Browsing by Author "Kiliç I."
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item An image compression algorithm using hierarchical finite state vector quantization and zerotree wavelet structure; [Hiyerarşik sonlu durum vektör nicemleme ve sifir aǧaç dalgacik yapi ile imge kodlama](2005) Kiliç I.In this paper an image compression technique which is designed for high compression ratios is presented. The discrete wavelet transform (DWT) is combined with Lloyd Max (LM) quantization and zerotree wavelet (ZTW) structure. The novel and the most important feature of this coding scheme is the combination of hierarchical finite state vector quantization (HFSVQ) with the zerotree structure to encode the quantized wavelet coefficients. The wavelet coefficients belong to the standard images are compressed at high ratios. The simultion results show that this proposed new encoder performs better than the intra coding schemes of EZW, ZTE, MPEG-4/VM video encoders and JPEG image encoder. © 2005 IEEE.Item Using LBG algorithm for extracting the features of EMG signals; [EMG i̇şaretlerinin özniteliklerinin belirlenmesinde LBG algoritmasinin kullanimi](2008) Koçyiǧit Y.; Kiliç I.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. ©2008 IEEE.Item Fast global fuzzy C-means clustering for ECG signal classification; [EKG i̇şaretlerini siniflamak için hizli global bulanik C-ortalama öbekleşme](2010) Koçyiǧit Y.; Kiliç I.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. ©2010 IEEE.