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Current Advances in Sensor Design, Innovation, and Their Industry Applications
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Current Advances in Sensor Design, Innovation, and Their Industry Applications

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 3271

Special Issue Editor

Prof. Dr. Wilmar Hernandez

E-Mail Website
Guest Editor
Facultad de Ingeniería y Ciencias Aplicadas, Campus UDLAPARK, Universidad de Las Américas-Ecuador, Redondel del Ciclista, Antigua Vía a Nayón, Quito EC 170124, Ecuador
Interests: signal processing, estimation, and control for sensors; robust and optimal sensor systems and their applications; statistical analysis of the information obtained from sensor measurements; signal conditioning techniques for intelligent sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In today's data-rich environment, artificial intelligence (AI) tools are playing an increasingly pivotal role in many areas of science and engineering. This has led to a surge in data science, which is becoming a powerful force in decision making. It is therefore vital to provide users with reliable, optimal and robust information about the state of the variables that govern the dynamics of processes, nature and the environment. In this context, sensors are of critical importance, as they are responsible for monitoring the value of physical quantities and detecting changes. It is therefore essential to have reliable and robust sensor systems that can accurately and consistently measure these quantities. Furthermore, to gain a deeper understanding of the subject, it is essential to consider the role of the sensor, the signal conditioning circuits, the electronic instrumentation and the correct transmission of information in human–nature interactions. 

However, it is important to note that process data are often unavailable or affected by a significant amount of noise and random disturbances in both research and industry settings. It should be noted that these processes may also be multivariable, non-linear and time-varying. As a result, AI tools are not effective solutions for addressing the issues that impact these processes. The most effective method for controlling these processes is through a scientific approach that incorporates system identification, mathematical-physical modeling, robust and optimal control and the appropriate use of electronic instrumentation, sensors and actuators. 

The primary objective of this Special Session is to publish works focused on the design and construction of sensor systems with applications in various scientific and engineering fields, including electrical, mechanical, civil, chemical and interdisciplinary engineering, as well as subdisciplines within these fields. Finally, papers that address innovative solutions in sensor design as well as signal conditioning and signal processing techniques for sensors are welcome.

Prof. Dr. Wilmar Hernandez
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. 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

  • signal processing
  • estimation and control for sensor
  • novel signal conditioning techniques for sensors
  • statistical analysis of the information from sensor measurements
  • robust and optimal sensor systems
  • novel interface for sensors
  • applications of sensors in robotics, mechanics, telecommunications, environment, transportation, electrical circuits, hardware engineering, software engineering, IoT systems, acoustics, optics, biological systems and aerospace engineering, among other fields of science and engineering

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

25 pages, 6331 KiB  
Article
Substation Inspection Safety Risk Identification Based on Synthetic Data and Spatiotemporal Action Detection
by Chengcheng Liu, Weihua Zhang, Weijin Xu, Bo Lu, Weijie Li and Xuefeng Zhao
Sensors 2025, 25(9), 2720; https://doi.org/10.3390/s25092720 - 25 Apr 2025
Viewed by 65
Abstract
During substation inspection, operators are often exposed to hazardous working environments. It is necessary to use visual sensors to determine work status and perform action detection to distinguish between normal and dangerous actions in order to ensure the safety of operators. However, due [...] Read more.
During substation inspection, operators are often exposed to hazardous working environments. It is necessary to use visual sensors to determine work status and perform action detection to distinguish between normal and dangerous actions in order to ensure the safety of operators. However, due to information security, privacy protection, and the rarity of dangerous scenarios, there is a scarcity of related visual action datasets. To address this issue, this study first introduces a virtual work platform, which includes a controller for the parameterized control of scenarios and human resources. It can simulate realistic substation inspection operations and generate synthetic action datasets using domain randomization and behavior tree logic. Subsequently, a spatiotemporal action detection algorithm is utilized for action detection, employing YOLOv8 as the human detector, Vision Transformer as the backbone network, and SlowFast as the action detection architecture. Model training is conducted using three datasets: a real dataset, a synthetic dataset generated via a VWP, and a mixed dataset comprising both real and synthetic data. Finally, using the model trained on the real dataset as a baseline, the evaluation results on the test set shows that the use of synthetic datasets in training improves the model’s average precision by up to 10.7%, with a maximum average precision of 73.61%. This demonstrates the feasibility, effectiveness, and robustness of synthetic data. Full article
(This article belongs to the Special Issue Current Advances in Sensor Design, Innovation, and Their Industry Applications)
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23 pages, 1206 KiB  
Article
Air Pollution Monitoring Using Cost-Effective Devices Enhanced by Machine Learning
by Yanis Colléaux, Cédric Willaume, Bijan Mohandes, Jean-Christophe Nebel and Farzana Rahman
Sensors 2025, 25(5), 1423; https://doi.org/10.3390/s25051423 - 26 Feb 2025
Viewed by 1167
Abstract
Given the significant impact of air pollution on global health, the continuous and precise monitoring of air quality in all populated environments is crucial. Unfortunately, even in the most developed economies, current air quality monitoring networks are largely inadequate. The high cost of [...] Read more.
Given the significant impact of air pollution on global health, the continuous and precise monitoring of air quality in all populated environments is crucial. Unfortunately, even in the most developed economies, current air quality monitoring networks are largely inadequate. The high cost of monitoring stations has been identified as a key barrier to widespread coverage, making cost-effective air quality monitoring devices a potential game changer. However, the accuracy of the measurements obtained from low-cost sensors is affected by many factors, including gas cross-sensitivity, environmental conditions, and production inconsistencies. Fortunately, machine learning models can capture complex interdependent relationships in sensor responses and thus can enhance their readings and sensor accuracy. After gathering measurements from cost-effective air pollution monitoring devices placed alongside a reference station, the data were used to train such models. Assessments of their performance showed that models tailored to individual sensor units greatly improved measurement accuracy, boosting their correlation with reference-grade instruments by up to 10%. Nonetheless, this research also revealed that inconsistencies in the performance of similar sensor units can prevent the creation of a unified correction model for a given sensor type. Full article
(This article belongs to the Special Issue Current Advances in Sensor Design, Innovation, and Their Industry Applications)
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46 pages, 11722 KiB  
Article
A Signal Pattern Extraction Method Useful for Monitoring the Condition of Actuated Mechanical Systems Operating in Steady State Regimes
by Adriana Munteanu, Mihaita Horodinca, Neculai-Eduard Bumbu, Catalin Gabriel Dumitras, Dragos-Florin Chitariu, Constantin-Gheorghe Mihai, Mohammed Khdair and Lucian Oancea
Sensors 2025, 25(4), 1119; https://doi.org/10.3390/s25041119 - 12 Feb 2025
Viewed by 453
Abstract
The aim of this paper is to present an approach to condition monitoring of an actuated mechanical system operating in a steady-state regime. The state signals generated by the sensors placed on the mechanical system (a lathe headstock gearbox) operating in a steady-state [...] Read more.
The aim of this paper is to present an approach to condition monitoring of an actuated mechanical system operating in a steady-state regime. The state signals generated by the sensors placed on the mechanical system (a lathe headstock gearbox) operating in a steady-state regime contain a sum of periodic components, sometimes mixed with a small amount of noise. It is assumed that the state of a rotating part placed inside a mechanical system can be characterized by the shape of a periodic component within the state signal. This paper proposes a method to find the time domain description for the significant periodic components within these state signals, as patterns, based on the arithmetic averaging of signal samples selected at constant time regular intervals. This averaging has the same effect as a numerical filter with multiple narrow pass bands. The availability of this method for condition monitoring has been fully demonstrated experimentally. It has been applied to three different state signals: the active electrical power absorbed by an asynchronous AC electric motor driving a lathe headstock gearbox, the vibration of this gearbox, and the instantaneous angular speed of the output spindle. The paper presents some relevant patterns describing the behavior of different rotating parts within this gearbox, extracted from these state signals. Full article
(This article belongs to the Special Issue Current Advances in Sensor Design, Innovation, and Their Industry Applications)
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17 pages, 13115 KiB  
Article
Development, Verification and Assessment of a Laser Profilometer and Analysis Algorithm for Microtexture Assessment of Runway Surfaces
by Gadel Baimukhametov and Greg White
Sensors 2024, 24(23), 7661; https://doi.org/10.3390/s24237661 - 29 Nov 2024
Viewed by 769
Abstract
Runway surface friction is critically important to safe aircraft operations and mostly depends on the surface texture, which provides grip in the presence of contamination and directly affects the friction coefficient in general. Microtexture assessment is the most challenging part of texture assessment [...] Read more.
Runway surface friction is critically important to safe aircraft operations and mostly depends on the surface texture, which provides grip in the presence of contamination and directly affects the friction coefficient in general. Microtexture assessment is the most challenging part of texture assessment since there is no standardised pavement microtexture control method in runway maintenance and management practice. The purpose of this study was to develop a simple laser profilometer and analysis model and subsequent validation for use in runway friction surveys. To that end, a simple laser profilometer was developed, and a profile picture analysis and macrotexture filtration method were designed. Test results were compared to the stylus-based roughness tester and the British Pendulum Tester. The proposed profile picture analysis and profile smothering and filtration methodology, based on linear approximation, is simpler and more effective for the case of macrotexture filtration for the friction survey. The laser profilometer model results were highly correlated with the stylus-based roughness tester results (R2 = 0.99). The average roughness of the microtexture profile, after smothering and macrotexture filtration, also showed good correlation with the British Pendulum results (R2 = 0.78). The results from this study confirm the possibility of texture assessment for routine runway friction surveys using a simple and economical laser profilometer, which is not routinely available in current airport surface friction management. Full article
(This article belongs to the Special Issue Current Advances in Sensor Design, Innovation, and Their Industry Applications)
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