Researcher: Kozat, Süleyman Serdar
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Kozat, Süleyman Serdar
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Publication Metadata only Comparison of convex combination and affine combination of adaptive filters(Ieee, 2009) Singer, Andrew C.; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Kozat, Süleyman Serdar; Erdoğan, Alper Tunga; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; College of Engineering; 177972; 41624In the area of combination of adaptive filters, two main approaches, namely convex and affine combinations have been introduced. In this article, the relation between these two approaches is investigated. First, the problem of obtaining optimal convex combination coefficients is formulated as the projection of the optimal affine combination weights to the unit simplex in a weighted inner product space. Based on this formulation the closed form expressions for optimal combination weights and target MSE levels are obtained for two and three branch cases.Publication Metadata only Embedding and retrieving private metadata in electrocardiograms(Springer, 2009) Vlachos, Michail; Lucchese, Claudio; Van Herle, Helga; Yu, Philip S; Department of Electrical and Electronics Engineering; Kozat, Süleyman Serdar; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 177972Due to the recent explosion of 'identity theft' cases, the safeguarding of private data has been the focus of many scientific efforts. Medical data contain a number of sensitive attributes, whose access the rightful owner would ideally like to disclose only to authorized personnel. One way of providing limited access to sensitive data is through means of encryption. In this work we follow a different path, by proposing the fusion of the sensitive metadata within the medical data. Our work is focused on medical time-series signals and in particular on Electrocardiograms (ECG). We present techniques that allow the embedding and retrieval of sensitive numerical data, such as the patient's social security number or birth date, within the medical signal. The proposed technique not only allows the effective hiding of the sensitive metadata within the signal itself, but it additionally provides a way of authenticating the data ownership or providing assurances about the origin of the data. Our methodology builds upon watermarking notions, and presents the following desirable characteristics: (a) it does not distort important ECG characteristics, which are essential for proper medical diagnosis, (b) it allows not only the embedding but also the efficient retrieval of the embedded data, (c) it provides resilience and fault tolerance by employing multistage watermarks (both robust and fragile). Our experiments on real ECG data indicate the viability of the proposed scheme.Publication Metadata only Universal switching portfolios under transaction costs(Ieee, 2008) Singer, Andrew C.; Department of Electrical and Electronics Engineering; Kozat, Süleyman Serdar; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 177972In this paper, we consider online (sequential) portfolio selection in a competitive algorithm framework under transaction costs. We construct a sequential algorithm for portfolio selection that asymptotically achieves the wealth of the best piecewise constant rebalanced portfolio tuned to the underlying individual sequence of price relative vectors where we pay a fixed percent commission for each transaction. Without knowledge of the investment duration, the algorithm can perform as well as the best investment algorithm that can choose both the partitioning of the sequence of the price relative vectors as well as the best constant rebalanced portfolio within each segment based on knowledge of the sequence of price relative vectors in advance. We use a transition diagram similar to that in [1] to compete with an exponential number of switching investment strategies, using only linear complexity in the data length for combination.Publication Metadata only Low complexity turbo-equalization: a clustering approach(Institute of Electrical and Electronics Engineers (IEEE), 2014) Kim, Kyeongyeon; Choi, Jun Won; Singer, Andrew C.; Department of Electrical and Electronics Engineering; Kozat, Süleyman Serdar; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 177972We introduce a low complexity approach to iterative equalization and decoding, or "turbo equalization", which uses clustered models to better match the nonlinear relationship that exists between likelihood information from a channel decoder and the symbol estimates that arise in soft-input channel equalization. The introduced clustered turbo equalizer uses piecewise linear models to capture the nonlinear dependency of the linear minimum mean square error (MMSE) symbol estimate on the symbol likelihoods produced by the channel decoder and maintains a computational complexity that is only linear in the channel memory. By partitioning the space of likelihood information from the decoder based on either hard or soft clustering and using locally-linear adaptive equalizers within each clustered region, the performance gap between the linear MMSE turbo equalizers and low-complexity least mean square (LMS)-based linear turbo equalizers can be narrowed.Publication Metadata only Transient analysis of convexly constrained mixture methods(IEEE, 2012) N/A; Department of Electrical and Electronics Engineering; N/A; N/A; Kozat, Süleyman Serdar; Dönmez, Mehmet Ali; Özkan, Hüseyin; Faculty Member; Master Student; PhD Student; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; 177972; N/A; N/AWe study the transient performances of three convexly constrained adaptive combination methods that combine outputs of two adaptive filters running in parallel to model a desired unknown system. We propose a theoretical model for the mean and mean-square convergence behaviors of each algorithm. Specifically, we provide expressions for the time evolution of the mean and the variance of the combination parameters, as well as for the mean square errors. The accuracy of the theoretical models are illustrated through simulations in the case of a mixture of two LMS filters with different step sizes.Publication Metadata only Universal portfolios via context trees(IEEE, 2008) Singer, Andrew C.; Bean, Andrew J.; Department of Electrical and Electronics Engineering; Kozat, Süleyman Serdar; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 177972In this paper, we consider the sequential portfolio investment problem considered by Cover [3] and extend the results of [3] to the class of piecewise constant rebalanced portfolios that are tuned to the underlying sequence of price relatives. Here, the piecewise constant models are used to partition the space of past price relative vectors where we assign a different constant rebalanced portfolio to each region independently. We then extend these results where we compete against a doubly exponential number of piecewise constant portfolios that are represented by a context tree. We use the context tree to achieve the wealth of a portfolio selection algorithm that can choose both its partitioning of the space of the past price relatives and its constant rebalanced portfolio within each region of the partition, based on observing the entire sequence of price relatives in advance, uniformly, for every bounded deterministic sequence of price relative vectors. This performance is achieved with a portfolio algorithm whose complexity is only linear in the depth of the context tree per investment period. We demonstrate that the resulting portfolio algorithm achieves significant gains on historical stock pairs over the algorithm of [3] and the best constant rebalanced portfolio.Publication Metadata only Optimal distance bounds on time-series data(Society for Industrial and Applied Mathematics, 2009) Vlachos, Michail; Yu, Philip S.; Department of Electrical and Electronics Engineering; Kozat, Süleyman Serdar; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 177972Most data mining operations include an integral search component at their core. For example, the performance of similarity search or classification based on Nearest Neighbors is largely dependent on the underlying compression and distance estimation techniques. As data repositories grow larger, there is an explicit need not only for storing the data in a compressed form, but also for facilitating mining operations directly on the compressed data. Naturally, the quality or tightness of the estimated distances on the compressed objects directly affects the search performance. We motivate our work within the setting of search engine weblog repositories, where keyword demand trends over time are represented and stored as compressed time-series data. Search and analysis over such sequence data has important applications for the search engines, including discovery of important news events, keyword recommendation and efficient keyword-to-advertisement mapping. We present new mechanisms for very fast search operations over the compressed time-series data, with specific focus on weblog data. An important contribution of this work is the derivation of optimally tight bounds on the Euclidean distance estimation between compressed sequences. Since our methodology is applicable to sequential data in general, the proposed technique is of independent interest. Additionally, our distance estimation strategy is not tied to a specific compression methodology, but can be applied on top of any orthonormal based compression technique (Fourier, Wavelet, PCA, etc). The experimental results indicate that the new optimal bounds lead to a significant improvement in the pruning power of search compared to previous state-of-the-art, in many cases eliminating more than 80% of the candidate search sequences.Publication Metadata only Competitive randomized nonlinear prediction under additive noise(IEEE-Inst Electrical Electronics Engineers Inc, 2010) N/A; Department of Electrical and Electronics Engineering; Yılmaz, Yasin; Kozat, Süleyman Serdar; Master Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 177972We consider sequential nonlinear prediction of a bounded, real-valued and deterministic signal from its noise-corrupted past samples in a competitive algorithm framework. We introduce a randomized algorithm based on context-trees [1]. The introduced algorithm asymptotically achieves the performance of the best piecewise affine model that can both select the best partition of the past observations space (from a doubly exponential number of possible partitions) and the affine model parameters based on the desired clean signal in hindsight. Although the performance measure including the loss function is defined with respect to the noise-free clean signal, the clean signal, its past samples or prediction errors are not available for training or constructing predictions. We demonstrate the performance of the introduced algorithm when applied to certain chaotic signals.Publication Metadata only A performance-weighted mixture of LMS filters(Ieee, 2009) Singer, Andrew C.; Department of Electrical and Electronics Engineering; Kozat, Süleyman Serdar; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 177972In this paper, we explore the use of a particular multistage adaptation algorithm for a variety of adaptive filtering applications where the structure of the underlying process to be estimated is unknown. The proposed algorithm uses a performance-weighted mixture of LMS filters of various orders to construct its final output. The algorithm is analyzed in a stochastic context with respect to its convergence and mean-square error (MSE) behaviors and is shown to achieve the best MSE performance of the constituent algorithms in the mixture. Through simulations, it has been observed that the mixture structure can offer considerable performance improvement for both stationary and time varying observation sequences.Publication Metadata only Optimal portfolios under transaction costs in discrete time markets(IEEE, 2012) N/A; Department of Electrical and Electronics Engineering; N/A; N/A; Kozat, Süleyman Serdar; Dönmez, Mehmet Ali; Tunç, Sait; Faculty Member; Master Student; Master Student; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; 177972; N/A; N/AWe study portfolio investment problem from a probabilistic modeling perspective and study how an investor should distribute wealth over two assets in order to maximize the cumulative wealth. We construct portfolios that provide the optimal growth in i.i.d. discrete time two-asset markets under proportional transaction costs. As the market model, we consider arbitrary discrete distributions on the price relative vectors. To achieve optimal growth, we use threshold portfolios. We demonstrate that under the threshold rebalancing framework, the achievable set of portfolios elegantly form an irreducible Markov chain under mild technical conditions. We evaluate the corresponding stationary distribution of this Markov chain, which provides a natural and efficient method to calculate the cumulative expected wealth. Subsequently, the corresponding parameters are optimized using a brute force approach yielding the growth optimal portfolio under proportional transaction costs in i.i.d. discrete-time two-asset markets.