Journal Information
Computers & Electrical Engineering
Impact Factor:

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 AI-based Intelligent Networks - Models and Applications (VSI-ain)
Submission Date: 2020-06-10

In recent times, Artificial Intelligence (AI) has been integrated into almost all the Information and Communication Technologies (ICT) and applied in various day-to-day applications. The involvement of AI models, like machine learning and deep learning in networking applications have proven as the significant potential in computer communications, machine vision, autonomous vehicles, and enhanced performance optimization models. In this emerging era of 5G networks and edge computing, features like network resiliency, agility, efficient resource optimization, and security play a crucial role in developing intelligent network applications. The emerging technologies like Machine Learning (ML) and Deep Learning (DL) inherit a substantial background for automated networking and cognitive management in intelligent network applications, and extend the Quality of Service (QoS) and Quality of Experience (QoE). This motivates intelligent network researchers to embed AI models in the forthcoming intelligent networks built on cognitive, cloud and mobile-edge computing platforms. This special section aims to address the state-of-the-art theoretical models and practical results of intelligent networks by addressing various AI domains like machine learning and deep learning for network performance optimization, decision making, and control of future generation networks. The primary focus of this special section is on the application of deep learning tools in domains like smart cities, computer vision, bio-informatics, and knowledge management. The deep learning domain will help intelligent networks to explore the voluminous data and complicated network environments. Further research exploration of this domain will thoroughly address the dynamic and complex future-generation intelligent networks. Topics of interest include: AI for smart network management Deep Learning-based network optimization techniques Machine Learning-based network security techniques Innovative decision making models AI-based network modeling and analysis Quality of Service and Experience in future communication networks Centralized/distributed data processing in computer networks Machine Learning-based algorithms for smart networks AI-based networks and smart cities AI in the cloud and mobile edge networks AI-based big data processing in smart networks Scalability, resilience, and architecture of AI-enabled network applications Deep learning-based network resource optimization AI-based autonomic and adaptive networking applications
Last updated by Dou Sun in 2020-05-21
Special Issue on Sustainable Security Solutions for Internet of Vehicles
Submission Date: 2020-06-30

By enabling the vehicle to access the Internet or communicate with other vehicles, roadside units, personal devices or smart things, Internet of Vehicles (IoV) has been initiated to achieve information interaction among vehicles, humans and roadside units. Based on the interaction of information, the IoV can effectively guide and monitor the vehicle, while providing substantial multimedia and mobile Internet application services. Thus, the IoV has a good application prospect in increasing road safety level, optimizing transport efficiency, improving the driver’s experience and saving fuel, etc. Moreover, the IoV, which combines the advanced 5G communications capabilities, will push the vehicle network performance and capability requirements to the extremes with the coming of 5G communication era. In the near future, the emergence of many IoV-based applications, which play an essential part of the smart city in the near future, further magnifies the importance and irreplaceability of IoV. Considering the dynamic nature of entities in the IoV, there are a series of challenges including the security, privacy, decentralization, trust management and so on. Besides these challenges, climate change and carbon dioxide emission caused by the huge number of cars in the city are a source of concern. Therefore, it is imperative to design a green and sustainable security and privacy solutions for IoV (e.g., efficiency or reduced energy usage during data exchange and processing). The objective of the special section is to compile recent research efforts dedicated to study the sustainable security and privacy solutions of rapidly increasing IoV paradigms. The special section solicits high quality and unpublished work on recent advances in new methodologies empowering sustainable security solutions for IoV, and theories and technologies proposed to defend IoV-oriented applications against adversarial or malicious attacks. Specific topics include, but are not limited to: Efficient secure data dissemination mechanisms for IoV Energy-aware secure environment for IoV Optimization techniques for sustainable security solutions Energy-efficient and secure management/control of IoV resources Security and privacy for resource-constrained devices in IoV Energy-aware secure routing solutions for IoV Big data management, data processing and analytics in IoV Leveraging cloud/fog/edge computing in IoV New Intelligent Transportation Systems (ITS) and services based on IoV Secure communication architecture for GPS system, unmanned traffic management (UTM) and unmanned aerial vehicles (UAVs) in IoV Designing green-oriented protocols, techniques and services for IoV Sustainable security solutions for electric/hybrid vehicles Secure and privacy-preserving V2V and V2X communications Blockchain enabled sustainable security solutions for IoV
Last updated by Dou Sun in 2020-01-11
Special Issue on Research challenges and directions of Data Mining in Edge Computing systems (VSI-dmec)
Submission Date: 2020-06-30

An increasing traffic of valuable, heterogeneous and dynamic data constantly flows from billions of smart devices towards a plethora of innovative Internet-of-Things (IoT) applications. Pervasively deployed within the environment so to be involved in our daily activities, these devices typically represent precious information sources and/or actuators, often with limited resources. Therefore, an effective and efficient Data Mining activity at the network edge becomes necessary to address their computation, networking, mobility and energy issues, while still providing adequate timely information extraction. To this end, the adoption of distributed and decentralized computing paradigms is widely acknowledged as a suitable solution to alleviate the issues of scalability, latency and privacy, using centralized approaches, like Cloud Computing. In this special section, new Data Mining approaches particularly tailored for the IoT scenario will be investigated, in particular, with respect to the promising, emerging novel computing paradigm of Edge Computing. Indeed, conventional Data Mining techniques need to be adjusted for optimizing the long pipeline which eventually leads, across data collection, processing and communication, to the information extraction at the network edge. Authors are invited to submit outstanding and original unpublished research manuscripts focused on effective and efficient data mining techniques for the gathering, analysis and exploitation of the distributed data generated at the network edge that would adapt to high mobility, resource boundedness, dynamic topology and power constraints. The section will also emphasize the presentation of innovative aspects related to a simulation-based approach for quantitative evaluation by combining elements of information technology and telecommunications networking, the pros and cons of different Edge, Cloud or hybrid deployments in the light of the specific applications requirements. Both theoretical and experimental aspects are welcome. Suggested topics include: Systems for and applications of data gathering, integration and analysis from distributed smart devices IoT data stream mining Data Mining for Event Stream Processing (ESP) and Complex Event Processing (CEP) at network edge Fuzzy-knowledge retrieval from smart devices data Mobility- and scale-tolerant sensors data mining. Large-scale and evolutionary algorithms for big data mining at the network edge. Security and privacy of edge computing systems Models, architectures, applications, and tools for data mining at the edge. Simulation models and tools for edge computing systems Use cases of data mining at the edge in key IoT domains (Smart Grid, Smart City, Smart Health, Smart Manufacturing, etc.)
Last updated by Dou Sun in 2020-03-21
Special Issue on Edge Intelligence in Industrial Applications (VSI-eiia)
Submission Date: 2020-07-15

The development of cloud computing, big data, Internet of Things, high-performance computing, and other emerging technologies is dramatically influencing industrial applications. Specifically, the current scenario is usually an ecosystem made up of intelligent embedded systems and intelligent products. It combines lots of intelligent and autonomous devices, which can perform predictive analysis and human-machine collaboration, to improve the level of personalization, efficiency and reliability. With the expansion of the industrial system, the complexity of the system has also increased considerably. Centralized data centers cannot analyze such massive amounts of data in a timely fashion. Thus, the concept of edge computing is presented to solve the issue. With edge computing and analytics, the data is processed near the source, in sensors, controllers, machines, and gateways. In addition to the basic requirements for network latency, industrial applications also place requirements on self-monitoring, self-diagnosis, self-reconfiguration, and self-adaptation. This requires the edge not only to perform simple data processing, but also integrate intelligence. Therefore, the future trend is to integrate new intelligence algorithms into the edge, but research on this is still limited. The purpose of this special section is to present the advances in the field of edge-based industrial applications, to promote the development of the theory and practice in this area. This special section has a wide scope that includes the development of edge intelligence, current applications, analysis of key technologies, and methods to solve problems for future development of edge intelligence. Topics include: Multi-agent systems in industry Control with distributed edge intelligence Data interaction between edge and cloud platform Security and privacy in edge intelligence Human and machine intelligence fusion based on edge computing Intelligent decision-making systems for edge computing Edge intelligence for Industrial Internet of Things Data mining and knowledge discovery based on big data analysis from the aspect of edge intelligence Blockchain technology for edge intelligence Digital twins and digital shadows from the aspect of edge intelligence Mixed production planning and scheduling based on edge intelligence
Last updated by Dou Sun in 2020-03-21
Special Issue on Deep Learning-based Intelligent Systems: Theories, Algorithms, and Applications (SI-dlis)
Submission Date: 2020-07-31

Overview Deep learning has become a topic of increasing interest for researchers, from both academia and Industry, during the past decade. Unlike traditional machine learning methods, deep learning algorithms show the ability to learn and model very large-scale data sets. Deep learning techniques have achieved great success in different tasks in computer vision, natural language processing, robotics, and other areas. Recent years have witnessed a great development of the deep learning theory and various applications in the general field of artificial intelligence, including neural network structure, optimization, data representation, and deep reinforcement learning. Some extensions to the deep learning networks, e.g., attention mechanism, adversarial generative networks, and deep Q-network, were also developed, and remarkable achievements and successes have been achieved. Although deep learning has been well studied in recent years, there exist many challenges to apply deep learning techniques in intelligent systems. First, deep learning approaches require a huge and diverse amount of data as input to models, and have a large number of parameters for training. Second, the training of deep models is easy to fall into over-fitting problems, and the transfer learning of deep models to other fields is also challenging. Besides, since deep learning models have transparency or black-box issues, it is hard to understand how a given system makes a decision, which, however, is important in some domains such as financial trading or medical diagnosis. Topics This special section solicits high-quality papers reporting on deep learning-based intelligent systems, with the goals of highlighting new achievements and developments as well as feature outstanding open issues and promising new directions on theories, algorithms, and applications. Particularly, the principal technical areas could be: - Knowledge representation, storage, and processing - Optimization and decision-making - Learning-based reasoning techniques - Planning and scheduling - Cross-modal learning - Exploring new models and datasets - Transfer learning - Deep reinforcement learning - Attention mechanism - Adversarial learning - Multi-modal fusion and knowledge discovery - Intelligent transportation
Last updated by Dou Sun in 2020-01-04
Special Issue on Security and Privacy Issues in Smart Grid by Applying Deep Learning Techniques (SI-gridl)
Submission Date: 2020-07-31

Smart Grid is the next generation of the electrical-grid, which is envisioned to revolutionize the way electricity is generated, distributed and monitored. It is strongly believed that it will make the life of our next generations and us a lot safer and more sustainable. Therefore, many countries have already taken major steps towards its adoption so that benefits provided by Smart Grid can be reached to its citizens. However, there are a number of issues which needs to be addressed before this dream can be fully realized. Among the most pressing issues security and privacy are the most serious. Smart grid is exposed to a wide array of threats, including data theft, false data injection, denial of service, and insider attack. On the other hand, advancements in cryptography, differential privacy and secure multi-party computation have much promised. However, there is still much to be desired from these approaches. The integration of the cloud-fog-based computing model has also provided great prospects in moving towards the goals of Smart Grid, but we are still far behind achieving the desired goals. Machine learning-based approaches have also been adopted to address the security and privacy issues of smart grid. However, the cutting edge deep learning-based approaches have not been studied for addressing the security and privacy problems in smart grid. This special section will focus on these issues in the context of machine learning/deep learning models. Authors are invited to submit outstanding and original unpublished research manuscripts focused on the latest findings in security and privacy issues in smart grid by applying deep learning techniques. The topics of interest are: Robustness, fault-tolerance in smart grid using deep learning models Fault data injection attacks detection using deep learning models. Privacy preserving data aggregation and protection using deep learning models Privacy preserving using fully homomorphic encryption schemes in smart grids. Differential privacy and deep learning for smart grid communication. Fault prediction, diagnosis and avoidance using deep learning models. Deep learning empowered forensics techniques for smart grid. Deep learning empowered secure logging/provenance techniques for smart grid. Machine learning and deep learning for resilient and efficient smart grid working. Intelligent data collection and inspection models using deep learning Security and Privacy issues in Fog-enabled model for smart grid Fully homomorphic encryption based fault-tolerance in Fog-enabled model for smart grid. Secure Multiparty computation based fault-tolerance in Fog-enabled model for smart grid
Last updated by Dou Sun in 2020-03-18
Special Issue on Smart Green Applications: QoS and Security-Aware Solutions in Collaborative Wireless Networks (VSI-cwn)
Submission Date: 2020-08-15

In recent years, we have witnessed increasing interest in so-called green themes. In particular, most of this is aiming to search for concrete and efficient energy-aware approaches for new issues that have emerged as a result of the explosive growth of wireless scenarios. Smart solutions for reducing power consumption in wireless protocols has motivated us to develop and integrate new technologies and methods for intelligent ambient applications. The goal is to rapidly adapt and respond to existing changes in users’ (or environments’) demands for high-quality services. In fact, smart green application solutions are always deployed at core areas to meet or establish a good compromise between deadlines and Quality of Service (QoS)-aware requirements. The most challenging issue is that systems need real-time communication support to achieve strict latency requirements or deadlines. For instance, critical events in smart cities, smart home, and smart factories have proposed promising visions which are calling for industrial productions with rapid responses to demands. On the other hand, smart green application solutions can be vulnerable to various security threats and attacks if they are not secured. Consequently, it is necessary to study security aspects of those applications. Advanced technologies and approaches play important roles in the development of innovative solutions and/or optimization methods to achieve QoS and security-aware requirements. They also enable higher levels of adaptability and flexibility in collaborative wireless networks. Topics: This special section solicits high-quality unpublished work on recent advances in smart green applications which take into account QoS and security requirements in collaborative wireless networks. The main topics of interest include: ● Applications for smart space (city, home, industry, ...) ● Green communication architectures ● Smart green protocol designs ● Smart industrial IoT applications ● Sustainable design and solutions for green IoT ● System on-chip and network on-chip architectures ● Automation systems for real-time networks ● Resource-constrainedIoT devices optimizations ● Test-bed, prototype, and practical systems for green communication ● Security requirements and models in smart green applications ● Authentication and key management in smart green applications ● Security protocol and formal analysis in smart green applications
Last updated by Dou Sun in 2020-03-21
Special Issue on Autonomous computing and Its applications for self organizing networks (VSI-acson)
Submission Date: 2020-08-30

Autonomic computing is a self-managing computing model named after, and patterned on, the human body's autonomic nervous system. An autonomic computing system would control the functioning of computer applications and systems without input from the user, in the same way that the autonomic nervous system regulates body systems without conscious input from the individual. The goal of autonomic computing is to create systems that run themselves, capable of high-level functioning while keeping the system's complexity invisible to the user. The need for such autonomic system and application management is becoming critical as computing infrastructures become increasingly heterogeneous, integrating different classes of resources from high-end HPC systems to commodity clusters and clouds. Clouds are complex, large-scale, and heterogeneous distributed systems, management of whose resources is a challenging task. They need automated and integrated intelligent strategies for provisioning of resources to offer services that are secure, reliable, and cost-efficient. Hence, effective management of services becomes fundamental in software platforms that constitute the fabric of computing Clouds. Topics: Suggested topics include: Dependable Large Scale Distributed Systems (Cloud, Grid, P2P, Virtualization) for self organizing networks Autonomic Computing (Architectures and Systems, Theory and Models) for self organizing networks Autonomic computing and proactive computing for self organizing networks Self-optimizing software systems for computer networks Self-stabilization and dynamic stability criteria and mechanisms for self organizing networks Tools, languages and platforms for designing self organizing networks.
Last updated by Dou Sun in 2020-03-21
Special Issue on Visual Servoing and its Applications (VSI-ibvs)
Submission Date: 2020-10-15

Visual servoing task is to control the pose of a robotic system, relative to a target, using visual features extracted from an image. Advanced robot systems often integrate up-to-date sensors, vision systems, and visual servoing techniques to deal with non-static target objects of various shapes and colors. The environment a robot is immersed in its orientation, and its motion can be described through visual information. The camera may be carried by the robot or fixed in the surroundings, known as endpoint closed-loop (eye-in-hand) and endpoint open-loop, respectively. Visual servoing has proven to be useful in a wide range of real-world applications, such as military, medical devices, trade, search and rescue, security, among many others. For instance, visual servoing techniques can be applied on unmanned vehicles, which can be used for surveillance, road-traffic control, border inspection, and reserved areas supervision, to provide visual information from the surroundings. Nowadays, the study on enhancing the autonomy of these vehicles has focused on navigation and formation control, target recognition, and tracking, among many others, by improving their visual capabilities. For example, the formation control in unmanned aerial vehicle (UAV) swarms is used in applications like searching and mapping to fly in groups above vast areas for goods delivery, tracking and even locating and following military targets. Visual servoing must produces the necessary commands to maintain the vehicle attitude and define the flight path based on the provided information from onboard sensors. Improving the autonomy of robots is still one of the challenges facing visual servoing and there are various ongoing studies in this field, where each system may differ from others in size, appearance, type of power plant, and more; due to these differences, they may also show distinct characteristics, but the equipment employed for evaluating their position and orientation is usually the same, which consists of inertial measurement units (IMU) and vison sensors. One of the flaws of using inertial information for motion is the accumulation of small errors on estimating the robot position, which consequently results in drift and deviation in determining its location over time. In modern motion equipment, the global positioning system (GPS) information is used for continuously correcting the IMU estimations and solving this problem, but in the event of interference or disconnection of the GPS signal, the position and motion error should remain and therefore visual servoing can be used for guidance and navigation in different kind of applications to improve the robot autonomy by feedback control of pose and motion through information acquired by onboard vision sensors. Topics: The topics of interest, listed below, are aimed to show continuing efforts provided in the domain of Visual Serevoing applications. Control system design for camera-carrying moving platforms. Image processing algorithms for feature extraction. Stability improvement. Target location and recognition. Speed estimation Tracking Submission Guidelines: 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 special issue theme. Before submission, authors should carefully read over the journal's Author Guidelines, which is available at: Manuscripts must be submitted online at: by selecting "SI-ibvs" from the "Issues" pull-down menu during the submission process. Schedule: Submission of manuscript: October 15, 2020 First notification: January 15, 2021 Submission of revised manuscript: February 15, 2021 Notification of the re-review: March 15, 2021 Final notification: April 1, 2021 Final paper due: May 1, 2021 Guest Editors: E. Cabal-Yepez, PhD (Managing Guest Editor) Dean of the Department of Multidisciplinary Studies Engineering Division, Campus Irapuato-Salamanca, University of Guanajuato. Yuriria, Guanajuato, Mexico +52 4454589040 Ext. 1700. Email: He received his M.Eng. degree from Facultad de Ingenieria Mecanica Electrica y Electronica (FIMEE), Universidad de Guanajuato, Mexico, in 2001, and his Ph.D. degree from University of Sussex, United Kingdom, in 2007. In April 2008, he joined the Division de Ingenierias del Campus Irapuato-Salamanca de la Universidad de Guanajuato, where he is a Titular Professor and serves as the Dean of the Departamento de Estudios Multidisciplinarios. His current research interests are Digital Image and Signal Processing, Artificial Intelligence, Robotics, Smart Sensors, Real-Time Processing, Mechatronics, FPGAs, and Embedded Systems. He is a National Researcher with the Consejo Nacional de Ciencia y Tecnologia, Mexico. A. H. Mazinan, PhD Control Engineering Department, South Tehran Branch, Islamic Azad University (IAU), Tehran, Iran. Email: or He received the Ph.D. degree in 2009 in Control Engineering. He has been an Associate Professor and also the Director of Control Engineering Department at the Islamic Azad University, South Tehran Branch, Iran, since 2009. He is now an Associate Editor of Transactions of the Institute of Measurement and Control (Sage publisher), and an Associate Editor of Computers and Electrical Engineering (Elsevier Publisher). He is also a member of the Editorial Board in three international journals and also a member of programming committee in four international conferences. He has published more than 150 journal and conference papers. His current research interests include intelligent systems, model-based predictive control, over-actuated space systems modeling and control, time-frequency representation, filter banks, wavelet theory and image-video processing.
Last updated by Dou Sun in 2020-04-15
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 Authors should submit their papers through the journal's web submission tool at 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: 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
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