Browsing by Keywords "Particle swarm optimization (PSO)"
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3D electron density estimation in the ionosphere
(IEEE Computer Society, 2014)Ionosphere has ion distribution which is variable in space and time. There have been physical and empirical studies for modeling the ionosphere. International Reference Ionosphere extended to Plasmasphere (IRIPlas) is the ... 
The COST292 experimental framework for TRECVID 2007
(National Institute of Standards and Technology, 2007)In this paper, we give an overview of the four tasks submitted to TRECVID 2007 by COST292. In shot boundary (SB) detection task, four SB detectors have been developed and the results are merged using two merging algorithms. ... 
Detection of sparse targets with structurally perturbed echo dictionaries
(Elsevier, 2013)In this paper, a novel algorithm is proposed to achieve robust high resolution detection in sparse multipath channels. Currently used sparse reconstruction techniques are not immediately applicable in multipath channel ... 
Dipole source reconstruction of brain signals by using particle swarm optimization
(2009)Resolving the sources of neural activity is of prime importance in the analysis of Event Related Potentials (ERP). These sources can be modeled as effective dipoles. Identifying the dipole parameters from the measured ... 
Distributed detection by Particle Swarm Optimization
(2011)Nature inspired optimization methods have been finding many application areas in different disciplines due to their ease of use and high performance. In this study, Particle Swarm Optimization, a nature inspired optimization ... 
ERP source reconstruction by using Particle Swarm Optimization
(2009)Localization of the sources of Event Related Potentials (ERP) is a challenging inverse problem, especially to resolve sources of neural activity occurring simultaneously. By using an effective dipole source model, we propose ... 
Maximum likelihood estimation of Gaussian mixture models using particle swarm optimization
(IEEE, 2010)We present solutions to two problems that prevent the effective use of populationbased algorithms in clustering problems. The first solution presents a new representation for arbitrary covariance matrices that allows ... 
Multipath channel identification by using global optimization in ambiguity function domain
(ELSEVIER, 20110615)A new transform domain array signal processing technique is proposed for identification of multipath communication channels. The received array element outputs are transformed to delayDoppler domain by using the crossambiguity ... 
Optimal stochastic signaling for powerconstrained binary communications systems
(IEEE, 2010)Optimal stochastic signaling is studied under second and fourth moment constraints for the detection of scalarvalued binary signals in additive noise channels. Sufficient conditions are obtained to specify when the use ... 
Optimization of linear wire antenna arrays to increase MIMO capacity using swarm intelligence
(2007)Free standing linear arrays (FSLA) are analyzed and optimized to increase MIMO capacity. A MIMO channel model based on electric fields is used. The effects of mutual interactions among the array elements are included into ... 
Parameter estimation for synthetic TEC surfaces by using Particle Swarm Optimization
(2012)In this study, parameter estimation is made for global ionospheric Total Electron Content (TEC) on both noiseless and noisy synthetic surfaces by using modified Particle Swarm Optimization (PSO). In addition, the improvements ... 
Particle swarm optimization based channel identification in crossambiguity domain
(2010)In this paper, a new array signal processing technique by using particle swarm optimization (PSO) is proposed to identify multipath channel parameters. The proposed technique provides estimates to the channel parameters ... 
A particle swarm optimization based SAR motion compensation algorithm for target image reconstruction
(2010)A new SAR motion compensation algorithm is proposed for robust reconstruction of target images even under large deviations of the platform from intended flight path. Phase error due to flight path deviations is estimated ... 
Particle swarm optimization for SAGE maximization step in channel parameter estimation
(2007)This paper presents an application of particle swarm optimization (PSO) in space alternating generalized expectation maximization (SAGE) algorithm. SAGE algorithm is a powerful tool for estimating channel parameters like ... 
Particle swarm optimization of dipole arrays for superior MIMO capacity
(WILEY, 2009)The particle swarm optimization (PSO) technique is employed to design MIMO arrays for superior capacity. A channel model based on the method of moments solution of the electric field integral equation is utilized with PSO ... 
Piecewise constant line fitting on noisy ramped signals by particle swarm optimization
(2012)In this study, Particle Swarm Optimization(PSO) is proposed for change point (edge) detection on noisy ramped signals. By taking moving averages between detected edges, noise on ramped signals is filtered and desired ... 
Real time noisecancellation using ICA, PSO and PE
(2012)In order to provide noiseless transmission of speech in wireless communication systems a realtime implementable noise cancellation algorithm is developed. Speech and noise sources are not known but only their mixtures are ... 
Signal denoising by piecewise continuous polynomial fitting
(2010)Piecewise smooth signal denoising is cast as a nonlinear optimization problem in terms of transition boundaries and a parametric smooth signal family. Optimal transition boundaries for a given number of transitions are ... 
Successive cancelation approach for doppler frequency estimation in pulse doppler radar systems
(2010)In this paper, a successive cancelation approach is proposed to estimate Doppler frequencies of targets in pulse Doppler radar systems. This technique utilizes the Doppler domain waveform structure of the received signal ... 
Unsupervised classification of remotely sensed images using Gaussian mixture models and particle swarm optimization
(2010)Gaussian mixture models (GMM) are widely used for unsupervised classification applications in remote sensing. ExpectationMaximization (EM) is the standard algorithm employed to estimate the parameters of these models. ...