Browsing by Subject "Parameter extraction"

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    Man-made object classification in SAR images using 2-D cepstrum
    (IEEE, 2009-05) Eryildirim, A.; Çetin, A. Enis
    In this paper, a novel descriptive feature parameter extraction method from Synthetic Aperture Radar (SAR) images is proposed. The new method is based on the two-dimensional (2-D) real cepstrum. This novel 2-D cepstrum method is compared with principal component analysis (PCA) method by testing over the MSTAR image database. The extracted features are classified using Support Vector Machine (SVM). We demonstrate that discrimination of natural background (clutter) and man-made objects (metal objects) in SAR imagery is possible using the 2-D cepstrum feature parameters. In addition, the computational cost of the cepstrum method is lower than the PCA method. Experimental results are presented. ©2009 IEEE.
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    A performance-enhanced planar Schottky diode for Terahertz applications: an electromagnetic modeling approach
    (Cambridge University Press, 2017) Ghobadi, Amir; Khan, Talha Masood; Celik, Ozan Onur; Biyikli, Necmi; Okyay, Ali Kemal; Topalli, Kagan
    In this paper, we present the electromagnetic modeling of a performance-enhanced planar Schottky diode for applications in terahertz (THz) frequencies. We provide a systematic simulation approach for analyzing our Schottky diode based on finite element method and lumped equivalent circuit parameter extraction. Afterward, we use the developed model to investigate the effect of design parameters of the Schottky diode on parasitic capacitive and resistive elements. Based on this model, device design has been improved by deep-trench formation in the substrate and using a closed-loop junction to reduce the amount of parasitic capacitance and spreading resistance, respectively. The results indicate that cut-off frequency can be improved from 4.1 to 14.1 THz. Finally, a scaled version of the diode is designed, fabricated, and well characterized to verify the validity of this modeling approach.
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    Target detection and classification in SAR images using region covariance and co-difference
    (SPIE, 2009-04) Duman, Kaan; Eryıldırım, Abdulkadir; Çetin, A. Enis
    In this paper, a novel descriptive feature parameter extraction method from synthetic aperture radar (SAR) images is proposed. The new approach is based on region covariance (RC) method which involves the computation of a covariance matrix whose entries are used in target detection and classification. In addition the region co-difference matrix is also introduced. Experimental results of object detection in MSTAR (moving and stationary target recognition) database are presented. The RC and region co-difference method delivers high detection accuracy and low false alarm rates. It is also experimentally observed that these methods produce better results than the commonly used principal component analysis (PCA) method when they are used with different distance metrics introduced. © 2009 SPIE.