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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]

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, 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]

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]

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 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]

This article concerns the estimation algorithms of battery's parameters and state of charge (SOC) and it is twofold. First, we present a simple variable length block wise least square estimation algorithm by considering locally linear SOC and open circuit voltage (OCV) relation on the run. The proposed algorithm can estimate accurately the parameters and follow the parameter changes. After estimating the battery parameters, SOC is directly computed from the combination of estimated parameters and SOC-OCV relation. This estimation scheme, which assumes hysteresis effect is negligible, is an online method. Second, if the hysteresis effect is considered in the model, we present an additional least square optimization problem to estimate SOC accurately. In this case, some parameters are tuned up in an offline way. Our approach compared with the extended Kalman filter based estimation method. The algorithms are validated by experiments with real data obtained from lab tests. [ABSTRACT FROM AUTHOR]

A parametric estimation algorithm for a single-axis gradiometer is described. The most influential parameters are isolated and estimated; their bias and variance are discussed. Even though the system is closed-loop controlled, only plant parameters are estimated using an open-loop identification equation, which simplifies the identification procedure. The estimates are obtained using the method of instrumental variables (IV). The advantage of this method is that it remains bias-free even when the system states are correlated with the measurement noise. The problem is described in a one-dimensional frame, but it can be extended to the multivariate case if necessary. The estimated parameters are then used to reconstitute the closed-loop transfer function, which allows the input disturbance that caused the observed signal to be deduced. The estimation algorithm is applied to the laser interferometer space antenna (LISA) technology package, which is the scientific payload of the LISA Pathfinder spacecraft (SC), an ESA/NASA technology demonstrator to be launched in 2014. The analysis is operated on a simulated dataset produced with the mission end-to-end simulator (E2E) as well as with a simple linear simulator. The on-board software and hardware constraints are taken into account since they are important performance drivers. The results are detailed along with the experiment model. [ABSTRACT FROM PUBLISHER]

We study the use of the FitzHugh–Nagumo (FHN) model for capturing neural spiking. The FHN model is a widely used approximation of the Hodgkin–Huxley model that has significant limitations. In particular, it cannot produce the key spiking behavior of bursting. We illustrate that by allowing time-varying parameters for the FHN model, these limitations can be overcome while retaining its low-order complexity. This extension has applications in modeling neural spiking behaviors in the thalamus and the respiratory center. We demonstrate the use of the FHN model from an estimation perspective by presenting a novel parameter estimation method that exploits its multiple time-scale properties, and compare the performance of this method with the extended Kalman filter in several illustrative examples. We demonstrate that the dynamics of the spiking threshold can be recovered even in the absence of complete specifications for the system. [ABSTRACT FROM PUBLISHER]

We propose a novel algorithm based on the matrix pencil method to estimate the parameters of a class of signals modeled as damped power-law phase signals. This class arises primarily from the electromagnetic probing of dispersive geological and civil engineering materials as a consequence of the universal dielectric response. When stratified media are considered, direct application of conventional matrix-shifting methods is hindered not only by the nonlinear frequency dependency which destroys the desired shift-invariance property of the data matrix, but also by the stratified structure of the medium which introduces a cumulative effect. In this regard, the proposed algorithm restores recursively the Vandermonde structure of one mode vector at a time by means of a spline-interpolation technique and then orthogonally projects it to filter out its contribution before passing to another. The algorithm is tested on simulated and experimental data resulting from the probing of a stratified dispersive medium, and its performance is assessed against the Cramér-Rao lower bound. For the example of experimental data, collected from concrete cores by means of a cylindrical transition line, the permittivities at the reference frequency and the dispersion indices are determined using the new algorithm and compared with those of a nonlinear optimization scheme. [ABSTRACT FROM PUBLISHER]