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When a single-phase-to-ground (SPG) fault occurs in distribution networks, the existing faulty feeder detection techniques do not present reliable detection results. This paper introduces a novel faulty feeder detection method based on transient energy and cosine similarity. First, the fault characteristics of transient zero-sequence current (TZSC) are analyzed. It is found that the fault transient characteristics of transient components decrease as the grounding resistance increases. Second, to fully maintain the fault transient features, a fundamental frequency component (FFC) shifting method is introduced to remove FFCs. Next, the transient currents without containing FFCs are utilized to generate two fault indicators: transient energy within the first half power cycle and cosine similarity after the first half power cycle. After that step, to effectively merge the two fault indicators, the Laplace distribution is utilized. The fault possibility of each feeder can be quantitatively estimated by calculating the cumulative density function (CDF). Finally, the feeder with the maximum fault possibility is determined as the faulty feeder. The effectiveness and applicability of the proposed detection method are verified in the radial distribution network and the modified IEEE 34-bus distribution network. The results of the field test also indicate the feasibility of the proposed SPG detection method. [ABSTRACT FROM AUTHOR]

The Fuzzy transform is applied mainly to 1-D signals and 2-D data organized as a regular grid (e.g., 2-D images), thus, limiting its potential application to arbitrary data in terms of dimensionality and structure. This article defines and analyzes the properties of the data-driven F-transform, with a focus on the construction of the class of data-driven membership functions, which are multiscale, local, linearly independent, intrinsic, and robust to data discretization. Data-driven membership functions are defined by applying a 1-D filter to the Laplace–Beltrami operator, which encodes the geometric and topological properties of the input data. Then, we address the efficient computation of the data-driven F-transform through a polynomial or a rational polynomial approximation of the input filter. In this way, the computation of the data-driven F-transform is independent of the evaluation of the membership functions at any point of the input domain and reduces to the solution of a small set of sparse and symmetric linear systems. Finally, the data-driven F-transform is efficiently evaluated on large and arbitrary data, in terms of dimensionality, structure, and size. [ABSTRACT FROM AUTHOR]

The modified numerical inversion of the Laplace transform (NILT $n$) has been recently proposed as a fast and provably stable numerical simulation for general circuits. However, its efficiency has revealed challenges that are not typically seen in the conventional circuit simulators that rely on the classical SPICE engine. More particularly, the issue of estimating the approximation error is one problem that has not been adequately addressed using efficient approaches. The work presented in this article addresses this issue by deriving a formula that is used to estimate the approximation error for a given length of the time step. The proposed method is based on the relation between NILT $n$ and NILT0, which allows the error to be estimated via a series of forward/backward substitutions. The numerical simulations show the accuracy of the proposed method to predict the approximation error at an incremental computational cost. [ABSTRACT FROM AUTHOR]

In this paper, stochastic geometry tools are utilized to analyze the performance of a wireless network with Unmanned Aerial Vehicles (UAVs). The evaluation is based on the concept of a truncated octahedron that is the fundamental three-dimensional (3D) shape that models the basic cell in an entirely aerial cellular structure with UAV nodes only. The spatial locations of the aerial users, are modeled as a 3D Binomial Point Process (BPP). Subsequently, stochastic geometry analysis in a reference truncated octahedron located at the origin is performed and exact expressions for the distances between aerial users, are derived. To this end, expressions for evaluating the volume of the regions of the 3D cell in which an aerial user may fall, are derived. Then, by defining the signal-to-interference-plus-noise (SINR) ratio at the reference UAV-base station (UAV-BS) receiver, a performance analysis in terms of coverage probability is conducted under two interference scenarios, i.e., a pure intra-cell interference and an intra- plus inter-cell interference case. As intermediate step in the coverage probability analysis, expressions for relevant distance distributions and the Laplace transform of the aggregate interference power distribution are derived. For the sake of completeness, coverage probability analysis under intra-cell interference is also performed and compared to a 3D cell modeled as a sphere. The analytical expressions are also compared to simulation results to validate the accuracy of the proposed framework and reveal valuable insights. [ABSTRACT FROM AUTHOR]

The probabilistic prediction approach can be used not only for obtaining the maximum posterior probability solution but also for uncertainty evaluation. Its prior distribution has a significant impact on the prediction result. An improper prior assumption may lead to prediction deviation. To improve the prediction accuracy of elastic parameters, a Laplace prior with total variation (TV) constraint is introduced in the probabilistic prediction. First, the effect of TV constraint on the probability distribution of elastic parameters is analyzed in detail. Then, two approaches are proposed to handle the cases where the elastic parameters have blocky boundaries and no blocky boundaries: probabilistic prediction scheme for elastic parameters with blocky boundaries and probabilistic prediction scheme with blocky lithology prior constraint. The former imposes a sparse constraint on the elastic parameters, while the latter imposes a sparse constraint on the TV processing lithology. Their posterior probabilities are rederived. Considering that the discrete lithology is more likely to be blocky compared with the continuous elastic parameters, the sparse lithology constraint can handle more general cases. In addition, this approach allows for lithology prediction. The applications of numerical examples and field seismic data verify the feasibility of the proposed approaches. [ABSTRACT FROM AUTHOR]

A new approach to $L_{2}$ -consistent estimation of a general density functional using $k$ -nearest neighbor distances is proposed, where the functional under consideration is in the form of the expectation of some function $f$ of the densities at each point. The estimator is designed to be asymptotically unbiased, using the convergence of the normalized volume of a $k$ -nearest neighbor ball to a Gamma distribution in the large-sample limit, and naturally involves the inverse Laplace transform of a scaled version of the function $f$. Some instantiations of the proposed estimator recover existing $k$ -nearest neighbor based estimators of Shannon and Rényi entropies and Kullback–Leibler and Rényi divergences, and discover new consistent estimators for many other functionals such as logarithmic entropies and divergences. The $L_{2}$ -consistency of the proposed estimator is established for a broad class of densities for general functionals, and the convergence rate in mean squared error is established as a function of the sample size for smooth, bounded densities. [ABSTRACT FROM AUTHOR]

Although massive pruning methods are emerging for achieving structural sparsity in convolutional neural networks (CNN), most of them target structures such as ResNet. Meanwhile, previous works take more interest in pruning filters inside a residual block and keep the shortcut connection intact, leading to an imbalanced network structure. In this paper, we focus on the penalty-based method to prune already compact networks. In contrast to the broadly used $L_{1}$ constraint, which shrinks the parameters uniformly, we propose a novel penalty term that is similar in shape to an upside-down Laplace distribution. The penalty allows us to impose more pressure on potential weak channels but protects others during training to avoid damaging crucial channels, especially for compact architectures. We also design a candidate selection strategy to cooperate with the penalty-based training procedure. Besides, we address the residual block pruning problem by a scaling factor elimination skill, which is often ignored in other research. Our method reduces 50% parameters of MobileNet v1/v2 with a tolerable accuracy degradation. We further conduct pruning on MobileNetv1-SSDLite to compress parameters by 60%, manifesting the ability to generalize to different visual tasks. The experiment results demonstrate that our method outperforms pruning frameworks based on channel importance without a complicated tuning for hyper-parameters like search-based methods. [ABSTRACT FROM AUTHOR]

This paper is concerned with the robust parameter estimation for linear parameter varying (LPV) finite impulse response (FIR) model. The practical process data are typically polluted by outliers and conventional parameter estimation methods may fail to derive an unbiased estimate. In order to deal with outliers, the Laplace noise model is adopted and the robust system model for the described parameter estimation problem is established. The robust parameter estimation for LPV FIR model is formulated and solved in the EM algorithm scheme and the equations to estimate all the unknown parameters are derived. The efficacy of the proposed method is verified through a numerical simulation and a chemical unit.

Data collected in modern industrial processes often exhibit complex non-Gaussian and multimodal characteristics. In order to address these problems, a robust mixture probabilistic partial least squares (RMPPLS) model-based soft sensor is developed in this article, where two different kinds of hidden variables are introduced in the formulated model structure. The multivariate Laplace distribution is employed for robust modeling, and mixture form of the probabilistic partial least squares model is adopted for multimodal description. The unknown parameters are estimated in the expectation-maximization (EM) scheme and the corresponding soft sensor is finally constructed. A numerical example and the Tennessee Eastman (TE) process case study are explored to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper introduces a robust identification solution for the linear parameter varying Autoregressive Exogenous systems with outlier-contaminated outputs. The Laplace distribution with heavy tails and the expectation maximization algorithm are combined to build the robust system identification framework. To overcome the obstacles brought by the outliers, the Laplace distribution which can be decomposed into infinite Gaussian components, is applied to mathematically model the system noise. The problem of parameter estimation is solved using the expectation maximization algorithm, and the equations to infer the system model and noise parameters are simultaneously provided in the developed identification method. Finally, the verification tests performed on a numerical example and a mechanical unit are used to prove the validity of the developed identification method.

This brief develops a robust multiple model strategy for nonlinear system identification with system output data corrupted by outliers. The nonlinear system is described as a global model that combines multiple local nonlinear state-space models (SSMs) identified at the prechosen working points. Industrial data contaminated with outliers is a common problem in practical processes, which imposes great challenges for nonlinear processes modeling. In order to handle the outliers, the robust observation model based on Laplace distribution, instead of the conventional Gaussian distribution, is used to model the outliers corrupted output data. The approach to estimate parameters of all local models simultaneously is derived using the expectation maximization (EM) algorithm, and a particle filter (PF) is introduced to numerically calculate the cost function (Q-function) in the EM algorithm. The effectiveness of the proposed method is verified through a numerical example and the practical two-link robotic manipulator. [ABSTRACT FROM AUTHOR]

Bayesian methods are attracting increasing attention in the field of compressive sensing (CS), as they are applicable to recover signals from random measurements. However, these methods have limited use in many tensor-based cases such as hyperspectral Kronecker compressive sensing (HKCS), because they exploit the sparsity in only one dimension. In this paper, we propose a novel Bayesian model for HKCS in an attempt to overcome the above limitation. The model exploits multi-dimensional block-sparsity such that the information redundancies in all dimensions are eliminated. Laplace prior distributions are employed for sparse coefficients in each dimension, and their coupling is consistent with the multi-dimensional block-sparsity model. Based on the proposed model, we develop a tensor-based Bayesian reconstruction algorithm, which decouples the hyperparameters for each dimension via a low-complexity technique. Experimental results demonstrate that the proposed method is able to provide more accurate reconstruction than existing Bayesian methods at a satisfactory speed. Additionally, the proposed method can not only be used for HKCS, it also has the potential to be extended to other multi-dimensional CS applications and to multi-dimensional block-sparse-based data recovery. [ABSTRACT FROM AUTHOR]

Vector quantization is an integral part of modern speech and audio codecs. This study proposes the logarithmic pyramid vector quantizer (LPVQ), which is a gain-shape vector quantizer specifically designed for the quantization of Laplace distributed memoryless sources. The objective of the study is a theoretical analysis of the LPVQ: We determine the distortion of the shape quantizer with respect to rate and vector dimension for the quantization of an i.i.d. Laplace source. Furthermore, we derive formulas for the quantization signal-to-noise ratio (SNR) of the shape quantizer and the quantization SNR of the LPVQ, giving reliable results for an effective bit rate per vector coordinate of 2bits and higher. We study the SNR-behavior of the LPVQ for infinite dimensions and compare it with the maximal achievable SNR according to the rate-distortion bound. After having proposed a strategy for the allocation of the bit rate for the gain quantization and the quantization of the shape vector, respectively, we verify the derived formulas by simulations. [ABSTRACT FROM AUTHOR]

Using a well-known wiretap model, for the first time in this paper, closed-form solutions are achieved for wireless secrecy under dual correlated Rician fading. A novel wireless secrecy and end-to-end (e2e) system analysis employing decode-and-forward (DF) opportunistic relays and an adaptive encoder with on/off transmission are reported. A signal-to-noise ratio (SNR) distribution of dual correlated Rician fading is first obtained as a function of four nested infinite summations, which (i) converges for a finite number of terms, and (ii) is employed to compute secrecy outage probability for a passive wiretapper. Using two-dimensional Laplace transform of the SNR distribution of dual correlated Rician fading, an e2e system analysis is then given by deriving compact expressions for e2e SNR distributions, and e2e ergodic capacity. Relationships between important parameters are elaborated and explained. An asymptotic analysis and proof for infinite summation convergence are also given. It is shown that there is a trade-off between transmission quality and security via line-of-sight power. Simulation results are shown to match theoretical prediction, which validates this work. [ABSTRACT FROM AUTHOR]

The robust multimodel solution for multiple-input single-output nonlinear time-delay systems identification with polluted outputs is derived in this article. First, all the local autoregressive exogenous models are preidentified at the working points; then, the global system model is built by interpolating the local models with a smoothing strategy. The outliers and input time-delays which often increase the nonideality of process data are both considered. To cope with the outliers, the Laplace distribution is reutilized to describe the output measurement process and the negative impact resulted from each outlier imposed on parameter estimation can be suppressed through automatically assigned small weight. The parameter estimation procedure is realized with the expectation–maximization algorithm and the joint posterior probability of all input delays is also maximized to calculate the unknown input time-delays. With the verifications on a numerical example and the continuous fermentation process, the validity of the proposed approach is proved. [ABSTRACT FROM AUTHOR]

Overvoltages and dielectric stresses caused by transient conditions can result in the failure of transformer windings. Adequate prediction of these conditions is generally based on the implementation of accurate computer models, which, in turn, require an accurate computation of electrical parameters. This paper analyzes the computation of frequency-dependent core and proximity losses included in the transient model of transformer windings, using the finite element method and analytical expressions. A modified formula to calculate the proximity losses is presented. The results from a frequency-domain winding model are compared with experimental measurements on a disk-type prototype winding. These results show that including the frequency-dependent proximity losses is essential to accurately reproducing the experimental results. They also show that the core losses have a modest effect on the transient response of the winding when compared with measurements. [ABSTRACT FROM AUTHOR]

In this letter, Laplace distribution is used to model the prior for the direction of arrival (DoA) of sources. In order to incorporate the real and imaginary part of the received signal, we propose a method that pairwise estimates the hyperparameters for parts of the signal coefficients. In addition, we propose a multitask algorithm to extend the application of our method to the situation where multiple measurements are available. Nonuniform linear arrays are used to demonstrate the validity and advantages of the proposed method including its improved efficiency and accuracy compared with the state-of-art DoA estimation methods. [ABSTRACT FROM AUTHOR]

Assessment of disclosure risk is of paramount importance in data privacy research and applications. The concept of differential privacy (DP) formalizes privacy in probabilistic terms and provides a robust concept for privacy protection. Practical applications of DP involve development of DP mechanisms to release data at a pre-specified privacy budget. In this paper, we generalize the widely used Laplace mechanism to the family of generalized Gaussian (GG) mechanism based on the $l_p$ global sensitivity of statistical queries. We explore the theoretical requirement for the GG mechanism to reach DP at prespecified privacy parameters, and investigate the connections and differences between the GG mechanism and the Exponential mechanism based on the GG distribution. We also present a lower bound on the scale parameter of the Gaussian mechanism of $(\epsilon, \delta)$ -probabilistic DP as a special case of the GG mechanism, and compare the utility of sanitized results in the tail probability and dispersion between the Gaussian and Laplace mechanisms. Lastly, we apply the GG mechanism in three experiments and compare the accuracy of sanitized results in the $l_1$ distance and Kullback-Leibler divergence, and examine the prediction power of a SVM classifier constructed with the sanitized data relative to the original results. [ABSTRACT FROM AUTHOR]

Light-fidelity (LiFi) is a networked optical wireless communication (OWC) solution for high-speed indoor connectivity for fixed and mobile optical communications. Unlike conventional radio frequency wireless systems, the OWC channel is not isotropic, meaning that the device orientation affects the channel gain significantly, particularly for mobile users. However, due to the lack of a proper model for device orientation, many studies have assumed that the receiver is vertically upward and fixed. In this paper, a novel model for device orientation based on experimental measurements of 40 participants has been proposed. It is shown that the probability density function (PDF) of the polar angle can be modeled either based on a Laplace (for static users) or a Gaussian (for mobile users) distribution. In addition, a closed-form expression is obtained for the PDF of the cosine of the incidence angle based on which the line-of-sight (LOS) channel gain is described in OWC channels. An approximation of this PDF based on the truncated Laplace is proposed and the accuracy of this approximation is confirmed by the Kolmogorov–Smirnov distance. Moreover, the statistics of the LOS channel gain are calculated and the random orientation of a user equipment (UE) is modeled as a random process. The influence of the random orientation on signal-to-noise-ratio performance of OWC systems has been evaluated. Finally, an orientation-based random waypoint (ORWP) mobility model is proposed by considering the random orientation of the UE during the user’s movement. The performance of ORWP is assessed on the handover rate and it is shown that it is important to take the random orientation into account. [ABSTRACT FROM AUTHOR]

This paper presents an analytical model for the prediction of the performance in a spoke-type permanent magnet synchronous in-wheel motor with a trapezoid magnet accounting for tooth saturation. The impact of magnet shape on modeling is considered. To match the standard boundary conditions, the trapezoid magnet is simplified to sector magnets. According to the invariance principle of the magnetic field created by the magnet, the dimension of the sector magnet is calculated with a lumped magnetic circuit model. Then, the motor is divided into six subdomains, i.e., shaft, inner sector magnet, outer sector magnet, rotor slot, air-gap, and stator slot. Based on the boundary conditions, the magnetic field, back electromotive force, and torque are obtained by solving Poisson's and Laplace's equations. The effect of tooth saturation is considered. The finite-element method and experiments are employed to validate the accuracy of the analytical model. In addition, the proposed method can also be used to evaluate the performance of the spoke-type motor with any irregular magnet. [ABSTRACT FROM AUTHOR]

This paper deals with active sampling of graph nodes representing training data for binary classification. The graph may be given or constructed using similarity measures among nodal features. Leveraging the graph for classification builds on the premise that labels across neighboring nodes are correlated according to a categorical Markov random field (MRF). This model is further relaxed to a Gaussian (G)MRF with labels taking continuous values—an approximation that not only mitigates the combinatorial complexity of the categorical model, but also offers optimal unbiased soft predictors of the unlabeled nodes. The proposed sampling strategy is based on querying the node whose label disclosure is expected to inflict the largest change on the GMRF, and in this sense it is the most informative on average. Connections are established to other sampling methods including uncertainty sampling, variance minimization, and sampling based on the $\Sigma \text{-}$ optimality criterion. A simple yet effective heuristic is also introduced for increasing the exploration capabilities of the sampler, and reducing bias of the resultant classifier, by adjusting the confidence on the model label predictions. The novel sampling strategies are based on quantities that are readily available without the need for model retraining, rendering them computationally efficient and scalable to large graphs. Numerical tests using synthetic and real data demonstrate that the proposed methods achieve accuracy that is comparable or superior to the state of the art even at reduced runtime. [ABSTRACT FROM AUTHOR]

To cope up with the explosive growth of mobile data demand, the fifth-generation mobile network intents to exploit the available spectrum in millimeter-wave (mmWave) band to boost the communication capacity. However, for a potential mmWave communication to happen, challenges, as propagation losses and blockages, have to be dealt with. Abundant literature illustrating techniques to circumvent these challenges and increase the cellular capacity can be found. Among the approaches used to overcome the challenges in mmWave networks, optimal transmit precoding design, spatial reuse of mmWave base stations (BSs), and mmWave-overlaid microwave ( $\mu$ Wave) cellular networks are employed. This paper focuses on the performance analysis of mmWave-overlaid microwave cellular networks, from security perspective. We particularly developed a mathematical framework to analyze the connection outage probability, the secrecy outage probability, and the achievable average secrecy rate of the hybrid mmWave network, while taking fading and the impact of blockages into consideration. Moreover, based on the received signal strength, we formulated a scheme for a generic mobile user to be associated with either the mmWave or $\mu$ Wave network. The exact average secrecy rate of mmWave networks is also formulated using moment generating and Laplace functions as a tool. The derived analytic expressions are validated via simulation results; for different antenna gain, eavesdropper density, BS, and blockage density. [ABSTRACT FROM PUBLISHER]

In this study, representative motion profiles are analyzed to explain why smoother movement can enable a system to be silent, accurate, and reliable. Previous studies have indicated that abrupt changes in motion profiles are sources of system vibration and are directly connected to performance such as degraded efficiency and decreased machine's lifetime. Therefore, efforts to generate smoother motion profiles based on various functions such as polynomial and trigonometric functions are ongoing. However, a comprehensive analytical comparison of previous studies has not been performed. In this study, the relationship between the design of the motion profiles and their characteristics is analyzed in Laplace domain with well-defined comparison tools that are frequently employed in input shaping technique. From the analysis, the characteristics of each motion profile and the tradeoffs between the cost and benefit of generating smoother movement can be explained. [ABSTRACT FROM PUBLISHER]

Stationarity is a cornerstone property that facilitates the analysis and processing of random signals in the time domain. Although time-varying signals are abundant in nature, in many practical scenarios, the information of interest resides in more irregular graph domains. This lack of regularity hampers the generalization of the classical notion of stationarity to graph signals. This paper proposes a definition of weak stationarity for random graph signals that takes into account the structure of the graph where the random process takes place, while inheriting many of the meaningful properties of the classical time domain definition. Provided that the topology of the graph can be described by a normal matrix, stationary graph processes can be modeled as the output of a linear graph filter applied to a white input. This is shown equivalent to requiring the correlation matrix to be diagonalized by the graph Fourier transform; a fact that is leveraged to define a notion of power spectral density (PSD). Properties of the graph PSD are analyzed and a number of methods for its estimation are proposed. This includes generalizations of nonparametric approaches such as periodograms, window-based average periodograms, and filter banks, as well as parametric approaches, using moving-average, autoregressive, and ARMA processes. Graph stationarity and graph PSD estimation are investigated numerically for synthetic and real-world graph signals. [ABSTRACT FROM PUBLISHER]

To investigate the dynamic distribution of electrostatic charge and potential in oil tanks in the case of filling process, a novel calculation model is established. In the model, the oil tanks filling process is simulated by a gas-oil two-phase flow model. The electrostatic charge flowing into the tank is transported by diffusion, convection and dissipation. The distribution of electrostatic potential is obtained by solving Laplace's and Poisson's equations. The calculated surface potential, which changes with filling time, coincides with the experimental results well. The calculation results show that electrostatic charge isn't uniformly distributed in the tank, which is opposite to the assumption of uniform distribution in most researches. Surface potential reaches the maximum value at the filling fraction of 28%. [ABSTRACT FROM PUBLISHER]

It is challenging to quantitatively assess image quality in real time without a reference image while achieving human-level perception performance. In this paper, we present a no-reference (NR) image quality assessment (IQA) method called BNB (an acronym for blurriness, noisiness, and blockiness). Our BNB method quantifies the blurriness, noisiness, and blockiness of a given image, which are considered as three critical factors affecting users’ quality of experience. This method is rooted in the observation that for any image, the difference between any two adjacent pixel values follows a generalized Laplace distribution with zero mean. This Laplace distribution changes differently when the image experiences various types of artifacts, i.e., blurriness, noisiness, and blockiness. To construct a metric for each BNB artifact, we first extract features for each type of artifacting from the changing Laplace distribution and then identify the quantitative relationship between the feature value and the variation of the artifact. Given human perception scores of a popular image database, we use the k-nearest neighbor algorithm to map our three BNB metrics of an image to a human perception score. Experimental results reveal that the image quality score obtained from our BNB method has higher correlation with human perceptual scores in addition to requiring notably less computation compared with existing NR IQA methods. [ABSTRACT FROM PUBLISHER]

Electro-quasi-static (EQS) field problems involving nonlinear materials are commonly discretized in space using finite elements. In this paper, it is proposed to solve the resulting system of ordinary differential equations (ODEs) by an explicit Runge–Kutta–Chebyshev time-integration scheme. This mitigates the need for Newton–Raphson iterations, as they are necessary within fully implicit time integration schemes. However, the EQS system of ODE has a Laplace-type mass matrix such that parts of the explicit time-integration scheme remain implicit. An iterative solver with constant preconditioner is shown to efficiently solve the resulting multiple right-hand side problem. This approach allows an efficient parallel implementation on a system featuring multiple graphic processing units. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid magnetic model combining reluctance network and Laplace’s equation resolution using magnetic vector potential in the air gap. The coupling of the two models is performed by introducing fictive equivalent magnetomotive forces in the air gap. The model is applied to the study of dc machines used as a starter in automotive application. Good results are obtained compared with finite-element model and experimental measurements. [ABSTRACT FROM AUTHOR]

This paper develops a mathematical framework to study downlink error rates and throughput for half-duplex (HD) terminals served by a full-duplex (FD) base station. The developed model is used to motivate long term pairing for users that have a non-line of sight (NLOS) interfering link. Consequently, we study the interferer limited problem that appears between NLOS HD users-pair that are scheduled on the same FD-channel. The distribution of the interference is first characterized via its distribution function, which is derived in closed form. Then, a comprehensive performance assessment for the proposed pairing scheme is provided by assuming Extended Generalized- \mathcal K fading for the downlink and studying different modulation schemes. To this end, a unified closed-form expression for the average symbol error rate is derived. Furthermore, we show the effective downlink throughput gain harvested by the pairing NLOS users as a function of the average signal-to-interference-ratio when compared with an idealized HD scenario with neither interference nor noise. Finally, we show the minimum required channel gain pairing threshold to harvest downlink throughput via the FD operation when compared with the HD case for each modulation scheme. [ABSTRACT FROM PUBLISHER]

To improve the disturbance rejection capability of phase-locked loops (PLLs), which are undoubtedly the most common synchronization tool in power and energy applications, using different filtering techniques have been suggested in the literature. Among these filtering strategies, the delayed signal cancellation (DSC) operator is highly popular probably because it can be easily tailored for different grid scenarios. The DSC operator(s) can be used either as an in-loop filter in the PLL structure or as a preprocessing filter before the PLL input. The latter case is often preferred mainly because it results in a faster dynamic response in the extraction of grid voltage parameters. In this paper, a combination of an adaptive DSC operator with multiple nonadaptive DSC operators is suggested as the PLL preprocessing stage. To compensate for the phase and amplitude errors caused by the nonadaptive operators, a compensator is designed and cascaded with them. The proposed filter requires a low computational burden for the implementation and ensures a fast dynamic response and high filtering capability for the PLL. The effectiveness of this technique is verified through experimental results. [ABSTRACT FROM PUBLISHER]

This paper proposes a structure for the decoupling and performance improvement of a hybrid control system. Additionally, the application of this system to a motion-copying system (MCS) is introduced. MCSs are proposed as a means of preserving human-skilled techniques. In the motion-saving phase, a bilateral controller (a type of position/force hybrid control system) is utilized. A decoupling controller is required for bilateral control, for realizing precise force and position reproduction. A conventional hybrid controller for bilateral control systems has been developed, based on a force controller, by using a modal space disturbance observer (DOB). However, deterioration in the system performance during changes in the configuration of the manipulator is observed in force controllers based on workspace DOBs. It is also found that another conventional bilateral controller based on a hybrid matrix generates an interference term in the force control system, if there is a difference between the masses of the master and slave systems. This study shows that an acceleration controller can also realize decoupling control in the modal space. Furthermore, a novel DOB for enhancing the performance of an acceleration controller is proposed. Additionally, it is shown that a saved human motion can be reproduced accurately, while maintaining the high transparency of the bilateral control, by implementing a structure based on a hybrid controller and using the proposed DOB. The validity of the proposal is confirmed through analyses and experiments. [ABSTRACT FROM PUBLISHER]

In grid-connected inverters, a feedforward controller accurately compensates for a large number of the harmonic components of the injected current. This controller is fast and directly tackles the grid dynamic voltage disturbances without doing any further harmonic analysis. A feedforward controller was introduced to a three-phase grid connection with a wye configuration; however, for the widespread delta connections, such a scheme is less established. Moreover, the nonsinusoidal profile of the grid voltage waveform makes the online conversion of line-to-line values into phase values impossible. In this paper, implicit zero-sequence discontinuous pulse width modulation (IZDPWM) based feedforward control is implemented on the two-level grid-connected inverter. Regardless of the grid topology, the IZDPWM-based controller ensures sinusoidal current injection to the grid under balanced, unbalanced, and distorted conditions. [ABSTRACT FROM PUBLISHER]

A semidefinite programming (SDP) relaxation globally solves many optimal power flow (OPF) problems. For other OPF problems where the SDP relaxation only provides a lower bound on the objective value rather than the globally optimal decision variables, recent literature has proposed a penalization approach to find feasible points that are often nearly globally optimal. A disadvantage of this penalization approach is the need to specify penalty parameters. This paper presents an alternative approach that algorithmically determines a penalization appropriate for many OPF problems. The proposed approach constrains the generation cost to be close to the lower bound from the SDP relaxation. The objective function is specified using iteratively determined weights for a Laplacian matrix. This approach yields feasible points to the OPF problem that are guaranteed to have objective values near the global optimum due to the constraint on generation cost. The proposed approach is demonstrated on both small OPF problems and a variety of large test cases representing portions of European power systems. [ABSTRACT FROM AUTHOR]

In this paper, we provide new compact integral expressions and associated simple asymptotic approximations for converse and achievability bounds in the finite blocklength regime. The chosen converse and random coding union bounds were taken from the recent work of Polyanskyi–Poor–Verdù, and are investigated under parallel AWGN channels, the AWGN channel, the BI-AWGN channel, and the BSC. The technique we use, which is a generalization of some recent results available from the literature, is to map the probabilities of interest into a Laplace integral, and then solve (or approximate) the integral by use of a steepest descent technique. The proposed results are particularly useful for short packet lengths, where the normal approximation may provide unreliable results. [ABSTRACT FROM PUBLISHER]

The exact-repair regenerating codes for distributed storage system are studied in this work. A novel coding scheme is proposed for code construction for any $(n,k,d=k)$ system. It is shown that the proposed codes are optimum, in the sense that any operating point satisfying the lower bound for the storage-bandwidth trade-off can be achieved with the proposed construction. As a consequence, the optimum linear trade-off of exact-regenerating system is fully characterized for any systems with $d=k$ . The proposed codes are linear, and can be generated by multiplication of an encoder matrix with a so-called data matrix. The exact regenerating property is provided based on fundamental properties of matrix determinants, and in particular, generalized Laplace expansion for determinant. Thus the code is called determinant code. Importantly, the field size required for this code construction is $\Theta (n)$ , the total number of nodes in the system. [ABSTRACT FROM PUBLISHER]

A sound localization system consisting of a number of spatially distributed sensors can be employed to estimate the source bearing by measuring the relative time-difference-of-arrival (TDOA) of the transient acoustic signal. However, sensor measurements may contain nonnegligible consistent systematic biases, resulting in significant direction finding estimation error. In this paper, an efficient expectation-maximization (EM) algorithm is proposed for accurately estimating the direction-of-arrival (DOA) of the signal from a far field source in the presence of biased TDOA measurements. The unknown biases are treated as hidden variables, and nonlinear least square estimators are developed to jointly estimate the biases and DOA parameters for both the reference-free mode and the reference mode. TDOA error distribution is investigated and four different distributions [Gaussian distribution, Laplace distribution, Gaussian-Laplace distribution, and generalized normal distribution (GND)] are employed to fit the experimental data. It is observed that GND is the best for modeling the biased TDOA errors in terms of cumulative distribution function fitting root mean square error, while Laplace distribution offers a good tradeoff between the accuracy and complexity. Both the simulation and field experimental results demonstrate that the proposed EM-based estimators can considerably outperform the existing algorithms. [ABSTRACT FROM PUBLISHER]

The recent trends of densification and centralized signal processing in radio access networks suggest that future networks may comprise ubiquitous antennas coordinated to form a network-wide gigantic array, referred to as the ubiquitous array (UA). In this paper, the UA communication techniques are designed and analyzed based on a geometric model. Specifically, the UA is modeled as a continuous circular/spherical array enclosing target users and free-space propagation is assumed. First, consider the estimation of multiuser UA channels induced by user locations. Given single pilot symbols, a novel channel estimation scheme is proposed that decomposes training signals into Fourier/Laplace series and thereby translates multiuser channel estimation into peak detection of a derive function of location. The process is shown to suppress noise. Moreover, it is proved that estimation error due to interference diminishes with the increasing minimum user-separation distance following the power law, where the exponent is 1/3 and 1 for the circular and spherical UA, respectively. If orthogonal pilot sequences are used, channel estimation is found to be perfect. Next, consider channel-conjugate data transmission that maximizes received signal power. The power of interference between two users is shown to decay with the increasing user-separation distance sub-linearly and super-linearly for the circular and spherical UA, respectively. Furthermore, a novel multiuser precoding design is proposed by exciting different phase modes of the UA and controlling the mode weight factors to null interference. The number of available degrees of freedom for interference nulling using the UA is proved to be proportional to the minimum user-separation distance. [ABSTRACT FROM AUTHOR]

In this paper, a new model for retinal Optical Coherence Tomography (OCT) images is proposed. This statistical model is based on introducing a nonlinear Gaussianization transform to convert the probability distribution function (pdf) of each OCT intra-retinal layer to a Gaussian distribution. The retina is a layered structure and in OCT each of these layers has a specific pdf which is corrupted by speckle noise, therefore a mixture model for statistical modeling of OCT images is proposed. A Normal-Laplace distribution, which is a convolution of a Laplace pdf and Gaussian noise, is proposed as the distribution of each component of this model. The reason for choosing Laplace pdf is the monotonically decaying behavior of OCT intensities in each layer for healthy cases. After fitting a mixture model to the data, each component is gaussianized and all of them are combined by Averaged Maximum A Posterior (AMAP) method. To demonstrate the ability of this method, a new contrast enhancement method based on this statistical model is proposed and tested on thirteen healthy 3D OCTs taken by the Topcon 3D OCT and five 3D OCTs from Age-related Macular Degeneration (AMD) patients, taken by Zeiss Cirrus HD-OCT. Comparing the results with two contending techniques, the prominence of the proposed method is demonstrated both visually and numerically. Furthermore, to prove the efficacy of the proposed method for a more direct and specific purpose, an improvement in the segmentation of intra-retinal layers using the proposed contrast enhancement method as a preprocessing step, is demonstrated. [ABSTRACT FROM AUTHOR]

System modeling problems, such as the channel of a single-hop relay communication system in a flat-fading environment, require a cascade of two or more autoregressive (AR) processes to capture the entire system characteristics. However, for the purpose of system simulation and parameter estimations, it is more convenient if the entire system is modeled by a single AR model. In this paper, we consider a cascade system whose statistical characteristics of its subsystems is represented by two independent p th-order complex Gaussian AR processes, and model it by a single p th-order Laplace AR process. In our analysis, we first demonstrate that the marginal probability distribution functions of the real and imaginary components of a system described by a cascade of the two complex Gaussian AR processes are Laplace distributed. Then, to model the cascaded system, we develop a single complex Laplace AR process whose parameters are configured to match other statistical characteristics of the cascaded system. Specifically, we show that the autocorrelation of the developed Laplace AR process satisfies Yule–Walker type of equations and derive the steps for the design of its parameters through autocorrelation matching. [ABSTRACT FROM PUBLISHER]

In this paper, we study the statistics of phase processes and random frequency modulation (FM) noise encountered in double Rayleigh fading channels. The Rayleigh processes making up the double Rayleigh channel are assumed to be independent but not necessarily identically distributed. The Doppler power spectral densities of these processes are supposed to be symmetric about the carrier frequency. Under these fading conditions, we derive first an expression for the joint probability density function (jpdf) of the phase process and its rate of change. Capitalizing on this jpdf formula, we then investigate the probability density function (pdf) and cumulative distribution function (cdf) of random FM noise. Moreover, the average crossing rate of phase processes, as well as that of the Laplacian distributed in-phase and quadrature components of the complex channel gain, are obtained. Aside from the aforementioned statistics, we also provide exact expressions for the pdf, cdf, and second-order moment of the random FM noise at the output of a selection combiner, operating on independent and identically distributed (i.i.d.) branches. The validity of the analysis has been carried out by reducing the derived expressions to already-known results for single Rayleigh channels and by comparing our analytical results with the corresponding simulation results. [ABSTRACT FROM AUTHOR]

In this paper, we first present explicit expressions for the maximum likelihood estimates (MLEs) of the location, and scale parameters of the Laplace distribution based on a Type-II right censored sample under different cases. Then, after giving the exact density functions of the MLEs, and the expectations, we derive the exact density of the MLE of the quantile, and utilize it to develop exact confidence intervals for the population quantile. We also briefly discuss the MLEs of reliability and cumulative hazard functions, and how to develop exact confidence intervals for these functions. These results can also be extended to any linear estimators. Finally, we present two examples to illustrate the inferential methods developed here. [ABSTRACT FROM PUBLISHER]

The standard Gaussian Process regression (GP) is usually formulated under stationary hypotheses: The noise power is considered constant throughout the input space and the covariance of the prior distribution is typically modeled as depending only on the difference between input samples. These assumptions can be too restrictive and unrealistic for many real-world problems. Although nonstationarity can be achieved using specific covariance functions, they require a prior knowledge of the kind of nonstationarity, not available for most applications. In this paper we propose to use the Laplace approximation to make inference in a divisive GP model to perform nonstationary regression, including heteroscedastic noise cases. The log-concavity of the likelihood ensures a unimodal posterior and makes that the Laplace approximation converges to a unique maximum. The characteristics of the likelihood also allow to obtain accurate posterior approximations when compared to the Expectation Propagation (EP) approximations and the asymptotically exact posterior provided by a Markov Chain Monte Carlo implementation with Elliptical Slice Sampling (ESS), but at a reduced computational load with respect to both, EP and ESS. [ABSTRACT FROM PUBLISHER]

Automatic cell segmentation can hardly be flawless due to the complexity of image data particularly when time-lapse experiments last for a long time without biomarkers. To address this issue, we propose an interactive cell segmentation method by classifying feature-homogeneous superpixels into specific classes, which is guided by human interventions. Specifically, we propose to actively select the most informative superpixels by minimizing the expected prediction error which is upper bounded by the transductive Rademacher complexity, and then query for human annotations. After propagating the user-specified labels to the remaining unlabeled superpixels via an affinity graph, the error-prone superpixels are selected automatically and request for human verification on them; once erroneous segmentation is detected and subsequently corrected, the information is propagated efficiently over a gradually-augmented graph to un-labeled superpixels such that the analogous errors are fixed meanwhile. The correction propagation step is efficiently conducted by introducing a verification propagation matrix rather than rebuilding the affinity graph and re-performing the label propagation from the beginning. We repeat this procedure until most superpixels are classified into a specific category with high confidence. Experimental results performed on three types of cell populations validate that our interactive cell segmentation algorithm quickly reaches high quality results with minimal human interventions and is significantly more efficient than alternative methods, since the most informative samples are selected for human annotation/verification early. [ABSTRACT FROM PUBLISHER]

In this paper, we assess the potential of several forms of the postcoherent differential detectors for the detection of weak Global Navigation Satellite Systems (GNSS) signals. We analyze in detail two different detector forms, namely the pair-wise differential detector (PWD) and noncoherent differential detector (NCDD). First, we follow a novel approach to obtain analytic expressions to characterize statistically the PWD. Then, we use these results to propose a polynomial-like model fitted by simulation to the sensitivity loss experienced by the differential operation with respect to coherent summing. This sensitivity loss formula is also used to characterize the NCDD, which is shown to be more adequate than the PWD for the acquisition of GNSS signals. A comparison between the PWD, NCDD, and the traditional noncoherent detector (NCD) is also carried out in this study. The results highlight the superior performance of the NCDD over the NCD for the acquisition of weak signals. For the case of the PWD, its performance is sensitive to Doppler shift. The conclusions drawn from the simulation results are confirmed in the acquisition of real Global Positioning System L1 C/A signals. [ABSTRACT FROM AUTHOR]