Search

The temperature and irradiance dependent mathematical model for photovoltaic panel performances estimation is proposed in the paper. The base of the model is the mathematical function of the photovoltaic panel current–voltage curve. The model of the current–voltage curve is based on the sigmoid function with temperature and irradiance dependent parameters. The temperature and irradiance dependencies of the parameters are proposed in the form of analytic functions. The constant parameters are involved in the analytical functions. The constant parameters need to be estimated to get the temperature and irradiance dependent current–voltage curve. The mathematical model contains 12 constant parameters and they are estimated by using the evolutionary algorithm. The optimization problem is defined for this purpose. The optimization problem objective function is based on estimated and extracted (measured) current and voltage values. The current and voltage values are extracted from current–voltage curves given in datasheet of the photovoltaic panels. The new procedure for estimation of open circuit voltage value at any temperature and irradiance is proposed in the model. The performance of the proposed mathematical model is presented for three different photovoltaic panel technologies. The simulation results indicate that the proposed mathematical model is acceptable for estimation of temperature and irradiance dependent current–voltage curve and photovoltaic panel performances within temperature and irradiance ranges. [ABSTRACT FROM AUTHOR]

The notion of Architectural Vulnerability Factor (AVF) has been extensively used to evaluate various aspects of design robustness. While AVF has been a very popular way of assessing element resiliency, its calculation requires rigorous and extremely time-consuming experiments. Furthermore, recent radiation studies in 90 nm and 65 nm technology nodes demonstrate that up to 55 percent of Single Event Upsets (SEUs) result in Multiple Bit Upsets (MBUs), and thus the Single Bit Flip (SBF) model employed in computing AVF needs to be reassessed. In this paper, we present a method for calculating the vulnerability of modern microprocessors -using Statistical Fault Injection (SFI)- several orders of magnitude faster than traditional SFI techniques, while also using more realistic fault models which reflect the existence of MBUs. Our method partitions the design into various hierarchical levels and systematically performs incremental fault injections to generate vulnerability estimates. The presented method has been applied on an Intel microprocessor and an Alpha 21264 design, accelerating fault injection by 15×, on average, and reducing computational cost for investigating the effect of MBUs. Extensive experiments, focusing on the effect of MBUs in modern microprocessors, corroborate that the SBF model employed by current vulnerability estimation tools is not sufficient to accurately capture the increasing effect of MBUs in contemporary processes. [ABSTRACT FROM PUBLISHER]

It is well known that appropriately biasing an estimator can potentially lead to a lower mean square error (MSE) than the achievable MSE within the class of unbiased estimators. Nevertheless, the choice of an appropriate bias is generally unclear and only recently there have been attempts to systematize such a selection. These systematic approaches aim at introducing MSE bounds that are lower than the unbiased Cramér–Rao bound (CRB) for all values of the unknown parameters and at choosing biased estimators that beat the standard maximum-likelihood (ML) and/or least squares (LS) estimators in the finite sample case. In this paper, we take these approaches one step further and investigate the same problem from the aspect of an end-performance metric different than the classical MSE. This study is motivated by recent advances in the area of system identification indicating that the optimal experiment design should be done by taking into account the end-performance metric of interest and not by quantifying a quadratic distance of the unknown model from the true one. [ABSTRACT FROM AUTHOR]

The estimation of signal parameters using quantized data is a recurrent problem in electrical engineering. As an example, this includes the estimation of a noisy constant value and of the parameters of a sinewave, that is, its amplitude, initial record phase, and offset. Conventional algorithms, such as the arithmetic mean, in the case of the estimation of a constant, are known not to be optimal in the presence of quantization errors. They provide biased estimates if particular conditions regarding the quantization process are not met, as it usually happens in practice. In this paper, a quantile-based estimator is presented, which is based on the Gauss–Markov theorem. The general theory is first described and the estimator is then applied to both direct current and alternate current input signals with unknown characteristics. Using simulations and experimental results, it is shown that the new estimator outperforms conventional estimators in both problems, by removing the estimation bias. [ABSTRACT FROM PUBLISHER]

Calibration of a robot manipulator requires an external instrument to measure its end-effector locations. If the parameters are estimated directly from the measurements, it is called one-stage estimation. Otherwise, it is called two-stage estimation. It was previously observed that two-stage estimation has the drawback of error propagation. Theoretically, it is known that for linear system, the minimum variance is achieved using the best linear unbiased estimator in one-stage estimation. However, the two-stage estimation can also achieve the minimum variance under certain conditions. In this paper, the statistical properties of both are explored in detail under a newly established mathematical fact. The result can be extended to the nonlinear estimation problem via approximation. Simulations of robot calibration by a coordinate measuring machine and eye-in-hand camera are conducted and the results confirm the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper investigates the stress–strength reliability in the presence of fuzziness. The fuzzy membership function is defined as a function of the difference between stress and strength values, and the fuzzy reliability of single unit and multicomponent systems are calculated. The inclusion of fuzziness in the stress–strength interference enables the user to make more sensitive analysis. Illustrations are presented for various stress and strength distributions. [ABSTRACT FROM AUTHOR]

Orthogonal frequency division multiplexing (OFDM) over wireless channels is sensitive to carrier frequency offset (CFO), which destroys orthogonality amongst sub-carriers, giving rise to inter-carrier interference (ICI). Different techniques are available for estimating and compensating for the CFO at the receiver. However, in practice, a residual CFO remains at the receiver after CFO estimation, where the estimation accuracy depends primarily on the fractions of time and power used by the estimator. In this paper, we propose to measure the efficiency of OFDM systems with CFO estimation errors in terms of the spectral efficiency, which accounts for both, the degradation in signal-to-interference plus noise ratio (SINR) due to the residual CFO, and the penalty of the extra power and spectral resources allocated to achieve the desired CFO estimation accuracy. New accurate expressions are derived for the spectral efficiency of wireless OFDM systems in the presence of residual CFO and frequency-selective multipath fading channel. These are used to compare between two common CFO estimation methods in wireless OFDM systems, namely, the cyclic prefix based and the training symbols based CFO estimation techniques for fixed and variable pilot power. These results are further extended to include OFDM systems with transmit diversity techniques. In addition, the impact of imperfect channel estimation on the overall spectral efficiency is also included. Numerical results reveal that the cyclic prefix based CFO technique is more efficient than the training symbols based CFO technique when perfect channel state information (CSI) is known blindly at the receiver. Furthermore, fixed pilot power results in a spectral efficiency ceiling as SNR increases, whereas spectral efficiency increases with SNR without bound in the equal pilot and signal powers case. [ABSTRACT FROM PUBLISHER]

Acoustic tracking with propagation delay is a challenging problem for the following reasons: 1) It is difficult to perform an accurate state prediction, as the time interval between the current state and the previous state is varying and unknown due to the propagation delay; and 2) the target time (signal emission time) needs to be estimated in addition to the target position and velocity. With the state augmented with the target time, the state transition cannot be described by the commonly used explicit Gauss?Markov model. In this paper, we propose a new approach to solving this difficult problem by using the Gauss?Helmert model for the state transition, which consists of an implicit equation between two consecutive states. An unscented Gauss?Helmert filter is then developed based on this formulation. The new approach is applied to the bearings-only tracking problem with state-dependent propagation delay. Simulation tests are conducted to demonstrate the performance of the unscented Gauss?Helmert filter, which is shown to outperform other approaches in terms of estimation accuracy, especially when a target is moving at high speed. [ABSTRACT FROM PUBLISHER]

This paper presents a novel analytical redundancy approach for pitot tube failure accommodation using nonlinear Kalman filtering. This approach utilizes information from other sensors that are commonly implemented on aircraft in order to obtain an estimate of the airspeed which is independent from the pitot tube(s). This method was demonstrated to be independent of the aircraft model by providing experimental estimation results from two different unmanned aerial vehicle (UAV) research platforms. [ABSTRACT FROM PUBLISHER]

This paper presents a novel sigma-point unscented predictive filter (UPF) for relative position and attitude estimation of satellite formation taking into account the influence of J 2 . A coupled relative translational dynamics model is formulated to represent orbital motion of arbitrary feature points on the deputy spacecraft, and the relative attitude motion is formulated by considering a rotational dynamics for a satellite without gyros. Based on the proposed coupled dynamic model, the UPF is developed based on unscented transformation technique, extending the capability of a traditional predictive filter (PF). The algorithm flow of the UPF is described first. Then it is demonstrated that the estimation accuracy of the model error and system state for UPF is higher than that of the traditional PF. In addition, the unscented Kalman filter (UKF) is also employed in order to compare the performance of the proposed UPF with that of the UKF. Several different scenarios are simulated to validate the effectiveness of the coupled dynamics model and the performance of the proposed UPF. Through comparisons, the proposed UPF is shown to yield highly accurate estimation of relative position and attitude during satellite formation flying. [ABSTRACT FROM AUTHOR]

Internal combustion engines exhibit fast pulsating short-time dynamics due to the reciprocating cylinder motion, around mean operating points that change comparatively slow due to inputs such as throttle and load. Comparatively, simple mean value engine models (MVEM) describe the slow changes of the averaged states for automotive control and fault diagnosis. In this paper, a bank of state estimators based on MVEMs is used for fault residual generation. Three faults: 1) throttle mass air-flow sensor fault; 2) exhaust gas recirculation valve sensor fault; and 3) exhaust leak fault are considered here. These faults are significant as they affect emission levels. Optimized thresholds for residual classification are derived for minimizing false alarm rates and missed detection rates. The diagnosis logic, based on the principles of structured residuals proposed in literature, is extended here for multiple hypotheses testing. Furthermore, the Dempster–Shafer theory is used to associate a confidence measure with the decision conclusions and this is shown to improve isolation. Performance is demonstrated with automotive engine data obtained from a four-cylinder instantaneous spark-ignition engine (gasoline) system model, developed in the simulation software AMESim. [ABSTRACT FROM PUBLISHER]

This paper presents a distributed leader-assistive localization approach for a heterogeneous multirobotic system (MRS). The localization algorithm is formulated to estimate the position and orientation (pose) of a group of robots in a given reference coordinate frame (or global coordinate frame). It is assumed that the heterogeneous-MRS has one or a group of robots (which we refer as leader robots) with higher sensor payload, higher processing power, and larger memory capacity, enabling accurate self-localization capabilities. Robots with limited resources (which we refer as child robots) rely on leader robots, and inter-robot observations between leaders and themselves for localization. Finite-range sensing is a key challenge for such leader-assistive localization. This study presents a sensor sharing technique which virtually enhances the sensing range of leader robots. In the proposed method, each robot locally runs a cubature Kalman filter to estimate its own pose and hosts a low cost, lightweight, and low-power sensory system to periodically measure relative pose of neighbors. Each robot transmits these relative pose measurements to leader robots. Leader robots then combine available relative observations in order to synthesise global pose measurements and associated noise covariances for child robots. Child robots are acknowledged by the leader robots with the synthesized global pose measurements and fuse these measurements with their local belief in order to improve their localization. Theoretical developments are presented to virtually enhance the leader robots’ sensing range. The performance of the proposed distributed leader-assistive localization algorithm is evaluated on a multirobot simulation test-bed and on a publicly available multirobot localization and mapping data-set. The results illustrate that the proposed algorithm is capable of establishing accurate and consistent localization for the child robots even when they operate beyond the sensing range of the leader robots. [ABSTRACT FROM PUBLISHER]

Chatting with nearby interested strangers instantly in location-based mobile social network (LMSN) has become increasingly popular. Currently, friend recommendation relies only on the simple and limited user profiles, and is agnostic to users' offline behaviors in the real world. For the first time, we focus on utilizing the user's check-in behaviors in the real world, instead of the general acquaintance-based social circles, to instantly recommend nearby strangers to make friends. However, bridging nearby strangers with similar check-in behaviors instantly has some new characteristics, such as lack of common friends and interaction histories, temporal, spatial and user three-dimensional correlation, and sparseness of check-ins. Most existing work about friend recommendations mainly focuses on making friends within the acquaintance-based social circles, and has not fully considered these new characteristics mentioned above. Therefore, how to catch the ephemeral opportunity to recommend nearby interested strangers instantly remains a challenge. In this paper, we present to use “Encounter” probability to measure the behavior similarity of two strangers in the real world based on their check-in histories. To address the sparseness challenge of check-in data, a Kernel Density Estimation (KDE)-based user check-in probability estimation method considering the spatiotemporal dimensions is proposed to estimate each user's check-in probability distribution with time at each spot. Finally, we use a large-scale user check-in dataset of Gowalla to validate the effectiveness of this approach. The experimental results show that our approach outperforms other commonly used similarity computation methods. [ABSTRACT FROM PUBLISHER]

This paper revisits an existing method of constructing high-dimensional probability density functions (PDFs) based on the PDF at the output of a dimension-reducing feature transformation. We show how to modify the method so that it can provide the PDF with the highest entropy among all PDFs that generate the given low-dimensional PDF. The method is completely general and applies to arbitrary feature transformations. The chain-rule is described for multi-stage feature calculations typically used in signal processing. Examples are given including MFCC and auto-regressive features. Experimental verification of the results using simulated data is provided including a comparison with competing generative methods. [ABSTRACT FROM PUBLISHER]

In this paper, a novel discrete-time estimator is proposed, which is employed for simultaneous estimation of system states, and actuator/sensor faults in a discrete-time dynamic system. The existence of the discrete-time simultaneous estimator is proven mathematically. The systematic design procedure for the derivative and proportional observer gains is addressed, enabling the estimation error dynamics to be internally proper and stable, and robust against the effects from the process disturbances, measurement noise, and faults. Based on the estimated fault signals and system states, a discrete-time fault-tolerant design approach is addressed, by which the system may recover the system performance when actuator/sensor faults occur. Finally, the proposed integrated discrete-time fault estimation and fault-tolerant control technique is applied to the vehicle lateral dynamics, which demonstrates the effectiveness of the developed techniques. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel unified structure to estimate tire forces. The proposed structure uses estimation modules to calculate/estimate tire forces by means of nonlinear observers. The novelty in the proposed approach lies in the independence of the estimates from the vehicle tire model, thereby making the structure robust against variations in vehicle mass, tire parameters due to tire wear, and, most importantly, road surface conditions. In the proposed structure, we have a dedicated module to estimate the longitudinal tire forces and another to calculate the vertical tire forces. Subsequently, these forces are fed into a third module that utilizes a nonlinear observer to estimate lateral tire forces. The proposed structure is validated through experimental studies. [ABSTRACT FROM PUBLISHER]

This paper describes the measurement and nonparametric estimation of phase difference with stronger inhibition of spectral leakage. The phase difference is estimated by discrete Fourier transform coefficients around component peaks using Rife–Vincent windows class I (RV1) and the minimum side-lobe level (MSL) windows. Selecting a suitable window order, MSL windows demonstrate a very low bias error and lower noise error propagation than RV1 windows. A practical example evaluates the accuracy of the used windows in estimations. [ABSTRACT FROM PUBLISHER]

This paper describes a development framework for alternating current ratio standard working at high audio frequencies. To realize this, first, a calibration method for frequency-dependent balancing current generators (BCGs) producing error currents to balance current comparators (CCs) is proposed. Second, the BCG error correction is made not only for the current transformer (CT) calibration but also for the CC self-calibration. Third, practical capacitive error corrections are also proposed to be introduced for the CT and CC calibrations. Finally, in consideration of the BCG error, capacitive error, and compensated CC error, a proposed way of calculating CT error and estimating its uncertainty is described. [ABSTRACT FROM PUBLISHER]

Modeling intensity of facial action units from spontaneously displayed facial expressions is challenging mainly because of high variability in subject-specific facial expressiveness, head-movements, illumination changes, etc. These factors make the target problem highly context-sensitive. However, existing methods usually ignore this context-sensitivity of the target problem. We propose a novel Conditional Ordinal Random Field (CORF) model for context-sensitive modeling of the facial action unit intensity, where the W5+ (who, when , what, where, why and how) definition of the context is used. While the proposed model is general enough to handle all six context questions, in this paper we focus on the context questions: who (the observed subject), how (the changes in facial expressions), and when (the timing of facial expressions and their intensity). The context questions who and how are modeled by means of the newly introduced context-dependent covariate effects, and the context question when is modeled in terms of temporal correlation between the ordinal outputs, i.e., intensity levels of action units. We also introduce a weighted softmax-margin learning of CRFs from data with skewed distribution of the intensity levels, which is commonly encountered in spontaneous facial data. The proposed model is evaluated on intensity estimation of pain and facial action units using two recently published datasets (UNBC Shoulder Pain and DISFA) of spontaneously displayed facial expressions. Our experiments show that the proposed model performs significantly better on the target tasks compared to the state-of-the-art approaches. Furthermore, compared to traditional learning of CRFs, we show that the proposed weighted learning results in more robust parameter estimation from the imbalanced intensity data. [ABSTRACT FROM AUTHOR]

A large number of web data repositories are hidden behind restrictive web interfaces, making it an important challenge to enable data analytics over these hidden web databases. Most existing techniques assume a form-like web interface which consists solely of categorical attributes (or numeric ones that can be discretized). Nonetheless, many real-world web interfaces (of hidden databases) also feature checkbox interfaces—e.g., the specification of a set of desired features, such as A/C, navigation, etc., for a car-search website like Yahoo! Autos. We find that, for the purpose of data analytics, such checkbox-represented attributes differ fundamentally from the categorical/numerical ones that were traditionally studied. In this paper, we address the problem of data analytics over hidden databases with checkbox interfaces. Extensive experiments on both synthetic and real datasets demonstrate the accuracy and efficiency of our proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of tracking the two-dimensional (2-D) direction-of-arrivals (DOAs) of multiple moving targets with crossover points on their trajectories, and we propose a new computationally efficient cross-correlation based 2-D DOA estimation with automatic pair-matching (CODEC) method for noncoherent narrowband signals impinging on the L-shaped sensor array structured by two uniform linear arrays (ULAs) with omnidirectional sensors. The effectiveness of the proposed method and the theoretical analysis are substantiated through numerical examples. [ABSTRACT FROM AUTHOR]

This paper presents an important improvement on Sherman?s method for angle estimation of two unresolved targets when using monopulse radar. The graphical or numerical solution for the 6th-order polynomial equation is replaced with a quadratic equation that has exact algebraic solutions for nonfluctuating or slowly fluctuating targets. The proposed method can also estimate the angle for a single target without ambiguity and can estimate the angles when two targets are positioned at the same azimuth or elevation angle, in contrast with previous techniques. The proposed method is validated using simulations. [ABSTRACT FROM AUTHOR]

A structured least squares (SLS)?based ESPRIT algorithm for direction-of-arrival (DOA) estimation of strictly second-order noncircular signals using a crossed array is devised in this paper. Unlike the conventional ESPRIT for noncircular signals, which employs the least squares or total least squares, the proposed solution is able to exploit the overlapping structure of the subarray configurations to efficiently solve the shift-invariant equations (SIEs), ending up with the SLS solution for a three-axis crossed array. Moreover, an additional constraint requiring the SIEs of the three linear subarrays directed along the x-, y-, and z-axes to share the same set of eigenvectors is applied to solve the rank-deficiency problem. Consequently, the proposed approach provides more accurate DOA estimates, and its superiority over existing ESPRIT schemes is demonstrated via computer simulations. [ABSTRACT FROM AUTHOR]

Quadrature compressive sampling (QuadCS) is a recently introduced sub-Nyquist sampling scheme for effective acquisition of inphase and quadrature (I/Q) components of sparse radio frequency signals. In applications to pulse-Doppler radars, the QuadCS outputs can be arranged into a two-dimensional data format, in terms of slow time and virtual fast time, similar to that by Nyquist sampling. This paper develops a compressive sampling pulse-Doppler (CoSaPD) processing scheme which performs Doppler estimation/detection and range estimation from the sub-Nyquist data without recovering the Nyquist samples. The Doppler estimation is realized through a spectrum analyzer as in classical processing, whereas the detection is performed using the Doppler bin data. The range estimation is performed using sparse recovery algorithms only for the detected targets to reduce the computational load. A low detection threshold is used to improve the detection probability and the introduced false targets are then removed in the range estimation stage by exploiting the inherent target detection capability of the recovery algorithms. Simulation results verify the effectiveness of the proposed CoSaPD scheme, which requires only one-eighth of the Nyquist rate to achieve similar performance to the classical processing with Nyquist samples, provided that the input signal-to-noise ratio (SNR) is above ?25 dB. [ABSTRACT FROM AUTHOR]

In this paper, generalized forms of amplitude and intensity K-distributions are proposed to model multilook synthetic aperture radar (SAR) data. The approach is based upon a product model in which the amplitude and intensity distributions of the SAR backscatter component are assumed to be Laguerre orthogonal expansions of the traditional clutter distribution. The SAR intensity and amplitude probability density functions (pdfs) are derived and turn out to be the weighted combination of a series of K-distributions. Several theoretical and implementation issues regarding the proposed generalization are discussed. Comparative experimental results using real SAR data indicates that these generalized forms of K may allow better estimation of the SAR pdf than that of the traditional K distribution. [ABSTRACT FROM AUTHOR]

A three-dimensional (3D) bistatic inverse synthetic aperture radar (ISAR) imaging method is proposed in this paper. The proposed method makes use of interferometry and technically speaking, produces a 3D target reconstruction by estimating a scattering center position in 3D Cartesian space. The proposed method makes use of a combined ISAR/interferometry technique that also allows the ISAR image plane to orientation to be estimated. Cross- or L-shaped antenna configurations are discussed, and the effects of the baseline length along the horizontal and vertical direction on the scatterer?s position estimation are analyzed in detail. Finally, numerical simulations are used to evaluate the proposed method?s performance. [ABSTRACT FROM AUTHOR]

This paper presents applications of a generalized theoretical analysis of Tsui's phase measurement approach to detection of N - 1 ultraweak signals. A closed-form analytical expression is obtained for one application. A detection approach is presented for real-time estimation of the slope and curvature of sampled data of the instantaneous frequency. This approach provides the capability for early simultaneous signal detection and extraction of signal parameters. The capability for N = 4 simultaneous signals is desired for electronic warfare (EW) receivers. [ABSTRACT FROM AUTHOR]

This paper proposes a novel residual electrical endurance (REE) prediction modeling method for low-voltage electromagnetic alternating current (ac) contactors based on conditional density estimation (CDE). For a type of ac contactor, the current REE value is defined by the ratio of the current operation number against its maximum operation number. Voltage and current signals between contacts during breaking processes are acquired as the initial data. An arcing characteristic set of every breaking process is computed from the voltage/current signals, and the characteristic sets are grouped and averaged to achieve training and testing data sets. Four data schemes are applied and compared. CDE modeling with cross-validation is performed on the training data set to build REE prediction models, which are then applied to estimate the REEs of testing observations. The experiment proves that the modeling method is able to reach relatively precise predictions. [ABSTRACT FROM AUTHOR]

In this paper, some new nonlinear retarded integral inequalities of Gronwall–Bellman–Pachpatte type are investigated. Some applications are also presented to illustrate the usefulness of some of our results in estimation of solution of certain retarded integro-differential equations with the initial conditions. [ABSTRACT FROM AUTHOR]

This paper presents a cost-sensitive ordinal hyperplanes ranking algorithm for human age estimation based on face images. The proposed approach exploits relative-order information among the age labels for rank prediction. In our approach, the age rank is obtained by aggregating a series of binary classification results, where cost sensitivities among the labels are introduced to improve the aggregating performance. In addition, we give a theoretical analysis on designing the cost of individual binary classifier so that the misranking cost can be bounded by the total misclassification costs. An efficient descriptor, scattering transform, which scatters the Gabor coefficients and pooled with Gaussian smoothing in multiple layers, is evaluated for facial feature extraction. We show that this descriptor is a generalization of conventional bioinspired features and is more effective for face-based age inference. Experimental results demonstrate that our method outperforms the state-of-the-art age estimation approaches. [ABSTRACT FROM PUBLISHER]

This paper investigates a novel estimation method for iron losses considering hysteretic property and skin effect simultaneously. In the proposed method, iron losses considering both hysteretic property and skin effect are estimated by utilizing a 1-D finite element method as a postprocessing of the usual 2-D analyses based on ordinary magnetization curve. To demonstrate the effectiveness of the proposed iron loss estimation method, we compare numerical results with measured ones. Furthermore, the proposed method is applied to iron loss analysis of an interior permanent magnet motor fed by pulsewidth modulation inverter. [ABSTRACT FROM PUBLISHER]

This paper derives a novel initial alignment method for the strapdown inertial navigation system (SINS), which transforms the attitude alignment into an attitude estimation problem. The process model of the proposed initial alignment method by attitude estimation is established by decomposition of the attitude matrix. The measurement model is constructed based on a generalized velocity integration formula that can integrate the inertial measurements over certain fixed time intervals. The contributions of the work presented here are twofold. First, the attitude estimation-based structure enables the proposed method to estimate the gyroscope biases other than the attitude quaternion, which is celebrated for the low-cost SINS. The second is the application of the proposed generalized velocity integration formula to attenuate the accumulated errors in vector observations caused by the traditional velocity integration formula. Experimental road tests are performed with a low-cost SINS, which validate the efficacy of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper develops a Bayesian framework to assess the reliability and performance of multi-state systems (MSSs). An MSS consists of multiple multi-state components of which the degradation follows a Markov process. Due to the lack of sufficient data, and only vague knowledge from experts, the transition intensities of multi-state components between any pair of states and the state probabilities cannot be precisely estimated. The proposed Bayesian method can merge prior knowledge from experts' judgments with continuous and discontinuous inspection data to obtain posterior distributions of transition intensities. A simulation method embedded with the universal generating function (UGF) is developed to estimate the posterior state probabilities, the reliability, and the performance of the entire MSS. Two numerical experiments are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper describes strategies toward model-based automation of intravenous anaesthesia employing advanced control techniques. In particular, based on a detailed compartmental mathematical model featuring pharmacokinetic and pharmacodynamics information, two alternative model predictive control strategies are presented: a model predictive control strategy, based on online optimization, the extended predictive self-adaptive control and a multiparametric control strategy based on offline optimization, the multiparametric model predictive control. The multiparametric features to account for the effect of nonlinearity and the impact of estimation are also described. The control strategies are tested on a set of 12 virtually generated patient models for the regulation of the depth of anaesthesia by means of the bispectral index (BIS) using Propofol as the administrated anaesthetic. The simulations show fast response, suitability of dose, and robustness to induce and maintain the desired BIS setpoint. [ABSTRACT FROM PUBLISHER]

Real-time radial velocity estimation is a key challenge for moving target imaging and location in current single-antenna synthetic aperture radar (SAR)-ground moving target indication systems. Since the conventional methods suffer from ambiguity, complexity realization, or heavy computation load for fast moving target motion estimation, this paper emphasizes the estimation efficiency by simple realization. An efficient Radon transform (RT) estimation is proposed to estimate the radial velocity of fast moving target by utilizing the geometry information, and much more geometry information is exploited to realize clutter cancellation, noise cancellation, and estimation error minimizing in the RT domain, which is not proposed by the others. With only two to four angles used to calculate rather than search for the radial velocity of moving targets, the proposed methods simplify the conventional range and angle (2-D) searching procedure into several time range (1-D) searching procedure efficiently. The theoretical and experimental analysis provides qualitative and quantitative evaluations into the effectiveness of the proposed methods. In the single-antenna SAR system, the proposed methods can estimate the radial velocity of fast moving target efficiently and accurately in high signal to clutter plus noise ratio scenarios. [ABSTRACT FROM PUBLISHER]

Anomaly detection has been an important topic in hyperspectral image analysis. This technique is sometimes more preferable than the supervised target detection because it requires no a priori information for the interested materials. Many efforts have been made in this topic; however, they usually suffer from excessive time cost and a high false-positive rate. There are two major problems that lead to such a predicament. First, the construction of the background model and affinity estimation are often overcomplicated. Second, most of these methods have to impose a stringent assumption on the spectrum distribution of background; however, these assumptions cannot hold for all practical situations. Based on this consideration, this paper proposes a novel method allowing for fast yet accurate pixel-level hyperspectral anomaly detection. We claim the following main contributions: construct a high-order 2-D crossing approach to find the regions of rapid change in the spectrum, which runs without any a priori assumption; and design a low-complexity discrimination framework for fast hyperspectral anomaly detection, which can be implemented by a series of filtering operators with linear time cost. Experiments on three different hyperspectral images containing several pixel-level anomalies demonstrate the superiority of the proposed detector compared with the benchmark methods. [ABSTRACT FROM PUBLISHER]

In this paper, we study the distributed blind equalization of networked single-input multi-output (SIMO) systems. An indirect distributed equalization framework is presented, which estimates the transfer functions followed by the associated equalizers. Two distributed indirect equalization algorithms are proposed: one depends on multiple average consensus operations, and the other relies on the combination of innovation and one average consensus operation. The former generates an approximate equalizer for which the associated estimation error is determined by the number of average consensus operations, while the latter can provide an accurate equalizer estimation under some mild conditions. The proposed algorithms estimate the desired equalizer recursively and recover the source signal in real time. Furthermore, the distributed equalization under a time-varying topology is investigated as well. Convergence properties of the proposed algorithms are established and numerical simulations are carried out to show the performances of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a object alignment method that detects the landmarks of an object in 2D images. In the regression forests (RFs) framework, observations (patches) that are extracted at several image locations cast votes for the localization of several landmarks. We propose to refine the votes before accumulating them into the Hough space, by sieving and/or aggregating. In order to filter out false positive votes, we pass them through several sieves, each associated with a discrete or continuous latent variable. The sieves filter out votes that are not consistent with the latent variable in question, something that implicitly enforces global constraints. In order to aggregate the votes when necessary, we adjusts on-the-fly a proximity threshold by applying a classifier on middle-level features extracted from voting maps for the object landmark in question. Moreover, our method is able to predict the unreliability of an individual object landmark. This information can be useful for subsequent object analysis like object recognition. Our contributions are validated for two object alignment tasks, face alignment and car alignment, on data sets with challenging images collected in the wild, i.e., the Labeled Face in the Wild, the Annotated Facial Landmarks in the Wild, and the street scene car data set. We show that with the proposed approach, and without explicitly introducing shape models, we obtain performance superior or close to the state of the art for both tasks. [ABSTRACT FROM AUTHOR]

This paper considers estimation of a quantized constant in noise when using uniform and nonuniform quantizers. Estimators based on simple arithmetic averages, on sample statistical moments and on the maximum-likelihood procedure are considered. It provides expressions for the statistical efficiency of the arithmetic mean by comparing its variance to the proper Cramér–Rao lower bound. It is conjectured that the arithmetic mean is optimal among all estimators with an exactly known bias. Conditions under which its statistical performance are improved by the other estimation procedures when the exact bias is not known are found and analyzed. Using simulations and analysis of experimental data, it is shown that both moment-based and maximum-likelihood-based estimators provide better results, when the noise standard deviation is comparable with the quantization step and the noise model of quantization can not be applied. [ABSTRACT FROM PUBLISHER]

The effect of directional antenna elements in uniform circular arrays (UCAs) for direction of arrival (DOA) estimation is studied in this paper. While the vast majority of previous work assumes isotropic antenna elements or omnidirectional dipoles, this work demonstrates that improved DOA estimation accuracy and increased bandwidth is achievable with appropriately-designed directional antennas. The Cramer–Rao Lower Bound (CRLB) is derived for UCAs with directional antennas and is compared to isotropic antennas for 4- and 8-element arrays using a theoretical radiation pattern. The directivity that minimizes the CRLB is identified and microstrip patch antennas approximating the optimal theoretical gain pattern are designed to compare the resulting DOA estimation accuracy with a UCA using dipole antenna elements. Simulation results show improved DOA estimation accuracy and robustness using microstrip patch antennas as opposed to conventional dipoles. Additionally, it is shown that the bandwidth of a UCA for DOA estimation is limited only by the broadband characteristics of the directional antenna elements and not by the electrical size of the array as is the case with omnidirectional antennas. [ABSTRACT FROM PUBLISHER]

As a key component in the wind turbine system, the power electronic converter and its power semiconductors suffer from complicated power loadings related to environment, and are proven to have high failure rates. Therefore, correct lifetime estimation of wind power converter is crucial for the reliability improvement and also for cost reduction of wind power technology. Unfortunately, the existing lifetime estimation methods for the power electronic converter are not yet suitable in the wind power application, because the comprehensive mission profiles are not well specified and included. Consequently, a relative more advanced approach is proposed in this paper, which is based on the loading and strength analysis of devices and takes into account different time constants of the thermal behaviors in power converter. With the established methods for loading and lifetime estimation for power devices, more detailed information of the lifetime-related performance in wind power converter can be obtained. Some experimental results are also included to validate the thermal behavior of power device under different mission profiles. [ABSTRACT FROM AUTHOR]

Let us define A = Circr(a0, a1, . . . , an-1) to be a n × n r-circulant matrix. The entries in the first row of A = Circr (a0, a1, . . . , an-1) are ai = Fi, or ai = Li, or ai = FiLi, or ai = F2i, or ai = L2i(i = 0,1, . . . , n - 1),where Fi and Li are the ith Fibonacci and Lucas numbers, respectively. This paper gives an upper bound estimation of the spectral norm for r-circulant matrices with Fibonacci and Lucas numbers. The result is more accurate than the corresponding results of S Solak and S Shen, and of J Cen, and the numerical examples have provided further proof. [ABSTRACT FROM AUTHOR]

Nonlinear estimation problems, such as range-only and bearing-only target tracking, are often addressed using linearized estimators, e.g., the extended Kalman filter (EKF). These estimators generally suffer from linearization errors as well as the inability to track multimodal probability density functions. In this paper, we propose a bank of batch maximum a posteriori (MAP) estimators as a general estimation framework that provides relinearization of the entire state trajectory, multihypothesis tracking, and an efficient hypothesis generation scheme. Each estimator in the bank is initialized using a locally optimal state estimate for the current time step. Every time a new measurement becomes available, we relax the original batch-MAP problem and solve it incrementally. More specifically, we convert the relaxed one-step-ahead cost function into polynomial or rational form and compute all the local minima analytically. These local minima generate highly probable hypotheses for the target's trajectory and hence greatly improve the quality of the overall MAP estimate. Additionally, pruning of least probable hypotheses and marginalization of old states are employed to control the computational cost. Monte Carlo simulation and real-world experimental results show that the proposed approach significantly outperforms the standard EKF, the batch-MAP estimator, and the particle filter. [ABSTRACT FROM PUBLISHER]

A time or spatial series of drop counts is but one realization of a multiple stochastic process. In this paper, a method is presented that extracts more of the information contained in the time series of 1-min Joss-Waldvogel disdrometer counts in rain than a simple analysis of the magnitudes of the counts would provide. This is done by greatly increasing the size of a data set using a Bayesian analysis of drop count measurements in 17 size bins. Using the empirical copula statistical technique of probability density function transformations, a 1391-min time series of drop counts was expanded to the equivalent of 40 000 min. This dramatic increase in sample size permits a deeper characterization of the rain. Using this single disdrometer, it also allows one to translate these counts into a 200 × 200 grid filled at each point with drop size distributions of mean drop concentrations consistent with the observed statistical properties of the rain. Such a field can be used for remote sensing studies of the effect of partial beam filling and for algorithm development. Moreover, since there is nothing unique to this set of drop counts, this approach can be applied to any other set of count data, including snow and clouds. [ABSTRACT FROM PUBLISHER]

This paper presents a new preprocessing stage that allows for the reliable classification of digital amplitude-phase modulated signals in a practical scenario where: 1) the classifier has no knowledge of the timing (symbol transition epochs) of the received signal; 2) the noise added in the channel is non-Gaussian; and 3) the fading experienced by the signal is frequency selective. The proposed preprocessor, which is based on the Gibbs sampling algorithm, is used to acquire timing information and to estimate the channel state and noise distribution parameters blindly, i.e., without knowledge of the received symbol sequence and the modulation scheme used. With the obtained estimates, in a second processing stage, the signal is then classified by using an appropriate (likelihood- or feature-based) classification algorithm. To quantify the performance of the proposed preprocessor, the probability of correct classification obtained by using the preprocessor with different classification algorithms is presented. It is shown that, by using the proposed preprocessor, modulation classification algorithms can perform well compared with clairvoyant classifiers assumed to be symbol synchronous with the received signal and to have perfect knowledge of the channel state and noise distribution. [ABSTRACT FROM AUTHOR]

We present a novel two-pass framework for counting the number of people in an environment, where multiple cameras provide different views of the subjects. By exploiting the complementary information captured by the cameras, we can transfer knowledge between the cameras to address the difficulties of people counting and improve the performance. The contribution of this paper is threefold. First, normalizing the perspective of visual features and estimating the size of a crowd are highly correlated tasks. Hence, we treat them as a joint learning problem. The derived counting model is scalable and it provides more accurate results than existing approaches. Second, we introduce an algorithm that matches groups of pedestrians in images captured by different cameras. The results provide a common domain for knowledge transfer, so we can work with multiple cameras without worrying about their differences. Third, the proposed counting system is comprised of a pair of collaborative regressors. The first one determines the people count based on features extracted from intracamera visual information, whereas the second calculates the residual by considering the conflicts between intercamera predictions. The two regressors are elegantly coupled and provide an accurate people counting system. The results of experiments in various settings show that, overall, our approach outperforms comparable baseline methods. The significant performance improvement demonstrates the effectiveness of our two-pass regression framework. [ABSTRACT FROM AUTHOR]

Time-delay estimation determines the relative displacement between two ultrasound echo signals. In this paper, we propose a new time-delay estimation algorithm which uses only the sign function to obtain the corresponding timedelay estimate. The performance of the proposed algorithm was compared with two established algorithms, i.e., normalized cross-correlation (NCC) and sum of squared differences (SSD), using metrics such as statistical analysis and computational time. All simulated ultrasound echo signals were generated using ultrasound simulation software. The results indicated that overall, the proposed algorithm had similar accuracy and precision compared with the NCC and SSD algorithms; however, the computational time of the proposed algorithm was about 70% less than NCC and SSD, which showed a significant improvement. [ABSTRACT FROM AUTHOR]

Ultrasonic guided waves are one of the primary methods being investigated for structural health monitoring of plate-like components. A common practice is to collect measurements from a sparse transducer array using the pitch-catch method, which enables interrogation of defects from multiple directions. Thus, knowledge of how guided waves scatter from defects is very useful for detection, localization, and characterization of damage. One way to describe scattering patterns is with a matrix indexed by incident angle and scattered angle, and sparse array measurements essentially sample this matrix. A methodology is proposed in this paper to estimate the complete scattering matrix from these limited array measurements. First, recorded array signals are compensated for geometric spreading loss, wave packet spreading loss, and transducer differences. Initial scattering values are then extracted from the scattered wave packets after baseline subtraction and are augmented using transducer reciprocity and any a priori knowledge of defect geometric symmetry. Finally, radial basis function interpolation is performed on these values to obtain the complete scattering matrix. Scattering matrices are generated from experimental data by cutting notches of different lengths originating from a through-hole in an aluminum plate specimen that is instrumented with a sparse transducer array. The methodology is validated by laser vibrometry measurements performed on a nominally identical specimen for one notch length. [ABSTRACT FROM AUTHOR]