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Publication Metadata only A deterministic analysis of an online convex mixture of experts algorithm(Institute of Electrical and Electronics Engineers (IEEE), 2015) Özkan, Hüseyin; Dönmez, Mehmet A.; N/A; Tunç, Sait; Master Student; Graduate School of Sciences and Engineering; N/AWe analyze an online learning algorithm that adaptively combines outputs of two constituent algorithms (or the experts) running in parallel to estimate an unknown desired signal. This online learning algorithm is shown to achieve and in some cases outperform the mean-square error (MSE) performance of the best constituent algorithm in the steady state. However, the MSE analysis of this algorithm in the literature uses approximations and relies on statistical models on the underlying signals. Hence, such an analysis may not be useful or valid for signals generated by various real-life systems that show high degrees of nonstationarity, limit cycles and that are even chaotic in many cases. In this brief, we produce results in an individual sequence manner. In particular, we relate the time-accumulated squared estimation error of this online algorithm at any time over any interval to the one of the optimal convex mixture of the constituent algorithms directly tuned to the underlying signal in a deterministic sense without any statistical assumptions. In this sense, our analysis provides the transient, steady-state, and tracking behavior of this algorithm in a strong sense without any approximations in the derivations or statistical assumptions on the underlying signals such that our results are guaranteed to hold. We illustrate the introduced results through examples.Publication Metadata only A stochastic framework for rate-distortion optimized video coding over error-prone networks(IEEE-Inst Electrical Electronics Engineers Inc, 2007) Harmanci, Oztan; Department of Electrical and Electronics Engineering; Tekalp, Ahmet Murat; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 26207This paper proposes a complete stochastic framework for RD optimal encoder design for video over error-prone networks, which applies to any motion-compensated predictive video codec. The distortion measure has been taken as the mean square error over an ensemble of channels given an estimate of the instantaneous packet loss probability. We show that 1) the optimal motion compensated prediction, in the MSE sense, requires computation of the expected value of the reference frames, and 2) calculation of the MSE (distortion measure) requires computation of the second moment of the reference frames. We propose a recursive procedure for the computation of both the expected value and second moment of the reference frames, which are together called the stochastic frame buffer. Furthermore, we propose a stochastic RD optimization method for selection of the optimal macroblock mode and motion vectors given the instantaneous packet loss probability. If available, channel feedback can also be incorporated into the proposed stochastic framework. However, the proposed framework does not require a feedback channel to exist, and when it exists, it does not have to be lossless. In the absence of any packet losses, the proposed stochastic framework reduces to the well-known deterministic RD optimization procedures. One possible application of the optimal stochastic framework would be for multicast streaming to an ensemble of receivers. Experimental results indicate that the proposed framework outperforms other available error tracking and control schemes.Publication Metadata only Analysis of head gesture and prosody patterns for prosody-driven head-gesture animation(IEEE Computer Soc, 2008) Sargin, Mehmet Emre; Department of Computer Engineering; Department of Computer Engineering; Department of Electrical and Electronics Engineering; Yemez, Yücel; Erzin, Engin; Tekalp, Ahmet Murat; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Electrical and Electronics Engineering; College of Engineering; College of Engineering; College of Engineering; 107907; 34503; 26207We propose a new two-stage framework for joint analysis of head gesture and speech prosody patterns of a speaker toward automatic realistic synthesis of head gestures from speech prosody. In the first stage analysis, we perform Hidden Markov Model (HMM)-based unsupervised temporal segmentation of head gesture and speech prosody features separately to determine elementary head gesture and speech prosody patterns, respectively, for a particular speaker. In the second stage, joint analysis of correlations between these elementary head gesture and prosody patterns is performed using Multistream HMMs to determine an audio-visual mapping model. The resulting audio-visual mapping model is then employed to synthesize natural head gestures from arbitrary input test speech given a head model for the speaker. In the synthesis stage, the audio-visual mapping model is used to predict a sequence of gesture patterns from the prosody pattern sequence computed for the input test speech. The Euler angles associated with each gesture pattern are then applied to animate the speaker head model. Objective and subjective evaluations indicate that the proposed synthesis by analysis scheme provides natural looking head gestures for the speaker with any input test speech, as well as in "prosody transplant" and "gesture transplant" scenarios.Publication Metadata only Automatic soccer video analysis and summarization(Institute of Electrical and Electronics Engineers (IEEE), 2003) Ekin, Ahmet; Mehrotra, Rajiv; Department of Electrical and Electronics Engineering; Tekalp, Ahmet Murat; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 26207We propose a fully automatic and computationally efficient framework for analysis and summarization of soccer videos using cinematic and object-based features. The proposed framework includes some novel low-level soccer video processing algorithms, such as dominant color region detection, robust shot boundary detection, and shot classification, as well as some higher-level algorithms for goal detection, referee detection, and penalty-box detection. The system can output three types of summaries: i) all slow-motion segments in a game, ii) all goals in a game, and iii) slow-motion segments classified according to object-based features. The first two types of summaries are based on cinematic features only for speedy processing, while the summaries of the last type contain higher-level semantics. The proposed framework is efficient, effective, and robust for soccer video processing. It is efficient in the sense that there is no need to compute object-based features when cinematic features are sufficient for the detection of certain events, e.g., goals in soccer. It is effective in the sense that the framework can also employ object-based features when needed to increase accuracy (at the expense of more computation). The efficiency, effectiveness, and the robustness of the proposed framework are demonstrated over a large data set, consisting of more than 13 hours of soccer video, captured at different countries and conditions.Publication Metadata only Graphene supercapacitor as a voltage controlled saturable absorber for femtosecond pulse generation(Optical Society of America (OSA), 2014) Ozharar, Sarper; Ozan Polat E.; Kocabas, Coskun; N/A; N/A; Department of Physics; Toker, Işınsu Baylam; Çizmeciyan, Melisa Natali; Sennaroğlu, Alphan; PhD Student; PhD Student; Faculty Member; Department of Physics; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Sciences; N/A, N/A; 23851For the first time to our knowledge, we employed a graphene supercapacitor as a voltage controlled saturable absorber at bias voltages of 0.5-1V to generate 84-fs pulses from a solid-state laser near 1255 nm.Publication Metadata only Hierarchical watermarking for secure image authentication with localization(2002) Çelik, Mehmet Utku; Sharma, Gaurav; Saber, Eli; Department of Electrical and Electronics Engineering; Tekalp, Ahmet Murat; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 26207Several fragile watermarking schemes presented in the literature are either vulnerable to vector quantization (VQ) counterfeiting attacks or sacrifice localization accuracy to improve security. Using a hierarchical structure, we propose a method that thwarts the VQ attack while sustaining the superior localization properties of blockwise independent watermarking methods. In particular, we propose dividing the image into blocks in a multilevel hierarchy and calculating block signatures in this hierarchy. While signatures of small blocks on the lowest level of the hierarchy ensure superior accuracy of tamper localization, higher level block signatures provide increasing resistance to VQ attacks. At the top level, a signature calculated using the whole image completely thwarts the counterfeiting attack. Moreover, "sliding window" searches through the hierarchy enable the verification of untampered regions after an image has been cropped. We provide experimental results to demonstrate the effectiveness of our method.Publication Metadata only Local image registration by adaptive filtering(Institute of Electrical and Electronics Engineers (IEEE), 2006) Caner, Gülçin; Sharma, Gaurav; Heinzelman, Wendi; Department of Electrical and Electronics Engineering; Tekalp, Ahmet Murat; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 26207We propose a new adaptive filtering framework for local image registration, which compensates for the effect of local distortions/displacements without explicitly estimating a distortion/displacement field. To this effect, we formulate local image registration as a two-dimensional (2-D) system identification problem with spatially varying system parameters. We utilize a 2-D adaptive filtering framework to identify the locally varying system parameters, where a new block adaptive filtering scheme is introduced. We discuss the conditions under which the adaptive filter coefficients conform to a local displacement vector at each pixel. Experimental results demonstrate that the proposed 2-D adaptive filtering framework is very successful in modeling and compensation of both local distortions, such as Stirmark attacks, and local motion, such as in the presence of a parallax field. In particular, we show that the proposed method can provide image registration to: a) enable reliable detection of watermarks following a Stirmark attack in nonblind detection scenarios, b) compensate for lens distortions, and c) align multiview images with nonparametric local motion.Publication Metadata only Minimum energy data transmission for wireless networked control systems(Ieee-Inst Electrical Electronics Engineers Inc, 2014) Park, Pangun; N/A; Department of Electrical and Electronics Engineering; Şadi, Yalçın; Ergen, Sinem Çöleri; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; 246556; 7211The communication protocol design for wireless networked control systems brings the additional challenge of providing the guaranteed stability of the closed-loop control system compared to traditional wireless sensor networks. In this paper, we provide a framework for the joint optimization of controller and communication systems encompassing efficient abstractions of both systems. The objective of the optimization problem is to minimize the power consumption of the communication system due to the limited lifetime of the battery-operated wireless nodes. The constraints of the problem are the schedulability and maximum transmit power restrictions of the communication system, and the reliability and delay requirements of the control system to guarantee its stability. The formulation comprises communication system parameters including transmission power, rate and scheduling, and control system parameters including sampling period. The resulting problem is a Mixed-Integer Programming problem. However, analyzing the optimality conditions on the variables of the problem allows us to reduce it to an Integer Programming problem for which we propose an efficient solution method based on its relaxation. Simulations demonstrate that the proposed method performs very close to optimal and much better than the traditional separate design of these systems.Publication Metadata only Minimum-distortion isometric shape correspondence using em algorithm(IEEE Computer Soc, 2012) N/A; Department of Computer Engineering; Sahillioğlu, Yusuf; Yemez, Yücel; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; 215195; 107907We present a purely isometric method that establishes 3D correspondence between two (nearly) isometric shapes. Our method evenly samples high-curvature vertices from the given mesh representations, and then seeks an injective mapping from one vertex set to the other that minimizes the isometric distortion. We formulate the problem of shape correspondence as combinatorial optimization over the domain of all possible mappings, which then reduces in a probabilistic setting to a log-likelihood maximization problem that we solve via the Expectation-Maximization (EM) algorithm. The EM algorithm is initialized in the spectral domain by transforming the sampled vertices via classical Multidimensional Scaling (MDS). Minimization of the isometric distortion, and hence maximization of the log-likelihood function, is then achieved in the original 3D euclidean space, for each iteration of the EM algorithm, in two steps: by first using bipartite perfect matching, and then a greedy optimization algorithm. The optimal mapping obtained at convergence can be one-to-one or many-to-one upon choice. We demonstrate the performance of our method on various isometric (or nearly isometric) pairs of shapes for some of which the ground-truth correspondence is available.Publication Metadata only Object segmentation and labeling by learning from examples(2003) Xu, Yaowu; Saber, Eli; Department of Electrical and Electronics Engineering; Tekalp, Ahmet Murat; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 26207We propose a system that employs low-level image segmentation followed by color and two-dimensional (2-D) shape matching to automatically group those low-level segments into objects based on their similarity to a set of example object templates presented by the user. A hierarchical content tree data structure is used for each database image to store matching combinations of low-level regions as objects. The system automatically initializes the content tree with only "elementary nodes" representing homogeneous low-level regions. The "learning" phase refers to labeling of combinations of low-level regions that have resulted in successful color and/or 2-D shape matches with the example template(s). These combinations are labeled as "object nodes" in the hierarchical content tree. Once learning is performed, the speed of second-time retrieval of learned objects in the database increases significantly. The learning step can be performed off-line provided that example objects are given in the form of user interest profiles. Experimental results are presented to demonstrate the effectiveness of the proposed system with hierarchical content tree representation and learning by color and 2-D shape matching on collections of car and face images.