A Special Issue on: Unleashing the power of Edge AI for Scalable Image and Video Processing

Introduction

In the era of smart technologies, the fusion of Edge Artificial Intelligence (Edge AI) with image and video processing capabilities stands as a groundbreaking frontier, reshaping the landscape of scalable multimedia analytics. Traditionally, the centralized nature of cloud-based processing has posed challenges related to latency, bandwidth, and privacy concerns in the context of Scalable Image and Video Processing applications. The objectives include pushing the boundaries of visual data analytics by investigating state-of-the-art Edge AI techniques, displaying transformative impacts across diverse domains, tackling edge-specific challenges such as efficiency and latency, and catalyzing interdisciplinary collaboration. The emergence of Edge AI, however, has empowered devices at the network periphery to not only capture but also intelligently process and analyze visual data in real-time. This confluence of Edge Computing and AI introduces a transformative potential for applications ranging from smart cities and healthcare to industrial automation and surveillance.

In this context, our special issue invites researchers, practitioners, and visionaries to contribute their insights and findings to a collective exploration of the manifold dimensions of unleashing the power of Edge AI for scalable image and video processing. The spotlight is on scalable architectures, intelligent algorithms, and innovative applications that leverage the computational prowess of edge devices. We seek to unravel the intricacies of distributed Edge AI systems, delve into the energy-efficient dimensions of edge computing, and explore the symbiotic relationship between edge and cloud resources.

Recommended topics (but not limited to)

  • Edge AI architectures for scalable image and video processing
  • Edge AI algorithms for real-time image and video analytics
  • Edge AI for Visual Recognition and object detection in images and videos
  • Optimizing power consumption in Edge AI devices during image and video processing
  • Security and privacy in edge-based image and video processing
  • Edge AI with cloud-based processing for handling large-scale multimedia data
  • Approaches for managing diverse and heterogeneous image and video data at the edge
  • Real-time image and video processing through hardware acceleration and algorithmic efficiency
  • Real-time object recognition and tracking in AR environments at the edge
  • Scalable platforms and distributed systems for Edge AI

Important dates

Submission deadline: 15 September, 2024

Authors notification: 15 December, 2024

Revision submission: 15 February, 2025

Completion of Special Issue: March, 2025

Submission guidelines

Original and unpublished works on any of the topics aforementioned or related are welcome. The SCPE journal has a rigorous peer-reviewing process and papers will be reviewed by at least two referees. All submitted papers must be formatted according to the journal's instructions, which can be found here.

During submission please select a Special Issue that you want to submit to and provide this information in the Comments for the Editor field.

Guest Editors

Lead: Dr. V.DHILIP KUMAR, Professor and Head, Department of Artificial Intelligence and Data Science, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Chennai, India, email: dhilipkumarit@ieee.org

V. Dhilip Kumar, is an Professor and Head, Department of Artificial Intelligence and Data Science at Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai. He received the PhD at North Eastern Hill University (A Central University of INDIA) in 2018. He has more than 12 years’ experience of teaching as well as research. He did his B.Tech Information Technology and M.E. Computer science engineering under Anna University, Chennai. He has published various international journals and international conferences in the field of wireless communication and vehicular communication. He is an editorial board member and reviewer of various international journals and conferences such as Springer, Frontiers, MDPI, IGI Global etc. He has also been a guest editor of several special issues including the journals like Journal of Reliable Intelligent Environments (JRIE), International Journal of Speech Technology(IJST), Journal of Intelligent Enterprise (IJIE) and editing book in CRC press under the series of Electronics and Communications Technology. His area of interest is Soft Computing, Internet of things, Machine learning and Expert Systems etc. Currently working on Govt Funding Project DBT in the field of Precision Aquaculture using Machine Learning. He is Guiding 6 PhD Scholars in the field of Machine Learning for Healthcare and Computer Vision for Human Computer Interaction.

Dr. Oana Geman, Professor, University Stefan cel Mare of Suceava, Romania, email: oana.geman@usm.ro

Dr. Oana Geman is a Medical Bioengineer and PhD in Electronics and Telecommunication (Title of Doctoral Thesis: “Contributions To Knowledge-based Systems Using Nonlinear Analysis, With Medical Applications,” 2005) and a post-doctoral researcher in Computer Science (2012). She is currently an Associate Professor at the University of Suceava, Romania, and obtained Habilitation in Electronics and Telecommunication Field (2018). Within the past five years she has published 10 books, over 100 articles (65 articles in ISI Web of Science journals), 15 articles in ISI-indexed conference volumes as main author, and over 50 papers in Q1and Q2 Journals, with FI over 50. Her various works have been cited over 2000 times, and she has an H-index is 25. She served as chair or organizer of many internationals conferences as well as session chair and member in programs and technical committees. She is a senior member of IEEE. She has been a director or a member in 10 national and international grants. Her current research interests include: non-invasive measurements of biomedical signals, wireless sensors, signal processing, and processing information by way of artificial intelligence such as nonlinear dynamics analysis, stochastic networks and neuro-fuzzy methods, classification and prediction, data-mining, deep learning, intelligent systems, bioinformatics and biostatistics, and biomedical applications. Her current work is focused on identifying ways to contribute to the understanding of the role and predictive power of specific neural circuitries in the occurrence of neurological disorders and rehabilitation or other biomedical applications. This current project will involve research, dissemination, and transfer of knowledge in the area of bioengineering and medical engineering, with contributions to the development of system requirements, software development, testing the new system under laboratory conditions, as well as conducting reality simulations. She is a reviewer formany top journals, including IEEE Transactions, IEEE Access, IOT Journal, Sensors, and Symmetry, etc

Dr. David Asirvatham, Professor, School of Information Technology Taylor’s University, Selangor, Malaysia-47500, email: david.asirvatham@taylors.edu.my

Dr. David Asirvatham is currently the Head for the School of Computing and IT, Taylor’s University. Prior to this, he was the Director for the Centre of Information Technology at University of Malaya. He has held numerous posts such the Associate Dean for Faculty of Information Technology (Multimedia University), Project Manager for the Multimedia and IT Infrastructure Development for a university campus (US$14 million), Finance Committee for Multimedia University, SAP Advisory Council, Consultant for e-University Project and many more. Dr. David completed his Ph.D. from Multimedia University, M.Sc. (Digital System) from Brunel University (U.K.), and B.Sc. (Hons) Ed. and Post-Graduate Diploma in Computer Science from University of Malaya. He has been lecturing as well as managing ICT projects for the past 25 years. His area of expertise will include Neural Network, E-Learning, ICT Project Management, Multimedia Content Development and recently he has done some work on Big Data Analytics. At the national level, he was the Chairman, ICT Human Capital Development for 11th Malaysia Plan 2016-2020 (Prime Minister’s Office), Secretary for the Artificial Intelligence Society Malaysia and Country Representative for the Asia E-learning Network (AEN) based in Japan. Steering Committee Member for the Implementation of E-Learning for Malaysian Public Sector, and Member of the Malaysian Grid for Learning's (MyGfL) Standards Expert Group (SEG), 2003-2004. At International level, he worked on various ICT Projects and e-learning Workshops in South Africa, Sudan, Iran, Ghana, Kenya, Vietnam, Maldives, Bangladesh (World Bank Project), UAE, India and Brunei. On Research, currently he hold two Research Grants: (a) Newton Grant (RM800,000) and (b) Taylor’s University Grant for Travel Behavior Analytics (RM300,000). He is also leads the Data Analytics, Modelling and Visualisation Research Cluster at the university.

Dr. Nurzaman Ahmed, Engineering Research Scientist, Donald Danforth Plant Science Center, St. Louis, MO, USA, email: nahmed@danforthcenter.org

Nurzaman Ahmed is currently an Engineering Research Scientist at Donald Danforth Plant Science Center. Before joining Danforth he was a Postdoctoral Scholar at Dartmouth College. He also a Postdoctoral Fellow at Indian Institute of Science, Bangalore, and a Research Associate at Indian Institute of Technology, Kharagpur. He received his Doctor of Philosophy from North-Eastern Hill University, India, in 2020. He received Bachelor of Technology and Master of Technology in Information Technology from North-Eastern Hill University in 2013 and 2016, respectively. He have more than seven years of research experience working for Govt. of India and Govt. of US sponsored projects. He have more than 75 publications in reputed international journals, conferences, and book chapters and has patents. He have more than 1000+ citations for his publication. His current research interests include Internet of Things (IoT), Software-Defined Networks (SDN), and WiFi-based long-distance networks. I'm a member of IEEE and ACM and a graduate member of IEEE ComSoc.

Published in 2024