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State-of-the-Art Sensors Technology in Romania 2022

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 4692

Special Issue Editors


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Guest Editor
Department of Telecommunications, University Politehnica of Bucharest, 060042 Bucharest, Romania
Interests: internet of things; mobile communications; wireless networks; communications security; radio propagation
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Computer Science, University Politehnica of Bucharest, 060042 Bucharest, Romania
Interests: mobile wireless networks and computing applications, pervasive services, context-awareness, and people-centric sensing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Telecommunications, University Politehnica of Bucharest, 060042 Bucharest, Romania
Interests: signal processing; internet of things; mobile communications; wireless systems; communications security
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
1. Department of Science and Engineering of Oxide Materials and Nanomaterials, Faculty of Applied Chemistry and Materials Science, University Politehnica of Bucharest, 1-7 Gh. Polizu St., 011061 Bucharest, Romania
2. Academy of Romanian Scientists, 54 Splaiul Independenței St., Bucharest, Romania
Interests: bio(nano)materials; synthesis methods; materials processing and design; advanced coatings; tissue engineering; drug delivery; characterization methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will provide a comprehensive overview of state-of-the-art sensors science and technology and related topics in Romania. The theme of the Special Issue is “From Sensors to IoT and the Digital Revolution”, as it is intended to underscore the importance of IoT technologies in bringing about the digital revolution and making it a reality.

We invite research articles that will consolidate our understanding of the state-of-the-art in this area. The Special Issue will publish highly-rated full research and review manuscripts addressing the above topics.

The covered topics will span from sensing devices and principles (including physical, biological, chemical, optical and radiation sensors), to sensor technologies (including micro- and nanofabrication, film and printed technologies), to sensor systems (including sensor electronics, energy harvesting, sensor networks, sensory data processing and communications and Internet of Things), to applications in different scenarios (including industrial, automotive, environmental, food and agriculture, biomedical and other fields), electromagnetic compatibility and antennas, reflecting also the integration of all these aspects in unitary devices or systems.

Potential topics include but are not limited to the following:

  • Physical, chemical, and biological sensors and microsystems;
  • Sensors materials and technology;
  • Theory, modeling, design, and simulation;
  • Sensor electronics and signal processing;
  • Sensor calibration and measurements;
  • Wireless sensor networks;
  • Sensor systems and applications;
  • Actuators and micromachines;
  • Packaging and assembly technology;
  • Industrial sensors and IoT protocols;
  • Advanced communications and connectivity, and 5G technologies;
  • Distributed processing of sensory data and edge computing;
  • Sensors and the technology advances brought by artificial intelligence;
  • Cybersecurity, data security, and privacy technologies for sensory data;
  • Electromagnetic compatibility in an IoT enviromnents;
  • Antennas and antennas arrays as sensing devices;
  • Data and the Internet of Things—volume, velocity, and variability;
  • Green Technologies—the environment, sustainability, and the circular economy;
  • Sensors and biosensors addressing current emerging problems of clinical, food and environmental applications;
  • New sensing aspects in the COVID-19 pandemic context;
  • Sensors to sensor systems evolution.

Prof. Dr. Octavian Fratu
Prof. Dr. Ciprian Dobre
Prof. Dr. Simona Halunga
Prof. Dr. Anton Ficai
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

  • sensors
  • Internet of Things
  • sensors systems
  • sensors-based applications
  • sensory data processing and communications
  • sensors data analytics
  • integrated sensors systems
  • electromagnetic compatibility

Published Papers (2 papers)

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Research

18 pages, 3608 KiB  
Article
Robust OCC System Optimized for Low-Frame-Rate Receivers
by Robert-Alexandru Dobre, Radu-Ovidiu Preda and Radu-Alexandru Badea
Sensors 2022, 22(16), 5938; https://doi.org/10.3390/s22165938 - 09 Aug 2022
Cited by 1 | Viewed by 1375
Abstract
Light emitting diodes (LED) are becoming the dominant lighting elements due to their efficiency. Optical camera communications (OCC), the branch of visible light communications (VLC) that uses video cameras as receivers, is a suitable candidate in facilitating the development of new communication solutions [...] Read more.
Light emitting diodes (LED) are becoming the dominant lighting elements due to their efficiency. Optical camera communications (OCC), the branch of visible light communications (VLC) that uses video cameras as receivers, is a suitable candidate in facilitating the development of new communication solutions for the broader public because video cameras are available on almost any smartphone nowadays. Unfortunately, most OCC systems that have been proposed until now require either expensive and specialized high-frame-rate cameras as receivers, which are unavailable on smartphones, or they rely on the rolling shutter effect, being sensitive to camera movement and pointing direction, they produce light flicker when low-frame-rate cameras are used, or they must discern between more than two light intensity values, affecting the robustness of the decoding process. This paper presents in detail the design of an OCC system that overcomes these limitations, being designed for receivers capturing 120 frames per second and being easily adaptable for any other frame rate. The system does not rely on the rolling shutter effect, thus making it insensitive to camera movement during frame acquisition and less demanding about camera resolution. It can work with reflected light, requiring neither a direct line of sight to the light source nor high resolution image sensors. The proposed communication is invariant to the moment when the transmitter and the receiver are started as the communication is self-synchronized, without any other exchange of information between the transmitter and the receiver, without producing light flicker, and requires only two levels of brightness to be detected (light on and light off). The proposed system overcomes the challenge of not producing light flicker even when it is adapted to work with very low-frame-rate receivers. This paper presents the statistical analysis of the communication performance and discusses its implementation in an indoor localization system. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Romania 2022)
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14 pages, 4161 KiB  
Article
Upper and Lower Leaf Side Detection with Machine Learning Methods
by Rodica Gabriela Dawod and Ciprian Dobre
Sensors 2022, 22(7), 2696; https://doi.org/10.3390/s22072696 - 31 Mar 2022
Cited by 9 | Viewed by 1915
Abstract
Recent studies have approached the identification of foliar plant diseases using artificial intelligence, but in these works, classification is achieved using only one side of the leaf. Phytopathology specifies that there are diseases that show similar symptoms on the upper part of the [...] Read more.
Recent studies have approached the identification of foliar plant diseases using artificial intelligence, but in these works, classification is achieved using only one side of the leaf. Phytopathology specifies that there are diseases that show similar symptoms on the upper part of the leaf, but different ones on the lower side. An improvement in accuracy can be achieved if the symptoms of both sides of the leaf are considered when classifying plant diseases. In this context, it is necessary to establish whether the captured image represents the leaf on its upper or lower side. From the research conducted using botany books, we can conclude that a useful classification feature is color, because the sun-facing part is greener, while the opposite side is shaded. A second feature is the thickness of the primary and secondary veins. The veins of a leaf are more prominent on the lower side, compared to the upper side. A third feature corresponds to the concave shape of the leaf on its upper part and its convex shape on the lower part. In this study, we aim to achieve upper and lower leaf side classification using both deep learning methods and machine learning models. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Romania 2022)
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