ABSTRACT In this paper, a new model utilizing all the information derived from connectivity‐based sensor network localization is introduced. The connectivity information between any pair of nodes is modeled as convex and non‐convex constraints. The localization problem is solved by searching for a solution that would satisfy all the constraints established in the problem. A two‐objective evolutionary algorithm called Pareto Archived Evolution Strategy (PAES) is used to solve the localization problem. The solution can reach the most suitable configuration of the unknown nodes because the information on both convex and non‐convex constraints related to connectivity has been utilized. From simulation results, a relationship between the communication range and accuracy is obtained. Furthermore, a two‐level range connectivity‐based sensor network localization method is proposed to enrich the connectivity information. The two‐level range/indication of connectivity between each pair of nodes would indicate three levels of connectivity: strong, weak, or nil. A comparison on accuracy between the one‐level and two‐level ranges of connectivity is carried out by simulation using six different topological networks all containing 100 nodes. Simulation results have shown that better solution can be obtained by using two‐level range connectivity compared with the usual one‐level range connectivity‐based localization. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, our aim is to segment a foreground region into individual persons in crowded scenes. We will focus on the combination of multiple clues for crowd segmentation. To ensure a wide range of applications, few assumptions are needed on the scenarios. In the developed method, crowd segmentation is formulated as a process to group the feature points with a human model. It is assumed that a foreground region has been detected and that an informative foreground contour is not required. The approach adopts a block-based implicit shape model (B-ISM) to collect some typical patches from a human being and assess the possibility of their occurrence in each part of a body. The combination of appearance cues with coherent motion of the feature points in each individual is considered. Some results based on the USC-Campus sequence and the CAVIAR data set have been shown. The contributions of this paper are threefold. First, a new B-ISM model is developed, and it is combined with joint occlusion analysis for crowd segmentation. The requirement for an accurate foreground contour is reduced. In addition, ambiguity in a dense area can be handled by collecting the evidences inside the crowd region based on the B-ISM. Furthermore, motion cues-which are coherent moving trajectories of feature points from individuals"are combined with appearance cues to help segment the foreground region into individuals. The usage of motion cues can be an effective supplement to appearance cues, particularly when the background is cluttered or the crowd is dense. Third, three features have been proposed to distinguish points on rigid body parts from those with articulated movements. Coherent motion of feature points on each individual can be more reliably identified by excluding points with articulated motion.

We present a new time-domain simulation algorithm (named OPM) based on operational matrices, which naturally handles system models cast in ordinary differential equations (ODEs), differential algebraic equations (DAEs), high-order differential equations and fractional differential equations (FDEs). When applied to simulating linear systems (represented by ODEs or DAEs), OPM has similar performance to advanced transient analysis methods such as trapezoidal or Gear's method in terms of complexity and accuracy. On the other hand, OPM naturally handles FDEs without much extra effort, which can not be efficiently solved using existing time-domain methods. High-order differential systems, being special cases of FDEs, can also be simulated using OPM. Moreover, adaptive time step can be utilized in OPM to provide a more flexible simulation with low CPU time. Numerical results then validate OPM's wide applicability and superiority.

Power grid verification has become an indispensable step to guarantee a functional and robust chip design. Vectorless power grid verification methods, by solving linear programming (LP) problems under current constraints, enable worst-case voltage drop predictions at an early stage of design when the specific waveforms of current drains are unknown. In this paper, a novel power grid verification algorithm based on hierarchical constraints is proposed. By introducing novel power constraints, the proposed algorithm generates more realistic current patterns and provides less pessimistic voltage drop predictions. The model order reduction-based coefficient computation algorithm reduces the complexity of formulating the LP problems from being proportional to steps to being independent of steps. Utilizing the special hierarchical constraint structure, the submodular polyhedron greedy algorithm dramatically reduces the complexity of solving the LP problems from overO(km3) to roughlyO(kmlogkm), wherekmis the number of variables. Numerical results have shown that the proposed algorithm provides less pessimistic voltage drop prediction while at the same time achieves dramatic speedup.

The authors for the above titled paper were incorrectly listed. The correct list of authors is presented here.

Passivity is an important property of circuits and systems to guarantee stable global simulation. Nonetheless, nonpassive models may result from passive underlying structures due to numerical or measurement error/inaccuracy. A postprocessing passivity enforcement algorithm is therefore desirable to perturb the model to be passive under a controlled error. However, previous literature only reports such passivity enforcement algorithms for pole-residue models and regular systems (RSs). In this paper, passivity enforcement algorithms for descriptor systems (DSs, a superset of RSs) with possibly singular direct term (specifically,D+DTorI-DDT) are proposed. The proposed algorithms cover all kinds of state-space models (RSs or DSs, with direct terms being singular or nonsingular, in the immittance or scattering representation) and thus have a much wider application scope than existing algorithms. The passivity enforcement is reduced to two standard optimization problems that can be solved efficiently. The objective functions in both optimization problems are the error functions, hence perturbed models with adequate accuracy can be obtained. Numerical examples then verify the efficiency and robustness of the proposed algorithms.

Vectorless power grid verification algorithms, by solving linear programming (LP) problems under current constraints, enable worst-case voltage drop predictions at an early design stage. However, worst-case current patterns obtained by many existing vectorless algorithms are time-invariant (i.e., are constant throughout the simulation time), which may result in an overly pessimistic voltage drop prediction. In this paper, a more realistic power grid verification algorithm based on hierarchical current and power constraints is proposed. The proposed algorithm naturally handles general RCL power grid models. Currents at different time steps are treated as independent variables and additional power constraints are introduced; this results in more realistic time-varying worst-case current patterns and less pessimistic worst-case voltage drop predictions. Moreover, a sorting-deletion algorithm is proposed to speed up solving LP problems by utilizing the hierarchical constraint structure. Experimental results confirm that worst-case current patterns and voltage drops obtained by the proposed algorithm are more realistic, and that the sorting-deletion algorithm reduces runtime needed to solve LP problems by 85%.

Reliable people counting and human detection is an important problem in visual surveillance. In recent years, the field has seen many advances, but the solutions have restrictions: people must be moving, the background must be simple, and the image resolution must be high. This paper aims to develop an effective method for estimating the number of people and locate each individual in a low resolution image with complicated scenes. The contribution of this paper is threefold. First, postprocessing steps are performed on background subtraction results to estimate the number of people in a complicated scene, which includes people who are moving only slightly. Second, an Expectation Maximization (EM)-based method has been developed to locate individuals in a low resolution scene. In this method, a new cluster model is used to represent each person in the scene. The method does not require a very accurate foreground contour. Third, the number of people is used as a priori for locating individuals based on feature points. Hence, the methods for estimating the number of people and for locating individuals are connected. The developed methods have been validated based on a 4-hour video, with the number of people in the scene ranging from 36 to 222. The best result for estimating the number of people has an average error of 10% over 51 test cases. Based on the estimated number of people, some results of the EM-based method have also been shown.

Numerous algorithms to macromodel a linear time-invariant (LTI) system from its frequency-domain sampling data have been proposed in recent years [1, 2, 3, 4, 5, 6, 7, 8], among which Loewner matrix-based tangential interpolation proves to be especially suitable for modeling massive-port systems [6, 7, 8]. However, the existing Loewner matrix-based method follows vector-format tangential interpolation (VFTI), which fails to explore all the information contained in the frequency samples. In this paper, a novel matrix-format tangential interpolation (MFTI) is proposed, which requires much fewer samples to recover the system and yields better accuracy when handling under-sampled, noisy and/or ill-conditioned data. A recursive version of MFTI is proposed to further reduce the computational complexity. Numerical examples then confirm the superiority of MFTI over VFTI.

Passivity is a crucial property of macromodels to guarantee stable global (interconnected) simulation. However, weakly nonpassive models may be generated for passive circuits and systems in various contexts, such as data fitting, model order reduction (MOR) and electromagnetic (EM) macromodeling. Therefore, a post-processing passivity enforcement algorithm is desired. Most existing algorithms are designed to handle pole-residue models. The few algorithms for state space models only handle regular systems (RSs) with a nonsingular D+DTterm. To the authors' best knowledge, no algorithm has been proposed to enforce passivity for more general descriptor systems (DSs) and state space models with singular D+DTterms. In this paper, a new post-processing passivity enforcement algorithm based on perturbation of Hamiltonian-symplectic matrix pencil, PEDS, is proposed. PEDS, for the first time, can enforce passivity for DSs. It can also handle all kinds of state space models (both RSs and DSs) with singular D+DTterms. Moreover, a criterion to control the error of perturbation is devised, with which the optimal passive models with the best accuracy can be obtained. Numerical examples then verify that PEDS is efficient, robust and relatively cheap for passivity enforcement of DSs with mild passivity violations.

In this paper, our focus is to segment the foreground area for human detection. It is assumed that the foreground region has been detected. Accurate foreground contours are not required. The developed approach adopts a modified ISM (Implicit Shape Model) to collect some typical local patches of human being and their location information. Individuals are detected by grouping some local patches in the foreground area. The method can get good results in crowded scenes. Some examples based on CAVIAR dataset have been shown. A main contribution of the paper is that ISM model and joint occlusion analysis are combined for individual segmentation. There are mainly two advantages: First, with more sufficient information inside the foreground region, even the individuals inside a dense area can also be handled. Secondly, the method does not require an accurate foreground contour. A rough foreground area can be easily obtained in most situations.

This paper carries an extensive evaluation on the performance of a generalized motif-based method for detecting defects in 16 out of 17 wallpaper groups in 2-D patterned texture. The motif-based method evolves from the concept that every wallpaper group is defined by a lattice, which contains a further constituent-motif. It utilizes the symmetry properties of motifs to calculate the energy of moving subtraction and its variance among motifs. Decision boundaries are determined by learning the distribution of those values among the defect-free and defective patterns in the energy-variance space. In this paper, shape transform for irregular motif has been demonstrated according to the three basic motif shapes: rectangle, triangle, and parallelogram. An error analysis for the misclassifications has also been delivered. In the database of fabrics and other patterned textures, a total of 381 defect-free lattices are used for formulation of boundaries while further 340 defect-free and 233 defective lattices are for testing. The motif-based method has a consistent result and reaches a detection success rate of 93.86%.

Although there have been a lot of studies on human detection in recent years, most of them have some particular requirements. However, some videos may have complicated scenes with low resolution and include both moving and standing people. The paper aims for individual detection under the challenging situation. An EM (expectation-maximum) based method has been developed to detect individuals in a common outdoor scenario. A new cluster model is employed in the method for human detection. The method can work well even for low-resolution images. Both moving and standing people can be detected as long as they show some occasional movements, which is satisfied in most cases. Some promising results have been presented and analyzed in a scene with around 100 people.

This paper considers regularity analysis for patterned texture material inspection. Patterned texture-like fabric is built on a repetitive unit of a pattern. Regularity is one of the most important features in many textures. In this paper, a new patterned texture inspection approach called the regular bands (RB) method is described. First, the properties of textures and the meaning of regularity measurements are presented. Next, traditional regularity analysis for patterned textures is introduced. Many traditional approaches such as co-occurrence matrices, autocorrelation, traditional image subtraction and hash function are based on the concept of periodicity. These approaches have been applied for image retrieval, image synthesis, and defect detection of patterned textures. In this paper, a new measure of periodicity for patterned textures is described. The Regular Bands method is based on the idea of periodicity. A detailed description of the RB method with definitions, procedures, and explanations is given. There is also a detailed evaluation using the Regular Bands of some patterned textures. Three kinds of patterned fabric samples are used in the evaluation and a high detection success rate is achieved. Finally, there is a discussion of the method and some conclusions.

This paper proposed a patterned fabric defect detection method for sixteen out of seventeen wallpaper groups using a motif-based approach. From the symmetry properties of motifs, the energy of moving subtraction and its variance among motifs are mapped onto an energy-variance space. By learning the distribution of defect-free and defective patterns in this space, boundaries conditions can be determined for defect detection purpose. The proposed method is evaluated on four wallpaper categories, from which all 16 wallpaper groups can be generalized. Altogether, 160 defect-free lattices samples are used for learning the decision boundaries; and 200 other defect-free and 138 other defective samples are used for testing. An overall detection accuracy has reached 93.61%, which outperforms previous approaches.

This paper presents a gait analysis of the equilateral hexagonal model of hexapod robot. Mathematical analysis has been made on mobility, fault-tolerance, and stability. A comparison with the rectangular model of hexapod robot is also given, and it has shown that the hexagonal model shows better turning ability, a higher margin of stability during the fault-tolerant gait, and greater stride length in certain conditions.

The problem of automated defect detection in textured materials is investigated. A new approach for defect detection using linear FIR filters with optimized energy separation is proposed. The performance of different feature separation criteria with reference to fabric defects has been evaluated. The issues relating to the design of optimal filters for supervised and unsupervised web inspection are addressed. A general web inspection system based on the optimal filters is proposed. The experiments on this new approach have yielded excellent results. The low computational requirement confirms the usefulness of the approach for industrial inspection.

This paper studies the adaptive wavelet design for fabric defect detection. In order to achieve translation invariance and more flexible design, the wavelet design focuses on the nonsubsampled wavelet transform. We design the wavelet filters under the constraints that the analysis filters are power-complementary and the wavelet has only one vanishing moment, which corresponds to a multiscale edge detector. Based on lattice structure factorization, the design of the power-complementary filters turns out to be unconstrained optimization of lattice coefficients. Adaptive wavelets are designed for five kinds of fabric defects in the experiments. Comparing the proposed method with adaptive wavelet design for defect detection based on orthogonal wavelet transform energy between the defect area and the background, and achieve a robust and accurate detection of fabric defects.

In this paper, an effective method for the detection of moving cast shadow for visual traffic surveillance is proposed. Based on the cast shadow observations in luminance, chrominance, gradient density and geometry domains, a combined probability map, called the shadow confidence score, of the region belonging to the shadow is deduced. The object region is then separated from its shadow according to the score. The proposed method has been further tested on images taken under different lighting conditions (sunny and cloudy), viewing angles (roadside and overhead), and vehicle sizes (small, medium and large).

This paper is in the field of vehicle positioning technology for the intelligent transportation systems. The ideas of an innovative light-emitting diode (LED)-based location beacon system are developed and verified. The system developed is a combination of several latest technologies which include a CMOS vision sensor, high brightness LED, and digital image processing techniques. It belongs to a new kind of simplex communication link. A digital camera is used to capture images contained in the LED beacon signal. The captured digital images are processed by the algorithms developed and a location code is extracted. The location code can be used for calibration of a vehicle positioning system which may consist of a GPS, inertial navigation system (INS) and other sensors. The issues examined include the structure of the transmitter and the receiver, the signaling method, the transmission protocol of the LED panel, the relationship between the camera capturing rate and the LED pattern update rate, the digital camera exposure technology, and the efficiency of the image processing algorithms. Experiments using a prototype transmitter and a receiver were performed. The experimental results provide a good demonstration of the viability of the ideas and methodologies developed.