Sensors

18 pages, 2839 KiB

Open AccessArticle

Synergy Effect of Combined Near and Mid-Infrared Fibre Spectroscopy for Diagnostics of Abdominal Cancer

by Thaddäus Hocotz, Olga Bibikova, Valeria Belikova, Andrey Bogomolov, Iskander Usenov, Lukasz Pieszczek, Tatiana Sakharova, Olaf Minet, Elena Feliksberger, Viacheslav Artyushenko, Beate Rau and Urszula Zabarylo

Sensors 2020, 20(22), 6706; https://doi.org/10.3390/s20226706 - 23 Nov 2020

Cited by 7 | Viewed by 3393

Abstract

Cancers of the abdominal cavity comprise one of the most prevalent forms of cancers, with the highest contribution from colon and rectal cancers (12% of the human population), followed by stomach cancers (4%). Surgery, as the preferred choice of treatment, includes the selection [...] Read more.

Cancers of the abdominal cavity comprise one of the most prevalent forms of cancers, with the highest contribution from colon and rectal cancers (12% of the human population), followed by stomach cancers (4%). Surgery, as the preferred choice of treatment, includes the selection of adequate resection margins to avoid local recurrences due to minimal residual disease. The presence of functionally vital structures can complicate the choice of resection margins. Spectral analysis of tissue samples in combination with chemometric models constitutes a promising approach for more efficient and precise tumour margin identification. Additionally, this technique provides a real-time tumour identification approach not only for intraoperative application but also during endoscopic diagnosis of tumours in hollow organs. The combination of near-infrared and mid-infrared spectroscopy has advantages compared to individual methods for the clinical implementation of this technique as a diagnostic tool. Full article

(This article belongs to the Special Issue Multisensor Systems and Signal Processing in Analytical Chemistry)

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13 pages, 3236 KiB

Open AccessLetter

Speed Sensorless Control of Linear Ultrasonic Motors Based on Stator Vibration Amplitude Compensation

by Yuzhao Yan, Ming Yang, Tianyue Yang, Siwei Ye and Wanlu Jiang

Sensors 2020, 20(22), 6705; https://doi.org/10.3390/s20226705 - 23 Nov 2020

Cited by 4 | Viewed by 2415

Abstract

In some applications of linear ultrasonic motors (LUSMs), not installing speed/position sensors can reduce the size and cost of the system, changes in load will cause fluctuations in the speed of the LUSM. To eliminate the influence of load changes on speed, a [...] Read more.

In some applications of linear ultrasonic motors (LUSMs), not installing speed/position sensors can reduce the size and cost of the system, changes in load will cause fluctuations in the speed of the LUSM. To eliminate the influence of load changes on speed, a speed sensorless control scheme based on stator vibration amplitude compensation (SSCBVC) is proposed. This scheme is implemented under the framework of the stator vibration amplitude-based speed control (VBSC) and frequency tracking. Based on the stator vibration amplitude-speed and the output force-speed curves of the LUSM, the relationship between the load changes and stator vibration amplitude (SVA) to be compensated is established, realizing a speed sensorless control of the LUSM under variable load conditions. The experimental results show that the maximum fluctuation of the speed is about 2.2% when the output force changes from 0 to 6 N with SSCBVC. This scheme can effectively reduce the influence of load changes on the speed of the LUSM without using speed/position sensors. Full article

(This article belongs to the Section Intelligent Sensors)

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18 pages, 7267 KiB

Open AccessArticle

Automatic Detection of Freshwater Phytoplankton Specimens in Conventional Microscopy Images

by David Rivas-Villar, José Rouco, Manuel G. Penedo, Rafael Carballeira and Jorge Novo

Sensors 2020, 20(22), 6704; https://doi.org/10.3390/s20226704 - 23 Nov 2020

Cited by 5 | Viewed by 3423

Abstract

Water safety and quality can be compromised by the proliferation of toxin-producing phytoplankton species, requiring continuous monitoring of water sources. This analysis involves the identification and counting of these species which requires broad experience and knowledge. The automatization of these tasks is highly [...] Read more.

Water safety and quality can be compromised by the proliferation of toxin-producing phytoplankton species, requiring continuous monitoring of water sources. This analysis involves the identification and counting of these species which requires broad experience and knowledge. The automatization of these tasks is highly desirable as it would release the experts from tedious work, eliminate subjective factors, and improve repeatability. Thus, in this preliminary work, we propose to advance towards an automatic methodology for phytoplankton analysis in digital images of water samples acquired using regular microscopes. In particular, we propose a novel and fully automatic method to detect and segment the existent phytoplankton specimens in these images using classical computer vision algorithms. The proposed method is able to correctly detect sparse colonies as single phytoplankton candidates, thanks to a novel fusion algorithm, and is able to differentiate phytoplankton specimens from other image objects in the microscope samples (like minerals, bubbles or detritus) using a machine learning based approach that exploits texture and colour features. Our preliminary experiments demonstrate that the proposed method provides satisfactory and accurate results. Full article

(This article belongs to the Special Issue New Advances in Sensing and Artificial Intelligence for Medical Imaging)

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13 pages, 29254 KiB

Open AccessLetter

A U-Net Based Approach for Automating Tribological Experiments

by Benjamin Staar, Suleyman Bayrak, Dominik Paulkowski and Michael Freitag

Sensors 2020, 20(22), 6703; https://doi.org/10.3390/s20226703 - 23 Nov 2020

Cited by 2 | Viewed by 3069

Abstract

Tribological experiments (i.e., characterizing the friction and wear behavior of materials) are crucial for determining their potential areas of application. Automating such tests could hence help speed up the development of novel materials and coatings. Here, we utilize convolutional neural networks (CNNs) to [...] Read more.

Tribological experiments (i.e., characterizing the friction and wear behavior of materials) are crucial for determining their potential areas of application. Automating such tests could hence help speed up the development of novel materials and coatings. Here, we utilize convolutional neural networks (CNNs) to automate a common experimental setup whereby an endoscopic camera was used to measure the contact area between a rubber sample and a spherical counterpart. Instead of manually determining the contact area, our approach utilizes a U-Net-like CNN architecture to automate this task, creating a much more efficient and versatile experimental setup. Using a 5× random permutation cross validation as well as additional sanity checks, we show that we approached human-level performance. To ensure a flexible and mobile setup, we implemented the method on an NVIDIA Jetson AGX Xavier development kit where we achieved ~18 frames per second by employing mixed-precision training. Full article

(This article belongs to the Section Intelligent Sensors)

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18 pages, 8360 KiB

Open AccessArticle

SodSAR: A Tower-Based 1–10 GHz SAR System for Snow, Soil and Vegetation Studies

by Jorge Jorge Ruiz, Risto Vehmas, Juha Lemmetyinen, Josu Uusitalo, Janne Lahtinen, Kari Lehtinen, Anna Kontu, Kimmo Rautiainen, Riku Tarvainen, Jouni Pulliainen and Jaan Praks

Sensors 2020, 20(22), 6702; https://doi.org/10.3390/s20226702 - 23 Nov 2020

Cited by 10 | Viewed by 3534

Abstract

We introduce SodSAR, a fully polarimetric tower-based wide frequency (1–10 GHz) range Synthetic Aperture Radar (SAR) aimed at snow, soil and vegetation studies. The instrument is located in the Arctic Space Centre of the Finnish Meteorological Institute in Sodankylä, Finland. The system is [...] Read more.

We introduce SodSAR, a fully polarimetric tower-based wide frequency (1–10 GHz) range Synthetic Aperture Radar (SAR) aimed at snow, soil and vegetation studies. The instrument is located in the Arctic Space Centre of the Finnish Meteorological Institute in Sodankylä, Finland. The system is based on a Vector Network Analyzer (VNA)-operated scatterometer mounted on a rail allowing the formation of SAR images, including interferometric pairs separated by a temporal baseline. We present the description of the radar, the applied SAR focusing technique, the radar calibration and measurement stability analysis. Measured stability of the backscattering intensity over a three-month period was observed to be better than 0.5 dB, when measuring a target with a known radar cross section. Deviations of the estimated target range were in the order of a few cm over the same period, indicating also good stability of the measured phase. Interforometric SAR (InSAR) capabilities are also discussed, and as a example, the coherence of subsequent SAR acquisitions over the observed boreal forest stand are analyzed over increasing temporal baselines. The analysis shows good conservation of coherence in particular at L-band, while higher frequencies are susceptible to loss of coherence in particular for dense vegetation. The potential of the instrument for satellite calibration and validation activities is also discussed. Full article

(This article belongs to the Section Remote Sensors)

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16 pages, 81363 KiB

Open AccessArticle

Excess Air Ratio Management in a Diesel Engine with Exhaust Backpressure Compensation

by Piotr Kasprzyk, Jacek Hunicz, Arkadiusz Rybak, Michał S. Gęca and Maciej Mikulski

Sensors 2020, 20(22), 6701; https://doi.org/10.3390/s20226701 - 23 Nov 2020

Cited by 7 | Viewed by 3502

Abstract

The paper investigates the operation of a wideband universal exhaust gas oxygen (UEGO) sensor in a diesel engine under elevated exhaust backpressure. Although UEGO sensors provide the excess air ratio feedback signal primarily in spark ignition engines, they are also used in diesel [...] Read more.

The paper investigates the operation of a wideband universal exhaust gas oxygen (UEGO) sensor in a diesel engine under elevated exhaust backpressure. Although UEGO sensors provide the excess air ratio feedback signal primarily in spark ignition engines, they are also used in diesel engines to facilitate low-emission combustion. The excess air signal is used as an input for the fuel mass observer, as well as to run the engine in the low-emission regime and enable smokeless acceleration. To ensure a short response time and individual cylinder control, the UEGO sensor can be installed upstream of a turbocharger; however, this means that the exhaust gas pressure affects the measured oxygen concentration. Therefore, this study determines the sensor’s sensitivity to the exhaust pressure under typical conditions for lean burn low-emission diesel engines. Identification experiments are carried out on a supercharged single-cylinder diesel engine with an exhaust system mimicking the operation of the turbocharger. The apparent excess air measured with the UEGO sensor is compared to that obtained in a detailed exhaust gas analysis. The comparison of reference and apparent signals shows that the pressure compensation correlations used in gasoline engines do not provide the correct values for diesel engine conditions. Therefore, based on the data analysis, a new empirical formula is proposed, for which the suitability for lean burn diesel engines is verified. Full article

(This article belongs to the Section Physical Sensors)

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21 pages, 2735 KiB

Open AccessReview

Categorization and Characterization of Time Domain CMOS Temperature Sensors

by Sangjin Byun

Sensors 2020, 20(22), 6700; https://doi.org/10.3390/s20226700 - 23 Nov 2020

Cited by 5 | Viewed by 3356

Abstract

Time domain complementary metal-oxide-semiconductor (CMOS) temperature sensors estimate the temperature of a sensory device by measuring the frequency, period and/or delay time instead of the voltage and/or current signals that have been traditionally measured for a long time. In this paper, the time [...] Read more.

Time domain complementary metal-oxide-semiconductor (CMOS) temperature sensors estimate the temperature of a sensory device by measuring the frequency, period and/or delay time instead of the voltage and/or current signals that have been traditionally measured for a long time. In this paper, the time domain CMOS temperature sensors are categorized into twelve types by using the temperature estimation function which is newly defined as the ratio of two measured time domain signals. The categorized time domain CMOS temperature sensors, which have been published in literature, show different characteristics respectively in terms of temperature conversion rate, die area, process variation compensation, temperature error, power supply voltage sensitivity and so on. Based on their characteristics, we can choose the most appropriate one from twelve types to satisfy a given specification. Full article

(This article belongs to the Section Nanosensors)

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20 pages, 5708 KiB

Open AccessArticle

An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images

by Fei Sun, Fang Fang, Run Wang, Bo Wan, Qinghua Guo, Hong Li and Xincai Wu

Sensors 2020, 20(22), 6699; https://doi.org/10.3390/s20226699 - 23 Nov 2020

Cited by 10 | Viewed by 3005

Abstract

Imbalanced learning is a common problem in remote sensing imagery-based land-use and land-cover classifications. Imbalanced learning can lead to a reduction in classification accuracy and even the omission of the minority class. In this paper, an impartial semi-supervised learning strategy based on extreme [...] Read more.

Imbalanced learning is a common problem in remote sensing imagery-based land-use and land-cover classifications. Imbalanced learning can lead to a reduction in classification accuracy and even the omission of the minority class. In this paper, an impartial semi-supervised learning strategy based on extreme gradient boosting (ISS-XGB) is proposed to classify very high resolution (VHR) images with imbalanced data. ISS-XGB solves multi-class classification by using several semi-supervised classifiers. It first employs multi-group unlabeled data to eliminate the imbalance of training samples and then utilizes gradient boosting-based regression to simulate the target classes with positive and unlabeled samples. In this study, experiments were conducted on eight study areas with different imbalanced situations. The results showed that ISS-XGB provided a comparable but more stable performance than most commonly used classification approaches (i.e., random forest (RF), XGB, multilayer perceptron (MLP), and support vector machine (SVM)), positive and unlabeled learning (PU-Learning) methods (PU-BP and PU-SVM), and typical synthetic sample-based imbalanced learning methods. Especially under extremely imbalanced situations, ISS-XGB can provide high accuracy for the minority class without losing overall performance (the average overall accuracy achieves 85.92%). The proposed strategy has great potential in solving the imbalanced classification problems in remote sensing. Full article

(This article belongs to the Special Issue Remote Sensing Big Data for Improving the Urban Environment)

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22 pages, 19855 KiB

Open AccessArticle

Effect Evaluation of Spatial Characteristics on Map Matching-Based Indoor Positioning

by Shuaiwei Luo, Fuqiang Gu, Fan Xu and Jianga Shang

Sensors 2020, 20(22), 6698; https://doi.org/10.3390/s20226698 - 23 Nov 2020

Cited by 4 | Viewed by 3181

Abstract

Map-matching is a popular method that uses spatial information to improve the accuracy of positioning methods. The performance of map matching methods is closely related to spatial characteristics. Although several studies have demonstrated that certain map matching algorithms are affected by some spatial [...] Read more.

Map-matching is a popular method that uses spatial information to improve the accuracy of positioning methods. The performance of map matching methods is closely related to spatial characteristics. Although several studies have demonstrated that certain map matching algorithms are affected by some spatial structures (e.g., parallel paths), they focus on the analysis of single map matching method or few spatial structures. In this study, we explored how the most commonly-used four spatial characteristics (namely forks, open spaces, corners, and narrow corridors) affect three popular map matching methods, namely particle filtering (PF), hidden Markov model (HMM), and geometric methods. We first provide a theoretical analysis on how spatial characteristics affect the performance of map matching methods, and then evaluate these effects through experiments. We found that corners and narrow corridors are helpful in improving the positioning accuracy, while forks and open spaces often lead to a larger positioning error. We hope that our findings are helpful for future researchers in choosing proper map matching algorithms with considering the spatial characteristics. Full article

(This article belongs to the Collection Positioning and Navigation)

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21 pages, 6654 KiB

Open AccessArticle

Mobile Manipulation Integrating Enhanced AMCL High-Precision Location and Dynamic Tracking Grasp

by Huaidong Zhou, Wusheng Chou, Wanchen Tuo, Yongfeng Rong and Song Xu

Sensors 2020, 20(22), 6697; https://doi.org/10.3390/s20226697 - 23 Nov 2020

Cited by 12 | Viewed by 3977

Abstract

Mobile manipulation, which has more flexibility than fixed-base manipulation, has always been an important topic in the field of robotics. However, for sophisticated operation in complex environments, efficient localization and dynamic tracking grasp still face enormous challenges. To address these challenges, this paper [...] Read more.

Mobile manipulation, which has more flexibility than fixed-base manipulation, has always been an important topic in the field of robotics. However, for sophisticated operation in complex environments, efficient localization and dynamic tracking grasp still face enormous challenges. To address these challenges, this paper proposes a mobile manipulation method integrating laser-reflector-enhanced adaptive Monte Carlo localization (AMCL) algorithm and a dynamic tracking and grasping algorithm. First, by fusing the information of laser-reflector landmarks to adjust the weight of particles in AMCL, the localization accuracy of mobile platforms can be improved. Second, deep-learning-based multiple-object detection and visual servo are exploited to efficiently track and grasp dynamic objects. Then, a mobile manipulation system integrating the above two algorithms into a robotic with a 6-degrees-of-freedom (DOF) operation arm is implemented in an indoor environment. Technical components, including localization, multiple-object detection, dynamic tracking grasp, and the integrated system, are all verified in real-world scenarios. Experimental results demonstrate the efficacy and superiority of our method. Full article

(This article belongs to the Section Intelligent Sensors)

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19 pages, 9376 KiB

Open AccessArticle

Powered Two-Wheeler Riding Profile Clustering for an In-Depth Study of Bend-Taking Practices

by Mohamed Diop, Abderrahmane Boubezoul, Latifa Oukhellou and Stéphane Espié

Sensors 2020, 20(22), 6696; https://doi.org/10.3390/s20226696 - 23 Nov 2020

Cited by 5 | Viewed by 3688

Abstract

The understanding of rider/vehicle interaction modalities remains an issue, specifically in the case of bend-taking. This difficulty results both from the lack of adequate instrumentation to conduct this type of study and from the variety of practices of this population of road users. [...] Read more.

The understanding of rider/vehicle interaction modalities remains an issue, specifically in the case of bend-taking. This difficulty results both from the lack of adequate instrumentation to conduct this type of study and from the variety of practices of this population of road users. Riders have numerous explanations of strategies for controlling their motorcycles when taking bends. The objective of this paper is to develop a data-driven methodology in order to identify typical riding behaviors in bends by using clustering methods. The real dataset used for the experiments is collected within the VIROLO++ collaborative project to improve the knowledge of actual PTW riding practices, especially during bend taking, by collecting real data on this riding situation, including data on PTW dynamics (velocity, normal acceleration, and jerk), position on the road (road curvature), and handlebar actions (handlebar steering angle). A detailed analysis of the results is provided for both the Anderson–Darling test and clustering steps. Moreover, the clustering results are compared with the subjective data of subjects to highlight and contextualize typical riding tendencies. Finally, we perform an in-depth analysis of the bend-taking practices of one subject to highlight the differences between different methods of controlling the motorcycle (steering handlebar vs. rider’s lean) using the rider action measurements made by pressure sensors. Full article

(This article belongs to the Special Issue Wearable Sensor for Activity Analysis and Context Recognition)

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22 pages, 2658 KiB

Open AccessArticle

Experimental Comparison of IR-UWB Radar and FMCW Radar for Vital Signs

by Dingyang Wang, Sungwon Yoo and Sung Ho Cho

Sensors 2020, 20(22), 6695; https://doi.org/10.3390/s20226695 - 23 Nov 2020

Cited by 65 | Viewed by 11953

Abstract

In this paper, we compare the performances of impulse radio ultra-wideband (IR-UWB) and frequency modulation continuous wave (FMCW) radars in measuring noncontact vital signs such as respiration rate and heart rate. These two type radars have been widely used in various fields and [...] Read more.

In this paper, we compare the performances of impulse radio ultra-wideband (IR-UWB) and frequency modulation continuous wave (FMCW) radars in measuring noncontact vital signs such as respiration rate and heart rate. These two type radars have been widely used in various fields and have shown their applicability to extract vital signs in noncontact ways. IR-UWB radar can extract vital signs using distance information. On the other hand, FMCW radar requires phase information to estimate vital signs, and the result can be enhanced with Multi-input Multi-output (MIMO) antenna topologies. By using commercial radar chipsets, the operation of radars under different conditions and frequency bands will also affect the performance of vital sign detection capabilities. We compared the accuracy and signal-to-noise (SNR) ratios of IR-UWB and FMCW radars in various scenarios, such as distance, orientation, carotid pulse, harmonics, and obstacle penetration. In general, the IR-UWB radars offer a slightly better accuracy and higher SNR in comparison to FMCW radar. However, each radar system has its own unique advantages, with IR-UWB exhibiting fewer harmonics and a higher SNR, while FMCW can combine the results from each channel. Full article

(This article belongs to the Section Remote Sensors)

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30 pages, 2993 KiB

Open AccessReview

Semiconductor Gas Sensors: Materials, Technology, Design, and Application

by Maria Vesna Nikolic, Vladimir Milovanovic, Zorka Z. Vasiljevic and Zoran Stamenkovic

Sensors 2020, 20(22), 6694; https://doi.org/10.3390/s20226694 - 23 Nov 2020

Cited by 304 | Viewed by 29586

Abstract

This paper presents an overview of semiconductor materials used in gas sensors, their technology, design, and application. Semiconductor materials include metal oxides, conducting polymers, carbon nanotubes, and 2D materials. Metal oxides are most often the first choice due to their ease of fabrication, [...] Read more.

This paper presents an overview of semiconductor materials used in gas sensors, their technology, design, and application. Semiconductor materials include metal oxides, conducting polymers, carbon nanotubes, and 2D materials. Metal oxides are most often the first choice due to their ease of fabrication, low cost, high sensitivity, and stability. Some of their disadvantages are low selectivity and high operating temperature. Conducting polymers have the advantage of a low operating temperature and can detect many organic vapors. They are flexible but affected by humidity. Carbon nanotubes are chemically and mechanically stable and are sensitive towards NO and NH₃, but need dopants or modifications to sense other gases. Graphene, transition metal chalcogenides, boron nitride, transition metal carbides/nitrides, metal organic frameworks, and metal oxide nanosheets as 2D materials represent gas-sensing materials of the future, especially in medical devices, such as breath sensing. This overview covers the most used semiconducting materials in gas sensing, their synthesis methods and morphology, especially oxide nanostructures, heterostructures, and 2D materials, as well as sensor technology and design, application in advance electronic circuits and systems, and research challenges from the perspective of emerging technologies. Full article

(This article belongs to the Section Electronic Sensors)

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21 pages, 3617 KiB

Open AccessArticle

Multirate Audio-Integrated Feedback Active Noise Control Systems Using Decimated-Band Adaptive Filters for Reducing Narrowband Noises

by Antonius Siswanto, Cheng-Yuan Chang and Sen M. Kuo

Sensors 2020, 20(22), 6693; https://doi.org/10.3390/s20226693 - 23 Nov 2020

Cited by 3 | Viewed by 3669

Abstract

Audio-integrated feedback active noise control (AFANC) systems deliver wideband audio signals and cancel low frequency narrowband noises simultaneously. The conventional AFANC system uses single-rate processing with fullband adaptive active noise control (ANC) filter for generating anti-noise signal and fullband audio cancelation filter for [...] Read more.

Audio-integrated feedback active noise control (AFANC) systems deliver wideband audio signals and cancel low frequency narrowband noises simultaneously. The conventional AFANC system uses single-rate processing with fullband adaptive active noise control (ANC) filter for generating anti-noise signal and fullband audio cancelation filter for audio-interference cancelation. The conventional system requires a high sampling rate for audio processing. Thus, the fullband adaptive filters require long filter lengths, resulting in high computational complexity and impracticality in real-time system. This paper proposes a multirate AFANC system using decimated-band adaptive filters (DAFs) to decrease the required filter lengths. The decimated-band adaptive ANC filter is updated by the proposed decimated filtered-X least mean square (FXLMS) algorithm, and the decimated-band audio cancelation filter can be obtained by the proposed on-line and off-line decimated secondary-path modeling algorithms. The computational complexity can be decreased significantly in the proposed AFANC system with good enough noise reduction and fast convergence speed, which were verified in the analysis and computer simulations. The proposed AFANC system was implemented for an active headrest system, and the real-time performances were tested in real-time experiments. Full article

(This article belongs to the Special Issue Intelligent Acoustic Sensors and Its Applications)

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23 pages, 1931 KiB

Open AccessArticle

Q-Learning Based Joint Energy-Spectral Efficiency Optimization in Multi-Hop Device-to-Device Communication

by Muhidul Islam Khan, Luca Reggiani, Muhammad Mahtab Alam, Yannick Le Moullec, Navuday Sharma, Elias Yaacoub and Maurizio Magarini

Sensors 2020, 20(22), 6692; https://doi.org/10.3390/s20226692 - 23 Nov 2020

Cited by 5 | Viewed by 2689

Abstract

In scenarios, like critical public safety communication networks, On-Scene Available (OSA) user equipment (UE) may be only partially connected with the network infrastructure, e.g., due to physical damages or on-purpose deactivation by the authorities. In this work, we consider multi-hop Device-to-Device (D2D) communication [...] Read more.

In scenarios, like critical public safety communication networks, On-Scene Available (OSA) user equipment (UE) may be only partially connected with the network infrastructure, e.g., due to physical damages or on-purpose deactivation by the authorities. In this work, we consider multi-hop Device-to-Device (D2D) communication in a hybrid infrastructure where OSA UEs connect to each other in a seamless manner in order to disseminate critical information to a deployed command center. The challenge that we address is to simultaneously keep the OSA UEs alive as long as possible and send the critical information to a final destination (e.g., a command center) as rapidly as possible, while considering the heterogeneous characteristics of the OSA UEs. We propose a dynamic adaptation approach based on machine learning to improve a joint energy-spectral efficiency (ESE). We apply a Q-learning scheme in a hybrid fashion (partially distributed and centralized) in learner agents (distributed OSA UEs) and scheduler agents (remote radio heads or RRHs), for which the next hop selection and RRH selection algorithms are proposed. Our simulation results show that the proposed dynamic adaptation approach outperforms the baseline system by approximately

67 %

in terms of joint energy-spectral efficiency, wherein the energy efficiency of the OSA UEs benefit from a gain of approximately

30 %

. Finally, the results show also that our proposed framework with C-RAN reduces latency by approximately

50 %

w.r.t. the baseline. Full article

(This article belongs to the Special Issue Internet of Things for Smart Community Solutions)

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20 pages, 10448 KiB

Open AccessArticle

Design and Validation of an E-Textile-Based Wearable Sock for Remote Gait and Postural Assessment

by Federica Amitrano, Armando Coccia, Carlo Ricciardi, Leandro Donisi, Giuseppe Cesarelli, Edda Maria Capodaglio and Giovanni D’Addio

Sensors 2020, 20(22), 6691; https://doi.org/10.3390/s20226691 - 23 Nov 2020

Cited by 33 | Viewed by 5076

Abstract

This paper presents a new wearable e-textile based system, named SWEET Sock, for biomedical signals remote monitoring. The system includes a textile sensing sock, an electronic unit for data transmission, a custom-made Android application for real-time signal visualization, and a software desktop for [...] Read more.

This paper presents a new wearable e-textile based system, named SWEET Sock, for biomedical signals remote monitoring. The system includes a textile sensing sock, an electronic unit for data transmission, a custom-made Android application for real-time signal visualization, and a software desktop for advanced digital signal processing. The device allows the acquisition of angular velocities of the lower limbs and plantar pressure signals, which are postprocessed to have a complete and schematic overview of patient’s clinical status, regarding gait and postural assessment. In this work, device performances are validated by evaluating the agreement between the prototype and an optoelectronic system for gait analysis on a set of free walk acquisitions. Results show good agreement between the systems in the assessment of gait cycle time and cadence, while the presence of systematic and proportional errors are pointed out for swing and stance time parameters. Worse results were obtained in the comparison of spatial metrics. The “wearability” of the system and its comfortable use make it suitable to be used in domestic environment for the continuous remote health monitoring of de-hospitalized patients but also in the ergonomic assessment of health workers, thanks to its low invasiveness. Full article

(This article belongs to the Special Issue Wearables for Movement Analysis in Healthcare)

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22 pages, 5147 KiB

Open AccessArticle

Single Image Dehazing Algorithm Analysis with Hyperspectral Images in the Visible Range

by Miguel Ángel Martínez-Domingo, Eva M. Valero, Juan L. Nieves, Pedro Jesús Molina-Fuentes, Javier Romero and Javier Hernández-Andrés

Sensors 2020, 20(22), 6690; https://doi.org/10.3390/s20226690 - 23 Nov 2020

Cited by 5 | Viewed by 3521

Abstract

In foggy or hazy conditions, images are degraded due to the scattering and attenuation of atmospheric particles, reducing the contrast and visibility and changing the color. This degradation depends on the distance, the density of the atmospheric particles and the wavelength. We have [...] Read more.

In foggy or hazy conditions, images are degraded due to the scattering and attenuation of atmospheric particles, reducing the contrast and visibility and changing the color. This degradation depends on the distance, the density of the atmospheric particles and the wavelength. We have tested and applied five single image dehazing algorithms, originally developed to work on RGB images and not requiring user interaction and/or prior knowledge about the images, on a spectral hazy image database in the visible range. We have made the evaluation using two strategies: the first is based on the analysis of eleven state-of-the-art metrics and the second is two psychophysical experiments with 126 subjects. Our results suggest that the higher the wavelength within the visible range is, the higher the quality of the dehazed images. The quality increases for low haze/fog levels. The choice of the best performing algorithm depends on the criterion prioritized by the metric design strategy. The psychophysical experiment results show that the level of agreement between observers and metrics depends on the criterion set for the observers’ task. Full article

(This article belongs to the Special Issue Color & Spectral Sensors)

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13 pages, 596 KiB

Open AccessLetter

Defect Detection in Aerospace Sandwich Composite Panels Using Conductive Thermography and Contact Sensors

by David I. Gillespie, Andrew W. Hamilton, Robert C. Atkinson, Xavier Bellekens, Craig Michie, Ivan Andonovic and Christos Tachtatzis

Sensors 2020, 20(22), 6689; https://doi.org/10.3390/s20226689 - 23 Nov 2020

Cited by 9 | Viewed by 2953

Abstract

Sandwich panels consisting of two Carbon Fibre Reinforced Polymer (CFRP) outer skins and an aluminium honeycomb core are a common structure of surfaces on commercial aircraft due to the beneficial strength–weight ratio. Mechanical defects such as a crushed honeycomb core, dis-bonds and delaminations [...] Read more.

Sandwich panels consisting of two Carbon Fibre Reinforced Polymer (CFRP) outer skins and an aluminium honeycomb core are a common structure of surfaces on commercial aircraft due to the beneficial strength–weight ratio. Mechanical defects such as a crushed honeycomb core, dis-bonds and delaminations in the outer skins and in the core occur routinely under normal use and are repaired during aerospace Maintenance, Repair and Overhaul (MRO) processes. Current practices rely heavily on manual inspection where it is possible minor defects are not identified prior to primary repair and are only addressed after initial repairs intensify the defects due to thermal expansion during high temperature curing. This paper reports on the development and characterisation of a technique based on conductive thermography implemented using an array of single point temperature sensors mounted on one surface of the panel and the concomitant induced thermal profile generated by a thermal stimulus on the opposing surface to identify such defects. Defects are classified by analysing the differential conduction of thermal energy profiles across the surface of the panel. Results indicate that crushed core and impact damage are detectable using a stepped temperature profile of 80

^{\circ}

C The method is amenable to integration within the existing drying cycle stage and reduces the costs of executing the overall process in terms of time-to-repair and manual effort. Full article

(This article belongs to the Special Issue Damage Detection Systems for Aerospace Applications)

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19 pages, 970 KiB

Open AccessArticle

Fusion-ConvBERT: Parallel Convolution and BERT Fusion for Speech Emotion Recognition

by Sanghyun Lee, David K. Han and Hanseok Ko

Sensors 2020, 20(22), 6688; https://doi.org/10.3390/s20226688 - 23 Nov 2020

Cited by 25 | Viewed by 5662

Abstract

Speech emotion recognition predicts the emotional state of a speaker based on the person’s speech. It brings an additional element for creating more natural human–computer interactions. Earlier studies on emotional recognition have been primarily based on handcrafted features and manual labels. With the [...] Read more.

Speech emotion recognition predicts the emotional state of a speaker based on the person’s speech. It brings an additional element for creating more natural human–computer interactions. Earlier studies on emotional recognition have been primarily based on handcrafted features and manual labels. With the advent of deep learning, there have been some efforts in applying the deep-network-based approach to the problem of emotion recognition. As deep learning automatically extracts salient features correlated to speaker emotion, it brings certain advantages over the handcrafted-feature-based methods. There are, however, some challenges in applying them to the emotion recognition problem, because data required for properly training deep networks are often lacking. Therefore, there is a need for a new deep-learning-based approach which can exploit available information from given speech signals to the maximum extent possible. Our proposed method, called “Fusion-ConvBERT”, is a parallel fusion model consisting of bidirectional encoder representations from transformers and convolutional neural networks. Extensive experiments were conducted on the proposed model using the EMO-DB and Interactive Emotional Dyadic Motion Capture Database emotion corpus, and it was shown that the proposed method outperformed state-of-the-art techniques in most of the test configurations. Full article

(This article belongs to the Special Issue Sensor Fusion for Object Detection, Classification and Tracking)

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13 pages, 4063 KiB

Open AccessArticle

Facilitating Hotspot Alignment in Tip-Enhanced Raman Spectroscopy via the Silver Photoluminescence of the Probe

by Yuan Fan, Dan Jin, Xiuju Wu, Hui Fang and Xiaocong Yuan

Sensors 2020, 20(22), 6687; https://doi.org/10.3390/s20226687 - 23 Nov 2020

Cited by 4 | Viewed by 3070

Abstract

A tip-enhanced Raman spectroscopy (TERS) system based on an atomic force microscope (AFM) and radially polarized laser beam was developed. A TERS probe with plasmon resonance wavelength matching the excitation wavelength was prepared with the help of dark-field micrographs. The intrinsic photoluminescence (PL) [...] Read more.

A tip-enhanced Raman spectroscopy (TERS) system based on an atomic force microscope (AFM) and radially polarized laser beam was developed. A TERS probe with plasmon resonance wavelength matching the excitation wavelength was prepared with the help of dark-field micrographs. The intrinsic photoluminescence (PL) from the silver (Ag)-coated TERS probe induced by localized surface plasmon resonance contains information about the near-field enhanced electromagnetic field intensity of the probe. Therefore, we used the intensity change of Ag PL to evaluate the stability of the Ag-coated probe during TERS experiments. Tracking the Ag PL of the TERS probe was helpful to detect probe damage and hotspot alignment. Our setup was successfully used for the TERS imaging of single-walled carbon nanotubes, which demonstrated that the Ag PL of the TERS probe is a good criterion to assist in the hotspot alignment procedure required for TERS experiments. This method lowers the risk of contamination and damage of the precious TERS probe, making it worthwhile for wide adoption in TERS experiments. Full article

(This article belongs to the Section Nanosensors)

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11 pages, 2610 KiB

Open AccessArticle

Synthesis and Characterization of Ru-MOFs on Microelectrode for Trace Mercury Detection

by Chenyu Xiong, Yuhao Xu, Chao Bian, Ri Wang, Yong Xie, Mingjie Han and Shanhong Xia

Sensors 2020, 20(22), 6686; https://doi.org/10.3390/s20226686 - 23 Nov 2020

Cited by 10 | Viewed by 2984

Abstract

Mercury ions (Hg

^{2 +}

) pollution in the water environment can cause serious harm to human health. Trace Hg

^{2 +}

detection is of vital importance for environmental monitoring. Herein, we report a novel design of Ru-MOFs modified gold microelectrode for Hg [...] Read more.

Mercury ions (Hg

^{2 +}

) pollution in the water environment can cause serious harm to human health. Trace Hg

^{2 +}

detection is of vital importance for environmental monitoring. Herein, we report a novel design of Ru-MOFs modified gold microelectrode for Hg

^{2 +}

determination. Ru-MOFs are synthesized directly by the cathodic method on gold microelectrode, with the covered area accurately controlled. Cathodic synthesized Ru-MOFs show good conductivity and are suitable to be used as the electrode surface material directly. The synergy of the pre-deposition process and the adsorption process of Ru-MOFs can effectively improves the performance of the sensor. The results show good linearity (R

^{2}

= 0.996) from 0.1 ppb to 5 ppb, with a high sensitivity of 0.583

μ

A ppb

^{- 1}

mm

^{- 2}

. The limit of detection is found to be 0.08 ppb and the test process is within 6 min. Most importantly, the senor has a good anti-interference ability and the recoveries are satisfactory. This miniature electrochemical sensor has the potential for on-site detection of trace mercury in the field. Full article

(This article belongs to the Special Issue Advanced Micro- and Nano-Sensors)

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57 pages, 21474 KiB

Open AccessReview

Energy Harvesting Technologies for Structural Health Monitoring of Airplane Components—A Review

by Saša Zelenika, Zdenek Hadas, Sebastian Bader, Thomas Becker, Petar Gljušćić, Jiri Hlinka, Ludek Janak, Ervin Kamenar, Filip Ksica, Theodora Kyratsi, Loucas Louca, Miroslav Mrlik, Adnan Osmanović, Vikram Pakrashi, Ondrej Rubes, Oldřich Ševeček, José P.B. Silva, Pavel Tofel, Bojan Trkulja, Runar Unnthorsson, Jasmin Velagić and Željko Vrcan Show full author list Hide full author list

Sensors 2020, 20(22), 6685; https://doi.org/10.3390/s20226685 - 22 Nov 2020

Cited by 59 | Viewed by 12505

Abstract

With the aim of increasing the efficiency of maintenance and fuel usage in airplanes, structural health monitoring (SHM) of critical composite structures is increasingly expected and required. The optimized usage of this concept is subject of intensive work in the framework of the [...] Read more.

With the aim of increasing the efficiency of maintenance and fuel usage in airplanes, structural health monitoring (SHM) of critical composite structures is increasingly expected and required. The optimized usage of this concept is subject of intensive work in the framework of the EU COST Action CA18203 “Optimising Design for Inspection” (ODIN). In this context, a thorough review of a broad range of energy harvesting (EH) technologies to be potentially used as power sources for the acoustic emission and guided wave propagation sensors of the considered SHM systems, as well as for the respective data elaboration and wireless communication modules, is provided in this work. EH devices based on the usage of kinetic energy, thermal gradients, solar radiation, airflow, and other viable energy sources, proposed so far in the literature, are thus described with a critical review of the respective specific power levels, of their potential placement on airplanes, as well as the consequently necessary power management architectures. The guidelines provided for the selection of the most appropriate EH and power management technologies create the preconditions to develop a new class of autonomous sensor nodes for the in-process, non-destructive SHM of airplane components. Full article

(This article belongs to the Special Issue Damage Detection Systems for Aerospace Applications)

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14 pages, 3187 KiB

Open AccessArticle

Imaging Tremor Quantification for Neurological Disease Diagnosis

by Yuichi Mitsui, Thi Thi Zin, Nobuyuki Ishii and Hitoshi Mochizuki

Sensors 2020, 20(22), 6684; https://doi.org/10.3390/s20226684 - 22 Nov 2020

Cited by 4 | Viewed by 2614

Abstract

In this paper, we introduce a simple method based on image analysis and deep learning that can be used in the objective assessment and measurement of tremors. A tremor is a neurological disorder that causes involuntary and rhythmic movements in a human body [...] Read more.

In this paper, we introduce a simple method based on image analysis and deep learning that can be used in the objective assessment and measurement of tremors. A tremor is a neurological disorder that causes involuntary and rhythmic movements in a human body part or parts. There are many types of tremors, depending on their amplitude and frequency type. Appropriate treatment is only possible when there is an accurate diagnosis. Thus, a need exists for a technique to analyze tremors. In this paper, we propose a hybrid approach using imaging technology and machine learning techniques for quantification and extraction of the parameters associated with tremors. These extracted parameters are used to classify the tremor for subsequent identification of the disease. In particular, we focus on essential tremor and cerebellar disorders by monitoring the finger–nose–finger test. First of all, test results obtained from both patients and healthy individuals are analyzed using image processing techniques. Next, data were grouped in order to determine classes of typical responses. A machine learning method using a support vector machine is used to perform an unsupervised clustering. Experimental results showed the highest internal evaluation for distribution into three clusters, which could be used to differentiate the responses of healthy subjects, patients with essential tremor and patients with cerebellar disorders. Full article

(This article belongs to the Special Issue Advanced Sensing and Image Processing Techniques for Healthcare Applications)

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22 pages, 1058 KiB

Open AccessArticle

FarpScusn: Fully Anonymous Routing Protocol with Self-Healing Capability in Unstable Sensor Networks

by Fengyin Li, Ying Wang, Hongwei Ju, Yanli Wang, Zhaojie Wang and Huiyu Zhou

Sensors 2020, 20(22), 6683; https://doi.org/10.3390/s20226683 - 22 Nov 2020

Cited by 5 | Viewed by 2788

Abstract

Anonymous technology is an effective way for protecting users’ privacy. Anonymity in sensor networks is to prevent the unauthorized third party from revealing the identities of the communication parties. While, in unstable wireless sensor networks, frequent topology changes often lead to route-failure in [...] Read more.

Anonymous technology is an effective way for protecting users’ privacy. Anonymity in sensor networks is to prevent the unauthorized third party from revealing the identities of the communication parties. While, in unstable wireless sensor networks, frequent topology changes often lead to route-failure in anonymous communication. To deal with the problems of anonymous route-failure in unstable sensor networks, in this paper we propose a fully anonymous routing protocol with self-healing capability in unstable sensor networks by constructing a new key agreement scheme and proposing an anonymous identity scheme. The proposed protocol maintains full anonymity of sensor nodes with the self-healing capability of anonymous routes. The results from the performance analysis show that the proposed self-healing anonymity-focused protocol achieves full anonymity of source nodes, destination nodes, and communication association. Full article

(This article belongs to the Special Issue Blockchain for Trustworthy Internet of Things)

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17 pages, 4202 KiB

Open AccessArticle

Fluid Intake Monitoring System Using a Wearable Inertial Sensor for Fluid Intake Management

by Hsiang-Yun Huang, Chia-Yeh Hsieh, Kai-Chun Liu, Steen Jun-Ping Hsu and Chia-Tai Chan

Sensors 2020, 20(22), 6682; https://doi.org/10.3390/s20226682 - 22 Nov 2020

Cited by 16 | Viewed by 4569

Abstract

Fluid intake is important for people to maintain body fluid homeostasis. Inadequate fluid intake leads to negative health consequences, such as headache, dizziness and urolithiasis. However, people in busy lifestyles usually forget to drink sufficient water and neglect the importance of fluid intake. [...] Read more.

Fluid intake is important for people to maintain body fluid homeostasis. Inadequate fluid intake leads to negative health consequences, such as headache, dizziness and urolithiasis. However, people in busy lifestyles usually forget to drink sufficient water and neglect the importance of fluid intake. Fluid intake management is important to assist people in adopting individual drinking behaviors. This work aims to propose a fluid intake monitoring system with a wearable inertial sensor using a hierarchical approach to detect drinking activities, recognize sip gestures and estimate fluid intake amount. Additionally, container-dependent amount estimation models are developed due to the influence of containers on fluid intake amount. The proposed fluid intake monitoring system could achieve 94.42% accuracy, 90.17% sensitivity, and 40.11% mean absolute percentage error (MAPE) for drinking detection, gesture spotting and amount estimation, respectively. Particularly, MAPE of amount estimation is improved approximately 10% compared to the typical approaches. The results have demonstrated the feasibility and the effectiveness of the proposed fluid intake monitoring system. Full article

(This article belongs to the Special Issue Wearable Inertial Sensors)

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25 pages, 10422 KiB

Open AccessArticle

Voltage-Mode Multifunction Biquad Filter and Its Application as Fully-Uncoupled Quadrature Oscillator Based on Current-Feedback Operational Amplifiers

by San-Fu Wang, Hua-Pin Chen, Yitsen Ku and Ming-Xiu Zhong

Sensors 2020, 20(22), 6681; https://doi.org/10.3390/s20226681 - 22 Nov 2020

Cited by 23 | Viewed by 3139

Abstract

This research introduces a new multifunction biquad filter based on voltage mode (VM) current-feedback operational amplifier (CFOA) and a fully uncoupled quadrature oscillator (QO) based on the proposed VM multifunction biquad filter. The proposed VM multifunction biquad filter has high impedance to the [...] Read more.

This research introduces a new multifunction biquad filter based on voltage mode (VM) current-feedback operational amplifier (CFOA) and a fully uncoupled quadrature oscillator (QO) based on the proposed VM multifunction biquad filter. The proposed VM multifunction biquad filter has high impedance to the input voltage signal, and uses three CFOAs as active components, while using four resistors and two grounded capacitors as passive components. The VM CFOA-based multifunction biquad filter realizes band-reject, band-pass, and low-pass transfer functions at high-input impedance node simultaneously, which has the feature of easy cascading in VM operation without the need for additional voltage buffers. Additionally, the filter control factor parameter pole frequency (ω_o) and quality factor (Q) of the proposed VM multifunction biquad filter can be independently set by varying different resistors. By slightly modifying the VM multifunction biquad filter topology, a VM fully-uncoupled QO is easily obtained. The difference from the previous VM CFOA-based multifunction biquad filter is that the proposed VM CFOA-based multifunction biquad filter can be independently controlled by the filter control factor parameters, ω_o and Q. The proposed VM CFOA-based multifunction biquad filter can be transformed into a VM QO with fully-uncoupled adjustable of the oscillation condition and the oscillation frequency. The oscillation condition and the oscillation frequency can be fully-uncoupled and controlled by varying two sets of completely different resistors. The proposed VM fully-uncoupled QO solves the amplitude instability. The constant amplitude ratio of two quadrature sinusoidal waveforms can be realized when tuning FO. PSpice simulation and experimental results prove the performances of the proposed VM multifunction filter and VM fully-uncoupled QO. Simulation and experimental results confirm the theoretical analysis of the proposed circuits. Full article

(This article belongs to the Section Electronic Sensors)

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27 pages, 18625 KiB

Open AccessArticle

Efficient Deployment with Throughput Maximization for UAVs Communication Networks

by Mohd Abuzar Sayeed, Rajesh Kumar, Vishal Sharma and Mohd Asim Sayeed

Sensors 2020, 20(22), 6680; https://doi.org/10.3390/s20226680 - 22 Nov 2020

Cited by 11 | Viewed by 3495

Abstract

The article presents a throughput maximization approach for UAV assisted ground networks. Throughput maximization involves minimizing delay and packet loss through UAV trajectory optimization, reinforcing the congested nodes and transmission channels. The aggressive reinforcement policy is achieved by characterizing nodes, links, and overall [...] Read more.

The article presents a throughput maximization approach for UAV assisted ground networks. Throughput maximization involves minimizing delay and packet loss through UAV trajectory optimization, reinforcing the congested nodes and transmission channels. The aggressive reinforcement policy is achieved by characterizing nodes, links, and overall topology through delay, loss, throughput, and distance. A position-aware graph neural network (GNN) is used for characterization, prediction, and dynamic UAV trajectory enhancement. To establish correctness, the proposed approach is validated against optimized link state routing (OLSR) driven UAV assisted ground networks. The proposed approach considerably outperforms the classical approach by demonstrating significant gains in throughput and packet delivery ratio with notable decrements in delay and packet loss. The performance analysis of the proposed approach against software-defined UAVs (U-S) and UAVs as base stations (U-B) verifies the consistency and gains in average throughput while minimizing delay and packet loss. The scalability test of the proposed approach is performed by varying data rates and the number of UAVs. Full article

(This article belongs to the Section Sensor Networks)

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24 pages, 910 KiB

Open AccessArticle

Large Intelligent Surfaces Communicating Through Massive MIMO Rayleigh Fading Channels

by Ricardo Coelho Ferreira, Michelle S. P. Facina, Felipe A. P. de Figueiredo, Gustavo Fraidenraich and Eduardo Rodrigues de Lima

Sensors 2020, 20(22), 6679; https://doi.org/10.3390/s20226679 - 22 Nov 2020

Cited by 8 | Viewed by 3178

Abstract

Large intelligent surfaces (LIS) promises not only to improve the signal to noise ratio, and spectral efficiency but also to reduce the energy consumption during the transmission. We consider a base station equipped with an antenna array using the maximum ratio transmission (MRT), [...] Read more.

Large intelligent surfaces (LIS) promises not only to improve the signal to noise ratio, and spectral efficiency but also to reduce the energy consumption during the transmission. We consider a base station equipped with an antenna array using the maximum ratio transmission (MRT), and a large reflector array sending signals to a single user. Each subchannel is affected by the Rayleigh flat fading, and the reflecting elements perform non-perfect phase correction which introduces a Von Mises distributed phase error. Based on the central limit theorem (CLT), we conclude that the overall channel has an equivalent Gamma fading whose parameters are derived from the moments of the channel fading between the antenna array and LIS, and also from the LIS to the single user. Assuming that the equivalent channel can be modeled as a Gamma distribution, we propose very accurate closed-form expressions for the bit error probability and a very tight upper bound. For the case where the LIS is not able to perform perfect phase cancellation, that is, under phase errors, it is possible to analyze the system performance considering the analytical approximations and the simulated results obtained using the well known Monte Carlo method. The analytical expressions for the parameters of the Gamma distribution are very difficult to be obtained due to the complexity of the nonlinear transformations of random variables with non-zero mean and correlated terms. Even with perfect phase cancellation, all the fading coefficients are complex due to the link between the user and the base station that is not neglected in this paper. Full article

(This article belongs to the Section Intelligent Sensors)

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18 pages, 2925 KiB

Open AccessArticle

Dielectric Spectroscopy and Application of Mixing Models Describing Dielectric Dispersion in Clay Minerals and Clayey Soils

by Juan D. González-Teruel, Scott B. Jones, Fulgencio Soto-Valles, Roque Torres-Sánchez, Inmaculada Lebron, Shmulik P. Friedman and David A. Robinson

Sensors 2020, 20(22), 6678; https://doi.org/10.3390/s20226678 - 22 Nov 2020

Cited by 24 | Viewed by 5141

Abstract

The number of sensors, ground-based and remote, exploiting the relationship between soil dielectric response and soil water content continues to grow. Empirical expressions for this relationship generally work well in coarse-textured soils but can break down for high-surface area and intricate materials such [...] Read more.

The number of sensors, ground-based and remote, exploiting the relationship between soil dielectric response and soil water content continues to grow. Empirical expressions for this relationship generally work well in coarse-textured soils but can break down for high-surface area and intricate materials such as clayey soils. Dielectric mixing models are helpful for exploring mechanisms and developing new understanding of the dielectric response in porous media that do not conform to a simple empirical approach, such as clayey soils. Here, we explore the dielectric response of clay minerals and clayey soils using the mixing model approach in the frequency domain. Our modeling focuses on the use of mixing models to explore geometrical effects. New spectroscopic data are presented for clay minerals (talc, kaolinite, illite and montmorillonite) and soils dominated by these clay minerals in the 1 MHz–6 GHz bandwidth. We also present a new typology for the way water is held in soils that we hope will act as a framework for furthering discussion on sensor design. We found that the frequency-domain response can be mostly accounted for by adjusting model structural parameters, which needs to be conducted to describe the Maxwell–Wagner (MW) relaxation effects. The work supports the importance of accounting for soil structural properties to understand and predict soil dielectric response and ultimately to find models that can describe the dielectric–water content relationship in fine-textured soils measured with sensors. Full article

(This article belongs to the Special Issue Proximal and Remote Soil Sensing Technologies for Multiscale Soil Investigation)

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16 pages, 21700 KiB

Open AccessArticle

Open-Ended Coaxial Probe Measurements of Complex Dielectric Permittivity in Diesel-Contaminated Soil during Bioremediation

by Andrea Vergnano, Alberto Godio, Carla Maria Raffa, Fulvia Chiampo, Jorge A. Tobon Vasquez and Francesca Vipiana

Sensors 2020, 20(22), 6677; https://doi.org/10.3390/s20226677 - 22 Nov 2020

Cited by 11 | Viewed by 3528

Abstract

In the bioremediation field, geophysical techniques are commonly applied, at lab scale and field scale, to perform the characterization and the monitoring of contaminated soils. We propose a method for detecting the dielectric properties of contaminated soil during a process of bioremediation. An [...] Read more.

In the bioremediation field, geophysical techniques are commonly applied, at lab scale and field scale, to perform the characterization and the monitoring of contaminated soils. We propose a method for detecting the dielectric properties of contaminated soil during a process of bioremediation. An open-ended coaxial probe measured the complex dielectric permittivity (between 0.2 and 20 GHz) on a series of six soil microcosms contaminated by diesel oil (13.5% V_oil/V_tot). The microcosms had different moisture content (13%, 19%, and 24% V_w/V_tot) and different salinity due to the addition of nutrients (22 and 15 g/L). The real and the imaginary component of the complex dielectric permittivity were evaluated at the initial stage of contamination and after 130 days. In almost all microcosms, the real component showed a significant decrease (up to 2 units) at all frequencies. The results revealed that the changes in the real part of the dielectric permittivity are related to the amount of degradation and loss in moisture content. The imaginary component, mainly linked to the electrical conductivity of the soil, shows a significant drop to almost 0 at low frequencies. This could be explained by a salt depletion during bioremediation. Despite a moderate accuracy reduction compared to measurements performed on liquid media, this technology can be successfully applied to granular materials such as soil. The open-ended coaxial probe is a promising instrument to check the dielectric properties of soil to characterize or monitor a bioremediation process. Full article

(This article belongs to the Special Issue Electromagnetic and Electrical Methods for Environmental Engineering)

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