA) estimators designed for linear arrays. In an earlier companion paper [21], a first-order condition for zero DOA bias under such mapping was derived and was also used to construct a design algorithm for the mapping matrix that minimized the DOA estimate bias. This bias-minimizing theory is now extended to minimize not only bias, but also to consider finite sample effects due to noise and reduce the DOA mean-square error (MSE). An analytical first-order expression for mapped DOA MSE is derived, and a design algorithm for the transformation matrix that minimizes this MSE is proposed. Generally, DOA MSE is not reduced by minimizing the size of the mapping errors but instead by rotating these errors and the associated noise subspace into optimal directions relative to a certain gradient of the DOA estimator criterion function. The analytical MSE expression and the design algorithm are supported by simulations that show not only conspicuous MSE improvements in relevant scenarios, but also a more robust preprocessing for low signal-to-noise ratios (SNRs) as compared with the pure bias-minimizing design developed in the previous paper. [ABSTRACT FROM AUTHOR]

A new probabilistic load-flow solution algorithm based on an efficient point estimate method is proposed in this paper. It is assumed that the uncertainties of bus injections and line parameters can be estimated or measured. This paper shows how to estimate the corresponding uncertainty in the load-flow solution. The proposed method can be used directly with any existing deterministic load-flow program. For a system with m uncertain parameters, it uses 2m calculations of load flow to calculate the statistical moments of load-flow solution distributions by weighting the value of the solution evaluated at 2m locations. The moments are then used in the probability distribution fitting. Performance of the proposed method is verified and compared with those obtained from Monte Carlo simulation technique and combined simulation and analytical method using several IEEE test systems. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of unsupervised Bayesian hidden Markov chain restoration. When the hidden chain is stationary, the classical "Hidden Markov Chain" (HMC) model is quite efficient, and associated unsupervised Bayesian restoration methods using the "Expectation-Maximization" (EM) algorithm work well. When the hidden chain is non stationary, on the other hand, the unsupervised restoration results using the HMC model can be poor, due to a bad match between the real and estimated models. The novelty of this paper is to offer a more appropriate model for hidden nonstationary Markov chains, via the theory of evidence. Using recent results relating to Triplet Markov Chains (TMCs), we show, via simulations, that the classical restoration results can be improved by the use of the theory of evidence and Dempster-Shafer fusion. The latter improvement is performed in an entirely unsupervised way using an original parameter estimation method. Some application examples to unsupervised image segmentation are also provided. [ABSTRACT FROM AUTHOR]