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Sensors, Volume 16, Issue 11 (November 2016)

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Open AccessArticle Simulation of SAW Humidity Sensors Based on ( 11 2 ¯ 0 ) ZnO/R-Sapphire Structures
Sensors 2016, 16(11), 1112; doi:10.3390/s16111112
Received: 30 March 2016 / Revised: 24 June 2016 / Accepted: 6 July 2016 / Published: 2 November 2016
Cited by 1 | PDF Full-text (3834 KB) | HTML Full-text | XML Full-text
Abstract
The characteristics of two types of surface acoustic waves SAWs (Rayleigh waves and Love waves) propagating in bilayered structures of (112¯0)ZnO/R-sapphire are simulated by a finite element method (FEM) model, in which both SAWs have crossed propagation
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The characteristics of two types of surface acoustic waves SAWs (Rayleigh waves and Love waves) propagating in bilayered structures of ( 11 2 ¯ 0 ) ZnO/R-sapphire are simulated by a finite element method (FEM) model, in which both SAWs have crossed propagation directions. Furthermore, based on the bilayered structures, the frequency responses of Rayleigh wave and Love wave humidity sensors are also simulated. Meanwhile, the frequency shifts, insertion loss changes and then the sensitivities of both humidity sensors induced by the adsorbed water layer perturbations, including the mechanical and electrical factors, are calculated numerically. Generally, the characteristics and performances of both sensors are strongly dependent on the thickness of the ZnO films. By appropriate selecting the ratio of the film thickness to SAW wavelength for each kind of the sensors, the performances of both sensors can be optimized. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Label-Free Fluorescent Detection of Trypsin Activity Based on DNA-Stabilized Silver Nanocluster-Peptide Conjugates
Sensors 2016, 16(11), 1477; doi:10.3390/s16111477
Received: 27 June 2016 / Revised: 2 August 2016 / Accepted: 2 August 2016 / Published: 9 November 2016
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Abstract
Trypsin is important during the regulation of pancreatic exocrine function. The detection of trypsin activity is currently limited because of the need for the substrate to be labeled with a fluorescent tag. A label-free fluorescent method has been developed to monitor trypsin activity.
[...] Read more.
Trypsin is important during the regulation of pancreatic exocrine function. The detection of trypsin activity is currently limited because of the need for the substrate to be labeled with a fluorescent tag. A label-free fluorescent method has been developed to monitor trypsin activity. The designed peptide probe consists of six arginine molecules and a cysteine terminus and can be conjugated to DNA-stabilized silver nanoclusters (DNA-AgNCs) by Ag-S bonding to enhance fluorescence. The peptide probe can also be adsorbed to the surface of graphene oxide (GO), thus resulting in the fluorescence quenching of DNA-AgNCs-peptide conjugate because of Förster resonance energy transfer. Once trypsin had degraded the peptide probe into amino acid residues, the DNA-AgNCs were released from the surface of GO, and the enhanced fluorescence of DNA-AgNCs was restored. Trypsin can be determined with a linear range of 0.0–50.0 ng/mL with a concentration as low as 1 ng/mL. This label-free method is simple and sensitive and has been successfully used for the determination of trypsin in serum. The method can also be modified to detect other proteases. Full article
(This article belongs to the Special Issue Colorimetric and Fluorescent Sensor)
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Open AccessArticle Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods
Sensors 2016, 16(11), 1483; doi:10.3390/s16111483
Received: 30 May 2016 / Revised: 29 July 2016 / Accepted: 9 August 2016 / Published: 26 October 2016
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Abstract
Biosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides in their fast performance, high sensitivity and continuous measuring capabilities; however, a full understanding is
[...] Read more.
Biosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides in their fast performance, high sensitivity and continuous measuring capabilities; however, a full understanding is still under research. This paper aims to contribute to this growing field of biotechnology, with a focus on Glucose-Oxidase Biosensor (GOB) modeling through statistical learning methods from a regression perspective. We model the amperometric response of a GOB with dependent variables under different conditions, such as temperature, benzoquinone, pH and glucose concentrations, by means of several machine learning algorithms. Since the sensitivity of a GOB response is strongly related to these dependent variables, their interactions should be optimized to maximize the output signal, for which a genetic algorithm and simulated annealing are used. We report a model that shows a good generalization error and is consistent with the optimization. Full article
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Open AccessArticle An SOI CMOS-Based Multi-Sensor MEMS Chip for Fluidic Applications
Sensors 2016, 16(11), 1608; doi:10.3390/s16111608
Received: 4 June 2016 / Revised: 1 September 2016 / Accepted: 2 September 2016 / Published: 4 November 2016
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Abstract
An SOI CMOS multi-sensor MEMS chip, which can simultaneously measure temperature, pressure and flow rate, has been reported. The multi-sensor chip has been designed keeping in view the requirements of researchers interested in experimental fluid dynamics. The chip contains ten thermodiodes (temperature sensors),
[...] Read more.
An SOI CMOS multi-sensor MEMS chip, which can simultaneously measure temperature, pressure and flow rate, has been reported. The multi-sensor chip has been designed keeping in view the requirements of researchers interested in experimental fluid dynamics. The chip contains ten thermodiodes (temperature sensors), a piezoresistive-type pressure sensor and nine hot film-based flow rate sensors fabricated within the oxide layer of the SOI wafers. The silicon dioxide layers with embedded sensors are relieved from the substrate as membranes with the help of a single DRIE step after chip fabrication from a commercial CMOS foundry. Very dense sensor packing per unit area of the chip has been enabled by using technologies/processes like SOI, CMOS and DRIE. Independent apparatuses were used for the characterization of each sensor. With a drive current of 10 µA–0.1 µA, the thermodiodes exhibited sensitivities of 1.41 mV/°C–1.79 mV/°C in the range 20–300 °C. The sensitivity of the pressure sensor was 0.0686 mV/(Vexcit kPa) with a non-linearity of 0.25% between 0 and 69 kPa above ambient pressure. Packaged in a micro-channel, the flow rate sensor has a linearized sensitivity of 17.3 mV/(L/min)−0.1 in the tested range of 0–4.7 L/min. The multi-sensor chip can be used for simultaneous measurement of fluid pressure, temperature and flow rate in fluidic experiments and aerospace/automotive/biomedical/process industries. Full article
(This article belongs to the Special Issue Microfluidic Sensors and Control Devices)
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Open AccessArticle A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks
Sensors 2016, 16(11), 1715; doi:10.3390/s16111715
Received: 1 June 2016 / Revised: 6 October 2016 / Accepted: 7 October 2016 / Published: 25 October 2016
Cited by 2 | PDF Full-text (659 KB) | HTML Full-text | XML Full-text
Abstract
Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive
[...] Read more.
Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches. Full article
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Open AccessArticle Design of the MEMS Piezoresistive Electronic Heart Sound Sensor
Sensors 2016, 16(11), 1728; doi:10.3390/s16111728
Received: 23 August 2016 / Revised: 3 October 2016 / Accepted: 11 October 2016 / Published: 7 November 2016
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Abstract
This paper proposes the electronic heart sound sensor, based on the piezoresistive principle and MEMS (Micro-Electro-Mechanical System) technology. Firstly, according to the characteristics of heart sound detection, the double-beam-block microstructure has been proposed, and the theoretical analysis and finite element method (FEM) simulation
[...] Read more.
This paper proposes the electronic heart sound sensor, based on the piezoresistive principle and MEMS (Micro-Electro-Mechanical System) technology. Firstly, according to the characteristics of heart sound detection, the double-beam-block microstructure has been proposed, and the theoretical analysis and finite element method (FEM) simulation have been carried out. Combined with the natural frequency response of the heart sound (20~600 Hz), its structure sizes have been determined. Secondly, the processing technology of the microstructure with the stress concentration grooves has been developed. The material and sizes of the package have been determined by the three-layer medium transmission principle. Lastly, the MEMS piezoresistive electronic heart sound sensor has been tested compared with the 3200-type electronic stethoscope from 3M (São Paulo, MN, USA). The test results show that the heart sound waveform tested by the MEMS electronic heart sound sensor are almost the same as that tested by the 3200-type electronic stethoscope. Moreover, its signal-to-noise ratio is significantly higher. Compared with the traditional stethoscope, the MEMS heart sound sensor can provide the first and second heart sounds containing more abundant information about the lesion. Compared with the 3200-type electronic stethoscope from 3M, it has better performance and lower cost. Full article
(This article belongs to the collection Modeling, Testing and Reliability Issues in MEMS Engineering)
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Open AccessArticle Development of an FPW Biosensor with Low Insertion Loss and High Fabrication Yield for Detection of Carcinoembryonic Antigen
Sensors 2016, 16(11), 1729; doi:10.3390/s16111729
Received: 1 August 2016 / Accepted: 3 October 2016 / Published: 8 November 2016
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Abstract
In the last two decades, various flexural plate-wave (FPW)-based biosensors with low phase velocity, low operation frequency, high sensitivity, and short response time, have been developed. However, conventional FPW transducers have low fabrication yield because controlling the thickness of silicon/isolation/metal/piezoelectric multilayer floating thin-plate
[...] Read more.
In the last two decades, various flexural plate-wave (FPW)-based biosensors with low phase velocity, low operation frequency, high sensitivity, and short response time, have been developed. However, conventional FPW transducers have low fabrication yield because controlling the thickness of silicon/isolation/metal/piezoelectric multilayer floating thin-plate is difficult. Additionally, conventional FPW devices usually have high insertion loss because of wave energy dissipation to the silicon substrate or outside area of the output interdigital transducers (IDTs). These two disadvantages hinder the application of FPW devices. To reduce the high insertion loss of FPW devices, we designed two focus-type IDTs (fan-shaped and circular, respectively) that can effectively confine the launched wave energy, and adopted a focus-type silicon-grooved reflective grating structure (RGS) that can reduce the wave propagation loss. To accurately control the thickness of the silicon thin-plate and substantially improve the fabrication yield of FPW transducers, a 60 °C/27 °C two-step anisotropic wet etching process was developed. Compared with conventional FPW devices (with parallel-type IDTs and without RGS), the proposed FPW devices have lower insertion loss (36.04 dB) and higher fabrication yield (63.88%). Furthermore, by using cystamine-based self-assembled monolayer (SAM) nanotechnology, we used the improved FPW device to develop a novel FPW-based carcinoembryonic antigen (CEA) biosensor for detection of colorectal cancer, and this FPW-CEA biosensor has a low detection limit (5 ng/mL), short response time (<10 min), high sensitivity (60.16–70.06 cm2/g), and high sensing linearity (R-square = 0.859–0.980). Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle In-Line Sorting of Harumanis Mango Based on External Quality Using Visible Imaging
Sensors 2016, 16(11), 1753; doi:10.3390/s16111753
Received: 2 June 2016 / Revised: 9 July 2016 / Accepted: 18 July 2016 / Published: 27 October 2016
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Abstract
The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its
[...] Read more.
The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its estimated mass. A group of images of mangoes of different size and shape was used as database set. Some important features such as length, height, centroid and parameter were extracted from each image. Fourier descriptor and size-shape parameters were used to describe the mango shape while the disk method was used to estimate the mass of the mango. Four features have been selected by stepwise discriminant analysis which was effective in sorting regular and misshapen mango. The volume from water displacement method was compared with the volume estimated by image processing using paired t-test and Bland-Altman method. The result between both measurements was not significantly different (P > 0.05). The average correct classification for shape classification was 98% for a training set composed of 180 mangoes. The data was validated with another testing set consist of 140 mangoes which have the success rate of 92%. The same set was used for evaluating the performance of mass estimation. The average success rate of the classification for grading based on its mass was 94%. The results indicate that the in-line sorting system using machine vision has a great potential in automatic fruit sorting according to its shape and mass. Full article
(This article belongs to the Special Issue Sensors for Agriculture)
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Open AccessArticle Integrating Virtual Worlds with Tangible User Interfaces for Teaching Mathematics: A Pilot Study
Sensors 2016, 16(11), 1775; doi:10.3390/s16111775
Received: 27 April 2016 / Revised: 17 October 2016 / Accepted: 18 October 2016 / Published: 25 October 2016
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Abstract
This article presents a pilot study of the use of two new tangible interfaces and virtual worlds for teaching geometry in a secondary school. The first tangible device allows the user to control a virtual object in six degrees of freedom. The second
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This article presents a pilot study of the use of two new tangible interfaces and virtual worlds for teaching geometry in a secondary school. The first tangible device allows the user to control a virtual object in six degrees of freedom. The second tangible device is used to modify virtual objects, changing attributes such as position, size, rotation and color. A pilot study on using these devices was carried out at the “Florida Secundaria” high school. A virtual world was built where students used the tangible interfaces to manipulate geometrical figures in order to learn different geometrical concepts. The pilot experiment results suggest that the use of tangible interfaces and virtual worlds allowed a more meaningful learning (concepts learnt were more durable). Full article
(This article belongs to the Special Issue Selected Papers from UCAmI, IWAAL and AmIHEALTH 2015)
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Open AccessArticle A Low Noise CMOS Readout Based on a Polymer-Coated SAW Array for Miniature Electronic Nose
Sensors 2016, 16(11), 1777; doi:10.3390/s16111777
Received: 30 July 2016 / Revised: 18 October 2016 / Accepted: 19 October 2016 / Published: 25 October 2016
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Abstract
An electronic nose (E-Nose) is one of the applications for surface acoustic wave (SAW) sensors. In this paper, we present a low-noise complementary metal–oxide–semiconductor (CMOS) readout application-specific integrated circuit (ASIC) based on an SAW sensor array for achieving a miniature E-Nose. The center
[...] Read more.
An electronic nose (E-Nose) is one of the applications for surface acoustic wave (SAW) sensors. In this paper, we present a low-noise complementary metal–oxide–semiconductor (CMOS) readout application-specific integrated circuit (ASIC) based on an SAW sensor array for achieving a miniature E-Nose. The center frequency of the SAW sensors was measured to be approximately 114 MHz. Because of interference between the sensors, we designed a low-noise CMOS frequency readout circuit to enable the SAW sensor to obtain frequency variation. The proposed circuit was fabricated in Taiwan Semiconductor Manufacturing Company (TSMC) 0.18 μm 1P6M CMOS process technology. The total chip size was nearly 1203 × 1203 μm2. The chip was operated at a supply voltage of 1 V for a digital circuit and 1.8 V for an analog circuit. The least measurable difference between frequencies was 4 Hz. The detection limit of the system, when estimated using methanol and ethanol, was 0.1 ppm. Their linearity was in the range of 0.1 to 26,000 ppm. The power consumption levels of the analog and digital circuits were 1.742 mW and 761 μW, respectively. Full article
(This article belongs to the Special Issue The Use of New and/or Improved Materials for Sensing Applications)
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Open AccessArticle A Camera-Based Target Detection and Positioning UAV System for Search and Rescue (SAR) Purposes
Sensors 2016, 16(11), 1778; doi:10.3390/s16111778
Received: 30 August 2016 / Revised: 15 October 2016 / Accepted: 19 October 2016 / Published: 25 October 2016
Cited by 3 | PDF Full-text (17406 KB) | HTML Full-text | XML Full-text
Abstract
Wilderness search and rescue entails performing a wide-range of work in complex environments and large regions. Given the concerns inherent in large regions due to limited rescue distribution, unmanned aerial vehicle (UAV)-based frameworks are a promising platform for providing aerial imaging. In recent
[...] Read more.
Wilderness search and rescue entails performing a wide-range of work in complex environments and large regions. Given the concerns inherent in large regions due to limited rescue distribution, unmanned aerial vehicle (UAV)-based frameworks are a promising platform for providing aerial imaging. In recent years, technological advances in areas such as micro-technology, sensors and navigation have influenced the various applications of UAVs. In this study, an all-in-one camera-based target detection and positioning system is developed and integrated into a fully autonomous fixed-wing UAV. The system presented in this paper is capable of on-board, real-time target identification, post-target identification and location and aerial image collection for further mapping applications. Its performance is examined using several simulated search and rescue missions, and the test results demonstrate its reliability and efficiency. Full article
(This article belongs to the Special Issue UAV-Based Remote Sensing)
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Open AccessArticle Elimination of Drifts in Long-Duration Monitoring for Apnea-Hypopnea of Human Respiration
Sensors 2016, 16(11), 1779; doi:10.3390/s16111779
Received: 22 July 2016 / Revised: 28 September 2016 / Accepted: 19 October 2016 / Published: 25 October 2016
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This paper reports a methodology to eliminate an uncertain baseline drift in respiratory monitoring using a thermal airflow sensor exposed in a high humidity environment. Human respiratory airflow usually contains a large amount of moisture (relative humidity, RH > 85%). Water vapors in
[...] Read more.
This paper reports a methodology to eliminate an uncertain baseline drift in respiratory monitoring using a thermal airflow sensor exposed in a high humidity environment. Human respiratory airflow usually contains a large amount of moisture (relative humidity, RH > 85%). Water vapors in breathing air condense gradually on the surface of the sensor so as to form a thin water film that leads to a significant sensor drift in long-duration respiratory monitoring. The water film is formed by a combination of condensation and evaporation, and therefore the behavior of the humidity drift is complicated. Fortunately, the exhale and inhale responses of the sensor exhibit distinguishing features that are different from the humidity drift. Using a wavelet analysis method, we removed the baseline drift of the sensor and successfully recovered the respiratory waveform. Finally, we extracted apnea-hypopnea events from the respiratory signals monitored in whole-night sleeps of patients and compared them with golden standard polysomnography (PSG) results. Full article
(This article belongs to the Special Issue Wearable Biomedical Sensors)
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Open AccessArticle Electro-Mechanical Properties of Multilayer Graphene-Based Polymeric Composite Obtained through a Capillary Rise Method
Sensors 2016, 16(11), 1780; doi:10.3390/s16111780
Received: 1 September 2016 / Revised: 15 October 2016 / Accepted: 20 October 2016 / Published: 25 October 2016
Cited by 1 | PDF Full-text (7307 KB) | HTML Full-text | XML Full-text
Abstract
A new sensor made of a vinyl-ester polymer composite filled with multilayer graphene nanoplatelets (MLG) is produced through an innovative capillary rise method for application in strain sensing and structural health monitoring. The new sensor is characterized by high stability of the piezoresistive
[...] Read more.
A new sensor made of a vinyl-ester polymer composite filled with multilayer graphene nanoplatelets (MLG) is produced through an innovative capillary rise method for application in strain sensing and structural health monitoring. The new sensor is characterized by high stability of the piezoresistive response under quasi-static consecutive loading/unloading cycles and monotonic tests. This is due to the peculiarity of the fabrication process that ensures a smooth and clean surface of the sensor, without the presence of filler agglomerates acting as micro- or macro-sized defects in the composite. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle One-Port Electronic Detection Strategies for Improving Sensitivity in Piezoelectric Resonant Sensor Measurements
Sensors 2016, 16(11), 1781; doi:10.3390/s16111781
Received: 12 July 2016 / Revised: 3 October 2016 / Accepted: 20 October 2016 / Published: 25 October 2016
Cited by 1 | PDF Full-text (5071 KB) | HTML Full-text | XML Full-text
Abstract
This paper describes a one-port mechanical resonance detection scheme utilized on a piezoelectric thin film driven silicon circular diaphragm resonator and discusses the limitations to such an approach in degenerate mode mass detection sensors. The sensor utilizes degenerated vibration modes of a radial
[...] Read more.
This paper describes a one-port mechanical resonance detection scheme utilized on a piezoelectric thin film driven silicon circular diaphragm resonator and discusses the limitations to such an approach in degenerate mode mass detection sensors. The sensor utilizes degenerated vibration modes of a radial symmetrical microstructure thereby providing both a sense and reference mode allowing for minimization of environmental effects on performance. The circular diaphragm resonator was fabricated with thickness of 4.5 µm and diameter of 140 µm. A PZT thin film of 0.75 µm was patterned on the top surface for the purposes of excitation and vibration sensing. The device showed a resonant frequency of 5.8 MHz for the (1, 1) mode. An electronic interface circuit was designed to cancel out the large static and parasitic capacitance allowing for electrical detection of the mechanical vibration thereby enabling the frequency split between the sense and reference mode to be measured accurately. The extracted motional current, proportional to the vibration velocity, was fed back to the drive to effectively increase the Q factor, and therefore device sensitivity, by more than a factor of 8. A software phase-locked loop was implemented to automatically track the resonant frequencies to allow for faster and accurate resonance detection. Results showed that by utilizing the absolute mode frequencies as an indication of sensor temperature, the variation in sensor temperature due to the heating from the drive electronics was accounted for and led to an ultimate measurement sensitivity of 2.3 Hz. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography
Sensors 2016, 16(11), 1782; doi:10.3390/s16111782
Received: 17 July 2016 / Revised: 18 October 2016 / Accepted: 21 October 2016 / Published: 25 October 2016
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Abstract
Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We
[...] Read more.
Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces. Full article
(This article belongs to the Special Issue Noninvasive Biomedical Sensors)
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Open AccessArticle Optimal Subset Selection of Time-Series MODIS Images and Sample Data Transfer with Random Forests for Supervised Classification Modelling
Sensors 2016, 16(11), 1783; doi:10.3390/s16111783
Received: 9 June 2016 / Revised: 23 August 2016 / Accepted: 19 October 2016 / Published: 25 October 2016
PDF Full-text (2341 KB) | HTML Full-text | XML Full-text
Abstract
Nowadays, various time-series Earth Observation data with multiple bands are freely available, such as Moderate Resolution Imaging Spectroradiometer (MODIS) datasets including 8-day composites from NASA, and 10-day composites from the Canada Centre for Remote Sensing (CCRS). It is challenging to efficiently use these
[...] Read more.
Nowadays, various time-series Earth Observation data with multiple bands are freely available, such as Moderate Resolution Imaging Spectroradiometer (MODIS) datasets including 8-day composites from NASA, and 10-day composites from the Canada Centre for Remote Sensing (CCRS). It is challenging to efficiently use these time-series MODIS datasets for long-term environmental monitoring due to their vast volume and information redundancy. This challenge will be greater when Sentinel 2–3 data become available. Another challenge that researchers face is the lack of in-situ data for supervised modelling, especially for time-series data analysis. In this study, we attempt to tackle the two important issues with a case study of land cover mapping using CCRS 10-day MODIS composites with the help of Random Forests’ features: variable importance, outlier identification. The variable importance feature is used to analyze and select optimal subsets of time-series MODIS imagery for efficient land cover mapping, and the outlier identification feature is utilized for transferring sample data available from one year to an adjacent year for supervised classification modelling. The results of the case study of agricultural land cover classification at a regional scale show that using only about a half of the variables we can achieve land cover classification accuracy close to that generated using the full dataset. The proposed simple but effective solution of sample transferring could make supervised modelling possible for applications lacking sample data. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Optimal Parameter Exploration for Online Change-Point Detection in Activity Monitoring Using Genetic Algorithms
Sensors 2016, 16(11), 1784; doi:10.3390/s16111784
Received: 6 May 2016 / Revised: 17 October 2016 / Accepted: 18 October 2016 / Published: 26 October 2016
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Abstract
In recent years, smart phones with inbuilt sensors have become popular devices to facilitate activity recognition. The sensors capture a large amount of data, containing meaningful events, in a short period of time. The change points in this data are used to specify
[...] Read more.
In recent years, smart phones with inbuilt sensors have become popular devices to facilitate activity recognition. The sensors capture a large amount of data, containing meaningful events, in a short period of time. The change points in this data are used to specify transitions to distinct events and can be used in various scenarios such as identifying change in a patient’s vital signs in the medical domain or requesting activity labels for generating real-world labeled activity datasets. Our work focuses on change-point detection to identify a transition from one activity to another. Within this paper, we extend our previous work on multivariate exponentially weighted moving average (MEWMA) algorithm by using a genetic algorithm (GA) to identify the optimal set of parameters for online change-point detection. The proposed technique finds the maximum accuracy and F_measure by optimizing the different parameters of the MEWMA, which subsequently identifies the exact location of the change point from an existing activity to a new one. Optimal parameter selection facilitates an algorithm to detect accurate change points and minimize false alarms. Results have been evaluated based on two real datasets of accelerometer data collected from a set of different activities from two users, with a high degree of accuracy from 99.4% to 99.8% and F_measure of up to 66.7%. Full article
(This article belongs to the Special Issue Selected Papers from UCAmI, IWAAL and AmIHEALTH 2015)
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Open AccessArticle Detection of Copper(II) Ions Using Glycine on Hydrazine-Adsorbed Gold Nanoparticles via Raman Spectroscopy
Sensors 2016, 16(11), 1785; doi:10.3390/s16111785
Received: 19 August 2016 / Revised: 7 October 2016 / Accepted: 13 October 2016 / Published: 26 October 2016
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Abstract
A facile, selective, and sensitive detection method for the Cu2+ ions in environmental and biological solutions has been newly developed by observing the unique CN stretching peaks at ~2108 cm−1 upon the dissociative adsorption of glycine (GLY) in hydrazine buffer on
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A facile, selective, and sensitive detection method for the Cu2+ ions in environmental and biological solutions has been newly developed by observing the unique CN stretching peaks at ~2108 cm−1 upon the dissociative adsorption of glycine (GLY) in hydrazine buffer on gold nanoparticles (AuNPs). The relative abundance of Cu species on AuNPs was identified from X-ray photoelectron spectroscopy analysis. UV-Vis spectra also indicated that the Au particles aggregated to result in the color change owing to the destabilization induced by the GLY-Cu2+ complex. The CN stretching band at ~2108 cm−1 could be observed to indicate the formation of the CN species from GLY on the hydrazine-covered AuNP surfaces. The other ions of Fe3+, Fe2+, Hg2+, Mg2+, Mn2+, Ni2+, Zn2+, Cr3+, Co2+, Cd2+, Pb2+, Ca2+, NH4+, Na+, and K+ at high concentrations of 50 µM did not produce such spectral changes. The detection limit based on the CN band for the determination of the Cu2+ ion could be estimated to be as low as 500 nM in distilled water and 1 µM in river water, respectively. We attempted to apply our method to estimate intracellular ion detection in cancer cells for more practical purposes. Full article
(This article belongs to the Special Issue Colorimetric and Fluorescent Sensor)
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Open AccessArticle Development and Experimental Comparison of Low-Cost, Reliable Capacitive Touch Sensing Boards
Sensors 2016, 16(11), 1786; doi:10.3390/s16111786
Received: 29 June 2016 / Revised: 18 October 2016 / Accepted: 19 October 2016 / Published: 26 October 2016
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Abstract
In this study, two types of direct interface capacitive sensors, self- and mutual-capacitance, were developed and compared experimentally. Electromagnetic Compatibility (EMC) tests—International Electrotechnical Commission (IEC) 61000-4-3, IEC 61000-4-4, IEC 61000-4-6—were applied in an accredited laboratory to measure the immunity of the sensors against
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In this study, two types of direct interface capacitive sensors, self- and mutual-capacitance, were developed and compared experimentally. Electromagnetic Compatibility (EMC) tests—International Electrotechnical Commission (IEC) 61000-4-3, IEC 61000-4-4, IEC 61000-4-6—were applied in an accredited laboratory to measure the immunity of the sensors against radiated and conducted interference. The frequency hopping algorithm could be implemented for the mutual-capacitance sensor without using any particular circuit. The effects of EMC disturbance were detected by means of a new noise detection algorithm and when the signal-to-noise ratio (SNR) became lower, the operation frequency of the sensors switched to an undisturbed frequency to ensure safe operation. For this purpose, a new noise detection algorithm was developed and frequency hopping was performed with a standard controller. Both cards were tested under several conditions and their performances compared. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Preparation, Characterization and Activity of a Peptide-Cellulosic Aerogel Protease Sensor from Cotton
Sensors 2016, 16(11), 1789; doi:10.3390/s16111789
Received: 15 July 2016 / Revised: 19 September 2016 / Accepted: 12 October 2016 / Published: 26 October 2016
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Abstract
Nanocellulosic aerogels (NA) provide a lightweight biocompatible material with structural properties, like interconnected high porosity and specific surface area, suitable for biosensor design. We report here the preparation, characterization and activity of peptide-nanocellulose aerogels (PepNA) made from unprocessed cotton and designed with protease
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Nanocellulosic aerogels (NA) provide a lightweight biocompatible material with structural properties, like interconnected high porosity and specific surface area, suitable for biosensor design. We report here the preparation, characterization and activity of peptide-nanocellulose aerogels (PepNA) made from unprocessed cotton and designed with protease detection activity. Low-density cellulosic aerogels were prepared from greige cotton by employing calcium thiocyanate octahydrate/lithium chloride as a direct cellulose dissolving medium. Subsequent casting, coagulation, solvent exchange and supercritical carbon dioxide drying afforded homogeneous cellulose II aerogels of fibrous morphology. The cotton-based aerogel had a porosity of 99% largely dominated by mesopores (2–50 nm) and an internal surface of 163 m2·g−1. A fluorescent tripeptide-substrate (succinyl-alanine-proline-alanine-4-amino-7-methyl-coumarin) was tethered to NA by (1) esterification of cellulose C6 surface hydroxyl groups with glycidyl-fluorenylmethyloxycarbonyl (FMOC), (2) deprotection and (3) coupling of the immobilized glycine with the tripeptide. Characterization of the NA and PepNA included techniques, such as elemental analysis, mass spectral analysis, attenuated total reflectance infrared imaging, nitrogen adsorption, scanning electron microscopy and bioactivity studies. The degree of substitution of the peptide analog attached to the anhydroglucose units of PepNA was 0.015. The findings from mass spectral analysis and attenuated total reflectance infrared imaging indicated that the peptide substrate was immobilized on to the surface of the NA. Nitrogen adsorption revealed a high specific surface area and a highly porous system, which supports the open porous structure observed from scanning electron microscopy images. Bioactivity studies of PepNA revealed a detection sensitivity of 0.13 units/milliliter for human neutrophil elastase, a diagnostic biomarker for inflammatory diseases. The physical properties of the aerogel are suitable for interfacing with an intelligent protease sequestrant wound dressing. Full article
(This article belongs to the Special Issue Point-of-Care Biosensors)
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Open AccessArticle Sensor Location Problem Optimization for Traffic Network with Different Spatial Distributions of Traffic Information
Sensors 2016, 16(11), 1790; doi:10.3390/s16111790
Received: 28 July 2016 / Revised: 13 October 2016 / Accepted: 23 October 2016 / Published: 27 October 2016
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Abstract
To obtain adequate traffic information, the density of traffic sensors should be sufficiently high to cover the entire transportation network. However, deploying sensors densely over the entire network may not be realistic for practical applications due to the budgetary constraints of traffic management
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To obtain adequate traffic information, the density of traffic sensors should be sufficiently high to cover the entire transportation network. However, deploying sensors densely over the entire network may not be realistic for practical applications due to the budgetary constraints of traffic management agencies. This paper describes several possible spatial distributions of traffic information credibility and proposes corresponding different sensor information credibility functions to describe these spatial distribution properties. A maximum benefit model and its simplified model are proposed to solve the traffic sensor location problem. The relationships between the benefit and the number of sensors are formulated with different sensor information credibility functions. Next, expanding models and algorithms in analytic results are performed. For each case, the maximum benefit, the optimal number and spacing of sensors are obtained and the analytic formulations of the optimal sensor locations are derived as well. Finally, a numerical example is proposed to verify the validity and availability of the proposed models for solving a network sensor location problem. The results show that the optimal number of sensors of segments with different model parameters in an entire freeway network can be calculated. Besides, it can also be concluded that the optimal sensor spacing is independent of end restrictions but dependent on the values of model parameters that represent the physical conditions of sensors and roads. Full article
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Open AccessArticle Nonenzymatic Glucose Sensor Based on In Situ Reduction of Ni/NiO-Graphene Nanocomposite
Sensors 2016, 16(11), 1791; doi:10.3390/s16111791
Received: 22 August 2016 / Revised: 8 October 2016 / Accepted: 17 October 2016 / Published: 26 October 2016
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Abstract
Ni/NiO nanoflower modified reduced graphene oxide (rGO) nanocomposite (Ni/NiO-rGO) was introduced to screen printed electrode (SPE) for the construction of a nonenzymatic electrochemical glucose biosensor. The Ni/NiO-rGO nanocomposite was synthesized by an in situ reduction process. Graphene oxide (GO) hybrid Nafion sheets first
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Ni/NiO nanoflower modified reduced graphene oxide (rGO) nanocomposite (Ni/NiO-rGO) was introduced to screen printed electrode (SPE) for the construction of a nonenzymatic electrochemical glucose biosensor. The Ni/NiO-rGO nanocomposite was synthesized by an in situ reduction process. Graphene oxide (GO) hybrid Nafion sheets first chemical adsorbed Ni ions and assembled on the SPE. Subsequently, GO and Ni ions were reduced by hydrazine hydrate. The electrochemical properties of such a Ni/NiO-rGO modified SPE were carefully investigated. It showed a high activity for electrocatalytic oxidation of glucose in alkaline medium. The proposed nonenzymatic sensor can be utilized for quantification of glucose with a wide linear range from 29.9 μM to 6.44 mM (R = 0.9937) with a low detection limit of 1.8 μM (S/N = 3) and a high sensitivity of 1997 μA/mM∙cm−2. It also exhibited good reproducibility as well as high selectivity. Full article
(This article belongs to the Special Issue Nanobiosensing for Sensors)
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Open AccessArticle Skeleton-Based Abnormal Gait Detection
Sensors 2016, 16(11), 1792; doi:10.3390/s16111792
Received: 5 August 2016 / Revised: 17 October 2016 / Accepted: 21 October 2016 / Published: 26 October 2016
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Abstract
Human gait analysis plays an important role in musculoskeletal disorder diagnosis. Detecting anomalies in human walking, such as shuffling gait, stiff leg or unsteady gait, can be difficult if the prior knowledge of such a gait pattern is not available. We propose an
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Human gait analysis plays an important role in musculoskeletal disorder diagnosis. Detecting anomalies in human walking, such as shuffling gait, stiff leg or unsteady gait, can be difficult if the prior knowledge of such a gait pattern is not available. We propose an approach for detecting abnormal human gait based on a normal gait model. Instead of employing the color image, silhouette, or spatio-temporal volume, our model is created based on human joint positions (skeleton) in time series. We decompose each sequence of normal gait images into gait cycles. Each human instant posture is represented by a feature vector which describes relationships between pairs of bone joints located in the lower body. Such vectors are then converted into codewords using a clustering technique. The normal human gait model is created based on multiple sequences of codewords corresponding to different gait cycles. In the detection stage, a gait cycle with normality likelihood below a threshold, which is determined automatically in the training step, is assumed as an anomaly. The experimental results on both marker-based mocap data and Kinect skeleton show that our method is very promising in distinguishing normal and abnormal gaits with an overall accuracy of 90.12%. Full article
(This article belongs to the collection Sensors for Globalized Healthy Living and Wellbeing)
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Open AccessArticle Detection and Identification of Multiple Stationary Human Targets Via Bio-Radar Based on the Cross-Correlation Method
Sensors 2016, 16(11), 1793; doi:10.3390/s16111793
Received: 18 August 2016 / Revised: 15 October 2016 / Accepted: 18 October 2016 / Published: 27 October 2016
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Abstract
Ultra-wideband (UWB) radar has been widely used for detecting human physiological signals (respiration, movement, etc.) in the fields of rescue, security, and medicine owing to its high penetrability and range resolution. In these applications, especially in rescue after disaster (earthquake, collapse, mine accident,
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Ultra-wideband (UWB) radar has been widely used for detecting human physiological signals (respiration, movement, etc.) in the fields of rescue, security, and medicine owing to its high penetrability and range resolution. In these applications, especially in rescue after disaster (earthquake, collapse, mine accident, etc.), the presence, number, and location of the trapped victims to be detected and rescued are the key issues of concern. Ample research has been done on the first issue, whereas the identification and localization of multi-targets remains a challenge. False positive and negative identification results are two common problems associated with the detection of multiple stationary human targets. This is mainly because the energy of the signal reflected from the target close to the receiving antenna is considerably stronger than those of the targets at further range, often leading to missing or false recognition if the identification method is based on the energy of the respiratory signal. Therefore, a novel method based on cross-correlation is proposed in this paper that is based on the relativity and periodicity of the signals, rather than on the energy. The validity of this method is confirmed through experiments using different scenarios; the results indicate a discernible improvement in the detection precision and identification of the multiple stationary targets. Full article
(This article belongs to the Special Issue Non-Contact Sensing)
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Open AccessArticle A 3D Optical Surface Profilometer Using a Dual-Frequency Liquid Crystal-Based Dynamic Fringe Pattern Generator
Sensors 2016, 16(11), 1794; doi:10.3390/s16111794
Received: 23 September 2016 / Revised: 22 October 2016 / Accepted: 24 October 2016 / Published: 27 October 2016
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Abstract
We propose a liquid crystal (LC)-based 3D optical surface profilometer that can utilize multiple fringe patterns to extract an enhanced 3D surface depth profile. To avoid the optical phase ambiguity and enhance the 3D depth extraction, 16 interference patterns were generated by the
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We propose a liquid crystal (LC)-based 3D optical surface profilometer that can utilize multiple fringe patterns to extract an enhanced 3D surface depth profile. To avoid the optical phase ambiguity and enhance the 3D depth extraction, 16 interference patterns were generated by the LC-based dynamic fringe pattern generator (DFPG) using four-step phase shifting and four-step spatial frequency varying schemes. The DFPG had one common slit with an electrically controllable birefringence (ECB) LC mode and four switching slits with a twisted nematic LC mode. The spatial frequency of the projected fringe pattern could be controlled by selecting one of the switching slits. In addition, moving fringe patterns were obtainable by applying voltages to the ECB LC layer, which varied the phase difference between the common and the selected switching slits. Notably, the DFPG switching time required to project 16 fringe patterns was minimized by utilizing the dual-frequency modulation of the driving waveform to switch the LC layers. We calculated the phase modulation of the DFPG and reconstructed the depth profile of 3D objects using a discrete Fourier transform method and geometric optical parameters. Full article
(This article belongs to the Special Issue Imaging: Sensors and Technologies) Printed Edition available
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Open AccessArticle Comparison of Ultrasonic Welding and Thermal Bonding for the Integration of Thin Film Metal Electrodes in Injection Molded Polymeric Lab-on-Chip Systems for Electrochemistry
Sensors 2016, 16(11), 1795; doi:10.3390/s16111795
Received: 5 September 2016 / Revised: 6 October 2016 / Accepted: 14 October 2016 / Published: 27 October 2016
Cited by 1 | PDF Full-text (2106 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
We compare ultrasonic welding (UW) and thermal bonding (TB) for the integration of embedded thin-film gold electrodes for electrochemical applications in injection molded (IM) microfluidic chips. The UW bonded chips showed a significantly superior electrochemical performance compared to the ones obtained using TB.
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We compare ultrasonic welding (UW) and thermal bonding (TB) for the integration of embedded thin-film gold electrodes for electrochemical applications in injection molded (IM) microfluidic chips. The UW bonded chips showed a significantly superior electrochemical performance compared to the ones obtained using TB. Parameters such as metal thickness of electrodes, depth of electrode embedding, delivered power, and height of energy directors (for UW), as well as pressure and temperature (for TB), were systematically studied to evaluate the two bonding methods and requirements for optimal electrochemical performance. The presented technology is intended for easy and effective integration of polymeric Lab-on-Chip systems to encourage their use in research, commercialization and education. Full article
(This article belongs to the Special Issue Microfluidics-Based Microsystem Integration Research)
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Open AccessArticle Portable Electronic Tongue Based on Microsensors for the Analysis of Cava Wines
Sensors 2016, 16(11), 1796; doi:10.3390/s16111796
Received: 30 August 2016 / Revised: 21 October 2016 / Accepted: 24 October 2016 / Published: 27 October 2016
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Abstract
Cava is a quality sparkling wine produced in Spain. As a product with a designation of origin, Cava wine has to meet certain quality requirements throughout its production process; therefore, the analysis of several parameters is of great interest. In this work, a
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Cava is a quality sparkling wine produced in Spain. As a product with a designation of origin, Cava wine has to meet certain quality requirements throughout its production process; therefore, the analysis of several parameters is of great interest. In this work, a portable electronic tongue for the analysis of Cava wine is described. The system is comprised of compact and low-power-consumption electronic equipment and an array of microsensors formed by six ion-selective field effect transistors sensitive to pH, Na+, K+, Ca2+, Cl, and CO32−, one conductivity sensor, one redox potential sensor, and two amperometric gold microelectrodes. This system, combined with chemometric tools, has been applied to the analysis of 78 Cava wine samples. Results demonstrate that the electronic tongue is able to classify the samples according to the aging time, with a percentage of correct prediction between 80% and 96%, by using linear discriminant analysis, as well as to quantify the total acidity, pH, volumetric alcoholic degree, potassium, conductivity, glycerol, and methanol parameters, with mean relative errors between 2.3% and 6.0%, by using partial least squares regressions. Full article
(This article belongs to the Special Issue Olfactory and Gustatory Sensors)
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Open AccessArticle Electrochemical Behavior and Determination of Chlorogenic Acid Based on Multi-Walled Carbon Nanotubes Modified Screen-Printed Electrode
Sensors 2016, 16(11), 1797; doi:10.3390/s16111797
Received: 12 July 2016 / Revised: 6 September 2016 / Accepted: 12 October 2016 / Published: 27 October 2016
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Abstract
In this paper, the multi-walled carbon nanotubes modified screen-printed electrode (MWCNTs/SPE) was prepared and the MWCNTs/SPE was employed for the electrochemical determination of the antioxidant substance chlorogenic acids (CGAs). A pair of well-defined redox peaks of CGA was observed at the MWCNTs/SPE in
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In this paper, the multi-walled carbon nanotubes modified screen-printed electrode (MWCNTs/SPE) was prepared and the MWCNTs/SPE was employed for the electrochemical determination of the antioxidant substance chlorogenic acids (CGAs). A pair of well-defined redox peaks of CGA was observed at the MWCNTs/SPE in 0.10 mol/L acetic acid-sodium acetate buffer (pH 6.2) and the electrode process was adsorption-controlled. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) methods for the determination of CGA were proposed based on the MWCNTs/SPE. Under the optimal conditions, the proposed method exhibited linear ranges from 0.17 to 15.8 µg/mL, and the linear regression equation was Ipa (µA) = 4.1993 C (×10−5 mol/L) + 1.1039 (r = 0.9976) and the detection limit for CGA could reach 0.12 µg/mL. The recovery of matrine was 94.74%–106.65% (RSD = 2.92%) in coffee beans. The proposed method is quick, sensitive, reliable, and can be used for the determination of CGA. Full article
(This article belongs to the Special Issue Recent Advances in Biosensors Based Screen Printed Platforms)
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Open AccessArticle Information Fusion of Conflicting Input Data
Sensors 2016, 16(11), 1798; doi:10.3390/s16111798
Received: 30 July 2016 / Revised: 30 September 2016 / Accepted: 19 October 2016 / Published: 29 October 2016
Cited by 4 | PDF Full-text (2083 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Sensors, and also actuators or external sources such as databases, serve as data sources in order to realise condition monitoring of industrial applications or the acquisition of characteristic parameters like production speed or reject rate. Modern facilities create such a large amount of
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Sensors, and also actuators or external sources such as databases, serve as data sources in order to realise condition monitoring of industrial applications or the acquisition of characteristic parameters like production speed or reject rate. Modern facilities create such a large amount of complex data that a machine operator is unable to comprehend and process the information contained in the data. Thus, information fusion mechanisms gain increasing importance. Besides the management of large amounts of data, further challenges towards the fusion algorithms arise from epistemic uncertainties (incomplete knowledge) in the input signals as well as conflicts between them. These aspects must be considered during information processing to obtain reliable results, which are in accordance with the real world. The analysis of the scientific state of the art shows that current solutions fulfil said requirements at most only partly. This article proposes the multilayered information fusion system MACRO (multilayer attribute-based conflict-reducing observation) employing the μBalTLCS (fuzzified balanced two-layer conflict solving) fusion algorithm to reduce the impact of conflicts on the fusion result. The performance of the contribution is shown by its evaluation in the scope of a machine condition monitoring application under laboratory conditions. Here, the MACRO system yields the best results compared to state-of-the-art fusion mechanisms. The utilised data is published and freely accessible. Full article
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
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Open AccessArticle A Novel Group Decision-Making Method Based on Sensor Data and Fuzzy Information
Sensors 2016, 16(11), 1799; doi:10.3390/s16111799
Received: 23 July 2016 / Revised: 2 October 2016 / Accepted: 24 October 2016 / Published: 28 October 2016
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Abstract
Algal bloom is a typical phenomenon of the eutrophication of rivers and lakes and makes the water dirty and smelly. It is a serious threat to water security and public health. Most scholars studying solutions for this pollution have studied the principles of
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Algal bloom is a typical phenomenon of the eutrophication of rivers and lakes and makes the water dirty and smelly. It is a serious threat to water security and public health. Most scholars studying solutions for this pollution have studied the principles of remediation approaches, but few have studied the decision-making and selection of the approaches. Existing research uses simplex decision-making information which is highly subjective and uses little of the data from water quality sensors. To utilize these data and solve the rational decision-making problem, a novel group decision-making method is proposed using the sensor data with fuzzy evaluation information. Firstly, the optimal similarity aggregation model of group opinions is built based on the modified similarity measurement of Vague values. Secondly, the approaches’ ability to improve the water quality indexes is expressed using Vague evaluation methods. Thirdly, the water quality sensor data are analyzed to match the features of the alternative approaches with grey relational degrees. This allows the best remediation approach to be selected to meet the current water status. Finally, the selection model is applied to the remediation of algal bloom in lakes. The results show this method’s rationality and feasibility when using different data from different sources. Full article
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
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Open AccessArticle An Optical Fiber Sensor and Its Application in UAVs for Current Measurements
Sensors 2016, 16(11), 1800; doi:10.3390/s16111800
Received: 4 July 2016 / Revised: 23 August 2016 / Accepted: 6 September 2016 / Published: 27 October 2016
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Abstract
In this paper, we propose and experimentally investigate an optical sensor based on a novel combination of a long-period fiber grating (LPFG) with a permanent magnet to measure electrical current in unmanned aerial vehicles (UAVs). The proposed device uses a neodymium magnet attached
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In this paper, we propose and experimentally investigate an optical sensor based on a novel combination of a long-period fiber grating (LPFG) with a permanent magnet to measure electrical current in unmanned aerial vehicles (UAVs). The proposed device uses a neodymium magnet attached to the grating structure, which suffers from an electromagnetic force produced when the current flows in the wire of the UAV engine. Therefore, it causes deformation on the sensor and thus, different shifts occur in the resonant bands of the transmission spectrum of the LPFG. Finally, the results show that it is possible to monitor electrical current throughout the entire operating range of the UAV engine from 0 A to 10 A in an effective and practical way with good linearity, reliability and response time, which are desirable characteristics in electrical current sensing. Full article
(This article belongs to the Special Issue Recent Advances in Fiber Bragg Grating Sensing)
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Open AccessArticle Comparison of Reflectance Measurements Acquired with a Contact Probe and an Integration Sphere: Implications for the Spectral Properties of Vegetation at a Leaf Level
Sensors 2016, 16(11), 1801; doi:10.3390/s16111801
Received: 9 August 2016 / Revised: 5 October 2016 / Accepted: 15 October 2016 / Published: 28 October 2016
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Abstract
Laboratory spectroscopy in visible and infrared regions is an important tool for studies dealing with plant ecophysiology and early recognition of plant stress due to changing environmental conditions. Leaf optical properties are typically acquired with a spectroradiometer coupled with an integration sphere (IS)
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Laboratory spectroscopy in visible and infrared regions is an important tool for studies dealing with plant ecophysiology and early recognition of plant stress due to changing environmental conditions. Leaf optical properties are typically acquired with a spectroradiometer coupled with an integration sphere (IS) in a laboratory or with a contact probe (CP), which has the advantage of operating flexibility and the provision of repetitive in-situ reflectance measurements. Experiments comparing reflectance spectra measured with different devices and device settings are rarely reported in literature. Thus, in our study we focused on a comparison of spectra collected with two ISs on identical samples ranging from a Spectralon and coloured papers as reference standards to vegetation samples with broadleaved (Nicotiana Rustica L.) and coniferous (Picea abies L. Karst.) leaf types. First, statistical measures such as mean absolute difference, median of differences, standard deviation and paired-sample t-test were applied in order to evaluate differences between collected reflectance values. The possibility of linear transformation between spectra was also tested. Moreover, correlation between normalised differential indexes (NDI) derived for each device and all combinations of wavelengths between 450 nm and 1800 nm were assessed. Finally, relationships between laboratory measured leaf compounds (total chlorophyll, carotenoids and water content), NDI and selected spectral indices often used in remote sensing were studied. The results showed differences between spectra acquired with different devices. While differences were negligible in the case of the Spectralon and they were possible to be modelled with a linear transformation in the case of coloured papers, the spectra collected with the CP and the ISs differed significantly in the case of vegetation samples. Regarding the spectral indices calculated from the reflectance data collected with the three devices, their mean values were in the range of the corresponding standard deviations in the case of broadleaved leaf type. Larger differences in optical leaf properties of spruce needles collected with the CP and ISs are implicated from the different measurement procedure due to needle-like leaf where shoots with spatially oriented needles were measured with the CP and individual needles with the IS. The study shows that a direct comparison between the spectra collected with two devices is not advisable as spectrally dependent offsets may likely exist. We propose that the future studies shall focus on standardisation of measurement procedures so that open access spectral libraries could serve as a reliable input for modelling of optical properties on a leaf level. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Complementary Split-Ring Resonator-Loaded Microfluidic Ethanol Chemical Sensor
Sensors 2016, 16(11), 1802; doi:10.3390/s16111802
Received: 4 August 2016 / Revised: 5 October 2016 / Accepted: 22 October 2016 / Published: 28 October 2016
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Abstract
In this paper, a complementary split-ring resonator (CSRR)-loaded patch is proposed as a microfluidic ethanol chemical sensor. The primary objective of this chemical sensor is to detect ethanol’s concentration. First, two tightly coupled concentric CSRRs loaded on a patch are realized on a
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In this paper, a complementary split-ring resonator (CSRR)-loaded patch is proposed as a microfluidic ethanol chemical sensor. The primary objective of this chemical sensor is to detect ethanol’s concentration. First, two tightly coupled concentric CSRRs loaded on a patch are realized on a Rogers RT/Duroid 5870 substrate, and then a microfluidic channel engraved on polydimethylsiloxane (PDMS) is integrated for ethanol chemical sensor applications. The resonant frequency of the structure before loading the microfluidic channel is 4.72 GHz. After loading the microfluidic channel, the 550 MHz shift in the resonant frequency is ascribed to the dielectric perturbation phenomenon when the ethanol concentration is varied from 0% to 100%. In order to assess the sensitivity range of our proposed sensor, various concentrations of ethanol are tested and analyzed. Our proposed sensor exhibits repeatability and successfully detects 10% ethanol as verified by the measurement set-up. It has created headway to a miniaturized, non-contact, low-cost, reliable, reusable, and easily fabricated design using extremely small liquid volumes. Full article
(This article belongs to the Special Issue Microfluidics-Based Microsystem Integration Research)
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Open AccessArticle Electrochemical Aptasensor for Myoglobin-Specific Recognition Based on Porphyrin Functionalized Graphene-Conjugated Gold Nanocomposites
Sensors 2016, 16(11), 1803; doi:10.3390/s16111803
Received: 9 September 2016 / Revised: 13 October 2016 / Accepted: 22 October 2016 / Published: 28 October 2016
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Abstract
In this work, a novel electrochemical aptasensor was developed for sensitive and selective detection of myoglobin based on meso-tetra (4-carboxyphenyl) porphyrin-functionalized graphene-conjugated gold nanoparticles (TCPP–Gr/AuNPs). Due to its good electric conductivity, large specific surface area, and excellent mechanical properties, TCPP–Gr/AuNPs can act as
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In this work, a novel electrochemical aptasensor was developed for sensitive and selective detection of myoglobin based on meso-tetra (4-carboxyphenyl) porphyrin-functionalized graphene-conjugated gold nanoparticles (TCPP–Gr/AuNPs). Due to its good electric conductivity, large specific surface area, and excellent mechanical properties, TCPP–Gr/AuNPs can act as an enhanced material for the electrochemical detection of myoglobin. Meanwhile, it provides an effective matrix for immobilizing myoglobin-binding aptamer (MbBA). The electrochemical aptasensor has a sensitive response to myoglobin in a linear range from 2.0 × 10−11 M to 7.7 × 10−7 M with a detection limit of 6.7 × 10−12 M (S/N = 3). Furthermore, the method has the merits of high sensitivity, low price, and high specificity. Our work will supply new horizons for the diagnostic applications of graphene-based materials in biomedicine and biosensors. Full article
(This article belongs to the Special Issue Nanobiosensing for Sensors)
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Open AccessArticle Two-Time Scale Virtual Sensor Design for Vibration Observation of a Translational Flexible-Link Manipulator Based on Singular Perturbation and Differential Games
Sensors 2016, 16(11), 1804; doi:10.3390/s16111804
Received: 1 August 2016 / Revised: 18 October 2016 / Accepted: 21 October 2016 / Published: 28 October 2016
PDF Full-text (11745 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Effective feedback control requires all state variable information of the system. However, in the translational flexible-link manipulator (TFM) system, it is unrealistic to measure the vibration signals and their time derivative of any points of the TFM by infinite sensors. With the rigid-flexible
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Effective feedback control requires all state variable information of the system. However, in the translational flexible-link manipulator (TFM) system, it is unrealistic to measure the vibration signals and their time derivative of any points of the TFM by infinite sensors. With the rigid-flexible coupling between the global motion of the rigid base and the elastic vibration of the flexible-link manipulator considered, a two-time scale virtual sensor, which includes the speed observer and the vibration observer, is designed to achieve the estimation for the vibration signals and their time derivative of the TFM, as well as the speed observer and the vibration observer are separately designed for the slow and fast subsystems, which are decomposed from the dynamic model of the TFM by the singular perturbation. Additionally, based on the linear-quadratic differential games, the observer gains of the two-time scale virtual sensor are optimized, which aims to minimize the estimation error while keeping the observer stable. Finally, the numerical calculation and experiment verify the efficiency of the designed two-time scale virtual sensor. Full article
(This article belongs to the Special Issue Advanced Robotics and Mechatronics Devices)
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Open AccessArticle Wheelchair Navigation System for Disabled and Elderly People
Sensors 2016, 16(11), 1806; doi:10.3390/s16111806
Received: 16 May 2016 / Revised: 19 October 2016 / Accepted: 21 October 2016 / Published: 28 October 2016
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Abstract
An intelligent wheelchair (IW) system is developed in order to support safe mobility for disabled or elderly people with various impairments. The proposed IW offers two main functions: obstacle detection and avoidance, and situation recognition. First, through a combination of a vision sensor
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An intelligent wheelchair (IW) system is developed in order to support safe mobility for disabled or elderly people with various impairments. The proposed IW offers two main functions: obstacle detection and avoidance, and situation recognition. First, through a combination of a vision sensor and eight ultrasonic ones, it detects diverse obstacles and produces occupancy grid maps (OGMs) that describe environmental information, including the positions and sizes of obstacles, which is then given to the learning-based algorithm. By learning the common patterns among OGMs assigned to the same directions, the IW can automatically find paths to prevent collisions with obstacles. Second, it distinguishes a situation whereby the user is standing on a sidewalk, traffic intersection, or roadway through analyzing the texture and shape of the images, which aids in preventing any accidents that would result in fatal injuries to the user, such as collisions with vehicles. From the experiments that were performed in various environments, we can prove the following: (1) the proposed system can recognize different types of outdoor places with 98.3% accuracy; and (2) it can produce paths that avoid obstacles with 92.0% accuracy. Full article
(This article belongs to the collection Sensors for Globalized Healthy Living and Wellbeing)
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Open AccessArticle When Ultrasonic Sensors and Computer Vision Join Forces for Efficient Obstacle Detection and Recognition
Sensors 2016, 16(11), 1807; doi:10.3390/s16111807
Received: 30 August 2016 / Revised: 24 October 2016 / Accepted: 25 October 2016 / Published: 28 October 2016
Cited by 3 | PDF Full-text (14141 KB) | HTML Full-text | XML Full-text
Abstract
In the most recent report published by the World Health Organization concerning people with visual disabilities it is highlighted that by the year 2020, worldwide, the number of completely blind people will reach 75 million, while the number of visually impaired (VI) people
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In the most recent report published by the World Health Organization concerning people with visual disabilities it is highlighted that by the year 2020, worldwide, the number of completely blind people will reach 75 million, while the number of visually impaired (VI) people will rise to 250 million. Within this context, the development of dedicated electronic travel aid (ETA) systems, able to increase the safe displacement of VI people in indoor/outdoor spaces, while providing additional cognition of the environment becomes of outmost importance. This paper introduces a novel wearable assistive device designed to facilitate the autonomous navigation of blind and VI people in highly dynamic urban scenes. The system exploits two independent sources of information: ultrasonic sensors and the video camera embedded in a regular smartphone. The underlying methodology exploits computer vision and machine learning techniques and makes it possible to identify accurately both static and highly dynamic objects existent in a scene, regardless on their location, size or shape. In addition, the proposed system is able to acquire information about the environment, semantically interpret it and alert users about possible dangerous situations through acoustic feedback. To determine the performance of the proposed methodology we have performed an extensive objective and subjective experimental evaluation with the help of 21 VI subjects from two blind associations. The users pointed out that our prototype is highly helpful in increasing the mobility, while being friendly and easy to learn. Full article
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
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Open AccessArticle 3D Tracking via Shoe Sensing
Sensors 2016, 16(11), 1809; doi:10.3390/s16111809
Received: 6 September 2016 / Revised: 15 October 2016 / Accepted: 20 October 2016 / Published: 28 October 2016
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Abstract
Most location-based services are based on a global positioning system (GPS), which only works well in outdoor environments. Compared to outdoor environments, indoor localization has created more buzz in recent years as people spent most of their time indoors working at offices and
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Most location-based services are based on a global positioning system (GPS), which only works well in outdoor environments. Compared to outdoor environments, indoor localization has created more buzz in recent years as people spent most of their time indoors working at offices and shopping at malls, etc. Existing solutions mainly rely on inertial sensors (i.e., accelerometer and gyroscope) embedded in mobile devices, which are usually not accurate enough to be useful due to the mobile devices’ random movements while people are walking. In this paper, we propose the use of shoe sensing (i.e., sensors attached to shoes) to achieve 3D indoor positioning. Specifically, a short-time energy-based approach is used to extract the gait pattern. Moreover, in order to improve the accuracy of vertical distance estimation while the person is climbing upstairs, a state classification is designed to distinguish the walking status including plane motion (i.e., normal walking and jogging horizontally), walking upstairs, and walking downstairs. Furthermore, we also provide a mechanism to reduce the vertical distance accumulation error. Experimental results show that we can achieve nearly 100% accuracy when extracting gait patterns from walking/jogging with a low-cost shoe sensor, and can also achieve 3D indoor real-time positioning with high accuracy. Full article
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
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Open AccessArticle Component-Based Modelling for Scalable Smart City Systems Interoperability: A Case Study on Integrating Energy Demand Response Systems
Sensors 2016, 16(11), 1810; doi:10.3390/s16111810
Received: 12 August 2016 / Revised: 19 October 2016 / Accepted: 24 October 2016 / Published: 28 October 2016
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Abstract
Smart city systems embrace major challenges associated with climate change, energy efficiency, mobility and future services by embedding the virtual space into a complex cyber-physical system. Those systems are constantly evolving and scaling up, involving a wide range of integration among users, devices,
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Smart city systems embrace major challenges associated with climate change, energy efficiency, mobility and future services by embedding the virtual space into a complex cyber-physical system. Those systems are constantly evolving and scaling up, involving a wide range of integration among users, devices, utilities, public services and also policies. Modelling such complex dynamic systems’ architectures has always been essential for the development and application of techniques/tools to support design and deployment of integration of new components, as well as for the analysis, verification, simulation and testing to ensure trustworthiness. This article reports on the definition and implementation of a scalable component-based architecture that supports a cooperative energy demand response (DR) system coordinating energy usage between neighbouring households. The proposed architecture, called refinement of Cyber-Physical Component Systems (rCPCS), which extends the refinement calculus for component and object system (rCOS) modelling method, is implemented using Eclipse Extensible Coordination Tools (ECT), i.e., Reo coordination language. With rCPCS implementation in Reo, we specify the communication, synchronisation and co-operation amongst the heterogeneous components of the system assuring, by design scalability and the interoperability, correctness of component cooperation. Full article
(This article belongs to the Special Issue Smart City: Vision and Reality)
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Open AccessArticle An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
Sensors 2016, 16(11), 1811; doi:10.3390/s16111811
Received: 25 August 2016 / Revised: 23 October 2016 / Accepted: 25 October 2016 / Published: 29 October 2016
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Abstract
With the development of localization technologies, researchers solve the indoor localization problems using diverse methods and equipment. Most localization techniques require either specialized devices or fingerprints, which are inconvenient for daily use. Therefore, we propose and implement an accurate, efficient and lightweight system
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With the development of localization technologies, researchers solve the indoor localization problems using diverse methods and equipment. Most localization techniques require either specialized devices or fingerprints, which are inconvenient for daily use. Therefore, we propose and implement an accurate, efficient and lightweight system for indoor direction finding using common smartphones and loudspeakers. Our method is derived from a key insight: By moving a smartphone in regular patterns, we can effectively emulate the sensitivity and functionality of a Uniform Antenna Array to estimate the angle of arrival of the target signal. Specifically, a user only needs to hold his smartphone still in front of him, and then rotate his body around 360 duration with the smartphone at an approximate constant velocity. Then, our system can provide accurate directional guidance and lead the user to their destinations (normal loudspeakers we preset in the indoor environment transmitting high frequency acoustic signals) after a few measurements. Major challenges in implementing our system are not only imitating a virtual antenna array by ordinary smartphones but also overcoming the detection difficulties caused by the complex indoor environment. In addition, we leverage the gyroscope of the smartphone to reduce the impact of a user’s motion pattern change to the accuracy of our system. In order to get rid of the multipath effect, we leverage multiple signal classification to calculate the direction of the target signal, and then design and deploy our system in various indoor scenes. Extensive comparative experiments show that our system is reliable under various circumstances. Full article
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Open AccessArticle Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar
Sensors 2016, 16(11), 1812; doi:10.3390/s16111812
Received: 3 August 2016 / Revised: 25 October 2016 / Accepted: 26 October 2016 / Published: 29 October 2016
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Abstract
People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker’s vocal tract
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People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker’s vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle An Efficient Method of Sharing Mass Spatio-Temporal Trajectory Data Based on Cloudera Impala for Traffic Distribution Mapping in an Urban City
Sensors 2016, 16(11), 1813; doi:10.3390/s16111813
Received: 20 August 2016 / Revised: 21 October 2016 / Accepted: 24 October 2016 / Published: 29 October 2016
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Abstract
The efficient sharing of spatio-temporal trajectory data is important to understand traffic congestion in mass data. However, the data volumes of bus networks in urban cities are growing rapidly, reaching daily volumes of one hundred million datapoints. Accessing and retrieving mass spatio-temporal trajectory
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The efficient sharing of spatio-temporal trajectory data is important to understand traffic congestion in mass data. However, the data volumes of bus networks in urban cities are growing rapidly, reaching daily volumes of one hundred million datapoints. Accessing and retrieving mass spatio-temporal trajectory data in any field is hard and inefficient due to limited computational capabilities and incomplete data organization mechanisms. Therefore, we propose an optimized and efficient spatio-temporal trajectory data retrieval method based on the Cloudera Impala query engine, called ESTRI, to enhance the efficiency of mass data sharing. As an excellent query tool for mass data, Impala can be applied for mass spatio-temporal trajectory data sharing. In ESTRI we extend the spatio-temporal trajectory data retrieval function of Impala and design a suitable data partitioning method. In our experiments, the Taiyuan BeiDou (BD) bus network is selected, containing 2300 buses with BD positioning sensors, producing 20 million records every day, resulting in two difficulties as described in the Introduction section. In addition, ESTRI and MongoDB are applied in experiments. The experiments show that ESTRI achieves the most efficient data retrieval compared to retrieval using MongoDB for data volumes of fifty million, one hundred million, one hundred and fifty million, and two hundred million. The performance of ESTRI is approximately seven times higher than that of MongoDB. The experiments show that ESTRI is an effective method for retrieving mass spatio-temporal trajectory data. Finally, bus distribution mapping in Taiyuan city is achieved, describing the buses density in different regions at different times throughout the day, which can be applied in future studies of transport, such as traffic scheduling, traffic planning and traffic behavior management in intelligent public transportation systems. Full article
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Open AccessArticle Whispering Gallery Mode Thermometry
Sensors 2016, 16(11), 1814; doi:10.3390/s16111814
Received: 1 September 2016 / Revised: 18 October 2016 / Accepted: 25 October 2016 / Published: 29 October 2016
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Abstract
This paper presents a state-of-the-art whispering gallery mode (WGM) thermometer system, which could replace platinum resistance thermometers currently used in many industrial applications, thus overcoming some of their well-known limitations and their potential for providing lower measurement uncertainty. The temperature-sensing element is a
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This paper presents a state-of-the-art whispering gallery mode (WGM) thermometer system, which could replace platinum resistance thermometers currently used in many industrial applications, thus overcoming some of their well-known limitations and their potential for providing lower measurement uncertainty. The temperature-sensing element is a sapphire-crystal-based whispering gallery mode resonator with the main resonant modes between 10 GHz and 20 GHz. In particular, it was found that the WGM around 13.6 GHz maximizes measurement performance, affording sub-millikelvin resolution and temperature stability of better than 1 mK at 0 °C. The thermometer system was made portable and low-cost by developing an ad hoc interrogation system (hardware and software) able to achieve an accuracy in the order of a few parts in 109 in the determination of resonance frequencies. Herein we report the experimental assessment of the measurement stability, repeatability and resolution, and the calibration of the thermometer in the temperature range from −74 °C to 85 °C. The combined standard uncertainty for a single temperature calibration point is found to be within 5 mK (i.e., comparable with state-of-the-art for industrial thermometry), and is mainly due to the employed calibration setup. The uncertainty contribution of the WGM thermometer alone is within a millikelvin. Full article
(This article belongs to the Special Issue Resonator Sensors)
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Open AccessArticle A Reference Model for Monitoring IoT WSN-Based Applications
Sensors 2016, 16(11), 1816; doi:10.3390/s16111816
Received: 25 July 2016 / Revised: 17 October 2016 / Accepted: 27 October 2016 / Published: 30 October 2016
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Abstract
The Internet of Things (IoT) is, at this moment, one of the most promising technologies that has arisen for decades. Wireless Sensor Networks (WSNs) are one of the main pillars for many IoT applications, insofar as they require to obtain context-awareness information. The
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The Internet of Things (IoT) is, at this moment, one of the most promising technologies that has arisen for decades. Wireless Sensor Networks (WSNs) are one of the main pillars for many IoT applications, insofar as they require to obtain context-awareness information. The bibliography shows many difficulties in their real implementation that have prevented its massive deployment. Additionally, in IoT environments where data producers and data consumers are not directly related, compatibility and certification issues become fundamental. Both problems would profit from accurate knowledge of the internal behavior of WSNs that must be obtained by the utilization of appropriate tools. There are many ad-hoc proposals with no common structure or methodology, and intended to monitor a particular WSN. To overcome this problem, this paper proposes a structured three-layer reference model for WSN Monitoring Platforms (WSN-MP), which offers a standard environment for the design of new monitoring platforms to debug, verify and certify a WSN’s behavior and performance, and applicable to every WSN. This model also allows the comparative analysis of the current proposals for monitoring the operation of WSNs. Following this methodology, it is possible to achieve a standardization of WSN-MP, promoting new research areas in order to solve the problems of each layer. Full article
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Open AccessArticle Signal Conditioning for the Kalman Filter: Application to Satellite Attitude Estimation with Magnetometer and Sun Sensors
Sensors 2016, 16(11), 1817; doi:10.3390/s16111817
Received: 3 August 2016 / Revised: 16 September 2016 / Accepted: 25 October 2016 / Published: 31 October 2016
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Abstract
Most satellites use an on-board attitude estimation system, based on available sensors. In the case of low-cost satellites, which are of increasing interest, it is usual to use magnetometers and Sun sensors. A Kalman filter is commonly recommended for the estimation, to simultaneously
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Most satellites use an on-board attitude estimation system, based on available sensors. In the case of low-cost satellites, which are of increasing interest, it is usual to use magnetometers and Sun sensors. A Kalman filter is commonly recommended for the estimation, to simultaneously exploit the information from sensors and from a mathematical model of the satellite motion. It would be also convenient to adhere to a quaternion representation. This article focuses on some problems linked to this context. The state of the system should be represented in observable form. Singularities due to alignment of measured vectors cause estimation problems. Accommodation of the Kalman filter originates convergence difficulties. The article includes a new proposal that solves these problems, not needing changes in the Kalman filter algorithm. In addition, the article includes assessment of different errors, initialization values for the Kalman filter; and considers the influence of the magnetic dipole moment perturbation, showing how to handle it as part of the Kalman filter framework. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Spain 2016)
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Open AccessArticle Modelling of XCO2 Surfaces Based on Flight Tests of TanSat Instruments
Sensors 2016, 16(11), 1818; doi:10.3390/s16111818
Received: 16 August 2016 / Revised: 25 October 2016 / Accepted: 26 October 2016 / Published: 1 November 2016
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Abstract
The TanSat carbon satellite is to be launched at the end of 2016. In order to verify the performance of its instruments, a flight test of TanSat instruments was conducted in Jilin Province in September, 2015. The flight test area covered a total
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The TanSat carbon satellite is to be launched at the end of 2016. In order to verify the performance of its instruments, a flight test of TanSat instruments was conducted in Jilin Province in September, 2015. The flight test area covered a total area of about 11,000 km2 and the underlying surface cover included several lakes, forest land, grassland, wetland, farmland, a thermal power plant and numerous cities and villages. We modeled the column-average dry-air mole fraction of atmospheric carbon dioxide (XCO2) surface based on flight test data which measured the near- and short-wave infrared (NIR) reflected solar radiation in the absorption bands at around 760 and 1610 nm. However, it is difficult to directly analyze the spatial distribution of XCO2 in the flight area using the limited flight test data and the approximate surface of XCO2, which was obtained by regression modeling, which is not very accurate either. We therefore used the high accuracy surface modeling (HASM) platform to fill the gaps where there is no information on XCO2 in the flight test area, which takes the approximate surface of XCO2 as its driving field and the XCO2 observations retrieved from the flight test as its optimum control constraints. High accuracy surfaces of XCO2 were constructed with HASM based on the flight’s observations. The results showed that the mean XCO2 in the flight test area is about 400 ppm and that XCO2 over urban areas is much higher than in other places. Compared with OCO-2’s XCO2, the mean difference is 0.7 ppm and the standard deviation is 0.95 ppm. Therefore, the modelling of the XCO2 surface based on the flight test of the TanSat instruments fell within an expected and acceptable range. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle A Photoactivated Gas Detector for Toluene Sensing at Room Temperature Based on New Coral-Like ZnO Nanostructure Arrays
Sensors 2016, 16(11), 1820; doi:10.3390/s16111820
Received: 5 September 2016 / Revised: 23 October 2016 / Accepted: 25 October 2016 / Published: 31 October 2016
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Abstract
A photoactivated gas detector operated at room temperature was microfabricated using a simple hydrothermal method. We report that the photoactivated gas detector can detect toluene using a UV illumination of 2 μW/cm2. By ultraviolet (UV) illumination, gas detectors sense toluene at
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A photoactivated gas detector operated at room temperature was microfabricated using a simple hydrothermal method. We report that the photoactivated gas detector can detect toluene using a UV illumination of 2 μW/cm2. By ultraviolet (UV) illumination, gas detectors sense toluene at room temperature without heating. A significant enhancement of detector sensitivity is achieved because of the high surface-area-to-volume ratio of the morphology of the coral-like ZnO nanorods arrays (NRAs) and the increased number of photo-induced oxygen ions under UV illumination. The corresponding sensitivity (ΔR/R0) of the detector based on coral-like ZnO NRAs is enhanced by approximately 1022% compared to that of thin-film detectors. The proposed detector greatly extends the dynamic range of detection of metal-oxide-based detectors for gas sensing applications. We report the first-ever detection of toluene with a novel coral-like NRAs gas detector at room temperature. A sensing mechanism model is also proposed to explain the sensing responses of gas detectors based on coral-like ZnO NRAs. Full article
(This article belongs to the Special Issue Materials and Applications for Sensors and Transducers)
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Open AccessArticle Sensitivity and 3 dB Bandwidth in Single and Series-Connected Tunneling Magnetoresistive Sensors
Sensors 2016, 16(11), 1821; doi:10.3390/s16111821
Received: 20 August 2016 / Revised: 10 October 2016 / Accepted: 12 October 2016 / Published: 31 October 2016
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Abstract
As single tunneling magnetoresistive (TMR) sensor performance in modern high-speed applications is limited by breakdown voltage and saturation of the sensitivity, for higher voltage applications (i.e., compatible to 1.8 V, 3.3 V or 5 V standards) practically only a series connection can be
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As single tunneling magnetoresistive (TMR) sensor performance in modern high-speed applications is limited by breakdown voltage and saturation of the sensitivity, for higher voltage applications (i.e., compatible to 1.8 V, 3.3 V or 5 V standards) practically only a series connection can be applied. Thus, in this study we focused on sensitivity, 3 dB bandwidth and sensitivity-bandwidth product (SBP) dependence on the DC bias voltage in single and series-connected TMR sensors. We show that, below breakdown voltage, the strong bias influence on sensitivity and the 3 dB frequency of a single sensor results in higher SBP than in a series connection. However, the sensitivity saturation limits the single sensor SBP which, under 1 V, reaches the same level of 2000 MHz∙V/T as in a series connection. Above the single sensor breakdown voltage, linear sensitivity dependence on the bias and the constant 3 dB bandwidth of the series connection enable increasing its SBP up to nearly 10,000 MHz∙V/T under 5 V. Thus, although by tuning bias voltage it is possible to control the sensitivity-bandwidth product, the choice between the single TMR sensor and the series connection is crucial for the optimal performance in the high frequency range. Full article
(This article belongs to the Special Issue Magnetoresistive Sensors under Extreme Conditions)
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Open AccessArticle Novel Networked Remote Laboratory Architecture for Open Connectivity Based on PLC-OPC-LabVIEW-EJS Integration. Application in Remote Fuzzy Control and Sensors Data Acquisition
Sensors 2016, 16(11), 1822; doi:10.3390/s16111822
Received: 12 September 2016 / Revised: 17 October 2016 / Accepted: 26 October 2016 / Published: 31 October 2016
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Abstract
In this paper the design and implementation of a network for integrating Programmable Logic Controllers (PLC), the Object-Linking and Embedding for Process Control protocol (OPC) and the open-source Easy Java Simulations (EJS) package is presented. A LabVIEW interface and the Java-Internet-LabVIEW (JIL) server
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In this paper the design and implementation of a network for integrating Programmable Logic Controllers (PLC), the Object-Linking and Embedding for Process Control protocol (OPC) and the open-source Easy Java Simulations (EJS) package is presented. A LabVIEW interface and the Java-Internet-LabVIEW (JIL) server complete the scheme for data exchange. This configuration allows the user to remotely interact with the PLC. Such integration can be considered a novelty in scientific literature for remote control and sensor data acquisition of industrial plants. An experimental application devoted to remote laboratories is developed to demonstrate the feasibility and benefits of the proposed approach. The experiment to be conducted is the parameterization and supervision of a fuzzy controller of a DC servomotor. The graphical user interface has been developed with EJS and the fuzzy control is carried out by our own PLC. In fact, the distinctive features of the proposed novel network application are the integration of the OPC protocol to share information with the PLC and the application under control. The user can perform the tuning of the controller parameters online and observe in real time the effect on the servomotor behavior. The target group is engineering remote users, specifically in control- and automation-related tasks. The proposed architecture system is described and experimental results are presented. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Fuzzy Neural Network-Based Interacting Multiple Model for Multi-Node Target Tracking Algorithm
Sensors 2016, 16(11), 1823; doi:10.3390/s16111823
Received: 28 June 2016 / Revised: 15 October 2016 / Accepted: 18 October 2016 / Published: 1 November 2016
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Abstract
An interacting multiple model for multi-node target tracking algorithm was proposed based on a fuzzy neural network (FNN) to solve the multi-node target tracking problem of wireless sensor networks (WSNs). Measured error variance was adaptively adjusted during the multiple model interacting output stage
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An interacting multiple model for multi-node target tracking algorithm was proposed based on a fuzzy neural network (FNN) to solve the multi-node target tracking problem of wireless sensor networks (WSNs). Measured error variance was adaptively adjusted during the multiple model interacting output stage using the difference between the theoretical and estimated values of the measured error covariance matrix. The FNN fusion system was established during multi-node fusion to integrate with the target state estimated data from different nodes and consequently obtain network target state estimation. The feasibility of the algorithm was verified based on a network of nine detection nodes. Experimental results indicated that the proposed algorithm could trace the maneuvering target effectively under sensor failure and unknown system measurement errors. The proposed algorithm exhibited great practicability in the multi-node target tracking of WSNs. Full article
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Open AccessArticle Millimetre Level Accuracy GNSS Positioning with the Blind Adaptive Beamforming Method in Interference Environments
Sensors 2016, 16(11), 1824; doi:10.3390/s16111824
Received: 13 August 2016 / Revised: 18 October 2016 / Accepted: 26 October 2016 / Published: 31 October 2016
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Abstract
The use of antenna arrays in Global Navigation Satellite System (GNSS) applications is gaining significant attention due to its superior capability to suppress both narrowband and wideband interference. However, the phase distortions resulting from array processing may limit the applicability of these methods
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The use of antenna arrays in Global Navigation Satellite System (GNSS) applications is gaining significant attention due to its superior capability to suppress both narrowband and wideband interference. However, the phase distortions resulting from array processing may limit the applicability of these methods for high precision applications using carrier phase based positioning techniques. This paper studies the phase distortions occurring with the adaptive blind beamforming method in which satellite angle of arrival (AoA) information is not employed in the optimization problem. To cater to non-stationary interference scenarios, the array weights of the adaptive beamformer are continuously updated. The effects of these continuous updates on the tracking parameters of a GNSS receiver are analyzed. The second part of this paper focuses on reducing the phase distortions during the blind beamforming process in order to allow the receiver to perform carrier phase based positioning by applying a constraint on the structure of the array configuration and by compensating the array uncertainties. Limitations of the previous methods are studied and a new method is proposed that keeps the simplicity of the blind beamformer structure and, at the same time, reduces tracking degradations while achieving millimetre level positioning accuracy in interference environments. To verify the applicability of the proposed method and analyze the degradations, array signals corresponding to the GPS L1 band are generated using a combination of hardware and software simulators. Furthermore, the amount of degradation and performance of the proposed method under different conditions are evaluated based on Monte Carlo simulations. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle An Integrated Wireless Wearable Sensor System for Posture Recognition and Indoor Localization
Sensors 2016, 16(11), 1825; doi:10.3390/s16111825
Received: 30 June 2016 / Revised: 22 October 2016 / Accepted: 24 October 2016 / Published: 31 October 2016
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Abstract
In order to provide better monitoring for the elderly or patients, we developed an integrated wireless wearable sensor system that can realize posture recognition and indoor localization in real time. Five designed sensor nodes which are respectively fixed on lower limbs and a
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In order to provide better monitoring for the elderly or patients, we developed an integrated wireless wearable sensor system that can realize posture recognition and indoor localization in real time. Five designed sensor nodes which are respectively fixed on lower limbs and a standard Kalman filter are used to acquire basic attitude data. After the attitude angles of five body segments (two thighs, two shanks and the waist) are obtained, the pitch angles of the left thigh and waist are used to realize posture recognition. Based on all these attitude angles of body segments, we can also calculate the coordinates of six lower limb joints (two hip joints, two knee joints and two ankle joints). Then, a novel relative localization algorithm based on step length is proposed to realize the indoor localization of the user. Several sparsely distributed active Radio Frequency Identification (RFID) tags are used to correct the accumulative error in the relative localization algorithm and a set-membership filter is applied to realize the data fusion. The experimental results verify the effectiveness of the proposed algorithms. Full article
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Open AccessArticle New Flexible Silicone-Based EEG Dry Sensor Material Compositions Exhibiting Improvements in Lifespan, Conductivity, and Reliability
Sensors 2016, 16(11), 1826; doi:10.3390/s16111826
Received: 13 June 2016 / Revised: 15 October 2016 / Accepted: 22 October 2016 / Published: 31 October 2016
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Abstract
This study investigates alternative material compositions for flexible silicone-based dry electroencephalography (EEG) electrodes to improve the performance lifespan while maintaining high-fidelity transmission of EEG signals. Electrode materials were fabricated with varying concentrations of silver-coated silica and silver flakes to evaluate their electrical, mechanical,
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This study investigates alternative material compositions for flexible silicone-based dry electroencephalography (EEG) electrodes to improve the performance lifespan while maintaining high-fidelity transmission of EEG signals. Electrode materials were fabricated with varying concentrations of silver-coated silica and silver flakes to evaluate their electrical, mechanical, and EEG transmission performance. Scanning electron microscope (SEM) analysis of the initial electrode development identified some weak points in the sensors’ construction, including particle pull-out and ablation of the silver coating on the silica filler. The newly-developed sensor materials achieved significant improvement in EEG measurements while maintaining the advantages of previous silicone-based electrodes, including flexibility and non-toxicity. The experimental results indicated that the proposed electrodes maintained suitable performance even after exposure to temperature fluctuations, 85% relative humidity, and enhanced corrosion conditions demonstrating improvements in the environmental stability. Fabricated flat (forehead) and acicular (hairy sites) electrodes composed of the optimum identified formulation exhibited low impedance and reliable EEG measurement; some initial human experiments demonstrate the feasibility of using these silicone-based electrodes for typical lab data collection applications. Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle 3D Buried Utility Location Using A Marching-Cross-Section Algorithm for Multi-Sensor Data Fusion
Sensors 2016, 16(11), 1827; doi:10.3390/s16111827
Received: 26 August 2016 / Revised: 15 October 2016 / Accepted: 24 October 2016 / Published: 2 November 2016
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Abstract
We address the problem of accurately locating buried utility segments by fusing data from multiple sensors using a novel Marching-Cross-Section (MCS) algorithm. Five types of sensors are used in this work: Ground Penetrating Radar (GPR), Passive Magnetic Fields (PMF), Magnetic Gradiometer (MG), Low
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We address the problem of accurately locating buried utility segments by fusing data from multiple sensors using a novel Marching-Cross-Section (MCS) algorithm. Five types of sensors are used in this work: Ground Penetrating Radar (GPR), Passive Magnetic Fields (PMF), Magnetic Gradiometer (MG), Low Frequency Electromagnetic Fields (LFEM) and Vibro-Acoustics (VA). As part of the MCS algorithm, a novel formulation of the extended Kalman Filter (EKF) is proposed for marching existing utility tracks from a scan cross-section (scs) to the next one; novel rules for initializing utilities based on hypothesized detections on the first scs and for associating predicted utility tracks with hypothesized detections in the following scss are introduced. Algorithms are proposed for generating virtual scan lines based on given hypothesized detections when different sensors do not share common scan lines, or when only the coordinates of the hypothesized detections are provided without any information of the actual survey scan lines. The performance of the proposed system is evaluated with both synthetic data and real data. The experimental results in this work demonstrate that the proposed MCS algorithm can locate multiple buried utility segments simultaneously, including both straight and curved utilities, and can separate intersecting segments. By using the probabilities of a hypothesized detection being a pipe or a cable together with its 3D coordinates, the MCS algorithm is able to discriminate a pipe and a cable close to each other. The MCS algorithm can be used for both post- and on-site processing. When it is used on site, the detected tracks on the current scs can help to determine the location and direction of the next scan line. The proposed “multi-utility multi-sensor” system has no limit to the number of buried utilities or the number of sensors, and the more sensor data used, the more buried utility segments can be detected with more accurate location and orientation. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle A Cross-Correlational Analysis between Electroencephalographic and End-Tidal Carbon Dioxide Signals: Methodological Issues in the Presence of Missing Data and Real Data Results
Sensors 2016, 16(11), 1828; doi:10.3390/s16111828
Received: 25 August 2016 / Revised: 21 October 2016 / Accepted: 27 October 2016 / Published: 31 October 2016
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Abstract
Electroencephalographic (EEG) irreducible artifacts are common and the removal of corrupted segments from the analysis may be required. The present study aims at exploring the effects of different EEG Missing Data Segment (MDS) distributions on cross-correlation analysis, involving EEG and physiological signals. The
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Electroencephalographic (EEG) irreducible artifacts are common and the removal of corrupted segments from the analysis may be required. The present study aims at exploring the effects of different EEG Missing Data Segment (MDS) distributions on cross-correlation analysis, involving EEG and physiological signals. The reliability of cross-correlation analysis both at single subject and at group level as a function of missing data statistics was evaluated using dedicated simulations. Moreover, a Bayesian-based approach for combining the single subject results at group level by considering each subject’s reliability was introduced. Starting from the above considerations, the cross-correlation function between EEG Global Field Power (GFP) in delta band and end-tidal CO2 (PETCO2) during rest and voluntary breath-hold was evaluated in six healthy subjects. The analysis of simulated data results at single subject level revealed a worsening of precision and accuracy in the cross-correlation analysis in the presence of MDS. At the group level, a large improvement in the results’ reliability with respect to single subject analysis was observed. The proposed Bayesian approach showed a slight improvement with respect to simple average results. Real data results were discussed in light of the simulated data tests and of the current physiological findings. Full article
(This article belongs to the Special Issue Noninvasive Biomedical Sensors)
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Open AccessArticle Microwave Chemical Sensor Using Substrate-Integrated-Waveguide Cavity
Sensors 2016, 16(11), 1829; doi:10.3390/s16111829
Received: 16 August 2016 / Revised: 10 October 2016 / Accepted: 27 October 2016 / Published: 31 October 2016
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Abstract
This research proposes a substrate-integrated waveguide (SIW) cavity sensor to detect several chemicals using the microwave frequency range. The frequency response of the presented SIW sensor is switched by filling a very small quantity of chemical inside of the fluidic channel, which also
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This research proposes a substrate-integrated waveguide (SIW) cavity sensor to detect several chemicals using the microwave frequency range. The frequency response of the presented SIW sensor is switched by filling a very small quantity of chemical inside of the fluidic channel, which also causes a difference in the effective permittivity. The fluidic channel on this structure is either empty or filled with a chemical; when it is empty the structure resonates at 17.08 GHz. There is always a different resonant frequency when any chemical is injected into the fluidic channel. The maximum amount of chemical after injection is held in the center of the SIW structure, which has the maximum magnitude of the electric field distribution. Thus, the objective of sensing chemicals in this research is achieved by perturbing the electric fields of the SIW structure. Full article
(This article belongs to the Special Issue Non-Contact Sensing)
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Open AccessArticle Gas Sensing Analysis of Ag-Decorated Graphene for Sulfur Hexafluoride Decomposition Products Based on the Density Functional Theory
Sensors 2016, 16(11), 1830; doi:10.3390/s16111830
Received: 16 August 2016 / Revised: 16 October 2016 / Accepted: 21 October 2016 / Published: 1 November 2016
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Abstract
Detection of decomposition products of sulfur hexafluoride (SF6) is one of the best ways to diagnose early latent insulation faults in gas-insulated equipment, and the occurrence of sudden accidents can be avoided effectively by finding early latent faults. Recently, functionalized graphene,
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Detection of decomposition products of sulfur hexafluoride (SF6) is one of the best ways to diagnose early latent insulation faults in gas-insulated equipment, and the occurrence of sudden accidents can be avoided effectively by finding early latent faults. Recently, functionalized graphene, a kind of gas sensing material, has been reported to show good application prospects in the gas sensor field. Therefore, calculations were performed to analyze the gas sensing properties of intrinsic graphene (Int-graphene) and functionalized graphene-based material, Ag-decorated graphene (Ag-graphene), for decomposition products of SF6, including SO2F2, SOF2, and SO2, based on density functional theory (DFT). We thoroughly investigated a series of parameters presenting gas-sensing properties of adsorbing process about gas molecule (SO2F2, SOF2, SO2) and double gas molecules (2SO2F2, 2SOF2, 2SO2) on Ag-graphene, including adsorption energy, net charge transfer, electronic state density, and the highest and lowest unoccupied molecular orbital. The results showed that the Ag atom significantly enhances the electrochemical reactivity of graphene, reflected in the change of conductivity during the adsorption process. SO2F2 and SO2 gas molecules on Ag-graphene presented chemisorption, and the adsorption strength was SO2F2 > SO2, while SOF2 absorption on Ag-graphene was physical adsorption. Thus, we concluded that Ag-graphene showed good selectivity and high sensitivity to SO2F2. The results can provide a helpful guide in exploring Ag-graphene material in experiments for monitoring the insulation status of SF6-insulated equipment based on detecting decomposition products of SF6. Full article
(This article belongs to the Special Issue The Use of New and/or Improved Materials for Sensing Applications)
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Open AccessArticle Estimation of Anonymous Email Network Characteristics through Statistical Disclosure Attacks
Sensors 2016, 16(11), 1832; doi:10.3390/s16111832
Received: 22 August 2016 / Revised: 24 October 2016 / Accepted: 26 October 2016 / Published: 1 November 2016
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Abstract
Social network analysis aims to obtain relational data from social systems to identify leaders, roles, and communities in order to model profiles or predict a specific behavior in users’ network. Preserving anonymity in social networks is a subject of major concern. Anonymity can
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Social network analysis aims to obtain relational data from social systems to identify leaders, roles, and communities in order to model profiles or predict a specific behavior in users’ network. Preserving anonymity in social networks is a subject of major concern. Anonymity can be compromised by disclosing senders’ or receivers’ identity, message content, or sender-receiver relationships. Under strongly incomplete information, a statistical disclosure attack is used to estimate the network and node characteristics such as centrality and clustering measures, degree distribution, and small-world-ness. A database of email networks in 29 university faculties is used to study the method. A research on the small-world-ness and Power law characteristics of these email networks is also developed, helping to understand the behavior of small email networks. Full article
(This article belongs to the Special Issue Topology Control in Emerging Sensor Networks)
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Open AccessArticle Flexible Graphene Electrodes for Prolonged Dynamic ECG Monitoring
Sensors 2016, 16(11), 1833; doi:10.3390/s16111833
Received: 12 July 2016 / Revised: 15 October 2016 / Accepted: 28 October 2016 / Published: 1 November 2016
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Abstract
This paper describes the development of a graphene-based dry flexible electrocardiography (ECG) electrode and a portable wireless ECG measurement system. First, graphene films on polyethylene terephthalate (PET) substrates and graphene paper were used to construct the ECG electrode. Then, a graphene textile was
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This paper describes the development of a graphene-based dry flexible electrocardiography (ECG) electrode and a portable wireless ECG measurement system. First, graphene films on polyethylene terephthalate (PET) substrates and graphene paper were used to construct the ECG electrode. Then, a graphene textile was synthesized for the fabrication of a wearable ECG monitoring system. The structure and the electrical properties of the graphene electrodes were evaluated using Raman spectroscopy, scanning electron microscopy (SEM), and alternating current impedance spectroscopy. ECG signals were then collected from healthy subjects using the developed graphene electrode and portable measurement system. The results show that the graphene electrode was able to acquire the typical characteristics and features of human ECG signals with a high signal-to-noise (SNR) ratio in different states of motion. A week-long continuous wearability test showed no degradation in the ECG signal quality over time. The graphene-based flexible electrode demonstrates comfortability, good biocompatibility, and high electrophysiological detection sensitivity. The graphene electrode also combines the potential for use in long-term wearable dynamic cardiac activity monitoring systems with convenience and comfort for use in home health care of elderly and high-risk adults. Full article
(This article belongs to the Special Issue Body Worn Behavior Sensing)
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Open AccessArticle Design and Analysis of A Beacon-Less Routing Protocol for Large Volume Content Dissemination in Vehicular Ad Hoc Networks
Sensors 2016, 16(11), 1834; doi:10.3390/s16111834
Received: 15 August 2016 / Revised: 23 October 2016 / Accepted: 24 October 2016 / Published: 1 November 2016
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Abstract
Large volume content dissemination is pursued by the growing number of high quality applications for Vehicular Ad hoc NETworks(VANETs), e.g., the live road surveillance service and the video-based overtaking assistant service. For the highly dynamical vehicular network topology, beacon-less routing protocols have been
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Large volume content dissemination is pursued by the growing number of high quality applications for Vehicular Ad hoc NETworks(VANETs), e.g., the live road surveillance service and the video-based overtaking assistant service. For the highly dynamical vehicular network topology, beacon-less routing protocols have been proven to be efficient in achieving a balance between the system performance and the control overhead. However, to the authors’ best knowledge, the routing design for large volume content has not been well considered in the previous work, which will introduce new challenges, e.g., the enhanced connectivity requirement for a radio link. In this paper, a link Lifetime-aware Beacon-less Routing Protocol (LBRP) is designed for large volume content delivery in VANETs. Each vehicle makes the forwarding decision based on the message header information and its current state, including the speed and position information. A semi-Markov process analytical model is proposed to evaluate the expected delay in constructing one routing path for LBRP. Simulations show that the proposed LBRP scheme outperforms the traditional dissemination protocols in providing a low end-to-end delay. The analytical model is shown to exhibit a good match on the delay estimation with Monte Carlo simulations, as well. Full article
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Open AccessArticle Multi-Sensor Fusion with Interaction Multiple Model and Chi-Square Test Tolerant Filter
Sensors 2016, 16(11), 1835; doi:10.3390/s16111835
Received: 26 July 2016 / Revised: 23 October 2016 / Accepted: 25 October 2016 / Published: 2 November 2016
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Abstract
Motivated by the key importance of multi-sensor information fusion algorithms in the state-of-the-art integrated navigation systems due to recent advancements in sensor technologies, telecommunication, and navigation systems, the paper proposes an improved and innovative fault-tolerant fusion framework. An integrated navigation system is considered
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Motivated by the key importance of multi-sensor information fusion algorithms in the state-of-the-art integrated navigation systems due to recent advancements in sensor technologies, telecommunication, and navigation systems, the paper proposes an improved and innovative fault-tolerant fusion framework. An integrated navigation system is considered consisting of four sensory sub-systems, i.e., Strap-down Inertial Navigation System (SINS), Global Navigation System (GPS), the Bei-Dou2 (BD2) and Celestial Navigation System (CNS) navigation sensors. In such multi-sensor applications, on the one hand, the design of an efficient fusion methodology is extremely constrained specially when no information regarding the system’s error characteristics is available. On the other hand, the development of an accurate fault detection and integrity monitoring solution is both challenging and critical. The paper addresses the sensitivity issues of conventional fault detection solutions and the unavailability of a precisely known system model by jointly designing fault detection and information fusion algorithms. In particular, by using ideas from Interacting Multiple Model (IMM) filters, the uncertainty of the system will be adjusted adaptively by model probabilities and using the proposed fuzzy-based fusion framework. The paper also addresses the problem of using corrupted measurements for fault detection purposes by designing a two state propagator chi-square test jointly with the fusion algorithm. Two IMM predictors, running in parallel, are used and alternatively reactivated based on the received information form the fusion filter to increase the reliability and accuracy of the proposed detection solution. With the combination of the IMM and the proposed fusion method, we increase the failure sensitivity of the detection system and, thereby, significantly increase the overall reliability and accuracy of the integrated navigation system. Simulation results indicate that the proposed fault tolerant fusion framework provides superior performance over its traditional counterparts. Full article
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
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Open AccessArticle Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting
Sensors 2016, 16(11), 1836; doi:10.3390/s16111836
Received: 25 August 2016 / Revised: 14 October 2016 / Accepted: 21 October 2016 / Published: 2 November 2016
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Abstract
A lensless blood cell counting system integrating microfluidic channel and a complementary metal oxide semiconductor (CMOS) image sensor is a promising technique to miniaturize the conventional optical lens based imaging system for point-of-care testing (POCT). However, such a system has limited resolution, making
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A lensless blood cell counting system integrating microfluidic channel and a complementary metal oxide semiconductor (CMOS) image sensor is a promising technique to miniaturize the conventional optical lens based imaging system for point-of-care testing (POCT). However, such a system has limited resolution, making it imperative to improve resolution from the system-level using super-resolution (SR) processing. Yet, how to improve resolution towards better cell detection and recognition with low cost of processing resources and without degrading system throughput is still a challenge. In this article, two machine learning based single-frame SR processing types are proposed and compared for lensless blood cell counting, namely the Extreme Learning Machine based SR (ELMSR) and Convolutional Neural Network based SR (CNNSR). Moreover, lensless blood cell counting prototypes using commercial CMOS image sensors and custom designed backside-illuminated CMOS image sensors are demonstrated with ELMSR and CNNSR. When one captured low-resolution lensless cell image is input, an improved high-resolution cell image will be output. The experimental results show that the cell resolution is improved by 4×, and CNNSR has 9.5% improvement over the ELMSR on resolution enhancing performance. The cell counting results also match well with a commercial flow cytometer. Such ELMSR and CNNSR therefore have the potential for efficient resolution improvement in lensless blood cell counting systems towards POCT applications. Full article
(This article belongs to the Special Issue Microfluidics-Based Microsystem Integration Research)
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Open AccessArticle Feature Mining and Health Assessment for Gearboxes Using Run-Up/Coast-Down Signals
Sensors 2016, 16(11), 1837; doi:10.3390/s16111837
Received: 30 September 2016 / Revised: 27 October 2016 / Accepted: 28 October 2016 / Published: 2 November 2016
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Abstract
Vibration signals measured in the run-up/coast-down (R/C) processes usually carry rich information about the health status of machinery. However, a major challenge in R/C signals analysis lies in how to exploit more diagnostic information, and how this information could be properly integrated to
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Vibration signals measured in the run-up/coast-down (R/C) processes usually carry rich information about the health status of machinery. However, a major challenge in R/C signals analysis lies in how to exploit more diagnostic information, and how this information could be properly integrated to achieve a more reliable maintenance decision. Aiming at this problem, a framework of R/C signals analysis is presented for the health assessment of gearbox. In the proposed methodology, we first investigate the data preprocessing and feature selection issues for R/C signals. Based on that, a sparsity-guided feature enhancement scheme is then proposed to extract the weak phase jitter associated with gear defect. In order for an effective feature mining and integration under R/C, a generalized phase demodulation technique is further established to reveal the evolution of modulation feature with operating speed and rotation angle. The experimental results indicate that the proposed methodology could not only detect the presence of gear damage, but also offer a novel insight into the dynamic behavior of gearbox. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle An Analysis of the Influence of Flight Parameters in the Generation of Unmanned Aerial Vehicle (UAV) Orthomosaicks to Survey Archaeological Areas
Sensors 2016, 16(11), 1838; doi:10.3390/s16111838
Received: 28 July 2016 / Revised: 20 October 2016 / Accepted: 26 October 2016 / Published: 1 November 2016
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Abstract
This article describes the configuration and technical specifications of a multi-rotor unmanned aerial vehicle (UAV) using a red–green–blue (RGB) sensor for the acquisition of images needed for the production of orthomosaics to be used in archaeological applications. Several flight missions were programmed as
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This article describes the configuration and technical specifications of a multi-rotor unmanned aerial vehicle (UAV) using a red–green–blue (RGB) sensor for the acquisition of images needed for the production of orthomosaics to be used in archaeological applications. Several flight missions were programmed as follows: flight altitudes at 30, 40, 50, 60, 70 and 80 m above ground level; two forward and side overlap settings (80%–50% and 70%–40%); and the use, or lack thereof, of ground control points. These settings were chosen to analyze their influence on the spatial quality of orthomosaicked images processed by Inpho UASMaster (Trimble, CA, USA). Changes in illumination over the study area, its impact on flight duration, and how it relates to these settings is also considered. The combined effect of these parameters on spatial quality is presented as well, defining a ratio between ground sample distance of UAV images and expected root mean square of a UAV orthomosaick. The results indicate that a balance between all the proposed parameters is useful for optimizing mission planning and image processing, altitude above ground level (AGL) being main parameter because of its influence on root mean square error (RMSE). Full article
(This article belongs to the Special Issue UAV-Based Remote Sensing)
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Open AccessArticle A Stretchable Radio-Frequency Strain Sensor Using Screen Printing Technology
Sensors 2016, 16(11), 1839; doi:10.3390/s16111839
Received: 13 September 2016 / Revised: 25 October 2016 / Accepted: 31 October 2016 / Published: 2 November 2016
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Abstract
In this paper, we propose a stretchable radio-frequency (RF) strain sensor fabricated with screen printing technology. The RF sensor is designed using a half-wavelength patch that resonates at 3.7 GHz. The resonant frequency is determined by the length of the patch, and it
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In this paper, we propose a stretchable radio-frequency (RF) strain sensor fabricated with screen printing technology. The RF sensor is designed using a half-wavelength patch that resonates at 3.7 GHz. The resonant frequency is determined by the length of the patch, and it therefore changes when the patch is stretched. Polydimethylsiloxane (PDMS) is used to create the substrate, because of its stretchable and screen-printable surface. In addition, Dupont PE872 (Dupont, NC, American) silver conductive ink is used to create the stretchable conductive patterns. The sensor performance is demonstrated both with full-wave simulations and with measurements carried out on a fabricated sample. When the length of the patch sensor is increased by a 7.8% stretch, the resonant frequency decreases from 3.7 GHz to 3.43 GHz, evidencing a sensitivity of 3.43 × 107 Hz/%. Stretching the patch along its width does not change the resonant frequency. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Complex IoT Systems as Enablers for Smart Homes in a Smart City Vision
Sensors 2016, 16(11), 1840; doi:10.3390/s16111840
Received: 15 August 2016 / Revised: 26 October 2016 / Accepted: 28 October 2016 / Published: 2 November 2016
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Abstract
The world is entering a new era, where Internet-of-Things (IoT), smart homes, and smart cities will play an important role in meeting the so-called big challenges. In the near future, it is foreseen that the majority of the world’s population will live their
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The world is entering a new era, where Internet-of-Things (IoT), smart homes, and smart cities will play an important role in meeting the so-called big challenges. In the near future, it is foreseen that the majority of the world’s population will live their lives in smart homes and in smart cities. To deal with these challenges, to support a sustainable urban development, and to improve the quality of life for citizens, a multi-disciplinary approach is needed. It seems evident, however, that a new, advanced Information and Communications Technology ICT infrastructure is a key feature to realize the “smart” vision. This paper proposes a specific solution in the form of a hierarchical layered ICT based infrastructure that handles ICT issues related to the “big challenges” and seamlessly integrates IoT, smart homes, and smart city structures into one coherent unit. To exemplify benefits of this infrastructure, a complex IoT system has been deployed, simulated and elaborated. This simulation deals with wastewater energy harvesting from smart buildings located in a smart city context. From the simulations, it has been found that the proposed infrastructure is able to harvest between 50% and 75% of the wastewater energy in a smart residential building. By letting the smart city infrastructure coordinate and control the harvest time and duration, it is possible to achieve considerable energy savings in the smart homes, and it is possible to reduce the peak-load for district heating plants. Full article
(This article belongs to the Special Issue Smart City: Vision and Reality)
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Open AccessArticle Pixel-Level and Robust Vibration Source Sensing in High-Frame-Rate Video Analysis
Sensors 2016, 16(11), 1842; doi:10.3390/s16111842
Received: 3 August 2016 / Revised: 13 October 2016 / Accepted: 26 October 2016 / Published: 2 November 2016
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Abstract
We investigate the effect of appearance variations on the detectability of vibration feature extraction with pixel-level digital filters for high-frame-rate videos. In particular, we consider robust vibrating object tracking, which is clearly different from conventional appearance-based object tracking with spatial pattern recognition in
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We investigate the effect of appearance variations on the detectability of vibration feature extraction with pixel-level digital filters for high-frame-rate videos. In particular, we consider robust vibrating object tracking, which is clearly different from conventional appearance-based object tracking with spatial pattern recognition in a high-quality image region of a certain size. For 512 × 512 videos of a rotating fan located at different positions and orientations and captured at 2000 frames per second with different lens settings, we verify how many pixels are extracted as vibrating regions with pixel-level digital filters. The effectiveness of dynamics-based vibration features is demonstrated by examining the robustness against changes in aperture size and the focal condition of the camera lens, the apparent size and orientation of the object being tracked, and its rotational frequency, as well as complexities and movements of background scenes. Tracking experiments for a flying multicopter with rotating propellers are also described to verify the robustness of localization under complex imaging conditions in outside scenarios. Full article
(This article belongs to the Special Issue Vision-Based Sensors in Field Robotics)
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Open AccessArticle Microwave Moisture Sensing of Seedcotton: Part 1: Seedcotton Microwave Material Properties
Sensors 2016, 16(11), 1843; doi:10.3390/s16111843
Received: 6 September 2016 / Revised: 21 October 2016 / Accepted: 25 October 2016 / Published: 2 November 2016
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Abstract
Moisture content at harvest is a key parameter that impacts quality and how well the cotton crop can be stored without degrading before processing. It is also a key parameter of interest for harvest time field trials as it can directly influence the
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Moisture content at harvest is a key parameter that impacts quality and how well the cotton crop can be stored without degrading before processing. It is also a key parameter of interest for harvest time field trials as it can directly influence the quality of the harvested crop as well as skew the results of in-field yield and quality assessments. Microwave sensing of moisture has several unique advantages over lower frequency sensing approaches. The first is that microwaves are insensitive to variations in conductivity, due to presence of salts or minerals. The second advantage is that microwaves can peer deep inside large bulk packaging to assess the internal moisture content without performing a destructive tear down of the package. To help facilitate the development of a microwave moisture sensor for seedcotton; research was performed to determine the basic microwave properties of seedcotton. The research was performed on 110 kg micro-modules, which are of direct interest to research teams for use in ongoing field-based research projects. It should also prove useful for the enhancement of existing and future yield monitor designs. Experimental data was gathered on the basic relations between microwave material properties and seedcotton over the range from 1.0 GHz to 2.5 GHz and is reported on herein. This research is part one of a two-part series that reports on the fundamental microwave properties of seedcotton as moisture and density vary naturally during the course of typical harvesting operations; part two will utilize this data to formulate a prediction algorithm to form the basis for a prototype microwave moisture sensor. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Towards Autonomous Modular UAV Missions: The Detection, Geo-Location and Landing Paradigm
Sensors 2016, 16(11), 1844; doi:10.3390/s16111844
Received: 27 August 2016 / Revised: 21 October 2016 / Accepted: 25 October 2016 / Published: 3 November 2016
Cited by 1 | PDF Full-text (7915 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Nowadays, various unmanned aerial vehicle (UAV) applications become increasingly demanding since they require real-time, autonomous and intelligent functions. Towards this end, in the present study, a fully autonomous UAV scenario is implemented, including the tasks of area scanning, target recognition, geo-location, monitoring, following
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Nowadays, various unmanned aerial vehicle (UAV) applications become increasingly demanding since they require real-time, autonomous and intelligent functions. Towards this end, in the present study, a fully autonomous UAV scenario is implemented, including the tasks of area scanning, target recognition, geo-location, monitoring, following and finally landing on a high speed moving platform. The underlying methodology includes AprilTag target identification through Graphics Processing Unit (GPU) parallelized processing, image processing and several optimized locations and approach algorithms employing gimbal movement, Global Navigation Satellite System (GNSS) readings and UAV navigation. For the experimentation, a commercial and a custom made quad-copter prototype were used, portraying a high and a low-computational embedded platform alternative. Among the successful targeting and follow procedures, it is shown that the landing approach can be successfully performed even under high platform speeds. Full article
(This article belongs to the Special Issue UAV-Based Remote Sensing)
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Open AccessArticle RSS Fingerprint Based Indoor Localization Using Sparse Representation with Spatio-Temporal Constraint
Sensors 2016, 16(11), 1845; doi:10.3390/s16111845
Received: 3 August 2016 / Revised: 28 September 2016 / Accepted: 17 October 2016 / Published: 3 November 2016
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Abstract
The Received Signal Strength (RSS) fingerprint-based indoor localization is an important research topic in wireless network communications. Most current RSS fingerprint-based indoor localization methods do not explore and utilize the spatial or temporal correlation existing in fingerprint data and measurement data, which is
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The Received Signal Strength (RSS) fingerprint-based indoor localization is an important research topic in wireless network communications. Most current RSS fingerprint-based indoor localization methods do not explore and utilize the spatial or temporal correlation existing in fingerprint data and measurement data, which is helpful for improving localization accuracy. In this paper, we propose an RSS fingerprint-based indoor localization method by integrating the spatio-temporal constraints into the sparse representation model. The proposed model utilizes the inherent spatial correlation of fingerprint data in the fingerprint matching and uses the temporal continuity of the RSS measurement data in the localization phase. Experiments on the simulated data and the localization tests in the real scenes show that the proposed method improves the localization accuracy and stability effectively compared with state-of-the-art indoor localization methods. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle An Effective Massive Sensor Network Data Access Scheme Based on Topology Control for the Internet of Things
Sensors 2016, 16(11), 1846; doi:10.3390/s16111846
Received: 20 September 2016 / Revised: 23 October 2016 / Accepted: 28 October 2016 / Published: 3 November 2016
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Abstract
This paper considers the distributed access and control problem of massive wireless sensor networks’ data access center for the Internet of Things, which is an extension of wireless sensor networks and an element of its topology structure. In the context of the arrival
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This paper considers the distributed access and control problem of massive wireless sensor networks’ data access center for the Internet of Things, which is an extension of wireless sensor networks and an element of its topology structure. In the context of the arrival of massive service access requests at a virtual data center, this paper designs a massive sensing data access and control mechanism to improve the access efficiency of service requests and makes full use of the available resources at the data access center for the Internet of things. Firstly, this paper proposes a synergistically distributed buffer access model, which separates the information of resource and location. Secondly, the paper divides the service access requests into multiple virtual groups based on their characteristics and locations using an optimized self-organizing feature map neural network. Furthermore, this paper designs an optimal scheduling algorithm of group migration based on the combination scheme between the artificial bee colony algorithm and chaos searching theory. Finally, the experimental results demonstrate that this mechanism outperforms the existing schemes in terms of enhancing the accessibility of service requests effectively, reducing network delay, and has higher load balancing capacity and higher resource utility rate. Full article
(This article belongs to the Special Issue Topology Control in Emerging Sensor Networks)
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Open AccessArticle Wearable IMU for Shoulder Injury Prevention in Overhead Sports
Sensors 2016, 16(11), 1847; doi:10.3390/s16111847
Received: 11 August 2016 / Revised: 21 October 2016 / Accepted: 26 October 2016 / Published: 3