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Publication Metadata only 300 GHz broadband transceiver design for low-THz band wireless communications in indoor internet of things(Ieee, 2017) N/A; Department of Electrical and Electronics Engineering; N/A; Department of Electrical and Electronics Engineering; Khalid, Nabil; Abbasi, Naveed Ahmed; Akan, Özgür Barış; Researcher; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 6647This paper presents the architectural design of a 300 GHz transceiver system that can be used to explore the high speed communication opportunities offered by the Terahertz (THz) band for advanced applications of Internet-of-Things (IoT). We use low cost industry ready components to prepare a fully customizable THz band communication system that provides a bandwidth of 20 GHz that is easily extendable up to 40 GHz. Component parameters arc carefully observed and used in simulations to predict the system performance while the compatibility of different components is ensured to produce a reliable design. Our results show that the receiver provides a conversion gain of 51 dB with a noise figure (NE) of 9.56 dB to achieve a data rate of 90.31 Gbps at an operation range of 2 meters, which is suitable for high speed indoor IoT nodes. The flexible design of the transceiver provides groundwork for further research efforts in 5G IoT applications and pushing boundaries of throughputs to the order of terabits per second (Tbps).Publication Metadata only A bi-criteria optimization model to analyze the impacts of electric vehicles on costs and emissions(Elsevier, 2017) N/A; N/A; Department of Industrial Engineering; Kabatepe, Bora; Türkay, Metin; Master Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 24956Electric vehicles (EV) are emerging as a mobility solution to reduce emissions in the transportation sector. The studies environmental impact analysis of EVs in the literature are based on the average energy mix or pre-defined generation scenarios and construct policy recommendations with a cost minimization objective. However, the environmental performance of EVs depends on the source of the marginal electricity provided to the grid and single objective models do not provide a thorough analysis on the economic and environmental impacts of EVs. In this paper, these gaps are addressed by a four step methodology that analyzes the effects of EVs under different charging and market penetration scenarios. The methodology includes a bi-criteria optimization model representing the electricity market operations. The results from a real-life case analysis show that EVs decrease costs and emissions significantly compared to conventional vehicles.Publication Metadata only A computational-graph partitioning method for training memory-constrained DNNs(Elsevier, 2021) Wahib, Mohamed; Dikbayir, Doga; Belviranli, Mehmet Esat; N/A; Department of Computer Engineering; Qararyah, Fareed Mohammad; Erten, Didem Unat; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 219274Many state-of-the-art Deep Neural Networks (DNNs) have substantial memory requirements. Limited device memory becomes a bottleneck when training those models. We propose ParDNN, an automatic, generic, and non-intrusive partitioning strategy for DNNs that are represented as computational graphs. ParDNN decides a placement of DNN's underlying computational graph operations across multiple devices so that the devices' memory constraints are met and the training time is minimized. ParDNN is completely independent of the deep learning aspects of a DNN. It requires no modification neither at the model nor at the systems level implementation of its operation kernels. ParDNN partitions DNNs having billions of parameters and hundreds of thousands of operations in seconds to few minutes. Our experiments with TensorFlow on 16 GPUs demonstrate efficient training of 5 very large models while achieving superlinear scaling for both the batch size and training throughput. ParDNN either outperforms or qualitatively improves upon the related work.Publication Metadata only A consensus protocol with deterministic finality(Ieee, 2021) N/A; N/A; N/A; Hassanzadeh-Nazarabadi, Yahya; Boshrooyeh, Sanaz Taheri; PhD Student; PhD Student; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; N/A; N/AProof-of-Validation (PoV) is a fair, immutable, and fully decentralized blockchain consensus protocol with an O(1) asymptotic message complexity. The original PoV proposal lacks deterministic finality, which guarantees that a valid block will not be revoked once it is committed to the blockchain. Supporting deterministic finality yields a fork-resistant blockchain. In this extended abstract, we pitch the architectural outline of our proposed Finalita, which is the extension of PoV that enables deterministic finality. Blockchains running with Finalita feature deterministic finality, in addition to all qualities supported by the original PoV.Publication Metadata only A containerized proof-of-concept implementation of LightChain system(Ieee, 2020) N/A; N/A; Department of Computer Engineering; N/A; Department of Computer Engineering; Department of Computer Engineering; Hassanzadeh-Nazarabadi, Yahya; Nayal, Nazir; Hamdan, Shadi Sameh; Özkasap, Öznur; Küpçü, Alptekin; PhD Student; Faculty Member; Master Student; Faculty Member; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; N/A; 113507; 168060LightChain is the first Distributed Hash Table (DHT)-based blockchain with a logarithmic asymptotic message and memory complexity. In this demo paper, we present the software architecture of our open-source implementation of LightChain, as well as a novel deployment scenario of the entire LightChain system on a single machine aiming at results reproducibility.Publication Metadata only A deep learning approach for data driven vocal tract area function estimation(IEEE, 2018) N/A; Department of Computer Engineering; Asadiabadi, Sasan; Erzin, Engin; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 34503In this paper we present a data driven vocal tract area function (VTAF) estimation using Deep Neural Networks (DNN). We approach the VTAF estimation problem based on sequence to sequence learning neural networks, where regression over a sliding window is used to learn arbitrary non-linear one-to-many mapping from the input feature sequence to the target articulatory sequence. We propose two schemes for efficient estimation of the VTAF; (1) a direct estimation of the area function values and (2) an indirect estimation via predicting the vocal tract boundaries. We consider acoustic speech and phone sequence as two possible input modalities for the DNN estimators. Experimental evaluations are performed over a large data comprising acoustic and phonetic features with parallel articulatory information from the USC-TIMIT database. Our results show that the proposed direct and indirect schemes perform the VTAF estimation with mean absolute error (MAE) rates lower than 1.65 mm, where the direct estimation scheme is observed to perform better than the indirect scheme.Publication Metadata only A dynamic path planning approach for multirobot sensor-based coverage considering energy constraints(IEEE-Inst Electrical Electronics Engineers Inc, 2014) Yazici, Ahmet; Parlaktuna, Osman; Sipahioglu, Aydin; N/A; Kirlik, Gökhan; PhD Student; Graduate School of Sciences and Engineering; N/AMultirobot sensor-based coverage path planning determines a tour for each robot in a team such that every point in a given workspace is covered by at least one robot using its sensors. In sensor-based coverage of narrow spaces, i.e., obstacles lie within the sensor range, a generalized Voronoi diagram (GVD)-based graph can be used to model the environment. A complete sensor-based coverage path plan for the robot team can be obtained by using the capacitated arc routing problem solution methods on the GVD-based graph. Unlike capacitated arc routing problem, sensor-based coverage problem requires to consider two types of edge demands. Therefore, modified Ulusoy algorithm is used to obtain mobile robot tours by taking into account two different energy consumption cases during sensor-based coverage. However, due to the partially unknown nature of the environment, the robots may encounter obstacles on their tours. This requires a replanning process that considers the remaining energy capacities and the current positions of the robots. In this paper, the modified Ulusoy algorithm is extended to incorporate this dynamic planning problem. A dynamic path-planning approach is proposed for multirobot sensor-based coverage of narrow environments by considering the energy capacities of the mobile robots. The approach is tested in a laboratory environment using Pioneer 3-DX mobile robots. Simulations are also conducted for a larger test environment.Publication Metadata only A front tracking method for direct numerical simulation of evaporation process in a multiphase system(Academic Press Inc Elsevier Science, 2017) N/A; N/A; Department of Mechanical Engineering; Irfan, Muhammad; Muradoğlu, Metin; PhD Student; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 46561A front-tracking method is developed for the direct numerical simulation of evaporation process in a liquid-gas multiphase system. One-field formulation is used to solve the flow, energy and species equations in the framework of the front tracking method, with suitable jump conditions at the interface. Both phases are assumed to be incompressible; however, the divergence-free velocity field condition is modified to account for the phase-change/mass-transfer at the interface. Both temperature and species gradient driven evaporation/phase-change processes are simulated. For the species gradient driven phase change process, the Clausius-Clapeyron equilibrium relation is used to find the vapor mass fraction and subsequently the evaporation mass flux at the interface. A number of benchmark cases are first studied to validate the implementation. The numerical results are found to be in excellent agreement with the analytical solutions for all the studied cases. The methods are then applied to study the evaporation of a static as well as a single and two droplets systems falling in the gravitational field. The methods are demonstrated to be grid convergent and the mass is globally conserved during the phase change process for both the static and moving droplet cases.Publication Metadata only A front tracking method for particle-resolved simulation of evaporation and combustion of a fuel droplet(Pergamon-Elsevier Science Ltd, 2018) N/A; N/A; Department of Mechanical Engineering; Irfan, Muhammad; Muradoğlu, Metin; PhD Student; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 46561A front-tracking method is developed for the particle-resolved simulations of droplet evaporation and combustion in a liquid-gas multiphase system. One field formulation of the governing equations is solved in the whole computational domain by incorporating suitable jump conditions at the interface. Both phases are assumed to be incompressible but the divergence-free velocity condition is modified to account for the phase change at the interface. A temperature gradient based evaporation model is used. An operator-splitting approach is employed to advance temperature and species mass fractions in time. The CHEMKIN package is incorporated into the solver to handle the chemical kinetics. The multiphase flow solver and the evaporation model are first validated using the benchmark problems. The method is then applied to study combustion of a n-heptane droplet using a single-step chemistry model and a reduced chemical kinetics mechanism involving 25-species and 26-reactions. The results are found to be in good agreement with the experimental data and the previous numerical simulations for the time history of the normalized droplet size, the gasification rate, the peak temperature and the ignition delay times. The initial flame diameter and the profile of the flame standoff ratio are also found to be compatible with the results in the literature. The method is finally applied to simulate a burning droplet moving due to gravity at various ambient temperatures and interesting results are observed about the flame blow-off.Publication Metadata only A hexagonal grid based human blockage model for the 5G low terahertz band communications(IEEE, 2018) N/A; N/A; Ertürk, Onur; Yılmaz, Türker; PhD Student; PhD Student; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; N/A; N/AUsers continuously demand higher connection speeds and data traffic from wireless communication networks. the newly required network capacity should be provided by higher frequency bands, because legacy sub-6 GHz bands are already operating using advanced communication techniques that provide very high spectral efficiencies. Consequently, millimeter wave communication standards are either complete or ongoing, and general submillimeter wave applications are next in line. accordingly, this paper proposes a motion model in hexagonal grid of a person carrying a user equipment. Electromagnetic wave blockage analyses by utilizing channel characteristics of the low-THz band are presented. Lastly, the communication and blockage probabilities of an exemplary system are both theoretically examined and numerically simulated.