Fikret Ari

Square root unscented based FastSlam optimized by particle swarm optimization passive congregation

Simultaneous localization and mapping (SLAM) is known to be a problem for autonomous vehicles/robots. Different solutions have recently been proposed on this subject. The best known of these are FastSlam based approaches. In this study, two improved FastSlam based methods are proposed to solve the SLAM problem. In the first method, square root unscented (Sru) Kalman filter is used instead of extended Kalman filter in robot position prediction/update for each particle filter samples and feature updates. The second method uses Sru - Kalman filter with particle swarm optimization passive congregation (PSO-PC) for robot/feature position estimations. In the second method, particle swarm optimization passive congregation (PSO-PC) is used to optimize particle samples in case of sampling stage. The experimental results were compared with FastSlamII and unscented U-FastSlam. It is seen that proposed methods are an alternative for the solution of SLAM problem. The best results were obtained by Sru - based PSO-PC optimized FastSlam approach for the vehicle position and heading angle mean square errors.

Snore-related sound classification based on time-domain features by using ANFIS model

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.

Detection of sleep apnea with chaotic sound features

Snoring is one of the most important symptom of the Obstructive Sleep Apnea Syndrome (OSAS). When apnea is able to be diagnosed only using the snore sounds, recording and analysis of snore signals will be able to perform in home environment without the necessity of laboratory. Thus, diagnosing snore apnea by benefiting from snore signal has great importance. In this study, based on chaotic structure of the snore sounds, Largest Lyapunov Exponent (LLE) and mean value of divergence curves parameters are used as features for classification of snore sounds. OSAS/simple snoring situations are classified by means of a feed forward neural network. When the two features used as inputs of the neural network, total classifier performance rate was obtained as %96,58.

Square root unscented filter based FastSLAM approach for SLAM problem solution

There are different Bayesian based approaches proposed for the solution of simultaneous localization and mapping (SLAM) problem in the literature. In this study, square root unscented Kalman based (Sru)-FastSLAM and square root unscented particle filter based (SruPf) - FastSLAM were proposed for the SLAM problem solution. The first method used Sru - Kalman filter for estimating the robot position, the landmarks location and particle weights. The second method with the help of FastSlam II uses Sru-Kalman filter for each particle. FastSLAM II, unscented particle filter based (Upf) FastSlam II, unscented (U) FastSlam, unscented Kalman aided (UAided) FastSLAM, Sru- FastSlam and SruPf - FastSLAM were used for comparison of filter performance in the experimental results. It is seen that Sru - FastSlam and SruPf-FastSLAM are alternative to solving the problem of SLAM. The best results for heading, position error of robot/ vehicle and uncertainty of position of landmarks were obtained by Sru-FastSlam II.

Chaotic analysis of snore related sounds

Snoring is a noise related sound, which occurs while sleeping owing to narrow upper airway cause hard to breathing. There are a lot of studies available related to diagnosis and specifying the how harmful for people bodies. Some studies have used these sounds depending on acoustical features; the others have used polysomnography devices that produce some data to analyze the illness. In this study, we have used largest Lyapunov exponents (LLE) for chaotic analysis of snore related sounds in case of different snoring stage on sleeping. Estimated feature values were used for classifying of these sounds with adaptive neuro fuzzy inference system (ANFIS) classifier. These estimated feature values was used input for ANFIS classifier. ANFIS can also classify these sounds with the highest accuracy of 97% training and 89.08% testing results.

Super-resolution image construction from video images based on block matching technique

In this study, it is intended to construct a super-resolution image from video images by using motion compensation technique based on block matching and its performance is compared with performances of various motion compensation techniques available in literature for super-resolution. Experimental results show that results of block matching based motion compensation technique are better than results of other motion compensation techniques. Furthermore, a mask which introduces differences between the images to be combined together, is suggested in order to improve the success of motion compensation technique based on block matching, as a result of this image quality has increased remarkably.

Real time pupil-corneal reflection following with Labview

Eye tracking in human-computer interaction is a situation often encountered in recent years. Pupil and eye gaze detection is used in health problems, security systems, ergonomic design, psychological analysis, market research, military areas and control applications. In this study, A realtime pupil-corneal reflection tracking with free head position is made by using Labview and Labview Vision Development Module. Literature in pursuit eye racking, we see that many different methods and algorithms used. Many of them use circle and ellipse detection algorithms. This is the long processing time of algorithms is one of the difficulties. With the proposed method in this study, pupil-corneal reflection is detected in 11 ms time at free head position.

Finding target fixation point based on pupil center and corneal reflection

In this study, a system for detection and tracking of eye movements has been developed. The system includes a video camera capable of making Infrared (IR) imaging, near-IR illumination system and record/analysis software. System tracks eye movements by detecting the location of pupil based on features of anatomic structure of the eye and the reflection on the cornea of the light source as the first corneal reflection. Experimental results show that system might successfully be used in different applications related to human computer interaction.