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Publication Metadata only 3D model retrieval using probability density-based shape descriptors(IEEE Computer Society, 2009) Akgul, Ceyhun Burak; Sankur, Buelent; Schmitt, Francis; Department of Computer Engineering; Yemez, Yücel; Faculty Member; Department of Computer Engineering; College of Engineering; 107907We address content-based retrieval of complete 3D object models by a probabilistic generative description of local shape properties. The proposed shape description framework characterizes a 3D object with sampled multivariate probability density functions of its local surface features. This density-based descriptor can be efficiently computed via kernel density estimation (KDE) coupled with fast Gauss transform. The nonparametric KDE technique allows reliable characterization of a diverse set of shapes and yields descriptors which remain relatively insensitive to small shape perturbations and mesh resolution. Density-based characterization also induces a permutation property which can be used to guarantee invariance at the shape matching stage. As proven by extensive retrieval experiments on several 3D databases, our framework provides state-of-the-art discrimination over a broad and heterogeneous set of shape categories.Publication Open Access A challenging design case study for interactive media design education: interactive media for individuals with autism(Springer, 2014) Orhun, Simge Esin; Çimen, Ayça Ünlüer; Department of Media and Visual Arts; Yantaç, Asım Evren; Faculty Member; Department of Media and Visual Arts; College of Social Sciences and Humanities; 52621Since 1999, research for creativity triggering education solutions for interactive media design (IMD) undergraduate level education in Yildiz Technical University leaded to a variety of rule breaking exercises. Among many approaches, the method of designing for disabling environment, in which the students design for the users with one or more of their senses disabled, brought the challenge of working on developing interactive solutions for the individuals with autism spectrum conditions (ASC). With the aim of making their life easier, the design students were urged to find innovative yet functional interaction solutions for this focused user group, whose communicational disability activate due to the deficiencies in their senses and/or cognition. Between 2011 and 2012, this project brief supported by participatory design method motivated 26 students highly to develop design works to reflect the perfect fit of interaction design to this challenging framework involving the defective social communication cases of autism.Publication Open Access A DASH7-based power metering system(Institute of Electrical and Electronics Engineers (IEEE), 2015) Çetinkaya, Oktay; Akan, Özgür Barış; Researcher; College of EngineeringConsidering the inability of the existing energy resources to satisfy the current needs, the right and efficient. use of the energy has become compulsory. To make energy sustainability permanent, management and planning activities should be carried out by arranging the working hours and decreasing the energy wasting. For all these, power metering, managing and controlling systems or plugs has been proposed in recent efforts. Starting from this point, a new DASH7-based Smart Plug (D7SP) is designed and implemented to achieve a better structure compared to ZigBee equipped models and reduce the drawbacks of current applications. DASH7 technology reaches nearly 6 times farther distances in comparison with 2.4 GHz based protocols and provides multi-year battery life as a result of using limited energy during transmission. Performing in the 433 MHz band prevents the possible interference from overcrowded 2.4 GHz and the other frequencies which helps to gather a more reliable working environment. To shorten the single connection delays and human oriented failures, the MCU was shifted directly into the plug from the rear-end device. Working hours arrangement and standby power cutting off algorithms are implemented in addition to these energy saving targeted improvements to enhance more efficient systems. With the collaboration of the conducted hardware and software oriented adjustments and DASH7-based improvements, a more reliable, mobile and efficient system has been obtained in this work.Publication Open Access A deep learning approach for data driven vocal tract area function estimation(Institute of Electrical and Electronics Engineers (IEEE), 2018) Department of Computer Engineering; Department of Electrical and Electronics Engineering; Erzin, Engin; Asadiabadi, Sasan; Faculty Member; Department of Computer Engineering; Department of Electrical and Electronics Engineering; College of Sciences; Graduate School of Sciences and Engineering; 34503; N/AIn 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 front-tracking method for computational modeling of impact and spreading of viscous droplets on solid walls(Pergamon-Elsevier Science Ltd, 2010) N/A; Department of Mechanical Engineering; Department of Mechanical Engineering; Muradoğlu, Metin; Taşoğlu, Savaş; Faculty Member; Faculty Member; Department of Mechanical Engineering; College of Engineering; College of Engineering; 46561; 291971A finite-difference/front-tracking method is developed for computational modeling of impact and spreading of a viscous droplet on dry solid walls. The contact angle is specified dynamically using the empirical correlation given by Kistler (1993). The numerical method is general and can treat non-wetting, partially wetting and fully wetting cases but the focus here is placed on the partially wetting substrates. Here the method is implemented for axisymmetric problems but it is straightforward to extend it to three dimensional cases. Grid convergence of the method is demonstrated and the validity of the dynamic contact angle method is examined. The method is first tested for the spreading and relaxation of a droplet from the initial spherical shape to its final equilibrium conditions for various values of Eotvos number. Then it is applied to impact and spreading of glycerin droplets on wax and glass substrates and, the results are compared with experimental data of Sikalo et al. (2005). The numerical results are found in a good agreement with the experimental data. Finally the effects of governing non-dimensional numbers on the spreading rate, apparent contact angle and deformation of the droplet are investigated.Publication Open Access A gated fusion network for dynamic saliency prediction(Institute of Electrical and Electronics Engineers (IEEE), 2022) Kocak, Aysun; Erdem, Erkut; Department of Computer Engineering; Erdem, Aykut; Faculty Member; Department of Computer Engineering; College of Engineering; 20331Predicting saliency in videos is a challenging problem due to complex modeling of interactions between spatial and temporal information, especially when ever-changing, dynamic nature of videos is considered. Recently, researchers have proposed large-scale data sets and models that take advantage of deep learning as a way to understand what is important for video saliency. These approaches, however, learn to combine spatial and temporal features in a static manner and do not adapt themselves much to the changes in the video content. In this article, we introduce the gated fusion network for dynamic saliency (GFSalNet), the first deep saliency model capable of making predictions in a dynamic way via the gated fusion mechanism. Moreover, our model also exploits spatial and channelwise attention within a multiscale architecture that further allows for highly accurate predictions. We evaluate the proposed approach on a number of data sets, and our experimental analysis demonstrates that it outperforms or is highly competitive with the state of the art. Importantly, we show that it has a good generalization ability, and moreover, exploits temporal information more effectively via its adaptive fusion scheme.Publication Metadata only A mechanical transduction-based molecular communication receiver for ınternet of nano things (IoNT)(Assoc Computing Machinery, 2021) N/A; Department of Electrical and Electronics Engineering; Aktaş, Dilara; Akan, Özgür Barış; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 6647Molecular conununication (MC) is one of the most promising technology to enable nanonetworks. Despite many aspects of MC have been investigated broadly, the physical design of the MC receiver has gained little interest. High-performance MC receivers based on bioFETs are proposed and extensively analyzed. However, they have some challenges such as limited detection with charged molecules, Debye screening, and the need for reference electrodes. To overcome these shortcomings, we propose a mechanical-based transducing scheme. In particular, we focus on a Flexure field-effect transistor (FET)-based MC receiver architecture, which provides exponentially high sensitivity by utilizing a nonlinear electromechanical coupling. In addition, the detection of neutral molecules with much simpler instrumentation is possible. In this paper, we analyze its fundamental performance metrics; sensitivity, noise power, signal-to-noise ratio, and the symbol error probability, from an MC theoretical perspective.Publication Metadata only A multi-start granular skewed variable neighborhood tabu search for the roaming salesman problem(Elsevier, 2021) Shahmanzari, Masoud; Department of Business Administration; Aksen, Deniz; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 40308This paper presents a novel hybrid metaheuristic algorithm for the Roaming Salesman Problem (RSP), called Multi-Start Granular Skewed Variable Neighborhood Tabu Search (MS-GSVNTS). The objective in RSP is to design daily tours for a traveling campaigner who collects rewards from activities in cities during a fixed planning horizon. RSP exhibits a number of exclusive features: It is selective which implies that not every node needs a visit. The rewards of cities are time-dependent. Daily tours can be either an open or a closed tour which implies the absence of a fixed depot. Instead, there is a campaign base that is to be attended frequently. Multiple visits are allowed for certain cities. The proposed method MS-GSVNTS is tested on 45 real-life instances from Turkey which are built with actual travel distances and times and on 10 large scale instances. Computational results suggest that MS-GSVNTS is superior to the existing solution methods developed for RSP. It produces 50 best known solutions including 18 ties and 32 new ones. The performance of MS-GSVNTS can be attributed to its multi-start feature, rich neighborhood structures, skewed moves, and granular neighborhoods.Publication Open Access A new haptic interaction and visualization approach for rigid molecular docking in virtual environments(Massachusetts Institute of Technology (MIT) Press, 2008) Department of Mechanical Engineering; Subaşı, Erk; Başdoğan, Çağatay; Faculty Member; Department of Mechanical Engineering; College of Engineering; N/A; 125489Many biological activities take place through the physicochemical interaction of two molecules. This interaction occurs when one of the molecules finds a suitable location on the surface of the other for binding. This process is known as molecular docking, and it has applications to drug design. If we can determine which drug molecule binds to a particular protein, and how the protein interacts with the bonded molecule, we can possibly enhance or inhibit its activities. This information, in turn, can be used to develop new drugs that are more effective against diseases. In this paper, we propose a new approach based on a human-computer interaction paradigm for the solution of the rigid body molecular docking problem. In our approach, a rigid ligand molecule (i.e., drug) manipulated by the user is inserted into the cavities of a rigid protein molecule to search for the binding cavity, while the molecular interaction forces are conveyed to the user via a haptic device for guidance. We developed a new visualization concept, Active Haptic Workspace (AHW), for the efficient exploration of the large protein surface in high resolution using a haptic device having a small workspace. After the discovery of the true binding site and the rough alignment of the ligand molecule inside the cavity by the user, its final configuration is calculated off-line through time stepping molecular dynamics (MD) simulations. At each time step, the optimum rigid body transformations of the ligand molecule are calculated using a new approach, which minimizes the distance error between the previous rigid body coordinates of its atoms and their new coordinates calculated by the MD simulations. The simulations are continued until the ligand molecule arrives at the lowest energy configuration. Our experimental studies conducted with six human subjects testing six different molecular complexes demonstrate that given a ligand molecule and five potential binding sites on a protein surface, the subjects can successfully identify the true binding site using visual and haptic cues. Moreover, they can roughly align the ligand molecule inside the binding cavity such that the final configuration of the ligand molecule can be determined via the proposed MD simulations.Publication Metadata only A new robust consistent hybrid finite-volume/particle method for solving the PDF model equations of turbulent reactive flows(Pergamon-Elsevier Science Ltd, 2014) Department of Mechanical Engineering; Sheikhsarmast, Reza Mokhtarpoor; Türkeri, Hasret; Muradoğlu, Metin; PhD Student; PhD Student; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 46561A new robust hybrid finite-volume (FV)/particle method is developed for solving joint probability density function (JPDF) model equations of statistically stationary turbulent reacting flows. The method is designed to remedy the deficiencies of the hybrid algorithm developed by Muradoglu et al. (1999, 2001). The density-based FV solver in the original hybrid algorithm has been found to be excessively dissipative and yet not very robust. To remedy these deficiencies, a pressure-based PISO algorithm in the open source FV package, OpenFOAM, is used to solve the Favre-averaged mean mass and momentum equations while a particle-based Monte Carlo algorithm is employed to solve the fluctuating velocity-turbulence frequency-compositions JPDF transport equation. The mean density is computed as a particle field and passed to the FV method. Thus the redundancy of the density fields in the original hybrid method is removed making the new hybrid algorithm more consistent at the numerical solution level. The new hybrid algorithm is first applied to simulate non-swirling cold and reacting bluff-body flows. The convergence of the method is demonstrated. In contrast with the original hybrid method, the new hybrid algorithm is very robust with respect to grid refinement and achieves grid convergence without any unphysical vortex shedding in the cold bluff-body flow case. In addition, the results are found to be in good agreement with the earlier PDF calculations and also with the available experimental data. Finally the new hybrid algorithm is successfully applied to simulate the more complicated Sydney swirling bluff-body flame 'SM1'. The method is also very robust for this difficult test case and the results are in good agreement with the available experimental data. In all the cases, the PISO-FV solver is found to be highly resilient to the noise in the mean density field extracted from the particles.