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This paper considers the problem of secure parameter estimation when an estimation algorithm is prone to causative attacks. Causative attacks, in general, target decision-making algorithms (e.g., inference or learning algorithm) to alter their decisions in specific scenarios (e.g., distort parameter estimates for specific ranges of the parameter of interest). Such attacks influence the decisions via tampering with the mechanisms through which an algorithm acquires the statistical model of the population about which it aims to form a decision. Such attacks are viable, for instance, by contaminating the historical or training data, or by compromising an expert who provides the statistical model. In the presence of causative attacks, inference algorithms operate under a distorted statistical model for the data samples. This paper introduces a notion of secure parameter estimation and formalizes a framework under which secure estimation can be formulated and analyzed. The central premise underlying the secure estimation framework is that forming secure estimates introduces a new dimension to the estimation objective, pertaining to detecting attacks and isolating the true model. Since detection and isolation decisions themselves are imperfect, their inclusion induces an inherent coupling between the desired secure estimation objective and the auxiliary detection and isolation decisions that need to be formed in conjunction with the estimates. This paper establishes the fundamental interplay among these decisions, and characterizes the general decision rules in closed-forms for any desired estimation cost function. Furthermore, to circumvent the computational complexity associated with growing parameter dimension or attack complexity, a scalable estimation algorithm is provided, which is shown to enjoy certain optimality guarantees. Finally, the theory developed is applied to secure parameter estimation in sensor networks. [ABSTRACT FROM AUTHOR]

The accurate models of photovoltaic systems are the core of solar energy studies that are describing the system performance and behavior under different operating conditions. The improving of photovoltaic models based on optimizing method is recently the main simulation tool to construct I-V and P–V characteristic curves with the aid of system parameters. These parameters are extracted by using powerful optimal techniques that are building-up from datasheet of manufacturers or experimental data. This paper presents a novel proposed photovoltaic model based on Marine Predators Algorithm to estimate the optimal model parameters of solar cells or modules. Also, it can extract parameters of single diode, double diode and three diode models. Moreover, the Route Mean Square Error value between each model computed parameters and measured results of photovoltaic components are considered as the objective function. R.T.C. France Solar Cell, Photowatt-PWP201 and thin-film photovoltaic modules have been implemented to extract parameters for all three previously mentioned models at different irradiance intensities or temperature degrees. The proposed algorithm results of such cells and modules for each diode model are compared with other research works and manufacturer's results. Moreover, the evaluation of proposed algorithm has been presented considering the complexity analysis and statistical tests. The compared results show that, the accuracy of algorithm results is the best and their I-V and P–V characteristic curves are highly coinciding with manufacturer's curves. Therefore, the results of the proposed algorithm are satisfied with high superiority and better reliability to optimize parameters under different operating conditions. [ABSTRACT FROM AUTHOR]

Recently, maximum correntropy criterion (MCC) has been widely and successfully used in robust signal processing and machine learning, in which the correntropy is maximized instead of minimizing the popular mean square error (MSE) to improve the robustness with respect to outliers or impulsive noises. A lot of efforts have been devoted to derive different adaptive algorithms under MCC, but to date, little insight has been gained as to how the MCC solution will be influenced by outliers. In this paper, we investigate this problem and our focus is mainly on the parameter estimation of a simple linear errors-in-variables (EIVs) model with scalar variables. Under some conditions, we derive an upper bound on the absolute value of the estimation error and show that the MCC solution can get very close to the true value of the unknown parameter even with arbitrarily large outliers in both the input and output variables. Illustrative examples are provided to verify and clarify the theory. [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]

This paper proposes a light, flexible, and yet reliable multiparametric estimation algorithm for grid-connected voltage-source-converter-based systems. This approach combines the nonlinear least squares (NLSQ) fitting algorithm and the steady-state extended harmonic domain (EHD) model to take into account the harmonic effects. The EHD model is used to take advantage of the harmonic content of the electrical signals to provide robustness and improved performance to the NLSQ estimation methodology. In addition, this harmonic formulation reduces the necessity of synchronized measurements. Details of the implementation are provided by an experimental case study, in which the grid equivalent (Thevenin voltage, inductance, and resistance), the ac-side filter (inductance and resistance), and the converter switching and conduction loss resistance are accurately estimated for three different operating conditions. [ABSTRACT FROM AUTHOR]

Effective monitoring and management applications on modern distribution networks (DNs) require a sound network model and the knowledge of line parameters. Network line impedances are used, among other things, for state estimation and protection relay setting. Phasor measurement units (PMUs) give synchronized voltage and current phasor measurements, referred to a common time reference (coordinated universal time). All synchrophasor measurements can thus be temporally aligned and coordinated across the network. This feature, along with high accuracy and reporting rates, could make PMUs useful for the evaluation of network parameters. However, instrument transformer behavior strongly affects the parameter estimation accuracy. In this paper, a new PMU-based iterative line parameter estimation algorithm for DNs, which includes in the estimation model systematic measurement errors, is presented. This method exploits the simultaneous measurements given by PMUs on different nodes and branches of the network. A complete analysis of uncertainty sources is also performed, allowing the evaluation of estimation uncertainty. Issues related to operating conditions, topology, and measurement uncertainty are thoroughly discussed and referenced to a realistic model of a DN to show how a full network estimator is possible. [ABSTRACT FROM AUTHOR]

In some complex environments, nonline-of-sight (or multipath) usually introduces an additional path delay to the pseudorange, which is known to be one of the most dominant sources of accuracy degradation in global navigation satellite systems (GNSSs). The additional path error is non-Gaussian. Different techniques, including antenna-based, receiver-based, and poster-receiver, have been proposed for mitigating the influence of multipath. However, the estimated additional path delay obtained by classical algorithms, such as maximum likelihood or Bayesian estimation, often has a certain deviation from the real-additional path delay. For more accurate estimation, we first restrict the additional path error in a relatively wide interval; then, the position estimation is converted into a constrained separable nonlinear least squares problem. We propose, in this paper, a two-stage estimation algorithm for global positioning system positioning. At the first stage, we ignore the additional path error and obtain a relatively accurate initial position. Then, based on the result obtained at the first stage, we include the additional path error in the estimation problem and estimate it by variable projection (VP) method. Numerical results show that the proposed algorithm based on the VP method can effectively mitigate multipath interference. [ABSTRACT FROM AUTHOR]

In this paper, we present new algorithms and analysis for the linear inverse sensor placement and scheduling problems over multiple time instances with power and communications constraints. The proposed algorithms, which deal directly with minimizing the mean squared error (MSE), are based on the convex relaxation approach to address the binary optimization scheduling problems that are formulated in sensor network scenarios. We propose to balance the energy and communications demands of operating a network of sensors over time while we still guarantee a minimum level of estimation accuracy. We measure this accuracy by the MSE for which we provide average case and lower bounds analyses that hold in general, irrespective of the scheduling algorithm used. We show experimentally how the proposed algorithms perform against state-of-the-art methods previously described in the literature. [ABSTRACT FROM AUTHOR]

Channel encoder, which includes a forward error correcting (FEC) code followed by an interleaver, plays a vital role in improving the error performance of digital storage and communication systems. In most of the applications, the FEC code and interleaver parameters are known at the receiver to decode and de-interleave the information bits, respectively. But the blind/semi-blind estimation of code and interleaver parameters at the receiver will provide additional advantages in applications, such as adaptive modulation and coding, cognitive radio, non-cooperative systems, etc. The algorithms for the blind estimation of code parameters at the receiver had previously been proposed and investigated for known FEC codes. In this paper, we propose algorithms for the joint recognition of the type of FEC codes and interleaver parameters without knowing any information about the channel encoder. The proposed algorithm classify the incoming data symbols among block coded, convolutional coded, and uncoded symbols. Further, we suggest analytical and histogram approaches for setting the threshold value to perform code classification and parameter estimation. It is observed from the simulation results that the code classification and interleaver parameter estimation are performed successfully over erroneous channel conditions. The proposed histogram approach is more robust against the analytical approach for noisy transmission environment and system latency is one of the important challenges for the histogram approach to achieve better performance. [ABSTRACT FROM PUBLISHER]

Distribution systems require ad hoc estimators, distribution system state estimation (DSSE) techniques, to acquire knowledge about the system status. An incorrect evaluation of the accuracy of the DSSE creates decision risks in network management. The possible variations in the network parameter values and the decays of the metrological characteristics of the measurement system elements are uncertainty sources very often not considered. Considering these possible lacks of accuracy, this paper focuses on the robustness of distributed measurement systems aimed to obtain accurate DSSE results. The problem of the proper assessment of the accuracy of the DSSE results obtained through a weighted least squares (WLS) approach is faced. A method capable of including different uncertainty sources in the uncertainty estimation of the WLS approach is presented. Furthermore, this paper proposes an optimal meter placement algorithm robust with respect to possible malfunctions in measurement system components. The results obtained on a portion of an Italian distribution network, along with their accuracy, are presented and discussed. [ABSTRACT FROM AUTHOR]

In this paper, we present Gaussian Process Gauss–Newton (GPGN), an algorithm for non-parametric, continuous-time, nonlinear, batch state estimation. This work adapts the methods of Gaussian process (GP) regression to address the problem of batch simultaneous localization and mapping (SLAM) by using the Gauss–Newton optimization method. In particular, we formulate the estimation problem with a continuous-time state model, along with the more conventional discrete-time measurements. Two derivations are presented in this paper, reflecting both the weight-space and function-space approaches from the GP regression literature. Validation is conducted through simulations and a hardware experiment, which utilizes the well-understood problem of two-dimensional SLAM as an illustrative example. The performance is compared with the traditional discrete-time batch Gauss–Newton approach, and we also show that GPGN can be employed to estimate motion with only range/bearing measurements of landmarks (i.e. no odometry), even when there are not enough measurements to constrain the pose at a given timestep. [ABSTRACT FROM PUBLISHER]

This paper presents a global task coordinate frame (TCF) (GTCF)-based integrated direct/indirect adaptive robust contouring controller for linear-motor-driven biaxial systems that achieves both stringent contouring performance and accurate parameter estimations. In contrast to the past research works those use various locally defined TCFs “attached” to the desired contour to approximately calculate the contouring error for feedback controller designs, this paper first formulates the contouring control problem using a recently developed GTCF, in which calculation of the contouring error is rather accurate and not affected by the curvature of the desired contour. A physical-model-based indirect-type parameter-estimation algorithm is then synthesized to obtain accurate online estimates of unknown physical model parameters. An integrated direct/indirect adaptive robust controller with dynamic-compensation-type fast adaptation is also constructed to preserve the excellent transient and steady-state performance of the direct adaptive robust control (ARC) designs. Comparative experimental results show that the proposed GTCF-based integrated direct/indirect ARC algorithm not only achieves the best contouring performance but also possesses rather accurate estimations of physical parameters. [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 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]

The Indian Mesosphere–Stratosphere–Troposphere (MST) radar, located at Gadanki, Andhra Pradesh, serves the purpose of providing data regarding atmospheric movements. In order to obtain information on the wind parameters, the signals collected from the radar are to be analyzed, which mainly involves the estimation of power spectrum. Parametric and nonparametric methods for spectrum estimation were applied on the complex radar data and were found to fail in accurately estimating the Doppler spectrum, particularly in the height range of 14–17 km. This made way for the introduction of a new category of spectrum estimation methods called semiparametric. This paper aims at spectral analysis of MST radar signals using a sparse spectrum estimation algorithm termed as SemiParametric/sparse Iterative Covariance-based Estimation (SPICE). This algorithm was found to successfully estimate the spectrum for simulated data even in the scenario of low SNR. For the MST radar data, the zonal $U$, meridional $V$, and wind velocity $W$ components have been estimated from the Doppler spectrum. For the purpose of validation, the obtained wind speed has been compared with the Global Positioning System radiosonde data, along with the wind speed acquired using the previously attempted methods of spectrum estimation. [ABSTRACT FROM AUTHOR]

In this paper, we investigate parameter estimation for generalized frequency division multiplexing (GFDM) signals over frequency-selective fading channels. Based on the characteristics of the second-order cyclostationary statistics of GFDM signals, we develop algorithms to blindly estimate pure block duration, overall block duration, symbol duration, and number of subcarriers. The proposed algorithms are robust to timing, phase, and carrier frequency uncertainties and do not require any a priori knowledge of the received signal. Simulation results verify the feasibility of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Imaging the acoustical coefficient of nonlinearity, β, is of interest in several healthcare interventional applications. It is an important feature that can be used for discriminating tissues. In this paper, we propose a nonlinearity characterization method with the goal of locally estimating the coefficient of nonlinearity. The proposed method is based on a 1-D solution of the nonlinear lossy Westerfelt equation, thereby deriving a local relation between β and the pressure wave field. Based on several assumptions, a β imaging method is then presented that is based on the ratio between the harmonic and fundamental fields, thereby reducing the effect of spatial amplitude variations of the speckle pattern. By testing the method on simulated ultrasound pressure fields and an in vitro B-mode ultrasound acquisition, we show that the designed algorithm is able to estimate the coefficient of nonlinearity, and that the tissue types of interest are well discriminable. The proposed imaging method provides a new approach to β estimation, not requiring a special measurement setup or transducer, that seems particularly promising for in vivo imaging. [ABSTRACT FROM AUTHOR]

The use of unmanned aerial vehicles (UAVs) in commercial and warfare activities has intensified over the last decade. One of the main challenges is to enable UAVs to become as autonomous as possible. A vital component toward this direction is the robust and accurate estimation of the egomotion of the UAV. Egomotion estimation can be enhanced by equipping the UAV with a video camera, which enables a vision-based egomotion estimation. However, the high computational requirements of vision-based egomotion algorithms, combined with the real-time performance and low power consumption requirements that are related to such an application, cannot be met by general-purpose processing units. This paper presents a system architecture that employs a field-programmable gate array as the main processing platform connected to a low-power CPU that targets the problem of vision-based egomotion estimation in a UAV. The performance evaluation of the proposed system, using real data captured by a UAV's on-board camera, demonstrates the ability of the system to render accurate estimation of the egomotion parameters, meeting at the same time the real-time requirements imposed by the application. [ABSTRACT FROM PUBLISHER]

Abstract: Piezoelectric fuel injectors provide a means to reduce fuel consumption, noise and emissions in modern IC engines. This paper summarizes an effort resulting in within-a-cycle estimation of injected fuel flow rate for “rate shaped” injection profiles. A reduced order model is derived by simplifying a physically based, experimentally validated simulation model. A within-a-cycle FPGA-based estimation algorithm is derived from the model and experimentally demonstrated at a “loop time” of . [Copyright &y& Elsevier]

This paper presents a performance comparison of two classes of synchrophasor estimators recently proposed in the literature, i.e., the frequency-domain algorithms known as interpolated discrete Fourier transform (IpDFT), and the time-domain algorithms based on the weighted least squares (WLSs) approach. The analysis is focused on fast phasor estimation only and it is performed under steady-state, dynamic, and transient conditions, when the length of the observation interval is equal to one or two nominal cycles of the acquired electric waveform. The considered testing conditions include not only the worst-case scenarios specified in the IEEE Standard C37.118.1-2011, but they also combine the effect of static and dynamic disturbances. As accuracy performance parameters, the total vector error as well as the amplitude and phase estimation errors are considered and evaluated. Estimator responsiveness is analyzed instead in terms of response times, when amplitude or phase steps occur. The preferable one- and two-cycles IpDFT and WLS algorithms are first determined by choosing the windows and the number of Taylor's series terms (in the WLS case only) assuring the best accuracy. It is shown that, when the time-domain approach is considered and one-cycle or two-cycle intervals are observed, the best accuracy is achieved by the WLS algorithm based on the rectangular window or the two-term one-cycle maximum image tone interference rejection (MIR) window, respectively. Conversely, when the frequency-domain approach is adopted, the best accuracy is achieved by the IpDFT-based on the MIR window related to the observed interval length. The estimates produced by the selected WLS and IpDFT algorithms are then compared against the boundaries specified in the Standard IEEE C37.118.1-2011 for P-and M-class compliance. During the discussion, advantages and disadvantages of both time-domain and frequency-domain approaches are highlighted. [ABSTRACT FROM AUTHOR]

It is well known that in a photovoltaic (PV) plant, the modules are connected to switch-mode power converters to enhance the power output in every environmental condition. This task is performed by the maximum power point tracker (MPPT), which provides a current or voltage reference to the converter. Traditional perturb and observe or incremental conductance algorithms are not efficient in rapidly changing conditions, whereas a model-based (MB) MPPT offers a better dynamic performance. Because it is relatively easy to obtain an accurate model of a single PV panel, thus predicting the maximum power point voltage for given environmental conditions, MB MPPTs seemed to be attractive for employing in module integrated converters. Conventional MB MPPT algorithms, however, usually require an expensive pyranometer to properly operate. In this paper, starting from a new set of equations modeling a PV module, a novel MB MPPT technique, which does not require the direct measurement of the solar radiation, is proposed and experimentally validated. [ABSTRACT FROM PUBLISHER]

This paper presents a weighted least squares-based intrafield de-interlacing algorithm. First, we formulate the estimation of the missing pixels as a maximum a posteriori (MAP) framework. We deduce the weighted least squares structure from MAP based on the analysis of the statistic model of the original high-resolution images and the associated statistical model of the given low-resolution images and original high-resolution images. The weights affect the estimation of the statistical model. We also design adaptive weights to match regions with different properties. The method is compared with other de-interlacing algorithms in terms of PSNR and SSIM objective quality measures and de-interlacing speed. It was found to provide excellent performance and the best quality–speed tradeoff among the methods studied. [ABSTRACT FROM AUTHOR]

This paper presents a novel iterative calibration algorithm for triaxial magnetometers. The proposed algorithm estimates and compensates the effects of deterministic interference parameters using only nine distinct measurements. The results of our extensive simulations and empirical evaluations confirm that the proposed method outperforms conventional ellipsoid fitting based models both in terms of accuracy and reliability even in the presence of a moderate wideband noise. The algorithm also achieves considerably faster convergence, which makes it suitable for real-time applications. The algorithm performance is also shown to be independent of the initial guesses of the interference parameters. [ABSTRACT FROM PUBLISHER]

A computational problem that arises frequently in computer vision is that of estimating the parameters of a model from data that have been contaminated by noise and outliers. More generally, any practical system that seeks to estimate quantities from noisy data measurements must have at its core some means of dealing with data contamination. The random sample consensus (RANSAC) algorithm is one of the most popular tools for robust estimation. Recent years have seen an explosion of activity in this area, leading to the development of a number of techniques that improve upon the efficiency and robustness of the basic RANSAC algorithm. In this paper, we present a comprehensive overview of recent research in RANSAC-based robust estimation by analyzing and comparing various approaches that have been explored over the years. We provide a common context for this analysis by introducing a new framework for robust estimation, which we call Universal RANSAC (USAC). USAC extends the simple hypothesize-and-verify structure of standard RANSAC to incorporate a number of important practical and computational considerations. In addition, we provide a general-purpose C++ software library that implements the USAC framework by leveraging state-of-the-art algorithms for the various modules. This implementation thus addresses many of the limitations of standard RANSAC within a single unified package. We benchmark the performance of the algorithm on a large collection of estimation problems. The implementation we provide can be used by researchers either as a stand-alone tool for robust estimation or as a benchmark for evaluating new techniques. [ABSTRACT FROM PUBLISHER]

Abstract: The cylinder pressure is arguably the most important variable characterizing the combustion process in internal combustion engines. In light of the recent advances in combustion technologies and in engine control, the use of cylinder pressure is now frequently considered as a feedback signal for closed-loop combustion control algorithms. In order to generate an accurate pressure trace for real-time combustion control and diagnostics, the output of the in-cylinder pressure transducer must be conditioned with signal processing methods to mitigate the well-known issues of offset and noise. While several techniques have been proposed for processing the cylinder pressure signal with limited computational burden, most of the available methods still require one to apply low-pass filters or moving average windows in order to mitigate the noise. This ultimately limits the opportunity of exploiting the in-cylinder pressure feedback for a cycle-by-cycle control of the combustion process. To this extent, this paper presents an estimation algorithm that extracts the pressure signal from the in-cylinder sensor in real-time, allowing for estimating the 50% burn rate location and IMEP on a cycle-by-cycle basis. The proposed approach relies on a model-based estimation algorithm whose starting point is a crank-angle based engine combustion model that predicts the in-cylinder pressure from the definition of a burn rate function. Linear parameter varying (LPV) techniques are then used to expand the region of estimation to cover the engine operating map, as well as allowing for real-time cylinder estimation during transients. The estimator is tested on the experimental data collected on an engine dynamometer as well as on a high-fidelity engine simulator. The results obtained show the effectiveness of the estimator in reconstructing the cylinder pressure on a crank-angle basis and in rejecting measurement noise and modeling errors, with considerably low computation effort. [Copyright &y& Elsevier]

The Levenberg–Marquardt (LM) algorithm is a nonlinear parameter learning algorithm that converges accurately and quickly. This paper demonstrates for the first time to our knowledge, a real-time implementation of the LM algorithm on field programmable gate arrays (FPGAs). It was used to train neural networks to solve the eXclusive Or function (XOR), and for 3D-to-2D camera calibration parameter estimation. A Xilinx Virtex-5 ML506 was used to implement the LMA as a hardware-in-the-loop system. The XOR function was approximated in only 13 iterations from zero initial conditions, usually the same function is approximated in thousands of iterations using the error backpropagation algorithm. Also, this type of training not only reduced the number of iterations but also achieved a speed up in excess of 3 \times 10^6 when compared to the software implementation. A real-time camera calibration and parameter estimation was performed successfully on FPGAs. Compared to the software implementation the FPGA implementation led to an increase in the mean squared error and standard deviation by only 17.94% and 8.04% respectively. The FPGA increased the calibration speed by a factor of 1.41 \times 10^6. There are a wide range of systems problems solved via nonlinear parameter optimization, this study demonstrated that a hardware solution for systems such as automated optical inspection systems or systems dealing with projective geometry estimation and motion compensation systems in robotic vision systems is possible in real time. [ABSTRACT FROM PUBLISHER]

The development of an in situ efficiency estimation technique is a challenging task where the lowest level of intrusion and the highest possible accuracy are required. In this paper, a new algorithm is discussed for the in situ efficiency estimation of induction machines under unbalanced power supplies. Prior work in the literature has concentrated on balanced supplies. In addition, to have a nonintrusive speed measurement, a specific adaptive nonlinear algorithm is applied for the extraction of the speed-dependent current harmonics from the measured current signal. A similar algorithm is used to extract the symmetrical components from the current and voltage signals to handle the unbalanced supply conditions. Experimental results with two different machines are used to prove the effectiveness and generality of the proposed method. Measurement error analysis, as well as repeatability tests, has been done to determine the credibility of the proposed method. [ABSTRACT FROM AUTHOR]

Numerous techniques with different levels of intrusion and accuracy have been proposed for in situ efficiency estimation. Among them, optimization-based techniques and the air-gap torque (AGT) method show promise when unbalanced supply conditions exist. In this paper, an optimization-based algorithm is proposed for in situ efficiency estimation of induction machines operating with over-/undervoltage and unbalanced supplies. In addition, a comprehensive study is done on the functionality and accuracy of the nonintrusive AGT method which is claimed to be one of the most promising methods in the literature. It is shown that the efficiency calculated by this method under field conditions cannot be used in the decision making process on replacement of the existing machines as well as the relevant calculations regarding the payback period. The research is supported by experimental results on two different induction machines. [ABSTRACT FROM AUTHOR]

This paper presents a technology fusion of encoderless control technique and encoderless-specialized motor applied for servo drive applications. First, a fast encoderless control technique which employs a high-frequency pulsating signal injection method with square-wave pulse voltage at d-axis is proposed. The estimation principle is almost the same as that of a resolver, and the control technique includes a fast initial position estimation algorithm based on magnetic hysteresis phenomenon. Next, a prototype interior permanent-magnet synchronous motor is designed to act like a resolver. Then, evaluation results of the prototype encoderless drive system are reported. Finally, the prototype system is confirmed to fulfill major servo specifications of about 10-b net resolution of the position detection at standstill and more than 60-Hz speed control bandwidth in operation speed from 0 up to 1000 \min^-1. In conclusion, the proposed encoderless drive system is verified to achieve major control performances almost comparable to those of encoder-assisted servo drive systems. [ABSTRACT FROM AUTHOR]

This paper presents a new method for online symmetrical components and phase-angle extraction from high-voltage transmission-line faults. This method is based on the Adaline neural networks and the instantaneous power theory, also known as the p-q method. A new current decomposition is proposed in order to derive the direct, inverse, and homopolar current components. The average and oscillating terms of powers in the \alpha \beta frame are separated by using four Adaline neural networks. The Adalines use a cosine and sine as inputs in order to learn the linear combination of the powers. The resulting symmetrical components are used by three other Adalines for phase-angle estimation between direct and inverse current components. These phase angles permit classifying the fault types. The neural networks use an online learning process-based Widrow–Hoff algorithm and can adapt their weight parameters to the power-supply evolution. Simulation results show the performance and the robustness of this method and provide a perspective for protection relay improvement. [ABSTRACT FROM AUTHOR]

Super-resolution is a widely applied technique that improves the resolution of input images by software methods. Most conventional reconstruction-based super-resolution algorithms assume accurate dense optical flow fields between the input frames, and their performance degrades rapidly when the motion estimation result is not accurate enough. However, optical flow estimation is usually difficult, particularly when complicated motion is presented in real-world videos. In this paper, we explore a new way to solve this problem by using sparse feature point correspondences between the input images. The feature point correspondences, which are obtained by matching a set of feature points, are usually precise and much more robust than dense optical flow fields. This is because the feature points represent well-selected significant locations in the image, and performing matching on the feature point set is usually very accurate. In order to utilize the sparse correspondences in conventional super-resolution, we extract an adaptive support region with a reliable local flow field from each corresponding feature point pair. The normalized prior is also proposed to increase the visual consistency of the reconstructed result. Extensive experiments on real data were carried out, and results show that the proposed algorithm produces high-resolution images with better quality, particularly in the presence of large-scale or complicated motion fields. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to the image blind super-resolution (BSR) problem in the case of affine interframe motion. Although the tasks of image registration, blur identification, and high-resolution (HR) image reconstruction are coupled in the imaging process, when dealing with nonisometric interframe motion or without the exact knowledge of the blurring process, classic SR techniques generally have to tackle them (maybe in some combinations) separately. The main difficulty is that state-of-the-art deconvolution methods cannot be straightforwardly generalized to cope with the space-variant motion. We prove that the operators of affine warping and blur commute with some additional transforms and derive an equivalent form of the BSR observation model. Using this equivalent form, we develop an iterative algorithm to jointly estimate the triple-coupled variables, i.e., the motion parameters, blur kernels, and HR image. Experiments on synthetic and real-life images illustrate the performance of the proposed technique in modeling the space-variant degradation process and restoring local textures. [ABSTRACT FROM AUTHOR]

In this paper, we analyze and compare the properties of different well-known and also new nonparametric discrete Fourier transform (DFT)-based methods for resonant frequency and logarithmic decrement estimation in application to mechanical spectroscopy. We derive a new DFT interpolation algorithm for a signal analyzed with Rife–Vincent class-I windows and also propose new formulas that extend Bertocco and Yoshida methods. We study errors of the resonant frequency and logarithmic decrement estimation in realistic conditions that include measurement noise and a zero-point drift. We also investigate the systematic errors of the estimation methods of interest. A nonlinear least squares time-domain parametric signal fitting is used to determine the boundaries of statistical efficiency in all tests. [ABSTRACT FROM AUTHOR]

In this paper, the problem of the simultaneous estimation of frequency and power components is investigated. It is solved through a new recursive estimator capable of estimating the unknown model parameters during severe dynamic changes in the system. By this, a simple nonlinear parameter model for the instantaneous power, taking into account the fundamental components of voltage and current, is used as a starting point for the power components and frequency estimation. In the model, the system frequency is considered as an unknown model parameter, and it is simultaneously estimated with other unknown parameters. This resulted in an efficient numerical algorithm for the measurement of power components, which is not sensitive to variations of the system frequency. This is an important achievement, which improves the accuracy of the method during network transients. The new estimator has been improved by the strategy for tuning its forgetting factor according to the system dynamics, and it is tested through computer simulations and by using data records obtained under laboratory conditions. The algorithm was developed for a single-phase system, and the approach might be easily extended to multiphase systems. [ABSTRACT FROM AUTHOR]

Abstract: Heat transfer problems in homogeneous medium obey to diffusion phenomenon. In this paper, we study heat conduction through a sphere which can be modelled with the help of fractional systems. We use models based on an original fractional integrator whose order acts only over a limited spectral band. Simulations of front face thermal characterization consisting in measuring the inner surface temperature of the sphere where a random heat flux is applied are carried out. These simulations allow to validate the ability of the proposed estimation algorithms not only to rebuild the inner surface temperature but also to identify with a good accuracy the heat conductivity and diffusivity. Monte-Carlo simulations are also performed in order to provide statistical properties of the thermo-physical parameter estimates. [ABSTRACT FROM AUTHOR]

In this paper, we propose and present an algorithm for total variation (TV)-based blind deconvolution. Both the unknown image and blur can be estimated within an alternating minimization framework. With the generalized cross-validation (GCV) method, the regularization parameters associated with the unknown image and blur can be updated in alternating minimization steps. Experimental results confirm that the performance of the proposed algorithm is better than variational Bayesian blind deconvolution algorithms with Student's-t priors or a total variation prior. [ABSTRACT FROM AUTHOR]

This paper outlines a more powerful formulation of a previously published new concept in blind seismic deconvolution, referred to as principle phase decomposition (PPD). In this new PPD filter formulation, an iterative forward modeling (IFM) algorithm is incorporated, which facilitates the estimation of parameters defining the source wave (i.e., dominant frequency, phase, and decay) and the overlapping source waves (i.e., reflection coefficients' corresponding arrival times and amplitudes). This IFM integrated PPD algorithm allows for a significantly more accurate approach in estimating the source wave and corresponding reflection series compared to the previously published technique of sequentially estimating the source wave and overlapping source waves utilizing a Rao–Blackwellized particle filter. In general terms, the source wave is modeled as an amplitude-modulated sinusoid, and the overlapping source waves are treated as known inputs within the Kalman filter formulation based on the current source wave and reflection series IFM parameter estimates. The source wave and reflection series parameters are obtained by iteratively minimizing a cost function defined to be the rms difference between the measured seismogram and the synthesized seismogram within the IFM algorithm. [ABSTRACT FROM AUTHOR]

Across the field of inverse problems in image and video processing, nearly all algorithms have various parameters which need to be set in order to yield good results. In practice, usually the choice of such parameters is made empirically with trial and error if no “ground-truth” reference is available. Some analytical methods such as cross-validation and Stein's unbiased risk estimate (SURE) have been successfully used to set such parameters. However, these methods tend to be strongly reliant on restrictive assumptions on the noise, and also computationally heavy. In this paper, we propose a no-reference metric Q which is based upon singular value decomposition of local image gradient matrix, and provides a quantitative measure of true image content (i.e., sharpness and contrast as manifested in visually salient geometric features such as edges,) in the presence of noise and other disturbances. This measure 1) is easy to compute, 2) reacts reasonably to both blur and random noise, and 3) works well even when the noise is not Gaussian. The proposed measure is used to automatically and effectively set the parameters of two leading image denoising algorithms. Ample simulated and real data experiments support our claims. Furthermore, tests using the TID2008 database show that this measure correlates well with subjective quality evaluations for both blur and noise distortions. [ABSTRACT FROM AUTHOR]

In this paper, an extended COvariance Matching Estimation Techniques–Extend Invariance Principle (COMET-EXIP) method is proposed to estimate interferometric synthetic aperture radar or interferometric synthetic aperture sonar (InSAS) interferometric phase in the presence of large coregistration errors, even up to one pixel. First, the extended COMET-EXIP method is presented for the application of joint-pixel-model-based interferogram estimation, through choosing a novel “unstructured model” in terms of the parameters to be estimated and decoupling the interesting parameters from the uninteresting “nuisance parameters.” Then, a fast algorithm of COMET-EXIP is proposed for the interferometric phase estimation. Finally, the ambiguity problem of the COMET-EXIP method is solved without introducing performance degradation. The simulated data and real data from the trial InSAS and X-SAR are used to verify the validity of the method. The results show that the method is robust for a wide range of signal-to-noise ratio and has a good performance on both fringe preserving and noise suppressing. In addition, the same computational speed level of the proposed method as that of the pivoting mean filtering is very attractive. [ABSTRACT FROM AUTHOR]

In this paper, we consider iterative space-time multiuser detection and channel parameter estimation in a bit-interleaved coded modulation scheme for asynchronous direct-sequence code division multiple access (DS-CDMA) transmission over frequency selective, slowly fading channels. Accurate estimation of the channel parameters is critical as it has great impact on the overall BER performance. We present an iterative space-time multiuser (STMU) turbo detection and estimation scheme, based on space alternating generalized expectation maximization (SAGE) algorithm. This algorithm operates on the coded symbols by exchanging soft information between the detector and the estimator. We show through computer simulations that the proposed low complexity STMU receiver considerably outperforms conventional estimation schemes and achieves excellent performance, both in terms of BER and estimation error variance. Finally, we will consider different mapping strategies and investigate their impact on the performance and complexity of the estimator. [ABSTRACT FROM AUTHOR]