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Töreyin, Behçet Uğur

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Profile URL
https://hdl.handle.net/20.500.12416/9315
Name Variants
Töreyin, B.U.
Töreyin, Behçer Uğur
Toreyin, B. Ugur
Toreyin, Behcet Ugur
Job Title
Yrd. Doç. Dr.
Email Address
toreyin@cankaya.edu.tr
Main Affiliation
06.03. Elektrik-Elektronik Mühendisliği
Elektrik-Elektronik Mühendisliği
06. Mühendislik Fakültesi
01. Çankaya Üniversitesi
Status
Former Staff
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Scopus Author ID
Turkish CoHE Profile ID
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WoS Researcher ID

Sustainable Development Goals

11

SUSTAINABLE CITIES AND COMMUNITIES
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3

GOOD HEALTH AND WELL-BEING
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9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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1

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6

CLEAN WATER AND SANITATION
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14

LIFE BELOW WATER
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12

RESPONSIBLE CONSUMPTION AND PRODUCTION
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8

DECENT WORK AND ECONOMIC GROWTH
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1

NO POVERTY
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4

QUALITY EDUCATION
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5

GENDER EQUALITY
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10

REDUCED INEQUALITIES
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16

PEACE, JUSTICE AND STRONG INSTITUTIONS
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15

LIFE ON LAND
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1

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7

AFFORDABLE AND CLEAN ENERGY
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1

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13

CLIMATE ACTION
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17

PARTNERSHIPS FOR THE GOALS
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ZERO HUNGER
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Scholarly Output

21

Articles

10

Views / Downloads

730/19

Supervised MSc Theses

0

Supervised PhD Theses

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WoS Citation Count

191

Scopus Citation Count

258

WoS h-index

7

Scopus h-index

8

Patents

0

Projects

0

WoS Citations per Publication

9.10

Scopus Citations per Publication

12.29

Open Access Source

3

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0

JournalCount
2012 20th Signal Processing and Communications Applications Conference, SIU 2012, Proceedings -- 2012 20th Signal Processing and Communications Applications Conference, SIU 2012 -- 18 April 2012 through 20 April 2012 -- Fethiye, Mugla -- 907863
2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings -- 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 -- 16 May 2015 through 19 May 2015 -- Malatya -- 1130521
22nd IEEE Signal Processing and Communications Applications Conference (SIU) -- APR 23-25, 2014 -- Karadeniz Teknik Univ, Trabzon, TURKEY1
23nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu Univ, Malatya, TURKEY1
6th Workshop on Hyperspectral Image and Signal Processing - Evolution in Remote Sensing (WHISPERS) -- JUN 24-27, 2014 -- Lausanne, SWITZERLAND1
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Now showing 1 - 10 of 21
  • Thumbnail Image
    Article
    Citation - WoS: 8
    Citation - Scopus: 10
    Sparse Coding of Hyperspectral Imagery Using Online Learning
    (Springer London Ltd, 2015) Toreyin, Behcet Ugur; Ulku, Irem
    Sparse coding ensures to express the data in terms of a few nonzero dictionary elements. Since the data size is large for hyperspectral imagery, it is reasonable to use sparse coding for compression of hyperspectral images. In this paper, a hyperspectral image compression method is proposed using a discriminative online learning-based sparse coding algorithm. Compression and anomaly detection tests are performed on hyperspectral images from the AVIRIS dataset. Comparative rate-distortion analyses indicate that the proposed method is superior to the state-of-the-art hyperspectral compression techniques.
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    Conference Object
    Citation - Scopus: 6
    Wavelet Based Flame Detection Using Differential Pir Sensors
    (2012) Töreyin, B.U.; Soyer, E.B.; Inaç, I.; Günay, O.; Köse, K.; Çetin, A.E.; Erden, F.
    In this paper, a flame detection system using a differential Pyro-electric Infrared (PIR) sensor is proposed. 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 differential PIR sensor signal is used for feature extraction and feature vectors are fed to Markov models trained with uncontrolled fire flames and walking person. The model yielding the highest probability is chosen. Results suggest that the system can be used in spacious rooms for uncontrolled fire flame detection. © 2012 IEEE.
  • Thumbnail Image
    Article
    Citation - WoS: 10
    Citation - Scopus: 13
    Sparse Representations for Online-Learning Hyperspectral Image Compression
    (Optical Soc Amer, 2015) Toreyin, Behcet Ugur; Ulku, Irem
    Sparse models provide data representations in the fewest possible number of nonzero elements. This inherent characteristic enables sparse models to be utilized for data compression purposes. Hyperspectral data is large in size. In this paper, a framework for sparsity-based hyperspectral image compression methods using online learning is proposed. There are various sparse optimization models. A comparative analysis of sparse representations in terms of their hyperspectral image compression performance is presented. For this purpose, online-learning-based hyperspectral image compression methods are proposed using four different sparse representations. Results indicate that, independent of the sparsity models, online-learning-based hyperspectral data compression schemes yield the best compression performances for data rates of 0.1 and 0.3 bits per sample, compared to other state-of-the-art hyperspectral data compression techniques, in terms of image quality measured as average peak signal-to-noise ratio. (c) 2015 Optical Society of America
  • Thumbnail Image
    Conference Object
    Entropy Functional Based Adaptive Decision Fusion Framework
    (2012) Günay, O.; Töreyin, B.U.; Köse, K.; Çetin, A.E.
    In this paper, an entropy functional based online adaptive decision fusion framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several sub-algorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular sub-algorithm. Decision values are linearly combined with weights which are updated online according to an active fusion method based on performing entropic projections onto convex sets describing sub-algorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video based wildfire detection system was developed to evaluate the performance of the decision fusion algorithm. © 2012 IEEE.
  • Thumbnail Image
    Conference Object
    Evaluation of Clustering Performance of Hyperspectral Bands
    (Ieee, 2015) Sakarya, Ufuk; Toreyin, Behcet Ugur; Haliloglu, Onur; Haliloʇlu, Onur
    Hyperspectral images have huge data volume that contains spectral and spatial information. This high data volume leads to processing, storage, and transmission problems. Moreover, insufficient training data results in Hughes phenomenon. It is possible to solve these problems with the help of feature selection. In this paper, a method that evaluates the clustering performance of spectral bands is proposed as a pre-processing operation in order to realize feature selection. This method is clustering each spectral band based on "dominant sets" technique and it evaluates the clustering performance of each band. The proposed method is time efficient since it works on a small set of training data instead of the whole hyperspectral data. In this study, "dominant sets" technique is first applied to hyperspectral image processing as a clustering method.
  • Thumbnail Image
    Conference Object
    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
    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.
  • Thumbnail Image
    Article
    Citation - WoS: 72
    Citation - Scopus: 85
    Entropy-Functional Online Adaptive Decision Fusion Framework With Application To Wildfire Detection in Video
    (Ieee-inst Electrical Electronics Engineers inc, 2012) Toreyin, Behcet Ugur; Kose, Kivanc; Cetin, A. Enis; Gunay, Osman
    In this paper, an entropy-functional-based online adaptive decision fusion (EADF) framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular subalgorithm. Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing entropic projections onto convex sets describing subalgorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video-based wildfire detection system was developed to evaluate the performance of the decision fusion algorithm. In this case, image data arrive sequentially, and the oracle is the security guard of the forest lookout tower, verifying the decision of the combined algorithm. The simulation results are presented.
  • Thumbnail Image
    Article
    Flow structure and fish passage performance of a brush-type fish way: a field study in the yidere River, Turkey
    (Csiro Publishing, 2019) Küçükali, Serhat; Verep, Bülent; Alp, Ahmet; Turan, Davut; Mutlu, Tanju; Kaya, Cüneyt; Yıldırım, Yasin; Töreyin, Behçet Uğur; Özelçi, Dursun
    The fish passage performance and flow structure of a brush fish pass were investigated at the ncirli Small Hydropower Plant on the yidere River, located in the East Black Sea region of Turkey. The spatial distributions of velocity vectors, power velocity, Froude number and turbulent kinetic energy are presented. The flow is quasi-uniform and subcritical, which provides different migration corridors with favourable hydraulic conditions; importantly for the fish, these corridors continue through the complete fish pass. The flow-bristle interaction creates a reduced velocity and low-turbulence resting zones. In addition, the passage efficiency of the brush fish pass was assessed using passive integrated transponder telemetry. The results clearly showed that upstream passage efficiency differs between fish species: Salmo coruhensis performed better than Alburnoides fasciatus on the same fish passage. The passage efficiency for the target fish species S. coruhensis was calculated to be 82.4%. The data revealed that the brush fish passage provides passage for small-bodied fish (total body length <15 cm) in a high-gradient channel with a slope of 10%. The monitoring data revealed that bristles as flexible hydraulic elements are beneficial for migrating fish.
  • Thumbnail Image
    Article
    Citation - WoS: 8
    Citation - Scopus: 9
    Joint Parameter and State Estimation of the Hemodynamic Model by Iterative Extended Kalman Smoother
    (Elsevier Sci Ltd, 2016) Akin, Ata; Aslan, Serdar; Cemgil, Ali Taylan; Aslan, Murat Samil; Toreyin, Behcet Ugur
    The joint estimation of the parameters and the states of the hemodynamic model from the blood oxygen level dependent (BOLD) signal is a challenging problem. In the functional magnetic resonance imaging (fMRI) literature, quite interestingly, many proposed algorithms work only as a filtering method. This makes the estimation of hidden states and parameters less reliable compared with the algorithms that use smoothing. In standard implementations, smoothing is performed only once. However, joint state and parameter estimation can be improved substantially by iterating smoothing schemes such as the extended Kalman smoother (IEKS). In the fMRI literature, extended Kalman filtering is thought to be less accurate than standard particle filtering (PF). We compared EKF with PF and observed that the contrary is true. We improved the EKF performance by adding smoother. By iterative scheme joint hemodynamic and parameter estimation is improved substantially. We compared IEKS performance with the square-root cubature Kalman smoother (SCKS) algorithm. We show that its accuracy for the state and the parameter estimation is better and much faster than iterative SCKS. SCKS was found to be a better estimator than the dynamic expectation maximization (DEM), EKF, local linearization filter (LLF) and PP methods. We show in this paper that IEKS is a better estimator than iterative SCKS under different process and measurement noise conditions. As a result, IEKS seems to be the best method we evaluated in all aspects. (C) 2015 Elsevier Ltd. All rights reserved.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 5
    Lossy Compression of Hyperspectral Images Using Online Learning Based Sparse Coding
    (Ieee, 2014) Toreyin, B. Ugur; Ulku, Irem
    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.