Ilker Kilic

GENETiK - LBG ALGORİTMASI İLE SAYISAL GÖRÜNTÜLERİN SIKIŞTIRILMASI - DIGITAL IMAGE COMPRESSION BY GENETIC – LBG ALGORITHM

GENETiK - LBG ALGORITMASI ILE SAYISAL GORUNTULERIN SIKIŞTIRILMASI Bu calismada K-Ortalamalar(KO), LBG ve Bulanik C Ortalamalar(BCO) guncel kumeleme algoritmalari yardimi ile bulunan merkezler uzerinden gerceklestirilen kayipli goruntu sikistirma algoritmalarinin performanslari, onerilen Genetik LBG Algoritmasi (GA-LBG) ile iyilestirilmistir. Onerilen yeni algoritma standart goruntuler uzerinde denenmis, klasik yontemlerden hem OKH(Ortalama karesel hata) degerleri, hem de sikistirilip acilan goruntu kalitesi acisindan ustun oldugu gozlenmistir. DIGITAL IMAGE COMPRESSION BY GENETIC – LBG ALGORITHM In this study, using cluster centers of the popular clustering algorithms such as K-Means, LBG and Fuzzy C-Means a lossy compression is performed. The performances of these algorithms are improved by the proposed Genetic LBG algorithm. The new algorithm is applied on the standard images and seen that it is better than the classical methods according to both MSE values and visual assessments.

Fast global Fuzzy C-Means clustering for ECG signal classification

Fuzzy clustering plays an important role in solving problems in the areas of pattern recognition and fuzzy model identification. The Fuzzy C-Means algorithm is one of widely used algorithms. It is based on optimizing an objective function, being responsive to initial conditions; the algorithm usually leads to local minimum results. Aiming at above problem, the fast global Fuzzy C-Means clustering algorithm (FGFCM) has been proposed, which is an incremental approach to clustering, and does not depend on any initial conditions. The algorithm was applied on ECG signals to classification.

Using LBG algorithm for extracting the features of EMG signals

The Electromyographic (EMG) signals observed at the surface of the skin is the sum of many small action potentials generated in the muscle fibers. There is only a pattern for each EMG signals, which are generated by biceps and triceps muscles. There are different types of signal processing in order to find out the feature values for true classification in this pattern. In this study, the Feature values belong to 4 different arm movements are obtained by using clustering methods, i.e K-means, Fuzzy C-means, and LBG after applying Wavelet Transform to EMG signals . Then these feature values are compared each other by KEYK and Quadratic Discriminant Analysis classifier.