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Sensors, Volume 20, Issue 8 (April-2 2020) – 278 articles

Cover Story (view full-size image): “Virtus Unita Fortior!” Synthetic sensing materials are some of the most attractive components of chemical/biosensors because of their long-term stability and low-cost of production. For the construction of artificial material-based sensors, the bottom-up assembly of these materials is one of the effective methods. This is because the driving forces of molecular recognition on the receptors could be enhanced by the integration of such kinds of materials at the ‘interfaces’. Thus, synthetic receptor membrane-based nanosensors can be applied to powerful tools for high-throughput analyses of the required targets. In this review, we summarize a comprehensive overview that includes the preparation techniques for molecular assemblies, the characterization methods of the interfaces, and a few examples of receptor assembly-based chemical/biosensing platforms on each transduction mechanism.View this paper.
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15 pages, 15725 KiB  
Article
Shedding Light on People Action Recognition in Social Robotics by Means of Common Spatial Patterns
by Itsaso Rodríguez-Moreno, José María Martínez-Otzeta, Izaro Goienetxea, Igor Rodriguez-Rodriguez and Basilio Sierra
Sensors 2020, 20(8), 2436; https://doi.org/10.3390/s20082436 - 24 Apr 2020
Cited by 15 | Viewed by 3531
Abstract
Action recognition in robotics is a research field that has gained momentum in recent years. In this work, a video activity recognition method is presented, which has the ultimate goal of endowing a robot with action recognition capabilities for a more natural social [...] Read more.
Action recognition in robotics is a research field that has gained momentum in recent years. In this work, a video activity recognition method is presented, which has the ultimate goal of endowing a robot with action recognition capabilities for a more natural social interaction. The application of Common Spatial Patterns (CSP), a signal processing approach widely used in electroencephalography (EEG), is presented in a novel manner to be used in activity recognition in videos taken by a humanoid robot. A sequence of skeleton data is considered as a multidimensional signal and filtered according to the CSP algorithm. Then, characteristics extracted from these filtered data are used as features for a classifier. A database with 46 individuals performing six different actions has been created to test the proposed method. The CSP-based method along with a Linear Discriminant Analysis (LDA) classifier has been compared to a Long Short-Term Memory (LSTM) neural network, showing that the former obtains similar or better results than the latter, while being simpler. Full article
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<p>Interaction example.</p>
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<p>Proposed approach.</p>
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<p>Pepper’s RGB cameras position and orientation.</p>
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<p>Skeleton’s joint positions and matrix representation of the extracted signals.</p>
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<p>Frame sequence examples for different categories.</p>
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<p>Linear interpolation example.</p>
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18 pages, 2821 KiB  
Article
Medication Adherence and Liquid Level Tracking System for Healthcare Provider Feedback
by Nolan Payne, Rahul Gangwani, Kira Barton, Alanson P. Sample, Stephen M. Cain, David T. Burke, Paula Anne Newman-Casey and K. Alex Shorter
Sensors 2020, 20(8), 2435; https://doi.org/10.3390/s20082435 - 24 Apr 2020
Cited by 10 | Viewed by 4781
Abstract
A common problem for healthcare providers is accurately tracking patients’ adherence to medication and providing real-time feedback on the management of their medication regimen. This is a particular problem for eye drop medications, as the current commercially available monitors focus on measuring adherence [...] Read more.
A common problem for healthcare providers is accurately tracking patients’ adherence to medication and providing real-time feedback on the management of their medication regimen. This is a particular problem for eye drop medications, as the current commercially available monitors focus on measuring adherence to pills, and not to eye drops. This work presents an intelligent bottle sleeve that slides onto a prescription eye drop medication bottle. The intelligent sleeve is capable of detecting eye drop use, measuring fluid level, and sending use information to a healthcare team to facilitate intervention. The electronics embedded into the sleeve measure fluid level, dropper orientation, the state of the dropper top (on/off), and rates of angular motion during an application. The sleeve was tested with ten patients (age ≥65) and successfully identified and timestamped 94% of use events. On-board processing enabled event detection and the measurement of fluid levels at a 0.4 mL resolution. These data were communicated to the healthcare team using Bluetooth and Wi-Fi in real-time, enabling rapid feedback to the subject. The healthcare team can therefore monitor a log of medication use behavior to make informed decisions on treatment or support for the patient. Full article
(This article belongs to the Special Issue Wearable Sensors and Systems in the IOT)
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<p>Overview of information flow in the system. (<b>A</b>) The prescription bottle is placed in the sleeve with the embedded sensors and electronics. (<b>B</b>) Data from the sensors detect use and monitor fluid level. (<b>C</b>) Data and usage information are transmitted from the system to a smart phone or another Bluetooth connected device. (<b>D</b>) Healthcare providers use this information to inform clinical decisions.</p>
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<p>Mechanical layout of the bottle and sleeve assembly. (<b>1</b>) Bar magnets placed in the cap, and (<b>2</b>) reed switches in the sleeve are used to sense the cap removal. Electronics are embedded in the base of the system and were designed around an (<b>4</b>) nRF51422 system-on-a-chip. (<b>5</b>) BLE was used to transfer data, and the system was powered using (<b>6</b>) a single rechargeable coin cell battery. (<b>3</b>) The two-part capacitive sensor consisted of two rectangular copper sheets (<b>a</b>) and (<b>b</b>) surrounding the bottle. The bottom left view illustrates the electric field measured by the capacitance sensor.</p>
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<p>Fluid level sensor calibration results for changes over the entire bottle volume (<b>left</b>) and a higher resolution test with 0.2 mL increments (<b>right</b>). Results indicate a linear relationship between fluid volume and capacitance reading with a resolution of approximately 0.4 mL.</p>
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<p>Example Institutional Review Board (IRB) trial data for <span class="html-italic">z</span>-axis accelerometer from one participant. (<b>1</b>) Participant walked with the system in a pocket/purse for one minute. (<b>2</b>) Participant dispensed medication five times while in a standing position and placed the eyedropper on the table between each use. (<b>3</b>) Participant removed the cap from the eye dropper and placed the eye dropper on the table without dispensing medication five times. (<b>4</b>) Participant shook the sleeve with the cap still on five times. (<b>5</b>) Participant removed eye dropper cap and executed a simulated eye drop event five times. (<b>6</b>) Participant dispensed medication five times while in a reclined position and placed the eyedropper on the table between each use.</p>
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<p>Raw capacitance data of a use event (<b>left</b>) and a simulated use (<b>right</b>). For both cases, the capacitance dropped as the bottle was flipped (<b>1</b>) because the fluid flowed out of the bottle body and into the nozzle. There was a gradual increase in capacitance for the use case (<b>2</b>), which was caused by the participant squeezing the bottle. Then, there was an occasional sharp drop caused by the suction of air after the participant finished the squeezing action (<b>3</b>). Neither the slope nor the spike was present in the simulated use case (<b>4</b>).</p>
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<p>Data from the sleeve during three consecutive use events. Reed switch status indicates cap state (on or off). When the bottle was inverted, there was a change in orientation most clearly visible in the accelerometer data. The changing angular velocity at the beginning and end of each application was present in the recorded angular velocity. Data from the capacitance sensor showed the initial drop in capacitance was due to movement of the fluid into the top of the bottle, and then the additional drop in was capacitance created as the droplets were dispensed.</p>
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<p>Receiver operating characteristic (ROC) curves of each algorithm. ROC curves plot the false positive rate (FPR) versus the true positive rate (TPR). Therefore, the closer the ROC curve is to the upper left corner, the higher the overall accuracy of the model.</p>
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<p>Information gained (IG) is the percentage of total information the model gained by a feature in classifying use events. Using the average IG of all features with respect to the sensor, the importance of a sensor in classifying a use event can be determined. Sensors ranked were the accelerometer (A), magnetometer (M), gyroscope (W), and capacitance sensor (C). Data from sensors related to the orientation of the bottle and the capacitance were the most important. Reed switches were not included in importance rankings because the built in algorithm only runs when the reed switches indicate the cap has been removed.</p>
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21 pages, 2359 KiB  
Article
Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems: A Resource-Based Pricing and User Risk-Awareness Approach
by Giorgos Mitsis, Eirini Eleni Tsiropoulou and Symeon Papavassiliou
Sensors 2020, 20(8), 2434; https://doi.org/10.3390/s20082434 - 24 Apr 2020
Cited by 36 | Viewed by 5013
Abstract
Unmanned Aerial Vehicle (UAV)-assisted Multi-access Edge Computing (MEC) systems have emerged recently as a flexible and dynamic computing environment, providing task offloading service to the users. In order for such a paradigm to be viable, the operator of a UAV-mounted MEC server should [...] Read more.
Unmanned Aerial Vehicle (UAV)-assisted Multi-access Edge Computing (MEC) systems have emerged recently as a flexible and dynamic computing environment, providing task offloading service to the users. In order for such a paradigm to be viable, the operator of a UAV-mounted MEC server should enjoy some form of profit by offering its computing capabilities to the end users. To deal with this issue in this paper, we apply a usage-based pricing policy for allowing the exploitation of the servers’ computing resources. The proposed pricing mechanism implicitly introduces a more social behavior to the users with respect to competing for the UAV-mounted MEC servers’ computation resources. In order to properly model the users’ risk-aware behavior within the overall data offloading decision-making process the principles of Prospect Theory are adopted, while the exploitation of the available computation resources is considered based on the theory of the Tragedy of the Commons. Initially, the user’s prospect-theoretic utility function is formulated by quantifying the user’s risk seeking and loss aversion behavior, while taking into account the pricing mechanism. Accordingly, the users’ pricing and risk-aware data offloading problem is formulated as a distributed maximization problem of each user’s expected prospect-theoretic utility function and addressed as a non-cooperative game among the users. The existence of a Pure Nash Equilibrium (PNE) for the formulated non-cooperative game is shown based on the theory of submodular games. An iterative and distributed algorithm is introduced which converges to the PNE, following the learning rule of the best response dynamics. The performance evaluation of the proposed approach is achieved via modeling and simulation, and detailed numerical results are presented highlighting its key operation features and benefits. Full article
(This article belongs to the Special Issue Optimization and Communication in UAV Networks)
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<p>UAV-assisted multi-access edge computing system.</p>
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<p>Probability of failure vs <span class="html-italic">x</span> when <math display="inline"><semantics> <mrow> <mi>P</mi> <mi>r</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <msup> <mrow> <mo>(</mo> <mo>−</mo> <mn>1</mn> <mo>+</mo> <mfrac> <mn>2</mn> <mrow> <mn>1</mn> <mo>+</mo> <msup> <mi>e</mi> <mrow> <mo>−</mo> <mi>x</mi> </mrow> </msup> </mrow> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </semantics></math>.</p>
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<p>Amount of data offloaded by each user vs. iterations.</p>
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<p>Expected utility of each user vs. iterations.</p>
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<p>Pricing imposed by the server on each user vs. iterations.</p>
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<p>Average users’ expected utility and average users’ pricing vs. iterations.</p>
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<p>Probability of failure of MEC server vs. iterations.</p>
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<p>Probability of failure of MEC server vs. the pricing factor.</p>
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<p>Average expected utility, offloaded data, and pricing vs. the pricing factor.</p>
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<p>Users’ average expected utility, users’ average offloaded data and pricing at the PNE vs. number of users on the system.</p>
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<p>Users’ average data offloading vs. iterations for different numbers of users.</p>
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<p>Average offloading data and PoF vs. sensitivity parameter <math display="inline"><semantics> <msub> <mi>α</mi> <mi>n</mi> </msub> </semantics></math>.</p>
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<p>Average offloading data and PoF vs. loss aversion index <math display="inline"><semantics> <msub> <mi>k</mi> <mi>n</mi> </msub> </semantics></math>.</p>
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<p>Fragility under Competition vs. no. of users for different sensitivity parameters <math display="inline"><semantics> <msub> <mi>α</mi> <mi>n</mi> </msub> </semantics></math>.</p>
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<p>Fragility under Competition vs. no. of users for different loss aversion indices <math display="inline"><semantics> <msub> <mi>k</mi> <mi>n</mi> </msub> </semantics></math>.</p>
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16 pages, 5926 KiB  
Article
Fault Diagnosis of Planetary Gearbox Based on Adaptive Order Bispectrum Slice and Fault Characteristics Energy Ratio Analysis
by Zhaoyang Shen, Zhanqun Shi, Dong Zhen, Hao Zhang and Fengshou Gu
Sensors 2020, 20(8), 2433; https://doi.org/10.3390/s20082433 - 24 Apr 2020
Cited by 6 | Viewed by 3058
Abstract
The vibration of a planetary gearbox (PG) is complex and mutually modulated, which makes the weak features of incipient fault difficult to detect. To target this problem, a novel method, based on an adaptive order bispectrum slice (AOBS) and the fault characteristics energy [...] Read more.
The vibration of a planetary gearbox (PG) is complex and mutually modulated, which makes the weak features of incipient fault difficult to detect. To target this problem, a novel method, based on an adaptive order bispectrum slice (AOBS) and the fault characteristics energy ratio (FCER), is proposed. The order bispectrum (OB) method has shown its effectiveness in the feature extraction of bearings and fixed-shaft gearboxes. However, the effectiveness of the PG still needs to be explored. The FCER is developed to sum up the fault information, which is scattered by mutual modulation. In this method, the raw vibration signal is firstly converted to that in the angle domain. Secondly, the characteristic slice of AOBS is extracted. Different from the conventional OB method, the AOBS is extracted by searching for a characteristic carrier frequency adaptively in the sensitive range of signal coupling. Finally, the FCER is summed up and calculated from the fault features that were dispersed in the characteristic slice. Experimental data was processed, using both the AOBS-FCER method, and the method that combines order spectrum analysis with sideband energy ratio (OSA-SER), respectively. Results indicated that the new method is effective in incipient fault feature extraction, compared with the methods of OB and OSA-SER. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis and Prognostics)
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<p>The formation of bispectral peaks on the carrier frequency and modulation frequency plane.</p>
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<p>Waveforms of simulation signal: (<b>a</b>) Group a; (<b>b</b>) Group b; (<b>c</b>) Group c; (<b>d</b>) Group d.</p>
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<p>Simulation signals in frequency domain: (<b>a</b>) Group a; (<b>b</b>) Group b; (<b>c</b>) Group c; (<b>d</b>) Group d.</p>
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<p>Modulated frequencies on the characteristic slice: (<b>a</b>) Group a; (<b>b</b>) Group b; (<b>c</b>) Group c; (<b>d</b>) Group d.</p>
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<p>Flowchart of the proposed AOBS-FCER method.</p>
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<p>The planetary gearbox experiment system.</p>
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<p>The pitting fault on sun gear.</p>
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<p>The spectrums of vibration signal under different speed conditions: (<b>a</b>) Healthy PG under 20% rated speed; (<b>b</b>) Sun gear pitting fault PG under 20% rated speed; (<b>c</b>) Healthy PG under 30% rated speed; (<b>d</b>) Sun gear pitting fault PG under 30% rated speed; (<b>e</b>) Healthy PG under 40% rated speed; (<b>f</b>) Sun gear pitting fault PG under 40% rated speed.</p>
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<p>The OB values distribution under different carrier frequency ranges: (<b>a</b>) From 5 Order to 15 Order; (<b>b</b>) From 10 Order to 30 Order; (<b>c</b>) From 20 Order to 40 Order; (<b>d</b>) From 30 Order to 50 Order; (<b>e</b>) From 40 Order to 60 Order; (<b>f</b>) From 50 Order to 70 Order.</p>
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<p>The characteristic slice under 20% rated speed conditions: (<b>a</b>) Healthy PG at 0% rated load; (<b>b</b>) Pitting fault on sun gear of PG at 0% rated load; (<b>c</b>) Healthy PG at 25% rated load; (<b>d</b>) Pitting fault on sun gear of PG at 25% rated load; (<b>e</b>) Healthy PG at 50% rated load; (<b>f</b>) Pitting fault on sun gear of PG at 50% rated load; (<b>g</b>) Healthy PG at 75% rated load; (<b>h</b>) Pitting fault on sun gear of PG at 75% rated load; (<b>i</b>) Healthy PG at 90% rated load; (<b>j</b>) Pitting fault on sun gear of PG at 90% rated load.</p>
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<p>The characteristic slice under 30% rated speed conditions: (<b>a</b>) Healthy PG at 0% rated load; (<b>b</b>) Pitting fault on sun gear of PG at 0% rated load; (<b>c</b>) Healthy PG at 25% rated load; (<b>d</b>) Pitting fault on sun gear of PG at 25% rated load; (<b>e</b>) Healthy PG at 50% rated load; (<b>f</b>) Pitting fault on sun gear of PG at 50% rated load; (<b>g</b>) Healthy PG at 75% rated load; (<b>h</b>) Pitting fault on sun gear of PG at 75% rated load; (<b>i</b>) Healthy PG at 90% rated load; (<b>j</b>) Pitting fault on sun gear of PG at 90% rated load.</p>
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<p>The characteristic slice under 40% rated speed conditions: (<b>a</b>) Healthy PG at 0% rated load; (<b>b</b>) Pitting fault on sun gear of PG at 0% rated load; (<b>c</b>) Healthy PG at 25% rated load; (<b>d</b>) Pitting fault on sun gear of PG at 25% rated load; (<b>e</b>) Healthy PG at 50% rated load; (<b>f</b>) Pitting fault on sun gear of PG at 50% rated load; (<b>g</b>) Healthy PG at 75% rated load; (<b>h</b>) Pitting fault on sun gear of PG at 75% rated load; (<b>i</b>) Healthy PG at 90% rated load; (<b>j</b>) Pitting fault on sun gear of PG at 90% rated load.</p>
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<p>Feature extraction by different methods under different operating conditions: (<b>a</b>) Second sun gear order by order bispectrum; (<b>b</b>) Third sun gear order by order bispectrum; (<b>c</b>) Fourth sun gear order by order bispectrum; (<b>d</b>) SER values based on OSA; (<b>e</b>) FCER values based on AOBS.</p>
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20 pages, 10972 KiB  
Article
SGC-VSLAM: A Semantic and Geometric Constraints VSLAM for Dynamic Indoor Environments
by Shiqiang Yang, Guohao Fan, Lele Bai, Cheng Zhao and Dexin Li
Sensors 2020, 20(8), 2432; https://doi.org/10.3390/s20082432 - 24 Apr 2020
Cited by 19 | Viewed by 4753
Abstract
As one of the core technologies for autonomous mobile robots, Visual Simultaneous Localization and Mapping (VSLAM) has been widely researched in recent years. However, most state-of-the-art VSLAM adopts a strong scene rigidity assumption for analytical convenience, which limits the utility of these algorithms [...] Read more.
As one of the core technologies for autonomous mobile robots, Visual Simultaneous Localization and Mapping (VSLAM) has been widely researched in recent years. However, most state-of-the-art VSLAM adopts a strong scene rigidity assumption for analytical convenience, which limits the utility of these algorithms for real-world environments with independent dynamic objects. Hence, this paper presents a semantic and geometric constraints VSLAM (SGC-VSLAM), which is built on the RGB-D mode of ORB-SLAM2 with the addition of dynamic detection and static point cloud map construction modules. In detail, a novel improved quadtree-based method was adopted for SGC-VSLAM to enhance the performance of the feature extractor in ORB-SLAM (Oriented FAST and Rotated BRIEF-SLAM). Moreover, a new dynamic feature detection method called semantic and geometric constraints was proposed, which provided a robust and fast way to filter dynamic features. The semantic bounding box generated by YOLO v3 (You Only Look Once, v3) was used to calculate a more accurate fundamental matrix between adjacent frames, which was then used to filter all of the truly dynamic features. Finally, a static point cloud was estimated by using a new drawing key frame selection strategy. Experiments on the public TUM RGB-D (Red-Green-Blue Depth) dataset were conducted to evaluate the proposed approach. This evaluation revealed that the proposed SGC-VSLAM can effectively improve the positioning accuracy of the ORB-SLAM2 system in high-dynamic scenarios and was also able to build a map with the static parts of the real environment, which has long-term application value for autonomous mobile robots. Full article
(This article belongs to the Special Issue Intelligent Systems and Sensors for Robotics)
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<p>Overall-architecture for SGC-VSLAM. The improved quadtree-based, semantic and geometric constraints’ methods are integrated into the tracking thread, among which only the static features are used to calculate and optimize the camera pose. A semantic detection thread is added to detect objects labelled as dynamic, and a static point cloud map is created from the point cloud map creation thread.</p>
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<p>A schematic diagram of the quadtree-based algorithm. The circles denote the feature points. The grey area in (<b>a</b>) is the motion blur region, and the green circles represent the features with the highest Harris response values in their nodes. The desired features in (<b>b</b>) and (<b>c</b>) are set as 9.</p>
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<p>Flow chart of the semantic and geometric constraints’ algorithm. Features extracted from objects labelled as dynamic are first screened by their semantic information. Then, the stable fundamentals are calculated by the remaining features. Finally, all the real dynamic features are filtered out by the epipolar constraint.</p>
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<p>The schematic diagram of the epipolar constraint.</p>
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<p>The SLAM trajectory in the high-dynamic sequence. Three systems were tested on the high dynamic sequences. The same row in the figure is the test results of the systems on the same test sequence. The first column in the figure represents the test results of ORB-SLAM2, the second column denotes SGC-VSLAM(IQ) and the third column is the test trajectory of SGC-VSLAM.</p>
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<p>The SLAM trajectory in the low-dynamic sequence. Three systems were tested on the low dynamic sequences. The same row in the figure is the test results of the systems on the same test sequence. The first column in the figure represents the test results of ORB-SLAM2, the second column denotes SGC-VSLAM (IQ) and the third column is the test trajectory of SGC-VSLAM.</p>
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<p>Results of the point cloud map creation. The two map construction strategies were tested on experimental sequences. (<b>a</b>–<b>c</b>) and (<b>g</b>–<b>i</b>) are the test results of MKF. Maps built by MDKF and filtered out dynamic objects are shown in (<b>d</b>–<b>f</b>) and (<b>j</b>–<b>l</b>).</p>
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13 pages, 4293 KiB  
Article
Combined Long-Period Fiber Grating and Microcavity In-Line Mach–Zehnder Interferometer for Refractive Index Measurements with Limited Cross-Sensitivity
by Monika Janik, Marcin Koba, Krystian Król, Predrag Mikulic, Wojtek J. Bock and Mateusz Śmietana
Sensors 2020, 20(8), 2431; https://doi.org/10.3390/s20082431 - 24 Apr 2020
Cited by 15 | Viewed by 3227
Abstract
This work discusses sensing properties of a long-period grating (LPG) and microcavity in-line Mach–Zehnder interferometer (µIMZI) when both are induced in the same single-mode optical fiber. LPGs were either etched or nanocoated with aluminum oxide (Al2O3) to increase its [...] Read more.
This work discusses sensing properties of a long-period grating (LPG) and microcavity in-line Mach–Zehnder interferometer (µIMZI) when both are induced in the same single-mode optical fiber. LPGs were either etched or nanocoated with aluminum oxide (Al2O3) to increase its refractive index (RI) sensitivity up to ≈2000 and 9000 nm/RIU, respectively. The µIMZI was machined using a femtosecond laser as a cylindrical cavity (d = 60 μm) in the center of the LPG. In transmission measurements for various RI in the cavity and around the LPG we observed two effects coming from the two independently working sensors. This dual operation had no significant impact on either of the devices in terms of their functional properties, especially in a lower RI range. Moreover, due to the properties of combined sensors two major effects can be distinguished—sensitivity to the RI of the volume and sensitivity to the RI at the surface. Considering also the negligible temperature sensitivity of the µIMZI, it makes the combination of LPG and µIMZI sensors a promising approach to limit cross-sensitivity or tackle simultaneous measurements of multiple effects with high efficiency and reliability. Full article
(This article belongs to the Special Issue Optical Fiber Sensors for Biomedical Applications)
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<p>Schematic visualization of the sample preparation, where (<b>A1</b>) represents a long-period grating (LPG) etched with hydrofluoric (HF) acid; (<b>A2</b>)—an LPG nanocoated with aluminum oxide (Al<sub>2</sub>O<sub>3</sub>); (<b>B1</b>)—a combination of the microcavity in-line Mach–Zehnder interferometer (µIMZI) and etched LPG; (<b>B2</b>)—a combination of the µIMZI and nanocoated LPG; (<b>C1</b>)—a LPG-µIMZI coated with 30 nm Al<sub>2</sub>O<sub>3</sub> layer as a simulation of a biofilm formation; (<b>C2</b>)—a LPG-µIMZI after etching of the Al<sub>2</sub>O<sub>3</sub> layer with sodium hydroxide. Features are not to scale.</p>
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<p>Schematic representation of the measurement setup.</p>
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<p>Spectral response of the investigated structures (<b>A</b>) LPG and (<b>B</b>) Al<sub>2</sub>O<sub>3</sub>-LPG to changes in refractive index (RI). The arrows indicate a spectral shift with external RI.</p>
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<p>Spectral response of the reference µIMZI with cavity diameter <span class="html-italic">d</span> = 60 µm. An arrow indicates the shift of the minimum with RI.</p>
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<p>Spectral response of the LPG-µIMZI structure to RI in ranges: (<b>A</b>) from 1.3333 to 1.3527 RIU and (<b>B</b>) from 1.3553 to 1.3874 RIU. The effects corresponding to the LPG and µIMZI are marked in the figures.</p>
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<p>Spectral responses of the Al<sub>2</sub>O<sub>3</sub>-LPG-µIMZI structure to RI in its range (<b>A</b>) from 1.3333 to 1.3416 RIU and (<b>B</b>) from 1.3416 to 1.3718 RIU. The effects corresponding to the Al<sub>2</sub>O<sub>3</sub>-LPG and µIMZI are marked.</p>
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<p>The response of LPG-µIMZI to Al<sub>2</sub>O<sub>3</sub> film etching recorded for measurements in water. The plot shows the evolution of the transmission spectrum during the process of the response before the deposition. Due to the low effectiveness of the etching process, the last two etching rounds were performed in 1 M NaOH.</p>
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<p>A schematic showing LPG-µIMZI influenced by (<b>A</b>) high external RI, (<b>B</b>) thin film and lower external RI, and (<b>C</b>) thick film and the lowest external RI. For this specific combination of external RIs (n<sub>1</sub>, n<sub>2</sub>, and n<sub>3</sub>) and film thicknesses, only the response of µIMZI would allow discriminating the cases.</p>
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<p>Transmission spectra of µIMZI at different temperatures (T) of water in the cavity.</p>
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<p>Spectral responses of the Al<sub>2</sub>O<sub>3</sub>-LPG-µIMZI structure in water to variations of T.</p>
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17 pages, 7095 KiB  
Article
Time-Interleaved SAR ADC with Background Timing-Skew Calibration for UWB Wireless Communication in IoT Systems
by Kiho Seong, Dong-Kyu Jung, Dong-Hyun Yoon, Jae-Soub Han, Ju-Eon Kim, Tony Tae-Hyoung Kim, Woojoo Lee and Kwang-Hyun Baek
Sensors 2020, 20(8), 2430; https://doi.org/10.3390/s20082430 - 24 Apr 2020
Cited by 16 | Viewed by 6359
Abstract
Ultra-wideband (UWB) wireless communication is prospering as a powerful partner of the Internet-of-things (IoT). Due to the ongoing development of UWB wireless communications, the demand for high-speed and medium resolution analog-to-digital converters (ADCs) continues to grow. The successive approximation register (SAR) ADCs are [...] Read more.
Ultra-wideband (UWB) wireless communication is prospering as a powerful partner of the Internet-of-things (IoT). Due to the ongoing development of UWB wireless communications, the demand for high-speed and medium resolution analog-to-digital converters (ADCs) continues to grow. The successive approximation register (SAR) ADCs are the most powerful candidate to meet these demands, attracting both industries and academia. In particular, recent time-interleaved SAR ADCs show that multi-giga sample per second (GS/s) can be achieved by overcoming the challenges of high-speed implementation of existing SAR ADCs. However, there are still critical issues that need to be addressed before the time-interleaved SAR ADCs can be applied in real commercial applications. The most well-known problem is that the time-interleaved SAR ADC architecture requires multiple sub-ADCs, and the mismatches between these sub-ADCs can significantly degrade overall ADC performance. And one of the most difficult mismatches to solve is the sampling timing skew. Recently, research to solve this timing-skew problem has been intensively studied. In this paper, we focus on the cutting-edge timing-skew calibration technique using a window detector. Based on the pros and cons analysis of the existing techniques, we come up with an idea that increases the benefits of the window detector-based timing-skew calibration techniques and minimizes the power and area overheads. Finally, through the continuous development of this idea, we propose a timing-skew calibration technique using a comparator offset-based window detector. To demonstrate the effectiveness of the proposed technique, intensive works were performed, including the design of a 7-bit, 2.5 GS/s 5-channel time-interleaved SAR ADC and various simulations, and the results prove excellent efficacy of signal-to-noise and distortion ratio (SNDR) and spurious-free dynamic range (SFDR) of 40.79 dB and 48.97 dB at Nyquist frequency, respectively, while the proposed window detector occupies only 6.5% of the total active area, and consumes 11% of the total power. Full article
(This article belongs to the Section Communications)
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<p>(<b>a</b>) Basic structure of a time-interleaved successive approximation register analog-to-digital converters (SAR ADC) and (<b>b</b>) timing-skew error in a time-interleaved SAR ADC.</p>
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<p>Signal-to-noise ratio (SNR) degradation of the 7-bit time-interleaved ADC due to the timing skews.</p>
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<p>Concept of the window detector-based timing-skew calibration (<b>a</b>) without timing skew (<b>b</b>) with timing skew.</p>
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<p>Relationship between the comparison time and input voltage.</p>
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<p>(<b>a</b>) Basic structure of the SAR-based window detector and its operating principle when (<b>b</b>) outside the window and (<b>c</b>) inside the window. The timing diagram is shown in (<b>d</b>).</p>
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<p>Block diagram of the proposed time-interleaved SAR ADC.</p>
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<p>Block diagram of (<b>a</b>) the sub-channel 2b/cycle SAR ADC and (<b>b</b>) window detector SAR ADC.</p>
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<p>Schematic of the differential comparator with offset calibration transistors.</p>
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<p>Block diagram of the offset calibration logic with the majority voting scheme.</p>
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<p>The input cross-coupled comparators (<b>a</b>) without and (<b>b</b>) with offset, and the comparator outputs equality (<b>c</b>) inside, and (<b>d</b>) outside window.</p>
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<p>Relationship between window width, calibration accuracy and convergence time.</p>
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<p>The offset calibration after C<sub>WD</sub> switching to force desired offset.</p>
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<p>Histograms of <span class="html-italic">D<sub>out</sub></span> without and with timing skew.</p>
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<p>Block diagram of the proposed timing-skew calibration.</p>
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<p>Top layout of the proposed time-interleaved SAR ADC.</p>
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<p>Differential non-linearity (DNL) and integral non-linearity (INL) with post-layout extraction of CDAC and an additional 1% random mismatch.</p>
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<p>The offset calibration convergence for differential-difference comparators (DDCs).</p>
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<p>(<b>a</b>) The coarse-fine offset calibration convergence for window detecting comparator, and (<b>b</b>) its corresponding forced offset voltage.</p>
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<p>The coarse-fine timing-skew calibration convergence.</p>
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<p>Fast Fourier transform (FFT) analysis of a sinusoidal input signal at Nyquist frequency (<b>a</b>) without calibration, (<b>b</b>) with offset calibration only, and (<b>c</b>) with both offset and timing-skew calibration (4096 points for FFT).</p>
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26 pages, 9492 KiB  
Article
Implementation of System Operation Modes for Health Management and Failure Prognosis in Cyber-Physical Systems
by Santiago Ruiz-Arenas, Zoltán Rusák, Ricardo Mejía-Gutiérrez and Imre Horváth
Sensors 2020, 20(8), 2429; https://doi.org/10.3390/s20082429 - 24 Apr 2020
Cited by 5 | Viewed by 2618
Abstract
Cyber-physical systems (CPSs) have sophisticated control mechanisms that help achieve optimal system operations and services. These mechanisms, imply considering multiple signal inputs in parallel, to timely respond to varying working conditions. Despite the advantages that control mechanisms convey, they bring new challenges in [...] Read more.
Cyber-physical systems (CPSs) have sophisticated control mechanisms that help achieve optimal system operations and services. These mechanisms, imply considering multiple signal inputs in parallel, to timely respond to varying working conditions. Despite the advantages that control mechanisms convey, they bring new challenges in terms of failure prevention. The compensatory action the control exerts cause a fault masking effect, hampering fault diagnosis. Likewise, the multiple information inputs CPSs have to process can affect the timely system response to faults. This article proposes a failure prognosis method, which combines time series-based forecasting methods with statistically based classification techniques in order to investigate system degradation and failure forming on system levels. This method utilizes a new approach based on the concept of the system operation mode (SOM) that offers a novel perspective for health management that allows monitoring the system behavior, through the frequency and duration of SOMs. Validation of this method was conducted by systematically injecting faults in a cyber-physical greenhouse testbed. The obtained results demonstrate that the degradation and fault forming process can be monitored by analyzing the changes of the frequency and duration of SOMs. These indicators made possible to estimate the time to failure caused by various failures in the conducted experiments. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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<p>Procedure of the proposed computational failure forecasting method.</p>
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<p>Tasks versus time diagram.</p>
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<p>Pseudo-algorithm of step ii.</p>
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<p>Pseudo-algorithm of step iii.</p>
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<p>Pseudo-algorithm of step iv.</p>
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<p>Pseudo-algorithm of the training process corresponding to step v.</p>
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<p>Forecasting notation.</p>
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<p>Pseudo-algorithm for the diagnosis process in step v.</p>
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<p>Architecture of the testbed.</p>
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<p>Description of the system units.</p>
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<p>Instrumented cyber-physical greenhouse testbed.</p>
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<p>Evolution of the greenhouse ‘Tank leak’ failure mode (F_1).</p>
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<p>Comparison between the variations observed in the frequency of system operation mode (SOM), when subjected to a tank-leak, versus the variation observed in the fault-free state.</p>
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<p>Fault progression processes.</p>
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<p>Evolution of failure prediction for failure-leak <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mrow> <msub> <mi>F</mi> <mn>1</mn> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>.</p>
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<p>Failure forecasting for tank leak; (<b>a</b>) time-to-failure (TTF) of the failure mode and (<b>b</b>) forecasting of the failure mode.</p>
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16 pages, 7342 KiB  
Article
Research on Visualization and Error Compensation of Demolition Robot Attachment Changing
by Qian Deng, Shuliang Zou, Hongbin Chen and Weixiong Duan
Sensors 2020, 20(8), 2428; https://doi.org/10.3390/s20082428 - 24 Apr 2020
Cited by 4 | Viewed by 3424
Abstract
Attachment changing in demolition robots has a high docking accuracy requirement, so it is hard for operators to control this process remotely through the perspective of a camera. To solve this problem, this study investigated positioning error and proposed a method of error [...] Read more.
Attachment changing in demolition robots has a high docking accuracy requirement, so it is hard for operators to control this process remotely through the perspective of a camera. To solve this problem, this study investigated positioning error and proposed a method of error compensation to achieve a highly precise attachment changing process. This study established a link parameter model for the demolition robot, measured the error in the attachment changing, introduced a reference coordinate system to solve the coordinate transformation from the dock spot of the robot’s quick-hitch equipment to the dock spot of the attachment, and realized error compensation. Through calculation and experimentation, it was shown that the error compensation method proposed in this study reduced the level of error in attachment changing from the centimeter to millimeter scale, thereby meeting the accuracy requirements for attachment changing. This method can be applied to the remote-controlled attachment changing process of demolition robots, which provides the basis for the subsequent automatic changing of attachments. This has the potential to be applied in nuclear facility decommissioning and dismantling, as well as other radioactive environments. Full article
(This article belongs to the Section Intelligent Sensors)
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<p>Attachment changing procedure: (<b>a</b>) initialization, (<b>b</b>) preparation, (<b>c</b>) range alignment, and (<b>d</b>) angle alignment.</p>
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<p>Coordinate frame of a BROKK 160 robot. {B} is the robot’s base coordinate frame, {W} is the quick-hitch dock spot coordinate frame, {C} is the camera coordinate frame, and {T} is the attachment dock spot coordinate frame (the red axis is the X-axis, the green axis is the Y-axis, and the blue axis is the Z-axis).</p>
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<p>Coordinate frame transformation during the attachment changing process.</p>
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<p>Errors during the attachment changing process.</p>
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<p>(<b>a</b>) Measured position error data of <math display="inline"><semantics> <mrow> <msub> <mi>e</mi> <mi>W</mi> </msub> </mrow> </semantics></math>. (<b>b</b>) Distance and angle error analysis of <math display="inline"><semantics> <mrow> <msub> <mi>e</mi> <mi>W</mi> </msub> </mrow> </semantics></math>.</p>
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<p>Introduction of the reference coordinate frame. (1) Tag of the reference coordinate frame {R}, (2) quick-hitch equipment, (3) hydraulic quick coupling (male), (4) tags of the attachment dock spot coordinate frame {T}, and (5) attachment dock spot.</p>
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<p>Error compensation in the process of attachment changing.</p>
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<p>Block diagram of the error compensation algorithm.</p>
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<p>(<b>a</b>) Beginning of the experiment. (<b>b</b>) End of the first stage. (<b>c</b>) End of the second stage. (<b>d</b>) End of the third stage. (<b>e</b>) End of the fourth stage. (<b>f</b>) End of the experiment.</p>
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<p>(<b>a</b>) Beginning of the experiment. (<b>b</b>) End of the first stage. (<b>c</b>) End of the second stage. (<b>d</b>) End of the third stage. (<b>e</b>) End of the fourth stage. (<b>f</b>) End of the experiment.</p>
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<p>(<b>a</b>) Beginning of the experiment. (<b>b</b>) End of the first stage. (<b>c</b>) End of the second stage. (<b>d</b>) End of the third stage. (<b>e</b>) End of the fourth stage. (<b>f</b>) End of the experiment.</p>
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<p>Error analysis of the experiment.</p>
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16 pages, 2401 KiB  
Article
Intra- and Inter-Rater Reliability of Manual Feature Extraction Methods in Movement Related Cortical Potential Analysis
by Gemma Alder, Nada Signal, Usman Rashid, Sharon Olsen, Imran Khan Niazi and Denise Taylor
Sensors 2020, 20(8), 2427; https://doi.org/10.3390/s20082427 - 24 Apr 2020
Cited by 1 | Viewed by 3918
Abstract
Event related potentials (ERPs) provide insight into the neural activity generated in response to motor, sensory and cognitive processes. Despite the increasing use of ERP data in clinical research little is known about the reliability of human manual ERP labelling methods. Intra-rater and [...] Read more.
Event related potentials (ERPs) provide insight into the neural activity generated in response to motor, sensory and cognitive processes. Despite the increasing use of ERP data in clinical research little is known about the reliability of human manual ERP labelling methods. Intra-rater and inter-rater reliability were evaluated in five electroencephalography (EEG) experts who labelled the peak negativity of averaged movement related cortical potentials (MRCPs) derived from thirty datasets. Each dataset contained 50 MRCP epochs from healthy people performing cued voluntary or imagined movement, or people with stroke performing cued voluntary movement. Reliability was assessed using the intraclass correlation coefficient and standard error of measurement. Excellent intra- and inter-rater reliability was demonstrated in the voluntary movement conditions in healthy people and people with stroke. In comparison reliability in the imagined condition was low to moderate. Post-hoc secondary epoch analysis revealed that the morphology of the signal contributed to the consistency of epoch inclusion; potentially explaining the differences in reliability seen across conditions. Findings from this study may inform future research focused on developing automated labelling methods for ERP feature extraction and call to the wider community of researchers interested in utilizing ERPs as a measure of neurophysiological change or in the delivery of EEG-driven interventions. Full article
(This article belongs to the Special Issue Biomedical Signal Processing)
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<p>An overview of the study design. The three conditions and the 10 datasets within a condition were pseudo-randomized for each expert at each separate evaluation session. Epochs within a single dataset remained in the order in which they were recorded. HV-DF = healthy voluntary dorsiflexion; HI-DF = healthy imagined dorsiflexion; SV-DF = stroke voluntary dorsiflexion. PN1 = evaluation session 1 day 1; PN2 = evaluation session 2 day 1; PN3 = evaluation session 3 day 2.</p>
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<p>An illustration of the set up for continuous electroencephalography (EEG) recordings where a participant executes either voluntary or imagined ballistic dorsiflexion movements in time with a visual cue displayed on a computer monitor.</p>
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<p>Movement related cortical potential (MRCP) averages with 95% confidence intervals obtained from averaging filtered epochs from (<b>a</b>) a healthy participant performing voluntary dorsiflexion, (<b>b</b>) a healthy participant performing imagined dorsiflexion and (<b>c</b>) a participant with stroke performing voluntary dorsiflexion. Sample number 1500 corresponds to the onset of the cue to move.</p>
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<p>The relationship between the probability of experts obtaining a matched epoch and the cosine similarity. Data for each movement condition are presented (healthy voluntary (HV), stroke voluntary (SV), healthy imagined (HI)) with their 95% confidence intervals at intra-session (PN1 and PN2) and inter-session (PN1 and PN3) evaluations.</p>
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<p>The relationship between the probability of experts obtaining a matched epoch and the cosine similarity data for each movement condition are presented (healthy voluntary (HV), stroke voluntary (SV), healthy imagined (HI)) with their 95% confidence intervals at each of the three evaluation sessions.</p>
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17 pages, 2066 KiB  
Article
Adversarial Networks for Scale Feature-Attention Spectral Image Reconstruction from a Single RGB
by Pengfei Liu and Huaici Zhao
Sensors 2020, 20(8), 2426; https://doi.org/10.3390/s20082426 - 24 Apr 2020
Cited by 15 | Viewed by 3453
Abstract
Hyperspectral images reconstruction focuses on recovering the spectral information from a single RGBimage. In this paper, we propose two advanced Generative Adversarial Networks (GAN) for the heavily underconstrained inverse problem. We first propose scale attention pyramid UNet (SAPUNet), which uses U-Net with dilated [...] Read more.
Hyperspectral images reconstruction focuses on recovering the spectral information from a single RGBimage. In this paper, we propose two advanced Generative Adversarial Networks (GAN) for the heavily underconstrained inverse problem. We first propose scale attention pyramid UNet (SAPUNet), which uses U-Net with dilated convolution to extract features. We establish the feature pyramid inside the network and use the attention mechanism for feature selection. The superior performance of this model is due to the modern architecture and capturing of spatial semantics. To provide a more accurate solution, we propose another distinct architecture, named W-Net, that builds one more branch compared to U-Net to conduct boundary supervision. SAPUNet and scale attention pyramid WNet (SAPWNet) provide improvements on the Interdisciplinary Computational Vision Lab at Ben Gurion University (ICVL) datasetby 42% and 46.6%, and 45% and 50% in terms of root mean square error (RMSE) and relative RMSE, respectively. The experimental results demonstrate that our proposed models are more accurate than the state-of-the-art hyperspectral recovery methods Full article
(This article belongs to the Section Remote Sensors)
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<p>Overview of our two adversarial feature pyramid spectral reconstruction models. SAM, scale attention modules.</p>
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<p>Complete architecture of the Scale Attention Pyramid U-Net with dilated convolution at different rates.</p>
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<p>Architecture of the Attention Module Block.</p>
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<p>Complete architecture of the Scale Attention Pyramid W-Net with dilated convolution at different rates.</p>
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<p>Reconstruction images from the ICVL dataset [<a href="#B3-sensors-20-02426" class="html-bibr">3</a>]. From top to bottom, ground-truth, SAPWNet-GAN, SCAUNet-GAN [<a href="#B30-sensors-20-02426" class="html-bibr">30</a>], sparse coding [<a href="#B3-sensors-20-02426" class="html-bibr">3</a>], and error map of the SAPWNet-GAN.</p>
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<p>Visual comparison of the hyperspectral recovery of four selected images at 570 nm from the ICVL dataset [<a href="#B3-sensors-20-02426" class="html-bibr">3</a>]. From top to bottom: sparse coding [<a href="#B3-sensors-20-02426" class="html-bibr">3</a>], SCAUNet-GAN [<a href="#B30-sensors-20-02426" class="html-bibr">30</a>], SAPUNet-GAN, SAPWNet-GAN, and ground-truth.</p>
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<p>Spectral signatures of four selected spatial points identified by the colored dots from <a href="#sensors-20-02426-f006" class="html-fig">Figure 6</a> over 400–700 nm.</p>
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<p>Quantitative evaluation on the Chakrabarti dataset in comparison with several state-of-the-art hyperspectral reconstructing methods: Yan et al. [<a href="#B16-sensors-20-02426" class="html-bibr">16</a>], Arad et al. [<a href="#B3-sensors-20-02426" class="html-bibr">3</a>], Nguyen et al. [<a href="#B14-sensors-20-02426" class="html-bibr">14</a>], Kawakami et al. [<a href="#B20-sensors-20-02426" class="html-bibr">20</a>].</p>
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24 pages, 471 KiB  
Review
Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges
by Jonas Fausing Olesen and Hamid Reza Shaker
Sensors 2020, 20(8), 2425; https://doi.org/10.3390/s20082425 - 24 Apr 2020
Cited by 70 | Viewed by 13152
Abstract
Thermal power plants are an important asset in the current energy infrastructure, delivering ancillary services, power, and heat to their respective consumers. Faults on critical components, such as large pumping systems, can lead to material damage and opportunity losses. Pumps plays an essential [...] Read more.
Thermal power plants are an important asset in the current energy infrastructure, delivering ancillary services, power, and heat to their respective consumers. Faults on critical components, such as large pumping systems, can lead to material damage and opportunity losses. Pumps plays an essential role in various industries and as such clever maintenance can ensure cost reductions and high availability. Prognostics and Health Management, PHM, is the study utilizing data to estimate the current and future conditions of a system. Within the field of PHM, Predictive Maintenance, PdM, has been gaining increased attention. Data-driven models can be built to estimate the remaining-useful-lifetime of complex systems that would be difficult to identify by man. With the increased attention that the Predictive Maintenance field is receiving, review papers become increasingly important to understand what research has been conducted and what challenges need to be addressed. This paper does so by initially conceptualising the PdM field. A structured overview of literature in regard to application within PdM is presented, before delving into the domain of thermal power plants and pump systems. Finally, related challenges and trends will be outlined. This paper finds that a large number of experimental data-driven models have been successfully deployed, but the PdM field would benefit from more industrial case studies. Furthermore, investigations into the scale-ability of models would benefit industries that are looking into large-scale implementations. Here, examining a method for automatic maintenance of the developed model will be of interest. This paper can be used to understand the PdM field as a broad concept but does also provide a niche understanding of the domain in focus. Full article
(This article belongs to the Section Intelligent Sensors)
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<p>The published papers on predictive maintenance from 2000 to 2020 on MDPI, ScienceDirect and IEEE.</p>
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<p>A representation of model types that can be developed and utilised within the PdM field.</p>
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<p>An overview of various methods that can be chosen depending on what information is desired and to what extent data is available.</p>
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<p>An overview of the process for developing a successful machine learning model, be it in a PdM setting or another.</p>
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<p>A suggested Taxonomy for Data Preprocessing. The exact order does not necessarily matter and each step should be considered whether necessary for the application at hand.</p>
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<p>A simple figure of a centrifugal pump. From left to right; water enters the impeller through the suction nozzle, where kinetic energy will be applied to the liquid through turning of the shaft. The chasing keeps the water within the system, while the mechanical seals make sure there is no leakage. The bearing reduces the friction between the moving and stationary parts and is found in several places within a pumping system. The water exits at the discharge nozzle.</p>
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24 pages, 3132 KiB  
Article
Wearable Sensor-Based Gait Analysis for Age and Gender Estimation
by Md Atiqur Rahman Ahad, Thanh Trung Ngo, Anindya Das Antar, Masud Ahmed, Tahera Hossain, Daigo Muramatsu, Yasushi Makihara, Sozo Inoue and Yasushi Yagi
Sensors 2020, 20(8), 2424; https://doi.org/10.3390/s20082424 - 24 Apr 2020
Cited by 41 | Viewed by 7370
Abstract
Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender [...] Read more.
Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the 12th IAPR International Conference on Biometrics (ICB), 2019. In this competition, 18 teams initially registered from 14 countries. The goal of this challenge was to find some smart approaches to deal with age and gender estimation from sensor-based gait data. For this purpose, we employed a large wearable sensor-based gait dataset, which has 745 subjects (357 females and 388 males), from 2 to 78 years old in the training dataset; and 58 subjects (19 females and 39 males) in the test dataset. It has several walking patterns. The gait data sequences were collected from three IMUZ sensors, which were placed on waist-belt or at the top of a backpack. There were 67 solutions from ten teams—for age and gender estimation. This paper extensively analyzes the methods and achieved-results from various approaches. Based on analysis, we found that deep learning-based solutions lead the competitions compared with conventional handcrafted methods. We found that the best result achieved 24.23% prediction error for gender estimation, and 5.39 mean absolute error for age estimation by employing angle embedded gait dynamic image and temporal convolution network. Full article
(This article belongs to the Special Issue Inertial Sensors for Activity Recognition and Classification)
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<p>Setup of the sensor-based human gait data capturing system: (<b>a</b>) Waist-belt (uncovered) having three IMUZ sensors; (<b>b</b>) three axes of a typical IMUZ sensor; (<b>c</b>) Sensors’ attachment at left, right, and center-back position; and (<b>d</b>) Real data collection image, where a subject is wearing a belt, and flat ground, stairs and slope are highlighted in the environment. (This Figure was previously published in [<a href="#B17-sensors-20-02424" class="html-bibr">17</a>] as Figure 8. Hence, it is reprinted from [<a href="#B17-sensors-20-02424" class="html-bibr">17</a>], Copyright (2015), with permission from Elsevier).</p>
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<p>Distribution of subjects in training dataset—by age group and gender. The histogram demonstrates a non-uniform distribution of age groups though the distributions of both sexes are almost equally distributed.</p>
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<p>An example of sensor orientation inconsistency: within and among subjects.</p>
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<p>An example of three IMUZ sensors in the backpack for the test dataset. The sensors are attached to the top of the backpack.</p>
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<p>Distribution of subjects in test dataset—by age group and gender. The histogram demonstrates a much non-uniform distribution of age groups and gender than the training dataset.</p>
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<p>Examples of test signal sequences for gyroscope data, and accelerometer data.</p>
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<p>Examples of accelerometer data that appear only in testing.</p>
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<p>Gender prediction results for the 10 teams.</p>
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<p>Top 10 algorithms, irrespective of any team to predict errors for gender estimation. ‘T’ stands for ‘Team’ and ‘A’ stands for ‘Algorithm’.</p>
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<p>Comparison of different algorithms by teams in terms of the distribution of prediction error for gender estimation.</p>
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<p>Age prediction results by age groups for the 10 teams.</p>
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<p>Top 10 algorithms, irrespective of any team for age prediction results by age groups. ‘T’ stands for ‘Team’ and ‘A’ stands for ‘Algorithm’.</p>
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<p>Comparison of different algorithms by teams in terms of the distribution of prediction error for age estimation.</p>
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13 pages, 4995 KiB  
Article
PM2.5 Concentration Estimation Based on Image Processing Schemes and Simple Linear Regression
by Jiun-Jian Liaw, Yung-Fa Huang, Cheng-Hsiung Hsieh, Dung-Ching Lin and Chin-Hsiang Luo
Sensors 2020, 20(8), 2423; https://doi.org/10.3390/s20082423 - 24 Apr 2020
Cited by 14 | Viewed by 3933
Abstract
Fine aerosols with a diameter of less than 2.5 microns (PM2.5) have a significant negative impact on human health. However, their measurement devices or instruments are usually expensive and complicated operations are required, so a simple and effective way for measuring [...] Read more.
Fine aerosols with a diameter of less than 2.5 microns (PM2.5) have a significant negative impact on human health. However, their measurement devices or instruments are usually expensive and complicated operations are required, so a simple and effective way for measuring the PM2.5 concentration is needed. To relieve this problem, this paper attempts to provide an easy alternative approach to PM2.5 concentration estimation. The proposed approach is based on image processing schemes and a simple linear regression model. It uses images with a high and low PM2.5 concentration to obtain the difference between these images. The difference is applied to find the region with the greatest impact. The approach is described in two stages. First, a series of image processing schemes are employed to automatically select the region of interest (RoI) for PM2.5 concentration estimation. Through the selected RoI, a single feature is obtained. Second, by employing the single feature, a simple linear regression model is used and applied to PM2.5 concentration estimation. The proposed approach is verified by the real-world open data released by Taiwan’s government. The proposed scheme is not expected to replace component analysis using physical or chemical techniques. We have tried to provide a cheaper and easier way to conduct PM2.5 estimation with an acceptable performance more efficiently. To achieve this, further work will be conducted and is summarized at the end of this paper. Full article
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<p>A block diagram of the proposed approach.</p>
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<p>A flowchart of the proposed automatic region of interest (RoI) selection.</p>
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<p>A sample image pair. (<b>a</b>) <b><span class="html-italic">I</span></b><sub>1</sub> (low PM<sub>2.5</sub> concentration, <math display="inline"><semantics> <mrow> <mrow> <mn>1</mn> <mtext> </mtext> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">g</mi> </mrow> <mo>/</mo> <msup> <mi mathvariant="normal">m</mi> <mn>3</mn> </msup> </mrow> </semantics></math>); (<b>b</b>) <b><span class="html-italic">I</span></b><sub>2</sub> (high PM<sub>2.5</sub> concentration, <math display="inline"><semantics> <mrow> <mrow> <mn>75</mn> <mtext> </mtext> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">g</mi> </mrow> <mo>/</mo> <msup> <mi mathvariant="normal">m</mi> <mn>3</mn> </msup> </mrow> </semantics></math>).</p>
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<p>Images after Sobel edge detection. (<b>a</b>) <b><span class="html-italic">I</span></b><sub>1</sub><sub>,s</sub> (low PM<sub>2.5</sub> concentration); (<b>b</b>) <b><span class="html-italic">I</span></b><sub>2</sub><sub>,s</sub> (high PM<sub>2.5</sub> concentration); (<b>c</b>) the difference of (<b>a</b>) and (<b>b</b>).</p>
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<p>Images after Otsu thresholding. (<b>a</b>) <b><span class="html-italic">I</span></b><sub>1</sub><sub>,so</sub> (low PM<sub>2.5</sub> concentration); (<b>b</b>) <b><span class="html-italic">I</span></b><sub>2</sub><sub>,so</sub> (high PM<sub>2.5</sub> concentration).</p>
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<p>Images after morphological dilation. (<b>a</b>) <b><span class="html-italic">I</span></b><sub>1,som</sub> (low PM<sub>2.5</sub> concentration); (<b>b</b>) <b><span class="html-italic">I</span></b><sub>2,som</sub> (high PM<sub>2.5</sub> concentration).</p>
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<p>The difference image <b><span class="html-italic">I</span></b><sub>d</sub> after image subtraction.</p>
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<p>The image <b><span class="html-italic">I</span></b><sub>dl</sub> after labeling.</p>
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<p>The three candidate regions of interest indicated by red boxes.</p>
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<p>A box plot for three candidate regions of interest.</p>
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<p>The scatter plots for (<b>a</b>) the whole image; (<b>b</b>) Region 1 (selected); (<b>c</b>) Region 2; (<b>d</b>) Region 3.</p>
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11 pages, 11711 KiB  
Letter
Safe Helicopter Landing on Unprepared Terrain Using Onboard Interferometric Radar
by Pavel E. Shimkin, Alexander I. Baskakov, Aleksey A. Komarov and Min-Ho Ka
Sensors 2020, 20(8), 2422; https://doi.org/10.3390/s20082422 - 24 Apr 2020
Cited by 3 | Viewed by 3182
Abstract
This letter proposes a radar interferometric survey system for the ground surface of helicopter landing sites. This system generates high-quality three-dimensional terrain surface topography data and estimates the slope of the site with the required accuracy. This study presents the processing algorithms of [...] Read more.
This letter proposes a radar interferometric survey system for the ground surface of helicopter landing sites. This system generates high-quality three-dimensional terrain surface topography data and estimates the slope of the site with the required accuracy. This study presents the processing algorithms of the radar system for safe helicopter landing using an interferometric method and also demonstrates the efficiency of the proposed approach based on computer simulation results. The results of the calculated potential accuracy characteristics of such a system are presented, as well as one of the variants of the algorithmic implementation of a simulation computer model implemented on MATLAB. Visual results of modeling using an example of a helicopter landing on a non-uniform surface relief similar to a real case are shown. The study focuses on the simulation of a unique on-board radar system, which allows helicopters to land on an unprepared site with a high degree of safety, having previously determined the presence of dangerous irregularities, inclines, foreign objects, and mechanisms on the site. Full article
(This article belongs to the Special Issue InSAR Signal and Data Processing)
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<p>Imaging geometry of radar safe landing system of a helicopter (RSLSH).</p>
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<p>Height estimation error due phase estimation error on the baseline length at different look angles: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0</mn> <mo>°</mo> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>45</mn> <mo>°</mo> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>90</mn> <mo>°</mo> </mrow> </semantics></math>.</p>
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<p>(<b>a</b>) Digital elevation model (DEM) for acquisition 1; (<b>b</b>) optical image of DEM for acquisition 1; (<b>c</b>) radar cross section (RCS) for some types of surface; (<b>d</b>) RCS of DEM for acquisition 1.</p>
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<p>Interferometric phase difference (IPD) of DEM of (<b>a</b>) acquisition 1; (<b>b</b>) acquisition 2; (<b>c</b>) acquisition 3; (<b>d</b>) acquisition 4.</p>
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<p>IPD after removing the flat Earth IPD of (<b>a</b>) acquisition 1; (<b>b</b>) acquisition 2; (<b>c</b>) acquisition 3; (<b>d</b>) acquisition 4.</p>
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<p>Estimation of DEM of (<b>a</b>) acquisition 1; (<b>b</b>) acquisition 2; (<b>c</b>) acquisition 3; (<b>d</b>) acquisition 4.</p>
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<p>(<b>a</b>) combined DEM; (<b>b</b>) standard deviation of the errors; and (<b>c</b>) its histograms.</p>
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12 pages, 1103 KiB  
Article
Perception of a Haptic Stimulus Presented Under the Foot Under Workload
by Landry Delphin Chapwouo Tchakoute and Bob-Antoine J. Menelas
Sensors 2020, 20(8), 2421; https://doi.org/10.3390/s20082421 - 24 Apr 2020
Cited by 3 | Viewed by 2783
Abstract
It is clear that the haptic channel can be exploited as a communication medium for several tasks of everyday life. Here we investigated whether such communication can be altered in a cognitive load situation. We studied the perception of a vibrotactile stimulus presented [...] Read more.
It is clear that the haptic channel can be exploited as a communication medium for several tasks of everyday life. Here we investigated whether such communication can be altered in a cognitive load situation. We studied the perception of a vibrotactile stimulus presented under the foot when the attention is loaded by another task (cognitive load). The results demonstrated a significant influence of workload on the perception of the vibrotactile stimulus. Overall, we observed that the average score in the single-task (at rest) condition was greater than the overall mean score in the dual-task conditions (counting forwards, counting backwards, and walking). The walking task was the task that most influenced the perception of the vibrotactile stimulus presented under the foot. Full article
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<p>The wearable device worn on the left foot with a strap to hold the haptuator. (<b>I</b>) The device component used for the experiment. The haptuator is located under the arch of the second toe fixed by the black strap. (<b>II</b>) The electronic diagram of the device showing how the components are joined together in order to deliver the vibrotactile stimulus.</p>
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<p>Score mean perception of each condition (with mean value inside the bars).</p>
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<p>Overall score mean perception of single task compared to dual task (with mean value inside the bars).</p>
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<p>Box plot of score perception between conditions: at rest; counting forwards (CF); counting backwards (CB); and walking.</p>
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<p>Normality test results: residual plots of conditions at rest, counting forward (CF), counting backward (CB), and walking.</p>
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12 pages, 2083 KiB  
Article
Fast Screening of Whole Blood and Tumor Tissue for Bladder Cancer Biomarkers Using Stochastic Needle Sensors
by Raluca-Ioana Stefan-van Staden, Damaris-Cristina Gheorghe, Viorel Jinga, Cristian Sorin Sima and Marius Geanta
Sensors 2020, 20(8), 2420; https://doi.org/10.3390/s20082420 - 24 Apr 2020
Cited by 13 | Viewed by 3123
Abstract
Bladder cancer is one of the most common urologic malignancies, which is more frequent in men than in women. The early diagnosis for this type of cancer still remains a challenge, therefore, the development of a fast screening test for whole blood and [...] Read more.
Bladder cancer is one of the most common urologic malignancies, which is more frequent in men than in women. The early diagnosis for this type of cancer still remains a challenge, therefore, the development of a fast screening test for whole blood and tumor tissue samples may save lives. Four biomarkers, p53, E-cadherin, bladder tumor antigen (BTA), and hyaluronic acid were considered for the screening tests using stochastic needle sensors. Three stochastic needle sensors, based on graphite powder and modified with three types of chitosan, were designed and characterized for the screening test. The proposed sensors showed low limits of quantification, and high sensitivity and selectivity levels. The recoveries of p53, E-cadherin, BTA, and hyaluronic acid in whole blood samples and tissue samples were higher than 95.00% with a relative standard deviation lower than 1.00%. Full article
(This article belongs to the Special Issue Graphene-Based Sensors for Pharmaceutical and Biomedical Analysis)
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<p>Pattern recognition of p53, E-cadherin, BTA, and hyaluronic acid in whole blood samples using the stochastic needle sensor based on chitosan I/graphite (<b>a</b>), chitosan II/graphite (<b>b</b>), and chitosan III/graphite (<b>c</b>).</p>
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<p>Pattern recognition of p53, E-cadherin, BTA, and hyaluronic acid in tissue samples using the stochastic needle sensor based on chitosan I/graphite (<b>a</b>), chitosan II/graphite (<b>b</b>), and chitosan III/graphite (<b>c</b>).</p>
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<p>Needle stochastic sensor design, and tissue and whole blood measurements set-up.</p>
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<p>Current development for stochastic sensors, example for the detection of one biomarker.</p>
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10 pages, 2944 KiB  
Article
Enhancement of Photoemission on p-Type GaAs Using Surface Acoustic Waves
by Boqun Dong, Andrei Afanasev, Rolland Johnson and Mona Zaghloul
Sensors 2020, 20(8), 2419; https://doi.org/10.3390/s20082419 - 24 Apr 2020
Cited by 6 | Viewed by 3033
Abstract
We demonstrate that photoemission properties of p-type GaAs can be altered by surface acoustic waves (SAWs) generated on the GaAs surface due to dynamical piezoelectric fields of SAWs. Multiphysics simulations indicate that charge-carrier recombination is greatly reduced, and electron effective lifetime in p-doped [...] Read more.
We demonstrate that photoemission properties of p-type GaAs can be altered by surface acoustic waves (SAWs) generated on the GaAs surface due to dynamical piezoelectric fields of SAWs. Multiphysics simulations indicate that charge-carrier recombination is greatly reduced, and electron effective lifetime in p-doped GaAs may increase by a factor of 10× to 20×. It implies a significant increase, by a factor of 2× to 3×, of quantum efficiency (QE) for GaAs photoemission applications, like GaAs photocathodes. Conditions of different SAW wavelengths, swept SAW intensities, and varied incident photon energies were investigated. Essential steps in SAW device fabrication on a GaAs substrate are demonstrated, including deposition of an additional layer of ZnO for piezoelectric effect enhancement, measurements of current–voltage (I–V) characteristics of the SAW device, and ability to survive high-temperature annealing. Results obtained and reported in this study provide the potential and basis for future studies on building SAW-enhanced photocathodes, as well as other GaAs photoelectric applications. Full article
(This article belongs to the Special Issue Surface Acoustic Wave and Bulk Acoustic Wave Sensors 2019)
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<p>(<b>a</b>) The bottom is a three-dimensional (3D) schematic view showing the photoemission from p-type doped GaAs surface with a thin negative electron affinity (NEA) coating. The inset is a band diagram demonstration of the three-step photoemission mechanism: I. photoexcitation, II. transport, III. emission from the surface. (<b>b</b>) The 3D schematic view at bottom shows the concept and structure of our surface acoustic wave (SAW) device used to generate SAWs on p-type doped GaAs substrate. Top left—the result of photoexcitation without SAWs. Top right—the band bending effect caused by SAWs. In this case, the electrons and holes are spatially separated, thus the recombination is suppressed.</p>
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<p>(<b>a</b>) Simulation structure of bulk p-type doped GaAs with interdigital transducers (IDTs). (<b>b</b>) Comparison of recombination rates with and without SAWs (<span class="html-italic">V<sub>0</sub></span> = 1.0 V). (<b>c</b>) Comparison of surface electron concentrations with and without SAWs (<span class="html-italic">V<sub>0</sub></span> = 1.0 V). (<b>d</b>) Increase of electron concentrations vs. AC voltage and SAW intensity. (<b>e</b>) Electron concentrations along absorption depth for SAW wavelength (λ<sub>SAW</sub> = 9.2 μm) that was larger than the absorption depth (1.5 μm) in bulk GaAs. (<b>f</b>) Same as (<b>e</b>), but for λ<sub>SAW</sub> = 0.5 μm, which was smaller than the absorption depth. (<b>g</b>) Enhancement of quantum efficiency (QE) under different amplitudes of AC voltage. (<b>h</b>) Enhancement of QE under different incident light wavelength (<span class="html-italic">V<sub>0</sub></span> = 1.0 V).</p>
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<p>(<b>a</b>) Simulation structure of thin film GaAs with IDTs. (<b>b</b>) Comparison of recombination rates in GaAs thin film with and without SAWs (<span class="html-italic">V<sub>0</sub></span> = 1.0 V). (<b>c</b>) Comparison of surface electron concentrations with and without SAWs (<span class="html-italic">V<sub>0</sub></span> = 1.0 V). (<b>d</b>) Increase of electron concentrations caused by SAWs for different amplitudes of AC voltage. (<b>e</b>) Enhancement of QE caused by SAWs under different transmission probability <span class="html-italic">P<sub>E</sub></span>.</p>
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<p>(<b>a</b>) Fabrication process flow of the device: deposition of ZnO thin layer, e-beam lithography and e-beam metal evaporation for aluminum IDTs, etch ZnO to open a window for exposing GaAs surface in center. (<b>b</b>) Scanning electron microscope (SEM) image of the top surface view of IDT fingers placed on the ZnO film. (<b>c</b>) High-resolution SEM image showing the surface morphology of the aluminum IDT finger and the c-axis oriented ZnO layer. (<b>d</b>) Current–voltage (I–V) characteristics of IDTs fabricated on different materials. (<b>e</b>) Optical microscope images of the IDTs taken before annealing. (<b>f</b>) Optical microscope images of the IDTs taken after annealing. (<b>g</b>) Comparison of transmission property S<sub>21</sub> of SAWs generated before and after annealing.</p>
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26 pages, 18576 KiB  
Article
An IoT Platform Based on Microservices and Serverless Paradigms for Smart Farming Purposes
by Sergio Trilles, Alberto González-Pérez and Joaquín Huerta
Sensors 2020, 20(8), 2418; https://doi.org/10.3390/s20082418 - 24 Apr 2020
Cited by 70 | Viewed by 9963
Abstract
Nowadays, the concept of “Everything is connected to Everything” has spread to reach increasingly diverse scenarios, due to the benefits of constantly being able to know, in real-time, the status of your factory, your city, your health or your smallholding. This wide variety [...] Read more.
Nowadays, the concept of “Everything is connected to Everything” has spread to reach increasingly diverse scenarios, due to the benefits of constantly being able to know, in real-time, the status of your factory, your city, your health or your smallholding. This wide variety of scenarios creates different challenges such as the heterogeneity of IoT devices, support for large numbers of connected devices, reliable and safe systems, energy efficiency and the possibility of using this system by third-parties in other scenarios. A transversal middleware in all IoT solutions is called an IoT platform. the IoT platform is a piece of software that works like a kind of “glue” to combine platforms and orchestrate capabilities that connect devices, users and applications/services in a “cyber-physical” world. In this way, the IoT platform can help solve the challenges listed above. This paper proposes an IoT agnostic architecture, highlighting the role of the IoT platform, within a broader ecosystem of interconnected tools, aiming at increasing scalability, stability, interoperability and reusability. For that purpose, different paradigms of computing will be used, such as microservices architecture and serverless computing. Additionally, a technological proposal of the architecture, called SEnviro Connect, is presented. This proposal is validated in the IoT scenario of smart farming, where five IoT devices (SEnviro nodes) have been deployed to improve wine production. A comprehensive performance evaluation is carried out to guarantee a scalable and stable platform. Full article
(This article belongs to the Special Issue Smart Agricultural Applications with Internet of Things)
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<p>IoT data life cycle: <span class="html-italic">Capture</span>, <span class="html-italic">Communicate</span>, <span class="html-italic">Analyse</span> and <span class="html-italic">Act</span>.</p>
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<p>An IoT generic architecture stack.</p>
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<p>An agnostic-technology IoT architecture.</p>
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<p>A general overview of the full system from a technological point of view.</p>
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<p>The wireless communication model used to connect devices to IoT platform.</p>
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<p>A sequence diagram from the IoT node perspective.</p>
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<p>A sequence diagram from the user perspective.</p>
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<p>An example of <span class="html-italic">SEnviro</span> node deployed on a smallholding.</p>
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<p>A screen capture of the <span class="html-italic">SEnviro</span> connect, where it shows all <span class="html-italic">SEnviro</span> nodes deployed.</p>
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<p>A screen capture of the <span class="html-italic">SEnviro</span> connect in a responsive design.</p>
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<p>A screen capture of the <span class="html-italic">SEnviro</span> connect, where it shows the IoT device management view.</p>
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<p>A screen capture of the <span class="html-italic">SEnviro</span> connect, where the wizard to add IoT devices is shown.</p>
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<p>A screen capture of the <span class="html-italic">SEnviro</span> connect, showing the data and alerts for each IoT device.</p>
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<p>Average and standard deviation throughput of the observations injection with rates of 10, 25, 50, 100 and 200 requests per second.</p>
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<p>Average and standard deviation response time of API queries with a rate of 5, 10, 15 and 20 requests per second.</p>
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<p>Average and standard deviation throughput of the API queries with a rate of 5, 10, 15 and 20 requests per second.</p>
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<p>CPU utilisation of the observations injection with a rate of requests 10, 25, 50, 100 and 200 requests per second.</p>
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<p>Memory usage of the observations injection with a rate of requests 10, 25, 50, 100 and 200 requests per second.</p>
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<p>CPU utilisation of the API queries with a rate 5, 10, 15 and 20 requests per second.</p>
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<p>Memory usage of the API queries with a rate of 5, 10, 15 and 20 requests per second.</p>
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15 pages, 499 KiB  
Article
Covert Timing Channel Analysis Either as Cyber Attacks or Confidential Applications
by Shorouq Al-Eidi, Omar Darwish and Yuanzhu Chen
Sensors 2020, 20(8), 2417; https://doi.org/10.3390/s20082417 - 24 Apr 2020
Cited by 12 | Viewed by 5127
Abstract
Covert timing channels are an important alternative for transmitting information in the world of the Internet of Things (IoT). In covert timing channels data are encoded in inter-arrival times between consecutive packets based on modifying the transmission time of legitimate traffic. Typically, the [...] Read more.
Covert timing channels are an important alternative for transmitting information in the world of the Internet of Things (IoT). In covert timing channels data are encoded in inter-arrival times between consecutive packets based on modifying the transmission time of legitimate traffic. Typically, the modification of time takes place by delaying the transmitted packets on the sender side. A key aspect in covert timing channels is to find the threshold of packet delay that can accurately distinguish covert traffic from legitimate traffic. Based on that we can assess the level of dangerous of security threats or the quality of transferred sensitive information secretly. In this paper, we study the inter-arrival time behavior of covert timing channels in two different network configurations based on statistical metrics, in addition we investigate the packet delaying threshold value. Our experiments show that the threshold is approximately equal to or greater than double the mean of legitimate inter-arrival times. In this case covert timing channels become detectable as strong anomalies. Full article
(This article belongs to the Special Issue Machine Learning for IoT Applications and Digital Twins)
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<p>Scheme of encoding binary symbols to the inter arrival time.</p>
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<p>Representation of binary symbols in the packet inter-arrival times.</p>
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<p>Accuracy of distinguishing covert traffic from legitimate traffic.</p>
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<p>Percentage of data loss at different time windows.</p>
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<p>Transmitted bit rates of network configuration 1 (left) and network configuration 2 (right).</p>
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<p>Transmitted bit rates of network configuration 1 (left) and network configuration 2 (right).</p>
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24 pages, 11694 KiB  
Article
A Game-Based Rehabilitation System for Upper-Limb Cerebral Palsy: A Feasibility Study
by Mohammad I. Daoud, Abdullah Alhusseini, Mostafa Z. Ali and Rami Alazrai
Sensors 2020, 20(8), 2416; https://doi.org/10.3390/s20082416 - 24 Apr 2020
Cited by 17 | Viewed by 4446
Abstract
Game-based rehabilitation systems provide an effective tool to engage cerebral palsy patients in physical exercises within an exciting and entertaining environment. A crucial factor to ensure the effectiveness of game-based rehabilitation systems is to assess the correctness of the movements performed by the [...] Read more.
Game-based rehabilitation systems provide an effective tool to engage cerebral palsy patients in physical exercises within an exciting and entertaining environment. A crucial factor to ensure the effectiveness of game-based rehabilitation systems is to assess the correctness of the movements performed by the patient during the game-playing sessions. In this study, we propose a game-based rehabilitation system for upper-limb cerebral palsy that includes three game-based exercises and a computerized assessment method. The game-based exercises aim to engage the participant in shoulder flexion, shoulder horizontal abduction/adduction, and shoulder adduction physical exercises that target the right arm. Human interaction with the game-based rehabilitation system is achieved using a Kinect sensor that tracks the skeleton joints of the participant. The computerized assessment method aims to assess the correctness of the right arm movements during each game-playing session by analyzing the tracking data acquired by the Kinect sensor. To evaluate the performance of the computerized assessment method, two groups of participants volunteered to participate in the game-based exercises. The first group included six cerebral palsy children and the second group included twenty typically developing subjects. For every participant, the computerized assessment method was employed to assess the correctness of the right arm movements in each game-playing session and these computer-based assessments were compared with matching gold standard evaluations provided by an experienced physiotherapist. The results reported in this study suggest the feasibility of employing the computerized assessment method to evaluate the correctness of the right arm movements during the game-playing sessions. Full article
(This article belongs to the Special Issue Smart Sensors for Healthcare and Medical Applications)
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<p>A cerebral palsy patient playing one of the game-based rehabilitation exercises provided by our proposed system.</p>
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<p>The architecture of the proposed bespoke game-based rehabilitation system.</p>
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<p>(<b>a</b>,<b>b</b>): (<b>a</b>) The shoulder flexion physical exercise and (<b>b</b>) the corresponding shoulder flexion game-based exercise. (<b>c</b>,<b>d</b>): (<b>c</b>) The shoulder horizontal abduction physical exercise followed by the shoulder horizontal adduction physical exercise and (<b>d</b>) the corresponding shoulder horizontal abduction/adduction game-based exercise. (<b>e</b>,<b>f</b>): (<b>e</b>) The shoulder adduction physical exercise and (<b>f</b>) the corresponding shoulder adduction game-based exercise.</p>
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<p>(<b>a</b>) The twenty five skeleton joints that are tracked by the Kinect for Windows v2 sensor. (<b>b</b>) The body-attached coordinate system of the Extended Motion-Pose Geometric Descriptor (E-MPGD).</p>
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<p>(<b>a</b>–<b>c</b>) The combination-based occurrence frequency of the features (CBOFF) values computed for the six cerebral palsy children during their participation in (<b>a</b>) the shoulder flexion, (<b>b</b>) the shoulder horizontal abduction/adduction, and (<b>c</b>) the shoulder adduction game-based exercises. (<b>d</b>–<b>f</b>) The participant-based occurrence frequency of the important features (PBOFIF) values computed for the six cerebral palsy children during their participation in (<b>d</b>) the shoulder flexion, (<b>e</b>) the shoulder horizontal abduction/adduction, and (<b>f</b>) the shoulder adduction game-based exercises.</p>
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<p>(<b>a</b>–<b>c</b>) The CBOFF values computed for the twenty typically developing subjects during their participation in (<b>a</b>) the shoulder flexion, (<b>b</b>) the shoulder horizontal abduction/adduction, and (<b>c</b>) the shoulder adduction game-based exercises. (<b>d</b>–<b>f</b>) The PBOFIF values computed for the twenty typically developing subjects during their participation in (<b>d</b>) the shoulder flexion, (<b>e</b>) the shoulder horizontal abduction/adduction, and (<b>f</b>) the shoulder adduction game-based exercises.</p>
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5 pages, 158 KiB  
Editorial
Special Issue “Fibre Optic Sensors for Structural and Geotechnical Monitoring”
by Michele Arturo Caponero
Sensors 2020, 20(8), 2415; https://doi.org/10.3390/s20082415 - 24 Apr 2020
Cited by 5 | Viewed by 2452
Abstract
In this editorial on the special issue “Fibre Optic Sensors for Structural and Geotechnical Monitoring” a review of the contribution papers selected for publication is given. Each paper is briefly summarized, presenting its objective and methods, then a comment is given about the [...] Read more.
In this editorial on the special issue “Fibre Optic Sensors for Structural and Geotechnical Monitoring” a review of the contribution papers selected for publication is given. Each paper is briefly summarized, presenting its objective and methods, then a comment is given about the relevance of the work with respect to the advance and the spreading of the fibre optic technology for monitoring applications. Full article
(This article belongs to the Special Issue Fiber Optic Sensors for Structural and Geotechnical Monitoring)
12 pages, 2237 KiB  
Article
Software Sensor for Airflow Modulation and Noise Detection by Cyclostationary Tools
by Mohamad Alkoussa Dit Albacha, Laurent Rambault, Anas Sakout, Kamel Abed Meraim, Erik Etien, Thierry Doget and Sebastien Cauet
Sensors 2020, 20(8), 2414; https://doi.org/10.3390/s20082414 - 23 Apr 2020
Cited by 1 | Viewed by 2948
Abstract
The paper presents tools to model low speed airflow coming from a turbulent machine. This low speed flow have instabilities who generate noise disturbances in the environment. The aim of the study proposed in this paper, is the using of cyclostationary tools with [...] Read more.
The paper presents tools to model low speed airflow coming from a turbulent machine. This low speed flow have instabilities who generate noise disturbances in the environment. The aim of the study proposed in this paper, is the using of cyclostationary tools with audio signals to model this airflow and detect the noisy frequencies to eliminate this noise. This paper also deals with the extraction in real time of the frequency corresponding to the noise nuisance. This extraction makes it possible to build a software sensor. This software sensor can be used to estimate the air flow rate and also to control a future actuator which will reduce the intensity of the noise nuisance. This paper focuses on the characteristic of the sound signal (property of cyclostationarity) and on the development of a software sensor. The results are established using an experimental setup representative of the physical phenomenon to be characterised. Full article
(This article belongs to the Section Intelligent Sensors)
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<p>Bloc diagram of the algorithm.</p>
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<p>Linearization of the algorithm.</p>
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<p>Experimental Set Up.</p>
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<p>Schematic description of experimental Set Up.</p>
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<p>Measurement from microphone (8).</p>
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<p>Measurement from microphone (8).</p>
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<p>The cyclic spectral density.</p>
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<p>The cyclic spectral coherence.</p>
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<p>The theoretical relations between “<math display="inline"><semantics> <mi>α</mi> </semantics></math>” and “<span class="html-italic">f</span>” for the model (<a href="#FD9-sensors-20-02414" class="html-disp-formula">9</a>) when <math display="inline"><semantics> <mrow> <mi>U</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> is stationary.</p>
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<p>The theoretical relations between “<math display="inline"><semantics> <mi>α</mi> </semantics></math>” and “<span class="html-italic">f</span>” for the model (<a href="#FD9-sensors-20-02414" class="html-disp-formula">9</a>) when <math display="inline"><semantics> <mrow> <mi>U</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> is cyclostationary.</p>
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<p>Real time extraction of frequency.</p>
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13 pages, 3555 KiB  
Article
Performance Evaluation and Compensation Method of Trigger Probes in Measurement Based on the Abbé Principle
by Guoying Ren, Xinghua Qu and Xiangjun Chen
Sensors 2020, 20(8), 2413; https://doi.org/10.3390/s20082413 - 23 Apr 2020
Cited by 4 | Viewed by 4381
Abstract
Trigger probes are widely used in precision manufacturing industries such as coordinate measuring machines (CMM) and high-end computer numerical control(CNC) machine tools for quality control. Their performance and accuracy often determine the measurement results and the quality of the product manufacturing. However, because [...] Read more.
Trigger probes are widely used in precision manufacturing industries such as coordinate measuring machines (CMM) and high-end computer numerical control(CNC) machine tools for quality control. Their performance and accuracy often determine the measurement results and the quality of the product manufacturing. However, because there is no accurate measurement of the trigger force in different directions of the probe, and no special measuring device to calibrate the characteristic parameters of the probe in traditional measurement methods, it is impossible to exactly compensate for the measurement error caused by the trigger force of the probe in the measurement process. The accuracy of the measurement of the equipment can be improved by abiding by the Abbé principle. Thus, in order to better evaluate the performance parameters of the probe and realize the accurate compensation for its errors, this paper presents a method which can directly measure the performance parameters of the trigger probe based on the Abbé measurement principle, expounds the measurement principle, the establishment of the mathematical model, and the calibration system, and finishes with an experimental verification and measurement uncertainty analysis. The experimental results show that this method can obtain the exact calibration errors of the performance parameters of the trigger probe intuitively, realize the compensation for the errors of the probe in the measurement process, and effectively improve the measurement accuracy. Full article
(This article belongs to the Special Issue Sensors for Manufacturing Process Monitoring)
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<p>An illustration of an Abbé error.</p>
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<p>Structure of the touch trigger probe: (<b>a</b>) 3D structure drawing of the probe; (<b>b</b>) schematic diagram of each part of the probe; (<b>c</b>) stress deformation diagram of the probe under the trigger force <span class="html-italic">F<sub>T</sub></span>; (<b>d</b>) diagram of five degrees of freedom distribution of the probe.</p>
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<p>A schematic diagram of the probe parameter calibration system.</p>
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<p>Measurement time sequence diagram of the calibration system.</p>
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<p>Displacement change and stress analysis diagram of the probe in the process of triggering. (<b>a</b>) force analysis in the process of probe triggering; (<b>b</b>) equivalent diagram of force and rigid displacement change of the probe in the process of probe triggering; (<b>c</b>) displacement change of each stage in the process of probe triggering.</p>
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<p>Simulation diagram of pre-travel and its components changing with trigger force. (<b>a</b>) various displacement changes in the whole trigger force range; (<b>b</b>) enlarged view of the red marked area in <a href="#sensors-20-02413-f006" class="html-fig">Figure 6</a>a.</p>
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<p>A picture of the probe parameter calibration system.</p>
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<p>Error distribution of probe parameter calibration in different directions. (<b>a</b>) the repeatability error distribution of the trigger position of the probe; (<b>b</b>) the pre-travel distribution of the probe; (<b>c</b>) the measurement force distribution of the probe at the moment of trigger.</p>
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<p>Error distribution of length measurement of the probe parameter calibration system. (<b>a</b>) error distribution within 1 mm; (<b>b</b>) error distribution within 5 mm.</p>
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18 pages, 6959 KiB  
Article
Acoustic Emission Analysis of Corroded Reinforced Concrete Columns under Compressive Loading
by Qiang Li, Xianyu Jin, Dan Wu and Hailong Ye
Sensors 2020, 20(8), 2412; https://doi.org/10.3390/s20082412 - 23 Apr 2020
Cited by 6 | Viewed by 3153
Abstract
In this work, the failure process of non-corroded and corroded reinforced concrete (RC) columns under eccentric compressive loading is studied using the acoustic emission (AE) technique. The results show that reinforcement corrosion considerably affects the mechanical failure process of RC columns. The corrosion [...] Read more.
In this work, the failure process of non-corroded and corroded reinforced concrete (RC) columns under eccentric compressive loading is studied using the acoustic emission (AE) technique. The results show that reinforcement corrosion considerably affects the mechanical failure process of RC columns. The corrosion of reinforcement in RC columns leads to highly active AE signals at the initial stage of loading, in comparison to the non-corroded counterparts. Also, a continuous AE hit pattern with a higher number of cumulative hits is observed for corroded RC columns. The spatial distribution and evolution of AE events indicate that the reinforcement corrosion noticeably accelerates the initiation and propagation of cracking in the RC columns during compressive loading. The AE characteristics of corroded RC columns are in agreement with the macroscopic failure behaviors observed during the damage and failure process. A damage evolution model of corroded RC columns based on the AE parameters is proposed. Full article
(This article belongs to the Section Physical Sensors)
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<p>Configuration of the specimen (unit: mm), the thickness of concrete covers was 15 mm.</p>
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<p>Corrosion setup.</p>
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<p>Schematic of the test system. (Abbreviation: acoustic emission (AE))</p>
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<p>Photograph of the test setup.</p>
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<p>Distribution of AE sensors on specimens.</p>
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<p>Typical AE signal and its characteristics [<a href="#B44-sensors-20-02412" class="html-bibr">44</a>].</p>
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<p>Corrosion-induced crack patterns: (<b>a</b>) cracking maps of corroded reinforced concrete (RC) column; (<b>b</b>) distribution of cracks and leakage of rust from reinforcement corrosion on the surfaces of the RC column specimens. Unit: mm.</p>
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<p>Load-vertical displacement response of RC specimens.</p>
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<p>Load-mid-span flexural deflection response of RC specimens.</p>
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<p>Load-strain response: (<b>a</b>) non-corroded column, and (<b>b</b>) corroded column.</p>
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<p>AE energy graph: (<b>a</b>) non-corroded column, and (<b>b</b>) corroded column.</p>
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<p>Cracks due to volumetric increase by rust formation on the surfaces of the RC column specimens.</p>
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<p>Visual observation of failure characteristics at the surface of RC columns: (<b>a</b>) non-corroded column, and (<b>b</b>) corroded column.</p>
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<p>AE ring-down count graph: (<b>a</b>) non-corroded column, and (<b>b</b>) corroded column.</p>
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<p>The damage in the RC column specimen caused by corrosion of the reinforcement.</p>
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<p>Correlation between AE cumulative energy and loads.</p>
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<p>Testing results of AE events location at stages for non-corroded specimens with different load levels (<b>a</b>) 20%; (<b>b</b>) 40%; (<b>c</b>) 60%; (<b>d</b>) 80%; (<b>e</b>) 100% load levels.</p>
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<p>Testing results of AE events location at stages for corroded specimens with different load levels (<b>a</b>) 20%; (<b>b</b>) 40%; (<b>c</b>) 60%; (<b>d</b>) 80%; (<b>e</b>) 100% load levels.</p>
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<p>Correlation of load level and accumulated AE energy: (<b>a</b>) non-corroded column, and (<b>b</b>) corroded column.</p>
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<p>The modeled relationships between the load level and the damage factor for corroded and non-corroded RC columns.</p>
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12 pages, 5032 KiB  
Article
3-D Terrain Node Coverage of Wireless Sensor Network Using Enhanced Black Hole Algorithm
by Jeng-Shyang Pan, Qing-Wei Chai, Shu-Chuan Chu and Ning Wu
Sensors 2020, 20(8), 2411; https://doi.org/10.3390/s20082411 - 23 Apr 2020
Cited by 32 | Viewed by 3823
Abstract
In this paper, a new intelligent computing algorithm named Enhanced Black Hole (EBH) is proposed to which the mutation operation and weight factor are applied. In EBH, several elites are taken as role models instead of only one in the original Black Hole [...] Read more.
In this paper, a new intelligent computing algorithm named Enhanced Black Hole (EBH) is proposed to which the mutation operation and weight factor are applied. In EBH, several elites are taken as role models instead of only one in the original Black Hole (BH) algorithm. The performance of the EBH algorithm is verified by the CEC 2013 test suit, and shows better results than the original BH. In addition, the EBH and other celebrated algorithms can be used to solve node coverage problems of Wireless Sensor Network (WSN) in 3-D terrain with satisfactory performance. Full article
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<p>Terrain for Deploying Sensor Nodes (The units of x, y, z-axis is meter).</p>
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<p>A Simple Paradigm about LOS.</p>
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<p>The Value Setting of Dimensions of Individual.</p>
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<p>Results of Simulation Experiments (1).</p>
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<p>Results of Simulation Experiments (2).</p>
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17 pages, 2170 KiB  
Article
Use of Spectroscopic Techniques for a Rapid and Non-Destructive Monitoring of Thermal Treatments and Storage Time of Sous-Vide Cooked Cod Fillets
by Abdo Hassoun, Janna Cropotova, Turid Rustad, Karsten Heia, Stein-Kato Lindberg and Heidi Nilsen
Sensors 2020, 20(8), 2410; https://doi.org/10.3390/s20082410 - 23 Apr 2020
Cited by 13 | Viewed by 4565
Abstract
In this work, the potential of spectroscopic techniques was studied to investigate heat-induced changes occurring during the application of thermal treatments on cod (Gadus morhua L.) fillets. Vacuum-packed samples were thermally treated in a water bath at 50, 60, 70 and 80 [...] Read more.
In this work, the potential of spectroscopic techniques was studied to investigate heat-induced changes occurring during the application of thermal treatments on cod (Gadus morhua L.) fillets. Vacuum-packed samples were thermally treated in a water bath at 50, 60, 70 and 80 °C for 5 and 10 min, and further stored for one, four, and eight days at 4 ± 1 °C before analysis. Several traditional (including cooking loss, drip loss, texture, protein solubility, protein oxidation, and color) and spectroscopic (fluorescence and diffuse reflectance hyperspectral imaging) measurements were conducted on the same samples. The results showed a decrease in fluorescence intensity with increasing cooking temperature and storage time, while the impact of cooking time was only noticeable at low temperatures. Diffuse reflectance data exhibited a decrease in absorbance, possibly as a result of protein denaturation and increased scattering at higher cooking temperatures. Both fluorescence and diffuse reflectance data were highly correlated with color parameters, whereas moderate correlations were observed with most other traditional parameters. Support vector machine models performed better than partial least square ones for both classification of cod samples cooked at different temperatures and in prediction of the cooking temperature. The best classification result was obtained on fluorescence data, achieving an accuracy of 92.5%, while the prediction models resulted in a root mean square error of prediction of cooking temperature lower than 5 °C. Overall, the classification and prediction models showed good results, indicating that spectroscopic techniques, especially fluorescence hyperspectral imaging, have a high potential for monitoring thermal treatments in cod fillets. Full article
(This article belongs to the Special Issue Fluorescence-Based Sensors)
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<p>Cooking loss (<b>A</b>) and drip loss (<b>B</b>) obtained on the cod samples as a function of cooking temperature; T, cooking time; t, and storage days; D (V; vacuum-packed samples, A; air-packed samples).</p>
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<p>Total carbonyls in sarcoplasmic (<b>A</b>) and myofibrillar proteins (<b>B</b>), obtained on the cod samples as a function of cooking temperature; T, cooking time; t, and storage days; D (V; vacuum-packed samples, A; air-packed samples).</p>
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<p>Color parameters: lightness (<b>A</b>), redness (<b>B</b>), and yellowness (<b>C</b>), obtained on the cod samples as a function of cooking temperature; T, cooking time; t, and storage days; D (V; vacuum-packed samples, A; air-packed samples).</p>
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<p>Mean fluorescence (<b>A</b>,<b>B</b>) and diffuse reflectance (<b>C</b>,<b>D</b>) spectra of the control (A and C) and the heat treated samples (B and D), obtained as a function of storage days (D), packaging types (V; vacuum-packed samples, A; air-packed samples), and cooking conditions; cooking temperature; T, cooking time; t.</p>
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<p>Principal Components Analysis (PCA) (<b>A</b>,<b>C</b>) and predicted cross-validated classes resulted from Support Vector Machine Classification (SVMC) analysis (<b>B</b>,<b>D</b>) applied respectively to the fluorescence (A,B) and the diffuse reflectance (C,D) data, obtained on the control and heat treated cod samples as a function of cooking temperatures (T50, T60, T70, T80).</p>
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<p>Partial Least Square Regression (PLSR) (<b>A</b>,<b>B</b>) and Support Vector Machine Regression (SVMR) (<b>C</b>,<b>D</b>) results of predicted cooking temperatures, applied respectively to the fluorescence (A and C) and the diffuse reflectance (B and D) data obtained on the control (T20) and heat treated cod samples as a function of cooking temperatures (T50, T60, T70, T80).</p>
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11 pages, 5441 KiB  
Article
Measurement for the Thickness of Water Droplets/Film on a Curved Surface with Digital Image Projection (DIP) Technique
by Lingwei Zeng, Hanfeng Wang, Ying Li and Xuhui He
Sensors 2020, 20(8), 2409; https://doi.org/10.3390/s20082409 - 23 Apr 2020
Viewed by 3746
Abstract
Digital image projection (DIP) with traditional vertical calibration cannot be used for measuring the water droplets/film on a curved surface, because significant systematic error will be introduced. An improved DIP technique with normal calibration is proposed in the present paper, including the principles, [...] Read more.
Digital image projection (DIP) with traditional vertical calibration cannot be used for measuring the water droplets/film on a curved surface, because significant systematic error will be introduced. An improved DIP technique with normal calibration is proposed in the present paper, including the principles, operation procedures and analysis of systematic errors, which was successfully applied to measuring the water droplets/film on a curved surface. By comparing the results of laser profiler, traditional DIP, improved DIP and theoretical analysis, advantages of the present improved DIP technique are highlighted. Full article
(This article belongs to the Special Issue Camera as a Smart-Sensor (CaaSS))
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<p>Schematic diagram of the digital image projection (DIP) technique (Zhang et al. [<a href="#B12-sensors-20-02409" class="html-bibr">12</a>]).</p>
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<p>Schematic diagram of (<b>a</b>) a droplet on the cylindrical surface and (<b>b</b>) dependence of the systematic error on <span class="html-italic">α</span> for different <span class="html-italic">d</span>/<span class="html-italic">r</span>.</p>
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<p>Typical geometric configurations of the DIP measurement on curved surface: (<b>a</b>) 90°−<span class="html-italic">α</span> ≥ <span class="html-italic">θ</span> (convex surface); (<b>b</b>) 90°−<span class="html-italic">α</span> ≤ <span class="html-italic">θ</span> (convex surface); (<b>c</b>) 90°−<span class="html-italic">α</span> ≥ <span class="html-italic">θ</span> (concave surface); (<b>d</b>) 90°−α ≤ θ (concave surface)</p>
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<p>Relationship between <span class="html-italic">d</span>* and the corrected coefficient <span class="html-italic">k</span><sub>2</sub>.</p>
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<p>Maximum systematic error with different combinations of <math display="inline"><semantics> <mi mathvariant="italic">θ</mi> </semantics></math> and <math display="inline"><semantics> <mi mathvariant="italic">α</mi> </semantics></math>: (<b>a</b>) <span class="html-italic">d</span><sub>max</sub>/<span class="html-italic">r</span> = 1/3; (<b>b</b>) <span class="html-italic">d</span><sub>max</sub>/<span class="html-italic">r</span> = 1/10; (<b>c</b>) <span class="html-italic">d</span><sub>max</sub>/<span class="html-italic">r</span> = 1/33.</p>
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<p>Systematic error under the condition of <span class="html-italic">d</span><sub>max</sub>/<span class="html-italic">r</span> = <math display="inline"><semantics> <mrow> <mfrac> <mn>1</mn> <mrow> <mn>10</mn> </mrow> </mfrac> </mrow> </semantics></math>, <math display="inline"><semantics> <mi mathvariant="italic">θ</mi> </semantics></math> = 60° utilizing (<b>a</b>) standard reference axis or (<b>b</b>) arbitrary reference axis.</p>
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<p>Projected image with random scattered particles: (<b>a</b>) low grayscale; (<b>b</b>) high grayscale.</p>
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<p>Root Mean Square (RMS) error as function of the interrogation window size.</p>
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<p>Measurement for a curved shell: (<b>a</b>) experimental models; (<b>b</b>) results along the central cross section.</p>
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<p>Measuring results of a droplet on the flat plane: (<b>a</b>) droplet on a flat surface; (<b>b</b>) result of 2D laser profiler; (<b>c</b>) result of DIP; (<b>d</b>) thickness along Line 1; (<b>e</b>) thickness along Line 2.</p>
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<p>Measurement results of a droplet on the cylindrical surface: (<b>a</b>) sketch of the experimental setup; (<b>b</b>) thickness along Line 1.</p>
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10 pages, 3404 KiB  
Article
The Design of a Frame-Like ZnO FBAR Sensor for Achieving Uniform Mass Sensitivity Distributions
by Xueli Zhao, Zinan Zhao, Bin Wang, Zhenghua Qian and Tingfeng Ma
Sensors 2020, 20(8), 2408; https://doi.org/10.3390/s20082408 - 23 Apr 2020
Cited by 6 | Viewed by 3029
Abstract
In this paper, an infinite circular ZnO thin film bulk acoustic resonator (FBAR) with a frame-like electrode operating at the thickness-extensional (TE) mode is studied. Two-dimensional scalar differential equations established for the problem in the Cartesian coordinate system are successfully solved by transforming [...] Read more.
In this paper, an infinite circular ZnO thin film bulk acoustic resonator (FBAR) with a frame-like electrode operating at the thickness-extensional (TE) mode is studied. Two-dimensional scalar differential equations established for the problem in the Cartesian coordinate system are successfully solved by transforming them into normal Bessel equations and modified Bessel equations in the cylindrical coordinate system. Resonant frequencies and vibration distributions are obtained for this frame-like FBAR sensor. A nearly uniform mass sensitivity distribution in the active area is achieved by designing proper electrode size and mass ratio of the driving electrode to the ZnO film. Numerical results show that compared with the reported ring electrode FBAR sensor, the novel frame-like electrode FBAR can achieve a maximum optimization ratio (up to 97.90%) on the uniformity of the mass sensitivity distribution in the active area under the same structural parameters, which is also higher than the optimization ratio 77.63% obtained by the reported double-ring electrode design. Moreover, the mechanism to achieve a very uniform mass sensitivity distribution in the active area by the frame-like electrode is explained in detail according to dispersion curves. Namely, when the resonant frequency of the FBAR sensor is close to the cut-off frequency of the active region in the dispersion curve, the mass sensitivity distribution is nearly uniform. These conclusions provide a theoretical guidance for the design and optimization of ZnO FBAR mass sensors with high performance. Full article
(This article belongs to the Special Issue Surface Acoustic Wave and Bulk Acoustic Wave Sensors 2019)
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<p>The schematic of a ZnO film bulk acoustic resonator (FBAR) with a circular frame-like driving electrode. It consists of a driving electrode, a ZnO film, a ground electrode, and a Si substrate, where <span class="html-italic">h</span> indicates the thickness of each region and <span class="html-italic">r</span> represents the electrode size.</p>
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<p>2D top view of the fundamental thickness-extensional (TE) mode in the normal circular electrode (energy-trapped) FBAR sensor (<b>a</b>), the ring electrode FBAR sensor (<b>b</b>), and the frame-like electrode FBAR sensor (<b>c</b>). Normalized mass sensitivity distributions of these three cases can be seen in (<b>d</b>).</p>
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<p>Comparison of the frame-like electrode FBAR (<b>a</b>) and the ring electrode FBAR (<b>b</b>) [<a href="#B20-sensors-20-02408" class="html-bibr">20</a>] for different <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mn>1</mn> </msub> </mrow> </semantics></math>, when <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mn>2</mn> </msub> </mrow> </semantics></math> is fixed as <math display="inline"><semantics> <mrow> <mn>60</mn> <mtext> </mtext> <mrow> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">m</mi> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>R</mi> <mo>˜</mo> </mover> <mo>’</mo> </mrow> </semantics></math> is equal to 0.04. With the decreasing of <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mn>1</mn> </msub> </mrow> </semantics></math>, the mass sensitivity distributions in the active region for the frame-like electrode become more uniform, but the distributions for the ring electrode are still concave.</p>
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<p>Comparison between the frame-like electrode FBAR (<b>a</b>) and the ring electrode FBAR (<b>b</b>) [<a href="#B20-sensors-20-02408" class="html-bibr">20</a>] for different <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mn>2</mn> </msub> </mrow> </semantics></math>, when <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mn>1</mn> </msub> </mrow> </semantics></math> is fixed as 30<math display="inline"><semantics> <mrow> <mrow> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">m</mi> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>R</mi> <mo>˜</mo> </mover> <mo>’</mo> </mrow> </semantics></math> is equal to 0.04. With the increasing of <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mn>2</mn> </msub> </mrow> </semantics></math>, more uniform mass sensitivity distributions are observed for the frame-like electrode. However, it is hard for the ring electrode to achieve uniform distributions.</p>
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<p>Effects of the mass ratio <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>R</mi> <mo>˜</mo> </mover> <mo>’</mo> </mrow> </semantics></math> of the overlap electrode to ZnO piezoelectric thin film on the mass sensitivity distributions in the frame-like electrode FBAR sensor, when <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mn>1</mn> </msub> </mrow> </semantics></math> is fixed as 30<math display="inline"><semantics> <mrow> <mrow> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">m</mi> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>r</mi> <mn>2</mn> </msub> </mrow> </semantics></math> is fixed as 60<math display="inline"><semantics> <mrow> <mrow> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">m</mi> </mrow> </mrow> </semantics></math>. With the increasing of <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>R</mi> <mo>˜</mo> </mover> <mo>’</mo> </mrow> </semantics></math>, the shape of the distributions change to the concave from convex. The uniform distributions in the active region are obtained for <math display="inline"><semantics> <mrow> <mover accent="true"> <msup> <mi>R</mi> <mo>′</mo> </msup> <mo>˜</mo> </mover> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>, indicating that the uniform mass sensitivity distributions can be successfully achieved by selecting proper mass ratio through the novel frame-like electrode design.</p>
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<p>Schematic of the dispersion curve of the thickness extensional (TE) wave modes in the active, overlap, and outside areas.</p>
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12 pages, 1574 KiB  
Article
KickStat: A Coin-Sized Potentiostat for High-Resolution Electrochemical Analysis
by Orlando S. Hoilett, Jenna F. Walker, Bethany M. Balash, Nicholas J. Jaras, Sriram Boppana and Jacqueline C. Linnes
Sensors 2020, 20(8), 2407; https://doi.org/10.3390/s20082407 - 23 Apr 2020
Cited by 63 | Viewed by 14661
Abstract
The demand for wearable and point-of-care devices has led to an increase in electrochemical sensor development to measure an ever-increasing array of biological molecules. In order to move from the benchtop to truly portable devices, the development of new biosensors requires miniaturized instrumentation [...] Read more.
The demand for wearable and point-of-care devices has led to an increase in electrochemical sensor development to measure an ever-increasing array of biological molecules. In order to move from the benchtop to truly portable devices, the development of new biosensors requires miniaturized instrumentation capable of making highly sensitive amperometric measurements. To meet this demand, we have developed KickStat, a miniaturized potentiostat that combines the small size of the integrated Texas Instruments LMP91000 potentiostat chip (Texas Instruments, Dallas, TX, USA) with the processing power of the ARM Cortex-M0+ SAMD21 microcontroller (Microchip Technology, Chandler, AZ, USA) on a custom-designed 21.6 mm by 20.3 mm circuit board. By incorporating onboard signal processing via the SAMD21, we achieve 1 mV voltage increment resolution and an instrumental limit of detection of 4.5 nA in a coin-sized form factor. This elegant engineering solution allows for high-resolution electrochemical analysis without requiring extensive circuitry. We measured the faradaic current of an anti-cocaine aptamer using cyclic voltammetry and square wave voltammetry and demonstrated that KickStat’s response was within 0.6% of a high-end benchtop potentiostat. To further support others in electrochemical biosensors development, we have made KickStat’s design and firmware available in an online GitHub repository. Full article
(This article belongs to the Special Issue Amperometric Sensing)
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<p>(<b>A</b>) Photograph of the assembled KickStat: Button Cell Rev B. The device features the LMP91000 along with a SAMD21 microcontroller running an Arduino bootloader, (<b>B</b>) functional block diagram of KickStat: Button Cell Rev A highlighting the essential subcomponents, (<b>C</b>) block diagram of the LMP91000 highlighting its internal features and characteristics (diagram recreated from the chip’s datasheet). Details of the LMP91000 can be found in the chip’s datasheet.</p>
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<p>Open circuit current measurements with calculated input-referred noise. Noise decreases as the gain resistor increases.</p>
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<p>Quantitative comparisons between KickStat (blue) and the commercial device (red) while measuring 5 mM potassium ferricyanide with different electrochemical techniques. (<b>a</b>) Cyclic voltammetry, (<b>b</b>) square wave voltammetry, (<b>c</b>) chronoamperometry, and (<b>d</b>) normal pulse voltammetry. Peak values of the current are within 9% for each measurement across each electrochemical technique. Each data point shown for each device is the average of 3 sequential runs. Error bars represent standard deviation and are smaller than the points plotted. Voltages are referenced against an Ag/AgCl reference electrode.</p>
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<p>Qualitative comparisons between KickStat (blue) and the commercial device (red) while measuring cocaine biosensor. (<b>a</b>) Comparative readout of the cyclic voltammogram for the cocaine aptamer in phosphate-buffered saline (PBS) displaying minimal redox peak separation characteristic of an adsorbed species, indicating successful functionalization of the electrode, (<b>b</b>) Cyclic voltammogram with the lower resolution LMP91000 stock voltage reference generator and corresponding points using the commercial device. Peaks are not discernible by eye or by commercial device’s software, making analysis of the electrochemical current virtually impossible, (<b>c</b>) Square wave voltammograms in PBS and 0.5 mM cocaine hydrochloride. Data points shown for each device are the average of 3 sequential runs. Error bars represent standard deviation and are smaller than the points plotted in many cases. Voltages are referenced against an Ag/AgCl reference electrode.</p>
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