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The classic expectation-maximization (EM) algorithm in maximum-likelihood direction finding updates the complete-data sufficient statistics by finding their conditional expectations. Besides, from the perspective of alternating maximization (AM) these sufficient statistics can also be updated by maximizing the complete-data log-likelihood function with respect to only the complete data, based on which both deterministic and stochastic signal models are considered. Theoretical analysis indicates that the proposed AM algorithm is equivalent to the EM algorithm for the deterministic signal model while outperforming the EM algorithm for the stochastic signal model. On this foundation, a sequential AM (SAM) algorithm and two iterative weighting schemes are proposed to improve the convergence of the AM algorithm. Numerical results show that the SAM algorithm yields faster convergence and the two iterative weighting schemes can be used to avoid the convergence of the EM and AM algorithms to an unwanted limit point efficiently. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of hybrid state estimation, which relies on data provided by both conventional SCADA systems and phasor measurement units. A previously proposed cascaded architecture is employed, whose first estimation module processes the asynchronous measurements through a conventional, nonlinear state estimator. The focus, however, is on the second estimation module, where the SCADA-based estimates are combined with synchrophasor data in order to enhance the quality of the state estimates. For the benefit of computational performance, that task must be performed by a non-iterative and numerically robust algorithm. It is equally important to avoid awkward uncorrelatedness assumptions while defining measurement weighting factors, since they may severely compromise the statistical characterization of the final solution. To accomplish both objectives, a generalized 2 × 2 block version of the 3-multiplier Givens rotations has been developed and is introduced in this paper. This leads to numerically stable solutions, whose statistical properties are preserved. The features and advantages of the proposed two-stage hybrid state estimator are illustrated through case studies conducted on the IEEE 14-bus, 30-bus, 57-bus, and 300-bus test systems. [ABSTRACT FROM AUTHOR]

In this article, a kind of predictive control (PC) for permanent magnet synchronous motor (PMSM) rotor position control is introduced, and more stable and rapid performances are achieved comparing with other PCs. Nonlinear predictive control (NLPC) is shown to outperform the linear in terms of performance for nonlinear controlled plants because of containing nonlinear part and longer prediction horizons. A novel direct speed NLPC scheme for PMSM is presented based on this strategy. The response and reference signals are predicted with Taylor series expansion to guarantee the accuracy of the system. Prediction horizons are selected according to Shannon with its maximum value to eliminate steady-state error and delay between actual and reference signals. Furthermore, the proposed method is designed and analyzed in detail, and tested by simulations and experiments. Results with respect to the rotor position trace tracking, control performance, and weighting factors sensitivity demonstrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

OSMAR-S, a portable high-frequency surface wave radar (HFSWR), has been in operation for ten years in China for current mapping and shares more than 80% of the domestic HFSWR radar market. In this article, comprehensive quantitative validation and intercomparison of sea state parameter estimation from the OSMAR-S radar with multiple other sensors over a period of two months are presented. These sensors include five buoys temporarily deployed at range from 10 to 85 km away from the radar and another two types of commercially available phased-array (PA) radar made in China. A performance validation method of HFSWR compared with buoys deployed in both the high-precision area (HPA) and the edge area of the radar coverage is proposed for a full evaluation in a wide range area. A quantitative evaluation method using a variety of statistical metrics and their weights is proposed to obtain a comprehensive score for comparing the performance of different types of HFSWR. The good agreements with buoy data in a wide coverage area demonstrate the good capability of sea state parameters measurement using the OSMAR-S radar. Compared with the other two PA radars, OSMAR-S also shows a better performance in terms of accuracy and stability. [ABSTRACT FROM AUTHOR]

The state-of-the-art techniques have demonstrated that coherence error degrades the performance of synthetic aperture radar (SAR) interferometry (InSAR) for distributed scatterers (DSs). This article aims at fully evaluating the influence of coherence error on DS InSAR time-series analysis. In particular, we present a methodology to increase the estimation accuracy of DS interferometry, with emphasis on spatiotemporal coherence refinement. The motive behind this is that bias removal and variance mitigation of sample coherence matrix impose optimum weighting for estimating phase series and geophysical parameters of interest, whereas maximization of temporal coherence in a reference network can avoid spatial error propagation during the least-squares adjustment. Rather than developing independent processing chains, we integrate this method into SqueeSAR technique and simultaneously take the advantage of StaMPS into consideration. Using simulation and real data over southwestern China, comprehensive comparisons before and after spatiotemporal coherence refinement are performed over various coherence scenarios. The results tested from different phase and displacement rate estimators validate the effectiveness of the presented method. [ABSTRACT FROM AUTHOR]

Recently, a hypergraph constructed from functional magnetic resonance imaging (fMRI) was utilized to explore brain functional connectivity networks (FCNs) for the classification of neurodegenerative diseases. Each edge of a hypergraph (called hyperedge) can connect any number of brain regions-of-interest (ROIs) instead of only two ROIs, and thus characterizes high-order relations among multiple ROIs that cannot be uncovered by a simple graph in the traditional graph based FCN construction methods. Unlike the existing hypergraph based methods where all hyperedges are assumed to have equal weights and only certain topological features are extracted from the hypergraphs, we propose a hypergraph learning based method for FCN construction in this paper. Specifically, we first generate hyperedges from fMRI time series based on sparse representation, then employ hypergraph learning to adaptively learn hyperedge weights, and finally define a hypergraph similarity matrix to represent the FCN. In our proposed method, weighting hyperedges results in better discriminative FCNs across subjects, and the defined hypergraph similarity matrix can better reveal the overall structure of brain network than using those hypergraph topological features. Moreover, we propose a multi-hypergraph learning based method by integrating multi-paradigm fMRI data, where the hyperedge weights associated with each fMRI paradigm are jointly learned and then a unified hypergraph similarity matrix is computed to represent the FCN. We validate the effectiveness of the proposed method on the Philadelphia Neurodevelopmental Cohort dataset for the classification of individuals’ learning ability from three paradigms of fMRI data. Experimental results demonstrate that our proposed approach outperforms the traditional graph based methods (i.e., Pearson’s correlation and partial correlation with the graphical Lasso) and the existing unweighted hypergraph based methods, which sheds light on how to optimize estimation of FCNs for cognitive and behavioral study. [ABSTRACT FROM AUTHOR]

This paper proposes a set of fitting features that enables identifying propagation function of cables accurately in the form of multi-delay rational transfer functions while maintaining reduced order of approximation. Frequency domain partitioning and adaptive weighting techniques are applied directly in phase domain for the evaluation of poles and residues simultaneously to ensure the precision of fitting of all entries including the low-magnitude off-diagonal elements. The objective is not only to obtain a precise fitting in phase domain but also to account for intrinsic modal decomposition so that integration errors in time domain are not magnified. The order of approximation is considerably reduced by post-processing the fitting using the balanced realization technique. When the proposed fitting approach is combined with a precise integration technique in time domain, it leads to accurate evaluation of transients as demonstrated in this paper, eliminating the spurious oscillations or numerical instabilities that may be encountered in the universal line model regardless of the integration or interpolation technique. [ABSTRACT FROM AUTHOR]

In information retrieval (IR) and related tasks, term weighting approaches typically consider the frequency of the term in the document and in the collection in order to compute a score reflecting the importance of the term for the document. In tasks characterized by the presence of training data (such as text classification) it seems logical that the term weighting function should take into account the distribution (as estimated from training data) of the term across the classes of interest. Although “supervised term weighting” approaches that use this intuition have been described before, they have failed to show consistent improvements. In this article, we analyze the possible reasons for this failure, and call consolidated assumptions into question. Following this criticism, we propose a novel supervised term weighting approach that, instead of relying on any predefined formula, learns a term weighting function optimized on the training set of interest; we dub this approach Learning to Weight (LTW). The experiments that we run on several well-known benchmarks, and using different learning methods, show that our method outperforms previous term weighting approaches in text classification. [ABSTRACT FROM AUTHOR]

A multiinput-single-output converter is presented herein, which is applied to harvesting heat energy. Such a converter utilizes interleaved switches to solve an inherent problem in this circuit so that each thermoelectric generator has its own maximum power point tracking. In addition, this converter has low voltage stresses on switches, a high step-up voltage gain, etc. In this study, the basic principles of the proposed converter are first described and then analyzed. Afterward, the proposed converter is applied to the thermoelectric energy conversion system with the three-point-weighting method used to achieve the maximum power point tracking. Finally, some experimental results are provided to demonstrate the effectiveness of this thermoelectric energy conversion system. [ABSTRACT FROM AUTHOR]

In a paired-point rigid registration, target registration error (TRE) is deemed to be the most important quality metric. TRE usually cannot be directly measured, and thus many TRE estimation algorithms have been proposed. However, target localization errors (TLEs) in two spaces are not considered in the definition of TRE. In this paper, we propose a new type of evaluation metric that is referred to as total TRE (TTRE) at a given target point with TLE incorporated. Statistics including mean, root mean square (rms), and covariance matrix of TTRE are derived without making any assumption of the TLE magnitude. TTRE and fiducial registration error (FRE) are proved to be uncorrelated when an ideal weighting scheme is adopted in solving the registration problem. The proposed error model is validated through extensive experiments. In the first experiment with random fiducials and targets, in 90% of the test cases, there shows no difference between the predicted and simulated TTRE statistics when six fiducials are used. In the second experiment of deep-brain stimulation surgery, the mean value of CC(TTRE,FRE) being $8.9246\times 10^{-4}\pm 0.0389 $ was observed, which indicates that TTRE and FRE are uncorrelated. In the third experiment of surgical tool-tip tracking, the mean and standard deviation of percentage differences between predicted and simulated TTRE rms values are $2.22\%\pm 0.77\%$ for the planar tool and $2.62\%\pm 0.59\%$ for the textral tool. In summary, our proposed algorithm can well model the TTRE metric. Note to Practitioners—This paper was motivated by considering the existence of target localization error in two spaces to be registered in a rigid point-based registration. Given fiducial localization error (FLE) and TLE probability distributions, the proposed TTRE model gives the covariance matrix and root mean square of TTRE at a given target point. The possible applications of the proposed TTRE model in an image-guided surgery (IGS) include: 1) estimation of surgical tool-tip tracking error distribution when pivot calibration uncertainty is considered; 2) accuracy evaluation of an image-to-patient registration; 3) updating of a preoperative surgical plan on a computed tomography/MRI image to consider the registration error at a given target point; and 4) estimation of real-time tool-tip error in touching a desired “true” surgical target, such as a tumor during surgery. To use the proposed error estimation algorithm, FLE and TLE in two spaces to be registered have to be known or estimated in advance to a registration process. The proposed error model is validated through extensive simulations. [ABSTRACT FROM AUTHOR]

In the application of industrial cone-beam computed tomography (CBCT), the displaced detector scanning mode can significantly enlarge the field of view (FOV) of the CT system. However, when this mode is applied to inspect a large object, the connecting line between the X-ray focus and the center-of-rotation projection may not be perpendicular to the real flat-panel detector, which makes most of the existing reconstruction methods impractical. The purpose of this article is to explain the work done in developing an image reconstruction method under the displaced detector scanning mode. We first establish a virtual detector to make the connecting line be perpendicular to the virtual detector, and the real projection data are then converted into the virtual detector. The geometrical parameters of the real CT system are converted into the CT system with the virtual detector, in which the virtual detector and the twist angles of the reconstructed images are effectively corrected by adjusting angles of the projection sequence. Based on the corrected projection data and the converted geometry parameters, the cross sections of the object are accurately reconstructed through the FDK reconstruction algorithm combined with the Wang weighting function. Computer simulation results and CBCT experiment results demonstrate that the image artifacts and the twist angles of the reconstructed images are well corrected. Through this method, the FOV of the CT system is enlarged by 78% without changing the original geometric layout of the system, which is significant for engineering applications. [ABSTRACT FROM AUTHOR]

This paper investigates the benefits of intentionally adding noise to a Bayesian estimator, which comprises a linear combination of a number of individual Bayesian estimators that are perturbed by mutually independent noise sources and multiplied by a set of adjustable weighting coefficients. We prove that the Bayes risk for the mean square error (MSE) criterion is minimized when the same optimum weighting coefficients are assigned to the identical estimators in the combiner. This property leads to a simplified analysis of the noise benefit to the MSE of the combined Bayesian estimator even when the number of individual estimators tends to infinity. It is shown that, for a sufficiently large number of individual estimators, the MSE of the designed Bayesian estimator approaches a plateau for a wide range of added noise levels. This robust feature facilitates the incorporation of the added noise into the design of Bayesian estimators without tuning the noise level. For an easily implementable Bayesian estimator composed of quantizers, the benefit of the symmetric scale-family noise is demonstrated, and the optimal noise probability density function is approximated by solving a constrained nonlinear optimization problem. We further extend this potential Bayesian estimator to the nonlinear filter design. Finally, examples of the noise benefits in random parameter estimation and nonlinear filtering support the theoretical analyses. [ABSTRACT FROM AUTHOR]

Traditional cluster ensemble approaches have several limitations. (1) Few make use of prior knowledge provided by experts. (2) It is difficult to achieve good performance in high-dimensional datasets. (3) All of the weight values of the ensemble members are equal, which ignores different contributions from different ensemble members. (4) Not all pairwise constraints contribute to the final result. In the face of this situation, we propose double weighting semi-supervised ensemble clustering based on selected constraint projection(DCECP) which applies constraint weighting and ensemble member weighting to address these limitations. Specifically, DCECP first adopts the random subspace technique in combination with the constraint projection procedure to handle high-dimensional datasets. Second, it treats prior knowledge of experts as pairwise constraints, and assigns different subsets of pairwise constraints to different ensemble members. An adaptive ensemble member weighting process is designed to associate different weight values with different ensemble members. Third, the weighted normalized cut algorithm is adopted to summarize clustering solutions and generate the final result. Finally, nonparametric statistical tests are used to compare multiple algorithms on real-world datasets. Our experiments on 15 high-dimensional datasets show that DCECP performs better than most clustering algorithms. [ABSTRACT FROM AUTHOR]

Handle-driven image warping based on linear blending is widely used in many applications because of its merits on intuitiveness, efficiency, and ease of implementation. In this paper, we develop a method to compute high-quality weights within a closed domain for image warping. The property of C2 continuity in weights is guaranteed by the carefully formulated basis functions. The efficiency of our algorithm is ensured by a closed-form formulation of the computation for weights. The cost of inserting a new handle is only the time to evaluate the distances from the new handle to all other sample points in the domain. A virtual handle insertion algorithm is developed to allow users to freely place handles within the domain while preserving the satisfaction of all expected criteria on weights for linear blending. Experimental examples for real-time applications are shown to demonstrate the effectiveness of this method. [ABSTRACT FROM PUBLISHER]

In this paper, we mathematically analyze one of the most challenging issues in color image-guided depth upsampling: the texture copy artifacts. The optimal guidance weights denoted by balanced weights are proposed to best suppress texture copy artifacts. To both suppress texture copy artifacts and preserve depth discontinuities, a new general weighting scheme called variable bandwidth weighting is proposed. The variable bandwidth weighting scheme is able to adjust guidance weights according to the local depth smoothness. A new concept called relative smoothness is proposed for measuring the local depth smoothness. Given this quantitative smoothness measurement, the proposed weighting scheme can adaptively adjust the bandwidth for calculating the guidance weights in the existing methods. As we use the computationally efficient balanced weights instead of the guidance weights of a large bandwidth, the proposed method can speed up the upsampling process for about $2\times \sim 5\times $ when compared with the original upsampling methods. Experimental results show the effectiveness and efficiency of the proposed method in suppressing texture copy artifacts, preserving the depth discontinuities and reducing the computational cost at the same time. [ABSTRACT FROM PUBLISHER]

Intravascular ultrasound (IVUS) has been frequently used for coronary artery imaging clinically. More importantly, IVUS is the fundamental image modality for most advanced multimodality intravascular imaging techniques, since it provides a more comprehensive picture of vessel anatomy on which other imaging data can be superimposed. However, image quality in the deeper region is poor because of the downgraded lateral resolution and contrast-to-noise ratio (CNR). In this paper, we report on the application of an ultrasound beamforming method that combines virtual source synthetic aperture (VSSA) focusing and coherence factor weighting (CFW) to improve the IVUS image quality. The natural focal point of conventional IVUS transducer was treated as a virtual source that emits spherical waves within a certain region. Mono-static synthetic aperture focusing was conducted to achieve higher resolution. Coherence factor was calculated using delayed RF signals and applied to the synthesized beam to increase the CNR and focusing quality. The proposed method was tested through simulations in Field II and imaging experiments in both linear and rotational scans. The lateral resolution for linear scan mode is improved from 165–524 to 126–143 \mu m ; resolution for rotational scan mode improves by up to 42%. CNR improvement by up to 1.5 was observed on the anechoic cysts of different sizes and at different locations. Herein, it is demonstrated that the beamforming method, which combines VSSA and CFW, can significantly improve the IVUS image quality. This approach can be readily integrated into the current IVUS imaging system for enhanced clinical diagnosis. [ABSTRACT FROM PUBLISHER]

In practical scenarios with random arrival and departure of primary users (PUs), existing simultaneous sensing and transmission schemes allocated the same weight to each sample, and did not consider low signal-to-noise ratio (SNR) situations. This paper proposes an adaptive weighted sensing scheme with simultaneous transmission for dynamic PU traffic. It uses a power function based on the corresponding sampling sequence and could reveal the actual PU state in near real time. The power exponent is further adjusted to the sensing situations to achieve lowest false alarm probability under a certain detection probability constraint. Then, an analytical model considering all possible PU state transitions is developed to evaluate achievable interference, throughput, and energy efficiency. Furthermore, the optimal frame duration yielding both optimal false alarm probability and throughput is computed. After that, a fast search algorithm is proposed to track the optimal duration at an exponential convergence rate. Simulation results are provided to validate the analytical model and demonstrate the improvement in low SNR. The results indicate that the proposed scheme can achieve lower false alarm probability and higher energy efficiency over a wide SNR range than that of the existing weighting schemes, which are based on probability, geometric sequence, and equal weighting. [ABSTRACT FROM AUTHOR]

The two-bias method typically used to calculate the mobility-lifetime product ( $\mu \tau $ ) in single-polarity charge sensing detectors, uses induced signals from cathode-side events to measure the $\mu \tau $ . This method assumes an ideal weighting potential (exactly zero through the bulk and a rapid rise to unity at the anode). When a non-ideal weighting potential (e.g., from a pixelated detector) is used, the $\mu \tau $ is systematically overestimated. In this work, we characterize this overestimation and present a simple correction factor $k$ that can be applied to pixelated electrode configurations. It was found that the correction factor is only dependent on the pixel-pitch to detector thickness ratio. [ABSTRACT FROM PUBLISHER]

In this paper, we study a classification problem in which sample labels are randomly corrupted. In this scenario, there is an unobservable sample with noise-free labels. However, before being observed, the true labels are independently flipped with a probability $\rho \in [0,0.5)$ , and the random label noise can be class-conditional. Here, we address two fundamental problems raised by this scenario. The first is how to best use the abundant surrogate loss functions designed for the traditional classification problem when there is label noise. We prove that any surrogate loss function can be used for classification with noisy labels by using importance reweighting, with consistency assurance that the label noise does not ultimately hinder the search for the optimal classifier of the noise-free sample. The other is the open problem of how to obtain the noise rate \rho$ . We show that the rate is upper bounded by the conditional probability P(\hat{Y}|X) of the noisy sample. Consequently, the rate can be estimated, because the upper bound can be easily reached in classification problems. Experimental results on synthetic and real datasets confirm the efficiency of our methods. [ABSTRACT FROM PUBLISHER]

This paper presents a new random weighting method to deal with the systematic error of the kinematic model for dynamic navigation. This method incorporates random weights in the kinematic model to control the systematic error of the kinematic model for improving the navigation accuracy. A theory of random weighting estimation is established, showing that 1) the random weighting estimation of the kinematic model?s systematic error is unbiased and 2) the covariance matrix of the predicted state vector can be controlled by adjusting the covariance matrices of the predicted residual vector and estimated state vector to improve the accuracy of state prediction. Random weighting estimations are also constructed for the systematic error of the kinematic model as well as the covariance matrices of predicted residual vector, predicted state vector, and state noise vector. Experimental results demonstrate the effectiveness of the proposed random weighting method in resisting the disturbances of the kinematic model noise for improving the accuracy of dynamic navigation. [ABSTRACT FROM PUBLISHER]

Based on the works of Yahya Rahmat-Samii, a multiobjective optimization model is developed to analyze the effects of surface errors’ nonuniformity along the radius on the reflector’s radiation characteristic. Through the optimization model, Pareto front for trading off the peak gain loss (PGL) and the first sidelobe level increase (SLLI) is obtained. Simulations clearly show that for the same root mean square (rms) error for the whole reflector, different distributions along the radius result in different PGLs and first SLLIs. Then, the weighting function of the rms errors is considered and the evaluation formula of PGL is successfully applied to nonuniform errors. It is concluded that besides the radius, the amplitude aperture distribution, and the ratio of focal length to diameter (F/D), errors’ nonuniformity is also an important factor that should be considered in the procedure of the quality evaluation of the reflector surface. [ABSTRACT FROM PUBLISHER]

Spectral centroid from the backscattered ultrasound provides important information about the attenuation properties of soft tissues and Doppler effects of blood flows. Because the spectral centroid is originally determined from the power spectrum of backscattered ultrasound signals in the frequency domain, it is natural to calculate it after converting time-domain signals into spectral domain signals, using the fast Fourier transform (FFT). Recent research, however, derived the time-domain equations for calculating the spectral centroid using a Parseval's theorem, to avoid the calculation of the Fourier transform. The work only presented the final result, which showed that the computational time of the proposed time-domain method was 4.4 times faster than that of the original FFT-based method, whereas the average estimation error was negligible. In this paper, we present the optimal design of the autocorrelation weighting function, which is used for the timedomain spectral centroid estimation process, to reduce the computational time significantly. We also carry out a comprehensive analysis of the computational complexities of the FFTbased and time-domain methods with respect to the length of ultrasound signal segments. The simulation results using numerical phantoms show that, with the optimized autocorrelation weighting function, we only need approximately 3% of the full set of data points. In addition to that, because the proposed optimization technique requires a fixed number of data points to calculate the spectral centroid, the execution time is constant as the length of the data segment increases, whereas the execution time of the conventional FFT-based method is increased. Analysis of the computational complexities between the proposed method and the conventional FFT-based method presents O(N) and O(Nlog2N), respectively. [ABSTRACT FROM AUTHOR]

Some people may believe that the filtered backprojection (FBP) algorithm does not work if the projection data are measured non-uniformly. Some may also believe that iterative algorithms can automatically handle the non-uniformly sampled data in the projector/backprojector. This paper claims that the FBP algorithm can effectively handle the situation where the angular sampling is not uniform. This paper compares the images that are reconstructed by both the FBP and the iterative Landweber algorithms when the angular sampling is non-uniform. When the iteration number is low, the iterative algorithms do not handle the non-uniform sampling properly. A weighting strategy is then suggested and it makes the image resolution more isotropic. In few-view tomography, the FBP and iterative algorithms both perform poorly if no other prior information is used. We have made the following observations: 1) When using an iterative algorithm, one must use early solutions due to noise amplification. 2) An early solution can have anisotropic spatial resolution if the angular sampling is not uniform. 3) The anisotropic resolution problem can be solved by introducing angle dependent weighting, which is not noise dependent. 4) The weighting is not effective when the iteration number is large. The weighting only affects the early solutions, and does not affect the converged solution. 5) When the iteration number is large, the model-mismatch errors are amplified and cause artifacts in the image. 6) The FBP algorithm is not sensitive to the model-mismatch errors, and does not have the “early solution” problems. 7) In few-view tomography, both FBP and iterative algorithms perform poorly, while the FBP algorithm gives a sharper image than the iterative algorithm does. [ABSTRACT FROM PUBLISHER]

Each scatterer responds to the incident wave in a unique manner. The backscattered signal from the scatterer is affected by two distinct phenomena: early-time response which emanates from the scattering centers of the scatterer and late-time response which is the summation of the complex natural resonances (CNRs) with some weighting residues. Based on the singularity expansion method (SEM), CNRs are aspect-independent parameters which include some structural information of the scattering target. Based on this fact, the data can be embedded on the RFID tag as CNRs associated to the structural parameters. In this paper, a low-profile chipless RFID tag as a suitable device for high-density data is introduced. By incorporating several slots on the chipless RFID tag, the data can be encoded as CNRs on the structure. The designed RFID tag operates in the frequency band of 3.1–10.6 GHz. The data are decoded in the reader by applying short-time matrix pencil method (STMPM) to the transient backscattered response of the tag. In addition, the turn-on times, damping factors, and resonant frequencies of the poles are distinguished. After studying the effects of various parameters of the tag, a 24-bit tag is designed on the area of 24 \,\times\, 24 mm ^2, simulated and measured. The measurement is performed in the frequency domain and, after applying inverse fast Fourier transform (IFFT), STMPM is employed to extract the signature of the tag. The tag is successfully read remotely via its scattered fields. [ABSTRACT FROM PUBLISHER]

Service composition is emerging as an effective vehicle for integrating existing web services to create value-added and personalized composite services. As web services with similar functionality are expected to be provided by competing providers, a key challenge is to find the “best” web services to participate in the composition. When multiple quality aspects (e.g., response time, fee, etc.) are considered, a weighting mechanism is usually adopted by most existing approaches, which requires users to specify their preferences as numeric values. We propose to exploit the dominance relationship among service providers to find a set of “best” possible composite services, referred to as a composite service skyline. We develop efficient algorithms that allow us to find the composite service skyline from a significantly reduced searching space instead of considering all possible service compositions. We propose a novel bottom-up computation framework that enables the skyline algorithm to scale well with the number of services in a composition. We conduct a comprehensive analytical and experimental study to evaluate the effectiveness, efficiency, and scalability of the composite skyline computation approaches. [ABSTRACT FROM AUTHOR]

This paper proposes TW-$(k)$-means, an automated two-level variable weighting clustering algorithm for multiview data, which can simultaneously compute weights for views and individual variables. In this algorithm, a view weight is assigned to each view to identify the compactness of the view and a variable weight is also assigned to each variable in the view to identify the importance of the variable. Both view weights and variable weights are used in the distance function to determine the clusters of objects. In the new algorithm, two additional steps are added to the iterative $(k)$-means clustering process to automatically compute the view weights and the variable weights. We used two real-life data sets to investigate the properties of two types of weights in TW-$(k)$-means and investigated the difference between the weights of TW-$(k)$-means and the weights of the individual variable weighting method. The experiments have revealed the convergence property of the view weights in TW-$(k)$-means. We compared TW-$(k)$-means with five clustering algorithms on three real-life data sets and the results have shown that the TW-$(k)$-means algorithm significantly outperformed the other five clustering algorithms in four evaluation indices. [ABSTRACT FROM AUTHOR]

Cooperative coevolution is a successful trend of evolutionary computation which allows us to define partitions of the domain of a given problem, or to integrate several related techniques into one, by the use of evolutionary algorithms. It is possible to apply it to the development of advanced classification methods, which integrate several machine learning techniques into a single proposal. A novel approach integrating instance selection, instance weighting, and feature weighting into the framework of a coevolutionary model is presented in this paper. We compare it with a wide range of evolutionary and nonevolutionary related methods, in order to show the benefits of the employment of coevolution to apply the techniques considered simultaneously. The results obtained, contrasted through nonparametric statistical tests, show that our proposal outperforms other methods in the comparison, thus becoming a suitable tool in the task of enhancing the nearest neighbor classifier. [ABSTRACT FROM PUBLISHER]

Erasure entropy rate differs from Shannon's entropy rate in that the conditioning occurs with respect to both the past and the future, as opposed to only the past (or the future). In this paper, consistent universal algorithms for estimating erasure entropy rate are proposed based on the basic and extended context-tree weighting (CTW) algorithms. Simulation results for those algorithms applied to Markov sources, tree sources, and English texts are compared to those obtained by fixed-order plug-in estimators with different orders. [ABSTRACT FROM AUTHOR]

Performing optimal bit-allocation with 3-D wavelet coding methods is difficult because energy is not conserved after applying the motion-compensated temporal filtering (MCTF) process and the spatial wavelet transform. The problem cannot be solved by extending the 2-D wavelet coefficients weighting method directly and then applying the result to 3-D wavelet coefficients, since this approach does not consider the complicated pixel connectivity that results from the lifting-based MCTF process. In this paper, we propose a novel weighting method, which takes account of the pixel connectivity, to solve the problem and derive the effect of the quantization error of a subband on the reconstruction error of a group of pictures. We employ the proposed method on a 2-D + t structure with different temporal filters, namely the 5-3 filter and the 9-7 filter. Experiments on various coding parameters and sequences show that the proposed approach improves the bit-allocation performance over that obtained by using the weightings derived without considering the pixel connectivity in the MCTF process. [ABSTRACT FROM AUTHOR]

Computation of the undetected error probability for error detecting codes over the Z-channel is an important issue, explored only in part in previous literature. In this paper, Varshamov-Tenengol'ts (VT) codes are considered. First, an exact formula for the probability of undetected errors is given. It can be explicitly computed for small code lengths (up to approximately 25). Next, some lower bounds that can be explicitly computed up to almost twice this length are studied. A comparison to the Hamming codes is given. It is further shown that heuristic arguments give a very good approximation that can easily be computed even for large lengths. Finally, Monte Carlo methods are used to estimate performance for long code lengths. [ABSTRACT FROM AUTHOR]

In this paper, redundant random ensembles are defined and their average stopping set (SS) weight distributions are analyzed. A redundant random ensemble consists of a set of binary matrices with linearly dependent rows. These linearly dependent rows significantly reduce the number of stopping sets (SS) of small size. Upper and lower bounds on the average SS weight distribution of the redundant random ensembles are proved based on a combinatorial argument. Asymptotic forms of these bounds reveal asymptotic behavior of the average SS weight distributions. From these bounds, a tradeoff between the number of redundant rows (corresponding to decoding complexity of belief propagation on binary erasure channel) and the average SS weight distribution (corresponding to decoding performance) can be derived. [ABSTRACT FROM AUTHOR]

We describe an algorithm for the efficient annotation of events of interest in video microscopy. The specific application involves the detection and tracking of multiple possibly overlapping vesicles in total internal reflection fluorescent microscopy images. A statistical model for the dynamic image data of vesicle configurations allows us to properly weight various hypotheses online. The goal is to find the most likely trajectories given a sequence of images. The computational challenge is addressed by defining a sequence of coarse-to-fine tests, derived from the statistical model, to quickly eliminate most candidate positions at each time frame. The computational load of the tests is initially very low and gradually increases as the false positives become more difficult to eliminate. Only at the last step are state variables estimated from a complete time-dependent model. Processing time thus mainly depends on the number of vesicles in the image and not on image size. [ABSTRACT FROM AUTHOR]

We demonstrate a new weighting kernel for linear time-of-flight (TOF) positron emission tomography (PET) reconstruction that produces lower image noise variance in finite-sized objects than "confidence weighting" (CW) or any other kernel previously reported in the literature. The proposed kernel is adaptive to object size, but is always broader than the CW kernel. For 1.25 nsec TOF resolution, the new kernel improves image noise variance in a 10 cm diameter cylinder by 8%. Small but significant gains in image noise variance are also seen in clinical reconstructions. [ABSTRACT FROM AUTHOR]

In this paper, we present an in-depth study of two collaborative-localization methods, called the multidimensional scaling (MDS) and maximum-likelihood estimator (MLE), for wireless sensor networks. From theoretical analysis, it is shown that MLE is more appropriate than MDS, given the underlying assumption of statistical signal models of the received-signal-strength-based localization problem. We also show that MDS can approximately achieve asymptotic efficiency with appropriate weighting schemes in some scenarios. From extensive simulation results, it is noted that the nonlinear least square algorithms that are commonly used to determine MLE are not as efficient as the iterative MDS algorithms. Thus, we propose a new integrated method MDS-MLE to effectively benefit from the strength of both methods. In the new method, MDS is used as an initialization method for MLE. With the solution of MDS as an initial value, MLE converges much faster and achieves significantly better performance than with random initial values. Superior performance of the new method is clearly demonstrated through simulation results. The effects of the deployment density of sensor nodes and reference nodes (RNs), as well as the deployment structure of RNs, are also studied through various simulations. [ABSTRACT FROM AUTHOR]

This paper presents a simulation-based study to evaluate the performance of 12 defuzzification-based approaches for solving the general fuzzy multiattribute decision-making (MADM) problem requiring cardinal ranking of decision alternatives. These approaches are generated based on six defuzzification methods in conjunction with the simple additive weighting (SAW) method and the technique for order preference by similarity to the ideal solution method. The consistency and effectiveness of these approaches are examined in terms of four new objective performance measures, which are based on five evaluation indexes. The simulation result shows that the approaches, which are capable of using all the available information on fuzzy numbers effectively in the defuzzification process, produce more consistent ranking outcomes. In particular, the SAW method with the degree of dominance defuzzification is proved to be the overall best performed approach, which is followed by the SAW method with the area center defuzzification. These findings are of practical significance in real-world settings where the selection of the defuzzification-based approaches is required in solving the general fuzzy MADM problems under specific decision contexts. [ABSTRACT FROM AUTHOR]

The fuzzy c-means (FCM) algorithm is one of the most frequently used clustering algorithms. The weighting exponent m is a parameter that greatly influences the performance of the FCM. But there has been no theoretical basis for selecting the proper weighting exponent in the literature. In this paper, we develop a new theoretical approach to selecting the weighting exponent in the FCM. Based on this approach, we reveal the relation between the stability of the fixed points of the FCM and the data set itself. This relation provides the theoretical basis for selecting the weighting exponent in the FCM. The numerical experiments verify the effectiveness of our theoretical conclusion. [ABSTRACT FROM AUTHOR]