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
Basic Performance Evaluation of a Radiation Survey Meter That Uses a Plastic-Scintillation Sensor
Sensors 2024, 24(10), 2973; https://doi.org/10.3390/s24102973 - 07 May 2024
Abstract
After the Fukushima nuclear power plant accident in 2011, many types of survey meters were used, including Geiger–Müller (GM) survey meters, which have long been used to measure β-rays. Recently, however, a novel radiation survey meter that uses a plastic-scintillation sensor has been
[...] Read more.
After the Fukushima nuclear power plant accident in 2011, many types of survey meters were used, including Geiger–Müller (GM) survey meters, which have long been used to measure β-rays. Recently, however, a novel radiation survey meter that uses a plastic-scintillation sensor has been developed. Although manufacturers’ catalog data are available for these survey meters, there have been no user reports on performance. In addition, the performance of commercial plastic-scintillation survey meters has not been evaluated. In this study, we experimentally compared the performance of a plastic-scintillation survey meter with that of a GM survey meter. The results show that the two instruments performed very similarly in most respects. The GM survey meter exhibited count losses when the radiation count rate was high, whereas the plastic-scintillation survey meter remained accurate under such circumstances, with almost no count loss at high radiation rates. For measurements at background rates (i.e., low counting rates), the counting rates of the plastic-scintillation and GM survey meters were similar. Therefore, an advantage of plastic-scintillation survey meters is that they are less affected by count loss than GM survey meters. We conclude that the plastic-scintillation survey meter is a useful β-ray measuring/monitoring instrument.
Full article
(This article belongs to the Section Physical Sensors)
Open AccessArticle
Shoulder Movement-Centered Measurement and Estimation Scheme for Underarm-Throwing Motions
by
Geunho Lee, Yusuke Hayakawa, Takuya Watanabe and Yasuhiro Bonkobara
Sensors 2024, 24(10), 2972; https://doi.org/10.3390/s24102972 - 07 May 2024
Abstract
Underarm throwing motions are crucial in various sports, including boccia. Unlike healthy players, people with profound weakness, spasticity, athetosis, or deformity in the upper limbs may struggle or find it difficult to control their hands to hold or release a ball using their
[...] Read more.
Underarm throwing motions are crucial in various sports, including boccia. Unlike healthy players, people with profound weakness, spasticity, athetosis, or deformity in the upper limbs may struggle or find it difficult to control their hands to hold or release a ball using their fingers at the proper timing. To help them, our study aims to understand underarm throwing motions. We start by defining the throwing intention in terms of the launch angle of a ball, which goes hand-in-hand with the timing for releasing the ball. Then, an appropriate part of the body is determined in order to estimate ball-throwing intention based on the swinging motion. Furthermore, the geometric relationship between the movements of the body part and the release angle is investigated by involving multiple subjects. Based on the confirmed correlation, a calibration-and-estimation model that considers individual differences is proposed. The proposed model consists of calibration and estimation modules. To begin, as the calibration module is performed, individual prediction states for each subject are updated online. Then, in the estimation module, the throwing intention is estimated employing the updated prediction. To verify the effectiveness of the model, extensive experiments were conducted with seven subjects. In detail, two evaluation directions were set: (1) how many balls need to be thrown in advance to achieve sufficient accuracy; and (2) whether the model can reach sufficient accuracy despite individual differences. From the evaluation tests, by throwing 20 balls in advance, the model could account for individual differences in the throwing estimation. Consequently, the effectiveness of the model was confirmed when focusing on the movements of the shoulder in the human body during underarm throwing. In the near future, we expect the model to expand the means of supporting disabled people with ball-throwing disabilities.
Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Sensors in Sports Safety and NextGen Rehabilitation)
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Open AccessCommunication
Improving the Accuracy of Direction of Arrival Estimation with Multiple Signal Inputs Using Deep Learning
by
Yihan Lu, Hengchao Guan, Kun Yang, Tong Peng, Chengyuan Wen and Xin Li
Sensors 2024, 24(10), 2971; https://doi.org/10.3390/s24102971 - 07 May 2024
Abstract
In this paper, an innovative cyclic noise reduction method and an improved CAPON algorithm (also the called minimum variance distortionless response (MVDR) algorithm) are proposed to improve the accuracy and reliability of DOA (direction of arrival) estimation. By processing the eigenvalues obtained from
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In this paper, an innovative cyclic noise reduction method and an improved CAPON algorithm (also the called minimum variance distortionless response (MVDR) algorithm) are proposed to improve the accuracy and reliability of DOA (direction of arrival) estimation. By processing the eigenvalues obtained from the covariance matrix of the received signal, the signal-to-noise ratio (SNR) can be increased by up to 5 dB by the cyclic noise reduction method, which will improve the DOA estimation accuracy. The improved CAPON algorithm has a convolution neural network (CNN) structure, whose input is the processed covariance matrix of the received signal, and the CAPON spectral value is used as the training label to obtain the estimated spatial spectrum. It retains the advantages of the CAPON algorithm, which can achieve blind source estimation, and simulations show that the proposed algorithm exhibits a better performance than the traditional algorithm in conditions of various SNRs and snapshot numbers. The simulation results show that, based on a certain SNR, the root mean square error (RMSE) of the improved CAPON algorithm can be reduced from 0.86° to 0.8° compared to traditional algorithms, and the angle estimation error can be decreased by up to about 0.3°. With the help of the cyclic noise reduction method, the angle estimation error decreases from 0.04° to 0.02°.
Full article
(This article belongs to the Special Issue Novel Antennas for Wireless Communication and Intelligent Sensing)
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Open AccessArticle
Data-Fusion-Based Quality Enhancement for HR Measurements Collected by Wearable Sensors
by
Shenghao Xia, Shu-Fen Wung, Chang-Chun Chen, Jude Larbi Kwesi Coompson, Janet Roveda and Jian Liu
Sensors 2024, 24(10), 2970; https://doi.org/10.3390/s24102970 - 07 May 2024
Abstract
The advancements of Internet of Things (IoT) technologies have enabled the implementation of smart and wearable sensors, which can be employed to provide older adults with affordable and accessible continuous biophysiological status monitoring. The quality of such monitoring data, however, is unsatisfactory due
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The advancements of Internet of Things (IoT) technologies have enabled the implementation of smart and wearable sensors, which can be employed to provide older adults with affordable and accessible continuous biophysiological status monitoring. The quality of such monitoring data, however, is unsatisfactory due to excessive noise induced by various disturbances, such as motion artifacts. Existing methods take advantage of summary statistics, such as mean or median values, for denoising, without taking into account the biophysiological patterns embedded in data. In this research, a functional data analysis modeling method was proposed to enhance the data quality by learning individual subjects’ diurnal heart rate (HR) patterns from historical data, which were further improved by fusing newly collected data. This proposed data-fusion approach was developed based on a Bayesian inference framework. Its effectiveness was demonstrated in an HR analysis from a prospective study involving older adults residing in assisted living or home settings. The results indicate that it is imperative to conduct personalized healthcare by estimating individualized HR patterns. Furthermore, the proposed calibration method provides a more accurate (smaller mean errors) and more precise (smaller error standard deviations) HR estimation than raw HR and conventional methods, such as the mean.
Full article
(This article belongs to the Special Issue Wearable Sensors for Continuous Health Monitoring and Analysis)
Open AccessArticle
Dual-Frequency Soil Moisture Meter Method for Simultaneous Estimation of Soil Moisture and Conductivity
by
Jerzy S. Witkowski and Andrzej F. Grobelny
Sensors 2024, 24(10), 2969; https://doi.org/10.3390/s24102969 - 07 May 2024
Abstract
The measurement of soil water content is a very important factor in plant cultivation, both from an economic and ecological point of view. Proper estimation of moisture content not only allows for proper yields but can also contribute to ecologically appropriate use of
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The measurement of soil water content is a very important factor in plant cultivation, both from an economic and ecological point of view. Proper estimation of moisture content not only allows for proper yields but can also contribute to ecologically appropriate use of fresh water, of which the world’s resources are limited. It is important, for example, that the moisture content in the root area of plants is optimal for their growth, while over-watering can result in losses in the form of water, which seeps below the root layer and is lost. The novel, inexpensive electronic meter for measuring soil moisture is presented in the article. The meter, based on a capacitive method, uses an optimization algorithm to calculate soil electrical permeability and a simplified new formula between soil electrical permeability and volumetric moisture content. Moreover, by using two high-frequency signals for measurements, it is possible not only to estimate moisture content but also soil conductivity. Both readings obtained from the meter not only allow for rational management of crop optimization for economic reasons but are also important for environmental protection. In addition, the inexpensive meter, based on the principle of operation presented, can be made as an IoT module, which allows for its wide application.
Full article
(This article belongs to the Special Issue Electronic Sensors for Industrial or Environmental Monitoring Applications)
Open AccessArticle
Integrated Communication, Sensing, and Computation Framework for 6G Networks
by
Xu Chen, Zhiyong Feng, J. Andrew Zhang, Zhaohui Yang, Xin Yuan, Xinxin He and Ping Zhang
Sensors 2024, 24(10), 2968; https://doi.org/10.3390/s24102968 - 07 May 2024
Abstract
In the sixth generation (6G) era, intelligent machine network (IMN) applications, such as intelligent transportation, require collaborative machines with communication, sensing, and computation (CSC) capabilities. This article proposes an integrated communication, sensing, and computation (ICSAC) framework for 6G to achieve the reciprocity among
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In the sixth generation (6G) era, intelligent machine network (IMN) applications, such as intelligent transportation, require collaborative machines with communication, sensing, and computation (CSC) capabilities. This article proposes an integrated communication, sensing, and computation (ICSAC) framework for 6G to achieve the reciprocity among CSC functions to enhance the reliability and latency of communication, accuracy and timeliness of sensing information acquisition, and privacy and security of computing to realize the IMN applications. Specifically, the sensing and communication functions can merge into unified platforms using the same transmit signals, and the acquired real-time sensing information can be exploited as prior information for intelligent algorithms to enhance the performance of communication networks. This is called the computing-empowered integrated sensing and communications (ISAC) reciprocity. Such reciprocity can further improve the performance of distributed computation with the assistance of networked sensing capability, which is named the sensing-empowered integrated communications and computation (ICAC) reciprocity. The above ISAC and ICAC reciprocities can enhance each other iteratively and finally lead to the ICSAC reciprocity. To achieve these reciprocities, we explore the potential enabling technologies for the ICSAC framework. Finally, we present the evaluation results of crucial enabling technologies to show the feasibility of the ICSAC framework.
Full article
(This article belongs to the Special Issue Communication, Sensing and Localization in 6G Systems)
Open AccessArticle
Porcine Model of Cerebral Ischemic Stroke Utilizing Intracortical Recordings for the Continuous Monitoring of the Ischemic Area
by
Thomas Gomes Nørgaard dos Santos Nielsen, Numa Dancause, Taha Al Muhammadee Janjua, Felipe Rettore Andreis, Benedict Kjærgaard and Winnie Jensen
Sensors 2024, 24(10), 2967; https://doi.org/10.3390/s24102967 - 07 May 2024
Abstract
Purpose: Our aim was to use intracortical recording to enable the tracking of ischemic infarct development over the first few critical hours of ischemia with a high time resolution in pigs. We employed electrophysiological measurements to obtain quick feedback on neural function, which
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Purpose: Our aim was to use intracortical recording to enable the tracking of ischemic infarct development over the first few critical hours of ischemia with a high time resolution in pigs. We employed electrophysiological measurements to obtain quick feedback on neural function, which might be useful for screening, e.g., for the optimal dosage and timing of agents prior to further pre-clinical evaluation. Methods: Micro-electrode arrays containing 16 (animal 1) or 32 electrodes (animal 2–7) were implanted in the primary somatosensory cortex of seven female pigs, and continuous electrical stimulation was applied at 0.2 Hz to a cuff electrode implanted on the ulnar nerve. Ischemic stroke was induced after 30 min of baseline recording by injection of endothelin-1 onto the cortex adjacent to the micro-electrode array. Evoked responses were extracted over a moving window of 180 s and averaged across channels as a measure of cortical excitability. Results: Across the animals, the cortical excitability was significantly reduced in all seven 30 min segments following endothelin-1 injection, as compared to the 30 min preceding this intervention. This difference was not explained by changes in the anesthesia, ventilation, end-tidal CO2, mean blood pressure, heart rate, blood oxygenation, or core temperature, which all remained stable throughout the experiment. Conclusions: The animal model may assist in maturing neuroprotective approaches by testing them in an accessible model of resemblance to human neural and cardiovascular physiology and body size. This would constitute an intermediate step for translating positive results from rodent studies into human application, by more efficiently enabling effective optimization prior to chronic pre-clinical studies in large animals.
Full article
(This article belongs to the Special Issue Feature Papers in Biosensors Section 2024)
Open AccessCommunication
Radon Exposure Assessment in Occupational and Environmental Settings: An Overview of Instruments and Methods
by
Mota Kholopo and Phoka Caiphus Rathebe
Sensors 2024, 24(10), 2966; https://doi.org/10.3390/s24102966 - 07 May 2024
Abstract
Radon is a naturally occurring noble radioactive gas that poses significant health risks, particularly lung cancer, due to its colorless, odorless, and tasteless nature, which makes detection challenging without formal testing. It is found in soil, rock, and water, and it infiltrates indoor
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Radon is a naturally occurring noble radioactive gas that poses significant health risks, particularly lung cancer, due to its colorless, odorless, and tasteless nature, which makes detection challenging without formal testing. It is found in soil, rock, and water, and it infiltrates indoor environments, necessitating regulatory standards and guidelines from organizations such as the Environmental Protection Agency, the World Health Organization, and the Occupational Health and Safety Agency to mitigate exposure. In this paper, we present various methods and instruments for radon assessment in occupational and environmental settings. Discussion on long- and short-term monitoring, including grab sampling, radon dosimetry, and continuous real-time monitoring, is provided. The comparative analysis of detection techniques—active versus passive—is highlighted from real-time data and long-term exposure assessment, including advances in sensor technology, data processing, and public awareness, to improve radon exposure evaluation techniques.
Full article
(This article belongs to the Special Issue Detection and Measurement of Radioactive Noble Gases)
Open AccessArticle
Optical-OFDM VLC System: Peak-to-Average Power Ratio Enhancement and Performance Evaluation
by
Yasser A. Zenhom, Ehab K. I. Hamad, Mohammed Alghassab and Mohamed M. Elnabawy
Sensors 2024, 24(10), 2965; https://doi.org/10.3390/s24102965 - 07 May 2024
Abstract
Visible Light Communication (VLC) systems are favoured for numerous applications due to their extensive bandwidth and resilience to electromagnetic interference. This study delineates various constructions of Optical Orthogonal Frequency Division Multiplexing (O-OFDM) approaches employed in VLC systems. Various factors are elaborated within this
[...] Read more.
Visible Light Communication (VLC) systems are favoured for numerous applications due to their extensive bandwidth and resilience to electromagnetic interference. This study delineates various constructions of Optical Orthogonal Frequency Division Multiplexing (O-OFDM) approaches employed in VLC systems. Various factors are elaborated within this context to ascertain a more effective O-OFDM approach, including constellation size, data arrangement and spectral efficiency, power efficiency, computational complexity, bit error rate (BER), and peak-to-average power ratio (PAPR). This paper seeks to assess these approaches’ BER and PAPR performance across varying modulation orders. Regrettably, in VLC systems based on OFDM methodology, the superposition of multiple subcarriers results in a high PAPR. Therefore, this study aims to diminish the PAPR in VLC systems, enhancing system performance. We propose a non-distorting PAPR reduction technique, namely the Vandermonde-Like Matrix (VLM) precoding technique. The suggested technique is implemented across various O-OFDM approaches, including DCO-OFDM, ADO-OFDM, ACO-OFDM, FLIP-OFDM, ASCO-OFDM, and LACO-OFDM. Notably, this method does not affect the system’s data rate because it does not require the mandatory transmission of side information. Furthermore, this technique can decrease the PAPR without impacting the system’s BER performance. This study compares the proposed PAPR reduction technique against established methods documented in the literature to evaluate their efficacy and validity rigorously.
Full article
(This article belongs to the Special Issue Optical Wireless Communications and Positioning)
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Open AccessArticle
Simplified Beam Hardening Correction for Ultrafast X-ray CT Imaging of Binary Granular Mixtures
by
Martina Bieberle, Theodoros Nestor Papapetrou, Gregory Lecrivain, Dominic Windisch, André Bieberle, Michael Wagner and Uwe Hampel
Sensors 2024, 24(10), 2964; https://doi.org/10.3390/s24102964 - 07 May 2024
Abstract
Ultrafast X-ray computed tomography is an advanced imaging technique for multiphase flows. It has been used with great success for studying gas–liquid as well as gas–solid flows. Here, we apply this technique to analyze density-driven particle segregation in a rotating drum as an
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Ultrafast X-ray computed tomography is an advanced imaging technique for multiphase flows. It has been used with great success for studying gas–liquid as well as gas–solid flows. Here, we apply this technique to analyze density-driven particle segregation in a rotating drum as an exemplary use case for analyzing industrial particle mixing systems. As glass particles are used as the denser of two granular species to be mixed, beam hardening artefacts occur and hamper the data analysis. In the general case of a distribution of arbitrary materials, the inverse problem of image reconstruction with energy-dependent attenuation is often ill-posed. Consequently, commonly known beam hardening correction algorithms are often quite complex. In our case, however, the number of materials is limited. We therefore propose a correction algorithm simplified by taking advantage of the known material properties, and demonstrate its ability to improve image quality and subsequent analyses significantly.
Full article
(This article belongs to the Special Issue Tomographic and Multi-Dimensional Sensors)
Open AccessArticle
Single-Pixel Imaging Based on Deep Learning Enhanced Singular Value Decomposition
by
Youquan Deng, Rongbin She, Wenquan Liu, Yuanfu Lu and Guangyuan Li
Sensors 2024, 24(10), 2963; https://doi.org/10.3390/s24102963 - 07 May 2024
Abstract
We propose and demonstrate a single-pixel imaging method based on deep learning network enhanced singular value decomposition. The theoretical framework and the experimental implementation are elaborated and compared with the conventional methods based on Hadamard patterns or deep convolutional autoencoder network. Simulation and
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We propose and demonstrate a single-pixel imaging method based on deep learning network enhanced singular value decomposition. The theoretical framework and the experimental implementation are elaborated and compared with the conventional methods based on Hadamard patterns or deep convolutional autoencoder network. Simulation and experimental results show that the proposed approach is capable of reconstructing images with better quality especially under a low sampling ratio down to 3.12%, or with fewer measurements or shorter acquisition time if the image quality is given. We further demonstrate that it has better anti-noise performance by introducing noises in the SPI systems, and we show that it has better generalizability by applying the systems to targets outside the training dataset. We expect that the developed method will find potential applications based on single-pixel imaging beyond the visible regime.
Full article
(This article belongs to the Section Sensing and Imaging)
Open AccessReview
Gas Sensors Based on Semiconductor Metal Oxides Fabricated by Electrospinning: A Review
by
Hao Chen, Huayang Chen, Jiabao Chen and Mingxin Song
Sensors 2024, 24(10), 2962; https://doi.org/10.3390/s24102962 - 07 May 2024
Abstract
Electrospinning has revolutionized the field of semiconductor metal oxide (SMO) gas sensors, which are pivotal for gas detection. SMOs are known for their high sensitivity, rapid responsiveness, and exceptional selectivity towards various types of gases. When synthesized via electrospinning, they gain unmatched advantages.
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Electrospinning has revolutionized the field of semiconductor metal oxide (SMO) gas sensors, which are pivotal for gas detection. SMOs are known for their high sensitivity, rapid responsiveness, and exceptional selectivity towards various types of gases. When synthesized via electrospinning, they gain unmatched advantages. These include high porosity, large specific surface areas, adjustable morphologies and compositions, and diverse structural designs, improving gas-sensing performance. This review explores the application of variously structured and composed SMOs prepared by electrospinning in gas sensors. It highlights strategies to augment gas-sensing performance, such as noble metal modification and doping with transition metals, rare earth elements, and metal cations, all contributing to heightened sensitivity and selectivity. We also look at the fabrication of composite SMOs with polymers or carbon nanofibers, which addresses the challenge of high operating temperatures. Furthermore, this review discusses the advantages of hierarchical and core-shell structures. The use of spinel and perovskite structures is also explored for their unique chemical compositions and crystal structure. These structures are useful for high sensitivity and selectivity towards specific gases. These methodologies emphasize the critical role of innovative material integration and structural design in achieving high-performance gas sensors, pointing toward future research directions in this rapidly evolving field.
Full article
(This article belongs to the Special Issue Electrospun Composite Nanofibers: Sensing and Biosensing Applications)
Open AccessArticle
Enhanced Magnetoimpedance Effect in Co-Based Micron Composite CoFeNiSiB Ribbon Strips Coated by Carbon and FeCoGa Nanofilms for Sensing Applications
by
Zhen Yang, Mengyu Liu, Jingyuan Chen, Xuecheng Sun, Chong Lei, Yuanwei Shen, Zhenbao Wang, Mengjiao Zhu and Ziqin Meng
Sensors 2024, 24(10), 2961; https://doi.org/10.3390/s24102961 - 07 May 2024
Abstract
Quenched Co-based ribbon strips are widely used in the fields of magnetic amplifier, magnetic head material, magnetic shield, electric reactor, inductance core, sensor core, anti-theft system label, and so on. In this study, Co-based composite CoFeNiSiB ribbon strips with a micron width were
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Quenched Co-based ribbon strips are widely used in the fields of magnetic amplifier, magnetic head material, magnetic shield, electric reactor, inductance core, sensor core, anti-theft system label, and so on. In this study, Co-based composite CoFeNiSiB ribbon strips with a micron width were fabricated by micro-electro-mechanical systems (MEMS) technology. The carbon and FeCoGa nanofilms were deposited for surface modification. The effect of carbon and FeCoGa nanofilm coatings on the crystal structure, surface morphology, magnetic properties, and magnetoimpedance (MI) effect of composite ribbon strips were systematically investigated. The results show that the surface roughness and coercivity of the composite ribbon strips are minimum at a thickness of the carbon coating of 60 nm. The maximum value of MI effect is 41% at 2 MHz, which is approximately 2.4 times greater than plain ribbon and 1.6 times greater than FeCoGa-coated composite ribbon strip. The addition of a carbon layer provides a conductive path for high frequency currents, which effectively reduces the characteristic frequency of the composite ribbon strip. The FeCoGa coating is able to close the flux path and reduce the coercivity, which, in turn, increases the transverse permeability and improves the MI effect. The findings indicate that a successful combination of carbon layer and magnetostrictive FeCoGa nanofilm layer can improve the MI effect and magnetic field sensitivity of the ribbon strips, demonstrating the potential of the composite strips for local and micro area field sensing applications.
Full article
(This article belongs to the Special Issue Smart Sensors and Integration Technology for MEMS Devices)
Open AccessArticle
Laser Tracker and Terrestrial Laser Scanner Range Error Evaluation by Stitching
by
Bala Muralikrishnan, Braden Czapla, Vincent Lee, Craig Shakarji, Daniel Sawyer and Matthias Saure
Sensors 2024, 24(10), 2960; https://doi.org/10.3390/s24102960 - 07 May 2024
Abstract
Laser trackers (LTs) are dimensional measurement instruments commonly employed in the manufacture and assembly of large structures. Terrestrial laser scanners (TLSs) are a related class of dimensional measurement instruments more commonly employed in surveying, reverse engineering, and forensics. Commercially available LTs typically have
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Laser trackers (LTs) are dimensional measurement instruments commonly employed in the manufacture and assembly of large structures. Terrestrial laser scanners (TLSs) are a related class of dimensional measurement instruments more commonly employed in surveying, reverse engineering, and forensics. Commercially available LTs typically have measurement ranges of up to 80 m. The measurement ranges of TLSs vary from about 50 m to several hundred meters, with some extending as far as several kilometers. It is difficult, if not impossible, to construct long reference lengths to evaluate the ranging performances of these instruments over that distance. In this context, we explore the use of stitching errors (i.e., stacking errors in adjoining or overlapping short lengths) and stitching lengths (i.e., constructing long reference lengths from multiple positions of a reference instrument by registration) to evaluate these instruments. Through experimental data and a discussion on uncertainty, we show that stitching is indeed a viable option to evaluate the ranging performances of LTs and TLSs.
Full article
(This article belongs to the Section Optical Sensors)
Open AccessArticle
Numerical Simulation and Deformation Prediction of Deep Pit Based on PSO-BP Neural Network Inversion of Soil Parameters
by
Qingwang Li, Feng Cheng and Xinran Zhang
Sensors 2024, 24(10), 2959; https://doi.org/10.3390/s24102959 - 07 May 2024
Abstract
The finite element numerical simulation results of deep pit deformation are greatly influenced by soil layer parameters, which are crucial in determining the accuracy of deformation prediction results. This study employs the orthogonal experimental design to determine the combinations of various soil layer
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The finite element numerical simulation results of deep pit deformation are greatly influenced by soil layer parameters, which are crucial in determining the accuracy of deformation prediction results. This study employs the orthogonal experimental design to determine the combinations of various soil layer parameters in deep pits. Displacement values at specific measurement points were calculated using PLAXIS 3D under these varying parameter combinations to generate training samples. The nonlinear mapping ability of the Back Propagation (BP) neural network and Particle Swarm Optimization (PSO) were used for sample global optimization. Combining these with actual onsite measurements, we inversely calculate soil layer parameter values to update the input parameters for PLAXIS 3D. This allows us to conduct dynamic deformation prediction studies throughout the entire excavation process of deep pits. The results indicate that the use of the PSO-BP neural network for inverting soil layer parameters effectively enhances the convergence speed of the BP neural network model and avoids the issue of easily falling into local optimal solutions. The use of PLAXIS 3D to simulate the excavation process of the pit accurately reflects the dynamic changes in the displacement of the retaining structure, and the numerical simulation results show good agreement with the measured values. By updating the model parameters in real-time and calculating the pile displacement under different working conditions, the absolute errors between the measured and simulated values of pile top vertical displacement and pile body maximum horizontal displacement can be effectively reduced. This suggests that inverting soil layer parameters using measured values from working conditions is a feasible method for dynamically predicting the excavation process of the pit. The research results have some reference value for the selection of soil layer parameters in similar areas.
Full article
(This article belongs to the Section Smart Agriculture)
Open AccessReview
AI-Driven Sensing Technology: Review
by
Long Chen, Chenbin Xia, Zhehui Zhao, Haoran Fu and Yunmin Chen
Sensors 2024, 24(10), 2958; https://doi.org/10.3390/s24102958 - 07 May 2024
Abstract
Machine learning and deep learning technologies are rapidly advancing the capabilities of sensing technologies, bringing about significant improvements in accuracy, sensitivity, and adaptability. These advancements are making a notable impact across a broad spectrum of fields, including industrial automation, robotics, biomedical engineering, and
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Machine learning and deep learning technologies are rapidly advancing the capabilities of sensing technologies, bringing about significant improvements in accuracy, sensitivity, and adaptability. These advancements are making a notable impact across a broad spectrum of fields, including industrial automation, robotics, biomedical engineering, and civil infrastructure monitoring. The core of this transformative shift lies in the integration of artificial intelligence (AI) with sensor technology, focusing on the development of efficient algorithms that drive both device performance enhancements and novel applications in various biomedical and engineering fields. This review delves into the fusion of ML/DL algorithms with sensor technologies, shedding light on their profound impact on sensor design, calibration and compensation, object recognition, and behavior prediction. Through a series of exemplary applications, the review showcases the potential of AI algorithms to significantly upgrade sensor functionalities and widen their application range. Moreover, it addresses the challenges encountered in exploiting these technologies for sensing applications and offers insights into future trends and potential advancements.
Full article
(This article belongs to the Section State-of-the-Art Sensors Technologies)
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Open AccessArticle
A Machine Learning-Based Tropospheric Prediction Approach for High-Precision Real-Time GNSS Positioning
by
Jianping Chen and Yang Gao
Sensors 2024, 24(10), 2957; https://doi.org/10.3390/s24102957 - 07 May 2024
Abstract
For high-precision positioning applications, various GNSS errors need to be mitigated, including the tropospheric error, which remains a significant error source as it can reach up to a few meters. Although some commercial GNSS correction data providers, such as the Quasi-Zenith Satellite System
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For high-precision positioning applications, various GNSS errors need to be mitigated, including the tropospheric error, which remains a significant error source as it can reach up to a few meters. Although some commercial GNSS correction data providers, such as the Quasi-Zenith Satellite System (QZSS) Centimeter Level Augmentation Service (CLAS), have developed real-time precise regional troposphere products, the service is available only in limited regional areas. The International GNSS Service (IGS) has provided precise troposphere correction data in TRO format post-mission, but its long latency of 1 to 2 weeks makes it unable to support real-time applications. In this work, a real-time troposphere prediction method based on the IGS post-processing products was developed using machine learning techniques to eliminate the long latency problem. The test results from tropospheric predictions over a year using the proposed method indicate that the new method can achieve a prediction accuracy (RMSE) of 2 cm, making it suitable for real-time applications.
Full article
(This article belongs to the Special Issue GNSS Signals and Precise Point Positioning)
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Open AccessArticle
A Systematic Optimization Method for Permanent Magnet Synchronous Motors Based on SMS-EMOA
by
Bo Yuan, Ping Chen, Ershen Wang, Jianrui Yu and Jian Wang
Sensors 2024, 24(9), 2956; https://doi.org/10.3390/s24092956 - 06 May 2024
Abstract
The efficient design of Permanent Magnet Synchronous Motors (PMSMs) is crucial for their operational performance. A key design parameter, cogging torque, is significantly influenced by various structural parameters of the motor, complicating the optimization of motor structures. This paper proposes an optimization method
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The efficient design of Permanent Magnet Synchronous Motors (PMSMs) is crucial for their operational performance. A key design parameter, cogging torque, is significantly influenced by various structural parameters of the motor, complicating the optimization of motor structures. This paper proposes an optimization method for PMSM structures based on heuristic optimization algorithms, named the Permanent Magnet Synchronous Motor Self-Optimization Lift Algorithm (PMSM-SLA). Initially, a dataset capturing the efficiency of motors under various structural parameter scenarios is created using finite element simulation methods. Building on this dataset, a batch optimization solution aimed at PMSM structure optimization was introduced to identify the set of structural parameters that maximize motor efficiency. The approach presented in this study enhances the efficiency of optimizing PMSM structures, overcoming the limitations of traditional trial-and-error methods and supporting the industrial application of PMSM structural design.
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(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Reliability and Detectability of Emergency Management Systems in Smart Cities under Common Cause Failures
by
Thiago C. Jesus, Paulo Portugal, Daniel G. Costa and Francisco Vasques
Sensors 2024, 24(9), 2955; https://doi.org/10.3390/s24092955 - 06 May 2024
Abstract
Urban areas are undergoing significant changes with the rise of smart cities, with technology transforming how cities develop through enhanced connectivity and data-driven services. However, these advancements also bring new challenges, especially in dealing with urban emergencies that can disrupt city life and
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Urban areas are undergoing significant changes with the rise of smart cities, with technology transforming how cities develop through enhanced connectivity and data-driven services. However, these advancements also bring new challenges, especially in dealing with urban emergencies that can disrupt city life and infrastructure. The emergency management systems have become crucial elements for enabling cities to better handle urban emergencies, although ensuring the reliability and detectability of such system remains critical. This article introduces a new method to perform reliability and detectability assessments. By using Fault Tree Markov chain models, this article evaluates their performance under extreme conditions, providing valuable insights for designing and operating urban emergency systems. These analyses fill a gap in the existing research, offering a comprehensive understanding of emergency management systems functionality in complex urban settings.
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(This article belongs to the Section Sensor Networks)
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Open AccessArticle
Improving Adversarial Robustness of ECG Classification Based on Lipschitz Constraints and Channel Activation Suppression
by
Xin Chen, Yujuan Si, Zhanyuan Zhang, Wenke Yang and Jianchao Feng
Sensors 2024, 24(9), 2954; https://doi.org/10.3390/s24092954 - 06 May 2024
Abstract
Deep neural networks (DNNs) are increasingly important in the medical diagnosis of electrocardiogram (ECG) signals. However, research has shown that DNNs are highly vulnerable to adversarial examples, which can be created by carefully crafted perturbations. This vulnerability can lead to potential medical accidents.
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Deep neural networks (DNNs) are increasingly important in the medical diagnosis of electrocardiogram (ECG) signals. However, research has shown that DNNs are highly vulnerable to adversarial examples, which can be created by carefully crafted perturbations. This vulnerability can lead to potential medical accidents. This poses new challenges for the application of DNNs in the medical diagnosis of ECG signals. This paper proposes a novel network Channel Activation Suppression with Lipschitz Constraints Net (CASLCNet), which employs the Channel-wise Activation Suppressing (CAS) strategy to dynamically adjust the contribution of different channels to the class prediction and uses the 1-Lipschitz’s ℓ∞ distance network as a robust classifier to reduce the impact of adversarial perturbations on the model itself in order to increase the adversarial robustness of the model. The experimental results demonstrate that CASLCNet achieves scores of 91.03% and 83.01% when subjected to PGD attacks on the MIT-BIH and CPSC2018 datasets, respectively, which proves that the proposed method in this paper enhances the model’s adversarial robustness while maintaining a high accuracy rate.
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(This article belongs to the Special Issue Sensors Technology and Application in ECG Signal Processing)
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