Next Issue
Volume 22, May-1
Previous Issue
Volume 22, April-1
 
 
sensors-logo

Journal Browser

Journal Browser

Sensors, Volume 22, Issue 8 (April-2 2022) – 281 articles

Cover Story (view full-size image): In situ weather observations are essential for understanding the past, present, and future climate of Mars and for the preparation of human exploration. The MEDA meteorological station is onboard of the Perseverance rover with this aim. Suspended dust in the Martian atmosphere is one of the main drivers of the planet's climate. Its optical properties and vertical distribution affect the absorption and reflection of solar radiation, modulating the planet’s energy balance. The Radiation and Dust Sensor within MEDA will provide unprecedented insights on it. This article describes the sensor development cycle starting from the scientific objectives and continuing with the different design solutions adopted, the qualification, the calibration, and the validation of the retrieval methods with two campaigns performed in an Earth Martian analog. View this paper.
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Select all
Export citation of selected articles as:
14 pages, 2744 KiB  
Article
A Transformer-Based Bridge Structural Response Prediction Framework
by Ziqi Li, Dongsheng Li and Tianshu Sun
Sensors 2022, 22(8), 3100; https://doi.org/10.3390/s22083100 - 18 Apr 2022
Cited by 2 | Viewed by 2262
Abstract
Structural response prediction with desirable accuracy is considerably essential for the health monitoring of bridges. However, it appears to be difficult in accurately extracting structural response features on account of complex on-site environment and noise disturbance, resulting in poor prediction accuracy of the [...] Read more.
Structural response prediction with desirable accuracy is considerably essential for the health monitoring of bridges. However, it appears to be difficult in accurately extracting structural response features on account of complex on-site environment and noise disturbance, resulting in poor prediction accuracy of the response values. To address this issue, a Transformer-based bridge structural response prediction framework was proposed in this paper. The framework contains multi-layer encoder modules and attention modules that can precisely capture the history-dependent features in time-series data. The effectiveness of the proposed method was validated with the use of six-month strain response data of a concrete bridge, and the results are also compared with those of the most commonly used Long Short-Term Memory (LSTM)-based structural response prediction framework. The analysis indicated that the proposed method was effective in predicting structural response, with the prediction error less than 50% of the LSTM-based framework. The proposed method can be applied in damage diagnosis and disaster warning of bridges. Full article
(This article belongs to the Section Intelligent Sensors)
Show Figures

Figure 1

18 pages, 12831 KiB  
Article
Assessment for Different Neural Networks with FeatureSelection in Classification Issue
by Joy Iong-Zong Chen and Chung-Sheng Pi
Sensors 2022, 22(8), 3099; https://doi.org/10.3390/s22083099 - 18 Apr 2022
Viewed by 1812
Abstract
In general, the investigation of NN (neural network) computing systems requires the management of a significant number of simultaneous distinct algorithms, such as parallel computing, fault tolerance, classification, and data optimization. Supervised learning for NN originally comes from certain parameters, such as self-revised [...] Read more.
In general, the investigation of NN (neural network) computing systems requires the management of a significant number of simultaneous distinct algorithms, such as parallel computing, fault tolerance, classification, and data optimization. Supervised learning for NN originally comes from certain parameters, such as self-revised learning, input learning datasets, and multiple second learning processes. Specifically, the operation continues to adjust the NN connection synapses’ weight to achieve a self-learning computer system. The current article is aimed at developing the CC (correlation coefficient) assignment scheme adaptively joint with the FS (feature selection) categories to pursue the solutions utilized in solving the restrictions of NN computing. The NN computing system is expected to solve high-dimensional data, data overfitting, and strict FS problems. Hence, the Fruits-360 dataset is applied in the current article, that is, the variety of fruits, the sameness of color, and the differences in appearance features are utilized to examine the NN system accuracy, performance, and loss rate. Accordingly, there are 120 different kinds with a total of 20,860 fruit image datasets collected from AlexNet, GoogLeNet, and ResNet101, which were implemented in the CC assignment scheme proposed in this article. The results are employed to verify that the accuracy rate can be improved by reducing strict FS. Finally, the results of accuracy rate from the training held for the three NN frameworks are discussed. It was discovered that the GoogLeNet model presented the most significant FS performance. The demonstrated outcomes validate that the proposed CC assignment schemes are absolutely worthwhile in designing and choosing an NN training model for feature discrimination. From the simulation results, it has been observed that the FS-based CC assignment improves the accurate rate of recognition compared to the existing state-of-the-art approaches. Full article
(This article belongs to the Special Issue Feature Extraction and Forensic Image Analysis)
Show Figures

Figure 1

12 pages, 1901 KiB  
Article
Investigation of Impact of Walking Speed on Forces Acting on a Foot–Ground Unit
by Barbara Jasiewicz, Ewa Klimiec, Piotr Guzdek, Grzegorz Kołaszczyński, Jacek Piekarski, Krzysztof Zaraska and Tomasz Potaczek
Sensors 2022, 22(8), 3098; https://doi.org/10.3390/s22083098 - 18 Apr 2022
Cited by 1 | Viewed by 2289
Abstract
Static and dynamic methods can be used to assess the way a foot is loaded. The research question is how the pressure on the feet would vary depending on walking/running speed. This study involved 20 healthy volunteers. Dynamic measurement of foot pressure was [...] Read more.
Static and dynamic methods can be used to assess the way a foot is loaded. The research question is how the pressure on the feet would vary depending on walking/running speed. This study involved 20 healthy volunteers. Dynamic measurement of foot pressure was performed using the Ortopiezometr at normal, slow, and fast paces of walking. Obtained data underwent analysis in a “Steps” program. Based on the median, the power generated by the sensors during the entire stride period is the highest during a fast walk, whereas based on the average; a walk or slow walk prevails. During a fast walk, the difference between the mean and the median of the stride period is the smallest. Regardless of the pace of gait, the energy released per unit time does not depend on the paces of the volunteers’ gaits. Conclusions: Ortopiezometr is a feasible tool for the dynamic measurement of foot pressure. For investigations on walking motions, the plantar pressure analysis system, which uses the power generated on sensors installed in the insoles of shoes, is an alternative to force or energy measurements. Regardless of the pace of the walk, the amounts of pressure applied to the foot during step are similar among healthy volunteers. Full article
(This article belongs to the Topic Human Movement Analysis)
Show Figures

Figure 1

22 pages, 9642 KiB  
Article
Detection of Surface and Subsurface Flaws with Miniature GMR-Based Gradiometer
by Huu-Thang Nguyen, Jen-Tzong Jeng, Van-Dong Doan, Chinh-Hieu Dinh, Xuan Thang Trinh and Duy-Vinh Dao
Sensors 2022, 22(8), 3097; https://doi.org/10.3390/s22083097 - 18 Apr 2022
Viewed by 2078
Abstract
The eddy-current (EC) testing method is frequently utilized in the nondestructive inspection of conductive materials. To detect the minor and complex-shaped defects on the surface and in the underlying layers of a metallic sample, a miniature eddy-current probe with high sensitivity is preferred [...] Read more.
The eddy-current (EC) testing method is frequently utilized in the nondestructive inspection of conductive materials. To detect the minor and complex-shaped defects on the surface and in the underlying layers of a metallic sample, a miniature eddy-current probe with high sensitivity is preferred for enhancing the signal quality and spatial resolution of the obtained eddy-current images. In this work, we propose a novel design of a miniature eddy-current probe using a giant magnetoresistance (GMR) sensor fabricated on a silicon chip. The in-house-made GMR sensor comprises two cascaded spin-valve elements in parallel with an external variable resistor to form a Wheatstone bridge. The two elements on the chip are excited by the alternating magnetic field generated by a tiny coil aligned to the position that balances the background output of the bridge sensor. In this way, the two GMR elements behave effectively as an axial gradiometer with the bottom element sensitive to the surface and near-surface defects on a conductive specimen. The performance of the EC probe is verified by the numerical simulation and the corresponding experiments with machined defects on metallic samples. With this design, the geometric characteristics of the defects are clearly visualized with a spatial resolution of about 1 mm. The results demonstrate the feasibility and superiority of the proposed miniature GMR EC probe for characterizing the small and complex-shaped defects in multilayer conductive samples. Full article
(This article belongs to the Special Issue Advanced Sensors for Intelligent Control Systems)
Show Figures

Figure 1

19 pages, 3181 KiB  
Article
A Covariance Matrix Reconstruction Approach for Single Snapshot Direction of Arrival Estimation
by Murdifi Muhammad, Minghui Li, Qammer Abbasi, Cindy Goh and Muhammad Ali Imran
Sensors 2022, 22(8), 3096; https://doi.org/10.3390/s22083096 - 18 Apr 2022
Cited by 6 | Viewed by 2267
Abstract
Achieving accurate single snapshot direction of arrival (DOA) information significantly improves communication performance. This paper investigates an accurate and high-resolution DOA estimation technique by enabling single snapshot data collection and enhancing DOA estimation results compared to multiple snapshot methods. This is carried out [...] Read more.
Achieving accurate single snapshot direction of arrival (DOA) information significantly improves communication performance. This paper investigates an accurate and high-resolution DOA estimation technique by enabling single snapshot data collection and enhancing DOA estimation results compared to multiple snapshot methods. This is carried out by manipulating the incoming signal covariance matrix while suppressing undesired additive white Gaussian noise (AWGN) by actively updating and estimating the antenna array manifold vector. We demonstrated the estimation performance in simulation that our proposed technique supersedes the estimation performance of existing state-of-the-art techniques in various signal-to-noise ratio (SNR) scenarios and single snapshot sampling environments. Our proposed covariance-based single snapshot (CbSS) technique yields the lowest root-mean-squared error (RMSE) against the true DOA compared to root-MUSIC and the partial relaxation (PR) approach for multiple snapshots and a single signal source environment. In addition, our proposed technique presents the lowest DOA estimation performance degradation in a multiple uncorrelated and coherent signal source environment by up to 25.5% with nearly negligible bias. Lastly, our proposed CbSS technique presents the best DOA estimation results for a single snapshot and single-source scenario with an RMSE of 0.05° against the true DOA compared to root-MUSIC and the PR approach with nearly negligible bias as well. A potential application for CbSS would be in a scenario where accurate DOA estimation with a small antenna array form factor is a limitation, such as in the intelligent transportation system industry and wireless communication. Full article
(This article belongs to the Section Intelligent Sensors)
Show Figures

Figure 1

24 pages, 2652 KiB  
Article
Minimizing Global Buffer Access in a Deep Learning Accelerator Using a Local Register File with a Rearranged Computational Sequence
by Minjae Lee, Zhongfeng Zhang, Seungwon Choi and Jungwook Choi
Sensors 2022, 22(8), 3095; https://doi.org/10.3390/s22083095 - 18 Apr 2022
Cited by 1 | Viewed by 2119
Abstract
We propose a method for minimizing global buffer access within a deep learning accelerator for convolution operations by maximizing the data reuse through a local register file, thereby substituting the local register file access for the power-hungry global buffer access. To fully exploit [...] Read more.
We propose a method for minimizing global buffer access within a deep learning accelerator for convolution operations by maximizing the data reuse through a local register file, thereby substituting the local register file access for the power-hungry global buffer access. To fully exploit the merits of data reuse, this study proposes a rearrangement of the computational sequence in a deep learning accelerator. Once input data are read from the global buffer, repeatedly reading the same data is performed only through the local register file, saving significant power consumption. Furthermore, different from prior works that equip local register files in each computation unit, the proposed method enables sharing a local register file along the column of the 2D computation array, saving resources and controlling overhead. The proposed accelerator is implemented on an off-the-shelf field-programmable gate array to verify the functionality and resource utilization. Then, the performance improvement of the proposed method is demonstrated relative to popular deep learning accelerators. Our evaluation indicates that the proposed deep learning accelerator reduces the number of global-buffer accesses to nearly 86.8%, consequently saving up to 72.3% of the power consumption for the input data memory access with a minor increase in resource usage compared to a conventional deep learning accelerator. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensors II)
Show Figures

Figure 1

24 pages, 1870 KiB  
Article
Deep Residual Network for Smartwatch-Based User Identification through Complex Hand Movements
by Sakorn Mekruksavanich and Anuchit Jitpattanakul
Sensors 2022, 22(8), 3094; https://doi.org/10.3390/s22083094 - 18 Apr 2022
Cited by 39 | Viewed by 3023
Abstract
Wearable technology has advanced significantly and is now used in various entertainment and business contexts. Authentication methods could be trustworthy, transparent, and non-intrusive to guarantee that users can engage in online communications without consequences. An authentication system on a security framework starts with [...] Read more.
Wearable technology has advanced significantly and is now used in various entertainment and business contexts. Authentication methods could be trustworthy, transparent, and non-intrusive to guarantee that users can engage in online communications without consequences. An authentication system on a security framework starts with a process for identifying the user to ensure that the user is permitted. Establishing and verifying an individual’s appearance usually requires a lot of effort. Recent years have seen an increase in the usage of activity-based user identification systems to identify individuals. Despite this, there has not been much research into how complex hand movements can be used to determine the identity of an individual. This research used a one-dimensional residual network with squeeze-and-excitation (SE) configurations called the 1D-ResNet-SE model to investigate hand movements and user identification. According to the findings, the SE modules have enhanced the one-dimensional residual network’s identification ability. As a deep learning model, the proposed methodology is capable of effectively identifying features from the input smartwatch sensor and could be utilized as an end-to-end model to clarify the modeling process. The 1D-ResNet-SE identification model is superior to the other models. Hand movement assessment based on deep learning is an effective technique to identify smartwatch users. Full article
(This article belongs to the Special Issue Security for Mobile Sensing Networks)
Show Figures

Figure 1

18 pages, 5908 KiB  
Article
Performance Analysis of Optically Pumped 4He Magnetometers vs. Conventional SQUIDs: From Adult to Infant Head Models
by Saeed Zahran, Mahdi Mahmoudzadeh, Fabrice Wallois, Nacim Betrouni, Philippe Derambure, Matthieu Le Prado, Agustin Palacios-Laloy and Etienne Labyt
Sensors 2022, 22(8), 3093; https://doi.org/10.3390/s22083093 - 18 Apr 2022
Cited by 10 | Viewed by 2768
Abstract
Optically pumped magnetometers (OPMs) are new, room-temperature alternatives to superconducting quantum interference devices (SQUIDs) for measuring the brain’s magnetic fields. The most used OPM in MagnetoEncephaloGraphy (MEG) are based on alkali atoms operating in the spin-exchange relaxation-free (SERF) regime. These sensors do not [...] Read more.
Optically pumped magnetometers (OPMs) are new, room-temperature alternatives to superconducting quantum interference devices (SQUIDs) for measuring the brain’s magnetic fields. The most used OPM in MagnetoEncephaloGraphy (MEG) are based on alkali atoms operating in the spin-exchange relaxation-free (SERF) regime. These sensors do not require cooling but have to be heated. Another kind of OPM, based on the parametric resonance of 4He atoms are operated at room temperature, suppressing the heat dissipation issue. They also have an advantageous bandwidth and dynamic range more suitable for MEG recordings. We quantitatively assessed the improvement (relative to a SQUID magnetometers array) in recording the magnetic field with a wearable 4He OPM-MEG system through data simulations. The OPM array and magnetoencephalography forward models were based on anatomical MRI data from an adult, a nine-year-old child, and 10 infants aged between one month and two years. Our simulations showed that a 4He OPMs array offers markedly better spatial specificity than a SQUID magnetometers array in various key performance areas (e.g., signal power, information content, and spatial resolution). Our results are also discussed regarding previous simulation results obtained for alkali OPM. Full article
Show Figures

Figure 1

11 pages, 2662 KiB  
Article
High-Precision Log-Ratio Spot Position Detection Algorithm with a Quadrant Detector under Different SNR Environments
by Li Huo, Zhiyong Wu, Jiabin Wu, Shijie Gao, Yunshan Chen, Yinuo Song and Shuaifei Wang
Sensors 2022, 22(8), 3092; https://doi.org/10.3390/s22083092 - 18 Apr 2022
Cited by 3 | Viewed by 1869
Abstract
In atmospheric laser communication, a beam is transmitted through an atmospheric channel, and the photocurrent output from a quadrant detector (QD) used as the tracking sensor fluctuates significantly. To ensure uninterrupted communication and to adapt to such fluctuations, in this paper we apply [...] Read more.
In atmospheric laser communication, a beam is transmitted through an atmospheric channel, and the photocurrent output from a quadrant detector (QD) used as the tracking sensor fluctuates significantly. To ensure uninterrupted communication and to adapt to such fluctuations, in this paper we apply logarithmic amplifiers to process the output signals of a QD. To further improve the measurement accuracy of the spot position, we firstly propose an integral infinite log-ratio algorithm (IILRA) and an integral infinity log-ratio algorithm based on the signal-to-noise ratio (BSNR-IILRA) through analysis of the factors influencing the measurement error considering the signal-to-noise ratio (SNR) parameter. Secondly, the measurement error of the two algorithms under different SNRs and their variations are analyzed. Finally, a spot position detection experiment platform is built to correctly and efficiently verify the two algorithms. The experimental results show that when the SNR is 54.10 dB, the maximum error and root mean square error of the spot position of the IILRA are 0.0054 mm and 0.0039 mm, respectively, which are less than half those of the center approximation algorithm (CAA). When the SNR is 23.88 dB, the maximum error and root mean square error of the spot position of the BSNR-IILRA are 0.0046 mm and 0.0034 mm, respectively, which are one-thirtieth and one-twentieth of the CAA, respectively. The spot position measurement accuracy of the two proposed algorithms is significantly improved compared with the CAA. Full article
(This article belongs to the Section Communications)
Show Figures

Figure 1

16 pages, 9616 KiB  
Article
Indoor Localization Using Uncooperative Wi-Fi Access Points
by Berthold K. P. Horn
Sensors 2022, 22(8), 3091; https://doi.org/10.3390/s22083091 - 18 Apr 2022
Cited by 6 | Viewed by 2837
Abstract
Indoor localization using fine time measurement (FTM) round-trip time (RTT) with respect to cooperating Wi-Fi access points (APs) has been shown to work well and provide 1–2 m accuracy in both 2D and 3D applications. This approach depends on APs implementing the IEEE [...] Read more.
Indoor localization using fine time measurement (FTM) round-trip time (RTT) with respect to cooperating Wi-Fi access points (APs) has been shown to work well and provide 1–2 m accuracy in both 2D and 3D applications. This approach depends on APs implementing the IEEE 802.11-2016 (also known as IEEE 802.11mc) Wi-Fi standard (“two-sided” RTT). Unfortunately, the penetration of this Wi-Fi protocol has been slower than anticipated, perhaps because APs tend not to be upgraded as often as other kinds of electronics, in particular in large institutions—where they would be most useful. Recently, Google released Android 12, which also supports an alternative “one-sided” RTT method that will work with legacy APs as well. This method cannot subtract out the “turn-around” time of the signal, and so, produces distance estimates that have much larger offsets than those seen with two-sided RTT—and the results are somewhat less accurate. At the same time, this method makes possible distance measurements for many APs that previously could not be used. This increased accessibility can compensate for the decreased accuracy of individual measurements. We demonstrate here indoor localization using one-sided RTT with respect to legacy APs that do not support IEEE 802.11-2016. The accuracy achieved is 3–4 m in cluttered environments with few line-of-sight readings (and using only 20 MHz bandwidths). This is not as good as for two-sided RTT, where 1–2 m accuracy has been achieved (using 80 MHz bandwidths), but adequate for many applications A wider Wi-Fi channel bandwidth would increase the accuracy further. As before, Bayesian grid update is the preferred method for determining position and positional accuracy, but the observation model now is different from that for two-sided RTT. As with two-sided RTT, the probability of an RTT measurement below the true distance is very low, but, in the other direction, the range of measurements for a given distance can be much wider (up to well over twice the actual distance). We describe methods for formulating useful observation models. As with two-sided RTT, the offset or bias in distance measurements has to be subtracted from the reported measurements. One difference is that here, the offsets are large (typically in the 2400–2700 m range) because of the “turn-around time” of roughly 16 μs (i.e., about two orders of magnitude larger than the time of flight one is attempting to measure). We describe methods for estimating these offsets and for minimizing the effort required to do so when setting up an installation with many APs. Full article
(This article belongs to the Special Issue Indoor Wi-Fi Positioning: Techniques and Systems)
Show Figures

Figure 1

2 pages, 161 KiB  
Correction
Correction: Bellenger et al. Wrist-Based Photoplethysmography Assessment of Heart Rate and Heart Rate Variability: Validation of WHOOP. Sensors 2021, 21, 3571
by Clint R. Bellenger, Dean J. Miller, Shona L. Halson, Gregory D. Roach and Charli Sargent
Sensors 2022, 22(8), 3090; https://doi.org/10.3390/s22083090 - 18 Apr 2022
Viewed by 1309
Abstract
The authors wish to correct the following errors in the original paper [...] Full article
(This article belongs to the Section Wearables)
15 pages, 2696 KiB  
Communication
Standing-Wave Feeding for High-Gain Linear Dielectric Resonator Antenna (DRA) Array
by Kerlos Atia Abdalmalak, Ayman Abdulhadi Althuwayb, Choon Sae Lee, Gabriel Santamaría Botello, Enderson Falcón-Gómez, Luis Emilio García-Castillo and Luis Enrique García-Muñoz
Sensors 2022, 22(8), 3089; https://doi.org/10.3390/s22083089 - 18 Apr 2022
Cited by 7 | Viewed by 2966
Abstract
A novel feeding method for linear DRA arrays is presented, illuminating the use of the power divider, transitions, and launchers, and keeping uniform excitation to array elements. This results in a high-gain DRA array with low losses with a design that is simple, [...] Read more.
A novel feeding method for linear DRA arrays is presented, illuminating the use of the power divider, transitions, and launchers, and keeping uniform excitation to array elements. This results in a high-gain DRA array with low losses with a design that is simple, compact and inexpensive. The proposed feeding method is based on exciting standing waves using discrete metallic patches in a simple design procedure. Two arrays with two and four DRA elements are presented as a proof of concept, which provide high gains of 12 and 15dBi, respectively, which are close to the theoretical limit based on array theory. The radiation efficiency for both arrays is about 93%, which is equal to the array element efficiency, confirming that the feeding method does not add losses as in the case of standard methods. To facilitate the fabrication process, the entire array structure is 3D-printed, which significantly decreases the complexity of fabrication and alignment. Compared to state-of-the-art feeding techniques, the proposed method provides higher gain and higher efficiency with a smaller electrical size. Full article
(This article belongs to the Topic Antennas)
Show Figures

Figure 1

17 pages, 1762 KiB  
Article
FDNet: Knowledge and Data Fusion-Driven Deep Neural Network for Coal Burst Prediction
by Anye Cao, Yaoqi Liu, Xu Yang, Sen Li and Yapeng Liu
Sensors 2022, 22(8), 3088; https://doi.org/10.3390/s22083088 - 18 Apr 2022
Cited by 5 | Viewed by 2304
Abstract
Coal burst prediction is an important research hotspot in coal mine production safety. This paper presents FDNet, which is a knowledge and data fusion-driven deep neural network for coal burst prediction. The main idea of FDNet is to extract explicit features based on [...] Read more.
Coal burst prediction is an important research hotspot in coal mine production safety. This paper presents FDNet, which is a knowledge and data fusion-driven deep neural network for coal burst prediction. The main idea of FDNet is to extract explicit features based on the existing mine seismic physical model and utilize deep learning to automatically extract the implicit features of mine microseismic data. The key innovations of FDNet include an expert knowledge indicator selection method based on a subset search strategy, a mine microseismic data extraction method based on a deep convolutional neural network, and a feature deep fusion method of mine microseismic data based on an attention mechanism. We conducted a set of engineering experiments in Gaojiapu Coal Mine to evaluate the performance of FDNet. The results show that compared with the state-of-the-art data-driven machines and knowledge-driven methods, the prediction accuracy of FDNet is improved by 5% and 16%, respectively. Full article
Show Figures

Figure 1

14 pages, 2496 KiB  
Article
Muscle Mass Measurement Using Machine Learning Algorithms with Electrical Impedance Myography
by Kuo-Sheng Cheng, Ya-Ling Su, Li-Chieh Kuo, Tai-Hua Yang, Chia-Lin Lee, Wenxi Chen and Shing-Hong Liu
Sensors 2022, 22(8), 3087; https://doi.org/10.3390/s22083087 - 18 Apr 2022
Cited by 2 | Viewed by 3225
Abstract
Sarcopenia is a wild chronic disease among elderly people. Although it does not entail a life-threatening risk, it will increase the adverse risk due to the associated unsteady gait, fall, fractures, and functional disability. The import factors in diagnosing sarcopenia are muscle mass [...] Read more.
Sarcopenia is a wild chronic disease among elderly people. Although it does not entail a life-threatening risk, it will increase the adverse risk due to the associated unsteady gait, fall, fractures, and functional disability. The import factors in diagnosing sarcopenia are muscle mass and strength. The examination of muscle mass must be carried in the clinic. However, the loss of muscle mass can be improved by rehabilitation that can be performed in non-medical environments. Electronic impedance myography (EIM) can measure some parameters of muscles that have the correlations with muscle mass and strength. The goal of this study is to use machine learning algorithms to estimate the total mass of thigh muscles (MoTM) with the parameters of EIM and body information. We explored the seven major muscles of lower limbs. The feature selection methods, including recursive feature elimination (RFE) and feature combination, were used to select the optimal features based on the ridge regression (RR) and support vector regression (SVR) models. The optimal features were the resistance of rectus femoris normalized by the thigh circumference, phase of tibialis anterior combined with the gender, and body information, height, and weight. There were 96 subjects involved in this study. The performances of estimating the MoTM used the regression coefficient (r2) and root-mean-square error (RMSE), which were 0.800 and 0.929, and 1.432 kg and 0.980 kg for RR and SVR models, respectively. Thus, the proposed method could have the potential to support people examining their muscle mass in non-medical environments. Full article
Show Figures

Figure 1

12 pages, 4356 KiB  
Article
The Size Dependence of Microwave Permeability of Hollow Iron Particles
by Anastasia V. Artemova, Sergey S. Maklakov, Alexey V. Osipov, Dmitriy A. Petrov, Artem O. Shiryaev, Konstantin N. Rozanov and Andrey N. Lagarkov
Sensors 2022, 22(8), 3086; https://doi.org/10.3390/s22083086 - 18 Apr 2022
Cited by 2 | Viewed by 1804
Abstract
Hollow ferromagnetic powders of iron were obtained by means of ultrasonic spray pyrolysis. A variation in the conditions of the synthesis allows for the adjustment of the mean size of the hollow iron particles. Iron powders were obtained by this technique, starting from [...] Read more.
Hollow ferromagnetic powders of iron were obtained by means of ultrasonic spray pyrolysis. A variation in the conditions of the synthesis allows for the adjustment of the mean size of the hollow iron particles. Iron powders were obtained by this technique, starting from the aqueous solution of iron nitrate of two different concentrations: 10 and 20 wt.%. This was followed by a reduction in hydrogen. An increase in the concentration of the solution increased the mean particle size from 0.6 to 1.0 microns and widened particle size distribution, but still produced hollow particles. Larger particles appeared problematic for the reduction, although admixture of iron oxides did not decrease the microwave permeability of the material. The paraffin wax-based composites filled with obtained powders demonstrated broadband magnetic loss with a complex structure for lesser particles, and single-peak absorption for particles of 1 micron. Potential applications are 5G technology, electromagnetic compatibility designs, and magnetic field sensing. Full article
(This article belongs to the Special Issue Sensors and Biosensors Related to Magnetic Nanoparticles)
Show Figures

Figure 1

28 pages, 13568 KiB  
Article
AI Prediction of Brain Signals for Human Gait Using BCI Device and FBG Based Sensorial Platform for Plantar Pressure Measurements
by Asad Muhammad Butt, Hassan Alsaffar, Muhannad Alshareef and Khurram Karim Qureshi
Sensors 2022, 22(8), 3085; https://doi.org/10.3390/s22083085 - 18 Apr 2022
Cited by 4 | Viewed by 2981
Abstract
Artificial intelligence (AI) in developing modern solutions for biomedical problems such as the prediction of human gait for human rehabilitation is gaining ground. An attempt was made to use plantar pressure information through fiber Bragg grating (FBG) sensors mounted on an in-sole, in [...] Read more.
Artificial intelligence (AI) in developing modern solutions for biomedical problems such as the prediction of human gait for human rehabilitation is gaining ground. An attempt was made to use plantar pressure information through fiber Bragg grating (FBG) sensors mounted on an in-sole, in tandem with a brain-computer interface (BCI) device to predict brain signals corresponding to sitting, standing and walking postures of a person. Posture classification was attained with an accuracy range between 87–93% from FBG and BCI signals using machine learning models such as K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and naïve Bayes (NB). These models were used to identify electrodes responding to sitting, standing and walking activities of four users from a 16 channel BCI device. Six electrode positions based on the 10–20 system for electroencephalography (EEG) were identified as the most sensitive to plantar activities and found to be consistent with clinical investigations of the sensorimotor cortex during foot movement. A prediction of brain EEG corresponding to given FBG data with lowest mean square error (MSE) values (0.065–0.109) was made with the selection of a long-short term memory (LSTM) machine learning model when compared to the recurrent neural network (RNN) and gated recurrent unit (GRU) models. Full article
(This article belongs to the Special Issue Wearable Medical Sensors and Artificial Intelligence)
Show Figures

Figure 1

25 pages, 7258 KiB  
Article
Development of a Soft Sensor for Flow Estimation in Water Supply Systems Using Artificial Neural Networks
by Robson Pacífico Guimarães Lima, Juan Moises Mauricio Villanueva, Heber Pimentel Gomes and Thommas Kevin Sales Flores
Sensors 2022, 22(8), 3084; https://doi.org/10.3390/s22083084 - 18 Apr 2022
Cited by 4 | Viewed by 2554
Abstract
A water supply system is considered an essential service to the population as it is about providing an essential good for life. This system typically consists of several sensors, transducers, pumps, etc., and some of these elements have high costs and/or complex installation. [...] Read more.
A water supply system is considered an essential service to the population as it is about providing an essential good for life. This system typically consists of several sensors, transducers, pumps, etc., and some of these elements have high costs and/or complex installation. The indirect measurement of a quantity can be used to obtain a desired variable, dispensing with the use of a specific sensor in the plant. Among the contributions of this technique is the design of the pressure controller using the adaptive control, as well as the use of an artificial neural network for the construction of nonlinear models using inherent system parameters such as pressure, engine rotation frequency and control valve angle, with the purpose of estimating the flow. Among the various contributions of the research, we can highlight the suppression in the acquisition of physical flow meters, the elimination of physical installation and others. The validation was carried out through tests in an experimental bench located in the Laboratory of Energy and Hydraulic Efficiency in Sanitation of the Federal University of Paraiba. The results of the soft sensor were compared with those of an electromagnetic flux sensor, obtaining a maximum error of 10%. Full article
(This article belongs to the Topic Artificial Intelligence in Sensors)
Show Figures

Figure 1

24 pages, 7651 KiB  
Article
Atomicity and Regularity Principles Do Not Ensure Full Resistance of ECC Designs against Single-Trace Attacks
by Ievgen Kabin, Zoya Dyka and Peter Langendoerfer
Sensors 2022, 22(8), 3083; https://doi.org/10.3390/s22083083 - 18 Apr 2022
Cited by 4 | Viewed by 1792
Abstract
Elliptic curve cryptography (ECC) is one of the commonly used standard methods for encrypting and signing messages which is especially applicable to resource-constrained devices such as sensor nodes that are networked in the Internet of Things. The same holds true for wearable sensors. [...] Read more.
Elliptic curve cryptography (ECC) is one of the commonly used standard methods for encrypting and signing messages which is especially applicable to resource-constrained devices such as sensor nodes that are networked in the Internet of Things. The same holds true for wearable sensors. In these fields of application, confidentiality and data integrity are of utmost importance as human lives depend on them. In this paper, we discuss the resistance of our fast dual-field ECDSA accelerator against side-channel analysis attacks. We present our implementation of a design supporting four different NIST elliptic curves to allow the reader to understand the discussion of the resistance aspects. For two different target platforms—ASIC and FPGA—we show that the application of atomic patterns, which is considered to ensure resistance against simple side-channel analysis attacks in the literature, is not sufficient to prevent either simple SCA or horizontal address-bit DPA attacks. We also evaluated an approach which is based on the activity of the field multiplier to increase the inherent resistance of the design against attacks performed. Full article
Show Figures

Figure 1

23 pages, 7366 KiB  
Article
Butterfly: μW Level ULP Sensor Nodes with High Task Throughput
by Chong Zhang, Li Lu, Yihang Song, Qianhe Meng, Junqin Zhang, Xiandong Shao, Guangyuan Zhang and Mengshu Hou
Sensors 2022, 22(8), 3082; https://doi.org/10.3390/s22083082 - 17 Apr 2022
Viewed by 2364
Abstract
The rapid development of Internet of Things (IoT) applications calls for light-weight IoT sensor nodes with both low-power consumption and excellent task execution efficiency. However, in the existing system framework, designers must make trade-offs between these two. In this paper, we propose an [...] Read more.
The rapid development of Internet of Things (IoT) applications calls for light-weight IoT sensor nodes with both low-power consumption and excellent task execution efficiency. However, in the existing system framework, designers must make trade-offs between these two. In this paper, we propose an “edge-to-end integration” design paradigm, Butterfly, which assists sensor nodes to perform sensing tasks more efficiently with lower power consumption through their (high-performance) network infrastructures (i.e., a gateway). On the one hand, to optimize the power consumption, Butterfly offloads the energy-intensive computational tasks from the nodes to the gateway with only microwatt-level power budget, thereby eliminating the power-consuming Microcontroller (MCU) from the node. On the other hand, we address three issues facing the optimization of task execution efficiency. To start with, we buffer the frequently used instructions and data to minimize the volume of data transmitted on the downlink. Furthermore, based on our investigation on typical sensing data structures, we present a novel last-bit transmission and packaging mechanism to reduce the data amount on the uplink. Finally, we design a task prediction mechanism on the gateway to support efficient scheduling of concurrent tasks on multiple MCU-free Butterfly nodes. The experiment results show that Butterfly can speed up the task rate by 4.91 times and reduce the power consumption of each node by 94.3%, compared to the benchmarks. In addition, Butterfly nodes have natural security advantages (e.g., anti-capture) as they offload the control function with all application information up to the gateway. Full article
(This article belongs to the Special Issue Trustworthy Sensing with Human-and-Environment-in-the-Loop)
Show Figures

Figure 1

13 pages, 1539 KiB  
Article
Three-Dimensional Kinematics during Shoulder Scaption in Asymptomatic and Symptomatic Subjects by Inertial Sensors: A Cross-Sectional Study
by Cristina Roldán-Jiménez, Antonio I. Cuesta-Vargas and Jaime Martín-Martín
Sensors 2022, 22(8), 3081; https://doi.org/10.3390/s22083081 - 17 Apr 2022
Viewed by 2299
Abstract
Shoulder kinematics is a measure of interest in the clinical setting for diagnosis, evaluating treatment, and quantifying possible changes. The aim was to compare shoulder scaption kinematics between symptomatic and asymptomatic subjects by inertial sensors. Methods: Scaption kinematics of 27 subjects with shoulder [...] Read more.
Shoulder kinematics is a measure of interest in the clinical setting for diagnosis, evaluating treatment, and quantifying possible changes. The aim was to compare shoulder scaption kinematics between symptomatic and asymptomatic subjects by inertial sensors. Methods: Scaption kinematics of 27 subjects with shoulder symptomatology and 16 asymptomatic subjects were evaluated using four inertial sensors placed on the humerus, scapula, forearm, and sternum. Mobility, velocity, and acceleration were obtained from each sensor and the vector norm was calculated from the three spatial axis (x,y,Z). Shoulder function was measured by Upper Limb Functional Index and Disabilities of the Arm, Shoulder, and Hand questionnaires. One way ANOVA was calculated to test differences between the two groups. Effect size was calculated by Cohen’s d with 95% coefficient Intervals. Pearson’s correlation analysis was performed between the vector norms humerus and scapula kinematics against DASH and ULFI results in symptomatic subjects. Results: The asymptomatic group showed higher kinematic values, especially in the humerus and forearm. Symptomatic subjects showed significantly lower values of mobility for scapular protraction-retraction (Cohen’s d 2.654 (1.819–3.489) and anteriorisation-posteriorisation (Cohen’s d 1.195 (0.527–1.863). Values were also lower in symptomatic subjects for velocity in all scapular planes of motion. Negative correlation showed that subjects with higher scores in ULFI or DASH had lower kinematics values. Conclusion: Asymptomatic subjects tend to present greater kinematics in terms of mobility, velocity, and linear acceleration of the upper limb, and lower humerus and scapula kinematics in symptomatic subjects is associated with lower levels of function. Full article
(This article belongs to the Special Issue Wearable Sensors for Physical Activity Monitoring and Motion Control)
Show Figures

Figure 1

21 pages, 3727 KiB  
Article
On Mechanical and Electrical Coupling Determination at Piezoelectric Harvester by Customized Algorithm Modeling and Measurable Properties
by Irene Perez-Alfaro, Daniel Gil-Hernandez, Nieves Murillo and Carlos Bernal
Sensors 2022, 22(8), 3080; https://doi.org/10.3390/s22083080 - 17 Apr 2022
Cited by 2 | Viewed by 2538
Abstract
Piezoelectric harvesters use the actuation potential of the piezoelectric material to transform mechanical and vibrational energies into electrical power, scavenging energy from their environment. Few research has been focused on the development and understanding of the piezoelectric harvesters from the material themselves and [...] Read more.
Piezoelectric harvesters use the actuation potential of the piezoelectric material to transform mechanical and vibrational energies into electrical power, scavenging energy from their environment. Few research has been focused on the development and understanding of the piezoelectric harvesters from the material themselves and the real piezoelectric and mechanical properties of the harvester. In the present work, the authors propose a behavior real model based on the experimentally measured electromechanical parameters of a homemade PZT bimorph harvester with the aim to predict its Vrms output. To adjust the harvester behavior, an iterative customized algorithm has been developed in order to adapt the electromechanical coupling coefficient, finding the relationship between the harvester actuator and generator behavior. It has been demonstrated that the harvester adapts its elongation and its piezoelectric coefficients combining the effect of the applied mechanical strain and the electrical behavior as a more realistic behavior due to the electromechanical nature of the material. The complex rms voltage output of the homemade bimorph harvester in the frequency domain has been successfully reproduced by the proposed model. The Behavior Real Model, BRM, developed could become a powerful tool for the design and manufacturing of a piezoelectric harvester based on its customized dimensions, configuration, and the piezoelectric properties of the smart materials. Full article
(This article belongs to the Special Issue Sensors and Sensing Systems for Condition Monitoring)
Show Figures

Figure 1

15 pages, 2233 KiB  
Article
Quantitative Evaluation of EEG-Biomarkers for Prediction of Sleep Stages
by Iqram Hussain, Md Azam Hossain, Rafsan Jany, Md Abdul Bari, Musfik Uddin, Abu Raihan Mostafa Kamal, Yunseo Ku and Jik-Soo Kim
Sensors 2022, 22(8), 3079; https://doi.org/10.3390/s22083079 - 17 Apr 2022
Cited by 60 | Viewed by 6473
Abstract
Electroencephalography (EEG) is immediate and sensitive to neurological changes resulting from sleep stages and is considered a computing tool for understanding the association between neurological outcomes and sleep stages. EEG is expected to be an efficient approach for sleep stage prediction outside a [...] Read more.
Electroencephalography (EEG) is immediate and sensitive to neurological changes resulting from sleep stages and is considered a computing tool for understanding the association between neurological outcomes and sleep stages. EEG is expected to be an efficient approach for sleep stage prediction outside a highly equipped clinical setting compared with multimodal physiological signal-based polysomnography. This study aims to quantify the neurological EEG-biomarkers and predict five-class sleep stages using sleep EEG data. We investigated the three-channel EEG sleep recordings of 154 individuals (mean age of 53.8 ± 15.4 years) from the Haaglanden Medisch Centrum (HMC, The Hague, The Netherlands) open-access public dataset of PhysioNet. The power of fast-wave alpha, beta, and gamma rhythms decreases; and the power of slow-wave delta and theta oscillations gradually increases as sleep becomes deeper. Delta wave power ratios (DAR, DTR, and DTABR) may be considered biomarkers for their characteristics of attenuation in NREM sleep and subsequent increase in REM sleep. The overall accuracy of the C5.0, Neural Network, and CHAID machine-learning models are 91%, 89%, and 84%, respectively, for multi-class classification of the sleep stages. The EEG-based sleep stage prediction approach is expected to be utilized in a wearable sleep monitoring system. Full article
(This article belongs to the Section Wearables)
Show Figures

Figure 1

11 pages, 1124 KiB  
Article
Towards Dynamic Model-Based Agile Architecting of Cyber-Physical Systems
by Alexander Vodyaho, Nataly Zhukova, Alexey Subbotin and Fahem Anaam
Sensors 2022, 22(8), 3078; https://doi.org/10.3390/s22083078 - 17 Apr 2022
Cited by 4 | Viewed by 2156
Abstract
A model-based approach to large-scale distributed system architecting is suggested, which is based on the use of dynamic digital twins. This approach can be considered as an integration of known paradigms, such as digital twins, evolutionary architecture and agile architecture. It can also [...] Read more.
A model-based approach to large-scale distributed system architecting is suggested, which is based on the use of dynamic digital twins. This approach can be considered as an integration of known paradigms, such as digital twins, evolutionary architecture and agile architecture. It can also be considered as one of the possible realizations of the digital thread paradigm. As part of this approach, a three-level digital thread reference architecture is suggested, which includes the following levels: (i) digital thread support level; (ii) agile architecture support level; (iii) digital shadow support level. This approach has been used in the development of a number of real systems, and has shown its effectiveness in supporting system agility at the exploitation and modernization stages. The proposed approach is focused on building digital twin-based systems. This article may be interesting for specialists engaged in research and development in the domain of IoT- and IIoT-based information systems, primarily architects. Full article
Show Figures

Figure 1

27 pages, 9243 KiB  
Article
Ultrasonic Sound Guide System with Eyeglass Device for the Visually Impaired
by Kevin Kim, Saea Kim and Anthony Choi
Sensors 2022, 22(8), 3077; https://doi.org/10.3390/s22083077 - 17 Apr 2022
Cited by 1 | Viewed by 3412
Abstract
The ultrasonic sound guide system presents the audio broadcasting system based on the inaudible ultrasonic sound to assist the indoor and outdoor navigation of the visually impaired. The transmitters are placed at the point of interest to propagate the frequency modulated voice signal [...] Read more.
The ultrasonic sound guide system presents the audio broadcasting system based on the inaudible ultrasonic sound to assist the indoor and outdoor navigation of the visually impaired. The transmitters are placed at the point of interest to propagate the frequency modulated voice signal in ultrasonic sound range. The dual channel receiver device is carried by the visually impaired person in the form of eyeglasses to receive the ultrasonic sound for the voice signal via demodulation. Since the ultrasonic sound demonstrates the acoustic properties, the velocity, directivity, attenuation, and superposition of ultrasonic sound provide the acoustic clue to the user for localizing the multiple transmitter positions by binaural localization capability. The visually impaired hear the designated voice signal and follow the signal attributions to arrive at the specific location. Due to the low microphone gain from side addressing, the time delay between the receiver channels demonstrates the high variance and high bias in end directions. However, the perception experiment shows the further prediction accuracy in end directions as compared to the center direction outcomes. The overall evaluations show the precise directional prediction for narrow- and wide-angle situations. The ultrasonic sound guide system is a useful device to localize places in the near field without touching braille. Full article
(This article belongs to the Special Issue Acoustic Sensing Systems and Their Applications in Smart Environments)
Show Figures

Figure 1

15 pages, 3938 KiB  
Article
Super-Resolution Ultrasound Imaging Scheme Based on a Symmetric Series Convolutional Neural Network
by Lakpa Dorje Tamang and Byung-Wook Kim
Sensors 2022, 22(8), 3076; https://doi.org/10.3390/s22083076 - 16 Apr 2022
Cited by 2 | Viewed by 2613
Abstract
In this paper, we propose a symmetric series convolutional neural network (SS-CNN), which is a novel deep convolutional neural network (DCNN)-based super-resolution (SR) technique for ultrasound medical imaging. The proposed model comprises two parts: a feature extraction network (FEN) and an up-sampling layer. [...] Read more.
In this paper, we propose a symmetric series convolutional neural network (SS-CNN), which is a novel deep convolutional neural network (DCNN)-based super-resolution (SR) technique for ultrasound medical imaging. The proposed model comprises two parts: a feature extraction network (FEN) and an up-sampling layer. In the FEN, the low-resolution (LR) counterpart of the ultrasound image passes through a symmetric series of two different DCNNs. The low-level feature maps obtained from the subsequent layers of both DCNNs are concatenated in a feed forward manner, aiding in robust feature extraction to ensure high reconstruction quality. Subsequently, the final concatenated features serve as an input map to the latter 2D convolutional layers, where the textural information of the input image is connected via skip connections. The second part of the proposed model is a sub-pixel convolutional (SPC) layer, which up-samples the output of the FEN by multiplying it with a multi-dimensional kernel followed by a periodic shuffling operation to reconstruct a high-quality SR ultrasound image. We validate the performance of the SS-CNN with publicly available ultrasound image datasets. Experimental results show that the proposed model achieves a high-quality reconstruction of the ultrasound image over the conventional methods in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), while providing compelling SR reconstruction time. Full article
(This article belongs to the Special Issue Ultrasonic Systems for Biomedical Sensing)
Show Figures

Figure 1

21 pages, 17764 KiB  
Article
A Novel DEM Block Adjustment Method for Spaceborne InSAR Using Constraint Slices
by Rui Wang, Huiming Chai, Bin Guo, Li Zhang and Xiaolei Lv
Sensors 2022, 22(8), 3075; https://doi.org/10.3390/s22083075 - 16 Apr 2022
Cited by 1 | Viewed by 1892
Abstract
The lack and uneven distribution of Ground Control Points (GCPs) will lead to the deterioration of Digital Elevation Model (DEM) block adjustment results in the bistatic Interferometric Synthetic Aperture Radar (InSAR) system. Given this issue, we first explain the relationship between the stability [...] Read more.
The lack and uneven distribution of Ground Control Points (GCPs) will lead to the deterioration of Digital Elevation Model (DEM) block adjustment results in the bistatic Interferometric Synthetic Aperture Radar (InSAR) system. Given this issue, we first explain the relationship between the stability of adjustment parameters and the GCP distribution pattern theoretically using matrix perturbation theory. Second, we put forward the Constraint Slices (CSs) concept and first introduce CSs into the adjustment optimization model as constraint conditions rather than actual values as GCPs. Finally, we propose a novel DEM block adjustment method for spaceborne InSAR using CSs based on an optimization model with nonlinear constraints. The simulated experiment shows the instability of the conventional method and validates the proposed method under different parallel baseline errors. Four groups of real experiments were carried out according to the size of the uncontrolled area using twelve Co-registered Single-look Slant–range Complex (CoSSC) datasets for Henan Province, China. The adjustment results verified by the ICESat-2 ATL08 data demonstrate that the performance of the proposed method is better than the conventional method in the uncontrolled area; the corresponding improvements in adjustment accuracies compared with the conventional method are 0.13 m, 1.02 m, 2.12 m, and 8.18 m, respectively. At the same time, the proposed method can enhance the height consistency in overlapping areas, which is vital for seamless DEM production. Full article
(This article belongs to the Section Remote Sensors)
Show Figures

Figure 1

19 pages, 886 KiB  
Article
Rethinking Power Efficiency for Next-Generation Processor-Free Sensing Devices
by Yihang Song, Songfan Li, Chong Zhang, Shengyu Li and Li Lu
Sensors 2022, 22(8), 3074; https://doi.org/10.3390/s22083074 - 16 Apr 2022
Cited by 2 | Viewed by 2390
Abstract
The last decade has seen significant advances in power optimization for IoT sensors. The conventional wisdom considers that if we reduce the power consumption of each component (e.g., processor, radio) into μW-level of power, the IoT sensors could achieve overall ultra-low power [...] Read more.
The last decade has seen significant advances in power optimization for IoT sensors. The conventional wisdom considers that if we reduce the power consumption of each component (e.g., processor, radio) into μW-level of power, the IoT sensors could achieve overall ultra-low power consumption. However, we show that this conventional wisdom is overturned, as bus communication can take significant power for exchanging data between each component. In this paper, we analyze the power efficiency of bus communication and ask whether it is possible to reduce the power consumption for bus communication. We observe that existing bus architectures in mainstream IoT devices can be classified into either push-pull or open-drain architecture. push-pull only adapts to unidirectional communication, whereas open-drain inherently fits for bidirectional communication which benefits simplifying bus topology and reducing hardware costs. However, open-drain consumes more power than push-pull due to the high leakage current consumption while communicating on the bus. We present Turbo, a novel approach introducing low power to the open-drain based buses by reducing the leakage current created on the bus. We instantiate Turbo on I2C bus and evaluate it with commercial off-the-shelf (COTS) sensors. The results show a 76.9% improvement in power efficiency in I2C communication. Full article
(This article belongs to the Special Issue Trustworthy Sensing with Human-and-Environment-in-the-Loop)
Show Figures

Figure 1

14 pages, 3045 KiB  
Article
High-Speed Continuous Wavelet Transform Processor for Vital Signal Measurement Using Frequency-Modulated Continuous Wave Radar
by Chanhee Bae, Seongjoo Lee and Yunho Jung
Sensors 2022, 22(8), 3073; https://doi.org/10.3390/s22083073 - 16 Apr 2022
Cited by 7 | Viewed by 2443
Abstract
This paper proposes a high-speed continuous wavelet transform (CWT) processor to analyze vital signals extracted from a frequency-modulated continuous wave (FMCW) radar sensor. The proposed CWT processor consists of a fast Fourier transform (FFT) module, complex multiplier module, and inverse FFT (IFFT) module. [...] Read more.
This paper proposes a high-speed continuous wavelet transform (CWT) processor to analyze vital signals extracted from a frequency-modulated continuous wave (FMCW) radar sensor. The proposed CWT processor consists of a fast Fourier transform (FFT) module, complex multiplier module, and inverse FFT (IFFT) module. For high-throughput processing, the FFT and IFFT modules are designed with the pipeline FFT architecture of radix-2 single-path delay feedback (R2SDF) and mixed-radix multipath delay commutator (MRMDC) architecture, respectively. In addition, the IFFT module and the complex multiplier module perform a four-channel operation to reduce the processing time from repeated operations. Simultaneously, the MRMDC IFFT module minimizes the circuit area by reducing the number of non-trivial multipliers by using a mixed-radix algorithm. In addition, the proposed CWT processor can support variable lengths of 8, 16, 32, 64, 128, 256, 512, and 1024 to analyze various vital signals. The proposed CWT processor was implemented in a field-programmable gate array (FPGA) device and verified through the measurement of heartbeat and respiration from an FMCW radar sensor. Experimental results showed that the proposed CWT processor can reduce the processing time by 48.4-fold and 40.7-fold compared to MATLAB software with Intel i7 CPU. Moreover, it can be confirmed that the proposed CWT processor can reduce the processing time by 73.3% compared to previous FPGA-based implementations. Full article
(This article belongs to the Section Physical Sensors)
Show Figures

Figure 1

15 pages, 1715 KiB  
Article
Deep Learning Approach to UAV Detection and Classification by Using Compressively Sensed RF Signal
by Yongguang Mo, Jianjun Huang and Gongbin Qian
Sensors 2022, 22(8), 3072; https://doi.org/10.3390/s22083072 - 16 Apr 2022
Cited by 18 | Viewed by 3637
Abstract
Recently, the frequent occurrence of the misuse and intrusion of UAVs has made it a research challenge to identify and detect them effectively, and relatively high bandwidth and pressure on data transmission and real-time processing exist when sampling UAV communication signals using the [...] Read more.
Recently, the frequent occurrence of the misuse and intrusion of UAVs has made it a research challenge to identify and detect them effectively, and relatively high bandwidth and pressure on data transmission and real-time processing exist when sampling UAV communication signals using the RF detection method. In this paper, firstly, for data sampling, we chose a compressed sensing technique to replace the traditional sampling theorem and used a multi-channel random demodulator to sample the signal; secondly, for the detection and identification of the presence, type, and flight pattern of UAVs, a multi-stage deep learning-based UAV identification and detection method was proposed by exploiting the difference in communication signals between UAVs and controllers under different circumstances. The data samples are first passed by detectors that detect the presence of UAVs, then classifiers are used to identify the type of UAVs, and finally flight patterns are judged by the corresponding classifiers, for which two neural network structures (DNN and CNN) are constructed by deep learning algorithms and evaluated and validated by a 10-fold cross-validation method, with the DNN network used for detectors and the CNN network for subsequent type and flying mode classification. The experimental results demonstrate, first, the effectiveness of using compressed sensing for sampling the communication signals of UAVs and controllers; and second, the detecting method with multi-stage DL detects higher efficiency and accuracy compared with existing detecting methods, detecting the presence, type, and flight model of UAVs with an accuracy of over 99%. Full article
(This article belongs to the Section Remote Sensors)
Show Figures

Figure 1

8 pages, 455 KiB  
Brief Report
Head and Trunk Kinematics during Activities of Daily Living with and without Mechanical Restriction of Cervical Motion
by Angela R. Weston, Brian J. Loyd, Carolyn Taylor, Carrie Hoppes and Leland E. Dibble
Sensors 2022, 22(8), 3071; https://doi.org/10.3390/s22083071 - 16 Apr 2022
Viewed by 1695
Abstract
Alterations in head and trunk kinematics during activities of daily living can be difficult to recognize and quantify with visual observation. Incorporating wearable sensors allows for accurate and measurable assessment of movement. The aim of this study was to determine the ability of [...] Read more.
Alterations in head and trunk kinematics during activities of daily living can be difficult to recognize and quantify with visual observation. Incorporating wearable sensors allows for accurate and measurable assessment of movement. The aim of this study was to determine the ability of wearable sensors and data processing algorithms to discern motion restrictions during activities of daily living. Accelerometer data was collected with wearable sensors from 10 healthy adults (age 39.5 ± 12.47) as they performed daily living simulated tasks: coin pick up (pitch plane task), don/doff jacket (yaw plane task), self-paced community ambulation task [CAT] (pitch and yaw plane task) without and with a rigid cervical collar. Paired t-tests were used to discern differences between non-restricted (no collared) performance and restricted (collared) performance of tasks. Significant differences in head rotational velocity (jacket p = 0.03, CAT-pitch p < 0.001, CAT-yaw p < 0.001), head rotational amplitude (coin p = 0.03, CAT-pitch p < 0.001, CAT-yaw p < 0.001), trunk rotational amplitude (jacket p = 0.01, CAT-yaw p = 0.005), and head–trunk coupling (jacket p = 0.007, CAT-yaw p = 0.003) were captured by wearable sensors between the two conditions. Alterations in turning movement were detected at the head and trunk during daily living tasks. These results support the ecological validity of using wearable sensors to quantify movement alterations during real-world scenarios. Full article
(This article belongs to the Section Wearables)
Show Figures

Figure 1

Previous Issue
Next Issue
Back to TopTop