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Sensors, Volume 18, Issue 5 (May 2018)

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Cover Story (view full-size image) The miniature sensor integrates a printed slot antenna, a low-noise amplifier and an active mixer [...] Read more.
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Open AccessArticle Transmitting Pulse Encoding for Beyond-PRT Retransmitting Deception Jamming Detection in Spaceborne Synthetic Aperture Radar (SAR)
Sensors 2018, 18(5), 1666; https://doi.org/10.3390/s18051666
Received: 12 April 2018 / Revised: 19 May 2018 / Accepted: 21 May 2018 / Published: 22 May 2018
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Abstract
Retransmitting deception jamming (RDJ) degrades and misleads the Synthetic Aperture Radar (SAR) image interpretation by forming false targets. The beyond-Pulse Repetition Time (PRT) RDJ enlarges the effective jamming area without constraining the jammer location to reduce the spaceborne SAR working effectiveness. In order
[...] Read more.
Retransmitting deception jamming (RDJ) degrades and misleads the Synthetic Aperture Radar (SAR) image interpretation by forming false targets. The beyond-Pulse Repetition Time (PRT) RDJ enlarges the effective jamming area without constraining the jammer location to reduce the spaceborne SAR working effectiveness. In order to detect the beyond-PRT RDJ and enhance the working efficiency in electronic countermeasure environment, the transmitting pulse encoding method for use in spaceborne SAR is proposed based on the geometry and signal models of beyond-PRT RDJ. Optimum binary codes with maximum number of detection windows are determined by the encoding procedure. The detected area is found to be proportional to the code length and the encoding efficiencies of even and odd codes are analyzed. The simulation results validate the effectiveness of the transmitting pulse encoding method for beyond-PRT RDJ detection in spaceborne SAR. Full article
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Open AccessArticle Strategies to Improve Activity Recognition Based on Skeletal Tracking: Applying Restrictions Regarding Body Parts and Similarity Boundaries
Sensors 2018, 18(5), 1665; https://doi.org/10.3390/s18051665
Received: 4 April 2018 / Revised: 11 May 2018 / Accepted: 17 May 2018 / Published: 22 May 2018
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Abstract
This paper aims to improve activity recognition systems based on skeletal tracking through the study of two different strategies (and its combination): (a) specialized body parts analysis and (b) stricter restrictions for the most easily detectable activities. The study was performed using the
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This paper aims to improve activity recognition systems based on skeletal tracking through the study of two different strategies (and its combination): (a) specialized body parts analysis and (b) stricter restrictions for the most easily detectable activities. The study was performed using the Extended Body-Angles Algorithm, which is able to analyze activities using only a single key sample. This system allows to select, for each considered activity, which are its relevant joints, which makes it possible to monitor the body of the user selecting only a subset of the same. But this feature of the system has both advantages and disadvantages. As a consequence, in the past we had some difficulties with the recognition of activities that only have a small subset of the joints of the body as relevant. The goal of this work, therefore, is to analyze the effect produced by the application of several strategies on the results of an activity recognition system based on skeletal tracking joint oriented devices. Strategies that we applied with the purpose of improve the recognition rates of the activities with a small subset of relevant joints. Through the results of this work, we aim to give the scientific community some first indications about which considered strategy is better. Full article
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Open AccessArticle Design of a Novel MEMS Microgripper with Rotatory Electrostatic Comb-Drive Actuators for Biomedical Applications
Sensors 2018, 18(5), 1664; https://doi.org/10.3390/s18051664
Received: 27 March 2018 / Revised: 10 May 2018 / Accepted: 18 May 2018 / Published: 22 May 2018
Cited by 1 | PDF Full-text (7609 KB) | HTML Full-text | XML Full-text
Abstract
Primary tumors of patients can release circulating tumor cells (CTCs) to flow inside of their blood. The CTCs have different mechanical properties in comparison with red and white blood cells, and their detection may be employed to study the efficiency of medical treatments
[...] Read more.
Primary tumors of patients can release circulating tumor cells (CTCs) to flow inside of their blood. The CTCs have different mechanical properties in comparison with red and white blood cells, and their detection may be employed to study the efficiency of medical treatments against cancer. We present the design of a novel MEMS microgripper with rotatory electrostatic comb-drive actuators for mechanical properties characterization of cells. The microgripper has a compact structural configuration of four polysilicon layers and a simple performance that control the opening and closing displacements of the microgripper tips. The microgripper has a mobile arm, a fixed arm, two different actuators and two serpentine springs, which are designed based on the SUMMiT V surface micromachining process from Sandia National Laboratories. The proposed microgripper operates at its first rotational resonant frequency and its mobile arm has a controlled displacement of 40 µm at both opening and closing directions using dc and ac bias voltages. Analytical models are developed to predict the stiffness, damping forces and first torsional resonant frequency of the microgripper. In addition, finite element method (FEM) models are obtained to estimate the mechanical behavior of the microgripper. The results of the analytical models agree very well respect to FEM simulations. The microgripper has a first rotational resonant frequency of 463.8 Hz without gripped cell and it can operate up to with maximum dc and ac voltages of 23.4 V and 129.2 V, respectively. Based on the results of the analytical and FEM models about the performance of the proposed microgripper, it could be used as a dispositive for mechanical properties characterization of circulating tumor cells (CTCs). Full article
(This article belongs to the Special Issue MEMS Resonators)
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Open AccessArticle An Identity-Based Anti-Quantum Privacy-Preserving Blind Authentication in Wireless Sensor Networks
Sensors 2018, 18(5), 1663; https://doi.org/10.3390/s18051663
Received: 15 April 2018 / Revised: 18 May 2018 / Accepted: 19 May 2018 / Published: 22 May 2018
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Abstract
With the development of wireless sensor networks, IoT devices are crucial for the Smart City; these devices change people’s lives such as e-payment and e-voting systems. However, in these two systems, the state-of-art authentication protocols based on traditional number theory cannot defeat a
[...] Read more.
With the development of wireless sensor networks, IoT devices are crucial for the Smart City; these devices change people’s lives such as e-payment and e-voting systems. However, in these two systems, the state-of-art authentication protocols based on traditional number theory cannot defeat a quantum computer attack. In order to protect user privacy and guarantee trustworthy of big data, we propose a new identity-based blind signature scheme based on number theorem research unit lattice, this scheme mainly uses a rejection sampling theorem instead of constructing a trapdoor. Meanwhile, this scheme does not depend on complex public key infrastructure and can resist quantum computer attack. Then we design an e-payment protocol using the proposed scheme. Furthermore, we prove our scheme is secure in the random oracle, and satisfies confidentiality, integrity, and non-repudiation. Finally, we demonstrate that the proposed scheme outperforms the other traditional existing identity-based blind signature schemes in signing speed and verification speed, outperforms the other lattice-based blind signature in signing speed, verification speed, and signing secret key size. Full article
(This article belongs to the Special Issue Threat Identification and Defence for Internet-of-Things)
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Open AccessArticle Flying Real-Time Network to Coordinate Disaster Relief Activities in Urban Areas
Sensors 2018, 18(5), 1662; https://doi.org/10.3390/s18051662
Received: 24 April 2018 / Revised: 15 May 2018 / Accepted: 18 May 2018 / Published: 22 May 2018
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Abstract
While there have been important advances within wireless communication technology, the provision of communication support during disaster relief activities remains an open issue. The literature in disaster research reports several major restrictions to conducting first response activities in urban areas, given the limitations
[...] Read more.
While there have been important advances within wireless communication technology, the provision of communication support during disaster relief activities remains an open issue. The literature in disaster research reports several major restrictions to conducting first response activities in urban areas, given the limitations of telephone networks and radio systems to provide digital communication in the field. In search-and-rescue operations, the communication requirements are increased, since the first responders need to rely on real-time and reliable communication to perform their activities and coordinate their efforts with other teams. Therefore, these limitations open the door to improvisation during disaster relief efforts. In this paper, we argue that flying ad-hoc networks can provide the communication support needed in these scenarios, and propose a new solution towards that goal. The proposal involves the use of flying witness units, implemented using drones, that act as communication gateways between first responders working at different locations of the affected area. The proposal is named the Flying Real-Time Network, and its feasibility to provide communication in a disaster scenario is shown by presenting both a real-time schedulability analysis of message delivery, as well as simulations of the communication support in a physical scenario inspired by a real incident. The obtained results were highly positive and consistent, therefore this proposal represents a step forward towards the solution of this open issue. Full article
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Open AccessFeature PaperArticle Magnetorelaxometry in the Presence of a DC Bias Field of Ferromagnetic Nanoparticles Bearing a Viscoelastic Corona
Sensors 2018, 18(5), 1661; https://doi.org/10.3390/s18051661
Received: 16 April 2018 / Revised: 16 May 2018 / Accepted: 18 May 2018 / Published: 22 May 2018
Cited by 1 | PDF Full-text (605 KB) | HTML Full-text | XML Full-text
Abstract
With allowance for orientational Brownian motion, the magnetorelaxometry (MRX) signal, i.e., the decay of magnetization generated by an ensemble of ferromagnet nanoparticles, each of which bears a macromolecular corona (a loose layer of polymer gel) is studied. The rheology of corona is modelled
[...] Read more.
With allowance for orientational Brownian motion, the magnetorelaxometry (MRX) signal, i.e., the decay of magnetization generated by an ensemble of ferromagnet nanoparticles, each of which bears a macromolecular corona (a loose layer of polymer gel) is studied. The rheology of corona is modelled by the Jeffreys scheme. The latter, although comprising only three phenomenological parameters, enables one to describe a wide spectrum of viscoelastic media: from linearly viscous liquids to weakly-fluent gels. The “transverse” configuration of MRX is considered where the system is subjected to a DC (constant bias) field, whereas the probing field is applied perpendicularly to the bias one. The analysis shows that the rate of magnetization decay strongly depends on the state of corona and slows down with enhancement of the corona elasticity. In addition, for the case of “transverse” MRX, we consider the integral time, i.e., the characteristic that is applicable to relaxation processes with an arbitrary number of decay modes. Expressions for the dependence of the integral time on the corona elasticity parameter and temperature are derived. Full article
(This article belongs to the Special Issue Magnetic Materials Based Biosensors)
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Open AccessArticle In Situ Determination of Bisphenol A in Beverage Using a Molybdenum Selenide/Reduced Graphene Oxide Nanoparticle Composite Modified Glassy Carbon Electrode
Sensors 2018, 18(5), 1660; https://doi.org/10.3390/s18051660
Received: 3 April 2018 / Revised: 10 May 2018 / Accepted: 11 May 2018 / Published: 22 May 2018
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Abstract
Due to the endocrine disturbing effects of bisphenol A (BPA) on organisms, rapid detection has become one of the most important techniques for monitoring its levels in the aqueous solutions associated with plastics and human beings. In this paper, a glassy carbon electrode
[...] Read more.
Due to the endocrine disturbing effects of bisphenol A (BPA) on organisms, rapid detection has become one of the most important techniques for monitoring its levels in the aqueous solutions associated with plastics and human beings. In this paper, a glassy carbon electrode (GCE) modified with molybdenum selenide/reduced graphene oxide (MoSe2/rGO) was fabricated for in situ determination of bisphenol A in several beverages. The surface area of the electrode dramatically increases due to the existence of ultra-thin nanosheets in a flower-like structure of MoSe2. Adding phosphotungstic acid in the electrolyte can significantly enhance the repeatability (RSD = 0.4%) and reproducibility (RSD = 2.2%) of the electrode. Under the optimized condition (pH = 6.5), the linear range of BPA was from 0.1 μM–100 μM and the detection limit was 0.015 μM (S/N = 3). When using the as-prepared electrode for analyzing BPA in beverage samples without any pretreatments, the recoveries ranged from 98–107%, and the concentrations were from below the detection limit to 1.7 μM, indicating its potential prospect for routine analysis of BPA. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle A Nonlinear Calibration Algorithm Based on Harmonic Decomposition for Two-Axis Fluxgate Sensors
Sensors 2018, 18(5), 1659; https://doi.org/10.3390/s18051659
Received: 27 April 2018 / Revised: 16 May 2018 / Accepted: 18 May 2018 / Published: 22 May 2018
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Abstract
Nonlinearity is a prominent limitation to the calibration performance for two-axis fluxgate sensors. In this paper, a novel nonlinear calibration algorithm taking into account the nonlinearity of errors is proposed. In order to establish the nonlinear calibration model, the combined effort of all
[...] Read more.
Nonlinearity is a prominent limitation to the calibration performance for two-axis fluxgate sensors. In this paper, a novel nonlinear calibration algorithm taking into account the nonlinearity of errors is proposed. In order to establish the nonlinear calibration model, the combined effort of all time-invariant errors is analyzed in detail, and then harmonic decomposition method is utilized to estimate the compensation coefficients. Meanwhile, the proposed nonlinear calibration algorithm is validated and compared with a classical calibration algorithm by experiments. The experimental results show that, after the nonlinear calibration, the maximum deviation of magnetic field magnitude is decreased from 1302 nT to 30 nT, which is smaller than 81 nT after the classical calibration. Furthermore, for the two-axis fluxgate sensor used as magnetic compass, the maximum error of heading is corrected from 1.86° to 0.07°, which is approximately 11% in contrast with 0.62° after the classical calibration. The results suggest an effective way to improve the calibration performance of two-axis fluxgate sensors. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Automatic Identification of Alpine Mass Movements by a Combination of Seismic and Infrasound Sensors
Sensors 2018, 18(5), 1658; https://doi.org/10.3390/s18051658
Received: 18 April 2018 / Revised: 2 May 2018 / Accepted: 5 May 2018 / Published: 22 May 2018
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Abstract
The automatic detection and identification of alpine mass movements such as debris flows, debris floods, or landslides have been of increasing importance for devising mitigation measures in densely populated and intensively used alpine regions. Since these mass movements emit characteristic seismic and acoustic
[...] Read more.
The automatic detection and identification of alpine mass movements such as debris flows, debris floods, or landslides have been of increasing importance for devising mitigation measures in densely populated and intensively used alpine regions. Since these mass movements emit characteristic seismic and acoustic waves in the low-frequency range (<30 Hz), several approaches have already been developed for detection and warning systems based on these signals. However, a combination of the two methods, for improving detection probability and reducing false alarms, is still applied rarely. This paper presents an update and extension of a previously published approach for a detection and identification system based on a combination of seismic and infrasound sensors. Furthermore, this work evaluates the possible early warning times at several test sites and aims to analyze the seismic and infrasound spectral signature produced by different sediment-related mass movements to identify the process type and estimate the magnitude of the event. Thus, this study presents an initial method for estimating the peak discharge and total volume of debris flows based on infrasound data. Tests on several catchments show that this system can detect and identify mass movements in real time directly at the sensor site with high accuracy and a low false alarm ratio. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Segment-Tube: Spatio-Temporal Action Localization in Untrimmed Videos with Per-Frame Segmentation
Sensors 2018, 18(5), 1657; https://doi.org/10.3390/s18051657
Received: 23 April 2018 / Revised: 16 May 2018 / Accepted: 16 May 2018 / Published: 22 May 2018
Cited by 1 | PDF Full-text (5256 KB) | HTML Full-text | XML Full-text
Abstract
Inspired by the recent spatio-temporal action localization efforts with tubelets (sequences of bounding boxes), we present a new spatio-temporal action localization detector Segment-tube, which consists of sequences of per-frame segmentation masks. The proposed Segment-tube detector can temporally pinpoint the starting/ending frame of each
[...] Read more.
Inspired by the recent spatio-temporal action localization efforts with tubelets (sequences of bounding boxes), we present a new spatio-temporal action localization detector Segment-tube, which consists of sequences of per-frame segmentation masks. The proposed Segment-tube detector can temporally pinpoint the starting/ending frame of each action category in the presence of preceding/subsequent interference actions in untrimmed videos. Simultaneously, the Segment-tube detector produces per-frame segmentation masks instead of bounding boxes, offering superior spatial accuracy to tubelets. This is achieved by alternating iterative optimization between temporal action localization and spatial action segmentation. Experimental results on three datasets validated the efficacy of the proposed method, including (1) temporal action localization on the THUMOS 2014 dataset; (2) spatial action segmentation on the Segtrack dataset; and (3) joint spatio-temporal action localization on the newly proposed ActSeg dataset. It is shown that our method compares favorably with existing state-of-the-art methods. Full article
(This article belongs to the Special Issue Visual Sensors)
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Open AccessArticle Automated Field-of-View, Illumination, and Recognition Algorithm Design of a Vision System for Pick-and-Place Considering Colour Information in Illumination and Images
Sensors 2018, 18(5), 1656; https://doi.org/10.3390/s18051656
Received: 28 March 2018 / Revised: 15 May 2018 / Accepted: 18 May 2018 / Published: 22 May 2018
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Abstract
Machine vision is playing an increasingly important role in industrial applications, and the automated design of image recognition systems has been a subject of intense research. This study has proposed a system for automatically designing the field-of-view (FOV) of a camera, the illumination
[...] Read more.
Machine vision is playing an increasingly important role in industrial applications, and the automated design of image recognition systems has been a subject of intense research. This study has proposed a system for automatically designing the field-of-view (FOV) of a camera, the illumination strength and the parameters in a recognition algorithm. We formulated the design problem as an optimisation problem and used an experiment based on a hierarchical algorithm to solve it. The evaluation experiments using translucent plastics objects showed that the use of the proposed system resulted in an effective solution with a wide FOV, recognition of all objects and 0.32 mm and 0.4° maximal positional and angular errors when all the RGB (red, green and blue) for illumination and R channel image for recognition were used. Though all the RGB illumination and grey scale images also provided recognition of all the objects, only a narrow FOV was selected. Moreover, full recognition was not achieved by using only G illumination and a grey-scale image. The results showed that the proposed method can automatically design the FOV, illumination and parameters in the recognition algorithm and that tuning all the RGB illumination is desirable even when single-channel or grey-scale images are used for recognition. Full article
(This article belongs to the Special Issue Visual Sensors)
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Open AccessArticle Suspended Carbon Nanotubes for Humidity Sensing
Sensors 2018, 18(5), 1655; https://doi.org/10.3390/s18051655
Received: 10 April 2018 / Revised: 11 May 2018 / Accepted: 11 May 2018 / Published: 22 May 2018
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Abstract
A room temperature microfabrication technique using SU8, an epoxy-based highly functional photoresist as a sacrificial layer, is developed to obtain suspended aligned carbon nanotube beams. The humidity-sensing characteristics of aligned suspended single-walled carbon nanotube films are studied. A comparative study between suspended and
[...] Read more.
A room temperature microfabrication technique using SU8, an epoxy-based highly functional photoresist as a sacrificial layer, is developed to obtain suspended aligned carbon nanotube beams. The humidity-sensing characteristics of aligned suspended single-walled carbon nanotube films are studied. A comparative study between suspended and non-suspended architectures is done by recording the resistance change in the nanotubes under humidity. For the tests, the humidity was varied from 15% to 98% RH. A comparative study between suspended and non-suspended devices shows that the response and recovery times of the suspended devices was found to be almost 3 times shorter than the non-suspended devices. The suspended devices also showed minimal hysteresis even after 10 humidity cycles, and also exhibit enhanced sensitivity. Repeatability tests were performed by subjecting the sensors to continuous humidification cycles. All tests reported here have been performed using pristine non-functionalized nanotubes. Full article
(This article belongs to the Section Biosensors)
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Open AccessFeature PaperReview Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry
Sensors 2018, 18(5), 1654; https://doi.org/10.3390/s18051654
Received: 31 March 2018 / Revised: 13 May 2018 / Accepted: 18 May 2018 / Published: 22 May 2018
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Abstract
Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment
[...] Read more.
Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment of fall risk. Body-worn sensors such as accelerometers can provide valuable insight into fall risk. Currently, biomechanical features derived from accelerometer data are used for the assessment of fall risk. Here, we studied whether deep learning methods from machine learning are suited to automatically derive features from raw accelerometer data that assess fall risk. We used an existing dataset of 296 older adults. We compared the performance of three deep learning model architectures (convolutional neural network (CNN), long short-term memory (LSTM) and a combination of these two (ConvLSTM)) to each other and to a baseline model with biomechanical features on the same dataset. The results show that the deep learning models in a single-task learning mode are strong in recognition of identity of the subject, but that these models only slightly outperform the baseline method on fall risk assessment. When using multi-task learning, with gender and age as auxiliary tasks, deep learning models perform better. We also found that preprocessing of the data resulted in the best performance (AUC = 0.75). We conclude that deep learning models, and in particular multi-task learning, effectively assess fall risk on the basis of wearable sensor data. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle Dual Sensor Control Scheme for Multi-Target Tracking
Sensors 2018, 18(5), 1653; https://doi.org/10.3390/s18051653
Received: 1 May 2018 / Revised: 18 May 2018 / Accepted: 18 May 2018 / Published: 21 May 2018
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Abstract
Sensor control is a challenging issue in the field of multi-target tracking. It involves multi-target state estimation and the optimal control of the sensor. To maximize the overall utility of the surveillance system, we propose a dual sensor control scheme. This work is
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Sensor control is a challenging issue in the field of multi-target tracking. It involves multi-target state estimation and the optimal control of the sensor. To maximize the overall utility of the surveillance system, we propose a dual sensor control scheme. This work is formulated in the framework of partially observed Markov decision processes (POMDPs) with Mahler’s finite set statistics (FISST). To evaluate the performance associated with each control action, a key element is to design an appropriate metric. From a task-driven perspective, we utilize a metric to minimize the posterior distance between the sensor and the target. This distance-related metric promotes the design of a dual sensor control scheme. Moreover, we introduce a metric to maximize the predicted average probability of detection, which will improve the efficiency by avoiding unnecessary update processes. Simulation results indicate that the performance of the proposed algorithm is significantly superior to the existing methods. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle A High Noise Immunity, 28 × 16-Channel Finger Touch Sensing IC Using OFDM and Frequency Translation Technique
Sensors 2018, 18(5), 1652; https://doi.org/10.3390/s18051652
Received: 5 April 2018 / Revised: 11 May 2018 / Accepted: 17 May 2018 / Published: 21 May 2018
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Abstract
In this paper, a high noise immunity, 28 × 16-channel finger touch sensing IC for an orthogonal frequency division multiplexing (OFDM) touch sensing scheme is presented. In order to increase the signal-to-noise ratio (SNR), the OFDM sensing scheme is proposed. The transmitter (TX)
[...] Read more.
In this paper, a high noise immunity, 28 × 16-channel finger touch sensing IC for an orthogonal frequency division multiplexing (OFDM) touch sensing scheme is presented. In order to increase the signal-to-noise ratio (SNR), the OFDM sensing scheme is proposed. The transmitter (TX) transmits the orthogonal signal to each channels of the panel. The receiver (RX) detects the magnitude of the orthogonal frequency to be transmitted from the TX. Due to the orthogonal characteristics, it is robust to narrowband interference and noise. Therefore, the SNR can be improved. In order to reduce the noise effect of low frequencies, a mixer and high-pass filter are proposed as well. After the noise is filtered, the touch SNR attained is 60 dB, from 20 dB before the noise is filtered. The advantage of the proposed OFDM sensing scheme is its ability to detect channels of the panel simultaneously with the use of multiple carriers. To satisfy the linearity of the signal in the OFDM system, a high-linearity mixer and a rail-to-rail amplifier in the TX driver are designed. The proposed design is implemented in 90 nm CMOS process. The SNR is approximately 60 dB. The area is 13.6 mm2, and the power consumption is 62.4 mW. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Registration of Panoramic/Fish-Eye Image Sequence and LiDAR Points Using Skyline Features
Sensors 2018, 18(5), 1651; https://doi.org/10.3390/s18051651
Received: 24 March 2018 / Revised: 5 May 2018 / Accepted: 18 May 2018 / Published: 21 May 2018
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Abstract
We propose utilizing a rigorous registration model and a skyline-based method for automatic registration of LiDAR points and a sequence of panoramic/fish-eye images in a mobile mapping system (MMS). This method can automatically optimize original registration parameters and avoid the use of manual
[...] Read more.
We propose utilizing a rigorous registration model and a skyline-based method for automatic registration of LiDAR points and a sequence of panoramic/fish-eye images in a mobile mapping system (MMS). This method can automatically optimize original registration parameters and avoid the use of manual interventions in control point-based registration methods. First, the rigorous registration model between the LiDAR points and the panoramic/fish-eye image was built. Second, skyline pixels from panoramic/fish-eye images and skyline points from the MMS’s LiDAR points were extracted, relying on the difference in the pixel values and the registration model, respectively. Third, a brute force optimization method was used to search for optimal matching parameters between skyline pixels and skyline points. In the experiments, the original registration method and the control point registration method were used to compare the accuracy of our method with a sequence of panoramic/fish-eye images. The result showed: (1) the panoramic/fish-eye image registration model is effective and can achieve high-precision registration of the image and the MMS’s LiDAR points; (2) the skyline-based registration method can automatically optimize the initial attitude parameters, realizing a high-precision registration of a panoramic/fish-eye image and the MMS’s LiDAR points; and (3) the attitude correction values of the sequences of panoramic/fish-eye images are different, and the values must be solved one by one. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Random Weighting, Strong Tracking, and Unscented Kalman Filter for Soft Tissue Characterization
Sensors 2018, 18(5), 1650; https://doi.org/10.3390/s18051650
Received: 1 April 2018 / Revised: 17 May 2018 / Accepted: 17 May 2018 / Published: 21 May 2018
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Abstract
This paper presents a new nonlinear filtering method based on the Hunt-Crossley model for online nonlinear soft tissue characterization. This method overcomes the problem of performance degradation in the unscented Kalman filter due to contact model error. It adopts the concept of Mahalanobis
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This paper presents a new nonlinear filtering method based on the Hunt-Crossley model for online nonlinear soft tissue characterization. This method overcomes the problem of performance degradation in the unscented Kalman filter due to contact model error. It adopts the concept of Mahalanobis distance to identify contact model error, and further incorporates a scaling factor in predicted state covariance to compensate identified model error. This scaling factor is determined according to the principle of innovation orthogonality to avoid the cumbersome computation of Jacobian matrix, where the random weighting concept is adopted to improve the estimation accuracy of innovation covariance. A master-slave robotic indentation system is developed to validate the performance of the proposed method. Simulation and experimental results as well as comparison analyses demonstrate that the efficacy of the proposed method for online characterization of soft tissue parameters in the presence of contact model error. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle An Observation Capability Semantic-Associated Approach to the Selection of Remote Sensing Satellite Sensors: A Case Study of Flood Observations in the Jinsha River Basin
Sensors 2018, 18(5), 1649; https://doi.org/10.3390/s18051649
Received: 12 March 2018 / Revised: 14 May 2018 / Accepted: 17 May 2018 / Published: 21 May 2018
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Abstract
Observation schedules depend upon the accurate understanding of a single sensor’s observation capability and the interrelated observation capability information on multiple sensors. The general ontologies for sensors and observations are abundant. However, few observation capability ontologies for satellite sensors are available, and no
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Observation schedules depend upon the accurate understanding of a single sensor’s observation capability and the interrelated observation capability information on multiple sensors. The general ontologies for sensors and observations are abundant. However, few observation capability ontologies for satellite sensors are available, and no study has described the dynamic associations among the observation capabilities of multiple sensors used for integrated observational planning. This limitation results in a failure to realize effective sensor selection. This paper develops a sensor observation capability association (SOCA) ontology model that is resolved around the task-sensor-observation capability (TSOC) ontology pattern. The pattern is developed considering the stimulus-sensor-observation (SSO) ontology design pattern, which focuses on facilitating sensor selection for one observation task. The core aim of the SOCA ontology model is to achieve an observation capability semantic association. A prototype system called SemOCAssociation was developed, and an experiment was conducted for flood observations in the Jinsha River basin in China. The results of this experiment verified that the SOCA ontology based association method can help sensor planners intuitively and accurately make evidence-based sensor selection decisions for a given flood observation task, which facilitates efficient and effective observational planning for flood satellite sensors. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Design and Test of a Soil Profile Moisture Sensor Based on Sensitive Soil Layers
Sensors 2018, 18(5), 1648; https://doi.org/10.3390/s18051648
Received: 2 April 2018 / Revised: 16 May 2018 / Accepted: 16 May 2018 / Published: 21 May 2018
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Abstract
To meet the demand of intelligent irrigation for accurate moisture sensing in the soil vertical profile, a soil profile moisture sensor was designed based on the principle of high-frequency capacitance. The sensor consists of five groups of sensing probes, a data processor, and
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To meet the demand of intelligent irrigation for accurate moisture sensing in the soil vertical profile, a soil profile moisture sensor was designed based on the principle of high-frequency capacitance. The sensor consists of five groups of sensing probes, a data processor, and some accessory components. Low-resistivity copper rings were used as components of the sensing probes. Composable simulation of the sensor’s sensing probes was carried out using a high-frequency structure simulator. According to the effective radiation range of electric field intensity, width and spacing of copper ring were set to 30 mm and 40 mm, respectively. A parallel resonance circuit of voltage-controlled oscillator and high-frequency inductance-capacitance (LC) was designed for signal frequency division and conditioning. A data processor was used to process moisture-related frequency signals for soil profile moisture sensing. The sensor was able to detect real-time soil moisture at the depths of 20, 30, and 50 cm and conduct online inversion of moisture in the soil layer between 0–100 cm. According to the calibration results, the degree of fitting (R2) between the sensor’s measuring frequency and the volumetric moisture content of soil sample was 0.99 and the relative error of the sensor consistency test was 0–1.17%. Field tests in different loam soils showed that measured soil moisture from our sensor reproduced the observed soil moisture dynamic well, with an R2 of 0.96 and a root mean square error of 0.04. In a sensor accuracy test, the R2 between the measured value of the proposed sensor and that of the Diviner2000 portable soil moisture monitoring system was higher than 0.85, with a relative error smaller than 5%. The R2 between measured values and inversed soil moisture values for other soil layers were consistently higher than 0.8. According to calibration test and field test, this sensor, which features low cost, good operability, and high integration, is qualified for precise agricultural irrigation with stable performance and high accuracy. Full article
(This article belongs to the Special Issue Smart Sensors for Water Systems and Networks)
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Open AccessArticle Sensitivity and Resolution Improvement in RGBW Color Filter Array Sensor
Sensors 2018, 18(5), 1647; https://doi.org/10.3390/s18051647
Received: 26 April 2018 / Revised: 17 May 2018 / Accepted: 19 May 2018 / Published: 21 May 2018
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Abstract
Recently, several red-green-blue-white (RGBW) color filter arrays (CFAs), which include highly sensitive W pixels, have been proposed. However, RGBW CFA patterns suffer from spatial resolution degradation owing to the sensor composition having more color components than the Bayer CFA pattern. RGBW CFA demosaicing
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Recently, several red-green-blue-white (RGBW) color filter arrays (CFAs), which include highly sensitive W pixels, have been proposed. However, RGBW CFA patterns suffer from spatial resolution degradation owing to the sensor composition having more color components than the Bayer CFA pattern. RGBW CFA demosaicing methods reconstruct resolution using the correlation between white (W) pixels and pixels of other colors, which does not improve the red-green-blue (RGB) channel sensitivity to the W channel level. In this paper, we thus propose a demosaiced image post-processing method to improve the RGBW CFA sensitivity and resolution. The proposed method decomposes texture components containing image noise and resolution information. The RGB channel sensitivity and resolution are improved through updating the W channel texture component with those of RGB channels. For this process, a cross multilateral filter (CMF) is proposed. It decomposes the smoothness component from the texture component using color difference information and distinguishes color components through that information. Moreover, it decomposes texture components, luminance noise, color noise, and color aliasing artifacts from the demosaiced images. Finally, by updating the texture of the RGB channels with the W channel texture components, the proposed algorithm improves the sensitivity and resolution. Results show that the proposed method is effective, while maintaining W pixel resolution characteristics and improving sensitivity from the signal-to-noise ratio value by approximately 4.5 dB. Full article
(This article belongs to the Special Issue Image Sensors)
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Open AccessArticle Efficient Convex Optimization for Energy-Based Acoustic Sensor Self-Localization and Source Localization in Sensor Networks
Sensors 2018, 18(5), 1646; https://doi.org/10.3390/s18051646
Received: 10 April 2018 / Revised: 13 May 2018 / Accepted: 17 May 2018 / Published: 21 May 2018
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Abstract
The energy reading has been an efficient and attractive measure for collaborative acoustic source localization in practical application due to its cost saving in both energy and computation capability. The maximum likelihood problems by fusing received acoustic energy readings transmitted from local sensors
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The energy reading has been an efficient and attractive measure for collaborative acoustic source localization in practical application due to its cost saving in both energy and computation capability. The maximum likelihood problems by fusing received acoustic energy readings transmitted from local sensors are derived. Aiming to efficiently solve the nonconvex objective of the optimization problem, we present an approximate estimator of the original problem. Then, a direct norm relaxation and semidefinite relaxation, respectively, are utilized to derive the second-order cone programming, semidefinite programming or mixture of them for both cases of sensor self-location and source localization. Furthermore, by taking the colored energy reading noise into account, several minimax optimization problems are formulated, which are also relaxed via the direct norm relaxation and semidefinite relaxation respectively into convex optimization problems. Performance comparison with the existing acoustic energy-based source localization methods is given, where the results show the validity of our proposed methods. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle A Modified Empirical Wavelet Transform for Acoustic Emission Signal Decomposition in Structural Health Monitoring
Sensors 2018, 18(5), 1645; https://doi.org/10.3390/s18051645
Received: 14 April 2018 / Revised: 13 May 2018 / Accepted: 18 May 2018 / Published: 21 May 2018
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Abstract
The acoustic emission (AE) method is useful for structural health monitoring (SHM) of composite structures due to its high sensitivity and real-time capability. The main challenge, however, is how to classify the AE data into different failure mechanisms because the detected signals are
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The acoustic emission (AE) method is useful for structural health monitoring (SHM) of composite structures due to its high sensitivity and real-time capability. The main challenge, however, is how to classify the AE data into different failure mechanisms because the detected signals are affected by various factors. Empirical wavelet transform (EWT) is a solution for analyzing the multi-component signals and has been used to process the AE data. In order to solve the spectrum separation problem of the AE signals, this paper proposes a novel modified separation method based on local window maxima (LWM) algorithm. It searches the local maxima of the Fourier spectrum in a proper window, and automatically determines the boundaries of spectrum segmentations, which helps to eliminate the impact of noise interference or frequency dispersion in the detected signal and obtain the meaningful empirical modes that are more related to the damage characteristics. Additionally, both simulation signal and AE signal from the composite structures are used to verify the effectiveness of the proposed method. Finally, the experimental results indicate that the proposed method performs better than the original EWT method in identifying different damage mechanisms of composite structures. Full article
(This article belongs to the Special Issue Mechatronic Systems for Automatic Vehicles)
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Open AccessArticle A Novel Displacement and Tilt Detection Method Using Passive UHF RFID Technology
Sensors 2018, 18(5), 1644; https://doi.org/10.3390/s18051644
Received: 26 April 2018 / Revised: 17 May 2018 / Accepted: 18 May 2018 / Published: 21 May 2018
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Abstract
The displacement and tilt angle of an object are useful information for wireless monitoring applications. In this paper, a low-cost detection method based on passive radio frequency identification (RFID) technology is proposed. This method uses a standard ultrahigh-frequency (UHF) RFID reader to measure
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The displacement and tilt angle of an object are useful information for wireless monitoring applications. In this paper, a low-cost detection method based on passive radio frequency identification (RFID) technology is proposed. This method uses a standard ultrahigh-frequency (UHF) RFID reader to measure the phase variation of the tag response and detect the displacement and tilt angle of RFID tags attached to the targeted object. An accurate displacement result can be detected by the RFID system with a linearly polarized (LP) reader antenna. Based on the displacement results, an accurate tilt angle can also be detected by the RFID system with a circularly polarized (CP) reader antenna, which has been proved to have a linear relationship with the phase parameter of the tag’s backscattered wave. As far as accuracy is concerned, the mean absolute error (MAE) of displacement is less than 2 mm and the MAE of the tilt angle is less than 2.5° for an RFID system with 500 mm working range. Full article
(This article belongs to the Special Issue RFID-Based Sensors for IoT Applications)
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Open AccessArticle Analyzing Cyber-Physical Threats on Robotic Platforms
Sensors 2018, 18(5), 1643; https://doi.org/10.3390/s18051643
Received: 19 April 2018 / Revised: 9 May 2018 / Accepted: 18 May 2018 / Published: 21 May 2018
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Abstract
Robots are increasingly involved in our daily lives. Fundamental to robots are the communication link (or stream) and the applications that connect the robots to their clients or users. Such communication link and applications are usually supported through client/server network connection. This networking
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Robots are increasingly involved in our daily lives. Fundamental to robots are the communication link (or stream) and the applications that connect the robots to their clients or users. Such communication link and applications are usually supported through client/server network connection. This networking system is amenable of being attacked and vulnerable to the security threats. Ensuring security and privacy for robotic platforms is thus critical, as failures and attacks could have devastating consequences. In this paper, we examine several cyber-physical security threats that are unique to the robotic platforms; specifically the communication link and the applications. Threats target integrity, availability and confidential security requirements of the robotic platforms, which use MobileEyes/arnlServer client/server applications. A robot attack tool (RAT) was developed to perform specific security attacks. An impact-oriented approach was adopted to analyze the assessment results of the attacks. Tests and experiments of attacks were conducted in simulation environment and physically on the robot. The simulation environment was based on MobileSim; a software tool for simulating, debugging and experimenting on MobileRobots/ActivMedia platforms and their environments. The robot platform PeopleBotTM was used for physical experiments. The analysis and testing results show that certain attacks were successful at breaching the robot security. Integrity attacks modified commands and manipulated the robot behavior. Availability attacks were able to cause Denial-of-Service (DoS) and the robot was not responsive to MobileEyes commands. Integrity and availability attacks caused sensitive information on the robot to be hijacked. To mitigate security threats, we provide possible mitigation techniques and suggestions to raise awareness of threats on the robotic platforms, especially when the robots are involved in critical missions or applications. Full article
(This article belongs to the Special Issue Security, Trust and Privacy for Sensor Networks)
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Open AccessArticle Research on the Forward and Reverse Calculation Based on the Adaptive Zero-Velocity Interval Adjustment for the Foot-Mounted Inertial Pedestrian-Positioning System
Sensors 2018, 18(5), 1642; https://doi.org/10.3390/s18051642
Received: 10 April 2018 / Revised: 5 May 2018 / Accepted: 15 May 2018 / Published: 21 May 2018
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Abstract
Pedestrian-positioning technology based on the foot-mounted micro inertial measurement unit (MIMU) plays an important role in the field of indoor navigation and has received extensive attention in recent years. However, the positioning accuracy of the inertial-based pedestrian-positioning method is rapidly reduced because of
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Pedestrian-positioning technology based on the foot-mounted micro inertial measurement unit (MIMU) plays an important role in the field of indoor navigation and has received extensive attention in recent years. However, the positioning accuracy of the inertial-based pedestrian-positioning method is rapidly reduced because of the relatively low measurement accuracy of the measurement sensor. The zero-velocity update (ZUPT) is an error correction method which was proposed to solve the cumulative error because, on a regular basis, the foot is stationary during the ordinary gait; this is intended to reduce the position error growth of the system. However, the traditional ZUPT has poor performance because the time of foot touchdown is short when the pedestrians move faster, which decreases the positioning accuracy. Considering these problems, a forward and reverse calculation method based on the adaptive zero-velocity interval adjustment for the foot-mounted MIMU location method is proposed in this paper. To solve the inaccuracy of the zero-velocity interval detector during fast pedestrian movement where the contact time of the foot on the ground is short, an adaptive zero-velocity interval detection algorithm based on fuzzy logic reasoning is presented in this paper. In addition, to improve the effectiveness of the ZUPT algorithm, forward and reverse multiple solutions are presented. Finally, with the basic principles and derivation process of this method, the MTi-G710 produced by the XSENS company is used to complete the test. The experimental results verify the correctness and applicability of the proposed method. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessReview Registration of Laser Scanning Point Clouds: A Review
Sensors 2018, 18(5), 1641; https://doi.org/10.3390/s18051641
Received: 21 March 2018 / Revised: 9 May 2018 / Accepted: 16 May 2018 / Published: 21 May 2018
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Abstract
The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used
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The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Effects of Center Metals in Porphines on Nanomechanical Gas Sensing
Sensors 2018, 18(5), 1640; https://doi.org/10.3390/s18051640
Received: 18 April 2018 / Revised: 15 May 2018 / Accepted: 16 May 2018 / Published: 21 May 2018
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Abstract
Porphyrin is one of the most promising materials for realizing a practical artificial olfactory sensor system. In this study, we focus on non-substituted porphyrins—porphines—as receptor materials of nanomechanical membrane-type surface stress sensors (MSS) to investigate the effect of center metals on gas sensing.
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Porphyrin is one of the most promising materials for realizing a practical artificial olfactory sensor system. In this study, we focus on non-substituted porphyrins—porphines—as receptor materials of nanomechanical membrane-type surface stress sensors (MSS) to investigate the effect of center metals on gas sensing. By omitting the substituents on the tetrapyrrole macrocycle of porphyrin, the peripheral interference by substituents can be avoided. Zinc, nickel, and iron were chosen for the center metals as these metalloporphines show different properties compared to free-base porphine. The present study revealed that iron insertion enhanced sensitivity to various gases, while zinc and nickel insertion led to equivalent or less sensitivity than free-base porphine. Based on the experimental results, we discuss the role of center metals for gas uptake from the view point of molecular interaction. We also report the high robustness of the iron porphine to humidity, showing the high feasibility of porphine-based nanomechanical sensor devices for practical applications in ambient conditions. Full article
(This article belongs to the Special Issue Artificial Olfaction and Taste)
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Open AccessArticle Recognition of a Person Wearing Sport Shoes or High Heels through Gait Using Two Types of Sensors
Sensors 2018, 18(5), 1639; https://doi.org/10.3390/s18051639
Received: 9 April 2018 / Revised: 14 May 2018 / Accepted: 18 May 2018 / Published: 21 May 2018
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Abstract
Biometrics is currently an area that is both very interesting as well as rapidly growing. Among various types of biometrics the human gait recognition seems to be one of the most intriguing. However, one of the greatest problems within this field of biometrics
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Biometrics is currently an area that is both very interesting as well as rapidly growing. Among various types of biometrics the human gait recognition seems to be one of the most intriguing. However, one of the greatest problems within this field of biometrics is the change in gait caused by footwear. A change of shoes results in a significant lowering of accuracy in recognition of people. The following work presents a method which uses data gathered by two sensors: force plates and Microsoft Kinect v2 to reduce this problem. Microsoft Kinect is utilized to measure the body height of a person which allows the reduction of the set of recognized people only to those whose height is similar to that which has been measured. The entire process is preceded by identifying the type of footwear which the person is wearing. The research was conducted on data obtained from 99 people (more than 3400 strides) and the proposed method allowed us to reach a Correct Classification Rate (CCR) greater than 88% which, in comparison to earlier methods reaching CCR’s of <80%, is a significant improvement. The work presents advantages as well as limitations of the proposed method. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle Cold-Rolled Strip Steel Stress Detection Technology Based on a Magnetoresistance Sensor and the Magnetoelastic Effect
Sensors 2018, 18(5), 1638; https://doi.org/10.3390/s18051638
Received: 17 April 2018 / Revised: 14 May 2018 / Accepted: 18 May 2018 / Published: 21 May 2018
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Abstract
Driven by the demands for contactless stress detection, technologies are being used for shape control when producing cold-rolled strips. This paper presents a novel contactless stress detection technology based on a magnetoresistance sensor and the magnetoelastic effect, enabling the detection of internal stress
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Driven by the demands for contactless stress detection, technologies are being used for shape control when producing cold-rolled strips. This paper presents a novel contactless stress detection technology based on a magnetoresistance sensor and the magnetoelastic effect, enabling the detection of internal stress in manufactured cold-rolled strips. An experimental device was designed and produced. Characteristics of this detection technology were investigated through experiments assisted by theoretical analysis. Theoretically, a linear correlation exists between the internal stress of strip steel and the voltage output of a magneto-resistive sensor. Therefore, for this stress detection system, the sensitivity of the stress detection was adjusted by adjusting the supply voltage of the magnetoresistance sensor, detection distance, and other relevant parameters. The stress detection experimental results showed that this detection system has good repeatability and linearity. The detection error was controlled within 1.5%. Moreover, the intrinsic factors of the detected strip steel, including thickness, carbon percentage, and crystal orientation, also affected the sensitivity of the detection system. The detection technology proposed in this research enables online contactless detection and meets the requirements for cold-rolled steel strips. Full article
(This article belongs to the Special Issue Magnetic Sensors)
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Open AccessArticle An Interactive Real-Time Locating System Based on Bluetooth Low-Energy Beacon Network
Sensors 2018, 18(5), 1637; https://doi.org/10.3390/s18051637
Received: 5 May 2018 / Revised: 16 May 2018 / Accepted: 18 May 2018 / Published: 21 May 2018
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Abstract
The ubiquity of Bluetooth-enabled smartphones and peripherals has brought tremendous convenience to our daily life. In recent years, Bluetooth beacons have also been gaining popularity in implementing a variety of innovative location-based services such as self-guided systems in exhibition centers. However, the broadcast-based
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The ubiquity of Bluetooth-enabled smartphones and peripherals has brought tremendous convenience to our daily life. In recent years, Bluetooth beacons have also been gaining popularity in implementing a variety of innovative location-based services such as self-guided systems in exhibition centers. However, the broadcast-based beacon technology can only provide unidirectional communication. In case smartphone users would like to respond to the beacon messages, they have to rely on their own mobile Internet connections to send the information back to the backend system. Nevertheless, mobile Internet services may not be always available or too costly. In this work, we develop a real-time locating system based only on the Bluetooth low energy (BLE) technology to support interactive communications by combining the broadcast and mesh topology options to extend the applicability of beacon solutions. Specifically, we turn the smartphone into a beacon device and augment the beacon devices with the capability of forming a mesh network. The implementation result shows that our beacon devices can detect the presence of specific users at specific locations, and then the presence state can be sent to the application server via the relay of beacon devices. Moreover, the application server can send personalized location-based messages to the users, again via the relay of beacon devices. With the capability of relaying messages between the beacon devices, it would be convenient for developers to implement a variety of interactive applications such as tracking VIP customers at the airport, or tracking an elder with Alzheimer’s disease in the neighborhood. Full article
(This article belongs to the Section Sensor Networks)
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