KeynoteSpeakers


Mehmet Celenk
School of Electrical Engineering & Computer Science, Ohio University, OH, USA

Mehmet Celenk received the B.S. and M.S. degrees from Istanbul Technical University, in 1974 and 1976, in Electrical and Communications Engineering, and the Ph.D. degree from Stevens Institute of Technology in Electrical Engineering and Computer Science (EECS), in 1983, where he was the Robert Crook Stanley Graduate Fellow in 1985. He served on the Turkish Army in 1984-85 as a lieutenant and joined Ohio University (OU) in 1985, where he is currently a Professor of the School of EECS. He has published 300 articles, received $600K hypercube processor grant, participated in $450K Tubitak Autonomous Vehicle Design and Development grant, and secured $120K fund for visiting scholars’ R&D projects. He directed 35 MS/Ph.D. theses/dissertations in the School of EECS of OU. He received the distinguished service award from the Signal School in Ankara in 1984 for his R&D work and launching the Communications Journal. He was the recipient of the 1988 Fritz & Dolores Russ Research Award of the Russ College of ENT of OU, and awarded the OU Avionics Academic Challenge Faculty Fellowship in 1988-92. He has been an active reviewer for numerous professional societies (e.g., IEEE, IEE, IET, SPIE, IS&T, IAPR), journals/transactions, publishers, and funding agencies (NSF, NYSTAR 2002-07). He has been an Associate Editor (AE) of the IEEET on SMCA (currently SMC: Systems) since 2005, an AE of the Electronic Letters of the IET since 2015, an AE of EURASIP J. on Advances in Signal Processing since 2015, and the recipient of the Best Associate Award of the IEEE SMC Society in 2010. He has served on the Editorial Board of the J. Recent Patents on Signal Proc. 2008-14, on the Editorial Board of J. of Biometrics and its Applications in 2014-15, and on TCM of numerous international conferences. He is selected to be an IEEE Life Member (Jan.1, 2018), IEEE Senior member (March 7, 2018), a member of Eta Kappa Nu, and former member of SPIE, IS&T, ACM, ASEE, OE. He was awarded Certificate of Appreciation by SPIE’s Electronic Imaging J. and Optical Engineering for his review services in 2012-17 and by INSTICC&ICPRAM for his review services in 2015-17. His research area includes image/video processing, computer-vision, pattern recognition and machine learning, multi-sensory networking, data fusion, distributed computing, medical imaging, and digital systems.

Speech title: Autonomous Vehicle Guidance

Abstract: The aim of this paper* is to investigate a novel method for detection of road lane markers in conjunction with the determination of positioning of the self-driving vehicle relative to lane markers and road boundaries during travel in inclement weather conditions continues to be of paramount importance. This research considers the detection performance and associated parameters using experimental data that demonstrates the accurate results during various conditions. This work presents an investigation and associated results where road land boundary markers are detected in conjunction with the ability decipher the horizon when the front view of the vehicle’s path is degraded. Degradation of driving scenes can be attributed to such weather conditions as heavy rain, fog, snow or dust storms. The detection of lane markers and road boundaries is especially important for roads that exhibit severe curves, aggressive uphill slopes and downhill valleys, respectively. We present a model to predict deviations from reference distances associated with roads with such design constraints. To address self-driving objectives a method is proposed based on the Least Mean Square (LMS) optimization and the orthogonality principle. The paper also presents a design methodology of the concepts to address autonomous operation of passenger vehicles with some promising experimental results. Specifically, error curves are computed and presented for the actual verses predicted lane markers by integrating salient features of the Principal Component Analysis (PCA) and Gradient Specturm Matching (GSM) methods. Multi IR-sensory based fusion is selected as an implementation test bed for the development of an embedded system for autonomous convoy guidance with promising experimental results.

*H. B. Riley, A. Parajuli, and M. Celenk, “Autonomous Vehicle Guidance,” IEEE Trans. on ITS, (submitted on Jan. 20, 2018)