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In this paper, an adaptive nonlinear differential limiter (ANDL) is proposed to efficiently alleviate the impact of impulsive noise (IN) in a communication system. Unlike existing nonlinear methods, the ANDL is implemented in the analog domain where the broader acquisition bandwidth makes outliers more detectable and consequently it is easier to remove them. While the proposed ANDL behaves like a linear filter when there is no outlier, it exhibits intermittent nonlinearity in response to IN. Therefore, the structure of the matched filter in the receiver is modified to compensate the filtering effect of the ANDL in the linear regime. In this paper, we quantify the performance of the ANDL by deriving a closed-form analytical bound for the average signal-to-noise ratio at the output of the filter. The calculation is based on the idea that the ANDL can be perceived as a time-variant linear filter whose bandwidth is modified based on the intensity of the IN. In addition, by linearizing the filter time parameter variations, we treat the ANDL as a set of linear filters where the exact operating filter at a given time depends upon the magnitude of the outliers. The theoretical average bit error rate is validated through simulations and the performance gains relative to classical methods such as blanking and clipping are quantified. [ABSTRACT FROM AUTHOR]

The paper addresses a sequential changepoint detection problem for a general stochastic model, assuming that the observed data may be non-i.i.d. (i.e., dependent and non-identically distributed) and prior distribution of the change point is arbitrary. Tartakovsky and Veeravalli (2005), Baron and Tartakovsky (2006), and, more recently, Tartakovsky (2017) developed a general asymptotic theory of changepoint detection for non-i.i.d. stochastic models, assuming certain stability of the log-likelihood ratio process, in the case of simple hypotheses when both pre-change and post-change models are completely specified. However, in most applications, the post-change distribution is not completely known. In the present paper, we generalize previous results to the case of parametric uncertainty, assuming that the parameter of the post-change distribution is unknown. We introduce two detection rules based on mixtures–the mixture Shiryaev rule and the mixture Shiryaev–Roberts rule–and study their asymptotic properties in the Bayesian context. In particular, we provide sufficient conditions under which these rules are first-order asymptotically optimal, minimizing moments of the delay to detection as the probability of false alarm approaches zero. [ABSTRACT FROM AUTHOR]

Avionics and air traffic management (ATM) systems are evolving with the introduction of progressively higher levels of automation, toward attaining the ambitious operational, technical, and safety enhancements required to sustain the present growth of global air traffic. This paper presents novel 4-Dimensional Trajectory (4DT) functionalities that are being developed for integration in ATM and avionics systems to support trajectory based operations (TBO). The 4DT planning process, which is the main focus of the paper, is supported by a custom multiobjective variant of state-of-the-art optimal control algorithms, incorporating various operational, economic, and environmental factors. Capitalizing on the higher theoretical accuracy offered by optimal control algorithms compared to other methods, a key feature of the proposed approach is the introduction of a postprocessing stage to ensure that the mathematically optimal trajectories are translated into a set of standardized 4DT descriptors, which can be flown by state-of-the-art automatic flight control systems. Additionally, to support air-ground 4DT intent negotiation and validation in the TBO context, the 4DT postprocessing ensures that optimal trajectories are synthetically described by a limited number of parameters, minimizing the bandwidth requirements imposed on airborne data links. Simulation-based verification activities addressing operational efficiency improvements and computational performance in the terminal area ATM context support the viability of the proposed 4DT planning functionality for online tactical TBO. [ABSTRACT FROM AUTHOR]

This paper presents a decentralized solution algorithm for network-constrained unit commitment (NCUC) in multiregional power systems. The proposed algorithm is based on our previous work in which a local NCUC was formulated for each control entity (i.e., region) and an analytical target cascading (ATC) based distributed but partially parallelized algorithm requiring a central coordinator was presented. The primary objective of this paper is to present a decentralized approach that relaxes the need for any form of central coordinator in ATC and allows fully parallelized solutions of the local NCUCs. To achieve this objective, we formulate a bilevel optimization problem for each control entity. While the upper level solves the NCUC problem of the control entity, the lower level seeks to coordinate the control entity with its neighboring regions. The lower level is a convex optimization, which can be further replaced in the upper level problem by the Karush–Kuhn–Tucker conditions. The control entities communicate directly with each other and synchronously solve their local NCUCs. Having no need for any form of central coordinator, the proposed algorithm is potentially less vulnerable to cyber-attacks and communication failures than the distributed methods utilizing a coordinator. [ABSTRACT FROM AUTHOR]

A fusion of inertial navigation system and global positioning system (GPS) has been proposed to implement a robust navigation system for a transporter. In several cases, the GPS signals are not reliable to localize a transporter. In order to overcome this difficulty, a fault detection and isolation (FDI) algorithm has been used to detect and isolate the contaminated satellite signals. When the FDI algorithm is used alone, and the number of reliable satellite signals becomes less than four owing to GPS signal errors, the receiver cannot estimate the position precisely. In this paper, a modified FDI algorithm has been proposed and applied for the navigation of a transporter on a flat yard in order to avoid losing its positioning while it passes through a structured area. In the modified FDI algorithm, signal-to-noise ratio, measurement of quality indicator, and Doppler shift data are incorporated. In order to verify the effectiveness of the proposed algorithm, the navigation experiments have been conducted on a campus using a mobile robot to simulate the transporter on a shipbuilding yard and the results are demonstrated in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a technique for the full-wave analysis of large problems using the method of moments, in combination with the multilevel fast multipole algorithm, based on a decomposition of the scenario into two domains using a ray-tracing analysis. These domains are computed considering the proximity to the critical points. The currents over the main scenario are obtained first and used to modify the excitation of the secondary domain, which in turn can be analyzed using a relaxed iteration error that very noticeably reduces the iteration time without a significant impact on the accuracy. The procedure used for the separation of both domains minimizes the amount of information exchanged between them. [ABSTRACT FROM AUTHOR]

Continuous top-$k$ query over streaming data is a fundamental problem in database. In this paper, we focus on the sliding window scenario, where a continuous top- $k$ query returns the top- $k$ objects within each query window on the data stream. Existing algorithms support this type of queries via incrementally maintaining a subset of objects in the window and try to retrieve the answer from this subset as much as possible whenever the window slides. However, since all the existing algorithms are sensitive to query parameters and data distribution, they all suffer from expensive incremental maintenance cost. In this paper, we propose a self-adaptive partition framework to support continuous top-$k$ query. It partitions the window into sub-windows and only maintains a small number of candidates with highest scores in each sub-window. Based on this framework, we have developed several partition algorithms to cater for different object distributions and query parameters. To our best knowledge, it is the first algorithm that achieves logarithmic complexity w.r.t. $k$ for incrementally maintaining the candidate set even in the worst case scenarios. [ABSTRACT FROM PUBLISHER]

Tribological tests are adopted to estimate the kinetic friction coefficient of the material of interest according to the ASTM standard. Typically, for the measurement process, several replicas as well as a postprocessing data treatment are necessary to take into account the observed casual variability of the measurand. This paper describes a novel statistical approach aiming to highlight the most significant time intervals during each test replica. The experimental activity has been carried out with reference to the ball-on-flat configuration of the tribometer in order to verify the suggested procedure in terms of both accuracy and reliability. Numerical simulation and comparison of the measurement results with the output of a different outlier detection algorithm are also included to show the improvement allowed by the authors’ proposal about the dry friction coefficient measurement. [ABSTRACT FROM AUTHOR]

Fractional interpolation is used to provide sub-pixel level references for motion compensation in the interprediction of video coding, which attempts to remove temporal redundancy in video sequences. Traditional handcrafted fractional interpolation filters face the challenge of modeling discontinuous regions in videos, while existing deep learning-based methods are either designed for a single quantization parameter (QP), only generating half-pixel samples, or need to train a model for each sub-pixel position. In this paper, we present a one-for-all fractional interpolation method based on a grouped variation convolutional neural network (GVCNN). Our method can deal with video frames coded using different QPs and is capable of generating all sub-pixel positions at one sub-pixel level. Also, by predicting variations between integer-position pixels and sub-pixels, our network offers more expressive power. Moreover, we perform specific measurements in training data generation to simulate practical situations in video coding, including blurring the down-sampled sub-pixel samples to avoid aliasing effects and coding integer pixels to simulate reconstruction errors. In addition, we analyze the impact of the size of blur kernels theoretically. Experimental results verify the efficiency of GVCNN. Compared with HEVC, our method achieves 2.2% in bit saving on average and up to 5.2% under low-delay P configuration. [ABSTRACT FROM AUTHOR]

In this paper, we establish a framework for enabling ultra-reliable and low-latency communications in the control and non-payload communications (CNPC) links of the unmanned aerial vehicle (UAV) communication systems. We first derive the available range of the CNPC links between UAVs and a ground control station. The available range is defined as the maximal horizontal communication distance within which the round-trip delay and the overall packet loss probability can be ensured with a required probability. To exploit the macro-diversity gain of the distributed multi-antenna systems (DAS) and the array gain of the centralized multi-antenna systems (CAS), we consider a modified DAS (M-DAS), where the ground control station is equipped with the distributed access points (APs), and each AP can have multiple antennas. We then show that the available range can be maximized by judiciously optimizing the altitude of UAVs, the duration of the uplink and downlink phases, and the antenna configuration. To solve the non-convex problem, we propose an algorithm that can converge to the optimal solution in DAS and CAS, and then extend it into more general M-DAS. The simulation and numerical results validate our analysis and show that the available range of M-DAS can be significantly larger than those of the DAS and CAS. [ABSTRACT FROM AUTHOR]

2-D linear discriminant analysis (2DLDA) has been widely used in pattern recognition and image classification. 2DLDA selects discriminative features from the up and left corner of images. However, 2DLDA uses the Frobenius norm (F-norm), which is sensitive to noise or outliers in data, as a metric. In this paper, we propose a novel framework, called horizontal and vertical nuclear norm-based 2DLDA (HVNN-2DLDA) for image representation. In the proposed framework, HVNN-2DLDA methods (i.e., HNN-2DLDA and VNN-2DLDA) are proposed, and both use the nuclear norm as a criterion. The nuclear norm can provide more structure and global information for the reconstruction of noisy images. HNN-2DLDA and VNN-2DLDA represent images in the row and column directions, respectively. In addition, by combining the row and column directions, we propose a bilateral nuclear norm-based 2DLDA method called BNN-2DLDA. The advantage of BNN-2DLDA over HNN-2DLDA and VNN-2DLDA is that an image sample can be represented by both the row and the column directions instead of only the row or column direction. HVNN-2DLDA learns a set of local optimal projection vectors by maximizing the ratio of the nuclear norm of the between-class scatter matrix and the nuclear norm of the within-class scatter matrix. To verify the robustness and recognition performance in image classification of HVNN-2DLDA, six public image databases are used for experiments. The experimental results demonstrate the effectiveness and the feasibility of the proposed framework. [ABSTRACT FROM AUTHOR]

Effective monitoring and management applications on modern distribution networks (DNs) require a sound network model and the knowledge of line parameters. Network line impedances are used, among other things, for state estimation and protection relay setting. Phasor measurement units (PMUs) give synchronized voltage and current phasor measurements, referred to a common time reference (coordinated universal time). All synchrophasor measurements can thus be temporally aligned and coordinated across the network. This feature, along with high accuracy and reporting rates, could make PMUs useful for the evaluation of network parameters. However, instrument transformer behavior strongly affects the parameter estimation accuracy. In this paper, a new PMU-based iterative line parameter estimation algorithm for DNs, which includes in the estimation model systematic measurement errors, is presented. This method exploits the simultaneous measurements given by PMUs on different nodes and branches of the network. A complete analysis of uncertainty sources is also performed, allowing the evaluation of estimation uncertainty. Issues related to operating conditions, topology, and measurement uncertainty are thoroughly discussed and referenced to a realistic model of a DN to show how a full network estimator is possible. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing has attracted much attention in recent years. Single sparse unmixing assumes that a pixel in a hyperspectral image consists of a relatively small number of spectral signatures from large, ever-growing, and available spectral libraries. Joint-sparsity (or row-sparsity) model typically enforces all pixels in a neighborhood to share the same set of spectral signatures. The two sparse models are widely used in the literature. In this paper, we propose a joint-sparsity-blocks model for abundance estimation problem. Namely, the abundance matrix of size $m\times n$ is partitioned to have one row block and $s$ column blocks and each column block itself is joint-sparse. It generalizes both the single (i.e., $s=n$) and the joint (i.e., $s=1$) sparsities. Moreover, concatenating the proposed joint-sparsity-blocks structure and low rankness assumption on the abundance coefficients, we develop a new algorithm called joint-sparse-blocks and low-rank unmixing. In particular, for the joint-sparse-blocks regression problem, we develop a two-level reweighting strategy to enhance the sparsity along the rows within each block. Simulated and real-data experiments demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose two groups of recursive codes, namely the Hadamard recursive carrier interferometry (HRCI) codes and diagonal recursive carrier interferometry (DRCI) codes, to simultaneously reduce the peak-to-average power ratio (PAPR) and suppress the phase noises and residual carrier frequency offset (RCFO) for high-speed orthogonal frequency division multiplexing (OFDM) systems. We exploit the recursion property of the Hadamard and diagonal matrices to constitute the proposed HRCI code and DRCI code using the carrier interferometry (CI) code, and present the system model in details to spread OFDM symbols in the frequency domain. Then, we prove that both HRCI and DRCI generation matrices keep their invertibility during the recursions. As a direct result of the new code design, the phase intervals are enlarged and the signals are shifted in the time domain, thus both the PAPR and the phase noises are reduced, and the RCFO can be mitigated. Moreover, the enlarged phase intervals enable the transceiver blocks including the channel estimator, which estimate the phase noise based on the received signals, to improve the performance. Furthermore, we present the expressions for the HRCI and DRCI embedded transmitted and received signals, and derive the analytical signal-to-interference-plus-noise ratio (SINR) expressions for bit error rate (BER) performance analysis. Then, we analyze the effects of presented codes on the phase differences and provide the computational complexity analysis. Numerical simulations verify the effectiveness of our theoretical analysis of SINR. Additionally, under different parameter settings, which consider the working conditions of practical systems, we demonstrate that the presented robust HRCI and DRCI coded OFDM systems can suppress the PAPR satisfactorily and better BER performances can be achieved by the recursive CI-aided spread OFDM systems with phase noise rejection and RCFO compensation when compared with counterpart systems. [ABSTRACT FROM AUTHOR]

In this paper, we propose a lattice-based robust distributed source coding system for two correlated sources and provide a detailed performance analysis under the high resolution assumption. It is shown, among other things, that, in the asymptotic regime where: 1) the side distortion approaches 0 and 2) the ratio between the central and side distortions approaches 0, our scheme is capable of achieving the information-theoretic limit of quadratic multiple description coding when the two sources are identical, whereas a variant of the random coding scheme by Chen and Berger with Gaussian codes has a performance loss of 0.5 bits relative to this limit. [ABSTRACT FROM AUTHOR]

Algorithms for secure encryption in a post-quantum world are currently receiving a lot of attention in the research community. One of the most promising such algorithms is the code-based scheme called QC-MDPC, which has excellent performance and a small public key size. In this paper, we present a very efficient key recovery attack on the QC-MDPC scheme using the fact that decryption uses an iterative decoding step, and this can fail with some small probability. We identify a dependence between the secret key and the failure in decoding. This can be used to build what we refer to as a distance spectrum for the secret key, which is the set of all distances between any two ones in the secret key. In a reconstruction step, we then determine the secret key from the distance spectrum. The attack has been implemented and tested on a proposed instance of QC-MDPC for 80-bit security. It successfully recovers the secret key in minutes. A slightly modified version of the attack can be applied on proposed versions of the QC-MDPC scheme that provides IND-CCA security. The attack is a bit more complex in this case, but still very much below the security level. The reason why we can break schemes with proved CCA security is that the model for these proofs typically does not include the decoding error possibility. At last, we present several algorithms for key reconstruction from an empirical distance spectrum. We first improve the naïve algorithm for key reconstruction by a factor of about 3 0000, when the parameters for 80-bit security are implemented. We further develop the algorithm to deal with errors in the distance spectrum. This ultimately reduces the requirement on the number of ciphertexts that need to be collected for a successful key recovery. [ABSTRACT FROM AUTHOR]

Dynamic robust principal component analysis (PCA) refers to the dynamic (time-varying) extension of robust PCA (RPCA). It assumes that the true (uncorrupted) data lie in a low-dimensional subspace that can change with time, albeit slowly. The goal is to track this changing subspace over time in the presence of sparse outliers. We develop and study a novel algorithm, which we call simple-ReProCS, based on the recently introduced Recursive Projected Compressive Sensing (ReProCS) framework. This paper provides the first guarantee for dynamic RPCA that holds under weakened versions of standard RPCA assumptions, slow subspace change, and a lower bound assumption on most outlier magnitudes. Our result is significant because: 1) it removes the strong assumptions needed by the two previous complete guarantees for ReProCS-based algorithms; 2) it shows that it is possible to achieve significantly improved outlier tolerance, compared with all existing RPCA or dynamic RPCA solutions by exploiting the above-mentioned two simple extra assumptions; and 3) it proves that simple-ReProCS is online (after initialization), fast, and, has near-optimal memory complexity. [ABSTRACT FROM AUTHOR]

Many statistical estimation procedures lead to nonconvex optimization problems. Algorithms to solve these problems are often guaranteed to output a stationary point of the optimization problem. Oracle inequalities are an important theoretical instrument to assess the statistical performance of an estimator. Oracle results have focused on the theoretical properties of the uncomputable (global) minimum or maximum. In this paper, a general framework used for convex optimization problems to derive oracle inequalities for stationary points is extended. A main new ingredient of these oracle inequalities is that they are sharp: they show closeness to the best approximation within the model plus a remainder term. We apply this framework to different estimation problems. [ABSTRACT FROM AUTHOR]

The use of the method of moments to solve surface integral equations is one of the most popular numerical techniques in electromagnetic modeling and analysis. This method requires the accurate and efficient numerical evaluation of iterated surface integrals over both source and testing domains. In this paper, we propose a scheme for evaluating these 4-D interaction integrals between pairs of arbitrarily positioned and oriented elements. The approach is based on applying the surface divergence theorem twice, once on the source and once on the test domain. When the integrations are reordered as two outer contour integrals plus two inner radial integrals, the initial radial integrations provide significant smoothing of the underlying singular integrands. The method is numerically validated for static and dynamic kernels arising in the electric field integral equation, i.e., for kernels with $1/R$ singularities, and linear basis functions. The proposed formula to evaluate 4-D reaction integrals can be extended to different kernels and to different elements, e.g., to curved or volumetric elements, and to basis functions of higher order. [ABSTRACT FROM AUTHOR]

Direct geolocation of radio emitters has traditionally been formulated using short measurement intervals over which delay and Doppler frequency shift are approximated as constant. In this paper, we consider maximum likelihood position estimation using long measurement intervals for which the Doppler is time varying. A series of assumptions and approximations are used to develop an efficient solution involving coherent combining of traditional, complex ambiguity function processing outputs. Simulation results are used to demonstrate potential gains. [ABSTRACT FROM AUTHOR]

This paper is devoted to the terrain aided navigation (TAN) systems based on the state estimation algorithms. In particular, the emphasis is laid on the design of the Bayesian Rao–Blackwellized point-mass filter for nonlinear state-space models of a specific structure typically used in the terrain aided navigation. The proposed filter preserves advantages of the point-mass filter, including the high estimation accuracy, robust initialization, deterministic nature of the algorithm, and predictable computational complexity, while the computational complexity is significantly reduced. The performance of the Rao–Blackwellized point-mass filter is illustrated using a simulated numerical example and a TAN system based on the real data. [ABSTRACT FROM AUTHOR]

The emerging of depth-camera technology is paving the way for a variety of new applications and it is believed that plane detection is one of them. In fact, planes are common in man-made living structures, thus their accurate detection can benefit many visual-based applications. The use of depth information allows detecting planes characterized by complex pattern and texture, where the texture-based plane detection algorithms usually fail. In this paper, we propose a robust depth-driven plane detection (DPD) algorithm which consists of two parts: the growing-based plane detection and a two-stage refinement. The proposed approach starts from the seed patch with the highest planarity and uses the estimated equation of the growing plane and a dynamic threshold function to steer the growing process. Aided with this mechanism, each seed patch can grow to its maximum extent, and then the next seed patch starts to grow. This process is iteratively repeated so as to detect all the planes. Moreover, the refinement is proposed to tackle two common problems suffered by growing-based approaches, the over-growing problem, and the under-growing problem. Validated by extensive experiments, the proposed DPD algorithm is able to accurately detect planes and robust to various testing conditions. In terms of applications, it can be used as the pre-processing step for a variety of applications, such as, planar object recognition, super-resolution of the time-of-flight depth images with intrinsically low resolution. [ABSTRACT FROM AUTHOR]

Frame rate up-conversion (FRUC) is a technology for generating a higher frame rate video from one with a lower frame rate. The multi-view plus depth map (MVD) system has been recently considered a promising solution for three-dimensional video systems. Accordingly, an FRUC efficient solution for the MVD system is greatly needed. In this paper, we thus propose an FRUC approach that considers the MVD system characteristics. It divides a frame into multiple layers using a depth map, and it reconstructs occluded regions using neighboring views and depth map information. With reconstructed information, it accurately performs motion estimation when a region occlusion occurs. Experimental results demonstrated that the proposed method significantly improved the quality of interpolated frames, and it can effectively complement other conventional FRUC approaches. [ABSTRACT FROM AUTHOR]

This paper formally constructs navigation functions with time-varying destinations on star worlds. The construction is based on appropriate diffeomorphic transformations and extends an earlier sphere-world formulation. A new obstacle modeling method is also introduced, reducing analytical complexity and offering unified expressions of common classes of $n$ -dimensional obstacles. The method allows for dynamic target tracking and is validated through simulations and experiments. [ABSTRACT FROM AUTHOR]

An alternative approach to standard computed torque with feedback linearization is proposed in this paper to control cable-driven parallel robots (CDPRs) with highly flexible cables. Exteroceptive feedback is used to measure the end-effector Cartesian position at a high sampling rate. Stability is demonstrated using singular perturbation theory. The proposed control scheme is experimentally validated on a planar 3-degree-of-freedom CDPR and its efficiency is assessed by comparison with a simple kinematic control law. [ABSTRACT FROM AUTHOR]

This paper deals with lower bounds on aperiodic correlation of sequences. It intends to solve two open questions. The first one is on the validity of the Levenshtein bound for a set of sequences other than binary sequences or those over the roots of unity. Although this result could be a priori extended to polyphase sequences, a formal demonstration is presented here, proving that it does actually hold for these sequences. The second open question is on the possibility to find a bound tighter than Welch’s, in the case of a set consisting of two sequences $M = 2$. By including the specific structure of correlation sequences, a tighter lower bound is introduced for this case. Besides, this method also provides in the cases $M = 3$ and $M = 4$ a tighter bound than the up-to-now tightest bound provided by Liu et al. [ABSTRACT FROM AUTHOR]

Representation-based classification (RC) methods such as sparse RC (SRC) have attracted great interest in pattern recognition recently. Despite their empirical success, few theoretical results are reported to justify their effectiveness. In this paper, we establish the theoretical guarantees for a general unified framework termed as atomic representation-based classification (ARC), which includes most RC methods as special cases. We introduce a new condition called atomic classification condition (ACC), which reveals important geometric insights for the theory of ARC. We show that under such condition ARC is provably effective in correctly recognizing any new test sample, even corrupted with noise. Our theoretical analysis significantly broadens the range of conditions under which RC methods succeed for classification in the following two aspects: (1) prior theoretical advances of RC are mainly concerned with the single SRC method while our theory can apply to the general unified ARC framework, including SRC and many other RC methods; and (2) previous works are confined to the analysis of noiseless test data while we provide theoretical guarantees for ARC using both noiseless and noisy test data. Numerical results are provided to validate and complement our theoretical analysis of ARC and its important special cases for both noiseless and noisy test data. [ABSTRACT FROM AUTHOR]

In this paper, a computationally efficient algorithm for Bayesian joint change point detection (CPD) in multiple time series is presented. The data generation model includes a number of change configurations (CC), each affecting a unique subset of the time series, which introduces correlation between the positions of change points (CPs) in the monitored time series. The inference objective is to identify joint changes and the associated CC. The algorithm consists of two stages: First, a univariate CPD algorithm is applied separately to each of the involved time series. The outcomes of this step are maximum a posteriori (MAP) detected CPs and posterior distributions of CPs conditioned on the MAP CPs. These outcomes are used in combination to approximate the posterior for the CCs. In the second algorithm stage, dynamic programming is used to find the maxima of this approximate CC posterior. The algorithm is applied to synthetic data, and it is shown to be both significantly faster and more accurate compared to a previously proposed algorithm designed to solve similar problems. Also, the initial algorithm is extended with steps from the maximization–maximization algorithm, which allows the hyperparameters of the data generation model to be estimated jointly with the CCs, and we show that these estimates coincide with estimates obtained from a Markov chain Monte Carlo algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the structure and performance of the maximum entropy temperature-emissivity separation (MaxEnTES) algorithm for assessing land temperature and emissivity from thermal infrared hyperspectral images. This procedure derives the emissivity spectrum and the temperature of the target adopting the maximum entropy (MaxEnt) estimation approach. The main advantage of the MaxEnt statistical inference is the absence of any external hypothesis, which is, instead, the main critical point characterizing any other temperature-emissivity separation (TES) algorithm. The MaxEnTES algorithm carries out the TES task adopting a modified version of the subgradient Shor's r-algorithm adopted for numerical optimization of a MaxEnt objective function. For this purpose, we have utilized the C/C++ Solvopt code from the University of Gratz to develop a practical data processing implementation. In this paper, we discuss the mathematical structure of the MaxEnTES algorithm and analyze its performance in depth using numerical simulations and remote sensing Multispectral Infrared/Visible Imaging Spectrometer images. We show that the MaxEnTES algorithm provides improved accuracy for temperature and emissivity estimation, lowering the standard estimation error to a fraction of degree Kelvin. In agreement with previous investigations, we find that the estimation accuracy grows when increasing the number of available spectral channels. The systematic errors affecting the temperature estimates (e.g., bias) are thoroughly evaluated. We prove that the MaxEnTES algorithm retrieves the correct shape of the target emissivity spectrum even in presence of a significant temperature estimation error. [ABSTRACT FROM PUBLISHER]

Space-time adaptive processing (STAP) is a family of algorithms frequently employed in surface moving target indication radar systems to enable detection of moving objects in the presence of fixed (i.e., nonmoving) clutter. In this paper, two new closed-form expressions that quantify the loss associated with the STAP notch centered on clutter are derived in terms of system parameters of interest. Although there are many excellent reports, books, and papers focused on STAP, a simple yet accurate approximation for the STAP notch has not previously appeared. It is also shown that a new, accurate approximation for the important STAP metric known as minimum detectable velocity may be derived from the STAP notch expression. Furthermore, accurate expressions are derived that predict when ?aperture-limited? STAP performance may be obtained. This work provides the first analytical, unifying connection between these STAP metrics. As they are implemented in compact closed-form expressions, the new results are attractive for system design. The significant computational benefits associated with the new results can be very advantageous in trade studies or large simulations in which STAP performance estimates must be computed thousands of times. It is expected that this work will be of great use to both readers with a background in STAP and readers new to this field. For readers with STAP experience, this work provides a new understanding of the drivers of STAP performance and of the interconnections between STAP performance measures. For readers new to the STAP field, this work removes the mystery associated with STAP by providing intuition into how STAP works. With accurate, analytical expressions, system engineers can now implement accurate predictions of STAP performance without the necessity of constructing patch-based analysis tools that estimate STAP performance by computing the notch associated with hundreds to thousands of clutter patches. [ABSTRACT FROM AUTHOR]

This paper considers a heavy loaded full-duplex (FD) small cell consisting of one FD base station and multiple half-duplex users where the number of subchannels is less than the number of uplink and downlink users. With the objective of maximizing the total sum-rate, a joint power allocation, and user pairing problem is formulated. To solve this problem, a novel decomposition method is proposed, which decouples the power allocation and user pairing problem into two subproblems. It is proven that the optimal solution of subproblems is the optimal solution of the original problem. Then, optimal algorithms are developed for the two subproblems. Numerical results show that the proposed algorithm achieves better performance compared with the baselines of the greedy algorithm and random pairing algorithm. [ABSTRACT FROM AUTHOR]

To preserve accuracy in a grid with arbitrary subgrids, a finite-difference time-domain (FDTD) subgridding scheme, in general, would result in an unsymmetric numerical system. Such a numerical system can have complex-valued eigenvalues, which will render a traditional explicit time marching of FDTD absolutely unstable. In this paper, we develop an accurate FDTD subgridding algorithm suitable for arbitrary subgridding settings with arbitrary contrast ratios between the normal grid and the subgrid. Although the resulting system matrix is also unsymmetric, we develop a time-marching method to overcome the stability problem without sacrificing the matrix-free merit of the original FDTD. This method is general, which is also applicable to other subgridding algorithms whose underlying numerical systems are unsymmetric. The proposed FDTD subgridding algorithm is then further made unconditionally stable, thus permitting the use of a time step independent of space step. Extensive numerical experiments involving both 2- and 3-D subgrids with various contrast ratios have demonstrated the accuracy, stability, and efficiency of the proposed subgridding algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a novel agent based dynamic Volt/Var control approach is introduced, by which the voltage security (i.e., voltage profile and mid- and long-term stability) can be improved and also power loss is reduced under postdisturbance condition and in a wide-area manner. The proposed scheme employs organized multiagent system to preserve voltage magnitude of all busbars within permissible range and reduction of power losses after any disturbance through appropriate coordination of reactive power sources. To accelerate exploitation of on-load tap changers (OLTCs) as well as suppressing the possibility of encountering voltage instability which may be initiated by them, a preventive scheme based on sensitivity analysis is proposed which determines efficient number of multitap changes of OLTCs. To evaluate the effectiveness of the introduced algorithm, different scenarios have been performed in Nordic 32 test system, where the numerical results in all cases demonstrate the effectiveness and reliability of the proposed scheme. [ABSTRACT FROM AUTHOR]

High-dynamic-range (HDR) images require tone mapping to be displayed properly on lower dynamic range devices. In this paper, a tone-mapping algorithm that uses histogram of luminance to construct a lookup table (LUT) for tone mapping is presented. Characteristics of the human visual system (HVS) are used to give more importance to visually distinguishable intensities while constructing the histogram bins. The method begins with constructing a histogram of the luminance channel, using bins that are perceived to be uniformly spaced by the HVS. Next, a refinement step is used, which removes the pixels from the bins that are indistinguishable by the HVS. Finally, the available display levels are distributed among the bins proportionate to the pixels counts thus giving due consideration to the visual contribution of each bin in the image. Quality assessment using both quantitative evaluations and user studies suggests that the presented algorithm produces tone-mapped images that are visually pleasant and preserve details of the original image better than the existing methods. Finally, implementation details of the algorithm on GPU for parallel processing are presented, which could achieve a significant gain in speed over CPU-based implementation. [ABSTRACT FROM AUTHOR]

Frequency response analysis (FRA) has recently been developed as a widely accepted tool for power transformer winding mechanical deformation diagnosis, and has proven to be effective and powerful in many cases. However, there still exist problems regarding the application of FRA. FRA is a comparative method in which the measured FRA signature should be compared with its fingerprint. Small differences of FRA signatures in certain frequency bands might be produced by external disturbance, which hinders fault diagnosis. Additionally, the existing correlation coefficient indicator recommended by power industry standards cannot reflect key information of signatures, namely the extreme points. This paper proposes an improved FRA based on binary morphology and extreme point variation. Binary morphology is first introduced to extract the certain frequency bands of signatures with significant difference. A composite indicator of extreme point variation is adopted to realize the diagnosis of fault level. A ternary diagram is constructed by the area proportions of the binary image to identify winding faults, which has a potential to realize cluster analysis of fault types. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of cluster formation control for a networked multi-agent system (MAS) in the simultaneous presence of aperiodic sampling and communication delays. First, to fulfill multiple formation tasks, a group of agents are decomposed into $M$ distinct and nonoverlapping clusters. The agents in each cluster are then driven to achieve a desired formation, whereas the MAS as a whole accomplishes $ M $ cluster formations. Second, by a proper modeling of aperiodic sampling and communication delays, an aperiodic sampled-data cluster formation protocol (CFP) is delicately constructed such that the information exchanges among neighboring agents only occur intermittently at discrete instants of time. Third, a detailed theoretical analysis of cluster formability is carried out and a sufficient and necessary condition is provided such that the system is $M$-cluster formable. Furthermore, a discontinuous Lyapunov functional approach is developed to derive a design criterion on the existence of an admissible sampled-data CFP. Finally, numerical simulations on a team of nonholonomic mobile robots are given to illustrate the effectiveness of the obtained theoretical result. [ABSTRACT FROM AUTHOR]

In string photovoltaic (PV) systems, the power–voltage (P–V) characteristic under partial shading conditions (PSC) displays multiple peaks requiring global maximum power point (GMPP) tracking (GMPPT) algorithms to identify the PV array GMPP. Several GMPPT algorithms scan wide voltage tracks to detect the GMPP, even larger than a P–V curve full scan. Moreover, many algorithms have been tested only for simple PSC that lead to three well distinct peaks at most. This paper initially presents a trapezoidal area, called the maximum power trapezium (MPT), in the P–V plane that contains all possible GMPPs for any PSC. Using the MPT and information about the minimum voltage difference between adjacent local peaks, a GMPPT algorithm is proposed, which tracks the GMPP of a string PV array under any severe PSC reducing its voltage track and so its tracking time. Simulation and experimental results show the superior performance of the proposed algorithm, in comparison to the state-of-art algorithms, in terms of tracking time, energy loss, and total voltage track during the search for the GMPP. [ABSTRACT FROM AUTHOR]

This paper presents two new implementations of finite control set model predictive control (FCS-MPC) methods applied to ac–dc converters with an inductive.capacitive.inductive (LCL) filter. The LCL filter, despite its advantages, can cause a strong resonance in the grid current and also pose a substantially more complex control problem. The new solutions improve the FCS-MPC with an active damping algorithm by eliminating low-order grid current harmonics and decreasing sensitivity to grid voltage distortion. The new methods, i.e., PCi1i2uc and PCi1i2uc-2steps propose multivariable approaches using converter-side current, line-side current, and capacitor voltage. Another improvement involves extending the prediction horizon in PCi1 i2uc-2steps. Both methods have been tested and compared in steady and transient states as well as under grid voltage disturbances. The methods were also compared with a predictive control algorithm with active damping. The simulation results and experimental measurements validate the developed control schemes and show high quality of grid current (low THDi value), high dynamic performance, and immunity under distorted grid voltage conditions. [ABSTRACT FROM AUTHOR]

Generalization is an effective technique for protecting confidential information of individuals, and has been studied by proposing numerous algorithms. However, the previous works do not separate the protection against identity disclosure and sensitive disclosure. Thus, when the requirement of attribute protection is higher than that of identity protection, generalization for $l$ -diversity causes overprotection for identity and large mounts of information utility loss. This paper presents a novel approach, called cross-bucket generalization, as a solution to meet the problem. The rationale is to divide microdata into equivalence groups and buckets. First, it provides separate protection for identity and sensitive values, and the level of protection can be flexibly adjusted based on actual demands. Second, the sizes of equivalence groups and buckets are minimized as far as possible by only satisfying the protection requirements, which avoid the overprotection for identity and reduce information loss. The experiments we conducted illustrate the effectiveness of our solution. [ABSTRACT FROM AUTHOR]

Computing shortest distances is a central task in many domains. The growing number of applications dealing with dynamic graphs calls for incremental algorithms, as it is impractical to recompute shortest distances from scratch every time updates occur. In this paper, we address the problem of maintaining all-pairs shortest distances in dynamic graphs. We propose efficient incremental algorithms to process sequences of edge deletions/insertions/updates and vertex deletions/insertions. The proposed approach relies on some general operators that can be easily “instantiated” both in main memory and on top of different underlying DBMSs. We provide complexity analyses of the proposed algorithms. Experimental results on several real-world datasets show that current main-memory algorithms become soon impractical, disk-based ones are needed for larger graphs, and our approach significantly outperforms state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Privacy concern in data sharing especially for health data gains particularly increasing attention nowadays. Now, some patients agree to open their information for research use, which gives rise to a new question of how to effectively use the public information to better understand the private dataset without breaching privacy. In this paper, we specialize this question as selecting an optimal subset of the public dataset for M-estimators in the framework of differential privacy (DP) in [1] . From a perspective of non-interactive learning, we first construct the weighted private density estimation from the hybrid datasets under DP. Along the same line as [2] , we analyze the accuracy of the DP M-estimators based on the hybrid datasets. Our main contributions are (i) we find that the bias-variance tradeoff in the performance of our M-estimators can be characterized in the sample size of the released dataset; (ii) based on this finding, we develop an algorithm to select the optimal subset of the public dataset to release under DP. Our simulation studies and application to the real datasets confirm our findings and set a guideline in the real application. [ABSTRACT FROM AUTHOR]

The problem of deriving lower and upper bounds for the edit distance between undirected, labeled graphs has recently received increasing attention. However, only one algorithm has been proposed that allegedly computes not only an upper but also a lower bound for non-uniform edit costs and incorporates information about both node and edge labels. In this paper, we demonstrate that this algorithm is incorrect. We present a corrected version \mathsf B\scriptstyleRANCH that runs in \mathcalO(n^2\Delta ^3+n^3) time, where \Delta is the maximum of the maximum degrees of input graphs G and H . We also develop a speed-up that runs in \mathcal{O}(n^2\Delta ^2+n^3) time and computes an only slightly less accurate lower bound. The lower bounds produced by \mathsf B\scriptstyleRANCH and \mathsf {B\scriptstyle{RANCH}}\mathsf{F\scriptstyle{AST}} are shown to be pseudo-metrics on a collection of graphs. Finally, we suggest an anytime algorithm \mathsf B\scriptstyleRANCH\mathsfT\scriptstyleIGHT that iteratively improves \mathsf B\scriptstyleRANCH ’s lower bound. \mathsf B\scriptstyleRANCH\mathsfT\scriptstyleIGHT runs in \mathcalO(n^3\Delta ^2+I(n^2\Delta ^3+n^3)) time, where the number of iterations $I$ is controlled by the user. A detailed experimental evaluation shows that all suggested algorithms are Pareto optimal, that they are very effective when used as filters for edit distance range queries, and that they perform excellently when used within classification frameworks. [ABSTRACT FROM AUTHOR]

Today, many scientific data sets are open to the public. For their operators, it is important to know what the users are interested in. In this paper, we study the problem of extracting and analyzing patterns from the query log of a database. We focus on design errors (antipatterns), which typically lead to unnecessary SQL statements. Such antipatterns do not only have a negative effect on performance. They also introduce bias on any subsequent analysis of the SQL log. We propose a framework designed to discover patterns and antipatterns in arbitrary SQL query logs and to clean antipatterns. To study the usefulness of our approach and to reveal insights regarding the existence of antipatterns in real-world systems, we examine the SQL log of the SkyServer project, containing more than 40 million queries. Among the top 15 patterns, we have found six antipatterns. This result as well as other ones gives way to the conclusion that antipatterns might falsify refactoring and any other downstream analyses. [ABSTRACT FROM AUTHOR]

Given a sequence of snapshots of flu propagating over a population network, can we find a segmentation when the patterns of the disease spread change, possibly due to interventions? In this paper, we study the problem of segmenting graph sequences with labeled nodes. Memes on the Twitter network, diseases over a contact network, movie-cascades over a social network, etc. are all graph sequences with labeled nodes. Most related work on this subject is on plain graphs and hence ignores the label dynamics. Others require fix parameters or feature engineering. We propose SnapNETS, to automatically find segmentations of such graph sequences, with different characteristics of nodes of each label in adjacent segments. It satisfies all the desired properties (being parameter free, comprehensive and scalable) by leveraging a principled, multi-level, flexible framework which maps the problem to a path optimization problem over a weighted DAG. Also, we develop the parallel framework of SnapNETS which speeds up its running time. Finally, we propose an extension of SnapNETS to handle the dynamic graph structures and use it to detect anomalies (and events) in network sequences. Extensive experiments on several diverse real datasets show that it finds cut points matching ground-truth or meaningful external signals and detects anomalies outperforming non-trivial baselines. We also show that the segmentations are easily interpretable, and that SnapNETS scales near-linearly with the size of the input. Finally, we show how to use SnapNETS to detect anomaly in a sequence of dynamic networks. [ABSTRACT FROM AUTHOR]

Document network is a kind of intriguing dataset which can provide both topical (textual content) and topological (relational link) information. A key point in modeling such datasets is to discover proper denominators beneath the text and link. Most previous work introduces the assumption that documents closely linked with each other share common latent topics. However, the heterophily (i.e., tendency to link to different others) of nodes is neglected, which is pervasive in social networks. In this paper, we simultaneously incorporate community detection and topic modeling in a unified framework, and appeal to Canonical Correlation Analysis (CCA) to capture the latent semantic correlations between the two heterogeneous factors, community and topic. Despite of the homophily (i.e., tendency to link to similar others) or heterophily, CCA can properly capture the inherent correlations which fit the dataset itself without any prior hypothesis. We also impose auxiliary word embeddings to improve the quality of topics. The effectiveness of our proposed model is comprehensively verified on three different types of datasets which are hyperlinked networks of web pages, social networks of friends, and coauthor networks of publications. Experimental results show that our approach achieves significant improvements compared with the current state of the art. [ABSTRACT FROM AUTHOR]

This paper presents a novel algorithm for the analysis of piecewise homogeneous, possibly lossy, waveguide circuits. The algorithm is based on the segmentation technique to split the circuit into homogeneous building blocks, combined with the representation of the generalized admittance matrix (GAM) of each block by the boundary integral-resonant mode expansion (BI-RME) method. The GAMs are then recombined by a circuital cascading procedure. The core of the method is the calculation of the quasi-static Green’s function of a rectangular box, which is required to determine the terms of the BI-RME pole-expansion of the GAM. The expressions of the quasi-static Green’s function, manipulated by Ewald’s technique to improve the convergence, are fully described, and numerical considerations for their efficient and accurate calculation are provided, with a particular focus on the proper selection of the splitting parameter. To demonstrate the correctness of the given formulas, the algorithm is developed and applied in the limited case of circuits segmentable into boxed building blocks. Numerical examples demonstrate the accuracy and efficiency of the proposed technique. [ABSTRACT FROM PUBLISHER]