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Sensors, Volume 19, Issue 10 (May-2 2019)

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Open AccessArticle
SAFE-MAC: Speed Aware Fairness Enabled MAC Protocol for Vehicular Ad-hoc Networks
Sensors 2019, 19(10), 2405; https://doi.org/10.3390/s19102405 (registering DOI)
Received: 1 April 2019 / Revised: 16 May 2019 / Accepted: 21 May 2019 / Published: 26 May 2019
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Abstract
Highly dynamic geographical topology, two-direction mobility, and varying traffic density can lead to fairness issues in Vehicular Ad-hoc Networks (VANETs). The Medium Access Control (MAC) protocol plays a vital role in sharing the common wireless channel efficiently between vehicles in a VANET system. [...] Read more.
Highly dynamic geographical topology, two-direction mobility, and varying traffic density can lead to fairness issues in Vehicular Ad-hoc Networks (VANETs). The Medium Access Control (MAC) protocol plays a vital role in sharing the common wireless channel efficiently between vehicles in a VANET system. However, ensuring fairness between vehicles can be a challenge in designing MAC protocols for VANET systems. The existing protocol, IEEE 802.11 DCF, ensures that the packet transmission rate for a particular vehicle is directly proportional to the amount of time a vehicle spends within a service area, but it does not guarantee that faster vehicles will be able to send the minimum number of packets. Other existing MAC protocols based on IEEE 802.11 are able to provide a minimum amount of data transmission regardless of velocity, but are unable to provide an amount of data transmission that is more proportionate to the time a vehicle spends in the service area. To address the above limitations, we propose a Speed Aware Fairness Enabled MAC (SAFE-MAC) protocol that calculates the residence time of a vehicle in a service area by using mobility metrics such as position, direction, and speed to synthesize the transmission probability of each individual vehicle with respect to its residence time. This is achieved by dynamically altering the values of parameters such as minimum contention window, maximum backoff stage, and retransmission limit in the MAC protocol. We then develop an analytical model to compare the performance of our proposed protocol with contemporary MAC protocols. Numerical analysis results show that our proposed protocol significantly improves fairness among the speed-varying vehicles in VANET. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle
A Distributed Approach for Collision Avoidance between Multirotor UAVs Following Planned Missions
Sensors 2019, 19(10), 2404; https://doi.org/10.3390/s19102404 (registering DOI)
Received: 15 April 2019 / Revised: 22 May 2019 / Accepted: 24 May 2019 / Published: 26 May 2019
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Abstract
As the number of potential applications for Unmanned Aerial Vehicles (UAVs) keeps rising steadily, the chances that these devices get close to each other during their flights also increases, causing concerns regarding potential collisions. This paper proposed the Mission Based Collision Avoidance Protocol [...] Read more.
As the number of potential applications for Unmanned Aerial Vehicles (UAVs) keeps rising steadily, the chances that these devices get close to each other during their flights also increases, causing concerns regarding potential collisions. This paper proposed the Mission Based Collision Avoidance Protocol (MBCAP), a novel UAV collision avoidance protocol applicable to all types of multicopters flying autonomously. It relies on wireless communications in order to detect nearby UAVs, and to negotiate the procedure to avoid any potential collision. Experimental and simulation results demonstrated the validity and effectiveness of the proposed solution, which typically introduces a small overhead in the range of 15 to 42 s for each risky situation successfully handled. Full article
(This article belongs to the Special Issue UAV-Based Applications in the Internet of Things (IoT))
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Open AccessArticle
Approximate Optimal Deployment of Barrier Coverage on Heterogeneous Bistatic Radar Sensors
Sensors 2019, 19(10), 2403; https://doi.org/10.3390/s19102403 (registering DOI)
Received: 29 March 2019 / Revised: 6 May 2019 / Accepted: 17 May 2019 / Published: 26 May 2019
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Abstract
Heterogeneous Bistatic Radars (BR) have different sensing ranges and couplings of sensing regions, which provide more flexible coverage for the boundary at complex terrain such as across rivers and valleys. Due to the Cassini oval sensing region of a BR and the coupling [...] Read more.
Heterogeneous Bistatic Radars (BR) have different sensing ranges and couplings of sensing regions, which provide more flexible coverage for the boundary at complex terrain such as across rivers and valleys. Due to the Cassini oval sensing region of a BR and the coupling of sensing regions among different BRs, the coverage problem of BR sensor networks is very challenging. Existing works in BR barrier coverage focus mainly on homogeneous BR sensor networks. This paper studies the heterogeneous BR placement problem on a line barrier to achieve optimal coverage. 1) We investigate coverage differences of the basic placement sequences of heterogeneous BRs on the line barrier, and prove the optimal basic placement spacing patterns of heterogeneous BRs. 2) We study the coverage coupling effect among adjacent BRs on the line barrier, and determine that different placement sequences of heterogeneous BR transmitters will affect the barrier’s coverage performance and length. The optimal placement sequence of heterogeneous BR barrier cannot be solved through the greedy algorithm. 3) We propose an optimal BRs placement algorithm on a line barrier when the heterogeneous BR transmitters’ placement sequence is predetermined on the barrier, and prove it to be optimal. Through simulation experiments, we determine that the different placement sequences of heterogeneous BR transmitters have little influence on the barrier’s maximum length. Then, we propose an approximate algorithm to optimize the BR placement spacing sequence on the heterogeneous line barrier. 4) As a heterogeneous barrier case study, a minimum cost coverage algorithm of heterogeneous BR barrier is presented. We validate the effectiveness of the proposed algorithms through theory analysis and extensive simulation experiments. Full article
(This article belongs to the Special Issue Collaborative and Cooperative Sensors)
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Open AccessArticle
Minimizing the Adverse Effects of Asymmetric Links: A Novel Cooperative Asynchronous MAC Protocol for Wireless Sensor Networks
Sensors 2019, 19(10), 2402; https://doi.org/10.3390/s19102402 (registering DOI)
Received: 20 March 2019 / Revised: 20 May 2019 / Accepted: 22 May 2019 / Published: 26 May 2019
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Abstract
As Wireless Sensor Networks (WSNs) grow in popularity, researchers are now focusing more on some challenging issues that significantly degrade overall performance, such as energy hole mitigation, link asymmetry minimization, etc. Link asymmetry is a problem that arises when the coverage distance between [...] Read more.
As Wireless Sensor Networks (WSNs) grow in popularity, researchers are now focusing more on some challenging issues that significantly degrade overall performance, such as energy hole mitigation, link asymmetry minimization, etc. Link asymmetry is a problem that arises when the coverage distance between two adjacent nodes varies. It creates an obstacle to overcome when designing an efficient Medium Access Control (MAC) protocol for WSNs with low duty-cycling. This phenomenon poses an especially difficult challenge for receiver-initiated asynchronous MAC protocols, which are popular due to their relatively higher energy efficiency. Exploiting the benefits of cooperative communication has emerged as one of the viable solutions to overcome this limitation. Cooperative communication in WSNs has received a lot of attention in recent years. Many researchers have worked to create a MAC layer supporting cooperative communication. However, the association of cooperative communication with an asymmetric link is not studied in the literature. In this research work, COASYM-MAC, a cooperative asynchronous MAC protocol for WSNs, is proposed based on a receiver-initiated MAC protocol that uses the fact that nodes have alternate paths between them to reduce link asymmetry. A key feature of the proposed protocol is that the optimal helper node is selected automatically in case of link asymmetry. Simulations exhibited that COASYM-MAC performs significantly better than a state-of-the-art MAC protocol for WSNs that handles asymmetric links, ASYM-MAC. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle
Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region
Sensors 2019, 19(10), 2401; https://doi.org/10.3390/s19102401 (registering DOI)
Received: 25 March 2019 / Revised: 22 May 2019 / Accepted: 24 May 2019 / Published: 26 May 2019
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Abstract
Crop-type identification is very important in agricultural regions. Most researchers in this area have focused on exploring the ability of synthetic-aperture radar (SAR) sensors to identify crops. This paper uses multi-source (Sentinel-1, Sentinel-2, and Landsat-8) and multi-temporal data to identify crop types. The [...] Read more.
Crop-type identification is very important in agricultural regions. Most researchers in this area have focused on exploring the ability of synthetic-aperture radar (SAR) sensors to identify crops. This paper uses multi-source (Sentinel-1, Sentinel-2, and Landsat-8) and multi-temporal data to identify crop types. The change detection method was used to analyze spectral and indices information in time series. Significant differences in crop growth status during the growing season were found. Then, three obviously differentiated time features were extracted. Three advanced machine learning algorithms (Support Vector Machine, Artificial Neural Network, and Random Forest, RF) were used to identify the crop types. The results showed that the detection of (Vertical-vertical) VV, (Vertical-horizontal) VH, and Cross Ratio (CR) changes was effective for identifying land cover. Moreover, the red-edge changes were obviously different according to crop growth periods. Sentinel-2 and Landsat-8 showed different normalized difference vegetation index (NDVI) changes also. By using single remote sensing data to classify crops, Sentinel-2 produced the highest overall accuracy (0.91) and Kappa coefficient (0.89). The combination of Sentinel-1, Sentinel-2, and Landsat-8 data provided the best overall accuracy (0.93) and Kappa coefficient (0.91). The RF method had the best performance in terms of identity classification. In addition, the indices feature dominated the classification results. The combination of phenological period information with multi-source remote sensing data can be used to explore a crop area and its status in the growing season. The results of crop classification can be used to analyze the density and distribution of crops. This study can also allow to determine crop growth status, improve crop yield estimation accuracy, and provide a basis for crop management. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Crop Phenotyping Application)
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Open AccessArticle
Detecting Variable Resistance by Fluorescence Intensity Ratio Technology
Sensors 2019, 19(10), 2400; https://doi.org/10.3390/s19102400 (registering DOI)
Received: 24 April 2019 / Revised: 18 May 2019 / Accepted: 21 May 2019 / Published: 26 May 2019
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Abstract
We report a new method for detecting variable resistance during short time intervals by using an optical method. A novel variable-resistance sensor composed of up-conversion nanoparticles (NaYF4:Yb3+,Er3+) and reduced graphene oxide (RGO) is designed based on characteristics [...] Read more.
We report a new method for detecting variable resistance during short time intervals by using an optical method. A novel variable-resistance sensor composed of up-conversion nanoparticles (NaYF4:Yb3+,Er3+) and reduced graphene oxide (RGO) is designed based on characteristics of a negative temperature coefficient (NTC) resistive element. The fluorescence intensity ratio (FIR) technology based on green and red emissions is used to detect variable resistance. Combining the Boltzmann distributing law with Steinhart–Hart equation, the FIR and relative sensitivity SR as a function of resistance can be defined. The maximum value of SR is 1.039 × 10−3/Ω. This work reports a new method for measuring variable resistance based on the experimental data from fluorescence spectrum. Full article
(This article belongs to the Section Optical Sensors)
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Open AccessArticle
Maximizing Coverage Quality with Budget Constrained in Mobile Crowd-Sensing Network for Environmental Monitoring Applications
Sensors 2019, 19(10), 2399; https://doi.org/10.3390/s19102399 (registering DOI)
Received: 27 March 2019 / Revised: 8 May 2019 / Accepted: 23 May 2019 / Published: 26 May 2019
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Abstract
The Mobile Crowd-sensing Network is a novel cyber–physical–social network which has received great attention recently and can be used as a powerful tool to monitor the phenomenon of the field of interest. Due to the limited budget, how to choose appropriate participants to [...] Read more.
The Mobile Crowd-sensing Network is a novel cyber–physical–social network which has received great attention recently and can be used as a powerful tool to monitor the phenomenon of the field of interest. Due to the limited budget, how to choose appropriate participants to maximize the coverage quality is one of the most important issues when the mobile crowd-sensing network applies to practical application, such as air quality monitoring. In this paper, given the number of available participants, the traverse path and the reward of each participant, we investigate the problem of how to choose suitable participants to monitor an environment of a critical region by a crowd-sensing network, while the total rewards for all selected participants is not larger than the limited budget. In our solution, we first divide a big critical region such as a city into smaller regions of different size, and select some sampling points in the smaller region; the collected data of those sampling points represents the collected data of the whole smaller region. Then, we design a greedy algorithm to select participants to cover the maximum sampling points while the total rewards of selected participants does not exceed the limited budget. Finally, we evaluate the validity and efficiency of the proposed algorithm by conducting extensive simulations. The simulation results show that the greedy algorithm outperforms an existing scheme. Full article
(This article belongs to the Special Issue Exploiting the IoT within Cyber Physical Social System)
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Open AccessArticle
Deep Convolutional Neural Network for Mapping Smallholder Agriculture Using High Spatial Resolution Satellite Image
Sensors 2019, 19(10), 2398; https://doi.org/10.3390/s19102398 (registering DOI)
Received: 19 April 2019 / Revised: 21 May 2019 / Accepted: 23 May 2019 / Published: 25 May 2019
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Abstract
In classification of satellite images acquired over smallholder agricultural landscape with complex spectral profiles of various crop types, exploring image spatial information is important. The deep convolutional neural network (CNN), originally designed for natural image recognition in the computer vision field, can automatically [...] Read more.
In classification of satellite images acquired over smallholder agricultural landscape with complex spectral profiles of various crop types, exploring image spatial information is important. The deep convolutional neural network (CNN), originally designed for natural image recognition in the computer vision field, can automatically explore high level spatial information and thus is promising for such tasks. This study tried to evaluate different CNN structures for classification of four smallholder agricultural landscapes in Heilongjiang, China using pan-sharpened 2 m GaoFen-1 (meaning high resolution in Chinese) satellite images. CNN with three pooling strategies: without pooling, with max pooling and with average pooling, were evaluated and compared with random forest. Two different numbers (~70,000 and ~290,000) of CNN learnable parameters were examined for each pooling strategy. The training and testing samples were systematically sampled from reference land cover maps to ensure sample distribution proportional to the reference land cover occurrence and included 60,000–400,000 pixels to ensure effective training. Testing sample classification results in the four study areas showed that the best pooling strategy was the average pooling CNN and that the CNN significantly outperformed random forest (2.4–3.3% higher overall accuracy and 0.05–0.24 higher kappa coefficient). Visual examination of CNN classification maps showed that CNN can discriminate better the spectrally similar crop types by effectively exploring spatial information. CNN was still significantly outperformed random forest using training samples that were evenly distributed among classes. Furthermore, future research to improve CNN performance was discussed. Full article
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
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Open AccessArticle
Knowledge Preserving OSELM Model for Wi-Fi-Based Indoor Localization
Sensors 2019, 19(10), 2397; https://doi.org/10.3390/s19102397 (registering DOI)
Received: 6 March 2019 / Revised: 25 April 2019 / Accepted: 21 May 2019 / Published: 25 May 2019
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Abstract
Wi-Fi has shown enormous potential for indoor localization because of its wide utilization and availability. Enabling the use of Wi-Fi for indoor localization necessitates the construction of a fingerprint and the adoption of a learning algorithm. The goal is to enable the use [...] Read more.
Wi-Fi has shown enormous potential for indoor localization because of its wide utilization and availability. Enabling the use of Wi-Fi for indoor localization necessitates the construction of a fingerprint and the adoption of a learning algorithm. The goal is to enable the use of the fingerprint in training the classifiers for predicting locations. Existing models of machine learning Wi-Fi-based localization are brought from machine learning and modified to accommodate for practical aspects that occur in indoor localization. The performance of these models varies depending on their effectiveness in handling and/or considering specific characteristics and the nature of indoor localization behavior. One common behavior in the indoor navigation of people is its cyclic dynamic nature. To the best of our knowledge, no existing machine learning model for Wi-Fi indoor localization exploits cyclic dynamic behavior for improving localization prediction. This study modifies the widely popular online sequential extreme learning machine (OSELM) to exploit cyclic dynamic behavior for achieving improved localization results. Our new model is called knowledge preserving OSELM (KP-OSELM). Experimental results conducted on the two popular datasets TampereU and UJIndoorLoc conclude that KP-OSELM outperforms benchmark models in terms of accuracy and stability. The last achieved accuracy was 92.74% for TampereU and 72.99% for UJIndoorLoc. Full article
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Open AccessArticle
Development of an Autonomous Unmanned Aerial Manipulator Based on a Real-Time Oriented-Object Detection Method
Sensors 2019, 19(10), 2396; https://doi.org/10.3390/s19102396 (registering DOI)
Received: 5 March 2019 / Revised: 27 April 2019 / Accepted: 23 May 2019 / Published: 25 May 2019
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Abstract
Autonomous Unmanned Aerial Manipulators (UAMs) have shown promising potential in mobile 3-dimensional grasping applications, but they still suffer from some difficulties impeding their board applications, such as target detection and indoor positioning. For the autonomous grasping mission, the UAMs need ability to recognize [...] Read more.
Autonomous Unmanned Aerial Manipulators (UAMs) have shown promising potential in mobile 3-dimensional grasping applications, but they still suffer from some difficulties impeding their board applications, such as target detection and indoor positioning. For the autonomous grasping mission, the UAMs need ability to recognize the objects and grasp them. Considering the efficiency and precision, we present a novel oriented-object detection method called Rotation-SqueezeDet. This method can run on embedded-platforms in near real-time. Besides, this method can give the oriented bounding box of an object in images to enable a rotation-aware grasping. Based on this method, a UAM platform was designed and built. We have given the formulation, positioning, control, and planning of the whole UAM system. All the mechanical designs are fully provided as open-source hardware for reuse by the community. Finally, the effectiveness of the proposed scheme was validated in multiple experimental trials, highlighting its applicability of autonomous aerial rotational grasping in Global Positioning System (GPS) denied environments. We believe this system can be deployed to many potential workplaces which need UAM to accomplish difficult manipulation tasks. Full article
Open AccessArticle
Data Storage Mechanism Based on Blockchain with Privacy Protection in Wireless Body Area Network
Sensors 2019, 19(10), 2395; https://doi.org/10.3390/s19102395 (registering DOI)
Received: 15 April 2019 / Revised: 21 May 2019 / Accepted: 22 May 2019 / Published: 25 May 2019
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Abstract
Wireless body area networks (WBANs) are expected to play a vital role in the field of patient-health monitoring shortly. They provide a convenient way to collect patient data, but they also bring serious problems which are mainly reflected in the safe storage of [...] Read more.
Wireless body area networks (WBANs) are expected to play a vital role in the field of patient-health monitoring shortly. They provide a convenient way to collect patient data, but they also bring serious problems which are mainly reflected in the safe storage of the collected data. The privacy and security of data storage in WBAN devices cannot meet the needs of WBAN users. Therefore, this paper adopts blockchain technology to store data, which improves the security of the collected data. Moreover, a storage model based on blockchain in WBAN is proposed in our solution. However, blockchain storage brings new problems, for example, that the storage space of blockchain is small, and the stored content is open to unauthorized attackers. To solve the problems above, this paper proposed a sequential aggregate signature scheme with a designated verifier (DVSSA) to ensure that the user’s data can only be viewed by the designated person and to protect the privacy of the users of WBAN. In addition, the new signature scheme can also compress the size of the blockchain storage space. Full article
(This article belongs to the Special Issue Wireless Body Area Networks: Applications and Technologies)
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Open AccessArticle
Towards an Autonomous Industry 4.0 Warehouse: A UAV and Blockchain-Based System for Inventory and Traceability Applications in Big Data-Driven Supply Chain Management
Sensors 2019, 19(10), 2394; https://doi.org/10.3390/s19102394 (registering DOI)
Received: 31 March 2019 / Revised: 30 April 2019 / Accepted: 22 May 2019 / Published: 25 May 2019
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Abstract
Industry 4.0 has paved the way for a world where smart factories will automate and upgrade many processes through the use of some of the latest emerging technologies. One of such technologies is Unmanned Aerial Vehicles (UAVs), which have evolved a great deal [...] Read more.
Industry 4.0 has paved the way for a world where smart factories will automate and upgrade many processes through the use of some of the latest emerging technologies. One of such technologies is Unmanned Aerial Vehicles (UAVs), which have evolved a great deal in the last years in terms of technology (e.g., control units, sensors, UAV frames) and have significantlyr educed their cost. UAVs can help industry in automatable and tedious tasks, like the ones performed on a regular basis for determining the inventory and for preserving item traceability. In such tasks, especially when it comes from untrusted third parties, it is essential to determine whether the collected information is valid or true. Likewise, ensuring data trustworthiness is a key issue in order to leverage Big Data analytics to supply chain efficiency and effectiveness. In such a case, blockchain, another Industry 4.0 technology that has become very popular in other fields like finance, has the potential to provide a higher level of transparency, security, trust and efficiency in the supply chain and enable the use of smart contracts. Thus, in this paper, we present the design and evaluation of a UAV-based system aimed at automating inventory tasks and keeping the traceability of industrial items attached to Radio-Frequency IDentification (RFID) tags. To confront current shortcomings, such a system is developed under a versatile, modular and scalable architecture aimed to reinforce cyber security and decentralization while fostering external audits and big data analytics. Therefore, the system uses a blockchain and a distributed ledger to store certain inventory data collected by UAVs, validate them, ensure their trustworthiness and make them available to the interested parties. In order to show the performance of the proposed system, different tests were performed in a real industrial warehouse, concluding that the system is able to obtain the inventory data really fast in comparison to traditional manual tasks, while being also able to estimate the position of the items when hovering over them thanks to their tag’s signal strength. In addition, the performance of the proposed blockchain-based architecture was evaluated in different scenarios. Full article
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Open AccessArticle
Epitaxial Graphene Sensors Combined with 3D-Printed Microfluidic Chip for Heavy Metals Detection
Sensors 2019, 19(10), 2393; https://doi.org/10.3390/s19102393 (registering DOI)
Received: 17 April 2019 / Revised: 13 May 2019 / Accepted: 22 May 2019 / Published: 25 May 2019
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Abstract
In this work, we investigated the sensing performance of epitaxial graphene on Si-face 4H-SiC (EG/SiC) for liquid-phase detection of heavy metals (e.g., Pb and Cd), showing fast and stable response and low detection limit. The sensing platform proposed includes 3D-printed microfluidic devices, which [...] Read more.
In this work, we investigated the sensing performance of epitaxial graphene on Si-face 4H-SiC (EG/SiC) for liquid-phase detection of heavy metals (e.g., Pb and Cd), showing fast and stable response and low detection limit. The sensing platform proposed includes 3D-printed microfluidic devices, which incorporate all features required to connect and execute lab-on-chip (LOC) functions. The obtained results indicate that EG exhibits excellent sensing activity towards Pb and Cd ions. Several concentrations of Pb2+ solutions, ranging from 125 nM to 500 µM, were analyzed showing Langmuir correlation between signal and Pb2+ concentrations, good stability, and reproducibility over time. Upon the simultaneous presence of both metals, sensor response is dominated by Pb2+ rather than Cd2+ ions. To explain the sensing mechanisms and difference in adsorption behavior of Pb2+ and Cd2+ ions on EG in water-based solutions, we performed van-der-Waals (vdW)-corrected density functional theory (DFT) calculations and non-covalent interaction (NCI) analysis, extended charge decomposition analysis (ECDA), and topological analysis. We demonstrated that Pb2+ and Cd2+ ions act as electron-acceptors, enhancing hole conductivity of EG, due to charge transfer from graphene to metal ions, and Pb2+ ions have preferential ability to binding with graphene over cadmium. Electrochemical measurements confirmed the conductometric results, which additionally indicate that EG is more sensitive to lead than to cadmium. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle
Machine Learning Techniques for Chemical Identification Using Cyclic Square Wave Voltammetry
Sensors 2019, 19(10), 2392; https://doi.org/10.3390/s19102392 (registering DOI)
Received: 25 April 2019 / Revised: 16 May 2019 / Accepted: 22 May 2019 / Published: 25 May 2019
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Abstract
Electroanalytical techniques are useful for detection and identification because the instrumentation is simple and can support a wide variety of assays. One example is cyclic square wave voltammetry (CSWV), a practical detection technique for different classes of compounds including explosives, herbicides/pesticides, industrial compounds, [...] Read more.
Electroanalytical techniques are useful for detection and identification because the instrumentation is simple and can support a wide variety of assays. One example is cyclic square wave voltammetry (CSWV), a practical detection technique for different classes of compounds including explosives, herbicides/pesticides, industrial compounds, and heavy metals. A key barrier to the widespread application of CSWV for chemical identification is the necessity of a high performance, generalizable classification algorithm. Here, machine and deep learning models were developed for classifying samples based on voltammograms alone. The highest performing models were Long Short-Term Memory (LSTM) and Fully Convolutional Networks (FCNs), depending on the dataset against which performance was assessed. When compared to other algorithms, previously used for classification of CSWV and other similar data, our LSTM and FCN-based neural networks achieve higher sensitivity and specificity with the area under the curve values from receiver operating characteristic (ROC) analyses greater than 0.99 for several datasets. Class activation maps were paired with CSWV scans to assist in understanding the decision-making process of the networks, and their ability to utilize this information was examined. The best-performing models were then successfully applied to new or holdout experimental data. An automated method for processing CSWV data, training machine learning models, and evaluating their prediction performance is described, and the tools generated provide support for the identification of compounds using CSWV from samples in the field. Full article
(This article belongs to the Special Issue Machine Learning for Sensing and Healthcare)
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Open AccessArticle
Numerical Simulation and Experimental Study of Fluid-Solid Coupling-Based Air-Coupled Ultrasonic Detection of Stomata Defect of Lithium-Ion Battery
Sensors 2019, 19(10), 2391; https://doi.org/10.3390/s19102391 (registering DOI)
Received: 5 April 2019 / Revised: 30 April 2019 / Accepted: 15 May 2019 / Published: 25 May 2019
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Abstract
Aiming at the characteristics of the periodic stacking structure of a lithium-ion battery core and the corresponding relationship between the air-coupled ultrasonic transmission initial wave and the wave propagation mode in each layer medium of a lithium-ion battery, the homogenized finite element model [...] Read more.
Aiming at the characteristics of the periodic stacking structure of a lithium-ion battery core and the corresponding relationship between the air-coupled ultrasonic transmission initial wave and the wave propagation mode in each layer medium of a lithium-ion battery, the homogenized finite element model of a lithium-ion battery was developed based on the theory of pressure acoustics and solid mechanics. This model provided a reliable method and basis for solving the visualization of ultrasonic propagation in a lithium-ion battery and the analysis of ultrasonic time-frequency domain characteristics. The finite element simulation analysis and experimental verification of a lithium-ion battery with a near-surface stomata defect, near-bottom stomata defect and middle-layer stomata defect were performed. The results showed that the air-coupled ultrasonic transmission signal can effectively characterize the stomata defect inside a lithium-ion battery. The energy of an air-coupled ultrasonic transmission signal is concentrated between 350–450 kHz, and the acoustic diffraction effect has an important influence on the effect of the ultrasonic and stomata defect. Based on the amplitude response characteristics of the air-coupled ultrasonic transmission wave in the stomata defect area, a C-scan of the lithium-ion battery was performed. The C-scan result verified that air-coupled ultrasonic testing technology can accurately and effectively detect the pre-embedded stomata defect and natural stomata defect in a lithium-ion battery, which is able to promote and expand the application of the technology in the field of electric energy security. Full article
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Open AccessArticle
SmartFire: Intelligent Platform for Monitoring Fire Extinguishers and Their Building Environment
Sensors 2019, 19(10), 2390; https://doi.org/10.3390/s19102390 (registering DOI)
Received: 12 April 2019 / Revised: 17 May 2019 / Accepted: 23 May 2019 / Published: 25 May 2019
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Abstract
Due to fire protection regulations, a minimum number of fire extinguishers must be available depending on the surface area of each building, industrial establishment or workplace. There is also a set of rules that establish where the fire extinguisher should be placed: always [...] Read more.
Due to fire protection regulations, a minimum number of fire extinguishers must be available depending on the surface area of each building, industrial establishment or workplace. There is also a set of rules that establish where the fire extinguisher should be placed: always close to the points that are most likely to be affected by a fire and where they are visible and accessible for use. Fire extinguishers are pressure devices, which means that they require maintenance operations that ensure they will function properly in the case of a fire. The purpose of manual and periodic fire extinguisher checks is to verify that their labeling, installation and condition comply with the standards. Security seals, inscriptions, hose and other seals are thoroughly checked. The state of charge (weight and pressure) of the extinguisher, the bottle of propellant gas (if available), and the state of all mechanical parts (nozzle, valves, hose, etc.) are also checked. To ensure greater safety and reduce the economic costs associated with maintaining fire extinguishers, it is necessary to develop a system that allows monitoring of their status. One of the advantages of monitoring fire extinguishers is that it will be possible to understand what external factors affect them (for example, temperature or humidity) and how they do so. For this reason, this article presents a system of soft agents that monitors the state of the extinguishers, collects a history of the state of the extinguisher and environmental factors and sends notifications if any parameter is not within the range of normal values.The results rendered by the SmartFire prototype indicate that its accuracy in calculating pressure changes is equivalent to that of a specific data acquisition system (DAS). The comparative study of the two curves (SmartFire and DAS) shows that the average error between the two curves is negligible: 8% in low pressure measurements (up to 3 bar) and 0.3% in high pressure (above 3 bar). Full article
(This article belongs to the Special Issue Artificial Intelligence and Blockchain in Wireless Sensors Networks)
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Open AccessArticle
Performance and Analysis of Feature Tracking Approaches in Laser Speckle Instrumentation
Sensors 2019, 19(10), 2389; https://doi.org/10.3390/s19102389 (registering DOI)
Received: 29 March 2019 / Revised: 8 May 2019 / Accepted: 21 May 2019 / Published: 24 May 2019
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Abstract
This paper investigates the application of feature tracking algorithms as an alternative data processing method for laser speckle instrumentation. The approach is capable of determining both the speckle pattern translation and rotation and can therefore be used to detect the in-plane rotation and [...] Read more.
This paper investigates the application of feature tracking algorithms as an alternative data processing method for laser speckle instrumentation. The approach is capable of determining both the speckle pattern translation and rotation and can therefore be used to detect the in-plane rotation and translation of an object simultaneously. A performance assessment of widely used feature detection and matching algorithms from the computer vision field, for both translation and rotation measurements from laser speckle patterns, is presented. The accuracy of translation measurements using the feature tracking approach was found to be similar to that of correlation-based processing with accuracies of 0.025–0.04 pixels and a typical precision of 0.02–0.09 pixels depending upon the method and image size used. The performance for in-plane rotation measurements are also presented with rotation measurement accuracies of <0.01 found to be achievable over an angle range of ±10 and of <0.1 over a range of ± 25 , with a typical precision between 0.02 and 0.08 depending upon method and image size. The measurement range is found to be limited by the failure to match sufficient speckles at larger rotation angles. An analysis of each stage of the process was conducted to identify the most suitable approaches for use with laser speckle images and areas requiring further improvement. A quantitative approach to assessing different feature tracking methods is described, and reference data sets of experimentally translated and rotated speckle patterns from a range of surface finishes and surface roughness are presented. As a result, three areas that lead to the failure of the matching process are identified as areas for future investigation: the inability to detect the same features in partially decorrelated images leading to unmatchable features, the variance of computed feature orientation between frames leading to different descriptors being calculated for the same feature, and the failure of the matching processes due to the inability to discriminate between different features in speckle images. Full article
(This article belongs to the Section Optical Sensors)
Open AccessArticle
Data Stream Mining Applied to Maximum Wind Forecasting in the Canary Islands
Sensors 2019, 19(10), 2388; https://doi.org/10.3390/s19102388 (registering DOI)
Received: 6 April 2019 / Revised: 15 May 2019 / Accepted: 21 May 2019 / Published: 24 May 2019
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Abstract
The Canary Islands are a well known tourist destination with generally stable and clement weather conditions. However, occasionally extreme weather conditions occur, which although very unusual, may cause severe damage to the local economy. The ViMetRi-MAC EU funded project has among its goals, [...] Read more.
The Canary Islands are a well known tourist destination with generally stable and clement weather conditions. However, occasionally extreme weather conditions occur, which although very unusual, may cause severe damage to the local economy. The ViMetRi-MAC EU funded project has among its goals, managing climate-change-associated risks. The Spanish National Meteorology Agency (AEMET) has a network of weather stations across the eight Canary Islands. Using data from those stations, we propose a novel methodology for the prediction of maximum wind speed in order to trigger an early alert for extreme weather conditions. The methodology proposed has the added value of using an innovative kind of machine learning that is based on the data stream mining paradigm. This type of machine learning system relies on two important features: models are learned incrementally and adaptively. That means the learner tunes the models gradually and endlessly as new observations are received and also modifies it when there is concept drift (statistical instability), in the modeled phenomenon. The results presented seem to prove that this data stream mining approach is a good fit for this kind of problem, clearly improving the results obtained with the accumulative non-adaptive version of the methodology. Full article
(This article belongs to the Special Issue New Trends in Tourism Business Intelligence)
Open AccessFeature PaperReview
Raman Sensing and Its Multimodal Combination with Optoacoustics and OCT for Applications in the Life Sciences
Sensors 2019, 19(10), 2387; https://doi.org/10.3390/s19102387 (registering DOI)
Received: 28 February 2019 / Revised: 10 May 2019 / Accepted: 15 May 2019 / Published: 24 May 2019
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Abstract
Currently, many optical modalities are being investigated, applied, and further developed for non-invasive analysis and sensing in the life sciences. To befit the complexity of the study objects and questions in this field, the combination of two or more modalities is attempted. We [...] Read more.
Currently, many optical modalities are being investigated, applied, and further developed for non-invasive analysis and sensing in the life sciences. To befit the complexity of the study objects and questions in this field, the combination of two or more modalities is attempted. We review our work on multimodal sensing concepts for applications ranging from non-invasive quantification of biomolecules in the living organism to supporting medical diagnosis showing the combined capabilities of Raman spectroscopy, optical coherence tomography, and optoacoustics. Full article
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Germany)
Open AccessArticle
Machine Learning Techniques for Undertaking Roundabouts in Autonomous Driving
Sensors 2019, 19(10), 2386; https://doi.org/10.3390/s19102386 (registering DOI)
Received: 14 March 2019 / Revised: 16 May 2019 / Accepted: 21 May 2019 / Published: 24 May 2019
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Abstract
This article presents a machine learning-based technique to build a predictive model and generate rules of action to allow autonomous vehicles to perform roundabout maneuvers. The approach consists of building a predictive model of vehicle speeds and steering angles based on collected data [...] Read more.
This article presents a machine learning-based technique to build a predictive model and generate rules of action to allow autonomous vehicles to perform roundabout maneuvers. The approach consists of building a predictive model of vehicle speeds and steering angles based on collected data related to driver–vehicle interactions and other aggregated data intrinsic to the traffic environment, such as roundabout geometry and the number of lanes obtained from Open-Street-Maps and offline video processing. The study systematically generates rules of action regarding the vehicle speed and steering angle required for autonomous vehicles to achieve complete roundabout maneuvers. Supervised learning algorithms like the support vector machine, linear regression, and deep learning are used to form the predictive models. Full article
(This article belongs to the Special Issue Perception Sensors for Road Applications)
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Open AccessArticle
Adaptive Segmentation of Remote Sensing Images Based on Global Spatial Information
Sensors 2019, 19(10), 2385; https://doi.org/10.3390/s19102385 (registering DOI)
Received: 22 April 2019 / Revised: 9 May 2019 / Accepted: 20 May 2019 / Published: 24 May 2019
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Abstract
The problem of image segmentation can be reduced to the clustering of pixels in the intensity space. The traditional fuzzy c-means algorithm only uses pixel membership information and does not make full use of spatial information around the pixel, so it is not [...] Read more.
The problem of image segmentation can be reduced to the clustering of pixels in the intensity space. The traditional fuzzy c-means algorithm only uses pixel membership information and does not make full use of spatial information around the pixel, so it is not ideal for noise reduction. Therefore, this paper proposes a clustering algorithm based on spatial information to improve the anti-noise and accuracy of image segmentation. Firstly, the image is roughly clustered using the improved Lévy grey wolf optimization algorithm (LGWO) to obtain the initial clustering center. Secondly, the neighborhood and non-neighborhood information around the pixel is added into the target function as spatial information, the weight between the pixel information and non-neighborhood spatial information is adjusted by information entropy, and the traditional Euclidean distance is replaced by the improved distance measure. Finally, the objective function is optimized by the gradient descent method to segment the image correctly. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing and Analysis)
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Open AccessArticle
400 °C Sensor Based on Ni/4H-SiC Schottky Diode for Reliable Temperature Monitoring in Industrial Environments
Sensors 2019, 19(10), 2384; https://doi.org/10.3390/s19102384 (registering DOI)
Received: 25 April 2019 / Revised: 16 May 2019 / Accepted: 22 May 2019 / Published: 24 May 2019
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Abstract
This paper presents a high-temperature probe suitable for operating in harsh industrial applications as a reliable alternative to low-lifespan conventional solutions, such as thermocouples. The temperature sensing element is a Schottky diode fabricated on 4H-SiC wafers, with Ni as the Schottky metal, which [...] Read more.
This paper presents a high-temperature probe suitable for operating in harsh industrial applications as a reliable alternative to low-lifespan conventional solutions, such as thermocouples. The temperature sensing element is a Schottky diode fabricated on 4H-SiC wafers, with Ni as the Schottky metal, which allows operation at temperatures up to 400 °C, with sensitivities over 2 mV/°C and excellent linearity (R2 > 99.99%). The temperature probe also includes dedicated circuitry for signal acquisition and conversion to the 4 mA–20 mA industrial standard output signal. This read-out circuit can be calibrated for linear response over a tunable temperature detection range. The entire system is designed for full electrical and mechanical compatibility with existing conventional probe casings, allowing for seamless implementation in a factory’s sensor network. Such sensors are tested alongside standard thermocouples, with matching temperature monitoring results, over several months, in real working conditions (a cement factory), up to 400 °C. Full article
(This article belongs to the Special Issue Temperature Sensors 2019)
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Open AccessArticle
Underdetermined DOA Estimation of Wideband LFM Signals Based on Gridless Sparse Reconstruction in the FRF Domain
Sensors 2019, 19(10), 2383; https://doi.org/10.3390/s19102383 (registering DOI)
Received: 18 April 2019 / Revised: 18 May 2019 / Accepted: 18 May 2019 / Published: 24 May 2019
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Abstract
An underdetermined direction of arrival (DOA) estimation method of wideband linear frequency modulated (LFM) signals is proposed without grid mismatch. According to the concentration property of LFM signal in the fractional Fourier (FRF) domain, the received sparse model of wideband signals with time-variant [...] Read more.
An underdetermined direction of arrival (DOA) estimation method of wideband linear frequency modulated (LFM) signals is proposed without grid mismatch. According to the concentration property of LFM signal in the fractional Fourier (FRF) domain, the received sparse model of wideband signals with time-variant steering vector is firstly derived based on a coprime array. Afterwards, by interpolating virtual sensors, a virtual extended uniform linear array (ULA) is constructed with more degrees of freedom, and its covariance matrix in the FRF domain is recovered by employing sparse matrix reconstruction. Meanwhile, in order to avoid the grid mismatch problem, the modified atomic norm minimization is used to retrieve the covariance matrix with the consecutive basis. Different from the existing methods that approximately assume the frequency and the steering vector of the wideband signals are time-invariant in every narrowband frequency bin, the proposed method not only can directly solve more DOAs of LFM signals than the number of physical sensors with time-variant frequency and steering vector, but also obtain higher resolution and more accurate DOA estimation performance by the gridless sparse reconstruction. Simulation results demonstrate the effectiveness of the proposed method. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle
Magnetostrictive Sensor for Blockage Detection in Pipes Subjected to High Temperatures
Sensors 2019, 19(10), 2382; https://doi.org/10.3390/s19102382 (registering DOI)
Received: 22 April 2019 / Revised: 14 May 2019 / Accepted: 21 May 2019 / Published: 24 May 2019
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Abstract
The use of solar thermal power plants is considered a cost-effective alternative to produce renewable energy. Unlike other energy installations, in this type of plants the transfer and storage of energy has been solved by using molten salts. These salts run between two [...] Read more.
The use of solar thermal power plants is considered a cost-effective alternative to produce renewable energy. Unlike other energy installations, in this type of plants the transfer and storage of energy has been solved by using molten salts. These salts run between two tanks through the steam generation system that feeds the turbine. Although the use of salts as a heat transfer fluid is considered an adequate solution, they are not without problems. One of them is the formation of blockages in the pipes due to a partial solidification of the salt, which leads to the shutdown of the installation, with the consequent economic losses. Fast location of these blockages in a minimally intrusive way is the objective pursued in this work. The method to achieve this is based on the use of a new magnetostrictive sensor that simplifies previous designs. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
A Lighted Deep Convolutional Neural Network Based Fault Diagnosis of Rotating Machinery
Sensors 2019, 19(10), 2381; https://doi.org/10.3390/s19102381 (registering DOI)
Received: 23 April 2019 / Revised: 17 May 2019 / Accepted: 21 May 2019 / Published: 24 May 2019
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Abstract
To improve the fault diagnosis performance for rotating machinery, an efficient, noise-resistant end-to-end deep learning (DL) algorithm is proposed based on the advantages of the wavelet packet transform in vibration signal processing (the capability to extract multiscale information and more spectral distribution features) [...] Read more.
To improve the fault diagnosis performance for rotating machinery, an efficient, noise-resistant end-to-end deep learning (DL) algorithm is proposed based on the advantages of the wavelet packet transform in vibration signal processing (the capability to extract multiscale information and more spectral distribution features) and deep convolutional neural networks (good classification performance, data-driven design and high transfer-learning ability). First, a vibration signal is subjected to pyramid wavelet packet decomposition, and each sub-band coefficient is used as the input for each channel of a deep convolutional network (DCN). Then, based on the lightweight modeling requirements and techniques, a new DCN structure is designed for the fault diagnosis. The proposed algorithm is compared with the support vector machine algorithm and the published DL algorithms based on a bearing dataset produced by Case Western Reserve University. The experimental results show that the proposed algorithm is superior to the existing algorithms in terms of accuracy, memory space, computational complexity, noise resistance, and transfer performance, producing good results. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle
Wearable Enzymatic Alcohol Biosensor
Sensors 2019, 19(10), 2380; https://doi.org/10.3390/s19102380 (registering DOI)
Received: 4 April 2019 / Revised: 9 May 2019 / Accepted: 20 May 2019 / Published: 24 May 2019
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Abstract
Transdermal alcohol biosensors have the ability to detect the alcohol that emanates from the bloodstream and diffuses through the skin. However, previous biosensors have suffered from long-term fouling of the sensor element and drift in the resulting sensor readings over time. Here, we [...] Read more.
Transdermal alcohol biosensors have the ability to detect the alcohol that emanates from the bloodstream and diffuses through the skin. However, previous biosensors have suffered from long-term fouling of the sensor element and drift in the resulting sensor readings over time. Here, we report a wearable alcohol sensor platform that solves the problem of sensor fouling by enabling drift-free signals in vivo for up to 24 h and an interchangeable cartridge connection that enables consecutive days of measurement. We demonstrate how alcohol oxidase enzyme and Prussian Blue can be combined to prevent baseline drift above 25 nA, enabling sensitive detection of transdermal alcohol. Laboratory characterization of the enzymatic alcohol sensor demonstrates that the sensor is mass-transfer-limited by a diffusion-limiting membrane of lower permeability than human skin and a linear sensor range between 0 mM and 50 mM. Further, we show continuous transdermal alcohol data recorded with a human subject for two consecutive days. The non-invasive sensor presented here is an objective alternative to the self-reports used commonly to quantify alcohol consumption in research studies. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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Open AccessReview
Copper(I)-Catalyzed Click Chemistry as a Tool for the Functionalization of Nanomaterials and the Preparation of Electrochemical (Bio)Sensors
Sensors 2019, 19(10), 2379; https://doi.org/10.3390/s19102379 (registering DOI)
Received: 24 April 2019 / Revised: 20 May 2019 / Accepted: 22 May 2019 / Published: 24 May 2019
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Abstract
Proper functionalization of electrode surfaces and/or nanomaterials plays a crucial role in the preparation of electrochemical (bio)sensors and their resulting performance. In this context, copper(I)-catalyzed azide-alkyne cycloaddition (CuAAC) has been demonstrated to be a powerful strategy due to the high yields achieved, absence [...] Read more.
Proper functionalization of electrode surfaces and/or nanomaterials plays a crucial role in the preparation of electrochemical (bio)sensors and their resulting performance. In this context, copper(I)-catalyzed azide-alkyne cycloaddition (CuAAC) has been demonstrated to be a powerful strategy due to the high yields achieved, absence of by-products and moderate conditions required both in aqueous medium and under physiological conditions. This particular chemistry offers great potential to functionalize a wide variety of electrode surfaces, nanomaterials, metallophthalocyanines (MPcs) and polymers, thus providing electrochemical platforms with improved electrocatalytic ability and allowing the stable, reproducible and functional integration of a wide range of nanomaterials and/or different biomolecules (enzymes, antibodies, nucleic acids and peptides). Considering the rapid progress in the field, and the potential of this technology, this review paper outlines the unique features imparted by this particular reaction in the development of electrochemical sensors through the discussion of representative examples of the methods mainly reported over the last five years. Special attention has been paid to electrochemical (bio)sensors prepared using nanomaterials and applied to the determination of relevant analytes at different molecular levels. Current challenges and future directions in this field are also briefly pointed out. Full article
(This article belongs to the Special Issue Electrochemical Nanobiosensors)
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Open AccessArticle
CONE: A Connected Dominating Set-Based Flooding Protocol for Wireless Sensor Networks
Sensors 2019, 19(10), 2378; https://doi.org/10.3390/s19102378
Received: 5 May 2019 / Revised: 20 May 2019 / Accepted: 21 May 2019 / Published: 23 May 2019
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Abstract
Wireless sensor networks (WSNs) play a significant role in a large number of applications, e.g., healthcare and industry. A WSN typically consists of a large number of sensor nodes which rely on limited power sources in many applications. Therefore, improving the energy efficiency [...] Read more.
Wireless sensor networks (WSNs) play a significant role in a large number of applications, e.g., healthcare and industry. A WSN typically consists of a large number of sensor nodes which rely on limited power sources in many applications. Therefore, improving the energy efficiency of WSNs becomes a crucial topic in the research community. As a fundamental service in WSNs, network flooding offers the advantages that information can be distributed fast and reliably throughout an entire network. However, network flooding suffers from low energy efficiency due to the large number of redundant transmissions in the network. In this work, we exploit connected dominating sets (CDS) to enhance the energy efficiency of network flooding by reducing the number of transmissions. For this purpose, we propose a connected dominating set-based flooding protocol (CONE). CONE inhibits nodes that are not in the CDS from rebroadcasting packets during the flooding process. Furthermore, we evaluate the performance of CONE in both simulations and a real-world testbed, and then we compare CONE to a baseline protocol. Experimental results show that CONE improves the end-to-end reliability and reduces the duty cycle of network flooding in the simulations. Additionally, CONE reduces the average energy consumption in the FlockLab testbed by 15%. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle
A Novel Eye Movement Data Transformation Technique that Preserves Temporal Information: A Demonstration in a Face Processing Task
Sensors 2019, 19(10), 2377; https://doi.org/10.3390/s19102377
Received: 16 April 2019 / Revised: 7 May 2019 / Accepted: 22 May 2019 / Published: 23 May 2019
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Abstract
Existing research has shown that human eye-movement data conveys rich information about underlying mental processes, and that the latter may be inferred from the former. However, most related studies rely on spatial information about which different areas of visual stimuli were looked at, [...] Read more.
Existing research has shown that human eye-movement data conveys rich information about underlying mental processes, and that the latter may be inferred from the former. However, most related studies rely on spatial information about which different areas of visual stimuli were looked at, without considering the order in which this occurred. Although powerful algorithms for making pairwise comparisons between eye-movement sequences (scanpaths) exist, the problem is how to compare two groups of scanpaths, e.g., those registered with vs. without an experimental manipulation in place, rather than individual scanpaths. Here, we propose that the problem might be solved by projecting a scanpath similarity matrix, obtained via a pairwise comparison algorithm, to a lower-dimensional space (the comparison and dimensionality-reduction techniques we use are ScanMatch and t-SNE). The resulting distributions of low-dimensional vectors representing individual scanpaths can be statistically compared. To assess if the differences result from temporal scanpath features, we propose to statistically compare the cross-validated accuracies of two classifiers predicting group membership: (1) based exclusively on spatial metrics; (2) based additionally on the obtained scanpath representation vectors. To illustrate, we compare autistic vs. typically-developing individuals looking at human faces during a lab experiment and find significant differences in temporal scanpath features. Full article
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Open AccessFeature PaperArticle
The Effect of UVB Irradiation and Oxidative Stress on the Skin Barrier—A New Method to Evaluate Sun Protection Factor Based on Electrical Impedance Spectroscopy
Sensors 2019, 19(10), 2376; https://doi.org/10.3390/s19102376
Received: 17 April 2019 / Revised: 16 May 2019 / Accepted: 21 May 2019 / Published: 23 May 2019
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Abstract
Sunlight is vital for several biochemical processes of the skin organ. However, acute or chronic exposure to ultraviolet radiation (UVR) has several harmful effects on the skin structure and function, especially in the case of the failing function of antioxidative enzymes, which may [...] Read more.
Sunlight is vital for several biochemical processes of the skin organ. However, acute or chronic exposure to ultraviolet radiation (UVR) has several harmful effects on the skin structure and function, especially in the case of the failing function of antioxidative enzymes, which may lead to substantial tissue damage due to the increased presence of reactive oxygen species (ROS). The aim of this work was to investigate the combined effect of ultraviolet B (UVB) irradiation and oxidative stress on the skin barrier integrity. For this, we employed electrical impedance spectroscopy (EIS) to characterize changes of the electrical properties of excised pig skin membranes after various exposure conditions of UVB irradiation, oxidative stress, and the inhibition of antioxidative enzymatic processes. The oxidative stress was regulated by adding hydrogen peroxide (H2O2) as a source of ROS, while sodium azide (NaN3) was used as an inhibitor of the antioxidative enzyme catalase, which is naturally present throughout the epidermis. By screening for the combined effect of UVB and oxidative stress on the skin membrane electrical properties, we developed a new protocol for evaluating these parameters in a simple in vitro setup. Strikingly, the results show that exposure to extreme UVB irradiation does not affect the skin membrane resistance, implying that the skin barrier remains macroscopically intact. Likewise, exposure to only oxidative stress conditions, without UVB irradiation, does not affect the skin membrane resistance. In contrast to these observations, the combination of UVB irradiation and oxidative stress conditions results in a drastic decrease of the skin membrane resistance, indicating that the integrity of the skin barrier is compromised. Further, the skin membrane effective capacitance remained more or less unaffected by UVB exposure, irrespective of simultaneous exposure of oxidative stress. The EIS results were concluded to be associated with clear signs of macroscopic tissue damage of the epidermis as visualized with microscopy after exposure to UVB irradiation under oxidative stress conditions. Finally, the novel methodology was tested by performing an assessment of cosmetic sunscreen formulations with varying sun protection factor (SPF), with an overall successful outcome, showing good correlation between SPF value and protection capacity in terms of skin resistance change. The results from this study allow for the development of new skin sensors based on EIS for the detection of skin tissue damage from exposure to UVB irradiation and oxidative stress and provide a new, more comprehensive methodology, taking into account both the influence of UVB irradiation and oxidative stress, for in vitro determination of SPF in cosmetic formulations. Full article
(This article belongs to the Special Issue Skin Sensors)
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