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This paper proposes the design of robust repetitive control with time-varying sampling periods. First, it develops a new frequency domain method to design a low-order, stable, robust, and causal IIR repetitive compensator using an optimization method to achieve fast convergence and high tracking accuracy. As such, a new stable and causal repetitive controller can be implemented independently to reduce the design complexity. The comprehensive analysis and comparison study are presented. Then, this paper extends the method to design a robust repetitive controller, which compensates time-varying periodic signals in a known range. A complete series of experiments is successfully carried out to demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the network resource allocation in multiuser downlink wireless systems where the base station and the mobile stations are equipped with multiple antennas to provide fair and efficient transmission services to the mobile users. We focus on packet scheduling, given that it has a significant impact on the overall performance of a multiple-input-multiple-output (MIMO) system. Most previous schedulers designed at the packet level do not take into account the traffic characteristics (different packet lengths and the arrival process parameters); consequently, they fall short of simultaneously providing fairness and a low average packet transmission delay. We are making use of a flexible packet transmission algorithm at the medium access control (MAC) layer to develop and propose a novel scheduler, which is referred to as MIMO packet-based proportional fairness (MP-PF). The new scheduler is designed with the goal of providing high performance in terms of a low average packet transmission delay and time and service fairness among the users based on the concept of proportional fairness. The scheduler also conserves work and takes into consideration the packet length, the user queue length, the user transmission rate (related to its channel quality), and the service guarantees for heterogeneous users. The well-known ideal service fair scheduler called max-min can also significantly be improved using our framework by taking into consideration the traffic characteristics. We also provide an analysis for the fairness of the new scheduler in terms of time and service allocation, which is the key contribution of this paper. Simulations that consider the traffic characteristics and the mobility of users show the relatively low average packet transmission delay and demonstrate the time and service fairness capabilities of MP-PF, compared with other well-known MIMO schedulers. [ABSTRACT FROM AUTHOR]

This paper solves the variation-aware decoupling capacitance (decap) budgeting problem. Unlike previous works which only consider worst case design, for the first time, we consider the input of both process variation and operation variation for decap budgeting. A novel stochastic current model is proposed that efficiently and accurately captures temporal correlation between clock cycles, logic-induced correlation between ports, and current variation due to process variation with spatial correlation. An iterative alternative programming algorithm that is applicable to a variety of current models is then developed. Compared with the baseline model which assumes maximum current peaks at all ports, the model considering temporal correlation reduces noise by up to 5×, and the model considering both temporal and logic-induced correlations reduces noise by up to 17×. Compared with using deterministic process parameters, considering process variation (in particular Leff variation) reduces the mean noise by up to 4× and 3σ noise by up to 13× when both applying the current model with temporal and logic-induced correlations. Note that stochastic optimization has been used mainly for process variation in the literature, but this paper convincingly demonstrate that stochastic optimization considering operation variation is effective to reduce overdesign introduced by worst case design for power integrity. Such stochastic optimization has a wide scope of applications to design problems. To the best of our knowledge, this is the first in-depth study on decap insertion for power network design considering current correlations including process variation. [ABSTRACT FROM AUTHOR]

This paper presents a new low-power test scheme integrated with the embedded deterministic test environment. The key contribution of this paper is a flexible test cube encoding scheme, which, in conjunction with a continuous flow decompressor, allows one to significantly reduce toggling rates when test patterns are fed into scan chains. The proposed solution requires neither additional design for testability logic nor modifications to the circuit under test. Experimental results obtained for industrial designs indicate that using this nonintrusive technique reduces switching activity to such extent that the resultant scan-in power consumption is similar to that of the functional mode, thus alleviating problems that are related to average and peak power dissipation, overheating, and risk of reliability degradation. Our approach seamlessly integrates with test logic synthesis flow, and it does not compromise compression ratios. Moreover, it fits well into various design paradigms, including modular design flow where modules come with individual decompressors and compactors. [ABSTRACT FROM AUTHOR]

This paper departs from the popular usage of the Backus-Gilbert inversion (BGI) method as a tool for inversion of antenna temperature measurements in microwave radiometry. The BGI method is applied in this paper to enhance the information content of an existing set of oversampled brightness-temperature (TB) data. The purpose is to isolate the inversion process from its resolution enhancement counterpart. The advantage gained is that the resolution enhancement can be performed in a simplified way and in a different level of processing that starts with the scan-mode TB data product and simply requires with it the knowledge of the antenna gain pattern and the sensor's scan geometry. The technique is demonstrated with the 19.35-GHz Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) channel, which provides oversampled TB data. The radiometric resemblance of this channel with that of the 37 GHz and geocollocation of their TB footprints facilitate validation of the enhancement of features. The significance of oversampling the low-frequency (LF) radiometer channels is underscored in the process, which gives the authors the confidence to propose oversampling of the LF data for the forthcoming sensor Microwave Analysis and Detection of Rain and Atmospheric Structures (MADRAS) onboard the Megha-Tropiques mission, which is a joint ISRO-CNES collaboration (due for launch in 2009). [ABSTRACT FROM AUTHOR]

In this paper, the duality in differential form is developed between a 3D primal surface and its dual manifold formed by the surface's tangent planes, that is, each tangent plane of the primal surface is represented as a four-dimensional vector that constitutes a point on the dual manifold. The iterated dual theorem shows that each tangent plane of the dual manifold corresponds to a point on the original 3D surface, that is, the "dual" of the "dual" goes back to the "primal." This theorem can be directly used to reconstruct 3D surface from image edges by estimating the dual manifold from these edges. In this paper, we further develop the work in our original conference papers resulting in the robust differential dual operator. We argue that the operator makes good use of the information available in the image data by using both points of intensity discontinuity and their edge directions; we provide a simple physical interpretation of what the abstract algorithm is actually estimating and why it makes sense in terms of estimation accuracy; our algorithm operates on all edges in the images, including silhouette edges, self occlusion edges, and texture edges, without distinguishing their types (thus, resulting in improved accuracy and handling locally concave surface estimation if texture edges are present); the algorithm automatically handles various degeneracies; and the algorithm incorporates new methodologies for implementing the required operations such as appropriately relating edges in pairs of images, evaluating and using the algorithm's sensitivity to noise to determine the accuracy of an estimated 3D point. Experiments with both synthetic and real images demonstrate that the operator is accurate, robust to degeneracies and noise, and general for reconstructing free-form objects from occluding edges and texture edges detected in calibrated images or video sequences. [ABSTRACT FROM AUTHOR]

Continual scaling of transistors and interconnects has exacerbated the power and thermal management problems in the design of ultralarge-scale integrated (ULSI) circuits. This pa- per presents an efficient thermal-analysis method of O(N lg N) complexity, where N is the number of blocks that discretize the heat-source or temperature-observation regions. The method is named LOTAGre and formulated using the Green's function for heat conduction through multiple-layer materials, which account for the structure of ULSI chips and the accompanying heat sinks and mounting accessories. In addition to analyzing the thermal effects of the distributive heat sources, LOTAGre also considers the ambient temperature effects that are generally excluded in conventional Green's function-based thermal-analysis tools in order to avoid the concomitant analytical complexity. By employing the well-known eigen-expansion technique and classical transmission-line theory, fully analytical and explicit formulas are derived in thus paper for the multilayer Green's function with the inclusion of the s-domain version, the homogeneous and inhomogeneous solutions to the heat-conduction equation. Then, the discrete cosine transform and its inversion are employed to accelerate the numerical computation of the homogeneous and inhomogeneous solutions. This paper includes extensive experimental results to demonstrate that LOTAGre can be as accurate as FLUENT, a sophisticated computational fluid dynamics tool, while speeding up the simulation run time by two to three orders of magnitude in comparison to FLUENT as well as conventional Green's function-based thermal-analysis methods. This paper also discusses the limitations of using the traditional single-layer thermal model in thermal analysis for approximating a multilayer chip structure. [ABSTRACT FROM AUTHOR]

We consider a statistical model for the problem of finding subgraphs with specified topology in an otherwise random graph. This task plays an important role in the analysis of social and biological networks. In these types of networks, small subgraphs with a specific structure have important functional roles, and they are referred to as motifs. Within this model, one or multiple copies of a subgraph is added (planted) in an Erdős–Renyi random graph with $n$ vertices and edge probability $q_{0}$. We ask whether the resulting graph can be distinguished reliably from a pure Erdős–Renyi random graph, and we present two types of result. First we investigate the question from a purely statistical perspective, and ask whether there isanytest that can distinguish between the two graph models. We provide necessary and sufficient conditions that are essentially tight for small enough subgraphs. Next we study two polynomial-time algorithms for solving the same problem: a spectral algorithm and a semidefinite programming (SDP) relaxation. For the spectral algorithm, we establish sufficient conditions under which it distinguishes the two graph models with high probability. Under the same conditions the spectral algorithm indeed identifies the hidden subgraph. The spectral algorithm is substantially sub-optimal with respect to the optimal test. We show that a similar gap is present for the more sophisticated SDP approach. [ABSTRACT FROM AUTHOR]

As the technology nodes scale down to sub-22 nm, double patterning lithography has been considered as a practical solution for layout manufacturing. Compared with litho-etch-litho-etch, self-aligned double patterning (SADP) has better overlay control. To have a better SADP layout decomposability of routing patterns, we consider SADP during detailed routing stage. Two major types of SADP processes are considered: 1) spacer-is-dielectric type and 2) spacer-is-metal type. Different from previous works, the idea of color preassignment is adopted for SADP-aware detailed routing. An elegant graph model is proposed to capture both routing and SADP manufacturing cost. They greatly simplify the problem to maintain SADP design rules in detailed routing. We apply a negotiated congestion based rip-up and reroute scheme to achieve better routability while maintaining SADP design rules. Compared with other state-of-the-art academic works, our approach does not produce any side overlay error and no SADP design rules violation is reported. Meanwhile, a better solution in terms of total wirelength, routability, and runtime is achieved. [ABSTRACT FROM AUTHOR]

We propose a generalized Sparse Representation-based Classification (SRC) algorithm for open set recognition where not all classes presented during testing are known during training. The SRC algorithm uses class reconstruction errors for classification. As most of the discriminative information for open set recognition is hidden in the tail part of the matched and sum of non-matched reconstruction error distributions, we model the tail of those two error distributions using the statistical Extreme Value Theory (EVT). Then we simplify the open set recognition problem into a set of hypothesis testing problems. The confidence scores corresponding to the tail distributions of a novel test sample are then fused to determine its identity. The effectiveness of the proposed method is demonstrated using four publicly available image and object classification datasets and it is shown that this method can perform significantly better than many competitive open set recognition algorithms. [ABSTRACT FROM PUBLISHER]

Pooling plays an important role in generating a discriminative video representation. In this paper, we propose a new semantic pooling approach for challenging event analysis tasks (e.g., event detection, recognition, and recounting) in long untrimmed Internet videos, especially when only a few shots/segments are relevant to the event of interest while many other shots are irrelevant or even misleading. The commonly adopted pooling strategies aggregate the shots indifferently in one way or another, resulting in a great loss of information. Instead, in this work we first define a novel notion of semantic saliency that assesses the relevance of each shot with the event of interest. We then prioritize the shots according to their saliency scores since shots that are semantically more salient are expected to contribute more to the final event analysis. Next, we propose a new isotonic regularizer that is able to exploit the constructed semantic ordering information. The resulting nearly-isotonic support vector machine classifier exhibits higher discriminative power in event analysis tasks. Computationally, we develop an efficient implementation using the proximal gradient algorithm, and we prove new and closed-form proximal steps. We conduct extensive experiments on three real-world video datasets and achieve promising improvements. [ABSTRACT FROM PUBLISHER]

We study optimal multirobot path planning on graphs (\textMPP ) over four minimization objectives: the makespan (last arrival time), the maximum (single-robot traveled) distance, the total arrival time, and the total distance. Having established previously that these objectives are distinct and NP-hard to optimize, in this paper, we focus on efficient algorithmic solutions for solving these optimal \textMPP problems. Toward this goal, we first establish a one-to-one solution mapping between \textMPP and a special type of multiflow network. Based on this equivalence and integer linear programming (ILP), we design novel and complete algorithms for optimizing over each of the four objectives. In particular, our exact algorithm for computing optimal makespan solutions is a first that is capable of solving extremely challenging problems with robot-vertex ratios as high as $100\%$ . Then, we further improve the computational performance of these exact algorithms through the introduction of principled heuristics, at the expense of slight optimality loss. The combination of ILP model based algorithms and the heuristics proves to be highly effective, allowing the computation of $1.x$-optimal solutions for problems containing hundreds of robots, densely populated in the environment, often in just seconds. [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]

In this paper, we address the theoretical aspects of coherence region modeling and its inversion methods for polarimetric interferometric synthetic aperture radar (PolInSAR). Instead of only considering the geometrical shape of the coherence region, we focus on directly modeling the whitened interferometric cross-correlation matrix of the PolInSAR data. In particular, we consider three classes of generic scattering models that respectively contain single, double, and triple phase centers within one resolution cell. We then demonstrate, for each class, that the modeled whitened interferometric cross-correlation matrix is normal. Based on this property, we propose, for each case, efficient algorithms to obtain the best normal matrix approximation solution for model inversion from the observed whitened interferometric cross-correlation matrix. In addition, we show how a simple and effective criterion can be designed from the derived solutions for model validation. Finally, we verify the solutions with both simulated and real data. The results prove the proposed method to be a very promising tool for PolInSAR applications. [ABSTRACT FROM PUBLISHER]

Topic modeling has become a widely used tool for document management. However, there are few topic models distinguishing the importance of documents on different topics. In this paper, we propose a framework LIMTopic to incorporate link based importance into topic modeling. To instantiate the framework, RankTopic and HITSTopic are proposed by incorporating topical pagerank and topical HITS into topic modeling respectively. Specifically, ranking methods are first used to compute the topical importance of documents. Then, a generalized relation is built between link importance and topic modeling. We empirically show that LIMTopic converges after a small number of iterations in most experimental settings. The necessity of incorporating link importance into topic modeling is justified based on KL-Divergences between topic distributions converted from topical link importance and those computed by basic topic models. To investigate the document network summarization performance of topic models, we propose a novel measure called log-likelihood of ranking-integrated document-word matrix. Extensive experimental results show that LIMTopic performs better than baseline models in generalization performance, document clustering and classification, topic interpretability and document network summarization performance. Moreover, RankTopic has comparable performance with relational topic model (RTM) and HITSTopic performs much better than baseline models in document clustering and classification. [ABSTRACT FROM AUTHOR]

This paper introduces the minimum-time arbitrarily constrained distributed scheduling problem. The optimal distributed nonsequential backtracking algorithm is used by agents to schedule tasks where the tasks are related by constraints and where the assignment of tasks to agents may change during scheduling. The problem is distributed in that no agent has full knowledge either of the communication network or of the scheduling constraints. Each agent knows the assignments of the agents with which it can communicate and the scheduled times of the tasks assigned to those agents. The minimum-time arbitrarily constrained distributed scheduling problem is for the agents to use this local knowledge to find a schedule that satisfies all constraints and minimizes the time needed to complete all tasks. The optimal distributed nonsequential backtracking algorithm is able to find minimum-time schedules that satisfy all constraints. This algorithm is designed to work concurrently with a distributed assignment algorithm that satisfies certain communication requirements detailed in this paper. This paper presents proof of the algorithm’s correctness, completeness, and optimality. [ABSTRACT FROM PUBLISHER]

Model checking is a formal method for verifying whether the system satisfies a user-defined specification. Compared to simulation, model checking is restricted in capacity. On the other hand, simulation is weak in detecting bugs that require long and complex sequences of events to be exposed. This paper combines model checking and simulation in an abstraction-refinement scheme to mitigate the problems of both methods. Abstraction refinement iteratively constructs a simplified model to verify the original model. While a simplified model mitigates the weakness of model checking, the set of simplified error traces model helps guide simulation toward deep bugs. In abstraction refinement, concretization—a process of deriving an error trace in the original model from the abstract ones—is used to invalidate spurious abstract error traces or to refute a property. In this paper, we describe a novel concretization algorithm that combines simulation with satisfiability to efficiently refute properties with very long error traces. [ABSTRACT FROM AUTHOR]

This paper focuses on a lot merging–splitting problem in a semiconductor wafer fabrication facility in which a relatively large number of wafer types are produced according to orders with different due dates. In the fab, two or more lots can be merged into a single lot if routes and all processing conditions of the lots are the same for a number of subsequent operations, and the merged lot is split into the original lots at the point where the routes or processing conditions become different. We suggest lot merging–splitting algorithms to reduce the total tardiness of the orders and the cycle times of the lots. The suggested algorithms are evaluated through a series of simulation experiments and the result shows that the algorithms work better than a method used in a real fab. [ABSTRACT FROM PUBLISHER]

Gyroscopes are integral components of inertial measurement units, which are used for guidance and stabilization of many platforms. This paper presents an algorithm for estimating the statistical parameters that govern the performance of rate gyros, i.e., the spectral densities R and Q of the angle random walk and rate random walk components, respectively. Previous work on gyro modeling is based on computing the Allan variance of a gyro signal and using a well-known formula for its mean. The algorithm in this paper uses these as well as the following quantities, which are derived in this paper: the theoretical variance of the Allan variance and the covariance between different Allan variance points. The algorithm is developed using the formulation of the best linear unbiased estimator from statistical estimation theory. The performance of the algorithm is demonstrated using simulated and experimental data. A bound on the error in the integral of the gyro output, as a function of Q and R, is also derived. [ABSTRACT FROM PUBLISHER]

Effect-based weapon–target pairing assigns weapons to targets for the given desired effects on such targets. The most obvious and natural effects on targets are represented by the percentages of damage of these targets. In this paper, we focus on the generation of input for effect-based weapon–target pairing optimization. One way to generate such input is based on the Joint Munition Effectiveness Manual (JMEM). JMEM allows the evaluation of the weapons. It is a database that contains many tables, and each table contains many different data fields. Because of the sheer size of JMEM, the optimization of weapon–target pairing based on JMEM is currently focused mainly on one target at a time. In other words, the optimization of weapon–target pairing for many targets and weapons is not directly supported by JMEM, although all the necessary data is there. In this paper, we derive an input based on the given JMEM and desired effect(s), which should be useful in the follow-on effect-based weapon–target pairing optimization that is not limited to a single weapon or target. In particular, effect-based weapon–target pairing will rely on the scanning of the attack guidance table that we derive from JMEM to determine a preferred set of weapon combinations for engaging a given set of targets. [ABSTRACT FROM PUBLISHER]

This paper presents a wireless risk assessment method to help an administrator manage wireless network security. The assessment method consists of a risk model and an assessment measure. The risk model is in charge of modeling the wireless network risk. Security requirements, wireless attacks, and system configurations are considered in the model. The assessment measure is an algorithm which determines the risk value of the wireless network according to the risk model. Our risk model is developed upon an extended analytic hierarchy process, which contains the 4 layers: the risk layer, the requirement layer, the attack layer, and the configuration layer. The separate layers of the risk model are helpful in dealing with the dynamics of a wireless network because only the related layers are introduced to the assessment measure when changes of the network are detected. Based on the risk model per device, our assessment measure evaluates the wireless network risk in consideration of the relations between devices, attacks, and configurations. Hence, our risk assessment method, composed of the risk model and the assessment measure, can determine the wireless network risk efficiently while considering the dependencies in the wireless network. Two examples are introduced in this paper to examine the feasibility of our method. In the first example, we demonstrate that the risk values derived by our method meet the ground truth by performing practical experiments. The second example shows that our method can evaluate the risk of a changing wireless network with efficiency, and can distinguish disparities in different wireless networks. [ABSTRACT FROM PUBLISHER]

The video surveillance market is increasingly moving toward cheaper, efficient, portable, and high resolution systems. A typical video surveillance system consists of several cameras, which issue warnings or initiate smart reactions according to the analysis results of the captured video data. Many algorithms in video surveillance systems assume fixed lighting conditions for the monitored area. The performance of these algorithms is severely affected by the illumination changes of the monitored area. This paper presents an efficient intensity correction algorithm for high definition video surveillance applications. The new algorithm corrects both the global and the local intensity changes. It uses an apparent gain factor to correct the global intensity changes. In addition it corrects the local intensity changes using the local intensity mean and standard deviation. The proposed algorithm shows a promising performance when compared with other intensity correction algorithms. It has a low computational cost that makes it a suitable choice for real-time hardware implementation. This paper also presents a hardware implementation of the proposed algorithm using Xilinx Spartan3A digital signal processor (DSP) XC3SD3400A device. The targeted resolution is 1920\,\times\,1080 at 30 f/s. The hardware prototype utilizes 17% of the slices, 21% of the block random access memories, and 24% of the DSP48As available in Spartan3A DSP XC3SD3400A device. [ABSTRACT FROM AUTHOR]

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]

The unceasing demand for continuous situational awareness calls for innovative and large-scale signal processing algorithms, complemented by collaborative and adaptive sensing platforms to accomplish the objectives of layered sensing and control. Towards this goal, the present paper develops a spline-based approach to field estimation, which relies on a basis expansion model of the field of interest. The model entails known bases, weighted by generic functions estimated from the field's noisy samples. A novel field estimator is developed based on a regularized variational least-squares (LS) criterion that yields finite-dimensional (function) estimates spanned by thin-plate splines. Robustness considerations motivate well the adoption of an overcomplete set of (possibly overlapping) basis functions, while a sparsifying regularizer augmenting the LS cost endows the estimator with the ability to select a few of these bases that “better” explain the data. This parsimonious field representation becomes possible, because the sparsity-aware spline-based method of this paper induces a group-Lasso estimator for the coefficients of the thin-plate spline expansions per basis. A distributed algorithm is also developed to obtain the group-Lasso estimator using a network of wireless sensors, or, using multiple processors to balance the load of a single computational unit. The novel spline-based approach is motivated by a spectrum cartography application, in which a set of sensing cognitive radios collaborate to estimate the distribution of RF power in space and frequency. Computer simulations and tests on real data corroborate that the estimated power spectrum density atlas yields the desired RF state awareness, since the maps reveal spatial locations where idle frequency bands can be reused for transmission, even when fading and shadowing effects are pronounced. [ABSTRACT FROM PUBLISHER]

A typical low- or medium-voltage distribution feeder consists of numerous branches as well as laterals and heterogenic conductor lines. The lack of measurement points and the presence of unbalanced loads make it more complicated for the construction of fault-location algorithms. In this paper, a brief and comprehensive review is presented which introduces and compares published papers in this area to date. In addition, the authors have devised a single-end fault-location algorithm using the distributed parameter model to overcome all of the aforementioned limitations in distribution feeders. The validity of the devised algorithm is studied within the PSCAD-EMTDC environment. This model provides more accurate results as the distributed nature of losses and capacitive effects are considered whereas in the available algorithms, these are ignored. A comparison which is made between the proposed method and two of the most complete available algorithms shows the superiority of our algorithm. Also, the proposed algorithm is able to locate various fault types in the network that has different phase laterals unbalanced loads and heterogeneity of the feeder line. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive method for tracking the amplitude, phase, and frequency of a time-varying sinusoid in white noise. Although the conventional techniques like adaptive linear elements and discrete or fast Fourier transforms are still widely used in many applications, their accuracy and convergence speed pose serious limitations under sudden supply frequency drift, fundamental amplitude, or phase variations. This paper, therefore, proposes a fast and low-complexity multiobjective Gauss–Newton algorithm for estimating the fundamental phasor and frequency of the power signal instantly and accurately. Further, the learning parameters in the proposed algorithm are tuned iteratively to provide faster convergence and better accuracy. The proposed method can also be extended to include time-varying harmonics and interharmonics mixed with noise of low signal-to-noise ratio with a great degree of accuracy. Numerical and experimental results are presented in support of the effectiveness of the new approach. [ABSTRACT FROM AUTHOR]

This paper focuses on the optimization of a linear actuator by the “output space mapping (OSM)” method. The underlying objective of this work lies in the minimization of the time required for the achievement of this optimal design. Indeed, in addition to the sole costs of optimization processes strictly speaking, the time needed for the developpement of the models is taken into account. In the context of OSM, two different finite-element models of the same actuator are used. This paper presents these modeling solutions and considers their corresponding accuracy. Results of this multi-objective optimization method are presented and compared with those obtained by the sequential simplex (SS) method based solely on the fine model. Both approaches give similar results. However, the comparison of their performances clearly shows that the OSM algorithm is an effective technique for reducing the computation time of optimization studies, even in the case of relatively simple electromagnetic structures. Hence, this approach leads to an original and effective optimization methodology. [ABSTRACT FROM AUTHOR]

This paper proposes a spectral-graph-based algorithm for face image repairing, which can improve the recognition performance on occluded faces. The face completion algorithm proposed in this paper includes three main procedures: 1) sparse representation for partially occluded face classification; 2) image-based data mining; and 3) graph Laplace (GL) for face image completion. The novel part of the proposed framework is GL, as named from graphical models and the Laplace equation, and can achieve a high-quality repairing of damaged or occluded faces. The relationship between the GL and the traditional Poisson equation is proven. We apply our face repairing algorithm to produce completed faces, and use face recognition to evaluate the performance of the algorithm. Experimental results verify the effectiveness of the GL method for occluded face completion. [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]

In this paper, we present a novel feature-based neural network (NN) approach for estimation of blood pressure (BP) from wrist oscillometric measurements. Unlike previous methods that use the raw oscillometric waveform envelope (OMWE) as input to the NN, in this paper, we propose to use features extracted from the envelope. The OMWE is mathematically modeled as a sum of two Gaussian functions. The optimum parameters of this model are found by minimizing the least squares error between the model and the OMWE using the Levenberg–Marquardt algorithm and are used as features. Two separate feed-forward NNs (FFNNs) are then designed to estimate the systolic and diastolic BPs using these features. The FFNNs are trained using the resilient backpropagation learning algorithm and tested on a data set of BP measurements recorded from 85 subjects. The performance is then compared with that of the conventional maximum amplitude algorithm, adaptive neuro-fuzzy inference system, and already published NN-based methods. It is found that the proposed approach achieves lower values of mean absolute error and standard deviation of error in the estimation of BP. In addition, the proposed approach has the following advantages: lower complexity with respect to the design parameters, smaller training data set, and lower computational load. [ABSTRACT FROM PUBLISHER]

Maximum power point (MPP) tracking (MPPT) techniques are widely applied in photovoltaic (PV) systems to make PV array generate peak power which depends on solar irradiation. Among all the MPPT strategies, the incremental-conductance (INC) algorithm is widely employed due to easy implementation and high tracking accuracy. In this paper, a novel variable step-size incremental-resistance MPPT algorithm is introduced, which not only has the merits of INC but also automatically adjusts the step size to track the PV array MPP. Compared with the variable step-size INC method, the proposed scheme can greatly improve the MPPT response speed and accuracy at steady state simultaneously. Moreover, it is more suitable for practical operating conditions due to a wider operating range. This paper provides the theoretical analysis and the design principle of the proposed MPPT strategy. Simulation and experimental results verify its feasibility. [ABSTRACT FROM AUTHOR]

Automatic differentiation (AD) is introduced as a powerful technique to compute derivatives of functions given in the form of computer programs in high-level programming languages such as FORTRAN, C, or C++. This paper applies AD to compute error-free gradients of electromagnetic device sizing models. Then, the obtained gradients are exploited in optimization to size electromagnetic devices by means of minimizing a cost function with constrained parameters and performances. Often, the electromagnetic devices models have to be described not only by analytical formulas, but also by algorithms. This paper proposes an electromagnetic model of a linear actuator dealing with implicit equations solved by numerical algorithms. The ADOL-C package is considered for automatic differentiation. [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]

This paper describes a new, physically interpretable, fully automatic algorithm for removal of tissue autofluorescence (AF) from fluorescence microscopy images, by non-negative matrix factorization. Measurement of signal intensities from the concentration of certain fluorescent reporter molecules at each location within a sample of biological tissue is confounded by fluorescence produced by the tissue itself (autofluorescence). Spectral mixing models use mixing coefficients to specify how much fluorescence from each source is present and unmixing algorithms separate the two fluorescent sources. Current spectral unmixing methods for AF removal often require a priori knowledge of mixing coefficients. Those which do not, such as principal component analysis, generate negative mixing coefficients that are not physically meaningful. Non-negative matrix factorization constrains mixing coefficients to be non-negative, and has been used for spectral unmixing, but not AF removal. This paper describes a novel non-negative matrix factorization algorithm which separates fluorescent images into true signal and AF components utilizing an estimate of the dark current. We also present a test-bed, based on fluorescent beads, to compare the performance of different AF removal algorithms. Our algorithm out-performed previous state of the art on validation images. [ABSTRACT FROM PUBLISHER]

Evaluating multistate flow network reliability and reducing system cost are important tasks when planning and designing systems. Existing methods are based on (d, c)-minimal paths ((d, c)-MP), which are vectors, such that d units of flow transmit between two specified nodes with a total cost that does not exceed c. However, these methods only work for directed networks. This correspondence paper finds all (d, c)-MPs before calculating network reliability under budget constraints using a novel method. The proposed algorithm is easier to understand and implement and is superior to existing algorithms. This correspondence paper analyzes and proves the correctness of the proposed algorithm, using two examples to demonstrate how to generate, verify, and implement all (d, c)-MPs to solve multistate flow network reliabilities under budget constraints using the proposed algorithm. [ABSTRACT FROM AUTHOR]

Hyperspectral images exhibit strong dependencies across spatial and spectral neighbors, which have been proved to be very useful for hyperspectral image classification. The recently defined conditional random field (CRF) can effectively model and use the dependencies for classification of hyperspectral images in a unified probabilistic framework. However, in order to be computationally tractable, the usual CRFs are limited to incorporate only pairwise potentials. Thus, the usual CRFs can capture only pairwise interactions and neglect higher order dependencies, which are potentially useful high-level properties particularly for the classification of hyperspectral image consisting of complex components. This paper overcomes this limitation by developing hyperspectral image classification algorithm based on a CRF with sparse higher order potentials, which are specially designed to incorporate complex characteristics of hyperspectral images. To efficiently implement the CRF model at training step, this paper develops an efficient local method under the piecewise training framework, while at inference step, this proposes a simple strategy to combine the piecewisely trained model to overcome the possible over-counting problems. Moreover, the combined model with the specially defined potentials can be efficiently inferred by graph cut method. Experiments on the real-world data attest to the accuracy, effectiveness, and efficiency of the proposed model on modeling and classifying hyperspectral images. [ABSTRACT FROM AUTHOR]

This paper deals with the susceptibility of electronic units to radiated electromagnetic (EM) interference and specifically, it focuses on the parasitic coupling of EM fields with printed circuit board interconnects like microstrip lines. To this purpose, a uniform lossless microstrip line illuminated by an EM plane wave is considered and the voltages at the line terminations are evaluated referring to the Baum–Liu–Tesche equations. Based on this, a new algorithm to identify, frequency-by-frequency, the incidence angles and the polarization of the impinging field that gives rise to the maximum induced voltages at the line terminations is presented. [ABSTRACT FROM AUTHOR]

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

The paper proposes a new message passing algorithm for cycle-free factor graphs. The proposed “entropy message passing” (EMP) algorithm may be viewed as sum-product message passing over the entropy semiring, which has previously appeared in automata theory. The primary use of EMP is to compute the entropy of a model. However, EMP can also be used to compute expressions that appear in expectation maximization and in gradient-descent algorithms. [ABSTRACT FROM AUTHOR]

Considering an asynchronous system made up of n processes and where up to t of them can crash, finding the weakest assumption that such a system has to satisfy for a common leader to be eventually elected is one of the holy grail quests of fault-tolerant asynchronous computing. This paper is a step in that direction. It has two contributions. Considering a simple and general asynchronous system model where processes generate asynchronous pulses during which they send and receive messages, it first introduces an additional assumption that allows to elect an eventual leader in all the runs that satisfy that assumption. That assumption is captured by the notion of asynchronous intermittent rotating t-star. An x-star is made up of one process p (the center of the star) plus a sequence of sets of x processes (the successive points of the star), which satisfies some properties. Intuitively, the intermittent rotating t-star assumption means that there are a process p, a subset of pulse numbers pn, and associated sets of processes Q(pn) such that each process of Q(pn) receives from p a message sent in pulse pn in a timely manner or among the first (n - t) messages tagged pn it ever receives. The t-star is called rotating because the setQ(pn) is allowed to change with pn; it is intermittent because it can disappear during finite periods; it is asynchronous because the points of a star are not required to be simultaneously at the same pulse. (This assumption combines and generalizes several synchrony and time-free assumptions that have been previously proposed to elect an eventual leader, e.g., eventual t-source, eventual t-moving source, and message pattern assumption.) The second contribution of the paper is an algorithm that eventually elects a common leader in the systems that satisfy the asynchronous intermittent rotating t-star assumption. This algorithm enjoys, among others, two noteworthy properties. First, from a design point of view, it is simple. Second, from a cost point of view, only the pulse numbers increase without bound. This means that, even in infinite executions, be links timely or not (or have the corresponding sender crashed or not), all the other local variables (including the timers) and message fields have a finite domain. [ABSTRACT FROM AUTHOR]

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

Unlike pattern-based biclustering methods that focus on grouping objects in the same subset of dimensions, in this paper, we propose a novel model of coherent clustering for time-series gene expression data, i.e., time-delayed cluster (td-cluster). Under this model, objects can be coherent in different subsets of dimensions if these objects follow a certain time-delayed relationship. Such a cluster can discover the cycle time of gene expression, which is essential in revealing gene regulatory networks. This paper is the first attempt to mine time-delayed gene expression patterns from microarray data. A novel algorithm is also presented and implemented to mine all significant td-clusters. Our experimental results show following two results: 1) the td-cluster algorithm can detect a significant amount of clusters that were missed by previous models, and these clusters are potentially of high biological significance and 2) the td-cluster model and algorithm can easily be extended to 3-D gene × sample × time data sets to identify 3-D td-clusters. [ABSTRACT FROM AUTHOR]

Controlling gene regulatory networks (GRNs) is an important and hard problem. As it is the case in all control problems, the curse of dimensionality is the main issue in real applications. It is possible that hundreds of genes may regulate one biological activity in an organism; this implies a huge state space, even in the case of Boolean models. This is also evident in the literature that shows that only models of small portions of the genome could be used in control applications. In this paper, we empower our framework for controlling GRNs by eliminating the need for expert knowledge to specify some crucial threshold that is necessary for producing effective results. Our framework is characterized by applying the factored Markov decision problem(FMDP) method to the control problem of GRNs. The FMDP is a suitable framework for large state spaces as it represents the probability distribution of state transitions using compact models so that more space and time efficient algorithms could be devised for solving control problems. We successfully mapped the GRN control problem to an FMDP and propose a model reduction algorithm that helps find approximate solutions for large networks by using existing FMDP solvers. The test results reported in this paper demonstrate the efficiency and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

An algorithm that is capable not only of tracking multiple targets but also of "track management"-meaning that it does not need to know the number of targets as a user input-is of considerable interest. In this paper we devise a recursive track-managed filter via a quantized state-space ("bin") model. In the limit, as the discretization implied by the bins becomes as refined as possible (infinitesimal bins) we find that the filter equations are identical to Mahier's probability hypothesis density (PHD) filter, a novel trackmanaged filtering scheme that is attracting increasing attention. Thus, one contribution of this paper is an interpretation of, if not the PHD itself, at least what the PHD is doing. This does offer some intuitive appeal, but has some practical use as well: with this model it is possible to identify the PHD's "target-death" problem, and also the statistical inference structures of the PHD filters. To obviate the target death problem, PHD originator Mahler developed a new "cardinalized" version of PHD (CPHD). The second contribution of this paper is to extend the "bin-occupancy" model such that the resulting recursive filter is identical to the cardinalized PHD filter. [ABSTRACT FROM AUTHOR]

This paper considers the noncooperative maximization of mutual information in the vector Gaussian interference channel in a fully distributed fashion via game theory. This problem has been widely studied in a number of works during the past decade for frequency-selective channels, and recently for the more general multiple-input multiple-output (MIMO) case, for which the state-of-the art results are valid only for nonsingular square channel matrices. Surprisingly, these results do not hold true when the channel matrices are rectangular and/or rank deficient matrices. The goal of this paper is to provide a complete characterization of the MIMO game for arbitrary channel matrices, in terms of conditions guaranteeing both the uniqueness of the Nash equilibrium and the convergence of asynchronous distributed iterative waterfilling algorithms. Our analysis hinges on new technical intermediate results, such as a new expression for the MIMO waterfilling projection valid (also) for singular matrices, a mean-value theorem for complex matrix-valued functions, and a general contraction theorem for the multiuser MIMO watefilling mapping valid for arbitrary channel matrices. The quite surprising result is that uniqueness/convergence conditions in the case of tall (possibly singular) channel matrices are more restrictive than those required in the case of (full rank) fat channel matrices. We also propose a modified game and algorithm with milder conditions for the uniqueness of the equilibrium and con- vergence, and virtually the same performance (in terms of Nash equilibria) of the original game. [ABSTRACT FROM AUTHOR]

A layout design algorithm for a variable-width inductor is proposed to minimize metal resistance. For a given chip area, the proposed algorithm can rapidly design metal widths of each coil in a planar inductor due to the analytical form. Two on-chip inductors with identical chip areas and inductance are fabricated to verify the proposed method in foundry 90-nm CMOS technology. Measurement results demonstrate that the improvement of metal resistance in the proposed device is approximately 19%. The results of this paper provide an effective algorithm to design a high-Q inductor for RFIC applications. [ABSTRACT FROM AUTHOR]