Browsing by Author "Teke, Oğuzhan"

Now showing 1 - 6 of 6

Open Access
Autofocus method in thermal cameras based on image histogram
(IEEE, 2011) Turgay, E.; Teke, Oğuzhan
In this paper, a new histogram based auto-focusing method for thermal cameras is proposed. This proposed method is realized by FPGA (Field Programmable Gate Array) and DSP (Digital Signal Processor) working together and simultaneously. HF (High Frequency) component, obtained from real-time image flow by FPGA and DSP is used for auto-focusing process. Proposed method is able to determine the focus direction from the HF component produced in the process of histogram equalization by FPGA, unlike Fourier transform and pixel differenve based methods in the literature. With this superiority, proposed method requires no extra calculation for thermal cameras for which histogram equalization is necessary. Analysis show that proposed method is successful on the simulations and scanning thermal cameras.
Open Access
A new OMP technique for sparse recovery
(IEEE, 2012) Teke, Oğuzhan; Gürbüz, A.C.; Arıkan, Orhan
Compressive Sensing (CS) theory details how a sparsely represented signal in a known basis can be reconstructed using less number of measurements. However in reality there is a mismatch between the assumed and the actual bases due to several reasons like discritization of the parameter space or model errors. Due to this mismatch, a sparse signal in the actual basis is definitely not sparse in the assumed basis and current sparse reconstruction algorithms suffer performance degradation. This paper presents a novel orthogonal matching pursuit algorithm that has a controlled perturbation mechanism on the basis vectors, decreasing the residual norm at each iteration. Superior performance of the proposed technique is shown in detailed simulations. © 2012 IEEE.
Open Access
A recursive way for sparse reconstruction of parametric spaces
(IEEE, 2015-11) Teke, Oğuzhan; Gürbüz, A. C.; Arıkan, Orhan
A novel recursive framework for sparse reconstruction of continuous parameter spaces is proposed by adaptive partitioning and discretization of the parameter space together with expectation maximization type iterations. Any sparse solver or reconstruction technique can be used within the proposed recursive framework. Experimental results show that proposed technique improves the parameter estimation performance of classical sparse solvers while achieving Cramér-Rao lower bound on the tested frequency estimation problem. © 2014 IEEE.
Open Access
Robust compressive sensing techniques
(2014) Teke, Oğuzhan
Compressive Sensing theory details how a sparsely represented signal in a known basis can be reconstructed from an underdetermined linear measurements. However, in reality there is a mismatch between the assumed and the actual dictionary due to factors such as discretization of the parameter space defining basis components, sampling jitter in A/D conversion, and model errors. Due to this mismatch, a signal may not be sparse in the assumed basis, which causes signifi- cant performance degradation in sparse reconstruction algorithms. To eliminate the mismatch problem, this thesis presents two novel robust algorithm and an adaptive discretization framework that can obtain successful sparse representations. In the proposed techniques, the selected dictionary atoms are perturbed towards directions to decrease the orthogonal residual norm. The first algorithm named as Parameter Perturbed Orthogonal Matching Pursuit (PPOMP) targets the off-grid problem and the parameters of the selected dictionary atoms are perturbed. The second algorithm named as Perturbed Orthogonal Matching Pursuit (POMP) targets the unstructured basis mismatch problem and performs controlled rotation based perturbation of selected dictionary atoms. Based on detailed mathematical analysis, conditions for successful reconstruction are derived. Simulations show that robust results with much smaller reconstruction errors in the case of both parametric and unstructured basis mismatch problem can be obtained as compared to standard sparse reconstruction techniques. Different from the proposed perturbation approaches, the proposed adaptive framework discretizes the continuous parameter space depending on the estimated sparsity level. Once a provisional solution is obtained with a sparse solver, the framework recursively splits the problem into sparser sub-problems so that each sub-problem is exposed to less severe off-grid problem. In the presented recursive framework, any sparse reconstruction technique can be used. As illustrated over commonly used applications, the error in the estimated parameters of sparse signal components almost achieve the Cram´er-Rao lower bound in the proposed framework.
Open Access
Seyrek sinyallerin geri çatımına özyineli bir yaklaşım
(IEEE, 2014-04) Teke, Oğuzhan; Arıkan, Orhan; Gürbüz, A. C.
Sıkıştırılmış Algılama (SA) kuramı, bilinen bir tabanda seyrek olan bir sinyalin az sayıda ölçüm ile nasıl geri çatılacağını inceler. Çoğu pratik sistemdeki ölçüm sinyallerinin sürekli bir parametre uzayında seyrek bir tanıma sahip olması, SA kuramı altında geliştirilmiş tekniklerin kullanılabilme olasılığını ortaya çıkarır. Ancak, SA tekniklerinin uygulanabilmesi için sürekli parametre uzayının ayrıklaştırılması gerekir. Bu ayrıklaştırma sonucunda da iyi bilinen ızgara-dışılık problemi ortaya çıkar. Izgara-dışılık problemini engellemek için bu çalışmada, parametre alanını degişken ve uyarlamalı bir şekilde ayrıklaştıran özyineli bir yaklaşım sunulmuştur. Önerilen yaklaşımın çok yakın şekilde konumlanmış hedefleri dahi yüksek hassasiyetle kestirebildiği benzetim çalışmalarıyla gösterilmiştir.
Open Access
Sparse delay-Doppler image reconstruction under off-grid problem
(IEEE, 2014-06) Teke, Oğuzhan; Gürbüz, A. C.; Arıkan, Orhan
Pulse-Doppler radar has been successfully applied to surveillance and tracking of both moving and stationary targets. For efficient processing of radar returns, delay-Doppler plane is discretized and FFT techniques are employed to compute matched filter output on this discrete grid. However, for targets whose delay-Doppler values do not coincide with the computation grid, the detection performance degrades considerably. Especially for detecting strong and closely spaced targets this causes miss detections and false alarms. Although compressive sensing based techniques provide sparse and high resolution results at sub-Nyquist sampling rates, straightforward application of these techniques is significantly more sensitive to the off-grid problem. Here a novel and OMP based sparse reconstruction technique with parameter perturbation, named as PPOMP, is proposed for robust delay-Doppler radar processing even under the off-grid case. In the proposed technique, the selected dictionary parameters are perturbed towards directions to decrease the orthogonal residual norm. A new performance metric based on Kull-back-Leibler Divergence (KLD) is proposed to better characterize the error between actual and reconstructed parameter spaces. © 2014 IEEE.