Snoring, which may be decisive for many diseases, is an important indicator especially for sleep disorders. In recent years, many studies have been performed on the snore related sounds (SRSs) due to producing useful results for detection of sleep apnea/hypopnea syndrome (SAHS). The first important step of these studies is the detection of snore from SRSs by using different time and frequency domain features. The SRSs have a complex nature that is originated from several physiological and physical conditions. The nonlinear characteristics of SRSs can be examined with chaos theory methods which are widely used to evaluate the biomedical signals and systems, recently. The aim of this study is to classify the SRSs as snore/breathing/silence by using the largest Lyapunov exponent (LLE) and entropy with multiclass support vector machines (SVMs) and adaptive network fuzzy inference system (ANFIS). Two different experiments were performed for different training and test data sets. Experimental results show that the multiclass SVMs can produce the better classification results than ANFIS with used nonlinear quantities. Additionally, these nonlinear features are carrying meaningful information for classifying SRSs and are able to be used for diagnosis of sleep disorders such as SAHS.

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Obstructive sleep apnea/hypopnea (OSAH) is a highly prevalent disease which causes collapse in upper airway while sleeping. The purpose of this study is to classify snore related sounds into snore/non-snore episodes using adaptive neuro fuzzy inference system (ANFIS). Time-domain features which are entropy, energy and zero crossing rates were used and applied to data for ANFIS classifier model. At first, apnea and normal snore related sounds obtained from different patients are segmented. After segmentation, energy, entropy and zero crossing rates are calculated. Unlike the previous studies, entropy information was firstly used for snoring classification. Then, ANFIS was used to classify episodes as snore/non-snore. Experimental results have shown that ANFIS is able to classify snore segments with accuracy rate 97.08%. In conclusion, the results prove that ANFIS has good performance for classifying snore related sounds.

This study presents a new approach to improve the performance of FastSLAM. The aim of the study is to obtain a more robust algorithm for FastSLAM applications by using a Kalman filter that uses Stirling's polynomial interpolation formula. In this paper, some new improvements have been proposed; the first approach is the square

ÖZETÇEEşzamanlı konum belirleme ve harita oluşturma (EKBHO) robotlar veya özerk araçlar tarafından, bilinmeyen bir çevre içersinde mevcut yer ile birlikte çevrenin haritasını çıkarma veya bilinen bir ortamda verilen harita bilgisinin güncellenmesi için kullanılan bir yöntemdir. Özellikle son yıllarda bu tür araçlar için büyük önem arz eden bir problem olarak görülmektedir. Bu problemi çözmek için farklı istatistiksel metotlar kullanılmıştır. Bunlardan en çok bilinenleri beklenti en büyültme, Kalman tabanlı filtreler ve parçacık filtreleridir. Bu çalışmada anılan problemin çözümü için Kalman tabanlı filtrelerden kara kök merkez fark Kalman filtresi kullanılmıştır. Filtre modelinde temel iki iyileştirme yapılmıştır. İlk iyileştirme Q ve R tasarım matrislerinin ayarlanabilir bulanık mantık çıkarımı sistemiyle ayarlanabilir olması ikincisi ise Rauch-Tung-Striebel düzenleyicisinin filtre tahmin doğruluğunu iyileştirmesi amaçlıdır. Simülasyon sonuçlarının daha önceden elde edilen genişletilmiş, kokusuz (unscented), kara kök kokusuz Kalman filtreleri ve parçacık tabanlı FASTSLAM II modeli sonuçlarına göre daha başarılı olduğu gözlenmiştir. ABSTRACT Simultaneous Localization and Mapping (SLAM) is a method employed by robots and autonomous vehicles to build up a map within an unknown environment or to update a map within a known environment.In recent years, SLAM has been a significant problem with autonomous. There have been different statistical methods used for solving this problem ranging from expectation maximization method to Kalman based filters and particle filters. In this study, square root uncented Kalman filter has been utilized to address the SLAM problem. Two basic improvements have been achieved with the proposed method i) tuning Q and R design matrices using adaptive neuro fuzzy inference system (ANFIS), ii) Rauch-Tung-Striebel smoother for enhancing the filter's prediction. Simulation results have shown that the proposed filter is more successful compared with the extended, unscented, square root uncented Kalman filters and particle based FASTSLAM II model.

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