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Electronics
Special Issues
Sensor Based Big Data Analysis
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Sensor Based Big Data Analysis

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 May 2024) | Viewed by 6527

Special Issue Editor

Dr. Daniela Isidori

E-Mail Website
Guest Editor
Independent Researcher, Pedaso, 63827 Marche, Italy
Interests: monitoring systems; sensors; wireless sensors network; data analysis; numerical simulations; structural health monitoring systems; energy monitoring systems; renewable energies; machine learning; net-zero-energy buildings; artificial intelligence; smart things; smart buildings; smart cities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensor data analysis, together with the design of novel and reliable modelling and simulation techniques, allow for the development of a smart monitoring system that can be applied across a large range of applications, such as buildings, infrastructure, smart cities, precision agriculture, industrial components, and autonomous mobility, etc., for resilience, decision-making, proficient implementation of operational control, maintenance strategies, and sustainability in various sectors.

Sensor data analysis includes cutting-edge sensor technologies, data acquisition systems, data analysis techniques, machine learning (ML), and artificial intelligence (AI) solutions. Despite recent successful examples, more research is needed to collect, store, and manage big datasets from robust sensor networks and data acquisition systems, as well as to create reliable and efficient algorithms for data analysis and trustful numerical simulations. This Special Issue “Sensor Based Big Data Analysis” focuses on recent advances in data analysis methods for smart monitoring systems and their applications and is calling for high-impact submissions in the following areas:

  • Data analysis methods;
  • Machine learning methods;
  • Modelling of sensor networks (wired and wireless);
  • Real-time data collection with dynamic inputs;
  • Time synchronization;
  • Simulation techniques;
  • Embedded machine learning methods and applications;
  • Novel applications of smart monitoring systems from all areas;
  • All other related areas.

Dr. Daniela Isidori
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. Electronics 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 2400 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

  • data analysis
  • sensor networks
  • machine learning
  • artificial intelligence
  • wireless sensors
  • numerical model
  • simulation techniques
  • monitoring system

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Published Papers (3 papers)

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Research

15 pages, 26053 KiB  
Article
Module Tester for Positron Emission Tomography and Particle Physics
by David Baranyai, Stefan Oniga, Balazs Gyongyosi, Balazs Ujvari and Attia Mohamed
Electronics 2024, 13(15), 3066; https://doi.org/10.3390/electronics13153066 - 2 Aug 2024
Cited by 1 | Viewed by 1002
Abstract
The combination of high-density, high-time-resolution inorganic scintillation crystals such as Lutetium Yttrium Oxyorthosilicate (LYSO), Yttrium Orthosilicate (YSO) and Bismuth Germanate (BGO) with Silicon Photomultiplier (SiPM) sensors is widely employed in medical imaging, particularly in Positron Emission Tomography (PET), as well as in modern [...] Read more.
The combination of high-density, high-time-resolution inorganic scintillation crystals such as Lutetium Yttrium Oxyorthosilicate (LYSO), Yttrium Orthosilicate (YSO) and Bismuth Germanate (BGO) with Silicon Photomultiplier (SiPM) sensors is widely employed in medical imaging, particularly in Positron Emission Tomography (PET), as well as in modern particle physics detectors for precisely timing sub-detectors and calorimeters. During the assembly of each module, following individual component testing, the crystals and SiPMs are bonded together using optical glue and enclosed in a light-tight, temperature-controlled cooling box. After integration with the readout electronics, the bonding is initially tested. The final readout electronics typically comprise Application-Specific Integrated Circuits (ASICs) or low-power Analog-to-Digital Converters (ADCs) and amplifiers, designed not to heat up the temperature-sensitive SiPM sensors. However, these setups are not optimal for testing the optical bonding. Specific setups were developed to test the LYSO + SiPM modules that are already bonded but not enclosed in a box. Through large data collection, small deviations in bonding can be detected if the SiPMs and LYSOs have been thoroughly tested before our measurement. The Monte Carlo simulations we used to study how parameters—which are difficult to measure in the laboratory (LYSO absorption length, refractive index of the coating)—affect the final result. Our setups for particle physics and PET applications are already in use by research institutes and industrial partners. Full article
(This article belongs to the Special Issue Sensor Based Big Data Analysis)
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22 pages, 8354 KiB  
Article
Driving Reality vs. Simulator: Data Distinctions
by Natalia Piaseczna, Rafał Doniec, Szymon Sieciński, Klaudia Barańska, Marek Jędrychowski and Marcin Grzegorzek
Electronics 2024, 13(14), 2708; https://doi.org/10.3390/electronics13142708 - 10 Jul 2024
Viewed by 1885
Abstract
As the automotive industry undergoes a phase of rapid transformation driven by technological advancements, the integration of driving simulators stands out as an important tool for research and development. The usage of such simulators offers a controlled environment for studying driver behavior; the [...] Read more.
As the automotive industry undergoes a phase of rapid transformation driven by technological advancements, the integration of driving simulators stands out as an important tool for research and development. The usage of such simulators offers a controlled environment for studying driver behavior; the alignment of data, however, remains a complex aspect that warrants a thorough investigation. This research investigates driver state classification using a dataset obtained from real-road and simulated conditions, recorded through JINS MEME ES_R smart glasses. The dataset encompasses electrooculography signals, with a focus on standardizing and processing the data for subsequent analysis. For this purpose, we used a recurrent neural network model, which yielded a high accuracy on the testing dataset (86.5%). The findings of this study indicate that the proposed methodology could be used in real scenarios and that it could be used for the development of intelligent transportation systems and driver monitoring technology. Full article
(This article belongs to the Special Issue Sensor Based Big Data Analysis)
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18 pages, 1206 KiB  
Article
A Reference Paper Collection System Using Web Scraping
by Inzali Naing, Soe Thandar Aung, Khaing Hsu Wai and Nobuo Funabiki
Electronics 2024, 13(14), 2700; https://doi.org/10.3390/electronics13142700 - 10 Jul 2024
Cited by 1 | Viewed by 3108
Abstract
Collecting reference papers from the Internet is one of the most important activities for progressing research and writing papers about their results. Unfortunately, the current process using Google Scholar may not be efficient, since a lot of paper files cannot be accessed directly [...] Read more.
Collecting reference papers from the Internet is one of the most important activities for progressing research and writing papers about their results. Unfortunately, the current process using Google Scholar may not be efficient, since a lot of paper files cannot be accessed directly by the user. Even if they are accessible, their effectiveness needs to be checked manually. In this paper, we propose a reference paper collection system using web scraping to automate paper collections from websites. This system can collect or monitor data from the Internet, which is considered as the environment, using Selenium, a popular web scraping software, as the sensor; this examines the similarity against the search target by comparing the keywords using the Bert model. The Bert model is a deep learning model for natural language processing (NLP) that can understand context by analyzing the relationships between words in a sentence bidirectionally. The Python Flask is adopted at the web application server, where Angular is used for data presentations. For the evaluation, we measured the performance, investigated the accuracy, and asked members of our laboratory to use the proposed method and provide their feedback. Their results confirm the method’s effectiveness. Full article
(This article belongs to the Special Issue Sensor Based Big Data Analysis)
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