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This paper proposes and evaluates two robust sheet tension estimation algorithms based on unwind and rewind variables, respectively. The proper sheet tension estimation is guaranteed by a continuous monitoring of the differences between the actual estimated values and the values predicted from the last calibration of the estimation parameters. The evaluation was performed with the aid of a developed winder emulator that allowed the offline testing in a real-time field environment, including the initial setting of the estimation constants, and evaluation of both four- and six-shipping-roll cycles. The sheet tension estimation results confirm the precise and stable operation of the proposed robust estimation algorithms, in tests conducted with signals from a winder model, and with tests involving actual field signals from an operational winder. [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]

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]

In this paper, we consider the problem of learning undirected graphical models from data generated according to the Glauber dynamics (also known as the Gibbs sampler). The Glauber dynamics is a Markov chain that sequentially updates individual nodes (variables) in a graphical model and it is frequently used to sample from the stationary distribution (to which it converges given sufficient time). Additionally, the Glauber dynamics is a natural dynamical model in a variety of settings. This paper deviates from the standard formulation of graphical model learning in the literature, where one assumes access to independent identically distributed samples from the distribution. Much of the research on graphical model learning has been directed toward finding algorithms with low computational cost. As the main result of this paper, we establish that the problem of reconstructing binary pairwise graphical models is computationally tractable when we observe the Glauber dynamics. Specifically, we show that a binary pairwise graphical model on p nodes with maximum degree d can be learned in time f(d)p^2\log p , for a function f(d) defined explicitly in this paper, using nearly the information-theoretic minimum number of samples. [ABSTRACT FROM AUTHOR]

Online sensing of sheet modulus of elasticity in the dry-end spans of printing grade paper machines (PMs) will permit the machine operators to control the mechanical properties of the produced paper. This paper develops an integrated sensorless tension control and sheet modulus of elasticity estimation algorithm, which is tested using field-recorded signals from a production PM and using a full model of the dry-end sections of this machine. Results show the ability of the proposed estimation algorithm to predict the sheet tension (tensile force) and sheet modulus of elasticity even in the presence of rapid changes in the speed and/or in other process operating conditions. The developed algorithms offer the papermaker new insights into the papermaking process. The ability to understand how modulus changes in real time provides a pathway to optimizing the operation of the PM drive system. [ABSTRACT FROM PUBLISHER]

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]

Recently, several high-resolution parameter estimation algorithms have been developed to exploit the structure of strictly second-order (SO) non-circular (NC) signals. They achieve a higher estimation accuracy and can resolve up to twice as many signal sources compared to the traditional methods for arbitrary signals. As a benchmark for these NC methods, we derive the closed-form deterministic $R$ -D NC Cramér-Rao bound (NC CRB) for the multi-dimensional parameter estimation of strictly non-circular (rectilinear) signal sources in this paper. Assuming a separable centro-symmetric $R$-D array, we show that in some special cases, the deterministic $R$-D NC CRB reduces to the existing deterministic $R$-D CRB for arbitrary signals. This suggests that no gain from strictly non-circular sources (NC gain) can be achieved under the deterministic data assumption in these cases. For more general scenarios, finding an analytical expression of the NC gain for an arbitrary number of sources is very challenging. Thus, in this paper, we simplify the derived NC CRB and the existing CRB for the special case of two closely-spaced strictly non-circular sources captured by a uniform linear array (ULA). Subsequently, we use these simplified CRB expressions to analytically compute the maximum achievable asymptotic NC gain for the considered two source case. The resulting expression only depends on the various physical parameters and we find the conditions that provide the largest NC gain. Our analysis is supported by extensive simulation results. [ABSTRACT FROM PUBLISHER]

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]

This paper investigates the injecting power stabilization of nonlinear dc microgrids (MGs) with constant power loads (CPLs). By considering a centralized controller scheme, the limitations of the communication utilities are considered. Therefore, limited information is transferred through the nonideal noisy communication network. Consequently, a cubature Kalman filter (CKF) with a third degree is proposed to mitigate the effect of the noisy measurement and the noisy network on the system's information. Moreover, an estimation-based robust feedback controller is developed to design an optimal value for the injecting power. The considered CKF algorithm is robust against the system uncertainty and noisy environments and has a low computational time for high-order dc MGs with a high number of sources and CPLs. In addition, a systematic procedure to compute the feedback gain of the controller is presented, which can be numerically solved by linear matrix inequality techniques. Hardware-in-the-loop real-time simulation results verify the simplicity of the controller implementation, enhanced performance for the case of limited information, and better robustness against the noisy measurements compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm that calibrates multiple cameras scattered across a broad area. The key idea of the proposed method is “using the position of an omnidirectional camera as a reference point.” The common approach to calibrating multiple cameras assumes that the cameras capture at least some common points. This means calibration becomes quite difficult if there are no shared points in each camera’s field of view (FOV). The proposed method uses the position of an omnidirectional camera to determine point correspondence. The position of an omnidirectional camera relative to the calibrated camera is estimated by the theory of epipolar geometry, even if the omnidirectional camera is placed outside the camera’s FOV. This property makes our method applicable to multiple cameras scattered across a broad area. Qualitative and quantitative evaluations using synthesized and real data, e.g., a sports field, demonstrate the advantages of the proposed method. [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]

Detecting periodicity in a short sequence is an important problem, with many applications across science and engineering. Several efficient algorithms have been proposed for this over the years. There is a wide choice available today in terms of the tradeoff between algorithmic complexity and estimation accuracy. In spite of such a rich history, one particular aspect of period estimation has received very little attention from a fundamental perspective. Namely, given a discrete time periodic signal and a list of candidate integer periods, what is the absolute minimum datalength required to estimate its integer period? Notice that the answer we seek must be a fundamental bound, i.e., independent of any particular period estimation technique. Common intuition suggests the minimum datalength as twice the largest expected period. However, this is true only under some special contexts. This paper derives the exact necessary and sufficient bounds to this problem. The above-mentioned question is also extended to the case of mixtures of periodic signals. First, a careful mathematical formulation discussing the unique identifiability of the component periods (hidden integer periods) is presented. Once again, a rigorous theoretical framework in this regard is missing in the existing literature but is a necessary platform to derive precise bounds on the minimum necessary datalength. The bounds given here are generic, that is, independent of the algorithms used. Specific algorithm-dependent bounds are also presented in the end for the case of dictionary-based integer period estimation reported in recent years. [ABSTRACT FROM AUTHOR]

In this paper, a novel force-sensing method is proposed for a high-performance force control system based on friction-free and noise-free force observation. A periodic signal is inserted into the control system for friction reduction. A combination of a high-order disturbance observer (DOB) (HDOB) and a Kalman filter is constructed to perform the force sensing. The HDOB is designed to estimate the force and reject oscillatory disturbances in the estimation. The force-sensing bandwidth is improved through effective noise suppression by the Kalman filter. Additionally, this paper proposes the application of the HDOB to the bilateral control system of a different master–slave mechanism. All the control algorithms are implemented in a field-programmable gate array with a high sampling rate that also enables the widening of the bandwidth of the force control system. The effectiveness of the proposed method is verified by experimental results. [ABSTRACT FROM PUBLISHER]

Courses at the Master's level in automatic control and signal processing cover mathematical theories and algorithms for control, estimation, and filtering. However, giving students practical experience in how to use these algorithms is also an important part of these courses. A goal is that the students should not only be able to understand and derive these algorithms, but also be able to apply them to real-life technical problems. The latter is achieved by assigning more time to the laboratory tutorials and designing them in such a way that the exercises are open for interpretation; an example of this would be giving the students more freedom to decide how to acquire the data needed to solve the given exercises. The students are asked to hand in a laboratory report in which they describe how they solved the exercises. This paper presents a double-blind peer-review process for laboratory reports, introduced at the Department of Electrical Engineering, Linköping University, Sweden. A survey was administered to students, and the results are summarized in this paper. Also discussed are the teachers' experiences of peer review and of how students perform later in their education in writing their Master's theses. [ABSTRACT FROM AUTHOR]

Recent studies show that individuals in a social network can be divided into different groups of densely connected communities, and these individuals who bridge different communities, referred to as structural hole spanners, have great potential to acquire resources/information from communities and thus benefit from the access. Structural hole spanners are crucial in many real applications such as community detections, diffusion controls, viral marketing, etc. In spite of their importance, little attention has been paid to them. Particularly, how to accurately characterize the structural hole spanners and how to devise efficient yet scalable algorithms to find them in a large social network are fundamental issues. In this paper, we study the top-$k$ structural hole spanner problem. We first provide a novel model to measure the quality of structural hole spanners through exploiting the structural hole spanner properties. Due to its NP-hardness, we then devise two efficient yet scalable algorithms, by developing innovative filtering techniques that can filter out unlikely solutions as quickly as possible, while the proposed techniques are built up on fast estimations of the upper and lower bounds on the cost of an optimal solution and make use of articulation points in real social networks. We finally conduct extensive experiments to validate the effectiveness of the proposed model, and to evaluate the performance of the proposed algorithms using real world datasets. The experimental results demonstrate that the proposed model can capture the characteristics of structural hole spanners accurately, and the structural hole spanners found by the proposed algorithms are much better than those by existing algorithms in all considered social networks, while the running times of the proposed algorithms are very fast. [ABSTRACT FROM AUTHOR]

This paper presents a novel jointly sparse signal reconstruction algorithm for the DOA estimation problem, aiming to achieve faster convergence rate and better estimation accuracy compared to existing \ell 2,1-norm minimization approaches. The proposed greedy block coordinate descent (GBCD) algorithm shares similarity with the standard block coordinate descent method for \ell 2,1-norm minimization, but adopts a greedy block selection rule which gives preference to sparsity. Although greedy, the proposed algorithm is proved to also have global convergence in this paper. Through theoretical analysis we demonstrate its stability in the sense that all nonzero supports found by the proposed algorithm are the actual ones under certain conditions. Last, we move forward to propose a weighted form of the block selection rule based on the MUSIC prior. The refinement greatly improves the estimation accuracy especially when two point sources are closely spaced. Numerical experiments show that the proposed GBCD algorithm has several notable advantages over the existing DOA estimation methods, such as fast convergence rate, accurate reconstruction, and noise resistance. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the stabilization problem of a class of networked control systems, where the plant measurements and the control signals are transmitted over a network and encounter both time delays and packet losses. To deal with the nonzero disturbance rejection issue, two kinds of networked optimal state feedback integral control (SFIC) methods are proposed in this paper: One is a networked guaranteed-cost SFIC method, and the other one is an SFIC method based on estimation of a distribution algorithm. It has been shown that the proposed methods have better nonzero disturbance rejection capability and can achieve zero steady-state error. Simulation and experimental results are given to demonstrate the effectiveness and applicability of the proposed approach. [ABSTRACT FROM PUBLISHER]

A “condensate load” (condensate in \% of a totally full cylinder) over 1\% accumulated inside the dryer cylinders of a paper machine is likely to produce a drive overload, eventually increasing the acceleration time or even producing an overload trip. This paper proposes and evaluates a confident online condensate estimation algorithm that gives updated information at preset speeds and when the transition to the rimming stage is detected. The experimental evaluation performed on a 500-mm-diameter cylinder, with condensate load between 0.5 \% and 25\% , confirmed the ability of the algorithm to give accurate estimations within 0.5 \% intervals of condensate loads. The developed algorithm is a useful tool for the operators, allowing them to take the appropriate decisions according to the severity of the condensate accumulation detected. [ABSTRACT FROM PUBLISHER]

This paper proposes a Kalman filter-based attitude (i.e., roll and pitch) estimation algorithm using an inertial sensor composed of a triaxial accelerometer and a triaxial gyroscope. In particular, the proposed algorithm has been developed for accurate attitude estimation during dynamic conditions, in which external acceleration is present. Although external acceleration is the main source of the attitude estimation error and despite the need for its accurate estimation in many applications, this problem that can be critical for the attitude estimation has not been addressed explicitly in the literature. Accordingly, this paper addresses the combined estimation problem of the attitude and external acceleration. Experimental tests were conducted to verify the performance of the proposed algorithm in various dynamic condition settings and to provide further insight into the variations in the estimation accuracy. Furthermore, two different approaches for dealing with the estimation problem during dynamic conditions were compared, i.e., threshold-based switching approach versus acceleration model-based approach. Based on an external acceleration model, the proposed algorithm was capable of estimating accurate attitudes and external accelerations for short accelerated periods, showing its high effectiveness during short-term fast dynamic conditions. Contrariwise, when the testing condition involved prolonged high external accelerations, the proposed algorithm exhibited gradually increasing errors. However, as soon as the condition returned to static or quasi-static conditions, the algorithm was able to stabilize the estimation error, regaining its high estimation accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we present a stereo visual-inertial odometry algorithm assembled with three separated Kalman filters, i.e., attitude filter, orientation filter, and position filter. Our algorithm carries out the orientation and position estimation with three filters working on different fusion intervals, which can provide more robustness even when the visual odometry estimation fails. In our orientation estimation, we propose an improved indirect Kalman filter, which uses the orientation error space represented by unit quaternion as the state of the filter. The performance of the algorithm is demonstrated through extensive experimental results, including the benchmark KITTI datasets and some challenging datasets captured in a rough terrain campus. [ABSTRACT FROM PUBLISHER]

Since its introduction in 1977, the expectation maximization (EM) algorithm has been one of the most important and widely used estimation method in estimating parameters of distributions in the presence of incomplete information. In this paper, a variant of the EM algorithm, the expectation conditional maximization (ECM) algorithm, is introduced for the first time and it provides a promising alternative in estimating the parameters of nonhomogeneous poisson (NHPP) software reliability growth models (SRGM). This algorithm circumvents the difficult M-step of the EM algorithm by replacing it by a series of conditional maximization steps. The utility of the ECM approach is demonstrated in the estimation of parameters of several well-known models for both time domain and time interval software failure data. Numerical examples with real-data indicate that the ECM algorithm performs well in estimating parameters of NHPP SRGM with complex mean value functions and can produce a faster rate of convergence. [ABSTRACT FROM AUTHOR]

Stereo matching, a key element towards extracting depth information from stereo images, is widely used in several embedded consumer electronic and multimedia systems. Such systems demand high processing performance and accurate depth perception, while their deployment in embedded and mobile environments implies that cost, energy and memory overheads need to be minimized. Hardware acceleration has been demonstrated in efficient embedded stereo vision systems. To this end, this paper presents the design and implementation of a hardware-based stereo matching system able to provide high accuracy and concurrently high performance for embedded vision devices, which are associated with limited hardware and power budget. We first implemented a compact and efficient design of the guided image filter, an edge-preserving filter, which reduces the hardware complexity of the implemented stereo algorithm, while at the same time maintains high-quality results. The guided filter design is used in two parts of the stereo matching pipeline, showing that it can simplify the hardware complexity of the Adaptive Support Weight aggregation step, and efficiently enable a powerful disparity refinement unit, which improves matching accuracy, even though cost aggregation is based on simple, fixed support strategies. We implemented several variants of our design on a Kintex-7 FPGA board, which was able to process HD video (1,280 × 720) in real-time (60 fps), using ∼57.5k and ∼71k of the FPGA's logic (CLB) and register resources, respectively. Additionally, the proposed stereo matching design delivers leading accuracy when compared to state-of-the-art hardware implementations based on the Middlebury evaluation metrics (at least 1.5 percent less bad matching pixels). [ABSTRACT FROM PUBLISHER]

Sparsity has been shown to be very useful in source separation of multichannel observations. However, in most cases, the sources of interest are not sparse in their current domain and one needs to sparsify them using a known transform or dictionary. If such a priori about the underlying sparse domain of the sources is not available, then the current algorithms will fail to successfully recover the sources. In this paper, we address this problem and attempt to give a solution via fusing the dictionary learning into the source separation. We first define a cost function based on this idea and propose an extension of the denoising method in the work of Elad and Aharon to minimize it. Due to impracticality of such direct extension, we then propose a feasible approach. In the proposed hierarchical method, a local dictionary is adaptively learned for each source along with separation. This process improves the quality of source separation even in noisy situations. In another part of this paper, we explore the possibility of adding global priors to the proposed method. The results of our experiments are promising and confirm the strength of the proposed approach. [ABSTRACT FROM AUTHOR]

In the context of low-cost video encoding, distributed video coding (DVC) has recently emerged as a potential candidate for uplink-oriented applications. This paper builds on a concept of correlation channel (CC) modeling, which expresses the correlation noise as being statistically dependent on the side information (SI). Compared with classical side-information-independent (SII) noise modeling adopted in current DVC solutions, it is theoretically proven that side-information-dependent (SID) modeling improves the Wyner–Ziv coding performance. Anchored in this finding, this paper proposes a novel algorithm for online estimation of the SID CC parameters based on already decoded information. The proposed algorithm enables bit-plane-by-bit-plane successive refinement of the channel estimation leading to progressively improved accuracy. Additionally, the proposed algorithm is included in a novel DVC architecture that employs a competitive hash-based motion estimation technique to generate high-quality SI at the decoder. Experimental results corroborate our theoretical gains and validate the accuracy of the channel estimation algorithm. The performance assessment of the proposed architecture shows remarkable and consistent coding gains over a germane group of state-of-the-art distributed and standard video codecs, even under strenuous conditions, i.e., large groups of pictures and highly irregular motion content. [ABSTRACT FROM AUTHOR]

Location estimation and tracking for mobile stations have attracted a significant amount of attention in recent years. Different types of signal sources are considered available to provide measurement inputs for location estimation and tracking in heterogeneous wireless networks. Various techniques have been studied and combined for location tracking, e.g., the least square methods for location estimation associated with the Kalman filters for location tracking. In this paper, the hybrid unified Kalman tracking (HUKT) technique is proposed to provide an integrated algorithm for precise location tracking based on both time of arrival (TOA) and time difference of arrival (TDOA) measurements. A new variable is incorporated as an additional state within the Kalman filtering formulation to consider the nonlinear behavior in the measurement update process. The relationship between this new variable and the desired location estimate is applied in the state update process of the Kalman filter. Three different designs of hybrid factor are proposed to adaptively adjust the weighting value between the TOA and TDOA measurements. Moreover, similar concepts are also utilized in the design of unified Kalman tracking schemes for pure TOA and TDOA measurement inputs in this paper. Compared with existing schemes, numerical results illustrate that the proposed HUKT algorithm can achieve enhanced accuracy for mobile location tracking, particularly under environments with an insufficient number of measurements in one of the signal paths. [ABSTRACT FROM PUBLISHER]

The stability of sparse signal reconstruction with respect to measurement noise is investigated in this paper. We design efficient algorithms to verify the sufficient condition for unique \ell1 sparse recovery. One of our algorithms produces comparable results with the state-of-the-art technique and performs orders of magnitude faster. We show that the \ell1-constrained minimal singular value (\ell1-CMSV) of the measurement matrix determines, in a very concise manner, the recovery performance of \ell1-based algorithms such as the Basis Pursuit, the Dantzig selector, and the LASSO estimator. Compared to performance analysis involving the Restricted Isometry Constant, the arguments in this paper are much less complicated and provide more intuition on the stability of sparse signal recovery. We show also that, with high probability, the subgaussian ensemble generates measurement matrices with \ell1-CMSVs bounded away from zero, as long as the number of measurements is relatively large. To compute the \ell1-CMSV and its lower bound, we design two algorithms based on the interior point algorithm and the semidefinite relaxation. [ABSTRACT FROM AUTHOR]

This paper addresses a transmission energy problem for distributed (or decentralized) estimation in multihop wireless sensor networks (WSN). A primary advantage of distributed estimation is its energy efficiency when compared to centralized estimation. Two distributed estimation schemes are considered in this paper: progressive estimation and consensus estimation. We develop a generalized energy planning algorithm for a progressive estimation method which exploits routing tree and channel state information. We also analyze the energy cost for a consensus estimation method used in broadcast multihop WSN. We demonstrate by analysis and simulation that, subject to an equivalent performance, the total energy cost for consensus estimation is typically much higher than that for progressive estimation, but the peak energy for the former can be less than that for the latter. [ABSTRACT FROM PUBLISHER]

Performance evaluation of multi-target tracking algorithms is of great practical importance in the design, parameter optimization and comparison of tracking systems. The goal of performance evaluation is to measure the distance between two sets of tracks: the ground truth tracks and the set of estimated tracks. This paper proposes a mathematically rigorous metric for this purpose. The basis of the proposed distance measure is the recently formulated consistent metric for performance evaluation of multi-target filters, referred to as the OSPA metric. Multi-target filters sequentially estimate the number of targets and their position in the state space. The OSPA metric is therefore defined on the space of finite sets of vectors. The distinction between filtering and tracking is that tracking algorithms output tracks and a track represents a labeled temporal sequence of state estimates, associated with the same target. The metric proposed in this paper is therefore defined on the space of finite sets of tracks and incorporates the labeling error. Numerical examples demonstrate that the proposed metric behaves in a manner consistent with our expectations. [ABSTRACT FROM PUBLISHER]

Facial age classification is an approach to classify face images into one of several predefined age groups. One of the difficulties in applying learning techniques to the age classification problem is the large amount of labeled training data required. Acquiring such training data is very costly in terms of age progress, privacy, human time, and effort. Although unlabeled face images can be obtained easily, it would be expensive to manually label them on a large scale and getting the ground truth. The frugal selection of the unlabeled data for labeling to quickly reach high classification performance with minimal labeling efforts is a challenging problem. In this paper, we present an active learning approach based on an online incremental bilateral two-dimension linear discriminant analysis (IB2DLDA) which initially learns from a small pool of labeled data and then iteratively selects the most informative samples from the unlabeled set to increasingly improve the classifier. Specifically, we propose a novel data selection criterion called the furthest nearest-neighbor (FNN) that generalizes the margin-based uncertainty to the multiclass case and which is easy to compute, so that the proposed active learning algorithm can handle a large number of classes and large data sizes efficiently. Empirical experiments on FG-NET and Morph databases together with a large unlabeled data set for age categorization problems show that the proposed approach can achieve results comparable or even outperform a conventionally trained active classifier that requires much more labeling effort. Our IB2DLDA-FNN algorithm can achieve similar results much faster than random selection and with fewer samples for age categorization. It also can achieve comparable results with active SVM but is much faster than active SVM in terms of training because kernel methods are not needed. The results on the face recognition database and palmprint/palm vein database showed that our approach can handle problems with large number of classes. Our contributions in this paper are twofold. First, we proposed the IB2DLDA-FNN, the FNN being our novel idea, as a generic on-line or active learning paradigm. Second, we showed that it can be another viable tool for active learning of facial age range classification. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel feature-based neural network (NN) approach for estimation of blood pressure (BP) from wrist oscillometric measurements. Unlike previous methods that use the raw oscillometric waveform envelope (OMWE) as input to the NN, in this paper, we propose to use features extracted from the envelope. The OMWE is mathematically modeled as a sum of two Gaussian functions. The optimum parameters of this model are found by minimizing the least squares error between the model and the OMWE using the Levenberg–Marquardt algorithm and are used as features. Two separate feed-forward NNs (FFNNs) are then designed to estimate the systolic and diastolic BPs using these features. The FFNNs are trained using the resilient backpropagation learning algorithm and tested on a data set of BP measurements recorded from 85 subjects. The performance is then compared with that of the conventional maximum amplitude algorithm, adaptive neuro-fuzzy inference system, and already published NN-based methods. It is found that the proposed approach achieves lower values of mean absolute error and standard deviation of error in the estimation of BP. In addition, the proposed approach has the following advantages: lower complexity with respect to the design parameters, smaller training data set, and lower computational load. [ABSTRACT FROM PUBLISHER]

This paper investigates a very practical and commonly encountered problem in state estimation. It is related to the placement of measurements in order to make an otherwise unobservable system observable. Typically, this is a situation that is frequently encountered during the daily operation as one or more of the measurements become unavailable due to various unexpected reasons. Under such circumstances, the state estimator resorts to the use of pseudo-measurements in order to recover observability and solve the state estimation problem. Pseudo-measurements that will make the system observable can be selected by applying one of the well-developed network observability analysis techniques in a repetitive fashion adding one candidate measurement at a time until the system is no longer unobservable. As the system size increases, this approach may prove computationally expensive. A nonrepetitive solution has also been proposed. However, its computational requirements grow rapidly with increasing number of observable islands that may exist in large scale systems. This paper presents an alternative approach which will be shown to be suitable for large scale power systems providing a compromise between the two existing techniques. Examples are given to highlight the main benefits of the proposed method. [ABSTRACT FROM PUBLISHER]

In this paper, we address the super resolution (SR) problem from a set of degraded low resolution (LR) images to obtain a high resolution (HR) image. Accurate estimation of the sub-pixel motion between the LR images significantly affects the performance of the reconstructed HR image. In this paper, we propose novel super resolution methods where the HR image and the motion parameters are estimated simultaneously. Utilizing a Bayesian formulation, we model the unknown HR image, the acquisition process, the motion parameters and the unknown model parameters in a stochastic sense. Employing a variational Bayesian analysis, we develop two novel algorithms which jointly estimate the distributions of all unknowns. The proposed framework has the following advantages: 1) Through the incorporation of uncertainty of the estimates, the algorithms prevent the propagation of errors between the estimates of the various unknowns; 2) the algorithms are robust to errors in the estimation of the motion parameters; and 3) using a fully Bayesian formulation, the developed algorithms simultaneously estimate all algorithmic parameters along with the HR image and motion parameters, and therefore they are fully-automated and do not require parameter tuning. We also show that the proposed motion estimation method is a stochastic generalization of the classical Lucas-Kanade registration algorithm. Experimental results demonstrate that the proposed approaches are very effective and compare favorably to state-of-the-art SR algorithms. [ABSTRACT FROM PUBLISHER]

This paper deals with subspace-based target detection in hyperspectral images. Specifically, it focuses on a general detection scheme where, first, background is suppressed through orthogonal-subspace projection and then target detection is accomplished. An adequate estimation of the background subspace is essential to a successful outcome. The background subspace has been typically estimated globally. However, global approaches may be ineffective for small-target-detection applications since they tend to overestimate the background interference affecting a given target. This may result in a low target residual energy after background suppression that is detrimental to detection performance. In this paper, we propose a novel and fully automatic algorithm for local background-subspace estimation (LBSE). Local background has typically a lower inherent complexity than that of global background. By estimating the background subspace over a local neighborhood of the test pixel, the resulting background-subspace dimension is expected to be low, thus resulting in a higher target residual energy after suppression which benefits the detection performance. Specifically, the proposed LBSE acts on a per-pixel basis, thus adaptively tailoring the estimated basis to the local complexity of background. Both simulated and real hyperspectral data are employed to investigate the detection-performance improvements offered by LBSE with respect to both global and local methodologies previously presented. [ABSTRACT FROM AUTHOR]

In this paper, exponential signals in the frequency domain are accurately analyzed by an algorithm, and the peaks of the discrete Fourier transform (DFT) result are adopted to obtain parameters that include amplitudes, frequencies, dampings, and phases. There are two steps for this algorithm: interpolated DFT and leakage elimination. Interpolated DFT refers to the three neighboring spectral lines at the peak of each mode used to calculate an approximate result, with the purpose of leakage elimination being to eliminate the influence of leakage on these data. After some iteration, this algorithm will obtain accurate parameters of the exponential components that are derived from their DFT results. The comparison of different algorithms is also discussed in this paper. [ABSTRACT FROM PUBLISHER]

Detection and delineation of P- and T-waves are important issues in the analysis and interpretation of electrocardiogram (ECG) signals. This paper addresses this problem by using Bayesian inference to represent a priori relationships among ECG wave components. Based on the recently introduced partially collapsed Gibbs sampler principle, the wave delineation and estimation are conducted simultaneously by using a Bayesian algorithm combined with a Markov chain Monte Carlo method. This method exploits the strong local dependency of ECG signals. The proposed strategy is evaluated on the annotated QT database and compared to other classical algorithms. An important feature of this paper is that it allows not only for the detection of P- and T-wave peaks and boundaries, but also for the accurate estimation of waveforms for each analysis window. This can be useful for some ECG analysis that require wave morphology information. [ABSTRACT FROM AUTHOR]

This paper considers the problem of density estimation and clustering in sensor networks. It is assumed that measurements of the sensors can statistically be modeled by a common Gaussian mixture model (GMM). In this paper, a distributed expectation maximization (DEM) algorithm is developed to estimate the model order and the parameters of this model. Scalability and fault tolerance are two important advantages of this method. In the E-step of this algorithm, each node calculates local sufficient statistics using its local observations. A distributed averaging approach is then used to diffuse local sufficient statistics to neighboring nodes and estimate global sufficient statistics in each node. In the M-step, each node updates parameters of the GMM using the estimated global sufficient statistics. Diffusion speed and convergence of the proposed algorithm are also studied. The proposed method is then used for environmental monitoring and also distributed target classification. Simulation results approve the promising performance of this algorithm. [ABSTRACT FROM PUBLISHER]

This paper considers the statistical performance of the MUSIC method under the condition that two closely spaced sources impinging on an array of sensors are effectively resolved, i.e., the spectrum exhibits two peaks in the neighborhood of the true directions-of-arrival (DOA). The MUSIC algorithm is known to have an infinite resolution power in theory. However, in the presence of modeling errors, sources can not be resolved with certainty, even if the array correlation matrix is perfectly known. The focus of this paper is to predict the bias and variance of the DOA estimates taking into account the possible resolution failure of MUSIC. This performance prediction, based on our recent mathematical investigation, is new to the best of our knowledge. A general mathematical framework to derive closed form expressions of the bias and variance versus the model mismatch, conditioned on a general statistical resolution test is proposed. In order to illustrate our mathematical approach, statistical tests with one and two conditions, respectively, are investigated. The accuracy of the performance prediction is illustrated in a simulation study. It is found that the proposed approach outperforms the "classical" technique, which ignores the possible resolution failure of the MUSIC algorithm. Therefore, our results provide better tools for determining the necessary antenna calibration accuracy to achieve some targeted specifications on the estimator performance. [ABSTRACT FROM AUTHOR]

Motion-compensated frame rate up-conversion (MC-FRUC) consists of two key elements: motion estimation and motion-compensated frame interpolation. The motion estimation algorithm presented in [1], which is used in the MC-FRUC method proposed in this paper, provides unidirectional motion trajectories. The advantage of this motion estimation algorithm, besides its accuracy, is that it provides information on occlusions. However, motion compensation along unidirectional motion trajectories yields overlaps, holes, and blocking artifacts. To solve these problems, this paper presents two new algorithms for unidirectional motion-compensated frame interpolation: irregular-grid expanded-block weighted motion compensation (DIEM) and block-wise directional hole interpolation (BUDDHISM). The DIEM is used to reduce the blocking artifacts and solve the problem of overlapping blocks. The BUDDHISM preserves local texture and edges while filling holes. Experimental results show that the DIEM outperforms conventional motion compensation, and the BUDDHISM is better than the repeated median filter that is often used to fill holes. The performance of the proposed MC-FRUC, that uses the two new algorithms and the unidirectional motion estimation algorithm, is evaluated against three existing MC-FRUC techniques: a typical bi-directional algorithm, an object-based algorithm, and a commercial plug-in product. Experimental results show that the quality of the pictures interpolated using the proposed MC-FRUC method is much higher than those interpolated using the three existing MC-FRUC techniques. [ABSTRACT FROM AUTHOR]

This paper considers the problem of time difference-of-arrival (TDOA) source localization when the TDOA measurements from multiple disjoint sources are subject to the same sensor position displacements from the available sensor positions. This is a challenging problem and closed-form solution with good localization accuracy has yet to be found. This paper proposes an estimator that can achieve this purpose. The pro- posed algorithm jointly estimates the unknown source and sensor positions to take the advantage that the TDOAs from different sources have the same sensor position displacements. The joint estimation is a highly nonlinear problem due to the coupling of source and sensor positions in the measurement equations. We introduce the novel idea of hypothesized source locations in the algorithm development to enable the formulation of psuedolinear equations, thereby leading to the establishment of closed-form solution for source location estimates. Besides the advantage of closed-form, the newly developed algorithm is shown analytically, under the condition that the TDOA measurement noise and the sensor position errors are sufficiently small, to reach the CRLB accuracy. For clarity, the localization of two disjoint sources is used in the algorithm development. The developed algorithm is then examined under the special case of a single source and extended to the more general case of more than two unknown sources. The theoretical developments are supported by simulations. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of signal subspace estimation for dimensionality reduction (DR) in hyperspectral images in the presence of rare pixels, i.e., pixels that are scarcely represented in the image and containing spectral components that are linearly independent of the background. Most of the classical methods proposed in the literature are based on the analysis second-order statistics (SOS), which are weakly influenced by the rare signals. Therefore, such techniques estimate the signal subspace addressing mostly the background and ignoring the presence of rare pixels. This may reduce the target/background spectral contrast, thus decreasing the detection performance when DR is adopted as preprocessing task in small-target detection applications. In this paper, a new robust algorithm, namely, robust signal subspace estimation (RSSE), is developed, which preserves both abundant and rare signal components. It combines the analysis of SOS and a recent approach based on the analysis of the l2∞ norm. The novel contribution of this paper is twofold. First, the RSSE algorithm is presented, which includes a new iterative procedure to derive the signal subspace and an original statistical method to estimate the data dimensionality. Second, an ad simulation strategy is proposed to assess the performance of signal subspace estimation methods in the presence of rare signal components. The procedure is adopted to compare the RSSE algorithm with a classical technique based on the analysis of SOS. The results obtained by applying the two methods on a real Airborne Visible Infrared Imaging Spectrometer hyperspectral image also presented and discussed. [ABSTRACT FROM AUTHOR]

This paper develops a Square Root Unscented Kalman Filter (SRUKF) for performing video-rate visual simultaneous localization and mapping (SLAM) using a single camera. The conventional UKF has been proposed previously for SLAM, improving the handling of nonlinearities compared with the more widely used Extended Kalman Filter (EKF). However, no account was taken of the comparative complexity of the algorithms: In SLAM, the UKF scales as O(N³) in the state length, compared to the EKF's O(N²), making it unsuitable for video-rate applications with other than unrealistically few scene points. Here, it is shown that the SRUKF provides the same results as the UKF to within machine accuracy and that it can be reposed with complexity O(N²) for state estimation in visual SLAM. This paper presents results from video-rate experiments on live imagery. Trials using synthesized data show that the consistency of the SRUKF is routinely better than that of the EKF, but that its overall cost settles at an order of magnitude greater than the EKF for large scenes. [ABSTRACT FROM AUTHOR]

This paper describes a channel estimation algorithm for the advanced terrestrial digital multimedia broadcasting (T-DMB) transmission system which is fully backward compatible with the conventional T-DMB standard. The advanced T-DMB has enhanced features which provide higher transmission data rate for increasing program channel capacity and better multimedia A/V service quality than the conventional T-DMB system by adopting a hierarchical modulation and scalable audio/video CODEC. To support the higher data rate transmission for the advanced T-DMB, high performance channel estimation and equalization techniques should be adopted into the advanced T-DMB receivers. In this paper, we propose a new channel estimation technique for the Advanced T-DMB to compensate channel distortion under dynamic multipath environment. [ABSTRACT FROM AUTHOR]

This paper proves that the mathematical complexity of running filters on semi-groups is C(p) = 3 - (6/(p + 1)) operations per sample, where p is the filter window. On other algebraic structures, the mathematical complexity of the filtering can be lower. Two such cases are investigated: the running filtering on max/mm lattice and on additions group. They correspond to max/mm and moving average filters. It is shown that the algorithms developed for semi-groups are of interest in such cases, too. Thus, the fastest deterministic algorithm for max/mm filtering known so far is based on an algorithm for running filtering on semi-groups. First, an optimized version is proposed in this paper. Then, further improvement for data-dependent max/mm algorithms is proposed. Finally, the case of moving average and exponential smoothing is investigated. Their implementation by using the algorithms for semi-groups is of lower complexity than the classical recursive implementation for the particular case of small size windows (p = 3 and p = 4). [ABSTRACT FROM AUTHOR]

In this paper, we describe an approach to the problem of simultaneously enhancing image sequences and tracking the objects of interest represented by the latter. The enhancement part of the algorithm is based on Bayesian wavelet denoising, which has been chosen due to its exceptional ability to incorporate diverse a priori information into the process of image recovery. In particular, we demonstrate that, in dynamic settings, useful statistical priors can come both from some reasonable assumptions on the properties of the image to be enhanced as well as from the images that have already been observed before the current scene. Using such priors forms the main contribution of the present paper which is the proposal of the dynamic denoising as a tool for simultaneously enhancing and tracking image sequences. Within the proposed framework, the previous observations of a dynamic scene are employed to enhance its present observation. The mechanism that allows the fusion of the information within successive image frames is Bayesian estimation, while transferring the useful information between the images is governed by a Kalman filter that is used for both prediction and estimation of the dynamics of tracked objects. Therefore, in this methodology, the processes of target tracking and image enhancement "collaborate" in an interlacing manner, rather than being applied separately. The dynamic denoising is demonstrated on several examples of SAR imagery. The results demonstrated in this paper indicate a number of advantages of the proposed dynamic denoising over "static" approaches, in which the tracking images are enhanced independently of each other. [ABSTRACT FROM AUTHOR]

This paper addresses object tracking in ultrasound images using a robust multiple model tracker. The proposed tracker has the following features: 1) it uses multiple dynamic models to track the evolution of the object boundary, and 2) it models invalid observations (outliers), reducing their influence on the shape estimates. The problem considered in this paper is the tracking of the left ventricle which is known to be a challenging problem. The heart motion presents two phases (diastole and systole) with different dynamics, the multiple models used in this tracker try to solve this difficulty. In addition, ultrasound images are corrupted by strong multiplicative noise which prevents the use of standard deformable models. Robust estimation techniques are used to address this difficulty. The multiple model data association (MMDA) tracker proposed in this paper is based on a bank of nonlinear filters, organized in a tree structure. The algorithm determines which model is active at each instant of time and updates its state by propagating the probability distribution, using robust estimation techniques. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of blind identification of a convolutive multiple-input-multiple-output (MIMO) system with No outputs and Ni inputs. While many methods have been proposed to blindly identify convolutive MIMO systems with No ≥ Ni (overdetermined), very scarce results exist for the case of No < Ni (underdetermined), all of which refer to systems that either have some special structure or special No and Ni values. In this paper, we show that, as long as min(No, Ni) ≥ 2, independent of whether the system is overdetermined or underdetermined, we can always find the appropriate order of statistics that guarantees identifiability of the system response within trivial ambiguities. We also propose an algorithm to reach the solution, that consists of parallel factorization (PARAFAC) of a K-way tensor containing K th-order statistics of the system outputs, followed by an iterative scheme. For a certain order of statistics K, we provide the description of the class of identifiable MIMO systems. We also show that this class can be expanded by applying PARAFAC decomposition to a pair of tensors instead of one tensor. The proposed approach constitutes a novel scheme for estimation of underdetermined systems, and improves over existing approaches for overdetermined systems. [ABSTRACT FROM AUTHOR]

Standard formulations of image/signal deconvolution under wavelet-based priors/regularizers lead to very high-dimensional optimization problems involving the following difficulties: the non-Gaussian (heavy-tailed) wavelet priors lead to objective functions which are nonquadratic, usually nondifferentiable, and sometimes even nonconvex; the presence of the convolution operator destroys the separability which underlies the simplicity of wavelet-based denoising. This paper presents a unified view of several recently proposed algorithms for handling this class of optimization problems, placing them in a common majorization-minimization (MM) framework. One of the classes of algorithms considered (when using quadratic bounds on non- differentiable log-priors) shares the infamous "singularity issue" (SI) of "iteratively reweighted least squares" (IRLS) algorithms: the possibility of having to handle infinite weights, which may cause both numerical and convergence issues. In this paper, we prove several new results which strongly support the claim that the SI does not compromise the usefulness of this class of algorithms. Exploiting the unified MM perspective, we introduce a new algorithm, resulting from using £1 bounds for nonconvex regularizers; the experiments confirm the superior performance of this method, when compared to the one based on quadratic majorization. Finally, an experimental comparison of the several algorithms, reveals their relative merits for different standard types of scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we present a new parametric modeling of three-dimensional homogeneous volumes of textured images (3-D texture) based on 3-D Wold decomposition for discrete processes. This decomposition additively separates any 3-D discrete homogeneous process into three mutually orthogonal components: a purely indeterministic, a "remote past"—type deterministic and an evanescent component. The evanescent component can be further decomposed into two orthogonal components called type 1 evanescent and type 2 evanescent. Assuming that the 3-D image volume is a finite realization of a discrete homogeneous stochastic process, the 3-D texture field can be uniquely represented as a sum of four mutually orthogonal components with different spectral properties. The aim of this paper is to present new parametric models able to describe both the spectral support and spatial characteristics of each component. A spectral decomposition algorithm to separate a 3-D texture into structured and unstructured parts is also proposed. [ABSTRACT FROM AUTHOR]