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Artificial Intelligence and Deep Learning in Sensors and Applications: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 3533

Special Issue Editor

Prof. Dr. Shyan-Ming Yuan

E-Mail Website
Guest Editor
Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
Interests: distributed system; middleware kernel; application; platform solution; web & wireless technologies and application; applied education system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

To effectively solve the increasingly complex problems experienced by human beings, the latest development trend is to apply a large number of different types of sensors to collect data in order to establish effective solutions based on deep learning and Artificial Intelligence.

This not only creates a huge demand for sensors, providing business opportunities, but also creates new challenges for the development of sensor devices and their related applications. These technological developments that combine AI and sensors are being actively used in various application fields such as healthcare, manufacturing, agriculture and fisheries, transportation, construction, environmental monitoring, etc.

In this Special Issue, we aim to solicit high-quality original research papers and surveys that explore new developments in AI (deep learning) and sensor technology in various fields as well as to share ideas, designs, data-driven applications, and production and deployment experiences and challenges.

Topics of interest include, but are not limited to, the following:

  • Applications and sensors for manufacturing, machinery, and semiconductors and related industries such as quality inspection, defect detection, predictive maintenance, yield control, and related applications.
  • Smart applications and sensors for architecture, construction, buildings, e-learning, and recommendation systems.
  • Applications and sensors for autonomous vehicles, surveillance systems, traffic monitoring, suspicious tracking, and transportation.
  • Object recognition, image classification, object detection, speech processing, human behavior analysis, and related sensing applications.
  • Safety in nuclear power plants, drone-based delivery, medical systems, automation systems, security systems, smart farming, sensor performance optimization, thermal imaging (infection detection).
  • Sensor group-based communication for collective task operations, i.e., vehicle platooning, AI drones, manufacturing synchronization, etc.
  • Autonomous sensor devices in edge networks performing AI-based applications.

All other applications related to AI and sensors.

Prof. Dr. Shyan-Ming Yuan
Guest Editor

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

  • sensors
  • deep learning
  • big data
  • reinforcement learning
  • Artificial Intelligence

Related Special Issue

Published Papers (4 papers)

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Research

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24 pages, 2022 KiB  
Article
Random Convolutional Kernel Transform with Empirical Mode Decomposition for Classification of Insulators from Power Grid
by Anne Carolina Rodrigues Klaar, Laio Oriel Seman, Viviana Cocco Mariani and Leandro dos Santos Coelho
Sensors 2024, 24(4), 1113; https://doi.org/10.3390/s24041113 - 08 Feb 2024
Viewed by 722
Abstract
The electrical energy supply relies on the satisfactory operation of insulators. The ultrasound recorded from insulators in different conditions has a time series output, which can be used to classify faulty insulators. The random convolutional kernel transform (Rocket) algorithms use convolutional filters to [...] Read more.
The electrical energy supply relies on the satisfactory operation of insulators. The ultrasound recorded from insulators in different conditions has a time series output, which can be used to classify faulty insulators. The random convolutional kernel transform (Rocket) algorithms use convolutional filters to extract various features from the time series data. This paper proposes a combination of Rocket algorithms, machine learning classifiers, and empirical mode decomposition (EMD) methods, such as complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), empirical wavelet transform (EWT), and variational mode decomposition (VMD). The results show that the EMD methods, combined with MiniRocket, significantly improve the accuracy of logistic regression in insulator fault diagnosis. The proposed strategy achieves an accuracy of 0.992 using CEEMDAN, 0.995 with EWT, and 0.980 with VMD. These results highlight the potential of incorporating EMD methods in insulator failure detection models to enhance the safety and dependability of power systems. Full article
(This article belongs to the Special Issue Artificial Intelligence and Deep Learning in Sensors and Applications: 2nd Edition)
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16 pages, 6328 KiB  
Article
Training Universal Deep-Learning Networks for Electromagnetic Medical Imaging Using a Large Database of Randomized Objects
by Fei Xue, Lei Guo, Alina Bialkowski and Amin Abbosh
Sensors 2024, 24(1), 8; https://doi.org/10.3390/s24010008 - 19 Dec 2023
Viewed by 743
Abstract
Deep learning has become a powerful tool for solving inverse problems in electromagnetic medical imaging. However, contemporary deep-learning-based approaches are susceptible to inaccuracies stemming from inadequate training datasets, primarily consisting of signals generated from simplified and homogeneous imaging scenarios. This paper introduces a [...] Read more.
Deep learning has become a powerful tool for solving inverse problems in electromagnetic medical imaging. However, contemporary deep-learning-based approaches are susceptible to inaccuracies stemming from inadequate training datasets, primarily consisting of signals generated from simplified and homogeneous imaging scenarios. This paper introduces a novel methodology to construct an expansive and diverse database encompassing domains featuring randomly shaped structures with electrical properties representative of healthy and abnormal tissues. The core objective of this database is to enable the training of universal deep-learning techniques for permittivity profile reconstruction in complex electromagnetic medical imaging domains. The constructed database contains 25,000 unique objects created by superimposing from 6 to 24 randomly sized ellipses and polygons with varying electrical attributes. Introducing randomness in the database enhances training, allowing the neural network to achieve universality while reducing the risk of overfitting. The representative signals in the database are generated using an array of antennas that irradiate the imaging domain and capture scattered signals. A custom-designed U-net is trained by using those signals to generate the permittivity profile of the defined imaging domain. To assess the database and confirm the universality of the trained network, three distinct testing datasets with diverse objects are imaged using the designed U-net. Quantitative assessments of the generated images show promising results, with structural similarity scores consistently exceeding 0.84, normalized root mean square errors remaining below 14%, and peak signal-to-noise ratios exceeding 33 dB. These results demonstrate the practicality of the constructed database for training deep learning networks that have generalization capabilities in solving inverse problems in medical imaging without the need for additional physical assistant algorithms. Full article
(This article belongs to the Special Issue Artificial Intelligence and Deep Learning in Sensors and Applications: 2nd Edition)
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13 pages, 5055 KiB  
Article
A Generative Adversarial Network to Synthesize 3D Magnetohydrodynamic Distortions for Electrocardiogram Analyses Applied to Cardiac Magnetic Resonance Imaging
by Maroua Mehri, Guillaume Calmon, Freddy Odille, Julien Oster and Alain Lalande
Sensors 2023, 23(21), 8691; https://doi.org/10.3390/s23218691 - 24 Oct 2023
Viewed by 893
Abstract
Recently, deep learning (DL) models have been increasingly adopted for automatic analyses of medical data, including electrocardiograms (ECGs). Large, available ECG datasets, generally of high quality, often lack specific distortions, which could be helpful for enhancing DL-based algorithms. Synthetic ECG datasets could overcome [...] Read more.
Recently, deep learning (DL) models have been increasingly adopted for automatic analyses of medical data, including electrocardiograms (ECGs). Large, available ECG datasets, generally of high quality, often lack specific distortions, which could be helpful for enhancing DL-based algorithms. Synthetic ECG datasets could overcome this limitation. A generative adversarial network (GAN) was used to synthesize realistic 3D magnetohydrodynamic (MHD) distortion templates, as observed during magnetic resonance imaging (MRI), and then added to available ECG recordings to produce an augmented dataset. Similarity metrics, as well as the accuracy of a DL-based R-peak detector trained with and without data augmentation, were used to evaluate the effectiveness of the synthesized data. Three-dimensional MHD distortions produced by the proposed GAN were similar to the measured ones used as input. The precision of a DL-based R-peak detector, tested on actual unseen data, was significantly enhanced by data augmentation; its recall was higher when trained with augmented data. Using synthesized MHD-distorted ECGs significantly improves the accuracy of a DL-based R-peak detector, with a good generalization capacity. This provides a simple and effective alternative to collecting new patient data. DL-based algorithms for ECG analyses can suffer from bias or gaps in training datasets. Using a GAN to synthesize new data, as well as metrics to evaluate its performance, can overcome the scarcity issue of data availability. Full article
(This article belongs to the Special Issue Artificial Intelligence and Deep Learning in Sensors and Applications: 2nd Edition)
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Review

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14 pages, 779 KiB  
Review
A Review of Machine Learning Approaches for the Personalization of Amplification in Hearing Aids
by Nafisa Zarrin Tasnim, Aoxin Ni, Edward Lobarinas and Nasser Kehtarnavaz
Sensors 2024, 24(5), 1546; https://doi.org/10.3390/s24051546 - 28 Feb 2024
Viewed by 704
Abstract
This paper provides a review of various machine learning approaches that have appeared in the literature aimed at individualizing or personalizing the amplification settings of hearing aids. After stating the limitations associated with the current one-size-fits-all settings of hearing aid prescriptions, a spectrum [...] Read more.
This paper provides a review of various machine learning approaches that have appeared in the literature aimed at individualizing or personalizing the amplification settings of hearing aids. After stating the limitations associated with the current one-size-fits-all settings of hearing aid prescriptions, a spectrum of studies in engineering and hearing science are discussed. These studies involve making adjustments to prescriptive values in order to enable preferred and individualized settings for a hearing aid user in an audio environment of interest to that user. This review gathers, in one place, a comprehensive collection of works that have been conducted thus far with respect to achieving the personalization or individualization of the amplification function of hearing aids. Furthermore, it underscores the impact that machine learning can have on enabling an improved and personalized hearing experience for hearing aid users. This paper concludes by stating the challenges and future research directions in this area. Full article
(This article belongs to the Special Issue Artificial Intelligence and Deep Learning in Sensors and Applications: 2nd Edition)
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