Image deconvolution is an important subject in image processing. It is an ill-posed inverse problem, so regularization techniques are used to solve this problem by adding constraints to the objective function. Various...
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We want to implement a new optimal approach for image restoration problem, which is useful for neutron radiography images enhancement to assist the physician interpreter on his evaluation. Our approach is based on usi...
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Broadband power-line communications (PLC) is considered as an enabling technology for the Internet-of-Things (IoT) systems. However, PLC faces some transmission issues as a power line was not designed to transmit comm...
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This paper investigates optimal classification of lightning strike maps (LSMs), using regular and fuzzy K-means classifiers. It also describes a novel approach for measurements of the physical LSMs. Lightning strikes ...
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This paper investigates optimal classification of lightning strike maps (LSMs), using regular and fuzzy K-means classifiers. It also describes a novel approach for measurements of the physical LSMs. Lightning strikes are physical phenomena, which have adverse effects on power transmission. Modelling and classification of the LSMs can lead to prediction of their time-space behaviour, and enhance the protection of the existing and new power systems and transmission lines. Prediction requires the classification of the simulated results and physical data, and comparing them together using characterization. Since the LSMs are highly nonlinear, nonstationary, and stochastic, ordinary models and analyses are incapable of simulating and characterizing these phenomena. Since self-affinity of such maps is an indication of multifractality, percolation models and complexity measures such as the Renyi fractal dimension spectrum (RS) and Mandelbrot singularity spectrum (MS) are utilized. The data of the LSMs have been obtained through the Canadian Lightning Detection Network (CLDN) for Manitoba in the year 2002. The data have been characterized by both the RS and MS. Features have been extracted using the RS. Classification employed the regular and fuzzy K-means methods. For each classification method, 50 tests have been utilized, and classification performance for 2 to 15 classes has been investigated through Davis-Bouldin criterion. The fuzzy K-means classifier identified 6 optimal classes with a confidence level of 0.78, while the regular K-means algorithm could not distinguish the classes.
This paper proposes a low-complexity predistorter (PD) for compensation of both the AM/AM and the AM/PM conversions with memory. The nonlinear power amplifier (PA) is modeled as a Wiener type nonlinearity. The quasi-s...
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This paper proposes a low-complexity predistorter (PD) for compensation of both the AM/AM and the AM/PM conversions with memory. The nonlinear power amplifier (PA) is modeled as a Wiener type nonlinearity. The quasi-static nonlinearities are modeled using a class of piecewise linear (PWL) functions. The PWL function facilitates an efficient PD identification algorithm. The proposed algorithm involves a novel inverse coordinate mapping (ICM) method that maps the nonlinear characteristics of the PA to that of the PD, and parameter estimations that do not require matrix inversion. The indirect learning architecture is used to provide an on-line compensation of thememory effect of the PA. Simulation results show that the PD that compensates also the AM/PM distortion performs significantly better than one that considers only the AM/AM nonlinearity. The proposed PD is also shown to outperform the orthogonal polynomial PD in both adjacent channel interference suppression and inband distortion compensation.
This paper introduces a new method to track articulator movements, specifically jaw position and angle, using 5 degree of freedom (5 DOF) orientation data. The approach uses a quaternion rotation method to accomplish ...
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On the basis of human muscle fiber tissues’ characteristics,it is first proposed to establish the analytical model of galvanic coupling intra-body communication channel. In this model,the parallel and the transverse ...
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For signals containing discontinuities, the usual assumptions of Gauss-Markov distributed signal sources do not hold. To preserve edges, non-Gaussian prior models have been developed for use in Bayesian restoration. T...
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In this paper, an improved dead-beat controller (DBC) for a single-phase LC-coupling hybrid active power filter (LC-HAPF) is proposed to achieve both low steady-state error and fast transient response. The conventiona...
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This paper focuses on multifractal characterization and feature extraction of nonstationary temporal signals, particularly the turn-on transients for radio transmitter classification purposes. A transient signal conta...
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This paper focuses on multifractal characterization and feature extraction of nonstationary temporal signals, particularly the turn-on transients for radio transmitter classification purposes. A transient signal contaminated by noise can be considered as an output from a chaotic dynamical system. The trajectory of such a system in phase space is often attracted to a bounded fractal object called strange attractor. Transient signals are preprocessed to obtain the corresponding strange attractor in phase space. Multifractal measures based on a generalized entropy are then applied to the strange attractor and features are extracted in terms of a fractal dimension spectrum. Experimental results show that there exists a strange attractor for the transient signal and the fractal dimension spectrum can characterize a transient signal uniquely, even with a relatively short time duration.
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