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Sensors, Volume 17, Issue 11 (November 2017)

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Cover Story Zhang and Or report a small-scale, standalone, and high-performance magnetoelectric (ME) transverse [...] Read more.
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Open AccessArticle Securing Color Fidelity in 3D Architectural Heritage Scenarios
Sensors 2017, 17(11), 2437; doi:10.3390/s17112437
Received: 28 August 2017 / Revised: 7 October 2017 / Accepted: 22 October 2017 / Published: 25 October 2017
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Abstract
Ensuring color fidelity in image-based 3D modeling of heritage scenarios is nowadays still an open research matter. Image colors are important during the data processing as they affect algorithm outcomes, therefore their correct treatment, reduction and enhancement is fundamental. In this contribution, we
[...] Read more.
Ensuring color fidelity in image-based 3D modeling of heritage scenarios is nowadays still an open research matter. Image colors are important during the data processing as they affect algorithm outcomes, therefore their correct treatment, reduction and enhancement is fundamental. In this contribution, we present an automated solution developed to improve the radiometric quality of an image datasets and the performances of two main steps of the photogrammetric pipeline (camera orientation and dense image matching). The suggested solution aims to achieve a robust automatic color balance and exposure equalization, stability of the RGB-to-gray image conversion and faithful color appearance of a digitized artifact. The innovative aspects of the article are: complete automation, better color target detection, a MATLAB implementation of the ACR scripts created by Fraser and the use of a specific weighted polynomial regression. A series of tests are presented to demonstrate the efficiency of the developed methodology and to evaluate color accuracy (‘color characterization’). Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Defect Detection of Adhesive Layer of Thermal Insulation Materials Based on Improved Particle Swarm Optimization of ECT
Sensors 2017, 17(11), 2440; doi:10.3390/s17112440
Received: 1 September 2017 / Revised: 10 October 2017 / Accepted: 20 October 2017 / Published: 25 October 2017
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Abstract
This paper studies the defect detection problem of adhesive layer of thermal insulation materials. A novel detection method based on an improved particle swarm optimization (PSO) algorithm of Electrical Capacitance Tomography (ECT) is presented. Firstly, a least squares support vector machine is applied
[...] Read more.
This paper studies the defect detection problem of adhesive layer of thermal insulation materials. A novel detection method based on an improved particle swarm optimization (PSO) algorithm of Electrical Capacitance Tomography (ECT) is presented. Firstly, a least squares support vector machine is applied for data processing of measured capacitance values. Then, the improved PSO algorithm is proposed and applied for image reconstruction. Finally, some experiments are provided to verify the effectiveness of the proposed method in defect detection for adhesive layer of thermal insulation materials. The performance comparisons demonstrate that the proposed method has higher precision by comparing with traditional ECT algorithms. Full article
(This article belongs to the Special Issue Intelligent Sensing Technologies for Nondestructive Evaluation)
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Open AccessArticle Chronotropic Competence Indices Extracted from Wearable Sensors for Cardiovascular Diseases Management
Sensors 2017, 17(11), 2441; doi:10.3390/s17112441
Received: 30 August 2017 / Revised: 7 October 2017 / Accepted: 19 October 2017 / Published: 25 October 2017
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Abstract
Chronotropic incompetence (CI) has been proven to be an important factor in the diagnosis and management of cardiovascular diseases. In this paper, we extend the existing CI parameters and propose chronotropic competence indices (CCI) to describe the exercise response of the cardiopulmonary system.
[...] Read more.
Chronotropic incompetence (CI) has been proven to be an important factor in the diagnosis and management of cardiovascular diseases. In this paper, we extend the existing CI parameters and propose chronotropic competence indices (CCI) to describe the exercise response of the cardiopulmonary system. A cardiac chronotropic competence Test (3CT), dedicated to CCI measurement using a wearable device, is also presented. Preliminary clinical trials are presented for the validation of 3CT measurement accuracy, and to show the potential of CCI in the prevention and rehabilitation of cardiovascular diseases. Full article
(This article belongs to the Special Issue Sensors and Analytics for Precision Medicine)
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Open AccessArticle Visual Localization across Seasons Using Sequence Matching Based on Multi-Feature Combination
Sensors 2017, 17(11), 2442; doi:10.3390/s17112442
Received: 29 August 2017 / Revised: 18 October 2017 / Accepted: 19 October 2017 / Published: 25 October 2017
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Abstract
Visual localization is widely used in autonomous navigation system and Advanced Driver Assistance Systems (ADAS). However, visual-based localization in seasonal changing situations is one of the most challenging topics in computer vision and the intelligent vehicle community. The difficulty of this task is
[...] Read more.
Visual localization is widely used in autonomous navigation system and Advanced Driver Assistance Systems (ADAS). However, visual-based localization in seasonal changing situations is one of the most challenging topics in computer vision and the intelligent vehicle community. The difficulty of this task is related to the strong appearance changes that occur in scenes due to weather or season changes. In this paper, a place recognition based visual localization method is proposed, which realizes the localization by identifying previously visited places using the sequence matching method. It operates by matching query image sequences to an image database acquired previously (video acquired during traveling period). In this method, in order to improve matching accuracy, multi-feature is constructed by combining a global GIST descriptor and local binary feature CSLBP (Center-symmetric local binary patterns) to represent image sequence. Then, similarity measurement according to Chi-square distance is used for effective sequences matching. For experimental evaluation, the relationship between image sequence length and sequences matching performance is studied. To show its effectiveness, the proposed method is tested and evaluated in four seasons outdoor environments. The results have shown improved precision–recall performance against the state-of-the-art SeqSLAM algorithm. Full article
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Open AccessArticle Towards Intelligent Interpretation of Low Strain Pile Integrity Testing Results Using Machine Learning Techniques
Sensors 2017, 17(11), 2443; doi:10.3390/s17112443
Received: 4 August 2017 / Revised: 10 October 2017 / Accepted: 13 October 2017 / Published: 25 October 2017
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Abstract
Low strain pile integrity testing (LSPIT), due to its simplicity and low cost, is one of the most popular NDE methods used in pile foundation construction. While performing LSPIT in the field is generally quite simple and quick, determining the integrity of the
[...] Read more.
Low strain pile integrity testing (LSPIT), due to its simplicity and low cost, is one of the most popular NDE methods used in pile foundation construction. While performing LSPIT in the field is generally quite simple and quick, determining the integrity of the test piles by analyzing and interpreting the test signals (reflectograms) is still a manual process performed by experienced experts only. For foundation construction sites where the number of piles to be tested is large, it may take days before the expert can complete interpreting all of the piles and delivering the integrity assessment report. Techniques that can automate test signal interpretation, thus shortening the LSPIT’s turnaround time, are of great business value and are in great need. Motivated by this need, in this paper, we develop a computer-aided reflectogram interpretation (CARI) methodology that can interpret a large number of LSPIT signals quickly and consistently. The methodology, built on advanced signal processing and machine learning technologies, can be used to assist the experts in performing both qualitative and quantitative interpretation of LSPIT signals. Specifically, the methodology can ease experts’ interpretation burden by screening all test piles quickly and identifying a small number of suspected piles for experts to perform manual, in-depth interpretation. We demonstrate the methodology’s effectiveness using the LSPIT signals collected from a number of real-world pile construction sites. The proposed methodology can potentially enhance LSPIT and make it even more efficient and effective in quality control of deep foundation construction. Full article
(This article belongs to the Special Issue Intelligent Sensing Technologies for Nondestructive Evaluation)
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Open AccessArticle Linear Extended State Observer-Based Motion Synchronization Control for Hybrid Actuation System of More Electric Aircraft
Sensors 2017, 17(11), 2444; doi:10.3390/s17112444
Received: 17 August 2017 / Revised: 8 October 2017 / Accepted: 18 October 2017 / Published: 25 October 2017
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Abstract
Moving towards the more electric aircraft (MEA), a hybrid actuator configuration provides an opportunity to introduce electromechanical actuator (EMA) into primary flight control. In the hybrid actuation system (HAS), an electro-hydraulic servo actuator (EHSA) and an EMA operate on the same control surface.
[...] Read more.
Moving towards the more electric aircraft (MEA), a hybrid actuator configuration provides an opportunity to introduce electromechanical actuator (EMA) into primary flight control. In the hybrid actuation system (HAS), an electro-hydraulic servo actuator (EHSA) and an EMA operate on the same control surface. In order to solve force fighting problem in HAS, this paper proposes a novel linear extended state observer (LESO)-based motion synchronization control method. To cope with the problem of unavailability of the state signals required by the motion synchronization controller, LESO is designed for EHSA and EMA to observe the state variables. Based on the observed states of LESO, motion synchronization controllers could enable EHSA and EMA to simultaneously track the desired motion trajectories. Additionally, nonlinearities, uncertainties and unknown disturbances as well as the coupling term between EHSA and EMA can be estimated and compensated by using the extended state of the proposed LESO. Finally, comparative simulation results indicate that the proposed LESO-based motion synchronization controller could reduce significant force fighting between EHSA and EMA. Full article
(This article belongs to the Special Issue Mechatronic Systems for Automatic Vehicles)
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Open AccessArticle Arrhythmia Evaluation in Wearable ECG Devices
Sensors 2017, 17(11), 2445; doi:10.3390/s17112445
Received: 19 September 2017 / Revised: 20 October 2017 / Accepted: 21 October 2017 / Published: 25 October 2017
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Abstract
This study evaluates four databases from PhysioNet: The American Heart Association database (AHADB), Creighton University Ventricular Tachyarrhythmia database (CUDB), MIT-BIH Arrhythmia database (MITDB), and MIT-BIH Noise Stress Test database (NSTDB). The ANSI/AAMI EC57:2012 is used for the evaluation of the algorithms for the
[...] Read more.
This study evaluates four databases from PhysioNet: The American Heart Association database (AHADB), Creighton University Ventricular Tachyarrhythmia database (CUDB), MIT-BIH Arrhythmia database (MITDB), and MIT-BIH Noise Stress Test database (NSTDB). The ANSI/AAMI EC57:2012 is used for the evaluation of the algorithms for the supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), atrial fibrillation (AF), and ventricular fibrillation (VF) via the evaluation of the sensitivity, positive predictivity and false positive rate. Sample entropy, fast Fourier transform (FFT), and multilayer perceptron neural network with backpropagation training algorithm are selected for the integrated detection algorithms. For this study, the result for SVEB has some improvements compared to a previous study that also utilized ANSI/AAMI EC57. In further, VEB sensitivity and positive predictivity gross evaluations have greater than 80%, except for the positive predictivity of the NSTDB database. For AF gross evaluation of MITDB database, the results show very good classification, excluding the episode sensitivity. In advanced, for VF gross evaluation, the episode sensitivity and positive predictivity for the AHADB, MITDB, and CUDB, have greater than 80%, except for MITDB episode positive predictivity, which is 75%. The achieved results show that the proposed integrated SVEB, VEB, AF, and VF detection algorithm has an accurate classification according to ANSI/AAMI EC57:2012. In conclusion, the proposed integrated detection algorithm can achieve good accuracy in comparison with other previous studies. Furthermore, more advanced algorithms and hardware devices should be performed in future for arrhythmia detection and evaluation. Full article
(This article belongs to the Special Issue Biomedical Sensors and Systems 2017)
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Open AccessArticle Magnetoelectric Transverse Gradient Sensor with High Detection Sensitivity and Low Gradient Noise
Sensors 2017, 17(11), 2446; doi:10.3390/s17112446
Received: 24 September 2017 / Revised: 15 October 2017 / Accepted: 23 October 2017 / Published: 25 October 2017
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Abstract
We report, theoretically and experimentally, the realization of a high detection performance in a novel magnetoelectric (ME) transverse gradient sensor based on the large ME effect and the magnetic field gradient (MFG) technique in a pair of magnetically-biased, electrically-shielded, and mechanically-enclosed ME composites
[...] Read more.
We report, theoretically and experimentally, the realization of a high detection performance in a novel magnetoelectric (ME) transverse gradient sensor based on the large ME effect and the magnetic field gradient (MFG) technique in a pair of magnetically-biased, electrically-shielded, and mechanically-enclosed ME composites having a transverse orientation and an axial separation. The output voltage of the gradient sensor is directly obtained from the transverse MFG-induced difference in ME voltage between the two ME composites and is calibrated against transverse MFGs to give a high detection sensitivity of 0.4–30.6 V/(T/m), a strong common-mode magnetic field noise rejection rate of <−14.5 dB, a small input-output nonlinearity of <10 ppm, and a low gradient noise of 0.16–620 nT/m/ Hz in a broad frequency range of 1 Hz–170 kHz under a small baseline of 35 mm. An analysis of experimental gradient noise spectra obtained in a magnetically-unshielded laboratory environment reveals the domination of the pink (1/f) noise, dielectric loss noise, and power-frequency noise below 3 kHz, in addition to the circuit noise above 3 kHz, in the gradient sensor. The high detection performance, together with the added merit of passive and direct ME conversion by the large ME effect in the ME composites, makes the gradient sensor suitable for the passive, direct, and broadband detection of transverse MFGs. Full article
(This article belongs to the Special Issue Magnetic Sensors and Their Applications)
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Open AccessArticle Dolphin Sounds-Inspired Covert Underwater Acoustic Communication and Micro-Modem
Sensors 2017, 17(11), 2447; doi:10.3390/s17112447
Received: 11 August 2017 / Revised: 6 October 2017 / Accepted: 13 October 2017 / Published: 25 October 2017
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Abstract
A novel portable underwater acoustic modem is proposed in this paper for covert communication between divers or underwater unmanned vehicles (UUVs) and divers at a short distance. For the first time, real dolphin calls are used in the modem to realize biologically inspired
[...] Read more.
A novel portable underwater acoustic modem is proposed in this paper for covert communication between divers or underwater unmanned vehicles (UUVs) and divers at a short distance. For the first time, real dolphin calls are used in the modem to realize biologically inspired Covert Underwater Acoustic Communication (CUAC). A variety of dolphin whistles and clicks stored in an SD card inside the modem helps to realize different biomimetic CUAC algorithms based on the specified covert scenario. In this paper, the information is conveyed during the time interval between dolphin clicks. TMS320C6748 and TLV320AIC3106 are the core processors used in our unique modem for fast digital processing and interconnection with other terminals or sensors. Simulation results show that the bit error rate (BER) of the CUAC algorithm is less than 10 5 when the signal to noise ratio is over ‒5 dB. The modem was tested in an underwater pool, and a data rate of 27.1 bits per second at a distance of 10 m was achieved. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Noise Maps for Quantitative and Clinical Severity Towards Long-Term ECG Monitoring
Sensors 2017, 17(11), 2448; doi:10.3390/s17112448
Received: 17 September 2017 / Revised: 15 October 2017 / Accepted: 20 October 2017 / Published: 25 October 2017
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Abstract
Noise and artifacts are inherent contaminating components and are particularly present in Holter electrocardiogram (ECG) monitoring. The presence of noise is even more significant in long-term monitoring (LTM) recordings, as these are collected for several days in patients following their daily activities; hence,
[...] Read more.
Noise and artifacts are inherent contaminating components and are particularly present in Holter electrocardiogram (ECG) monitoring. The presence of noise is even more significant in long-term monitoring (LTM) recordings, as these are collected for several days in patients following their daily activities; hence, strong artifact components can temporarily impair the clinical measurements from the LTM recordings. Traditionally, the noise presence has been dealt with as a problem of non-desirable component removal by means of several quantitative signal metrics such as the signal-to-noise ratio (SNR), but current systems do not provide any information about the true impact of noise on the ECG clinical evaluation. As a first step towards an alternative to classical approaches, this work assesses the ECG quality under the assumption that an ECG has good quality when it is clinically interpretable. Therefore, our hypotheses are that it is possible (a) to create a clinical severity score for the effect of the noise on the ECG, (b) to characterize its consistency in terms of its temporal and statistical distribution, and (c) to use it for signal quality evaluation in LTM scenarios. For this purpose, a database of external event recorder (EER) signals is assembled and labeled from a clinical point of view for its use as the gold standard of noise severity categorization. These devices are assumed to capture those signal segments more prone to be corrupted with noise during long-term periods. Then, the ECG noise is characterized through the comparison of these clinical severity criteria with conventional quantitative metrics taken from traditional noise-removal approaches, and noise maps are proposed as a novel representation tool to achieve this comparison. Our results showed that neither of the benchmarked quantitative noise measurement criteria represent an accurate enough estimation of the clinical severity of the noise. A case study of long-term ECG is reported, showing the statistical and temporal correspondences and properties with respect to EER signals used to create the gold standard for clinical noise. The proposed noise maps, together with the statistical consistency of the characterization of the noise clinical severity, paves the way towards forthcoming systems providing us with noise maps of the noise clinical severity, allowing the user to process different ECG segments with different techniques and in terms of different measured clinical parameters. Full article
(This article belongs to the Special Issue Sensors for Health Monitoring and Disease Diagnosis)
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Open AccessArticle A Robust Dynamic Heart-Rate Detection Algorithm Framework During Intense Physical Activities Using Photoplethysmographic Signals
Sensors 2017, 17(11), 2450; doi:10.3390/s17112450
Received: 5 September 2017 / Revised: 17 October 2017 / Accepted: 21 October 2017 / Published: 25 October 2017
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Abstract
Dynamic accurate heart-rate (HR) estimation using a photoplethysmogram (PPG) during intense physical activities is always challenging due to corruption by motion artifacts (MAs). It is difficult to reconstruct a clean signal and extract HR from contaminated PPG. This paper proposes a robust HR-estimation
[...] Read more.
Dynamic accurate heart-rate (HR) estimation using a photoplethysmogram (PPG) during intense physical activities is always challenging due to corruption by motion artifacts (MAs). It is difficult to reconstruct a clean signal and extract HR from contaminated PPG. This paper proposes a robust HR-estimation algorithm framework that uses one-channel PPG and tri-axis acceleration data to reconstruct the PPG and calculate the HR based on features of the PPG and spectral analysis. Firstly, the signal is judged by the presence of MAs. Then, the spectral peaks corresponding to acceleration data are filtered from the periodogram of the PPG when MAs exist. Different signal-processing methods are applied based on the amount of remaining PPG spectral peaks. The main MA-removal algorithm (NFEEMD) includes the repeated single-notch filter and ensemble empirical mode decomposition. Finally, HR calibration is designed to ensure the accuracy of HR tracking. The NFEEMD algorithm was performed on the 23 datasets from the 2015 IEEE Signal Processing Cup Database. The average estimation errors were 1.12 BPM (12 training datasets), 2.63 BPM (10 testing datasets) and 1.87 BPM (all 23 datasets), respectively. The Pearson correlation was 0.992. The experiment results illustrate that the proposed algorithm is not only suitable for HR estimation during continuous activities, like slow running (13 training datasets), but also for intense physical activities with acceleration, like arm exercise (10 testing datasets). Full article
(This article belongs to the Special Issue Sensors for Health Monitoring and Disease Diagnosis)
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Open AccessArticle 3-Axis Fully-Integrated Capacitive Tactile Sensor with Flip-Bonded CMOS on LTCC Interposer
Sensors 2017, 17(11), 2451; doi:10.3390/s17112451
Received: 25 September 2017 / Revised: 19 October 2017 / Accepted: 24 October 2017 / Published: 25 October 2017
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Abstract
This paper reports a 3-axis fully integrated differential capacitive tactile sensor surface-mountable on a bus line. The sensor integrates a flip-bonded complementary metal-oxide semiconductor (CMOS) with capacitive sensing circuits on a low temperature cofired ceramic (LTCC) interposer with Au through vias by Au-Au
[...] Read more.
This paper reports a 3-axis fully integrated differential capacitive tactile sensor surface-mountable on a bus line. The sensor integrates a flip-bonded complementary metal-oxide semiconductor (CMOS) with capacitive sensing circuits on a low temperature cofired ceramic (LTCC) interposer with Au through vias by Au-Au thermo-compression bonding. The CMOS circuit and bonding pads on the sensor backside were electrically connected through Au bumps and the LTCC interposer, and the differential capacitive gap was formed by an Au sealing frame. A diaphragm for sensing 3-axis force was formed in the CMOS substrate. The dimensions of the completed sensor are 2.5 mm in width, 2.5 mm in length, and 0.66 mm in thickness. The fabricated sensor output coded 3-axis capacitive sensing data according to applied 3-axis force by three-dimensional (3D)-printed pins. The measured sensitivity was as high as over 34 Count/mN for normal force and 14 to 15 Count/mN for shear force with small noise, which corresponds to less than 1 mN. The hysteresis and the average cross-sensitivity were also found to be less than 2% full scale and 11%, respectively. Full article
(This article belongs to the Special Issue Tactile Sensors and Sensing)
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Open AccessArticle Surface Acoustic Wave (SAW)-Enhanced Chemical Functionalization of Gold Films
Sensors 2017, 17(11), 2452; doi:10.3390/s17112452
Received: 29 September 2017 / Revised: 20 October 2017 / Accepted: 24 October 2017 / Published: 26 October 2017
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Abstract
Surface chemical and biochemical functionalization is a fundamental process that is widely applied in many fields to add new functions, features, or capabilities to a material’s surface. Here, we demonstrate that surface acoustic waves (SAWs) can enhance the chemical functionalization of gold films.
[...] Read more.
Surface chemical and biochemical functionalization is a fundamental process that is widely applied in many fields to add new functions, features, or capabilities to a material’s surface. Here, we demonstrate that surface acoustic waves (SAWs) can enhance the chemical functionalization of gold films. This is shown by using an integrated biochip composed by a microfluidic channel coupled to a surface plasmon resonance (SPR) readout system and by monitoring the adhesion of biotin-thiol on the gold SPR areas in different conditions. In the case of SAW-induced streaming, the functionalization efficiency is improved 5 times with respect to the case without SAWs. The technology here proposed can be easily applied to a wide variety of biological systems (e.g., proteins, nucleic acids) and devices (e.g., sensors, devices for cell cultures). Full article
(This article belongs to the Special Issue Surface Plasmon Resonance Sensing)
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Open AccessArticle Prostate Cancer Detection with a Tactile Resonance Sensor—Measurement Considerations and Clinical Setup
Sensors 2017, 17(11), 2453; doi:10.3390/s17112453
Received: 12 September 2017 / Revised: 16 October 2017 / Accepted: 24 October 2017 / Published: 26 October 2017
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Abstract
Tumors in the human prostate are usually stiffer compared to surrounding non-malignant glandular tissue, and tactile resonance sensors measuring stiffness can be used to detect prostate cancer. To explore this further, we used a tactile resonance sensor system combined with a rotatable sample
[...] Read more.
Tumors in the human prostate are usually stiffer compared to surrounding non-malignant glandular tissue, and tactile resonance sensors measuring stiffness can be used to detect prostate cancer. To explore this further, we used a tactile resonance sensor system combined with a rotatable sample holder where whole surgically removed prostates could be attached to detect tumors on, and beneath, the surface ex vivo. Model studies on tissue phantoms made of silicone and porcine tissue were performed. Finally, two resected human prostate glands were studied. Embedded stiff silicone inclusions placed 4 mm under the surface could be detected in both the silicone and biological tissue models, with a sensor indentation of 0.6 mm. Areas with different amounts of prostate cancer (PCa) could be distinguished from normal tissue (p < 0.05), when the tumor was located in the anterior part, whereas small tumors located in the dorsal aspect were undetected. The study indicates that PCa may be detected in a whole resected prostate with an uneven surface and through its capsule. This is promising for the development of a clinically useful instrument to detect prostate cancer during surgery. Full article
(This article belongs to the Special Issue Tactile Sensors and Sensing)
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Open AccessArticle Precise Aperture-Dependent Motion Compensation with Frequency Domain Fast Back-Projection Algorithm
Sensors 2017, 17(11), 2454; doi:10.3390/s17112454
Received: 20 August 2017 / Revised: 12 October 2017 / Accepted: 24 October 2017 / Published: 26 October 2017
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Abstract
Precise azimuth-variant motion compensation (MOCO) is an essential and difficult task for high-resolution synthetic aperture radar (SAR) imagery. In conventional post-filtering approaches, residual azimuth-variant motion errors are generally compensated through a set of spatial post-filters, where the coarse-focused image is segmented into overlapped
[...] Read more.
Precise azimuth-variant motion compensation (MOCO) is an essential and difficult task for high-resolution synthetic aperture radar (SAR) imagery. In conventional post-filtering approaches, residual azimuth-variant motion errors are generally compensated through a set of spatial post-filters, where the coarse-focused image is segmented into overlapped blocks concerning the azimuth-dependent residual errors. However, image domain post-filtering approaches, such as precise topography- and aperture-dependent motion compensation algorithm (PTA), have difficulty of robustness in declining, when strong motion errors are involved in the coarse-focused image. In this case, in order to capture the complete motion blurring function within each image block, both the block size and the overlapped part need necessary extension leading to degeneration of efficiency and robustness inevitably. Herein, a frequency domain fast back-projection algorithm (FDFBPA) is introduced to deal with strong azimuth-variant motion errors. FDFBPA disposes of the azimuth-variant motion errors based on a precise azimuth spectrum expression in the azimuth wavenumber domain. First, a wavenumber domain sub-aperture processing strategy is introduced to accelerate computation. After that, the azimuth wavenumber spectrum is partitioned into a set of wavenumber blocks, and each block is formed into a sub-aperture coarse resolution image via the back-projection integral. Then, the sub-aperture images are straightforwardly fused together in azimuth wavenumber domain to obtain a full resolution image. Moreover, chirp-Z transform (CZT) is also introduced to implement the sub-aperture back-projection integral, increasing the efficiency of the algorithm. By disusing the image domain post-filtering strategy, robustness of the proposed algorithm is improved. Both simulation and real-measured data experiments demonstrate the effectiveness and superiority of the proposal. Full article
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Open AccessArticle Inferring Interaction Force from Visual Information without Using Physical Force Sensors
Sensors 2017, 17(11), 2455; doi:10.3390/s17112455
Received: 13 August 2017 / Revised: 30 September 2017 / Accepted: 24 October 2017 / Published: 26 October 2017
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Abstract
In this paper, we present an interaction force estimation method that uses visual information rather than that of a force sensor. Specifically, we propose a novel deep learning-based method utilizing only sequential images for estimating the interaction force against a target object, where
[...] Read more.
In this paper, we present an interaction force estimation method that uses visual information rather than that of a force sensor. Specifically, we propose a novel deep learning-based method utilizing only sequential images for estimating the interaction force against a target object, where the shape of the object is changed by an external force. The force applied to the target can be estimated by means of the visual shape changes. However, the shape differences in the images are not very clear. To address this problem, we formulate a recurrent neural network-based deep model with fully-connected layers, which models complex temporal dynamics from the visual representations. Extensive evaluations show that the proposed learning models successfully estimate the interaction forces using only the corresponding sequential images, in particular in the case of three objects made of different materials, a sponge, a PET bottle, a human arm, and a tube. The forces predicted by the proposed method are very similar to those measured by force sensors. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Modeling of Thermal Phase Noise in a Solid Core Photonic Crystal Fiber-Optic Gyroscope
Sensors 2017, 17(11), 2456; doi:10.3390/s17112456
Received: 5 September 2017 / Revised: 10 October 2017 / Accepted: 23 October 2017 / Published: 26 October 2017
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Abstract
A theoretical model of the thermal phase noise in a square-wave modulated solid core photonic crystal fiber-optic gyroscope has been established, and then verified by measurements. The results demonstrate a good agreement between theory and experiment. The contribution of the thermal phase noise
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A theoretical model of the thermal phase noise in a square-wave modulated solid core photonic crystal fiber-optic gyroscope has been established, and then verified by measurements. The results demonstrate a good agreement between theory and experiment. The contribution of the thermal phase noise to the random walk coefficient of the gyroscope is derived. A fiber coil with 2.8 km length is used in the experimental solid core photonic crystal fiber-optic gyroscope, showing a random walk coefficient of 9.25 × 10−5 deg/√h. Full article
(This article belongs to the Special Issue Integrated Photonic Technologies for Sensing Applications)
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Open AccessArticle ESPRIT-Like Two-Dimensional DOA Estimation for Monostatic MIMO Radar with Electromagnetic Vector Received Sensors under the Condition of Gain and Phase Uncertainties and Mutual Coupling
Sensors 2017, 17(11), 2457; doi:10.3390/s17112457
Received: 30 August 2017 / Revised: 9 October 2017 / Accepted: 25 October 2017 / Published: 26 October 2017
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Abstract
In this paper, we focus on the problem of two-dimensional direction of arrival (2D-DOA) estimation for monostatic MIMO Radar with electromagnetic vector received sensors (MIMO-EMVSs) under the condition of gain and phase uncertainties (GPU) and mutual coupling (MC). GPU would spoil the invariance
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In this paper, we focus on the problem of two-dimensional direction of arrival (2D-DOA) estimation for monostatic MIMO Radar with electromagnetic vector received sensors (MIMO-EMVSs) under the condition of gain and phase uncertainties (GPU) and mutual coupling (MC). GPU would spoil the invariance property of the EMVSs in MIMO-EMVSs, thus the effective ESPRIT algorithm unable to be used directly. Then we put forward a C-SPD ESPRIT-like algorithm. It estimates the 2D-DOA and polarization station angle (PSA) based on the instrumental sensors method (ISM). The C-SPD ESPRIT-like algorithm can obtain good angle estimation accuracy without knowing the GPU. Furthermore, it can be applied to arbitrary array configuration and has low complexity for avoiding the angle searching procedure. When MC and GPU exist together between the elements of EMVSs, in order to make our algorithm feasible, we derive a class of separated electromagnetic vector receiver and give the S-SPD ESPRIT-like algorithm. It can solve the problem of GPU and MC efficiently. And the array configuration can be arbitrary. The effectiveness of our proposed algorithms is verified by the simulation result. Full article
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Open AccessArticle Soft Sensing of Non-Newtonian Fluid Flow in Open Venturi Channel Using an Array of Ultrasonic Level Sensors—AI Models and Their Validations
Sensors 2017, 17(11), 2458; doi:10.3390/s17112458
Received: 27 September 2017 / Revised: 20 October 2017 / Accepted: 23 October 2017 / Published: 26 October 2017
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Abstract
In oil and gas and geothermal installations, open channels followed by sieves for removal of drill cuttings, are used to monitor the quality and quantity of the drilling fluids. Drilling fluid flow rate is difficult to measure due to the varying flow conditions
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In oil and gas and geothermal installations, open channels followed by sieves for removal of drill cuttings, are used to monitor the quality and quantity of the drilling fluids. Drilling fluid flow rate is difficult to measure due to the varying flow conditions (e.g., wavy, turbulent and irregular) and the presence of drilling cuttings and gas bubbles. Inclusion of a Venturi section in the open channel and an array of ultrasonic level sensors above it at locations in the vicinity of and above the Venturi constriction gives the varying levels of the drilling fluid in the channel. The time series of the levels from this array of ultrasonic level sensors are used to estimate the drilling fluid flow rate, which is compared with Coriolis meter measurements. Fuzzy logic, neural networks and support vector regression algorithms applied to the data from temporal and spatial ultrasonic level measurements of the drilling fluid in the open channel give estimates of its flow rate with sufficient reliability, repeatability and uncertainty, providing a novel soft sensing of an important process variable. Simulations, cross-validations and experimental results show that feedforward neural networks with the Bayesian regularization learning algorithm provide the best flow rate estimates. Finally, the benefits of using this soft sensing technique combined with Venturi constriction in open channels are discussed. Full article
(This article belongs to the Special Issue Soft Sensors and Intelligent Algorithms for Data Fusion)
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Open AccessArticle Waveguide Bragg Gratings in Ormocer®s for Temperature Sensing
Sensors 2017, 17(11), 2459; doi:10.3390/s17112459
Received: 28 September 2017 / Revised: 17 October 2017 / Accepted: 20 October 2017 / Published: 26 October 2017
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Abstract
Embedded channel waveguide Bragg gratings are fabricated in the Ormocer® hybrid polymers OrmoComp®, OrmoCore, and OrmoClad by employing a single writing step technique based on phase mask technology and KrF excimer laser irradiation. All waveguide Bragg gratings exhibit well-defined reflection
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Embedded channel waveguide Bragg gratings are fabricated in the Ormocer® hybrid polymers OrmoComp®, OrmoCore, and OrmoClad by employing a single writing step technique based on phase mask technology and KrF excimer laser irradiation. All waveguide Bragg gratings exhibit well-defined reflection peaks within the telecom wavelengths range with peak heights of up to 35 dB and −3 dB-bandwidths of down to 95 pm. Furthermore, the dependency of the fabricated embedded channel waveguide Bragg gratings on changes of the temperature and relative humidity are investigated. Here, we found that the Bragg grating in OrmoComp® is significantly influenced by humidity variations, while the Bragg gratings in OrmoCore and OrmoClad exhibit linear and considerably high temperature sensitivities of up to −250 pm/ C and a linear dependency on the relative humidity in the range of −9 pm/%. Full article
(This article belongs to the Special Issue Fiber Bragg Grating Based Sensors)
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Open AccessArticle BeiDou Geostationary Satellite Code Bias Modeling Using Fengyun-3C Onboard Measurements
Sensors 2017, 17(11), 2460; doi:10.3390/s17112460
Received: 23 August 2017 / Revised: 23 October 2017 / Accepted: 25 October 2017 / Published: 27 October 2017
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Abstract
This study validated and investigated elevation- and frequency-dependent systematic biases observed in ground-based code measurements of the Chinese BeiDou navigation satellite system, using the onboard BeiDou code measurement data from the Chinese meteorological satellite Fengyun-3C. Particularly for geostationary earth orbit satellites, sky-view coverage
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This study validated and investigated elevation- and frequency-dependent systematic biases observed in ground-based code measurements of the Chinese BeiDou navigation satellite system, using the onboard BeiDou code measurement data from the Chinese meteorological satellite Fengyun-3C. Particularly for geostationary earth orbit satellites, sky-view coverage can be achieved over the entire elevation and azimuth angle ranges with the available onboard tracking data, which is more favorable to modeling code biases. Apart from the BeiDou-satellite-induced biases, the onboard BeiDou code multipath effects also indicate pronounced near-field systematic biases that depend only on signal frequency and the line-of-sight directions. To correct these biases, we developed a proposed code correction model by estimating the BeiDou-satellite-induced biases as linear piece-wise functions in different satellite groups and the near-field systematic biases in a grid approach. To validate the code bias model, we carried out orbit determination using single-frequency BeiDou data with and without code bias corrections applied. Orbit precision statistics indicate that those code biases can seriously degrade single-frequency orbit determination. After the correction model was applied, the orbit position errors, 3D root mean square, were reduced from 150.6 to 56.3 cm. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle A Green Microbial Fuel Cell-Based Biosensor for In Situ Chromium (VI) Measurement in Electroplating Wastewater
Sensors 2017, 17(11), 2461; doi:10.3390/s17112461
Received: 24 September 2017 / Revised: 20 October 2017 / Accepted: 24 October 2017 / Published: 27 October 2017
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Abstract
The extensive use of Cr(VI) in many industries and the disposal of Cr(VI)-containing wastes have resulted in Cr(VI)-induced environmental contamination. Cr(VI) compounds are associated with increased cancer risks; hence, the detection of toxic Cr(VI) compounds is crucial. Various methods have been developed for
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The extensive use of Cr(VI) in many industries and the disposal of Cr(VI)-containing wastes have resulted in Cr(VI)-induced environmental contamination. Cr(VI) compounds are associated with increased cancer risks; hence, the detection of toxic Cr(VI) compounds is crucial. Various methods have been developed for Cr(VI) measurement, but they are often conducted offsite and cannot provide real-time toxicity monitoring. A microbial fuel cell (MFC) is an eco-friendly and self-sustaining device that has great potential as a biosensor for in situ Cr(VI) measurement, especially for wastewater generated from different electroplating units. In this study, Exiguobacterium aestuarii YC211, a facultatively anaerobic, Cr(VI)-reducing, salt-tolerant, and exoelectrogenic bacterium, was isolated and inoculated into an MFC to evaluate its feasibility as a Cr(VI) biosensor. The Cr(VI) removal efficiency of E. aestuarii YC211 was not affected by the surrounding environment (pH 5–9, 20–35 °C, coexisting ions, and salinity of 0–15 g/L). The maximum power density of the MFC biosensor was 98.3 ± 1.5 mW/m2 at 1500 Ω. A good linear relationship (r2 = 0.997) was observed between the Cr(VI) concentration (2.5–60 mg/L) and the voltage output. The developed MFC biosensor is a simple device that can accurately measure Cr(VI) concentrations in the actual electroplating wastewater that is generated from different electroplating units within 30 min with low deviations (−6.1% to 2.2%). After treating the actual electroplating wastewater with the MFC, the predominant family in the biofilm was found to be Bacillaceae (95.3%) and was further identified as the originally inoculated E. aestuarii YC211 by next generation sequencing (NGS). Thus, the MFC biosensor can measure Cr(VI) concentrations in situ in the effluents from different electroplating units, and it can potentially help in preventing the violation of effluent regulations. Full article
(This article belongs to the Special Issue Environmental Monitoring Biosensors)
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Open AccessArticle Tightly-Coupled Integration of Multi-GNSS Single-Frequency RTK and MEMS-IMU for Enhanced Positioning Performance
Sensors 2017, 17(11), 2462; doi:10.3390/s17112462
Received: 25 September 2017 / Revised: 25 October 2017 / Accepted: 25 October 2017 / Published: 27 October 2017
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Abstract
Dual-frequency Global Positioning System (GPS) Real-time Kinematics (RTK) has been proven in the past few years to be a reliable and efficient technique to obtain high accuracy positioning. However, there are still challenges for GPS single-frequency RTK, such as low reliability and ambiguity
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Dual-frequency Global Positioning System (GPS) Real-time Kinematics (RTK) has been proven in the past few years to be a reliable and efficient technique to obtain high accuracy positioning. However, there are still challenges for GPS single-frequency RTK, such as low reliability and ambiguity resolution (AR) success rate, especially in kinematic environments. Recently, multi-Global Navigation Satellite System (multi-GNSS) has been applied to enhance the RTK performance in terms of availability and reliability of AR. In order to further enhance the multi-GNSS single-frequency RTK performance in terms of reliability, continuity and accuracy, a low-cost micro-electro-mechanical system (MEMS) inertial measurement unit (IMU) is adopted in this contribution. We tightly integrate the single-frequency GPS/BeiDou/GLONASS and MEMS-IMU through the extended Kalman filter (EKF), which directly fuses the ambiguity-fixed double-differenced (DD) carrier phase observables and IMU data. A field vehicular test was carried out to evaluate the impacts of the multi-GNSS and IMU on the AR and positioning performance in different system configurations. Test results indicate that the empirical success rate of single-epoch AR for the tightly-coupled single-frequency multi-GNSS RTK/INS integration is over 99% even at an elevation cut-off angle of 40°, and the corresponding position time series is much more stable in comparison with the GPS solution. Besides, GNSS outage simulations show that continuous positioning with certain accuracy is possible due to the INS bridging capability when GNSS positioning is not available. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Design of a Matching Network for a High-Sensitivity Broadband Magnetic Resonance Sounding Coil Sensor
Sensors 2017, 17(11), 2463; doi:10.3390/s17112463
Received: 9 September 2017 / Revised: 17 October 2017 / Accepted: 23 October 2017 / Published: 27 October 2017
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Abstract
The magnetic resonance sounding (MRS) technique is a non-invasive geophysical method that can provide unique insights into the hydrological properties of groundwater. The Cu coil sensor is the preferred choice for detecting the weak MRS signal because of its high sensitivity, low fabrication
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The magnetic resonance sounding (MRS) technique is a non-invasive geophysical method that can provide unique insights into the hydrological properties of groundwater. The Cu coil sensor is the preferred choice for detecting the weak MRS signal because of its high sensitivity, low fabrication complexity and low cost. The tuned configuration was traditionally used for the MRS coil sensor design because of its high sensitivity and narrowband filtering. However, its narrow bandwidth may distort the MRS signals. To address this issue, a non-tuned design exhibiting a broad bandwidth has emerged recently, however, the sensitivity decreases as the bandwidth increases. Moreover, the effect of the MRS applications is often seriously influenced by power harmonic noises in the developed areas, especially low-frequency harmonics, resulting in saturation of the coil sensor, regardless of the tuned or non-tuned configuration. To solve the two aforementioned problems, we propose a matching network consisting of an LC broadband filter in parallel with a matching capacitor and provide a design for a coil sensor with a matching network (CSMN). The theoretical parameter calculations and the equivalent schematic of the CSMN with noise sources are investigated, and the sensitivity of the CSMN is evaluated by the Allan variance and the signal-to-noise ratio (SNR). Correspondingly, we constructed the CSMN with a 3 dB bandwidth, passband gain, normalized equivalent input noise and sensitivity (detection limit) of 1030 Hz, 4.6 dB, 1.78 nV/(Hz)1/2 @ 2 kHz and 3 nV, respectively. Experimental tests in the laboratory show that the CSMN can not only improve the sensitivity, but also inhibit the signal distortion by suppressing power harmonic noises in the strong electromagnetic interference environment. Finally, a field experiment is performed with the CSMN to show a valid measurement of the signals of an MRS instrument system. Full article
(This article belongs to the Special Issue Magnetic Sensors and Their Applications)
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Open AccessArticle Electrochemical Detection of Plasma Immunoglobulin as a Biomarker for Alzheimer’s Disease
Sensors 2017, 17(11), 2464; doi:10.3390/s17112464
Received: 5 September 2017 / Revised: 20 October 2017 / Accepted: 20 October 2017 / Published: 27 October 2017
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Abstract
The clinical diagnosis and treatment of Alzheimer’s disease (AD) represent a challenge to clinicians due to the variability of clinical symptomatology as well as the unavailability of reliable diagnostic tests. In this study, the development of a novel electrochemical assay and its potential
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The clinical diagnosis and treatment of Alzheimer’s disease (AD) represent a challenge to clinicians due to the variability of clinical symptomatology as well as the unavailability of reliable diagnostic tests. In this study, the development of a novel electrochemical assay and its potential to detect peripheral blood biomarkers to diagnose AD using plasma immunoglobulins is investigated. The immunosensor employs a gold electrode as the immobilizing substrate, albumin depleted plasma immunoglobulin as the biomarker, and polyclonal rabbit Anti-human immunoglobulin (against IgA, IgG, IgM) as the receptor for plasma conjugation. The assay showed good response, sensitivity and reproducibility in differentiating plasma immunoglobulin from AD and control subjects down to 10−9 dilutions of plasma immunoglobulin representing plasma content concentrations in the pg mL−1 range. The newly developed assay is highly sensitive, less time consuming, easy to handle, can be easily modified to detect other dementia-related biomarkers in blood samples, and can be easily integrated into portable devices. Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle Determination of Fluid Density and Viscosity by Analyzing Flexural Wave Propagations on the Vibrating Micro-Cantilever
Sensors 2017, 17(11), 2466; doi:10.3390/s17112466
Received: 19 September 2017 / Revised: 16 October 2017 / Accepted: 24 October 2017 / Published: 27 October 2017
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Abstract
The determination of fluid density and viscosity using most cantilever-based sensors is based on changes in resonant frequency and peak width. Here, we present a wave propagation analysis using piezoelectrically excited micro-cantilevers under distributed fluid loading. The standing wave shapes of microscale-thickness cantilevers
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The determination of fluid density and viscosity using most cantilever-based sensors is based on changes in resonant frequency and peak width. Here, we present a wave propagation analysis using piezoelectrically excited micro-cantilevers under distributed fluid loading. The standing wave shapes of microscale-thickness cantilevers partially immersed in liquids (water, 25% glycerol, and acetone), and nanoscale-thickness microfabricated cantilevers fully immersed in gases (air at three different pressures, carbon dioxide, and nitrogen) were investigated to identify the effects of fluid-structure interactions to thus determine the fluid properties. This measurement method was validated by comparing with the known fluid properties, which agreed well with the measurements. The relative differences for the liquids were less than 4.8% for the densities and 3.1% for the viscosities, and those for the gases were less than 6.7% for the densities and 7.3% for the viscosities, showing better agreements in liquids than in gases. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Monitoring of Pre-Load on Rock Bolt Using Piezoceramic-Transducer Enabled Time Reversal Method
Sensors 2017, 17(11), 2467; doi:10.3390/s17112467
Received: 10 September 2017 / Revised: 19 October 2017 / Accepted: 23 October 2017 / Published: 27 October 2017
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Abstract
Rock bolts ensure structural stability for tunnels and many other underground structures. The pre-load on a rock bolt plays an important role in the structural reinforcement and it is vital to monitor the pre-load status of rock bolts. In this paper, a rock
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Rock bolts ensure structural stability for tunnels and many other underground structures. The pre-load on a rock bolt plays an important role in the structural reinforcement and it is vital to monitor the pre-load status of rock bolts. In this paper, a rock bolt pre-load monitoring method based on the piezoceramic enabled time reversal method is proposed. A lead zirconate titanate (PZT) patch transducer, which works as an actuator to generate stress waves, is bonded onto the anchor plate of the rock bolt. A smart washer, which is fabricated by sandwiching a PZT patch between two metal rings, is installed between the hex nut and the anchor plate along the rock bolt. The smart washer functions as a sensor to detect the stress wave. With the increase of the pre-load values on the rock bolt, the effective contact surface area between the smart washer and the anchor plate, benefiting the stress wave propagation crossing the contact surface. With the help of time reversal technique, experimental results reveal that the magnitude of focused signal clearly increases with the increase of the pre-load on a rock bolt before the saturation which happens beyond a relatively high value of the pre-load. The proposed method provides an innovative and real time means to monitor the pre-load level of a rock bolt. By employing this method, the pre-load degradation process on a rock bolt can be clearly monitored. Please note that, currently, the proposed method applies to only new rock bolts, on which it is possible to install the PZT smart washer. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Trail-Based Search for Efficient Event Report to Mobile Actors in Wireless Sensor and Actor Networks
Sensors 2017, 17(11), 2468; doi:10.3390/s17112468
Received: 7 September 2017 / Revised: 17 October 2017 / Accepted: 24 October 2017 / Published: 27 October 2017
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Abstract
In wireless sensor and actor networks, when an event is detected, the sensor node needs to transmit an event report to inform the actor. Since the actor moves in the network to execute missions, its location is always unavailable to the sensor nodes.
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In wireless sensor and actor networks, when an event is detected, the sensor node needs to transmit an event report to inform the actor. Since the actor moves in the network to execute missions, its location is always unavailable to the sensor nodes. A popular solution is the search strategy that can forward the data to a node without its location information. However, most existing works have not considered the mobility of the node, and thus generate significant energy consumption or transmission delay. In this paper, we propose the trail-based search (TS) strategy that takes advantage of actor’s mobility to improve the search efficiency. The main idea of TS is that, when the actor moves in the network, it can leave its trail composed of continuous footprints. The search packet with the event report is transmitted in the network to search the actor or its footprints. Once an effective footprint is discovered, the packet will be forwarded along the trail until it is received by the actor. Moreover, we derive the condition to guarantee the trail connectivity, and propose the redundancy reduction scheme based on TS (TS-R) to reduce nontrivial transmission redundancy that is generated by the trail. The theoretical and numerical analysis is provided to prove the efficiency of TS. Compared with the well-known expanding ring search (ERS), TS significantly reduces the energy consumption and search delay. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Inverse Source Data-Processing Strategies for Radio-Frequency Localization in Indoor Environments
Sensors 2017, 17(11), 2469; doi:10.3390/s17112469
Received: 21 September 2017 / Revised: 22 October 2017 / Accepted: 24 October 2017 / Published: 27 October 2017
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Abstract
Indoor positioning of mobile devices plays a key role in many aspects of our daily life. These include real-time people tracking and monitoring, activity recognition, emergency detection, navigation, and numerous location based services. Despite many wireless technologies and data-processing algorithms have been developed
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Indoor positioning of mobile devices plays a key role in many aspects of our daily life. These include real-time people tracking and monitoring, activity recognition, emergency detection, navigation, and numerous location based services. Despite many wireless technologies and data-processing algorithms have been developed in recent years, indoor positioning is still a problem subject of intensive research. This paper deals with the active radio-frequency (RF) source localization in indoor scenarios. The localization task is carried out at the physical layer thanks to receiving sensor arrays which are deployed on the border of the surveillance region to record the signal emitted by the source. The localization problem is formulated as an imaging one by taking advantage of the inverse source approach. Different measurement configurations and data-processing/fusion strategies are examined to investigate their effectiveness in terms of localization accuracy under both line-of-sight (LOS) and non-line of sight (NLOS) conditions. Numerical results based on full-wave synthetic data are reported to support the analysis. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Fast Detection of Striped Stem-Borer (Chilo suppressalis Walker) Infested Rice Seedling Based on Visible/Near-Infrared Hyperspectral Imaging System
Sensors 2017, 17(11), 2470; doi:10.3390/s17112470
Received: 20 September 2017 / Revised: 22 October 2017 / Accepted: 24 October 2017 / Published: 27 October 2017
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Abstract
Striped stem-borer (SSB) infestation is one of the most serious sources of damage to rice growth. A rapid and non-destructive method of early SSB detection is essential for rice-growth protection. In this study, hyperspectral imaging combined with chemometrics was used to detect early
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Striped stem-borer (SSB) infestation is one of the most serious sources of damage to rice growth. A rapid and non-destructive method of early SSB detection is essential for rice-growth protection. In this study, hyperspectral imaging combined with chemometrics was used to detect early SSB infestation in rice and identify the degree of infestation (DI). Visible/near-infrared hyperspectral images (in the spectral range of 380 nm to 1030 nm) were taken of the healthy rice plants and infested rice plants by SSB for 2, 4, 6, 8 and 10 days. A total of 17 characteristic wavelengths were selected from the spectral data extracted from the hyperspectral images by the successive projection algorithm (SPA). Principal component analysis (PCA) was applied to the hyperspectral images, and 16 textural features based on the gray-level co-occurrence matrix (GLCM) were extracted from the first two principal component (PC) images. A back-propagation neural network (BPNN) was used to establish infestation degree evaluation models based on full spectra, characteristic wavelengths, textural features and features fusion, respectively. BPNN models based on a fusion of characteristic wavelengths and textural features achieved the best performance, with classification accuracy of calibration and prediction sets over 95%. The accuracy of each infestation degree was satisfactory, and the accuracy of rice samples infested for 2 days was slightly low. In all, this study indicated the feasibility of hyperspectral imaging techniques to detect early SSB infestation and identify degrees of infestation. Full article
(This article belongs to the Special Issue Sensors in Agriculture)
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Open AccessArticle A New Scale Factor Adjustment Method for Magnetic Force Feedback Accelerometer
Sensors 2017, 17(11), 2471; doi:10.3390/s17112471
Received: 26 September 2017 / Revised: 25 October 2017 / Accepted: 25 October 2017 / Published: 27 October 2017
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Abstract
A new and simple method to adjust the scale factor of a magnetic force feedback accelerometer is presented, which could be used in developing a rotating accelerometer gravity gradient instrument (GGI). Adjusting and matching the acceleration-to-current transfer function of the four accelerometers automatically
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A new and simple method to adjust the scale factor of a magnetic force feedback accelerometer is presented, which could be used in developing a rotating accelerometer gravity gradient instrument (GGI). Adjusting and matching the acceleration-to-current transfer function of the four accelerometers automatically is one of the basic and necessary technologies for rejecting the common mode accelerations in the development of GGI. In order to adjust the scale factor of the magnetic force rebalance accelerometer, an external current is injected and combined with the normal feedback current; they are then applied together to the torque coil of the magnetic actuator. The injected current could be varied proportionally according to the external adjustment needs, and the change in the acceleration-to-current transfer function then realized dynamically. The new adjustment method has the advantages of no extra assembly and ease of operation. Changes in the scale factors range from 33% smaller to 100% larger are verified experimentally by adjusting the different external coefficients. The static noise of the used accelerometer is compared under conditions with and without the injecting current, and the experimental results find no change at the current noise level, which further confirms the validity of the presented method. Full article
(This article belongs to the Special Issue Inertial Sensors for Positioning and Navigation)
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Open AccessArticle DEEP-SEE: Joint Object Detection, Tracking and Recognition with Application to Visually Impaired Navigational Assistance
Sensors 2017, 17(11), 2473; doi:10.3390/s17112473
Received: 28 August 2017 / Revised: 5 October 2017 / Accepted: 25 October 2017 / Published: 28 October 2017
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Abstract
In this paper, we introduce the so-called DEEP-SEE framework that jointly exploits computer vision algorithms and deep convolutional neural networks (CNNs) to detect, track and recognize in real time objects encountered during navigation in the outdoor environment. A first feature concerns an object
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In this paper, we introduce the so-called DEEP-SEE framework that jointly exploits computer vision algorithms and deep convolutional neural networks (CNNs) to detect, track and recognize in real time objects encountered during navigation in the outdoor environment. A first feature concerns an object detection technique designed to localize both static and dynamic objects without any a priori knowledge about their position, type or shape. The methodological core of the proposed approach relies on a novel object tracking method based on two convolutional neural networks trained offline. The key principle consists of alternating between tracking using motion information and predicting the object location in time based on visual similarity. The validation of the tracking technique is performed on standard benchmark VOT datasets, and shows that the proposed approach returns state-of-the-art results while minimizing the computational complexity. Then, the DEEP-SEE framework is integrated into a novel assistive device, designed to improve cognition of VI people and to increase their safety when navigating in crowded urban scenes. The validation of our assistive device is performed on a video dataset with 30 elements acquired with the help of VI users. The proposed system shows high accuracy (>90%) and robustness (>90%) scores regardless on the scene dynamics. Full article
(This article belongs to the Special Issue Video Analysis and Tracking Using State-of-the-Art Sensors)
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Open AccessArticle An Electronic System for the Contactless Reading of ECG Signals
Sensors 2017, 17(11), 2474; doi:10.3390/s17112474
Received: 25 September 2017 / Revised: 23 October 2017 / Accepted: 27 October 2017 / Published: 28 October 2017
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Abstract
The aim of this work is the development of a contactless capacitive sensory system for the detection of (Electrocardiographic) ECG-like signals. The acquisition approach is based on a capacitive coupling with the patient body performed by electrodes integrated in a front-end circuit. The
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The aim of this work is the development of a contactless capacitive sensory system for the detection of (Electrocardiographic) ECG-like signals. The acquisition approach is based on a capacitive coupling with the patient body performed by electrodes integrated in a front-end circuit. The proposed system is able to detect changes in the electric charge related to the heart activity. Due to the target signal weakness and to the presence of other undesired signals, suitable amplification stages and analogue filters are required. Simulated results allowed us to evaluate the effectiveness of the approach, whereas experimental measurements, recorded without contact to the skin, have validated the practical effectiveness of the proposed architecture. The system operates with a supply voltage of ±9 V with an overall power consumption of about 10 mW. The analogue output of the electronic interface is connected to an ATmega328 microcontroller implementing the A/D conversion and the data acquisition. The collected data can be displayed on any multimedia support for real-time tracking applications. Full article
(This article belongs to the Special Issue Sensors for Health Monitoring and Disease Diagnosis)
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Open AccessArticle Road Lane Detection Robust to Shadows Based on a Fuzzy System Using a Visible Light Camera Sensor
Sensors 2017, 17(11), 2475; doi:10.3390/s17112475
Received: 22 August 2017 / Revised: 21 October 2017 / Accepted: 25 October 2017 / Published: 28 October 2017
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Abstract
Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous
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Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous vehicles for maintaining the safety of semi-autonomous and fully autonomous systems. Unlike traffic signs, road lanes are easily damaged by both internal and external factors such as road quality, occlusion (traffic on the road), weather conditions, and illumination (shadows from objects such as cars, trees, and buildings). Obtaining clear road lane markings for recognition processing is a difficult challenge. Therefore, we propose a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor. Experimental results from three open databases, Caltech dataset, Santiago Lanes dataset (SLD), and Road Marking dataset, showed that our method outperformed conventional lane detection methods. Full article
(This article belongs to the Special Issue Mechatronic Systems for Automatic Vehicles)
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Open AccessArticle Indoor Air Quality Analysis Using Deep Learning with Sensor Data
Sensors 2017, 17(11), 2476; doi:10.3390/s17112476
Received: 21 July 2017 / Revised: 7 September 2017 / Accepted: 25 October 2017 / Published: 28 October 2017
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Abstract
Indoor air quality analysis is of interest to understand the abnormal atmospheric phenomena and external factors that affect air quality. By recording and analyzing quality measurements, we are able to observe patterns in the measurements and predict the air quality of near future.
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Indoor air quality analysis is of interest to understand the abnormal atmospheric phenomena and external factors that affect air quality. By recording and analyzing quality measurements, we are able to observe patterns in the measurements and predict the air quality of near future. We designed a microchip made out of sensors that is capable of periodically recording measurements, and proposed a model that estimates atmospheric changes using deep learning. In addition, we developed an efficient algorithm to determine the optimal observation period for accurate air quality prediction. Experimental results with real-world data demonstrate the feasibility of our approach. Full article
(This article belongs to the Special Issue Air Pollution Sensors: A New Class of Tools to Measure Air Quality)
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Open AccessArticle Fault Detection of Bearing Systems through EEMD and Optimization Algorithm
Sensors 2017, 17(11), 2477; doi:10.3390/s17112477
Received: 31 August 2017 / Revised: 20 October 2017 / Accepted: 23 October 2017 / Published: 28 October 2017
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Abstract
This study proposes a fault detection and diagnosis method for bearing systems using ensemble empirical mode decomposition (EEMD) based feature extraction, in conjunction with particle swarm optimization (PSO), principal component analysis (PCA), and Isomap. First, a mathematical model is assumed to generate vibration
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This study proposes a fault detection and diagnosis method for bearing systems using ensemble empirical mode decomposition (EEMD) based feature extraction, in conjunction with particle swarm optimization (PSO), principal component analysis (PCA), and Isomap. First, a mathematical model is assumed to generate vibration signals from damaged bearing components, such as the inner-race, outer-race, and rolling elements. The process of decomposing vibration signals into intrinsic mode functions (IMFs) and extracting statistical features is introduced to develop a damage-sensitive parameter vector. Finally, PCA and Isomap algorithm are used to classify and visualize this parameter vector, to separate damage characteristics from healthy bearing components. Moreover, the PSO-based optimization algorithm improves the classification performance by selecting proper weightings for the parameter vector, to maximize the visualization effect of separating and grouping of parameter vectors in three-dimensional space. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Distributed Channel Allocation and Time Slot Optimization for Green Internet of Things
Sensors 2017, 17(11), 2479; doi:10.3390/s17112479
Received: 23 September 2017 / Revised: 18 October 2017 / Accepted: 27 October 2017 / Published: 28 October 2017
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Abstract
In sustainable smart cities, power saving is a severe challenge in the energy-constrained Internet of Things (IoT). Efficient utilization of limited multiple non-overlap channels and time resources is a promising solution to reduce the network interference and save energy consumption. In this paper,
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In sustainable smart cities, power saving is a severe challenge in the energy-constrained Internet of Things (IoT). Efficient utilization of limited multiple non-overlap channels and time resources is a promising solution to reduce the network interference and save energy consumption. In this paper, we propose a joint channel allocation and time slot optimization solution for IoT. First, we propose a channel ranking algorithm which enables each node to rank its available channels based on the channel properties. Then, we propose a distributed channel allocation algorithm so that each node can choose a proper channel based on the channel ranking and its own residual energy. Finally, the sleeping duration and spectrum sensing duration are jointly optimized to maximize the normalized throughput and satisfy energy consumption constraints simultaneously. Different from the former approaches, our proposed solution requires no central coordination or any global information that each node can operate based on its own local information in a total distributed manner. Also, theoretical analysis and extensive simulations have validated that when applying our solution in the network of IoT: (i) each node can be allocated to a proper channel based on the residual energy to balance the lifetime; (ii) the network can rapidly converge to a collision-free transmission through each node’s learning ability in the process of the distributed channel allocation; and (iii) the network throughput is further improved via the dynamic time slot optimization. Full article
(This article belongs to the Special Issue Advances in Sensors for Sustainable Smart Cities and Smart Buildings)
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Open AccessArticle An Adaptive Transmitting Scheme for Interrupted Sampling Repeater Jamming Suppression
Sensors 2017, 17(11), 2480; doi:10.3390/s17112480
Received: 12 August 2017 / Revised: 11 October 2017 / Accepted: 23 October 2017 / Published: 29 October 2017
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Abstract
The interrupted sampling repeater jamming (ISRJ) based on a digital radio frequency memory (DRFM) device is a new type of coherent jamming. This kind of jamming usually occurs as main-lobe jamming and has the advantages of low power requirements and easy parameter adjustment,
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The interrupted sampling repeater jamming (ISRJ) based on a digital radio frequency memory (DRFM) device is a new type of coherent jamming. This kind of jamming usually occurs as main-lobe jamming and has the advantages of low power requirements and easy parameter adjustment, posing a serious threat to the modern radar systems. In order to suppress the ISRJ, this paper proposes an adaptive transmitting scheme based on a phase-coded signal. The scheme firstly performs jamming perception to estimate the jamming parameters, then, on this basis, optimizes the waveform with genetic algorithm. With the optimized waveform, the jamming signal is orthogonal to the target echo, thus it can be easily suppressed with pulse compression. Simulation experiments are performed to verify the effectiveness of the scheme and the results suggest that the peak-to-side-lobe ratio (PSR) and integrated side-lobe level (ISL) of the pulse compression can be improved by about 16 dB and 15 dB, respectively, for the case where the jamming-to-signal ratio (JSR) is 13 dB. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Electrical and Self-Sensing Properties of Ultra-High-Performance Fiber-Reinforced Concrete with Carbon Nanotubes
Sensors 2017, 17(11), 2481; doi:10.3390/s17112481
Received: 24 September 2017 / Revised: 20 October 2017 / Accepted: 27 October 2017 / Published: 29 October 2017
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Abstract
This study examined the electrical and self-sensing capacities of ultra-high-performance fiber-reinforced concrete (UHPFRC) with and without carbon nanotubes (CNTs). For this, the effects of steel fiber content, orientation, and pore water content on the electrical and piezoresistive properties of UHPFRC without CNTs were
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This study examined the electrical and self-sensing capacities of ultra-high-performance fiber-reinforced concrete (UHPFRC) with and without carbon nanotubes (CNTs). For this, the effects of steel fiber content, orientation, and pore water content on the electrical and piezoresistive properties of UHPFRC without CNTs were first evaluated. Then, the effect of CNT content on the self-sensing capacities of UHPFRC under compression and flexure was investigated. Test results indicated that higher steel fiber content, better fiber orientation, and higher amount of pore water led to higher electrical conductivity of UHPFRC. The effects of fiber orientation and drying condition on the electrical conductivity became minor as sufficiently high amount of steel fibers, 3% by volume, was added. Including only steel fibers did not impart UHPFRC with piezoresistive properties. Addition of CNTs substantially improved the electrical conductivity of UHPFRC. Under compression, UHPFRC with a CNT content of 0.3% or greater had a self-sensing ability that was activated by the formation of cracks, and better sensing capacity was achieved by including greater amount of CNTs. Furthermore, the pre-peak flexural behavior of UHPFRC was precisely simulated with a fractional change in resistivity when 0.3% CNTs were incorporated. The pre-cracking self-sensing capacity of UHPFRC with CNTs was more effective under tensile stress state than under compressive stress state. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Motion-Blur-Free High-Speed Video Shooting Using a Resonant Mirror
Sensors 2017, 17(11), 2483; doi:10.3390/s17112483
Received: 15 September 2017 / Revised: 22 October 2017 / Accepted: 25 October 2017 / Published: 29 October 2017
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Abstract
This study proposes a novel concept of actuator-driven frame-by-frame intermittent tracking for motion-blur-free video shooting of fast-moving objects. The camera frame and shutter timings are controlled for motion blur reduction in synchronization with a free-vibration-type actuator vibrating with a large amplitude at hundreds
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This study proposes a novel concept of actuator-driven frame-by-frame intermittent tracking for motion-blur-free video shooting of fast-moving objects. The camera frame and shutter timings are controlled for motion blur reduction in synchronization with a free-vibration-type actuator vibrating with a large amplitude at hundreds of hertz so that motion blur can be significantly reduced in free-viewpoint high-frame-rate video shooting for fast-moving objects by deriving the maximum performance of the actuator. We develop a prototype of a motion-blur-free video shooting system by implementing our frame-by-frame intermittent tracking algorithm on a high-speed video camera system with a resonant mirror vibrating at 750 Hz. It can capture 1024 × 1024 images of fast-moving objects at 750 fps with an exposure time of 0.33 ms without motion blur. Several experimental results for fast-moving objects verify that our proposed method can reduce image degradation from motion blur without decreasing the camera exposure time. Full article
(This article belongs to the Special Issue Video Analysis and Tracking Using State-of-the-Art Sensors)
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Open AccessArticle Model-Based Localization and Tracking Using Bluetooth Low-Energy Beacons
Sensors 2017, 17(11), 2484; doi:10.3390/s17112484
Received: 29 September 2017 / Revised: 22 October 2017 / Accepted: 26 October 2017 / Published: 29 October 2017
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Abstract
We introduce a high precision localization and tracking method that makes use of cheap Bluetooth low-energy (BLE) beacons only. We track the position of a moving sensor by integrating highly unreliable and noisy BLE observations streaming from multiple locations. A novel aspect of
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We introduce a high precision localization and tracking method that makes use of cheap Bluetooth low-energy (BLE) beacons only. We track the position of a moving sensor by integrating highly unreliable and noisy BLE observations streaming from multiple locations. A novel aspect of our approach is the development of an observation model, specifically tailored for received signal strength indicator (RSSI) fingerprints: a combination based on the optimal transport model of Wasserstein distance. The tracking results of the entire system are compared with alternative baseline estimation methods, such as nearest neighboring fingerprints and an artificial neural network. Our results show that highly accurate estimation from noisy Bluetooth data is practically feasible with an observation model based on Wasserstein distance interpolation combined with the sequential Monte Carlo (SMC) method for tracking. Full article
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Open AccessArticle Development of a Shipboard Remote Control and Telemetry Experimental System for Large-Scale Model’s Motions and Loads Measurement in Realistic Sea Waves
Sensors 2017, 17(11), 2485; doi:10.3390/s17112485
Received: 1 October 2017 / Revised: 27 October 2017 / Accepted: 27 October 2017 / Published: 29 October 2017
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Abstract
Wave-induced motion and load responses are important criteria for ship performance evaluation. Physical experiments have long been an indispensable tool in the predictions of ship’s navigation state, speed, motions, accelerations, sectional loads and wave impact pressure. Currently, majority of the experiments are conducted
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Wave-induced motion and load responses are important criteria for ship performance evaluation. Physical experiments have long been an indispensable tool in the predictions of ship’s navigation state, speed, motions, accelerations, sectional loads and wave impact pressure. Currently, majority of the experiments are conducted in laboratory tank environment, where the wave environments are different from the realistic sea waves. In this paper, a laboratory tank testing system for ship motions and loads measurement is reviewed and reported first. Then, a novel large-scale model measurement technique is developed based on the laboratory testing foundations to obtain accurate motion and load responses of ships in realistic sea conditions. For this purpose, a suite of advanced remote control and telemetry experimental system was developed in-house to allow for the implementation of large-scale model seakeeping measurement at sea. The experimental system includes a series of technique sensors, e.g., the Global Position System/Inertial Navigation System (GPS/INS) module, course top, optical fiber sensors, strain gauges, pressure sensors and accelerometers. The developed measurement system was tested by field experiments in coastal seas, which indicates that the proposed large-scale model testing scheme is capable and feasible. Meaningful data including ocean environment parameters, ship navigation state, motions and loads were obtained through the sea trial campaign. Full article
(This article belongs to the Special Issue Smart Sensors for Mechatronic and Robotic Systems)
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Open AccessArticle A Laboratory Experimental Study: An FBG-PVC Tube Integrated Device for Monitoring the Slip Surface of Landslides
Sensors 2017, 17(11), 2486; doi:10.3390/s17112486
Received: 6 September 2017 / Revised: 24 October 2017 / Accepted: 26 October 2017 / Published: 30 October 2017
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Abstract
A new detection device was designed by integrating fiber Bragg grating (FBG) and polyvinyl chloride (PVC) tube in order to monitor the slip surface of a landslide. Using this new FBG-based device, a corresponding slope model with a pre-set slip surface was designed,
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A new detection device was designed by integrating fiber Bragg grating (FBG) and polyvinyl chloride (PVC) tube in order to monitor the slip surface of a landslide. Using this new FBG-based device, a corresponding slope model with a pre-set slip surface was designed, and seven tests with different soil properties were carried out in laboratory conditions. The FBG sensing fibers were fixed on the PVC tube to measure strain distributions of PVC tube at different elevation. Test results indicated that the PVC tube could keep deformation compatible with soil mass. The new device was able to monitor slip surface location before sliding occurrence, and the location of monitored slip surface was about 1–2 cm above the pre-set slip surface, which basically agreed with presupposition results. The monitoring results are expected to be used to pre-estimate landslide volume and provide a beneficial option for evaluating the potential impact of landslides on shipping safety in the Three Gorges area. Full article
(This article belongs to the Special Issue Fiber Bragg Grating Based Sensors)
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Open AccessArticle Village Building Identification Based on Ensemble Convolutional Neural Networks
Sensors 2017, 17(11), 2487; doi:10.3390/s17112487
Received: 15 September 2017 / Revised: 25 October 2017 / Accepted: 26 October 2017 / Published: 30 October 2017
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Abstract
In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for
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In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86. Full article
(This article belongs to the Special Issue Sensors and Smart Sensing of Agricultural Land Systems)
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Open AccessArticle Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV)
Sensors 2017, 17(11), 2488; doi:10.3390/s17112488
Received: 26 September 2017 / Revised: 23 October 2017 / Accepted: 24 October 2017 / Published: 30 October 2017
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Abstract
Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential (Ψstem). However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability.
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Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential (Ψstem). However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on unmanned aerial vehicle (UAV) is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI) that use information between 500–800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN) models derived from multispectral images to predict the Ψstem spatial variability of a drip-irrigated Carménère vineyard in Talca, Maule Region, Chile. The coefficient of determination (R2) obtained between ANN outputs and ground-truth measurements of Ψstem were between 0.56–0.87, with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model could estimate Ψstem with a mean absolute error (MAE) of 0.1 MPa, root mean square error (RMSE) of 0.12 MPa, and relative error (RE) of −9.1%. For the validation of the CMI, the MAE, RMSE and RE values were between 0.26–0.27 MPa, 0.32–0.34 MPa and −24.2–25.6%, respectively. Full article
(This article belongs to the Special Issue UAV or Drones for Remote Sensing Applications)
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Open AccessFeature PaperArticle Ratiometric Decoding of Pheromones for a Biomimetic Infochemical Communication System
Sensors 2017, 17(11), 2489; doi:10.3390/s17112489
Received: 6 September 2017 / Revised: 13 October 2017 / Accepted: 20 October 2017 / Published: 30 October 2017
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Abstract
Biosynthetic infochemical communication is an emerging scientific field employing molecular compounds for information transmission, labelling, and biochemical interfacing; having potential application in diverse areas ranging from pest management to group coordination of swarming robots. Our communication system comprises a chemoemitter module that encodes
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Biosynthetic infochemical communication is an emerging scientific field employing molecular compounds for information transmission, labelling, and biochemical interfacing; having potential application in diverse areas ranging from pest management to group coordination of swarming robots. Our communication system comprises a chemoemitter module that encodes information by producing volatile pheromone components and a chemoreceiver module that decodes the transmitted ratiometric information via polymer-coated piezoelectric Surface Acoustic Wave Resonator (SAWR) sensors. The inspiration for such a system is based on the pheromone-based communication between insects. Ten features are extracted from the SAWR sensor response and analysed using multi-variate classification techniques, i.e., Linear Discriminant Analysis (LDA), Probabilistic Neural Network (PNN), and Multilayer Perception Neural Network (MLPNN) methods, and an optimal feature subset is identified. A combination of steady state and transient features of the sensor signals showed superior performances with LDA and MLPNN. Although MLPNN gave excellent results reaching 100% recognition rate at 400 s, over all time stations PNN gave the best performance based on an expanded data-set with adjacent neighbours. In this case, 100% of the pheromone mixtures were successfully identified just 200 s after they were first injected into the wind tunnel. We believe that this approach can be used for future chemical communication employing simple mixtures of airborne molecules. Full article
(This article belongs to the Special Issue Surface Acoustic Wave and Bulk Acoustic Wave Sensors)
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Open AccessArticle Extrinsic Parameter Calibration for Line Scanning Cameras on Ground Vehicles with Navigation Systems Using a Calibration Pattern
Sensors 2017, 17(11), 2491; doi:10.3390/s17112491
Received: 1 September 2017 / Revised: 19 October 2017 / Accepted: 25 October 2017 / Published: 30 October 2017
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Abstract
Line scanning cameras, which capture only a single line of pixels, have been increasingly used in ground based mobile or robotic platforms. In applications where it is advantageous to directly georeference the camera data to world coordinates, an accurate estimate of the camera’s
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Line scanning cameras, which capture only a single line of pixels, have been increasingly used in ground based mobile or robotic platforms. In applications where it is advantageous to directly georeference the camera data to world coordinates, an accurate estimate of the camera’s 6D pose is required. This paper focuses on the common case where a mobile platform is equipped with a rigidly mounted line scanning camera, whose pose is unknown, and a navigation system providing vehicle body pose estimates. We propose a novel method that estimates the camera’s pose relative to the navigation system. The approach involves imaging and manually labelling a calibration pattern with distinctly identifiable points, triangulating these points from camera and navigation system data and reprojecting them in order to compute a likelihood, which is maximised to estimate the 6D camera pose. Additionally, a Markov Chain Monte Carlo (MCMC) algorithm is used to estimate the uncertainty of the offset. Tested on two different platforms, the method was able to estimate the pose to within 0.06 m/1.05 and 0.18 m/2.39 . We also propose several approaches to displaying and interpreting the 6D results in a human readable way. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Multi-Source Cooperative Data Collection with a Mobile Sink for the Wireless Sensor Network
Sensors 2017, 17(11), 2493; doi:10.3390/s17112493
Received: 25 August 2017 / Revised: 22 October 2017 / Accepted: 25 October 2017 / Published: 30 October 2017
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Abstract
The multi-source cooperation integrating distributed low-density parity-check codes is investigated to jointly collect data from multiple sensor nodes to the mobile sink in the wireless sensor network. The one-round and two-round cooperative data collection schemes are proposed according to the moving trajectories of
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The multi-source cooperation integrating distributed low-density parity-check codes is investigated to jointly collect data from multiple sensor nodes to the mobile sink in the wireless sensor network. The one-round and two-round cooperative data collection schemes are proposed according to the moving trajectories of the sink node. Specifically, two sparse cooperation models are firstly formed based on geographical locations of sensor source nodes, the impairment of inter-node wireless channels and moving trajectories of the mobile sink. Then, distributed low-density parity-check codes are devised to match the directed graphs and cooperation matrices related with the cooperation models. In the proposed schemes, each source node has quite low complexity attributed to the sparse cooperation and the distributed processing. Simulation results reveal that the proposed cooperative data collection schemes obtain significant bit error rate performance and the two-round cooperation exhibits better performance compared with the one-round scheme. The performance can be further improved when more source nodes participate in the sparse cooperation. For the two-round data collection schemes, the performance is evaluated for the wireless sensor networks with different moving trajectories and the variant data sizes. Full article
(This article belongs to the Special Issue Smart Communication Protocols and Algorithms for Sensor Networks)
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Open AccessArticle Global Calibration of Multi-Cameras Based on Refractive Projection and Ray Tracing
Sensors 2017, 17(11), 2494; doi:10.3390/s17112494
Received: 30 September 2017 / Revised: 25 October 2017 / Accepted: 26 October 2017 / Published: 31 October 2017
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Abstract
Multi-camera systems are widely applied in the three dimensional (3D) computer vision, especially when multiple cameras are distributed on both sides of the measured object. The calibration methods of multi-camera systems are critical to the accuracy of vision measurement and the key is
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Multi-camera systems are widely applied in the three dimensional (3D) computer vision, especially when multiple cameras are distributed on both sides of the measured object. The calibration methods of multi-camera systems are critical to the accuracy of vision measurement and the key is to find an appropriate calibration target. In this paper, a high-precision camera calibration method for multi-camera systems based on transparent glass checkerboards and ray tracing is described, and is used to calibrate multiple cameras distributed on both sides of the glass checkerboard. Firstly, the intrinsic parameters of each camera are obtained by Zhang’s calibration method. Then, multiple cameras capture several images from the front and back of the glass checkerboard with different orientations, and all images contain distinct grid corners. As the cameras on one side are not affected by the refraction of glass checkerboard, extrinsic parameters can be directly calculated. However, the cameras on the other side are influenced by the refraction of glass checkerboard, and the direct use of projection model will produce a calibration error. A multi-camera calibration method using refractive projection model and ray tracing is developed to eliminate this error. Furthermore, both synthetic and real data are employed to validate the proposed approach. The experimental results of refractive calibration show that the error of the 3D reconstruction is smaller than 0.2 mm, the relative errors of both rotation and translation are less than 0.014%, and the mean and standard deviation of reprojection error of the four-camera system are 0.00007 and 0.4543 pixels, respectively. The proposed method is flexible, highly accurate, and simple to carry out. Full article
(This article belongs to the Special Issue Imaging Depth Sensors—Sensors, Algorithms and Applications)
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Open AccessArticle The Evaluation of a Low-Cost Colorimeter for Glucose Detection in Salivary Samples
Sensors 2017, 17(11), 2495; doi:10.3390/s17112495
Received: 4 September 2017 / Revised: 26 September 2017 / Accepted: 4 October 2017 / Published: 1 November 2017
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Abstract
Given the limited access to healthcare resources, low-income settings require the development of affordable technology. Here we present the design and evaluation of a low-cost colorimeter applied to the non-invasive monitoring of Diabetes Mellitus through the detection of glucose in salival fluid. Samples
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Given the limited access to healthcare resources, low-income settings require the development of affordable technology. Here we present the design and evaluation of a low-cost colorimeter applied to the non-invasive monitoring of Diabetes Mellitus through the detection of glucose in salival fluid. Samples were processed by the glucose oxidase-peroxidase enzymatic system and analyzed with the development equipment. A light emission diode of 532.5 nm was used as an excitation source and a RGB module was used as a receptor. A calibration curve to quantify the concentration of salivary glucose (0 to 18 mg/dL) was carried out by relating the RGB components registered with glucose concentrations, achieving a limit of detection of 0.17 mg/dL with a CV of 5% (n = 3). Salivary samples of diabetic and healthy volunteers were processed with the equipment showing an average concentration of 1.5519 ± 0.4511 mg/dL for the first and 4.0479 ± 1.6103 mg/dL for the last, allowing a discrimination between both groups. Results were validated against a UV-Vis-NIR spectrophotometer with a correspondence of R2 of 0.98194 between both instruments. Results suggest the potential application of the developed device to the sensitive detection of relevant analytes with a low-cost, user-friendly, low-power and portable instrumentation. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle Precise Measurement of Gas Volumes by Means of Low-Offset MEMS Flow Sensors with μL/min Resolution
Sensors 2017, 17(11), 2497; doi:10.3390/s17112497
Received: 6 October 2017 / Revised: 26 October 2017 / Accepted: 27 October 2017 / Published: 31 October 2017
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Abstract
Experiments devoted to evaluate the performance of a MEMS thermal flow sensor in measuring gas volumes are described. The sensor is a single-chip platform, including several sensing structures and a low-offset, low-noise readout interface. A recently proposed offset compensation approach is implemented obtaining
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Experiments devoted to evaluate the performance of a MEMS thermal flow sensor in measuring gas volumes are described. The sensor is a single-chip platform, including several sensing structures and a low-offset, low-noise readout interface. A recently proposed offset compensation approach is implemented obtaining low temperature drift and excellent long time stability. The sensor is fabricated by applying a simple micromachining procedure to a chip produced using the BCD6s process of STMicroelectronics. Application of a gas conveyor allowed inclusion of the sensing structure into a channel of sub-millimeter cross-section. The results of measurements performed by making controlled air volumes pass through the sensor channel in both directions at rates from 0.1 to 5 mL/min are described. Full article
(This article belongs to the Special Issue Integrated MEMS Sensors for the IoT Era)
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Open AccessArticle A Rule-Based Spatial Reasoning Approach for OpenStreetMap Data Quality Enrichment; Case Study of Routing and Navigation
Sensors 2017, 17(11), 2498; doi:10.3390/s17112498
Received: 4 August 2017 / Revised: 26 September 2017 / Accepted: 12 October 2017 / Published: 31 October 2017
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Abstract
Finding relevant geospatial information is increasingly critical because of the growing volume of geospatial data available within the emerging “Big Data” era. Users are expecting that the availability of massive datasets will create more opportunities to uncover hidden information and answer more complex
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Finding relevant geospatial information is increasingly critical because of the growing volume of geospatial data available within the emerging “Big Data” era. Users are expecting that the availability of massive datasets will create more opportunities to uncover hidden information and answer more complex queries. This is especially the case with routing and navigation services where the ability to retrieve points of interest and landmarks make the routing service personalized, precise, and relevant. In this paper, we propose a new geospatial information approach that enables the retrieval of implicit information, i.e., geospatial entities that do not exist explicitly in the available source. We present an information broker that uses a rule-based spatial reasoning algorithm to detect topological relations. The information broker is embedded into a framework where annotations and mappings between OpenStreetMap data attributes and external resources, such as taxonomies, support the enrichment of queries to improve the ability of the system to retrieve information. Our method is tested with two case studies that leads to enriching the completeness of OpenStreetMap data with footway crossing points-of-interests as well as building entrances for routing and navigation purposes. It is concluded that the proposed approach can uncover implicit entities and contribute to extract required information from the existing datasets. Full article
(This article belongs to the Special Issue Crowd-Sensing and Remote Sensing Technologies for Smart Cities)
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Open AccessArticle Feasibility Study of the Electromagnetic Damper for Cable Structures Using Real-Time Hybrid Simulation
Sensors 2017, 17(11), 2499; doi:10.3390/s17112499
Received: 23 August 2017 / Revised: 24 October 2017 / Accepted: 24 October 2017 / Published: 31 October 2017
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Abstract
Cable structure is a major component of long-span bridges, such as cable-stayed and suspension bridges, and it transfers the main loads of bridges to the pylons. As these cable structures are exposed to continuous external loads, such as vehicle and wind loads, vibration
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Cable structure is a major component of long-span bridges, such as cable-stayed and suspension bridges, and it transfers the main loads of bridges to the pylons. As these cable structures are exposed to continuous external loads, such as vehicle and wind loads, vibration control and continuous monitoring of the cable are required. In this study, an electromagnetic (EM) damper was designed and fabricated for vibration control and monitoring of the cable structure. EM dampers, also called regenerative dampers, consist of permanent magnets and coils. The electromagnetic force due to the relative motion between the coil and the permanent magnet can be used to control the vibration of the structure. The electrical energy can be used as a power source for the monitoring system. The effects of the design parameters of the damper were numerically analyzed and the damper was fabricated. The characteristics of the damper were analyzed with various external load changes. Finally, the vibration-control and energy-harvesting performances of the cable structure were evaluated through a hybrid simulation. The vibration-control and energy-harvesting performances for various loads were analyzed and the applicability to the cable structure of the EM damper was evaluated. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Identification of the Rice Wines with Different Marked Ages by Electronic Nose Coupled with Smartphone and Cloud Storage Platform
Sensors 2017, 17(11), 2500; doi:10.3390/s17112500
Received: 30 September 2017 / Revised: 23 October 2017 / Accepted: 26 October 2017 / Published: 31 October 2017
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Abstract
In this study, a portable electronic nose (E-nose) was self-developed to identify rice wines with different marked ages—all the operations of the E-nose were controlled by a special Smartphone Application. The sensor array of the E-nose was comprised of 12 MOS sensors and
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In this study, a portable electronic nose (E-nose) was self-developed to identify rice wines with different marked ages—all the operations of the E-nose were controlled by a special Smartphone Application. The sensor array of the E-nose was comprised of 12 MOS sensors and the obtained response values were transmitted to the Smartphone thorough a wireless communication module. Then, Aliyun worked as a cloud storage platform for the storage of responses and identification models. The measurement of the E-nose was composed of the taste information obtained phase (TIOP) and the aftertaste information obtained phase (AIOP). The area feature data obtained from the TIOP and the feature data obtained from the TIOP-AIOP were applied to identify rice wines by using pattern recognition methods. Principal component analysis (PCA), locally linear embedding (LLE) and linear discriminant analysis (LDA) were applied for the classification of those wine samples. LDA based on the area feature data obtained from the TIOP-AIOP proved a powerful tool and showed the best classification results. Partial least-squares regression (PLSR) and support vector machine (SVM) were applied for the predictions of marked ages and SVM (R2 = 0.9942) worked much better than PLSR. Full article
(This article belongs to the Special Issue Electronic Tongues and Electronic Noses)
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Open AccessArticle Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm
Sensors 2017, 17(11), 2501; doi:10.3390/s17112501
Received: 12 September 2017 / Revised: 26 October 2017 / Accepted: 27 October 2017 / Published: 31 October 2017
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Abstract
The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and
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The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route. Full article
(This article belongs to the Special Issue Smart Vehicular Mobile Sensing)
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Open AccessArticle COALA: A Protocol for the Avoidance and Alleviation of Congestion in Wireless Sensor Networks
Sensors 2017, 17(11), 2502; doi:10.3390/s17112502
Received: 29 September 2017 / Revised: 26 October 2017 / Accepted: 28 October 2017 / Published: 31 October 2017
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Abstract
The occurrence of congestion has an extremely deleterious impact on the performance of Wireless Sensor Networks (WSNs). This article presents a novel protocol, named COALA (COngestion ALleviation and Avoidance), which aims to act both proactively, in order to avoid the creation
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The occurrence of congestion has an extremely deleterious impact on the performance of Wireless Sensor Networks (WSNs). This article presents a novel protocol, named COALA (COngestion ALleviation and Avoidance), which aims to act both proactively, in order to avoid the creation of congestion in WSNs, and reactively, so as to mitigate the diffusion of upcoming congestion through alternative path routing. Its operation is based on the utilization of an accumulative cost function, which considers both static and dynamic metrics in order to send data through the paths that are less probable to be congested. COALA is validated through simulation tests, which exhibit its ability to achieve remarkable reduction of loss ratios, transmission delays and energy dissipation. Moreover, the appropriate adjustment of the weighting of the accumulative cost function enables the algorithm to adapt to the performance criteria of individual case scenarios. Full article
(This article belongs to the Special Issue Soft Sensors and Intelligent Algorithms for Data Fusion)
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Open AccessArticle Impedance-Based Living Cell Analysis for Clinical Diagnosis of Type I Allergy
Sensors 2017, 17(11), 2503; doi:10.3390/s17112503
Received: 30 August 2017 / Revised: 23 October 2017 / Accepted: 27 October 2017 / Published: 31 October 2017
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Abstract
Non-invasive real time evaluation of living cell conditions and functions are increasingly desired in the field of clinical diagnosis. For diagnosis of type I allergy, the identification of antigens that induces activation of mast cells and basophils is crucial to avoid symptoms of
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Non-invasive real time evaluation of living cell conditions and functions are increasingly desired in the field of clinical diagnosis. For diagnosis of type I allergy, the identification of antigens that induces activation of mast cells and basophils is crucial to avoid symptoms of allergic diseases. However, conventional tests, such as detection of antigen-specific IgE antibody and skin tests, are either of low reliability or are invasive. To overcome such problems, we hereby applied an impedance sensor for label-free and real-time monitoring of mast cell reactions in response to stimuli. When IgE-sensitized RBL-2H3 cells cultured on the electrodes were stimulated with various concentrations of antigens, dose-dependent cell index (CI) increases were detected. Moreover, we confirmed that the impedance sensor detected morphological changes rather than degranulation as the indicator of cell activation. Furthermore, the CI of human IgE receptor-expressing cells (RBL-48 cells) treated with serum of a sweat allergy-positive patient, but not with serum from a sweat allergy-negative patient, significantly increased in response to purified human sweat antigen. We thus developed a technique to detect the activation of living cells in response to stimuli without any labeling using the impedance sensor. This system may represent a high reliable tool for the diagnosis of type I allergy. Full article
(This article belongs to the Special Issue Sensors for Health Monitoring and Disease Diagnosis)
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Open AccessArticle A Novel Evidence Theory and Fuzzy Preference Approach-Based Multi-Sensor Data Fusion Technique for Fault Diagnosis
Sensors 2017, 17(11), 2504; doi:10.3390/s17112504
Received: 12 September 2017 / Revised: 27 October 2017 / Accepted: 27 October 2017 / Published: 31 October 2017
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Abstract
The multi-sensor data fusion technique plays a significant role in fault diagnosis and in a variety of such applications, and the Dempster–Shafer evidence theory is employed to improve the system performance; whereas, it may generate a counter-intuitive result when the pieces of evidence
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The multi-sensor data fusion technique plays a significant role in fault diagnosis and in a variety of such applications, and the Dempster–Shafer evidence theory is employed to improve the system performance; whereas, it may generate a counter-intuitive result when the pieces of evidence highly conflict with each other. To handle this problem, a novel multi-sensor data fusion approach on the basis of the distance of evidence, belief entropy and fuzzy preference relation analysis is proposed. A function of evidence distance is first leveraged to measure the conflict degree among the pieces of evidence; thus, the support degree can be obtained to represent the reliability of the evidence. Next, the uncertainty of each piece of evidence is measured by means of the belief entropy. Based on the quantitative uncertainty measured above, the fuzzy preference relations are applied to represent the relative credibility preference of the evidence. Afterwards, the support degree of each piece of evidence is adjusted by taking advantage of the relative credibility preference of the evidence that can be utilized to generate an appropriate weight with respect to each piece of evidence. Finally, the modified weights of the evidence are adopted to adjust the bodies of the evidence in the advance of utilizing Dempster’s combination rule. A numerical example and a practical application in fault diagnosis are used as illustrations to demonstrate that the proposal is reasonable and efficient in the management of conflict and fault diagnosis. Full article
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Open AccessArticle Design, Fabrication, and Modeling of a Novel Dual-Axis Control Input PZT Gyroscope
Sensors 2017, 17(11), 2505; doi:10.3390/s17112505
Received: 23 August 2017 / Revised: 10 October 2017 / Accepted: 26 October 2017 / Published: 31 October 2017
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Abstract
Conventional gyroscopes are equipped with a single-axis control input, limiting their performance. Although researchers have proposed control algorithms with dual-axis control inputs to improve gyroscope performance, most have verified the control algorithms through numerical simulations because they lacked practical devices with dual-axis control
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Conventional gyroscopes are equipped with a single-axis control input, limiting their performance. Although researchers have proposed control algorithms with dual-axis control inputs to improve gyroscope performance, most have verified the control algorithms through numerical simulations because they lacked practical devices with dual-axis control inputs. The aim of this study was to design a piezoelectric gyroscope equipped with a dual-axis control input so that researchers may experimentally verify those control algorithms in future. Designing a piezoelectric gyroscope with a dual-axis control input is more difficult than designing a conventional gyroscope because the control input must be effective over a broad frequency range to compensate for imperfections, and the multiple mode shapes in flexural deformations complicate the relation between flexural deformation and the proof mass position. This study solved these problems by using a lead zirconate titanate (PZT) material, introducing additional electrodes for shielding, developing an optimal electrode pattern, and performing calibrations of undesired couplings. The results indicated that the fabricated device could be operated at 5.5±1 kHz to perform dual-axis actuations and position measurements. The calibration of the fabricated device was completed by system identifications of a new dynamic model including gyroscopic motions, electromechanical coupling, mechanical coupling, electrostatic coupling, and capacitive output impedance. Finally, without the assistance of control algorithms, the “open loop sensitivity” of the fabricated gyroscope was 1.82 μV/deg/s with a nonlinearity of 9.5% full-scale output. This sensitivity is comparable with those of other PZT gyroscopes with single-axis control inputs. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Two-Stage Multi-Task Representation Learning for Synthetic Aperture Radar (SAR) Target Images Classification
Sensors 2017, 17(11), 2506; doi:10.3390/s17112506
Received: 24 August 2017 / Revised: 4 October 2017 / Accepted: 28 October 2017 / Published: 1 November 2017
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Abstract
In this paper, we propose a two-stage multi-task learning representation method for the classification of synthetic aperture radar (SAR) target images. The first stage of the proposed approach uses multi-features joint sparse representation learning, modeled as a 2,1-norm regularized
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In this paper, we propose a two-stage multi-task learning representation method for the classification of synthetic aperture radar (SAR) target images. The first stage of the proposed approach uses multi-features joint sparse representation learning, modeled as a 2 , 1 -norm regularized multi-task sparse learning problem, to find an effective subset of training samples. Then, a new dictionary is constructed based on the training subset. The second stage of the method is to perform target images classification based on the new dictionary, utilizing multi-task collaborative representation. The proposed algorithm not only exploits the discrimination ability of multiple features but also greatly reduces the interference of atoms that are irrelevant to the test sample, thus effectively improving classification performance. Conducted with the Moving and Stationary Target Acquisition and Recognition (MSTAR) public SAR database, experimental results show that the proposed approach is effective and superior to many state-of-the-art methods. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle A Pattern Recognition Approach to Acoustic Emission Data Originating from Fatigue of Wind Turbine Blades
Sensors 2017, 17(11), 2507; doi:10.3390/s17112507
Received: 19 September 2017 / Revised: 20 October 2017 / Accepted: 25 October 2017 / Published: 1 November 2017
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Abstract
The identification of particular types of damage in wind turbine blades using acoustic emission (AE) techniques is a significant emerging field. In this work, a 45.7-m turbine blade was subjected to flap-wise fatigue loading for 21 days, during which AE was measured by
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The identification of particular types of damage in wind turbine blades using acoustic emission (AE) techniques is a significant emerging field. In this work, a 45.7-m turbine blade was subjected to flap-wise fatigue loading for 21 days, during which AE was measured by internally mounted piezoelectric sensors. This paper focuses on using unsupervised pattern recognition methods to characterize different AE activities corresponding to different fracture mechanisms. A sequential feature selection method based on a k-means clustering algorithm is used to achieve a fine classification accuracy. The visualization of clusters in peak frequency−frequency centroid features is used to correlate the clustering results with failure modes. The positions of these clusters in time domain features, average frequency−MARSE, and average frequency−peak amplitude are also presented in this paper (where MARSE represents the Measured Area under Rectified Signal Envelope). The results show that these parameters are representative for the classification of the failure modes. Full article
(This article belongs to the Special Issue Sensor Technologies for Health Monitoring of Composite Structures)
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Open AccessArticle A Code Division Design Strategy for Multiplexing Fiber Bragg Grating Sensing Networks
Sensors 2017, 17(11), 2508; doi:10.3390/s17112508
Received: 28 September 2017 / Revised: 24 October 2017 / Accepted: 31 October 2017 / Published: 1 November 2017
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Abstract
In this paper, an encoding strategy is used to design specialized fiber Bragg grating (FBG) sensors. The encoding of each sensor requires two binary codewords to define the amplitude and phase patterns of each sensor. The combined pattern (amplitude and phase) makes each
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In this paper, an encoding strategy is used to design specialized fiber Bragg grating (FBG) sensors. The encoding of each sensor requires two binary codewords to define the amplitude and phase patterns of each sensor. The combined pattern (amplitude and phase) makes each sensor unique and therefore two or more sensors can be identified under spectral overlapping. In this way, we add another dimension to the multiplexing of FBG sensors, obtaining an increase factor ‘n’ to enhance the number of sensors that the system can handle. A proof-of-concept scenario with three sensors was performed, including the manufacturing of the encoded sensors. Furthermore, an interrogation setup to detect the sensors central wavelength was proposed and its working principle was theoretically developed. Results show that total identification of the central wavelength is performed under spectral overlapping between the manufactured sensors, achieving a three-time improvement of the system capacity. Finally, the error due to overlapping between the sensors was assessed obtaining approximately 3 pm, which makes the approach suitable for use in real measurement systems. Full article
(This article belongs to the Special Issue Fiber Bragg Grating Based Sensors)
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Open AccessArticle Design and Electro-Thermo-Mechanical Behavior Analysis of Au/Si3N4 Bimorph Microcantilevers for Static Mode Sensing
Sensors 2017, 17(11), 2510; doi:10.3390/s17112510
Received: 7 September 2017 / Revised: 23 October 2017 / Accepted: 29 October 2017 / Published: 1 November 2017
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Abstract
This paper presents a design optimization method based on theoretical analysis and numerical calculations, using a commercial multi-physics solver (e.g., ANSYS and ESI CFD-ACE+), for a 3D continuous model, to analyze the bending characteristics of an electrically heated bimorph microcantilever. The results from
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This paper presents a design optimization method based on theoretical analysis and numerical calculations, using a commercial multi-physics solver (e.g., ANSYS and ESI CFD-ACE+), for a 3D continuous model, to analyze the bending characteristics of an electrically heated bimorph microcantilever. The results from the theoretical calculation and numerical analysis are compared with those measured using a CCD camera and magnification lenses for a chip level microcantilever array fabricated in this study. The bimorph microcantilevers are thermally actuated by joule heating generated by a 0.4 μm thin-film Au heater deposited on 0.6 μm Si3N4 microcantilevers. The initial deflections caused by residual stress resulting from the thermal bonding of two metallic layers with different coefficients of thermal expansion (CTEs) are additionally considered, to find the exact deflected position. The numerically calculated total deflections caused by electrical actuation show differences of 10%, on average, with experimental measurements in the operating current region (i.e., ~25 mA) to prevent deterioration by overheating. Bimorph microcantilevers are promising components for use in various MEMS (Micro-Electro-Mechanical System) sensing applications, and their deflection characteristics in static mode sensing are essential for detecting changes in thermal stress on the surface of microcantilevers. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Smart Collaborative Caching for Information-Centric IoT in Fog Computing
Sensors 2017, 17(11), 2512; doi:10.3390/s17112512
Received: 12 September 2017 / Revised: 16 October 2017 / Accepted: 20 October 2017 / Published: 1 November 2017
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Abstract
The significant changes enabled by the fog computing had demonstrated that Internet of Things (IoT) urgently needs more evolutional reforms. Limited by the inflexible design philosophy; the traditional structure of a network is hard to meet the latest demands. However, Information-Centric Networking (ICN)
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The significant changes enabled by the fog computing had demonstrated that Internet of Things (IoT) urgently needs more evolutional reforms. Limited by the inflexible design philosophy; the traditional structure of a network is hard to meet the latest demands. However, Information-Centric Networking (ICN) is a promising option to bridge and cover these enormous gaps. In this paper, a Smart Collaborative Caching (SCC) scheme is established by leveraging high-level ICN principles for IoT within fog computing paradigm. The proposed solution is supposed to be utilized in resource pooling, content storing, node locating and other related situations. By investigating the available characteristics of ICN, some challenges of such combination are reviewed in depth. The details of building SCC, including basic model and advanced algorithms, are presented based on theoretical analysis and simplified examples. The validation focuses on two typical scenarios: simple status inquiry and complex content sharing. The number of clusters, packet loss probability and other parameters are also considered. The analytical results demonstrate that the performance of our scheme, regarding total packet number and average transmission latency, can outperform that of the original ones. We expect that the SCC will contribute an efficient solution to the related studies. Full article
(This article belongs to the Special Issue Next Generation Wireless Technologies for Internet of Things)
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Open AccessArticle High-Accuracy Readout Electronics for Piezoresistive Tactile Sensors
Sensors 2017, 17(11), 2513; doi:10.3390/s17112513
Received: 29 September 2017 / Revised: 27 October 2017 / Accepted: 31 October 2017 / Published: 1 November 2017
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Abstract
The typical layout in a piezoresistive tactile sensor arranges individual sensors to form an array with M rows and N columns. While this layout reduces the wiring involved, it does not allow the values of the sensor resistors to be measured individually due
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The typical layout in a piezoresistive tactile sensor arranges individual sensors to form an array with M rows and N columns. While this layout reduces the wiring involved, it does not allow the values of the sensor resistors to be measured individually due to the appearance of crosstalk caused by the nonidealities of the array reading circuits. In this paper, two reading methods that minimize errors resulting from this phenomenon are assessed by designing an electronic system for array reading, and the results are compared to those obtained using the traditional method, obviating the nonidealities of the reading circuit. The different models were compared by testing the system with an array of discrete resistors. The system was later connected to a tactile sensor with 8 × 7 taxels. Full article
(This article belongs to the Special Issue Tactile Sensors and Sensing)
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Open AccessArticle Multipass Target Search in Natural Environments
Sensors 2017, 17(11), 2514; doi:10.3390/s17112514
Received: 14 August 2017 / Revised: 6 October 2017 / Accepted: 18 October 2017 / Published: 2 November 2017
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Abstract
Consider a disaster scenario where search and rescue workers must search difficult to access buildings during an earthquake or flood. Often, finding survivors a few hours sooner results in a dramatic increase in saved lives, suggesting the use of drones for expedient rescue
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Consider a disaster scenario where search and rescue workers must search difficult to access buildings during an earthquake or flood. Often, finding survivors a few hours sooner results in a dramatic increase in saved lives, suggesting the use of drones for expedient rescue operations. Entropy can be used to quantify the generation and resolution of uncertainty. When searching for targets, maximizing mutual information of future sensor observations will minimize expected target location uncertainty by minimizing the entropy of the future estimate. Motion planning for multi-target autonomous search requires planning over an area with an imperfect sensor and may require multiple passes, which is hindered by the submodularity property of mutual information. Further, mission duration constraints must be handled accordingly, requiring consideration of the vehicle’s dynamics to generate feasible trajectories and must plan trajectories spanning the entire mission duration, something which most information gathering algorithms are incapable of doing. If unanticipated changes occur in an uncertain environment, new plans must be generated quickly. In addition, planning multipass trajectories requires evaluating path dependent rewards, requiring planning in the space of all previously selected actions, compounding the problem. We present an anytime algorithm for autonomous multipass target search in natural environments. The algorithm is capable of generating long duration dynamically feasible multipass coverage plans that maximize mutual information using a variety of techniques such as ϵ -admissible heuristics to speed up the search. To the authors’ knowledge this is the first attempt at efficiently solving multipass target search problems of such long duration. The proposed algorithm is based on best first branch and bound and is benchmarked against state of the art algorithms adapted to the problem in natural Simplex environments, gathering the most information in the given search time. Full article
(This article belongs to the Special Issue Remote Sensing and GIS for Geo-Hazards and Disasters)
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Open AccessArticle Concept and Evaluation of a New Piezoelectric Transducer for an Implantable Middle Ear Hearing Device
Sensors 2017, 17(11), 2515; doi:10.3390/s17112515
Received: 17 September 2017 / Revised: 31 October 2017 / Accepted: 31 October 2017 / Published: 2 November 2017
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Abstract
Implantable middle ear hearing devices (IMEHDs) have been developed as a new technology to overcome the limitations of conventional hearing aids. The piezoelectric cantilever transducers currently used in the IMEHDs have the advantages of low power consumption and ease of fabrication, but generate
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Implantable middle ear hearing devices (IMEHDs) have been developed as a new technology to overcome the limitations of conventional hearing aids. The piezoelectric cantilever transducers currently used in the IMEHDs have the advantages of low power consumption and ease of fabrication, but generate less high-frequency output. To address this problem, we proposed and designed a new piezoelectric transducer based on a piezoelectric stack for the IMEHD. This new transducer, attached to the incus body with a coupling rod, stimulates the ossicular chain in response to the expansion-and-contraction of its piezoelectric stack. To test its feasibility for hearing loss compensation, a bench testing of the transducer prototype and a temporal bone experiment were conducted, respectively. Bench testing results showed that the new transducer did have a broad frequency bandwidth. Besides, the transducer was found to have a low total harmonic distortion (<0.75%) in all frequencies, and small release time (1 ms). The temporal bone experiment further proved that the transducer has the capability to produce sufficient vibrations to compensate for severe sensorineural hearing loss, especially at high frequencies. This property benefits the treatment of the most common sloping high-frequency sensorineural hearing loss. To produce a 100 dB SPL equivalent sound pressure at 1 kHz, its power consumption is 0.49 mW, which is low enough for the transducer to be utilized in the IMEHD. Full article
(This article belongs to the Special Issue Implantable Sensors 2017)
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Open AccessArticle Experimental Investigation of the Piezoresistive Properties of Cement Composites with Hybrid Carbon Fibers and Nanotubes
Sensors 2017, 17(11), 2516; doi:10.3390/s17112516
Received: 27 September 2017 / Revised: 28 October 2017 / Accepted: 31 October 2017 / Published: 2 November 2017
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Abstract
Cement-based sensors with hybrid conductive fillers using both carbon fibers (CFs) and multi-walled carbon nanotubes (MWCNTs) were experimentally investigated in this study. The self-sensing capacities of cement-based composites with only CFs or MWCNTs were found based on preliminary tests. The results showed that
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Cement-based sensors with hybrid conductive fillers using both carbon fibers (CFs) and multi-walled carbon nanotubes (MWCNTs) were experimentally investigated in this study. The self-sensing capacities of cement-based composites with only CFs or MWCNTs were found based on preliminary tests. The results showed that the percolation thresholds of CFs and MWCNTs were 0.5–1.0 vol.% and 1.0 vol.%, respectively. Based on these results, the feasibility of self-sensing composites with four different amounts of CFs and MWCNTs was considered under cyclic compression loads. When the amount of incorporated CFs increased and the amount of incorporated MWCNTs decreased, the self-sensing capacity of the composites was reduced. It was concluded that cement-based composites containing both 0.1 vol.% CFs and 0.5 vol.% MWCNTs could be an alternative to cement-based composites with 1.0 vol.% MWCNTs in order to achieve equivalent self-sensing performance at half the price. The gauge factor (GF) for that composite was 160.3 with an R-square of 0.9274 in loading stages I and II, which was similar to the GF of 166.6 for the composite with 1.0 vol.% MWCNTs. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle