### Browsing by Subject "Optimization problems"

Now showing 1 - 16 of 16

###### Results Per Page

###### Sort Options

Item Open Access 3D electron density estimation in the ionosphere(IEEE, 2014) Tuna, Hakan; Arıkan, Orhan; Arıkan, F.Show more Ionosphere has ion distribution which is variable in space and time. There have been physical and empirical studies for modeling the ionosphere. International Reference Ionosphere extended to Plasmasphere (IRI-Plas) is the most recent model developed for this purpose. However, IRI-Plas presents a model about the ionosphere and its compliance with the instantaneous state of the ionosphere does not provide the accuracy needed for engineering purposes. One of the important information sources about the instantaneous state of the ionosphere is GPS signals. In this study, constructing the ionosphere which is compatible with both the instantaneous ionosphere measurements and the physical structure of the ionosphere is presented as an optimization problem, and solved by using the particle swarm optimization technique. The ionosphere over Turkey is investigated by using the proposed optimization method and the importance of the instantaneous ionosphere measurements obtained from GPS signals is demonstrated.Show more Item Open Access Average error in recovery of sparse signals and discrete fourier transform(IEEE, 2012-04) Özçelikkale, Ayça; Yüksel, S.; Özaktaş Haldun M.Show more In compressive sensing framework it has been shown that a sparse signal can be successfully recovered from a few random measurements. The Discrete Fourier Transform (DFT) is one of the transforms that provide the best performance guarantees regardless of which components of the signal are nonzero. This result is based on the performance criterion of signal recovery with high probability. Whether the DFT is the optimum transform under average error criterion, instead of high probability criterion, has not been investigated. Here we consider this optimization problem. For this purpose, we model the signal as a random process, and propose a model where the covariance matrix of the signal is used as a measure of sparsity. We show that the DFT is, in general, not optimal despite numerous results that suggest otherwise. © 2012 IEEE.Show more Item Open Access Birleşik sezim ve kestirim sistemlerinin gürültü ile geliştirilmesi(IEEE, 2014-04) Akbay, Abdullah Başar; Gezici, SinanShow more Belirli koşullar altında, optimal olmayan bazı sezici ve kestiricilerin performansını girdilerine gürültü ekleyerek geliştirmek mümkündür. Bu çalışmada, birleşik bir sezim ve kestirim sisteminin gürültü eklenerek geliştirilmesi incelenmektedir. Sistem performansının maksimizasyonu bir optimizasyon problemi olarak tanımlanmaktadır. Optimal toplanır gürültü dağılımının istatiksel özellikleri belirlenmektedir. Sistem performansının gürültü ile iyileştirilemeyeceği bir koşul elde edilmektedir.Önerilen optimizasyon probleminin, bir doğrusal programlama (DP) problemi olarak yaklaşımı sunulmaktadır. Bir sayısal örnek üzerinde, kuramsal bulguları desteklemek amacıyla, gürültü eklenmiş sistem ile orijinal sistemin performansları karşılaştırılmaktadır.Show more Item Open Access Çok kullanıcılı çok antenli sistemlerde iş birlikli iletim(IEEE, 2008-04) Yazarel, Y. K.; Aktaş, DefneShow more Bu çalışmada işbirlikli, çok kullanıcılı, ve çok antenli bir haberleşme sisteminde telsiz erişim terminallerinin en iyi veri iletimi tekniğine ortaklaşa karar vermeleri problemini inceliyoruz. Burada pek çok çalışmadan farklı olarak kullanıcıların bireysel başarım hedefleri ve anten başına iletim gücü kısıtlamaları olduğu durumu ele alıyoruz. Önceki bir çalışmamızda bu kısıtlamalar altında en iyi sonucu bulan döngülü bir algoritma sunmuştuk. Ancak bu algoritma merkezi bir yapıda olduğu için tam anlamıyla dağıtılmış şekilde gerçeklenememektedir. Bununla birlikte basit yaklaşıklıklar kullanarak en iyiye yakın bir başarım sağlayan ve kısıtlı ve yerel veri iletimi ile gerçeklenebilecek etkin bir algoritma öneriyoruz.Show more Item Open Access A complexity-reduced ML parametric signal reconstruction method(2011) Deprem, Z.; Leblebicioglu, K.; Arkan O.; Çetin, A.E.Show more The problem of component estimation from a multicomponent signal in additive white Gaussian noise is considered. A parametric ML approach, where all components are represented as a multiplication of a polynomial amplitude and polynomial phase term, is used. The formulated optimization problem is solved via nonlinear iterative techniques and the amplitude and phase parameters for all components are reconstructed. The initial amplitude and the phase parameters are obtained via time-frequency techniques. An alternative method, which iterates amplitude and phase parameters separately, is proposed. The proposed method reduces the computational complexity and convergence time significantly. Furthermore, by using the proposed method together with Expectation Maximization (EM) approach, better reconstruction error level is obtained at low SNR. Though the proposed method reduces the computations significantly, it does not guarantee global optimum. As is known, these types of non-linear optimization algorithms converge to local minimum and do not guarantee global optimum. The global optimum is initialization dependent. © 2011 Z. Deprem et al.Show more Item Open Access Effects of signal randomization on performance of binary communications systems(IEEE, 2010) Göken, Çağrı; Gezici, Sinan; Arıkan, OrhanShow more In this paper, effects of signal randomization are studied for binary communications systems. First, it is stated that the average probability of error for a power-constrained binary communications system is minimized when each symbol is randomized between at most two signal values. Then, a fixed detector is considered, and sufficient conditions under which its performance can or cannot be improved via signal randomization are presented. After that, the joint design of detectors and signal structures is studied, and an optimization problem is formulated to determine the optimal system parameters. Finally, numerical results are presented to exemplify the improvements via signal randomization. ©2010 IEEE.Show more Item Open Access Non-linear pricing by convex duality(Elsevier, 2015) Pınar, M. Ç.Show more We consider the pricing problem of a risk-neutral monopolist who produces (at a cost) and offers an infinitely divisible good to a single potential buyer that can be of a finite number of (single dimensional) types. The buyer has a non-linear utility function that is differentiable, strictly concave and strictly increasing. Using a simple reformulation and shortest path problem duality as in Vohra (2011) we transform the initial non-convex pricing problem of the monopolist into an equivalent optimization problem yielding a closed-form pricing formula under a regularity assumption on the probability distribution of buyer types. We examine the solution of the problem when the regularity condition is relaxed in different ways, or when the production function is non-linear and convex. For arbitrary type distributions, we offer a complete solution procedure.Show more Item Open Access On the accuracy of uniform polyhedral approximations of the copositive cone(Taylor & Francis, 2012) Yıldırım, A.Show more We consider linear optimization problems over the cone of copositive matrices. Such conic optimization problems, called copositive programs, arise from the reformulation of a wide variety of difficult optimization problems. We propose a hierarchy of increasingly better outer polyhedral approximations to the copositive cone. We establish that the sequence of approximations is exact in the limit. By combining our outer polyhedral approximations with the inner polyhedral approximations due to de Klerk and Pasechnik [SIAM J. Optim. 12 (2002), pp. 875-892], we obtain a sequence of increasingly sharper lower and upper bounds on the optimal value of a copositive program. Under primal and dual regularity assumptions, we establish that both sequences converge to the optimal value. For standard quadratic optimization problems, we derive tight bounds on the gap between the upper and lower bounds. We provide closed-form expressions of the bounds for the maximum stable set problem. Our computational results shed light on the quality of the bounds on randomly generated instances.Show more Item Open Access An optimal network dimensioning and initial energy assignment minimizing the monetary cost of a heterogeneous WSN(IEEE, 2009) Sevgi, Cüneyt; Kocyigit, A.Show more In this paper, a novel method is proposed to dimension a randomly deployed heterogeneous Wireless Sensor Network (WSN) of minimum monetary cost satisfying minimum coverage and minimum lifetime requirements. We consider WSNs consisting of two different types of nodes clusterheads and ordinary sensor nodes, randomly deployed over a sensing field. All devices are assumed to be stationary and have identical sensing capabilities. However, the clusterheads are more energetic and powerful in terms of processing and communication capabilities compared to sensor nodes. For such a network, finding minimum cost WSN problem is not a trivial one, since the distribution of the mixture of two different types of devices and the batteries with different initial energies in each type of device primarily determine the monetary cost of a WSN. Therefore, we formulated an optimization problem to minimize the monetary cost of a WSN for given coverage and lifetime requirements. The proposed optimization problem is solved for a certain scenario and the solution is validated by computer simulations. © 2009 IEEE.Show more Item Open Access Particle swarm optimization based channel identification in cross-ambiguity domain(IEEE, 2010) Güldoğan, Mehmet Burak; Arıkan, OrhanShow more In this paper, a new array signal processing technique by using particle swarm optimization (PSO) is proposed to identify multipath channel parameters. The proposed technique provides estimates to the channel parameters by finding a global minimum of an optimization problem. Since the optimization problem is formulated in the cross-ambiguity function (CAF) domain of the transmitted signal and the received array outputs, the proposed technique is called as PSO-CAF. The performance of the PSO-CAF is compared with the space alternating generalized expectation maximization (SAGE) technique and with another recently proposed PSO based technique for various SNR values. Simulation results indicate the superior performance of the PSO-CAF technique over mentioned techniques for all SNR values. ©2010 IEEE.Show more Item Open Access A particle swarm optimization based SAR motion compensation algorithm for target image reconstruction(IEEE, 2010) Uğur, Salih; Arıkan, OrhanShow more A new SAR motion compensation algorithm is proposed for robust reconstruction of target images even under large deviations of the platform from intended flight path. Phase error due to flight path deviations is estimated as a solution to an optimization problem in terms of the positions of the reflectivity centers of the target. Particle swarm optimization is used to obtain phase error estimates efficiently. The quality of the reconstructions is demonstrated by using simulation studies. © 2010 IEEE.Show more Item Open Access Pipelined fission for stream programs with dynamic selectivity and partitioned state(Academic Press, 2016) Gedik, B.; Özsema, H. G.; Öztürk, Ö.Show more There is an ever increasing rate of digital information available in the form of online data streams. In many application domains, high throughput processing of such data is a critical requirement for keeping up with the soaring input rates. Data stream processing is a computational paradigm that aims at addressing this challenge by processing data streams in an on-the-fly manner, in contrast to the more traditional and less efficient store-and-then process approach. In this paper, we study the problem of automatically parallelizing data stream processing applications in order to improve throughput. The parallelization is automatic in the sense that stream programs are written sequentially by the application developers and are parallelized by the system. We adopt the asynchronous data flow model for our work, which is typical in Data Stream Processing Systems (DSPS), where operators often have dynamic selectivity and are stateful. We solve the problem of pipelined fission, in which the original sequential program is parallelized by taking advantage of both pipeline parallelism and data parallelism at the same time. Our pipelined fission solution supports partitioned stateful data parallelism with dynamic selectivity and is designed for shared-memory multi-core machines. We first develop a cost-based formulation that enables us to express pipelined fission as an optimization problem. The bruteforce solution of this problem takes a long time for moderately sized stream programs. Accordingly, we develop a heuristic algorithm that can quickly, but approximately, solve the pipelined fission problem. We provide an extensive evaluation studying the performance of our pipelined fission solution, including simulations as well as experiments with an industrial-strength DSPS. Our results show good scalability for applications that contain sufficient parallelism, as well as close to optimal performance for the heuristic pipelined fission algorithm.Show more Item Open Access Projections onto convex sets (POCS) based optimization by lifting(IEEE, 2013) Çetin, A. Enis; Bozkurt, Alican; Günay, Osman; Habiboglu, Yusuf Hakan; Köse, K.; Onaran, İbrahim; Tofighi, Mohammad; Sevimli, Rasim AkınShow more A new optimization technique based on the projections onto convex space (POCS) framework for solving convex and some non-convex optimization problems are presented. The dimension of the minimization problem is lifted by one and sets corresponding to the cost function are defined. If the cost function is a convex function in RN the corresponding set which is the epigraph of the cost function is also a convex set in RN+1. The iterative optimization approach starts with an arbitrary initial estimate in R N+1 and an orthogonal projection is performed onto one of the sets in a sequential manner at each step of the optimization problem. The method provides globally optimal solutions in total-variation, filtered variation, l1, and entropic cost functions. It is also experimentally observed that cost functions based on lp; p < 1 may be handled by using the supporting hyperplane concept. The new POCS based method can be used in image deblurring, restoration and compressive sensing problems. © 2013 IEEE.Show more Item Open Access Quality control chart design under jidoka(John Wiley & Sons, Inc., 2009) Berk, E.; Toy, A. Ö.Show more We consider design of control charts in the presence of machine stoppages that are exogenously imposed (as under jidoka practices). Each stoppage creates an opportunity for inspection/repair at reduced cost. We first model a single machine facing opportunities arriving according to a Poisson process, develop the expressions for its operating characteristics and construct the optimization problem for economic design of a control chart. We, then, consider the multiple machine setting where individual machine stoppages may create inspection/repair opportunities for other machines. We develop exact expressions for the cases when all machines are either opportunity-takers or not. On the basis of an approximation for the all-taker case, we then propose an approximate model for the mixed case. In a numerical study, we examine the opportunity taking behavior of machines in both single and multiple machine settings and the impact of such practices on the design of an X̄ - Q C chart. Our findings indicate that incorporating inspection/repair opportunities into QC chart design may provide considerable cost savings.Show more Item Open Access Sparse ground-penetrating radar imaging method for off-the-grid target problem(SPIE, 2013) Gurbuz, A. C.; Teke, O.; Arıkan, OrhanShow more Spatial sparsity of the target space in subsurface or through-the-wall imaging applications has been successfully used within the compressive-sensing framework to decrease the data acquisition load in practical systems, while also generating highresolution images. The developed techniques in this area mainly discretize the continuous target space into grid points and generate a dictionary of model data that is used in image-reconstructing optimization problems. However, for targets that do not coincide with the computation grid, imaging performance degrades considerably. This phenomenon is known as the off-grid problem. This paper presents a novel sparse ground-penetrating radar imaging method that is robust for off-grid targets. The proposed technique is an iterative orthogonal matching pursuit-based method that uses gradientbased steepest ascent-type iterations to locate the off-grid target. Simulations show that robust results with much smaller reconstruction errors are obtained for multiple off-grid targets compared to standard sparse reconstruction techniques. © 2013 SPIE and IS&T.Show more Item Open Access Ultra-wideband orthogonal pulse shape set design by using Hermite-Gaussian functions(IEEE, 2012) Alp, Yaşar Kemal; Dedeoǧlu, Mehmet; Arıkan, OrhanShow more Ultra-Wideband (UWB) communication systems have been developed for short distance, high data rate communications. To avoid interfering with the existing systems in the same environment, very short duration pulses used by these systems should satisfy a predefined spectral mask. Data rate of UWB systems can be increased by using multiple pulse shapes simultaneously. Orthogonality of the simultaneously used pulse shapes simplifies the receiver design. In this work, design of orthogonal pulse shapes which satisfy the spectral mask is modelled as an optimization problem. First, it is converted to a convex optimization problem by constraining the pulse shapes to lie in a subspace spanned by the Hermite-Gaussian (HG) functions. Then the optimal solution is obtained. It is shown that a larger pulse shape set can be designed compared to the existing approaches, and hence, a higher data rate can be achieved. © 2012 IEEE.Show more