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
Button-Type Beam Position Monitor Development for Fourth-Generation Synchrotron Light Sources: Numerical Modeling and Test Bench Measurements
Sensors 2024, 24(9), 2726; https://doi.org/10.3390/s24092726 (registering DOI) - 25 Apr 2024
Abstract
This paper addresses the design of beam position monitor (BPM) devices suitable for fourth-generation diffraction-limited X-ray storage rings. Detailed investigations of the electromagnetic (EM) phenomena occurring inside the component under various working conditions are carried out by considering different BPM EM models defined
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This paper addresses the design of beam position monitor (BPM) devices suitable for fourth-generation diffraction-limited X-ray storage rings. Detailed investigations of the electromagnetic (EM) phenomena occurring inside the component under various working conditions are carried out by considering different BPM EM models defined by their geometry and materials. Moving from a theoretical characterization of the common round geometry, rhomboidal structures are studied through a careful numerical analysis relying on advanced computer-aided tools. Several critical elements, such as wakefields, pick-up signal extraction, and trapped and propagating modes, are explored from the simulation point of view and from the experimental one, by deploying a manufactured microwave test bench, which is employed to measure the radio frequency behavior of a BPM prototype built at Elettra Sincrotrone Trieste. The aim of the proposed study is to identify a satisfactory tradeoff between achievable performance and practical realizability for BPM devices operating in last-generation light sources.
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(This article belongs to the Section Sensors Development)
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Open AccessArticle
Development and Application of an IoT-Based System for Soil Water Status Monitoring in a Soil Profile
by
Alessandro Comegna, Shawcat Basel Mostafa Hassan and Antonio Coppola
Sensors 2024, 24(9), 2725; https://doi.org/10.3390/s24092725 (registering DOI) - 25 Apr 2024
Abstract
Soil water content (θ), matric potential (h) and hydraulic conductivity (K) are key parameters for hydrological and environmental processes. Several sensors have been developed for measuring soil θ–h–K relationships. The cost of such commercially available sensors
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Soil water content (θ), matric potential (h) and hydraulic conductivity (K) are key parameters for hydrological and environmental processes. Several sensors have been developed for measuring soil θ–h–K relationships. The cost of such commercially available sensors may vary over several orders of magnitude. In recent years, some sensors have been designed in the framework of Internet of Things (i.e., IoT) systems to make remote real-time soil data acquisition more straightforward, enabling low-cost field-scale monitoring at high spatio-temporal scales. In this paper, we introduce a new multi-parameter sensor designed for the simultaneous estimation of θ and h at different soil depths and, due to the sensor’s specific layout, the soil hydraulic conductivity function via the instantaneous profile method (IPM). Our findings indicate that a second-order polynomial function is the most suitable model (R2 = 0.99) for capturing the behavior of the capacitive-based sensor in estimating θ in the examined soil, which has a silty-loam texture. The effectiveness of low-cost capacitive sensors, coupled with the IPM method, was confirmed as a viable alternative to time domain reflectometry (TDR) probes. Notably, the layout of the sensor makes the IPM method less labor-intensive to implement. The proposed monitoring system consistently demonstrated robust performance throughout extended periods of data acquisition and is highly suitable for ongoing monitoring of soil water status.
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(This article belongs to the Section Environmental Sensing)
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Hardware Acceleration of Digital Pulse Shape Analysis Using FPGAs
by
César González, Mariano Ruiz, Antonio Carpeño, Alejandro Piñas, Daniel Cano-Ott, Julio Plaza, Trino Martinez and David Villamarin
Sensors 2024, 24(9), 2724; https://doi.org/10.3390/s24092724 (registering DOI) - 25 Apr 2024
Abstract
The BC501A sensor is a liquid scintillator frequently used in nuclear physics for detecting fast neutrons. This paper describes a hardware implementation of digital pulse shape analysis (DPSA) for real-time analysis. DPSA is an algorithm that extracts the physically relevant parameters from the
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The BC501A sensor is a liquid scintillator frequently used in nuclear physics for detecting fast neutrons. This paper describes a hardware implementation of digital pulse shape analysis (DPSA) for real-time analysis. DPSA is an algorithm that extracts the physically relevant parameters from the detected BC501A signals. The hardware solution is implemented in a MicroTCA system that provides the physical, mechanical, electrical, and cooling support for an AMC board (NAMC-ZYNQ-FMC) with a Xilinx ZYNQ Ultrascale-MP SoC. The Xilinx FPGA programmable logic implements a JESD204B interface to high-speed ADCs. The physical and datalink JESD204B layers are implemented using hardware description language (HDL), while the Xilinx high-level synthesis language (HLS) is used for the transport and application layers. The DPSA algorithm is a JESD204B application layer that includes a FIR filter and a constant fraction discriminator (CFD) function, a baseline calculation function, a peak detection function, and an energy calculation function. This architecture achieves an analysis mean time of less than 100 µs per signal with an FPGA resource utilization of about 50% of its most used resources. This paper presents a high-performance DPSA embedded system that interfaces with a 1 GS/s ADC and performs accurate calculations with relatively low latency.
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(This article belongs to the Special Issue Advanced Interface Circuits for Sensor Systems (Volume II))
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Improving the Signal-to-Noise Ratio of Axial Displacement Measurements of Microspheres Based on Compound Digital Holography Microscopy Combined with the Reconstruction Centering Method
by
Yanan Zeng, Qihang Guo, Xiaodong Hu, Junsheng Lu, Xiaopan Fan, Haiyun Wu, Xiao Xu, Jun Xie and Rui Ma
Sensors 2024, 24(9), 2723; https://doi.org/10.3390/s24092723 (registering DOI) - 24 Apr 2024
Abstract
In 3D microsphere tracking, unlike in-plane motion that can be measured directly by a microscope, axial displacements are resolved by optical interference or a diffraction model. As a result, the axial results are affected by the environmental noise. The immunity to environmental noise
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In 3D microsphere tracking, unlike in-plane motion that can be measured directly by a microscope, axial displacements are resolved by optical interference or a diffraction model. As a result, the axial results are affected by the environmental noise. The immunity to environmental noise increases with measurement accuracy and the signal-to-noise ratio (SNR). In compound digital holography microscopy (CDHM)-based measurements, precise identification of the tracking marker is critical to ensuring measurement precision. The reconstruction centering method (RCM) was proposed to suppress the drawbacks caused by installation errors and, at the same time, improve the correct identification of the tracking marker. The reconstructed center is considered to be the center of the microsphere, rather than the center of imaging in conventional digital holographic microscopy. This method was verified by simulation of rays tracing through microspheres and axial moving experiments. The axial displacements of silica microspheres with diameters of 5 μm and 10 μm were tested by CDHM in combination with the RCM. As a result, the SNR of the proposed method was improved by around 30%. In addition, the method was successfully applied to axial displacement measurements of overlapped microspheres with a resolution of 2 nm.
Full article
(This article belongs to the Special Issue Digital Holography in Optics: Techniques and Applications)
Open AccessArticle
Design of A Transformer Oil Viscosity, Density, and Dielectric Constant Simultaneous Measurement System Based on A Quartz Tuning Fork
by
Hao Yang, Shijie Chen and Jiafeng Ding
Sensors 2024, 24(9), 2722; https://doi.org/10.3390/s24092722 (registering DOI) - 24 Apr 2024
Abstract
Transformer oil, crucial for transformer and power system safety, demands effective monitoring. Aiming to address the problems of expensive and bulky equipment, poor real-time performance, and single parameter detection of traditional measurement methods, this study proposes a quartz tuning fork-based simultaneous measurement system
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Transformer oil, crucial for transformer and power system safety, demands effective monitoring. Aiming to address the problems of expensive and bulky equipment, poor real-time performance, and single parameter detection of traditional measurement methods, this study proposes a quartz tuning fork-based simultaneous measurement system for online monitoring of the density, viscosity, and dielectric constant of transformer oil. Based on the Butterworth–Van Dyke quartz tuning fork equivalent circuit model, a working mechanism of transformer oil density, viscosity, and dielectric constant was analyzed, and a measurement model for oil samples was obtained. A miniaturized simultaneous measurement system was designed based on a dedicated chip for vector current-voltage impedance analysis for data acquisition and a Savitzky–Golay filter for data filtering. A transformer oil test platform was built to verify the simultaneous measurement system. The results showed that the system has good repeatability, and the measurement errors of density, viscosity, and dielectric constant are lower than 2.00%, 5.50%, and 3.20%, respectively. The online and offline results showed that the system meets the requirements of the condition maintenance system for online monitoring accuracy and real-time detection.
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(This article belongs to the Section Physical Sensors)
Open AccessArticle
A Continuous Non-Invasive Blood Pressure Prediction Method Based on Deep Sparse Residual U-Net Combined with Improved Squeeze and Excitation Skip Connections
by
Kaixuan Lai, Xusheng Wang and Congjun Cao
Sensors 2024, 24(9), 2721; https://doi.org/10.3390/s24092721 (registering DOI) - 24 Apr 2024
Abstract
Arterial blood pressure (ABP) serves as a pivotal clinical metric in cardiovascular health assessments, with the precise forecasting of continuous blood pressure assuming a critical role in both preventing and treating cardiovascular diseases. This study proposes a novel continuous non-invasive blood pressure prediction
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Arterial blood pressure (ABP) serves as a pivotal clinical metric in cardiovascular health assessments, with the precise forecasting of continuous blood pressure assuming a critical role in both preventing and treating cardiovascular diseases. This study proposes a novel continuous non-invasive blood pressure prediction model, DSRUnet, based on deep sparse residual U-net combined with improved SE skip connections, which aim to enhance the accuracy of using photoplethysmography (PPG) signals for continuous blood pressure prediction. The model first introduces a sparse residual connection approach for path contraction and expansion, facilitating richer information fusion and feature expansion to better capture subtle variations in the original PPG signals, thereby enhancing the network’s representational capacity and predictive performance and mitigating potential degradation in the network performance. Furthermore, an enhanced SE-GRU module was embedded in the skip connections to model and weight global information using an attention mechanism, capturing the temporal features of the PPG pulse signals through GRU layers to improve the quality of the transferred feature information and reduce redundant feature learning. Finally, a deep supervision mechanism was incorporated into the decoder module to guide the lower-level network to learn effective feature representations, alleviating the problem of gradient vanishing and facilitating effective training of the network. The proposed DSRUnet model was trained and tested on the publicly available UCI-BP dataset, with the average absolute errors for predicting systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean blood pressure (MBP) being 3.36 ± 6.61 mmHg, 2.35 ± 4.54 mmHg, and 2.21 ± 4.36 mmHg, respectively, meeting the standards set by the Association for the Advancement of Medical Instrumentation (AAMI), and achieving Grade A according to the British Hypertension Society (BHS) Standard for SBP and DBP predictions. Through ablation experiments and comparisons with other state-of-the-art methods, the effectiveness of DSRUnet in blood pressure prediction tasks, particularly for SBP, which generally yields poor prediction results, was significantly higher. The experimental results demonstrate that the DSRUnet model can accurately utilize PPG signals for real-time continuous blood pressure prediction and obtain high-quality and high-precision blood pressure prediction waveforms. Due to its non-invasiveness, continuity, and clinical relevance, the model may have significant implications for clinical applications in hospitals and research on wearable devices in daily life.
Full article
(This article belongs to the Section Biomedical Sensors)
Open AccessArticle
A Robust Interacting Multi-Model Multi-Bernoulli Mixture Filter for Maneuvering Multitarget Tracking under Glint Noise
by
Benru Yu, Hong Gu and Weimin Su
Sensors 2024, 24(9), 2720; https://doi.org/10.3390/s24092720 (registering DOI) - 24 Apr 2024
Abstract
In practical radar systems, changes in the target aspect toward the radar will result in glint noise disturbances in the measurement data. The glint noise has heavy-tailed characteristics and cannot be perfectly modeled by the Gaussian distribution widely used in conventional tracking algorithms.
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In practical radar systems, changes in the target aspect toward the radar will result in glint noise disturbances in the measurement data. The glint noise has heavy-tailed characteristics and cannot be perfectly modeled by the Gaussian distribution widely used in conventional tracking algorithms. In this article, we investigate the challenging problem of tracking a time-varying number of maneuvering targets in the context of glint noise with unknown statistics. By assuming a set of models for the possible motion modes of each single-target hypothesis and leveraging the multivariate Laplace distribution to model measurement noise, we propose a robust interacting multi-model multi-Bernoulli mixture filter based on the variational Bayesian method. Within this filter, the unknown noise statistics are adaptively learned while filtering and the predictive likelihood is approximately calculated by means of the variational lower bound. The effectiveness and superiority of our proposed filter is verified via computer simulations.
Full article
(This article belongs to the Special Issue Radar Sensors for Target Tracking and Localization)
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Signal Processing Using a Circular Sensor Array to Measure the Torsional Angle of a Bolted Joint
by
Thorben Schüthe, Karl-Ragmar Riemschneider and Andreas Meyer-Eschenbach
Sensors 2024, 24(9), 2719; https://doi.org/10.3390/s24092719 (registering DOI) - 24 Apr 2024
Abstract
This study presents a new approach to determining the preload force of bolted joints. The concept involves measuring the torsional angle without contact. For this purpose, we present a circular magnetic sensor array integrated into the torque wrench. The torsional angle in bolted
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This study presents a new approach to determining the preload force of bolted joints. The concept involves measuring the torsional angle without contact. For this purpose, we present a circular magnetic sensor array integrated into the torque wrench. The torsional angle in bolted joints depends on the dimensions of the screw and the materials used and is typically less than four degrees. For this reason, one requirement is a high angular resolution so that a continuous recording of the torsion angle is feasible during the assembly process. This can be achieved using the circular sensor array and adapted signal processing methods. Two signal processing approaches are utilized. First, the direct method uses the discrete Fourier transformation to calculate the rotation angle from the signal phase. This approach is robust to signal distortion and does not depend on signal amplitude. Second, the method with a learning phase employs Gaussian process regression to minimize the angle error. In an experiment, both approaches were applied within a test bench and showed promising results. The direct method demonstrated a very good angular resolution without training and calibration. For mobile and less-complex applications where a reference system is unavailable, the direct method is preferable. However, in complex measurement systems where reference systems can be utilized initially, significant enhancements to an excellent resolution can be achieved through prior training.
Full article
(This article belongs to the Special Issue Magnetic Sensor and Its Applications)
Open AccessArticle
Adaptation of Postural Sway in a Standing Position during Tilted Video Viewing Using Virtual Reality: A Comparison between Younger and Older Adults
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Tsubasa Tashiro, Noriaki Maeda, Takeru Abekura, Rami Mizuta, Yui Terao, Satoshi Arima, Satoshi Onoue and Yukio Urabe
Sensors 2024, 24(9), 2718; https://doi.org/10.3390/s24092718 (registering DOI) - 24 Apr 2024
Abstract
This study aimed to investigate the effects of wearing virtual reality (VR) with a head-mounted display (HMD) on body sway in younger and older adults. A standing posture with eyes open without an HMD constituted the control condition. Wearing an HMD and viewing
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This study aimed to investigate the effects of wearing virtual reality (VR) with a head-mounted display (HMD) on body sway in younger and older adults. A standing posture with eyes open without an HMD constituted the control condition. Wearing an HMD and viewing a 30°-tilt image and a 60°-tilt image in a resting standing position were the experimental conditions. Measurements were made using a force plate. All conditions were performed three times each and included the X-axis trajectory length (mm), Y-axis trajectory length (mm), total trajectory length (mm), trajectory length per unit time (mm/s), outer peripheral area (mm2), and rectangular area (mm2). The results showed a significant interaction between generation and condition in Y-axis trajectory length (mm) and total trajectory length (mm), with an increased body center-of-gravity sway during the viewing of tilted VR images in older adults than in younger adults in both sexes. The results of this study show that body sway can be induced by visual stimulation alone with VR without movement, suggesting the possibility of providing safe and simple balance training to older adults.
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(This article belongs to the Section Intelligent Sensors)
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Open AccessReview
The Role of Interdigitated Electrodes in Printed and Flexible Electronics
by
Shayma Habboush, Sara Rojas, Noel Rodríguez and Almudena Rivadeneyra
Sensors 2024, 24(9), 2717; https://doi.org/10.3390/s24092717 - 24 Apr 2024
Abstract
Flexible electronics, also referred to as printable electronics, represent an interesting technology for implementing electronic circuits via depositing electronic devices onto flexible substrates, boosting their possible applications. Among all flexible electronics, interdigitated electrodes (IDEs) are currently being used for different sensor applications since
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Flexible electronics, also referred to as printable electronics, represent an interesting technology for implementing electronic circuits via depositing electronic devices onto flexible substrates, boosting their possible applications. Among all flexible electronics, interdigitated electrodes (IDEs) are currently being used for different sensor applications since they offer significant benefits beyond their functionality as capacitors, like the generation of high output voltage, fewer fabrication steps, convenience of application of sensitive coatings, material imaging capability and a potential of spectroscopy measurements via electrical excitation frequency variation. This review examines the role of IDEs in printed and flexible electronics since they are progressively being incorporated into a myriad of applications, envisaging that the growth pattern will continue in the next generations of flexible circuits to come.
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(This article belongs to the Section Electronic Sensors)
Open AccessArticle
Hardware and Software Setup for Quantitative 23Na Magnetic Resonance Imaging at 3T: A Phantom Study
by
Giulio Giovannetti, Alessandra Flori, Nicola Martini, Filippo Cademartiri, Giovanni Donato Aquaro, Alessandro Pingitore and Francesca Frijia
Sensors 2024, 24(9), 2716; https://doi.org/10.3390/s24092716 - 24 Apr 2024
Abstract
Magnetic resonance (MR) with sodium (23Na) is a noninvasive tool providing quantitative biochemical information regarding physiology, cellular metabolism, and viability, with the potential to extend MR beyond anatomical proton imaging. However, when using clinical scanners, the low detectable 23Na signal
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Magnetic resonance (MR) with sodium (23Na) is a noninvasive tool providing quantitative biochemical information regarding physiology, cellular metabolism, and viability, with the potential to extend MR beyond anatomical proton imaging. However, when using clinical scanners, the low detectable 23Na signal and the low 23Na gyromagnetic ratio require the design of dedicated radiofrequency (RF) coils tuned to the 23Na Larmor frequency and sequences, as well as the development of dedicated phantoms for testing the image quality, and an MR scanner with multinuclear spectroscopy (MNS) capabilities. In this work, we propose a hardware and software setup for evaluating the potential of 23Na magnetic resonance imaging (MRI) with a clinical scanner. In particular, the reliability of the proposed setup and the reproducibility of the measurements were verified by multiple acquisitions from a 3T MR scanner using a homebuilt RF volume coil and a dedicated sequence for the imaging of a phantom specifically designed for evaluating the accuracy of the technique. The final goal of this study is to propose a setup for standardizing clinical and research 23Na MRI protocols.
Full article
(This article belongs to the Special Issue Recent Advances in the Acquisition and Processing of Biomedical Signals and Images)
Open AccessArticle
Design and Experiment of an Unoccupied Control System for a Tracked Grain Vehicle
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Jiahui Pan, Lizhang Xu, En Lu, Buwang Dai, Tiaotiao Chen, Weiming Sun, Zhihong Cui and Jinpeng Hu
Sensors 2024, 24(9), 2715; https://doi.org/10.3390/s24092715 - 24 Apr 2024
Abstract
In order to enhance crop harvesting efficiency, an automatic-driving tracked grain vehicle system was designed. Based on the harvester chassis, we designed the mechanical structure of a tracked grain vehicle with a loading capacity of 4.5 m3 and a grain unloading hydraulic
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In order to enhance crop harvesting efficiency, an automatic-driving tracked grain vehicle system was designed. Based on the harvester chassis, we designed the mechanical structure of a tracked grain vehicle with a loading capacity of 4.5 m3 and a grain unloading hydraulic system. Using the BODAS hydraulic controller, we implemented the design of an electronic control system that combines the manual and automatic operation of the chassis walking mechanism and grain unloading mechanism. We utilized a hybrid A* algorithm to plan the traveling path of the tracked grain vehicle, and the path-tracking controller of the tracked grain vehicle was designed by combining fuzzy control and pure pursuit algorithms. Leveraging binocular vision technology and semantic segmentation technology, we designed an automatic grain unloading control system with functions of grain tank recognition and grain unloading regulation control. Finally, we conducted experiments on automatic grain unloading control and automatic navigation control in the field. The results showed that both the precision of the path-tracking control and the automatic unloading system meet the requirements for practical unoccupied operations of the tracked grain vehicle.
Full article
(This article belongs to the Section Smart Agriculture)
Open AccessArticle
Curved and Annular Diaphragm Coupled Piezoelectric Micromachined Ultrasonic Transducers for High Transmit Biomedical Applications
by
Yun Zhang, Tong Jin, Zijie Zhao, Chenfang Yan, Xinchao Lu, Hang Gao and Chengjun Huang
Sensors 2024, 24(9), 2714; https://doi.org/10.3390/s24092714 - 24 Apr 2024
Abstract
In this paper, we present a novel three-dimensional (3D) coupled configuration of piezoelectric micromachined ultrasound transducers (pMUTs) by combing a curved and an annular diaphragm for transmit performance optimization in biomedical applications. An analytical equivalent circuit model (EQC) is developed with varied excitation
[...] Read more.
In this paper, we present a novel three-dimensional (3D) coupled configuration of piezoelectric micromachined ultrasound transducers (pMUTs) by combing a curved and an annular diaphragm for transmit performance optimization in biomedical applications. An analytical equivalent circuit model (EQC) is developed with varied excitation methods to incorporate the acoustic–structure coupling of the curved and annular diaphragm-coupled pMUTs (CAC-pMUTs). The model-derived results align well with the reference simulated by the finite element method (FEM). Using this EQC model, we optimize the key design parameters of the CAC-pMUTs in order to improve the output sound pressure, including the width of the annular membrane, the thickness of the passive layer, and the phase difference of the driving voltage. In the anti-phase mode, the designed CAC-pMUTs demonstrate a transmit efficiency 285 times higher than that of single annular pMUTs. This substantial improvement underscores the potential of CAC-pMUTs for large array applications.
Full article
(This article belongs to the Section Physical Sensors)
Open AccessArticle
Impact of Rainfall on the Detection Performance of Non-Contact Safety Sensors for UAVs/UGVs
by
Yasushi Sumi, Bong Keun Kim, Takuya Ogure, Masato Kodama, Naoki Sakai and Masami Kobayashi
Sensors 2024, 24(9), 2713; https://doi.org/10.3390/s24092713 - 24 Apr 2024
Abstract
This study comprehensively investigates how rain and drizzle affect the object-detection performance of non-contact safety sensors, which are essential for the operation of unmanned aerial vehicles and ground vehicles in adverse weather conditions. In contrast to conventional sensor-performance evaluation based on the amount
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This study comprehensively investigates how rain and drizzle affect the object-detection performance of non-contact safety sensors, which are essential for the operation of unmanned aerial vehicles and ground vehicles in adverse weather conditions. In contrast to conventional sensor-performance evaluation based on the amount of precipitation, this paper proposes spatial transmittance and particle density as more appropriate metrics for rain environments. Through detailed experiments conducted under a variety of precipitation conditions, it is shown that sensor performance is significantly affected by the density of small raindrops rather than the total amount of precipitation. This finding challenges traditional sensor-evaluation metrics in rainfall environments and suggests a paradigm shift toward the use of spatial transmittance as a universal metric for evaluating sensor performance in rain, drizzle, and potentially other adverse weather scenarios.
Full article
(This article belongs to the Special Issue Advanced UAV-Based Sensor Technologies)
Open AccessArticle
Modified RTK-GNSS for Challenging Environments
by
Ellarizza Fredeluces, Tomohiro Ozeki, Nobuaki Kubo and Ahmed El-Mowafy
Sensors 2024, 24(9), 2712; https://doi.org/10.3390/s24092712 - 24 Apr 2024
Abstract
Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS) is currently the premier technique for achieving centimeter-level accuracy quickly and easily. However, the robustness of RTK-GNSS diminishes in challenging environments due to severe multipath effects and a limited number of available GNSS signals. This is
[...] Read more.
Real-Time Kinematic Global Navigation Satellite System (RTK-GNSS) is currently the premier technique for achieving centimeter-level accuracy quickly and easily. However, the robustness of RTK-GNSS diminishes in challenging environments due to severe multipath effects and a limited number of available GNSS signals. This is a pressing issue, especially for GNSS users in the navigation industry. This paper proposes and evaluates several methodologies designed to overcome these issues by enhancing the availability and reliability of RTK-GNSS solutions in urban environments. Our novel approach involves the integration of conventional methods with a new technique that leverages surplus satellites—those not initially used for positioning—to more reliably detect incorrect fix solutions. We conducted three tests in densely built-up areas within the Tokyo region. The results demonstrate that our approach not only surpasses the fix rate of the latest commercial receivers and a popular open-source RTK-GNSS program but also improves positional reliability to levels comparable to or exceeding those of the aforementioned commercial technology.
Full article
(This article belongs to the Special Issue GNSS Signals and Precise Point Positioning)
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Open AccessArticle
Color and Luminance Separated Enhancement for Low-Light Images with Brightness Guidance
by
Feng Zhang, Xinran Liu, Changxin Gao and Nong Sang
Sensors 2024, 24(9), 2711; https://doi.org/10.3390/s24092711 - 24 Apr 2024
Abstract
Existing retinex-based low-light image enhancement strategies focus heavily on crafting complex networks for Retinex decomposition but often result in imprecise estimations. To overcome the limitations of previous methods, we introduce a straightforward yet effective strategy for Retinex decomposition, dividing images into colormaps and
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Existing retinex-based low-light image enhancement strategies focus heavily on crafting complex networks for Retinex decomposition but often result in imprecise estimations. To overcome the limitations of previous methods, we introduce a straightforward yet effective strategy for Retinex decomposition, dividing images into colormaps and graymaps as new estimations for reflectance and illumination maps. The enhancement of these maps is separately conducted using a diffusion model for improved restoration. Furthermore, we address the dual challenge of perturbation removal and brightness adjustment in illumination maps by incorporating brightness guidance. This guidance aids in precisely adjusting the brightness while eliminating disturbances, ensuring a more effective enhancement process. Extensive quantitative and qualitative experimental analyses demonstrate that our proposed method improves the performance by approximately on the LOL dataset compared to other state-of-the-art diffusion-based methods, while also validating the model’s generalizability across multiple real-world datasets.
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(This article belongs to the Topic Applied Computer Vision and Pattern Recognition: 2nd Volume)
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Open AccessArticle
Enhanced Lightweight YOLOX for Small Object Wildfire Detection in UAV Imagery
by
Tian Luan, Shixiong Zhou, Guokang Zhang, Zechun Song, Jiahui Wu and Weijun Pan
Sensors 2024, 24(9), 2710; https://doi.org/10.3390/s24092710 - 24 Apr 2024
Abstract
Target detection technology based on unmanned aerial vehicle (UAV)-derived aerial imagery has been widely applied in the field of forest fire patrol and rescue. However, due to the specificity of UAV platforms, there are still significant issues to be resolved such as severe
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Target detection technology based on unmanned aerial vehicle (UAV)-derived aerial imagery has been widely applied in the field of forest fire patrol and rescue. However, due to the specificity of UAV platforms, there are still significant issues to be resolved such as severe omission, low detection accuracy, and poor early warning effectiveness. In light of these issues, this paper proposes an improved YOLOX network for the rapid detection of forest fires in images captured by UAVs. Firstly, to enhance the network’s feature-extraction capability in complex fire environments, a multi-level-feature-extraction structure, CSP-ML, is designed to improve the algorithm’s detection accuracy for small-target fire areas. Additionally, a CBAM attention mechanism is embedded in the neck network to reduce interference caused by background noise and irrelevant information. Secondly, an adaptive-feature-extraction module is introduced in the YOLOX network’s feature fusion part to prevent the loss of important feature information during the fusion process, thus enhancing the network’s feature-learning capability. Lastly, the CIoU loss function is used to replace the original loss function, to address issues such as excessive optimization of negative samples and poor gradient-descent direction, thereby strengthening the network’s effective recognition of positive samples. Experimental results show that the improved YOLOX network has better detection performance, with mAP@50 and mAP@50_95 increasing by 6.4% and 2.17%, respectively, compared to the traditional YOLOX network. In multi-target flame and small-target flame scenarios, the improved YOLO model achieved a mAP of 96.3%, outperforming deep learning algorithms such as FasterRCNN, SSD, and YOLOv5 by 33.5%, 7.7%, and 7%, respectively. It has a lower omission rate and higher detection accuracy, and it is capable of handling small-target detection tasks in complex fire environments. This can provide support for UAV patrol and rescue applications from a high-altitude perspective.
Full article
(This article belongs to the Special Issue Advances on UAV-Based Sensing and Imaging)
Open AccessArticle
The Influence of Nonlinear High-Intensity Dynamic Processes on the Standing Wave Precession of a Non-Ideal Hemispherical Resonator
by
Wei Cheng, Shunqing Ren, Boqi Xi, Zhen Tian, Youhuan Ning and Yan Huo
Sensors 2024, 24(9), 2709; https://doi.org/10.3390/s24092709 - 24 Apr 2024
Abstract
The properties of small size, low noise, high performance and no wear-out have made the hemispherical resonator gyroscope a good choice for high-value space missions. To enhance the precision of the hemispherical resonator gyroscope for use in tasks with large angular velocities and
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The properties of small size, low noise, high performance and no wear-out have made the hemispherical resonator gyroscope a good choice for high-value space missions. To enhance the precision of the hemispherical resonator gyroscope for use in tasks with large angular velocities and angular accelerations, this paper investigates the standing wave precession of a non-ideal hemispherical resonator under nonlinear high-intensity dynamic conditions. Based on the thin shell theory of elasticity, a dynamic model of a hemispherical resonator is established by using Lagrange’s second kind equation. Then, the dynamic model is equivalently transformed into a simple harmonic vibration model of a point mass in two-dimensional space, which is analyzed using a method of averaging that separates the slow variables from the fast variables. The results reveal that taking the nonlinear terms about the square of the angular velocity and the angular acceleration in the dynamic equation into account can weaken the influence of the 4th harmonic component of a mass defect on standing wave drift, and the extent of this weakening effect varies with the dimensions of the mass defects, which is very important for steering the development of the high-precision hemispherical resonator gyroscope.
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(This article belongs to the Section Physical Sensors)
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Open AccessArticle
A Multiple Attention Convolutional Neural Networks for Diesel Engine Fault Diagnosis
by
Xiao Yang, Fengrong Bi, Jiangang Cheng, Daijie Tang, Pengfei Shen and Xiaoyang Bi
Sensors 2024, 24(9), 2708; https://doi.org/10.3390/s24092708 - 24 Apr 2024
Abstract
Fault diagnosis can improve the safety and reliability of diesel engines. An end-to-end method based on a multi-attention convolutional neural network (MACNN) is proposed for accurate and efficient diesel engine fault diagnosis. By optimizing the arrangement and kernel size of the channel and
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Fault diagnosis can improve the safety and reliability of diesel engines. An end-to-end method based on a multi-attention convolutional neural network (MACNN) is proposed for accurate and efficient diesel engine fault diagnosis. By optimizing the arrangement and kernel size of the channel and spatial attention modules, the feature extraction capability is improved, and an improved convolutional block attention module (ICBAM) is obtained. Vibration signal features are acquired using a feature extraction model alternating between the convolutional neural network (CNN) and ICBAM. The feature map is recombined to reconstruct the sequence order information. Next, the self-attention mechanism (SAM) is applied to learn the recombined sequence features directly. A Swish activation function is introduced to solve “Dead ReLU” and improve the accuracy. A dynamic learning rate curve is designed to improve the convergence ability of the model. The diesel engine fault simulation experiment is carried out to simulate three kinds of fault types (abnormal valve clearance, abnormal rail pressure, and insufficient fuel supply), and each kind of fault varies in different degrees. The comparison results show that the accuracy of MACNN on the eight-class fault dataset at different speeds is more than 97%. The testing time of the MACNN is much less than the machine running time (for one work cycle). Therefore, the proposed end-to-end fault diagnosis method has a good application prospect.
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(This article belongs to the Special Issue Advanced Sensing Systems for Structural Monitoring and Damage Detection)
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Coupling of Modes in Step-Index Plastic Optical Fibers by Using D-Shape Technique
by
Cláudio Márcio F. Silva, Gefeson M. Pacheco, Jognes Panasiewicz and Luis A. Rabanal Ramirez
Sensors 2024, 24(9), 2707; https://doi.org/10.3390/s24092707 - 24 Apr 2024
Abstract
This article presents a technique for reducing the stabilization length of steady-state modes in step-index plastic optical fibers (POFs) that is important for sensor networks, Internet of Things, and signal processing and data fusion in sensor systems. The results obtained with the computational
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This article presents a technique for reducing the stabilization length of steady-state modes in step-index plastic optical fibers (POFs) that is important for sensor networks, Internet of Things, and signal processing and data fusion in sensor systems. The results obtained with the computational tool developed suggest that the D-shape created in the POF effectively reduces the stabilization length of the modes and, by extension, minimizes the dispersion effects of the modes by filtering out high-order modes. Applying the analysis to commercial POFs, the authors experimentally verified a reduction in the stabilization length of modes from 27 to 10 m and from 20 m to 5 m. Reducing the mode stabilization length minimizes the bit error rate (BER) in short-length SI-POF-based optical links operating at 250 Mbp/s. A reduction from 7.6 × 10−7 to 3.7 × 10−10 was achieved.
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(This article belongs to the Section Optical Sensors)
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