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Electromagnetic devices containing permanent magnet (PM) are common and typical electromagnetic devices that have advantages of low-power consumption, small size, high sensitivity, and its optimization problem has aroused widespread concern. Calculation accuracy of electromagnetic properties and efficiency of optimization processes are two important factors that affect the optimization effect of electromagnetic devices. A fast calculation method was proposed in this paper that aimed at electromagnetic devices with PM characteristics of strong non-linearity and low solving precision. The response surface method was used to derive the basis function, and the error between actual measured values and calculated values was modified by the Kriging method. On the basis of the calculation model, the distributed collaborative strategy was adopted to fulfill the parallel execution of the fast computation. Simultaneously, the updating strategy of particles was improved, and the robustness of the multi-objective particle swarm optimization algorithm in solving the optimization problem of electromagnetic devices with PM was enhanced. Effectiveness of the method proposed in this paper was verified by the case study of a specific type of electromagnetic relay with PM. [ABSTRACT FROM AUTHOR]

Cross-validation (CV) is a widely adopted approach for selecting the optimal model. However, the computation of empirical cross-validation error (CVE) has high complexity due to multiple times of learner training. In this paper, we develop a novel approximation theory of CVE and present an approximate approach to CV based on the Bouligand influence function (BIF) for kernel-based algorithms. We first represent the BIF and higher order BIFs in Taylor expansions, and approximate CV via the Taylor expansions. We then derive an upper bound of the discrepancy between the original and approximate CV. Furthermore, we provide a novel computing method to calculate the BIF for general distribution, and evaluate BIF criterion for sample distribution to approximate CV. The proposed approximate CV requires training on the full data set only once and is suitable for a wide variety of kernel-based algorithms. Experimental results demonstrate that the proposed approximate CV is sound and effective. [ABSTRACT FROM AUTHOR]

Koblitz curves have been an important subject of consideration for both theoretical and practical interests. The window τ-adic algorithm of Solinas (window τNAF) is the most powerful method for computing point multiplication for Koblitz curves. Pre-computation plays an important role in improving the performance of point multiplication. In this paper, the concept of optimal pre-computation for window τNAF is formulated. In this setting, an optimal pre-computation has some mathematically natural and clean forms, and requires 2w−2−1 point additions and two evaluations of the Frobenius map τ, where w is the window width. One of the main results of this paper is to construct an optimal pre-computation scheme for each window width w from 4 to 15 (more than practical needs).These pre-computations can be easily incorporated into implementations of window τNAF. The ideas in the paper can also be used to construct other suitable pre-computations. This paper includes a discussion of coefficient sets for window τNAF and the divisibility by powers of τ through different approaches. Some issues of implementation are also discussed. [ABSTRACT FROM PUBLISHER]

There are more and more structured traffic flows (a.k.a coflow) in today's data center networks. Completing a coflow is extremely important for various applications, e.g., MapReduce. To reduce the coflow completion time or CCT, one may increase the link capacity by applying advanced optical circuit switches in data center networks. Due to special features of optical circuit switches, both traffic scheduling and circuit scheduling will influence the CCT. However, previous solutions have some significant limitations: they consider either coflow scheduling, or circuit scheduling for only one optical circuit switch, which are both insufficient. In this paper, we study the integrated coflow and circuit scheduling (GCCS) problem with the objective to minimize the CCT, and prove its NP-hardness. We present an integrated algorithm which includes two steps, coflow scheduling and circuit scheduling, respectively. We also analyze that the proposed algorithm can achieve the approximation ratio $O(h)$O(h) in most practical situations, where $h$h is the maximum number of ports among all lightpaths. Through large-scale simulations, we demonstrate that the integrated solution can significantly reduce the CCT by about 43-70 percent compared with the state-of-the-art coflow scheduler for optical networks. [ABSTRACT FROM AUTHOR]

This paper presents a macromodeling technique for modeling distributed circuits characterized by noisy frequency-domain data. The proposed method is based on an iterative Loewner matrix (LM) approach. Using the LM approximation of previous iterations, the singular values and orthonormal matrices of the Loewner pencil are made to be more accurate. This approach is shown to minimize the biasing effect of the noisy data due to the nonlinearity of the singular value decomposition operation, resulting in more accurate poles and residues. Numerical examples illustrate that for noisy frequency-domain data, the proposed algorithm creates more accurate macromodels when compared with the traditional LM approach. [ABSTRACT FROM AUTHOR]

Milano Retinex is a family of spatial color algorithms inspired by Retinex and mainly devoted to the image enhancement. In the so-called point-based sampling Milano Retinex algorithms, this task is accomplished by processing the color of each image pixel based on a set of colors sampled in its surround. This paper presents STAR, a segmentation based approximation of the point-based sampling Milano Retinex approaches: it replaces the pixel-wise image sampling by a novel, computationally efficient procedure that detects once for all the color and spatial information relevant to image enhancement from clusters of pixels output by a segmentation. The experiments reported here show that STAR performs similarly to previous point-based sampling Milano Retinex approaches and that STAR enhancement improves the accuracy of the well-known algorithm scale-invariant feature transform on the description and matching of photographs captured under difficult light conditions. [ABSTRACT FROM AUTHOR]

In this paper, we present a new low-complexity iterative joint phase estimation and decoding algorithm which can be applied to low-density-parity-check (or turbo) codes for channels affected by strong phase noise. The algorithm exhibits a very good performance and a very low complexity even in strong phase noise, high code rate, and high-order constellations. The proposed algorithm works by first obtaining a preliminary pilots-aided phase estimation. Next, the expressions for the belief propagation messages are approximated around that reference and reduced to a Gaussian canonical form. This results in a simple closed form of a recursive update. Simulations results for phase-shift-keying modulation of size 8 and quadrature-amplitude modulation of size 64 are presented and compared with other algorithms in the literature. [ABSTRACT FROM AUTHOR]

Visually analyzing citation networks poses challenges to many fields of the data mining research. How can we summarize a large citation graph according to the user’s interest? In particular, how can we illustrate the impact of a highly influential paper through the summarization? Can we maintain the sensory node-link graph structure while revealing the flow-based influence patterns and preserving a fine readability? The state-of-the-art influence maximization algorithms can detect the most influential node in a citation network, but fail to summarize a graph structure to account for its influence. On the other hand, existing graph summarization methods fold large graphs into clustered views, but can not reveal the hidden influence patterns underneath the citation network. In this paper, we first formally define the Influence Graph Summarization problem on citation networks. Second, we propose a matrix decomposition based algorithm pipeline to solve the IGS problem. Our method can not only highlight the flow-based influence patterns, but also easily extend to support the rich attribute information. A prototype system called VEGAS implementing this pipeline is also developed. Third, we present a theoretical analysis on our main algorithm, which is equivalent to the kernel k-mean clustering. It can be proved that the matrix decomposition based algorithm can approximate the objective of the proposed IGS problem. Last, we conduct comprehensive experiments with real-world citation networks to compare the proposed algorithm with classical graph summarization methods. Evaluation results demonstrate that our method significantly outperforms the previous ones in optimizing both the quantitative IGS objective and the quality of the visual summarizations. [ABSTRACT FROM PUBLISHER]

As net-load becomes less predictable there is a lot of pressure in changing decision models for power markets such that they account explicitly for future scenarios in making commitment decisions. This paper proposes to make commitment decisions with multiple gate closures. Our proposed model also leverages a state-space formulation for the commitment variables, through which the operational constraints of generation units participating in the market are respected. We also study the problem of constructing scenario tree approximations for stochastic processes and evaluate our algorithms on scenario tree libraries derived from real net-load data. [ABSTRACT FROM AUTHOR]

In this paper, we explore low-complexity probabilistic algorithms for soft symbol detection in high-dimensional multiple-input multiple-output (MIMO) systems. We present a novel algorithm based on the expectation consistency (EC) framework, which describes the approximate inference problem as an optimization over a nonconvex function. EC generalizes algorithms such as belief propagation and expectation propagation. For the MIMO symbol detection problem, we discuss feasible methods to find stationary points of the EC function and explore their tradeoffs between accuracy and speed of convergence. The accuracy is studied, first in terms of input–output mutual information and show that the proposed EC MIMO detector greatly improves state-of-the-art methods, with a complexity order cubic in the number of transmitting antennas. Second, these gains are corroborated by combining the probabilistic output of the EC detector with a low-density parity-check channel code. [ABSTRACT FROM PUBLISHER]

In this paper, we address the dimension-reduced space-time adaptive processing (STAP) techniques for ground clutter suppression in airborne radar from the viewpoint of approximation theory. The weights in the optimum STAP technique can be naturally expressible as the weight matrix. An efficient dimension-reduced space-time adaptive clutter suppression (STACS) algorithm based on lower-rank approximation to weight matrix is established, which finds a set of space-time separable filters to approximate the optimum STAP processor. By exploiting a lower-rank approximation to weight matrix, we make the quadratic cost function used in the classical optimum STAP processor be converted into a biquadratic cost function. To seek a minimum point of the biquadratic cost function, this paper develops an efficient multistage bi-iterative algorithm and the corresponding multistage dimension-reduced technique with a modular structure, where each stage finds an orthogonal component for approximating to the weight matrix. The effectiveness of the STACS algorithm is tested via several experiments. [ABSTRACT FROM PUBLISHER]

Probabilistic principal component regression (PPCR) has been introduced for soft sensor modeling as a probabilistic projection regression method, which is effective in handling data collinearity and random noises. However, the linear limitation of data relationships may cause its performance deterioration when applied to nonlinear processes. Therefore, a novel weighted PPCR (WPPCR) algorithm is proposed in this paper for soft sensing of nonlinear processes. In WPPCR, by including the most relevant samples for local modeling, different weights will be assigned to these samples according to their similarities with the testing sample. Then, a weighted log-likelihood function is constructed, and expectation-maximization algorithm can be carried out iteratively to obtain the optimal model parameters. In this way, the nonlinear data relationship can be locally approximated by WPPCR. The effectiveness and flexibility of the proposed method are validated on a numerical example and an industrial process. [ABSTRACT FROM AUTHOR]

Small cells are introduced to cellular systems to enhance coverage and improve capacity. Densely deploying small cells can not only offload the traffic of macrocells but also provide an energy- and cost-efficient way to meet the sharp increase in traffic demands in mobile networks. However, such a cell deployment paradigm also leads to heterogeneous network (HetNet) infrastructure and raises new challenges for cell planning. In this paper, we study the cell planning issue in the HetNet. Our optimization task is to select a subset of candidate sites to deploy macro or small cells to minimize the total cost of ownership (TCO) or the energy consumption of the cellular system while satisfying practical constraints. We introduce approximation algorithms to cope with two different cell-planning cases, which are both NP-hard. First, we discuss the macrocell-only case. Our proposed algorithm achieves an approximation ratio of O(\log R) in this scenario, where R is the maximum achievable capacity of macrocells. Then, we introduce an O(\log \widetildeR)-approximation algorithm to the small-cell scenario, where \widetildeR is the maximum achievable capacity of a macrocell with small cells overlaid on it. Numerical results indicate that the HetNet can significantly reduce the TCO and the energy consumption of the cellular system. [ABSTRACT FROM PUBLISHER]

Geometrical wavelets have already proved their strength in approximation, compression, and denoising of piecewise constant and piecewise linear images. In this paper, we extend this family by introducing planelets toward an effective representation of indoor depth images. It uses a linear fractional model to capture non-linearity of depth values in the planar regions of the output images of Kinect-like sensors. A block-based compression framework based on planelet approximation is then presented, which uses quadtree decomposition along with spatial predictions as an effective intra-coding scheme. Compared with both classical geometric wavelets and some state-of-the-art image coding algorithms, our method provides desirable quality by explicitly representing edges and planar patches. [ABSTRACT FROM AUTHOR]

The maximum operator in statistical static timing analysis (SSTA) is a decent approximation for timing sign-off, but often causes significant error in SSTA applications. This paper presents a timing criticality computation method based on non-maximum analytic operators in a parameterized SSTA. After an SSTA run, the proposed method computes the criticality for all edges and nodes in a single graph traversal. Although we do not employ the max operator in the computation process, the error in the maximum operator still degrades the accuracy of the computed criticality because the criticality is a joint probability of expressions, including arrival times, which are computed by the maximum operator during SSTA. To address this issue, we employ the refactoring technique, which was recently proposed to reduce common path pessimism in combinational circuits. This paper shows that refactoring is also very useful in reducing the maximum-induced error in arrival times, and how existing graph-based algorithms can be geared toward refactoring. Our experimental results show that the proposed method reduces the error of the criticality significantly compared to the conventional cutset-based method. [ABSTRACT FROM AUTHOR]

Determining the minimum distance of a linear code is one of the most important problems in algorithmic coding theory. The exact version of the problem was shown to be NP-complete by Vardy. The gap version of the problem was shown to be NP-hard for any constant factor under a randomized reduction in an earlier work. It was shown in the same paper that the minimum distance problem is not approximable in randomized polynomial time to the factor 2^\log^1-\epsilonn unless NP\subseteq RTIME(2^polylog(n)). In this paper, we derandomize the reduction and thus prove that there is no deterministic polynomial time algorithm to approximate the minimum distance to any constant factor unless P=NP. We also prove that the minimum distance is not approximable in deterministic polynomial time to the factor 2^\log^1-\epsilonn unless NP\subseteq DTIME(2^polylog(n)). As the main technical contribution, for any constant 2/3 < \rho < 1, we present a deterministic algorithm that given a positive integer s, runs in time poly(s) and constructs a code \cal C of length poly(s) with an explicit Hamming ball of radius \rho d(\cal C), such that the projection at the first s coordinates sends the codewords in the ball surjectively onto a linear subspace of dimension s, where d(\cal C) denotes the minimum distance of \cal C. The codes are obtained by concatenating Reed–Solomon codes with Hadamard codes. [ABSTRACT FROM PUBLISHER]

The Booth multiplier has been widely used for high performance signed multiplication by encoding and thereby reducing the number of partial products. A multiplier using the radix-4 (or modified Booth) algorithm is very efficient due to the ease of partial product generation, whereas the radix-8 Booth multiplier is slow due to the complexity of generating the odd multiples of the multiplicand. In this paper, this issue is alleviated by the application of approximate designs. An approximate 2-bit adder is deliberately designed for calculating the sum of 1× and 2× of a binary number. This adder requires a small area, a low power and a short critical path delay. Subsequently, the 2-bit adder is employed to implement the less significant section of a recoding adder for generating the triple multiplicand with no carry propagation. In the pursuit of a trade-off between accuracy and power consumption, two signed 16 × 16 bit approximate radix-8 Booth multipliers are designed using the approximate recoding adder with and without the truncation of a number of less significant bits in the partial products. The proposed approximate multipliers are faster and more power efficient than the accurate Booth multiplier. The multiplier with 15-bit truncation achieves the best overall performance in terms of hardware and accuracy when compared to other approximate Booth multiplier designs. Finally, the approximate multipliers are applied to the design of a low-pass FIR filter and they show better performance than other approximate Booth multipliers. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a digital implementation of the 3-D shearlet transform and illustrate its application to problems of video denoising and enhancement. The shearlet representation is a multiscale pyramid of well-localized waveforms defined at various locations and orientations, which was introduced to overcome the limitations of traditional multiscale systems in dealing with multidimensional data. While the shearlet approach shares the general philosophy of curvelets and surfacelets, it is based on a very different mathematical framework, which is derived from the theory of affine systems and uses shearing matrices rather than rotations. This allows a natural transition from the continuous setting to the digital setting and a more flexible mathematical structure. The 3-D digital shearlet transform algorithm presented in this paper consists in a cascade of a multiscale decomposition and a directional filtering stage. The filters employed in this decomposition are implemented as finite-length filters, and this ensures that the transform is local and numerically efficient. To illustrate its performance, the 3-D discrete shearlet transform is applied to problems of video denoising and enhancement, and compared against other state-of-the-art multiscale techniques, including curvelets and surfacelets. [ABSTRACT FROM AUTHOR]

Observations in many applications consist of counts of discrete events, such as photons hitting a detector, which cannot be effectively modeled using an additive bounded or Gaussian noise model, and instead require a Poisson noise model. As a result, accurate reconstruction of a spatially or temporally distributed phenomenon (\mmb f^\star) from Poisson data (\mmb y) cannot be effectively accomplished by minimizing a conventional penalized least-squares objective function. The problem addressed in this paper is the estimation of \mmb f^\star from \mmb y in an inverse problem setting, where the number of unknowns may potentially be larger than the number of observations and \mmb f^\star admits sparse approximation. The optimization formulation considered in this paper uses a penalized negative Poisson log-likelihood objective function with nonnegativity constraints (since Poisson intensities are naturally nonnegative). In particular, the proposed approach incorporates key ideas of using separable quadratic approximations to the objective function at each iteration and penalization terms related to \mmb \ell1 norms of coefficient vectors, total variation seminorms, and partition-based multiscale estimation methods. [ABSTRACT FROM AUTHOR]

A concave finite element-boundary integral-multilevel fast multipole algorithm (FE-BI-MLFMA) is presented for scattering by a large body with nonuniform deep cavities. Different from the conventional FE-BI-MLFMA, the boundary integral equation in this concave FE-BI-MLFMA is established on a concave surface to reduce the region of the finite element method (FEM), which can significantly reduce the dispersion error from the FEM and improve the efficiency of FE-BI-MLFMA especially for nonuniform cavities. To eliminate the problem of slow convergence caused by concave surface, an efficient preconditioner based on the sparse approximate inverse (SAI) is constructed in this paper. Numerical performance of the constructed preconditioner based on the SAI is investigated in detail. Numerical experiments demonstrate the accuracy and efficiency of this SAI preconditioned concave FE-BI-MLFMA for nonuniform deep and large cavites. This SAI preconditioned concave FE-BI-MLFMA is parallelized to further improve its capability in this paper. An extremely big and deep nonuniform cavity has been calculated, demonstrating the great capability of this parallel concave FE-BI-MLFMA. [ABSTRACT FROM PUBLISHER]

This paper presents a global task coordinate frame (TCF) (GTCF)-based integrated direct/indirect adaptive robust contouring controller for linear-motor-driven biaxial systems that achieves both stringent contouring performance and accurate parameter estimations. In contrast to the past research works those use various locally defined TCFs “attached” to the desired contour to approximately calculate the contouring error for feedback controller designs, this paper first formulates the contouring control problem using a recently developed GTCF, in which calculation of the contouring error is rather accurate and not affected by the curvature of the desired contour. A physical-model-based indirect-type parameter-estimation algorithm is then synthesized to obtain accurate online estimates of unknown physical model parameters. An integrated direct/indirect adaptive robust controller with dynamic-compensation-type fast adaptation is also constructed to preserve the excellent transient and steady-state performance of the direct adaptive robust control (ARC) designs. Comparative experimental results show that the proposed GTCF-based integrated direct/indirect ARC algorithm not only achieves the best contouring performance but also possesses rather accurate estimations of physical parameters. [ABSTRACT FROM PUBLISHER]

In this paper, the problem of adaptive distributed learning in diffusion networks is considered. The algorithms are developed within the convex set theoretic framework. More specifically, they are based on computationally simple geometric projections onto closed convex sets. The paper suggests a novel combine-project-adapt protocol for cooperation among the nodes of the network; such a protocol fits naturally with the philosophy that underlies the projection-based rationale. Moreover, the possibility that some of the nodes may fail is also considered and it is addressed by employing robust statistics loss functions. Such loss functions can easily be accommodated in the adopted algorithmic framework; all that is required from a loss function is convexity. Under some mild assumptions, the proposed algorithms enjoy monotonicity, asymptotic optimality, asymptotic consensus, strong convergence and linear complexity with respect to the number of unknown parameters. Finally, experiments in the context of the system-identification task verify the validity of the proposed algorithmic schemes, which are compared to other recent algorithms that have been developed for adaptive distributed learning. [ABSTRACT FROM PUBLISHER]

Most partitioning algorithms iteratively partition a space into cells that contain underlying linear or nonlinear structures using linear partitioning strategies. The compactness of each cell depends on how well the (locally) linear partitioning strategy approximates the intrinsic structure. To partition a compact structure for complex data in a nonlinear context, this paper proposes a nonlinear partition strategy. This is a principal curve tree (PC-tree), which is implemented iteratively. Given that a PC passes through the middle of the data distribution, it allows for partitioning based on the arc length of the PC. To enhance the partitioning of a given space, a residual version of the PC-tree algorithm is developed, denoted here as the principal component analysis tree (PCR-tree) algorithm. Because of its residual property, the PCR-tree can yield the intrinsic dimension of high-dimensional data. Comparisons presented in this paper confirm that the proposed PC-tree and PCR-tree approaches show a better performance than several other competing partitioning algorithms in terms of vector quantization error and nearest neighbor search. The comparison also shows that the proposed algorithms outperform competing linear methods in total average coverage which measures the nonlinear compactness of partitioning algorithms. [ABSTRACT FROM AUTHOR]

Recently, multipath solutions have been proposed to improve the quality-of-service of the source to destination (s,t)-path in communication networks (CNs). This paper describes the \lambdaDP/DR problem to obtain \lambda\geq 1 edge-disjoint (s,t)-paths (\lambdaDP) such that its reliability is maximized while keeping its delay no longer than a delay constraint D\geq 1. This problem is NP-hard, and thus this paper proposes an approximate solution using Lagrange-relaxation. Our algorithm generates \lambdaDP with \delta(\lambdaDP)\leqD, and reliability bounded by \vert\log(R\min)\vert\leq\vert\log(\rho(\lambdaDP))\vert\leq(1+k)^{\ast}\vert\log(R\min)\vert, where R\min is the minimum reliability of any (s,t)-path in the CN, and k\geq 1. Our simulations on forty random CNs and large grid networks show that our solution produces \lambdaDP with delay and reliability comparable to those obtained by the optimal but exponential time algorithm. [ABSTRACT FROM AUTHOR]

In many real applications, graph data is subject to uncertainties due to incompleteness and imprecision of data. Mining such uncertain graph data is semantically different from and computationally more challenging than mining conventional exact graph data. This paper investigates the problem of mining uncertain graph data and especially focuses on mining frequent subgraph patterns on an uncertain graph database. A novel model of uncertain graphs is presented, and the frequent subgraph pattern mining problem is formalized by introducing a new measure, called expected support. This problem is proved to be NP-hard. An approximate mining algorithm is proposed to find a set of approximately frequent subgraph patterns by allowing an error tolerance on expected supports of discovered subgraph patterns. The algorithm uses efficient methods to determine whether a subgraph pattern can be output or not and a new pruning method to reduce the complexity of examining subgraph patterns. Analytical and experimental results show that the algorithm is very efficient, accurate, and scalable for large uncertain graph databases. To the best of our knowledge, this paper is the first one to investigate the problem of mining frequent subgraph patterns from uncertain graph data. [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]

Low-rank matrix approximation has applications in many fields, such as 2-D filter design and 3-D reconstruction from an image sequence. In this paper, one issue with low-rank matrix approximation is investigated: heteroscedastic noise. In most of previous research, the covariance matrix of the heteroscedastic noise is assumed to be positive definite. This requirement restricts the usefulness of results derived from such research. In this paper, we extend the Wiberg algorithm, which originally deals with the missing data problem with low-rank approximation, to the cases, where the heteroscedastic noise has a singular covariance matrix. Experiments show that the proposed Wiberg algorithm converges much faster than the bilinear approach, and consequently avoids many nonconvergent cases in the bilinear approach. Experiments also show that, to some extent, the Wiberg algorithm can tolerate outliers and is not sensitive to parameter variation. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of finding a B-term wavelet representation of a given discrete function f ϵ Rn whose distance from f is minimized. The problem is well understood when we seek to minimize the Euclidean distance between f and its representation. The first-known algorithms for finding provably approximate representations minimizing general lp distances (including l∞ under a wide variety of compactly supported wavelet bases are presented in this paper. For the Haar basis, a polynomial time approximation scheme is demonstrated. These algorithms are applicable in the one-pass sublinear-space data stream model of computation. They generalize naturally to multiple dimensions and weighted norms. A universal representation that provides a provable approximation guarantee under all p-norms simultaneously; and the first approximation algorithms for bit-budget versions of the problem, known as adaptive quantization, are also presented. Further, it is shown that the algorithms presented here can be used to select a basis from a tree-structured dictionary of bases and find a B-term representation of the given function that provably approximates its best dictionary-basis representation. [ABSTRACT FROM AUTHOR]

This paper is concerned with tracking control problems for nonlinear systems that are not affine in the control signal and that contain unknown nonlinearities in the system dynamic equations. This paper develops a piecewise linear approximation to the unknown functions during the system operation. New control and parameter adaptation algorithms are designed and analyzed using Lyapunov-like methods. The objectives are to achieve semiglobal stability of the state, accurate tracking of bounded reference signals contained within a known domain D, and at least boundedness of the function approximator parameter estimates. Numerical simulations are included to illustrate the effectiveness of the learning algorithm. [ABSTRACT FROM AUTHOR]

This paper concentrates on two critical aspects to improve the performance of maximum power point tracking (MPPT). One improvement is to accurately locate the position of the maximum power point (MPP) by using the centered differentiation. Another effort is to reduce the oscillation around the MPP in steady state by controlling active perturbations. This paper also adopts the method of steepest descent for MPPT, which shows faster dynamic response and smoother steady state than the method of hill climbing. Comprehensive experimental evaluations have successfully illustrated the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In dealing with large data sets, the reduced support vector machine (RSVM) was proposed for the practical objective to overcome some computational difficulties as well as to reduce the model complexity. In this paper, we study the RSVM from the viewpoint of sampling design, its robustness, and the spectral analysis of the reduced kernel. We consider the nonlinear separating surface as a mixture of kernels. Instead of a full model, the RSVM uses a reduced mixture with kernels sampled from certain candidate set. Our main results center on two major themes. One is the robustness of the random subset mixture model. The other is the spectral analysis of the reduced kernel. The robustness is judged by a few criteria as follows: 1) model variation measure; 2) model bias (deviation) between the reduced model and the full model; and 3) test power in distinguishing the reduced model from the full one. For the spectral analysis, we compare the eigenstructures of the full kernel matrix and the approximation kernel matrix. The approximation kernels are generated by uniform random subsets. The small discrepancies between them indicate that the approximation kernels can retain most of the relevant information for learning tasks in the full kernel. We focus on some statistical theory of the reduced set method mainly in the context of the RSVM. The use of a uniform random subset is not limited to the RSVM. This approach can act as a supplemental algorithm on top of a basic optimization algorithm, wherein the actual optimization takes place on the subset-approximated data. The statistical properties discussed in this paper are still valid. [ABSTRACT FROM AUTHOR]

A deinterlacing algorithm that is based on Rough Sets theory is researched and applied in this paper. The fundamental concepts of Rough Sets, with upper and lower approximations, offer a powerful means of representing uncertain boundary regions in image processing. However, there are a few studies that discuss the effectiveness of the Rough Sets concept in the field of engineering. Moreover, the studies involving deinterlacing systems that are based on Rough Sets have not been proposed yet. Thus, this paper proposes a deinterlacing method that will reliably confirm that the method being tested is the most suitable for the sequence, with almost perfect reliability. This proposed deinterlacing approach employs a size reduction of the database system, keeping only the essential information for the process. Decision making and interpolation results are presented. The results of computer simulations show that the proposed method outperforms a number of methods presented in the literature. [ABSTRACT FROM AUTHOR]

Linear dispersion (LD) codes are a good candidate for high-data-rate multiple-input multple-ouput (MIMO) signaling. Traditionally LD codes were designed by maximizing the average mutual information, which cannot guarantee good error performance. This paper presents a new design scheme for LD codes that directly minimizes the block error rate (BLER) in MIMO channels with arbitrary fading statistics and various detection algorithms. For MIMO systems employing LD codes, the error rate does not admit an explicit form. Therefore, we cannot use deterministic optimization methods to design the minimum-error-rate LD codes. In this paper, we propose a simulation-based optimization methodology for the design of LD codes through stochastic approximation and simulation-based gradient estimation. The gradient estimation is done using the score function method originally developed in the discrete-event-system community. The proposed method can be applied to design the minimum-error-rate LD codes for a variety of detector structures including the maximum-likelihood (ML) detector and several sub-optimal detectors. It can also design optimal codes under arbitrary fading channel statistics; in particular, it can take into account the knowledge of spatial fading correlation at the transmitter and receiver ends. Simulation results show that codes generated by the proposed new design paradigm generally outperform the codes designed based on algebraic number theory. [ABSTRACT FROM AUTHOR]

This paper proposes, analyzes, and illustrates several best basis search algorithms for dictionaries consisting of lapped orthogonal bases. It improves upon the best local cosine basis selection based on a dyadic tree [10], [11] by considering larger dictionaries of bases. It is shown that this can result in sparser representations and approximate shift invariance. An algorithm that is strictly shift invariant is also provided. The experiments in this paper suggest that the new dictionaries can be advantageous for time-frequency analysis, compression, and noise removal. Accelerated versions of the basic algorithm are provided that explore various tradeoffs between computational efficiency and adaptability. It is shown that the proposed algorithms are in fact applicable to any finite dictionary comprised of lapped orthogonal bases. One such novel dictionary is proposed that constructs the best local cosine representation in the frequency domain, and it is shown that the new dictionary is better suited for representing certain types of signals. [ABSTRACT FROM AUTHOR]

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

This paper proposes a simple closed-form formula of the ground-return impedance of horizontal parallel wires above a lossy ground plane. It is derived by a double logarithmic approximation of the integral term with a semi-infinite interval in Carson's ground-return impedance formula. The proposed double logarithmic approximation gives better accuracy compared to a similar approximation proposed by Pizarro and Eriksson, and this is achieved by an advanced optimization technique used for determining the coefficients in the double logarithmic approximation and also by incorporating an additional variable which minimizes errors. In this paper, it is shown, using practical examples, that the derived formula gives more accurate results compared with other existing approximate formulas. It is also shown that a graphical interpretation of the double logarithmic approximation is to replace a homogeneous lossy ground with a pair of perfectly conducting return planes located at different complex depths. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a tomographical system suitable for imaging absorbers in strongly scattering media using a single light source-detector pair. Their reflective alignment would make it possible to implement both source and detector into a compact scan-head. Suitable probes would be soft tissue, some strongly scattering biological probes, or translucent technical materials, for example. An intensity-modulation technique is utilized to take ad- vantage of the amplitude and delay alterations between the source and the detector. The propagation of the photons is modeled by the Helmholtz equation and then appropriate inverse calculations of several measurements reveal the distribution of the absorption co-efficient inside the investigated volume. Therefore a reconstruction algorithm employing a Born approximation of higher order and a Tikhonov-regularization scheme were used to solve the deduced ill-posed and underdetermined system of equations. In this paper, we present results of simulations as well as of measurements. [ABSTRACT FROM AUTHOR]

This paper presents a novel learning algorithm for efficient construction of the radial basis function (RBF) networks that can deliver the same level of accuracy as the support vector machines (SVMs) in data classification applications. The proposed learning algorithm works by constructing one RBF subnetwork to approximate the probability density function of each class of objects in the training data set. With respect to algorithm design, the main distinction of the proposed learning algorithm is the novel kernel density estimation algorithm that features an average time complexity of O(n log n), where vi is the number of samples in the training data set. One important advantage of the proposed learning algorithm, in comparison with the SVM, is that the proposed learning algorithm generally takes far less time to construct a data classifier with an optimized parameter setting. This feature is of significance for many contemporary applications, in particular, for those applications in which new objects are continuously added into an already large database. Another desirable feature of the proposed learning algorithm is that the RBF networks constructed are capable of carrying out data classification with more than two classes of objects in one single run. In other words, unlike with the SVM, there is no need to resort to mechanisms such as one-against-one or one-against-all for handling datasets with more than two classes of objects. The comparison with SVM is of particular interest, because it has been shown in a number of recent studies that SVM generally are able to deliver higher classification accuracy than the other existing data classification algorithms. As the proposed learning algorithm is instance-based, the data reduction issue is also addressed in this paper. One interesting observation in this regard is that, for all three data sets used in data reduction experiments, the number of training samples remaining after a naïve data reduction mechanism is applied is quite close to the number of support vectors identified by the SVM software. This paper also compares the performance of the RBF networks constructed with the proposed learning algorithm and those constructed with a conventional cluster-based learning algorithm. The most interesting observation learned is that, with respect to data classification, the distributions of training samples near the bound- aries between different classes of objects carry more crucial information than the distributions of samples in the inner parts of the clusters. [ABSTRACT FROM AUTHOR]

This paper presents a new sequential learning algorithm for radial basis function (RBF) networks referred to as generalized growing and pruning algorithm for RBF (GGAP-RBF). The paper first introduces the concept of significance for the hidden neurons and then uses it in the learning algorithm to realize parsimonious networks. The growing and pruning strategy of GGAP-RBF is based on linking the required learning accuracy with the significance of the nearest or intentionally added new neuron. Significance of a neuron is a measure of the average information content of that neuron. The GGAP-RBF algorithm can be used for any arbitrary sampling density for training samples and is derived from a rigorous statistical point of view. Simulation re- suits for bench mark problems in the function approximation area show that the GGAP-RBF outperforms several other sequential learning algorithms in terms of learning speed, network size and generalization performance regardless of the sampling density function of the training data. [ABSTRACT FROM AUTHOR]

CANDECOMP/PARAFAC (CP) approximates multiway data by sum of rank-1 tensors. Unlike matrix decomposition, the procedure which estimates the best rank-R tensor approximation through R sequential best rank-1 approximations does not work for tensors, because the deflation does not always reduce the tensor rank. In this paper, we propose a novel deflation method for the problem. When one factor matrix of a rank-R CP decomposition is of full column rank, the decomposition can be performed through (R-1) rank-1 reductions. At each deflation stage, the residue tensor is constrained to have a reduced multilinear rank. For decomposition of order-3 tensors of size R\times R\times R and rank-R, estimation of one rank-1 tensor has a computational cost of \cal O(R^3) per iteration which is lower than the cost \cal O(R^4) of the ALS algorithm for the overall CP decomposition. The method can be extended to tracking one or a few rank-one tensors of slow changes, or inspect variations of common patterns in individual datasets. [ABSTRACT FROM PUBLISHER]

Dynamic spectrum leasing (DSL) is a promising method to improve the spectrum efficiency. A lot of work has been done about spectrum sharing issues with static models. In this paper, we want to study the behavior of the primary users (PUs) under uncertainty. For this objective, we propose a model with time-varying parameters. The model captures both technical and economic features. We study the long-term behavior of the PUs in the model with stochastic Cournot game theory and variational inequality theory. We show that the PUs can maximize their long-term utilities with a static supply of spectrum. In addition, we also propose two algorithms based on stochastic approximation theory. The convergence and performance of the algorithms are demonstrated theoretically and numerically. The proposed model and analytical framework can be used to study and predict the behaviors of PUs given a DSL system and provide design insights for the DSL system. [ABSTRACT FROM AUTHOR]

This paper focuses on two special aspects regarding fast partial inductance computations in the nonorthogonal partial element equivalent circuit (PEEC) method. First, a multifunction PEEC algorithm is proposed, which is able to calculate partial inductances as efficient as possible for mixed environments with nonorthogonal as well as orthogonal cells. Second, a new numerical integration routine for the self-inductance terms of nonorthogonal cells is focused on by using averaged orthogonal subelements. It is presented that the slow convergence caused by the singularities is avoided and a fast evaluation of the self-terms is enabled consequently. The approach is verified by two examples where a good agreement is obtained when comparing the proposed results with analytical solutions as well as finite-element method reference results. [ABSTRACT FROM AUTHOR]

Information searches are the most common application within social networks. Normally, the social network is modeled as a network graph, consisting of nodes (In the rest of the paper, unless otherwise specified, we will use the terms “user” and “node” interchangeably.) representing users within the network and edges representing relationships between users. Choosing the appropriate nodes to form an auxiliary structure for supporting the effective query message spreading can reduce the troublesome repeated queries. To accomplish this, a hybrid search (HS) scheme is proposed. If the query message is received by a node belonging the auxiliary structure constructed by dynamic weighted distributed label clustering (DW-DLC), it would be flooded to all neighbors of the visited node; otherwise, it would be forwarded to one neighbor of the visited node. The DW-DLC based auxiliary structure can accelerate the process of obtaining required information within the network. The simulation results show that the HS+DW-DLC scheme can reduce the average searching delay time, even in a required-information-scarce social network. In addition, the proposed scheme can generate a relatively low amount of repeated messages to lower repeatedly asking social network users. [ABSTRACT FROM PUBLISHER]

This paper presents a derivation of four radix-2 division algorithms by digit recurrence. Each division algorithm selects a quotient digit from the over-redundant digit set {−2, −1, 0, 1, 2}, and the selection of each quotient digit depends only on the two most-significant digits of the partial remainder in a redundant representation. Two algorithms use a two’s complement representation for the partial remainder and carry-save additions, and the other two algorithms use a binary signed-digit representation for the partial remainder and carry-free additions. Three algorithms are novel. The fourth algorithm has been presented before. Results from the synthesized netlists show that two of our fastest algorithms achieve an improvement of 10 percent in latency per iteration over a standard radix-2 SRT algorithm at the cost of 36 percent more power and 50 percent more area. [ABSTRACT FROM PUBLISHER]