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Intelligent Sensors and Advanced Computing: Developments in the Era of Industry 4.0

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 6789

Special Issue Editors


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Guest Editor
Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, 62124 Serres, Greece
Interests: digital design; FPGAs; image processing; semiconductors

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Guest Editor
Research Institute for Intelligent Computer Systems, Department of Information Computer Systems and Control, West Ukrainian National University, 46020 Ternopil, Ukraine
Interests: precision sensor measuring systems; artificial neural network applications; wireless sensor networks; intelligent cybersecurity systems; image processing and pattern recognition
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Faculty of Computer Science, University of Applied Sciences and Arts, 44227 Dortmund, Germany
Interests: digital transformation; competence development; agile project management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue aims to collect high-quality, timely contributions at the interface between modern data acquisition systems and new computing frameworks that are used to build and deploy intelligent integrated systems. This requires expert knowledge and large-scale evaluations in various subdomains that include, but are not limited to: advanced instrumentation and data acquisition systems, advanced mathematical methods for data acquisition and computing systems, bio-informatics, computational intelligence for instrumentation and data acquisition systems, computer systems for healthcare and medicine, data analysis and modeling, embedded systems, intelligent distributed systems and remote control, intelligent information systems, data mining and ontology, intelligent software systems and tools, intelligent instrumentation and data acquisition systems in advanced manufacturing for Industry 4.0, big data, internet of things, pattern recognition, digital image and signal processing, virtual instrumentation systems, 5G networks technologies and security, advanced automatic control and information technology, cyber security, advanced computer architectures and embedded systems, design and testing of advanced measuring and computer systems, human–computer interaction, intelligent robotics and sensors, machine learning, smart building and smart cities systems, wireless systems, virtual and augmented reality, cyber physical systems, and IoT dependability and resilience.

Contributions will strengthen the scientific profile of the Sensors journal, considering the growing role of advanced algorithms in the design and validation of intelligent sensor systems. Emphasis is on experimental laboratory systems and meaningful real-world applications with extensive evaluation for replicable outcomes.

The authors of selected papers presented at the 12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2023) are invited to submit significantly revised and extended versions to this Special Issue. Contributions from other researchers also working in this area of critical interest are very welcome.

Prof. Dr. John Kalomiros
Prof. Dr. Anatoliy Sachenko
Prof. Dr. Carsten Wolff
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

  • intelligent sensors
  • data acquisition
  • advanced computing systems
  • information processing
  • wireless sensor networks
  • Internet of Things

Published Papers (6 papers)

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Research

19 pages, 5285 KiB  
Article
Optimizing Occupant Comfort in a Room Using the Predictive Control Model as a Thermal Control Strategy
by Mihaela-Gabriela Boicu, Grigore Stamatescu, Ioana Făgărăşan, Mihaela Vasluianu, Giorgian Neculoiu and Marius-Alexandru Dobrea
Sensors 2024, 24(12), 3857; https://doi.org/10.3390/s24123857 - 14 Jun 2024
Viewed by 219
Abstract
Thermal comfort strategies represent a very important aspect when it comes to achieving thermal comfort conditions. At the same time, recently, there has been a growing interest in user-centered building control concepts. Thus, this work focuses on developing a thermal control strategy that [...] Read more.
Thermal comfort strategies represent a very important aspect when it comes to achieving thermal comfort conditions. At the same time, recently, there has been a growing interest in user-centered building control concepts. Thus, this work focuses on developing a thermal control strategy that combines the restrictions related to achieving thermal comfort, expressed in terms of environmental parameters and specific factors of personal perception, with the objective of reducing energy consumption. This case study aims at implementing this strategy in a laboratory room located within the Technical University of Civil Engineering Bucharest. The strategy proposed by the authors is based on implementing a combination of a Model Predictive Control (MPC) model and a fuzzy system, which presents constraints related to the room occupancy level. Relevant observations regarding the parameterization of fuzzy systems are also highlighted. Full article
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15 pages, 5119 KiB  
Article
Tracing State Structure for Ecological Processes in Soil Including Greenhouse Gas Exchange with Lower Atmosphere
by Miki Sirola, Markku Koskinen, Tatu Polvinen and Mari Pihlatie
Sensors 2024, 24(11), 3507; https://doi.org/10.3390/s24113507 - 29 May 2024
Viewed by 384
Abstract
Exploring data aids in the comprehension of the dataset and the system’s essence. Various approaches exist for managing numerous sensors. This study perceives operational states to clarify the physical dynamics within a soil environment. Utilizing Principal Component Analysis (PCA) enables dimensionality reduction, offering [...] Read more.
Exploring data aids in the comprehension of the dataset and the system’s essence. Various approaches exist for managing numerous sensors. This study perceives operational states to clarify the physical dynamics within a soil environment. Utilizing Principal Component Analysis (PCA) enables dimensionality reduction, offering an alternative perspective on the spring soil dataset. The K-means algorithm clusters data densities, forming the groundwork for an operational state description. Soil data, integral to an ecosystem, entails evident attributes. Employing dynamic visualization, including animations, constitutes a vital exploration angle. Greenhouse gas variables have been added to PCA to achieve more understanding in the interconnection of gas exchange and soil properties. Pit data and flux data are analysed both separately and together using a data-driven approach. The results look promising, showing the potential to add new values and more detailed state structures to ecological models. All experiments are conducted within the Jupyter programming environment, utilizing Python 3. The relevant literature on data visualization is examined. Through combined techniques and tools, the potential features of the soil ecosystem are observed and identified. Full article
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16 pages, 1612 KiB  
Article
Using a Slit to Suppress Optical Aberrations in Laser Triangulation Sensors
by Steven Pigeon and Benjamin Lapointe-Pinel
Sensors 2024, 24(8), 2662; https://doi.org/10.3390/s24082662 - 22 Apr 2024
Viewed by 2184
Abstract
In this paper, we present a laser triangulation sensor to measure the distance between the sensor and an object without contact using a diffraction slit rather than a traditional lens. We show that by replacing the lens with a slit, we can exploit [...] Read more.
In this paper, we present a laser triangulation sensor to measure the distance between the sensor and an object without contact using a diffraction slit rather than a traditional lens. We show that by replacing the lens with a slit, we can exploit the resulting diffraction pattern to have finer and yet simpler image analysis, yielding better estimation of the distance to the object. To test our hypothesis, we build a precision position table and a laser triangulation sensor, generate large data sets to test different estimation algorithms on various materials, and compare data acquisition using a traditional lens versus using a slit. We show that position estimation when using a slit is both more precise and more accurate than comparable methods using a lens. Full article
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16 pages, 2816 KiB  
Article
A Risk Evaluation Framework in System Control Subject to Sensor Degradation and Failure
by Tangxiao Yuan, Weilin Xu, Kondo Hloindo Adjallah, Huifen Wang, Linyan Liu and Junshan Xu
Sensors 2024, 24(5), 1550; https://doi.org/10.3390/s24051550 - 28 Feb 2024
Viewed by 689
Abstract
Sensor degradation and failure often undermine users’ confidence in adopting a new data-driven decision-making model, especially in risk-sensitive scenarios. A risk assessment framework tailored to classification algorithms is introduced to evaluate the decision-making risks arising from sensor degradation and failures in such scenarios. [...] Read more.
Sensor degradation and failure often undermine users’ confidence in adopting a new data-driven decision-making model, especially in risk-sensitive scenarios. A risk assessment framework tailored to classification algorithms is introduced to evaluate the decision-making risks arising from sensor degradation and failures in such scenarios. The framework encompasses various steps, including on-site fault-free data collection, sensor failure data collection, fault data generation, simulated data-driven decision-making, risk identification, quantitative risk assessment, and risk prediction. Leveraging this risk assessment framework, users can evaluate the potential risks of decision errors under the current data collection status. Before model adoption, ranking risk sensitivity to sensor data provides a basis for optimizing data collection. During the use of decision algorithms, considering the expected lifespan of sensors enables the prediction of potential risks the system might face, offering comprehensive information for sensor maintenance. This method has been validated through a case study involving an access control. Full article
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18 pages, 1642 KiB  
Article
Advancing Digital Twin-Based Collision Avoidance: A Comprehensive Analysis of Communication Networks for Safety-Critical Applications in Industry 4.0
by Christian Moldovan, Silas Ulrich, Volker Köster, Janis Tiemann and Andreas Lewandowski
Sensors 2024, 24(5), 1405; https://doi.org/10.3390/s24051405 - 22 Feb 2024
Cited by 1 | Viewed by 729
Abstract
This study presents a theoretical framework for defining the performance level of wireless safety functions within industrial environments. While acknowledging the simplifications inherent in our approach—primarily based on packet loss rates as a measure of system performance—the study underscores the dynamic challenges posed [...] Read more.
This study presents a theoretical framework for defining the performance level of wireless safety functions within industrial environments. While acknowledging the simplifications inherent in our approach—primarily based on packet loss rates as a measure of system performance—the study underscores the dynamic challenges posed by real-world warehouses. Through an in situ measurement study of a forklift truck safety system, we validate the proposed method and emphasize the need for a more nuanced examination of wireless communication in complex settings. The study advocates for an expanded theoretical framework that considers fluctuations in warehouse dynamics, accounting for their impact on packet loss rates and, consequently, the precision of performance-level assessments. Furthermore, the research highlights the complexity introduced by wireless system characteristics not addressed in the simplified model, urging future investigations to incorporate these factors for a comprehensive understanding of wireless safety systems. The absence of specific criteria for wireless systems within existing standards emphasizes the necessity for a specialized framework in addressing safety aspects unique to wireless applications. Full article
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22 pages, 14168 KiB  
Article
Lung-DT: An AI-Powered Digital Twin Framework for Thoracic Health Monitoring and Diagnosis
by Roberta Avanzato, Francesco Beritelli, Alfio Lombardo and Carmelo Ricci
Sensors 2024, 24(3), 958; https://doi.org/10.3390/s24030958 - 1 Feb 2024
Cited by 2 | Viewed by 1298
Abstract
The integration of artificial intelligence (AI) with Digital Twins (DTs) has emerged as a promising approach to revolutionize healthcare, particularly in terms of diagnosis and management of thoracic disorders. This study proposes a comprehensive framework, named Lung-DT, which leverages IoT sensors and AI [...] Read more.
The integration of artificial intelligence (AI) with Digital Twins (DTs) has emerged as a promising approach to revolutionize healthcare, particularly in terms of diagnosis and management of thoracic disorders. This study proposes a comprehensive framework, named Lung-DT, which leverages IoT sensors and AI algorithms to establish the digital representation of a patient’s respiratory health. Using the YOLOv8 neural network, the Lung-DT system accurately classifies chest X-rays into five distinct categories of lung diseases, including “normal”, “covid”, “lung_opacity”, “pneumonia”, and “tuberculosis”. The performance of the system was evaluated employing a chest X-ray dataset available in the literature, demonstrating average accuracy of 96.8%, precision of 92%, recall of 97%, and F1-score of 94%. The proposed Lung-DT framework offers several advantages over conventional diagnostic methods. Firstly, it enables real-time monitoring of lung health through continuous data acquisition from IoT sensors, facilitating early diagnosis and intervention. Secondly, the AI-powered classification module provides automated and objective assessments of chest X-rays, reducing dependence on subjective human interpretation. Thirdly, the twin digital representation of the patient’s respiratory health allows for comprehensive analysis and correlation of multiple data streams, providing valuable insights as to personalized treatment plans. The integration of IoT sensors, AI algorithms, and DT technology within the Lung-DT system demonstrates a significant step towards improving thoracic healthcare. By enabling continuous monitoring, automated diagnosis, and comprehensive data analysis, the Lung-DT framework has enormous potential to enhance patient outcomes, reduce healthcare costs, and optimize resource allocation. Full article
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