Elektrik Elektronik Mühendisliği Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/411
Browse
Browsing Elektrik Elektronik Mühendisliği Bölümü Yayın Koleksiyonu by Author "19325"
Now showing 1 - 5 of 5
- Results Per Page
- Sort Options
Conference Object Citation - WoS: 1Citation - Scopus: 3Hyperspectral image compression using an online learning method(Spie-int Soc Optical Engineering, 2015) Ulku, Irem; Toreyin, B. Ugur; 19325A 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.Article Citation - WoS: 8Citation - Scopus: 9Joint parameter and state estimation of the hemodynamic model by iterative extended Kalman smoother(Elsevier Sci Ltd, 2016) Aslan, Serdar; Töreyin, Behçet Uğur; Cemgil, Ali Taylan; Aslan, Murat Samil; Toreyin, Behcet Ugur; Akin, Ata; 19325; Elektrik-Elektronik MühendisliğiThe 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.Article Citation - WoS: 8Citation - Scopus: 10Sparse coding of hyperspectral imagery using online learning(Springer London Ltd, 2015) Ulku, Irem; Töreyin, Behçet Uğur; Toreyin, Behcet Ugur; 17575; 19325; Elektrik-Elektronik MühendisliğiSparse 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.Article Citation - WoS: 10Citation - Scopus: 12Sparse representations for online-learning-based hyperspectral image compression(Optical Soc Amer, 2015) Ulku, Irem; Töreyin, Behçet Uğur; Toreyin, Behcet Ugur; 17575; 19325; Elektrik-Elektronik MühendisliğiSparse 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 AmericaConference Object Citation - Scopus: 6Wavelet based flame detection using differential PIR sensors(2012) Erden, F.; Töreyin, Behçet Uğur; Töreyin, B.U.; Soyer, E.B.; Inaç, I.; Günay, O.; Köse, K.; Çetin, A.E.; 19325; Elektrik-Elektronik MühendisliğiIn 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.
