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Sensors, Volume 20, Issue 19 (October-1 2020) – 277 articles

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Cover Story (view full-size image) The digitization of the manufacturing industry has led to more efficient production, under the [...] Read more.
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Open AccessArticle
Rough or Noisy? Metrics for Noise Estimation in SfM Reconstructions
Sensors 2020, 20(19), 5725; https://doi.org/10.3390/s20195725 - 08 Oct 2020
Viewed by 344
Abstract
Structure from Motion (SfM) can produce highly detailed 3D reconstructions, but distinguishing real surface roughness from reconstruction noise and geometric inaccuracies has always been a difficult problem to solve. Existing SfM commercial solutions achieve noise removal by a combination of aggressive global smoothing [...] Read more.
Structure from Motion (SfM) can produce highly detailed 3D reconstructions, but distinguishing real surface roughness from reconstruction noise and geometric inaccuracies has always been a difficult problem to solve. Existing SfM commercial solutions achieve noise removal by a combination of aggressive global smoothing and the reconstructed texture for smaller details, which is a subpar solution when the results are used for surface inspection. Other noise estimation and removal algorithms do not take advantage of all the additional data connected with SfM. We propose a number of geometrical and statistical metrics for noise assessment, based on both the reconstructed object and the capturing camera setup. We test the correlation of each of the metrics to the presence of noise on reconstructed surfaces and demonstrate that classical supervised learning methods, trained with these metrics can be used to distinguish between noise and roughness with an accuracy above 85%, with an additional 5–6% performance coming from the capturing setup metrics. Our proposed solution can easily be integrated into existing SfM workflows as it does not require more image data or additional sensors. Finally, as part of the testing we create an image dataset for SfM from a number of objects with varying shapes and sizes, which are available online together with ground truth annotations. Full article
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Open AccessArticle
Study on Propagation Depth of Ultrasonic Longitudinal Critically Refracted (LCR) Wave
Sensors 2020, 20(19), 5724; https://doi.org/10.3390/s20195724 - 08 Oct 2020
Viewed by 335
Abstract
The accurate measurement of stress at different depths in the end face of a high-pressure compressor rotor is particularly important, as it is directly related to the assembly quality and overall performance of aero-engines. The ultrasonic longitudinal critically refracted (LCR) wave is sensitive [...] Read more.
The accurate measurement of stress at different depths in the end face of a high-pressure compressor rotor is particularly important, as it is directly related to the assembly quality and overall performance of aero-engines. The ultrasonic longitudinal critically refracted (LCR) wave is sensitive to stress and can measure stress at different depths, which has a prominent advantage in stress non-destructive measurements. In order to accurately characterize the propagation depth of LCR waves and improve the spatial resolution of stress measurement, a finite element model suitable for the study of LCR wave propagation depths was established based on a wave equation and Snell law, and the generation and propagation process of LCR waves are analyzed. By analyzing the blocking effect of grooves with different depths on the wave, the propagation depth of the LCR wave at seven specific frequencies was determined in turn. On this basis, the LCR wave propagation depth model is established, and the effects of wedge materials, piezoelectric element diameters, and excitation voltages on the propagation depth of LCR waves are discussed. This study is of great significance to improve the spatial resolution of stress measurements at different depths in the end face of the aero-engine rotor. Full article
(This article belongs to the Special Issue Ultrasonic Sensors and Technology for Material Characterization)
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Open AccessLetter
Influence of Nivolumab for Intercellular Adhesion Force between a T Cell and a Cancer Cell Evaluated by AFM Force Spectroscopy
Sensors 2020, 20(19), 5723; https://doi.org/10.3390/s20195723 - 08 Oct 2020
Viewed by 339
Abstract
The influence of nivolumab on intercellular adhesion forces between T cells and cancer cells was evaluated quantitatively using atomic force microscopy (AFM). Two model T cells, one expressing high levels of programmed cell death protein 1 (PD-1) (PD-1high Jurkat) and the other [...] Read more.
The influence of nivolumab on intercellular adhesion forces between T cells and cancer cells was evaluated quantitatively using atomic force microscopy (AFM). Two model T cells, one expressing high levels of programmed cell death protein 1 (PD-1) (PD-1high Jurkat) and the other with low PD-1 expression levels (PD-1low Jurkat), were analyzed. In addition, two model cancer cells, one expressing programmed death-ligand 1 (PD-L1) on the cell surface (PC-9, PD-L1+) and the other without PD-L1 (MCF-7, PD-L1), were also used. A T cell was attached to the apex of the AFM cantilever using a cup-attached AFM chip, and the intercellular adhesion forces were measured. Although PD-1high T cells adhered strongly to PD-L1+ cancer cells, the adhesion force was smaller than that with PD-L1 cancer cells. After the treatment of PD-1high T cells with nivolumab, the adhesion force with PD-L1+ cancer cells increased to a similar level as with PD-L1 cancer cells. These results can be explained by nivolumab influencing the upregulation of the adhesion ability of PD-1high T cells with PD-L1+ cancer cells. These results were obtained by measuring intercellular adhesion forces quantitatively, indicating the usefulness of single-cell AFM analysis. Full article
(This article belongs to the Special Issue Nanosensors for Biomedical Applications)
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Open AccessArticle
Evaluation of Inertial Sensor Data by a Comparison with Optical Motion Capture Data of Guitar Strumming Gestures
Sensors 2020, 20(19), 5722; https://doi.org/10.3390/s20195722 - 08 Oct 2020
Viewed by 288
Abstract
Computing technologies have opened up a myriad of possibilities for expanding the sonic capabilities of acoustic musical instruments. Musicians nowadays employ a variety of rather inexpensive, wireless sensor-based systems to obtain refined control of interactive musical performances in actual musical situations like live [...] Read more.
Computing technologies have opened up a myriad of possibilities for expanding the sonic capabilities of acoustic musical instruments. Musicians nowadays employ a variety of rather inexpensive, wireless sensor-based systems to obtain refined control of interactive musical performances in actual musical situations like live music concerts. It is essential though to clearly understand the capabilities and limitations of such acquisition systems and their potential influence on high-level control of musical processes. In this study, we evaluate one such system composed of an inertial sensor (MetaMotionR) and a hexaphonic nylon guitar for capturing strumming gestures. To characterize this system, we compared it with a high-end commercial motion capture system (Qualisys) typically used in the controlled environments of research laboratories, in two complementary tasks: comparisons of rotational and translational data. For the rotations, we were able to compare our results with those that are found in the literature, obtaining RMSE below 10° for 88% of the curves. The translations were compared in two ways: by double derivation of positional data from the mocap and by double integration of IMU acceleration data. For the task of estimating displacements from acceleration data, we developed a compensative-integration method to deal with the oscillatory character of the strumming, whose approximative results are very dependent on the type of gestures and segmentation; a value of 0.77 was obtained for the average of the normalized covariance coefficients of the displacement magnitudes. Although not in the ideal range, these results point to a clearly acceptable trade-off between the flexibility, portability and low cost of the proposed system when compared to the limited use and cost of the high-end motion capture standard in interactive music setups. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Motion Analysis)
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Open AccessLetter
A Novel Approach for Measurement of Composition and Temperature of N-Decane/Butanol Blends Using Two-Color Laser-Induced Fluorescence of Nile Red
Sensors 2020, 20(19), 5721; https://doi.org/10.3390/s20195721 - 08 Oct 2020
Viewed by 326
Abstract
In this work, the possibility of using a two-color LIF (laser-induced fluorescence) approach for fuel composition and temperature measurements using nile red dissolved in n-decane/butanol blends is investigated. The studies were conducted in a specially designed micro cell enabling the detection of the [...] Read more.
In this work, the possibility of using a two-color LIF (laser-induced fluorescence) approach for fuel composition and temperature measurements using nile red dissolved in n-decane/butanol blends is investigated. The studies were conducted in a specially designed micro cell enabling the detection of the spectral LIF intensities over a wide range of temperatures (283–423 K) and butanol concentrations (0–100 vol.%) in mixtures with n-decane. Furthermore, absorption spectra were analyzed for these fuel mixtures. At constant temperature, the absorption and LIF signals exhibit a large spectral shift toward higher wavelengths with increasing butanol concentration. Based on this fact, a two-color detection approach is proposed that enables the determination of the butanol concentration. This is reasonable when temperature changes and evaporation effects accompanied with dye enrichment can be neglected. For n-decane, no spectral shift and broadening of the spectrum are observed for various temperatures. However, for butanol admixture, two-color thermometry is possible as long as the dye and butanol concentrations are kept constant. For example, the LIF spectrum shows a distinct broadening for B20 (i.e., 80 vol.% n-decane, 20 vol.% butanol) and a shift of the peak toward lower wavelengths of about 40 nm for temperature variations of 140 K. Full article
(This article belongs to the Section Optical Sensors)
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Open AccessArticle
A Smart Sensing and Routing Mechanism for Wireless Sensor Networks
Sensors 2020, 20(19), 5720; https://doi.org/10.3390/s20195720 - 08 Oct 2020
Viewed by 340
Abstract
Wireless sensor networks (WSNs) have long been used for many applications. The efficiency of a WSN is subject to its monitoring accuracy and limited energy capacity. Thus, accurate detection and limited energy are two crucial problems for WSNs. Some studies have focused on [...] Read more.
Wireless sensor networks (WSNs) have long been used for many applications. The efficiency of a WSN is subject to its monitoring accuracy and limited energy capacity. Thus, accurate detection and limited energy are two crucial problems for WSNs. Some studies have focused on building energy-efficient transmission mechanisms to extend monitoring lifetimes, and others have focused on building additional systems to support monitoring for enhanced accuracy. Herein, we propose a distributed cooperative mechanism where neighboring sensors mutually confirm event occurrences for improved monitoring accuracy. Moreover, the mechanism transmits events in a time- and energy-efficient manner by using smart antennae to extend monitoring lifetimes. The results of the simulations reveal that monitoring lifetime is extended and time for event notifications is shortened under the proposed mechanism. The evaluations also demonstrate that the monitoring accuracy of the proposed mechanism is much higher than that of other existing mechanisms. Full article
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Open AccessArticle
Comparative Experiments of V2X Security Protocol Based on Hash Chain Cryptography
Sensors 2020, 20(19), 5719; https://doi.org/10.3390/s20195719 - 08 Oct 2020
Viewed by 372
Abstract
Vehicle-to-everything (V2X) is the communication technology designed to support road safety for drivers and autonomous driving. The light-weight security solution is crucial to meet the real-time needs of on-board V2X applications. However, most of the recently proposed V2X security protocols—based on the Elliptic [...] Read more.
Vehicle-to-everything (V2X) is the communication technology designed to support road safety for drivers and autonomous driving. The light-weight security solution is crucial to meet the real-time needs of on-board V2X applications. However, most of the recently proposed V2X security protocols—based on the Elliptic Curve Digital Signature Algorithm (ECDSA)—are not efficient enough to support fast processing and reduce the communication overhead between vehicles. ECDSA provides a high-security level at the cost of excessive communication and computation overhead, which motivates us to propose a light-weight message authentication and privacy preservation protocol for V2X communications. The proposed protocol achieves highly secure message authentication at a substantially lower cost by introducing a hash chain of secret keys for a Message Authentication Code (MAC). We implemented the proposed protocol using commercial V2X devices to prove its performance advantages over the standard and non-standard protocols. We constructed real V2X networks using commercial V2X devices that run our implemented protocol. Our extensive experiments with real networks demonstrate that the proposed protocol reduces the communication overhead by 6 times and computation overhead by more than 100 times compared with the IEEE1609.2 standard. Moreover, the proposed protocol reduces the communication overhead by 4 times and the computation overhead by up to 100 times compared with a non-standard security protocol, TESLA. The proposed protocol substantially reduces the average end-to-end delay to 2.5 ms, which is a 24- and 28-fold reduction, respectively, compared with the IEEE1609 and TESLA protocols. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessFeature PaperArticle
Network Optimisation and Performance Analysis of a Multistatic Acoustic Navigation Sensor
Sensors 2020, 20(19), 5718; https://doi.org/10.3390/s20195718 - 08 Oct 2020
Viewed by 304
Abstract
This paper addresses some of the existing research gaps in the practical use of acoustic waves for navigation of autonomous air and surface vehicles. After providing a characterisation of ultrasonic transducers, a multistatic sensor arrangement is discussed, with multiple transmitters broadcasting their respective [...] Read more.
This paper addresses some of the existing research gaps in the practical use of acoustic waves for navigation of autonomous air and surface vehicles. After providing a characterisation of ultrasonic transducers, a multistatic sensor arrangement is discussed, with multiple transmitters broadcasting their respective signals in a round-robin fashion, following a time division multiple access (TDMA) scheme. In particular, an optimisation methodology for the placement of transmitters in a given test volume is presented with the objective of minimizing the position dilution of precision (PDOP) and maximizing the sensor availability. Additionally, the contribution of platform dynamics to positioning error is also analysed in order to support future ground and flight vehicle test activities. Results are presented of both theoretical and experimental data analysis performed to determine the positioning accuracy attainable from the proposed multistatic acoustic navigation sensor. In particular, the ranging errors due to signal delays and attenuation of sound waves in air are analytically derived, and static indoor positioning tests are performed to determine the positioning accuracy attainable with different transmitter–receiver-relative geometries. Additionally, it is shown that the proposed transmitter placement optimisation methodology leads to increased accuracy and better coverage in an indoor environment, where the required position, velocity, and time (PVT) data cannot be delivered by satellite-based navigation systems. Full article
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Open AccessArticle
Mapping Small-Scale Horizontal Velocity Field in Panzhinan Waterway by Coastal Acoustic Tomography
Sensors 2020, 20(19), 5717; https://doi.org/10.3390/s20195717 - 08 Oct 2020
Viewed by 273
Abstract
Mapping small-scale high-precision velocity fields is of great significance to oceanic environment research. Coastal acoustic tomography (CAT) is a frontier technology used to observe large-scale velocity field in the horizontal slice. Nonetheless, it is difficult to observe the velocity field using the CAT [...] Read more.
Mapping small-scale high-precision velocity fields is of great significance to oceanic environment research. Coastal acoustic tomography (CAT) is a frontier technology used to observe large-scale velocity field in the horizontal slice. Nonetheless, it is difficult to observe the velocity field using the CAT in small-scale areas, specifically where the flow field is complex such as ocean ranch and artificial upwelling areas. This paper conducted a sound transmission experiment using four 50 kHz CAT systems in the Panzhinan waterway. Notably, sound transmission based on the round-robin method was recommended for small-scale CAT observation. The travel time between stations, obtained by correlation of raw data, was applied to reconstruct the horizontal velocity fields using Tapered Least Square inversion. The minimum net volume transport was 8.7 m3/s at 12:32, 1.63% of the total inflow volume transport indicating that the observational errors were acceptable. The relative errors of the range-average velocity calculated by differential travel time were 1.54% (path 2) and 0.92% (path 6), respectively. Moreover, the inversion velocity root-mean-square errors (RMSEs) were 0.5163, 0.1494, 0.2103, 0.2804 and 0.2817 m/s for paths 1, 2, 3, 4 and 6, respectively. The feasibility and acceptable accuracy of the CAT method in the small-scale velocity profiling measurement were validated. Furthermore, a three-dimensional (3-D) velocity field mapping should be performed with combined analysis in horizontal and vertical slices. Full article
(This article belongs to the Special Issue Intelligent Sound Measurement Sensor and System)
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Open AccessReview
A Review of Microelectronic Systems and Circuit Techniques for Electrical Neural Recording Aimed at Closed-Loop Epilepsy Control
Sensors 2020, 20(19), 5716; https://doi.org/10.3390/s20195716 - 08 Oct 2020
Viewed by 283
Abstract
Closed-loop implantable electronics offer a new trend in therapeutic systems aimed at controlling some neurological diseases such as epilepsy. Seizures are detected and electrical stimulation applied to the brain or groups of nerves. To this aim, the signal recording chain must be very [...] Read more.
Closed-loop implantable electronics offer a new trend in therapeutic systems aimed at controlling some neurological diseases such as epilepsy. Seizures are detected and electrical stimulation applied to the brain or groups of nerves. To this aim, the signal recording chain must be very carefully designed so as to operate in low-power and low-latency, while enhancing the probability of correct event detection. This paper reviews the electrical characteristics of the target brain signals pertaining to epilepsy detection. Commercial systems are presented and discussed. Finally, the major blocks of the signal acquisition chain are presented with a focus on the circuit architecture and a careful attention to solutions to issues related to data acquisition from multi-channel arrays of cortical sensors. Full article
(This article belongs to the Special Issue Integrated Circuits and Systems for Smart Sensory Applications)
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Open AccessArticle
Determination of the Inaccuracies of Calculated EEG Indices
Sensors 2020, 20(19), 5715; https://doi.org/10.3390/s20195715 - 08 Oct 2020
Viewed by 299
Abstract
The data obtained as a result of an EEG measurement are burdened with inaccuracies related to the measurement process itself and the need to remove recorded disturbances. The article presents an example of how to calculate the Approach-Withdraw Index (EEG-AW) and Memorization Index [...] Read more.
The data obtained as a result of an EEG measurement are burdened with inaccuracies related to the measurement process itself and the need to remove recorded disturbances. The article presents an example of how to calculate the Approach-Withdraw Index (EEG-AW) and Memorization Index (MI) indices in such a way that their inaccuracy resulting from the removal of artifacts can be periodically calculated. This inaccuracy is expressed in terms of standard deviation. This allows you to determine the reliability of the obtained conclusions in the context of examining elements in a 2D computer game created in the Unity engine. Full article
(This article belongs to the Section Wearables)
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Open AccessArticle
The Effect of Deflections and Elastic Deformations on Geometrical Deviation and Shape Profile Measurements of Large Crankshafts with Uncontrolled Supports
Sensors 2020, 20(19), 5714; https://doi.org/10.3390/s20195714 - 08 Oct 2020
Viewed by 299
Abstract
This article presents a multi-criteria analysis of the errors that may occur while measuring the geometric deviations of crankshafts that require multi-point support. The analysis included in the paper confirmed that the currently used conventional support method—in which the journals of large crankshafts [...] Read more.
This article presents a multi-criteria analysis of the errors that may occur while measuring the geometric deviations of crankshafts that require multi-point support. The analysis included in the paper confirmed that the currently used conventional support method—in which the journals of large crankshafts rest on a set of fixed rigid vee-blocks—significantly limits the detectability of their geometric deviations, especially those of the main journal axes’ positions. Insights for performing practical measurements, which will improve measurement procedures and increase measurement accuracy, are provided. The results are presented both graphically and as discrete amplitude spectra to make a visual, qualitative comparison, which is complemented by a quantitative assessment based on correlation analysis. Full article
(This article belongs to the Special Issue Measurement Methods in the Operation of Ships and Offshore Facilities)
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Open AccessArticle
A Compact and Flexible UHF RFID Tag Antenna for Massive IoT Devices in 5G System
Sensors 2020, 20(19), 5713; https://doi.org/10.3390/s20195713 - 08 Oct 2020
Viewed by 328
Abstract
Upcoming 5th-generation (5G) systems incorporate physical objects (referred to as things), which sense the presence of components such as gears, gadgets, and sensors. They may transmit many kinds of states in the smart city context, such as new deals at malls, safe distances [...] Read more.
Upcoming 5th-generation (5G) systems incorporate physical objects (referred to as things), which sense the presence of components such as gears, gadgets, and sensors. They may transmit many kinds of states in the smart city context, such as new deals at malls, safe distances on roads, patient heart rhythms (especially in hospitals), and logistic control at aerodromes and seaports around the world. These serve to form the so-called future internet of things (IoT). From this futuristic perspective, everything should have its own identity. In this context, radio frequency identification (RFID) plays a specific role, which provides wireless communications in a secure manner. Passive RFID tags carry out work using the energy harvested among massive systems. RFID has been habitually realized as a prerequisite for IoT, the combination of which is called IoT RFID (I-RFID). For the current scenario, such tags should be productive, low-profile, compact, easily mountable, and have eco-friendly features. The presently available tags are not cost-effective and have not been proven as green tags for environmentally friendly IoT in 5G systems nor are they suitable for long-range communications in 5G systems. The proposed I-RFID tag uses the meandering angle technique (MAT) to construct a design that satisfies the features of a lower-cost printed antenna over the worldwide UHF RFID band standard (860–960 MHz). In our research, tag MAT antennas are fabricated on paper-based Korsnäs by screen- and flexo-printing, which have lowest simulated effective outcomes with dielectric variation due to humidity and have a plausible read range (RR) for European (EU; 866–868 MHz) and North American (NA; 902–928 MHz) UHF band standards. The I-RFID tag size is reduced by 36% to 38% w.r.t. a previously published case, the tag gain has been improved by 23.6% to 33.12%, and its read range has been enhanced by 50.9% and 59.6% for EU and NA UHF bands, respectively. It provides impressive performance on some platforms (e.g., plastic, paper, and glass), thereby providing a new state-of-the-art I-RFID tag with better qualities in 5G systems. Full article
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Open AccessArticle
Determining UAV Flight Trajectory for Target Recognition Using EO/IR and SAR
Sensors 2020, 20(19), 5712; https://doi.org/10.3390/s20195712 - 08 Oct 2020
Viewed by 329
Abstract
The paper presents the concept of planning the optimal trajectory of fixed-wing unmanned aerial vehicle (UAV) of a short-range tactical class, whose task is to recognize a set of ground objects as a part of a reconnaissance mission. Tasks carried out by such [...] Read more.
The paper presents the concept of planning the optimal trajectory of fixed-wing unmanned aerial vehicle (UAV) of a short-range tactical class, whose task is to recognize a set of ground objects as a part of a reconnaissance mission. Tasks carried out by such systems are mainly associated with an aerial reconnaissance using Electro-Optical/Infrared (EO/IR) systems and Synthetic Aperture Radars (SARs) to support military operations. Execution of a professional reconnaissance of the indicated objects requires determining the UAV flight trajectory in the close neighborhood of the target, in order to collect as much interesting information as possible. The paper describes the algorithm for determining UAV flight trajectories, which is tasked with identifying the indicated objectives using the sensors specified in the order. The presence of UAV threatening objects is taken into account. The task of determining the UAV flight trajectory for recognition of the target is a component of the planning process of the tactical class UAV mission, which is also presented in the article. The problem of determining the optimal UAV trajectory has been decomposed into several subproblems: determining the reconnaissance flight method in the vicinity of the currently recognized target depending on the sensor used and the required parameters of the recognition product (photo, film, or SAR scan), determining the initial possible flight trajectory that takes into account potential UAV threats, and planning detailed flight trajectory considering the parameters of the air platform based on the maneuver planning algorithm designed for tactical class platforms. UAV route planning algorithms with time constraints imposed on the implementation of individual tasks were used to solve the task of determining UAV flight trajectories. The problem was formulated in the form of a Mixed Integer Linear Problem (MILP) model. For determining the flight path in the neighborhood of the target, the optimal control algorithm was also presented in the form of a MILP model. The determined trajectory is then corrected based on the construction algorithm for determining real UAV flight segments based on Dubin curves. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle
Real-Time Impedance Monitoring of Epithelial Cultures with Inkjet-Printed Interdigitated-Electrode Sensors
Sensors 2020, 20(19), 5711; https://doi.org/10.3390/s20195711 - 08 Oct 2020
Viewed by 340
Abstract
From electronic devices to large-area electronics, from individual cells to skin substitutes, printing techniques are providing compelling applications in wide-ranging fields. Research has thus fueled the vision of a hybrid, printing platform to fabricate sensors/electronics and living engineered tissues simultaneously. Following this interest, [...] Read more.
From electronic devices to large-area electronics, from individual cells to skin substitutes, printing techniques are providing compelling applications in wide-ranging fields. Research has thus fueled the vision of a hybrid, printing platform to fabricate sensors/electronics and living engineered tissues simultaneously. Following this interest, we have fabricated interdigitated-electrode sensors (IDEs) by inkjet printing to monitor epithelial cell cultures. We have fabricated IDEs using flexible substrates with silver nanoparticles as a conductive element and SU-8 as the passivation layer. Our sensors are cytocompatible, have a topography that simulates microgrooves of 300 µm width and ~4 µm depth, and can be reused for cellular studies without detrimental in the electrical performance. To test the inkjet-printed sensors and demonstrate their potential use for monitoring laboratory-growth skin tissues, we have developed a real-time system and monitored label-free proliferation, migration, and detachment of keratinocytes by impedance spectroscopy. We have found that variations in the impedance correlate linearly to cell densities initially seeded and that the main component influencing the total impedance is the isolated effect of the cell membranes. Results obtained show that impedance can track cellular migration over the surface of the sensors, exhibiting a linear relationship with the standard method of image processing. Our results provide a useful approach for non-destructive in-situ monitoring of processes related to both in vitro epidermal models and wound healing with low-cost ink-jetted sensors. This type of flexible sensor as well as the impedance method are promising for the envisioned hybrid technology of 3D-bioprinted smart skin substitutes with built-in electronics. Full article
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Open AccessArticle
A Hybrid Intrusion Detection Model Combining SAE with Kernel Approximation in Internet of Things
Sensors 2020, 20(19), 5710; https://doi.org/10.3390/s20195710 - 08 Oct 2020
Viewed by 288
Abstract
Owing to the constraints of time and space complexity, network intrusion detection systems (NIDSs) based on support vector machines (SVMs) face the “curse of dimensionality” in a large-scale, high-dimensional feature space. This study proposes a joint training model that combines a stacked autoencoder [...] Read more.
Owing to the constraints of time and space complexity, network intrusion detection systems (NIDSs) based on support vector machines (SVMs) face the “curse of dimensionality” in a large-scale, high-dimensional feature space. This study proposes a joint training model that combines a stacked autoencoder (SAE) with an SVM and the kernel approximation technique. The training model uses the SAE to perform feature dimension reduction, uses random Fourier features to perform kernel approximation, and then random Fourier mapping is explicitly applied to the sub-sample to generate the random feature space, making it possible to apply a linear SVM to uniformly approximate to the Gaussian kernel SVM. Finally, the SAE performs joint training with the efficient linear SVM. We studied the effects of an SAE structure and a random Fourier feature on classification performance, and compared that performance with that of other training models, including some without kernel approximation. At the same time, we compare the accuracy of the proposed model with that of other models, which include basic machine learning models and the state-of-the-art models in other literatures. The experimental results demonstrate that the proposed model outperforms the previously proposed methods in terms of classification performance and also reduces the training time. Our model is feasible and works efficiently on large-scale datasets. Full article
(This article belongs to the Section Internet of Things)
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Open AccessArticle
Force Shadows: An Online Method to Estimate and Distribute Vertical Ground Reaction Forces from Kinematic Data
Sensors 2020, 20(19), 5709; https://doi.org/10.3390/s20195709 - 08 Oct 2020
Viewed by 374
Abstract
Kinetic models of human motion rely on boundary conditions which are defined by the interaction of the body with its environment. In the simplest case, this interaction is limited to the foot contact with the ground and is given by the so called [...] Read more.
Kinetic models of human motion rely on boundary conditions which are defined by the interaction of the body with its environment. In the simplest case, this interaction is limited to the foot contact with the ground and is given by the so called ground reaction force (GRF). A major challenge in the reconstruction of GRF from kinematic data is the double support phase, referring to the state with multiple ground contacts. In this case, the GRF prediction is not well defined. In this work we present an approach to reconstruct and distribute vertical GRF (vGRF) to each foot separately, using only kinematic data. We propose the biomechanically inspired force shadow method (FSM) to obtain a unique solution for any contact phase, including double support, of an arbitrary motion. We create a kinematic based function, model an anatomical foot shape and mimic the effect of hip muscle activations. We compare our estimations with the measurements of a Zebris pressure plate and obtain correlations of 0.39r0.94 for double support motions and 0.83r0.87 for a walking motion. The presented data is based on inertial human motion capture, showing the applicability for scenarios outside the laboratory. The proposed approach has low computational complexity and allows for online vGRF estimation. Full article
(This article belongs to the Special Issue Sensor-Based Systems for Kinematics and Kinetics)
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Open AccessArticle
Condition-Based Maintenance with Reinforcement Learning for Dry Gas Pipeline Subject to Internal Corrosion
Sensors 2020, 20(19), 5708; https://doi.org/10.3390/s20195708 - 07 Oct 2020
Cited by 1 | Viewed by 384
Abstract
Gas pipeline systems are one of the largest energy infrastructures in the world and are known to be very efficient and reliable. However, this does not mean they are prone to no risk. Corrosion is a significant problem in gas pipelines that imposes [...] Read more.
Gas pipeline systems are one of the largest energy infrastructures in the world and are known to be very efficient and reliable. However, this does not mean they are prone to no risk. Corrosion is a significant problem in gas pipelines that imposes large risks such as ruptures and leakage to the environment and the pipeline system. Therefore, various maintenance actions are performed routinely to ensure the integrity of the pipelines. The costs of the corrosion-related maintenance actions are a significant portion of the pipeline’s operation and maintenance costs, and minimizing this large cost is a highly compelling subject that has been addressed by many studies. In this paper, we investigate the benefits of applying reinforcement learning (RL) techniques to the corrosion-related maintenance management of dry gas pipelines. We first address the rising need for a simulated testbed by proposing a test bench that models corrosion degradation while interacting with the maintenance decision-maker within the RL environment. Second, we propose a condition-based maintenance management approach that leverages a data-driven RL decision-making methodology. An RL maintenance scheduler is applied to the proposed test bench, and the results show that applying the proposed condition-based maintenance management technique can reduce up to 58% of the maintenance costs compared to a periodic maintenance policy while securing pipeline reliability. Full article
(This article belongs to the Special Issue The Application of Sensors in Fault Diagnosis and Prognosis)
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Open AccessLetter
A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition
Sensors 2020, 20(19), 5707; https://doi.org/10.3390/s20195707 - 07 Oct 2020
Viewed by 450
Abstract
Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Human Activity Recognition (HAR) is one of the fields that has seen an explosive research interest among the ML community due to its [...] Read more.
Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Human Activity Recognition (HAR) is one of the fields that has seen an explosive research interest among the ML community due to its wide range of applications. HAR is one of the most helpful technology tools to support the elderly’s daily life and to help people suffering from cognitive disorders, Parkinson’s disease, dementia, etc. It is also very useful in areas such as transportation, robotics and sports. Deep learning (DL) is a branch of ML based on complex Artificial Neural Networks (ANNs) that has demonstrated a high level of accuracy and performance in HAR. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two types of DL models widely used in the recent years to address the HAR problem. The purpose of this paper is to investigate the effectiveness of their integration in recognizing daily activities, e.g., walking. We analyze four hybrid models that integrate CNNs with four powerful RNNs, i.e., LSTMs, BiLSTMs, GRUs and BiGRUs. The outcomes of our experiments on the PAMAP2 dataset indicate that our proposed hybrid models achieve an outstanding level of performance with respect to several indicative measures, e.g., F-score, accuracy, sensitivity, and specificity. Full article
(This article belongs to the Special Issue Sensors for Activity Recognition)
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Open AccessArticle
Accuracy–Power Controllable LiDAR Sensor System with 3D Object Recognition for Autonomous Vehicle
Sensors 2020, 20(19), 5706; https://doi.org/10.3390/s20195706 - 07 Oct 2020
Viewed by 381
Abstract
Light detection and ranging (LiDAR) sensors help autonomous vehicles detect the surrounding environment and the exact distance to an object’s position. Conventional LiDAR sensors require a certain amount of power consumption because they detect objects by transmitting lasers at a regular interval according [...] Read more.
Light detection and ranging (LiDAR) sensors help autonomous vehicles detect the surrounding environment and the exact distance to an object’s position. Conventional LiDAR sensors require a certain amount of power consumption because they detect objects by transmitting lasers at a regular interval according to a horizontal angular resolution (HAR). However, because the LiDAR sensors, which continuously consume power inefficiently, have a fatal effect on autonomous and electric vehicles using battery power, power consumption efficiency needs to be improved. In this paper, we propose algorithms to improve the inefficient power consumption of conventional LiDAR sensors, and efficiently reduce power consumption in two ways: (a) controlling the HAR to vary the laser transmission period (TP) of a laser diode (LD) depending on the vehicle’s speed and (b) reducing the static power consumption using a sleep mode, depending on the surrounding environment. The proposed LiDAR sensor with the HAR control algorithm reduces the power consumption of the LD by 6.92% to 32.43% depending on the vehicle’s speed, compared to the maximum number of laser transmissions (Nx.max). The sleep mode with a surrounding environment-sensing algorithm reduces the power consumption by 61.09%. The algorithm of the proposed LiDAR sensor was tested on a commercial processor chip, and the integrated processor was designed as an IC using the Global Foundries 55 nm CMOS process. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle
Does the Position of Foot-Mounted IMU Sensors Influence the Accuracy of Spatio-Temporal Parameters in Endurance Running?
Sensors 2020, 20(19), 5705; https://doi.org/10.3390/s20195705 - 07 Oct 2020
Viewed by 387
Abstract
Wearable sensor technology already has a great impact on the endurance running community. Smartwatches and heart rate monitors are heavily used to evaluate runners’ performance and monitor their training progress. Additionally, foot-mounted inertial measurement units (IMUs) have drawn the attention of sport scientists [...] Read more.
Wearable sensor technology already has a great impact on the endurance running community. Smartwatches and heart rate monitors are heavily used to evaluate runners’ performance and monitor their training progress. Additionally, foot-mounted inertial measurement units (IMUs) have drawn the attention of sport scientists due to the possibility to monitor biomechanically relevant spatio-temporal parameters outside the lab in real-world environments. Researchers developed and investigated algorithms to extract various features using IMU data of different sensor positions on the foot. In this work, we evaluate whether the sensor position of IMUs mounted to running shoes has an impact on the accuracy of different spatio-temporal parameters. We compare both the raw data of the IMUs at different sensor positions as well as the accuracy of six endurance running-related parameters. We contribute a study with 29 subjects wearing running shoes equipped with four IMUs on both the left and the right shoes and a motion capture system as ground truth. The results show that the IMUs measure different raw data depending on their position on the foot and that the accuracy of the spatio-temporal parameters depends on the sensor position. We recommend to integrate IMU sensors in a cavity in the sole of a running shoe under the foot’s arch, because the raw data of this sensor position is best suitable for the reconstruction of the foot trajectory during a stride. Full article
(This article belongs to the Special Issue Technologies for Sports Engineering and Analytics)
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Open AccessLetter
Detection of Small Magnetic Fields Using Serial Magnetic Tunnel Junctions with Various Geometrical Characteristics
Sensors 2020, 20(19), 5704; https://doi.org/10.3390/s20195704 - 07 Oct 2020
Viewed by 333
Abstract
Thanks to their high magnetoresistance and integration capability, magnetic tunnel junction-based magnetoresistive sensors are widely utilized to detect weak, low-frequency magnetic fields in a variety of applications. The low detectivity of MTJs is necessary to obtain a high signal-to-noise ratio when detecting small [...] Read more.
Thanks to their high magnetoresistance and integration capability, magnetic tunnel junction-based magnetoresistive sensors are widely utilized to detect weak, low-frequency magnetic fields in a variety of applications. The low detectivity of MTJs is necessary to obtain a high signal-to-noise ratio when detecting small variations in magnetic fields. We fabricated serial MTJ-based sensors with various junction area and free-layer electrode aspect ratios. Our investigation showed that their sensitivity and noise power are affected by the MTJ geometry due to the variation in the magnetic shape anisotropy. Their MR curves demonstrated a decrease in sensitivity with an increase in the aspect ratio of the free-layer electrode, and their noise properties showed that MTJs with larger junction areas exhibit lower noise spectral density in the low-frequency region. All of the sensors were able detect a small AC magnetic field (Hrms = 0.3 Oe at 23 Hz). Among the MTJ sensors we examined, the sensor with a square-free layer and large junction area exhibited a high signal-to-noise ratio (4792 ± 646). These results suggest that MTJ geometrical characteristics play a critical role in enhancing the detectivity of MTJ-based sensors. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle
Multibeam Characteristics of a Negative Refractive Index Shaped Lens
Sensors 2020, 20(19), 5703; https://doi.org/10.3390/s20195703 - 07 Oct 2020
Viewed by 239
Abstract
Narrow beam width, higher gain and multibeam characteristics are demanded in 5G technology. Array antennas that are utilized in the existing mobile base stations have many drawbacks when operating at upper 5G frequency bands. For example, due to the high frequency operation, the [...] Read more.
Narrow beam width, higher gain and multibeam characteristics are demanded in 5G technology. Array antennas that are utilized in the existing mobile base stations have many drawbacks when operating at upper 5G frequency bands. For example, due to the high frequency operation, the antenna elements become smaller and thus, in order to provide higher gain, more antenna elements and arrays are required, which will cause the feeding network design to be more complex. The lens antenna is one of the potential candidates to replace the current structure in mobile base station. Therefore, a negative refractive index shaped lens is proposed to provide high gain and narrow beamwidth using energy conservation and Abbe’s sine principle. The aim of this study is to investigate the multibeam characteristics of a negative refractive index shaped lens in mobile base station applications. In this paper, the feed positions for the multibeam are selected on the circle from the center of the lens and the accuracy of the feed position is validated through Electromagnetic (EM) simulation. Based on the analysis performed in this study, a negative refractive index shaped lens with a smaller radius and slender lens than the conventional lens is designed, with the additional capability of performing wide-angle beam scanning. Full article
(This article belongs to the Special Issue Antenna Design for 5G and Beyond)
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Open AccessArticle
GNSS-Based Non-Negative Absolute Ionosphere Total Electron Content, its Spatial Gradients, Time Derivatives and Differential Code Biases: Bounded-Variable Least-Squares and Taylor Series
Sensors 2020, 20(19), 5702; https://doi.org/10.3390/s20195702 - 07 Oct 2020
Viewed by 211
Abstract
Global navigation satellite systems (GNSS) allow estimating total electron content (TEC). However, it is still a problem to calculate absolute ionosphere parameters from GNSS data: negative TEC values could appear, and most of existing algorithms does not enable to estimate TEC spatial gradients [...] Read more.
Global navigation satellite systems (GNSS) allow estimating total electron content (TEC). However, it is still a problem to calculate absolute ionosphere parameters from GNSS data: negative TEC values could appear, and most of existing algorithms does not enable to estimate TEC spatial gradients and TEC time derivatives. We developed an algorithm to recover the absolute non-negative vertical and slant TEC, its derivatives and its gradients, as well as the GNSS equipment differential code biases (DCBs) by using the Taylor series expansion and bounded-variable least-squares. We termed this algorithm TuRBOTEC. Bounded-variable least-squares fitting ensures non-negative values of both slant TEC and vertical TEC. The second order Taylor series expansion could provide a relevant TEC spatial gradients and TEC time derivatives. The technique validation was performed by using independent experimental data over 2014 and the IRI-2012 and IRI-plas models. As a TEC source we used Madrigal maps, CODE (the Center for Orbit Determination in Europe) global ionosphere maps (GIM), the IONOLAB software, and the SEEMALA-TEC software developed by Dr. Seemala. For the Asian mid-latitudes TuRBOTEC results agree with the GIM and IONOLAB data (root-mean-square was < 3 TECU), but they disagree with the SEEMALA-TEC and Madrigal data (root-mean-square was >10 TECU). About 9% of vertical TECs from the TuRBOTEC estimates exceed (by more than 1 TECU) those from the same algorithm but without constraints. The analysis of TEC spatial gradients showed that as far as 10–15° on latitude, TEC estimation error exceeds 10 TECU. Longitudinal gradients produce smaller error for the same distance. Experimental GLObal Navigation Satellite System (GLONASS) DCB from TuRBOTEC and CODE peaked 15 TECU difference, while GPS DCB agrees. Slant TEC series indicate that the TuRBOTEC data for GLONASS are physically more plausible. Full article
(This article belongs to the Special Issue GNSS Signals and Sensors)
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Open AccessArticle
Defect-Induced Gas-Sensing Properties of a Flexible SnS Sensor under UV Illumination at Room Temperature
Sensors 2020, 20(19), 5701; https://doi.org/10.3390/s20195701 - 07 Oct 2020
Viewed by 287
Abstract
Tin sulfide (SnS) is known for its effective gas-detecting ability at low temperatures. However, the development of a portable and flexible SnS sensor is hindered by its high resistance, low response, and long recovery time. Like other chalcogenides, the electronic and gas-sensing properties [...] Read more.
Tin sulfide (SnS) is known for its effective gas-detecting ability at low temperatures. However, the development of a portable and flexible SnS sensor is hindered by its high resistance, low response, and long recovery time. Like other chalcogenides, the electronic and gas-sensing properties of SnS strongly depend on its surface defects. Therefore, understanding the effects of its surface defects on its electronic and gas-sensing properties is a key factor in developing low-temperature SnS gas sensors. Herein, using thin SnS films annealed at different temperatures, we demonstrate that SnS exhibits n-type semiconducting behavior upon the appearance of S vacancies. Furthermore, the presence of S vacancies imparts the n-type SnS sensor with better sensing performance under UV illumination at room temperature (25 °C) than that of a p-type SnS sensor. These results are thoroughly investigated using various experimental analysis techniques and theoretical calculations using density functional theory. In addition, n-type SnS deposited on a polyimide substrate can be used to fabricate high-stability flexible sensors, which can be further developed for real applications. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessLetter
Interference Spreading through Random Subcarrier Allocation Technique and Its Error Rate Performance in Cognitive Radio Networks
Sensors 2020, 20(19), 5700; https://doi.org/10.3390/s20195700 - 07 Oct 2020
Viewed by 233
Abstract
In this letter, we investigate the idea of interference spreading and its effect on bit error rate (BER) performance in a cognitive radio network (CRN). The interference spreading phenomenon is caused because of the random allocation of subcarriers in an orthogonal frequency division [...] Read more.
In this letter, we investigate the idea of interference spreading and its effect on bit error rate (BER) performance in a cognitive radio network (CRN). The interference spreading phenomenon is caused because of the random allocation of subcarriers in an orthogonal frequency division multiplexing (OFDM)-based CRN without any spectrum-sensing mechanism. The CRN assumed in this work is of underlay configuration, where the frequency bands are accessed concurrently by both primary users (PUs) and secondary users (SUs). With random allocation, subcarrier collisions occur among the carriers of primary users (PUs) and secondary users (SUs), leading to interference among subcarriers. This interference caused by subcarrier collisions spreads out across multiple subcarriers of PUs rather than on an individual PU, therefore avoiding high BER for an individual PU. Theoretical and simulated signal to interference and noise ratio (SINR) for collision and no-collision cases are validated for M-quadrature amplitude modulation (M-QAM) techniques. Similarly, theoretical BER performance expressions are found and compared for M-QAM modulation orders under Rayleigh fading channel conditions. The BER for different modulation orders of M-QAM are compared and the relationship of average BER with interference temperature is also explored further. Full article
(This article belongs to the Special Issue Advances in Cognitive Radio Networks)
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Open AccessReview
Vital Sign Monitoring in Car Seats Based on Electrocardiography, Ballistocardiography and Seismocardiography: A Review
Sensors 2020, 20(19), 5699; https://doi.org/10.3390/s20195699 - 06 Oct 2020
Viewed by 395
Abstract
This paper focuses on a thorough summary of vital function measuring methods in vehicles. The focus of this paper is to summarize and compare already existing methods integrated into car seats with the implementation of inter alia capacitive electrocardiogram (cECG), mechanical motion analysis [...] Read more.
This paper focuses on a thorough summary of vital function measuring methods in vehicles. The focus of this paper is to summarize and compare already existing methods integrated into car seats with the implementation of inter alia capacitive electrocardiogram (cECG), mechanical motion analysis Ballistocardiography (BCG) and Seismocardiography (SCG). In addition, a comprehensive overview of other methods of vital sign monitoring, such as camera-based systems or steering wheel sensors, is also presented in this article. Furthermore, this work contains a very thorough background study on advanced signal processing methods and their potential application for the purpose of vital sign monitoring in cars, which is prone to various disturbances and artifacts occurrence that have to be eliminated. Full article
(This article belongs to the Section Biomedical Sensors)
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Open AccessArticle
A Pilot Study of the Impact of Microwave Ablation on the Dielectric Properties of Breast Tissue
Sensors 2020, 20(19), 5698; https://doi.org/10.3390/s20195698 - 06 Oct 2020
Viewed by 306
Abstract
Percutaneous microwave ablation (MWA) is a promising technology for patients with breast cancer, as it may help treat individuals who have less aggressive cancers or do not respond to targeted therapies in the neoadjuvant or pre-surgical setting. In this study, we investigate changes [...] Read more.
Percutaneous microwave ablation (MWA) is a promising technology for patients with breast cancer, as it may help treat individuals who have less aggressive cancers or do not respond to targeted therapies in the neoadjuvant or pre-surgical setting. In this study, we investigate changes to the microwave dielectric properties of breast tissue that are induced by MWA. While similar changes have been characterized for relatively homogeneous tissues, such as liver, those prior results are not directly translatable to breast tissue because of the extreme tissue heterogeneity present in the breast. This study was motivated, in part by the expectation that the changes in the dielectric properties of the microwave antenna’s operation environment will be impacted by tissue composition of the ablation target, which includes not only the tumor, but also its margins. Accordingly, this target comprises a heterogeneous mix of malignant, healthy glandular, and adipose tissue. Therefore, knowledge of MWA impact on breast dielectric properties is essential for the successful development of MWA systems for breast cancer. We performed ablations in 14 human ex-vivo prophylactic mastectomy specimens from surgeries that were conducted at the UW Hospital and monitored the temperature in the vicinity of the MWA antenna during ablation. After ablation we measured the dielectric properties of the tissue and analyzed the tissue samples to determine both the tissue composition and the extent of damage due to the ablation. We observed that MWA induced cell damage across all tissue compositions, and found that the microwave frequency-dependent relative permittivity and conductivity of damaged tissue are lower than those of healthy tissue, especially for tissue with high fibroglandular content. The results provide information for future developments on breast MWA systems. Full article
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Open AccessArticle
Clustering-Based Component Fraction Estimation in Solid–Liquid Two-Phase Flow in Dredging Engineering
Sensors 2020, 20(19), 5697; https://doi.org/10.3390/s20195697 - 06 Oct 2020
Viewed by 250
Abstract
Component fraction (CF) is one of the most important parameters in multiple-phase flow. Due to the complexity of the solid–liquid two-phase flow, the CF estimation remains unsolved both in scientific research and industrial application for a long time. Electrical resistance tomography (ERT) is [...] Read more.
Component fraction (CF) is one of the most important parameters in multiple-phase flow. Due to the complexity of the solid–liquid two-phase flow, the CF estimation remains unsolved both in scientific research and industrial application for a long time. Electrical resistance tomography (ERT) is an advanced type of conductivity detection technique due to its low-cost, fast-response, non-invasive, and non-radiation characteristics. However, when the existing ERT method is used to measure the CF value in solid–liquid two-phase flow in dredging engineering, there are at least three problems: (1) the dependence of reference distribution whose CF value is zero; (2) the size of the detected objects may be too small to be found by ERT; and (3) there is no efficient way to estimate the effect of artifacts in ERT. In this paper, we proposed a method based on the clustering technique, where a fast-fuzzy clustering algorithm is used to partition the ERT image to three clusters that respond to liquid, solid phases, and their mixtures and artifacts, respectively. The clustering algorithm does not need any reference distribution in the CF estimation. In the case of small solid objects or artifacts, the CF value remains effectively computed by prior information. To validate the new method, a group of typical CF estimations in dredging engineering were implemented. Results show that the new method can effectively overcome the limitations of the existing method, and can provide a practical and more accurate way for CF estimation. Full article
(This article belongs to the Special Issue Selected papers from ISMTMF-2019)
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Open AccessReview
A Review of Measurement Calibration and Interpretation for Seepage Monitoring by Optical Fiber Distributed Temperature Sensors
Sensors 2020, 20(19), 5696; https://doi.org/10.3390/s20195696 - 06 Oct 2020
Viewed by 508
Abstract
Seepage flow through embankment dams and their sub-base is a crucial safety concern that can initiate internal erosion of the structure. The thermometric method of seepage monitoring employs the study of heat transfer characteristics in the soils, as the temperature distribution in earth-filled [...] Read more.
Seepage flow through embankment dams and their sub-base is a crucial safety concern that can initiate internal erosion of the structure. The thermometric method of seepage monitoring employs the study of heat transfer characteristics in the soils, as the temperature distribution in earth-filled structures can be influenced by the presence of seepage. Thus, continuous temperature measurements can allow detection of seepage flows. With the recent advances in optical fiber temperature sensor technology, accurate and fast temperature measurements, with relatively high spatial resolution, have been made possible using optical fiber distributed temperature sensors (DTSs). As with any sensor system, to obtain a precise temperature, the DTS measurements need to be calibrated. DTS systems automatically calibrate the measurements using an internal thermometer and reference section. Additionally, manual calibration techniques have been developed which are discussed in this paper. The temperature data do not provide any direct information about the seepage, and this requires further processing and analysis. Several methods have been developed to interpret the temperature data for the localization of the seepage and in some cases to estimate the seepage quantity. An efficient DTS application in seepage monitoring strongly depends on the following factors: installation approach, calibration technique, along with temperature data interpretation and post-processing. This paper reviews the different techniques for calibration of DTS measurements as well as the methods of interpretation of the temperature data. Full article
(This article belongs to the Special Issue Environmental Sensors and Their Applications)
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