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This paper introduces a new framework to construct fast and efficient sensing matrices for practical compressive sensing, called Structurally Random Matrix (SRM). In the proposed framework, we prerandomize the sensing signal by scrambling its sample locations or flipping its sample signs and then fast-transform the randomized samples and finally, subsample the resulting transform coefficients to obtain the final sensing measurements. SRM is highly relevant for large-scale, real-time compressive sensing applications as it has fast computation and supports block-based processing. In addition, we can show that SRM has theoretical sensing performance comparable to that of completely random sensing matrices. Numerical simulation results verify the validity of the theory and illustrate the promising potentials of the proposed sensing framework. [ABSTRACT FROM PUBLISHER]

The direct torque control (DTC) technique of permanent-magnet synchronous motors (PMSMs) receives increasing attention due to its advantages in eliminating the current controllers and quicker dynamic response, compared with other motor control algorithms. However, high torque and stator flux ripples remain in the system when using DTC technologies. This means large stator voltage and current harmonic contents exist in the PM motors. Since the variation of motor electromagnetic torque is related to the voltages that are applied to the motor, by analyzing the relationships between stator flux, torque, and voltages, a PMSM torque predictive control scheme is proposed in this paper. In each digital signal processor cycle, the optimized voltage is utilized to reduce torque ripple, and the voltage vector angle is determined by the output of torque and flux hysteresis controllers. The proposed scheme is simulated and experimentally verified. Both simulation and experimental results have shown that low torque ripple and reduced stator current harmonics are achieved by using the proposed scheme. [ABSTRACT FROM PUBLISHER]

In this paper, we deal with the problem of adaptive detection of distributed targets embedded in colored noise modeled in terms of a compound-Gaussian process and without assuming that a set of secondary data is available. The covariance matrices of the data under test share a common structure while having different power levels. A Bayesian approach is proposed here, where the structure and possibly the power levels are assumed to be random, with appropriate distributions. Within this framework we propose GLRT-based and ad-hoc detectors. Some simulation studies are presented to illustrate the performances of the proposed algorithms. The analysis indicates that the Bayesian framework could be a viable means to alleviate the need for secondary data, a critical issue in heterogeneous scenarios. [ABSTRACT FROM AUTHOR]

The goal of this paper is to propose a bio-inspired radio access mechanism for cognitive networks mimicking the behavior of a flock of birds swarming in search for food in a cohesive fashion without colliding with each other. The equivalence between swarming and radio resource allocation is established by modeling the interference distribution in the resource domain, e.g., frequency and time, as the spatial distribution of food, while the position of the single bird represents the radio resource chosen by each radio node. The swarming mechanism is enforced by letting every node allocate its resources (power/bits) in the time-frequency regions where the interference is minimum (the food density is maximum), avoiding collisions with other nodes (birds), yet limiting the spread in the time-frequency domain (i.e., maintaining the swarm cohesion). The solution is given as the distributed minimization of a functional, borrowed from social foraging swarming models, containing the average interference plus repulsion and attraction terms that help to avoid conflicts and maintain cohesiveness, respectively. [ABSTRACT FROM AUTHOR]

This paper presents an improved mean-square deviation (MSD) analysis of the standard affine projection algorithm (APA) based on two distinctive features. First, the propagation model of the error covariance includes the cross-correlation between the current weight error vector and the prior measurement noises associated with the reused inputs; such a cross-correlation has merely been considered previously. Second, the analysis based on n most recent accumulated iterations, rather than a typical analysis based on a current single iteration, is suggested to reveal a previously unseen phenomenon, where n denotes the tap-length of the filter. Simulation results are in better agreement with the proposed theoretical results, than the previous theoretical ones, over a wide range of parameters such as tap-length, projection order, and step-size. [ABSTRACT FROM AUTHOR]

Electromagnetic scattering by penetrable bodies often is modelled by the Poggio-Miller-Chan-Harrington-Wu-Tsai (PMCHWT) integral equation. Unfortunately the spectrum of the operator involved in this equation is bounded neither from above or below. This implies that the equation suffers from dense discretization breakdown; that is, the condition numbers of the matrix resulting upon discretizing the equation rise with the mesh density. The electric field integral equation, often used to model scattering by perfect electrically conducting bodies, is susceptible to a similar breakdown phenomenon. Recently, this breakdown was cured by leveraging the Calderón identities. In this paper, a Calderón preconditioned PMCHWT integral equation is introduced. By constructing a Calderón identity for the PMCHWT operator, it is shown that the new equation does not suffer from dense discretization breakdown. A consistent discretization scheme involving both Rao-Wilton-Glisson and Buffa-Christiansen functions is introduced. This scheme amounts to the application of a multiplicative matrix preconditioner to the classical PMCHWT system, and therefore is compatible with existing boundary element codes and acceleration schemes. The efficiency and accuracy of the algorithm are corroborated by numerical examples. [ABSTRACT FROM AUTHOR]

Fire-flame detection using a video camera is difficult because a flame has irregular characteristics, i.e., vague shapes and color patterns. Therefore, in this paper, we propose a novel fire-flame detection method using fuzzy finite automata (FFA) with probability density functions based on visual features, thereby providing a systemic approach to handling irregularity in computational systems and the ability to handle continuous spaces by combining the capabilities of automata with fuzzy logic. First, moving regions are detected via background subtraction, and the candidate flame regions are then identified by applying flame color models. In general, flame regions have a continuous irregular pattern; therefore, probability density functions are generated for the variation in intensity, wavelet energy, and motion orientation and applied to the FFA. The proposed algorithm is successfully applied to various fire/non-fire videos, and its detection performance is better than that of other methods. [ABSTRACT FROM AUTHOR]

An accurate estimation of critical load levels (CLLs) is beneficial in evaluating the confidence level for the predicted locational marginal prices (LMPs) in power markets. This paper proposes an efficient algorithm to quickly identify CLLs under the with-loss-DCOPF framework. The proposed method approximates the trajectory of DCOPF solutions with respect to the system load variations. The quadratic coefficients of the trajectory can be efficiently obtained by solving a small-scale nonlinear system of equations formed by the variable substitution method, which does not require additional OPF runs and is thus more computationally efficient than existing methods such as binary search and interpolation. The obtained trajectory can be used for a quick calculation of OPF solutions without re-running OPF. Further, through trajectory extrapolation, new congestions and other binding constraints can be identified simultaneously with the CLL predictions. Particularly, the proposed method is not restricted to a specific DCOPF-based model and can be easily applied to other DCOPF-based variants. Numeric studies on a revised IEEE 30-bus system are presented to illustrate the high computational efficiency and accuracy of the approach. It is further tested on the IEEE 118-bus system using four different DCOPF-based models to illustrate its model adaptability. [ABSTRACT FROM PUBLISHER]

Dynamic system modeling plays a crucial role in the development of techniques for stationary and nonstationary signal processing. Due to the inherent physical characteristics of systems under investigation, nonnegativity is a desired constraint that can usually be imposed on the parameters to estimate. In this paper, we propose a general method for system identification under nonnegativity constraints. We derive the so-called nonnegative least-mean-square algorithm (NNLMS) based on stochastic gradient descent, and we analyze its convergence. Experiments are conducted to illustrate the performance of this approach and consistency with the analysis. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new demosaicing algorithm that can be used for various sensor images captured by digital cameras equipped with various red–green–blue color filter arrays. Our algorithm enhances the universal demosaicing algorithm of Lukac et al by defining a new spectral interpolation model that exploits not only the information on the color of pixels but also the relative distance between neighboring pixels within an image. Moreover, we include an edge-detection model that makes our algorithm adaptive and reduces the presence of color shifts and artifacts. A series of tests has been made on images of the Kodak database, and our algorithm performs better than the universal demosaicing algorithm with regard to both subjective and objective evaluation. The versatility of our demosaicing algorithm is also highlighted through an application to the issue of color image resampling, and we obtain conclusive experimental results. [ABSTRACT FROM PUBLISHER]

This paper presents an adaptive tracking algorithm by learning hybrid object templates online in video. The templates consist of multiple types of features, each of which describes one specific appearance structure, such as flatness, texture, or edge/corner. Our proposed solution consists of three aspects. First, in order to make the features of different types comparable with each other, a unified statistical measure is defined to select the most informative features to construct the hybrid template. Second, we propose a simple yet powerful generative model for representing objects. This model is characterized by its simplicity since it could be efficiently learnt from the currently observed frames. Last, we present an iterative procedure to learn the object template from the currently observed frames, and to locate every feature of the object template within the observed frames. The former step is referred to as feature pursuit, and the latter step is referred to as feature alignment, both of which are performed over a batch of observations. We fuse the results of feature alignment to locate objects within frames. The proposed solution to object tracking is in essence robust against various challenges, including background clutters, low-resolution, scale changes, and severe occlusions. Extensive experiments are conducted over several publicly available databases and the results with comparisons show that our tracking algorithm clearly outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Three-dimensional face landmarking aims at automatically localizing facial landmarks and has a wide range of applications (e.g., face recognition, face tracking, and facial expression analysis). Existing methods assume neutral facial expressions and unoccluded faces. In this paper, we propose a general learning-based framework for reliable landmark localization on 3-D facial data under challenging conditions (i.e., facial expressions and occlusions). Our approach relies on a statistical model, called 3-D statistical facial feature model, which learns both the global variations in configurational relationships between landmarks and the local variations of texture and geometry around each landmark. Based on this model, we further propose an occlusion classifier and a fitting algorithm. Results from experiments on three publicly available 3-D face databases (FRGC, BU-3-DFE, and Bosphorus) demonstrate the effectiveness of our approach, in terms of landmarking accuracy and robustness, in the presence of expressions and occlusions. [ABSTRACT FROM AUTHOR]

This paper introduces a tensor framework to solve the problem of nonunitary joint block diagonalization (JBD) of a set of real or complex valued matrices. We show that JBD can be seen as a particular case of the block-component-decomposition (BCD) of a third-order tensor. The resulting tensor model fitting problem does not require the block-diagonalizer to be a square matrix: the over- and underdetermined cases can be handled. To compute the tensor decomposition, we build an efficient nonlinear conjugate gradient (NCG) algorithm. In the over- and exactly determined cases, we show that exact JBD can be computed by a closed-form solution based on eigenvalue analysis. In approximate JBD problems, this solution can be used to efficiently initialize any iterative JBD algorithm such as NCG. Finally, we illustrate the performance of our technique in the context of independent subspace analysis (ISA) based on second-order statistics (SOS). [ABSTRACT FROM PUBLISHER]

In this paper, the problem of the simultaneous estimation of frequency and power components is investigated. It is solved through a new recursive estimator capable of estimating the unknown model parameters during severe dynamic changes in the system. By this, a simple nonlinear parameter model for the instantaneous power, taking into account the fundamental components of voltage and current, is used as a starting point for the power components and frequency estimation. In the model, the system frequency is considered as an unknown model parameter, and it is simultaneously estimated with other unknown parameters. This resulted in an efficient numerical algorithm for the measurement of power components, which is not sensitive to variations of the system frequency. This is an important achievement, which improves the accuracy of the method during network transients. The new estimator has been improved by the strategy for tuning its forgetting factor according to the system dynamics, and it is tested through computer simulations and by using data records obtained under laboratory conditions. The algorithm was developed for a single-phase system, and the approach might be easily extended to multiphase systems. [ABSTRACT FROM AUTHOR]

Motion capture provides the best interface to the understanding of trajectory planning and formations in the central nervous system, which has enormously benefited the research in interactive game and learning, animation, film special effects, health care, and navigation. The existing human motion capture techniques, which use structured multiple high-resolution cameras in a dedicated studio, are complicated and expensive. With the rapid development of microsensors on chip, a ubiquitous real-time low-cost human motion capture system that uses wearable microsensors becomes possible. Because of the agility in movement, upper limb motion estimation has been regarded as the most difficult problem in human motion capture. In this paper, we take the upper limb as our research subject and present a novel upper limb motion estimation algorithm by hierarchical fusion of sensor data and human skeleton constraints. Our method represents the orientations of upper limb segments in quaternion, which is computationally effective and can avoid the singularity problem. To address the nonlinear human body segment motion, a particle filter is proposed to fuse inertial and magnetic sensor data. To compensate for the drift, which is the most challenging issue in motion estimation using inertial sensors, we present a novel solution by modeling the geometrical constraint in elbow joint and fuse the constraint in the framework of particle filter to revise the sensor fusion results and improve the estimation accuracy. The experimental results have shown that the proposed algorithm can provide accurate results compared to the ground truth. [ABSTRACT FROM AUTHOR]

The discrete complex image method (DCIM) has been well-established and widely employed as a tool to compute spatial-domain Green's functions in planar multilayered media. Although the use of the DCIM in conjunction with the method of moments has been quite satisfactory for the efficient analysis of printed structures in layered media, it has not been considered much as a legitimate tool for commercial applications. Up until recently, there has been mainly two reasons for the reluctance of its acceptance in commercial products: the lack of a proper error criterion in the spatial domain and the manual selection of the order of images. As the former shortcoming has recently been eliminated by establishing a proper error criterion in the spatial domain, this paper intends to propose a viable solution for the latter problem, i.e., for the choice of the number of images. Note that, thus far, this choice has been based on the number of significant singular values obtained as a by-product of the method, which is neither rigorous, nor optimum in any sense, and renders it nonrobust and unreliable. As a remedy for this remaining shortcoming, a model selection approach known as minimum description length (MDL) has been introduced into the algorithm of the DCIM with success. The MDL approach, exploiting the regularities in the data, selects the order of the model based on the tradeoff between the quality of fitting and complexity of the model. [ABSTRACT FROM AUTHOR]

Computational color constancy is a fundamental prerequisite for many computer vision applications. This paper presents a survey of many recent developments and state-of-the-art methods. Several criteria are proposed that are used to assess the approaches. A taxonomy of existing algorithms is proposed and methods are separated in three groups: static methods, gamut-based methods, and learning-based methods. Further, the experimental setup is discussed including an overview of publicly available datasets. Finally, various freely available methods, of which some are considered to be state of the art, are evaluated on two datasets. [ABSTRACT FROM PUBLISHER]

Sensor fusion of multiple sources plays an important role in vehicular systems to achieve refined target position and velocity estimates. In this paper, we address the general registration problem, which is a key module for a fusion system to accurately correct systematic errors of sensors. A fast maximum a posteriori (FMAP) algorithm for joint registration–tracking is presented. The algorithm uses a recursive two-step optimization that involves orthogonal factorization to ensure numerically stability. Statistical efficiency analysis based on Cramèr–Rao lower bound theory is presented to show asymptotical optimality of FMAP. In addition, Givens rotation is used to derive a fast implementation with complexity O(n), with n being the number of tracked targets. Simulations and experiments are presented to demonstrate the promise and effectiveness of FMAP. [ABSTRACT FROM PUBLISHER]

In this paper, we analyze the accuracy of indoor localization measurement based on a wireless sensor network. The position estimation procedure is based on the received-signal-strength measurements collected in a real indoor environment. Two different classes of low-computational-effort algorithms based on the centroid concept are considered, i.e., the weighted centroid localization method and the relative-span exponential weighted localization method. In particular, different sources of measurement uncertainty are analyzed by means of theoretical simulations and experimental results. [ABSTRACT FROM PUBLISHER]

The weighted multi-commodity multistate unreliable network (WMMUN) is an extension of the multistate network. It is a new network composed of multistate unreliable components (such as arcs and nodes) with various weight capacities which is able to transmit different types of commodities. Currently, the method used to calculate the direct WMMUN reliability is derived from algorithms based on D-minimal path (D-MP). The best-known method may fail to find real D-MPs, and therefore requires more comparison and verification. A very simple algorithm based on the sequential decomposition method has been developed for finding all real D-MPs before calculating the WMMUN reliability. The relationships among the different versions of WMMUN reliability problems have also been clarified. The correctness and computational complexity of the proposed algorithm will be analysed and proven in this paper. An example will be given to illustrate how the WMMUN reliability is evaluated using the proposed algorithm. Computational results compare favorably with existing methods in terms of the running time, the number of d-MPs, and the number of D-MP candidates. [ABSTRACT FROM AUTHOR]

Mock circulatory loops (MCLs) have been widely used to test left ventricular assist devices. The hydraulic properties of the mock systemic arterial system are usually described by two alternative four-element windkessel (W4) models. Compared with three-element windkessel model, their parameters, especially the inertial term, are much more difficult to estimate. In this paper, an estimator based on the iterated unscented Kalman filter (IUKF) algorithm is proposed to identify model parameters. Identifiability of these parameters for different measurements is described. Performance of the estimator for different model structures is first evaluated using numerical simulation data contaminated with artificial noise. An MCL is developed to test the proposed algorithm. Parameter estimates for different models are compared with the calculated values derived from the mechanical and hydraulic properties of the MCL to validate model structures. In conclusion, the W4 model with an inertance and an aortic characteristic resistance arranged in series is proposed to represent the mock systemic arterial system. Once model structure is appropriately selected, IUKF can provide reasonable estimation accuracy in a limited time and may be helpful for future clinical applications. [ABSTRACT FROM PUBLISHER]

This paper presents a novel method that is very efficient in solving multidimensional real and complex eigenvalue problems, commonly employed in electromagnetic analysis, which can be transformed into a nonlinear equation. The concept is realized as root tracing process of a real or complex function of N variables in the constrained space. We assume that the roots of the continuous function of N variables lie on the continuous (N-1)-dimensional hyperplane. The method uses regular N and (N-1)-Simplexes, at which vertices the considered function changes its sign. Based on (N-1)-Simplex, the function is evaluated at two new points that are vertices of new regular N-Simplexes for which (N-1)-Simplex is one of its (N-1)-faces. The algorithm, with the usage of stack, runs in an iterative mode tracing the roots inside the volume of the considered simplexes. As a result, the algorithm creates a chain of simplexes in the constrained region. The proposed algorithm is optimal in the sense of the number of function evaluations. The numerical results, real and complex dispersion characteristics of chosen microwave guides, have proven the versatility and efficiency of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

This paper investigates the possibility of providing aggregated regulation services with small loads, such as water heaters or air conditioners. A direct-load control algorithm is presented to aggregate the water heater load for the purpose of regulation. A dual-element electric water heater model is developed, which accounts for both thermal dynamics and users' water consumption. A realistic regulation signal was used to evaluate the number of water heaters needed and the operational characteristics of a water heater when providing 2-MW regulation service. Modeling results suggest that approximately 33 000 water heaters are needed to provide a 2-MW regulation service 24 hours a day. However, if water heaters only provide regulation from 6:00 to 24:00, approximately 20 000 will be needed. Because the control algorithm has considered the thermal setting of the water heater, customer comfort is maintained. Therefore, the aggregated regulation service provided by water heater loads can become a major source of revenue for load-serving entities when the smart grid enables the direct load control. [ABSTRACT FROM PUBLISHER]

This paper presents a method for determining the double outage contingencies that threaten the system without solving the full contingency set. Two methods for contingency screening with complementary properties are presented. The results of the algorithms are compared to the full double outage contingency analysis results for a large North American case. The results show that the screening algorithms are able to detect nearly all of the contingencies that will result in violations, while requiring only a small fraction of the contingencies to be solved. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel approach to optimize a generator's profit in an offer-based electricity market cleared by an optimal power flow (OPF) program. A generator's offer is optimized based on its transmission-constrained residual demand derivative (TCRDD), which avoids representing the full network model in the optimization. The TCRDD can be easily calculated based on a solved OPF without changing existing OPF algorithms and programs. As demonstrated with an IEEE 118-bus example, the TCRDD approach is computationally efficient. The TCRDD approach can help market participants to bid into electricity markets, and help market monitors to diagnose bidding behaviors in the presence of transmission constraints. [ABSTRACT FROM PUBLISHER]

In the modern power systems operating at lower stability margins, conventional non-adaptive schemes cannot offer adequate protection for securing the power system, especially against combinational events. For major disturbances, the active power deficit is usually accompanied by reactive power deficit and frequency stability and voltage stability of the system are jeopardized simultaneously. In this paper, three adaptive combinational load shedding methods are proposed to improve operation of the conventional underfrequency load shedding scheme in order to enhance power system stability following severe disturbances. The proposed methods use locally measured frequency and voltage signals to counteract such events. In the proposed algorithms, load shedding is started from the locations which have higher voltage decay for longer period of time. The speed, location, and amount of load shedding are changed adaptively depending on the disturbance location, voltage status of the system, and the rate of frequency decline. Operation of the conventional and the proposed load shedding methods have been simulated in an actual large network. Obtained simulation results confirm that the proposed methods provide considerable enhancement in the power system voltage stability margin, and by using the proposed algorithms, various power system blackouts could be prevented. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid model for an internal combustion engine, with the power generated due to combustion as the input and the crankshaft speed fluctuations as the output. The individual cylinders of the engine are considered as subsystems for which a nonlinear model, based on the physical principles, is derived. The proposed model is linearized at an operating point, and a switched linear model is formed. The simulation results of the proposed model are validated by matching the results with the experimentally observed data. Using the properties of the validated model, it is shown that the crankshaft speed variations observed in the engine are a Markov process. A novel algorithm that is based on the Markov chain is proposed to detect the misfire in the spark ignition engines. In the ensuing engine rig experiments, an igniter misfire is introduced in the system and is successfully detected. The analysis of the data shows that the engine also has an air leakage in a cylinder (a developing misfire), which is experimentally confirmed later. [ABSTRACT FROM PUBLISHER]

Antilock braking systems (ABSs) are usually designed based on controlling the wheel slip ratio so as to maintain each wheel in a presumed stable region. Since the optimal slip ratio (which results in maximum tire–road friction) varies with the type of road, current methods are not efficient in the sense of achieving the shortest possible stopping distance. This paper introduces an efficient ABS, in which the brake commands directly maximize the longitudinal component of tire–road friction at each wheel of the vehicle independently. The tire–road friction is estimated using a torque balance equation at each wheel, and within those equations, real-time estimates of the effective radius of tire are used. A step-by-step algorithm for computing the brake commands that maximize the tire–road friction is also presented. Three challenging braking scenarios were tested using the comprehensive CarSim simulation environment. The results show that, in comparison to conventional ABS, our method significantly reduces the stopping distance and improves the vehicle stability. [ABSTRACT FROM PUBLISHER]

Liquid crystal display (LCD) devices are well known for their slow responses due to the physical limitations of liquid crystals. Therefore, fast moving objects in a scene are often perceived as blurred. This effect is known as the LCD motion blur. In order to reduce LCD motion blur, an accurate LCD model and an efficient deblurring algorithm are needed. However, existing LCD motion blur models are insufficient to reflect the limitation of human-eye-tracking system. Also, the spatiotemporal equivalence in LCD motion blur models has not been proven directly in the discrete 2-D spatial domain, although it is widely used. There are three main contributions of this paper: modeling, analysis, and algorithm. First, a comprehensive LCD motion blur model is presented, in which human-eye-tracking limits are taken into consideration. Second, a complete analysis of spatiotemporal equivalence is provided and verified using real video sequences. Third, an LCD motion blur reduction algorithm is proposed. The proposed algorithm solves an l_1-norm regularized least-squares minimization problem using a subgradient projection method. Numerical results show that the proposed algorithm gives higher peak SNR, lower temporal error, and lower spatial error than motion-compensated inverse filtering and Lucy–Richardson deconvolution algorithm, which are two state-of-the-art LCD deblurring algorithms. [ABSTRACT FROM PUBLISHER]

This paper presents a methodology for optimizing sample point selection in the context of model order reduction (MOR). The procedure iteratively selects samples from a large candidate set in order to identify a projection subspace that accurately captures system behavior. Samples are selected in an efficient and automatic manner based on their relevance measured through an error estimator. Projection vectors are computed only for the best samples according to the given criteria, thus minimizing the number of expensive solves. The scheme makes no prior assumptions on the system behavior, is general, and valid for single and multiple dimensions, with applicability on linear and parameterized MOR methodologies. The proposed approach is integrated into a multi-point MOR algorithm, with automatic sample and order selection based on a transfer function error estimation. Different implementations and improvements are proposed, and a wide range of results on a variety of industrial examples demonstrate the accuracy and robustness of the methodology. [ABSTRACT FROM PUBLISHER]

In this paper, we study the problem of optimal trajectory generation for a team of heterogeneous robots moving in a plane and tracking a moving target by processing relative observations, i.e., distance and/or bearing. Contrary to previous approaches, we explicitly consider limits on the robots’ speed and impose constraints on the minimum distance at which the robots are allowed to approach the target. We first address the case of a single tracking sensor and seek the next sensing location in order to minimize the uncertainty about the target’s position. We show that although the corresponding optimization problem involves a nonconvex objective function and a nonconvex constraint, its global optimal solution can be determined analytically. We then extend the approach to the case of multiple sensors and propose an iterative algorithm, i.e., the Gauss–Seidel relaxation (GSR), to determine the next best sensing location for each sensor. Extensive simulation results demonstrate that the GSR algorithm, whose computational complexity is linear in the number of sensors, achieves higher tracking accuracy than gradient descent methods and has performance that is indistinguishable from that of a grid-based exhaustive search, whose cost is exponential in the number of sensors. Finally, through experiments, we demonstrate that the proposed GSR algorithm is robust and applicable to real systems. [ABSTRACT FROM AUTHOR]

Adaptive dynamic programming (ADP) is a promising research field for design of intelligent controllers, which can both learn on-the-fly and exhibit optimal behavior. Over the past decades, several generations of ADP design have been proposed in the literature, which have demonstrated many successful applications in various benchmarks and industrial applications. While many of the existing researches focus on multiple-inputs-single-output system with steepest descent search, in this paper we investigate a generalized multiple-input-multiple-output (GMIMO) ADP design for online learning and control, which is more applicable to a wide range of practical real-world applications. Furthermore, an improved weight-updating algorithm based on recursive Levenberg–Marquardt methods is presented and embodied in the GMIMO approach to improve its performance. Finally, we test the performance of this approach based on a practical complex system, namely, the learning and control of the tension and height of the looper system in a hot strip mill. Experimental results demonstrate that the proposed approach can achieve effective and robust performance. [ABSTRACT FROM AUTHOR]

Analog very large scale integration implementations of neural networks can compute using a fraction of the size and power required by their digital counterparts. However, intrinsic limitations of analog hardware, such as device mismatch, charge leakage, and noise, reduce the accuracy of analog arithmetic circuits, degrading the performance of large-scale adaptive systems. In this paper, we present a detailed mathematical analysis that relates different parameters of the hardware limitations to specific effects on the convergence properties of linear perceptrons trained with the least-mean-square (LMS) algorithm. Using this analysis, we derive design guidelines and introduce simple on-chip calibration techniques to improve the accuracy of analog neural networks with a small cost in die area and power dissipation. We validate our analysis by evaluating the performance of a mixed-signal complementary metal-oxide-semiconductor implementation of a 32-input perceptron trained with LMS. [ABSTRACT FROM AUTHOR]

Requirements for system availability for ultrahigh reliability electronic systems such as airborne and space electronic systems are driving the need for advanced health monitoring techniques for the early detection of the onset of damage. Aerospace electronic systems usually face a very harsh environment, requiring them to survive the high strain rates, e.g., during launch and reentry, and thermal environments, including extremely low and high temperatures. Traditional health monitoring methodologies have relied on reactive methods of failure detection often providing little or no insight into the remaining useful life of the system. In this paper, a mathematical approach for the interrogation of the system state under cyclic thermomechanical stresses has been developed for six different lead-free solder alloy systems. Data have been collected for leading indicators of failure for alloy systems, including \Sn3\Ag0.5\Cu, \Sn0.3\Ag0.7\Cu, \Sn1\Ag0.5\Cu, \Sn0.3\Ag0.5\Cu0.1\Bi, \Sn0.2\Ag0.5\Cu0.1\Bi0.1\Ni, and 96.5\Sn3.5\Ag second-level interconnects under the application of cyclic thermomechanical loads. The methodology presented resides in the prefailure space of the system in which no macroindicators such as cracks or delamination exist. Systems subjected to thermomechanical damage have been interrogated for the system state and the computed damage state correlated with the known imposed damage. The approach involves the use of condition monitoring devices which can be interrogated for damage proxies at finite time intervals. The interrogation techniques are based on the derivation of damage proxies and system prior-damage-based nonlinear least square methods, including the Levenberg–Marquardt algorithm. The system's residual life is computed based on residual-life computation algorithms. [ABSTRACT FROM AUTHOR]

This paper studies a problem of image restoration that observed images are contaminated by Gaussian and impulse noise. Existing methods for this problem in the literature are based on minimizing an objective functional having the \ell^1 fidelity term and the Mumford–Shah regularizer. We present an algorithm on this problem by minimizing a new objective functional. The proposed functional has a content-dependent fidelity term which assimilates the strength of fidelity terms measured by the \ell^1 and \ell^2 norms. The regularizer in the functional is formed by the \ell^1 norm of tight framelet coefficients of the underlying image. The selected tight framelet filters are able to extract geometric features of images. We then propose an iterative framelet-based approximation/sparsity deblurring algorithm (IFASDA) for the proposed functional. Parameters in IFASDA are adaptively varying at each iteration and are determined automatically. In this sense, IFASDA is a parameter-free algorithm. This advantage makes the algorithm more attractive and practical. The effectiveness of IFASDA is experimentally illustrated on problems of image deblurring with Gaussian and impulse noise. Improvements in both PSNR and visual quality of IFASDA over a typical existing method are demonstrated. In addition, Fast_IFASDA, an accelerated algorithm of IFASDA, is also developed. [ABSTRACT FROM AUTHOR]

Reliably detecting objects in broad-area overhead or satellite images has become an increasingly pressing need, as the capabilities for image acquisition are growing rapidly. The problem is particularly difficult in the presence of large intraclass variability, e.g., finding “boats” or “buildings,” where model-based approaches tend to fail because no good model or template can be defined for the highly variable targets. This paper explores an automatic approach to detect and classify targets in high-resolution broad-area satellite images, which relies on detecting statistical signatures of targets, in terms of a set of biologically-inspired low-level visual features. Broad-area images are cut into small image chips, analyzed in two complementary ways: “attention/saliency” analysis exploits local features and their interactions across space, while “gist” analysis focuses on global nonspatial features and their statistics. Both feature sets are used to classify each chip as containing target(s) or not, using a support vector machine. Four experiments were performed to find “boats” (Experiments 1 and 2), “buildings” (Experiment 3) and “airplanes” (Experiment 4). In experiment 1, 14 416 image chips were randomly divided into training (300 boat, 300 nonboat) and test sets (13 816), and classification was performed on the test set (ROC area: 0.977 \pm 0.003). In experiment 2, classification was performed on another test set of 11 385 chips from another broad-area image, keeping the same training set as in experiment 1 (ROC area: 0.952 \pm 0.006). In experiment 3, 600 training chips (300 for each type) were randomly selected from 108 885 chips, and classification was conducted (ROC area: 0.922 \pm 0.005). In experiment 4, 20 training chips (10 for each type) were randomly selected to classify the remaining 2581 chips (ROC area: 0.976 \pm 0.003). The proposed algorithm outperformed the state-of-the-art SIFT, HMAX, and hidden-scale salient structure methods, and previous gist-only features in all four experiments. This study shows that the proposed target search method can reliably and effectively detect highly variable target objects in large image datasets. [ABSTRACT FROM AUTHOR]

Power electronics systems are becoming larger and more complex, involving a wide variety of loads, power sources, and power conditioning equipment. Simple behavioral models are required to perform a system-level analysis. These models can be parameterized from simple measurements or data supplied by the manufacturer. One of the emerging power sources are the fuel cells, which are being used in different power levels and applications. Some commercial products include not only the fuel cell stack, but also a dc–dc converter to provide a regulated output voltage despite the operation conditions of the fuel cell. In this paper, a behavioral model of a commercial fuel cell with a built-in front-end dc–dc converter is developed, based on load step tests. Model identification is carried out from experimental measurements using well-known identification algorithms. A validation of the obtained model has been performed by means of applying the same current patterns to the model and the actual fuel cell: steps; pulses; and sinusoids. Moreover, a simple power system has been built in order to validate the proposed model. A good trade-off between simplicity and accuracy is achieved. [ABSTRACT FROM AUTHOR]

In this paper a model selection algorithm for a nonlinear system identification method is proposed to study functional magnetic resonance imaging (fMRI) effective connectivity. Unlike most other methods, this method does not need a pre-defined structure/model for effective connectivity analysis. Instead, it relies on selecting significant nonlinear or linear covariates for the differential equations to describe the mapping relationship between brain output (fMRI response) and input (experiment design). These covariates, as well as their coefficients, are estimated based on a least angle regression (LARS) method. In the implementation of the LARS method, Akaike's information criterion corrected (AICc) algorithm and the leave-one-out (LOO) cross-validation method were employed and compared for model selection. Simulation comparison between the dynamic causal model (DCM), nonlinear identification method, and model selection method for modelling the single-input-single-output (SISO) and multiple-input multiple-output (MIMO) systems were conducted. Results show that the LARS model selection method is faster than DCM and achieves a compact and economic nonlinear model simultaneously. To verify the efficacy of the proposed approach, an analysis of the dorsal and ventral visual pathway networks was carried out based on three real datasets. The results show that LARS can be used for model selection in an fMRI effective connectivity study with phase-encoded, standard block, and random block designs. It is also shown that the LOO cross-validation method for nonlinear model selection has less residual sum squares than the AICc algorithm for the study. [ABSTRACT FROM AUTHOR]

Human action recognition and annotation is an active research topic in computer vision. How to model various actions, varying with time resolution, visual appearance, and others, is a challenging task. In this paper, we propose a boosted exemplar learning (BEL) approach to model various actions in a weakly supervised manner, i.e., only action bag-level labels are provided but action instance level ones are not. The proposed BEL method can be summarized as three steps. First, for each action category, amount of class-specific candidate exemplars are learned through an optimization formulation considering their discrimination and co-occurrence. Second, each action bag is described as a set of similarities between its instances and candidate exemplars. Instead of simply using a heuristic distance measure, the similarities are decided by the exemplar-based classifiers through the multiple instance learning, in which a positive (or negative) video or image set is deemed as a positive (or negative) action bag and those frames similar to the given exemplar in Euclidean Space as action instances. Third, we formulate the selection of the most discriminative exemplars into a boosted feature selection framework and simultaneously obtain an action bag-based detector. Experimental results on two publicly available datasets: the KTH dataset and Weizmann dataset, demonstrate the validity and effectiveness of the proposed approach for action recognition. We also apply BEL to learn representations of actions by using images collected from the Web and use this knowledge to automatically annotate action in YouTube videos. Results are very impressive, which proves that the proposed algorithm is also practical in unconstraint environments. [ABSTRACT FROM AUTHOR]

Although the recently proposed single-implicit-equation-based input voltage equations (IVEs) for the independent double-gate (IDG) MOSFET promise faster computation time than the earlier proposed coupled-equations-based IVEs, it is not clear how those equations could be solved inside a circuit simulator as the conventional Newton–Raphson (NR)-based root finding method will not always converge due to the presence of discontinuity at the G-zero point (GZP) and nonremovable singularities in the trigonometric IVE. In this paper, we propose a unique algorithm to solve those IVEs, which combines the Ridders algorithm with the NR-based technique in order to provide assured convergence for any bias conditions. Studying the IDG MOSFET operation carefully, we apply an optimized initial guess to the NR component and a minimized solution space to the Ridders component in order to achieve rapid convergence, which is very important for circuit simulation. To reduce the computation budget further, we propose a new closed-form solution of the IVEs in the near vicinity of the GZP. The proposed algorithm is tested with different device parameters in the extended range of bias conditions and successfully implemented in a commercial circuit simulator through its Verilog-A interface. [ABSTRACT FROM PUBLISHER]

Electronically controlled air suspension systems have been used in vehicles to improve ride comfort and handling safety by adjusting vehicle height. This paper proposes a new nonlinear controller to adjust the height of the vehicle sprung mass (height control) and to regulate the roll and pitch angles of the vehicle body (leveling control) by an air suspension system. A sliding mode control algorithm is designed to improve the tracking accuracy of the control and to overcome nonlinearities and uncertainties in the air suspension system. A mathematical model of the air suspension system is formulated in a nonlinear affine form to describe the dynamic behavior of the system and to derive the control algorithm. The sliding mode observer is also designed to estimate the pressures inside four air springs. The effectiveness and performance of the proposed control algorithm are verified by simulations and actual vehicle tests. [ABSTRACT FROM PUBLISHER]

Multiple object tracking (MOT) is a very challenging task yet of fundamental importance for many practical applications. In this paper, we focus on the problem of tracking multiple players in sports video which is even more difficult due to the abrupt movements of players and their complex interactions. To handle the difficulties in this problem, we present a new MOT algorithm which contributes both in the observation modeling level and in the tracking strategy level. For the observation modeling, we develop a progressive observation modeling process that is able to provide strong tracking observations and greatly facilitate the tracking task. For the tracking strategy, we propose a dual-mode two-way Bayesian inference approach which dynamically switches between an offline general model and an online dedicated model to deal with single isolated object tracking and multiple occluded object tracking integrally by forward filtering and backward smoothing. Extensive experiments on different kinds of sports videos, including football, basketball, as well as hockey, demonstrate the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a variational formulation for histogram transfer of two or more color images. We study an energy functional composed by three terms: one tends to approach the cumulative histograms of the transformed images, the other two tend to maintain the colors and geometry of the original images. By minimizing this energy, we obtain an algorithm that balances equalization and the conservation of features of the original images. As a result, they evolve while approaching an intermediate histogram between them. This intermediate histogram does not need to be specified in advance, but it is a natural result of the model. Finally, we provide experiments showing that the proposed method compares well with the state of the art. [ABSTRACT FROM AUTHOR]

This paper presents a multiphase fuzzy region competition model that takes into account spatial and frequency information for image segmentation. In the proposed energy functional, each region is represented by a fuzzy membership function and a data fidelity term that measures the conformity of spatial and frequency data within each region to (generalized) Gaussian densities whose parameters are determined jointly with the segmentation process. Compared with the classical region competition model, our approach gives soft segmentation results via the fuzzy membership functions, and moreover, the use of frequency data provides additional region information that can improve the overall segmentation result. To efficiently solve the minimization of the energy functional, we adopt an alternate minimization procedure and make use of Chambolle's fast duality projection algorithm. We apply the proposed method to synthetic and natural textures as well as real-world natural images. Experimental results show that our proposed method has very promising segmentation performance compared with the current state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In this paper, we present a probabilistic spatial approach to build compact 3-D representations of unknown objects probed by tactile sensors. Our approach exploits the high frame rates provided by modern tactile sensors and utilizes Kalman filters to build a probabilistic model of the contact point cloud that is efficiently stored in a kd-tree. The quality of generated shape representations is compared with a naive averaging approach, and we show that our method provides superior accuracy. We also evaluate the feasibility of object classification combining the generated object representations, together with the iterative closest point algorithm. [ABSTRACT FROM AUTHOR]

Minimization of total variation (TV) is a well-known method for image denoising. Recently, the relationship between TV minimization problems and binary MRF models has been much explored. This has resulted in some very efficient combinatorial optimization algorithms for the TV minimization problem in the discrete setting via graph cuts. To overcome limitations, such as staircasing effects, of the relatively simple TV model, variational models based upon higher order derivatives have been proposed. The Euler's elastica model is one such higher order model of central importance, which minimizes the curvature of all level lines in the image. Traditional numerical methods for minimizing the energy in such higher order models are complicated and computationally complex. In this paper, we will present an efficient minimization algorithm based upon graph cuts for minimizing the energy in the Euler's elastica model, by simplifying the problem to that of solving a sequence of easy graph representable problems. This sequence has connections to the gradient flow of the energy function, and converges to a minimum point. The numerical experiments show that our new approach is more effective in maintaining smooth visual results while preserving sharp features better than TV models. [ABSTRACT FROM PUBLISHER]

This paper presents a method for determination of the finite-element method model electrothermal parameters in a medium-voltage switchgear cell. The research includes a thermal calculation that is considered as a consequence of the eddy currents phenomena, which is a coupled numerical problem. Calibration of the numerical model is based on the multiobjective optimization procedure, which is carried out with the differential evolution algorithm (DE). The DE provides the best possible agreement of the measured temperature values on the test model and calculated values with the equivalent numerical model. The presented results are obtained for the linear and nonlinear case of eddy currents phenomena. The goal of the calibration is to determine the correct electrothermal parameters. [ABSTRACT FROM PUBLISHER]

In this paper, a model-based fault prognosis method is developed for hybrid systems with multiple incipient faults. The concept of augmented global analytical redundancy relations is proposed for the identification of degradation of components, such as sensors and actuators, which cannot be described by physical parameters. In addition, multiple incipient faults are considered in a complex hybrid system, and these faults can develop during a mode when the faults are not detectable. The unknown degradation characteristic of each incipient fault is identified with the closest matching one of some prescribed dynamic models. The resultant degradation model will serve as a base for prognosis. In the process of fault detection and isolation, the degradation models and faults are identified using a multiple-adaptive-hybrid-particle-swarm-optimization algorithm. The proposed methodology and algorithm are verified with simulation as well as experiments. [ABSTRACT FROM PUBLISHER]

This correspondence paper presents an algorithm in which the independent components of link lengths are used as a medium to analyze the forward kinematics of a six-degree-of-freedom Stewart platform. The link lengths are firstly transformed into independent components through independent component analysis. Then, the value of positional variables is computed by using the Nelder–Mead algorithm by taking advantage of the relationships between the independent components and the positional variables. Simulations have been conducted to test the proposed algorithm. The experimental results show that the proposed algorithm can achieve a better performance than the other published algorithms. [ABSTRACT FROM PUBLISHER]

Recent years have witnessed extensive research activity in modeling biological phenomena as well as in developing intervention strategies for such phenomena. S-systems, which offer a good compromise between accuracy and mathematical flexibility, are a promising framework for modeling the dynamical behavior of biological phenomena. In this paper, two different intervention strategies, namely direct and indirect, are proposed for the S-system model. In the indirect approach, the prespecified desired values for the target variables are used to compute the reference values for the control inputs, and two control algorithms, namely simple sampled-data control and model predictive control (MPC), are developed for transferring the control variables from their initial values to the computed reference ones. In the direct approach, a MPC algorithm is developed that directly guides the target variables to their desired values. The proposed intervention strategies are applied to the glycolytic–glycogenolytic pathway and the simulation results presented demonstrate the effectiveness of the proposed schemes. [ABSTRACT FROM PUBLISHER]