Browsing by Subject "principal component analysis"

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Open Access
Accumulation trends of metals and a metalloid in the freshwater crayfish Astacus leptodactylus from Lake Yeniçağa (Turkey)
(2013) Tunca, E.; Üçüncü, E.; Kurtuluş, B.; Ozkan, A.D.; Atasagun, S.
This study aims to determine the extent of metal pollution in Lake Yeniçaǧa (Bolu, Turkey) by investigating the accumulation trends of five metals (Al, Cu, Fe, Ni and Zn) and a metalloid (As) in gills, exoskeleton, hepatopancreas and abdominal muscles of the freshwater crayfish Astacus leptodactylus. Principal component analysis (PCA), cluster analysis (CA), correlation analysis and analysis of variance (ANOVA) were utilised to determine the accumulation profiles of each element over four seasons. The greatest element accumulation was found to occur in the gills. All elements in exoskeletal tissue displayed positive correlations with each other, a similar trend was also observed in the hepatopancreas samples. Strong (r=0.868) and very strong (r=0.960) positive correlations were found between the accumulations of Al and Fe in gills and the exoskeleton, respectively. Correlations in tissue accumulation rates are discussed in the context of metabolic roles and impacts associated with the elements tested. Elemental compositions of Yeniçaǧa water and sediment samples were also investigated to determine whether the composition of the surrounding environment matches the metal accumulation trends of tissue samples. We demonstrate that, by the criteria set by the United States Environmental Protection Agency, Lake Yeniçaǧa is heavily polluted in terms of As and Ni. © 2013 Taylor & Francis.
Open Access
Methods fro automatic target classification in radar
(2009) Eryıldırım, Abdülkadir
Automatic target recognition (ATR) using radar is an active research area. In this thesis, we develop new automatic radar target classification methods. We focus on two specific problems: (i) Synthetic Aperture Radar (SAR) target classification and (ii)Pulse-doppler radar (PDR) target classification. SAR and PDR target classification are extensively used for ground and battlefield surveillance tasks. In the first part of the thesis, a novel descriptive feature parameter extraction method from Synthetic Aperture Radar (SAR) images is proposed. Feature extraction and classification methods which were developed to handle optical images are usually inappropriate for SAR images because of the multiplicative nature of the severe speckle noise and imaging defects. In addition, SAR images of the same object taken at different aspect angles show great differences, which makes it hard to obtain satisfactory results. Consequently, feature parameter extraction method based on two-dimensional cepstrum is proposed and its object recognition results are compared with principal component analysis (PCA) and independent component analysis (ICA) methods. The extracted feature parameters are classified using Support Vector Machines (SVMs). Experimental results are presented. In the second part of the thesis, the automatic classification experiments over ground surveillance Pulse-doppler radar echo signal are investigated in order to overcome the limitations of human operators and improve the classification accuracy. Covariance method approach is introduced for PDR echo signal classification. To the best our knowledge, the use of covariance method-based classification is not investigated in radar automatic target classification problems. Furthermore, different approaches which involves SVMs are developed. As feature parameters, cepstrum and MFCCs are used. Performances of these two approaches are compared with the Gaussian Mixture Models (GMM) based classification scheme. Experimental results and conclusions are presented.