Watch Again | IEEE WIE UKI Ambassadors’ Scheme Early Career Talk 12 – An Inspirational and Empowering Webinar for Women in Engineering

Speaker: Nour Ghadban
Area of career: Academia

Talk topic: Revolutionizing Robotics: Harnessing the Power of 5G and Emerging Technologies for Next- Generation Applications

Title: Exploring Feature Extraction and AI in Revolutionizing Robotics with 5G and Emerging Technologies for Next-Generation Applications

This talk delves into the transformative impact of Artificial Intelligence (AI) and feature extraction in the realm of robotics, emphasizing the revolutionary role of 5G and other emerging technologies. It explores how AI enhances robotic capabilities through advanced feature extraction methods, enabling robots to interpret and interact with their environment more effectively. The integration of 5G technology is highlighted as a critical enabler for real-time data processing and improved connectivity, vital for advanced robotic applications. Furthermore, the presentation examines the synergy of AI with technologies like IoT and cloud computing, showcasing their combined potential in creating more sophisticated, efficient, and versatile robotic systems. This analysis culminates in a discussion of future trends and challenges in robotics, underlining the increasing collaboration between humans and robots in various sectors.

About the Speaker:

Nour is a postdoctoral researcher at the School of Engineering, University of Glasgow, where her specialisation is in AI, Feature extraction, speech recognition.

Prior to joining the University of Glasgow in 2022, she worked as a lecturer at Tishreen University in Syria (2013–2022), where she published numerous scientific works. She is also a fellow of the L’Oréal-UNESCO for Women Fellowship Science Levant Egypt (2017). In addition, worked as a researcher at Brno University (Sept 2018–Nov 2018), investigated the effects of non-invasive brain stimulation on hypokinetic dysarthria, micrographia, and brain plasticity in patients with Parkinson’s disease. This project was supported by the Czech Ministry of Health in collaboration with neurologists from the Central European Institute of Technology.

Her research interests have continued to evolve, from exploring the development of adaptive autonomous systems that can interact with unknown environments, to working on approaches to assist deaf or hard-of-hearing individuals in recognizing spoken words and sentences, even in noisy environments or when wearing masks. Her current research focuses on machine learning, deep learning, pattern recognition, and computer vision.

As a L’Oréal-UNESCO for Women in Science Levant and Egypt Fellow, a Senior Member of the Institute of Electrical and Electronics Engineers (SMIEEE), a Women’s Engineering Society Fellow (WES), a member of the Global Research Network, and a member of the Leadership Coach for For Women in Science (FWIS) 2023, my primary focus is on promoting and encouraging women in science. She is dedicated to providing support for collaborative research, publishing, policy projects, career development, and knowledge transfer to academics. To achieve her goals, She aims to expand her network and collaborate with like-minded professionals. She believes that diversity in higher education is crucial, and is eager to work with scholars from all disciplines worldwide to promote gender equality in the field of engineering and beyond.

Area of career: Academia

Research interest: Software Defined Networks, Blockchain, 5G/6G Networks, Digital Twin, IoT Networks

Talk Topic: Blockchain-based Software-Defined Networks

The current Fixed Wireless Access (FWA) cannot handle a huge amount of mobile data requirements. To meet this, service providers should invest new FWA extra OPEX/CAPEX. Software-defined network (SDN) controlled FWA is proposed to decrease costs by partial recovery on the current FWA. In each OpenFlow-based FWA, there are hardware and software-based tables, but network administrators are not allowed to configure software parts to result in deployment risks. Moreover, there is another challenge as not allowing to use of the same network equipment by different service providers at the same time. These challenges can be solved by blockchain-secured SDN that does not need for a central authority for network privacy. Here, the talk will be about the details of dynamically using OpenFlow-based FWA as a service by multiple providers with a smart contract stored in blockchain.

About the Speaker:

Asst. Prof. Dr. Müge Erel-Özçevik [S’13 M’19] is an Assistant Professor in the Software Engineering Department of Manisa Celal Bayar University. She had received her PhD, MSc, and BSc degrees in Computer Engineering from Istanbul Technical University, Turkey in 2019, 2015, and 2013 respectively. has been honored with third rank from Turkish Academy of Sciences (TÜBA) in Science and Technology category with her Ph.D. thesis in 2022. She has also been awarded the”Best Ph.D. Thesis” by IEEE Turkey Section and Istanbul Technical University in 2021 and 2019, respectively. She currently serves as a reviewer for IEEE Transactions on Vehicular Technology (TVT), IEEE Transactions in Wireless Communications (TWC), The International Journal of Communication Systems (IJCS), 2014-present; and The International Journal of Computer and Telecommunications Networking (COMNET), 2015-present; IEEE Transactions on Communications, Ad Hoc networks, IEEE Communication Letters, IEEE Computer Communications (COMCOM), 2017-present. She is the recipient of the IEEE CAMAD Best Paper Award (2016) and the IEEE INFOCOM Best Poster Paper Award (2015). She has been involved in several international conferences as a program co-chair, TCP member, and reviewer. Her current research includes Software-Defined Networking (SDN), Content-Delivery Networks (CDN), Blockchain, 5G Networks, and Ultra-Dense Networks (UDN). Moreover, she had been supported by ASELSAN as a Graduate Scholarship for Turkish Academicians between November 2016 and June 2019.

Please find below the link to her website: https://sdxrg.mcbu.edu.tr/

Area of career: Academia

Talk topic: Distributed learning in wireless networks, wireless communications and machine learning

Distributed learning techniques can efficiently support training machine learning models by exploiting the distributed computational resources. In this talk, distributed learning in wireless networks will be discussed, with focus on the most recent distributed learning approaches. Considering the diversity of wireless users in terms of different communication, computational and data resources, implementing distributed learning techniques in wireless networks is challenging. This talk starts from a comprehensive overview of the state-of-art distributed learning architectures in wireless communications. Then recent work on designing new hybrid distributed learning architectures and user scheduling schemes to address the challenges of diverse users in wireless networks is introduced. Moreover, energy-efficient user scheduling to improve energy and computation efficiency of wireless users will also be discussed.

About the Speaker:

Dr. Xiaolan Liu is a lecturer (assistant professor) at the Institute of Digital Technologies at Loughborough University in the London campus. She is also a visiting research scholar at King’s College London (KCL) and The Hong Kong University of Science and Technology (HKUST). She received her PhD degree in Computer Science from Queen Mary University of London (QMUL) in July 2021. Her current research interests include distributed learning for wireless communications, reinforcement learning in edge computing, and privacy-preserving machine learning. She has published more than 25 Journal and Conference papers in these areas with the H-index of 15. She was a research associate in KCL from August 2020 to July 2021. She is the editor of IEEE Internet of Things and IEEE Wireless Communication Letters. She has served as a TPC Member for IEEE conferences GLOBECOM 2022 and ICC 2022. She served as a TPC Session Chair of IEEE ICT 2021.

Area of career: Academic and Industrial

Research interests: Intelligent Robotics, Advanced Sensing, Green Communication

Talk topic: Revolutionizing Robotics with 5G and Emerging Technologies.

Revolutionising autonomous systems, our research focuses on the fusion of Machine learning, robotics, and 5G. With a core emphasis on knowledge aggregation, we unlock the potential of collaborative intelligent robotics through ML algorithms. Navigating the landscape of advanced sensing technologies in the realm of 5G, our work showcases applications that redefine robotics capabilities. Additionally, we explore green communication strategies, ensuring sustainability in this transformative synergy to create energy-efficient algorithms. Explore the forefront of technological convergence, where our research opens new horizons for unprecedented advancements in the field of robotics.

About the Speaker:

Muhammad Waqas Nawaz holds a B.Sc. (Hons.) in Mathematical Science in 2014 and an M.Sc. in Big Data, completed in 2020. I have 4 years of industrial experience as Associate Data Scientist and Research Assistant. Currently pursuing a Ph.D. in Autonomous Systems and Connectivity at Glasgow University, UK, funded by the Engineering and Physical Sciences Research Council. His research focuses on machine learning, the Internet of Things, autonomous connectivity, D2D communications, and UAV swarm networking to enhance communication adaptability in distributed environments. In the first year of his Ph.D., he has authored two conference papers accepted at VTC and WCNC and co-authored two journal papers.