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Distribution systems commonly operate with a radial topology, so all models of optimization problems in these distribution systems should consider radiality in their formulation. This work presents a literature review, a critical analysis, and a proposal for incorporating the radiality constraints in mathematical models of optimization problems for radial distribution systems. The objective is to show that the radiality constraints on such optimization problems can be considered in a simple and efficient way. The reconfiguration and expansion planning problems of distribution systems are used to test and verify the proposed radiality constraints. A generalization of radiality constraints is also examined. [ABSTRACT FROM PUBLISHER]

The state-transition matrix of the Kth order Taylor approximation to the dynamic phasor and its first derivatives leads to a plurality of state-space representations to approach the bandpass signal model of a power oscillation. With these truncated signal models, the Kalman filter algorithm can be applied to their state vectors in order to find observers able to estimate the dynamic phasor and its first derivatives. The estimates obtained through this technique, from oscillatory signals, are not only instantaneous (no delay) but also synchronous, an important attribute for control applications. They also provide frequency estimates. The new filters reduce the total vector error achieved with the traditional Kalman filter; are much more stable, with settling times five times lower; and improve the phasor estimates of oscillations with frequency offset. [ABSTRACT FROM PUBLISHER]

The scattering of the H- and E-polarized plane waves by a thin flat homogeneous magneto-dielectric strip is considered. Assuming the strip to be thinner than the wavelength, we shrink its cross-section to the median line where the generalized boundary conditions are imposed. The numerical solution is built on two singular integral equations discretized using Nystrom-type numerical algorithm. The obtained results demonstrate fast convergence and good agreement with data known for the limiting values of the strip parameters. This opens a way to the accurate numerical analysis of various striplike configurations simulating natural objects and electromagnetic circuit components, both in traditional microwave applications and nanophotonics. [ABSTRACT FROM AUTHOR]

A modified precise integration time-domain (PITD) method, called PITD(4) algorithm, is presented in order to mitigate the numerical dispersion errors of a recently proposed PITD method. The PITD(4) method is based on both the fourth-order accurate finite-difference scheme and the precise integration technique. Both the stability condition and the numerical dispersion relations of the PITD(4) method are derived analytically and the effects of spatial and time steps on the numerical dispersion are investigated in detail. It is found that with the precise integration technique, the stability condition of the PITD(4) method is much larger than the Courant-Friedrich-levy (CFL) stability condition of the conventional finite difference time domain; with the fourth-order accurate finite-difference scheme, the numerical dispersion errors of the PITD(4) method are much less than that of the PITD methods. Numerical examples are presented to validate the accuracy and the effectiveness of the PITD(4) method, and to verify our analysis of the numerical dispersion characteristics of the PITD(4) method. [ABSTRACT FROM AUTHOR]

Roll-to-roll (R2R) printing systems provide a continuous process of thin, flexible material (called web) through printing cylinders, where the required patterns are printed on the web. Tension fluctuation caused by the upstream register control quantity may lead to the downstream register errors. Thus, it is a challenge to develop a register control algorithm to remove the couplings among the angular velocity, tension fluctuation, and register error. In this article, the register error is defined as the marks position misalignment between the first and the current printing unit. Based on the definition, a mathematical model of registration is derived. Based on the model, a fully decoupled proportional-derivative (FDPD) control algorithm is proposed to completely remove the coupling between the upstream register control quantity and the downstream register error. Simulations and experiments are carried out to demonstrate the effectiveness of the proposed control algorithm. Compared with the other compensation control methods, FDPD is a complete decoupling control method, which can control the register errors of all downstream printing units (more than seven printing units) within $\pm 0.05$ mm, even if the upstream printing unit has a 3 mm register error. Decoupling formula included in the FDPD control is a first-order compensation formula, which is easy to implement in the actual industry. Furthermore, the proposed FDPD can deal with the situation where two adjacent gravure cylinders have different web lengths, which is not taken into account by other control methods. [ABSTRACT FROM AUTHOR]

Fault localization is one of the most important issues for the modular multilevel converters (MMC) consisting of lots of switches. This article proposes a switch open-circuit fault localization strategy for the MMC, where a sliding-time window (STW) based features extraction algorithm (FEA) is proposed to extract the features of the MMC based on the feature relationship between neighboring STWs. Based on the extracted features, the fault in the MMC can be easily located with the 2-D convolutional neural networks. The proposed STW-FEA-based fault localization strategy can constructs concise low-data-volume features samples for the MMC in both time domain and frequency domain, and accordingly it can locate the fault with short time and high accuracy for the MMC. In addition, it does not require the creation of complex mathematical models and manual setting of empirical thresholds. Simulation and experiment are conducted, and the results confirm the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

This letter proposes a method to determine the optimal shift frequency (OSF) for shifted frequency (SF)-based simulation. The SF-based simulation algorithm is briefly introduced, and the error of SF-based simulation is quantitatively analyzed. The OSF that minimizes the error is deduced, which can improve the simulation accuracy. Furthermore, the influence of time-step size on the OSF is investigated. Numerical test results demonstrate the effectiveness of the method in determining the OSF for SF-based simulation. [ABSTRACT FROM AUTHOR]

To handle different types of many-objective optimization problems (MaOPs), many-objective evolutionary algorithms (MaOEAs) need to simultaneously maintain convergence and population diversity in the high-dimensional objective space. In order to balance the relationship between diversity and convergence, we introduce a Kernel matrix and probability model called determinantal point processes (DPPs). Our MaOEA with DPPs (MaOEADPPs) is presented and compared with several state-of-the-art algorithms on various types of MaOPs with different numbers of objectives. The experimental results demonstrate that MaOEADPPs is competitive. [ABSTRACT FROM AUTHOR]

Excellent control performance and high robustness under different operating conditions are primary purposes pursued by many model predictive control algorithms. As a model-based control algorithm, the accuracy of the stator current mathematical model has a significant impact on the control performance of the predictive current control (PCC) method. To improve the current tracking accuracy and the robustness against parameter variation, a stator current mathematical model self-regulation strategy, which uses stator current prediction error to calculate parameter changes and design a parameter variation compensation strategy to correct the mathematical model in real-time at each control cycle, based on PCC algorithm is proposed to pursue desired performance. Consequently, the elimination of stator current prediction error, high controlled quality, and better robustness have been achieved in the proposed method. The comparative simulation and experiment results validate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Preserving a reduced feedback load in Multiuser Diversity systems is crucial. By establishing either single or multiple thresholds only in the energy domain, the Selective Multiuser Diversity (SMUD) schemes achieve a reduced feedback load while maintaining a high capacity performance. This paper introduces an enhanced joint time-energy domain scheduling approach for SMUD systems that maximizes the total spectral efficiency and preserves a minimal feedback load with just a few thresholds in the joint domain. This joint domain capitalizes on the energy and the non-fade duration statistics in order to establish a feedback policy that is focused on those users who have good channel condition and preserve it for a predetermined amount of time, providing thus an automatically more rational and fair use of the resources without a sophisticated scheduling with large polling/decision times. The analysis of this enhanced approach for the general case of Nakagami fading channels is developed and expressions for the average system capacity and the average feedback load are presented. The tradeoff between the feedback load and the spectral efficiency gain is analyzed. Two main scenarios are studied; the first when there are two thresholds in the energy domain side and one in the time domain side; the second when there are two thresholds in both domains. [ABSTRACT FROM PUBLISHER]

In this paper, based on Poisson point processes, two new methods for joint nonhomogeneous clutter background estimation and multitarget tracking are presented. In many scenarios, after the signal detection process, measurement points provided by the sensor (e.g., sonar, infrared sensor, radar) are not distributed uniformly in the surveillance region as assumed by most tracking algorithms. On the other hand, in order to obtain accurate results, the target tracking filter requires information about clutter's spatial intensity. Thus, nonhomogeneous clutter spatial intensity has to be estimated from the measurement set and the tracking filter's output. Also, in order to take advantage of existing tracking algorithms, it is desirable for the clutter estimation method to be integrated into the tracker itself. Nonhomogeneous Poisson point processes, whose intensity function are assumed to be a mixture of Gaussian functions, are used to model clutter points here. Based on this model, a recursive maximum likelihood (ML) method and an approximated Bayesian method are proposed to estimate the nonhomogeneous clutter spatial intensity. Both clutter estimation methods are integrated into the probability hypothesis density (PHD) filter, which itself also uses the Poisson point process assumption. The mean and the covariance of each Gaussian function are estimated and used to calculate the clutter density in the update equation of the PHD filter. Simulation results show that both methods are able to improve the performance of the PHD filter in the presence of slowly time-varying nonhomogeneous clutter background. [ABSTRACT FROM PUBLISHER]

Rendering a complex surface accurately and without aliasing requires the evaluation of an integral for each pixel, namely, a weighted average of the outgoing radiance over the pixel footprint on the surface. The outgoing radiance is itself given by a local illumination equation as a function of the incident radiance and of the surface properties. Computing all this numerically during rendering can be extremely costly. For efficiency, especially for real-time rendering, it is necessary to use precomputations. When the fine scale surface geometry, reflectance, and illumination properties are specified with maps on a coarse mesh (such as color maps, normal maps, horizon maps, or shadow maps), a frequently used simple idea is to prefilter each map linearly and separately. The averaged outgoing radiance, i.e., the average of the values given by the local illumination equation is then estimated by applying this equation to the averaged surface parameters. But this is really not accurate because this equation is nonlinear, due to self-occlusions, self-shadowing, nonlinear reflectance functions, etc. Some methods use more complex prefiltering algorithms to cope with these nonlinear effects. This paper is a survey of these methods. We start with a general presentation of the problem of prefiltering complex surfaces. We then present and classify the existing methods according to the approximations they make to tackle this difficult problem. Finally, an analysis of these methods allows us to highlight some generic tools to prefilter maps used in nonlinear functions, and to identify open issues to address the general problem. [ABSTRACT FROM PUBLISHER]

Many face recognition algorithms use “distance-based” methods: Feature vectors are extracted from each face and distances in feature space are compared to determine matches. In this paper, we argue for a fundamentally different approach. We consider each image as having been generated from several underlying causes, some of which are due to identity (latent identity variables, or LIVs) and some of which are not. In recognition, we evaluate the probability that two faces have the same underlying identity cause. We make these ideas concrete by developing a series of novel generative models which incorporate both within-individual and between-individual variation. We consider both the linear case, where signal and noise are represented by a subspace, and the nonlinear case, where an arbitrary face manifold can be described and noise is position-dependent. We also develop a “tied” version of the algorithm that allows explicit comparison of faces across quite different viewing conditions. We demonstrate that our model produces results that are comparable to or better than the state of the art for both frontal face recognition and face recognition under varying pose. [ABSTRACT FROM AUTHOR]

The underlying assumption in traditional machine learning algorithms is that instances are Independent and Identically Distributed (IID). These critical independence assumptions made in traditional machine learning algorithms prevent them from going beyond instance boundaries to exploit latent relations between features. In this paper, we develop a general approach to supervised learning by leveraging higher order dependencies between features. We introduce a novel Bayesian framework for classification termed Higher Order Naïve Bayes (HONB). Unlike approaches that assume data instances are independent, HONB leverages higher order relations between features across different instances. The approach is validated in the classification domain on widely used benchmark data sets. Results obtained on several benchmark text corpora demonstrate that higher order approaches achieve significant improvements in classification accuracy over the baseline methods, especially when training data is scarce. A complexity analysis also reveals that the space and time complexity of HONB compare favorably with existing approaches. [ABSTRACT FROM PUBLISHER]

The Pulse-Coupled Oscillator (PCO) is a novel protocol inspired by models used in mathematical biology to justify the emergence of synchrony in the natural world. Our paper introduces and demonstrates the efficacy of a new PCO protocol implementation that, by disabling all collision resolution mechanisms for a suitable portion of the node operations, lets the rapid establishment of a common clock and its maintenance. The key idea is to allow signals to be superimposed in time, a feature that is absent in previous implementations, because it is prevented by traditional medium access schemes. We map the PCO protocol into an event-driven asynchronous coloring algorithm, based on the local exchange of information to explain its convergence properties. The event-based description of the PCO protocol sets the stage for our experimental comparison with a competing decentralized network synchronization approach, namely, the Reference Broadcast Protocol (RBS). For comparison, we combined RBS with an asynchronous average consensus protocol, running exactly on the same MicaZ platforms. The experimental results showcase the better scalability of the PCO scheme compared to the competing method based on RBS, proving that the PCO primitive is a reasonable option to consider for wireless sensor network applications. [ABSTRACT FROM AUTHOR]

This paper presents a new partial discharge (PD) location technique for power cables. Unlike commonly used time-domain reflectometry (TDR) methods, this technique is based on the analysis of phase difference between the direct and reflected waves of a PD pulse measured at the cable end. The phase difference at various frequencies can be obtained from the cross Fourier spectral density function. After implementing a phase unwrapping algorithm, the defect is located. This technique has been tested using the Electromagnetic Transient Program (EMTP) on the model of a 20 kV, 1-km underground cable. The simulation takes into account the frequency-dependent parameters of the cable and the measurement accuracy of the A/D converter (8-bit). The signal-to-noise ratio (SNR) varies from 10 dB to -5 dB for all computer simulations. The statistical results are given to demonstrate the performance of the proposed technique. [ABSTRACT FROM AUTHOR]

We propose a unified mathematical model for multilayer-multiframe compressive light field displays that supports both attenuation-based and polarization-based architectures. We show that the light field decomposition of such a display can be cast as a bound constrained nonlinear matrix optimization problem. Efficient light field decomposition algorithms are developed using the limited-memory BFGS (L-BFGS) method for automultiscopic displays with high resolution and high image fidelity. In addition, this framework is the first to support multilayer polarization-based compressive light field displays with time multiplexing. This new architecture significantly reduces artifacts compared with attenuation-based multilayer-multiframe displays; thus, it can allow the requirements regarding the number of layers or the refresh rate to be relaxed. We verify the proposed methods by constructing two 3-layer prototypes using high-speed LCDs, one based on the attenuation architecture and one based on the polarization architecture. Moreover, an efficient CUDA-based program is implemented. Our displays can produce images with higher spatial resolution with thinner form factors compared with traditional automultiscopic displays in both simulations and experiments. [ABSTRACT FROM AUTHOR]

The essence and main content of a mathematical model for substantiating an optimal combat staff of state armed forces are described from the standpoint of the theory of prevented damage with allowance for economic factors. This article is a continuation of previous investigations of methodical aspects of substantiating the combat staff of state armed forces from the standpoint of the theory of prevented damage. [ABSTRACT FROM AUTHOR]

A second-order wave equation-based implicit discontinuous Galerkin time-domain (DGTD) method is proposed to efficiently model electrically small problems. The proposed method employs the second-order wave equation for electric field (or magnetic field) as the governing equation of the DG formulation, instead of the first-order Maxwell’s curl equations. A modified version of the Riemann solver (upwind flux) is introduced to evaluate the numerical flux resulting from the weak form of the wave equation. Compared with previous first-order Maxwell’s curl equation-based implicit DGTD methods, which typically solve all electric and magnetic field unknowns for each subdomain, the proposed method only needs to solve for the electric field unknowns plus the surface magnetic field unknowns at subdomain interfaces. This reduces the dimensions of the resultant linear system and thus allows for modeling larger problems. Furthermore, unlike element-based DGTD methods, the proposed method is subdomain-based. The computational region is divided into multiple subdomains based on the domain-decomposition method, and each subdomain may contain multiple elements. Different element types and orders of basis functions can be employed in different subdomains to exploit the geometry property of the model. A nonconformal mesh is allowed between different subdomains to increase meshing flexibility. The Newmark-beta time-integration scheme is used for implicit temporal discretization, and fast direct linear solvers, such as the lower-diagonal-upper decomposition algorithm, are employed to accelerate time integration when all the subdomains are in a sequential order. Numerical results show that the proposed method is more efficient in terms of CPU time, and also saves memory with respect to the previous implicit DGTD method when modeling electrically small problems. [ABSTRACT FROM PUBLISHER]

An efficient algorithm without flux correction for the simulation of corona discharges is proposed. The algorithm referred to as the position-state separation (POSS) method is used to solve convection-dominated continuity equations commonly present in corona discharges modeling. The proposed solution method combines an Eulerian scheme for the solution of the convective acceleration, the diffusion and the reaction subproblems, and a Lagrangian scheme for the solution of the linear convection subproblem. Several classical numerical experiments in different dimensions and coordinate systems are conducted to demonstrate the excellent performance of POSS regarding low computational cost, robustness, and high resolution. It is shown that the time complexity of the method when dealing with the convection of charged particles increases linearly with the number of unknowns. For the simulation of corona discharges where local electric fields do not change strongly in time, the time step of POSS could be much larger than the Courant–Friedrichs–Lewy time step. These special features enable POSS to have great potential in the modeling of corona discharges in long interelectrode gaps and for long simulation times. [ABSTRACT FROM AUTHOR]

BackgroundDiscontinuity of spirometry reference values from childhood into adulthood has been a problem with traditional reference values, thus modern modelling approaches using smoothing spline functions to better depict the transition during growth and ageing have been recently introduced. Following the publication of the new international Global Lung Initiative (GLI2012) reference values also new national Finnish reference values have been calculated using similar GAMLSS-modelling, with spline estimates for mean (Mspline) and standard deviation (Sspline) provided in tables. The aim of this study was to produce polynomial estimates for these spline functions to use in lieu of lookup tables and to assess their validity in the reference population of healthy non-smokers.MethodsLinear regression modelling was used to approximate the estimated values for Mspline and Sspline using similar polynomial functions as in the international GLI2012 reference values. Estimated values were compared to original calculations in absolute values, the derived predicted mean and individually calculated z-scores using both values.ResultsPolynomial functions were estimated for all 10 spirometry variables. The agreement between original lookup table-produced values and polynomial estimates was very good, with no significant differences found. The variation slightly increased in larger predicted volumes, but a range of −0.018 to +0.022 litres of FEV1 representing ± 0.4% of maximum difference in predicted mean.ConclusionsPolynomial approximations were very close to the original lookup tables and are recommended for use in clinical practice to facilitate the use of new reference values. [ABSTRACT FROM PUBLISHER]

One of the main factors having a significant effect on fuel and energy saving in the production of rolled ferrous metals is matching the operation of the continuous casting machines with that of the re-heating furnaces so called continuous technologies in the form of direct rolling or hot charging. In order to investigate the heat exchange processes, the opportunities for enhancing the energy efficiency and determining the optimal parameters of the flat product production process in ferrous metallurgy, some mathematical models of metal solidification and cooling in a continuous steel casting machine were determined as well as a mathematical model of metal heating in the re-heating furnaces. For efficient implementation of such technologies one common algorithm was built on the basis of the individual mathematical models, representing the continuous casting - rolling mill complex control technology, dynamically matching the operation of the individual units to the actual production conditions in on-line mode. The developed algorithm can be used as part of a system for analyzing the thermal condition of the blocks at any single moment for the purpose of optimization of the units' operation within the whole technological process. As a conclusion, considering the original developed algorithm, a 21-51% energy saving was noticed. [ABSTRACT FROM AUTHOR]

Background: While mathematical models are often used to predict progression of cancer and treatment outcomes, there is still uncertainty over how to best model tumor growth. Seven ordinary differential equation (ODE) models of tumor growth (exponential, Mendelsohn, logistic, linear, surface, Gompertz, and Bertalanffy) have been proposed, but there is no clear guidance on how to choose the most appropriate model for a particular cancer.Methods: We examined all seven of the previously proposed ODE models in the presence and absence of chemotherapy. We derived equations for the maximum tumor size, doubling time, and the minimum amount of chemotherapy needed to suppress the tumor and used a sample data set to compare how these quantities differ based on choice of growth model.Results: We find that there is a 12-fold difference in predicting doubling times and a 6-fold difference in the predicted amount of chemotherapy needed for suppression depending on which growth model was used.Conclusion: Our results highlight the need for careful consideration of model assumptions when developing mathematical models for use in cancer treatment planning. [ABSTRACT FROM AUTHOR]

A solution of the problem of increasing the accuracy of a data-measuring system for the nondestructive testing of the thermal properties of materials is proposed. An algorithm for correcting the technical imperfection of the structural components of the system, based on the use of a mathematical model of the measurement procedure and information on influencing destabilizing factors, is developed. [ABSTRACT FROM AUTHOR]

Numerical modeling of medical ultrasound has advanced tremendously in the past two decades. This opens up a great number of opportunities for medical ultrasound and associated technologies. Numerous new governing equations and algorithms have emerged and been applied to studying various medical ultrasound applications, including ultrasound imaging, photo-acoustic imaging, and therapeutic ultrasound. In addition, thanks to the rapid development of computers, modeling acoustic wave propagation in three-dimensional, large-scale domains has become a reality. This article will provide an indepth literature and technical review of recent progress on numerical modeling of medical ultrasound. Future challenges will also be discussed. [ABSTRACT FROM AUTHOR]

A collision avoidance algorithm for a quadrotor unmanned aerial vehicle using a stereo vision sensor is proposed. A mathematical model for the quadrotor is developed, and the underactuated problem of the quadrotor is treated by introducing virtual inputs. Stereo vision is used to obtain depth map information, which is used to detect an obstacle. The collision cone approach is adopted to avoid collisions between the quadrotor and the detected obstacle. The collision prediction is conducted by using the relation between the velocity vector of the quadrotor and the collision cone. Information of the detected obstacles is accumulated to determine the location and size of the obstacles, which is used to prevent the quadrotor from colliding with the obstacle. Multiple obstacles can also be managed by creating obstacle clusters in the image plane. A waypoint guidance law and controller are designed using a feedback linearization scheme and a linear quadratic tracker. Numerical simulations are performed to demonstrate the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A nonlinear model predictive control strategy is presented for unmanned aerial vehicle (UAV) trajectory determination. The objective is to find optimal paths in the atmosphere by maximizing the UAV?s energy (kinetic and potential) over a finite but receding horizon. The main assumption is that the updraft distribution is unknown, creating a realistic situation. The updrafts are only estimated online using standard on-board inertial sensors. Real-time implementation of the algorithm is shown to be possible in principle. [ABSTRACT FROM AUTHOR]

We infer peak currents from radiation electric field peaks of 48 positive return strokes acquired in Gainesville, FL, USA, from 2007 to 2008. In doing so, we use the transmission line model, National Lightning Detection Network (NLDN) -reported distances, and assumed return-stroke speed. From a similar analysis of negative subsequent strokes, it appears that the implied return-stroke speed in the NLDN field-to-current conversion equation is 1.8 × 10^8 m/s (the NLDN peak current estimation algorithm is calibrated for negative subsequent strokes). The NLDN uses the same field-to-current conversion procedure (and hence the same implied return-stroke speed) for positive return strokes. However, NLDN-reported peak currents for positive return strokes differ from peak currents predicted by the transmission line model with an assumed return-stroke speed of 1.8 × 10^8 m/s. The discrepancy between regression equations for negative and positive return strokes suggests that the NLDN procedure to compensate for field propagation effects and find the average range-normalized signal strength (RNSS) works differently for these two groups of strokes. We find that the difference can be explained by the bias toward NLDN sensor reports from larger distances for positive strokes combined with the higher relative sensor gain (the ratio of sensor's peak current estimate to the NLDN-reported peak current) at larger distances. [ABSTRACT FROM PUBLISHER]

Images of electrical conductivity and permittivity of tissues may be used for diagnostic purposes as well as for estimating local specific absorption rate distributions. Magnetic resonance electrical properties tomography (MREPT) aims at noninvasively obtaining conductivity and permittivity images at radio-frequency frequencies of magnetic resonance imaging systems. MREPT algorithms are based on measuring the B1 field which is perturbed by the electrical properties of the imaged object. In this study, the relation between the electrical properties and the measured B1 field is formulated for the first time as a well-known convection-reaction equation. The suggested novel algorithm, called “cr-MREPT,” is based on the solution of this equation on a triangular mesh, and in contrast to previously proposed algorithms, it is applicable in practice not only for regions where electrical properties are relatively constant but also for regions where they vary. The convective field of the convection-reaction equation depends on the spatial derivatives of the B1 field, and in the regions where its magnitude is low, a spot-like artifact is observed in the reconstructed electrical properties images. For eliminating this artifact, two different methods are developed, namely “constrained cr-MREPT” and “double-excitation cr-MREPT.” Successful reconstructions are obtained using noisy and noise-free simulated data, and experimental data from phantoms. [ABSTRACT FROM PUBLISHER]

This study proposes a semiparametric Bayesian approach to accelerated life test (ALT). The proposed accelerated life test model assumes a log-linear lifetime-stress relationship, without making any assumption on the parametric form of the failure-time distribution. A Dirichlet process mixture model with a Weibull kernel is employed to model the failure-time distribution at a given stress level. A simulation-based model fitting algorithm that implements Gibbs sampling is developed to analyze right-censored ALT data, and to predict the failure-time distribution at the normal stress level. The proposed model and algorithm are applied to two practical examples related to the reliability of nanoelectronic devices. The results have demonstrated that the proposed methodology is capable of providing accurate prediction of the failure-time distribution at the normal stress level without assuming any restrictive parametric failure-time distribution. [ABSTRACT FROM PUBLISHER]

The goal of pathway analysis is to identify the pathways significantly impacted in a given phenotype. Many current methods are based on algorithms that consider pathways as simple gene lists, dramatically under-utilizing the knowledge that such pathways are meant to capture. During the past few years, a plethora of methods claiming to incorporate various aspects of the pathway topology have been proposed. These topology-based methods, sometimes referred to as "third generation," have the potential to better model the phenomena described by pathways. Although there is now a large variety of approaches used for this purpose, no review is currently available to offer guidance for potential users and developers. This review covers 22 such topology-based pathway analysis methods published in the last decade. We compare these methods based on: type of pathways analyzed (e.g., signaling or metabolic), input (subset of genes, all genes, fold changes, gene p-values, etc.), mathematical models, pathway scoring approaches, output (one or more pathway scores, p-values, etc.) and implementation (web-based, standalone, etc.). We identify and discuss challenges, arising both in methodology and in pathway representation, including inconsistent terminology, different data formats, lack of meaningful benchmarks, and the lack of tissue and condition specificity. [ABSTRACT FROM AUTHOR]

We investigate whether it is possible to use the bidomain model and body surface potential maps (BSPMs) to compute the size and position of ischemic regions in the human heart. This leads to a severely ill posed inverse problem for a potential equation. We do not use the classical inverse problems of electrocardiography, in which the unknown sources are the epicardial potential distribution or the activation sequence. Instead we employ the bidomain theory to obtain a model that also enables identification of ischemic regions transmurally. This approach makes it possible to distinguish between subendocardial and transmural cases, only using the BSPM data. The main focus is on testing a previously published algorithm on clinical data, and the results are compared with images taken with perfusion scintigraphy. For the four patients involved in this study, the two modalities produce results that are rather similar: The relative differences between the center of mass and the size of the ischemic regions, suggested by the two modalities, are 10.8% \pm 4.4% and 7.1% \pm 4.6%, respectively. We also present some simulations which indicate that the methodology is robust with respect to uncertainties in important model parameters. However, in contrast to what has been observed in investigations only involving synthetic data, inequality constraints are needed to obtain sound results. [ABSTRACT FROM AUTHOR]

Existing approaches to image reconstruction in photoacoustic computed tomography (PACT) with acoustically heterogeneous media are limited to weakly varying media, are computationally burdensome, and/or cannot effectively mitigate the effects of measurement data incompleteness and noise. In this work, we develop and investigate a discrete imaging model for PACT that is based on the exact photoacoustic (PA) wave equation and facilitates the circumvention of these limitations. A key contribution of the work is the establishment of a procedure to implement a matched forward and backprojection operator pair associated with the discrete imaging model, which permits application of a wide-range of modern image reconstruction algorithms that can mitigate the effects of data incompleteness and noise. The forward and backprojection operators are based on the k-space pseudospectral method for computing numerical solutions to the PA wave equation in the time domain. The developed reconstruction methodology is investigated by use of both computer-simulated and experimental PACT measurement data. [ABSTRACT FROM AUTHOR]

For estimating the missing data of wireless sensor networks, an estimation algorithm called HD method, which can make use of sensoring space-time correlation of the data, was proposed based on mathematical Hermite and DESM statistical models. The algorithm not only can adaptively adjust the time and space weights, but also can accurately estimate the missing or unavailable data. The experimental results show that the algorithm has good stability and relatively high estimation accuracy. [ABSTRACT FROM AUTHOR]

A mathematical model of a circular disk-shaped brittle crack whose faces are acted upon by cohesive forces, whose magnitude depends on the distance between the faces. An algorithm of numerical solution of the singular nonlinear improper integral equation defining the crack profile was developed which can be used for other integral equations of this type. It is shown that taking into account the coupling forces between the crack faces leads to their gradual closing with distance from the center of the crack. [ABSTRACT FROM AUTHOR]

We present TransCut, a technique for interactive rendering of translucent objects undergoing fracturing and cutting operations. As the object is fractured or cut open, the user can directly examine and intuitively understand the complex translucent interior, as well as edit material properties through painting on cross sections and recombining the broken pieces—all with immediate and realistic visual feedback. This new mode of interaction with translucent volumes is made possible with two technical contributions. The first is a novel solver for the diffusion equation (DE) over a tetrahedral mesh that produces high-quality results comparable to the state-of-art finite element method (FEM) of Arbree et al. [1] but at substantially higher speeds. This accuracy and efficiency is obtained by computing the discrete divergences of the diffusion equation and constructing the DE matrix using analytic formulas derived for linear finite elements. The second contribution is a multiresolution algorithm to significantly accelerate our DE solver while adapting to the frequent changes in topological structure of dynamic objects. The entire multiresolution DE solver is highly parallel and easily implemented on the GPU. We believe TransCut provides a novel visual effect for heterogeneous translucent objects undergoing fracturing and cutting operations. [ABSTRACT FROM PUBLISHER]

Due to the variable characteristics of renewable generation, batteries used in renewable-power systems can undergo many irregular, partial charge/discharge cycles. In turn, this can also have a detrimental effect on battery lifetime and can increase project costs. This study presents a method of improving battery lifetime in a small-scale remote-area wind-power system by the use of a battery/supercapacitor hybrid energy storage system. The supervisory control algorithm and hardware implementation are described and projected long-term benefits of the proposed system are assessed by simulation. A representative dynamic model of the overall system, incorporating realistic wind-speed and load-power variations has been developed. An analysis is presented of the potential improvement in battery lifetime that is achievable by diverting short-term charge/discharge cycles to a supercapacitor energy-storage system. This study introduces a method by which supercapacitor energy storage systems and control algorithms can be evaluated and implemented in the application area considered. The composition of a prototype test system is described and experimental results are presented to demonstrate system feasibility. [ABSTRACT FROM AUTHOR]

Path planning considers the problem of designing the path a vehicle is supposed to follow. Along the designed path the objectives are to maximize the collected information (CI) from desired regions (DR), while avoiding flying over forbidden regions (FR) and reaching the destination. The path planning problem for a single unmanned air vehicle (UAV) is studied with the proposal of novel evolutionary operators: pull-to-desired-region (PTDR), push-from-forbidden-region (PFFR), and pull-to-final-point (PTFP). In addition to these newly proposed operators, standard mutation and crossover operators are used. The initial population seed-path is obtained by both utilizing the pattern search method and solving the traveling salesman problem (TSP). Using this seed-path the initial population of paths is generated by randomly selected heading angles. It should be emphasized that all of the paths in population in any generation of the genetic algorithm (GA) are constructed using the dynamical mathematical model of a UAV equipped with the autopilot and guidance algorithms. Simulations are realized in the MATLAB/Simulink environment. The path planning algorithm is tested with different scenarios, and the results are presented in Section VI. Although there are previous studies in this field, the focus here is on maximizing the CI instead of minimizing the total mission time. In addition it is observed that the proposed operators generate better paths than classical evolutionary operators. [ABSTRACT FROM PUBLISHER]

A partially perturbed particle swarm optimization (PPSO) has been proposed for identifying the parameters of the Beeler–Reuter (BR) equation from action potential data. In the PPSO algorithm, the 63 BR equation parameters are divided into groups, and parameter patterns are made from the combination of the groups. PPSO enhances the capability of conventional particle swarm optimization (CPSO) by partially perturbing the coordinates of the globally best particle with the patterns when the searching process is locally confined. “Experimental data” were produced for cardiac myocytes simulated by the BR equation and the equation of Luo and Rudy (1991), and were used to test the algorithm of PPSO. The test results show that PPSO was able to identify the parameters of the BR equation effectively for different cardiac myocytes, while still retaining the conceptual simplicity and easy implementation of CPSO. [ABSTRACT FROM PUBLISHER]

Deriving the proper density to achieve the region coverage for random sensors deployment is a fundamentally important problem in the area of wireless sensor networks. Most existing works on sensor coverage mainly concentrate on the two-dimensional (2D) plane coverage which assume that all the sensors are deployed on an ideal plane. In contrast, sensors are also deployed on the three-dimensional (3D) rolling surfaces in many real applications. Toward this end, we study the coverage problem of wireless sensor networks for the rolling terrains, and derive the expected coverage ratios under the stochastic sensors deployment. According to the different terrain features, we investigate two kinds of terrain coverage problems: the regular terrain coverage problem and the irregular terrain coverage problem. Specifically, we derive the general expression of the expected coverage ratio for an arbitrary surface z=f(x,y) and build two models, cone model and Cos-revolution model, to estimate the expected coverage ratios for regular terrains. For irregular terrains, we propose a digital elevation model (DEM) based method to calculate the expected coverage ratio and design an algorithm to estimate the expected coverage ratio of an interested region by using only the contour map of this region. We also conduct extensive simulations to validate and evaluate our proposed models and schemes. [ABSTRACT FROM AUTHOR]

A SPICE lumped circuit subcell model is formulated within the discontinuous Galerkin finite-element time-domain (DGFETD) discretization of Maxwell's equations. A fourth-order exponential time difference (ETD) algorithm is used for circuits that lead to stiff systems. The ETD method reduces to a standard fourth-order Runge–Kutta (RK4) time-integration for nonstiff regions. A number of test cases, including a microstrip transmission line terminated with general RLC networks, load arrays, and a diode detector are presented for the validation of the proposed hybrid DGFETD/SPICE solution method. [ABSTRACT FROM PUBLISHER]

Closed-loop decoder adaptation (CLDA) shows great promise to improve closed-loop brain–machine interface (BMI) performance. Developing adaptation algorithms capable of rapidly improving performance, independent of initial performance, may be crucial for clinical applications where patients have limited movement and sensory abilities due to motor deficits. Given the subject-decoder interactions inherent in closed-loop BMIs, the decoder adaptation time-scale may be of particular importance when initial performance is limited. Here, we present SmoothBatch, a CLDA algorithm which updates decoder parameters on a 1–2 min time-scale using an exponentially weighted sliding average. The algorithm was experimentally tested with one nonhuman primate performing a center-out reaching BMI task. SmoothBatch was seeded four ways with varying offline decoding power: 1) visual observation of a cursor ( n = 20), 2) ipsilateral arm movements ( n = 8), 3) baseline neural activity ( n = 17), and 4) arbitrary weights ( n = 11). SmoothBatch rapidly improved performance regardless of seeding, with performance improvements from 0.018 \pm 0.133 successes/min to >8 successes/min within 13.1 \pm 5.5 min ( n = 56). After decoder adaptation ceased, the subject maintained high performance. Moreover, performance improvements were paralleled by SmoothBatch convergence, suggesting that CLDA involves a co-adaptation process between the subject and the decoder. [ABSTRACT FROM PUBLISHER]

When dealing with motion blur, there is an inevitable tradeoff between the amount of blur and the amount of noise in the acquired images. The effectiveness of any restoration algorithm typically depends on these amounts, and it is difficult to find their best balance in order to ease the restoration task. To face this problem, we provide a methodology for deriving a statistical model of the restoration performance of a given deblurring algorithm in case of arbitrary motion. Each restoration-error model allows us to investigate how the restoration performance of the corresponding algorithm varies as the blur due to motion develops. Our modeling treats the point-spread-function trajectories as random processes and, following a Monte Carlo approach, expresses the restoration performance as the expectation of the restoration error conditioned on some motion-randomness descriptors and on the exposure time. This allows us to coherently encompass various imaging scenarios, including camera shake and uniform (rectilinear) motion, and, for each of these, identify the specific exposure time that maximizes the image quality after deblurring. [ABSTRACT FROM AUTHOR]

Detection of salient motion in a video involves determining which motion is attended to by the human visual system in the presence of background motion that consists of complex visuals that are constantly changing. Salient motion is marked by its predictability compared to the more complex unpredictable motion of the background such as fluttering of leaves, ripples in water, dispersion of smoke, and others. We introduce a novel approach to detect salient motion based on the concept of “observability” from the output pixels, when the video sequence is represented as a linear dynamical system. The group of output pixels with maximum saliency is further used to model the holistic dynamics of the salient region. The pixel saliency map is bolstered by two region-based saliency maps, which are computed based on the similarity of dynamics of the different spatiotemporal patches in the video with the salient region dynamics, in a global as well as a local sense. The resulting algorithm is tested on a set of challenging sequences and compared to state-of-the-art methods to showcase its superior performance on grounds of its computational efficiency and ability to detect salient motion. [ABSTRACT FROM PUBLISHER]

In this correspondence, our goal is to develop a visual tracking algorithm that is able to track moving objects in the presence of illumination variations in the scene and that is robust to occlusions. We treat the illumination and motion (x\ -y translation and scale) parameters as the unknown “state” sequence. The observation is the entire image, and the observation model allows for occasional occlusions (modeled as outliers). The nonlinearity and multimodality of the observation model necessitate the use of a particle filter (PF). Due to the inclusion of illumination parameters, the state dimension increases, thus making regular PFs impractically expensive. We show that the recently proposed approach using a PF with a mode tracker can be used here since, even in most occlusion cases, the posterior of illumination conditioned on motion and the previous state is unimodal and quite narrow. The key idea is to importance sample on the motion states while approximating importance sampling by posterior mode tracking for estimating illumination. Experiments demonstrate the advantage of the proposed algorithm over existing PF-based approaches for various face and vehicle tracking. We are also able to detect illumination model changes, e.g., those due to transition from shadow to sunlight or vice versa by using the generalized expected log-likelihood statistics and successfully compensate for it without ever loosing track. [ABSTRACT FROM AUTHOR]

We study the use of the FitzHugh–Nagumo (FHN) model for capturing neural spiking. The FHN model is a widely used approximation of the Hodgkin–Huxley model that has significant limitations. In particular, it cannot produce the key spiking behavior of bursting. We illustrate that by allowing time-varying parameters for the FHN model, these limitations can be overcome while retaining its low-order complexity. This extension has applications in modeling neural spiking behaviors in the thalamus and the respiratory center. We demonstrate the use of the FHN model from an estimation perspective by presenting a novel parameter estimation method that exploits its multiple time-scale properties, and compare the performance of this method with the extended Kalman filter in several illustrative examples. We demonstrate that the dynamics of the spiking threshold can be recovered even in the absence of complete specifications for the system. [ABSTRACT FROM PUBLISHER]

We have developed a new method of compensating for effects of partial volume and spillover in dual-modality imaging. The approach requires segmentation of just a few tissue types within a small volume-of-interest (VOI) surrounding a lesion; the algorithm estimates simultaneously, from projection data, the activity concentration within each segmented tissue inside the VOI. Measured emission projections were fitted to the sum of resolution-blurred projections of each such tissue, scaled by its unknown activity concentration, plus a global background contribution obtained by reprojection through the reconstructed image volume outside the VOI. The method was evaluated using multiple-pinhole \muSPECT data simulated for the MOBY mouse phantom containing two spherical lung tumors and one liver tumor, as well as using multiple-bead phantom data acquired on \muSPECT and \muCT scanners. Each VOI in the simulation study was 4.8 mm (12 voxels) cubed and, depending on location, contained up to four tissues (tumor, liver, heart, lung) with different values of relative ^99mTc concentration. All tumor activity estimates achieved <3\% bias after \sim15 ordered-subsets expectation maximization (OSEM) iterations (\times 10~\ subsets), with better than 8% precision (\leq25\% greater than the Cramer–Rao lower bound). The projection-based fitting approach also outperformed three standardized uptake value (SUV)-like metrics, one of which was corrected for count spillover. In the bead phantom experiment, the mean \pm standard deviation of the bias of VOI estimates of bead concentration were 0.9\pm 9.5\%, comparable to those of a perturbation geometric transfer matrix (pGTM) approach (-5.4\pm 8.6\%); however, VOI estimates were more stable with increasing iteration number than pGTM estimates, even in the presence of substantial axial misalignment between \muCT and \muSPECT image volumes. [ABSTRACT FROM PUBLISHER]