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) and International Society for the Measurement of Physical Behaviour (ISMPB) 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 (Instruments & Instrumentation) / CiteScore - Q1 (Instrumentation)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2023).
- 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, Automation, JCP and Targets.
Impact Factor:
3.9 (2022);
5-Year Impact Factor:
4.1 (2022)
Latest Articles
Can We Target Close Therapeutic Goals in the Gait Re-Education Algorithm for Stroke Patients at the Beginning of the Rehabilitation Process?
Sensors 2024, 24(11), 3416; https://doi.org/10.3390/s24113416 (registering DOI) - 25 May 2024
Abstract
(1) Background: The study aimed to determine the most important activities of the knee joints related to gait re-education in patients in the subacute period after a stroke. We focused on the tests that a physiotherapist could perform in daily clinical practice. (2)
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(1) Background: The study aimed to determine the most important activities of the knee joints related to gait re-education in patients in the subacute period after a stroke. We focused on the tests that a physiotherapist could perform in daily clinical practice. (2) Methods: Twenty-nine stroke patients (SG) and 29 healthy volunteers (CG) were included in the study. The patients underwent the 5-meter walk test (5mWT) and the Timed Up and Go test (TUG). Tests such as step up, step down, squat, step forward, and joint position sense test (JPS) were also performed, and the subjects were assessed using wireless motion sensors. (3) Results: We observed significant differences in the time needed to complete the 5mWT and TUG tests between groups. The results obtained in the JPS show a significant difference between the paretic and the non-paretic limbs compared to the CG group. A significantly smaller range of knee joint flexion (ROM) was observed in the paretic limb compared to the non-paretic and control limbs in the step down test and between the paretic and non-paretic limbs in the step forward test. (4) Conclusions: The described functional tests are useful in assessing a stroke patient’s motor skills and can be performed in daily clinical practice.
Full article
(This article belongs to the Special Issue Sensors for Recognition, Analysis, Assistance, and Training of Gait in Neurologic Disorders)
Open AccessArticle
Deploying Wireless Sensor Networks in Multi-Story Buildings toward Internet of Things-Based Intelligent Environments: An Empirical Study
by
Nurul I. Sarkar and Sonia Gul
Sensors 2024, 24(11), 3415; https://doi.org/10.3390/s24113415 (registering DOI) - 25 May 2024
Abstract
With the growing integration of the Internet of Things in smart buildings, it is crucial to ensure the precise implementation and operation of wireless sensor networks (WSNs). This paper aims to study the implementation aspect of WSNs in a commercial multi-story building, specifically
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With the growing integration of the Internet of Things in smart buildings, it is crucial to ensure the precise implementation and operation of wireless sensor networks (WSNs). This paper aims to study the implementation aspect of WSNs in a commercial multi-story building, specifically addressing the difficulty of dealing with the variable environmental conditions on each floor. This research addresses the disparity between simulated situations and actual deployments, offering valuable insights into the potential to significantly improve the efficiency and responsiveness of building management systems. We obtain real-time sensor data to analyze and evaluate the system’s performance. Our investigation is grounded in the growing importance of incorporating WSNs into buildings to create intelligent environments. We provide an in-depth analysis for scrutinizing the disparities and commonalities between the datasets obtained from real-world deployments and simulation. The results obtained show the significance of accurate simulation models for reliable data representation, providing a roadmap for further developments in the integration of WSNs into intelligent building scenarios. This research’s findings highlight the potential for optimizing living and working conditions based on the real-time monitoring of critical environmental parameters. This includes insights into temperature, humidity, and light intensity, offering opportunities for enhanced comfort and efficiency in intelligent environments.
Full article
(This article belongs to the Special Issue Sensors for Severe Environments)
Open AccessArticle
Device-Free Wireless Sensing for Gesture Recognition Based on Complementary CSI Amplitude and Phase
by
Zhijia Cai, Zehao Li, Zikai Chen, Hongyang Zhuo, Lei Zheng, Xianda Wu and Yong Liu
Sensors 2024, 24(11), 3414; https://doi.org/10.3390/s24113414 (registering DOI) - 25 May 2024
Abstract
By integrating sensing capability into wireless communication, wireless sensing technology has become a promising contactless and non-line-of-sight sensing paradigm to explore the dynamic characteristics of channel state information (CSI) for recognizing human behaviors. In this paper, we develop an effective device-free human gesture
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By integrating sensing capability into wireless communication, wireless sensing technology has become a promising contactless and non-line-of-sight sensing paradigm to explore the dynamic characteristics of channel state information (CSI) for recognizing human behaviors. In this paper, we develop an effective device-free human gesture recognition (HGR) system based on WiFi wireless sensing technology in which the complementary CSI amplitude and phase of communication link are jointly exploited. To improve the quality of collected CSI, a linear transform-based data processing method is first used to eliminate the phase offset and noise and to reduce the impact of multi-path effects. Then, six different time and frequency domain features are chosen for both amplitude and phase, including the mean, variance, root mean square, interquartile range, energy entropy and power spectral entropy, and a feature selection algorithm to remove irrelevant and redundant features is proposed based on filtering and principal component analysis methods, resulting in the construction of a feature subspace to distinguish different gestures. On this basis, a support vector machine-based stacking algorithm is proposed for gesture classification based on the selected and complementary amplitude and phase features. Lastly, we conduct experiments under a practical scenario with one transmitter and receiver. The results demonstrate that the average accuracy of the proposed HGR system is 98.3% and that the F1-score is over 97%.
Full article
(This article belongs to the Special Issue Techniques and Instrumentation for Microwave Sensing)
Open AccessArticle
Operational Deflection Shape Measurements on Bladed Disks with Continuous Scanning Laser Doppler Vibrometry
by
Cuihong Liu, Tengzhou Xu, Tao Chen, Shi Su, Jie Huang and Yijin Li
Sensors 2024, 24(11), 3413; https://doi.org/10.3390/s24113413 (registering DOI) - 25 May 2024
Abstract
The continuous scanning laser Doppler vibrometry (CSLDV) technique is usually used to evaluate the vibration operational deflection shapes (ODSs) of structures with continuous surfaces. In this paper, an extended CSLDV is demonstrated to measure the non-continuous surface of the bladed disk and to
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The continuous scanning laser Doppler vibrometry (CSLDV) technique is usually used to evaluate the vibration operational deflection shapes (ODSs) of structures with continuous surfaces. In this paper, an extended CSLDV is demonstrated to measure the non-continuous surface of the bladed disk and to obtain the ODS efficiently. For a bladed disk, the blades are uniformly distributed on a given disk. Although the ODS of each blade can be derived from its response data along the scanning path with CSLDV, the relative vibration direction between different blades cannot be determined from those data. Therefore, it is difficult to reconstruct the complete vibration mode of the whole blade disk. In order to measure the complete ODS of the bladed disk, a method based on ODS frequency response functions (ODS FRFs) has been proposed. While the ODS of each blade is measured by designing the suitable scanning paths in CSLDV, an additional response signal is obtained at a fixed point as the reference signal to identify the relative vibration phase between the blade and the blade of the bladed disk. Finally, a measurement is performed with a simple bladed disk and the results demonstrate the feasibility and effectiveness of the proposed extended CSLDV method.
Full article
(This article belongs to the Topic Advances in Non-Destructive Testing Methods, 2nd Volume)
Open AccessArticle
Caching Policy in Low Earth Orbit Satellite Mega-Constellation Information-Centric Networking for Internet of Things
by
Hongqiu Luo, Tingting Yan and Shengbo Hu
Sensors 2024, 24(11), 3412; https://doi.org/10.3390/s24113412 (registering DOI) - 25 May 2024
Abstract
Information-Centric Networking (ICN) is the emerging next-generation internet paradigm. The Low Earth Orbit (LEO) satellite mega-constellation based on ICN can achieve seamless global coverage and provide excellent support for Internet of Things (IoT) services. Additionally, in-network caching, typically characteristic of ICN, plays a
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Information-Centric Networking (ICN) is the emerging next-generation internet paradigm. The Low Earth Orbit (LEO) satellite mega-constellation based on ICN can achieve seamless global coverage and provide excellent support for Internet of Things (IoT) services. Additionally, in-network caching, typically characteristic of ICN, plays a paramount role in network performance. Therefore, the in-network caching policy is one of the hotspot problems. Especially, compared to caching traditional internet content, in-networking caching IoT content is more challenging, since the IoT content lifetime is small and transient. In this paper, firstly, the framework of the LEO satellite mega-constellation Information-Centric Networking for IoT (LEO-SMC-ICN-IoT) is proposed. Then, introducing the concept of “viscosity”, the proposed Caching Algorithm based on the Random Forest (CARF) policy of satellite nodes combines both content popularity prediction and satellite nodes location prediction, for achieving good cache matching between the satellite nodes and content. And using the matching rule, the Random Forest (RF) algorithm is adopted to predict the matching relationship among satellite nodes and content for guiding the deployment of caches. Especially, the content is cached in advance at the future satellite to maintain communication with the current ground segment at the time of satellite switchover. Additionally, the policy considers both the IoT content lifetime and the freshness. Finally, a simulation platform with LEO satellite mega-constellation based on ICN is developed in Network Simulator 3 (NS-3). The simulation results show that the proposed caching policy compared with the Leave Copy Everywhere (LCE), the opportunistic (OPP), the Leave Copy down (LCD), and the probabilistic algorithm which caches each content with probability 0.5 (prob 0.5) yield a significant performance improvement, such as the average number of hops, i.e., delay, cache hit rate, and throughput.
Full article
(This article belongs to the Special Issue Communication, Sensing and Localization in 6G Systems)
Open AccessReview
Towards Autonomous Driving: Technologies and Data for Vehicles-to-Everything Communication
by
Vygantas Ušinskis, Mantas Makulavičius, Sigitas Petkevičius, Andrius Dzedzickis and Vytautas Bučinskas
Sensors 2024, 24(11), 3411; https://doi.org/10.3390/s24113411 (registering DOI) - 25 May 2024
Abstract
Autonomous systems are becoming increasingly relevant in our everyday life. The transportation field is no exception and the smart cities concept raises new tasks and challenges for the development of autonomous systems development which has been progressively researched in literature. One of the
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Autonomous systems are becoming increasingly relevant in our everyday life. The transportation field is no exception and the smart cities concept raises new tasks and challenges for the development of autonomous systems development which has been progressively researched in literature. One of the main challenges is communication between different traffic objects. For instance, a mobile robot system can work as a standalone autonomous system reacting to a static environment and avoiding obstacles to reach a target. Nevertheless, more intensive communication and decision making is needed when additional dynamic objects and other autonomous systems are present in the same working environment. Traffic is a complicated environment consisting of vehicles, pedestrians, and various infrastructure elements. To apply autonomous systems in this kind of environment it is important to integrate object localization and to guarantee functional and trustworthy communication between each element. To achieve this, various sensors, communication standards, and equipment are integrated via the application of sensor fusion and AI machine learning methods. In this work review of vehicular communication systems is presented. The main focus is the researched sensors, communication standards, devices, machine learning methods, and vehicular-related data to find existing gaps for future vehicular communication system development. In the end, discussion and conclusions are presented.
Full article
(This article belongs to the Section Vehicular Sensing)
Open AccessArticle
Off-Design Operation and Cavitation Detection in Centrifugal Pumps Using Vibration and Motor Stator Current Analyses
by
Yuejiang Han, Jiamin Zou, Alexandre Presas, Yin Luo and Jianping Yuan
Sensors 2024, 24(11), 3410; https://doi.org/10.3390/s24113410 (registering DOI) - 25 May 2024
Abstract
Centrifugal pumps are essential in many industrial processes. An accurate operation diagnosis of centrifugal pumps is crucial to ensure their reliable operation and extend their useful life. In real industry applications, many centrifugal pumps lack flowmeters and accurate pressure sensors, and therefore, it
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Centrifugal pumps are essential in many industrial processes. An accurate operation diagnosis of centrifugal pumps is crucial to ensure their reliable operation and extend their useful life. In real industry applications, many centrifugal pumps lack flowmeters and accurate pressure sensors, and therefore, it is not possible to determine whether the pump is operating near its best efficiency point (BEP). This paper investigates the detection of off-design operation and cavitation for centrifugal pumps with accelerometers and current sensors. To this end, a centrifugal pump was tested under off-design conditions and various levels of cavitation. A three-axis accelerometer and three Hall-effect current sensors were used to collect vibration and stator current signals simultaneously under each state. Both kinds of signals were evaluated for their effectiveness in operation diagnosis. Signal processing methods, including wavelet threshold function, variational mode decomposition (VMD), Park vector modulus transformation, and a marginal spectrum were introduced for feature extraction. Seven families of machine learning-based classification algorithms were evaluated for their performance when used for off-design and cavitation identification. The obtained results, using both types of signals, prove the effectiveness of both approaches and the advantages of combining them in achieving the most reliable operation diagnosis results for centrifugal pumps.
Full article
(This article belongs to the Special Issue Fault Diagnosis and Vibration Signal Processing in Rotor Systems)
Open AccessArticle
A Video Mosaicing-Based Sensing Method for Chicken Behavior Recognition on Edge Computing Devices
by
Dmitrij Teterja, Jose Garcia-Rodriguez, Jorge Azorin-Lopez, Esther Sebastian-Gonzalez, Daliborka Nedić, Dalibor Leković, Petar Knežević, Dejan Drajić and Dejan Vukobratović
Sensors 2024, 24(11), 3409; https://doi.org/10.3390/s24113409 (registering DOI) - 25 May 2024
Abstract
Chicken behavior recognition is crucial for a number of reasons, including promoting animal welfare, ensuring the early detection of health issues, optimizing farm management practices, and contributing to more sustainable and ethical poultry farming. In this paper, we introduce a technique for recognizing
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Chicken behavior recognition is crucial for a number of reasons, including promoting animal welfare, ensuring the early detection of health issues, optimizing farm management practices, and contributing to more sustainable and ethical poultry farming. In this paper, we introduce a technique for recognizing chicken behavior on edge computing devices based on video sensing mosaicing. Our method combines video sensing mosaicing with deep learning to accurately identify specific chicken behaviors from videos. It attains remarkable accuracy, achieving 79.61% with MobileNetV2 for chickens demonstrating three types of behavior. These findings underscore the efficacy and promise of our approach in chicken behavior recognition on edge computing devices, making it adaptable for diverse applications. The ongoing exploration and identification of various behavioral patterns will contribute to a more comprehensive understanding of chicken behavior, enhancing the scope and accuracy of behavior analysis within diverse contexts.
Full article
(This article belongs to the Section Sensing and Imaging)
Open AccessArticle
Multi-View Metal Parts Pose Estimation Based on a Single Camera
by
Chen Chen and Xin Jiang
Sensors 2024, 24(11), 3408; https://doi.org/10.3390/s24113408 (registering DOI) - 25 May 2024
Abstract
Pose estimation of metal parts plays a vital role in industrial grasping areas. It is challenging to obtain complete point clouds of metal parts because of their reflective properties. This study introduces an approach for recovering the 6D pose of CAD-known metal parts
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Pose estimation of metal parts plays a vital role in industrial grasping areas. It is challenging to obtain complete point clouds of metal parts because of their reflective properties. This study introduces an approach for recovering the 6D pose of CAD-known metal parts from images captured by a single RGB camera. The proposed strategy only requires RGB images without depth information. The core idea of the proposed method is to use multiple views to estimate the metal parts’ pose. First, the pose of metal parts is estimated in the first view. Second, ray casting is employed to simulate additional views with the corresponding status of the metal parts, enabling the calculation of the camera’s next best viewpoint. The camera, mounted on a robotic arm, is then moved to this calculated position. Third, this study integrates the known camera transformations with the poses estimated from different viewpoints to refine the final scene. The results of this work demonstrate that the proposed method effectively estimates the pose of shiny metal parts.
Full article
(This article belongs to the Section Sensing and Imaging)
Open AccessArticle
Comparison of Sub-Ppm Instrument Response Suggests Higher Detection Limits Could Be Used to Quantify Methane Emissions from Oil and Gas Infrastructure
by
Stuart N. Riddick, Mercy Mbua, Ryan Brouwer, Ethan W. Emerson, Abhinav Anand, Elijah Kiplimo, Seunfunmi Ojomu, Jui-Hsiang Lo and Daniel J. Zimmerle
Sensors 2024, 24(11), 3407; https://doi.org/10.3390/s24113407 (registering DOI) - 25 May 2024
Abstract
Quantifying and controlling fugitive methane emissions from oil and gas facilities remains essential for addressing climate goals, but the costs associated with monitoring millions of production sites remain prohibitively expensive. Current thinking, supported by measurement and simple dispersion modelling, assumes single-digit parts-per-million instrumentation
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Quantifying and controlling fugitive methane emissions from oil and gas facilities remains essential for addressing climate goals, but the costs associated with monitoring millions of production sites remain prohibitively expensive. Current thinking, supported by measurement and simple dispersion modelling, assumes single-digit parts-per-million instrumentation is required. To investigate instrument response, the inlets of three trace-methane (sub-ppm) analyzers were collocated on a facility designed to release gas of known composition at known flow rates between 0.4 and 5.2 kg CH4 h−1 from simulated oil and gas infrastructure. Methane mixing ratios were measured by each instrument at 1 Hertz resolution over nine hours. While mixing ratios reported by a cavity ring-down spectrometer (CRDS)-based instrument were on average 10.0 ppm (range 1.8 to 83 ppm), a mid-infrared laser absorption spectroscopy (MIRA)-based instrument reported short-lived mixing ratios far larger than expected (range 1.8 to 779 ppm) with a similar nine-hour average to the CRDS (10.1 ppm). We suggest the peaks detected by the MIRA are likely caused by a micrometeorological phenomenon, where vortex shedding has resulted in heterogeneous methane plumes which only the MIRA can observe. Further analysis suggests an instrument like the MIRA (an optical-cavity-based instrument with cavity size ≤10 cm3 measuring at ≥2 Hz with air flow rates in the order of ≤0.3 slpm at distances of ≤20 m from the source) but with a higher detection limit (25 ppm) could detect enough of the high-concentration events to generate representative 20 min-average methane mixing ratios. Even though development of a lower-cost, high-precision, high-accuracy instrument with a 25 ppm detection threshold remains a significant problem, this has implications for the use of instrumentation with higher detection thresholds, resulting in the reduction in cost to measure methane emissions and providing a mechanism for the widespread deployment of effective leak detection and repair programs for all oil and gas infrastructure.
Full article
(This article belongs to the Special Issue Sensors and Sensor Systems for Atmospheric and Environmental Pollution Monitoring)
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Open AccessArticle
CyberSentinel: A Transparent Defense Framework for Malware Detection in High-Stakes Operational Environments
by
Mainak Basak and Myung-Mook Han
Sensors 2024, 24(11), 3406; https://doi.org/10.3390/s24113406 (registering DOI) - 25 May 2024
Abstract
Malware classification is a crucial step in defending against potential malware attacks. Despite the significance of a robust malware classifier, existing approaches reveal notable limitations in achieving high performance in malware classification. This study focuses on image-based malware detection, where malware binaries are
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Malware classification is a crucial step in defending against potential malware attacks. Despite the significance of a robust malware classifier, existing approaches reveal notable limitations in achieving high performance in malware classification. This study focuses on image-based malware detection, where malware binaries are transformed into visual representations to leverage image classification techniques. We propose a two-branch deep network designed to capture salient features from these malware images. The proposed network integrates faster asymmetric spatial attention to refine the extracted features of its backbone. Additionally, it incorporates an auxiliary feature branch to learn missing information about malware images. The feasibility of the proposed method has been thoroughly examined and compared with state-of-the-art deep learning-based classification methods. The experimental results demonstrate that the proposed method can surpass its counterparts across various evaluation metrics.
Full article
(This article belongs to the Special Issue Cyber Security and AI)
Open AccessArticle
Precise GDP Spatialization and Analysis in Built-Up Area by Combining the NPP-VIIRS-Like Dataset and Sentinel-2 Images
by
Zijun Chen, Wanning Wang, Haolin Zong and Xinyang Yu
Sensors 2024, 24(11), 3405; https://doi.org/10.3390/s24113405 (registering DOI) - 25 May 2024
Abstract
Spatialization and analysis of the gross domestic product of second and tertiary industries (GDP23) can effectively depict the socioeconomic status of regional development. However, existing studies mainly conduct GDP spatialization using nighttime light data; few studies specifically concentrated on the spatialization
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Spatialization and analysis of the gross domestic product of second and tertiary industries (GDP23) can effectively depict the socioeconomic status of regional development. However, existing studies mainly conduct GDP spatialization using nighttime light data; few studies specifically concentrated on the spatialization and analysis of GDP23 in a built-up area by combining multi-source remote sensing images. In this study, the NPP-VIIRS-like dataset and Sentinel-2 multi-spectral remote sensing images in six years were combined to precisely spatialize and analyze the variation patterns of the GDP23 in the built-up area of Zibo city, China. Sentinel-2 images and the random forest (RF) classification method based on PIE-Engine cloud platform were employed to extract built-up areas, in which the NPP-VIIRS-like dataset and comprehensive nighttime light index were used to indicate the nighttime light magnitudes to construct models to spatialize GDP23 and analyze their change patterns during the study period. The results found that (1) the RF classification method can accurately extract the built-up area with an overall accuracy higher than 0.90; the change patterns of built-up areas varied among districts and counties, with Yiyuan county being the only administrative region with an annual expansion rate of more than 1%. (2) The comprehensive nighttime light index is a viable indicator of GDP23 in the built-up area; the fitted model exhibited an R2 value of 0.82, and the overall relative errors of simulated GDP23 and statistical GDP23 were below 1%. (3) The year 2018 marked a significant turning point in the trajectory of GDP23 development in the study area; in 2018, Zhoucun district had the largest decrease in GDP23 at −52.36%. (4) GDP23 gradation results found that Zhangdian district exhibited the highest proportion of high GDP23 (>9%), while the proportions of low GDP23 regions in the remaining seven districts and counties all exceeded 60%. The innovation of this study is that the GDP23 in built-up areas were first precisely spatialized and analyzed using the NPP-VIIRS-like dataset and Sentinel-2 images. The findings of this study can serve as references for formulating improved city planning strategies and sustainable development policies.
Full article
(This article belongs to the Special Issue Application of Satellite Remote Sensing in Geospatial Monitoring)
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Open AccessArticle
Innovative Non-Invasive and Non-Intrusive Precision Thermometry in Stainless-Steel Tanks Using Ultrasound Transducers
by
Ahmed Bouzid, Saad Chidami, Tristan Quentin Lailler, Adrián Carrillo García, Tarek Ould-Bachir and Jamal Chaouki
Sensors 2024, 24(11), 3404; https://doi.org/10.3390/s24113404 (registering DOI) - 25 May 2024
Abstract
Measuring temperature inside chemical reactors is crucial to ensuring process control and safety. However, conventional methods face a number of limitations, such as the invasiveness and the restricted dynamic range. This paper presents a novel approach using ultrasound transducers to enable accurate temperature
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Measuring temperature inside chemical reactors is crucial to ensuring process control and safety. However, conventional methods face a number of limitations, such as the invasiveness and the restricted dynamic range. This paper presents a novel approach using ultrasound transducers to enable accurate temperature measurements. Our experiments, conducted within a temperature range of 28.8 to 83.8 °C, reveal a minimal temperature accuracy of 98.6% within the critical zone spanning between 70.5 and 75 °C, and an accuracy of over 99% outside this critical zone. The experiments focused on a homogeneous environment of distilled water within a stainless-steel tank. This approach will be extended in a future research in order to diversify the experimental media and non-uniform environments, while promising broader applications in chemical process monitoring and control.
Full article
(This article belongs to the Collection Instrument and Measurement)
Open AccessArticle
Variable Temporal Length Training for Action Recognition CNNs
by
Tan-Kun Li, Kwok-Leung Chan and Tardi Tjahjadi
Sensors 2024, 24(11), 3403; https://doi.org/10.3390/s24113403 (registering DOI) - 25 May 2024
Abstract
Most current deep learning models are suboptimal in terms of the flexibility of their input shape. Usually, computer vision models only work on one fixed shape used during training, otherwise their performance degrades significantly. For video-related tasks, the length of each video (i.e.,
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Most current deep learning models are suboptimal in terms of the flexibility of their input shape. Usually, computer vision models only work on one fixed shape used during training, otherwise their performance degrades significantly. For video-related tasks, the length of each video (i.e., number of video frames) can vary widely; therefore, sampling of video frames is employed to ensure that every video has the same temporal length. This training method brings about drawbacks in both the training and testing phases. For instance, a universal temporal length can damage the features in longer videos, preventing the model from flexibly adapting to variable lengths for the purposes of on-demand inference. To address this, we propose a simple yet effective training paradigm for 3D convolutional neural networks (3D-CNN) which enables them to process videos with inputs having variable temporal length, i.e., variable length training (VLT). Compared with the standard video training paradigm, our method introduces three extra operations during training: sampling twice, temporal packing, and subvideo-independent 3D convolution. These operations are efficient and can be integrated into any 3D-CNN. In addition, we introduce a consistency loss to regularize the representation space. After training, the model can successfully process video with varying temporal length without any modification in the inference phase. Our experiments on various popular action recognition datasets demonstrate the superior performance of the proposed method compared to conventional training paradigm and other state-of-the-art training paradigms.
Full article
(This article belongs to the Section Sensing and Imaging)
Open AccessArticle
Characterization of Gas–Liquid Two-Phase Slug Flow Using Distributed Acoustic Sensing in Horizontal Pipes
by
Sharifah Ali, Ge Jin and Yilin Fan
Sensors 2024, 24(11), 3402; https://doi.org/10.3390/s24113402 (registering DOI) - 25 May 2024
Abstract
This article discusses the use of distributed acoustic sensing (DAS) for monitoring gas–liquid two-phase slug flow in horizontal pipes, using standard telecommunication fiber optics connected to a DAS integrator for data acquisition. The experiments were performed in a 14 m long, 5 cm
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This article discusses the use of distributed acoustic sensing (DAS) for monitoring gas–liquid two-phase slug flow in horizontal pipes, using standard telecommunication fiber optics connected to a DAS integrator for data acquisition. The experiments were performed in a 14 m long, 5 cm diameter transparent PVC pipe with a fiber cable helically wrapped around the pipe. Using mineral oil and compressed air, the system captured various flow rates and gas–oil ratios. New algorithms were developed to characterize slug flow using DAS data, including slug frequency, translational velocity, and the lengths of slug body, slug unit, and the liquid film region that had never been discussed previously. This study employed a high-speed camera next to the fiber cable sensing section for validation purposes and achieved a good correlation among the measurements under all conditions tested. Compared to traditional multiphase flow sensors, this technology is non-intrusive and offers continuous, real-time measurement across long distances and in harsh environments, such as subsurface or downhole conditions. It is cost-effective, particularly where multiple measurement points are required. Characterizing slug flow in real time is crucial to many industries that suffer slug-flow-related issues. This research demonstrated the DAS’s potential to characterize slug flow quantitively. It will offer the industry a more optimal solution for facility design and operation and ensure safer operational practices.
Full article
(This article belongs to the Special Issue Advances in Fiber Optic Sensors for Energy Applications)
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Open AccessArticle
Influence of Front-End Electronics on Metrological Performance of QCM Systems
by
Ada Fort, Elia Landi, Riccardo Moretti, Marco Mugnaini, Consolatina Liguori, Vincenzo Paciello and Salvatore Dello Iacono
Sensors 2024, 24(11), 3401; https://doi.org/10.3390/s24113401 (registering DOI) - 25 May 2024
Abstract
Quartz Crystal Microbalances (QCMs) are versatile sensors employed in various fields, from environmental monitoring to biomedical applications, owing mainly to their very high sensitivity. However, the assessment of their metrological performance, including the impact of conditioning circuits, digital processing algorithms, and working conditions,
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Quartz Crystal Microbalances (QCMs) are versatile sensors employed in various fields, from environmental monitoring to biomedical applications, owing mainly to their very high sensitivity. However, the assessment of their metrological performance, including the impact of conditioning circuits, digital processing algorithms, and working conditions, is a complex and novel area of study. The purpose of this work is to investigate and understand the measurement errors associated with different QCM measurement techniques, specifically focusing on the influence of conditioning electronic circuits. Through a tailored and novel experimental setup, two measurement architectures—a Quartz Crystal Microbalance with dissipation monitoring (QCM-D) system and an oscillator-based QCM-R system—were compared under the same mechanical load conditions. Through rigorous experimentation and signal processing techniques, the study elucidated the complexities of accurately assessing QCM parameters, especially in liquid environments and under large mechanical loads. The comparison between the two different techniques allows for highlighting the critical aspects of the measurement techniques. The experimental results were discussed and interpreted based on models allowing for a deep understanding of the measurement problems encountered with QCM-based measurement systems. The performance of the different techniques was derived, showing that while the QCM-D technique exhibited higher accuracy, the QCM-R technique offered greater precision with a simpler design. This research advances our understanding of QCM-based measurements, providing insights for designing robust measurement systems adaptable to diverse conditions, thus enhancing their effectiveness in various applications.
Full article
(This article belongs to the Section Chemical Sensors)
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Cam-Unet: Print-Cam Image Correction for Zero-Bit Fourier Image Watermarking
by
Said Boujerfaoui, Hassan Douzi, Rachid Harba and Frédéric Ros
Sensors 2024, 24(11), 3400; https://doi.org/10.3390/s24113400 (registering DOI) - 25 May 2024
Abstract
Image watermarking often involves the use of handheld devices under non-structured conditions for authentication purposes, particularly in the print-cam process where smartphone cameras are used to capture watermarked printed images. However, these images frequently suffer from perspective distortions, making them unsuitable for automated
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Image watermarking often involves the use of handheld devices under non-structured conditions for authentication purposes, particularly in the print-cam process where smartphone cameras are used to capture watermarked printed images. However, these images frequently suffer from perspective distortions, making them unsuitable for automated information detection. To address this issue, Cam-Unet, an end-to-end neural network architecture, is presented to predict the mapping from distorted images to rectified ones, specifically tailored for print-cam challenges applied to ID images. Given the limited availability of large-scale real datasets containing ground truth distortions, we created an extensive synthetic dataset by subjecting undistorted images to print-cam attacks. The proposed network is trained on this dataset, using various data augmentation techniques to improve its generalization capabilities. Accordingly, this paper presents an image watermarking system for the print-cam process. The approach combines Fourier transform-based watermarking with Cam-Unet as perspective distortion correction. Results show that the proposed method outperforms existing watermarking approaches typically employed to counter print-cam attacks and achieves an optimal balance between efficiency and cost-effectiveness.
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(This article belongs to the Special Issue Digital Image Processing and Sensing Technologies)
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Open AccessArticle
An Adjustable Pneumatic Planter with Reduced Source Vibration for Better Precision in Field Seeding
by
Jyotirmay Mahapatra, Prem Shanker Tiwari, Krishna Pratap Singh, Balaji Murhari Nandede, Ramesh K. Sahni, Vikas Pagare, Jagjeet Singh, D. J. Shrinivasa and Sandip Mandal
Sensors 2024, 24(11), 3399; https://doi.org/10.3390/s24113399 (registering DOI) - 25 May 2024
Abstract
The growing demand for agricultural output and limited resources encourage precision applications to generate higher-order output by utilizing minimal inputs of seed, fertilizer, land, and water. An electronically operated planter was developed, considering problems like ground-wheel skidding, field vibration, and the lack of
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The growing demand for agricultural output and limited resources encourage precision applications to generate higher-order output by utilizing minimal inputs of seed, fertilizer, land, and water. An electronically operated planter was developed, considering problems like ground-wheel skidding, field vibration, and the lack of ease in field adjustments of ground-wheel-driven seed-metering plates. The seed-metering plate of each unit of the developed planter is individually driven by a brushless direct current (BLDC) motor, and a BLDC motor-based aspirator is attached for pneumatic suction of seeds. The revolutions per minute (RPM) of the seed-metering plate are controlled by a microcontroller as per the received data relating to RPM from the ground wheel and the current RPM of the seed-metering plate. A feedback loop with proportional integral derivative (PID) control is responsible for reducing the error. Additionally, each row unit is attached to a parallelogram-based depth control system that can provide depth between 0 and 100 mm. The suction pressure in each unit is regulated as per seed type using the RPM control knob of an individual BLDC motor-based aspirator. The row-to-row spacing can be changed from 350 mm to any desired spacing. The cotton variety selected for the study was RCH 659, and the crucial parameters like orifice size, vacuum pressure, and forward speed were optimized in the laboratory with the adoption of a central composite rotatable design. An orifice diameter of 2.947 mm with vacuum pressure of 3.961 kPa and forward speed of 4.261 km/h was found optimal. A quality feed index of 93% with a precision index of 8.01% was observed from laboratory tests under optimized conditions. Quality feed index and precision index values of 88.8 and 12.75%, respectively, were obtained from field tests under optimized conditions.
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(This article belongs to the Section Smart Agriculture)
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Open AccessArticle
A Novel Method of UAV-Assisted Trajectory Localization for Forestry Environments
by
Jian Huang and Xiansheng Guo
Sensors 2024, 24(11), 3398; https://doi.org/10.3390/s24113398 (registering DOI) - 25 May 2024
Abstract
Global positioning systems often fall short in dense forest environments, leading to increasing demand for innovative localization methods. Notably, existing methods suffer from the following limitations: (1) traditional localization frameworks necessitate several fixed anchors to estimate the locations of targets, which is difficult
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Global positioning systems often fall short in dense forest environments, leading to increasing demand for innovative localization methods. Notably, existing methods suffer from the following limitations: (1) traditional localization frameworks necessitate several fixed anchors to estimate the locations of targets, which is difficult to satisfy in complex and uncertain forestry environments; (2) the uncertain environment severely decreases the quality of signal measurements and thus the localization accuracy. To cope with these limitations, this paper proposes a new method of trajectory localization for forestry environments with the assistance of UAVs. Based on the multi-agent DRL technique, the topology of UAVs is optimized in real-time to cater for high-accuracy target localization. Then, with the aid of RSS measurements from UAVs to the target, the least squares algorithm is used to estimate the location, which is more flexible and reliable than existing localization systems. Furthermore, a shared replay memory is incorporated into the proposed multi-agent DRL system, which can effectively enhance learning performance and efficiency. Simulation results show that the proposed method can obtain a flexible and high-accuracy localization system with the aid of UAVs, which exhibits better robustness against high-dimensional heterogeneous data and is suitable for forestry environments.
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(This article belongs to the Section Navigation and Positioning)
Open AccessArticle
Monitoring of Curing Process of Epoxy Resin by Long-Period Fiber Gratings
by
Oleg V. Ivanov, Kaushal Bhavsar, Oliver Morgan-Clague and James M. Gilbert
Sensors 2024, 24(11), 3397; https://doi.org/10.3390/s24113397 (registering DOI) - 25 May 2024
Abstract
The curing of epoxy resin is a complex thermo-chemical process that is difficult to monitor using existing sensing systems. We monitored the curing process of an epoxy resin by using long-period fiber gratings. The refractive index of the epoxy resin increases during the
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The curing of epoxy resin is a complex thermo-chemical process that is difficult to monitor using existing sensing systems. We monitored the curing process of an epoxy resin by using long-period fiber gratings. The refractive index of the epoxy resin increases during the curing process and can be measured to determine the degree of curing. We employed long-period fiber gratings that are sensitive to the refractive index of an external medium for the measurement of refractive index changes in the resin. We observed that the resonances of long-period fiber gratings increased their depth with the increased refractive index of the resin, which was well described by our simulation taking the coupling to radiation modes into account. We demonstrated that the degree of cure can be estimated from the depth of the grating resonances using a phenomenological model. At the same time, long-period fiber gratings are sensitive to temperature variations and internal strains that are induced during curing. These factors may affect the measurements of curing degree and should also be addressed.
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(This article belongs to the Special Issue Advances in Applications of Optical Fiber Sensors)
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