Optical/electronic signal processing and transmission are essential in modern communication, imaging, sensing, etc. However, traditional methods generally rely on mathematical models and statistical techniques. They require a high cost to overcome real-world challenges like noise interference, signal distortion, and changing transmission conditions. The above situation may reduce classification accuracy, weaken noise suppression, and increase processing time. Therefore, the systems are less reliable, especially in high-demand applications like telecommunications, biomedical imaging, and remote sensing, these limitations. Moreover, conventional techniques also need manual tuning and do not adapt well to dynamic environments. AI-driven methods offer a better solution. Deep learning and machine learning models can learn patterns, classify signals, and remove noise automatically. They improve processing speed, accuracy, and adaptability. AI enhances the performance and robustness of optical and electronic signal systems, making it a necessary tool for overcoming current limitations.
The principal objective of this study area is to investigate AI-based classification or enhancement methods for optical/electrical signal processing and transmission. Conventional techniques are challenging for the effective and reliable processing or transmission of optical and electrical signals in intricate situations. Recent breakthroughs in artificial intelligence, including convolutional neural networks, transformers, reinforcement learning, and explainable models, have shown considerable capability in handling complex and noisy data. This special issue aims to investigate the categorization accuracy or denoising effect of AI-based processing and transmission systems. The main goal is to enhance interdisciplinary collaboration and stimulate innovation in AI-based signal processing technology.
This Research Topic invites contributions that explore AI applications in optical/electronic signal processing and transmission systems. We welcome original research articles, reviews, and perspectives that address the following themes:
• AI-based classification of optical/electronic signals
• AI-based enhancement of optical/electronic signals
• AI-based adaptive transmission strategies
• Noise reduction and real-time optical/electronic signal optimization
• Multimodal AI fusion of optical/electronic signals
• Hybrid AI processing techniques of optical/electronic signals
• Deep learning-based noise reduction and feature extraction
• Other themes of AI applications in optical/electronic signal processing and transmission.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Editorial
FAIR² Data
General Commentary
Mini Review
Opinion
Original Research
Perspective
Review
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Editorial
FAIR² Data
General Commentary
Mini Review
Opinion
Original Research
Perspective
Review
Technology and Code
Keywords: classification, signal enhancement, optical transmission, electrical imaging, deep learning, signal processing, transmission
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
