Date : 30th December 2022
Time: 07.15 PM – 08.30 PM (Indian Standard Time)
Zoom Credentials :

IEEE WIECON-ECE 2022 Ball Room

Meeting Link:

Meeting ID: 833 3259 2584
Pass: wiecon

Meet Our Invited Speakers

Dr. Maheshi Dissanayake, PhD(Surrey, UK)

B.ScEng(Peradeniya, SL)

Professor in Electrical and Electronic Engineering, Department of Electrical and Electronic Engineering,

Faculty of Engineering, University of Peradeniya, Sri Lanka.

Talk Title: Inter Symbol Interference in Molecular Communication Systems

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Prof. Maheshi B. Dissanayake received the B.Sc. Engineering degree with First Class Honors in Electrical and Electronic Engineering from the University of Peradeniya, Sri Lanka, in 2006, and the Ph.D. in electronic engineering from the University of Surrey, U.K., in 2010. She has been a senior lecturer from 2013 -2021 and a professor from 2021 to date with the Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Peradeniya. She has been a visiting research fellow at King’s College London from 2015-2017.

Prof. Dissanayake is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) and the recipient of the IEEE MGA Leadership Award 2022. She has served as an organizing committee member and TPC Member of many IEEE conferences, and as a reviewer in IEEE journals in the area of molecular communication and image processing

Her research interests include error correction codes, robust video communication, molecular communication, machine learning, and biomedical image analysis. She has co-authored nearly 75 conference and journal articles and has a citation record of more than 330. 

Prof. Dr. Mathini Sellathurai

Dean of Science and Engineering

Head Signal Processing and Communications Research Group

Heriot-Watt University, EH14 4AS, Edinburgh, UK.

Talk Title:  Machine Learning based channel/path-loss prediction for mm-wave channel exploiting contextual information.

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Large bandwidth at mm-wave is crucial for 5G and beyond. Still, the high path loss (PL) requires highly accurate PL prediction for network planning and optimization. Statistical models with slope-intercept fit fall short in capturing large variations seen in urban canyons, whereas raytracing, capable of characterizing site-specific features, faces challenges in describing foliage and street clutter and associated reflection/diffraction ray calculation. Machine learning (ML) is a promising technique but faces three critical challenges in PL prediction: 1) insufficient measurement data; 2) lack of extrapolation to new streets; 3) overwhelmingly complex features/models. The talk will describe an ML-based urban canyon PL prediction model based on extensive 28 GHz measurements from Manhattan. Street clutters modelled via a LiDAR point cloud dataset and buildings by a mesh-grid building dataset. We extract expert knowledge-driven street clutter features from the point cloud and aggressively compress 3D-building information using a convolutional-autoencoder. Using a new street-bystreet training and testing procedure to improve generalizability, the proposed model using both clutter and building features achieves a prediction error (RMSE) of 4.8±1.1 dB compared to 10.6±4.4 dB and 6.5±2.0 dB for 3GPP LOS and slope-intercept prediction, respectively, where the standard deviation indicates street-by-street variation. Only using the four most influential clutter features achieves RMSE of 5.5 ± 1.1 dB.

Mathini Sellathurai (Senior Member, IEEE) is currently a professor of signal processing and wireless communications and Dean of Science and Engineering with Heriot-Watt University, Edinburgh, UK She has been active in signal processing research for the past 20 years and has a strong international track record in multiple-input, multiple-output (MIMO) signal processing with applications in radar, healthcare, and wireless communications. She held visiting positions with Bell-Laboratories, Holmdel, NJ, USA, and The Canadian Communications Research Centre, Ottawa, Canada. She has published over 200 peer-reviewed papers in leading international journals and IEEE conferences, given invited talks and has written several book chapters and two research monographs (published by Wiley/IEEE and CRC press). Her research includes IoT for assistive care robots and hearing/visual aids, passive radar topography, localization, massive-MIMO, optimal coded-modulation designs, channel prediction, and mm-wave imaging and communications. She is a recipient of an IEEE Communication Society Fred W. Ellersick Best Paper Award in 2005, the Industry Canada Public Service Awards for contributions to Science and Technology in 2005, and Awards for contributions to Technology Transfers to Industry in 2004. She received the Natural Sciences and Engineering Research Council of Canada (NSERC) Doctoral Award for her PhD dissertation in 2002. She was an Editor for IEEE TRANSACTIONS ON SIGNAL PROCESSING from 2009 to 2014, and from 2015 to 2018, the General Co-Chair of IEEE SPAWC2016 in Edinburgh, and a member of IEEE SPCOM Technical Strategy Committee from 2014 to 2019. 

Azfar Adib, SMIEEE

ICT analyst & Activist

 PhD researcher, Concordia University, Canada.

Talk Title:  Utilizing Biometrics For Online Safety of Women and Kids.

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Azfar Adib is currently pursuing his PhD in the department of Electrical and Computer Engineering at Concordia University in Canada. He is also a leadership instructor for the university’s graduate students. Azfar completed his B.Sc. Eng from EEE Dept, BUET and MBA from IBA, DU in Dhaka. He worked for over 8.5 years for Grameenphone Ltd in Bangladesh, in different functional- managerial roles; notable leading IoT-M2M and ICT Partnership endeavours. Azfar has been a regular volunteer for IEEE Bangladesh section, previously serving as its Industrial Activity Co-ordinator during 2018 & 2019. He is currently the Student Activities Coordinator in IEEE Montreal Section.

Azfar is a Senior Member in IEEE. His current research area is anonymous age verification using ECG. Azfar is passionate about empowering people towards excellence, which he regularly promotes through public speaking-writing and other endeavors. He regularly writes for different think tanks and news media in Bangladesh and Canada. 

Dr. Nagham Saeed

Senior Lecturer in Electrical Engineering

School of Computing and Engineering

University of West London, UK.

Talk Title: Towards Sustainable Living in Smart Cities.

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Dr. Nagham Saeed leads the Industrial Internet of Things research group. Nagham has been working with intelligent systems since 2007. She completed her PhD in Wireless Communication Networks Optimization in 2011. Nagham has written extensively on intelligent systems and optimisations topics, with articles appearing in journals from disciplines including telecommunications, modelling and simulating, industry applications, information and smart power. Nagham has been serving as an editorial board member, guest editor, keynote speaker, plenary speaker, Technical Programme Committee member, and expert reviewer for many international scientific journals, conferences and workshops. Nagham IEEE senior member and IET member. She is the IEEE UK & Ireland Women in Engineering Chair and leads the IEEE UKI WiE Early Profession.