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Next Generation of Measurement Sensors and Instruments Based on Embedded Artificial Intelligence

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

Deadline for manuscript submissions: 30 August 2024 | Viewed by 2932

Special Issue Editors

Dr. Francesco Bonavolontà

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy
Interests: IoT; AR/VR-based distributed measurement systems; electrical and electronics engineering; measurement; signal processing; wireless sensor networks; embedded artificial intelligence; edge AI
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Flora Amato

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy
Interests: artificial intelligence; machine learning; deep learning; edge computing; data science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The combined use of the Internet of Things (IoT) and artificial intelligence (AI) has opened new, previously unimaginable opportunities. AI-based applications have resulted in being effective and flexible in disparate domains. IoT increases the value of AI through the ability to collect data with training models and algorithms, and vice versa, AI increases the value of IoT thanks to the transformation of collected data into useful information. In general, IoT and AI are physically distant from each other: IoT is close to the data source; AI still often runs into the cloud. This conventional cloud-based architecture can have critical issues, such as high latency times, lack of bandwidth, data loss, cloud congestion, and energy costs. These factors greatly reduce the user experience or, in the worst case, do not allow the implementation of AI-based real-time applications. To speed up the response, data produced by sensors and IoT devices must be analyzed directly at the edge, close to where the data are located and near the user, rather than being sent to a central location for later analysis. This new model is commonly identified as edge or embedded AI. It is possible to consider embedded artificial intelligence as an evolution of the IoT, by means of which connected objects are no longer just terminals intended to collect data to be conveyed to the cloud but natively exhibit characteristics peculiar to AI technologies. Embedded AI can be exploited to enhance both static and dynamic metrological characteristics of smart sensors, overcoming typical limitations they suffer from, and will find applications in numerous scenarios: industry, smart cities, smart agriculture, smart monitoring, etc. In the instrumentation and measurement field, for example, by leveraging embedded AI, meters will be able to manage better loads based on grid conditions and provide proactive recommendations in real time.

The purpose of this Special Issue is to gather a collection of papers reflecting the latest developments in the design of the next generation of measurement sensors and instruments that use embedded artificial intelligence, such as machine learning, Bayesian networks, neural networks, and fuzzy logic, to improve and enhance sensing, metering, and detection.

Dr. Francesco Bonavolontà
Prof. Dr. Flora Amato
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

  • AI-powered instruments
  • AI-enabled smart sensing and monitoring
  • human activity recognition
  • acoustic scene classification
  • fault detection and recognition
  • edge artificial intelligence
  • AI-enabled smart applications
  • predictive maintenance in manufacturing
  • embedded intelligence
  • visual recognition
  • AI for health
  • AI-enhanced smart sensors

Published Papers (2 papers)

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Research

26 pages, 19388 KiB  
Article
Condition Monitoring of Pneumatic Drive Systems Based on the AI Method Feed-Forward Backpropagation Neural Network
by Monica Tiboni and Carlo Remino
Sensors 2024, 24(6), 1783; https://doi.org/10.3390/s24061783 - 10 Mar 2024
Viewed by 421
Abstract
Machine condition monitoring is used in a variety of industries as a very efficient strategy for equipment maintenance. This paper presents a study on monitoring a pneumatic system using a feed-forward backpropagation neural network as a classifier and compares the results obtained with [...] Read more.
Machine condition monitoring is used in a variety of industries as a very efficient strategy for equipment maintenance. This paper presents a study on monitoring a pneumatic system using a feed-forward backpropagation neural network as a classifier and compares the results obtained with different sensor signals and associated extracted features as input for classification. The vibrations of the body of a pneumatic cylinder are acquired using both common industrial sensors and low-cost sensors integrated into an Arduino board. Pressure sensors for both chambers and a position sensor are also used. Power spectral density (PSD) is used to extract features from the acceleration signals, as well as statistical indices. Statistical indices are considered for pressure and position sensors. The results, which are based on experimental data obtained on a test bench, show that a feed-forward neural network makes it possible to identify the operating states with a good degree of reliability. Even with low-cost instrumentation, it is possible to realize reliable condition monitoring based on vibrations. This last result is particularly important as it can help to further increase the uptake of this maintenance approach in the industrial environment. Full article
(This article belongs to the Special Issue Next Generation of Measurement Sensors and Instruments Based on Embedded Artificial Intelligence)
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16 pages, 4692 KiB  
Article
A Deep Learning Approach to Organic Pollutants Classification Using Voltammetry
by Mario Molinara, Rocco Cancelliere, Alessio Di Tinno, Luigi Ferrigno, Mikhail Shuba, Polina Kuzhir, Antonio Maffucci and Laura Micheli
Sensors 2022, 22(20), 8032; https://doi.org/10.3390/s22208032 - 21 Oct 2022
Cited by 6 | Viewed by 2028
Abstract
This paper proposes a deep leaning technique for accurate detection and reliable classification of organic pollutants in water. The pollutants are detected by means of cyclic voltammetry characterizations made by using low-cost disposable screen-printed electrodes. The paper demonstrates the possibility of strongly improving [...] Read more.
This paper proposes a deep leaning technique for accurate detection and reliable classification of organic pollutants in water. The pollutants are detected by means of cyclic voltammetry characterizations made by using low-cost disposable screen-printed electrodes. The paper demonstrates the possibility of strongly improving the detection of such platforms by modifying them with nanomaterials. The classification is addressed by using a deep learning approach with convolutional neural networks. To this end, the results of the voltammetry analysis are transformed into equivalent RGB images by means of Gramian angular field transformations. The proposed technique is applied to the detection and classification of hydroquinone and benzoquinone, which are particularly challenging since these two pollutants have a similar electroactivity and thus the voltammetry curves exhibit overlapping peaks. The modification of electrodes by carbon nanotubes improves the sensitivity of a factor of about ×25, whereas the convolution neural network after Gramian transformation correctly classifies 100% of the experiments. Full article
(This article belongs to the Special Issue Next Generation of Measurement Sensors and Instruments Based on Embedded Artificial Intelligence)
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