Browsing by Author "Toreyin, B. Ugur"

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Citation - WoS: 34
Citation - Scopus: 45
Fall Detection Using Single-Tree Complex Wavelet Transform
(Elsevier, 2013) Keskin, Furkan; Toreyin, B. Ugur; Cetin, A. Enis; Yazar, Ahmet; 19325; 2147; 06.03. Elektrik-Elektronik Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
The goal of Ambient Assisted Living (AAL) research is to improve the quality of life of the elderly and handicapped people and help them maintain an independent lifestyle with the use of sensors, signal processing and telecommunications infrastructure. Unusual human activity detection such as fall detection has important applications. In this paper, a fall detection algorithm for a low cost AAL system using vibration and passive infrared (PIR) sensors is proposed. The single-tree complex wavelet transform (ST-CWT) is used for feature extraction from vibration sensor signal. The proposed feature extraction scheme is compared to discrete Fourier transform and mel-frequency cepstrum coefficients based feature extraction methods. Vibration signal features are classified into "fall" and "ordinary activity" classes using Euclidean distance, Mahalanobis distance, and support vector machine (SVM) classifiers, and they are compared to each other. The PIR sensor is used for the detection of a moving person in a region of interest. The proposed system works in real-time on a standard personal computer. (C) 2012 Elsevier B.V. All rights reserved.
Citation - WoS: 1
Citation - Scopus: 3
Hyperspectral Image Compression Using an Online Learning Method
(Spie-int Soc Optical Engineering, 2015) Ulku, Irem; Toreyin, B. Ugur; 19325; 06.03. Elektrik-Elektronik Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
A hyperspectral image compression method is proposed using an online dictionary learning approach. The online learning mechanism is aimed at utilizing least number of dictionary elements for each hyperspectral image under consideration. In order to meet this "sparsity constraint", basis pursuit algorithm is used. Hyperspectral imagery from AVIRIS datasets are used for testing purposes. Effects of non-zero dictionary elements on the compression performance are analyzed. Results indicate that, the proposed online dictionary learning algorithm may be utilized for higher data rates, as it performs better in terms of PSNR values, as compared with the state-of-the-art predictive lossy compression schemes.
Citation - WoS: 3
Citation - Scopus: 3
Image Analysis Based Fish Tail Beat Frequency Estimation for Fishway Efficiency
(Ieee Computer Soc, 2018) Kucukali, Serhat; Verep, Bulent; Turan, Davut; Alp, Ahmet; Yildirim, Yasin; Toreyin, B. Ugur; 20413; 06.03. Elektrik-Elektronik Mühendisliği; 06.05. İnşaat Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
In this paper, we propose image analysis based methods for estimating fish tail beat frequency, which is an indicator of fish energy consumption at fish passage structures. For this purpose, average magnitude difference and autocorrelation function based periodicity detection techniques are utilized. Actual fish images are acquired using a visible range camera installed in a brush type fish pass in Ikizdere River, near Rize, Turkey, which is very rich in biodiversity. Results show that image analysis based periodicity detection methods can be used for fishway efficiency evaluation purposes. To the best of authors' knowledge, this is the first study that automatically estimates fish tail beat frequency using image analysis. The findings of this study are expected to have implications for fish monitoring and fishway design.
Citation - Scopus: 5
Lossy Compression of Hyperspectral Images Using Online Learning Based Sparse Coding
(Ieee, 2014) Toreyin, B. Ugur; Ulku, Irem; 17575; 06.03. Elektrik-Elektronik Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
A lossy hyperspectral image compression method is proposed using online learning based sparse coding. The least number of coefficients are obtained to represent hyperspectral images by applying the sparse coding algorithm which is based on a dicriminative online dictionary learning method. Results indicate that a pre-analysis of the number of non-zero dictionary elements may help in improving the overall compression quality.
Citation - WoS: 34
Citation - Scopus: 39
Wavelet Based Flickering Flame Detector Using Differential Pir Sensors
(Elsevier Sci Ltd, 2012) Toreyin, B. Ugur; Soyer, E. Birey; Inac, Ihsan; Gunay, Osman; Kose, Kivanc; Cetin, A. Enis; Erden, Fatih; 19325; 2147; 243050; 06.03. Elektrik-Elektronik Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
A Pyro-electric Infrared (FIR) sensor based flame detection system is proposed using a Markovian decision algorithm. A differential PIR sensor is only sensitive to sudden temperature variations within its viewing range and it produces a time-varying signal. The wavelet transform of the FIR sensor signal is used for feature extraction from sensor signal and wavelet parameters are fed to a set of Markov models corresponding to the flame flicker process of an uncontrolled fire, ordinary activity of human beings and other objects. The final decision is reached based on the model yielding the highest probability among others. Comparative results show that the system can be used for fire detection in large rooms. (c) 2012 Elsevier Ltd. All rights reserved.