Sensor-Based Deep Learning Applications for Enhancing Situational Awareness
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 5007
Special Issue Editors
Interests: machine/deep learning; Internet of Things; wearable computing; signal processing
Special Issues, Collections and Topics in MDPI journals
Interests: distributed ledger technologies; blockchain; Internet of Things; identity/device management; edge computing
Special Issues, Collections and Topics in MDPI journals
Interests: antennas and propagation; microwaves; pattern recognition; optimization
Special Issues, Collections and Topics in MDPI journals
Interests: IoT; systems services; networks security
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, climate change and human intervention have contributed to an ever-increasing need to safeguard critical resources from natural disasters, such as floods, hurricanes and wildfires, while earthquakes are often triggering mass deterioration of urban networks. The rise of Deep Learning (DL) and Artificial Intelligence (AI) is promising to augment the capacity of first responders while being in the field, assisting them to process massive data from heterogeneous sensors and draw better inferences with better awareness of the situation. To this end, DL-based applications have been proposed over the years tο increase their situational awareness during an emergency by either monitoring their activity using wearable devices, including their mental (e.g., stress levels) and physical health status, or by extending their sensing (e.g., vision, smell) and environmental perception abilities with IoT sensors. However, there are several challenges including efficient sensor fusion, lack of data accuracy and reliability, low response time etc. Research papers reporting novel DL-driven sensor-based applications that will be used for disaster management are invited for submission to this Special Issue.
The scope and topic of this Special Issue includes but is not limited to:
- Deep learning on biometric data;
- Human/animal activity recognition using deep learning;
- Behavioral (including mental and physical) detection and forecasting based on deep learning analysis of sensory information;
- Deep learning on LiDAR/RADAR data for situational awareness;
- Deep learning-based audio scene analysis;
- Deep learning for camera-based (RGB-D, thermal, FLIR, etc.) surveillance systems;
- Earthquake forecasting using deep learning;
- Deep learning on environmental sensor data;
- Applications of Deep Learning on extreme weather phenomena and climate change.
Dr. Panagiotis Kasnesis
Dr. Dimitrios G. Kogias
Dr. Stelios A. Mitilineos
Dr. Charalampos Z. Patrikakis
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. Sensors 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 2600 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.
Keywords
- deep learning
- wearables
- sensor data
- sensor fusion
- computer vision
- signal processing.
