Topical Collection on "Artificial Intelligence and Advanced Technologies for Next-Generation Sensors and Computer Vision"

A topical collection of Digital Technologies Research and Applications (DTRA) (E-ISSN: 2754-5687).
Deadline for manuscript submissions: 25 July 2026

Collection Editor: 

Topical Collection Information:

Dear Colleagues,

In recent years, rapid progress in artificial intelligence (AI), sensor technologies, and computer vision has transformed the way machines perceive and interact with the physical world. Modern AI techniques have significantly improved perception, interpretation, and decision-making capabilities across diverse domains. At the same time, emerging sensor technologies ranging from optical and LiDAR systems to MEMS and multi-modal sensing platforms, have increased the richness, accuracy, and availability of environmental and physiological data.

The convergence of AI with advanced sensors and computer vision is enabling the development of intelligent systems capable of robust perception in complex, real-time, and safety-critical environments. These advancements are driving innovation in autonomous vehicles, robotics, smart manufacturing, healthcare monitoring, environmental sensing and human–machine interaction. In parallel, complementary technologies including embedded and edge computing, neuromorphic hardware, photonic sensors, digital twins, and the Internet of Things (IoT) are pushing intelligence closer to the edge, reducing latency and power consumption while enhancing privacy and scalability.

Despite substantial progress, many scientific and engineering challenges remain open. Key issues include sensor fusion across heterogeneous modalities, uncertainty quantification in perception pipelines, efficient on-device inference, robustness under environmental variability, long-term reliability, and explainability in AI-driven systems. Addressing these challenges requires interdisciplinary collaboration among experts in machine learning, signal processing, optics, electronics, embedded systems, robotics, and application-specific fields.

This Special Issue aims to bring together cutting-edge research contributions across these domains, promoting novel methodologies, system architectures, applications, and theoretical advancements that will shape the next generation of intelligent sensors and vision systems.

This Special Issue covers the following topics:

1. Applications for AI, Sensors and Computer Vision in various fields such as healthcare, automation and industry.
2. Novel AI models, algorithms, and learning paradigms that enhance sensing accuracy, perception robustness, data interpretation, and scene understanding.
3. Novel developments in next-generation sensor technologies, including multi-modal sensing, miniaturized and embedded sensing platforms, and emerging hardware innovations.
4. Interdisciplinary research on the integration of AI, sensing, and vision systems for real-world applications across domains such as autonomous systems, robotics, healthcare, smart manufacturing, transportation, security, and environmental monitoring.
5. Foster contributions that address challenges related to data fusion, uncertainty quantification, explainability, scalability, low-latency inference, energy efficiency, and system reliability.
6. Research bridging theory and practice, including benchmarks, real-world deployment, datasets, reproducible pipelines, and industry case studies

We welcome contributions that present novel algorithms, system architectures, datasets, experiments, theoretical insights, or application-driven case studies. Both research articles and comprehensive reviews are encouraged.

Dr. Minh Long Hoang

Keywords:

• Sensors
• AI
• Computer Vision
• Signal Processing
• Advanced Technology

Manuscript Submission Information:

Please visit the Submissions Guidelines page before submitting a manuscript. Submitted papers should be well formatted and use good English. Manuscripts should be submitted online through the online manuscript submission and editorial system. Additionally, please include a cover letter specifying that the manuscript is intended for the Topical Collection "Artificial Intelligence for Social Media Data: Algorithms, Accountability and Societal Impact" when submitting it online. Manuscripts can be submitted until the submission deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal and will be listed together on the Topical Collection website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract can be sent to the Editor Cecilia cecilia@ukscip.com for announcement on this website.

The Article Processing Charge (APC) for publication in this open access journal is 1800 USD. Authors who are unable to cover this cost or those who are invited to submit papers may be eligible for discounts or waivers.

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 double-blind peer-review process.

Planned Paper Information:

1: Title: AI-Driven Multi-Sensor Fusion in Human Activity Recognition for Healthcare Monitoring

Authors: Minh Long Hoang

Author: University of Parma, Italy; minhlong.hoang@unipr.it

Abstract: Human activity recognition (HAR) has emerged as a key enabling technology for healthcare monitoring, rehabilitation assessment, and ambient assisted living. However, real-world healthcare environments often require robust perception under heterogeneous sensing conditions, variability in patient behavior, and multi-modal data streams. This paper investigates AI-driven multi-sensor fusion approaches for HAR, leveraging data from wearable and ambient sensing platforms to improve recognition accuracy, temporal stability, and interpretability. Deep learning architectures and feature-level fusion strategies are explored to integrate inertial, biomechanical, and contextual information into a unified activity recognition framework. Experimental evaluations on healthcare datasets demonstrate improved performance for complex activities of daily living, including transitions and fine-grained motion patterns. The results indicate that multi-sensor AI fusion not only enhances classification robustness compared to single modality systems but also provides valuable insights for personalized healthcare and remote patient monitoring. This study highlights the potential of AI-driven HAR systems for next-generation smart healthcare solutions, emphasizing challenges and opportunities related to sensor heterogeneity, real-time processing, and clinical deployment.

2: Smart Sensor-Driven Optimization of PV Systems for Smart Grid Applications 

Author: Nicola Delmonte

University of Parma, Italy; nicola.delmonte@unipr.it

Abstract: The integration of photovoltaic (PV) systems into smart grids requires advanced monitoring, control, and optimization strategies to ensure energy reliability, stability, and efficiency. Smart sensors play a pivotal role in enabling real-time data acquisition, environmental awareness, and predictive control for PV generation systems. This paper investigates a smart sensor-driven framework for optimizing PV system performance within smart grid environments. The proposed approach utilizes sensor networks to collect high-resolution data on irradiance, temperature, load demand, and grid conditions, enabling enhanced forecasting, maximum power point tracking (MPPT), and adaptive energy management. By incorporating data-driven optimization algorithms and intelligent control mechanisms, the system improves energy yield, reduces operational losses, and enhances grid integration under dynamic environmental and load conditions. Experimental evaluations and simulation-based analyses demonstrate the effectiveness of smart sensor-assisted optimization in improving PV system efficiency, grid stability, and responsiveness to demand-side variations. The research highlights the potential of smart sensors as key enablers for next-generation PV-based smart grids, supporting sustainable energy infrastructures.