Research Outputs

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    Application-layer qos fairness in wireless video scheduling
    (IEEE, 2006) N/A; N/A; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Özçelebi, Tanır; Sunay, Mehmet Oğuz; Tekalp, Ahmet Murat; Civanlar, Mehmet Reha; PhD Student; Faculty Member; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; N/A; 26207; 16372
    In mobile video transmission systems, the initial delay for pre-fetching video at the client buffer needs to be short due to buffer limitations and application-layer user convenience. Therefore, an effective cross-layer wireless design is required that considers both physical and application layer aspects of such a system. We present a cross-layer optimized multi-user video adaptation and scheduling scheme for wireless video communication, where Quality-of-Service (QoS) fairness among users is provided while maximizing user convenience and video throughput. Application and physical layer aspects are jointly optimized using a Multi-Objective Optimization (MOO) framework that tries to schedule the user with the least remaining playback time and the highest video throughput (delivered video seconds per transmission slot) with maximum video quality. Experiments with the IS-856 (1xEV-DO) standard and ITU Pedestrian A and Vehicular B environments show the improvements over today's schedulers in terms of QoS fairness and user utility.
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    PublicationOpen Access
    Deep generation of 3D articulated models and animations from 2D stick figures
    (Elsevier, 2022) Akman, Alican; Sahillioğlu, Yusuf; Department of Computer Engineering; Sezgin, Tevfik Metin; Faculty Member; Department of Computer Engineering; College of Engineering; 18632
    Generating 3D models from 2D images or sketches is a widely studied important problem in computer graphics. We describe the first method to generate a 3D human model from a single sketched stick figure. In contrast to the existing human modeling techniques, our method does not require a statistical body shape model. We exploit Variational Autoencoders to develop a novel framework capable of transitioning from a simple 2D stick figure sketch, to a corresponding 3D human model. Our network learns the mapping between the input sketch and the output 3D model. Furthermore, our model learns the embedding space around these models. We demonstrate that our network can generate not only 3D models, but also 3D animations through interpolation and extrapolation in the learned embedding space. In addition to 3D human models, we produce 3D horse models in order to show the generalization ability of our framework. Extensive experiments show that our model learns to generate compatible 3D models and animations with 2D sketches.
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    PublicationOpen Access
    Federated dropout learning for hybrid beamforming with spatial path index modulation in multi-user MMWave-MIMO systems
    (Institute of Electrical and Electronics Engineers (IEEE), 2021) Mishra, Kumar Vijay; Department of Electrical and Electronics Engineering; Ergen, Sinem Çöleri; Elbir, Ahmet Musab; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 7211; N/A
    Millimeter wave multiple-input multiple-output (mmWave-MIMO) systems with small number of radio-frequency (RF) chains have limited multiplexing gain. Spatial path index modulation (SPIM) is helpful in improving this gain by utilizing additional signal bits modulated by the indices of spatial paths. In this paper, we introduce model-based and model-free frameworks for beamformer design in multi-user SPIM-MIMO systems. We first design the beamformers via model-based manifold optimization algorithm. Then, we leverage federated learning (FL) with dropout learning (DL) to train a learning model on the local dataset of users, who estimate the beamformers by feeding the model with their channel data. The DL randomly selects different set of model parameters during training, thereby further reducing the transmission overhead compared to conventional FL. Numerical experiments show that the proposed framework exhibits higher spectral efficiency than the state-of-the-art SPIM-MIMO methods and mmWave-MIMO, which relies on the strongest propagation path. Furthermore, the proposed FL approach provides at least 10 times lower transmission overhead than the centralized learning techniques.
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    FlexDPDP: flexlist-based optimized dynamic provable data possession
    (assoc Computing Machinery, 2016) N/A; N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Department of Computer Engineering; Esiner, Ertem; Kachkeev, Adilet; Küpçü, Alptekin; Özkasap, Öznur; Master Student; Master Student; N/A; Faculty Member; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; N/A; 168060; 113507
    With increasing popularity of cloud storage, efficiently proving the integrity of data stored on an untrusted server has become significant. authenticated skip lists and rank-based authenticated skip lists (RBaSL) have been used to provide support for provable data update operations in cloud storage. However, in a dynamic file scenario, An RBaSL based on block indices falls short when updates are not proportional to a fixed block size; such an update to the file, even if small, may result in O(n) updates on the data structure for a file with n blocks. To overcome this problem, we introduce FlexList, A flexible length-based authenticated skip list. FlexList translates variable-size updates to O(inverted right perpendicularu/Binverted left perpendicular) insertions, removals, or modifications, where u is the size of the update and B is the (average) block size. We further present various optimizations on the four types of skip lists (regular, Authenticated, rank-based authenticated, and FlexList). We build such a structure in O(n) time and parallelize this operation for the first time. We compute one single proof to answer multiple (non) membership queries and obtain efficiency gains of 35%, 35%, and 40% in terms of proof time, energy, and size, respectively. We propose a method of handling multiple updates at once, Achieving efficiency gains of up to 60% at the server side and 90% at the client side. We also deployed our implementation of FlexDPDP (dynamic provable data possession (DPDP) with FlexList instead of RBaSL) on PlanetLab, demonstrating that FlexDPDP performs comparable to the most efficient static storage scheme (provable data possession (PDP)) while providing dynamic data support.
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    Line segmentation of individual demographic data from Arabic handwritten population registers of Ottoman Empire
    (Springer International Publishing Ag, 2021) N/A; Department of History; Can, Yekta Said; Kabadayı, Mustafa Erdem; Researcher; Faculty Member; Department of History; College of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; 33267
    Recently, more and more studies have applied state-of-the-art algorithms for extracting information from handwritten historical documents. Line segmentation is a vital stage in the HTR systems; it directly affects the character segmentation stage, which affects the recognition success. In this study, we first applied deep learning-based layout analysis techniques to detect individuals in the first Ottoman population register series collected between the 1840s and 1860s. Then, we used a star path planning algorithm-based line segmentation to the demographic information of these detected individuals in these registers. We achieved encouraging results from the selected regions, which could be used to recognize the text in these registers.