Machine Learning for Thermal Sensing and Analysis
A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "A：Physics".
Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 484
Special Issue Editors
Interests: deep learning for intelligent sensing; thermal sensing; stretchable electronics
Interests: thermography nondestructive testing; vibration or infrared/visual visions; physics-constrained machine learning models for thermography post processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Temperature is one of the most fundamental parameters characterizing a physical system. The developments in thermal sensing over the past few decades have generated both direct and indirect thermal sensors with various performances and costs. Temperature sensing data can be 1D temporal data or 2D images for thermography. With the increasing sensing data and computing power, a new trend is to go beyond the quantification of the sensing events and integrate with machine learning algorithms. Such data-driven techniques have promising potential for improved automation and intelligent analysis in both industry and academia. This Special Issue aims to provide a platform to communicate and discuss the development of deep learning and AI techniques for analyzing thermal sensors and thermal imaging. We invite research papers, reviews, and communications that focus on the use of deep learning models for innovative applications related to thermal sensing.
Dr. Shuyu Wang
Dr. Hongjin Wang
Guest Editors
Manuscript Submission Information
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Keywords
- thermal sensors
- thermal imaging
- calorimeter
- machine learning
- deep learning
- intelligent sensing
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