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Stereo matching is one of the most popular techniques to estimate dense depth maps by finding the disparity between matching pixels on two, synchronized and rectified images. Alongside with the development of more accurate algorithms, the research community focused on finding good strategies to estimate the reliability, i.e., the confidence, of estimated disparity maps. This information proves to be a powerful cue to naively find wrong matches as well as to improve the overall effectiveness of a variety of stereo algorithms according to different strategies. In this paper, we review more than ten years of developments in the field of confidence estimation for stereo matching. We extensively discuss and evaluate existing confidence measures and their variants, from hand-crafted ones to the most recent, state-of-the-art learning based methods. We study the different behaviors of each measure when applied to a pool of different stereo algorithms and, for the first time in literature, when paired with a state-of-the-art deep stereo network. Our experiments, carried out on five different standard datasets, provide a comprehensive overview of the field, highlighting in particular both strengths and limitations of learning-based strategies. [ABSTRACT FROM AUTHOR]

This paper considers estimation of a random variable in Poisson noise with signal scaling coefficient and dark current as explicit parameters of the noise model. Specifically, the paper focuses on properties of the conditional mean estimator as a function of the scaling coefficient, the dark current parameter, the distribution of the input random variable and channel realizations. With respect to the scaling coefficient and the dark current, several identities in terms of derivatives are established. For example, it is shown that the gradient of the conditional mean estimator with respect to the scaling coefficient and dark current parameter is proportional to the conditional variance. Moreover, a score function is proposed and a Tweedie-like formula for the conditional expectation is recovered. With respect to the distribution, several regularity conditions are shown. For instance, it is shown that the conditional mean estimator uniquely determines the input distribution. Moreover, it is shown that if the conditional expectation is close to a linear function in terms of mean squared error, then the input distribution is approximately gamma in the Lévy distance. Furthermore, sufficient and necessary conditions for linearity are found. Interestingly, it is shown that the conditional mean estimator cannot be linear when the dark current parameter of the Poisson noise is non-zero. [ABSTRACT FROM AUTHOR]

This paper presents a novel method to estimate the number of shorted turns in a permanent magnet synchronous machine (PMSM) following the detection of interturn short-circuit (ITSC) fault and its location. In this proposed method, PMSM is excited through a low sinusoidal voltage at standstill condition to obtain the winding resistance and synchronous inductance by current response. It is shown that the ITSC fault introduces variation in the current response, which can be used to calculate the number of shorted turns under zero fault resistance assumption. Using this practical procedure, the fault severity can be estimated directly in a straight-forward manner. In other words, the severity estimation for a given machine can be done without complex machine modeling or experiments on ITSC prototype with multiple taps. The findings in this paper are essential for a comprehensive solution including fault mitigation algorithms and postfault operations. In order to verify the findings, a three-phase equivalent circuit model supported by finite element analysis results is used to take saturation and space harmonics into account. In addition, experimental results are presented to demonstrate the validity and practicability of the severity estimation. [ABSTRACT FROM AUTHOR]

The impulsive noise and the time-varying channel are two major detrimental factors which greatly constrain the performance of underwater acoustic communications (UACs). Utilizing the joint sparsity of the impulsive noise and the channel impulse response, the paper proposes the generalized approximate message passing (GAMP)-based sparse Bayesian learning (SBL) frameworks for joint impulsive noise mitigation and channel estimation and tracking for orthogonal frequency division multiplexing (OFDM) UACs. Firstly, the SBL framework for the joint estimation is employed. To reduce the computational complexity, the GAMP is introduced into the expectation-maximization (EM) algorithm, and a low-complexity GAMP based SBL framework is formulated without performance degradation. To further estimate and track the impulsive noise and channel state information in the slow time-varying scenarios, we propose a novel GAMP-based temporal SBL framework. The factor graph and GAMP are used to achieve the approximated estimation for the posterior statistics of both the channel state information and the impulsive noise. The algorithm formulates the message passing scheduling for the EM algorithm to solve the joint multiple sparse signal recovery problem. Simulations and sea-trial results demonstrate that the proposed algorithms significantly improve the performance in terms of the mean square error of channel estimation, impulsive noise estimation, bit error rate and computational complexity compared with their corresponding SBL-based counterparts. [ABSTRACT FROM AUTHOR]

This paper studies the capacity of an $n$ -dimensional vector Gaussian noise channel subject to the constraint that an input must lie in the ball of radius $R$ centered at the origin. It is known that in this setting, the optimizing input distribution is supported on a finite number of concentric spheres. However, the number, the positions, and the probabilities of the spheres are generally unknown. This paper characterizes necessary and sufficient conditions on the constraint $R$ , such that the input distribution supported on a single sphere is optimal. The maximum $\bar {R}_{n}$ , such that using only a single sphere is optimal, is shown to be a solution of an integral equation. Moreover, it is shown that $\bar {R}_{n}$ scales as $\sqrt {n}$ and the exact limit of $\frac {\bar {R}_{n}}{\sqrt {n}}$ is found. [ABSTRACT FROM AUTHOR]

A generalized framework of spectrum sensing disruption for a power-limited adversary is proposed in this paper. In the literature, a conventional sensing attack typically assumes that the adversary has perfect knowledge of the spectral usage status. The framework in this paper considers a more general case where there are uncertainties in the estimates at the adversary. These uncertainties are modeled utilizing the probability of detection and the probability of false alarm. Then, the sum of the conditional probabilities of false detection at the secondary within the spectral range of interest, conditioned on the adversary’s estimated spectrum usage status, is maximized. It is shown that the optimal sensing attack, given perfect estimation is a special case of the proposed framework. When the adversary has perfect spectrum usage information, this framework reduces to a previously demonstrated optimal sensing disruption. When the adversary has imperfect information on the spectral status, the proposed framework is significantly more robust than conventional sensing attacks. Further, when the adversary’s power budget increases, it asymptotically approaches the sensing disruption performance upper bound. [ABSTRACT FROM AUTHOR]

The problem of the leader-following formation control of nonholonomic mobile robots is addressed in this paper. A distributed formation control strategy using explicitly the coordination errors among robots is proposed without assuming that each follower robot knows the full state of the leader. First, a distributed estimation law is proposed for each follower robot to estimate the states, including the position, orientation, and linear velocity of the leader. The distributed formation control law is then designed based on the estimated states of the leader, and the neighborhood formation tracking error. Under some mild assumptions on the interaction graph among the leader and the follower robots, and the velocity of the leader, asymptotic convergence of formation tracking errors to zero can be achieved. Finally, some numerical simulations and experiments on a group of nonholonomic mobile robots are presented to demonstrate the effectiveness of the proposed strategy. Note to Practitioners—The motivation of this paper is to investigate a practical control strategy for the leader-following formation of multiple autonomous mobile robots subjected to nonholonomic constraints. In most of the existing leader-following formation control schemes for nonholonomic mobile robots, having access to the full state of the leader is a requirement. However, due to limitations in communication bandwidth and range, it is reasonable to assume that the information of the leader is available only to a subset of followers. Hence, this paper suggests a new distributed leader-following formation control strategy based on the distributed estimation of the leader’s states. Moreover, the coordination error between a pair of interacting robots is explicitly used in the control design to weaken the dependence on the estimated state of the leader, and enhance the decentralized nature of the proposed control scheme. The stability and convergence of the system are analyzed mathematically and the experiment using unicycles provides promising results. In ongoing research, we are addressing the issues of collision avoidance and communication delays to provide more realistic setup for the industrial applications of multivehicle systems. [ABSTRACT FROM AUTHOR]

Estimation of induction machine parameters, which are commonly used in efficiency evaluation and control methods, can be effectively achieved by utilizing genetic algorithms (GAs). One of the difficulties of using GAs for efficiency estimation is the determination of variable (parameter) constraints (ranges). This paper focuses on the range determination of the parameters (variables) for GA applications. A wide range of variables can cause unstable outcomes. Hence, it is essential to determine an acceptable range for each variable prior to a GA run to produce stable and repeatable results. In this paper, relationships based on the nameplate information and a large database of tested induction motors provided by Hydro-Québec are proposed to determine reasonable induction motor parameter ranges. The proposed method is applied to three different cases. The results of the three cases are compared to each other and also against the corresponding experimental results, which validate the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Terrestrial laser scanning is a promising tool for estimating leaf angle (including leaf inclination and azimuthal angles) distribution (LAD). However, previous studies focus on the retrieval of leaf inclination angle distribution, very few studies have considered the distribution of leaf azimuthal angle due to the restriction of measurement techniques. In this paper, we developed a new method to obtain more accurate leaf inclination and azimuthal angle estimations based on leaf point cloud segmentation and filtration and then fit LAD functions using two-parameter Beta-distribution model. In addition, we constructed a new projection coefficient model with two parameters $G(\theta $ , $\varphi$) using Nilson’s algorithm based on the accurate retrieval of LAD. To assess the influence of leaf numbers on leaf inclination and azimuthal angle estimations, we modeled 160 individual leaves and 10 trees with different leaf numbers. In addition, to validate the final results, we also sampled three magnolia trees with different leaf numbers and manually measured leaf inclination and azimuthal angles of all their leaves using an angle measurement device. All results showed that the method proposed in this paper can provide accurate leaf inclination and azimuthal angles (leaf inclination angle: $R^{2} = 0.98$ , RMSE = 24° and leaf azimuthal angle: $R^{2} = 0.99$ , RMSE = 3.44°). The simulated LAD and $G(\theta $ , $\varphi$) estimations based on these leaf inclination and azimuthal angles were strongly correlated with those obtained from ground truth measurements ($P >0.05$). [ABSTRACT FROM AUTHOR]

In this paper, the problem of estimating the unknown degree of sparsity from compressive measurements without the need to carry out a sparse recovery step is investigated. While the sparsity order can be directly inferred from the effective rank of the observation matrix in the multiple snapshot case, this appears to be impossible in the more challenging single snapshot case. It is shown that specially designed measurement matrices allow to rearrange the measurement vector into a matrix such that its effective rank coincides with the effective sparsity order. In fact, it is proven that matrices that are composed of a Khatri–Rao product of smaller matrices generate measurements that allow to infer the sparsity order. Moreover, if some samples are used more than once, one of the matrices needs to be Vandermonde. These structural constraints reduce the degrees of freedom in choosing the measurement matrix, which may incur in a degradation in the achievable coherence. Thus, this paper also addresses suitable choices of the measurement matrices. In particular, Khatri–Rao and Vandermonde matrices are analyzed in terms of their coherence and a new design for Vandermonde matrices that achieves a low coherence is proposed. [ABSTRACT FROM AUTHOR]

Hybrid cloud bursting (i.e., leasing temporary off-premise cloud resources to boost the overall capacity during peak utilization) can be a cost-effective way to deal with the increasing complexity of big data analytics, especially for iterative applications. However, the low throughput, high latency network link between the on-premise and off-premise resources (“weak link”) makes maintaining scalability difficult. While several data locality techniques have been designed for big data bursting on hybrid clouds, their effectiveness is difficult to estimate in advance. Yet such estimations are critical, because they help users decide whether the extra pay-as-you-go cost incurred by using the off-premise resources justifies the runtime speed-up. To this end, the current paper presents a performance model and methodology to estimate the runtime of iterative MapReduce applications in a hybrid cloud-bursting scenario. The paper focuses on the overhead incurred by the weak link at fine granularity, for both the map and the reduce phases. This approach enables high estimation accuracy, as demonstrated by extensive experiments at scale using a mix of real-world iterative MapReduce applications from standard big data benchmarking suites that cover a broad spectrum of data patterns. Not only are the produced estimations accurate in absolute terms compared with experimental results, but they are also up to an order of magnitude more accurate than applying state-of-art estimation approaches originally designed for single-site MapReduce deployments. [ABSTRACT FROM AUTHOR]

The concept of information fusion has gained a widespread interest in many fields due to its complementary properties. It makes systems more robust against uncertainty. This paper presents a new approach for the well-logging estimation problem by using a fusion methodology. The natural gammaray tool (NGT) is considered as an important instrument in the well logging. The NGT detects changes in natural radioactivity emerging from the variations in concentrations of micronutrients as uranium (U), thorium (Th), and potassium (K). The main goal of this paper is to have precise estimation of the concentrations of U, Th, and K. Four types of Kalman filters are designed to estimate the elements using the NGT sensor. Then, a fusion of the Kalman filters is utilized into an integrated framework by an ordered weighted averaging (OWA) operator to enhance the quality of the estimations. A real covariance of the output error based on the innovation matrix is utilized to design weighting factors for the OWA operator. The simulation studies indicate not only a reliable performance of the proposed method compared with the individual Kalman filters but also a better response in contrast with previous fusion methodologies. [ABSTRACT FROM AUTHOR]

Coarse attitude initialization procedure is used to determine the initial attitude of the inertial navigation system. To improve the accuracy of the attitude to a desired value, fine alignment (FA) process is employed. In stationary conditions, zero velocity updates are usually used to achieve such goal. In this paper, an analytic evaluation of the steady-state properties of the FA process is presented. The contribution of this paper is twofold: 1) closed-form analytic solutions to the error-state covariance of the FA process are derived and 2) semianalytic solutions to the convergence time of the error-state covariance are shown. With such closed-form solutions at hand, insight can be gained into the parameters involved in the FA process in preliminary design process. Simulation and field experiments are given to support the analytical expressions. [ABSTRACT FROM AUTHOR]

Objective: Vascular permeability (VP) is a mechanical parameter which plays an important role in cancer initiation, metastasis, and progression. To date, there are only a few non-invasive methods that can be used to image VP in solid tumors. Most of these methods require the use of contrast agents and are expensive, limiting widespread use. Methods: In this paper, we propose a new method to image VP in tumors, which is based on the use of ultrasound poroelastography. Estimation of VP by poroelastography requires knowledge of the Young's modulus (YM), Poisson's ratio (PR), and strain time constant (TC) in the tumors. In our method, we find the ellipse which best fits the tumor (regardless of its shape) using an eigen-system-based fitting technique and estimate the YM and PR using Eshelby's elliptic inclusion formulation. A Fourier method is used to estimate the axial strain TC, which does not require any initial guess and is highly robust to noise. Results: It is demonstrated that the proposed method can estimate VP in irregularly shaped tumors with an accuracy of above $\text{80}\%$ using ultrasound simulation data with signal-to-noise ratio of 20 dB or higher. In vivo feasibility of the proposed technique is demonstrated in an orthotopic mouse model of breast cancer. Conclusion: The proposed imaging method can provide accurate and localized estimation of VP in cancers non-invasively and cost-effectively. Significance: Accurate and non-invasive assessment of VP can have a significant impact on diagnosis, prognosis, and treatment of cancers. [ABSTRACT FROM AUTHOR]

It is an effective approach to estimate rotor position of doubly salient electromagnetic machine (DSEM) from self-inductances, which changes with rotor position. To overcome the restrictions of low estimation accuracy as well as the inevitable commutation lag in the conventional sensorless startup method, this paper proposes a novel rotor angular position estimating technique for the DSEM sensorless startup. It starts with injecting three test pulses A + C- (phase A is connected to the positive rail of dc bus, and phase C is connected to the ground rail), B + A- and C + B- on armature windings, so two-phase series self-inductances can be identified according to the resultant currents. Then, the estimating expression of the DSEM rotor angular position is further obtained based on the geometrical relation in the rectangle composed of self-inductances, and the inevitable commutation lag in startup process is likely to be eliminated with the estimated rotor angular position. Moreover, the error of rotor angular position introduced by the nonideal linearity of self-inductances are analyzed in detail, and the boundaries of neighboring sectors are distinguished from normal sectors to compensate the estimation error of rotor angular position. Finally, to enhance the output torque performance during the DSEM startup process, a novel selection method for test pulses concerning rotor sector is developed. Compared to the conventional method, a quicker and superior startup performance is achieved since no negative torque is generated by the test pulses. The experiments on a 12/8-pole DSEM validate the correctness and feasibility of the proposed sensorless control methods. [ABSTRACT FROM AUTHOR]

Set similarity search is a fundamental operation in a variety of applications. While many previous studies focus on threshold based set similarity search and join, few efforts have been paid for KNN set similarity search. In this paper, we propose a transformation based framework to solve the problem of KNN set similarity search, which given a collection of set records and a query set, returns $k$ k results with the largest similarity to the query. We devise an effective transformation mechanism to transform sets with various lengths to fixed length vectors which can map similar sets closer to each other. Then, we index such vectors with a tiny tree structure. Next, we propose efficient search algorithms and pruning strategies to perform exact KNN set similarity search. We also design an estimation technique by leveraging the data distribution to support approximate KNN search, which can speed up the search while retaining high recall. Experimental results on real world datasets show that our framework significantly outperforms state-of-the-art methods in both memory and disk based settings. [ABSTRACT FROM AUTHOR]

For airborne-phased array radar systems, space-time adaptive processing (STAP) is supposed to be a crucial technique for improving target detection performance in the strong clutter background. However, practical application environments are always heterogeneous and have offered a severe challenge to the implementation of STAP. To address this reality, a data-dependent reduced dimension STAP approach based on sparse recovery (SR) is proposed in this paper. To take a full account of the heterogeneous environments, we consider the extremely heterogeneous case in which only one single snapshot is available. There is no doubt that, compared with the clutter covariance matrix (CCM) calculated directly by a single snapshot, the SR technique can provide a more accurate estimate. However, we should come to realize that this estimation is not accurate enough to adaptive processing according to the presented simulation results in lots of literature, and it has been demonstrated that the performance of traditional SR-based STAP degrades dramatically when only a snapshot is available. In the proposed approach, the CCM estimated by SR technique is utilized to design the reduced dimension transformation matrix rather than to calculate adaptive weights as the traditional SR-based STAP. The relatively accurate CCM can provide better support for the design of reduced dimension transformation matrix. From the simulation results, the proposed approach can achieve great performance of clutter suppression and target detection with only a single snapshot compared with several typical STAP algorithms. It is worthwhile pointing out that the proposed approach can also be applied when multiple snapshots are available and the performance improves with increasing number of available snapshots. [ABSTRACT FROM AUTHOR]

Estimation of periodic signals, based on quantized data, is a topic of general interest in the area of instrumentation and measurement. Although several methods are available, new applications require low-power, low-complexity, and adequate estimation accuracy. In this paper, we consider the simplest possible quantization, that is, binary quantization, and describe a technique to estimate the parameters of a sampled periodic signal, using a fast algorithm. By neglecting the possibility that the sampling process is triggered by some signal-derived event, sampling is assumed to be asynchronous, that is, the ratio between the signal and the sampling periods is defined to be an irrational number. To preserve enough information at the quantizer output, additive Gaussian input noise is assumed as the information encoding mechanism. With respect to the published techniques addressing the same problem, the proposed approach does not rely on the numerical estimation of the maximum likelihood function but provides solutions that are very close to this estimate. At the same time, since the main estimator is based on matrix inversion, it proves to be less time-consuming than the numerical maximization of the likelihood function, especially when solving problems with a large number of parameters. The estimation procedure is described in detail and validated using both simulation and experimental results. The estimator performance limitations are also highlighted. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for the conversion of an implicit current–voltage (I–V) expression of a double diode model (DDM) photovoltaic (PV) module to an explicit expression of current in terms of voltage. A new method is proposed for the estimation of maximum power point (MPP) of a DDM PV module using the explicit I–V expression of a PV module under uniform irradiance condition. Further, a new mathematical model of a partially shaded PV array is developed using the proposed explicit expression. Since the explicit I–V expression is used for the estimation of MPP of a DDM PV module under uniform and nonuniform irradiance conditions, the estimation of MPP is simple, accurate, and efficient. The accuracy of the explicit expression is verified considering three different PV modules. A comparison of MPP of a DDM PV module extracted using the proposed method with different existing methods under different environmental conditions (DECs) is presented. The results obtained using the proposed method are validated with the experimental results under DEC. Also, the accuracy of the estimated global MPP of a PV array under partial shading conditions is verified. [ABSTRACT FROM AUTHOR]

This paper presents a novel motor current signature analysis algorithm for accurate detection and precise estimation of extremely small fault signature by utilizing multiple-phase-current signals in an electric machine. The two most critical challenges in developing a reliable diagnosis method are the detection of extreme small signals under extremely harsh environment, and the formulation of adaptive threshold under dynamic operation and noise variation of a motor. These challenges are addressed in this paper by logically and statistically utilizing multiple existing current sensors and current signals to make an accurate detection, and statistical/adaptive threshold for precise detection and decision making. The performance of the proposed algorithm has been mathematically and theoretically proved under modeled Gaussian noise conditions. Proposed theory is also experimentally verified on a 3-kW five-phase permanent-magnet-assisted synchronous reluctance motor. [ABSTRACT FROM PUBLISHER]

The product of the scanned responses of two colinear interleaved arrays estimates the spatial power spectral density of the received signal. This estimate has mathematical structure similar to the classical periodogram and hence is called the generalized periodogram. The generalized periodogram includes sparse arrays such as coprime arrays and two level nested arrays. This paper derives the analytical expected value of the generalized periodogram output for any signal. The generalized periodogram expected value is the convolution of the true spectral density with a windowing function. This result reveals that the expected product spectrum is not guaranteed to be positive definite. The paper also derives the covariance function of the generalized periodogram for a white Gaussian process. Finally, the paper illustrates the implicit equivalence between the product spectrum and estimation of the correlation function from the products of appropriately spaced pairs of sensors. [ABSTRACT FROM PUBLISHER]

Source localization based on time-difference-of-arrival (TDOA) measurements from spatially separated sensors is an important problem in sensor networks. In this paper, we study the optimal geometries of a team of networked sensors for the TDOA-based localization problem, taking into account the a priori uncertainty of the source location. Two types of TDOA sensor pairing methods, namely, centralized and decentralized , are considered. Analytic solutions to the optimal sensor geometries in the ideal case are derived in both static and movable source cases. In the movable source case, sensor geometry design is investigated under an extended Kalman filter (EKF) framework. Furthermore, under sensor motion and communication constraints, we extend this paper to an optimal sensor path planning problem. Simulations demonstrate its enhancement to the source localization and tracking performance. [ABSTRACT FROM AUTHOR]

Next-generation phasor measurement units (PMU) are expected to be more accurate than existing ones, especially to address the stricter requirements of future active distribution grids. From this perspective, the influence of acquisition wideband noise (which includes multiple contributions both in amplitude and in phase) has to be carefully evaluated. In this context, the contribution of this paper is twofold. First, it provides a general framework to evaluate the effect of wideband noise on synchrophasor amplitude, phase, frequency, and rate of change of frequency (ROCOF) estimation. The results of this analysis show that the influence of wideband noise can become critical for compliance with the requirements reported in the IEEE standards C37.118.1-2011 and C37.118.1a-2014, especially for frequency and ROCOF estimation. In addition, this paper reports general guidelines to choose the PMU effective resolution and sampling rate for which the impact of wideband noise on both P Class and M Class PMUs is negligible. [ABSTRACT FROM PUBLISHER]

In array processing, mutual coupling between sensors has an adverse effect on the estimation of parameters (e.g., DOA). Sparse arrays such as nested arrays, coprime arrays, and minimum redundancy arrays (MRA) have reduced mutual coupling compared to uniform linear arrays (ULAs). These arrays also have a difference coarray with O\left(N^2\right) virtual elements, where $N$ is the number of physical sensors, and can therefore resolve O\left(N^2\right) uncorrelated source directions. But these well-known sparse arrays have disadvantages: MRAs do not have simple closed-form expressions for the array geometry; coprime arrays have holes in the coarray; and nested arrays contain a dense ULA in the physical array, resulting in significantly higher mutual coupling than coprime arrays and MRAs. In a companion paper, a sparse array configuration called the (second-order) super nested array was introduced, which has many of the advantages of these sparse arrays, while removing most of the disadvantages. Namely, the sensor locations are readily computed for any $N$ (unlike MRAs), and the difference coarray is exactly that of a nested array, and therefore hole-free. At the same time, the mutual coupling is reduced significantly (unlike nested arrays). In this paper, a generalization of super nested arrays is introduced, called the $Q$th-order super nested array. This has all the properties of the second-order super nested array with the additional advantage that mutual coupling effects are further reduced for $Q > 2$. Many theoretical properties are proved and simulations are included to demonstrate the superior performance of these arrays. [ABSTRACT FROM PUBLISHER]

Cooperative tracking plays a key role in space situation awareness for scenarios with a limited number of observations or poor performance of a single sensor or both. To use the information from multiple networked sensors, both centralized and decentralized fusion algorithms can be used. Compared with centralized fusion algorithms, decentralized fusion algorithms are more robust in terms of communication failure and computational burden. One popular distributed estimation approach is based on the average consensus that asymptotically converges to the estimate by multiple exchanges of neighborhood information. Consensus-based algorithms have become popular in recent years due to the fact that they do not require global knowledge of the network or routing protocols. The main contributions of this paper are 1) an effective space-based object (SBO) measurement model that considers the geometric relation of the Sun, the space object, the SBO sensor, and the Earth; 2) two consensus-based filters, the information-weighted consensus filter (ICF) and the Kalman consensus filter (KCF), are used to track space objects by using multiple SBO sensors; and 3) the cubature rule-embedded ICF (Cub-ICF) and KCF (Cub-KCF) are proposed to improve the accuracy of the ICF and KCF. Three scenarios that contain one or two space objects and four SBOs are used to test proposed algorithms. We also compare the consensus-based space object tracking algorithms with the centralized extended information filter (centralized EIF) and the centralized cubature information filter (centralized Cub-IF). The simulation results indicate that 1) cooperative space object tracking algorithms provide better results than algorithms using a single sensor, 2) the consensus-based tracking algorithms can achieve performance close to that of the centralized algorithms, and 3) the Cub-ICF and Cub-KCF outperform the conventional ICF and KCF for a challenging space object tracking case shown in the paper. The proposed Cub-ICF and Cub-KCF algorithms should facilitate the application of using consensus-based filters for cooperative space object tracking. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel finite-memory (FM) estimation method for vehicle roll and road bank angles. We consider a three-degree-of-freedom model based on bicycle and vehicle roll models, where there exist uncertainties in the model parameters. The existing estimation methods may show poor performance for the considered model with uncertainties. The accumulated errors, which might be caused by modeling uncertainties and numerical errors, may degrade the performance of existing methods because they use all past measurements. To overcome the weaknesses of the existing methods, a new FM estimation method is proposed and can prevent accumulated errors by using recent finite measurements. Both the simulation and experimental results show that the proposed estimation method ensures more reliable and accurate performance than the existing methods under various driving conditions. [ABSTRACT FROM AUTHOR]

Process variability is one of the major concerns in semiconductor manufacturing in advanced technology nodes. This paper explains an all-digital technique to estimate the impact of threshold voltage (${V}_{\text{th}}$) of NMOS device under test (NDUT) using a reconfigurable ring oscillator (RRO) which consists of a chain of threshold voltage measurement cell (TVMC). The difference of periods of RRO due to two voltage levels, i.e., ${V}_{\text {dd}}$ and (${V}_{\text {dd}}-{V}_{\text {th}}$) at the intermediate node provides information about the impact of threshold voltage of an NDUT. A lognormal model is developed to extract the threshold voltage information from the RRO period difference. The maximum difference in predicting the threshold voltage from the proposed model and through SPICE simulation is found to be less than 1.5%. Measured results from 26 test chips in 130-nm process show the feasibility of estimating the $\ {V}_{\text {th}}$ variability from the proposed RRO test structure. The proposed test circuit with 300 samples of NDUT occupies a silicon area of ${280} \,\, {\times }\,\, {64}\,\, {\mu } \text {m}^{{2}}$. [ABSTRACT FROM AUTHOR]

In this paper, a sensorless rotor position estimation method based on the field current harmonic for a load-commutated inverter (LCI)-fed electrically excited synchronous motor (EESM) operating at zero or low speed is proposed. A detailed theoretical analysis and mathematical deduction are presented. The field current harmonic is used instead of the extra high-frequency signal injection. A reasonable harmonic frequency is selected, and a realizable field current control method is given. To simplify the complex signal processing and improve the measurement accuracy, a specific bandpass filter and a normalized quadrature phase-locked loop are designed. The phase change caused by the filters is compensated properly. The proposed approach is demonstrated through the simulation and experiment with a 15-kW LCI-fed EESM drive platform, confirming its feasibility and accuracy. [ABSTRACT FROM AUTHOR]

The estimation and prediction of state-of-health (SOH) and state-of-charge (SOC) of Lithium-ion batteries are two main functions of the battery management system (BMS). In order to reduce the computation cost and enable deployment of the BMS on the low-cost hardware, a Lebesgue-sampling-based extended Kalman filter (LS-EKF) is developed to estimate the SOH and SOC. An LS-EKF is able to eliminate unnecessary computations, especially when the states change slowly. In this paper, the SOH is first estimated and the remaining useful life is predicted by the LS-EKF. Then, the estimated SOH is used as the initial battery capacity for SOC estimation and prediction. The SOH and SOC estimation and prediction are calculated repeatedly in the whole battery service life. The proposed method is verified with the application to the capacity degradation of the Lithium-ion battery. The results show that the LS-EKF-based algorithm has a good performance in SOH and SOC estimation and prediction in terms of accuracy and computation cost. [ABSTRACT FROM AUTHOR]

Most man-made environments, such as urban and indoor scenes, consist of a set of parallel and orthogonal planar structures. These structures are approximated by the Manhattan world assumption, in which notion can be represented as a Manhattan frame (MF). Given a set of inputs such as surface normals or vanishing points, we pose an MF estimation problem as a consensus set maximization that maximizes the number of inliers over the rotation search space. Conventionally, this problem can be solved by a branch-and-bound framework, which mathematically guarantees global optimality. However, the computational time of the conventional branch-and-bound algorithms is rather far from real-time. In this paper, we propose a novel bound computation method on an efficient measurement domain for MF estimation, i.e., the extended Gaussian image (EGI). By relaxing the original problem, we can compute the bound with a constant complexity, while preserving global optimality. Furthermore, we quantitatively and qualitatively demonstrate the performance of the proposed method for various synthetic and real-world data. We also show the versatility of our approach through three different applications: extension to multiple MF estimation, 3D rotation based video stabilization, and vanishing point estimation (line clustering). [ABSTRACT FROM AUTHOR]

Fast and accurate frequency estimation of a complex sinusoidal plays an important role in many applications, including communications, radar, and sonar. This paper presents two methods for the frequency estimation of a complex sinusoidal in noisy environments. The proposed methods interpolate on shifted DFT coefficients to acquire the frequency estimates iteratively. The first method interpolates on the $q$ -shifted DFT coefficients of the signal, whose optimum iteration number is found to be a logarithmic function of the signal length. Furthermore, we also show that by appropriately selecting the value of the shift $q$ , the proposed method becomes asymptotically efficient. In the second method, we use hybrid half-shifted and $q$ -shifted DFT interpolators together, which converges in only two iterations. It is shown that both of the estimators are asymptotically unbiased with their mean square errors performing on the Crámer–Rao lower bound. Theoretical results are validated by extensive computer simulations. [ABSTRACT FROM AUTHOR]

The demand for automatic extraction of true information (i.e., truths) from conflicting multi-source data has soared recently. A variety of truth discovery methods have witnessed great successes via jointly estimating source reliability and truths. All existing truth discovery methods focus on providing a point estimator for each object's truth, but in many real-world applications, confidence interval estimation of truths is more desirable, since confidence interval contains richer information. To address this challenge, in this paper, we propose a novel truth discovery method (ETCIBoot) to construct confidence interval estimates as well as identify truths, where the bootstrapping techniques are nicely integrated into the truth discovery procedure. Due to the properties of bootstrapping, the estimators obtained by ETCIBoot are more accurate and robust compared with the state-of-the-art truth discovery approaches. The proposed framework is further adapted to deal with large-scale truth discovery task in distributed paradigm. Theoretically, we prove the asymptotical consistency of the confidence interval obtained by ETCIBoot. Experimentally, we demonstrate that ETCIBoot is not only effective in constructing confidence intervals but also able to obtain better truth estimates. [ABSTRACT FROM AUTHOR]

This paper proposes a scalable approach for counting pedestrians crossing a virtual line when the crowd is highly dynamic and possibly extremely dense. The approach mainly consists of two parts: local crowd density estimation and pedestrian counting based on accumulating local densities across the line. To obtain a fine estimation of local crowd densities, we divide the neighborhood at the line into a number of blocks. We enforce spatial consistency between local counts in the blocks and those in the enclosing regions to guarantee consistent estimation of local crowd densities. For scalability to various density levels in crowd density estimation, we propose a two-stage strategy: pre-classification of density levels and subsequent regression with overlapped operational ranges. To count pedestrians crossing the virtual line, we accumulate the crowd densities across the line according to the locally estimated velocities. Extensive experimental results demonstrate the effectiveness of the proposed approach and its scalability to crowdedness. [ABSTRACT FROM AUTHOR]

One of the core applications of light field imaging is depth estimation. To acquire a depth map, existing approaches apply a single photo-consistency measure to an entire light field. However, this is not an optimal choice because of the non-uniform light field degradations produced by limitations in the hardware design. In this paper, we introduce a pipeline that automatically determines the best configuration for photo-consistency measure, which leads to the most reliable depth label from the light field. We analyzed the practical factors affecting degradation in lenslet light field cameras, and designed a learning based framework that can retrieve the best cost measure and optimal depth label. To enhance the reliability of our method, we augmented an existing light field benchmark to simulate realistic source dependent noise, aberrations, and vignetting artifacts. The augmented dataset was used for the training and validation of the proposed approach. Our method was competitive with several state-of-the-art methods for the benchmark and real-world light field datasets. [ABSTRACT FROM AUTHOR]

This paper presents the design, implementation, and evaluation of four filters for the estimation of angle of attack (AOA) and angle of sideslip (AOS) of small unmanned aerial vehicles (UAVs). Specifically, two novel filters (a complementary filter and an extended Kalman filter) are proposed and evaluated without using direct flow angle and Global Positioning System measurements; two existing AOA/AOS filters are also implemented and evaluated. All filters are designed with minimal inputs and states to ensure the ease of implementation, simplicity of tuning, and computation efficiency. Both simulation and UAV flight test results show the performance of the proposed filters. Especially, flight test results from two different UAVs (a T-tail UAV and a flying wing UAV) show that the root mean square errors of estimated inertial AOA and AOS are less than 1.5 $^\circ$ under nominal flight conditions and around 2 $^\circ$ under aggressive maneuvers compared with direct flow angle measurements. [ABSTRACT FROM AUTHOR]

To address the classical selectivity estimation problem for OLAP queries in relational databases, a radically different approach calledPlanBouquetwas recently proposed in, wherein the estimation process is completely abandoned and replaced with a calibrated discovery mechanism. The beneficial outcome of this new construction is that provable guarantees on worst-case performance, measured as Maximum Sub-Optimality (MSO), are obtained thereby facilitating robust query processing. ThePlanBouquetformulation suffers, however, from a systemic drawback—the MSO bound is a function of not only the query, but also the optimizer's behavioral profile over the underlying database platform. As a result, there are adverse consequences: (i) the bound value becomes highly variable, depending on the specifics of the current operating environment, and (ii) it becomes infeasible to compute the value without substantial investments in preprocessing overheads. In this paper, we first presentSpillBound, a new query processing algorithm that retains the core strength of thePlanBouquetdiscovery process, but reduces the bound dependency to only the query. It does so by incorporating plan termination and selectivity monitoring mechanisms in the database engine. Specifically,SpillBounddelivers a worst-case multiplicative bound, of $D^2+3D$ , where $D$ is simply the number of error-prone predicates in the user query. Consequently, the bound value becomes independent of the optimizer and the database platform, and the guarantee can be issued simply by query inspection. We go on to prove thatSpillBoundis within an $O(D)$ factor of thebest possibledeterministic selectivity discovery algorithm in its class. We next devise techniques to bridge this quadratic-to-linear MSO gap by introducing the notion ofcontour alignment, a characterization of the nature of plan structures along theboundariesof the selectivity space. Specifically, we propose a variant ofSpillBound, calledAlignedBound, which exploits the alignment property and provides a guarantee in the range $\mathbf {[2D+2,D^2+3D]}$ . Finally, a detailed empirical evaluation over the standard decision-support benchmarks indicates that: (i)SpillBoundprovides markedly superior performance w.r.t. MSO as compared toPlanBouquet, and (ii)AlignedBoundprovides additional benefits for query instances that are challenging forSpillBound, often coming close to the ideal of MSO linearity in $D$ . From an absolute perspective,AlignedBoundevaluates virtually all the benchmark queries considered in our study with MSO of around10or lesser. Therefore, in an overall sense,SpillBoundandAlignedBoundoffer a substantive step forward in the long-standing quest for robust query processing. [ABSTRACT FROM AUTHOR]

Nodes in electric distribution networks are greatly differentiated and are very often nonlinear and/or unbalanced. They can create significant harmonic pollution, with harmonics that inevitably spread along the grid. Monitoring harmonic propagation and correlated power quality phenomena requires specific measurement devices and methodologies. Nevertheless, because of the unavailability of a rapid diffusion of synchronized and dedicated devices (due to technical and economic reasons) on every node and branch of the network, estimating the harmonic status of the entire grid by means of a complete or even redundant monitoring system can be practically unfeasible. A more feasible, though always meaningful, goal can thus be pursued, that is estimating the main harmonic sources in the network, rather than its complete harmonic status. This approach, of course, can be based on a simpler and cheaper upgrade of the distributed monitoring system. Even more, by considering the common scenario where the number of significant harmonic sources is lower than the number of loads connected to the grid, specific estimation procedures can be defined to further reduce the complexity of the monitoring system. In this scenario, this paper presents an efficient compressive sensing harmonics detector (CSHD) for the identification and the estimation of the principal pollution sources. The proposed CSHD method is validated by means of appropriate tests performed on an example of distribution grid. [ABSTRACT FROM AUTHOR]

Due to the popularity of 3D videos, 2D-to-3D video conversion has become a hot research topic for the past few years. The most critical issue in 3D video synthesis is the estimation of depth maps for the video frames. Numerous efforts have been devoted in fully automatic and semi-automatic depth estimation approaches, although the discontinuity of depth field and the ambiguity of motion boundary are still the main challenges in depth estimation. This paper proposes a semi-automatic structure-aware sparse-to-dense depth estimation method, which leverages the tensor voting at two different levels to propagate depth across frames. In the first level, a 4D tensor voting is performed to remove outliers caused by inaccurate motion estimation. Noticing that the 4D tensors of correctly matched points should lie on the smooth layer in the manifold, we utilize the variety saliency defined by the eigen-system of the tensor for outlier removal. In the second level, a high-dimensional tensor voting algorithm, incorporating spatial location, motion, and color into the tensor representation, is devised to propagate the depth from the sparse points to the entire image domain. By projecting the input feature into the tangent space, the relation between the location, motion, color, and the depth can be established by voting process. Extensive experiments on public data set validate the effectiveness of the proposed method in comparison with state-of-the-art depth estimation approaches. [ABSTRACT FROM AUTHOR]

Counting on-road vehicles in the highway is fundamental for intelligent transportation management. This paper presents the first highway vehicle counting method in compressed domain, aiming at achieving comparable estimation performance with the pixel-domain methods. Counting in compressed domain is rather challenging due to limited information about vehicles and large variance in vehicle numbers. To address this problem, we develop new low-level features to mitigate the challenge from insufficient information in compressed videos. The new proposed features can be easily extracted from the coding-related metadata. Then, we propose a hierarchical classification-based regression (HCR) model to estimate the number of vehicles from the compressed-domain low-level features for individual frame. HCR hierarchically divides the traffic scenes into different cases according to the density of vehicles such that the large variance of traffic scenes can be effectively captured. Beside the spatial regression in each frame, we propose a locally temporal regression model to further refine the counting results, which exploits the continuous variation characteristics of the traffic flow. We extensively evaluate the proposed method on real highway surveillance videos. The experimental results consistently show that the proposed method is very competitive compared with the pixel-domain methods, which can reach similar performance with much lower computational cost. [ABSTRACT FROM AUTHOR]

A reactive-power-based model reference adaptive system updating rotor time constant in induction motor drives has been researched for many years; however, it is still required to improve it further in some aspects for more wide applicability in practice. First, the information of stator frequency is incorporated into the adaptive mechanism, by which not only the effective range of the updating scheme is extended, but also the open-loop gain, otherwise relating with the quantity of stator frequency closely, is normalized facilitating the design of the adaptation gains. Second, two equilibrium points (EPs) of the reactive power model are found out and identified as the desired EP and zero-frequency EP, paving a way to stability analysis and refined design criterions. Third, since the high-nonlinearity characteristic of the system prevents the Routh–Hurwitz criterion from being used to the stability analysis, a small-signal model of the estimation system is established. It is revealed that the adaptive scheme, obtained through the Lyapunov theorem by neglecting rotor flux dynamics, is not always stable as commonly accepted. In this paper, the stability properties of the two EPs and the mechanism behind the instability phenomena are explored with the help of the developed small-signal model, which supplies theoretical basis to predict the stability of the reactive power model. Furthermore, the design strategy of adaptation proportional–integral gains is proposed, allowing the stability as well as the dynamic performance to be improved throughout the operation range. The proposed analysis has been validated through experiments. [ABSTRACT FROM AUTHOR]

Detection of moving objects in videos is a crucial step toward successful surveillance and monitoring applications. A key component for such tasks is called background subtraction and tries to extract regions of interest from the image background for further processing or action. For this reason, its accuracy and real-time performance are of great significance. Although effective background subtraction methods have been proposed, only a few of them take into consideration the special characteristics of thermal imagery. In this paper, we propose a background subtraction scheme, which models the thermal responses of each pixel as a mixture of Gaussians with unknown number of components. Following a Bayesian approach, our method automatically estimates the mixture structure, while simultaneously it avoids over-/underfitting. The pixel density estimate is followed by an efficient and highly accurate updating mechanism, which permits our system to be automatically adapted to dynamically changing operation conditions. We propose a reference implementation of our method in reconfigurable hardware achieving both adequate performance and low-power consumption. Adopting a high-level synthesis design and demanding floating point arithmetic operations are mapped in reconfigurable hardware, demonstrating fast prototyping and on-field customization at the same time. [ABSTRACT FROM AUTHOR]

Many state-of-the-art signal decomposition techniques rely on a low-rank factorization of a time–frequency (t–f) transform. In particular, nonnegative matrix factorization (NMF) of the spectrogram has been considered in many audio applications. This is an analysis approach in the sense that the factorization is applied to the squared magnitude of the analysis coefficients returned by the t–f transform. In this paper, we instead propose a synthesis approach, where low-rankness is imposed to the synthesis coefficients of the data signal over a given t–f dictionary (such as a Gabor frame). As such, we offer a novel modeling paradigm that bridges t–f synthesis modeling and traditional analysis based NMF approaches. The proposed generative model allows us in turn to design more sophisticated multilayer representations that can efficiently capture diverse forms of structure. Additionally, the generative modeling allows us to exploit t–f low-rankness for compressive sensing. We present efficient iterative shrinkage algorithms to perform estimation in the proposed models and illustrate the capabilities of the new modeling paradigm over audio signal processing examples. [ABSTRACT FROM AUTHOR]

Network systems often rely on distributed algorithms to achieve a global computation goal with iterative local information exchanges between neighbor nodes. To preserve data privacy, a node may add a random noise to its original data for information exchange at each iteration. Nevertheless, an eavesdropping node can estimate other’s original data based on the information it received. The estimation accuracy and data privacy can be measured in terms of $(\epsilon, \delta)$ -data-privacy, defined as the probability of $\epsilon $ -accurate estimate (the difference of an estimation and the original data is within $\epsilon $ ) is no larger than $\delta $ (the disclosure probability). How to optimize the estimation and analyze data privacy is a critical and open issue. In this paper, a theoretical framework is developed to investigate how to optimize the estimation of neighbor’s original data using the local information received, named optimal distributed estimation. Then, we study the disclosure probability under the optimal estimation for data privacy analysis. We further apply the developed framework to analyze the data privacy of the privacy-preserving average consensus algorithm and identify the optimal noises for the algorithm. [ABSTRACT FROM AUTHOR]

Suppose that we are given a time series where consecutive samples are believed to come from a probabilistic source, that the source changes from time to time and that the total number of sources is fixed. Our objective is to estimate the distributions of the sources. A standard approach to this problem is to model the data as a hidden Markov model (HMM). However, since the data often lacks the Markov or the stationarity properties of an HMM, one can ask whether this approach is still suitable or perhaps another approach is required. In this paper, we show that a maximum likelihood HMM estimator can be used to approximate the source distributions in a much larger class of models than HMMs. Specifically, we propose a natural and fairly general non-stationary model of the data, where the only restriction is that the sources do not change too often. Our main result shows that for this model, a maximum-likelihood HMM estimator produces the correct second moment of the data, and the results can be extended to higher moments. [ABSTRACT FROM AUTHOR]

Light-field cameras have now become available in both consumer and industrial applications, and recent papers have demonstrated practical algorithms for depth recovery from a passive single-shot capture. However, current light-field depth estimation methods are designed for Lambertian objects and fail or degrade for glossy or specular surfaces. The standard Lambertian photoconsistency measure considers the variance of different views, effectively enforcing point-consistency, i.e., that all views map to the same point in RGB space. This variance or point-consistency condition is a poor metric for glossy surfaces. In this paper, we present a novel theory of the relationship between light-field data and reflectance from the dichromatic model. We present a physically-based and practical method to estimate the light source color and separate specularity. We present a new photo consistency metric, line-consistency, which represents how viewpoint changes affect specular points. We then show how the new metric can be used in combination with the standard Lambertian variance or point-consistency measure to give us results that are robust against scenes with glossy surfaces. With our analysis, we can also robustly estimate multiple light source colors and remove the specular component from glossy objects. We show that our method outperforms current state-of-the-art specular removal and depth estimation algorithms in multiple real world scenarios using the consumer Lytro and Lytro Illum light field cameras. [ABSTRACT FROM PUBLISHER]

This paper proposes an online parameter estimation method based on a discrete-time dynamic model for the interior permanent-magnet synchronous motors (IPMSMs). The proposed estimation technique, which takes advantage of the difference in dynamics of motor parameters, consists of two affine projection algorithms. The first one is designed to accurately estimate the stator inductances, whereas the second one is designed to precisely estimate the stator resistance, rotor flux linkage, and load torque. In this paper, the adaptive decoupling proportional–integral (PI) controllers with the maximum torque per ampere operation, which utilize the previously identified parameters in real time, are chosen to verify the effectiveness of the proposed parameter estimation scheme. The simulation results via MATLAB/Simulink and the experimental results via a prototype IPMSM drive system with a TI TMS320F28335 DSP are presented under various conditions. A comparative study with the conventional decoupling PI control method is carried out to demonstrate the better performances (i.e., faster dynamic response, less steady-state error, more robustness, etc.) of the adaptive decoupling PI control scheme based on the proposed online parameter estimation technique. [ABSTRACT FROM PUBLISHER]

In this paper, we consider signal subspace estimation based on low-rank representation for hyperspectral imagery. It is often assumed that major signal sources occupy a low-rank subspace. Due to the mixed nature of hyperspectral remote sensing data, the underlying data structure may include multiple subspaces instead of a single subspace. Therefore, in this paper, we propose the use of low-rank subspace representation to estimate the number of subspaces in hyperspectral imagery. In particular, we develop simple estimation approaches without user-defined parameters because these parameters can be fixed as constants. Both real data experiments and computer simulations demonstrate excellent performance of the proposed approaches over those currently in the literature. [ABSTRACT FROM PUBLISHER]