Browsing by Author "Toreyin, Behcet Ugur"

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Adaptive Decision Fusion Based Framework for Short-Term Wind Speed and Turbulence Intensity Forecasting: Case Study for North West of Turkey
(2017) Toreyin, Behcet Ugur; Dinçkal, Çiğdem; Küçükali, Serhat; 20413; 26773; 06.05. İnşaat Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi; 06.03. Elektrik-Elektronik Mühendisliği
:In this paper, an online learning framework called adaptive decision fusion (ADF) is employed for short-term wind speed and turbulence intensity forecasting by use of wind speed data for each season for the city of ˙Izmit, located in the northwest of Turkey. Fixed-weight (FW) linear combination is derived and used for comparison with ADF. Wind speeds and turbulence intensities are predicted from the existing wind speed data and computed turbulence intensities, respectively, using the ADF and FW methods. Simulations are carried out for each season and the results are tested on mean absolute percentage error criterion. It is shown that the proposed model captured the system dynamic behavior and made accurate predictions based on the seasonal wind speed characteristics of the site. The procedure described here can be used to estimate the local velocity and turbulence intensity in a wind power plant during a storm.
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; 19325; 01. Çankaya Üniversitesi; 06. Mühendislik Fakültesi; 06.03. Elektrik-Elektronik Mühendisliği
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.
Evaluation of Clustering Performance of Hyperspectral Bands
(Ieee, 2015) Sakarya, Ufuk; Toreyin, Behcet Ugur; Haliloglu, Onur; 19325; 01. Çankaya Üniversitesi; 06. Mühendislik Fakültesi; 06.03. Elektrik-Elektronik Mühendisliği
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.
Citation - WoS: 1
Citation - Scopus: 4
Evaluation of On-Board Integer Wavelet Transform Based Spectral Decorrelation Schemes for Lossless Compression of Hyperspectral Images
(Ieee, 2014) Yilmaz, Ozan; Mert, Yakup Murat; Toreyin, Behcet Ugur; 19325; 01. Çankaya Üniversitesi; 06. Mühendislik Fakültesi; 06.03. Elektrik-Elektronik Mühendisliği
Integer-coefficient Discrete Wavelet Transformation (DWT) filters widely used in the literature are implemented and investigated as spectral decorrelator for on-board lossless hyperspectral image compression. As the performance of spectral decorrelation step has direct impact on the compression ratio (CR), it is important to employ the most convenient spectral decorrelator in terms of low computational complexity and high CR. Extensive tests using AVIRIS image data set are carried out and CRs corresponding to various subband decomposition levels are presented within a lossless hyperspectral compression framework. Results suggest that Cohen-Daubechies-Feauveau (CDF) 9/7 integer-coefficient wavelet transform with five levels of spectral subband decomposition would be an efficient spectral decorrelator for on-board lossless hyperspectral image compression.
Citation - WoS: 1
Citation - Scopus: 1
Graph-Cut Compression Algorithm for Compressed-Sensed Image Acquisition
(Ieee, 2014) Gulen, Seden Hazal; Trocan, Maria; Toreyin, Behcet Ugur; Alaydin, Julide Gulen; 19325; 01. Çankaya Üniversitesi; 06. Mühendislik Fakültesi; 06.03. Elektrik-Elektronik Mühendisliği
The purpose of the paper is to find the best quantizer allocation for compressed-sensed acquired images, by using a graph-cut quantizer allocation method. The compressed sensed acquisition is realized in a block-based manner, using a random projection matrix, and on the obtained block measurements a graph-cut-based quantizer allocation method is applied, in order to further reduce the bitrate associated to the measurements. Finally, the quantized measurements are reconstructed using a Smooth Projected Landweber recovery method. The proposed compression method for compressed sensed acquisition shows better results when compared to JPEG2000.
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; 19325; 01. Çankaya Üniversitesi; 06. Mühendislik Fakültesi; 06.03. Elektrik-Elektronik Mühendisliği; 06.01. Bilgisayar Mühendisliği
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.
Citation - WoS: 2
Citation - Scopus: 12
Lossless Hyperspectral Image Compression Using Wavelet Transform Based Spectral Decorrelation
(Ieee, 2015) Yilmaz, Ozan; Mert, Yakup Murat; Turk, Fethi; Toreyin, Behcet Ugur; 19325; 01. Çankaya Üniversitesi; 06. Mühendislik Fakültesi; 06.03. Elektrik-Elektronik Mühendisliği
Integer-coefficient Discrete Wavelet Transformation (DWT) filters widely used in the literature are implemented and investigated as spectral decorrelator. As the performance of spectral decorrelation step has direct impact on the compression ratio (CR), it is important to employ the most convenient spectral decorrelator in terms of computational complexity and CR. Tests using AVIRIS image data set are carried out and CRs corresponding to various subband decomposition levels are presented within a lossless hyperspectral compression framework. Two-dimensional images corresponding to each band is compressed using JPEG-LS algorithm. Results suggest that Cohen-Daubechies-Feauveau (CDF) 9/7 integer-coefficient wavelet transform with five levels of spectral subband decomposition would be an efficient spectral decorrelator for on-board lossless hyperspectral image compression.
Citation - WoS: 12
Citation - Scopus: 13
An Online Adaptive Cooperation Scheme for Spectrum Sensing Based on a Second-Order Statistical Method
(Ieee-inst Electrical Electronics Engineers inc, 2012) Toreyin, Behcet Ugur; Qaraqe, Khalid A.; Cetin, A. Enis; Yarkan, Serhan; 19325; 01. Çankaya Üniversitesi; 06. Mühendislik Fakültesi; 06.03. Elektrik-Elektronik Mühendisliği
Spectrum sensing is one of the most important features of cognitive radio (CR) systems. Although spectrum sensing can be performed by a single CR, it is shown in the literature that cooperative techniques, including multiple CRs/sensors, improve the performance and reliability of spectrum sensing. Existing cooperation techniques usually assume a static communication scenario between the unknown source and sensors along with a fixed propagation environment class. In this paper, an online adaptive cooperation scheme is proposed for spectrum sensing to maintain the level of sensing reliability and performance under changing channel and environmental conditions. Each cooperating sensor analyzes second-order statistics of the received signal, which undergoes both correlated fast and slow fading. Autocorrelation estimation data from sensors are fused together by an adaptive weighted linear combination at the fusion center. Weight update operation is performed online through the use of orthogonal projection onto convex sets. Numerical results show that the performance of the proposed scheme is maintained for dynamically changing characteristics of the channel between an unknown source and sensors, even under different physical propagation environments. In addition, it is shown that the proposed cooperative scheme, which is based on second-order detectors, yields better results compared with the same fusion mechanism that is based on conventional energy detectors.
Citation - WoS: 8
Citation - Scopus: 10
Sparse Coding of Hyperspectral Imagery Using Online Learning
(Springer London Ltd, 2015) Toreyin, Behcet Ugur; Ulku, Irem; 17575; 19325; 01. Çankaya Üniversitesi; 06. Mühendislik Fakültesi; 06.03. Elektrik-Elektronik Mühendisliği
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.
Citation - WoS: 10
Citation - Scopus: 12
Sparse Representations for Online-Learning Hyperspectral Image Compression
(Optical Soc Amer, 2015) Toreyin, Behcet Ugur; Ulku, Irem; 17575; 19325; 01. Çankaya Üniversitesi; 06. Mühendislik Fakültesi; 06.03. Elektrik-Elektronik Mühendisliği
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