Información de la Revista
Computers & Electrical Engineering
http://www.journals.elsevier.com/computers-and-electrical-engineering/
Factor de Impacto:
2.189
Editor:
Elsevier
ISSN:
0045-7906
Vistas:
16812
Seguidores:
35

Solicitud de Artículos
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
Última Actualización Por Dou Sun en 2019-11-24
Special Issues
Special Issue on Deep Learning-based Intelligent Systems: Theories, Algorithms, and Applications (SI-dlis)
Día de Entrega: 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
Última Actualización Por Dou Sun en 2020-01-04
Special Issue on Security and Privacy Issues in Smart Grid by Applying Deep Learning Techniques (SI-gridl)
Día de Entrega: 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
Última Actualización Por Dou Sun en 2020-03-18
Special Issue on Smart Green Applications: QoS and Security-Aware Solutions in Collaborative Wireless Networks (VSI-cwn)
Día de Entrega: 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
Última Actualización Por Dou Sun en 2020-03-21
Special Issue on Autonomous computing and Its applications for self organizing networks (VSI-acson)
Día de Entrega: 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.
Última Actualización Por Dou Sun en 2020-03-21
Special Issue on The Impact of Technological Advancements on Educational Innovation (VSI-tei)
Día de Entrega: 2020-08-30

Overview Advancement of computer technology and electrical engineering have revolutionized our lives specially our day-to-day interactions with the world. In theory, electronics and computer systems have significantly impacted what we call techno-based communications and/or high-performance computing. Computational techniques have found major importance in teaching and learning processes in normal-life settings and under special circumstances, including the current Coronavirus pandemic and subsequent economic crisis. The main contribution of such technologies involves development of mobile apps, online learning platforms, machine learning and artificial intelligent systems, game-based techniques, and e-learning, which play vital role in fostering Educational progress. The aim of this special section is to open a window of opportunity for submission of manuscripts that introduce new technologies, methods, and strategies for advancing the use of computer technology and electrical engineering for enhancing the quality of teaching and learning, and empowering Educational Innovation. This special section welcomes both original research and review articles from a wide spectrum of research, with a focus on the application of these technologies in all areas of Education and Educational Innovation. Manuscripts, or extended versions of papers presented at related conferences, are welcome as well. Submissions within the frame of following topics and related to the impact of COVID-19 on education are welcome. Topics: Suggested topics include: Academic pattern recognition Application of gamification in education Artificial intelligent methods and their impact in continuing education Design process of digital technologies in education E-learning and virtual environments Educational data mining, machine learning, and big data analytics Educational information systems and development Educational process intelligence Electroencephalographic signal processing for learning disabilities identification and attention deficit diagnosing Emotion recognition Human-computer interaction for learning strategies Impact of serious games in teaching and learning Innovative tools and technologies for lifelong learning Learning design and learning analytics Virtual reality, augmented reality, and computer vision approaches for teaching and learning Web and computer-based, and mobile-based technologies to improve collaboration across the education domain and among stakeholders
Última Actualización Por Dou Sun en 2020-07-16
Special Issue on Load Balancing of Sensory Data for IoT Deep Learning Applications with Edge/Fog Computing (VSI-lbdl)
Día de Entrega: 2020-09-01

Overview: In the last decade, evolution of Internet of Things (IoT) has controlled various application domains such as emergency management, industrial applications, health care systems, real-time systems, and has been foreseen to flourish in future. The populace of IoT-based devices has been extended to 30 billion and expected to surpass 500 billion by 2030. This fact has led to various challenges, like how to extract and manage huge amounts of data that are constantly generated by IoT devices. This gives rise to solutions for IoT based on edge/fog computing that can move the data processing towards the edge of network as cloud-based deployments are not able to meet the increasing demands of clients. Nowadays, the Deep learning (DL) approach is used to extract information from IoT devices that are deployed in edge/fog computing environment due to its multi-layer structuring. DL in IoT is performed as layered processing in a distributed manner between edge and fog by extracting new features automatically for different problems. For the extraction of information from huge volume of real time data of IoT devices, data processing needs to be done close to the end devices where the data is produced (the edge). Since the prevailing edge nodes have inadequate processing capability, it is necessary to design and develop efficient load balancing strategies to optimize the performance of IoT devices for deep learning applications with edge/fog computing. The aim of this special section is to deal with various challenges such as hardware design, theoretical modeling, system architecture and analysis of deep learning applications in IoT using edge/fog computing. The focus is therefore on high-quality original papers aiming at demonstrating effective and efficient DL approaches for edge/fog, which at the same time considers data, device and infrastructure perspectives and related issues. This special section will cover recent technical advances with respect to IoT edge/fog computing, including strategies and protocols, architectures, emerging models, test-beds, applications, systems and field deployments. Nevertheless, numerous challenges exist for synchronization of the edge devices with other edge devices and fog under vibrant network circumstances and diverse processing competencies to deal with privacy issue and application-level performance. Topics: The topics of interest include: Protocols, models and architectures Resource allocation and management Data processing, distribution, management, and storage Computation offloading from IoT deep learning applications with edge/fog computing Fog-Cloud computing for IoT systems Security, privacy and trust AI and machine learning Performance evaluation Services and applications Edge-based platforms for IoT deep learning applications Novel edge/fog based deep learning models for IoT Simulation of edge IoT mining Methodologies for driving deep learning-based IoT edge/fog systems development Real applications and systems of deep learning at the edge
Última Actualización Por Dou Sun en 2020-07-16
Special Issue on AI Models and Techniques for Edge-based Mission-Critical Applications (VSI-aie)
Día de Entrega: 2020-09-15

Recently, we have witnessed rapid deployment of various IoT-based applications, which is leading to Industry 4.0 revolution - the backbone of the underlying network infrastructure supported by 5G and upcoming 6G technologies. Some of the IoT-based applications (e.g., autonomous vehicles, e-healthcare, and smart surveillance systems) are being used for the benefits of the societies around the world. For example, doctors can diagnosis patients remotely in e-healthcare, autonomous vehicles can deliver necessary goods to customer premises, and smart robots can be used for surveillance. Most of these applications rely on AI-based control models and techniques to make independent decisions when necessary. However, most of these applications require “extra low-latency” and “ultra-high-speed” response time to provide Quality of Service (QoS) and Quality of Experience (QoE) to end users. It is, therefore, envisaged that 5G and 6G (i.e., beyond 5G - B5G) technologies can revolutionize the aforementioned applications in their respective domains in the years to come. Hence, with such infrastructure, low round trip delay is expected for most mission-critical applications such as, e-healthcare, robotic surgery, and surveillance. To support quick response time for mission-critical applications, quick decisions need to be made with respect to various data streams. For example, autonomous vehicles need to make quick decisions with respect to obstacles detection in their surroundings. In this context, AI-based techniques and models can be executed on edge servers, rather than remote cloud servers, to have real-time response. In B5G, various devices are expected to be inter-connected ubiquitously with the help of high communication coverage using dense deployed Heterogeneous Networks (HetNets) and machine-to-machine (M2M) communication. It creates high availability of the network resources close to the proximity of the end users, which in turn results in low latency for most of the applications. By off-loading different tasks directly to the edge servers, high performance gain can be achieved. Topics: The aim of this special section is to invite contributions from academia and industry on the following topics: mentioned below. AI models for network resources management using edge intelligence in 5G and 6G AI models for security and privacy preservation using edge intelligence in B5G AI models for location management of ubiquitous inter-connected devices using HetNets and M2M AI techniques for power and energy management using edge intelligence in B5G
Última Actualización Por Dou Sun en 2020-07-04
Special Issue on Advances in Smart Grids and Microgrids: operation, control, protection, and security (VSI-sgmg)
Día de Entrega: 2020-09-30

Due to environmental concerns, energy security risks, and fossil fuel issues, many countries around the world have decided to increase the penetration level of renewable energy resources (RERs) in their energy networks. Beside this, many countries are moving toward implementation of the smart grid concept, including microgrid and deregulation in their power systems to achieve reliable and secure operation of their power systems with high penetration level of renewable energy resources. In future smart grids, keeping the operation in stable mode requires new techniques and technologies for better control and security. Therefore, stability and security of smart grids should be well studied and analyzed. Moreover, new protection schemes are in demand in order to face any unexpected operational problems and contingencies in smart grid environment. This Special Section aims at encouraging researchers to address the technical issues and research gaps in smart grid and microgrid systems. The objective is to focus on the latest advances in smart grids and microgrids, especially on their operation, control, protection and security aspects. Energy systems are impacting our life, industrial productions and our way of life beyond our imagination. Various countries all over the world have launched national smart grids and microgrids initiatives, highlighting the significance and necessity of smart grids in their future smart cities, smart industries, and national securities. But at the same time, practical issues related to the operation, control, protection and security of modern and future smart grids cannot be ignored. Therefore, this special section would try to fill research gaps in smart grids and motivate research on such important topics impacting our lives. Topics: Suggested topics include: • Smart grids and microgrids; • The design, modeling, control, and management of smart grids and microgrids; • Smart grid and microgrid reliability, sustainability, flexibility, and resiliency; • Smart grid and microgrid dynamics, stability, protection and security; • Methodologies and applications of modern methods for the operation and control of smart grids; • Intelligent systems, solving methods, optimization, and advanced heuristics for Smart grids and microgrids; • The modeling, planning, and operating of renewable energy resources in Smart grids; • Business models for different electricity market players in Smart grids and microgrids; • Demand side management and demand response; • The sizing, placement, and operation of energy storage systems and electric vehicles in Smart grids; • Smart homes and building energy management; • Electricity market, electrical power, and energy systems; • The modeling, forecasting, and management of uncertainty in smart grids; • Microgrids and islanded networks; • Smart cities, smart energy, and IoT for the concept of Smart grids and microgrids; • Modern power systems and renewable energy resources.
Última Actualización Por Dou Sun en 2020-07-04
Special Issue on Visual Servoing and its Applications (VSI-ibvs)
Día de Entrega: 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: https://www.elsevier.com/journals/computers-and-electrical-engineering/0045-7906/guide-for-authors Manuscripts must be submitted online at: https://www.evise.com/profile/#/COMPELECENG/login 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: e.cabalyepez@gmail.com 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: ahmazinan@gmail.com or mazinan@azad.ac.ir 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.
Última Actualización Por Dou Sun en 2020-04-15
Special Issue on Big Data Analytics and Deep Learning Approaches for 5G and 6G Communication Networks (VSI-5g6g)
Día de Entrega: 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
Última Actualización Por Dou Sun en 2020-07-16
Special Issue on Artificial Intelligence and Robotics (VSI-air3)
Día de Entrega: 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.
Última Actualización Por Dou Sun en 2020-04-15
Special Issue on Recent Advances and Challenges in Intelligent Sliding Mode Control for Modern Industrial Systems: Soft Computing Solutions (VSI-smc)
Día de Entrega: 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
Última Actualización Por Dou Sun en 2020-07-16
Special Issue on Recent Advances and Challenges in Quantum-Dot Cellular Automata (VSI-qca)
Día de Entrega: 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.
Última Actualización Por Dou Sun en 2020-07-04
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