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Selected Papers from the 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (IEEE MetroXRAINE 2023)

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

Deadline for manuscript submissions: 5 June 2024 | Viewed by 1247

Special Issue Editors

Prof. Dr. Egidio De Benedetto

E-Mail Website
Guest Editor
Department of Information Technology and Electrical Engineering, University of Naples Federico II, 80138 Naples, Italy
Interests: dielectric characterization of materials; microwave reflectometry systems for monitoring applications; 4.0-based measurement systems; measurements for augmented reality systems
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio Esposito

E-Mail Website
Guest Editor
Department of Information Technology and Electrical Engineering, University of Naples Federico II, 80125 Naples, Italy
Interests: measurement theory; electronic measurements; electronic instrumentation devices; wearable brain computer interface
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering—IEEE MetroXRAINE 2023 will be held on OCTOBER 25-27, 2023, in Milano, Italy (https://metroxraine.org/). This will be an international event, mainly aimed at creating a synergy between experts in eXtended reality, the brain–computer interface, and artificial intelligence, with special attention to measurement. Authors of papers related to sensors presented at the conference are invited to submit extended versions of their work to this Special Issue for publication.

Areas of interest include, but are not limited to:

  • Instrumental solutions and measurement principles for enhancing the accuracy and robustness of XR-BCI systems.
  • Display technologies and human vision.
  • Wearable sensors for neuroimaging.
  • User experience, perception and interactions in XR and BCI.
  • Multisensory experiences and improved immersion.
  • Psychophysical condition monitoring.
  • Advanced machine learning techniques for XR-BCI.
  • Deep learning-based classification.
  • VR-supported mindfulness-based on EEG signals.
  • Immersive user experience with XR-BCI.
  • Human-in-the-loop AI.
  • Bioengineering and rehabilitation.
  • Biosignal processing.
  • Instrumental solutions and measurement principles for smart industry.
  • New challenge for metrology in the digital transformation scenario.

You may choose our Joint Special Issue in Metrology.

Prof. Dr. Egidio De Benedetto
Dr. Antonio Esposito
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.

Published Papers (2 papers)

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Research

23 pages, 1940 KiB  
Article
Performance Assessment for the Validation of Wireless Communication Engines in an Innovative Wearable Monitoring Platform
by Alessio Serrani and Andrea Aliverti
Sensors 2024, 24(9), 2782; https://doi.org/10.3390/s24092782 (registering DOI) - 26 Apr 2024
Abstract
In today’s health-monitoring applications, there is a growing demand for wireless and wearable acquisition platforms capable of simultaneously gathering multiple bio-signals from multiple body areas. These systems require well-structured software architectures, both to keep different wireless sensing nodes synchronized each other and to [...] Read more.
In today’s health-monitoring applications, there is a growing demand for wireless and wearable acquisition platforms capable of simultaneously gathering multiple bio-signals from multiple body areas. These systems require well-structured software architectures, both to keep different wireless sensing nodes synchronized each other and to flush collected data towards an external gateway. This paper presents a quantitative analysis aimed at validating both the wireless synchronization task (implemented with a custom protocol) and the data transmission task (implemented with the BLE protocol) in a prototype wearable monitoring platform. We evaluated seven frequencies for exchanging synchronization packets (10 Hz, 20 Hz, 30 Hz, 40 Hz, 50 Hz, 60 Hz, 70 Hz) as well as two different BLE configurations (with and without the implementation of a dynamic adaptation of the BLE Connection Interval parameter). Additionally, we tested BLE data transmission performance in five different use case scenarios. As a result, we achieved the optimal performance in the synchronization task (1.18 ticks as median synchronization delay with a Min-Max range of 1.60 ticks and an Interquartile range (IQR) of 0.42 ticks) when exploiting a synchronization frequency of 40 Hz and the dynamic adaptation of the Connection Interval. Moreover, BLE data transmission proved to be significantly more efficient with shorter distances between the communicating nodes, growing worse by 30.5% beyond 8 m. In summary, this study suggests the best-performing network configurations to enhance the synchronization task of the prototype platform under analysis, as well as quantitative details on the best placement of data collectors. Full article
(This article belongs to the Special Issue Selected Papers from the 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (IEEE MetroXRAINE 2023))
9 pages, 1983 KiB  
Communication
Investigation of Climate Effects on the Physiological Parameters of Dairy Livestock (Cow vs. Buffalo)
by Nadia Piscopo, Roberta Matera, Alessio Cotticelli, Lucia Trapanese, Oscar Tamburis, Roberta Cimmino and Angela Salzano
Sensors 2024, 24(4), 1164; https://doi.org/10.3390/s24041164 - 10 Feb 2024
Viewed by 597
Abstract
Nowadays climate change is affecting the planet’s biodiversity, and livestock practices must adapt themselves to improve production without affecting animal welfare. This work investigates the influence that some climatic parameters such as Environment Temperature, Relative Humidity, Thermal excursion and Temperature–Humidity Index (THI), can [...] Read more.
Nowadays climate change is affecting the planet’s biodiversity, and livestock practices must adapt themselves to improve production without affecting animal welfare. This work investigates the influence that some climatic parameters such as Environment Temperature, Relative Humidity, Thermal excursion and Temperature–Humidity Index (THI), can have on milk quantity and quality in two different dairy species (buffaloes and cows) raised on the same farm. A further aim was to understand if THI threshold used for cows could also be used for buffaloes. The climatic parameters were recorded daily through a meteorological station located inside the farm. Milk quantity (converted into ECM) and quality (Fat Percentage—FP; Protein Percentage—PP; Somatic Cell Count—SCC) were measured. Data were analyzed with Spearman’s correlation index, separately for buffaloes and cows. The results indicate a greater sensitivity of cows to heat stress and a strong negative correlation of the ECM with meteorological data (p < 0.01). The results of this study may stimulate the use of integrated technologies (sensors, software) in the dairy sector, since the IoT (sensors, software) helps to enhance animal well-being and to optimize process costs, with a precision livestock farming approach. Full article
(This article belongs to the Special Issue Selected Papers from the 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (IEEE MetroXRAINE 2023))
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