Journal Information
Computers & Electrical Engineering
http://www.journals.elsevier.com/computers-and-electrical-engineering/
Impact Factor:
2.189
Publisher:
ELSEVIER
ISSN:
0045-7906
Viewed:
15045
Tracked:
31

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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 Application of Artificial Intelligence in Security of Cyber Physical Systems
Submission Date: 2020-03-01

The deployment of smart technologies in the communication layer brings new challenges for online monitoring and control of Cyber-Physical Systems (CPS). In addition to the failure of physical infrastructure, CPS is sensitive to cyber attacks on its communication layer. There are many discussions about the role of security-aware design and analysis in the development of modern CPS, such as the smart grid using advanced Artificial Intelligence(AI), and machine learning techniques. Rapid advancement in AI technology enhances the scale, speed, and accuracy of the security in CPS. AI has the potential to be leveraged in different aspects of cyber security, cyber threat detection, and cyber threat intelligence. This special section will focus on the cutting edge, from both academia and industry, with a particular emphasis on novel practical and theoretical solutions for securing critical infrastructure and CPS using AI-based tools, techniques and procedures. The goal is to provide a sampling of recent advances and ideas on progress of research and the practical usage of AI technologies in addressing security of the CPS. Topics of interest include: Applications of intelligent data analytics in CPS Cyber investigation and threat intelligence utilizing AI solutions Applications of blockchain and smart contracts in securing CPS Threat intelligence techniques for cyber-attack detection, and reaction in CPS Application of AI in vulnerability and risk analysis for CPS AI-based security tools, techniques and procedures for the Internet of Things (IoT) Intelligent analysis of different types of data collected from different layers of CPS Intelligent access control and key management for CPS Application of AI in CPS system security and privacy modeling and simulation Intelligent AI-based control strategies to improve CPS performance Design, optimization and data-driven modeling of CPS Novel sensor placement methodologies for CPS AI-assisted digital investigation in CPS Cyber threat hunting and anomaly detection in CPS
Last updated by Dou Sun in 2019-11-12
Special Issue on Advances in Parallel Distributed Computing, Applications and Technologies (SI-pdcat3)
Submission Date: 2020-03-15

Parallel and distributed computing has been under many years of development, coupling with different research and application trends, such as grid computing, cloud computing, green computing, etc. Nowadays the theory, design, analysis, evaluation and application of parallel and distributed computing systems are still burgeoning to suit the increasing requirements on high efficiency and energy saving in global economy. Computers and Electrical Engineering announces a forthcoming Special Issue on “Parallel and Distributed Computing, Applications and Technologies”. The objective of this special issue is to publish and overview recent trends in the interdisciplinary area of parallel and distributed computing, applications and technologies. This special issue will include papers based on the presentation at the 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2019) and the 10th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP 2019), as well as papers submitted for this call for papers. All submitted papers are subjected to the same review process as those papers accepted for publication in the regular issues. Topics: Parallel/distributed architectures and algorithms Reliability and fault-tolerance Cloud computing Data center networks WSN and IoT Task scheduling and resource allocation Network routing and traffic analysis Cybersecurity and privacy protection High-performance computing and big data analytics Distributed AI and ML
Last updated by Dou Sun in 2020-02-23
Special Issue on Artificial Intelligence and Computer Vision
Submission Date: 2020-04-15

The integration of artificial intelligence and computer vision technologies has become a topic of increasing interest for both researchers and developers from academic fields and industries worldwide. It is foreseeable that artificial intelligence will be the main approach of the next generation of computer vision research. The explosive number of artificial intelligence algorithms and increasing computational power of computers have significantly extended the number of potential applications for computer vision. It has also brought new challenges to the vision community. This is the third special section of Computers and Electrical Engineering on artificial intelligence and computer vision: the first two were published in July 2017 and July 2018, and the next one will be published in January 2020. We expect to have more special sections on this topic as the area is open to new developments with many innovative and highly productive research. Authors are invited to submit outstanding and original unpublished research manuscripts focused on the latest findings in artificial intelligence and computer vision. The topics of interest are: Theoretical Foundations of Artificial Intelligence 3D Scene Reconstruction Explainable Deep Learning Pattern Recognition and Machine Vision Computational Imaging Object Tracking Computer Vision Soft Computing Internet of Visual Networks Robotic Vision Intelligent Medical Imaging and Processing Extreme Optical Vision User Experience for Big Multimedia Systems Vision-based Robotic Manipulation Mixed Reality/Augmented Reality for Computer Vision
Last updated by Dou Sun in 2019-10-20
Special Issue on Data Preprocessing for Big Biomedical Data in Deep Learning Models
Submission Date: 2020-04-15

Overview Due to numerous biomedical information sensing devices, such as, Photoacoustic Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, Single Photon Emission Computed Tomography (SPECT), Magnetic Resonance (MR) Imaging, Ultrasound, and Positron Emission Tomography (PET), Magnetic Particle Imaging, EE/MEG, Electron Tomography and Atomic Force Microscopy, etc. Large amounts of biomedical information were gathered these years. Many advanced methods are proposed like deep learning due to its excellent performance. However, a lot of issues appear in obtaining and preprocessing such big biomedical data, such as data heterogeneity, data missing, data imbalance and high dimensionality of data etc. Moreover, many biomedical data set simultaneously contain multiple issues. However, most of the current techniques can only deal with homogeneous, complete, and moderate sized-dimensional data, which makes the learning of big biomedical data difficult. Therefore, data processing including data representation learning, dimensionality reduction, missing value imputation should be developed to solve the big gap to make the deep learning methods used for the practical applications. The purpose of this special issue aims to provide a diverse, but complementary, set of contributions to demonstrate new developments and applications that cover existing above issues in data processing of big biomedical data. We would also like to accept successful applications of the new methods, including but not limited to data processing, analysis, and knowledge discovery of big biomedical data. Topics: Suggested topics include: Feature extraction by deep learning or sparse codes for biomedical data Data representation of biomedical data Dimensionality reduction techniques (subspace learning, feature selection, sparse screening, feature screening, feature merging, etc) for biomedical data Information retrieval for biomedical data Kernel-based learning for multi-source biomedical data Incremental learning or online learning for biomedical data. Data fusion for multi-source biomedical data Missing data imputation for multi-source biomedical data Data management and mining in biomedical data Web search and meta-search for biomedical data Web information retrieval for biomedical data Biomedical data quality assessment Transfer learning of biomedical data.
Last updated by Dou Sun in 2019-11-24
Special Issue on Power Grid Integration of Renewable Energy Systems (SI-IREC2020)
Submission Date: 2020-05-31

Overview: Adding renewable energy sources to the power grid is challenging in many countries. Delivering energy from distributed variable sources to consumers, and maintaining the stability and reliability of electrical systems requires further developments and studies. This is specially challenging for photovoltaic systems due to the inherent distributed characteristics of this source, and the quick variability in the presence of clouds. Therefore, the frequency of the power grid changes faster in case of any disturbance. The zero inertia generators of photovoltaic systems make it necessary to reduce the overall inertia of the system, which would force existing conventional generators to provide torque and inertia when trying to overcome any contingency event. These instability problems should be compensated with new ideas from Smart Grid. Topics: This special section will focus on all aspects related to the integration of photovoltaic and other renewable energy systems into existing electricity networks. The topics of interest are: Smart Grids Renewable Energy Stability of Power Systems Distributed energy resources Integration of Energy Storage with the Power Grid Virtual power plants Grid Frequency control
Last updated by Dou Sun in 2020-01-11
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 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
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