Journal Information
Computers & Electrical Engineering
http://www.journals.elsevier.com/computers-and-electrical-engineering/
Impact Factor:
2.189
Publisher:
Elsevier
ISSN:
0045-7906
Viewed:
18053
Tracked:
35

Call For Papers
The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency.

Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.

Specific topics of interest include:

    Applications of high-performance computing and novel computing systems

    Internet-based, multimedia, and wireless networks and applications

    Communications, especially wireless

    Signal processing architectures, algorithms, and applications

    Green technologies in information, computing, and communication systems

    Multi-disciplinary areas, including robotics, embedded systems, and security
Last updated by Dou Sun in 2019-11-24
Special Issues
Special Issue on Bio-inspired Computing – Emerging Theories and Industry Applications (VSI-bioc)
Submission Date: 2020-10-26

Bio-inspired computing is a field of study that abstracts computing ideas (data structures, operations with data, ways to control operations, computing models, artificial intelligence, multisource data driven and analysis, etc.) from the living phenomena or biological systems such as cells, tissue, the brain, neural network, immune system, ant colony, and evolution. Developing new and state-of-the-art computing solutions to address complex problems in science and engineering is one of the major challenges faced by researchers in a variety of fields. To construct the optimal IT solutions, many data domains are nonlinear and regulated by multiple constraints, such as time requirements, and high dimensionality. Bio-inspired computing has been a promising multi-disciplinary approach to solve complex industry data analysis problems, and we foresee increasing development and more successful applications. The aim of this special section is to demonstrate the power and impact of bio-inspired computing in real world by inviteing researchers from relevant fields to present their novel theories, methodologies, and applications of bio-inspired computing. Topics: This special issue welcome submissions of the emerging bio-inspired theoretical and application advancement / progressing on following topics: - Neural Networks - Brain-inspired Computing - Neuromorphic Computing and Architectures - Evolutionary Computing - Molecular and Genetic algorithm - Membrane Computing - Cellular Automata and Cellular Neural Networks - Ant Colonies Algorithm - Swarm Intelligence - Artificial Immune System and its Application in Security - Epidemiology and Communication Protocols - Sensory Organs and Sensor Networks - Cognitive Learning and Pattern Recognition - Artificial Intelligence
Last updated by Dou Sun in 2020-08-10
Special Issue on Big Data Analytics and Deep Learning Approaches for 5G and 6G Communication Networks (VSI-5g6g)
Submission Date: 2020-11-30

Overview: The next generation (5G and 6G) of communication networks will target unprecedented performance in terms of network capacity, quality of service, network availability, and user-experience. The convergence of the fifth-generation (5G) networks and big data analytics in today’s smart systems and devices is expected to disrupt the Information and Communications Technology (ICT) ecosystem. Advanced mathematical tools, such as those in the field of Big Data Analytics and Deep Learning (DL) also represent an extremely important opportunity to help in telecom, bioinformatics, healthcare, Internet of Things, social networks, and manufacturing. The possibility of efficiently leveraging large amounts of data, big data analytics, and Deep Learning tools, is expected to improve 5G and 6G networks through automation and self-optimization. The main focus of this Special Section is on the most recent applications of Deep Learning and Big Data Analytics to optimize data for next-generation networks. Topics: The topics of interest include: - Use of Deep Learning and data analytics in the Cloud, Mobile Edge Computing and Data Center Networks - Application of AI and ML for big data analysis in 5G networks - Network telemetry, monitoring, and data collection - Deep Learning for 5G-services traffic classification and forecast - Big data analytics to improve QoS in 5G networks - Role of IoT in Engineering, Tourism and Medical Applications - New Generation Approaches for 6G - Information and 5G Mobile Communications in Health Applications - Big Data Applications for Education - Big Data Analytics Utilization in Engineering - Deep Learning Solutions for Engineering Applications - Big data analytics in connected vehicles using 5G - Social media data analysis for 5G - Neural networks and reinforcement learning for big data analysis - Combined Solutions for Big Data in Engineering, Tourism and Health Care Applications
Last updated by Dou Sun in 2020-07-16
Special Issue on AI-enabled Internet of Things for Connected Community (VSI-aiot)
Submission Date: 2020-12-01

Overview Internet of Things (IoT) has revolutionized the digital landscape with embedded sensors, localized computing, and ubiquitous communication capabilities. It has opened new opportunities to enable smart cities and smart communities that support people with various services in improving their quality of life. However, in order to realize a more connected community that offers wider system-level and social-level collaboration, coordinated decision-making, resiliency, and improved interaction, IoT-based systems and services need to bring intelligence into their core. The interconnection between IoT and Artificial Intelligence (AI) can provide that edge. Interestingly, there has been a growing interest to incorporate AI into IoT-based deployments. The AI-IoT solutions can leverage the huge volume of IoT data, cluster and classify them, make predictions, find patterns and provide early observations that can solve various problems of connected community and its stakeholders. Although researchers individually have been making progresses in IoT (w.r.t. systems, services composition, connectivity, deployment, etc.), AI (w.r.t. data mining, machine learning, deep learning, etc.), and smart city (w.r.t. infrastructure, decision-making, interoperability, etc.), relatively less efforts have been invested to consider the AI-IoT duo in developing intelligent systems and services that strengthen the connections between communities, increase resilience, promote communities of interests, encourage voluntary computing, and address disconnections between communities toward a greater good. This special section aims to address the recent advances and ongoing improvements of AI-powered IoT solutions for connected communities. More specifically, various aspects related to AI-IoT deployments, technical issues, models, performance, and other aspects on building and supporting connected communities are of interest in the post-Covid-19 context. Topics: Suggested topics include: AI for prediction, clustering and classification in IoT-connected communities AI to understand mediation between virtual and real interactions in IoT-connected communities AI-powered modeling of friendship and support between IoT-enabled connected communities AI-powered dynamic communities of Interest for IoT in different context Resiliency in AI-IoT based connected communities to tackle abnormalities like COVID-19 Collaborative IoT for AI-powered connected communities AI to understand and analyze the effect of disconnected communities Security, privacy and trust issues in AI-enablement of IoT-connected community New challenges, opportunities, and applications of AI-IoT connected community
Last updated by Dou Sun in 2020-08-24
Special Issue on Artificial Intelligence and Robotics (VSI-air3)
Submission Date: 2020-12-30

Recently, many intelligent robots have been developed for the future society. Particularly, intelligent robots should continue to perform tasks in real environments such as homes, commercial facilities and public facilities. The growing needs to automate daily tasks combined with new robot technologies are driving the development of human-friendly robots. Intelligent robots should have human-like intelligence and cognitive capabilities to co-exist with people. Artificial intelligence is very important to provide human-friendly services by robots. Research on artificial intelligence, cognition computing, and soft computing has a long history. The concepts of adaptation, learning, and cognitive development should be introduced more intensively in the next generation robotics. Furthermore, the advent of Internet of Things, 5G wireless technology, and robotics technology may also bring brand-new emerging intelligence to robots. This special session focuses on the intelligence of robots emerging from the adaptation, learning, and cognitive development through the interaction with people and dynamic environments from the conceptual, theoretical, methodological, and technical points of view. It follows two earlier special sessions on the same topic (VSI-air, January 2019 and VSI-air2, November 2020). Topics The topics of interests in this special session include: - Robot Intelligence - Learning, Adaptation, and Evolution in Robotics - Human-Robot Interaction - Embodied Cognitive Science - Perception and Action - Intelligent Robots - Fuzzy, Neural, and Evolutionary Computation for Robotics - Evolutionary Robotics - Soft Computing for Vision and Learning Submission of manuscripts: Research articles must not have been published or submitted for publication elsewhere. All articles will be peer-reviewed and accepted based on quality, originality, novelty, and relevance to the theme of the special section. Before submission, authors should carefully read the Guide for Authors available at https://www.elsevier.com/journals/computers-and-electrical-engineering/0045-7906/guide-for-authors Authors should submit their papers through the journal's web submission tool at https://www.editorialmanager.com/compeleceng/default.aspx by selecting “VSI-air3” under the “Issues” tab. Schedule: Submission of manuscript: December 30, 2020 Submission of revised manuscript: March 1, 2021 Notification of the re-review: April 30, 2021 Final notification: July 30, 2021 Final paper due: August 15, 2021 Publication date: November, 2021 Guest Editor Dr. Huimin Lu, Kyushu Institute of Technology Email: dr.huimin.lu@ieee.org Huimin Lu received double M.S. degrees in Electrical Engineering from Kyushu Institute of Technology in 2011 and received a Ph.D. degree in Electrical Engineering from Kyushu Institute of Technology in 2014. From 2013 to 2016, he was a JSPS research fellow (DC2, PD, and FPD) at Kyushu Institute of Technology. Currently, he is an Associate Professor in Kyushu Institute of Technology and an Excellent Young Researcher of Ministry of Education, Culture, Sports, Science and Technology (MEXT)-Japan. He serves as area editor or associate editor for Computers & Electrical Engineering, Wireless Networks, Applied Soft Computing, etc. He is the Leading Guest Editor for Mobile Networks and Applications, Optics & Laser Technology, Multimedia Tools and Applications, IEEE Transactions on Network Science and Engineering, Pattern Recognition, ACM Transactions on Internet Technology, IEEE/CAA Journal of Automatica Sinica, IEEE Internet of Things Journal, etc. His research interests include artificial intelligence, machine vision, deep-sea observing, Internet of Things and robotics. He has authored or co-authored 100+ papers in peer-reviewed journals and conferences, which have received 3000+ citations, 10 ESI highly cited papers and 2 ESI hot papers. As the lead editor, he has edited 3 books and have 100K+ downloads. He has received 20+ awards and 20+ funds from the governments and associations. He is elected as the Fellow of European Alliance for Innovation (EAI) and Senior Member of The Institute of Electrical and Electronics Engineers (IEEE) in 2019.
Last updated by Dou Sun in 2020-04-15
Special Issue on Recent Advances and Challenges in Intelligent Sliding Mode Control for Modern Industrial Systems: Soft Computing Solutions (VSI-smc)
Submission Date: 2020-12-30

Overview In the rising trend of Industry 4.0, manufacturing industries have been experiencing significant changes with the increased untilization of machine learning, big data, aritificial intelligence, and intelligent automation. Modern industrial equipments and systems have been intensively used in wide applications to achieve a higher level of automation, e.g., for smart grids, renewable energy systems, robots, transportation and autotomotive industries. These changes requires better performance of the industrial systems in terms of robustness, reliablity, design and implementation simplicity, and intelligence. Sliding mode control (SMC), as an efficacious and powerful control methodology, is playing an essential role in meeting the performance requirements for modern industrial systems. The merits of SMC are high robustness against disturbances and parameter variations, reduced-order system design, simple control structure, computational simplicity for implementation, and fast dynamic response. Academics and engineers are working on further improving the convergence and robustness peformance, resulting in the dramatic development of the SMC methods. In spite of various research, the major technical problems of SMC are still challenging, particularly for modern industrial systems. As such, much of the recent SMC research has focused on the intergration with soft computing (SC) technologies, such that not only is the SMC more intelligent and flexible facing complex industrial environment, but also stronger robustness can be ensured. Meanwhile, the latest advances of microcontrollers, digital signal processors, sensors, etc. also facilitate the practical implementation of adavanced and intelligent SMC designs for complex industrial systems. The aim of this Special Section is to focus on the latest developments in the SC-based intelligent SMC for industrial systems, such as fuzzy logic (FL)-based SMC, neural network (NN)-based SMC, probabilistic reasoning (PR)-based SMC, SC integration-based SMC, etc. Meanwhile, practical technical issues and challenges of the intelligent SMC in various industrial applications should also be addressed. Topics: Suggested topics include: FL-based SMC techniques NN-based SMC techniques PR-based SMC techniques (evolutionary algorithms, chaos theory, belief networks, etc) SC methodology integration-based SMC techniques Intelligent sliding mode observer design techniques Applications of intelligent SMC in transportation, robots, automotive systems, mechatronic systems) Applications of intelligent SMC in industrial electronics (smart grid, renewable energy systems, power converters) Applications of intelligent SMC in networking and communication systems
Last updated by Dou Sun in 2020-07-16
Special Issue on Recent Advances and Challenges in Quantum-Dot Cellular Automata (VSI-qca)
Submission Date: 2020-12-31

Quantum computers promise dramatic improvements in our ability to efficiently solve classically intractable problems ranging from cryptosystems to simulation of quantum systems, and to optimization and machine learning. Quantum computing has attracted attention in the past two decades because it was found that computers exploiting quantum mechanics are able to outperform classical digital computers in certain areas like factoring integers and searching. Developments in the field of quantum computing have been strongly impacted by the paradigm of quantum-dot cellular automata (QCA), a scheme for molecular/metal/semiconductor electronics in which information is transmitted and processed through electrostatic interactions in an array of cells. QCA is a revolutionary computing paradigm that is well suited to nano-electronic implementation and scaling to molecular dimensions. In QCA, binary information is encoded in the position of single electrons among a group of dots forming a cell. This represents a significant break with the transistor-based paradigm in which information is encoded by the state of the transistor current switch. In QCA, electrons switch between quantum dots within a cell, but no current flows between cells. This leads to extremely low power dissipation, avoiding the problem of heat generation that ultimately limits the integration density of transistor circuits. QCA cells used for classical computing applications are mostly fully polarized during the operation. Dissipation plays a positive role helping the system to stay near the ground state. Unlike classical digital applications, quantum computing ideally needs coherence for correct operation. In the case of quantum computing, the cells are not fully polarized: they can be in a superposition of the P= +1 and -1 basis states. Similarly, a cell line can be in a superposition of the multi-qubit product states. In order to distinguish QCA applied for quantum computing from the classical digital QCA, the notion of coherent QCA (CQCA) can be explored. The aim of this special section is to explore solutions for major challenge in the area of QCA-based digital circuits. It includes the basics of new logic functions and novel digital circuit designs, Quantum Computing with QCA, new trends in quantum and quantum-inspired algorithms and applications, innovative layout methods, advanced EDA tools and algorithms to support QCA designers. Topics: Following are the main topic of interest: Quantum computer architecture; Performance evaluation methods for quantum networks New tools to design/build/optimize quantum hardware devices and quantum software; Design methodologies for and scalable quantum-computing systems; Emerging trends in quantum algorithms; Application case studies and evaluations; Testing, design for testability, built-in self-test in QCA technology. QCA-based logic structures and interconnections; Innovative clock schemes to control data flow directionality; Smart formulations of logic equations; Logic gates and digital circuits designs; Software development tools for the design and the characterization of QCA circuits; Area, power, and thermal analysis and design in QCA nano-technology.
Last updated by Dou Sun in 2020-07-04
Special Issue on Developments in Renewable Energy Generation and Automation (VSI-reg)
Submission Date: 2020-12-31

Overview Renewable energy resources pose a number of fundamental and practical challenges, such as cost and availability, that need to be addressed before significant levels of renewable penetration into the existing power-mix can be realized. The intermittency of the renewable energy sources results in exhibited changing dynamics and uncertainties. In addition, the behavior of these energy conversion systems is dominated by strong nonlinearities and the heavy interaction of continuous and discrete dynamics. This makes the application of classical control techniques, based on linearized models and purely continuous (or discrete) models, inadequate. The use of more efficient control and optimization strategies would not only enhance the performance of these systems, but would also reduce the cost per kilowatt-hour produced. Therefore, the optimal sizing/placement and the control applications of renewable energy systems are of great interest to researchers. This special section provides a forum for researchers and practitioners to share insights on innovation and development of different novel optimization techniques and the linear/nonlinear control methods for renewable energy systems, and the application of these advanced optimization/control approaches to enable more efficient operational capabilities of systems in the general area of renewable energy and Hybrid Power Systems (HPS). The current projections of rapid growth in energy consumption and vehicle use raise the worldwide dilemma of greenhouse gas emissions and relevant climate change impacts. Fuel, energy and their environment impacts are challenges of modern renewable energy sources (RESs) and new automotive system technologies. To cope with the technical, socio-economic and environmental challenges of global energy, further research is needed in areas like development of novel energy technologies, energy storage systems (ESSs), ecological energy generation through bio-fuel/hydrogen energy supply, as well as minimization of relevant power losses. This provides research potential, both for stationary and mobile applications. Recently, eco-sustainable renewable-based energy systems and fuel-efficient hybrid electric vehicles (HEVs) have combined the best features of different renewable energy sources besides the batteries and/or Fuel Cell (FC) technologies. For either battery or FC integration into RESs and HEVs, various aspects related to power/energy density, performance, durability, energy management and safety for stationary and mobility applications should be examined. Furthermore, the control applications and the optimal operations of renewable energy systems are of great interest to researchers. Topics: Suggested topics include: · Renewable energy (solar, wind, tidal, …); · Battery and/or fuel cells integration in mobile and stationary applications; · Hybridization in transportation applications; · Optimal sizing and placement research; · Intelligent energy and smart grids; · Green buildings; · Energy storage systems;
Last updated by Dou Sun in 2020-08-24
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