Edge Computing, Real-Time Data Processing, and Holistic Methods for Imaging Sensors
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".
Deadline for manuscript submissions: 20 August 2024 | Viewed by 219
Special Issue Editors
Interests: experimental physics; instrumentation
Interests: molecular physics; ultrafast X-ray spectroscopy; material response to electronic excitation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The interdisciplinary field of CMOS, other pixelated image sensors and their applications is rapidly growing, as is the need for real-time processing of the data they generate. Making image sensors smart is a must in many applications, and it is often a desired quality. Artificial intelligence/machine learning (AI/ML) is not only good for post-processing of CMOS data but also for real-time enhancement of CMOS sensors and measurements in situ and operando, also known as 'edge computing'. Holistic combinations of edge computing with CMOS and other imaging hardware lead to ‘smart or intelligent imaging’.
This Special Issue will publish original R&D results and review articles on Edge CMOS and smart image sensors that innovatively use edge computing, compressed sensing, and other real-time, data-driven methods including deep learning to enhance the image and pixelated sensor hardware, and holistically integrated approaches to data acquisition and data processing at the edge. Topics of interest include but are not limited to the following:
- AI/ML-enhanced CMOS and other imaging modalities for offline and real-time processing of image data;
- AI/ML deployment with CMOS and other imaging modalities for real-time collection of data;
- AI/ML for sparse imaging using CMOS and other sensors;
- Uncertainty quantification, error corrections in CMOS and other imaging modalities;
- Novel architectures to combine hardware (CPU, GPU, FPGA, ASIC) with
- algorithmic (ML, neural network) and augmented domain knowledge;
- Theoretical foundations to accelerate hardware and algorithmic (ML, NN) integration;
- Digital twin applications and other related topics.
Dr. Zhehui Wang
Dr. Ryan Coffee
Prof. Dr. Heinz Graafsma
Guest Editors
Manuscript Submission Information
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Keywords
- CMOS imaging
- smart vision
- edge sensing
- compressed sensing
- data-driven
- edge computing
- multi-modal imaging
- radiographic imaging and tomography
- reconstruction
- augmentation
- novel imaging architectures (hardware and algorithms)
