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This paper presents a new technique for estimating the two-dimensional direction of departure (2D-DOD) and direction of arrival (2D-DOA) in bistatic uniform planar array Multiple-Input Multiple-Output (MIMO) radar systems. The method is based on the reduced-dimension (RD) MUSIC algorithm, aiming to achieve improved precision and computational efficiency. Primarily, this pioneering approach efficiently transforms the four-dimensional (4D) estimation problem into two-dimensional (2D) searches, thus reducing the computational complexity typically associated with conventional MUSIC algorithms. Then, exploits the spatial diversity of array response vectors to construct a 4D spatial spectrum function, which is crucial in resolving the complex angular parameters of multiple simultaneous targets. Finally, the objective is to simplify the spatial spectrum to a 2D search within a 4D measurement space to achieve an optimal balance between efficiency and accuracy. Simulation results validate the effectiveness of our proposed algorithm compared to several existing approaches, demonstrating its robustness in accurately estimating 2D-DOD and 2D-DOA across various scenarios. The proposed technique shows significant computational savings and high-resolution estimations and maintains high precision, setting a new benchmark for future explorations in the field. [ABSTRACT FROM AUTHOR]

Sampling with the generalised atmospheric Rosenbluth method (GARM) is a technique for estimating the distributions of lattice polymer models that has had some success in the study of linear polymers and lattice polygons. In this paper we will explain how and why such sampling appears not to be effective for many models of branched polymers. Analysing the algorithm on a simple binary tree, we argue that the fundamental issue is an inherent bias towards extreme configurations that is costly to correct with reweighting techniques. We provide a solution to this by applying uniform sampling methods to the atmospheres that are central to GARM. We caution that the ensuing computational complexity often outweighs the improvements gained. [ABSTRACT FROM AUTHOR]

For linear optimization problems with a parametric objective, so-called parametric linear programs (PLP), we show that the optimal decision values are, under few technical restrictions, unimodal functions of the parameter, at least in the two-degrees-of-freedom case. Assuming that the parameter is random and follows a known probability distribution, this allows for an efficient algorithm to determe the quantiles of linear combinations of the optimal decisions. The novel results are demonstrated with probabilistic economic dispatch. For an example setup with uncertain fuel costs, quantiles of the resulting inter-regional power flows are computed. The approach is compared against Monte Carlo and piecewise computation techniques, proving significantly reduced computation times for the novel procedure. This holds especially when the feasible set is complex and/or extreme quantiles are desired. This work is limited to problems with two effective degrees of freedom and a one-dimensional uncertainty. Future extensions to higher dimensions could yield a key tool for the analysis of probabilistic PLPs and, specifically, risk management in energy systems. [ABSTRACT FROM AUTHOR]

We propose a fast algorithm for computing the entire ridge regression regularization path in nearly linear time. Our method constructs a basis on which the solution of ridge regression can be computed instantly for any value of the regularization parameter. Consequently, linear models can be tuned via cross-validation or other risk estimation strategies with substantially better efficiency. The algorithm is based on iteratively sketching the Krylov subspace with a binomial decomposition over the regularization path. We provide a convergence analysis with various sketching matrices and show that it improves the state-of-the-art computational complexity. We also provide a technique to adaptively estimate the sketching dimension. This algorithm works for both the over-determined and under-determined problems. We also provide an extension for matrix-valued ridge regression. The numerical results on real medium and large-scale ridge regression tasks illustrate the effectiveness of the proposed method compared to standard baselines which require super-linear computational time. [ABSTRACT FROM AUTHOR]

For dynamic networks with unknown diameter, we prove novel lower bounds on the time complexity of a range of basic distributed computing problems. Together with trivial upper bounds under dynamic networks with known diameter for these problems, our lower bounds show that the complexities of all these problems are sensitive to whether the diameter is known to the protocol beforehand: Not knowing the diameter increases the time complexities by a large poly(N) factor as compared to when the diameter is known, resulting in an exponential gap. Our lower bounds are obtained via communication complexity arguments and by reducing from the two-party DisjointnessCP problem. We further prove that sometimes this large poly(N) cost can be completely avoided if the protocol is given a good estimate on N. In other words, having such an estimate makes some problems no longer sensitive to unknown diameter. [ABSTRACT FROM AUTHOR]

Abstract Integration of radar and communication (IRC) is conducive to improving hardware resource utilization and spectrum utilization. This paper proposes a joint range and velocity estimation method for IRC based on multi‐symbol orthogonal frequency division multiplexing (OFDM) radar pulses. A two‐step parameters estimation method is applied, where estimating the signal parameters via rotational invariance technique (ESPRIT) and weighted subspace fitting (WSF) perform rough and fine estimations. We first build a receiving model for integrated signal based on element‐wise division. The ESPRIT method is then adopted based on the double Vandermonde structure array manifold for a rough estimation. It has relatively high estimation error but low computational complexity. The WSF method based on alternating projection (AP) algorithm is introduced to make the estimation more precise and reduce its error. It uses the estimation results in the first step and reduces the overall computational cost. Simulation results show that the proposed method reduces the computational complexity while improving the estimation accuracy compared with the WSF method.

The estimation of heritability has been an important question in statistical genetics. Due to the clear mathematical properties, the modified Haseman–Elston regression has been found a bridge that connects and develops various parallel heritability estimation methods. With the increasing sample size, estimating heritability for biobank-scale data poses a challenge for statistical computation, in particular that the calculation of the genetic relationship matrix is a huge challenge in statistical computation. Using the Haseman–Elston framework, in this study we explicitly analyzed the mathematical structure of the key term tr ( K T K ), the trace of high-order term of the genetic relationship matrix, a component involved in the estimation procedure. In this study, we proposed two estimators, which can estimate tr ( K T K ) with greatly reduced sampling variance compared to the existing method under the same computational complexity. We applied this method to 81 traits in UK Biobank data and compared the chromosome-wise partition heritability with the whole-genome heritability, also as an approach for testing polygenicity. [ABSTRACT FROM AUTHOR]

We present a technique to jointly estimate the channel taps and the frequency offset due to the Doppler effect of a special class of doubly dispersive aeronautical channels. The algorithm includes the use of pulse repetition techniques at the transmitter and the “power method” subspace iteration at the receiver. We show that transmitting constant-amplitude zero-autocorrelation sequences for channel sounding yields estimators with low computational complexity. Numerical simulations indicate performance comparable to the estimation-theoretic lower bound. [ABSTRACT FROM AUTHOR]

This paper proposes a closed-loop sparse channel estimation (CE) scheme for wideband millimeter-wave hybrid full-dimensional multiple-input multiple-output and time division duplexing based systems, which exploits the channel sparsity in both angle and delay domains. At the downlink CE stage, random transmit precoding matrix is designed at base station (BS) for channel sounding, and receive combining matrices at user devices (UDs) are designed whereby the hybrid array is visualized as a low-dimensional digital array for facilitating the multi-dimensional unitary ESPRIT (MDU-ESPRIT) algorithm to estimate respective angle-of-arrivals (AoAs). At the uplink CE stage, the estimated downlink AoAs, namely, uplink angle-of-departures (AoDs), are exploited to design multi-beam transmit precoding matrices at UDs to enable BS to estimate the uplink AoAs, i.e., the downlink AoDs, and delays of different UDs, whereby the MDU-ESPRIT algorithm is used based on the designed receive combining matrix at BS. Furthermore, a maximum likelihood approach is proposed to pair the channel parameters acquired at the two stages, and the path gains are then obtained using least squares estimator. According to spectrum estimation theory, our solution can acquire the super-resolution estimations of the AoAs/AoDs and delays of sparse multipath components with low training overhead. Simulation results verify the better CE performance and lower computational complexity of our solution over state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Min-Hash is a popular technique for efficiently estimating the Jaccard similarity of binary sets. Consistent Weighted Sampling (CWS) generalizes the Min-Hash scheme to sketch weighted sets and has drawn increasing interest from the community. Due to its constant-time complexity independent of the values of the weights, Improved CWS (ICWS) is considered as the state-of-the-art CWS algorithm. In this paper, we revisit ICWS and analyze its underlying mechanism to show that there actually exists dependence between the two components of the hash-code produced by ICWS, which violates the condition of independence. To remedy the problem, we propose an Improved ICWS (I $^2$ 2 CWS) algorithm which not only shares the same theoretical computational complexity as ICWS but also abides by the required conditions of the CWS scheme. The experimental results on a number of synthetic data sets and real-world text data sets demonstrate that our I $^2$ 2 CWS algorithm can estimate the Jaccard similarity more accurately, and also competes with or outperforms the compared methods, including ICWS, in classification and top- $K$ K retrieval, after relieving the underlying dependence. [ABSTRACT FROM AUTHOR]

Due to the ill-posed phenomenon of the classical physical model, single image dehazing based on the model has been a challenging vision task. In recent years, applying machine learning techniques to estimate a critical parameter transmission has proven to be an effective solution to this issue. Accordingly, the robustness and accuracy of learning-based transmission estimation model is extremely important, since it does impact on the final dehazing effects. The state-of-the-art dehazing algorithms by this means generally use haze-relevant features as the single input to their transmission estimation models. However, the used haze-relevant features sometimes are not sufficient and reliable in holding real intrinsic information related to haze due to their two shortcomings and ultimately bring about their less effectiveness for some dehazing cases. Based on related efforts on representation learning and deep convolutional neural networks, in this paper, we seek to achieve the robustness and accuracy of transmission estimation model for bolstering the effectiveness of single image dehazing. Specifically, we propose a hybrid model combining unsupervised and supervised learning in a considerably deep neural networks architecture, termed DeeptransMap, in order to achieve accurate transmission map from a single image. Experimental results demonstrate that our work performs favorably against several state-of-the-art dehazing methods with the same estimated goal and keeps efficient in terms of the computational complexity of transmission estimation. [ABSTRACT FROM AUTHOR]

The sensor geometry design problem of time difference of arrival (TDOA) localisation systems based on Cramer–Rao bound is studied. Sensor placement constraints are considered, which means the available placement area is limited. This makes sensor geometry design a sensor selection problem. In two‐dimensional (2D) TDOA localisation or 3D TDOA localisation on the earth surface, sensor selection can be implemented through solving a fractional integer programming problem. However, traditional fractional integer programming methods are either suboptimal or too time consuming. For this reason, a new method named path‐varying sphere decoding is proposed in two steps. In step one, the programming problem is relaxed into two sphere decoding (SD) subproblems. Solving these subproblems leads to the optimal solution, and the required computational complexity is much less than those of traditional optimal methods. In step two, the structure of the cost function is explored. This makes it possible to calculate the path‐varying upper‐bound of a quadratic function. Thus the quadratic function constraint used in one SD subproblem becomes tighter and the calculation speed is enhanced. Theory analyses and simulation results show that the proposed method is not only optimal but also much faster than traditional optimal methods when solving large‐scale programming problems. [ABSTRACT FROM AUTHOR]

This study concerns the parameter identification issues for a class of multivariable systems with moving average noise. The main contributions are to transform a multivariable system into several subsystems for reducing the computational complexity by means of the decomposition technique and to deal with the coloured noise for improving the estimation accuracy by using the maximum likelihood principle and the iterative estimation theory. As the maximum likelihood gradient-based iterative algorithm makes sufficient use of all the observed data at each iteration, the parameter estimation accuracy can be enhanced. The numerical simulation results demonstrate that the proposed algorithm has faster convergence rates and better tracking performance than the compared multivariable extended stochastic gradient algorithm. [ABSTRACT FROM AUTHOR]

Abstract Instantaneous frequency (IF) estimation of signals embedded in high noise is considered. A time-frequency (TF) approach based on the Wigner distribution (WD) is proposed, i.e. the WD is processed in order to enable accurate IF estimation of both monocomponent and multicomponent signals in high noise environment. To that end, a two-step procedure is introduced. In the first step, the auto terms are emphasized with respect to noise. In the second step, the influence of inner interferences, cross terms and noise is suppressed, yielding the final TF representation. The accuracy performance of the proposed method, validated on simulated and real data, is on par with the state-of-the-art methods. However, it is characterized by significantly lower calculation complexity. [ABSTRACT FROM AUTHOR]

Abstract Distance metric learning aims to learn a metric with the similarity of samples. However, the increasing scalability and complexity of dataset or complex application brings about inevitable label noise, which frustrates the distance metric learning. In this paper, we propose a resampling scheme robust to label noise, Re-KISSME, based on Keep It Simple and Straightforward Metric (KISSME) learning method. Specifically, we consider the data structure and the priors of labels as two resampling factors to correct the observed distribution. By introducing the true similarity as latent variable, these two factors are integrated into a maximum likelihood estimation model. As a result, Re-KISSME can reason the underlying similarity of each pair and reduce the influence of label noise to estimate the metric matrix. Our model is solved by iterative algorithm with low computational cost. With synthetic label noise, the experiments on UCI datasets and two application datasets of person re-identification confirm the effectiveness of our proposal. [ABSTRACT FROM AUTHOR]

Although advanced motion vector prediction (AMVP) modes based on motion estimation (ME) are selected significantly less due to the merge mode newly adopted in the high-efficiency video coding (HEVC), integer ME (IME) still occupies a large amount of computation in HEVC because the HEVC supports a highly flexible block partitioning structure. Introduction of the merge mode in HEVC substantially affects the optimal search algorithm of IME. Therefore, this gives a chance to reduce the computational complexity of ME with marginal drop of the compression efficiency. This paper proposes a new IME algorithm that significantly reduces its search ranges. Computational complexity of the proposed algorithm is reduced without serious degradation in coding efficiency by obtaining additional accurate motion vector predictors (MVPs) in a bottom-up order and searching narrow regions around multiple MVPs. These bottom-up MVPs are obtained from the prediction units (PUs) in the coding units (CUs) in the hierarchically lower level, which share the pixel area either completely or partially with the current PU. Although the encoder should perform the proposed IME in a bottom-up order from the smallest CUs to the larger CUs, several other processes, such as fractional ME and merges mode are performed in a top-down order to exploit several fast algorithms already adopted in the HEVC test model encoder. To keep compatibility with HEVC standard, the bottom-up MVPs are utilized for only IME. The bitstream is generated using standard AMVP. According to the simulation results, it is confirmed that the proposed algorithm improves the accuracy of MVPs, which leads to the reduction of IME computational complexity up to 81.95% on average with the Bjøntegaard delta bitrate of 0.39%. [ABSTRACT FROM AUTHOR]

In this paper, we propose a fast H.264-to-HEVC transcoder composed of a motion propagation algorithm and a fast mode decision framework. The motion propagation algorithm creates a motion vector candidate list at the coding tree unit (CTU) level and, thereafter, selects the best candidate at the prediction unit level. This method eliminates computational redundancy by pre-computing the prediction error of each candidate at the CTU level and reusing the information for various partition sizes. The fast mode decision framework is based on a post-order traversal of the CTU and includes several mode reduction techniques. In particular, the framework permits the early termination of the rate distortion cost computation, a highly complex task, when a mode is unpromising. Moreover, a novel method exploits the data created by the motion propagation algorithm to determine whether a coding unit must be split. This allows the pruning of unpromising sub-partitions. Compared with a cascaded pixel-domain transcoding approach, the experimental results show that the proposed solution using one reference frame is on average 8.5 times faster, for an average Bjøntegaard delta-rate (BD-Rate) of 2.63%. For a configuration with four reference frames, the average speed-up is 11.77 times and the average BD-Rate is 3.82%. [ABSTRACT FROM AUTHOR]

Fast and accurate fundamental phasor-estimation is a challenging task in digital relays. This letter proposes an improved discrete Fourier transform (DFT)-based phasor estimation algorithm. The accuracy of DFT-based methods is degraded owing to presence of decaying dc component (ddc) in fault current signals. To overcome the undesired effect of ddc, this letter introduces an auxiliary signal, which is generated by down-sampling of the fault current. The fundamental phasor is precisely estimated using sample-summation of the auxiliary signal besides DFT and sample-summation of the fault-current signal. Comprehensive simulations are performed for performance evaluation. The simulation results confirm that the proposed algorithm is fast and robust against ddc, harmonics, and noise. Moreover, the algorithm reduces the computational complexity, which makes it appropriate for digital relaying. [ABSTRACT FROM AUTHOR]

Highlights • Two new methods are proposed to estimate the number of clusters. • Both methods work only on the minimum inter-center distance. • The proposed methods have very low computation complexity O (k 2). • The method of the Last Leap excels at identifying well-separated clusters. • The second method the Last Major Leap identifies clusters of similar sizes. Abstract Center-based clustering methods like k -Means intend to identify closely packed clusters of data points by respectively finding the centers of each cluster. However, k -Means requires the user to guess the number of clusters, instead of estimating the same on the run. Hence, the incorporation of accurate automatic estimation of the natural number of clusters present in a data set is important to make a clustering method truly unsupervised. For k -Means, the minimum of the pairwise distance between cluster centers decreases as the user-defined number of clusters increases. In this paper, we observe that the last significant reduction occurs just as the user-defined number surpasses the natural number of clusters. Based on this insight, we propose two techniques: the Last Leap (LL) and the Last Major Leap (LML) to estimate the number of clusters for k -Means. Over a number of challenging situations, we show that LL accurately identifies the number of well-separated clusters, whereas LML identifies the number of equal-sized clusters. Any disparity between the values of LL and LML can thus inform a user about the underlying cluster structures present in the data set. The proposed techniques are independent of the size of the data set, making them especially suitable for large data sets. Experiments show that LL and LML perform competitively with the best cluster number estimation techniques while imposing drastically lower computational burden. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the fuzzy portfolio efficiency evaluation problem in different risk measures. Real frontier approach (RFA) is often used in portfolio performance assessment. However, the computation complexity and the real trading solution make it hard to achieve in practice. In this work, we first present three kinds of DEA (Data envelopment analysis) based fuzzy portfolio estimation models in different risk measures, i.e., possibilistic variance, possibilistic semi-variance, and possibilistic semi-absolute deviation, to evaluate the portfolio efficiency (PE). Furthermore, we carry out large amount of simulations with different sample sizes to compare our proposed models with RFA. All results demonstrate that with adequate sample size, the envelop frontier generated by our models can approximate the real effective portfolio frontier, and PE obtained by these two methods are highly related. [ABSTRACT FROM AUTHOR]

A new rank reduction (RARE)-based two-dimensional (2-D) direction of arrival (DOA) estimation algorithm is proposed considering a mixture of circular and strictly noncircular sources. To enhance array aperture, a geometry of three uniform linear arrays is considered and then treated as displaced arrays from a virtual array using a simple linear transformation. The received data and the conjugated counterpart are combined together, exploiting the noncircular property. Both sources can be estimated separately by designing and exploiting the distinctive nature of circular and noncircular steering vectors. However, a 2-D spectrum search would lead to a high computational complexity burden. To reduce this high computational complexity burden, a novel RARE-based method is proposed, which plays a vital role by decomposing 2-D observation space into two successive 1-D peak search functions. The proposed method has some distinctive advantages: it can enhance the array aperture utilization, it can provide better estimation accuracy when mixed sources are greater than the number of sensors, it can estimate a larger number of mixed sources than the number of sensors, and finally it can automatically pair 2-D DOAs without any complicated pairing formulation. Extensive simulation results are provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with a new design methodology of a reinforced interval type-2 fuzzy c-means (FCM) based fuzzy classifier (FC). The key point of this study is to reduce the computational complexity of type-2 fuzzy set-based models and to alleviate the deterioration of its generalization abilities through the synergistic effect of two algorithms: First, interval type-2 FCM (IT2FCM) is used in the hidden layer of the network and connections (weights) are adjusted by invoking the least squares error estimation method. Second, an L2-norm regularization is considered in the cost function to avoid the construction of the network suffering from overfitting. In more detail, the hidden layer of the proposed FC is realized by interval type-2 FCM clustering to deal with the factor of uncertainty involved in the problem. This type of clustering is realized by using two values of the fuzzification coefficient resulting in the interval type-2 membership functions. Once completing type reduction, the membership grades of IT2FCM are used as the outputs of the hidden layer. Instead of the backpropagation training, least squares estimator based learning is applied to adjust the functional connection being regarded as linear functions mapping the hidden layer to the output layer. In order to reduce potential overfitting, L2-norm regularization is taken into account. The effectiveness of the proposed classifier is analyzed with the aid of a number of machine learning datasets as well as face image datasets. Thorough comparative studies are also included. [ABSTRACT FROM AUTHOR]

• A computationally efficient method using inverse-free sparse Bayesian learning for direction-of-arrival (DOA) estimation is proposed. • An efficient gird refinement is provided to iteratively estimate the off-grid errors to improve the accuracy of the estimation of the off-grid DOAs. • Simulations and experimental results verify the improved computational efficiency of the proposed method compared with other state-of-art methods. The direction-of-arrival (DOA) estimation problem can be formulated in a Bayesian framework and solved well through sparse Bayesian learning (SBL). However, SBL-based methods often suffer from high computational complexity due to the matrix inversion of a large matrix in the Bayesian inference procedure. To address this issue, we propose an efficient DOA estimation method based on inverse-free sparse Bayesian learning (IFSBL). A real-valued vectorized array covariance model and a hierarchical Bayesian model are established. Then, a relaxed lower bound on the likelihood, namely relaxed evidence lower bound (relaxed-ELBO), is obtained as the optimization objective of the Bayesian inference. By utilizing the variational expectation–maximization method for the Bayesian inference, the estimation of the statistics of interest can be achieved, which is free of matrix inversion as the matrix to be inverted is diagonal. Moreover, when we take the off-grid DOAs into account, an efficient grid refinement is developed to iteratively modify the DOA estimates derived from the estimated spatial spectrum. The off-grid error obtained by the root of a simple polynomial is used to compensate for the corresponding DOA estimate. Simulations show good accuracy and improved computational efficiency of the proposed method compared with other state-of-art methods. Further experimental results verify the advantage in the runtime for the proposed method. [ABSTRACT FROM AUTHOR]

Recovering missing values (MVs) from incomplete data is an important problem for many real-world applications. Previous research efforts toward solving MVs problem primarily exploit the global and/or local structure of data. In this work, we propose a novel MVs imputation method by combing sample self-representation strategy and underlying local linear structure of data in a uniformed framework. Specifically, the proposed method consists of the following steps. First, an existing method is applied to obtain the first-round estimation of MVs. Then, a graph, characterizing local proximity structure of data, is constructed based on imputed data. Next, a novel model coined as graph regularized local self-representation (GRLSR) is proposed by integrating two crucial elements: local self-representation and graph regularization. The former assumes each sample can be well represented (reconstructed) by linearly combining the neighboring samples while the latter further requires the neighboring samples should not deviate too much from each other after reconstruction. By doing so, MVs can be more accurately restored due to the joint imputation as well as local linear reconstruction. We also develop an effective alternating optimization algorithm to solve GRLSR model, thereby achieving final imputation. The convergence and computational complexity analysis of our method are also presented. To evaluate our method, extensive experiments are conducted on both traffic flow dataset and UCI benchmark datasets. The results demonstrate the effectiveness of our proposed method compared with a set of widely-used competing methods. [ABSTRACT FROM AUTHOR]

We consider joint estimation of multiple graphical models arising from heterogeneous and high-dimensional observations. Unlike most previous approaches which assume that the cluster structure is given in advance, an appealing feature of our method is to learn cluster structure while estimating heterogeneous graphical models. This is achieved via a high dimensional version of Expectation Conditional Maximization (ECM) algorithm (Meng and Rubin, 1993). A joint graphical lasso penalty is imposed on the conditional maximization step to extract both homogeneity and heterogeneity components across all clusters. Our algorithm is computationally efficient due to fast sparse learning routines and can be implemented without unsupervised learning knowledge. The superior performance of our method is demonstrated by extensive experiments and its application to a Glioblastoma cancer dataset reveals some new insights in understanding the Glioblastoma cancer. In theory, a non-asymptotic error bound is established for the output directly from our high dimensional ECM algorithm, and it consists of two quantities: statistical error (statistical accuracy) and optimization error (computational complexity). Such a result gives a theoretical guideline in terminating our ECM iterations. [ABSTRACT FROM AUTHOR]

This paper applies the adaptive gradient descent method to the second-order generalized integrator (SOGI) filter in order to find an online estimation algorithm for the grid frequency, which leads to the proposal of three possible estimators. One of them is identical to the frequency-locked loop algorithm reported in the literature, which proves that it should be understood as a gradient descent estimation algorithm and not as a “frequency locked loop.” The proposed gradient descent estimators are simple and suitable to be implemented into a digital processor with small computational burden. However, due to the SOGI characteristics, the estimators show to be especially sensitive to subharmonic and dc-offset voltage distortion. These problems are removed adopting a cascaded double SOGI approach, which strongly increases the rejection capability to harmonics and enhances the response to voltage sags. Simulation and experimental results are provided to validate the proposed contribution. [ABSTRACT FROM PUBLISHER]

We investigate the issue of direction of arrival (DOA) estimation of noncircular signals for coprime linear array (CLA). The noncircular property enhances the degree of freedom and improves angle estimation performance, but it leads to a more complex angle ambiguity problem. To eliminate ambiguity, we theoretically prove that the actual DOAs of noncircular signals can be uniquely estimated by finding the coincide results from the two decomposed subarrays based on the coprimeness. We propose a locally reduced-dimensional (RD) Capon algorithm for DOA estimation of noncircular signals for CLA. The RD processing is used in the proposed algorithm to avoid two dimensional (2D) spectral peak search, and coprimeness is employed to avoid the global spectral peak search. The proposed algorithm requires one-dimensional locally spectral peak search, and it has very low computational complexity. Furthermore, the proposed algorithm needs no prior knowledge of the number of sources. We also derive the Crámer-Rao bound of DOA estimation of noncircular signals in CLA. Numerical simulation results demonstrate the effectiveness and superiority of the algorithm. [ABSTRACT FROM AUTHOR]

Relative Delay estimation is an important issue for asynchronous physical layer network coding (PLNC). In this paper, an fast Fourier transform (FFT) based channel and delay estimation method for asynchronous PLNC is proposed. The extended properties of discrete Fourier transform are exploited to design the pilots, which makes the pilots from different nodes orthogonal in frequency domain. Then channels between relay and different terminal nodes are estimated separately in frequency domain. Finally, after inverse FFT, the relative delay is extracted from the estimated time domain channel information. Compared with the existing approaches, the proposed method can achieve almost the same performance, but with significant improvement in computational complexity. [ABSTRACT FROM AUTHOR]

To solve the homography estimation problem containing outliers and noise, a fast, robust, and accurate method is proposed. In this method, the outliers are rejected based on the differing characteristics of algebraic errors between outliers and inliers, and the homography is estimated by minimising the residual vector. The advantage of this method is in integrating the outlier rejection into the estimation pipeline. The computational complexity of the proposed method is not increased, and the random sample consensus algorithm is not needed to extract the inliers, as was previously necessary. Since the outlier rejection process is based on an algebraic criterion without computing the re‐projection error at each step, the speed of the proposed method is improved. Several simulations based on synthetic and real images illustrate the performance of the proposed method in terms of subjective visual quality, objective quality measurement, and computational time. The experimental results demonstrate that the proposed method achieves accurate, efficient and robust homography estimation under different image transformation degrees and different outlier ratios. [ABSTRACT FROM AUTHOR]

We investigate the interval generalized Sylvester matrix equation A X B + C X D = F . We propose a necessary condition for its solutions, and also a sufficient condition for boundedness of the whole solution set. The main effort is performed to develop techniques for computing outer estimations of the so-called united solution set of this interval system. First, we propose a modified variant of the Krawczyk operator, reducing significantly computational complexity, compared to the Kronecker product form. We then propose an iterative technique for enclosing the solution set. These approaches are based on spectral decompositions of the midpoints of A , B , C and D and in both of them we suppose that the midpoints of A and C are simultaneously diagonalizable as well as for the midpoints of the matrices B and D . Numerical experiments are given to illustrate the performance of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper describes two approaches to optimization of the key design parameters, the support region threshold and the number of magnitude representation levels, of product polar companded quantizer (PPCQ) for Gaussian source of unit variance. The first approach is based on the exact performance analysis of PPCQ and on distortion optimization with respect to the key design parameters. Due to the optimization problem complexity we encountered with the first approach, some suitable approximations are introduced with the second one. As a result, much simpler asymptotic closed-form formula for distortion of PPCQ is derived as a function of the support region threshold. Although with this approach the closed-form formula for the support region threshold cannot be derived, the results of this approach indicate the useful support region threshold form. By combining the results of both approaches we propose, the worthy closed-form formulas for the support region threshold and signal to quantization noise ratio of a nearly optimal PPCQ are provided. Moreover, from the results of both analyses the lower and upper bound expressions for the number of magnitude representation levels are provided. The analysis presented in the paper is useful for designing PPCQ and is of great theoretical and practical importance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a vectorized noncircular MUSIC (VNCM) algorithm based on the concept of the coarray, which can construct the difference and sum (diff–sum) coarray, for direction finding of the noncircular (NC) quasi-stationary sources. Utilizing both the NC property and the concept of the Khatri–Rao product, the proposed method can be applied to not only the ULA but also sparse arrays. In addition, we utilize the quasi-stationary characteristic instead of the spatial smoothing method to solve the coherent issue generated by the Khatri–Rao product operation so that the available degree of freedom (DOF) of the constructed virtual array will not be reduced by half. Compared with the traditional NC virtual array obtained in the NC MUSIC method, the diff–sum coarray achieves a higher number of DOFs as it comprises both the difference set and the sum set. Due to the complementarity between the difference set and the sum set for the coprime array, we choose the coprime array with multiperiod subarrays (CAMpS) as the array model and summarize the properties of the corresponding diff–sum coarray. Furthermore, we develop a diff–sum coprime array with multiperiod subarrays (DsCAMpS) whose diff–sum coarray has a higher DOF. Simulation results validate the effectiveness of the proposed method and the high DOF of the diff–sum coarray. [ABSTRACT FROM AUTHOR]

Recently, many nonlinear spectral unmixing algorithms that use various bilinear mixture models (BMMs) have been proposed. However, the high computational complexity and intrinsic collinearity between true endmembers and virtual endmembers considerably decrease these algorithms' unmixing performances. In this paper, we come up with a novel abundance estimation algorithm based on the BMMs. Motivated by BMMs' geometric characteristics that are related to collinearity, we conduct a unique nonlinear vertex p to replace all the virtual endmembers. Unlike the virtual endmembers, this vertex p actually works as an additional true endmember that gives affine representations of pixels with other true endmembers. When the pixels' normalized barycentric coordinates with respect to true endmembers are obtained, they will be directly projected to be their approximate linear mixture components, which removes the collinearity effectively and enables further linear spectral unmixing. After that, based on the analysis of projection bias, two strategies using the projected gradient algorithm and a traditional linear spectral unmixing algorithm, respectively, are provided to correct the bias and estimate more accurate abundances. The experimental results on simulated and real hyperspectral data show that the proposed algorithm performs better compared with both traditional and state-of-the-art spectral unmixing algorithms. Both the unmixing accuracy and speed have been improved. [ABSTRACT FROM AUTHOR]

Electrical harmonic is an important issue on power quality of electrical systems. Therefore, estimation of power harmonics are essential to design filters to be utilized in electrical power systems for reducing harmonics and their effects on the electrical power systems. In this study, a power system harmonic estimation solution based on modified artificial bee colony (MABC) algorithm have been proposed. The proposed method in this study is different from other hybrid techniques previously reported ones since estimation processes are mainly focused on decreasing computational complexity and time. In order to provide more accurate and comprehensive estimation results for the proposed method, two different experiments are investigated. Obtained results by the performed experiments showed that the proposed harmonic estimator provides several advantages such as offering very low computational time, more accurate estimation of amplitude and phase values for all conditions and low complexity. [ABSTRACT FROM AUTHOR]

This work presents an efficient computational framework for prognostics by combining the particle filter-based prognostics principles with the technique of Subset Simulation, first developed in S.K. Au and J.L. Beck [ Probabilistic Engrg. Mech., 16 (2001), pp. 263-277 ], which has been named PFP-SubSim. The idea behind PFP-SubSim algorithm is to split the multi-step-ahead predicted trajectories into multiple branches of selected samples at various stages of the process, which correspond to increasingly closer approximations of the critical threshold. Following theoretical development, discussion and an illustrative example to demonstrate its efficacy, we report on experience using the algorithm for making predictions for the end-of-life and remaining useful life in the challenging application of fatigue damage propagation of carbon-fibre composite coupons using structural health monitoring data. Results show that PFP-SubSim algorithm outperforms the traditional particle filter-based prognostics approach in terms of computational efficiency, while achieving the same, or better, measure of accuracy in the prognostics estimates. It is also shown that PFP-SubSim algorithm gets its highest efficiency when dealing with rare-event simulation. [ABSTRACT FROM AUTHOR]

In recent years, visual object tracking has been widely used in military guidance, human-computer interaction, road traffic, scene monitoring and many other fields. The tracking algorithms based on correlation filters have shown good performance in terms of accuracy and tracking speed. However, their performance is not satisfactory in scenes with scale variation, deformation, and occlusion. In this paper, we propose a scene-aware adaptive updating mechanism for visual tracking via a kernel correlation filter (KCF). First, a low complexity scale estimation method is presented, in which the corresponding weight in five scales is employed to determine the final target scale. Then, the adaptive updating mechanism is presented based on the scene-classification. We classify the video scenes as four categories by video content analysis. According to the target scene, we exploit the adaptive updating mechanism to update the kernel correlation filter to improve the robustness of the tracker, especially in scenes with scale variation, deformation, and occlusion. We evaluate our tracker on the CVPR2013 benchmark. The experimental results obtained with the proposed algorithm are improved by 33.3%, 15%, 6%, 21.9% and 19.8% compared to those of the KCF tracker on the scene with scale variation, partial or long-time large-area occlusion, deformation, fast motion and out-of-view. [ABSTRACT FROM AUTHOR]

We propose a local measure of the relationship between parameter estimates and the moments of the data they depend on. Our measure can be computed at negligible cost even for complex structural models. We argue that reporting this measure can increase the transparency of structural estimates, making it easier for readers to predict the way violations of identifying assumptions would affect the results. When the key assumptions are orthogonality between error terms and excluded instruments, we show that our measure provides a natural extension of the omitted variables bias formula for nonlinear models. We illustrate with applications to published articles in several fields of economics. JEL Codes: C10, C52. [ABSTRACT FROM AUTHOR]

In this study, a novel robust measure of non‐linearity‐based cubature Kalman filter (RMoNCKF) is proposed to obtain good performance with lower computational burden. The proposed filter inherits the virtues of high accuracy of the high‐degree filter and computation efficiency of the low‐degree one. When the measure of non‐linearity (MoN) is evaluated and compared with the threshold in the dynamic system, the cubature rules nested in the RMoNCKF can be switched autonomously to decrease the computation complexity in the low non‐linear condition. Furthermore, the robust estimation technology can help to improve the value of MoN for the non‐Gaussian distributed case. Simulation results of target tracking and integrated navigation system demonstrate that the RMoNCKF can have a close performance to the fifth‐degree CKF with less computation time. In the circumstances of the time‐varying noise and contaminated Gaussian distributed noise, the RMoNCKF outperforms the UKF, the third‐degree CKF and fifth‐degree CKF. [ABSTRACT FROM AUTHOR]

Most current community detection methods for complex networks focus on partition. Community number estimation does not have the due attention it deserves, and the community number is only a by-product of community partition. In fact, knowing the community number in advance can speed up the partition process, especially for large scale and dynamic complex networks. This paper proposes a community number estimation method based on topology potential. In the topology potential field, the potential distribution of nodes shows a natural peak-valley structure, and each community corresponds to a local high potential area. The number of local maximum potential nodes is the estimated community number. Experiments on real world networks and artificial networks show that the proposed method gives very good performance in community number estimation. The more noticeable the peak-valley structure of the corresponding topology potential field is, the closer the estimated community number will be to the ground truth. Furthermore, compared with state-of-the-arts methods, our proposed method is not sensitive to the tuned parameter, and shows good efficiency. [ABSTRACT FROM AUTHOR]

Estimating the number of sources is a key task in many array signal processing applications. Conventional algorithms such as Akaike's information criterion (AIC) and minimum description length (MDL) suffer from underestimation and overestimation errors. In this study, the authors propose four algorithms to estimate the number of sources in white Gaussian noise. The authors' proposed algorithms are categorised into two main categories; namely, sample correlation matrix (CorrM) based and correlation coefficient matrix (CoefM) based. Their proposed algorithms are applied on the CorrM and CoefM eigenvalues. They propose to use two decision statistics, which are the moving increment and the moving standard deviation of the estimated eigenvalues as metrics to estimate the number of sources. For their two CorrM based algorithms, the decision statistics are compared to thresholds to decide on the number of sources. They show that the conventional process to estimate the threshold is mathematically tedious with high computational complexity. Alternatively, they define two threshold formulas through linear regression fitting. For their two CoefM based algorithms, they re‐define the problem as a simple maximum value search problem. Results show that the proposed algorithms perform on par or better than AIC and MDL as well as recently modified algorithms at medium and high signal‐to‐noise ratio (SNR) levels and better at low SNR levels and low number of samples, while using a lower complexity criterion function. [ABSTRACT FROM AUTHOR]

In order to improve the angle measurement precision of LFM signals with a low computational complexity, a direction of arrival (DOA) estimation algorithm STFT-MI-ESPRIT is proposed in this paper. The algorithm is based on short time Fourier transform (STFT) and multiple invariance estimation of signal parameters via rotational invariance techniques (MI-ESPRIT). Firstly, the STFT of the array element’s output is calculated and the signals are transformed to the time-frequency domain. Then the spatial time-frequency distribution matrix can be obtained through selecting multiple single-source time-frequency points in the time-frequency plane and the signal subspace can also be obtained using Eigen decomposition. Finally, the multiple rotational invariant equation of the array based on STFT is obtained and the closed-form solution is obtained using the multi-least-squares (MLS) criterion. The simulation results show that the proposed algorithm can improve the estimation precision greatly compared with the traditional ESPRIT-like algorithms and its computational complexity remains the same in general. This paper also proposes that the STFT-MI-ESPRIT algorithm can use partial rotational invariances of the array instead of all the rotational invariances, which can reduce the computational complexity on the basis of ensuring the estimation precision basically. The simulation results verify the effectiveness of the conclusion. [ABSTRACT FROM AUTHOR]

Color constancy is a phenomenon of perceiving the same color regardless of a scene illuminant. In the computational world, this is achieved by estimating the illuminant chromaticity and then correcting the captured image using a color correction technique. Already, various statistic- and learning-based unitary algorithms were proposed to estimate illuminant chromaticity. The computational time of the statistic-based methods varies with the statistical assumptions used and most of the learning-based methods have a very high computational time. In this article, we analyze the statistical results and computational time of different unitary methods on a benchmark data set to identify which method can estimate the illuminant chromaticity within permissible angular error at less computational time. This study indicates that there is no unique algorithm which can be considered to perform well on images with different settings and scenes. These findings motivated us to formulate a selection-based illuminant estimation method which chooses the fastest and optimum illuminant estimation method for the given image. In order to achieve this, the proposed method uses a classification-based hierarchical approach to organize the selection of a suitable algorithm based on computational time and performance. Therefore, the fastest unitary method at the very top, followed by remaining unitary methods are arranged in ascending order of computational time. This allows the illuminant estimation of the majority of images to be solved by using the fastest unitary method and remaining using complex methods which can perform well on a wide range of images. Experimental results on real world data set clearly demonstrate the effectiveness of hierarchical classification-based strategy in illuminant estimation. [ABSTRACT FROM AUTHOR]

In this paper, a novel two-dimensional (2D) direction-of-arrival (DOA) estimation algorithm for the mixed circular and strictly noncircular sources is proposed. A general array model with a mixture of signals is firstly built based on uniform rectangular arrays (URAs), and then, the approach, which uses the rank-reduction-based ROOT-MUSIC, can solve 2D DOA estimation problem. Besides, the theoretical error of the proposed algorithm, a criterion of the performance for evaluation, is analyzed by the first-order Taylor expression using second-order statistics. As verified by the simulation results, a better DOA estimation performance and a lower computational complexity are achieved by the proposed algorithm than the existing methods resorting to the noncircularity of the incoming signals. [ABSTRACT FROM AUTHOR]

A novel adaptive step-size matching pursuit algorithm (AStMP) is proposed. AStMP reduces the computational cost and increases the accuracy of reconstructing practical signals (i) whose sparsity K is unknown and/or (ii) may be corrupted by noise. AStMP accurately estimates K by combining the sparsity estimate of sparsity adaptive subspace pursuit (SASP) with the adaptively changing stepsize at each stage of sparsity adaptive matching pursuit (SAMP). Thus, AStMP can quickly achieve accurate estimation of the sparsity level and the true support set of the target signals. Meanwhile, a preselection is employed to reduce the computational complexity of each stage. When K is greater than half the number of measurements M, the probability of exact recovery is improved by analyzing the support set. Since the stepsize changes adaptively, AStMP can be applied in both the noiseless and noisy cases when the signal is not strictly sparse, allowing for exact or approximate signal recovery. Experimental results demonstrate that the AStMP is effective in fast and exact reconstruction and has good performance. [ABSTRACT FROM AUTHOR]

Chinese face reading has demonstrated the often satisfying capabilities to tell the characteristics (mostly exaggerated as fortune) of a person by reading his/her face, i.e. understanding the fine-grained facial attributes (e.g., length of nose, single/double-fold eyelid, density of eyebrows, etc.). Thus, a smart face reading system should estimate the fine-grained facial attributes well. Therefore, In this paper, we first study the fine-grained facial attribute estimation problem and propose a novel deep convolutional network equipped with a new facial region pooling layer (called FRP-net), to accurately estimate the fine-grained facial attributes. To capture the characteristics of fine-grained facial attributes, the embedded FRP layer implements the pooling operation on the searched facial region windows (locates the region of each facial attribute) instead of the commonly-used sliding windows. Further, we push the proposed fine-grained facial attribute estimation method into the face reading problem and present a computational face reader system to automatically infer the characteristics of a person based on his/her face. For example, it can estimate the attractive and easy-going characteristics of a Chinese person from his/her big eyes according to the Chinese anthroposcopy literature. The experimental results on facial attribute estimation demonstrate the superiority of the proposed FRP-net compared to the baselines, and the qualitative and quantitative evaluations on face reading validate the excellence of the presented face reader system. [ABSTRACT FROM AUTHOR]

This paper presents a Fuzzy Estimation Approach based on Decision Making ( FEADM ) to estimate point soil moisture, in order to develop a regional soil moisture estimate. This keeps the advantages of an automatic irrigation system but avoiding their operational complexity. In the FEADM 's first stage, the Fuzzy Analytical Process weigh five fuzzified environmental inputs used as assessment criteria. These criteria are used by a selective assessment stage, where only the relevant criteria for each alternative are assessed. As result, the best assessments of each alternative and the best assessed alternative are obtained. In the second stage, an output membership function is assigned to every alternative for its forward aggregation and defuzzification. This process reaches a soil moisture estimate. Unlike other models, FEADM allows estimating a quantitative value from a decision making process. Furthermore, its computational cost is lower than conventional fuzzy estimation models due to the selective assessment. [ABSTRACT FROM AUTHOR]