Invited Speakers

Assoc. Prof. Hovannes Kulhandjian
California State University, Fresno, USA

Hovannes Kulhandjian (Senior Member, IEEE) received the M.S. and Ph.D. degrees in electrical engineering from The State University of New York at Buffalo, Buffalo, NY, USA, in 2010 and 2014, respectively. From December 2014 to July 2015, he was an Associate Research Engineer with the Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA. He is currently an Associate Professor with the Department of Electrical and Computer Engineering, California State University, Fresno, Fresno, CA, USA. His current research interests include wireless communications, applied machine learning, intelligent transportation systems, precision agriculture underwater acoustic communications, visible light communications, robotics and artificial intelegence. He actively serves as a member of the Technical Program Committee for ACM and IEEE conferences, such as IEEE GLOBECOM, ICC, PIMRC, and ACM WUWNet, among others. He has served as a Guest Editor for IEEE ACCESS and MDPI journals.

 

Speech Title: Advancements in Multi-Sensor AI Systems for Enhanced Pedestrian and Driver Safety

Abstract: This invited talk explores cutting-edge developments in AI-based safety systems designed to enhance pedestrian and driver safety through multi-sensor data fusion and deep learning techniques. We present three key innovations:

1. Pedestrian Detection and Avoidance at Night: Utilizing a combination of video, infrared (IR), and micro-Doppler radar sensors, this system employs deep convolutional neural networks (DCNNs) to process and analyze RGB and IR images. With a high validation accuracy of 99.6% for RGB and 97.3% for IR, the system effectively detects pedestrians and triggers warning signals, ensuring safety during both day and night.

2. Drowsy Driver Detection: Integrating visual and radar sensors, this system achieves over 95% accuracy in detecting driver drowsiness. By leveraging deep learning and data fusion, it monitors biometric expressions and driver behavior in real-time, providing a robust solution to prevent accidents caused by drowsy driving under various conditions.

3. Smart Robot for Pedestrian Road Crossing: Addressing the critical issue of pedestrian safety, especially for vulnerable groups, this project introduces a smart robot equipped with advanced machine learning algorithms. Utilizing LiDAR and video camera data, the robot accurately identifies vehicles, pedestrians, and cyclists at intersections, making intelligent decisions to facilitate safe road crossings. These advancements demonstrate the significant potential of multi-sensor AI systems in creating safer transportation environments by enhancing detection capabilities and enabling intelligent decision-making for both pedestrians and drivers.

Prof. Psannis Konstantinos
University of Macedonia, Greece

Konstantinos E. Psannis was born and raised in Thessaloniki, Greece. He is currently Professor in Communications Systems and Networking at the Department of Applied Informatics, School of Information Sciences, University of Macedonia, Greece, Director of Mobility2net Research & Development & Consulting JP-EU Lab, member of the EU-JAPAN Centre for Industrial Cooperation and Visiting Consultant Professor, Graduate School of Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan. Professor Psannis' research spans a wide range of ubiquitous 6G AI-IoT/Big Data Cloud- Analytics/Digital Twins and communications. This work is supported by research grants and contracts from various government organisations. Konstantinos received the Ph.D. degree from the School of Engineering and Design, Department of Electronic and Computer Engineering of Brunel University, London, UK [awarded the British Chevening scholarship funded by the Foreign and Commonwealth Office (FCO) and partner organizations]. Dr. Psannis has several highly cited papers powered by Web of Science – Clarivate and received more than 7000 citations (h-index 33, i10-index 85) Dr. Psannis supervises 4 post-doc students and 12-PhD students and more than 300 M.Sc. Thesis. Professor Konstantinos E. Psannis has been included in the list of Top 2% influential researchers globally (prepared by Scientists from Stanford University USA), October 2020, October 2021, October 2022 and October 2023 [https://sites.uom.gr/kpsannis/].

 

Prof. Fahim Khan
Toyo University, Japan

Dr. Fahim Khan is a Professor at the Department of Information Networking for Innovation and Design (INIAD) in Toyo University, Tokyo, Japan. Prior to joining Toyo University, he served as a faculty member at the University of Tokyo, from where he also obtained his MS and PhD in Applied Computer Science. His current research focus includes, among others, developing security measures for IoT and smart spaces; designing distributed systems using machine learning, generative AI, and blockchain; and leveraging EdTech and learning sciences for CS, STEM and SDGs education. His research publications have won multiple best paper awards at IEEE conferences. He actively serves as a committee member in many IEEE and ACM conferences. A Senior Member of IEEE, Khan is a recipient of IEEE Japan Medal. He is also a globally selected member of ACM Future of Computing Academy (ACM-FCA), an initiative that brings together next-generation leaders in computing to carry the computing community into the future.

 

Speech Title: Leveraging Generative AI for Education: Opportunities and Challenges

Abstract: Comprising approximately 86 billion neurons and trillions of synapses among them, the human brain represents an incredibly complex structure with intricate folding and interconnected regions. In contrast, the neural network architectures of modern large language models (LLMs) appear deceptively simple. Yet, these LLMs astound us by emulating human-like languages with astonishing sophistication. Fueled by these powerful LLMs, the advent of generative AI (GenAI) has stunned the world with potential disruptive impacts across all industry sectors. The domain of education is no exception. According to Grand View Research, the AI education market revenue is projected to reach USD 32.27 billion by 2030, growing at a compound annual rate of 36% from 2022 to 2030. Indeed, GenAI holds immense promise for revolutionizing education, but it also presents challenges. This talk will explore how GenAI can be harnessed in education, examining both its potential benefits and associated risks. We will then delve into a case study featuring a GenAI powered application for language learning, grounded in pedagogical principles and learning sciences.

 

Assoc. Prof. Zhe Li
Soochow University, China

Zhe Li received the B.S. degree in telecommunication engineering from the Nanjing University of Posts and Telecommunication, Nanjing, China, in 2011, and the M.S. degree in software engineering and the Ph.D. degree in information and communication engineering from Southeast University, Nanjing, China, in 2014 and 2018, respectively. From 2016 to 2017, he was a Visiting Ph.D. Student with the Department of Electrical and Electronic Engineering, Imperial College London, London, U.K. Since 2018, he has been an Associate Professor with the School of Electronic and Information Engineering, Soochow University, Suzhou, China. He was the recipient of the Education Innovation Award at the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) in 2019. His research interests include complex and hyper-complex statistical signal processing and its applications in communications systems, power networks, and Internet of things.

Prof. Anand Nayyar
Duy Tan University, Vietnam

Dr. Anand Nayyar received Ph.D (Computer Science) from Desh Bhagat University in 2017 in the area of Wireless Sensor Networks, Swarm Intelligence and Network Simulation. He is currently working in School of Computer Science-Duy Tan University, Da Nang, Vietnam as Professor, Scientist, Vice-Chairman (Research) and Director- IoT and Intelligent Systems Lab. A Certified Professional with 125+ Professional certifications from CISCO, Microsoft, Amazon, EC-Council, Oracle, Google, Beingcert, EXIN, GAQM, Cyberoam and many more. Published more than 180+ Research Papers in various High-Quality ISI-SCI/SCIE/SSCI Impact Factor- Q1, Q2, Q3, Q4 Journals cum Scopus/ESCI indexed Journals, 70+ Papers in International Conferences indexed with Springer, IEEE and ACM Digital Library, 40+ Book Chapters in various SCOPUS/WEB OF SCIENCE Indexed Books with Springer, CRC Press, Wiley, IET, Elsevier with Citations: 11800+, H-Index: 57 and I-Index: 210.

 

Assoc. Prof. Fwen Hoon Wee
Universiti Malaysia Perlis (UniMAP), Malaysia

Fwen Hoon Wee, PhD, is an accomplished professional with a strong academic background. She obtained her B.Eng and PhD degrees in Communication Engineering from Universiti Malaysia Perlis in 2009 and 2013, respectively. Currently serving as an Associate Professor at the Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis (UniMAP), she has demonstrated her dedication to both academia and industry. Dr. Wee's career includes a significant industrial attachment at Keysight Technologies, Penang, Malaysia, focusing on instrument hardware development. Her research areas encompass dielectric resonator antennas, wearable antennas, 5G, automation, and dielectric material measurement. She has led numerous nationally and industrially funded projects and supervised several postgraduate students, contributing to the advancement of her field.

 

 

 

Assoc. Prof. Mohd Faizal Abdollah
University Teknikal Malaysia Melaka, Malaysia

Associate Profesor Dr Mohd Faizal Abdollah is currently a senior lecturer in University Teknikal Malaysia Melaka. The research area more focuses on network security, malware detection and network management. In cybersecurity, Dr Mohd Faizal led the sub project under CMERP project with the collaboration with Cyber Security Malaysia. This project more focuses on malware detection, eradication and mitigation. Currently, involve in developing EDR together with the Cybersecurity Malaysia. Others than that, Dr Mohd Faizal also involve in various grant sponsor by Ministry of Education, Industrial grant and University grant such as Fundamental Grant for detecting botnet activity, Transdisciplinary Grant for detecting the inside threat, ISIF grant for botnet detection using graph theory. He also teaches UTeM course such as Information Technology and IT Security, Network Management and Administration, Advanced Scalable Network and also manage to produce various conference paper and journal in cybersecurity related field.

Prof. Ghulam Abbas
GIK Institute of Engineering Sciences and Technology, Pakistan

GHULAM ABBAS received the B.S. degree in computer science from University of Peshawar, Pakistan, in 2003, and the M.S. degree in distributed systems and the Ph.D. degree in computer networks from University of Liverpool, U.K., in 2005 and 2010, respectively. From 2006 to 2010, he was Research Associate with Liverpool Hope University, U.K., where he was associated with the Intelligent & Distributed Systems Laboratory. Since 2011, he has been with the Faculty of Computer Science & Engineering, GIK Institute of Engineering Sciences and Technology, Pakistan. He is currently working as a full Professor, Head of Cybersecurity and Software Engineering Departments, and Director ICT Academy. Dr. Abbas is a co-founding member of the Telecommunications and Networking (TeleCoN) Research Center at GIK Institute. He is a Fellow of the Institute of Science & Technology, U.K., a Fellow of the British Computer Society, and a Senior Member of the IEEE. His research interests include computer networks and wireless and mobile communications.

Dr. Muhammad Waqas
University of Greenwich, UK

MUHAMMAD WAQAS (M’18, SM’22) received his B.Sc. and M.Sc. in Electrical Engineering from the Department of Electrical Engineering, University of Engineering and Technology, Peshawar, Pakistan, in 2009 and 2014, respectively. He received his PhD degree from the Department of Electronic Engineering, Tsinghua University, Beijing, China, in 2019. From Oct. 2019 to Mar. 2022, he was a Research Associate at the Faculty of Information Technology, Beijing University of Technology, Beijing, China. Currently, he is a Senior Lecturer in Cybersecurity at the School of Computing and Mathematical Sciences, Faculty of Engineering and Technology, University of Greenwich, London, UK.
His current research interests are in the areas of Wireless Communication, vehicular networks, Fog/Mobile Edge Computing, Internet of Things and Machine Learning. He has more than 100 research publications in reputed Journals and Conferences. He is an Associate Editor of the International Journal of Computing and Digital Systems and guest editor of Applied Sciences - MDPI. He is recognised as a Global Talent in the area of Wireless Communications by UK Research and Innovation and a Professional Member of Engineer Australia. He is a senior member of IEEE, a Professional Member of ACM, an IEEE Young Professional, a Member of the Pakistan Engineering Council and an approved supervisor by the Higher Education Commission of Pakistan.

Dr. Adam Wong Yoon Khang
Universiti Teknikal Malaysia Melaka, Malaysia

Adam Wong Yoon Khang received his Ph.D. Degree from Universiti Teknologi Malaysia in 2018. He is currently a Senior Lecturer in the Fakulti Teknologi Kejuruteraan Elektrik dan Elektronik, Universiti Teknikal Malaysia Melaka (UTeM). He is also a Professional Technologist for the Malaysia Board of Technologists (MBOT). Before joining the academia, he served various companies from 2007 until 2011 as a commercial engineer in industries ranging from manufacturing to service providers. His current research interests are the Internet of Things, Hybrid Optical Wireless, simulation optimization, ad hoc network and passive optical network but not limited to the mentioned topic here. He actively publishes research articles and received grants from the government and private sectors, universities and international collaboration.

 

Speech Title: Enhancing Deaf-Blind Accessibility with an American Sign Language System Powered by RF Signals and IoT

Abstract: The Internet of Things (IoT) has significantly enhanced people's lives, offering unprecedented convenience and connectivity across various devices and locations through the Internet. The creation of a portable American Sign Language (ASL) system using RF signals and IoT technology aims to address challenges faced by deaf-blind individuals, particularly in terms of slow learning and short-term memory loss. These difficulties often stem from the lack of engaging educational resources in the market. To remedy this, immediate measures are being taken to improve the learning experience for deaf-blind individuals. The American Sign Language visualizer has been developed to optimize the absorption of sign language, employing physical and interactive methods. The learning kit introduces two approaches: the first method involves selecting a pre-programmed Radio-Frequency Identification (RFID) sign language card, tapping it on an RFID reader connected to Raspberry Pi 4B, and viewing ASL alphabets on an LCD screen, accompanied by a video demonstrating finger movements. The second method is interactive, where students use a braille-embedded keyboard to trigger audio responses through ESpeak. Ultimately, the development of this ASL learning kit aims to foster a more engaging and effective learning experience, enhancing students' interest and proficiency in both ASL and Braille.