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Sensors, Volume 19, Issue 15 (August-1 2019)

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Cover Story (view full-size image) A burst images sequence of the Sentinel-1B raw dataset was acquired through the terrain observation [...] Read more.
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Open AccessArticle
A Novel Resource Allocation and Spectrum Defragmentation Mechanism for IoT-Based Big Data in Smart Cities
Sensors 2019, 19(15), 3443; https://doi.org/10.3390/s19153443
Received: 10 July 2019 / Revised: 1 August 2019 / Accepted: 5 August 2019 / Published: 6 August 2019
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Abstract
People’s demand for high-traffic applications and the need for Internet of Things (IoT) are enormous in smart cities. The amount of data generated by virtual reality, high-definition video, and other IoT applications is growing at an exponential rate that far exceeds our expectations, [...] Read more.
People’s demand for high-traffic applications and the need for Internet of Things (IoT) are enormous in smart cities. The amount of data generated by virtual reality, high-definition video, and other IoT applications is growing at an exponential rate that far exceeds our expectations, and the types of data are becoming more diverse. It has become critical to reliably accommodate IoT-based big data with reasonable resource allocation in optical backbone networks for smart cities. For the problem of reliable transmission and efficient resource allocation in optical backbone networks, a novel resource allocation and spectrum defragmentation mechanism for massive IoT traffic is presented in this paper. Firstly, a routing and spectrum allocation algorithm based on the distance-adaptive sharing protection mechanism (DASP) is proposed, to obtain sufficient protection and reduce the spectrum consumption. The DASP algorithm advocates applying different strategies to the resource allocation of the working and protection paths. Then, the protection path spectrum defragmentation algorithm based on OpenFlow is designed to improve spectrum utilization while providing shared protection for traffic demands. The lowest starting slot-index first (LSSF) algorithm is employed to remove and reconstruct the optical paths. Numerical results indicate that the proposal can effectively alleviate spectrum fragmentation and reduce the bandwidth-blocking probability by 44.68% compared with the traditional scheme. Full article
(This article belongs to the Special Issue Big Data Driven IoT for Smart Cities)
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Open AccessArticle
Iterative Trajectory Optimization for Physical-Layer Secure Buffer-Aided UAV Mobile Relaying
Sensors 2019, 19(15), 3442; https://doi.org/10.3390/s19153442
Received: 30 July 2019 / Accepted: 31 July 2019 / Published: 6 August 2019
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Abstract
With the fast development of commercial unmanned aerial vehicle (UAV) technology, there are increasing research interests on UAV communications. In this work, the mobility and deployment flexibility of UAVs are exploited to form a buffer-aided relaying system assisting terrestrial communication that is blocked. [...] Read more.
With the fast development of commercial unmanned aerial vehicle (UAV) technology, there are increasing research interests on UAV communications. In this work, the mobility and deployment flexibility of UAVs are exploited to form a buffer-aided relaying system assisting terrestrial communication that is blocked. Optimal UAV trajectory design of the UAV-enabled mobile relaying system with a randomly located eavesdropper is investigated from the physical-layer security perspective to improve the overall secrecy rate. Based on the mobility of the UAV relay, a wireless channel model that changes with the trajectory and is exploited for improved secrecy is established. The secrecy rate is maximized by optimizing the discretized trajectory anchor points based on the information causality and UAV mobility constraints. However, the problem is non-convex and therefore difficult to solve. To make the problem tractable, we alternatively optimize the increments of the trajectory anchor points iteratively in a two-dimensional space and decompose the problem into progressive convex approximate problems through the iterative procedure. Convergence of the proposed iterative trajectory optimization technique is proved analytically by the squeeze principle. Simulation results show that finding the optimal trajectory by iteratively updating the displacements is effective and fast converging. It is also shown by the simulation results that the distribution of the eavesdropper location influences the security performance of the system. Specifically, an eavesdropper further away from the destination is beneficial to the system’s overall secrecy rate. Furthermore, it is observed that eavesdropper being further away from the destination also results in shorter trajectories, which implies it being energy-efficient as well. Full article
(This article belongs to the Special Issue Selected Papers from CyberC 2018)
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Open AccessArticle
Simultaneous Measurement of Ear Canal Movement, Electromyography of the Masseter Muscle and Occlusal Force for Earphone-Type Occlusal Force Estimation Device Development
Sensors 2019, 19(15), 3441; https://doi.org/10.3390/s19153441
Received: 13 June 2019 / Revised: 30 July 2019 / Accepted: 5 August 2019 / Published: 6 August 2019
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Abstract
We intend to develop earphone-type wearable devices to measure occlusal force by measuring ear canal movement using an ear sensor that we developed. The proposed device can measure occlusal force during eating. In this work, we simultaneously measured the ear canal movement (ear [...] Read more.
We intend to develop earphone-type wearable devices to measure occlusal force by measuring ear canal movement using an ear sensor that we developed. The proposed device can measure occlusal force during eating. In this work, we simultaneously measured the ear canal movement (ear sensor value), the surface electromyography (EMG) of the masseter muscle and the occlusal force six times from five subjects as a basic study toward occlusal force meter development. Using the results, we investigated the correlation coefficient between the ear sensor value and the occlusal force, and the partial correlation coefficient between ear sensor values. Additionally, we investigated the average of the partial correlation coefficient and the absolute value of the average for each subject. The absolute value results indicated strong correlation, with correlation coefficients exceeding 0.9514 for all subjects. The subjects showed a lowest partial correlation coefficient of 0.6161 and a highest value of 0.8286. This was also indicative of correlation. We then estimated the occlusal force via a single regression analysis for each subject. Evaluation of the proposed method via the cross-validation method indicated that the root-mean-square error when comparing actual values with estimates for the five subjects ranged from 0.0338 to 0.0969. Full article
(This article belongs to the Special Issue Wearable Sensors and Devices for Healthcare Applications)
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Open AccessArticle
Artificial Intelligence Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 2. Prior Information Notice (PIN) Sensor Design and Simulation Results
Sensors 2019, 19(15), 3440; https://doi.org/10.3390/s19153440
Received: 12 June 2019 / Revised: 31 July 2019 / Accepted: 2 August 2019 / Published: 6 August 2019
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Abstract
The study continues the theoretical derivation from Part 1, and the experiment is carried out at a bus station equipped with six water-cooled chillers. Between 2012 and 2017, historical data collected from temperature and humidity sensors, as well as the energy consumption data, [...] Read more.
The study continues the theoretical derivation from Part 1, and the experiment is carried out at a bus station equipped with six water-cooled chillers. Between 2012 and 2017, historical data collected from temperature and humidity sensors, as well as the energy consumption data, were used to build artificial intelligence (AI) assisted heating ventilation and air conditioning (HVAC) control models. The AI control system, in conjunction with a specifically designed prior information notice (PIN) sensor, was used to improve the prediction accuracy. This data collected between 2012 and 2016 was used for AI training and PIN sensor testing. During the hottest week of 2017 in Taiwan, the PIN sensor was used to conduct temperature and humidity data predictions. A model-based predictive control was developed to obtain air conditioning energy consumption data. The comparative results between the predictive and actual data showed that the temperature and humidity prediction accuracies were between 95.5 and 96.6%, respectively. Additionally, energy savings amounting to 39.8% were achieved compared to the theoretical estimates of 44.6%, a difference of less than 5%. These results show that the experimental model supports the theoretical estimations. In the future, a PIN sensor will be installed in a chiller to further verify the energy savings of the AI assisted HVAC control. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
New Motion Intention Acquisition Method of Lower Limb Rehabilitation Robot Based on Static Torque Sensors
Sensors 2019, 19(15), 3439; https://doi.org/10.3390/s19153439
Received: 23 May 2019 / Revised: 20 July 2019 / Accepted: 31 July 2019 / Published: 6 August 2019
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Abstract
The rehabilitation robot is an application of robotic technology for people with limb disabilities. This paper investigates a new applicable and effective sitting/lying lower limb rehabilitation robot (the LLR-Ro). In order to improve the patient’s training initiative and accelerate the rehabilitation process, a [...] Read more.
The rehabilitation robot is an application of robotic technology for people with limb disabilities. This paper investigates a new applicable and effective sitting/lying lower limb rehabilitation robot (the LLR-Ro). In order to improve the patient’s training initiative and accelerate the rehabilitation process, a new motion intention acquisition method based on static torque sensors is proposed. This motion intention acquisition method is established through the dynamics modeling of human–machine coordination, which is built on the basis of Lagrangian equations. Combined with the static torque sensors installed on the mechanism leg joint axis, the LLR-Ro can obtain the active force from the patient’s leg. Based on the variation of the patient’s active force and the kinematic functional relationship of the patient’s leg end point, the patient motion intention is obtained and used in the proposed active rehabilitation training method. The simulation experiment demonstrates the correctness of mechanism leg dynamics equations through ADAMS software and MATLAB software. The calibration experiment of the joint torque sensors’ combining limit range filter with an average value filter provides the hardware support for active rehabilitation training. The consecutive variation of the torque sensors from just the mechanism leg weight, as well as both the mechanism leg and the patient leg weights, obtains the feasibility of lower limb motion intention acquisition. Full article
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Open AccessArticle
PARSUC: A Parallel Subsampling-Based Method for Clustering Remote Sensing Big Data
Sensors 2019, 19(15), 3438; https://doi.org/10.3390/s19153438
Received: 4 May 2019 / Revised: 23 July 2019 / Accepted: 27 July 2019 / Published: 5 August 2019
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Abstract
Remote sensing big data (RSBD) is generally characterized by huge volumes, diversity, and high dimensionality. Mining hidden information from RSBD for different applications imposes significant computational challenges. Clustering is an important data mining technique widely used in processing and analyzing remote sensing imagery. [...] Read more.
Remote sensing big data (RSBD) is generally characterized by huge volumes, diversity, and high dimensionality. Mining hidden information from RSBD for different applications imposes significant computational challenges. Clustering is an important data mining technique widely used in processing and analyzing remote sensing imagery. However, conventional clustering algorithms are designed for relatively small datasets. When applied to problems with RSBD, they are, in general, too slow or inefficient for practical use. In this paper, we proposed a parallel subsampling-based clustering (PARSUC) method for improving the performance of RSBD clustering in terms of both efficiency and accuracy. PARSUC leverages a novel subsampling-based data partitioning (SubDP) method to realize three-step parallel clustering, effectively solving the notable performance bottleneck of the existing parallel clustering algorithms; that is, they must cope with numerous repeated calculations to get a reasonable result. Furthermore, we propose a centroid filtering algorithm (CFA) to eliminate subsampling errors and to guarantee the accuracy of the clustering results. PARSUC was implemented on a Hadoop platform by using the MapReduce parallel model. Experiments conducted on massive remote sensing imageries with different sizes showed that PARSUC (1) provided much better accuracy than conventional remote sensing clustering algorithms in handling larger image data; (2) achieved notable scalability with increased computing nodes added; and (3) spent much less time than the existing parallel clustering algorithm in handling RSBD. Full article
(This article belongs to the Special Issue Computational Intelligence in Remote Sensing)
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Open AccessArticle
Monitoring of Chemical Risk Factors for Sudden Infant Death Syndrome (SIDS) by Hydroxyapatite-Graphene-MWCNT Composite-Based Sensors
Sensors 2019, 19(15), 3437; https://doi.org/10.3390/s19153437
Received: 30 May 2019 / Revised: 17 July 2019 / Accepted: 2 August 2019 / Published: 5 August 2019
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Abstract
Sensing properties of chemical sensors based on ternary hydroxyapatite-graphene-multiwalled carbon nanotube (HA-GN-MWCNT) nanocomposite in the detection of chemical substances representing risk factors for sudden infant death syndrome (SIDS), have been evaluated. Characterization data of the synthesized composite have shown that the graphene-MWCNT network [...] Read more.
Sensing properties of chemical sensors based on ternary hydroxyapatite-graphene-multiwalled carbon nanotube (HA-GN-MWCNT) nanocomposite in the detection of chemical substances representing risk factors for sudden infant death syndrome (SIDS), have been evaluated. Characterization data of the synthesized composite have shown that the graphene-MWCNT network serves as a matrix to uniformly disperse the hydroxyapatite nanoparticles and provide suitable electrical properties required for developing novel electrochemical and conductometric sensors. A HA-GN-MWCNT composite-modified glassy carbon electrode (HA-GN-MWCNT/GCE) has been fabricated and tested for the simultaneous monitoring of nicotine and caffeine by cyclic voltammetry (CV) and square wave voltammetry (SWV), whereas a HA-GN-MWCNT conductive gas sensor has been tested for the detection of CO2 in ambient air. Reported results suggest that the synergic combination of the chemical properties of HA and electrical/electrochemical characteristics of the mixed graphene-MWCNT network play a prominent role in enhancing the electrochemical and gas sensing behavior of the ternary HA-GN-MWCNT hybrid nanostructure. The high performances of the developed sensors make them suitable for monitoring unhealthy actions (e.g., smoking, drinking coffee) in breastfeeding women and environmental factors (bad air quality), which are associated with an enhanced risk for SIDS. Full article
(This article belongs to the Special Issue Sensors for Human Safety Monitoring)
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Open AccessArticle
Analysis of Byzantine Attacks for Target Tracking in Wireless Sensor Networks
Sensors 2019, 19(15), 3436; https://doi.org/10.3390/s19153436
Received: 11 June 2019 / Revised: 20 July 2019 / Accepted: 30 July 2019 / Published: 5 August 2019
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Abstract
Herein, the problem of target tracking in wireless sensor networks (WSNs) is investigated in the presence of Byzantine attacks. More specifically, we analyze the impact of Byzantine attacks on the performance of a tracking system. First, under the condition of jointly estimating the [...] Read more.
Herein, the problem of target tracking in wireless sensor networks (WSNs) is investigated in the presence of Byzantine attacks. More specifically, we analyze the impact of Byzantine attacks on the performance of a tracking system. First, under the condition of jointly estimating the target state and the attack parameters, the posterior Cramer–Rao lower bound (PCRLB) is calculated. Then, from the perspective of attackers, we define the optimal Byzantine attack and theoretically find a way to achieve such an attack with minimal cost. When the attacked nodes are correctly identified by the fusion center (FC), we further define the suboptimal Byzantine attack and also find a way to realize such an attack. Finally, in order to alleviate the negative impact of attackers on the system performance, a modified sampling importance resampling (SIR) filter is proposed. Simulation results show that the tracking results of the modified SIR filter can be close to the true trajectory of the moving target. In addition, when the quantization level increases, both the security performance and the estimation performance of the tracking system are improved. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle
UHF Partial Discharge Location in Power Transformers via Solution of the Maxwell Equations in a Computational Environment
Sensors 2019, 19(15), 3435; https://doi.org/10.3390/s19153435
Received: 27 June 2019 / Revised: 15 July 2019 / Accepted: 20 July 2019 / Published: 5 August 2019
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Abstract
This paper presents an algorithm for the localisation of partial discharge (PD) sources in power transformers based on the electromagnetic waves radiated by a PD pulse. The proposed algorithm is more accurate than existing methods, since it considers the effects of the reflection, [...] Read more.
This paper presents an algorithm for the localisation of partial discharge (PD) sources in power transformers based on the electromagnetic waves radiated by a PD pulse. The proposed algorithm is more accurate than existing methods, since it considers the effects of the reflection, refractions and diffractions undergone by the ultra-high frequency (UHF) signal within the equipment tank. The proposed method uses computational simulations of the electromagnetic waves generated by PD, and obtains the time delay of the signal between each point in the 3D space and the UHF sensors. The calculated signals can be compared with the signals measured in the field, so that the position of the PD source can be located based on the best correlation between the simulated propagation delay and the measured data. The equations used in the proposed method are defined as a 3D optimisation problem, so that the binary particle swarm optimisation algorithm can be used. To test and demonstrate the proposed algorithm, computational simulations were performed. The solutions were sufficient to identify not only the occurrence of defects, but also the winding and the region (top, centre or base) in which the defect occurred. In all cases, an accuracy of greater than 15 cm was obtained for the location, in a 180 MVA three-phase transformer. Full article
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Open AccessArticle
On-Device Deep Learning Inference for Efficient Activity Data Collection
Sensors 2019, 19(15), 3434; https://doi.org/10.3390/s19153434
Received: 9 July 2019 / Revised: 25 July 2019 / Accepted: 1 August 2019 / Published: 5 August 2019
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Abstract
Labeling activity data is a central part of the design and evaluation of human activity recognition systems. The performance of the systems greatly depends on the quantity and “quality” of annotations; therefore, it is inevitable to rely on users and to keep them [...] Read more.
Labeling activity data is a central part of the design and evaluation of human activity recognition systems. The performance of the systems greatly depends on the quantity and “quality” of annotations; therefore, it is inevitable to rely on users and to keep them motivated to provide activity labels. While mobile and embedded devices are increasingly using deep learning models to infer user context, we propose to exploit on-device deep learning inference using a long short-term memory (LSTM)-based method to alleviate the labeling effort and ground truth data collection in activity recognition systems using smartphone sensors. The novel idea behind this is that estimated activities are used as feedback for motivating users to collect accurate activity labels. To enable us to perform evaluations, we conduct the experiments with two conditional methods. We compare the proposed method showing estimated activities using on-device deep learning inference with the traditional method showing sentences without estimated activities through smartphone notifications. By evaluating with the dataset gathered, the results show our proposed method has improvements in both data quality (i.e., the performance of a classification model) and data quantity (i.e., the number of data collected) that reflect our method could improve activity data collection, which can enhance human activity recognition systems. We discuss the results, limitations, challenges, and implications for on-device deep learning inference that support activity data collection. Also, we publish the preliminary dataset collected to the research community for activity recognition. Full article
(This article belongs to the Special Issue Mobile Sensing: Platforms, Technologies and Challenges)
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Open AccessArticle
Congestion Control in CoAP Observe Group Communication
Sensors 2019, 19(15), 3433; https://doi.org/10.3390/s19153433
Received: 20 June 2019 / Revised: 31 July 2019 / Accepted: 3 August 2019 / Published: 5 August 2019
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Abstract
The Constrained Application Protocol (CoAP) is a simple and lightweight machine-to-machine (M2M) protocol for constrained devices for use in lossy networks which offers a small memory capacity and limited processing. Designed and developed by the Internet Engineering Task Force (IETF), it functions as [...] Read more.
The Constrained Application Protocol (CoAP) is a simple and lightweight machine-to-machine (M2M) protocol for constrained devices for use in lossy networks which offers a small memory capacity and limited processing. Designed and developed by the Internet Engineering Task Force (IETF), it functions as an application layer protocol and benefits from reliable delivery and simple congestion control. It is implemented for request/response message exchanges over the User Datagram Protocol (UDP) to support the Internet of Things (IoT). CoAP also provides a basic congestion control mechanism. In dealing with its own congestion, it relies on a fixed interval retransmission timeout (RTO) and binary exponential backoff (BEB). However, the default CoAP congestion control is considered to be unable to effectively perform group communication and observe resources, and it cannot handle rapid, frequent requests. This results in buffer overflow and packet loss. To overcome these problems, we proposed a new congestion control mechanism for CoAP Observe Group Communication, namely Congestion Control Random Early Detection (CoCo-RED), consisting of (1) determining and calculating an RTO timer, (2) a Revised Random Early Detection (RevRED) algorithm which has recently been developed and primarily based on the buffer management of TCP congestion control, and (3) a Fibonacci Pre-Increment Backoff (FPB) algorithm which waits for backoff time prior to retransmission. All the aforementioned algorithms were therefore implemented instead of the default CoAP mechanism. In this study, evaluations were carried out regarding the efficiency of the developed CoCo-RED using a Cooja simulator. The congestion control mechanism can quickly handle the changing behaviors of network communication, and thus it prevents the buffer overflow that leads to congestions. The results of our experiments indicate that CoCo-RED can control congestion more effectively than the default CoAP in every condition. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle
A Unique Interactive Nanostructure Knitting based Passive Sampler Adsorbent for Monitoring of Hg2+ in Water
Sensors 2019, 19(15), 3432; https://doi.org/10.3390/s19153432
Received: 5 July 2019 / Revised: 31 July 2019 / Accepted: 1 August 2019 / Published: 5 August 2019
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Abstract
This work reports the development of ultralight interwoven ultrathin graphitic carbon nitride (g-CN) nanosheets for use as a potential adsorbent in a passive sampler (PAS) designed to bind Hg2+ ions. The g-CN nanosheets were prepared from bulk g-CN synthesised via a modified [...] Read more.
This work reports the development of ultralight interwoven ultrathin graphitic carbon nitride (g-CN) nanosheets for use as a potential adsorbent in a passive sampler (PAS) designed to bind Hg2+ ions. The g-CN nanosheets were prepared from bulk g-CN synthesised via a modified high-temperature short-time (HTST) polycondensation process. The crystal structure, surface functional groups, and morphology of the g-CN nanosheets were characterised using a battery of instruments. The results confirmed that the as-synthesized product is composed of few-layered nanosheets. The adsorption efficiency of g-CN for binding Hg2+ (100 ng mL−1) in sea, river, rain, and Milli-Q quality water was 89%, 93%, 97%, and 100%, respectively, at natural pH. Interference studies found that the cations tested (Co2+, Ca2+, Zn2+, Fe2+, Mn2+, Ni2+, Bi3+, Na+, and K+) had no significant effect on the adsorption efficiency of Hg2+. Different parameters were optimised to improve the performance of g-CN such as pH, contact time, and amount of adsorbent. Optimum conditions were pH 7, 120 min incubation time and 10 mg of nanosheets. The yield of nanosheets was 72.5%, which is higher compared to other polycondensation processes using different monomers. The g-CN sheets could also be regenerated up to eight times with only a 20% loss in binding efficiency. Overall, nano-knitted g-CN is a promising low-cost green adsorbent for use in passive samplers or as a transducing material in sensor applications. Full article
(This article belongs to the Special Issue Global Mercury Assessment Sensing Strategies)
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Open AccessArticle
A Hybrid Two-Axis Force Sensor for the Mesoscopic Structural Superlubricity Studies
Sensors 2019, 19(15), 3431; https://doi.org/10.3390/s19153431
Received: 30 June 2019 / Revised: 26 July 2019 / Accepted: 2 August 2019 / Published: 5 August 2019
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Abstract
Structural superlubricity (SSL) is a state of nearly zero friction and zero wear between two directly contacted solid surfaces. Recently, SSL was achieved in mesoscale and thus opened the SSL technology which promises great applications in Micro-electromechanical Systems (MEMS), sensors, storage technologies, etc. [...] Read more.
Structural superlubricity (SSL) is a state of nearly zero friction and zero wear between two directly contacted solid surfaces. Recently, SSL was achieved in mesoscale and thus opened the SSL technology which promises great applications in Micro-electromechanical Systems (MEMS), sensors, storage technologies, etc. However, load issues in current mesoscale SSL studies are still not clear. The great challenge is to simultaneously measure both the ultralow shear forces and the much larger normal forces, although the widely used frictional force microscopes (FFM) and micro tribometers can satisfy the shear forces and normal forces requirements, respectively. Here we propose a hybrid two-axis force sensor that can well fill the blank between the capabilities of FFM and micro tribometers for the mesoscopic SSL studies. The proposed sensor can afford 1mN normal load with 10 nN lateral resolution. Moreover, the probe of the sensor is designed at the edge of the structure for the convenience of real-time optical observation. Calibrations and preliminary experiments are conducted to validate the performance of the design. Full article
(This article belongs to the Special Issue Sensors in Experimental Mechanics)
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Open AccessArticle
Smartphone-Based Platform for Affect Monitoring through Flexibly Managed Experience Sampling Methods
Sensors 2019, 19(15), 3430; https://doi.org/10.3390/s19153430
Received: 31 May 2019 / Revised: 26 July 2019 / Accepted: 31 July 2019 / Published: 5 August 2019
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Abstract
The identification of daily life events that trigger significant changes on our affective state has become a fundamental task in emotional research. To achieve it, the affective states must be assessed in real-time, along with situational information that could contextualize the affective data [...] Read more.
The identification of daily life events that trigger significant changes on our affective state has become a fundamental task in emotional research. To achieve it, the affective states must be assessed in real-time, along with situational information that could contextualize the affective data acquired. However, the objective monitoring of the affective states and the context is still in an early stage. Mobile technologies can help to achieve this task providing immediate and objective data of the users’ context and facilitating the assessment of their affective states. Previous works have developed mobile apps for monitoring affective states and context, but they use a fixed methodology which does not allow for making changes based on the progress of the study. This work presents a multimodal platform which leverages the potential of the smartphone sensors and the Experience Sampling Methods (ESM) to provide a continuous monitoring of the affective states and the context in an ubiquitous way. The platform integrates several elements aimed to expedite the real-time management of the ESM questionnaires. In order to show the potential of the platform, and evaluate its usability and its suitability for real-time assessment of affective states, a pilot study has been conducted. The results demonstrate an excellent usability level and a good acceptance from the users and the specialists that conducted the study, and lead to some suggestions for improving the data quality of mobile context-aware ESM-based systems. Full article
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Open AccessArticle
Real-Time Robust and Optimized Control of a 3D Overhead Crane System
Sensors 2019, 19(15), 3429; https://doi.org/10.3390/s19153429
Received: 8 July 2019 / Revised: 30 July 2019 / Accepted: 30 July 2019 / Published: 5 August 2019
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Abstract
A new and advanced control system for three-dimensional (3D) overhead cranes is proposed in this study using state feedback control in discrete time to deliver high performance trajectory tracking with minimum load swings in high-speed motions. By adopting the independent joint control strategy, [...] Read more.
A new and advanced control system for three-dimensional (3D) overhead cranes is proposed in this study using state feedback control in discrete time to deliver high performance trajectory tracking with minimum load swings in high-speed motions. By adopting the independent joint control strategy, a new and simplified model is developed where the overhead crane actuators are used to design the controller, with all the nonlinear equations of motions being viewed as disturbances affecting each actuator. A feedforward control is then designed to tackle these disturbances via computed torque control technique. A new load swing control is designed along with a new motion planning scheme to robustly minimize load swings as well as allowing fast load transportation without violating system’s constraints through updating reference trolley accelerations. The stability and performance analysis of the proposed discrete-time control system are demonstrated and validated analytically and practically. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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Open AccessArticle
A Node Density Control Learning Method for the Internet of Things
Sensors 2019, 19(15), 3428; https://doi.org/10.3390/s19153428
Received: 17 July 2019 / Revised: 31 July 2019 / Accepted: 2 August 2019 / Published: 5 August 2019
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Abstract
When examining density control learning methods for wireless sensor nodes, control time is often long and power consumption is usually very high. This paper proposes a node density control learning method for wireless sensor nodes and applies it to an environment based on [...] Read more.
When examining density control learning methods for wireless sensor nodes, control time is often long and power consumption is usually very high. This paper proposes a node density control learning method for wireless sensor nodes and applies it to an environment based on Internet of Things architectures. Firstly, the characteristics of wireless sensors networks and the structure of mobile nodes are analyzed. Combined with the flexibility of wireless sensor networks and the degree of freedom of real-time processing and configuration of field programmable gate array (FPGA) data, a one-step transition probability matrix is introduced. In addition, the probability of arrival of signals between any pair of mobile nodes is also studied and calculated. Finally, the probability of signal connection between mobile nodes is close to 1, approximating the minimum node density at T. We simulate using a fully connected network identifying a worst-case test environment. Detailed experimental results show that our novel proposed method has shorter completion time and lower power consumption than previous attempts. We achieve high node density control as well at close to 90%. Full article
(This article belongs to the Special Issue Big Data Driven IoT for Smart Cities)
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Open AccessArticle
Low-Cost and Highly Sensitive Wearable Sensor Based on Napkin for Health Monitoring
Sensors 2019, 19(15), 3427; https://doi.org/10.3390/s19153427
Received: 16 June 2019 / Revised: 21 July 2019 / Accepted: 3 August 2019 / Published: 5 August 2019
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Abstract
The development of sensors with high sensitivity, good flexibility, low cost, and capability of detecting multiple inputs is of great significance for wearable electronics. Herein, we report a napkin-based wearable capacitive sensor fabricated by a novel, low-cost, and facile strategy. The capacitive sensor [...] Read more.
The development of sensors with high sensitivity, good flexibility, low cost, and capability of detecting multiple inputs is of great significance for wearable electronics. Herein, we report a napkin-based wearable capacitive sensor fabricated by a novel, low-cost, and facile strategy. The capacitive sensor is composed of two pieces of electrode plates manufactured by spontaneous assembly of silver nanowires (NWs) on a polydimethylsiloxane (PDMS)-patterned napkin. The sensor possesses high sensitivity (>7.492 kPa−1), low cost, and capability for simultaneous detection of multiple signals. We demonstrate that the capacitive sensor can be applied to identify a variety of human physiological signals, including finger motions, eye blinking, and minute wrist pulse. More interestingly, the capacitive sensor comfortably attached to the temple can simultaneously monitor eye blinking and blood pulse. The demonstrated sensor shows great prospects in the applications of human–machine interface, prosthetics, home-based healthcare, and flexible touch panels. Full article
(This article belongs to the Special Issue Wearable Soft Sensors)
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Open AccessOpinion
Digital Health Sensing for Personalized Dermatology
Sensors 2019, 19(15), 3426; https://doi.org/10.3390/s19153426
Received: 10 June 2019 / Revised: 31 July 2019 / Accepted: 2 August 2019 / Published: 5 August 2019
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Abstract
The rapid evolution of technology, sensors and personal digital devices offers an opportunity to acquire health related data seamlessly, unobtrusively and in real time. In this opinion piece, we discuss the relevance and opportunities for using digital sensing in dermatology, taking eczema as [...] Read more.
The rapid evolution of technology, sensors and personal digital devices offers an opportunity to acquire health related data seamlessly, unobtrusively and in real time. In this opinion piece, we discuss the relevance and opportunities for using digital sensing in dermatology, taking eczema as an exemplar. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessReview
Recent Advances in Electric-Double-Layer Transistors for Bio-Chemical Sensing Applications
Sensors 2019, 19(15), 3425; https://doi.org/10.3390/s19153425
Received: 28 June 2019 / Revised: 25 July 2019 / Accepted: 1 August 2019 / Published: 5 August 2019
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Abstract
As promising biochemical sensors, ion-sensitive field-effect transistors (ISFETs) are used widely in the growing field of biochemical sensing applications. Recently, a new type of field-effect transistor gated by ionic electrolytes has attracted intense attention due to the extremely strong electric-double-layer (EDL) gating effect. [...] Read more.
As promising biochemical sensors, ion-sensitive field-effect transistors (ISFETs) are used widely in the growing field of biochemical sensing applications. Recently, a new type of field-effect transistor gated by ionic electrolytes has attracted intense attention due to the extremely strong electric-double-layer (EDL) gating effect. In such devices, the carrier density of the semiconductor channel can be effectively modulated by an ion-induced EDL capacitance at the semiconductor/electrolyte interface. With advantages of large specific capacitance, low operating voltage and sensitive interfacial properties, various EDL-based transistor (EDLT) devices have been developed for ultrasensitive portable sensing applications. In this article, we will review the recent progress of EDLT-based biochemical sensors. Starting with a brief introduction of the concepts of EDL capacitance and EDLT, we describe the material compositions and the working principle of EDLT devices. Moreover, the biochemical sensing performances of several important EDLTs are discussed in detail, including organic-based EDLTs, oxide-based EDLTs, nanomaterial-based EDLTs and neuromorphic EDLTs. Finally, the main challenges and development prospects of EDLT-based biochemical sensors are listed. Full article
(This article belongs to the Special Issue Potentiometric Bio/Chemical Sensing)
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Open AccessArticle
Artificial Neural Networks for Forecasting Passenger Flows on Metro Lines
Sensors 2019, 19(15), 3424; https://doi.org/10.3390/s19153424
Received: 1 July 2019 / Revised: 24 July 2019 / Accepted: 1 August 2019 / Published: 5 August 2019
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Abstract
Forecasting user flows on transportation networks is a fundamental task for Intelligent Transport Systems (ITSs). Indeed, most control and management strategies on transportation systems are based on the knowledge of user flows. For implementing ITS strategies, the forecast of user flows on some [...] Read more.
Forecasting user flows on transportation networks is a fundamental task for Intelligent Transport Systems (ITSs). Indeed, most control and management strategies on transportation systems are based on the knowledge of user flows. For implementing ITS strategies, the forecast of user flows on some network links obtained as a function of user flows on other links (for instance, where data are available in real time with sensors) may provide a significant contribution. In this paper, we propose the use of Artificial Neural Networks (ANNs) for forecasting metro onboard passenger flows as a function of passenger counts at station turnstiles. We assume that metro station turnstiles record the number of passengers entering by means of an automatic counting system and that these data are available every few minutes (temporal aggregation); the objective is to estimate onboard passengers on each track section of the line (i.e., between two successive stations) as a function of turnstile data collected in the previous periods. The choice of the period length may depend on service schedules. Artificial Neural Networks are trained by using simulation data obtained with a dynamic loading procedure of the rail line. The proposed approach is tested on a real-scale case: Line 1 of the Naples metro system (Italy). Numerical results show that the proposed approach is able to forecast the flows on metro sections with satisfactory precision. Full article
(This article belongs to the Special Issue Intelligent Transportation Related Complex Systems and Sensors)
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Open AccessArticle
Intelligent Rapid Adaptive Offloading Algorithm for Computational Services in Dynamic Internet of Things System
Sensors 2019, 19(15), 3423; https://doi.org/10.3390/s19153423
Received: 29 June 2019 / Revised: 1 August 2019 / Accepted: 3 August 2019 / Published: 4 August 2019
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Abstract
As restricted resources have seriously limited the computational performance of massive Internet of things (IoT) devices, better processing capability is urgently required. As an innovative technology, multi-access edge computing can provide cloudlet capabilities by offloading computation-intensive services from devices to a nearby edge [...] Read more.
As restricted resources have seriously limited the computational performance of massive Internet of things (IoT) devices, better processing capability is urgently required. As an innovative technology, multi-access edge computing can provide cloudlet capabilities by offloading computation-intensive services from devices to a nearby edge server. This paper proposes an intelligent rapid adaptive offloading (IRAO) algorithm for a dynamic IoT system to increase overall computational performance and simultaneously keep the fairness of multiple participants, which can achieve agile centralized control and solve the joint optimization problems related to offloading policy and resource allocation. For reducing algorithm execution time, we apply machine learning methods and construct an adaptive learning-based framework consisting of offloading decision-making, radio resource slicing and algorithm parameters updating. In particular, the offloading policy can be rapidly derived from an estimation algorithm based on a deep neural network, which uses an experience replay training method to improve model accuracy and adopts an asynchronous sampling trick to enhance training convergence performance. Extensive simulations with different parameters are conducted to maintain the trade-off between accuracy and efficiency of the IRAO algorithm. Compared with other candidates, the results illustrate that the IRAO algorithm can achieve superior performance in terms of scalability, effectiveness and efficiency. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
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Open AccessArticle
Machine Learning Classification for Assessing the Degree of Stenosis and Blood Flow Volume at Arteriovenous Fistulas of Hemodialysis Patients Using a New Photoplethysmography Sensor Device
Sensors 2019, 19(15), 3422; https://doi.org/10.3390/s19153422
Received: 31 May 2019 / Revised: 27 July 2019 / Accepted: 2 August 2019 / Published: 4 August 2019
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Abstract
The classifier of support vector machine (SVM) learning for assessing the quality of arteriovenous fistulae (AVFs) in hemodialysis (HD) patients using a new photoplethysmography (PPG) sensor device is presented in this work. In clinical practice, there are two important indices for assessing the [...] Read more.
The classifier of support vector machine (SVM) learning for assessing the quality of arteriovenous fistulae (AVFs) in hemodialysis (HD) patients using a new photoplethysmography (PPG) sensor device is presented in this work. In clinical practice, there are two important indices for assessing the quality of AVF: the blood flow volume (BFV) and the degree of stenosis (DOS). In hospitals, the BFV and DOS of AVFs are nowadays assessed using an ultrasound Doppler machine, which is bulky, expensive, hard to use, and time consuming. In this study, a newly-developed PPG sensor device was utilized to provide patients and doctors with an inexpensive and small-sized solution for ubiquitous AVF assessment. The readout in this sensor was custom-designed to increase the signal-to-noise ratio (SNR) and reduce the environment interference via maximizing successfully the full dynamic range of measured PPG entering an analog–digital converter (ADC) and effective filtering techniques. With quality PPG measurements obtained, machine learning classifiers including SVM were adopted to assess AVF quality, where the input features are determined based on optical Beer–Lambert’s law and hemodynamic model, to ensure all the necessary features are considered. Finally, the clinical experiment results showed that the proposed PPG sensor device successfully achieved an accuracy of 87.84% based on SVM analysis in assessing DOS at AVF, while an accuracy of 88.61% was achieved for assessing BFV at AVF. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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Open AccessArticle
An Event Recognition Method for Φ-OTDR Sensing System Based on Deep Learning
Sensors 2019, 19(15), 3421; https://doi.org/10.3390/s19153421
Received: 4 July 2019 / Revised: 29 July 2019 / Accepted: 1 August 2019 / Published: 4 August 2019
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Abstract
Phase-sensitive optical time domain reflectometer (Φ-OTDR) based distributed optical fiber sensing system has been widely used in many fields such as long range pipeline pre-warning, perimeter security and structure health monitoring. However, the lack of event recognition ability is always being the bottleneck [...] Read more.
Phase-sensitive optical time domain reflectometer (Φ-OTDR) based distributed optical fiber sensing system has been widely used in many fields such as long range pipeline pre-warning, perimeter security and structure health monitoring. However, the lack of event recognition ability is always being the bottleneck of Φ-OTDR in field application. An event recognition method based on deep learning is proposed in this paper. This method directly uses the temporal-spatial data matrix from Φ-OTDR as the input of a convolutional neural network (CNN). Only a simple bandpass filtering and a gray scale transformation are needed as the pre-processing, which achieves real-time. Besides, an optimized network structure with small size, high training speed and high classification accuracy is built. Experiment results based on 5644 events samples show that this network can achieve 96.67% classification accuracy in recognition of 5 kinds of events and the retraining time is only 7 min for a new sensing setup. Full article
(This article belongs to the Special Issue Fiber-Based Sensing Technology: Recent Progresses and New Challenges)
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Open AccessArticle
Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network
Sensors 2019, 19(15), 3420; https://doi.org/10.3390/s19153420
Received: 3 July 2019 / Revised: 28 July 2019 / Accepted: 2 August 2019 / Published: 4 August 2019
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Abstract
Blood pressure (BP) is a direct indicator of hypertension, a dangerous and potentially deadly condition. Regular monitoring of BP is thus important, but many people have aversion towards cuff-based devices, and their limitation is that they can only be used at rest. Using [...] Read more.
Blood pressure (BP) is a direct indicator of hypertension, a dangerous and potentially deadly condition. Regular monitoring of BP is thus important, but many people have aversion towards cuff-based devices, and their limitation is that they can only be used at rest. Using just a photoplethysmogram (PPG) to estimate BP is a potential solution investigated in our study. We analyzed the MIMIC III database for high-quality PPG and arterial BP waveforms, resulting in over 700 h of signals after preprocessing, belonging to 510 subjects. We then used the PPG alongside its first and second derivative as inputs into a novel spectro-temporal deep neural network with residual connections. We have shown in a leave-one-subject-out experiment that the network is able to model the dependency between PPG and BP, achieving mean absolute errors of 9.43 for systolic and 6.88 for diastolic BP. Additionally we have shown that personalization of models is important and substantially improves the results, while deriving a good general predictive model is difficult. We have made crucial parts of our study, especially the list of used subjects and our neural network code, publicly available, in an effort to provide a solid baseline and simplify potential comparison between future studies on an explicit MIMIC III subset. Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle
Investigation on Vortex-Induced Vibration Experiment of a Standing Variable-Tension Deepsea Riser Based on BFBG Sensor Technology
Sensors 2019, 19(15), 3419; https://doi.org/10.3390/s19153419
Received: 1 July 2019 / Revised: 1 August 2019 / Accepted: 2 August 2019 / Published: 4 August 2019
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Abstract
A vortex-induced vibration (VIV) experiment on a standing variable-tension deepsea riser was conducted to investigate the applicability and sensitivity of Bare Fiber Bragg Grating (BFBG) sensor technology for testing deepsea riser vibrations. The dominant frequencies, dimensionless displacements, in-line and cross-flow couplings of the [...] Read more.
A vortex-induced vibration (VIV) experiment on a standing variable-tension deepsea riser was conducted to investigate the applicability and sensitivity of Bare Fiber Bragg Grating (BFBG) sensor technology for testing deepsea riser vibrations. The dominant frequencies, dimensionless displacements, in-line and cross-flow couplings of the riser VIV under different top tensions were observed through wavelet transform and modal decomposition. The result indicated that, excited by the same external flow velocities, the cross-flow and in-line dominant frequencies of the riser both decreased with increasing top tension. In terms of displacement responses, increasing top tension caused the root mean square (RMS) displacement to decrease and the vibration amplitude to reduce. In terms of cross-flow and in-line coupling, the closer a location is to the ends of the riser, the smaller the trajectory is and the more standard the “8” becomes. During top tension increases, there exists a “lag” in the time when the riser’s vibration trajectory becomes an “8”. The Slalom Surround Installation approach can effectively prevent the local breakage of the optical fiber string. BFBG sensor technology can give an accurate presentation of the displacement time history, vibration amplitude and frequency of the riser, provides a clear picture of how the riser’s mode and VIV evolve as a function of flow velocity. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
A Hybrid Sliding Window Optimizer for Tightly-Coupled Vision-Aided Inertial Navigation System
Sensors 2019, 19(15), 3418; https://doi.org/10.3390/s19153418
Received: 6 June 2019 / Revised: 30 July 2019 / Accepted: 1 August 2019 / Published: 4 August 2019
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Abstract
The fusion of visual and inertial measurements for motion tracking has become prevalent in the robotic community, due to its complementary sensing characteristics, low cost, and small space requirements. This fusion task is known as the vision-aided inertial navigation system problem. We present [...] Read more.
The fusion of visual and inertial measurements for motion tracking has become prevalent in the robotic community, due to its complementary sensing characteristics, low cost, and small space requirements. This fusion task is known as the vision-aided inertial navigation system problem. We present a novel hybrid sliding window optimizer to achieve information fusion for a tightly-coupled vision-aided inertial navigation system. It possesses the advantages of both the conditioning-based method and the prior-based method. A novel distributed marginalization method was also designed based on the multi-state constraints method with significant efficiency improvement over the traditional method. The performance of the proposed algorithm was evaluated with the publicly available EuRoC datasets and showed competitive results compared with existing algorithms. Full article
(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)
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Open AccessArticle
A Novel Method for Soil Organic Matter Determination by Using an Artificial Olfactory System
Sensors 2019, 19(15), 3417; https://doi.org/10.3390/s19153417
Received: 11 July 2019 / Revised: 1 August 2019 / Accepted: 2 August 2019 / Published: 4 August 2019
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Abstract
Soil organic matter (SOM) is a major indicator of soil fertility and nutrients. In this study, a soil organic matter measuring method based on an artificial olfactory system (AOS) was designed. An array composed of 10 identical gas sensors controlled at different temperatures [...] Read more.
Soil organic matter (SOM) is a major indicator of soil fertility and nutrients. In this study, a soil organic matter measuring method based on an artificial olfactory system (AOS) was designed. An array composed of 10 identical gas sensors controlled at different temperatures was used to collect soil gases. From the response curve of each sensor, four features were extracted (maximum value, mean differential coefficient value, response area value, and the transient value at the 20th second). Then, soil organic matter regression prediction models were built based on back-propagation neural network (BPNN), support vector regression (SVR), and partial least squares regression (PLSR). The prediction performance of each model was evaluated using the coefficient of determination (R2), root-mean-square error (RMSE), and the ratio of performance to deviation (RPD). It was found that the R2 values between prediction (from BPNN, SVR, and PLSR) and observation were 0.880, 0.895, and 0.808. RMSEs were 14.916, 14.094, and 18.890, and RPDs were 2.837, 3.003, and 2.240, respectively. SVR had higher prediction ability than BPNN and PLSR and can be used to accurately predict organic matter contents. Thus, our findings offer brand new methods for predicting SOM. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle
3D-Printed Multilayer Sensor Structure for Electrical Capacitance Tomography
Sensors 2019, 19(15), 3416; https://doi.org/10.3390/s19153416
Received: 13 June 2019 / Revised: 25 July 2019 / Accepted: 29 July 2019 / Published: 4 August 2019
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Abstract
Presently, Electrical Capacitance Tomography (ECT) is positioned as a relatively mature and inexpensive tool for the diagnosis of non-conductive industrial processes. For most industrial applications, a hand-made approach for an ECT sensor and its 3D extended structure fabrication is used. Moreover, a hand-made [...] Read more.
Presently, Electrical Capacitance Tomography (ECT) is positioned as a relatively mature and inexpensive tool for the diagnosis of non-conductive industrial processes. For most industrial applications, a hand-made approach for an ECT sensor and its 3D extended structure fabrication is used. Moreover, a hand-made procedure is often inaccurate, complicated, and time-consuming. Another drawback is that a hand-made ECT sensor’s geometrical parameters, mounting base profile thickness, and electrode array shape usually depends on the structure of industrial test objects, tanks, and containers available on the market. Most of the traditionally fabricated capacitance tomography sensors offer external measurements only with electrodes localized outside of the test object. Although internal measurement is possible, it is often difficult to implement. This leads to limited in-depth scanning abilities and poor sensitivity distribution of traditionally fabricated ECT sensors. In this work we propose, demonstrate, and validate experimentally a new 3D ECT sensor fabrication process. The proposed solution uses a computational workflow that incorporates both 3D computer modeling and 3D-printing techniques. Such a 3D-printed structure can be of any shape, and the electrode layout can be easily fitted to a broad range of industrial applications. A developed solution offers an internal measurement due to negligible thickness of sensor mount base profile. This paper analyses and compares measurement capabilities of a traditionally fabricated 3D ECT sensor with novel 3D-printed design. The authors compared two types of the 3D ECT sensors using experimental capacitance measurements for a set of low-contrast and high-contrast permittivity distribution phantoms. The comparison demonstrates advantages and benefits of using the new 3D-printed spatial capacitance sensor regarding the significant fabrication time reduction as well as the improvement of overall measurement accuracy and stability. Full article
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Open AccessArticle
Magnetic Communication Using High-Sensitivity Magnetic Field Detectors
Sensors 2019, 19(15), 3415; https://doi.org/10.3390/s19153415
Received: 25 June 2019 / Revised: 24 July 2019 / Accepted: 2 August 2019 / Published: 4 August 2019
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Abstract
In this article, an innovative approach for magnetic data communication is presented. For this purpose, the receiver coil of a conventional magneto-inductive communication system is replaced by a high-sensitivity wideband magnetic field sensor. The results show decisive advantages offered by sensitive magnetic field [...] Read more.
In this article, an innovative approach for magnetic data communication is presented. For this purpose, the receiver coil of a conventional magneto-inductive communication system is replaced by a high-sensitivity wideband magnetic field sensor. The results show decisive advantages offered by sensitive magnetic field sensors, including a higher communication range for small receiver units. This approach supports numerous mobile applications where receiver size is limited, possibly in conjunction with multiple detectors. Numerical results are supported by a prototype implementation employing an anisotropic magneto-resistive sensor. Full article
(This article belongs to the Special Issue Magnetic Sensing Technology, Materials and Applications)
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Open AccessArticle
Effects of AlN and BCN Thin Film Multilayer Design on the Reaction Time of Ni/Ni-20Cr Thin Film Thermocouples on Thermally Sprayed Al2O3
Sensors 2019, 19(15), 3414; https://doi.org/10.3390/s19153414
Received: 5 July 2019 / Revised: 31 July 2019 / Accepted: 2 August 2019 / Published: 3 August 2019
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Abstract
Thin film thermocouples are widely used for local temperature determinations of surfaces. However, depending on the environment in which they are used, thin film thermocouples need to be covered by a wear or oxidation resistant top layer. With regard to the utilization in [...] Read more.
Thin film thermocouples are widely used for local temperature determinations of surfaces. However, depending on the environment in which they are used, thin film thermocouples need to be covered by a wear or oxidation resistant top layer. With regard to the utilization in wide-slit nozzles for plastic extrusion, Ni/Ni-20Cr thin film thermocouples were manufactured using direct-current (DC) magnetron sputtering combined with Aluminiumnitride (AlN) and Boron-Carbonitride (BCN) thin films. On the one hand, the deposition parameters of the nitride layers were varied to affect the chemical composition and morphology of the AlN and BCN thin films. On the other hand, the position of the nitride layers (below the thermocouple, above the thermocouple, around the thermocouple) was changed. Both factors were investigated concerning the influence on the Seebeck coefficient and the reaction behaviour of the thermocouples. Therefore, the impact of the nitride thin films on the morphology, physical structure, crystallite size, electrical resistance and hardness of the Ni and Ni-20Cr thin films is analysed. The investigations reveal that the Seebeck coefficient is not affected by the different architectures of the thermocouples. Nevertheless, the reaction time of the thermocouples can be significantly improved by adding a thermal conductive top coat over the thin films, whereas the top coat should have a coarse structure and low nitrogen content. Full article
(This article belongs to the Section Sensor Materials)
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