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Edge Computing, Real-Time Data Processing, and Holistic Methods for Imaging...
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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: 25 May 2025 | Viewed by 7540

Special Issue Editors

Dr. Zhehui Wang

E-Mail Website
Guest Editor
Los Alamos National Laboratory, Los Alamos, NM 87545, USA
Interests: experimental physics; instrumentation
Dr. Ryan Coffee

E-Mail Website
Guest Editor
LCLS-SLAC National Accelerator Lab, Menlo Park, CA, USA
Interests: molecular physics; ultrafast X-ray spectroscopy; material response to electronic excitation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Heinz Graafsma

E-Mail Website
Guest Editor
Deutsche Elektronen-Synchrotron, Hamburg, Germany
Interests: X-ray

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:

  1. AI/ML-enhanced CMOS and other imaging modalities for offline and real-time processing of image data;
  2. AI/ML deployment with CMOS and other imaging modalities for real-time collection of data;
  3. AI/ML for sparse imaging using CMOS and other sensors;
  4. Uncertainty quantification, error corrections in CMOS and other imaging modalities;
  5. Novel architectures to combine hardware (CPU, GPU, FPGA, ASIC) with
  6. algorithmic (ML, neural network) and augmented domain knowledge;
  7. Theoretical foundations to accelerate hardware and algorithmic (ML, NN) integration;
  8. Digital twin applications and other related topics. 

Dr. Zhehui Wang
Dr. Ryan Coffee
Prof. Dr. Heinz Graafsma
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

  • 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)

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Published Papers (4 papers)

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Research

17 pages, 7838 KiB  
Article
Safety After Dark: A Privacy Compliant and Real-Time Edge Computing Intelligent Video Analytics for Safer Public Transportation
by Johan Barthelemy, Umair Iqbal, Yan Qian, Mehrdad Amirghasemi and Pascal Perez
Sensors 2024, 24(24), 8102; https://doi.org/10.3390/s24248102 - 19 Dec 2024
Viewed by 911
Abstract
Public transportation systems play a vital role in modern cities, but they face growing security challenges, particularly related to incidents of violence. Detecting and responding to violence in real time is crucial for ensuring passenger safety and the smooth operation of these transport [...] Read more.
Public transportation systems play a vital role in modern cities, but they face growing security challenges, particularly related to incidents of violence. Detecting and responding to violence in real time is crucial for ensuring passenger safety and the smooth operation of these transport networks. To address this issue, we propose an advanced artificial intelligence (AI) solution for identifying unsafe behaviours in public transport. The proposed approach employs deep learning action recognition models and utilises technologies like NVIDIA DeepStream SDK, Amazon Web Services (AWS) DirectConnect, local edge computing server, ONNXRuntime and MQTT to accelerate the end-to-end pipeline. The solution captures video streams from remote train stations closed circuit television (CCTV) networks, processes the data in the cloud, applies the action recognition model, and transmits the results to a live web application. A temporal pyramid network (TPN) action recognition model was trained on a newly curated video dataset mixing open-source resources and live simulated trials to identify the unsafe behaviours. The base model was able to achieve a validation accuracy of 93% when trained using open-source dataset samples and was improved to 97% when live simulated dataset was included during the training. The developed AI system was deployed at Wollongong Train Station (NSW, Australia) and showcased impressive accuracy in detecting violence incidents during an 8-week test period, achieving a reliable false-positive (FP) rate of 23%. While the AI correctly identified 30 true-positive incidents, there were 6 cases of false negatives (FNs) where violence incidents were missed during the rainy weather suggesting more data in the training dataset related to bad weather. The AI model’s continuous retraining capability ensures its adaptability to various real-world scenarios, making it a valuable tool for enhancing safety and the overall passenger experience in public transport settings. Full article
(This article belongs to the Special Issue Edge Computing, Real-Time Data Processing, and Holistic Methods for Imaging Sensors)
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19 pages, 2630 KiB  
Article
Real-Time Pipeline Fault Detection in Water Distribution Networks Using You Only Look Once v8
by Goodnews Michael, Essa Q. Shahra, Shadi Basurra, Wenyan Wu and Waheb A. Jabbar
Sensors 2024, 24(21), 6982; https://doi.org/10.3390/s24216982 - 30 Oct 2024
Cited by 2 | Viewed by 1656
Abstract
Detecting faulty pipelines in water management systems is crucial for ensuring a reliable supply of clean water. Traditional inspection methods are often time-consuming, costly, and prone to errors. This study introduces an AI-based model utilizing images to detect pipeline defects, focusing on leaks, [...] Read more.
Detecting faulty pipelines in water management systems is crucial for ensuring a reliable supply of clean water. Traditional inspection methods are often time-consuming, costly, and prone to errors. This study introduces an AI-based model utilizing images to detect pipeline defects, focusing on leaks, cracks, and corrosion. The YOLOv8 model is employed for object detection due to its exceptional performance in detecting objects, segmentation, pose estimation, tracking, and classification. By training on a large dataset of labeled images, the model effectively learns to identify visual patterns associated with pipeline faults. Experiments conducted on a real-world dataset demonstrate that the AI-based model significantly outperforms traditional methods in detection accuracy. The model also exhibits robustness to various environmental conditions such as lighting changes, camera angles, and occlusions, ensuring reliable performance in diverse scenarios. The efficient processing time of the model enables real-time fault detection in large-scale water distribution networks implementing this AI-based model offers numerous advantages for water management systems. It reduces dependence on manual inspections, thereby saving costs and enhancing operational efficiency. Additionally, the model facilitates proactive maintenance through the early detection of faults, preventing water loss, contamination, and infrastructure damage. The results from the three conducted experiments indicate that the model from Experiment 1 achieves a commendable mAP50 of 90% in detecting faulty pipes, with an overall mAP50 of 74.7%. In contrast, the model from Experiment 3 exhibits superior overall performance, achieving a mAP50 of 76.1%. This research presents a promising approach to improving the reliability and sustainability of water management systems through AI-based fault detection using image analysis. Full article
(This article belongs to the Special Issue Edge Computing, Real-Time Data Processing, and Holistic Methods for Imaging Sensors)
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19 pages, 644 KiB  
Article
SMS Scam Detection Application Based on Optical Character Recognition for Image Data Using Unsupervised and Deep Semi-Supervised Learning
by Anjali Shinde, Essa Q. Shahra, Shadi Basurra, Faisal Saeed, Abdulrahman A. AlSewari and Waheb A. Jabbar
Sensors 2024, 24(18), 6084; https://doi.org/10.3390/s24186084 - 20 Sep 2024
Viewed by 2469
Abstract
The growing problem of unsolicited text messages (smishing) and data irregularities necessitates stronger spam detection solutions. This paper explores the development of a sophisticated model designed to identify smishing messages by understanding the complex relationships among words, images, and context-specific factors, areas that [...] Read more.
The growing problem of unsolicited text messages (smishing) and data irregularities necessitates stronger spam detection solutions. This paper explores the development of a sophisticated model designed to identify smishing messages by understanding the complex relationships among words, images, and context-specific factors, areas that remain underexplored in existing research. To address this, we merge a UCI spam dataset of regular text messages with real-world spam data, leveraging OCR technology for comprehensive analysis. The study employs a combination of traditional machine learning models, including K-means, Non-Negative Matrix Factorization, and Gaussian Mixture Models, along with feature extraction techniques such as TF-IDF and PCA. Additionally, deep learning models like RNN-Flatten, LSTM, and Bi-LSTM are utilized. The selection of these models is driven by their complementary strengths in capturing both the linear and non-linear relationships inherent in smishing messages. Machine learning models are chosen for their efficiency in handling structured text data, while deep learning models are selected for their superior ability to capture sequential dependencies and contextual nuances. The performance of these models is rigorously evaluated using metrics like accuracy, precision, recall, and F1 score, enabling a comparative analysis between the machine learning and deep learning approaches. Notably, the K-means feature extraction with vectorizer achieved 91.01% accuracy, and the KNN-Flatten model reached 94.13% accuracy, emerging as the top performer. The rationale behind highlighting these models is their potential to significantly improve smishing detection rates. For instance, the high accuracy of the KNN-Flatten model suggests its applicability in real-time spam detection systems, but its computational complexity might limit scalability in large-scale deployments. Similarly, while K-means with vectorizer excels in accuracy, it may struggle with the dynamic and evolving nature of smishing attacks, necessitating continual retraining. Full article
(This article belongs to the Special Issue Edge Computing, Real-Time Data Processing, and Holistic Methods for Imaging Sensors)
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27 pages, 19966 KiB  
Article
An Underwater Crack Detection System Combining New Underwater Image-Processing Technology and an Improved YOLOv9 Network
by Xinbo Huang, Chenxi Liang, Xinyu Li and Fei Kang
Sensors 2024, 24(18), 5981; https://doi.org/10.3390/s24185981 - 15 Sep 2024
Cited by 3 | Viewed by 1762
Abstract
Underwater cracks are difficult to detect and observe, posing a major challenge to crack detection. Currently, deep learning-based underwater crack detection methods rely heavily on a large number of crack images that are difficult to collect due to their complex and hazardous underwater [...] Read more.
Underwater cracks are difficult to detect and observe, posing a major challenge to crack detection. Currently, deep learning-based underwater crack detection methods rely heavily on a large number of crack images that are difficult to collect due to their complex and hazardous underwater environments. This study proposes a new underwater image-processing method that combines a novel white balance method and bilateral filtering denoising method to transform underwater crack images into high-quality above-water images with original crack features. Crack detection is then performed based on an improved YOLOv9-OREPA model. Through experiments, it is found that the new image-processing method proposed in this study significantly improves the evaluation indicators of new images, compared with other methods. The improved YOLOv9-OREPA also exhibits a significantly improved performance. The experimental results demonstrate that the method proposed in this study is a new approach suitable for detecting underwater cracks in dams and achieves the goal of transforming underwater images into above-water images. Full article
(This article belongs to the Special Issue Edge Computing, Real-Time Data Processing, and Holistic Methods for Imaging Sensors)
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