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Mixed Micro/Macro Cache for Device-to-Device Caching Systems in Multi-Operator Environments
Assessment of PM2.5 Concentration at University Transit Bus Stops Using Low-Cost Aerosol Monitors by Student Commuters
Remotely Sensed Maize Yield Estimation Model Dependency Analysis

Journal Description

Sensors

Sensors is an international, peer-reviewed, open access journal on the science and technology of sensors. Sensors is published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE), Japan Society of Photogrammetry and Remote Sensing (JSPRS), Spanish Society of Biomedical Engineering (SEIB), International Society for the Measurement of Physical Behaviour (ISMPB) and Chinese Society of Micro-Nano Technology (CSMNT) are affiliated with Sensors and their members receive a discount on the article processing charges.

Open Access — free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, MEDLINE, PMC, Ei Compendex, Inspec, Astrophysics Data System, and other databases.
Journal Rank: JCR - Q2 (Chemistry, Analytical) / CiteScore - Q1 (Instrumentation)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.8 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the first half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Testimonials: See what our editors and authors say about Sensors.
Companion journals for Sensors include: Chips, JCP and Targets.

Impact Factor: 3.4 (2023); 5-Year Impact Factor: 3.7 (2023)

Imprint Information Journal Flyer Open Access ISSN: 1424-8220

Latest Articles

25 pages, 842 KiB

Open AccessArticle

The Potential of Deep Learning in Underwater Wireless Sensor Networks and Noise Canceling for the Effective Monitoring of Aquatic Life

by Walaa M. Elsayed, Maazen Alsabaan, Mohamed I. Ibrahem and Engy El-Shafeiy

Sensors 2024, 24(18), 6102; https://doi.org/10.3390/s24186102 (registering DOI) - 20 Sep 2024

This paper describes a revolutionary design paradigm for monitoring aquatic life. This unique methodology addresses issues such as limited memory, insufficient bandwidth, and excessive noise levels by combining two approaches to create a comprehensive predictive filtration system, as well as multiple-transfer route analysis. [...] Read more.

This paper describes a revolutionary design paradigm for monitoring aquatic life. This unique methodology addresses issues such as limited memory, insufficient bandwidth, and excessive noise levels by combining two approaches to create a comprehensive predictive filtration system, as well as multiple-transfer route analysis. This work focuses on proposing a novel filtration learning approach for underwater sensor nodes. This model was created by merging two adaptive filters, the finite impulse response (FIR) and the adaptive line enhancer (ALE). The FIR integrated filter eliminates unwanted noise from the signal by obtaining a linear response phase and passes the signal without distortion. The goal of the ALE filter is to properly separate the noise signal from the measured signal, resulting in the signal of interest. The cluster head level filters are the adaptive cuckoo filter (ACF) and the Kalman filter. The ACF assesses whether an emitter node is part of a set or not. The Kalman filter improves the estimation of state values for a dynamic underwater sensor networking system. It uses distributed learning long short-term memory (LSTM-CNN) technology to ensure that the anticipated value of the square of the gap between the prediction and the correct state is the smallest possible. Compared to prior methods, our suggested deep filtering–learning model achieved 98.5% of the sensory filtration method in the majority of the obtained data and close to 99.1% of an adaptive prediction method, while also consuming little energy during lengthy monitoring. Full article

(This article belongs to the Section Sensor Networks)

16 pages, 19754 KiB

Open AccessArticle

Generic FPGA Pre-Processing Image Library for Industrial Vision Systems

by Diogo Ferreira, Filipe Moutinho , João P. Matos-Carvalho , Magno Guedes and Pedro Deusdado

Sensors 2024, 24(18), 6101; https://doi.org/10.3390/s24186101 (registering DOI) - 20 Sep 2024

Currently, there is a demand for an increase in the diversity and quality of new products reaching the consumer market. This fact imposes new challenges for different industrial sectors, including processes that integrate machine vision. Hardware acceleration and improvements in processing efficiency are [...] Read more.

Currently, there is a demand for an increase in the diversity and quality of new products reaching the consumer market. This fact imposes new challenges for different industrial sectors, including processes that integrate machine vision. Hardware acceleration and improvements in processing efficiency are becoming crucial for vision-based algorithms to follow the complexity growth of future industrial systems. This article presents a generic library of pre-processing filters for execution in field-programmable gate arrays (FPGAs) to reduce the overall image processing time in vision systems. An experimental setup based on the Zybo Z7 Pcam 5C Demo project was developed and used to validate the filters described in VHDL (VHSIC hardware description language). Finally, a comparison of the execution times using GPU and CPU platforms was performed as well as an evaluation of the integration of the current work in an industrial application. The results showed a decrease in the pre-processing time from milliseconds to nanoseconds when using FPGAs. Full article

(This article belongs to the Section Sensing and Imaging)

21 pages, 4410 KiB

Open AccessArticle

Triple-Camera Rectification for Depth Estimation Sensor

by Minkyung Jeon, Jinhong Park, Jin-Woo Kim and Sungmin Woo

Sensors 2024, 24(18), 6100; https://doi.org/10.3390/s24186100 (registering DOI) - 20 Sep 2024

In this study, we propose a novel rectification method for three cameras using a single image for depth estimation. Stereo rectification serves as a fundamental preprocessing step for disparity estimation in stereoscopic cameras. However, off-the-shelf depth cameras often include an additional RGB camera [...] Read more.

In this study, we propose a novel rectification method for three cameras using a single image for depth estimation. Stereo rectification serves as a fundamental preprocessing step for disparity estimation in stereoscopic cameras. However, off-the-shelf depth cameras often include an additional RGB camera for creating 3D point clouds. Existing rectification methods only align two cameras, necessitating an additional rectification and remapping process to align the third camera. Moreover, these methods require multiple reference checkerboard images for calibration and aim to minimize alignment errors, but often result in rotated images when there is significant misalignment between two cameras. In contrast, the proposed method simultaneously rectifies three cameras in a single shot without unnecessary rotation. To achieve this, we designed a lab environment with checkerboard settings and obtained multiple sample images from the cameras. The optimization function, designed specifically for rectification in stereo matching, enables the simultaneous alignment of all three cameras while ensuring performance comparable to traditional methods. Experimental results with real camera samples demonstrate the benefits of the proposed method and provide a detailed analysis of unnecessary rotations in the rectified images. Full article

(This article belongs to the Collection 3D Imaging and Sensing System)

19 pages, 2076 KiB

Open AccessArticle

Application of Indoor Positioning Systems in Nursing Homes: Enhancing Resident Safety and Staff Efficiency

by Chia-Rong Lee, Edward T.-H. Chu, Min-Jing Sie, Li-Tsai Lin, Mei-Zhen Hong and Ching-Chih Huang

Sensors 2024, 24(18), 6099; https://doi.org/10.3390/s24186099 (registering DOI) - 20 Sep 2024

Providing a safe and secure living environment for residents that is supported by a dedicated healthcare team is one of the core values of nursing homes. Nursing homes must protect residents from the risk of going missing, track quarantined residents and visitors to [...] Read more.

Providing a safe and secure living environment for residents that is supported by a dedicated healthcare team is one of the core values of nursing homes. Nursing homes must protect residents from the risk of going missing, track quarantined residents and visitors to control the spread of infection, and maintain proactive nursing rounds. However, recruiting and retaining qualified caregivers and medical staff has long been a challenge. Therefore, using advanced technology to ensure the safety and security of residents is highly desirable. In this work, we first demonstrate the applicability of indoor tracking applications in a nursing home, such as resident and asset tracking, nursing assistant management, visitor tracking, infection control, and vital-sign monitoring. To monitor the locations of residents and staff, Bluetooth tags were used, providing real-time data for location tracking. We then conduct a series of quantitative analyses to illustrate how indoor tracking data can support the management of nursing homes, including characterizing residents’ activities in daily living and assessing the performance and workload of nursing assistants. Finally, we use qualitative research to evaluate the acceptability of an indoor positioning system in the nursing home. The results show that the implemented indoor positioning applications can improve the quality of healthcare and working efficiency, thereby providing a safer and more secure living environment for residents. Full article

(This article belongs to the Special Issue Indoor Navigation: Indoor Positioning System Using Sensing Technologies)

26 pages, 2940 KiB

Open AccessArticle

Real-Time Air-Writing Recognition for Arabic Letters Using Deep Learning

by Aseel Qedear, Aldanh AlMatrafy, Athary Al-Sowat, Abrar Saigh and Asmaa Alayed

Sensors 2024, 24(18), 6098; https://doi.org/10.3390/s24186098 (registering DOI) - 20 Sep 2024

Learning to write the Arabic alphabet is crucial for Arab children’s cognitive development, enhancing their memory and retention skills. However, the lack of Arabic language educational applications may hamper the effectiveness of their learning experience. To bridge this gap, SamAbjd was developed, an [...] Read more.

Learning to write the Arabic alphabet is crucial for Arab children’s cognitive development, enhancing their memory and retention skills. However, the lack of Arabic language educational applications may hamper the effectiveness of their learning experience. To bridge this gap, SamAbjd was developed, an interactive web application that leverages deep learning techniques, including air-writing recognition, to teach Arabic letters. SamAbjd was tailored to user needs through extensive surveys conducted with mothers and teachers, and a comprehensive literature review was performed to identify effective teaching methods and models. The development process involved gathering data from three publicly available datasets, culminating in a collection of 31,349 annotated images of handwritten Arabic letters. To enhance the dataset’s quality, data preprocessing techniques were applied, such as image denoising, grayscale conversion, and data augmentation. Two models were experimented with using a convolution neural network (CNN) and Visual Geometry Group (VGG16) to evaluate their effectiveness in recognizing air-written Arabic characters. Among the CNN models tested, the standout performer was a seven-layer model without dropout, which achieved a high testing accuracy of 96.40%. This model also demonstrated impressive precision and F1-score, both around 96.44% and 96.43%, respectively, indicating successful fitting without overfitting. The web application, built using Flask and PyCharm, offers a robust and user-friendly interface. By incorporating deep learning techniques and user feedback, the web application meets educational needs effectively. Full article

(This article belongs to the Section Sensing and Imaging)

23 pages, 9501 KiB

Open AccessArticle

Visual Feature-Guided Diamond Convolutional Network for Finger Vein Recognition

by Qiong Yao, Dan Song, Xiang Xu and Kun Zou

Sensors 2024, 24(18), 6097; https://doi.org/10.3390/s24186097 (registering DOI) - 20 Sep 2024

Finger vein (FV) biometrics have garnered considerable attention due to their inherent non-contact nature and high security, exhibiting tremendous potential in identity authentication and beyond. Nevertheless, challenges pertaining to the scarcity of training data and inconsistent image quality continue to impede the effectiveness [...] Read more.

Finger vein (FV) biometrics have garnered considerable attention due to their inherent non-contact nature and high security, exhibiting tremendous potential in identity authentication and beyond. Nevertheless, challenges pertaining to the scarcity of training data and inconsistent image quality continue to impede the effectiveness of finger vein recognition (FVR) systems. To tackle these challenges, we introduce the visual feature-guided diamond convolutional network (dubbed `VF-DCN’), a uniquely configured multi-scale and multi-orientation convolutional neural network. The VF-DCN showcases three pivotal innovations: Firstly, it meticulously tunes the convolutional kernels through multi-scale Log-Gabor filters. Secondly, it implements a distinctive diamond-shaped convolutional kernel architecture inspired by human visual perception. This design intelligently allocates more orientational filters to medium scales, which inherently carry richer information. In contrast, at extreme scales, the use of orientational filters is minimized to simulate the natural blurring of objects at extreme focal lengths. Thirdly, the network boasts a deliberate three-layer configuration and fully unsupervised training process, prioritizing simplicity and optimal performance. Extensive experiments are conducted on four FV databases, including MMCBNU_6000, FV_USM, HKPU, and ZSC_FV. The experimental results reveal that VF-DCN achieves remarkable improvement with equal error rates (EERs) of

0.17 %

,

0.19 %

,

2.11 %

, and

0.65 %

, respectively, and Accuracy Rates (ACC) of

100 %

,

99.97 %

,

98.92 %

, and

99.36 %

, respectively. These results indicate that, compared with some existing FVR approaches, the proposed VF-DCN not only achieves notable recognition accuracy but also shows fewer number of parameters and lower model complexity. Moreover, VF-DCN exhibits superior robustness across diverse FV databases. Full article

(This article belongs to the Section Sensing and Imaging)

17 pages, 4097 KiB

Open AccessArticle

Reliability of Real-Time Kinematic (RTK) Positioning for Low-Cost Drones’ Navigation across Global Navigation Satellite System (GNSS) Critical Environments

by Luca Tavasci, Francesco Nex and Stefano Gandolfi

Sensors 2024, 24(18), 6096; https://doi.org/10.3390/s24186096 (registering DOI) - 20 Sep 2024

UAVs are nowadays used for several surveying activities, some of which imply flying close to tall walls, in and out of tunnels, under bridges, and so forth. In these applications, RTK GNSS positioning delivers results with very variable quality. It allows for centimetric-level [...] Read more.

UAVs are nowadays used for several surveying activities, some of which imply flying close to tall walls, in and out of tunnels, under bridges, and so forth. In these applications, RTK GNSS positioning delivers results with very variable quality. It allows for centimetric-level kinematic navigation in real time in ideal conditions, but limitations in sky visibility or strong multipath effects negatively impact the positioning quality. This paper aims at assessing the RTK positioning limitations for lightweight and low-cost drones carrying cheap GNSS modules when used to fly in some meaningful critical operational conditions. Three demanding scenarios have been set up simulating the trajectories of drones in tasks such as infrastructure (i.e., building or bridges) inspection. Different outage durations, flight dynamics, and obstacle sizes have been considered in this work to have a complete overview of the positioning quality. The performed tests have allowed us to define practical recommendations to safely fly drones in potentially critical environments just by considering common software and standard GNSS parameters. Full article

(This article belongs to the Section Navigation and Positioning)

26 pages, 1961 KiB

Open AccessArticle

Preservation and Protection of Cultural Heritage: Vibration Monitoring and Seismic Vulnerability of the Ruins of Carmo Convent (Lisbon)

by Nuno Mendes, Nicoletta Bianchini, Georgios Karanikoloudis, Anna Blyth, Jacopo Scacco, Luis Gerardo Flores Salazar, Cassie Cullimore and Lavina Jain

Sensors 2024, 24(18), 6095; https://doi.org/10.3390/s24186095 (registering DOI) - 20 Sep 2024

Preservation of cultural heritage sites is of paramount importance. The ruins of Carmo Convent in Lisbon stand as a poignant reminder of the city’s rich history, but challenges regarding structural integrity and safety are present in a highly populated and touristic area. In [...] Read more.

Preservation of cultural heritage sites is of paramount importance. The ruins of Carmo Convent in Lisbon stand as a poignant reminder of the city’s rich history, but challenges regarding structural integrity and safety are present in a highly populated and touristic area. In this paper, a comprehensive study of the Carmo Convent is presented, focused on non-destructive testing (NDT), structural health monitoring (SHM) and numerical modelling. Given its state of ruin and historical relevance, the study relied heavily on NDT. Additionally, a metro line passing underneath the convent raised concerns regarding potential hazards from induced vibrations. Thus, metro vibration monitoring (MVM) was implemented to assess the impact of induced vibrations on the structure. One of the challenges was the scarcity of standards specific to historic structures. However, through a combination of finite element method (FEM) and discrete element method (DEM) numerical modelling, valuable insights into the current condition of the structure were obtained. MVM revealed that the maximum velocity induced by metro activities remained within safe limits, indicating minimal impact. These results not only provide crucial information on structural preservation but also empower stakeholders to make informed decisions regarding the implementation of protective measures. Full article

(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2024)

18 pages, 2562 KiB

Open AccessArticle

An Adaptive Spatial Target Tracking Method Based on Unscented Kalman Filter

by Dandi Rong and Yi Wang

Sensors 2024, 24(18), 6094; https://doi.org/10.3390/s24186094 (registering DOI) - 20 Sep 2024

The spatial target motion model exhibits a high degree of nonlinearity. This leads to the fact that it is easy to diverge when the conventional Kalman filter is used to track the spatial target. The Unscented Kalman filter can be a good solution [...] Read more.

The spatial target motion model exhibits a high degree of nonlinearity. This leads to the fact that it is easy to diverge when the conventional Kalman filter is used to track the spatial target. The Unscented Kalman filter can be a good solution to this problem. This is because it conveys the statistical properties of the state vector by selecting sampling points to be mapped through the nonlinear model. In practice, however, the measurement noise is often time-varying or unknown. In this case, the filtering accuracy of the Unscented Kalman filter will be reduced. In order to reduce the influence of time-varying measurement noise on the spatial target tracking, while accurately representing the a posteriori mean and covariance of the spatial target state vector, this paper proposes an adaptive noise factor method based on the Unscented Kalman filter to adaptively adjust the covariance matrix of the measurement noise. In this paper, numerical simulations are performed using measurement models from a space-based infrared satellite and a ground-based radar. It is experimentally demonstrated that the adaptive noise factor method can adapt to time-varying measurement noise and thus improve the accuracy of spatial target tracking compared to the Unscented Kalman filter. Full article

(This article belongs to the Section Communications)

15 pages, 3616 KiB

Open AccessArticle

Assessing the Quality of Solvent-Assisted Lipid Bilayers Formed at Different Phases and Aqueous Buffer Media: A QCM-D Study

by Marta Lavrič, Laure Bar, Martin E. Villanueva, Patricia Losada-Pérez, Aleš Iglič, Nikola Novak and George Cordoyiannis

Sensors 2024, 24(18), 6093; https://doi.org/10.3390/s24186093 (registering DOI) - 20 Sep 2024

Supported lipid bilayers (SLBs) are low-complexity biomimetic membranes, serving as popular experimental platforms to study membrane organization and lipid transfer, membrane uptake of nanoparticles and biomolecules, and many other processes. Quartz crystal microbalance with dissipation monitoring has been utilized to probe the influence [...] Read more.

Supported lipid bilayers (SLBs) are low-complexity biomimetic membranes, serving as popular experimental platforms to study membrane organization and lipid transfer, membrane uptake of nanoparticles and biomolecules, and many other processes. Quartz crystal microbalance with dissipation monitoring has been utilized to probe the influence of several parameters on the quality of SLBs formed on Au- and SiO₂-coated sensors. The influence of the aqueous medium (i.e., buffer type) and the adsorption temperature, above and below the lipid melting point, is neatly explored for SLBs of 1,2-dimyristoyl-sn-glycero-3-phosphocholine and 1,2-dipalmitoyl-sn-glycero-3-phosphocholine formed by a solvent exchange. Below the lipid melting temperature, quality variations are observed upon the formation on Au and SiO₂ surfaces, with the SLBs being more homogeneous for the latter. We further investigate how the buffer affects the detection of lipid melting in SLBs, a transition that necessitates high-sensitivity and time-consuming surface-sensitive techniques to be detected. Full article

(This article belongs to the Special Issue Electronic, Mass-Sensitive and Thermal Transducers for Bio- and Chemosensing)

19 pages, 8508 KiB

Open AccessArticle

A Proposed Algorithm Based on Variance to Effectively Estimate Crack Source Localization in Solids

by Young-Chul Choi, Byunyoung Chung and Doyun Jung

Sensors 2024, 24(18), 6092; https://doi.org/10.3390/s24186092 (registering DOI) - 20 Sep 2024

Acoustic emissions (AEs) are produced by elastic waves generated by damage in solid materials. AE sensors have been widely used in several fields as a promising tool to analyze damage mechanisms such as cracking, dislocation movement, etc. However, accurately determining the location of [...] Read more.

Acoustic emissions (AEs) are produced by elastic waves generated by damage in solid materials. AE sensors have been widely used in several fields as a promising tool to analyze damage mechanisms such as cracking, dislocation movement, etc. However, accurately determining the location of damage in solids in a non-destructive manner is still challenging. In this paper, we propose a crack wave arrival time determination algorithm that can identify crack waves with low SNRs (signal-to-noise ratios) generated in rocks. The basic idea is that the variances in the crack wave and noise have different characteristics, depending on the size of the moving window. The results can be used to accurately determine the crack source location. The source location is determined by observing where the variance in the crack wave velocities of the true and imaginary crack location reach a minimum. By performing a pencil lead break test using rock samples, it was confirmed that the proposed method could successfully find wave arrival time and crack localization. The proposed algorithm for source localization can be used for evaluating and monitoring damage in tunnels or other underground facilities in real time. Full article

(This article belongs to the Section Physical Sensors)

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23 pages, 3378 KiB

Open AccessArticle

Research on Signal Noise Reduction and Leakage Localization in Urban Water Supply Pipelines Based on Northern Goshawk Optimization

by Xin Chen, Zhu Jiang, Jiale Li, Zhendong Zhao and Yunyun Cao

Sensors 2024, 24(18), 6091; https://doi.org/10.3390/s24186091 (registering DOI) - 20 Sep 2024

In order to enhance the accuracy and adaptability of urban water supply pipeline leak localization, based on the Northern Goshawk Optimization, a novel joint denoising method is proposed in this paper to reduce noise in negative pressure wave signals caused by leaks. Firstly, [...] Read more.

In order to enhance the accuracy and adaptability of urban water supply pipeline leak localization, based on the Northern Goshawk Optimization, a novel joint denoising method is proposed in this paper to reduce noise in negative pressure wave signals caused by leaks. Firstly, the Northern Goshawk Optimization optimizes the decomposition levels and penalty factors of Variational Mode Decomposition, and obtains their optimal combination. Subsequently, the optimized parameters are used to decompose the pressure signals into modal components, and the effective components and noise components are distinguished according to the correlation coefficients. Then, an optimized wavelet thresholding method is applied to the selected effective components for secondary denoising. Finally, the signal components that have been denoised twice are reconstructed with the effective signal components, and the denoised negative pressure wave signals are obtained. Simulation experiments demonstrate that compared to wavelet transforms and Empirical Mode Decomposition, our method achieves the highest signal-to-noise ratio improvement of 12.23 dB and normalized cross correlation of 0.991. It effectively preserves useful leak information in the signal while suppressing noise, laying a solid foundation for improving leak localization accuracy. After several leak simulation tests on the leakage simulation test platform, the test results verify the effectiveness of the proposed method. The minimum relative error of the leakage localization is 0.29%, and an average relative error is 1.64%, achieving accurate leakage localization. Full article

(This article belongs to the Section Navigation and Positioning)

18 pages, 3703 KiB

Open AccessArticle

Joint Spatio-Temporal-Frequency Representation Learning for Improved Sound Event Localization and Detection

by Baoqing Chen, Mei Wang and Yu Gu

Sensors 2024, 24(18), 6090; https://doi.org/10.3390/s24186090 (registering DOI) - 20 Sep 2024

Sound event localization and detection (SELD) is a crucial component of machine listening that aims to simultaneously identify and localize sound events in multichannel audio recordings. This task demands an integrated analysis of spatial, temporal, and frequency domains to accurately characterize sound events. [...] Read more.

Sound event localization and detection (SELD) is a crucial component of machine listening that aims to simultaneously identify and localize sound events in multichannel audio recordings. This task demands an integrated analysis of spatial, temporal, and frequency domains to accurately characterize sound events. The spatial domain pertains to the varying acoustic signals captured by multichannel microphones, which are essential for determining the location of sound sources. However, the majority of recent studies have focused on time-frequency correlations and spatio-temporal correlations separately, leading to inadequate performance in real-life scenarios. In this paper, we propose a novel SELD method that utilizes the newly developed Spatio-Temporal-Frequency Fusion Network (STFF-Net) to jointly learn comprehensive features across spatial, temporal, and frequency domains of sound events. The backbone of our STFF-Net is the Enhanced-3D (E3D) residual block, which combines 3D convolutions with a parameter-free attention mechanism to capture and refine the intricate correlations among these domains. Furthermore, our method incorporates the multi-ACCDOA format to effectively handle homogeneous overlaps between sound events. During the evaluation, we conduct extensive experiments on three de facto benchmark datasets, and our results demonstrate that the proposed SELD method significantly outperforms current state-of-the-art approaches. Full article

(This article belongs to the Section Physical Sensors)

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19 pages, 790 KiB

Open AccessArticle

Modeling and Analyzing the Availability of Technical Professional Profiles for the Success of Smart Cities Projects in Europe

by Inés López-Baldominos, Vera Pospelova, Luis Fernández-Sanz and Ana Castillo-Martínez

Sensors 2024, 24(18), 6089; https://doi.org/10.3390/s24186089 (registering DOI) - 20 Sep 2024

The success of developing and implementing Smart Cities (SC) projects depends on a varied set of factors, where the availability of a qualified technical workforce is a critical one. The combination of ICT requirements, like the effectiveness and quality of solutions merging IoT, [...] Read more.

The success of developing and implementing Smart Cities (SC) projects depends on a varied set of factors, where the availability of a qualified technical workforce is a critical one. The combination of ICT requirements, like the effectiveness and quality of solutions merging IoT, cloud computing, sensors, and communications with the work from many varied disciplines (e.g., civil engineering, architecture, etc.), mixed with aspects of environmental and business sustainability, makes the management of these projects really challenging. Reports forecast a scarcity of qualified candidates, given this complexity and the growth of activity in SC projects. The European project SMACITE has addressed the requirements of the qualification of an ICT workforce with an analysis of multiples sources of information from the labor market, feedback from involved stakeholders, and the literature. The goal was the development of two occupational ICT profiles as a reference for training and for the availability of candidates for job vacancies. The result is two ICT role profiles for engineers and technicians, mapped with the European skills frameworks ESCO and EN16234. The profiles determined the whole set of requirements, including not only the technical areas and soft skills, but also additional technical areas and sustainability and managerial skills and the analysis of different sources of information. Our work has also determined which existing ESCO occupations are similar to the two reference profiles, so they are better adapted to SC projects. The training activities of SMACITE have also suggested the amount of training expected for a varied sample of candidates who want to be qualified for SC projects. Full article

(This article belongs to the Special Issue Topical Advisory Panel Members’ Collection Series: Smart Cities in the Digital Age: Balancing Innovation and Security)

7 pages, 931 KiB

Open AccessCommunication

Development of Lasing Silica Microsphere for High-Speed DNA Molecular Detection

by Chan Seok Jun and Wonsuk Lee

Sensors 2024, 24(18), 6088; https://doi.org/10.3390/s24186088 (registering DOI) - 20 Sep 2024

Laser and molecular detection techniques that have been used to overcome the limitations of fluorescent DNA labeling have presented new challenges. To address some of these challenges, we developed a DNA laser that uses a solid-state silica microsphere as a ring resonator and [...] Read more.

Laser and molecular detection techniques that have been used to overcome the limitations of fluorescent DNA labeling have presented new challenges. To address some of these challenges, we developed a DNA laser that uses a solid-state silica microsphere as a ring resonator and a site for DNA-binding reactions, as well as a platform to detect and sequence target DNA molecules. We detected target DNA using laser emission from a DNA-labeling dye and a developed solid-state silica microsphere ring resonator. The microsphere was sensitive; a single base mismatch in the DNA resulted in the absence of an optical signal. As each individual microsphere can be utilized as a parallel DNA analysis chamber, this optical digital detection scheme allows for high-throughput and rapid analysis. More importantly, the solid-state DNA laser is free from deformation, which guarantees stable lasing characteristics, and can be manipulated freely outside the solution. Thus, this promising advanced DNA laser scheme can be implemented on platforms other than optofluidic chips. Full article

(This article belongs to the Special Issue Biomedical Sensors Based on Microfluidics)

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25 pages, 6653 KiB

Open AccessArticle

A Model-Based Systematic Innovative Design for Sonic Logging Instruments in Natural Gas Wells

by Chong Jiang, Wu Zhao, Miao Yu and Kai Zhang

Sensors 2024, 24(18), 6087; https://doi.org/10.3390/s24186087 (registering DOI) - 20 Sep 2024

With the continued development of natural gas extraction technologies, the accurate determination of downhole temperature and pressure has become increasingly important. It is crucial for the optimization of gas well production and an important measure to prevent accidents. However, existing logging instruments have [...] Read more.

With the continued development of natural gas extraction technologies, the accurate determination of downhole temperature and pressure has become increasingly important. It is crucial for the optimization of gas well production and an important measure to prevent accidents. However, existing logging instruments have a series of deficiencies in measurement and cannot adequately monitor modern natural gas. In response to these problems, in this paper, we propose a new model-based systematic innovation design method for designing logging instruments and simulations using finite element software. Our research results confirm the theoretical and practical utility of this model-based design method and provide a novel approach to designing logging instruments. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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25 pages, 3938 KiB

Open AccessArticle

Enhancing the Minimum Awareness Failure Distance in V2X Communications: A Deep Reinforcement Learning Approach

by Anthony Kyung Guzmán Leguel, Hoa-Hung Nguyen, David Gómez Gutiérrez, Jinwoo Yoo and Han-You Jeong

Sensors 2024, 24(18), 6086; https://doi.org/10.3390/s24186086 (registering DOI) - 20 Sep 2024

Vehicle-to-everything (V2X) communication is pivotal in enhancing cooperative awareness in vehicular networks. Typically, awareness is viewed as a vehicle’s ability to perceive and share real-time kinematic information. We present a novel definition of awareness in V2X communications, conceptualizing it as a multi-faceted concept [...] Read more.

Vehicle-to-everything (V2X) communication is pivotal in enhancing cooperative awareness in vehicular networks. Typically, awareness is viewed as a vehicle’s ability to perceive and share real-time kinematic information. We present a novel definition of awareness in V2X communications, conceptualizing it as a multi-faceted concept involving vehicle detection, tracking, and maintaining their safety distances. To enhance this awareness, we propose a deep reinforcement learning framework for the joint control of beacon rate and transmit power (DRL-JCBRTP). Our DRL−JCBRTP framework integrates LSTM-based actor networks and MLP-based critic networks within the Soft Actor-Critic (SAC) algorithm to effectively learn optimal policies. Leveraging local state information, the DRL-JCBRTP scheme uses an innovative reward function to increase the minimum awareness failure distance. Our SLMLab-Gym-VEINS simulations show that the DRL-JCBRTP scheme outperforms existing beaconing schemes in minimizing awareness failure probability and maximizing awareness distance, ultimately improving driving safety. Full article

(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communication Networks)

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12 pages, 2363 KiB

Open AccessArticle

The Determination of On-Water Rowing Stroke Kinematics Using an Undecimated Wavelet Transform of a Rowing Hull-Mounted Accelerometer Signal

by Daniel Geneau, Drew Commandeur, Ryan Brodie, Ming-Chang Tsai, Matt Jensen and Marc Klimstra

Sensors 2024, 24(18), 6085; https://doi.org/10.3390/s24186085 (registering DOI) - 20 Sep 2024

Boat acceleration profiles can provide valuable information for coaches and practitioners to make meaningful technical interventions and monitor the determinants of success in rowing. Previous studies have used simple feature detection methods to identify key phases within individual strokes, such as drive onset, [...] Read more.

Boat acceleration profiles can provide valuable information for coaches and practitioners to make meaningful technical interventions and monitor the determinants of success in rowing. Previous studies have used simple feature detection methods to identify key phases within individual strokes, such as drive onset, drive time, drive offset and stroke time. However, based on skill level, technique or boat class, the hull acceleration profile can differ, making robust feature detection more challenging. The current study’s purpose is to employ the undecimated wavelet transform (UWT) technique to detect individual features in the stroke acceleration profile from a single rowing hull-mounted accelerometer. In this investigation, the temporal and kinematic values obtained using the AdMos^TM sensor in conjunction with the UWT processing approach were strongly correlated with the comparative measures of the Peach™ instrumented oarlock system. The measures for stroke time displayed very strong agreeability between the systems for all boat classes, with ICC values of 0.993, 0.963 and 0.954 for the W8+, W4− and W1x boats, respectively. Similarly, the drive time was also very consistent, with strong to very strong agreeability, producing ICC values of 0.937, 0.901 and 0.881 for the W8+, W4™ and W1x boat classes. Further, a Bland–Altman analysis displayed little to no bias between the AdMos^TM-derived and Peach™ measures, indicating that there were no systematic discrepancies between signals. This single-sensor solution could form the basis for a simple, cost-effective and accessible alternative to multi-sensor instrumented systems for the determination of sub-stroke kinematic phases. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence for Analyzing Human Behavior in Sports and Physical Activity)

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19 pages, 632 KiB

Open AccessArticle

SMS Scam Detection Application Based on Optical Character Recognition for Image Data Using Unsupervised and Deep Semi-Supervised Learning

by Anjali Shinde, Essa Q. Shahra, Shadi Basurra, Faisal Saeed, Abdulrahman A. AlSewari and Waheb A. Jabbar

Sensors 2024, 24(18), 6084; https://doi.org/10.3390/s24186084 (registering DOI) - 20 Sep 2024

The growing problem of unsolicited text messages (smishing) and data irregularities necessitates stronger spam detection solutions. This paper explores the development of a sophisticated model designed to identify smishing messages by understanding the complex relationships among words, images, and context-specific factors, areas that [...] Read more.

The growing problem of unsolicited text messages (smishing) and data irregularities necessitates stronger spam detection solutions. This paper explores the development of a sophisticated model designed to identify smishing messages by understanding the complex relationships among words, images, and context-specific factors, areas that remain underexplored in existing research. To address this, we merge a UCI spam dataset of regular text messages with real-world spam data, leveraging OCR technology for comprehensive analysis. The study employs a combination of traditional machine learning models, including K-means, Non-Negative Matrix Factorization, and Gaussian Mixture Models, along with feature extraction techniques such as TF_IDF and PCA. Additionally, deep learning models like RNN-Flatten, LSTM, and Bi-LSTM are utilized. The selection of these models is driven by their complementary strengths in capturing both the linear and non-linear relationships inherent in smishing messages. Machine learning models are chosen for their efficiency in handling structured text data, while deep learning models are selected for their superior ability to capture sequential dependencies and contextual nuances. The performance of these models is rigorously evaluated using metrics like accuracy, precision, recall, and F1 score, enabling a comparative analysis between the machine learning and deep learning approaches. Notably, the K-means feature extraction with vectorizer achieved 91.01% accuracy, and the KNN-Flatten model reached 94.13% accuracy, emerging as the top performer. The rationale behind highlighting these models is their potential to significantly improve smishing detection rates. For instance, the high accuracy of the KNN-Flatten model suggests its applicability in real-time spam detection systems, but its computational complexity might limit scalability in large-scale deployments. Similarly, while K-means with vectorizer excels in accuracy, it may struggle with the dynamic and evolving nature of smishing attacks, necessitating continual retraining. Full article

(This article belongs to the Special Issue Edge Computing, Real-Time Data Processing, and Holistic Methods for Imaging Sensors)

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16 pages, 15870 KiB

Open AccessArticle

Active Region Mode Control for High-Power, Low-Linewidth Broadened Semiconductor Optical Amplifiers for Light Detection and Ranging

by Hui Tang, Meng Zhang, Lei Liang, Tianyi Zhang, Li Qin, Yue Song, Yuxin Lei, Peng Jia, Yubing Wang, Cheng Qiu, Chuantao Zheng, Xin Li, Yongyi Chen, Dan Li, Yongqiang Ning and Lijun Wang

Sensors 2024, 24(18), 6083; https://doi.org/10.3390/s24186083 (registering DOI) - 20 Sep 2024

This paper introduces a semiconductor optical amplifier (SOA) with high power and narrow linewidth broadening achieved through active region mode control. By integrating mode control with broad-spectrum epitaxial material design, the device achieves high gain, high power, and wide band output. At a [...] Read more.

This paper introduces a semiconductor optical amplifier (SOA) with high power and narrow linewidth broadening achieved through active region mode control. By integrating mode control with broad-spectrum epitaxial material design, the device achieves high gain, high power, and wide band output. At a wavelength of 1550 nm and an ambient temperature of 20 °C, the output power reaches 757 mW when the input power is 25 mW, and the gain is 21.92 dB when the input power is 4 mW. The 3 dB gain bandwidth is 88 nm, and the linewidth expansion of the input laser after amplification through the SOA is only 1.031 times. The device strikes a balance between high gain and high power, offering a new amplifier option for long-range light detection and ranging (LiDAR). Full article

(This article belongs to the Section Optical Sensors)

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