A Special Issue on: Next generation Pervasive Reconfigurable Computing for High Performance Real Time Applications

Introduction

Evolving aspects of scientific computing may alter the physical requirements of the fundamental hardware and software components. There is a huge demand for technical applications necessarily requires the use of high-performance platforms that can be architecturally updated for real-time applications. Traditional computing techniques rely on programmable general-purpose processors and continue to suffer from a deficiency of architectural alteration for required modification and speed computation during real-time applications. The pervasive reconfigurable computing approach is the solution for several real-time applications because it combines hardware speed and software flexibility on a single platform at a high level. Reconfigurable computing has the potential to significantly accelerate applications such as image processing, software designs, encryption, decryption, run-time operation, smart reconfigurable environments, sequence searching, matching, and other intensive computing applications.

For high performance applications, software programmed microprocessors are a more versatile choice. When compared to several signal processing algorithms, Artificial Intelligence (AI) reveals to be additional stable than conventional methods in noisy environments. To function effectively, AI relies heavily on reconfigurable design. Several hybrid machine learning techniques can also be used to improve the system's reliability without compromising its performance. In recent years, reconfigurable computing systems have provided a significant acceleration for many intensive computing algorithms when compared to various software-optimized versions, due to their parallel topologies. For computer vision problems, Deep Neural Architecture with flexible computation patterns can be used. Pattern recognition, high-performance machine learning algorithms, data manipulation, security threats, data mining, signal processing techniques and a few additional applications use Artificial Neural Networks (ANNs). Because of the desirability of ANN applications, a lot of research on software and hardware implementations of reconfigurable artificial neural networks is evolved.

This special issue intends to offer a detailed view of reconfigurable computing for high performance applications. Relatively new, innovative, and original research and review articles that focus on high performance applications are welcomed.

Recommended topics (but not limited to)

Authors are invited for original contributions on, but not limited to, the themes and topics in the following areas of research:

  • Design challenges in reconfigurable computing environments
  • Innovative embedded architectures for emerging technologies
  • Reconfigurable architectures for multimedia applications
  • Reconfigurable big data integrated circuits
  • Dynamic reconfigurable Network-on-Chip (NoC) Design
  • Reconfigurable hardware for Artificial Intelligence and Data Science
  • Reconfigurable programming technologies
  • Data-centric algorithms for reconfigurable computing
  • Reconfigurable algorithms for biomedical applications
  • Algorithms and systems for the Internet of Things (IoT)
  • Adjustable reconfigurable computing for intelligent environments
  • Performance analysis of network interface architectures
  • Reconfigurable data processing for cloud security
  • Machine learning-based reconfigurable architectures
  • Reconfigurable design techniques for light-weight medical systems

Important dates

Submission deadline: 30 September, 2023

Authors notification: 30 November, 2023

Revised version submission: 31 December 2023

Completion of Special Issue: March, 2024

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

Dr. C. Venkatesan (Managing Guest Editor) , Professor, Department of Electronics and Communication Engineering HKBK College of Engineering, Bangalore, Karnataka, India. E-mail: venkatesanc.ec@hkbk.edu.in

Prof. Dr. C. Venkatesan is currently working as Professor in the Department of Electronics and Communication Engineering, HKBK College of Engineering, Bangalore, Karnataka, India. He received Ph.D. in Information and Communication Engineering at Anna University, Chennai. He received a Bachelor of Engineering and Master of Technology from Anna University, India, and Government College of Technology, Coimbatore, India respectively. Furthermore, he is the author/co-author of more than 50 papers in journals, conferences, and book chapters, including Elsevier Biomedical Signal Processing and Control, IEEE Access, Springer National Academy of Science Letters, Springer Multimedia Tools and Applications, and Inderscience International Journal of Computational Science and Engineering. He is a reviewer in many journals such as IEEE, Elsevier, Springer, Taylor & Francis, Wiley, Emerald and IGI Global Journals and reviewed more than 250 research papers. He is currently serving as Lead Guest Editor in SCIE/Scopus/EI indexed Journals such as IEEE Instrumentation & Measurement Magazine, Emerald Sensor Review, Hindawi Journal of Sensors, Hindawi Security and Communication Networks, Emerald International Journal of Pervasive Computing and Communications, World Scientific Journal of Uncertain Systems and many more. He also served as keynote speaker, advisory committee member, and session chair for IEEE/Springer international conferences. He is currently a professional society member of IEEE, ISTE, IET, WASET, and IAENG. Not only that, but he received research grants from various Government funding agencies and was a recipient of the best faculty award and emerging researcher award for his contribution towards teaching and research. His current research interests include future Internet Technologies, Artificial Intelligence, Machine Learning and Wireless Sensor Networks.

Dr. Yu-Dong Zhang, Professor, Chair in Knowledge Discovery and Machine Learning, Department of Informatics, University of Leicester, United Kingdom. E-mail: yz461@leicester.ac.uk

Dr. Yudong Zhang worked as a postdoc from 2010 to 2012 with Columbia University, USA, and as an Assistant Research Scientist from 2012 to 2013 with the Research Foundation of Mental Hygiene (RFMH), USA. He served as a Full Professor from 2013 to 2017 with Nanjing Normal University. Now he serves as a professor at the School of Computing and Mathematical Sciences, University of Leicester, UK. His research interests include deep learning and medical image analysis. He is the Fellow of IET, Fellow of EAI, and Fellow of BCS. He is the Senior Member of IEEE, IES, and ACM. He is the Distinguished Speaker of ACM. He was included in Most Cited Chinese Researchers (Computer Science) by Elsevier from 2014 to 2018. He was the 2019, 2021 & 2022 recipient of Highly Cited Researcher by Clarivate. He won the Emerald Citation of Excellence 2017 and MDPI Top 10 Most Cited Papers 2015. He is included in Top Scientist in Research.com. He has (co)authored over 400 peer-reviewed articles in the journals JAMA Psychiatry, Inf Fus, IEEE TFS, IEEE TII, IEEE TIP, IEEE TMI, IEEE IoTJ, Neural Networks, IEEE TITS, Pattern Recognition, IEEE TGRS, IEEE JBHI, IEEE TCSVT, IEEE TETCI, IEEE TCSS, IEEE JSTARS, IEEE TNSRE, IEEE Sensors J, ACM TKDD, ACM TOMM, IEEE/ACM TCBB, IEEE TCAS-II, IEEE JTEHM, ACM TMIS, etc. There are more than 50 ESI Highly Cited Papers and 5 ESI Hot Papers in his (co)authored publications. His citation reached 22868 in Google Scholar (h-index 84). He has conducted many successful industrial projects and academic grants from NIH, Royal Society, GCRF, EPSRC, MRC, Hope, British Council, and NSFC. He has given over 120 invited talks at international conferences, universities, and companies, including Harvard University, University of Birmingham, University of Sheffield, De Montfort University, Polish Academy of Sciences, University of Warsaw, Hasselt University, etc. He has served as Chair for more than 60 international conferences and workshops. His research outputs have been reported by more than 50 news press, such as Reuters, BBC, Telegraph, Physics World, UK Today News, EurekAlert! Science News, India Times, Association of Optometrists (AOP) news, Medical Xpress, HospiMedica, Newsroom Odisha, etc.

Dr. Chow Chee Onn, Associate Professor, Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia. E-mail: cochow@um.edu.my

Dr. Chee-Onn Chow is an associate professor at the Department of Electrical Engineering, University of Malaya. He received his Bachelor of Engineering (Hons) and Master of Engineering Science degrees from University of Malaya in 1999 and 2001, respectively. He received his Doctorate of Engineering from the Tokai University, Japan in 2008. He has published more than 100 papers in reputable journals and conferences and completed more than 20 research projects funded by national and international organization. He is a reviewer in many journals such as IEEE, Elsevier, Springer, Taylor & Francis, Wiley, Emerald and IGI Global Journals and reviewed more than 500 research papers. His research interests include various issues related to wireless communications, deep learning, machine learning, mobile networks, genetic algorithms and big data. He is a Senior Member of IEEE (US) and a Professor Engineer (BEM, Malaysia).

Dr. Yong Shi, Associate Professor, Department of Computer Science, Kennesaw State University, Marietta, United States of America. E-mail: yshi5@kennesaw.edu

Dr. Yong Shi is an Associate Professor in the Department of Computer Science at Kennesaw State University, United States of America. He received his Ph.D. degree from the Department of Computer Science and Engineering at State University of New York at Buffalo (UB) emphasized in Data Mining. Before joining UB, he received B.S. and M.S. degrees in Computer Science from University of Science and Technology of China, Hefei, Anhui, China. He has published more than 80 papers in SCIE/ESCI/Scopus indexed journals and conferences and completed more than 10 research projects as principal and co-principal investigator funded by national and international organizations. He is a guest editor and reviewer in many publishers such as Springer, Emerald, IEEE, Wiley and IGI Global Journals and reviewed more than 500 research papers. Recently, he edited the special issues in Emerald International Journal of Intelligent Unmanned Systems, Springer Data Engineering and Communication Technologies, and Springer Neural Processing Letters etc. His research interest includes Cloud Computing, Security, Blockchain, Big Data Analytics, Mobile Computing, Parallel and Distributed Computing.

Articles published in 2024