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Your search keyword '"Ozanyan, Krikor B."' showing total 116 results
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116 results on '"Ozanyan, Krikor B."'

2. Deep Learning and Sensor Fusion Methods for Studying Gait Changes Under Cognitive Load in Males and Females

Author

Alharthi, Abdullah S., Ozanyan, Krikor B., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yin, Hujun, editor, Camacho, David, editor, Tino, Peter, editor, Tallón-Ballesteros, Antonio J., editor, Menezes, Ronaldo, editor, and Allmendinger, Richard, editor

Published
2019
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4. Inertial Sensor Based Modelling of Human Activity Classes: Feature Extraction and Multi-sensor Data Fusion Using Machine Learning Algorithms

Author

Zebin, Tahmina, Scully, Patricia J., Ozanyan, Krikor B., Akan, Ozgur, Series editor, Bellavista, Paolo, Series editor, Cao, Jiannong, Series editor, Coulson, Geoffrey, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin Sherman, Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert Y., Series editor, Giokas, Kostas, editor, Bokor, Laszlo, editor, and Hopfgartner, Frank, editor

Published
2017
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15. Deep learning and sensor fusion methods for cognitive load gait difference in males and females

Author

Alharthi, Abdullah S. and Ozanyan, Krikor B.

Subjects
Convolutional Neural Networks (CNN), Cognitive load gait, Ground Reaction Force (GRF), Layer-Wise Relevance Propagation (LRP)
Abstract

Human gait is the manner of walking in people. It is influenced by weight, age, health condition or the interaction with the surrounding environment. In this work, we study gait changes under cognitive load in healthy males and females, using machine learning methods. A deep learning model with multi-processing pipelining and back propagation techniques, is proposed for cognitive load gait analysis. The IMAGiMAT floor system enabling sensor fusion from plastic optical fiber (POF) elements, is utilized to record gait raw data on spatiotemporal ground reaction force (GRF). A deep parallel Convolutional Neural Network (CNN) is engineered for POF sensors fusion, and gait GRF classification. The Layer-Wise Relevance Propagation (LRP), is applied to reveal which gait events are relevant towards informing the parallel CNN prediction. The CNN differentiates between males and females with 95% weighted average precision, and cognitive load gait classification with 93% weighted average precision. These findings present a new hypothesis, whereas larger dataset holds promise for human activity analysis.

Published
2019

16. Toward in-cylinder absorption tomography in a production engine

Author

Wright, Paul, Garcia-Stewart, Charles A., Carey, Stephen J., Hindle, Francis P., Pegrum, Stephen H., Colbourne, Stephen M., Turner, Paul J., Hurr, William J., Litt, Tim J., Murray, Stuart C., Crossley, Sam D., Ozanyan, Krikor B., and McCann, Hugh

Subjects
Optics -- Research, Tomography -- Research, Automobiles -- Motors, engines, etc., Automobiles -- Research, Astronomy, Physics
Abstract

Design requirements for an 8000 frame/s dual-wavelength ratiometric chemical species tomography system, intended for hydrocarbon vapor imaging in one cylinder of a standard automobile engine, are examined. The design process is guided by spectroscopic measurements on iso-octane and by comprehensive results from laboratory phantoms and research engines, including results on temporal resolution performance. Novel image reconstruction techniques, necessary for this application, are presented. Recent progress toward implementation, including details of the optical access arrangement employed and signal-to-noise issues, is described. We present first cross-cylinder IR absorption measurements from a reduced channel-count (nontomographic) system and discuss the prospects for imaging. OCIS codes: 100.6950, 110.3080, 120.1740, 300.6260.

Published
2005

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