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Publication Open Access 3D microprinting of iron platinum nanoparticle-based magnetic mobile microrobots(Wiley, 2021) Giltinan, Joshua; Sridhar, Varun; Bozüyük, Uğur; Sheehan, Devin; Department of Mechanical Engineering; Sitti, Metin; Faculty Member; Department of Mechanical Engineering; School of Medicine; College of Engineering; 297104Wireless magnetic microrobots are envisioned to revolutionize minimally invasive medicine. While many promising medical magnetic microrobots are proposed, the ones using hard magnetic materials are not mostly biocompatible, and the ones using biocompatible soft magnetic nanoparticles are magnetically very weak and, therefore, difficult to actuate. Thus, biocompatible hard magnetic micro/nanomaterials are essential toward easy-to-actuate and clinically viable 3D medical microrobots. To fill such crucial gap, this study proposes ferromagnetic and biocompatible iron platinum (FePt) nanoparticle-based 3D microprinting of microrobots using the two-photon polymerization technique. A modified one-pot synthesis method is presented for producing FePt nanoparticles in large volumes and 3D printing of helical microswimmers made from biocompatible trimethylolpropane ethoxylate triacrylate (PETA) polymer with embedded FePt nanoparticles. The 30 mu m long helical magnetic microswimmers are able to swim at speeds of over five body lengths per second at 200Hz, making them the fastest helical swimmer in the tens of micrometer length scale at the corresponding low-magnitude actuation fields of 5-10mT. It is also experimentally in vitro verified that the synthesized FePt nanoparticles are biocompatible. Thus, such 3D-printed microrobots are biocompatible and easy to actuate toward creating clinically viable future medical microrobots.Publication Open Access An information theoretical analysis of multi-terminal neuro-spike communication network in spinal cord(Association for Computing Machinery (ACM), 2018) Department of Electrical and Electronics Engineering; Akan, Özgür Barış; Civaş, Meltem; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; 6647; N/ACommunication theoretical understanding of healthy and diseased connections in the spinal cord motor system is crucial for realizing future information and communication technology (ICT) based diagnosis and treatment techniques for spinal cord injuries (SCI). A spinal cord motor nucleus associated with a particular muscle constitutes an ideal candidate for studying to have an understanding of SCI. Typical spinal cord motor nucleus system contains pool of lower motor neurons (MNs) controlling a muscle by integrating synaptic inputs from spinal interneurons (INs), upper motor neurons (DNs) and sensory neurons (SNs). In this study, we consider this system from ICT perspective. Our aim is to quantify the rate of information flow across a spinal cord motor nucleus. To this end, we model an equivalent single-hop multiterminal network, where multiple transmitting nodes representing heterogeneous population of DNs, INs and SNs sen information to multiple receiving nodes corresponding to MNs. To identify the outputs at receiving nodes, we define corresponding neurospike communication channel and then find the bound on total rates across this network. Based on the network model, we analyze achievable rates for a particular motor nucleus system called Tibialis Anterior (TA) motor nucleus in the spinal cord numerically and simulate several spinal cord dysfunction scenarios. The numerical results reveal that decrease in the maximum total rates with the lower motor neuron injury causes weakness in the affected muscle.Publication Open Access Analysis of push-type epidemic data dissemination in fully connected networks(Elsevier, 2014) Sezer, Ali Devin; Department of Mathematics; Çağlar, Mine; Faculty Member; Department of Mathematics; College of Sciences; 105131Consider a fully connected network of nodes, some of which have a piece of data to be disseminated to the whole network. We analyze the following push-type epidemic algorithm: in each push round, every node that has the data, i.e., every infected node, randomly chooses c E Z. other nodes in the network and transmits, i.e., pushes, the data to them. We write this round as a random walk whose each step corresponds to a random selection of one of the infected nodes; this gives recursive formulas for the distribution and the moments of the number of newly infected nodes in a push round. We use the formula for the distribution to compute the expected number of rounds so that a given percentage of the network is infected and continue a numerical comparison of the push algorithm and the pull algorithm (where the susceptible nodes randomly choose peers) initiated in an earlier work. We then derive the fluid and diffusion limits of the random walk as the network size goes to infinity and deduce a number of properties of the push algorithm: (1) the number of newly infected nodes in a push round, and the number of random selections needed so that a given percent of the network is infected, are both asymptotically normal, (2) for large networks, starting with a nonzero proportion of infected nodes, a pull round infects slightly more nodes on average, (3) the number of rounds until a given proportion lambda of the network is infected converges to a constant for almost all lambda is an element of (0, 1). Numerical examples for theoretical results are provided.
Publication Metadata only 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; 16372In 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.Publication Open Access Audiovisual synchronization and fusion using canonical correlation analysis(Institute of Electrical and Electronics Engineers (IEEE), 2007) Department of Computer Engineering; Department of Electrical and Electronics Engineering; Sargın, Mehmet Emre; Yemez, Yücel; Erzin, Engin; Tekalp, Ahmet Murat; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 34503; 26207It is well-known that early integration (also called data fusion) is effective when the modalities are correlated, and late integration (also called decision or opinion fusion) is optimal when modalities are uncorrelated. In this paper, we propose a new multimodal fusion strategy for open-set speaker identification using a combination of early and late integration following canonical correlation analysis (CCA) of speech and lip texture features. We also propose a method for high precision synchronization of the speech and lip features using CCA prior to the proposed fusion. Experimental results show that i) the proposed fusion strategy yields the best equal error rates (EER), which are used to quantify the performance of the fusion strategy for open-set speaker identification, and ii) precise synchronization prior to fusion improves the EER; hence, the best EER is obtained when the proposed synchronization scheme is employed together with the proposed fusion strategy. We note that the proposed fusion strategy outperforms others because the features used in the late integration are truly uncorrelated, since they are output of the CCA analysis.Publication Open Access Automatic detection of road types from the third military mapping survey of Austria-Hungary historical map series with deep convolutional neural networks(Institute of Electrical and Electronics Engineers (IEEE), 2021) Department of History; Kabadayı, Mustafa Erdem; Can, Yekta Said; Gerrits, Petrus Johannes; Faculty Member; Department of History; College of Social Sciences and Humanities; 33267; N/A; N/AWith the increased amount of digitized historical documents, information extraction from them gains pace. Historical maps contain valuable information about historical, geographical and economic aspects of an era. Retrieving information from historical maps is more challenging than processing modern maps due to lower image quality, degradation of documents and the massive amount of non-annotated digital map archives. Convolutional Neural Networks (CNN) solved many image processing challenges with great success, but they require a vast amount of annotated data. For historical maps, this means an unprecedented scale of manual data entry and annotation. In this study, we first manually annotated the Third Military Mapping Survey of Austria-Hungary historical map series conducted between 1884 and 1918 and made them publicly accessible. We recognized different road types and their pixel-wise positions automatically by using a CNN architecture and achieved promising results.Publication Metadata only Automatic detection of road types from the third military mapping survey of Austria-Hungary historical map series with deep convolutional neural networks(IEEE-inst Electrical Electronics Engineers inc, 2021) N/A; N/A; Department of History; Can, Yekta Said; Gerrits, Petrus Johannes; Kabadayı, Mustafa Erdem; Resercher; Master Student; Faculty Member; Department of History; College of Social Sciences and Humanities; Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; N/A; 33267With the increased amount of digitized historical documents, information extraction from them gains pace. Historical maps contain valuable information about historical, geographical and economic aspects of an era. Retrieving information from historical maps is more challenging than processing modern maps due to lower image quality, degradation of documents and the massive amount of non-annotated digital map archives. Convolutional Neural Networks (CNN) solved many image processing challenges with great success, but they require a vast amount of annotated data. for historical maps, this means an unprecedented scale of manual data entry and annotation. in this study, we first manually annotated the Third Military Mapping Survey of austria-Hungary historical map series conducted between 1884 and 1918 and made them publicly accessible. We recognized different road types and their pixel-wise positions automatically by using a CNN architecture and achieved promising results.Publication Open Access Automatic estimation of age distributions from the first Ottoman Empire population register series by using deep learning(Multidisciplinary Digital Publishing Institute (MDPI), 2021) Department of History; Kabadayı, Mustafa Erdem; Can, Yekta Said; Faculty Member; Department of History; College of Social Sciences and Humanities; 33267; N/ARecently, an increasing number of studies have applied deep learning algorithms for extracting information from handwritten historical documents. In order to accomplish that, documents must be divided into smaller parts. Page and line segmentation are vital stages in the Handwritten Text Recognition systems; it directly affects the character segmentation stage, which in turn deter-mines 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 the 1860s. Then, we employed horizontal projection profile-based line segmentation to the demographic information of these detected individuals in these registers. We further trained a CNN model to recognize automatically detected ages of individuals and estimated age distributions of people from these historical documents. Extracting age information from these historical registers is significant because it has enormous potential to revolutionize historical demography of around 20 successor states of the Ottoman Empire or countries of today. We achieved approximately 60% digit accuracy for recognizing the numbers in these registers and estimated the age distribution with Root Mean Square Error 23.61.Publication Open Access Automatic road extraction from historical maps using deep learning techniques: a regional case study of Turkey in a German World War II map(Multidisciplinary Digital Publishing Institute (MDPI), 2021) Sertel, Elif; Department of History; Kabadayı, Mustafa Erdem; Ekim, Burak; Faculty Member; Department of History; College of Social Sciences and Humanities; 33267; N/AScanned historical maps are available from different sources in various scales and contents. Automatic geographical feature extraction from these historical maps is an essential task to derive valuable spatial information on the characteristics and distribution of transportation infrastructures and settlements and to conduct quantitative and geometrical analysis. In this research, we used the Deutsche Heereskarte 1:200,000 Türkei (DHK 200 Turkey) maps as the base geoinformation source to construct the past transportation networks using the deep learning approach. Five different road types were digitized and labeled to be used as inputs for the proposed deep learning-based segmentation approach. We adapted U-Net++ and ResneXt50_32×4d architectures to produce multi-class segmentation masks and perform feature extraction to determine various road types accurately. We achieved remarkable results, with 98.73% overall accuracy, 41.99% intersection of union, and 46.61% F1 score values. The proposed method can be implemented in DHK maps of different countries to automatically extract different road types and used for transfer learning of different historical maps.Publication Open Access Bridging the gap between pre-census and census-era historical data: devising a geo-sampling model to analyse agricultural production in the long run for Southeast Europe, 1840–1897(Edinburgh University Press, 2020) Gerrits, Piet; Department of History; Kabadayı, Mustafa Erdem; Boykov, Grigor; Faculty Member; Department of History; College of Social Sciences and Humanities; 33267; N/AThis research introduces a novel geo-spatial sampling model to overcome a major difficulty in historical economic geography of Bulgarian lands during a crucial period: immediately before and after the de facto independence of the territory from the Ottoman Empire in the second half of the nineteenth century. At its core it seeks to investigate the research question how the Bulgarian independence affected agricultural production in two regions (centered around the cities of Plovdiv and Ruse) of today's Bulgaria, for which there are conflicting yet empirically unsubstantiated claims concerning the economic impact of the political independence. Using our be-spoke geo-sampling strategy we believe, we have sampled regionally representative commensurable agricultural data from the 1840s Ottoman archival documentation, in accord with agricultural censuses conducted by the nascent nation state of Bulgaria in the 1890s.