Aims and scope

Journal of Electrical and Computer Engineering publishes papers that report advances from across the rapidly moving fields of both electrical engineering and computer engineering. Reflecting the prevalence of electrical systems and devices both in our everyday lives and in advanced technological environments, the journal welcomes submissions relating to their implementation in all settings and industries. Studies may relate to the design, production, operation, and testing of electrical devices and systems, or the engineering of their hardware, software and networking. Authors should submit to one of the following subject areas:

    Circuits and Systems
    Communications
    Power Systems
    Signal Processing

The journal will also consider articles that fall between these areas, or lie at the interface of these and another branch of engineering; please simply submit to the most appropriate one. As well as original research, Journal of Electrical and Computer Engineering also publishes focused review articles that examine the state of the art, identify emerging trends, and suggest future directions for developing fields.

Special Issue on Leveraging the Advancements in Chaotic Systems for Digital Distribution Network
Submission Date: 2024-11-01

Description

The use of chaotic systems in digital distribution networks has the potential to significantly improve the performance and efficiency of these networks. In recent years, there have been significant advancements in the understanding and application of chaotic systems, and these developments present several exciting opportunities for researchers and practitioners working in the field of digital distribution. In this special issue, we propose to explore the current state of chaotic systems and their application in digital distribution networks, as well as the recent advancements in this area and their potential impact on the field. Digital distribution networks play a vital role in the modern world, enabling the rapid and efficient transfer of data and information over the internet.

These networks are characterized by high levels of complexity and uncertainty, and they are subject to a wide range of external factors, such as traffic congestion, security threats, and hardware and software failures. To cope with these challenges and maintain reliable service, digital distribution networks must be able to adapt and respond to changing conditions in real-time. One promising approach to addressing these challenges is the use of chaotic systems. Chaotic systems are complex, nonlinear systems that exhibit sensitive dependence on initial conditions and are characterized by seemingly random behaviour. Despite their apparent randomness, chaotic systems can be highly predictable and controllable. They have been shown to be effective in various applications, including in fields such as physics, biology, and engineering. In the context of digital distribution networks, chaotic systems can potentially improve the performance and efficiency of these networks in several ways. For example, chaotic systems can be used to optimize traffic routing, reducing congestion and improving network performance. They can also enhance network security by generating unpredictable patterns difficult for attackers to predict or exploit. Finally, chaotic systems can improve data storage and retrieval by enabling the efficient organization and retrieval of large volumes of data. Despite the potential benefits of chaotic systems in digital distribution networks, some challenges must be addressed to fully realize their potential. One major challenge is the need for further research and development in this area to better understand the underlying principles of chaotic systems and how they can be applied to digital distribution networks. Another challenge is the need for robust and reliable hardware and software technologies to support the implementation of chaotic systems in these networks.

In this Special Issue, we propose to bring together researchers and practitioners from a range of disciplines to explore the current state of chaotic systems and their application in digital distribution networks, as well as the recent advancements in this area and their potential impact on the field. We envision this special issue as a forum for sharing ideas, knowledge, and experience and as a catalyst for further research and development in this exciting and rapidly evolving area.

Potential topics include but are not limited to the following:

    Overview of the current state of chaotic systems and their application in digital distribution networks
    Recent advancements in chaotic systems and their potential impact on digital distribution networks
    Case studies of successful implementation of chaotic systems in digital distribution networks
    Challenges and opportunities presented by chaotic systems in digital distribution networks
    An overview of the current state of chaotic systems and their application in digital distribution networks, including a review of the existing literature on the use of chaotic systems in such networks and an explanation of the key challenges and opportunities presented by these systems.
    A discussion of the recent advancements in chaotic systems and how they can be leveraged to improve the performance and efficiency of digital distribution networks. This could include advances in modelling and simulation techniques, as well as new hardware and software technologies enabling the development of more sophisticated chaotic systems.
    An exploration of the potential applications of chaotic systems in digital distribution networks, including in areas such as traffic routing, network security, and data storage. This could involve case studies of successful implementations of chaotic systems in these areas, as well as a discussion of the potential benefits and limitations of these approaches.
    A review of the current challenges facing the development and deployment of chaotic systems in digital distribution networks and suggestions for how these challenges can be overcome. This could include a discussion of the need for further research and development in this area, as well as the potential for collaboration between researchers and industry partners.

Editors

Lead Editor

Aceng Sambas1
1Universitas Muhammadiyah Tasikmalaya, Tasikmalaya, Indonesia

Guest Editors

Mohamad Afendee Mohamed1 | W. S. Mada Sanjaya2
1Sultan Zainal Abidin University, Kuala Terengganu, Malaysia
2Sunan Gunung Djati State Islamic University Bandung, Bandung, Indonesia

Special Issue on Energy-Efficient Computing and Sustainable Technologies
Submission Date: 2024-12-27

Description

The quest for energy-efficient computing and sustainable technologies has become a critical focus in the field of electrical and computer engineering. As the demand for computational power continues to escalate driven by advancements in artificial intelligence, big data, and the Internet of Things, the need for sustainable solutions is more pressing than ever. Innovations in energy-efficient computing not only promise to reduce the environmental impact of our growing digital footprint but also pave the way for more cost-effective and scalable technological infrastructures.

Despite significant advancements, several challenges persist in achieving truly energy-efficient and sustainable computing. Current systems often grapple with high energy consumption, limited battery life in portable devices, and the environmental impact of electronic waste. Furthermore, optimizing energy efficiency without compromising performance and speed remains a formidable hurdle. Researchers also face the complexities of integrating renewable energy sources into existing infrastructure and the need for novel materials and architectures that can enhance energy efficiency. Addressing these multifaceted challenges requires a concerted effort from academia, industry, and policymakers to develop holistic and innovative solutions.

The aim of this Special Issue is to bring together pioneering research that addresses the myriad challenges in energy-efficient computing and sustainable technologies. We welcome original research and review articles that delve into cutting-edge research and developments that are pushing the boundaries of energy efficiency and sustainability in computing technologies. We seek contributions that explore new theoretical frameworks, practical applications, and experimental studies that highlight breakthroughs in this domain. Overall, we aim to advance knowledge, promote innovation, and drive positive change towards a more energy-efficient and sustainable future in computing and related domains.

Potential topics include but are not limited to the following:

    Hardware and architecture design for energy-efficient computing.
    Low-power electronics and components.
    Energy-aware software design and optimization techniques.
    Green data centers and cloud computing.
    Renewable energy integration in computing systems.
    Energy-efficient networking protocols and infrastructure.
    Sustainable computing practices and methodologies.
    Life-cycle analysis of computing technologies.
    Environmental impact assessment of computing systems.
    Policy, regulations, and standards for sustainable computing.
    Algorithm-based optimization for sustainable material and manufacturing systems

Editors

Lead Editor

Sheila Mahapatra1
1Alliance University, Bengaluru, India

Guest Editors

Oluwasegun Julius Aroba1
1Durban University of Technology, Durban, South Africa

Special Issue on Recent Advances in Machine Learning for Signal Processing
Submission Date: 2024-12-27

Description

The Special Issue " Recent Advances in Machine Learning for Signal Processing" for the Journal of Electrical and Computer Engineering is a timely and pivotal collection addressing the convergence of two rapidly evolving fields. As machine learning techniques continue to advance, their application in signal processing has shown significant promise, offering new methods for efficiently analyzing, interpreting, and utilizing signals in various domains.

This issue highlights recent breakthroughs and innovative methodologies that leverage machine learning to enhance signal processing tasks such as denoising, feature extraction, and pattern recognition. With contributions from leading researchers, the issue explores how deep learning architectures, reinforcement learning, and unsupervised learning techniques are being tailored to address specific challenges in signal processing, driving forward the capabilities of modern electronic systems and communication technologies.

This Special Issue also underscores the practical implications and future directions of integrating machine learning with signal processing. It delves into real-world applications such as speech and image processing, biomedical signal analysis, and wireless communication, demonstrating the transformative potential of these technologies in improving accuracy, efficiency, and robustness. By presenting case studies and experimental results, the issue provides valuable insights into the scalability and adaptability of machine learning models in processing complex signals. As the demand for more intelligent and autonomous systems grows, the research presented in this issue offers a critical foundation for developing next-generation solutions that can meet the increasingly sophisticated needs of various industries.

Potential topics include but are not limited to the following:

    Machine Learning
    Signal Processing
    Image Processing
    Audio Processing
    Photodetector Technology
    Antenna Design
    Biosensors
    Computer Engineering
    Electrical Engineering
    Communication Systems

Editors

Lead Editor

Shonak Bansal1
1Chandigarh University, Chandigarh, India

Guest Editors

Gupta Dr. Anupma1 | Meet Kumari2 | Krishna Prakash3 | Payal Patial4

1Chitkara University, Punjab, India
2Chandigarh University, Chandigarh, India
3NRI Institute of Technology, Agiripalli, Andhra Pradesh, India
4Chandigarh University, Chandigarh, India

Special Issue on Advancements in Biosensors, Electromagnetic Antenna Design, and Machine Learning: Bridging Technologies for Next-Generation Applications
Submission Date: 2024-12-27

Description

The convergence of biosensors, electromagnetic antenna design, and machine learning represents a groundbreaking interdisciplinary frontier with profound implications across various domains. Biosensors have revolutionized healthcare, offering real-time monitoring capabilities for physiological parameters and biomarkers, thereby enabling early disease detection and personalized medicine.

Simultaneously, advancements in electromagnetic antenna design have enhanced communication systems, remote sensing, and radar technologies, driving innovation in wireless connectivity and remote sensing applications. Integrating machine learning with these technologies amplifies their capabilities, empowering data-driven decision-making, pattern recognition, and predictive modeling for enhanced performance and efficiency.

This Special Issue serves as a platform to explore the synergistic potential of biosensors, electromagnetic antenna design, and machine learning. By bringing together experts from diverse backgrounds, it aims to highlight recent advancements, foster interdisciplinary collaboration, and stimulate discussions on emerging trends and future directions. From improving healthcare diagnostics to optimizing wireless communication networks, this Special Issue underscores the transformative impact of integrating these innovative technologies, offering insights into their integration, challenges, and potential applications across various domains.

Potential topics include but are not limited to the following:

    Biosensors
    Electromagnetic antenna
    Machine learning applications
    Signal processing techniques
    Wireless communication
    Healthcare technology
    Data-driven analytics
    Sensing technology
    Remote monitoring
    Smart sensor networks

Editors

Lead Editor

Shonak Bansal1
1Chandigarh University, Chandigarh, India

Guest Editors

SACHIN KUMAR YADAV1 | Gupta Dr. Anupma2 | Meet Kumari3 | Kanwarpreet Kaur4

1GLA University, Mathura, Uttar Pradesh, India
2Chitkara University, Punjab, India
3Chandigarh University, Chandigarh, India
4Chandigarh University, Chandigarh, India