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Image feature correspondence selection is pivotal to many computer vision tasks from object recognition to 3D reconstruction. Although many correspondence selection algorithms have been developed in the past decade, there still lacks an in-depth evaluation and comparison in the open literature, which makes it difficult to choose the appropriate algorithm for a specific application. This paper attempts to fill this gap by evaluating eight competing correspondence selection algorithms including both classical methods and current state-of-the-art ones. In addition to preselected correspondences, we have compared different combinations of detector and descriptor on four standard datasets. The diversity of those datasets cover a wide range of uncertainty factors including zoom, rotation, blur, viewpoint change, JPEG compression, light change, different rendering styles and multiple structures. We have measured the quality of competing correspondence selection algorithms in terms of four performance metrics - i.e., precision, recall, F-measure and efficiency. Moreover, we propose to combine the strengths of eight competing methods by combining their correspondence selection results. Extensive experimental results are reported to demonstrate the superiority of several fusion strategies to individual methods, which suggests the possibility of adaptively combining those methods for even better performance. [ABSTRACT FROM AUTHOR]

This paper compares the performance of several boosting and bagging techniques in the context of learning from imbalanced and noisy binary-class data. Noise and class imbalance are two well-established data characteristics encountered in a wide range of data mining and machine learning initiatives. The learning algorithms studied in this paper, which include SMOTEBoost, RUSBoost, Exactly Balanced Bagging, and Roughly Balanced Bagging, combine boosting or bagging with data sampling to make them more effective when data are imbalanced. These techniques are evaluated in a comprehensive suite of experiments, for which nearly four million classification models were trained. All classifiers are assessed using seven different performance metrics, providing a complete perspective on the performance of these techniques, and results are tested for statistical significance via analysis-of-variance modeling. The experiments show that the bagging techniques generally outperform boosting, and hence in noisy data environments, bagging is the preferred method for handling class imbalance. [ABSTRACT FROM PUBLISHER]

The natural-element method (NEM) has proved to be able to provide more accurate and computationally efficient solutions than the finite-element method (FEM) for first-order consistency approximations. However, higher order approximations in the NEM framework are not obtained straightforwardly. This paper addresses a method of constructing higher order approximations out of the standard first-order NEM shape functions. This process is achieved through the de Boor algorithm. Accuracy of the scheme is compared with FEM. Results show that in second-order approximations context the NEM is still able to provide better accuracies for a given number of degrees of freedom. [ABSTRACT FROM PUBLISHER]

This paper introduces a new algorithm to approximate non-orthogonal joint diagonalization (NOJD) of a set of complex matrices. This algorithm is based on the Frobenius norm formulation of the joint diagonalization (JD) problem and takes advantage from combining Givens and Shear rotations to attempt the approximate JD. It represents a non-trivial generalization of the JDi (Joint Diagonalization) algorithm (Souloumiac 2009) to the complex case. The JDi is first slightly modified then generalized to the CJDi (i.e., Complex JDi) using complex to real matrix transformation. Several methods already exist in the literature. Consequently, we provide herein a brief overview of existing NOJD algorithms. Then we provide an extensive comparative study to illustrate the effectiveness and stability of the CJDi with respect to various system parameters and application contexts. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive parameter controlling non-dominated ranking differential evolution (A-NRDE) algorithm for multi-objective optimal design of electromagnetic problems. The variable parameters, such as mutation and crossover rates, are self-controlled based on the information of successful individuals and the number of Pareto optimal solutions in current iteration. In mutation step, the proposed algorithm incorporates multi-guiders to obtain a uniformly distributed Pareto front; the advantages of DE are combined with the mechanisms of non-dominated ranking and crowding distance sorting. The proposed A-NRDE algorithm is applied to a multi-objective version of TEAM 22 and five benchmark problems. Experimental results show that the proposed our approach is able to obtain a good distribution of Pareto front and convergence in all cases. Compared with several other state-of-the-art evolutionary algorithms, it achieves not only comparable results in terms of convergence and diversity metrics, but also a considerable reduction of the computational effort. [ABSTRACT FROM AUTHOR]

Validation is arguably the bottleneck in the diffusion magnetic resonance imaging (MRI) community. This paper evaluates and compares 20 algorithms for recovering the local intra-voxel fiber structure from diffusion MRI data and is based on the results of the “HARDI reconstruction challenge” organized in the context of the “ISBI 2012” conference. Evaluated methods encompass a mixture of classical techniques well known in the literature such as diffusion tensor, Q-Ball and diffusion spectrum imaging, algorithms inspired by the recent theory of compressed sensing and also brand new approaches proposed for the first time at this contest. To quantitatively compare the methods under controlled conditions, two datasets with known ground-truth were synthetically generated and two main criteria were used to evaluate the quality of the reconstructions in every voxel: correct assessment of the number of fiber populations and angular accuracy in their orientation. This comparative study investigates the behavior of every algorithm with varying experimental conditions and highlights strengths and weaknesses of each approach. This information can be useful not only for enhancing current algorithms and develop the next generation of reconstruction methods, but also to assist physicians in the choice of the most adequate technique for their studies. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate the computational affordability and accuracy of a leap-frog discontinuous Galerkin (LFDG) time-domain method for high intensity radiated fields assessment in electromagnetic compatibility for aerospace. The conformal truncation of the computational domain is discussed and formulated in the LFDG context. Numerical validations are performed on challenging test cases, in comparison to measurements and to other numerical methods, demonstrating the accuracy, efficiency, and scalability of the algorithm. [ABSTRACT FROM PUBLISHER]

In real-time systems, correctness depends not only on functionality but also on timeliness. A great number of scheduling theories have been developed for verification of the temporal correctness of jobs (software) in such systems. Among them, the Earliest Deadline first until Zero-Laxity (EDZL) scheduling algorithm has received growing attention thanks to its effectiveness in multicore real-time scheduling. However, the true potential of EDZL has not yet been fully exploited in its schedulability analysis as the state-of-the-art EDZL analysis techniques involve considerable pessimism. In this paper, we propose a new EDZL multicore schedulability test. We first introduce an interesting observation that suggests an insight toward pessimism reduction in the schedulability analysis of EDZL. We then incorporate it into a well-known existing Earliest Deadline First (EDF) schedulability test, resulting in a new EDZL schedulability test. We demonstrate that the proposed EDZL test not only has lower time complexity than existing EDZL schedulability tests, but also significantly improves the schedulability of EDZL by up to 36.6 percent compared to the best existing EDZL schedulability tests. [ABSTRACT FROM PUBLISHER]

Slicing is useful for many software engineering applications and has been widely studied for three decades, but there has been comparatively little work on slicing extended finite state machines (EFSMs). This paper introduces a set of dependence-based EFSM slicing algorithms and an accompanying tool. We demonstrate that our algorithms are suitable for dependence-based slicing. We use our tool to conduct experiments on 10 EFSMs, including benchmarks and industrial EFSMs. Ours is the first empirical study of dependence-based program slicing for EFSMs. Compared to the only previously published dependence-based algorithm, our average slice is smaller 40 percent of the time and larger only 10 percent of the time, with an average slice size of 35 percent for termination insensitive slicing. [ABSTRACT FROM PUBLISHER]

A particularly voluminous dataset in molecular genomics, known as whole genome alignments, has gained considerable importance over the last years. In this paper, we propose a compression modeling approach for the multiple sequence alignment (MSA) blocks, which make up most of these datasets. Our method is based on a mixture of finite-context models. Contrarily to other recent approaches, it addresses both the DNA bases and gap symbols at once, better exploring the existing correlations. For comparison with previous methods, our algorithm was tested in the multiz28way dataset. On average, it attained 0.94 bits per symbol, approximately 7% better than the previous best, for a similar computational complexity. We also tested the model in the most recent dataset, multiz46way. In this dataset, that contains alignments of 46 different species, our compression model achieved an average of 0.72 bits per MSA block symbol. [ABSTRACT FROM PUBLISHER]

This paper presents a comprehensive method for eccentricity fault diagnosis in switched reluctance machine (SRM) during offline and standstill modes. In this method, the fault signature is achieved by processing the differential currents resulted from injected high-frequency diagnostic pulses to the motor windings. For feasibility assessment of the proposed method, the correlation between differential current and eccentricity occurrence is analytically demonstrated. A 3-D transient finite-element method is utilized to analyze the proposal, and its results are validated by experimental tests on a prototype machine in the laboratory. According to the obtained results by experimental as well as numerical analysis, a pattern is introduced in order to detect fault presence. In this stage, an algorithm with two methods is proposed for the detection of fault location or faulty phase. Furthermore, the direction of fault is recognized by a simple comparative technique. Next, a normalized indicator is proposed in order to estimate the eccentricity fault severity as well as its occurrence. This indicator is not dependent of the healthy motor information, which makes it reliable and applicable tool for all sizes of SRMs. Finally, a simple procedure based on achieved pattern and indicator is implemented to distinguish the static, dynamic, and mixed eccentricity in the SRM. [ABSTRACT FROM AUTHOR]

Thermoacoustic tomography (TAT) is a novel, non-invasive medical imaging technique but has encountered obstacles in imaging through the cranium. In this paper we present two methods for transcranial TAT: Kirchhoff migration (KM) and reverse-time migration (RTM). The two methods' imaging qualities are verified and compared based on both synthetic and experimental data. RTM proves to have better velocity variance and imaging quality, and little noise with spatial aliasing. RTM is a promising approach for achieving transcranial TAT in further studies. [ABSTRACT FROM PUBLISHER]

In comparison with hard clustering methods, in which a pattern belongs to a single cluster, fuzzy clustering algorithms allow patterns to belong to all clusters with differing degrees of membership. This is important in domains such as sentence clustering, since a sentence is likely to be related to more than one theme or topic present within a document or set of documents. However, because most sentence similarity measures do not represent sentences in a common metric space, conventional fuzzy clustering approaches based on prototypes or mixtures of Gaussians are generally not applicable to sentence clustering. This paper presents a novel fuzzy clustering algorithm that operates on relational input data; i.e., data in the form of a square matrix of pairwise similarities between data objects. The algorithm uses a graph representation of the data, and operates in an Expectation-Maximization framework in which the graph centrality of an object in the graph is interpreted as a likelihood. Results of applying the algorithm to sentence clustering tasks demonstrate that the algorithm is capable of identifying overlapping clusters of semantically related sentences, and that it is therefore of potential use in a variety of text mining tasks. We also include results of applying the algorithm to benchmark data sets in several other domains. [ABSTRACT FROM AUTHOR]

In synthetic aperture radar (SAR) processing, there is a trade-off between accuracy and speed. The approximations in an algorithm help to increase the algorithm's speed but cause deterministic phase errors which directly affect the SAR image quality. This paper discusses the phase error calculations for bistatic fast backprojection (BiFBP) and bistatic fast factorized backprojection (BiFFBP) which are essential for setting their parameters. The phase error calculation principle for bistatic SAR in comparison to monostatic SAR is presented. This principle is used to derive the maximum phase error equation. [ABSTRACT FROM PUBLISHER]

The injection of software faults in software components to assess the impact of these faults on other components or on the system as a whole, allowing the evaluation of fault tolerance, is relatively new compared to decades of research on hardware fault injection. This paper presents an extensive experimental study (more than 3.8 million individual experiments in three real systems) to evaluate the representativeness of faults injected by a state-of-the-art approach (G-SWFIT). Results show that a significant share (up to 72 percent) of injected faults cannot be considered representative of residual software faults as they are consistently detected by regression tests, and that the representativeness of injected faults is affected by the fault location within the system, resulting in different distributions of representative/nonrepresentative faults across files and functions. Therefore, we propose a new approach to refine the faultload by removing faults that are not representative of residual software faults. This filtering is essential to assure meaningful results and to reduce the cost (in terms of number of faults) of software fault injection campaigns in complex software. The proposed approach is based on classification algorithms, is fully automatic, and can be used for improving fault representativeness of existing software fault injection approaches. [ABSTRACT FROM AUTHOR]

This paper deals with development of a sequential test for comparison of the mean times between failures (MTBF) of two items: a new one (subscript n) and another serving as a reference (subscript ref). In the test, the hypothesis is checked that MTBFn is not less than a given multiple of MTBFref. Prior of the test, information is available on a known quantity r of the time between failures (TBF) for the reference item, which is not involved in the test, and only the new item is tested. It is a common occurrence in industrial practice that a new, modernized item has to be checked versus a reference counterpart currently in use. We assume that the TBF of both items is distributed exponentially. The sequential probability ratio test (SPRT) is widely used for checking that an MTBFn is not less than a specified (target) value. The innovation here is that in the proposed sequential test the target is the MTBFref which is available not as an exact value but only as an estimate whose uncertainty depend on r. Accordingly, a search algorithm was developed for the decision boundaries in the proposed test, as well as for calculating the operating characteristic (OC), and the statistical parameters of the test duration and number of failures to stopping the test. Approximative relationships were obtained for the above, depending on the target OC. These straightforward relationships drastically simplify the planning process. Moreover, they will enable the reliability engineer to appreciate the need to take the above uncertainty into account, and the expediency of resorting to the test at an early stage of its planning. Its superior efficacy is demonstrated. [ABSTRACT FROM AUTHOR]

The synthetic aperture focusing technique (SAFT) has been shown to significantly improve the lateral resolution of monostatic imaging systems. To apply SAFT to interior imaging of cylindrical structures such as pipes or blood vessels, algorithms adapted to cylindrical measurement surfaces are needed. In this paper, we present a new algorithm for SAFT applied to such cylindrical scans. The derivation of the algorithm is based directly on the Fourier domain solutions to the wave equation in cylindrical coordinates, and in this sense, the algorithm is exact. Compared with existing methods, the proposed algorithm yields lower side lobes and better resolution for wide-beam transducers. The attainable angular resolution is shown to depend on the size of the transducer and the scanning radius R. For a flat, circular transducer of diameter D, the angular resolution is approximately D/(2R). It is also shown that using an angular step size of D/(4R) will keep grating lobes at least 40 dB below the main lobe, and we therefore recommend D/(4R) as a suitable step size for practical applications. [ABSTRACT FROM PUBLISHER]

Recent advances in digital microfluidics have led to the promise of miniaturized laboratories, with the associated advantages of high sensitivity and less human-induced errors. Front-end operations such as sample preparation play a pivotal role in biochemical laboratories, and in applications in biomedical engineering and life science. For fast and high-throughput biochemical applications, preparing samples of multiple target concentrations sequentially is inefficient and time-consuming. Therefore, it is critical to concurrently prepare samples of multiple target concentrations. In addition, since reagents used in biochemical reactions are expensive, reagent-saving has become an important consideration in sample preparation. Prior work in this area does not address the problem of reagent-saving and concurrent sample preparation for multiple target concentrations. In this paper, we propose the first reagent-saving mixing algorithm for biochemical samples of multiple target concentrations. The proposed algorithm not only minimizes the consumption of reagents, but it also reduces the number of waste droplets and the sample preparation time by preparing the target concentrations concurrently. The proposed algorithm is evaluated on both real biochemical experiments and synthetic test cases to demonstrate its effectiveness and efficiency. Compared to prior work, the proposed algorithm can achieve up to 41% reduction in the number of reagent droplets and waste droplets, and up to 50% reduction in sample preparation time. [ABSTRACT FROM AUTHOR]

The stability of low-rank matrix reconstruction with respect to noise is investigated in this paper. The \ell \ast-constrained minimal singular value (\ell \ast-CMSV) of the measurement operator is shown to determine the recovery performance of nuclear norm minimization-based algorithms. Compared with the stability results using the matrix restricted isometry constant, the performance bounds established using \ell \ast-CMSV are more concise, and their derivations are less complex. Isotropic and subgaussian measurement operators are shown to have \ell \ast-CMSVs bounded away from zero with high probability, as long as the number of measurements is relatively large. The \ell \ast-CMSV for correlated Gaussian operators are also analyzed and used to illustrate the advantage of \ell \ast-CMSV compared with the matrix restricted isometry constant. We also provide a fixed point characterization of \ell \ast-CMSV that is potentially useful for its computation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel coupled dictionary training method for single-image super-resolution (SR) based on patchwise sparse recovery, where the learned couple dictionaries relate the low- and high-resolution (HR) image patch spaces via sparse representation. The learning process enforces that the sparse representation of a low-resolution (LR) image patch in terms of the LR dictionary can well reconstruct its underlying HR image patch with the dictionary in the high-resolution image patch space. We model the learning problem as a bilevel optimization problem, where the optimization includes an \ell^1-norm minimization problem in its constraints. Implicit differentiation is employed to calculate the desired gradient for stochastic gradient descent. We demonstrate that our coupled dictionary learning method can outperform the existing joint dictionary training method both quantitatively and qualitatively. Furthermore, for real applications, we speed up the algorithm approximately 10 times by learning a neural network model for fast sparse inference and selectively processing only those visually salient regions. Extensive experimental comparisons with state-of-the-art SR algorithms validate the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

Coordination of directional overcurrent relays in a multiloop subtransmission or distribution network is formulated as an optimization problem. In this paper, the coordination of directional overcurrent relays is formulated as a mixed-integer nonlinear programming problem and is then solved by a new seeker optimization technique. Based on the act of human searching, in the proposed seeker technique, the search direction and step length are determined in an adaptive way. The proposed method is implemented in three different test cases. The results are compared with previously proposed analytic and evolutionary approaches. [ABSTRACT FROM AUTHOR]

The general method of calculation described in this paper is applicable to the calculation of the self and mutual inductance of thin-walled solenoids of general shape with parallel axes in both coaxial and non-coaxial relative positions. The method is based on the solution of integral equation, the validity of which is defined in the text and additionally the physical interpretation of each section of the equation is documented. The manner of utilization of the method is demonstrated for the cases known in systems with a circular solenoid and subsequently applied to the case of two solenoids of rectangular shape in a general relative position. In these cases, a similarly detailed algorithm is compiled and the measurements are then compared with the calculation results. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm that enhances the contrast of an input image using interpixel contextual information. The algorithm uses a 2-D histogram of the input image constructed using a mutual relationship between each pixel and its neighboring pixels. A smooth 2-D target histogram is obtained by minimizing the sum of Frobenius norms of the differences from the input histogram and the uniformly distributed histogram. The enhancement is achieved by mapping the diagonal elements of the input histogram to the diagonal elements of the target histogram. Experimental results show that the algorithm produces better or comparable enhanced images than four state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a novel algorithm for matching image interest points. Potential interest points are identified by searching for local peaks in Difference-of-Gaussian (DoG) images. We refine and assign rotation, scale and location for each keypoint by using the SIFT algorithm refid="ref1"/. Pseudo log-polar sampling grid is then applied to properly scaled image patches around each keypoint, and a weighted adaptive lifting scheme transform is designed for each ring of the log-polar grid. The designed adaptive transform for a ring in the reference keypoint and the general non-adaptive transform are applied to the corresponding ring in a test keypoint. Similarity measure is calculated by comparing the corresponding transform domain coefficients of the adaptive and non-adaptive transforms. We refer to the proposed versatile system of Rotation And Scale Invariant Matching as RASIM. Our experiments show that the accuracy of RASIM is more than SIFT, which is the most widely used interest point matching algorithm in the literature. RASIM is also more robust to image deformations while its computation time is comparable to SIFT. [ABSTRACT FROM AUTHOR]

A single captured image of a real-world scene is usually insufficient to reveal all the details due to under- or over-exposed regions. To solve this problem, images of the same scene can be first captured under different exposure settings and then combined into a single image using image fusion techniques. In this paper, we propose a novel probabilistic model-based fusion technique for multi-exposure images. Unlike previous multi-exposure fusion methods, our method aims to achieve an optimal balance between two quality measures, i.e., local contrast and color consistency, while combining the scene details revealed under different exposures. A generalized random walks framework is proposed to calculate a globally optimal solution subject to the two quality measures by formulating the fusion problem as probability estimation. Experiments demonstrate that our algorithm generates high-quality images at low computational cost. Comparisons with a number of other techniques show that our method generates better results in most cases. [ABSTRACT FROM AUTHOR]

This paper describes three data driven methods to monitor electromechanical oscillations in interconnected power system operation. The objective is to compare and contrast the performance of the methods. The accuracy of damping ratio and frequency of oscillations are the measures of the performance of the algorithms. The advantages and disadvantages of various techniques and their limitations to measurement noise have been considered while assessing performance. The target frequency and damping are computed using the Nordic power system simulation model. [ABSTRACT FROM PUBLISHER]

Local image features have become ubiquitous for a wide range of computer vision tasks. For embedded and low power devices, speed and memory efficiency is of main concern, and therefore, there have been several recent attempts to improve these issues. In this paper, we are concerned with the early components of the object recognition pipeline, namely, feature detection, feature description, and feature tracking. In particular, we propose a novel approach to speed up feature detectors and to inform feature tracking that speeds up the recognition process by using the concept of adaptive sampling. We select two examples of visual algorithms to be modified by adaptive sampling and present comparative results with and without modifications. We show how processing time and memory footprint can benefit by this approach with little impact on overall output quality. We implement our proposed methods on a chipset commonly found on smartphones and we discuss the obtained improvements. [ABSTRACT FROM AUTHOR]

This paper introduces a state-diagram-based protective algorithm for parallel circuit transmission lines using local information obtained at the relay bus. A new cross-differential technique is proposed which compares the currents in the corresponding phases of the parallel circuits in a 2-D space. A companion impedance-based algorithm is used to cover the evolving faults and some conditions in which the cross-differential technique is not able to discriminate the internal faults alone. The effect of the mutual coupling between parallel circuits on the measured impedances is adaptively compensated according to the status of the parallel circuit currents in the segmented 2-D space. The proposed state-diagram-based algorithm combines the outputs of the cross-differential and impedance-based techniques to provide a reliable response under all of the operating statuses of the parallel circuits. Extensive simulation studies using PSCAD/EMTDC software indicate that the proposed approach provides very reliable protection for the double-circuit transmission lines. It covers intercircuit and evolving faults, and is able to reliably select the faulted phases. [ABSTRACT FROM AUTHOR]

As the output power of photovoltaic (PV) array changes with the change of external environment and load, the maximum power point tracking (MPPT) technology is needed to bring the efficiency of PV device into full play. In this paper, the chaos search theory is first applied on the MPPT technology of PV system; the chaos search algorithm based on dual carrier increases the adequacy of chaos search and overcomes the blindness of traditional chaos search, thus improves the search efficiency and solves the problem of multiple MPP under partially shadowing conditions. Comparative tests show that the proposed method can track the step response quickly and accurately; at the same time get better optimization result. Simulation and experimental results showed the effectiveness and good performance of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we present an orientation inference framework for reconstructing implicit surfaces from unoriented point clouds. The proposed method starts from building a surface approximation hierarchy comprising of a set of unoriented local surfaces, which are represented as a weighted combination of radial basis functions. We formulate the determination of the globally consistent orientation as a graph optimization problem by treating the local implicit patches as nodes. An energy function is defined to penalize inconsistent orientation changes by checking the sign consistency between neighboring local surfaces. An optimal labeling of the graph nodes indicating the orientation of each local surface can, thus, be obtained by minimizing the total energy defined on the graph. The local inference results are propagated over the model in a front-propagation fashion to obtain the global solution. The reconstructed surfaces are consolidated by a simple and effective inspection procedure to locate the erroneously fitted local surfaces. A progressive reconstruction algorithm that iteratively includes more oriented points to improve the fitting accuracy and efficiently updates the RBF coefficients is proposed. We demonstrate the performance of the proposed method by showing the surface reconstruction results on some real-world 3-D data sets with comparison to those by using the previous methods. [ABSTRACT FROM AUTHOR]

This paper describes Olex, a novel method for the automatic induction of rule-based text classifiers. Olex supports a hypothesis language of the form "if T1 or … or Tn occurs in document d, and none of Tn+1,…Tn+m occurs in d, then classify d under category c," where each Ti is a conjunction of terms. The proposed method is simple and elegant. Despite this, the results of a systematic experimentation performed on the REUTERS-21578, the ORSUMED, and the ODP data collections show that Olex provides classifiers that are accurate, compact, and comprehensible. A comparative analysis conducted against some of the most well-known learning algorithms (namely, Naive Bayes, Ripper, C4.5, SVM, and Linear Logistic Regression) demonstrates that it is more than competitive in terms of both predictive accuracy and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we study the rate-distortion (RD) optimization of the H.264 main profile encoding. Specifically, a soft decision quantization (SDQ) algorithm is developed based on the context adaptive binary arithmetic coding (CABAC) method in the H.264 main profile. Given motion prediction and quantization step sizes, the proposed SDQ algorithm is proved to achieve near-optimal SDQ for residual coding in the sense of minimizing the true RD cost when the weak adjacent block dependency utilized in CABAC is ignored for optimization. The SDQ algorithm is then used in conjunction with a general RD optimization framework to jointly design motion prediction and residual coding for H.264 main profile coding given previously coded reference frames. Experiments have been conducted based on the reference encoder JM82 of H.264 main profile. Comparative studies show that the joint design method achieves on average 10% rate reduction at the same PSNR when compared with the RD method in the 11.264 main-profile reference software, with half of the reduction coming from the proposed SDQ algorithm, and 20% rate reduction at the same PSNR when compared with the RD method in the H.264 baseline-profile reference software. [ABSTRACT FROM AUTHOR]

Among the most exciting advances in early vision has been the development of efficient energy minimization algorithms for pixel-labeling tasks such as depth or texture computation. It has been known for decades that such problems can be elegantly expressed as Markov random fields, yet the resulting energy minimization problems have been widely viewed as intractable. Recently, algorithms such as graph cuts and loopy belief propagation (LBP) have proven to be very powerful: For example, such methods form the basis for almost all the top-performing stereo methods. However, the trade-offs among different energy minimization algorithms are still not well understood. In this paper, we describe a set of energy minimization benchmarks and use them to compare the solution quality and runtime of several common energy minimization algorithms. We investigate three promising recent methods—graph cuts, LBP, and tree-reweighted message passing—in addition to the well-known older iterated conditional mode (ICM) algorithm. Our benchmark problems are drawn from published energy functions used for stereo, image stitching, interactive segmentation, and denoising. We also provide a general-purpose software interface that allows vision researchers to easily switch between optimization methods. The benchmarks, code, images, and results are available at http://vision.middlebury.edu/MRF/. [ABSTRACT FROM AUTHOR]

Web users' clustering is a crucial task for mining information related to users' needs and preferences. Up to now, popular clustering approaches build clusters based on usage patterns derived from users' page preferences. This paper emphasizes the need to discover similarities in users' accessing behavior with respect to the time locality of their navigational acts. In this context, we present two time-aware clustering approaches for tuning and binding the page and time visiting criteria. The two tracks of the proposed algorithms define clusters with users that show similar visiting behavior at the same time period, by varying the priority given to page or time visiting. The proposed algorithms are evaluated using both synthetic and real data sets and the experimentation has shown that the new clustering schemes result in enriched clusters compared to those created by the conventional non-time-aware user clustering approaches. These clusters contain users exhibiting similar access behavior in terms not only of their page preferences but also of their access time. [ABSTRACT FROM AUTHOR]

Feature selection is an important method for improving the efficiency and accuracy of text categorization algorithms by removing redundant and irrelevant terms from the corpus. In this paper, we propose a new supervised feature selection method, named CHIR, which is based on the X2 statistic and new statistical data that can measure the positive term-category dependency. We a so propose a new text clustering algorithm, named Text Clustering with Feature Selection (TCFS). TCFS can incorporate CHIR to identity relevant features (i.e., terms) iteratively, and the clustering becomes a learning process. We compared TCFS and the K-means clustering algorithm in combination with different feature selection methods for various real data sets. Our experimental results show that TCFS with CHIR has better clustering accuracy in terms of the F-measure and the purity. [ABSTRACT FROM AUTHOR]

An orthogonal frequency-division-multiplexing signal is very sensitive to the nonlinear distortion of the power amplifier (PA) as a result of its high peak-to-average power ratio. Predistortion, which is an effective countermeasure for balancing off the nonlinearity of a PA, is usually necessary for the sake of mitigating the in-band distortion and the spectrum regrowth. In general, a feedback path is required to estimate the PA characteristic, and a memoryless polynomial is used in modeling the PA characteristic or constructing the predistorter. The polynomial coefficients are solved by least-square (LS) estimation or adaptive identification algorithms in the time domain. In this paper, we examine the estimation problem from the frequency domain and propose five predistortion schemes. The advantage of using frequency-domain estimation is that it is much easier to compensate the delay effect caused by the transmission and receiving filters in the feedback path. Two different criteria are used in the proposed algorithms. The first one is based on the minimization of the square error of the PA input, which is termed as the PA-input--LS criterion. The second one is based on the minimization of the square error between the input of the predistorter and the output of the PA, which is termed as PA-output-LS criterion. We also propose an easy method to cope with the delay effect caused by the transmission and receiving filters in the feedback path. The performances of the proposed schemes are compared in the simulation. The simulation results show that by using the proposed schemes, the power efficiency of the PA can be increased by at least 7 dB in the sense of total degradation for a practical IEEE 802.11a wireless local area network system with a 64-quadrature-amplitude-modulation signal constellation. [ABSTRACT FROM AUTHOR]

A key issue in software evolution analysis is the identification of particular changes that occur across several versions of a program, We present change distilling, a tree differencing algorithm for fine-grained source code change extraction. For that, we have improved the existing algorithm by Chawathe et al. for extracting changes in hierarchically structured data [8]. Our algorithm extracts changes by finding both a match between the nodes of the compared two abstract syntax trees and a minimum edit script that can transform one tree into the other given the computed matching. As a result, we can identify fine-grained change types between program versions according to our taxonomy of source code changes. We evaluated our change distilling algorithm with a benchmark that we developed, which consists of 1,064 manually classified changes in 219 revisions of eight methods from three different open source projects. We achieved significant improvements in extracting types of source code changes: Our algorithm approximates the minimum edit script 45 percent better than the original change extraction approach by Chawathe et al. We are able to find all occurring changes and almost reach the minimum conforming edit script, that is, we reach a mean absolute percentage error of 34 percent, compared to the 79 percent reached by the original algorithm. The paper describes both our change distilling algorithm and the results of our evaluation. [ABSTRACT FROM AUTHOR]

A new algorithm, the "shifted Rayleigh filter," is introduced for two- or three-dimensional bearings-only tracking problems. In common with other "moment matching" tracking algorithms such as the extended Kalman filter and its modern refinements, it approximates the prior conditional density of the target state by a normal density; the novel feature is that an exact calculation is then performed to update the conditional density in the light of the new measurement. The paper provides the theoretical justification of the algorithm. It also reports on simulations involving variants on two scenarios, which have been the basis of earlier comparative studies. The first is a "benign" scenario where the measurements are comparatively rich in range-related information; here the shifted Rayleigh filter is competitive with standard algorithms. The second is a more "extreme" scenario, involving multiple sensor platforms, high-dimensional models and noisy measurements; here the performance of the shifted Rayleigh filter matches the performance of a high-order bootstrap particle filter, while reducing the computational overhead by an order of magnitude. [ABSTRACT FROM AUTHOR]

Incorporating constraints into frequent itemset mining not only improves data mining efficiency, but also leads to concise and meaningful results. In this paper, a framework for closed constrained gradient itemset mining in retail databases is proposed by introducing the concept of gradient constraint into closed itemset mining. A tailored version of CLOSET+, LCLOSET, is first briefly introduced, which is designed for efficient closed itemset mining from sparse databases. Then, a newly proposed weaker but antimonotone measure, top-X average measure, is proposed and can be adopted to prune search space effectively. Experiments show that a combination of LCLOSET and the top-X average pruning provides an efficient approach to mining frequent closed gradient itemsets. [ABSTRACT FROM AUTHOR]

Linear spectral unmixing is a commonly accepted approach to mixed-pixel classification in hyperspectral imagery. This approach involves two steps. First, to find spectrally unique signatures of pure ground components, usually known as endmembers, and, second, to express mixed pixels as linear combinations of endmember materials. Over the past years, several algorithms have been developed for autonomous and supervised endmember extraction from hyperspectral data. Due to a lack of commonly accepted data and quantitative approaches to substantiate new algorithms, available methods have not been rigorously compared by using a unified scheme. In this paper, we present a comparative study of standard endmember extraction algorithms using a custom-designed quantitative and comparative framework that involves both the spectral and spatial information. The algorithms considered in this study represent substantiate different design choices. A database formed by simulated and real hyperspectral data collected by the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) is used to investigate the impact of noise, mixture complexity, and use of radiance/reflectance data on algorithm performance. The results obtained indicate that endmember selection and subsequent mixed-pixel interpretation by a linear mixture model are more successful when methods combining spatial and spectral information are applied. [ABSTRACT FROM AUTHOR]

Phantom measurements with glass inserts in a hot background are still used frequently for calibration and performance assessment of volume delineation algorithms in positron emission tomography (PET). Taking as an example the recent paper “Comparative study with new accuracy metrics for target volume contouring in PET image guided radiation therapy” (Shepherd, 2012), we demonstrate that this is not a valid approach due to the discontinuity introduced in the background by the cold walls of the glass inserts. We, moreover, emphasize that in order to define a sensible ground truth for performance assessment of contouring algorithms in patient data it is necessary to average over a sizable number of experienced observers and lesions in order to compensate for the substantial inter-observer variability. [ABSTRACT FROM AUTHOR]

RFID tag identification is a crucial problem in UHF RFID systems. Traditional tag identification algorithms can be classified into two categories, ALOHA-based and tree-based. Both of them are inefficient due to the incidental high coordination cost. In this paper, we bring CSMA into UHF RFID systems to enhance tag read rate by reducing coordination cost. However, it is not straightforward due to the simple hardware design of passive RFID tags, which is unable to sense the transmissions or collisions of other tags. To tackle this challenge, we propose receiver-based CSMA (RCSMA) in this paper. In RCSMA, the reader notifies the tags channel condition. According to different sensing results of reader's notifications, the tags take corresponding actions, e.g., random back off. RCSMA does not require special RFID tag hardware design. An absorbing Markov chain model is presented to analyze the performance of RCSMA and shown to be consistent with the simulation results. Compared with optimized ALOHA-based algorithms and optimized tree-based algorithms, RCSMA can enhance the tag read rate by 30-70 percent under different reader and tag data rates. [ABSTRACT FROM AUTHOR]

Comparison of image processing techniques is critically important in deciding which algorithm, method, or metric to use for enhanced image assessment. Image fusion is a popular choice for various image enhancement applications such as overlay of two image products, refinement of image resolutions for alignment, and image combination for feature extraction and target recognition. Since image fusion is used in many geospatial and night vision applications, it is important to understand these techniques and provide a comparative study of the methods. In this paper, we conduct a comparative study on 12 selected image fusion metrics over six multiresolution image fusion algorithms for two different fusion schemes and input images with distortion. The analysis can be applied to different image combination algorithms, image processing methods, and over a different choice of metrics that are of use to an image processing expert. The paper relates the results to an image quality measurement based on power spectrum and correlation analysis and serves as a summary of many contemporary techniques for objective assessment of image fusion algorithms. [ABSTRACT FROM AUTHOR]

For an undirected and weighted graph G=(V,E) and a terminal set S\subseteq V, the 2-connected Steiner minimal network (SMN) problem requires to compute a minimum-weight subgraph of G in which all terminals are 2-connected to each other. This problem has important applications in design of survivable networks and fault-tolerant communication, and is known MAXSNP-hard , a harder subclass of NP-hard problems for which no polynomial-time approximation scheme (PTAS) is known. This paper presents an efficient algorithm of O(\vert V\vert^2\vert S\vert^3) time for computing a 2-vertex connected Steiner network (2VSN) whose weight is bounded by two times of the optimal solution 2-vertex connected SMN (2VSMN). It compares favorably with the currently known 2--approximation solution to the 2VSMN problem based on that to the survivable network design problem], with a time complexity reduction of O(\vert V\vert^5\vert E\vert^7) for strongly polynomial time and O(\vert V\vert^5\gamma ) for weakly polynomial time where \gamma is determined by the sizes of input. Our algorithm applies a novel greedy approach to generate a 2VSN through progressive improvement on a set of vertex-disjoint shortest path pairs incident with each terminal of S. The algorithm can be directly deployed to solve the 2-edge connected SMN problem at the same approximation ratio within time O(\vert V\vert^2\vert S\vert^2). To the best of our knowledge, this result presents currently the most efficient 2-approximation algorithm for the 2-connected Steiner minimal network problem. [ABSTRACT FROM PUBLISHER]

Energy awareness for computation and protocol management is becoming a crucial factor in the design of protocols and algorithms. On the other hand, in order to support node mobility, scalable routing strategies have been designed and these protocols try to consider the path duration in order to respect some QoS constraints and to reduce the route discovery procedures. Often energy saving and path duration and stability can be two contrasting efforts and trying to satisfy both of them can be very difficult. In this paper, a novel routing strategy is proposed. This proposed approach tries to account for link stability and for minimum drain rate energy consumption. In order to verify the correctness of the proposed solution a biobjective optimization formulation has been designed and a novel routing protocol called Link-stAbility and Energy aware Routing protocols (LAER) is proposed. This novel routing scheme has been compared with other three protocols: PERRA, GPSR, and E-GPSR. The protocol performance has been evaluated in terms of Data Packet Delivery Ratio, Normalized Control Overhead, Link duration, Nodes lifetime, and Average energy consumption. [ABSTRACT FROM AUTHOR]

Cluster ensembles have recently emerged as a powerful alternative to standard cluster analysis, aggregating several input data clusterings to generate a single output clustering, with improved robustness and stability. From the early work, these techniques held great promise; however, most of them generate the final solution based on incomplete information of a cluster ensemble. The underlying ensemble-information matrix reflects only cluster-data point relations, while those among clusters are generally overlooked. This paper presents a new link-based approach to improve the conventional matrix. It achieves this using the similarity between clusters that are estimated from a link network model of the ensemble. In particular, three new link-based algorithms are proposed for the underlying similarity assessment. The final clustering result is generated from the refined matrix using two different consensus functions of feature-based and graph-based partitioning. This approach is the first to address and explicitly employ the relationship between input partitions, which has not been emphasized by recent studies of matrix refinement. The effectiveness of the link-based approach is empirically demonstrated over 10 data sets (synthetic and real) and three benchmark evaluation measures. The results suggest the new approach is able to efficiently extract information embedded in the input clusterings, and regularly illustrate higher clustering quality in comparison to several state-of-the-art techniques. [ABSTRACT FROM PUBLISHER]

In this paper, we introduce a novel method to solve shape alignment problems. We use gray-scale "images” to represent source shapes, and propose a novel two-component Gaussian Mixture (GM) distance map representation for target shapes. This asymmetric representation is a flexible image-based representation which is able to represent different kinds of shape data, including continuous contours, unstructured sparse point sets, edge maps, and even gray-scale gradient maps. Using this representation, a new energy function based on a novel two-component Gaussian Mixture distance model is proposed. The new energy function was empirically evaluated to be a more robust shape dissimilarity metric that can be computed efficiently. Such high efficiency is essential for global optimization methods. We adopt and modify one of them, the Particle Swarm Optimization (PSO), to effectively estimate the global optimum of the new energy function. Differently from the original PSO, several new strategies were employed to make the optimization more robust and prevent it from converging prematurely. The overall performance of the proposed framework as well as the properties of each algorithmic component were evaluated and compared with those of some state-of-the-art methods. Extensive experiments and comparison performed on generalized 2D and 3D shape data demonstrate the robustness and effectiveness of the method. [ABSTRACT FROM PUBLISHER]