Research Outputs
Permanent URI for this communityhttps://hdl.handle.net/20.500.14288/2
Browse
531 results
Filters
Advanced Search
Filter by
Settings
Search Results
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 Open Access 3D face recognition by projection based methods(Society of Photo-optical Instrumentation Engineers (SPIE), 2006) Dutaǧaci, Helin; Sankur, Bülent; Department of Computer Engineering; Yemez, Yücel; Faculty Member; Department of Computer Engineering; College of EngineeringIn this paper, we investigate recognition performances of various projection-based features applied on registered 3D scans of faces. Some features are data driven, such as ICA-based features or NNMF-based features. Other features are obtained using DFT or DCT-based schemes. We apply the feature extraction techniques to three different representations of registered faces, namely, 3D point clouds, 2D depth images and 3D voxel. We consider both global and local features. Global features are extracted from the whole face data, whereas local features are computed over the blocks partitioned from 2D depth images. The block-based local features are fused both at feature level and at decision level. The resulting feature vectors are matched using Linear Discriminant Analysis. Experiments using different combinations of representation types and feature vectors are conducted on the 3D-RMA dataset.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 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 blind separation approach for magnitude bounded sources(IEEE, 2005) Department of Electrical and Electronics Engineering; Erdoğan, Alper Tunga; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 41624A novel blind source separation approach for channels with and without memory is introduced. The proposed approach makes use of pre-whitening procedure to convert the original convolutive channel into a lossless and memoryless one. Then a blind subgradient algorithm, which corresponds to an l(infinity) norm based criterion, is used for the separation of sources. The proposed separation algorithm exploits the assumed boundedness of the original sources and it has a simple update rule. The typical performance of the algorithm is illustrated through simulation examples where separation is achieved with only small numbers of iterations.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 Metadata only A DASH7-based power metering system(IEEE, 2015) Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Çetinkaya, Oktay; Akan, Özgür Barış; Other; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; College of Engineering; N/A; 6647Considering 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 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 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.