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Sensors, Volume 17, Issue 10 (October 2017)

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Cover Story (view full-size image) Mirica and co-workers report a simple method for drawing chemiresistive sensors on paper using [...] Read more.
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Open AccessArticle Spatiotemporal Pixelization to Increase the Recognition Score of Characters for Retinal Prostheses
Sensors 2017, 17(10), 2439; https://doi.org/10.3390/s17102439
Received: 26 September 2017 / Revised: 21 October 2017 / Accepted: 22 October 2017 / Published: 24 October 2017
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Abstract
Most of the retinal prostheses use a head-fixed camera and a video processing unit. Some studies proposed various image processing methods to improve visual perception for patients. However, previous studies only focused on using spatial information. The present study proposes a spatiotemporal pixelization
[...] Read more.
Most of the retinal prostheses use a head-fixed camera and a video processing unit. Some studies proposed various image processing methods to improve visual perception for patients. However, previous studies only focused on using spatial information. The present study proposes a spatiotemporal pixelization method mimicking fixational eye movements to generate stimulation images for artificial retina arrays by combining spatial and temporal information. Input images were sampled with a resolution that was four times higher than the number of pixel arrays. We subsampled this image and generated four different phosphene images. We then evaluated the recognition scores of characters by sequentially presenting phosphene images with varying pixel array sizes (6 × 6, 8 × 8 and 10 × 10) and stimulus frame rates (10 Hz, 15 Hz, 20 Hz, 30 Hz, and 60 Hz). The proposed method showed the highest recognition score at a stimulus frame rate of approximately 20 Hz. The method also significantly improved the recognition score for complex characters. This method provides a new way to increase practical resolution over restricted spatial resolution by merging the higher resolution image into high-frame time slots. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
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Open AccessArticle Near-Field Three-Dimensional Planar Millimeter-Wave Holographic Imaging by Using Frequency Scaling Algorithm
Sensors 2017, 17(10), 2438; https://doi.org/10.3390/s17102438
Received: 23 August 2017 / Revised: 15 October 2017 / Accepted: 23 October 2017 / Published: 24 October 2017
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Abstract
In this paper, a fast three-dimensional (3-D) frequency scaling algorithm (FSA) with large depth of focus is presented for near-field planar millimeter-wave (MMW) holographic imaging. Considering the cross-range range coupling term which is neglected in the conventional range migration algorithm (RMA), we propose
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In this paper, a fast three-dimensional (3-D) frequency scaling algorithm (FSA) with large depth of focus is presented for near-field planar millimeter-wave (MMW) holographic imaging. Considering the cross-range range coupling term which is neglected in the conventional range migration algorithm (RMA), we propose an algorithm performing the range cell migration correction for de-chirped signals without interpolation by using a 3-D frequency scaling operation. First, to deal with the cross-range range coupling term, a 3-D frequency scaling operator is derived to eliminate the space variation of range cell migration. Then, a range migration correction factor is performed to compensate for the residual range cell migration. Finally, the imaging results are obtained by matched filtering in the cross-range direction. Compared with the conventional RMA, the proposed algorithm is comparable in accuracy but more efficient by using only chirp multiplications and fast Fourier transforms (FFTs). The algorithm has been tested with satisfying results by both simulation and experiment. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Development and Elucidation of a Novel Fluorescent Boron-Sensor for the Analysis of Boronic Acid-Containing Compounds
Sensors 2017, 17(10), 2436; https://doi.org/10.3390/s17102436
Received: 14 September 2017 / Revised: 10 October 2017 / Accepted: 20 October 2017 / Published: 24 October 2017
Cited by 1 | PDF Full-text (3507 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Novel boron-containing drugs have recently been suggested as a new class of pharmaceuticals. However, the majority of current boron-detection techniques require expensive facilities and/or tedious pretreatment methods. Thus, to develop a novel and convenient detection method for boron-based pharmaceuticals, imine-type boron-chelating-ligands were previously
[...] Read more.
Novel boron-containing drugs have recently been suggested as a new class of pharmaceuticals. However, the majority of current boron-detection techniques require expensive facilities and/or tedious pretreatment methods. Thus, to develop a novel and convenient detection method for boron-based pharmaceuticals, imine-type boron-chelating-ligands were previously synthesized for use in a fluorescent sensor for boronic acid containing compounds. However, the fluorescence quantum yield of the imine-type sensor was particularly low, and the sensor was easily decomposed in aqueous media. Thus, in this paper, we report the development of a novel, convenient, and stable fluorescent boron-sensor based on O- and N-chelation (i.e., 2-(pyridine-2yl)phenol), and a corresponding method for the quantitative and qualitative detection of boronic acid-containing compounds using this commercially available sensor is presented. Full article
(This article belongs to the Special Issue Fluorescent Probes and Sensors) Printed Edition available
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Open AccessArticle Detection of Stress Levels from Biosignals Measured in Virtual Reality Environments Using a Kernel-Based Extreme Learning Machine
Sensors 2017, 17(10), 2435; https://doi.org/10.3390/s17102435
Received: 6 September 2017 / Revised: 20 October 2017 / Accepted: 20 October 2017 / Published: 24 October 2017
Cited by 1 | PDF Full-text (5595 KB) | HTML Full-text | XML Full-text
Abstract
Virtual reality (VR) is a computer technique that creates an artificial environment composed of realistic images, sounds, and other sensations. Many researchers have used VR devices to generate various stimuli, and have utilized them to perform experiments or to provide treatment. In this
[...] Read more.
Virtual reality (VR) is a computer technique that creates an artificial environment composed of realistic images, sounds, and other sensations. Many researchers have used VR devices to generate various stimuli, and have utilized them to perform experiments or to provide treatment. In this study, the participants performed mental tasks using a VR device while physiological signals were measured: a photoplethysmogram (PPG), electrodermal activity (EDA), and skin temperature (SKT). In general, stress is an important factor that can influence the autonomic nervous system (ANS). Heart-rate variability (HRV) is known to be related to ANS activity, so we used an HRV derived from the PPG peak interval. In addition, the peak characteristics of the skin conductance (SC) from EDA and SKT variation can also reflect ANS activity; we utilized them as well. Then, we applied a kernel-based extreme-learning machine (K-ELM) to correctly classify the stress levels induced by the VR task to reflect five different levels of stress situations: baseline, mild stress, moderate stress, severe stress, and recovery. Twelve healthy subjects voluntarily participated in the study. Three physiological signals were measured in stress environment generated by VR device. As a result, the average classification accuracy was over 95% using K-ELM and the integrated feature (IT = HRV + SC + SKT). In addition, the proposed algorithm can embed a microcontroller chip since K-ELM algorithm have very short computation time. Therefore, a compact wearable device classifying stress levels using physiological signals can be developed. Full article
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Open AccessArticle Precise GNSS Positioning Using Smart Devices
Sensors 2017, 17(10), 2434; https://doi.org/10.3390/s17102434
Received: 24 August 2017 / Revised: 9 October 2017 / Accepted: 20 October 2017 / Published: 24 October 2017
Cited by 2 | PDF Full-text (2285 KB) | HTML Full-text | XML Full-text
Abstract
The recent access to GNSS (Global Navigation Satellite System) phase observations on smart devices, enabled by Google through its Android operating system, opens the possibility to apply precise positioning techniques using off-the-shelf, mass-market devices. The target of this work is to evaluate whether
[...] Read more.
The recent access to GNSS (Global Navigation Satellite System) phase observations on smart devices, enabled by Google through its Android operating system, opens the possibility to apply precise positioning techniques using off-the-shelf, mass-market devices. The target of this work is to evaluate whether this is feasible, and which positioning accuracy can be achieved by relative positioning of the smart device with respect to a base station. Positioning of a Google/HTC Nexus 9 tablet was performed by means of batch least-squares adjustment of L1 phase double-differenced observations, using the open source goGPS software, over baselines ranging from approximately 10 m to 8 km, with respect to both physical (geodetic or low-cost) and virtual base stations. The same positioning procedure was applied also to a co-located u-blox low-cost receiver, to compare the performance between the receiver and antenna embedded in the Nexus 9 and a standard low-cost single-frequency receiver with external patch antenna. The results demonstrate that with a smart device providing raw GNSS phase observations, like the Nexus 9, it is possible to reach decimeter-level accuracy through rapid-static surveys, without phase ambiguity resolution. It is expected that sub-centimeter accuracy could be achieved, as demonstrated for the u-blox case, if integer phase ambiguities were correctly resolved. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle mlCAF: Multi-Level Cross-Domain Semantic Context Fusioning for Behavior Identification
Sensors 2017, 17(10), 2433; https://doi.org/10.3390/s17102433
Received: 20 September 2017 / Revised: 16 October 2017 / Accepted: 19 October 2017 / Published: 24 October 2017
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Abstract
The emerging research on automatic identification of user’s contexts from the cross-domain environment in ubiquitous and pervasive computing systems has proved to be successful. Monitoring the diversified user’s contexts and behaviors can help in controlling lifestyle associated to chronic diseases using context-aware applications.
[...] Read more.
The emerging research on automatic identification of user’s contexts from the cross-domain environment in ubiquitous and pervasive computing systems has proved to be successful. Monitoring the diversified user’s contexts and behaviors can help in controlling lifestyle associated to chronic diseases using context-aware applications. However, availability of cross-domain heterogeneous contexts provides a challenging opportunity for their fusion to obtain abstract information for further analysis. This work demonstrates extension of our previous work from a single domain (i.e., physical activity) to multiple domains (physical activity, nutrition and clinical) for context-awareness. We propose multi-level Context-aware Framework (mlCAF), which fuses the multi-level cross-domain contexts in order to arbitrate richer behavioral contexts. This work explicitly focuses on key challenges linked to multi-level context modeling, reasoning and fusioning based on the mlCAF open-source ontology. More specifically, it addresses the interpretation of contexts from three different domains, their fusioning conforming to richer contextual information. This paper contributes in terms of ontology evolution with additional domains, context definitions, rules and inclusion of semantic queries. For the framework evaluation, multi-level cross-domain contexts collected from 20 users were used to ascertain abstract contexts, which served as basis for behavior modeling and lifestyle identification. The experimental results indicate a context recognition average accuracy of around 92.65% for the collected cross-domain contexts. Full article
(This article belongs to the Special Issue Context Aware Environments and Applications)
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Open AccessArticle Towards Harmonious Coexistence in the Unlicensed Spectrum: Rational Cooperation of Operators
Sensors 2017, 17(10), 2432; https://doi.org/10.3390/s17102432
Received: 22 August 2017 / Revised: 10 October 2017 / Accepted: 21 October 2017 / Published: 24 October 2017
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Abstract
5G New Radio (NR) operating in the unlicensed spectrum is accelerating the Fourth Industrial Revolution by supporting Internet of Things (IoT) networks or Industrial IoT deployments. Specifically, LTE-Advanced (LTE-A) is looking to achieve spectrum integration through coexistence with multi-radio access technology (RAT) systems
[...] Read more.
5G New Radio (NR) operating in the unlicensed spectrum is accelerating the Fourth Industrial Revolution by supporting Internet of Things (IoT) networks or Industrial IoT deployments. Specifically, LTE-Advanced (LTE-A) is looking to achieve spectrum integration through coexistence with multi-radio access technology (RAT) systems in the same unlicensed bands with both licensed-assisted and stand-alone access. The listen-before-talk (LBT) mechanism is mainly considered to enable an LTE operator to protect other incumbent unlicensed systems. In this article, we investigate the behaviors of multiple LTE operators along with the deployment of WiFi networks in the unlicensed spectrum from both short- and long-term points of view. In countries without mandatory LBT requirements, we show that an LTE operator is susceptible to collusion with another LTE operator, thus exploiting scarce spectrum resources by deceiving other wireless networks into thinking that channels are always busy; hence, mandatory usage of LTE with LBT is highly recommended at national level to achieve harmonious coexistence in the unlicensed spectrum. We discuss several possible coexistence scenarios to resolve the operator’s dilemmaas well as to improve unlicensed spectrum efficiency among multi-RAT systems, which is viable in the near future. Full article
(This article belongs to the Special Issue Green Wireless Networks in 5G-inspired Applications)
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Open AccessArticle Three-Axis Ground Reaction Force Distribution during Straight Walking
Sensors 2017, 17(10), 2431; https://doi.org/10.3390/s17102431
Received: 30 August 2017 / Revised: 12 October 2017 / Accepted: 19 October 2017 / Published: 24 October 2017
Cited by 1 | PDF Full-text (7855 KB) | HTML Full-text | XML Full-text
Abstract
We measured the three-axis ground reaction force (GRF) distribution during straight walking. Small three-axis force sensors composed of rubber and sensor chips were fabricated and calibrated. After sensor calibration, 16 force sensors were attached to the left shoe. The three-axis force distribution during
[...] Read more.
We measured the three-axis ground reaction force (GRF) distribution during straight walking. Small three-axis force sensors composed of rubber and sensor chips were fabricated and calibrated. After sensor calibration, 16 force sensors were attached to the left shoe. The three-axis force distribution during straight walking was measured, and the local features of the three-axis force under the sole of the shoe were analyzed. The heel area played a role in receiving the braking force, the base area of the fourth and fifth toes applied little vertical or shear force, the base area of the second and third toes generated a portion of the propulsive force and received a large vertical force, and the base area of the big toe helped move the body’s center of mass to the other foot. The results demonstrate that measuring the three-axis GRF distribution is useful for a detailed analysis of bipedal locomotion. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessReview Lithium Ion Sensors
Sensors 2017, 17(10), 2430; https://doi.org/10.3390/s17102430
Received: 20 September 2017 / Revised: 19 October 2017 / Accepted: 20 October 2017 / Published: 24 October 2017
Cited by 2 | PDF Full-text (3402 KB) | HTML Full-text | XML Full-text
Abstract
The detection and monitoring of lithium in environmental and clinical settings is becoming increasingly important. In this review, sensors incorporating conductive polymers and lithium bronzes are discussed, together with electrochemical and spectroscopic approaches. Ionophore-based methods have been employed extensively, with varying degrees of
[...] Read more.
The detection and monitoring of lithium in environmental and clinical settings is becoming increasingly important. In this review, sensors incorporating conductive polymers and lithium bronzes are discussed, together with electrochemical and spectroscopic approaches. Ionophore-based methods have been employed extensively, with varying degrees of selectivity and sensitivity, and these are discussed in depth. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle Fiber-Optic Point-Based Sensor Using Specklegram Measurement
Sensors 2017, 17(10), 2429; https://doi.org/10.3390/s17102429
Received: 14 September 2017 / Revised: 14 October 2017 / Accepted: 14 October 2017 / Published: 24 October 2017
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Abstract
Here, we report a fiber-optic point-based sensor to measure temperature and weight based on correlated specklegrams induced by spatial multimode interference. The device is realized simply by splicing a multimode fiber (MMF) to a single-mode fiber (SMF) with a core offset. A series
[...] Read more.
Here, we report a fiber-optic point-based sensor to measure temperature and weight based on correlated specklegrams induced by spatial multimode interference. The device is realized simply by splicing a multimode fiber (MMF) to a single-mode fiber (SMF) with a core offset. A series of experiments demonstrates the approximately linear relation between the correlation coefficient and variation. Furthermore, we show the potential applications of the refractive index sensing of our device by disconnecting the splicing point of MMF and SMF. A modification of the algorithm in order to improve the sensitivity of the sensor is also discussed at the end of the paper. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Comparison between Random Forests, Artificial Neural Networks and Gradient Boosted Machines Methods of On-Line Vis-NIR Spectroscopy Measurements of Soil Total Nitrogen and Total Carbon
Sensors 2017, 17(10), 2428; https://doi.org/10.3390/s17102428
Received: 10 August 2017 / Revised: 2 October 2017 / Accepted: 20 October 2017 / Published: 24 October 2017
Cited by 1 | PDF Full-text (4707 KB) | HTML Full-text | XML Full-text
Abstract
Accurate and detailed spatial soil information about within-field variability is essential for variable-rate applications of farm resources. Soil total nitrogen (TN) and total carbon (TC) are important fertility parameters that can be measured with on-line (mobile) visible and near infrared (vis-NIR) spectroscopy. This
[...] Read more.
Accurate and detailed spatial soil information about within-field variability is essential for variable-rate applications of farm resources. Soil total nitrogen (TN) and total carbon (TC) are important fertility parameters that can be measured with on-line (mobile) visible and near infrared (vis-NIR) spectroscopy. This study compares the performance of local farm scale calibrations with those based on the spiking of selected local samples from both fields into an European dataset for TN and TC estimation using three modelling techniques, namely gradient boosted machines (GBM), artificial neural networks (ANNs) and random forests (RF). The on-line measurements were carried out using a mobile, fiber type, vis-NIR spectrophotometer (305–2200 nm) (AgroSpec from tec5, Germany), during which soil spectra were recorded in diffuse reflectance mode from two fields in the UK. After spectra pre-processing, the entire datasets were then divided into calibration (75%) and prediction (25%) sets, and calibration models for TN and TC were developed using GBM, ANN and RF with leave-one-out cross-validation. Results of cross-validation showed that the effect of spiking of local samples collected from a field into an European dataset when combined with RF has resulted in the highest coefficients of determination (R2) values of 0.97 and 0.98, the lowest root mean square error (RMSE) of 0.01% and 0.10%, and the highest residual prediction deviations (RPD) of 5.58 and 7.54, for TN and TC, respectively. Results for laboratory and on-line predictions generally followed the same trend as for cross-validation in one field, where the spiked European dataset-based RF calibration models outperformed the corresponding GBM and ANN models. In the second field ANN has replaced RF in being the best performing. However, the local field calibrations provided lower R2 and RPD in most cases. Therefore, from a cost-effective point of view, it is recommended to adopt the spiked European dataset-based RF/ANN calibration models for successful prediction of TN and TC under on-line measurement conditions. Full article
(This article belongs to the Special Issue Sensors in Agriculture)
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Open AccessArticle A Novel Unsupervised Segmentation Quality Evaluation Method for Remote Sensing Images
Sensors 2017, 17(10), 2427; https://doi.org/10.3390/s17102427
Received: 29 August 2017 / Revised: 15 October 2017 / Accepted: 17 October 2017 / Published: 24 October 2017
Cited by 1 | PDF Full-text (22785 KB) | HTML Full-text | XML Full-text
Abstract
The segmentation of a high spatial resolution remote sensing image is a critical step in geographic object-based image analysis (GEOBIA). Evaluating the performance of segmentation without ground truth data, i.e., unsupervised evaluation, is important for the comparison of segmentation algorithms and the automatic
[...] Read more.
The segmentation of a high spatial resolution remote sensing image is a critical step in geographic object-based image analysis (GEOBIA). Evaluating the performance of segmentation without ground truth data, i.e., unsupervised evaluation, is important for the comparison of segmentation algorithms and the automatic selection of optimal parameters. This unsupervised strategy currently faces several challenges in practice, such as difficulties in designing effective indicators and limitations of the spectral values in the feature representation. This study proposes a novel unsupervised evaluation method to quantitatively measure the quality of segmentation results to overcome these problems. In this method, multiple spectral and spatial features of images are first extracted simultaneously and then integrated into a feature set to improve the quality of the feature representation of ground objects. The indicators designed for spatial stratified heterogeneity and spatial autocorrelation are included to estimate the properties of the segments in this integrated feature set. These two indicators are then combined into a global assessment metric as the final quality score. The trade-offs of the combined indicators are accounted for using a strategy based on the Mahalanobis distance, which can be exhibited geometrically. The method is tested on two segmentation algorithms and three testing images. The proposed method is compared with two existing unsupervised methods and a supervised method to confirm its capabilities. Through comparison and visual analysis, the results verified the effectiveness of the proposed method and demonstrated the reliability and improvements of this method with respect to other methods. Full article
(This article belongs to the Special Issue Spatial Analysis and Remote Sensing)
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Open AccessFeature PaperArticle Planar Microstrip Ring Resonators for Microwave-Based Gas Sensing: Design Aspects and Initial Transducers for Humidity and Ammonia Sensing
Sensors 2017, 17(10), 2422; https://doi.org/10.3390/s17102422
Received: 21 September 2017 / Revised: 16 October 2017 / Accepted: 17 October 2017 / Published: 24 October 2017
Cited by 1 | PDF Full-text (4071 KB) | HTML Full-text | XML Full-text
Abstract
A planar microstrip ring resonator structure on alumina was developed using the commercial FEM software COMSOL. Design parameters were evaluated, eventually leading to an optimized design of a miniaturized microwave gas sensor. The sensor was covered with a zeolite film. The device was
[...] Read more.
A planar microstrip ring resonator structure on alumina was developed using the commercial FEM software COMSOL. Design parameters were evaluated, eventually leading to an optimized design of a miniaturized microwave gas sensor. The sensor was covered with a zeolite film. The device was successfully operated at around 8.5 GHz at room temperature as a humidity sensor. In the next step, an additional planar heater will be included on the reverse side of the resonator structure to allow for testing of gas-sensitive materials under sensor conditions. Full article
(This article belongs to the collection Gas Sensors)
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Open AccessArticle A Search-and-Rescue Robot System for Remotely Sensing the Underground Coal Mine Environment
Sensors 2017, 17(10), 2426; https://doi.org/10.3390/s17102426
Received: 19 September 2017 / Revised: 17 October 2017 / Accepted: 20 October 2017 / Published: 23 October 2017
Cited by 3 | PDF Full-text (27304 KB) | HTML Full-text | XML Full-text
Abstract
This paper introduces a search-and-rescue robot system used for remote sensing of the underground coal mine environment, which is composed of an operating control unit and two mobile robots with explosion-proof and waterproof function. This robot system is designed to observe and collect
[...] Read more.
This paper introduces a search-and-rescue robot system used for remote sensing of the underground coal mine environment, which is composed of an operating control unit and two mobile robots with explosion-proof and waterproof function. This robot system is designed to observe and collect information of the coal mine environment through remote control. Thus, this system can be regarded as a multifunction sensor, which realizes remote sensing. When the robot system detects danger, it will send out signals to warn rescuers to keep away. The robot consists of two gas sensors, two cameras, a two-way audio, a 1 km-long fiber-optic cable for communication and a mechanical explosion-proof manipulator. Especially, the manipulator is a novel explosion-proof manipulator for cleaning obstacles, which has 3-degree-of-freedom, but is driven by two motors. Furthermore, the two robots can communicate in series for 2 km with the operating control unit. The development of the robot system may provide a reference for developing future search-and-rescue systems. Full article
(This article belongs to the Special Issue Smart Sensors for Mechatronic and Robotic Systems)
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Open AccessArticle Bias-Voltage Stabilizer for HVHF Amplifiers in VHF Pulse-Echo Measurement Systems
Sensors 2017, 17(10), 2425; https://doi.org/10.3390/s17102425
Received: 23 August 2017 / Revised: 6 October 2017 / Accepted: 16 October 2017 / Published: 23 October 2017
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Abstract
The impact of high-voltage–high-frequency (HVHF) amplifiers on echo-signal quality is greater with very-high-frequency (VHF, ≥100 MHz) ultrasound transducers than with low-frequency (LF, ≤15 MHz) ultrasound transducers. Hence, the bias voltage of an HVHF amplifier must be stabilized to ensure stable echo-signal amplitudes. We
[...] Read more.
The impact of high-voltage–high-frequency (HVHF) amplifiers on echo-signal quality is greater with very-high-frequency (VHF, ≥100 MHz) ultrasound transducers than with low-frequency (LF, ≤15 MHz) ultrasound transducers. Hence, the bias voltage of an HVHF amplifier must be stabilized to ensure stable echo-signal amplitudes. We propose a bias-voltage stabilizer circuit to maintain stable DC voltages over a wide input range, thus reducing the harmonic-distortion components of the echo signals in VHF pulse-echo measurement systems. To confirm the feasibility of the bias-voltage stabilizer, we measured and compared the deviations in the gain of the HVHF amplifier with and without a bias-voltage stabilizer. Between −13 and 26 dBm, the measured gain deviations of a HVHF amplifier with a bias-voltage stabilizer are less than that of an amplifier without a bias-voltage stabilizer. In order to confirm the feasibility of the bias-voltage stabilizer, we compared the pulse-echo responses of the amplifiers, which are typically used for the evaluation of transducers or electronic components used in pulse-echo measurement systems. From the responses, we observed that the amplitudes of the echo signals of a VHF transducer triggered by the HVHF amplifier with a bias-voltage stabilizer were higher than those of the transducer triggered by the HVHF amplifier alone. The second, third, and fourth harmonic-distortion components of the HVHF amplifier with the bias-voltage stabilizer were also lower than those of the HVHF amplifier alone. Hence, the proposed scheme is a promising method for stabilizing the bias voltage of an HVHF amplifier, and improving the echo-signal quality of VHF transducers. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
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