Advances and Challenges in AI/ML for Embedded Systems

Dear Colleagues,

We are pleased to announce a call for papers for the Special Issue titled "Advances and Challenges in AI/ML for Embedded Systems". This Special Issue will explore the latest developments and innovations in applying AI/ML to the design, development, testing, and deployment of embedded systems. We invite contributions that address both the opportunities and challenges associated with integrating AI/ML into embedded systems, with a focus on improving system design, testing, and performance in resource-constrained environments.

The Special Issue focuses on the integration of artificial intelligence (AI) and machine learning (ML) techniques into embedded systems. As embedded systems become increasingly complex and integral to various industries, there is a growing need for advanced methods that can improve the efficiency, reliability, and scalability of these systems. This issue aims to explore the latest AI and ML-driven innovations that are transforming how embedded systems are designed, optimized, tested, and maintained.

The scope of the issue covers a wide range of topics within the intersection of AI/ML and embedded systems. This includes, but is not limited to:

• Model Compression Techniques: Approaches for reducing the size of machine learning models to meet the resource constraints of embedded systems without compromising accuracy or functionality.
• AI-driven Design Automation for Embedded Systems: Tools and methodologies that use AI to automate and optimize the design process of embedded software, from architecture to code generation.
• Real-Time Machine Learning Applications in Embedded Systems: Implementing AI models in real-time environments, such as autonomous vehicles and robotics, where timing and reliability are critical.
• Self-Adaptive Embedded Systems: Designing embedded systems that can autonomously adjust their behaviour and performance based on environmental changes or operational feedback.
• AI for Predictive Maintenance and Fault Detection in Embedded Systems: Leveraging AI/ML to predict failures, optimize maintenance schedules, and enhance system reliability.
• Embedded Vision Systems: The use of AI-based computer vision techniques for object detection, facial recognition, and image classification in embedded devices.
• AI-Enhanced IoT Systems: Exploring how AI/ML can improve predictive analytics, decision-making, and automation within IoT systems featuring embedded software components.
• Low-Power AI/ML Algorithms: Developing energy-efficient AI/ML algorithms suitable for deployment on low-power embedded systems, with a focus on minimizing energy consumption while maintaining performance.
• Security and Privacy in AI-Driven Embedded Systems: Addressing vulnerabilities and risks in AI-powered embedded systems and exploring techniques to enhance security and privacy.
• Human-Machine Interaction in Embedded AI Systems: Designing intuitive interfaces and interactions for embedded systems incorporating AI, such as voice assistants, gesture recognition, and adaptive user interfaces.
• AI-Driven Testing Frameworks for Embedded Software: Development of AI-driven testing frameworks, methodologies, and tools that enhance the quality assurance and testing processes of embedded software.
• Standardized Practices for AI/ML in Embedded Software Engineering: Contributing to the development of standardized tools, processes, and best practices that integrate AI/ML into the embedded software lifecycle.
• Case Studies and Practical Applications: Presenting real-world case studies that highlight the challenges and successes of applying AI/ML in embedded software design, development, and testing.

We encourage the submission of original research, case studies, and reviews that offer insights into the application of AI/ML in embedded systems. Submissions should focus on practical solutions, innovative methodologies, or theoretical advancements that address the challenges posed by resource-constrained environments and the unique requirements of embedded systems.

The primary purpose of this Special Issue is to provide a platform for disseminating cutting-edge research and practical applications of AI and ML in the domain of embedded systems. By highlighting innovative approaches and emerging trends, the issue aims to bridge the gap between academic research and industrial practice, fostering collaboration and knowledge exchange. Additionally, it seeks to address current challenges and propose future directions for the effective integration of AI/ML in embedded systems.

Dr. Sahar Tahvili
Dr. Enislay Ramentol
Dr. Leo Hatvani
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• artificial intelligence (AI)
• machine learning (ML)
• embedded systems
• software design
• system testing
• automated testing
• predictive maintenance
• fault detection
• optimization
• real-time systems