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
Early-Stage Ice Detection Utilizing High-Order Ultrasonic Guided Waves
Sensors 2024, 24(9), 2850; https://doi.org/10.3390/s24092850 (registering DOI) - 29 Apr 2024
Abstract
Ice detection poses significant challenges in sectors such as renewable energy and aviation due to its adverse effects on aircraft performance and wind energy production. Ice buildup alters the surface characteristics of aircraft wings or wind turbine blades, inducing airflow separation and diminishing
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Ice detection poses significant challenges in sectors such as renewable energy and aviation due to its adverse effects on aircraft performance and wind energy production. Ice buildup alters the surface characteristics of aircraft wings or wind turbine blades, inducing airflow separation and diminishing the aerodynamic properties of these structures. While various approaches have been proposed to address icing effects, including chemical solutions, pneumatic systems, and heating systems, these solutions are often costly and limited in scope. To enhance the cost-effectiveness of ice protection systems, reliable information about current icing conditions, particularly in the early stages, is crucial. Ultrasonic guided waves offer a promising solution for ice detection, enabling integration into critical structures and providing coverage over larger areas. However, existing techniques primarily focus on detecting thick ice layers, leaving a gap in early-stage detection. This paper proposes an approach based on high-order symmetric modes to detect thin ice formation with thicknesses up to a few hundred microns. The method involves measuring the group velocity of the S1 mode at different temperatures and correlating velocity changes with ice layer formation. Experimental verification of the proposed approach was conducted using a novel group velocity dispersion curve reconstruction method, allowing for the tracking of propagating modes in the structure. Copper samples without and with special superhydrophobic multiscale coatings designed to prevent ice formation were employed for the experiments. The results demonstrated successful detection of ice formation and enabled differentiation between the coated and uncoated cases. Therefore, the proposed approach can be effectively used for early-stage monitoring of ice growth and evaluating the performance of anti-icing coatings, offering promising advancements in ice detection and prevention for critical applications.
Full article
(This article belongs to the Section Physical Sensors)
Open AccessArticle
Robust Tracking Control of Wheeled Mobile Robot Based on Differential Flatness and Sliding Active Disturbance Rejection Control: Simulations and Experiments
by
Amine Abadi, Amani Ayeb, Moussa Labbadi, David Fofi, Toufik Bakir and Hassen Mekki
Sensors 2024, 24(9), 2849; https://doi.org/10.3390/s24092849 (registering DOI) - 29 Apr 2024
Abstract
This paper proposes a robust tracking control method for wheeled mobile robot (WMR) against uncertainties, including wind disturbances and slipping. Through the application of the differential flatness methodology, the under-actuated WMR model is transformed into a linear canonical form, simplifying the design of
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This paper proposes a robust tracking control method for wheeled mobile robot (WMR) against uncertainties, including wind disturbances and slipping. Through the application of the differential flatness methodology, the under-actuated WMR model is transformed into a linear canonical form, simplifying the design of a stabilizing feedback controller. To handle uncertainties from wheel slip and wind disturbances, the proposed feedback controller uses sliding mode control (SMC). However, increased uncertainties lead to chattering in the SMC approach due to higher control inputs. To mitigate this, a boundary layer around the switching surface is introduced, implementing a continuous control law to reduce chattering. Although increasing the boundary layer thickness reduces chattering, it may compromise the robustness achieved by SMC. To address this challenge, an active disturbance rejection control (ADRC) is integrated with boundary layer sliding mode control. ADRC estimates lumped uncertainties via an extended state observer and eliminates them within the feedback loop. This combined feedback control method aims to achieve practical control and robust tracking performance. Stability properties of the closed-loop system are established using the Lyapunov theory. Finally, simulations and experimental results are conducted to compare and evaluate the efficiency of the proposed robust tracking controller against other existing control methods.
Full article
(This article belongs to the Special Issue Mobile Robots: Navigation, Control and Sensing)
Open AccessArticle
Toward Synthetic Physical Fingerprint Targets
by
Laurenz Ruzicka, Bernhard Strobl, Stephan Bergmann, Gerd Nolden, Tom Michalsky, Christoph Domscheit, Jannis Priesnitz, Florian Blümel, Bernhard Kohn and Clemens Heitzinger
Sensors 2024, 24(9), 2847; https://doi.org/10.3390/s24092847 (registering DOI) - 29 Apr 2024
Abstract
Biometric fingerprint identification hinges on the reliability of its sensors; however, calibrating and standardizing these sensors poses significant challenges, particularly in regards to repeatability and data diversity. To tackle these issues, we propose methodologies for fabricating synthetic 3D fingerprint targets, or phantoms, that
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Biometric fingerprint identification hinges on the reliability of its sensors; however, calibrating and standardizing these sensors poses significant challenges, particularly in regards to repeatability and data diversity. To tackle these issues, we propose methodologies for fabricating synthetic 3D fingerprint targets, or phantoms, that closely emulate real human fingerprints. These phantoms enable the precise evaluation and validation of fingerprint sensors under controlled and repeatable conditions. Our research employs laser engraving, 3D printing, and CNC machining techniques, utilizing different materials. We assess the phantoms’ fidelity to synthetic fingerprint patterns, intra-class variability, and interoperability across different manufacturing methods. The findings demonstrate that a combination of laser engraving or CNC machining with silicone casting produces finger-like phantoms with high accuracy and consistency for rolled fingerprint recordings. For slap recordings, direct laser engraving of flat silicone targets excels, and in the contactless fingerprint sensor setting, 3D printing and silicone filling provide the most favorable attributes. Our work enables a comprehensive, method-independent comparison of various fabrication methodologies, offering a unique perspective on the strengths and weaknesses of each approach. This facilitates a broader understanding of fingerprint recognition system validation and performance assessment.
Full article
(This article belongs to the Topic Applications in Image Analysis and Pattern Recognition)
Open AccessArticle
Image Super Resolution-Based Channel Estimation for Orthogonal Chirp Division Multiplexing on Shallow Water Underwater Acoustic Communications
by
Haoyang Liu, Chuanlin He, Yanting Yu, Yiqi Bai and Yufei Han
Sensors 2024, 24(9), 2846; https://doi.org/10.3390/s24092846 (registering DOI) - 29 Apr 2024
Abstract
Orthogonal chirp division multiplexing (OCDM) offers a promising modulation technology for shallow water underwater acoustic (UWA) communication systems due to multipath fading resistance and Doppler resistance. To handle the various channel distortions and interferences, obtaining accurate channel state information is vital for robust
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Orthogonal chirp division multiplexing (OCDM) offers a promising modulation technology for shallow water underwater acoustic (UWA) communication systems due to multipath fading resistance and Doppler resistance. To handle the various channel distortions and interferences, obtaining accurate channel state information is vital for robust and efficient shallow water UWA communication. In recent years, deep learning has attracted widespread attention in the communication field, providing a new way to improve the performance of physical layer communication systems. In this paper, the pilot-based channel estimation is transformed into a matrix completion problem, which is mathematically equivalent to the image super-resolution problem arising in the field of image processing. Simulation results show that the deep learning-based method can improve the channel distortion, outperforming the equalization performed by traditional estimator, the performance of Bit Error Rate is improved by 2.5 dB compared to the MMSE method in OCDM system. At the 7.5 to 20 dB region, it achieves better bit error rate performance than OFDM systems, and the bit error rate is reduced by approximately 53% compared to OFDM when the SNR value is 20, which is very useful in shallow water UWA channels with multipath extension and severe time-varying characteristics.
Full article
(This article belongs to the Special Issue Underwater Wireless Communications)
Open AccessArticle
Anomaly Detection for Asynchronous Multivariate Time Series of Nuclear Power Plants Using a Temporal-Spatial Transformer
by
Shuang Yi, Sheng Zheng, Senquan Yang, Guangrong Zhou and Jiajun Cai
Sensors 2024, 24(9), 2845; https://doi.org/10.3390/s24092845 (registering DOI) - 29 Apr 2024
Abstract
Industrial process monitoring is a critical application of multivariate time-series (MTS) anomaly detection, especially crucial for safety-critical systems such as nuclear power plants (NPPs). However, some current data-driven process monitoring approaches may not fully capitalize on the temporal-spatial correlations inherent in operational MTS
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Industrial process monitoring is a critical application of multivariate time-series (MTS) anomaly detection, especially crucial for safety-critical systems such as nuclear power plants (NPPs). However, some current data-driven process monitoring approaches may not fully capitalize on the temporal-spatial correlations inherent in operational MTS data. Particularly, asynchronous time-lagged correlations may exist among variables in actual NPPs, which further complicates this challenge. In this work, a reconstruction-based MTS anomaly detection approach based on a temporal-spatial transformer is proposed. It employs a two-stage temporal-spatial attention mechanism combined with a multi-scale strategy to learn the dependencies within normal operational data at various scales, thereby facilitating the extraction of temporal-spatial correlations from asynchronous MTS. Experiments on simulated datasets and real NPP datasets demonstrate that the proposed model possesses stronger feature learning capabilities, as evidenced by its improved performance in signal reconstruction and anomaly detection for asynchronous MTS data. Moreover, the proposed TS-Trans model enables earlier detection of anomalous events, which holds significant importance for enhancing operational safety and reducing potential losses in NPPs.
Full article
(This article belongs to the Special Issue AI-Assisted Condition Monitoring and Fault Diagnosis)
Open AccessArticle
Adaptive Fabrication of Electrochemical Chips with a Paste-Dispensing 3D Printer
by
Ten It Wong, Candy Ng, Shengxuan Lin, Zhong Chen and Xiaodong Zhou
Sensors 2024, 24(9), 2844; https://doi.org/10.3390/s24092844 (registering DOI) - 29 Apr 2024
Abstract
Electrochemical (EC) detection is a powerful tool supporting simple, low-cost, and rapid analysis. Although screen printing is commonly used to mass fabricate disposable EC chips, its mask is relatively expensive. In this research, we demonstrated a method for fabricating three-electrode EC chips using
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Electrochemical (EC) detection is a powerful tool supporting simple, low-cost, and rapid analysis. Although screen printing is commonly used to mass fabricate disposable EC chips, its mask is relatively expensive. In this research, we demonstrated a method for fabricating three-electrode EC chips using 3D printing of relatively high-viscosity paste. The electrodes consisted of two layers, with carbon paste printed over silver/silver chloride paste, and the printed EC chips were baked at 70 C for 1 h. Engineering challenges such as bulging of the tubing, clogging of the nozzle, dripping, and local accumulation of paste were solved by material selection for the tube and nozzle, and process optimization in 3D printing. The EC chips demonstrated good reversibility in redox reactions through cyclic voltammetry tests, and reliably detected heavy metal ions Pb(II) and Cd(II) in solutions using differential pulse anodic stripping voltammetry measurements. The results indicate that by optimizing the 3D printing of paste, EC chips can be obtained by maskless and flexible 3D printing techniques in lieu of screen printing.
Full article
(This article belongs to the Special Issue Sensing Technologies in Additive Manufacturing)
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Open AccessArticle
Rectenna System Development Using Harmonic Balance and S-Parameters for an RF Energy Harvester
by
Muhamad Nurarif Bin Md Jamil, Madiah Omar, Rosdiazli Ibrahim, Kishore Bingi and Mochammad Faqih
Sensors 2024, 24(9), 2843; https://doi.org/10.3390/s24092843 - 29 Apr 2024
Abstract
With the escalating demand for Radio Frequency Identification (RFID) technology and the Internet of Things (IoT), there is a growing need for sustainable and autonomous power solutions to energize low-powered devices. Consequently, there is a critical imperative to mitigate dependency on batteries during
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With the escalating demand for Radio Frequency Identification (RFID) technology and the Internet of Things (IoT), there is a growing need for sustainable and autonomous power solutions to energize low-powered devices. Consequently, there is a critical imperative to mitigate dependency on batteries during passive operation. This paper proposes the conceptual framework of rectenna architecture-based radio frequency energy harvesters’ performance, specifically optimized for low-power device applications. The proposed prototype utilizes the surroundings’ Wi-Fi signals within the 2.4 GHz frequency band. The design integrates a seven-stage Cockroft-Walton rectifier featuring a Schottky diode HSMS286C and MA4E2054B1-1146T, a low-pass filter, and a fractal antenna. Preliminary simulations conducted using Advanced Design System (ADS) reveal that a voltage of 3.53 V can be harvested by employing a 1.57 mm thickness Rogers 5880 printed circuit board (PCB) substrate with an MA4E2054B1-1146T rectifier prototype, given a minimum power input of −10 dBm (0.1 mW). Integrating the fabricated rectifier and fractal antenna successfully yields a 1.5 V DC output from Wi-Fi signals, demonstrable by illuminating a red LED. These findings underscore the viability of deploying a fractal antenna-based radio frequency (RF) harvester for empowering small electronic devices.
Full article
(This article belongs to the Special Issue Hardware Enablement of Integrated Sensing and Communication Systems)
Open AccessSystematic Review
Sensors and Sensing Devices Utilizing Electrorheological Fluids and Magnetorheological Materials—A Review
by
Yu-Jin Park and Seung-Bok Choi
Sensors 2024, 24(9), 2842; https://doi.org/10.3390/s24092842 - 29 Apr 2024
Abstract
This paper comprehensively reviews sensors and sensing devices developed or/and proposed so far utilizing two smart materials: electrorheological fluids (ERFs) and magnetorheological materials (MRMs) whose rheological characteristics such as stiffness and damping can be controlled by external stimuli; an electrical voltage for ERFs
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This paper comprehensively reviews sensors and sensing devices developed or/and proposed so far utilizing two smart materials: electrorheological fluids (ERFs) and magnetorheological materials (MRMs) whose rheological characteristics such as stiffness and damping can be controlled by external stimuli; an electrical voltage for ERFs and a magnetic field for MRMs, respectively. In this review article, the MRMs are classified into magnetorheological fluids (MRF), magnetorheological elastomers (MRE) and magnetorheological plastomers (MRP). To easily understand the history of sensing research using these two smart materials, the order of this review article is organized in a chronological manner of ERF sensors, MRF sensors, MRE sensors and MRP sensors. Among many sensors fabricated from each smart material, one or two sensors or sensing devices are adopted to discuss the sensing configuration, working principle and specifications such as accuracy and sensitivity. Some sensors adopted in this article include force sensors, tactile devices, strain sensors, wearable bending sensors, magnetometers, display devices and flux measurement sensors. After briefly describing what has been reviewed in a conclusion, several challenging future works, which should be undertaken for the practical applications of sensors or/and sensing devices, are discussed in terms of response time and new technologies integrating with artificial intelligence neural networks in which several parameters affecting the sensor signals can be precisely and optimally tuned. It is sure that this review article is very helpful to potential readers who are interested in creative sensors using not only the proposed smart materials but also different types of smart materials such as shape memory alloys and active polymers.
Full article
(This article belongs to the Special Issue Feature Review Papers in Physical Sensors)
Open AccessArticle
A Novel Method for Remaining Useful Life Prediction of RF Circuits Based on the Gated Recurrent Unit–Convolutional Neural Network Model
by
Wanyu Yang, Kunping Wu, Bing Long and Shulin Tian
Sensors 2024, 24(9), 2841; https://doi.org/10.3390/s24092841 - 29 Apr 2024
Abstract
The remaining useful life (RUL) prediction of RF circuits is an important tool for circuit reliability. Data-driven-based approaches do not require knowledge of the failure mechanism and reduce the dependence on knowledge of complex circuits, and thus can effectively realize RUL prediction. This
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The remaining useful life (RUL) prediction of RF circuits is an important tool for circuit reliability. Data-driven-based approaches do not require knowledge of the failure mechanism and reduce the dependence on knowledge of complex circuits, and thus can effectively realize RUL prediction. This manuscript proposes a novel RUL prediction method based on a gated recurrent unit–convolutional neural network (GRU-CNN). Firstly, the data are normalized to improve the efficiency of the algorithm; secondly, the degradation of the circuit is evaluated using the hybrid health score based on the Euclidean and Manhattan distances; then, the life cycle of the RF circuits is segmented based on the hybrid health scores; and finally, an RUL prediction is carried out for the circuits at each stage using the GRU-CNN model. The results show that the RMSE of the GRU-CNN model in the normal operation stage is only 3/5 of that of the GRU and CNN models, while the prediction uncertainty is minimized.
Full article
(This article belongs to the Special Issue Sensors and Analog Front-End Circuits for IoT Systems and High Sensitivity Measurements)
Open AccessArticle
Sub-Nyquist SAR Imaging and Error Correction Via an Optimization-Based Algorithm
by
Wenjiao Chen, Li Zhang, Xiaocen Xing, Xin Wen and Qiuxuan Zhang
Sensors 2024, 24(9), 2840; https://doi.org/10.3390/s24092840 - 29 Apr 2024
Abstract
Sub-Nyquist synthetic aperture radar (SAR) based on pseudo-random time–space modulation has been proposed to increase the swath width while preserving the azimuthal resolution. Due to the sub-Nyquist sampling, the scene can be recovered by an optimization-based algorithm. However, these methods suffer from some
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Sub-Nyquist synthetic aperture radar (SAR) based on pseudo-random time–space modulation has been proposed to increase the swath width while preserving the azimuthal resolution. Due to the sub-Nyquist sampling, the scene can be recovered by an optimization-based algorithm. However, these methods suffer from some issues, e.g., manually tuning difficulty and the pre-definition of optimization parameters, and a low signal–noise ratio (SNR) resistance. To address these issues, a reweighted optimization algorithm, named pseudo-ℒ0-norm optimization algorithm, is proposed for the sub-Nyquist SAR system in this paper. A modified regularization model is first built by applying the scene prior information to nearly acquire the number of nonzero elements based on Bayesian estimation, and then this model is solved by the Cauchy–Newton method. Additionally, an error correction method combined with our proposed pseudo-ℒ0-norm optimization algorithm is also present to eliminate defocusing in the motion-induced model. Finally, experiments with simulated signals and strip-map TerraSAR-X images are carried out to demonstrate the effectiveness and superiority of our proposed algorithm.
Full article
(This article belongs to the Special Issue Sensing and Signal Analysis in Synthetic Aperture Radar Systems)
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Open AccessCommunication
Mode-Locked Operation of High-Order Transverse Modes in a Vertical-External-Cavity Surface-Emitting Laser
by
Tao Wang, Yunjie Liu, Renjiang Zhu, Lidan Jiang, Huanyu Lu, Yanrong Song and Peng Zhang
Sensors 2024, 24(9), 2839; https://doi.org/10.3390/s24092839 - 29 Apr 2024
Abstract
Understanding the mechanism of mode-locking in a laser with high-order transverse mode is important for achieving an ultrashort pulses train under more complicated conditions. So far, mode-locking with high-order transverse mode has not been reported in other lasers except the multimode fiber laser.
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Understanding the mechanism of mode-locking in a laser with high-order transverse mode is important for achieving an ultrashort pulses train under more complicated conditions. So far, mode-locking with high-order transverse mode has not been reported in other lasers except the multimode fiber laser. This paper demonstrates robust mode-locking with high-order transverse mode in a Kerr-lens mode-locked vertical-external-cavity surface-emitting laser for the first time, to the best of our knowledge. While the longitudinal modes are locked, continuous mode-locking accompanied by high-order transverse mode up to TEM40 is observed. The threshold of the mode-locking is only a little bigger than that of the lasing. After the laser oscillation is built up, the mode-locked pulse train can be obtained almost immediately and maintained until the thermal rollover of the laser. Output powers of 717 mW under fundamental mode and 666 mW under high-order transverse mode are achieved with a 4.3 ps pulse duration and 1.1 GHz pulses repetition rate, and some phenomenological explanations to the related characteristics of the mode-locked operation of high-order transverse mode in the vertical-external-cavity surface-emitting laser are proposed.
Full article
(This article belongs to the Section Optical Sensors)
Open AccessArticle
Design of AD Converters in 0.35 µm SiGe BiCMOS Technology for Ultra-Wideband M-Sequence Radar Sensors
by
Miroslav Sokol, Pavol Galajda, Jan Saliga and Patrik Jurik
Sensors 2024, 24(9), 2838; https://doi.org/10.3390/s24092838 - 29 Apr 2024
Abstract
The article presents the analysis, design, and low-cost implementation of application-specific AD converters for M-sequence-based UWB applications to minimize and integrate the whole UWB sensor system. Therefore, the main goal of this article is to integrate the AD converter’s own design with the
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The article presents the analysis, design, and low-cost implementation of application-specific AD converters for M-sequence-based UWB applications to minimize and integrate the whole UWB sensor system. Therefore, the main goal of this article is to integrate the AD converter’s own design with the UWB analog part into the system-in-package (SiP) or directly into the system-on-a-chip (SoC), which cannot be implemented with commercial AD converters, or which would be disproportionately expensive. Based on the current and used UWB sensor system requirements, to achieve the maximum possible bandwidth in the proposed semiconductor technology, a parallel converter structure is designed and presented in this article. Moreover, 5-bit and 4-bit parallel flash AD converters were initially designed as part of the research and design of UWB M-sequence radar systems for specific applications, and are briefly introduced in this article. The requirements of the newly proposed specific UWB M-sequence systems were established based on the knowledge gained from these initial designs. After thorough testing and evaluation of the concept of the early proposed AD converters for these specific UWB M-sequence systems, the design of a new AD converter was initiated. After confirming sufficient characteristics based on the requirements of UWB M-sequence systems for specific applications, a 7-bit AD converter in low-cost 0.35 µm SiGe BiCMOS technology from AMS was designed, fabricated, and presented in this article. The proposed 7-bit AD converter achieves the following parameters: ENOB = 6.4 bits, SINAD = 38 dB, SFDR = 42 dBc, INL = ±2-bit LSB, and DNL = ±1.5 LSB. The maximum sampling rate reaches 1.4 Gs/s, the power consumption at 20 Ms/s is 1050 mW, and at 1.4 Gs/s is 1290 mW, with a power supply of −3.3 V.
Full article
(This article belongs to the Section Radar Sensors)
Open AccessCommunication
Impact of Residual Compositional Inhomogeneities on the MCT Material Properties for IR Detectors
by
Jan Sobieski, Małgorzata Kopytko, Kacper Matuszelański, Waldemar Gawron, Józef Piotrowski and Piotr Martyniuk
Sensors 2024, 24(9), 2837; https://doi.org/10.3390/s24092837 - 29 Apr 2024
Abstract
HgCdTe is a well-known material for state-of-the-art infrared photodetectors. The interd-iffused multilayer process (IMP) is used for Metal–Organic Chemical Vapor Deposition (MOCVD) of HgCdTe heterostructures, enabling precise control of composition. In this method, alternating HgTe and CdTe layers are deposited, and they homogenize
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HgCdTe is a well-known material for state-of-the-art infrared photodetectors. The interd-iffused multilayer process (IMP) is used for Metal–Organic Chemical Vapor Deposition (MOCVD) of HgCdTe heterostructures, enabling precise control of composition. In this method, alternating HgTe and CdTe layers are deposited, and they homogenize during growth due to interdiffusion, resulting in a near-uniform material. However, the relatively low (350 °C) IMP MOCVD growth temperature may result in significant residual compositional inhomogeneities. In this work, we have investigated the residual inhomogeneities in the IMP-grown HgCdTe layers and their influence on material properties. Significant IMP growth-related oscillations of composition have been revealed in as-grown epilayers with the use of a high-resolution Secondary Ion Mass Spectroscopy (SIMS). The oscillations can be minimized with post-growth annealing of the layers at a temperature exceeding that of growth. The electric and photoelectric characterizations showed a significant reduction in the background doping and an increase in the recombination time, which resulted in dramatic improvement of the spectral responsivity of photoconductors.
Full article
(This article belongs to the Section Optical Sensors)
Open AccessArticle
GBDT Method Integrating Feature-Enhancement and Active-Learning Strategies—Sea Ice Thickness Inversion in Beaufort Sea
by
Yanling Han, Junjie Huang, Zhenling Ma, Bowen Zheng, Jing Wang and Yun Zhang
Sensors 2024, 24(9), 2836; https://doi.org/10.3390/s24092836 - 29 Apr 2024
Abstract
Sea ice, as an important component of the Earth’s ecosystem, has a profound impact on global climate and human activities due to its thickness. Therefore, the inversion of sea ice thickness has important research significance. Due to environmental and equipment-related limitations, the number
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Sea ice, as an important component of the Earth’s ecosystem, has a profound impact on global climate and human activities due to its thickness. Therefore, the inversion of sea ice thickness has important research significance. Due to environmental and equipment-related limitations, the number of samples available for remote sensing inversion is currently insufficient. At high spatial resolutions, remote sensing data contain limited information and noise interference, which seriously affect the accuracy of sea ice thickness inversion. In response to the above issues, we conducted experiments using ice draft data from the Beaufort Sea and designed an improved GBDT method that integrates feature-enhancement and active-learning strategies (IFEAL-GBDT). In this method, the incident angle and time series are used to perform spatiotemporal correction of the data, reducing both temporal and spatial impacts. Meanwhile, based on the original polarization information, effective multi-attribute features are generated to expand the information content and improve the separability of sea ice with different thicknesses. Taking into account the growth cycle and age of sea ice, attributes were added for month and seawater temperature. In addition, we studied an active learning strategy based on the maximum standard deviation to select more informative and representative samples and improve the model’s generalization ability. The improved GBDT model was used for training and prediction, offering advantages in dealing with nonlinear, high-dimensional data, and data noise problems, further expanding the effectiveness of feature-enhancement and active-learning strategies. Compared with other methods, the method proposed in this paper achieves the best inversion accuracy, with an average absolute error of 8 cm and a root mean square error of 13.7 cm for IFEAL-GBDT and a correlation coefficient of 0.912. This research proves the effectiveness of our method, which is suitable for the high-precision inversion of sea ice thickness determined using Sentinel-1 data.
Full article
(This article belongs to the Topic Applications of Geodesy in Meteorological, Hydrological and Climatic Environments)
Open AccessArticle
The Impact of Various Cockpit Display Interfaces on Novice Pilots’ Mental Workload and Situational Awareness: A Comparative Study
by
Huimin Tang, Boon Giin Lee, Dave Towey and Matthew Pike
Sensors 2024, 24(9), 2835; https://doi.org/10.3390/s24092835 - 29 Apr 2024
Abstract
Future airspace is expected to become more congested with additional in-service cargo and commercial flights. Pilots will face additional burdens in such an environment, given the increasing number of factors that they must simultaneously consider while completing their work activities. Therefore, care and
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Future airspace is expected to become more congested with additional in-service cargo and commercial flights. Pilots will face additional burdens in such an environment, given the increasing number of factors that they must simultaneously consider while completing their work activities. Therefore, care and attention must be paid to the mental workload (MWL) experienced by operating pilots. If left unaddressed, a state of mental overload could affect the pilot’s ability to complete his or her work activities in a safe and correct manner. This study examines the impact of two different cockpit display interfaces (CDIs), the Steam Gauge panel and the G1000 Glass panel, on novice pilots’ MWL and situational awareness (SA) in a flight simulator-based setting. A combination of objective (EEG and HRV) and subjective (NASA-TLX) assessments is used to assess novice pilots’ cognitive states during this study. Our results indicate that the gauge design of the CDI affects novice pilots’ SA and MWL, with the G1000 Glass panel being more effective in reducing the MWL and improving SA compared with the Steam Gauge panel. The results of this study have implications for the design of future flight deck interfaces and the training of future pilots.
Full article
(This article belongs to the Special Issue Sensors Fusion in Digital Healthcare Applications)
Open AccessArticle
Accuracy Assessment of Geometric-Distortion Identification Methods for Sentinel-1 Synthetic Aperture Radar Imagery in Highland Mountainous Regions
by
Chao Shi, Xiaoqing Zuo, Jianming Zhang, Daming Zhu, Yongfa Li and Jinwei Bu
Sensors 2024, 24(9), 2834; https://doi.org/10.3390/s24092834 - 29 Apr 2024
Abstract
SAR imagery plays a crucial role in geological and environmental monitoring, particularly in highland mountainous regions. However, inherent geometric distortions in SAR images often undermine the precision of remote sensing analyses. Accurately identifying and classifying these distortions is key to analyzing their origins
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SAR imagery plays a crucial role in geological and environmental monitoring, particularly in highland mountainous regions. However, inherent geometric distortions in SAR images often undermine the precision of remote sensing analyses. Accurately identifying and classifying these distortions is key to analyzing their origins and enhancing the quality and accuracy of monitoring efforts. While the layover and shadow map (LSM) approach is commonly utilized to identify distortions, it falls short in classifying subtle ones. This study introduces a novel LSM ground-range slope (LG) method, tailored for the refined identification of minor distortions to augment the LSM approach. We implemented the LG method on Sentinel-1 SAR imagery from the tri-junction area where the Xiaojiang, Pudu, and Jinsha rivers converge at the Yunnan-Sichuan border. By comparing effective monitoring-point densities, we evaluated and validated traditional methods—LSM, R-Index, and P-NG—against the LG method. The LG method demonstrates superior performance in discriminating subtle distortions within complex terrains through its secondary classification process, which allows for precise and comprehensive recognition of geometric distortions. Furthermore, our research examines the impact of varying slope parameters during the classification process on the accuracy of distortion identification. This study addresses significant gaps in recognizing geometric distortions and lays a foundation for more precise SAR imagery analysis in complex geographic settings.
Full article
(This article belongs to the Section Sensing and Imaging)
Open AccessArticle
LSTM-Autoencoder Based Anomaly Detection Using Vibration Data of Wind Turbines
by
Younjeong Lee, Chanho Park, Namji Kim, Jisu Ahn and Jongpil Jeong
Sensors 2024, 24(9), 2833; https://doi.org/10.3390/s24092833 - 29 Apr 2024
Abstract
The problem of energy depletion has brought wind energy under consideration to replace oil- or chemical-based energy. However, the breakdown of wind turbines is a major concern. Accordingly, unsupervised learning was performed using the vibration signal of a wind power generator to achieve
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The problem of energy depletion has brought wind energy under consideration to replace oil- or chemical-based energy. However, the breakdown of wind turbines is a major concern. Accordingly, unsupervised learning was performed using the vibration signal of a wind power generator to achieve an outlier detection performance of 97%. We analyzed the vibration data through wavelet packet conversion and identified a specific frequency band that showed a large difference between the normal and abnormal data. To emphasize these specific frequency bands, high-pass filters were applied to maximize the difference. Subsequently, the dimensions of the data were reduced through principal component analysis, giving unique characteristics to the data preprocessing process. Normal data collected from a wind farm located in northern Sweden was first preprocessed, and a long short-term memory (LSTM) autoencoder, and outlier detection was performed. The LSTM Autoencoder is a model specialized for time-series data that learns the patterns of normal data and detects other data as outliers. Therefore, we propose a method for outlier detection through data preprocessing and unsupervised learning, utilizing the vibration signals from wind generators. This will facilitate the quick and accurate detection of wind power generator failures and provide alternatives to the problem of energy depletion.
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(This article belongs to the Special Issue Deep-Learning-Based Defect Detection for Smart Manufacturing)
Open AccessArticle
Experimental Study of Electroosmosis in Rock Cores Based on the Dual Pressure Sensor Method
by
Chenggang Yin, Wei Guan and Hengshan Hu
Sensors 2024, 24(9), 2832; https://doi.org/10.3390/s24092832 - 29 Apr 2024
Abstract
Electroosmotic experiments obtain the electroosmotic pressure coefficient of a rock sample by measuring the excitation voltage at both ends of the sample and the pressure difference caused by the excitation voltage. The electroosmotic pressure is very weak and buried in the background noise,
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Electroosmotic experiments obtain the electroosmotic pressure coefficient of a rock sample by measuring the excitation voltage at both ends of the sample and the pressure difference caused by the excitation voltage. The electroosmotic pressure is very weak and buried in the background noise, which is the most difficult signal to measure in the dynamic-electric coupling experiment, so it is necessary to improve its signal-to-noise ratio. In this paper, for the low signal-to-noise ratio of electroosmotic pressure, the dual pressure sensor method is proposed, i.e., two pressure sensors of the same type are used to measure electroosmotic pressure. Two different data extraction methods, Fast Fourier Transform and Locked Amplification, are utilized to compare the dual pressure sensor method of this paper with the existing single pressure sensor method. The relationship between the electroosmotic pressure coefficient and the excitation frequency, mineralization, permeability, and porosity is analyzed and discussed.
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(This article belongs to the Special Issue Sensors and Geophysical Electromagnetics)
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Open AccessArticle
Pulse Compression Shape-Based ADC/DAC Chain Synchronization Measurement Algorithm with Sub-Sampling Resolution
by
Xiangyu Hao, Hongji Fang, Wei Luo and Bo Zhang
Sensors 2024, 24(9), 2831; https://doi.org/10.3390/s24092831 - 29 Apr 2024
Abstract
In this article, we address the problem of synchronizing multiple analog-to-digital converter (ADC) and digital-to-analog converter (DAC) chains in a multi-channel system, which is constrained by the sampling frequency and inconsistencies among the components during system integration. To evaluate and compensate for the
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In this article, we address the problem of synchronizing multiple analog-to-digital converter (ADC) and digital-to-analog converter (DAC) chains in a multi-channel system, which is constrained by the sampling frequency and inconsistencies among the components during system integration. To evaluate and compensate for the synchronization differences, we propose a pulse compression shape-based algorithm to measure the entire delay parameter of the ADC/DAC chain, which achieves sub-sampling resolution by mapping the shape of the discrete pulse compression peak to the signal propagation delay. Moreover, owing to the matched filtering in the pulse compression process, the algorithm exhibits good noise performance and is suitable for wireless scenarios. Experiments verified that the algorithm can achieve precise measurements with sub-sampling resolution in scenarios where the signal-to-noise ratio (SNR) is greater than −10 dB.
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(This article belongs to the Special Issue Radar Receiver Design and Application)
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Open AccessArticle
DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features
by
Shakil Rana, Md Jabed Hosen, Tasnim Jahan Tonni, Md. Awlad Hossen Rony, Kaniz Fatema, Md. Zahid Hasan, Md. Tanvir Rahman, Risala Tasin Khan, Tony Jan and Md Whaiduzzaman
Sensors 2024, 24(9), 2830; https://doi.org/10.3390/s24092830 - 29 Apr 2024
Abstract
Lung diseases are the third-leading cause of mortality in the world. Due to compromised lung function, respiratory difficulties, and physiological complications, lung disease brought on by toxic substances, pollution, infections, or smoking results in millions of deaths every year. Chest X-ray images pose
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Lung diseases are the third-leading cause of mortality in the world. Due to compromised lung function, respiratory difficulties, and physiological complications, lung disease brought on by toxic substances, pollution, infections, or smoking results in millions of deaths every year. Chest X-ray images pose a challenge for classification due to their visual similarity, leading to confusion among radiologists. To imitate those issues, we created an automated system with a large data hub that contains 17 datasets of chest X-ray images for a total of 71,096, and we aim to classify ten different disease classes. For combining various resources, our large datasets contain noise and annotations, class imbalances, data redundancy, etc. We conducted several image pre-processing techniques to eliminate noise and artifacts from images, such as resizing, de-annotation, CLAHE, and filtering. The elastic deformation augmentation technique also generates a balanced dataset. Then, we developed DeepChestGNN, a novel medical image classification model utilizing a deep convolutional neural network (DCNN) to extract 100 significant deep features indicative of various lung diseases. This model, incorporating Batch Normalization, MaxPooling, and Dropout layers, achieved a remarkable 99.74% accuracy in extensive trials. By combining graph neural networks (GNNs) with feedforward layers, the architecture is very flexible when it comes to working with graph data for accurate lung disease classification. This study highlights the significant impact of combining advanced research with clinical application potential in diagnosing lung diseases, providing an optimal framework for precise and efficient disease identification and classification.
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(This article belongs to the Section Biomedical Sensors)
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