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Sensors, Volume 19, Issue 8 (April-2 2019)

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Open AccessArticle Optimization of Spectrum Utilization in Cooperative Spectrum Sensing
Sensors 2019, 19(8), 1922; https://doi.org/10.3390/s19081922 (registering DOI)
Received: 8 March 2019 / Revised: 12 April 2019 / Accepted: 19 April 2019 / Published: 23 April 2019
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Abstract
This paper presents an analytical framework for the probability of spectrum hole utilization (PSHU) of a cognitive radio system with soft cooperative spectrum sensing (CSS) under a practical consideration of fixed frame structure. In practical systems, the length of a time-frame is generally [...] Read more.
This paper presents an analytical framework for the probability of spectrum hole utilization (PSHU) of a cognitive radio system with soft cooperative spectrum sensing (CSS) under a practical consideration of fixed frame structure. In practical systems, the length of a time-frame is generally fixed, where the time-frame consists of sensing, reporting, and transmission durations. Thus, increasing sensing and reporting time duration in cooperative spectrum sensing improves the probability of successful detection of the primary user’s (PU) presence or the absence but reduces transmission time duration, which results in a lower PSHU. A large reporting duration is required when more secondary users (SUs) report their sensed information to the fusion center (FC) and/or multiple bits are used by each SU in soft cooperative spectrum sensing. Thus, reporting time in terms of the number of SUs and reporting bits also have a similar effect on PSHU. Based on this interesting trade-off between PSHU and the sensing and reporting time duration, this paper analyzes the impact of an increasing number of SUs and reporting bits on PSHU. Full article
(This article belongs to the Section Sensor Networks)
Open AccessArticle Radio Resource Allocation with The Fairness Metric for Low Density Signature OFDM in Underlay Cognitive Radio Networks
Sensors 2019, 19(8), 1921; https://doi.org/10.3390/s19081921 (registering DOI)
Received: 28 February 2019 / Revised: 29 March 2019 / Accepted: 15 April 2019 / Published: 23 April 2019
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Abstract
Low density signature orthogonal frequency division multiplexing (LDS-OFDM), one type of non-orthogonal multiple access (NOMA), is a special case of multi-carrier code division multiple access (MC-CDMA). In LDS-OFDM, each user is allowed to spread its symbols in a small set of subcarriers, and [...] Read more.
Low density signature orthogonal frequency division multiplexing (LDS-OFDM), one type of non-orthogonal multiple access (NOMA), is a special case of multi-carrier code division multiple access (MC-CDMA). In LDS-OFDM, each user is allowed to spread its symbols in a small set of subcarriers, and there is only a small group of users that are permitted to share the same subcarrier. In this paper, we study the resource allocation for LDS-OFDM as the multiple access model in cognitive radio networks. In our scheme, SUs are allocated to certain d v subcarriers based on minimum interference or higher SINR in each subcarrier. To overcome the problem where SUs were allocated less than the d v subcarriers, we propose interference limit-based resource allocation with the fairness metric (ILRA-FM). Simulation results show that, compared to the ILRA algorithm, the ILRA-FM algorithm has a lower outage probability and higher fairness metric value and also a higher throughput fairness index. Full article
(This article belongs to the Special Issue Selected Papers from TENCON 2018)
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Open AccessArticle Knowledge-Aided Doppler Beam Sharpening Super-Resolution Imaging by Exploiting the Spatial Continuity Information
Sensors 2019, 19(8), 1920; https://doi.org/10.3390/s19081920 (registering DOI)
Received: 16 January 2019 / Revised: 10 April 2019 / Accepted: 11 April 2019 / Published: 23 April 2019
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Abstract
This paper deals with the problem of high cross-range resolution Doppler beam sharpening (DBS) imaging for airborne wide-area surveillance (WAS) radar under short dwell time situations. A knowledge-aided DBS (KA-DBS) imaging algorithm is proposed. In the proposed KA-DBS framework, the DBS imaging model [...] Read more.
This paper deals with the problem of high cross-range resolution Doppler beam sharpening (DBS) imaging for airborne wide-area surveillance (WAS) radar under short dwell time situations. A knowledge-aided DBS (KA-DBS) imaging algorithm is proposed. In the proposed KA-DBS framework, the DBS imaging model for WAS radar is constructed and the cross-range resolution is analyzed. Since the radar illuminates the imaging scene continuously through the scanning movement of the antenna, there is strong spatial coherence between adjacent pulses. Based on this fact, forward and backward pulse information can be predicted, and the equivalent number of pulses in each coherent processing interval (CPI) will be doubled based on the autoregressive (AR) technique by taking advantage of the spatial continuity property of echoes. Finally, the predicted forward and backward pulses are utilized to merge with the initial pulses, then the newly merged pulses in each CPI are utilized to perform the DBS imaging. Since the number of newly merged pulses in KA-DBS is twice larger than that in the conventional DBS algorithm with the same dwell time, the cross-range resolution in the proposed KA-DBS algorithm can be improved by a factor of two. The imaging performance assessment conducted by resorting to real airborne data set, has verified the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Techniques and Applications)
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Open AccessArticle Minimum Connected Dominating Set Algorithms for Ad Hoc Sensor Networks
Sensors 2019, 19(8), 1919; https://doi.org/10.3390/s19081919 (registering DOI)
Received: 22 March 2019 / Revised: 20 April 2019 / Accepted: 21 April 2019 / Published: 23 April 2019
PDF Full-text (1707 KB)
Abstract
To achieve effective communication in ad hoc sensor networks, researchers have been working on finding a minimum connected dominating set (MCDS) as a virtual backbone network in practice. Presently, many approximate algorithms have been proposed to construct MCDS, the best among which is [...] Read more.
To achieve effective communication in ad hoc sensor networks, researchers have been working on finding a minimum connected dominating set (MCDS) as a virtual backbone network in practice. Presently, many approximate algorithms have been proposed to construct MCDS, the best among which is adopting the two-stage idea, that is, to construct a maximum independent set (MIS) firstly and then realize the connectivity through the Steiner tree construction algorithm. For the first stage, this paper proposes an improved collaborative coverage algorithm for solving maximum independent set (IC-MIS), which expands the selection of the dominating point from two-hop neighbor to three-hop neighbor. The coverage efficiency has been improved under the condition of complete coverage. For the second stage, this paper respectively proposes an improved Kruskal–Steiner tree construction algorithm (IK–ST) and a maximum leaf nodes Steiner tree construction algorithm (ML-ST), both of which can make the result closer to the optimal solution. Finally, the simulation results show that the algorithm proposed in this paper is a great improvement over the previous algorithm in optimizing the scale of the connected dominating set (CDS). Full article
Open AccessArticle Improved Reconstruction of MR Scanned Images by Using a Dictionary Learning Scheme
Sensors 2019, 19(8), 1918; https://doi.org/10.3390/s19081918 (registering DOI)
Received: 11 March 2019 / Revised: 16 April 2019 / Accepted: 21 April 2019 / Published: 23 April 2019
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Abstract
The application of compressed sensing (CS) to biomedical imaging is sensational since it permits a rationally accurate reconstruction of images by exploiting the image sparsity. The quality of CS reconstruction methods largely depends on the use of various sparsifying transforms, such as wavelets, [...] Read more.
The application of compressed sensing (CS) to biomedical imaging is sensational since it permits a rationally accurate reconstruction of images by exploiting the image sparsity. The quality of CS reconstruction methods largely depends on the use of various sparsifying transforms, such as wavelets, curvelets or total variation (TV), to recover MR images. As per recently developed mathematical concepts of CS, the biomedical images with sparse representation can be recovered from randomly undersampled data, provided that an appropriate nonlinear recovery method is used. Due to high under-sampling, the reconstructed images have noise like artifacts because of aliasing. Reconstruction of images from CS involves two steps, one for dictionary learning and the other for sparse coding. In this novel framework, we choose Simultaneous code word optimization (SimCO) patch-based dictionary learning that updates the atoms simultaneously, whereas Focal underdetermined system solver (FOCUSS) is used for sparse representation because of a soft constraint on sparsity of an image. Combining SimCO and FOCUSS, we propose a new scheme called SiFo. Our proposed alternating reconstruction scheme learns the dictionary, uses it to eliminate aliasing and noise in one stage, and afterwards restores and fills in the k-space data in the second stage. Experiments were performed using different sampling schemes with noisy and noiseless cases of both phantom and real brain images. Based on various performance parameters, it has been shown that our designed technique outperforms the conventional techniques, like K-SVD with OMP, used in dictionary learning based MRI (DLMRI) reconstruction. Full article
(This article belongs to the Section Biosensors)
Open AccessArticle Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing
Sensors 2019, 19(8), 1917; https://doi.org/10.3390/s19081917 (registering DOI)
Received: 29 January 2019 / Revised: 2 April 2019 / Accepted: 2 April 2019 / Published: 23 April 2019
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Abstract
We review some emerging trends in transduction, connectivity and data analytics for Point-of-Care Testing (POCT) of infectious and non-communicable diseases. The patient need for POCT is described along with developments in portable diagnostics, specifically in respect of Lab-on-chip and microfluidic systems. We describe [...] Read more.
We review some emerging trends in transduction, connectivity and data analytics for Point-of-Care Testing (POCT) of infectious and non-communicable diseases. The patient need for POCT is described along with developments in portable diagnostics, specifically in respect of Lab-on-chip and microfluidic systems. We describe some novel electrochemical and photonic systems and the use of mobile phones in terms of hardware components and device connectivity for POCT. Developments in data analytics that are applicable for POCT are described with an overview of data structures and recent AI/Machine learning trends. The most important methodologies of machine learning, including deep learning methods, are summarised. The potential value of trends within POCT systems for clinical diagnostics within Lower Middle Income Countries (LMICs) and the Least Developed Countries (LDCs) are highlighted. Full article
(This article belongs to the Section Biosensors)
Open AccessArticle A Trust-Based Formal Model for Fault Detection in Wireless Sensor Networks
Sensors 2019, 19(8), 1916; https://doi.org/10.3390/s19081916 (registering DOI)
Received: 24 January 2019 / Revised: 15 April 2019 / Accepted: 19 April 2019 / Published: 23 April 2019
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Abstract
Wireless Sensor Networks (WSNs) are prone to failures and malicious attacks. Trust evaluation is becoming a new method for fault detection in WSNs. In our previous work, a comprehensive trust model based on multi-factors was introduced for fault detection. This model was validated [...] Read more.
Wireless Sensor Networks (WSNs) are prone to failures and malicious attacks. Trust evaluation is becoming a new method for fault detection in WSNs. In our previous work, a comprehensive trust model based on multi-factors was introduced for fault detection. This model was validated by simulating. However, it needs to be redeployed when adjustment to network parameters is made. To address the redeployment issue, we propose a Trust-based Formal Model (TFM) that can describe the fault detection process and check faults without simulating and running a WSN. This model derives from Petri nets with the characteristics of time, weight, and threshold. Basic structures of TFM are presented with which compound structures for general purposes can be built. The transition firing and marking updating rules are both defined for further system analysis. An efficient TFM analysis algorithm is developed for structured detection models. When trust factor values, firing time, weights, and thresholds are loaded, precise assessment of the node can be obtained. Finally, we implement TFM with the Generic Modeling Environment (GME). With an example, we illustrate that TFM can efficiently describe the fault detection process and specify faults in advance for WSNs. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Building Corner Detection in Aerial Images with Fully Convolutional Networks
Sensors 2019, 19(8), 1915; https://doi.org/10.3390/s19081915 (registering DOI)
Received: 30 January 2019 / Revised: 13 April 2019 / Accepted: 20 April 2019 / Published: 23 April 2019
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Abstract
In aerial images, corner points can be detected to describe the structural information of buildings for city modeling, geo-localization, and so on. For this specific vision task, the existing generic corner detectors perform poorly, as they are incapable of distinguishing corner points on [...] Read more.
In aerial images, corner points can be detected to describe the structural information of buildings for city modeling, geo-localization, and so on. For this specific vision task, the existing generic corner detectors perform poorly, as they are incapable of distinguishing corner points on buildings from those on other objects such as trees and shadows. Recently, fully convolutional networks (FCNs) have been developed for semantic image segmentation that are able to recognize a designated kind of object through a training process with a manually labeled dataset. Motivated by this achievement, an FCN-based approach is proposed in the present work to detect building corners in aerial images. First, a DeepLab model comprised of improved FCNs and fully-connected conditional random fields (CRFs) is trained end-to-end for building region segmentation. The segmentation is then further improved by using a morphological opening operation to increase its accuracy. Corner points are finally detected on the contour curves of building regions by using a scale-space detector. Experimental results show that the proposed building corner detection approach achieves an F-measure of 0.83 in the test image set and outperforms a number of state-of-the-art corner detectors by a large margin. Full article
(This article belongs to the Special Issue Deep Learning Remote Sensing Data)
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Open AccessArticle Using a Balloon-Launched Unmanned Glider to Validate Real-Time WRF Modeling
Sensors 2019, 19(8), 1914; https://doi.org/10.3390/s19081914 (registering DOI)
Received: 23 February 2019 / Revised: 3 April 2019 / Accepted: 20 April 2019 / Published: 23 April 2019
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Abstract
The use of small unmanned aerial systems (sUAS) for meteorological measurements has expanded significantly in recent years. SUAS are efficient platforms for collecting data with high resolution in both space and time, providing opportunities for enhanced atmospheric sampling. Furthermore, advances in mesoscale weather [...] Read more.
The use of small unmanned aerial systems (sUAS) for meteorological measurements has expanded significantly in recent years. SUAS are efficient platforms for collecting data with high resolution in both space and time, providing opportunities for enhanced atmospheric sampling. Furthermore, advances in mesoscale weather research and forecasting (WRF) modeling and graphical processing unit (GPU) computing have enabled high resolution weather modeling. In this manuscript, a balloon-launched unmanned glider, complete with a suite of sensors to measure atmospheric temperature, pressure, and relative humidity, is deployed for validation of real-time weather models. This work demonstrates the usefulness of sUAS for validating and improving mesoscale, real-time weather models for advancements toward reliable weather forecasts to enable safe and predictable sUAS missions beyond visual line of sight (BVLOS). Full article
(This article belongs to the Special Issue Application of Unmanned Aircraft Systems for Atmospheric Science)
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Open AccessArticle Effects of Acetone Vapor on the Exciton Band Photoluminescence Emission from Single- and Few-Layer WS2 on Template-Stripped Gold
Sensors 2019, 19(8), 1913; https://doi.org/10.3390/s19081913 (registering DOI)
Received: 13 March 2019 / Revised: 17 April 2019 / Accepted: 18 April 2019 / Published: 23 April 2019
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Abstract
Two-dimensional (2D) materials are being used widely for chemical sensing applications due to their large surface-to-volume ratio and photoluminescence (PL) emission and emission exciton band tunability. To better understand how the analyte affects the PL response for a model 2D platform, we used [...] Read more.
Two-dimensional (2D) materials are being used widely for chemical sensing applications due to their large surface-to-volume ratio and photoluminescence (PL) emission and emission exciton band tunability. To better understand how the analyte affects the PL response for a model 2D platform, we used atomic force microscopy (AFM) and co-localized photoluminescence (PL) and Raman mapping to characterize tungsten disulfide (WS2) flakes on template-stripped gold (TSG) under acetone challenge. We determined the PL-based response from single- and few-layer WS2 arises from three excitons (neutral, A0; biexciton, AA; and the trion, A). The A0 exciton PL emission is the most strongly quenched by acetone whereas the A PL emission exhibits an enhancement. We find the PL behavior is also WS2 layer number dependent. Full article
(This article belongs to the Special Issue Two-Dimensional Materials Based Sensors)
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Open AccessArticle ANLoC: An Anomaly-Aware Node Localization Algorithm for WSNs in Complex Environments
Sensors 2019, 19(8), 1912; https://doi.org/10.3390/s19081912 (registering DOI)
Received: 13 March 2019 / Revised: 16 April 2019 / Accepted: 20 April 2019 / Published: 23 April 2019
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Abstract
Accurate and sufficient node location information is crucial for Wireless Sensor Networks (WSNs) applications. However, the existing range-based localization methods often suffer from incomplete and detorted range measurements. To address this issue, some methods based on low-rank matrix recovery have been proposed, which [...] Read more.
Accurate and sufficient node location information is crucial for Wireless Sensor Networks (WSNs) applications. However, the existing range-based localization methods often suffer from incomplete and detorted range measurements. To address this issue, some methods based on low-rank matrix recovery have been proposed, which usually assume noises follow single Gaussian distribution or/and single Laplacian distribution, and thus cannot handle the case with wider noise distributions beyond Gaussian and Laplacian ones. In this paper, a novel Anomaly-aware Node Localization (ANLoC) method is proposed to simultaneously impute missing range measurements and detect node anomaly in complex environments. Specifically, by utilizing inherent low-rank property of Euclidean Distance Matrix (EDM), we formulate range measurements imputation problem as a Robust 2 , 1 -norm Regularized Matrix Decomposition (RRMD) model, where complex noise is fitted by Mixture of Gaussian (MoG) distribution, and node anomaly is sifted by 2 , 1 -norm regularization. Meanwhile, an efficient optimization algorithm is designed to solve proposed RRMD model based on Expectation Maximization (EM) method. Furthermore, with the imputed EDM, all unknown nodes can be easily positioned by using Multi-Dimensional Scaling (MDS) method. Finally, some experiments are designed to evaluate performance of the proposed method, and experimental results demonstrate that our method outperforms three state-of-the-art node localization methods. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessReview Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Individuals with a Speech Disability
Sensors 2019, 19(8), 1911; https://doi.org/10.3390/s19081911
Received: 13 March 2019 / Revised: 13 April 2019 / Accepted: 18 April 2019 / Published: 22 April 2019
Viewed by 133 | PDF Full-text (836 KB)
Abstract
High-tech augmentative and alternative communication (AAC) methods are on a constant rise; however, the interaction between the user and the assistive technology is still challenged for an optimal user experience centered around the desired activity. This review presents a range of signal sensing [...] Read more.
High-tech augmentative and alternative communication (AAC) methods are on a constant rise; however, the interaction between the user and the assistive technology is still challenged for an optimal user experience centered around the desired activity. This review presents a range of signal sensing and acquisition methods utilized in conjunction with the existing high-tech AAC platforms for individuals with a speech disability, including imaging methods, touch-enabled systems, mechanical and electro-mechanical access, breath-activated methods, and brain–computer interfaces (BCI). The listed AAC sensing modalities are compared in terms of ease of access, affordability, complexity, portability, and typical conversational speeds. A revelation of the associated AAC signal processing, encoding, and retrieval highlights the roles of machine learning (ML) and deep learning (DL) in the development of intelligent AAC solutions. The demands and the affordability of most systems hinder the scale of usage of high-tech AAC. Further research is indeed needed for the development of intelligent AAC applications reducing the associated costs and enhancing the portability of the solutions for a real user’s environment. The consolidation of natural language processing with current solutions also needs to be further explored for the amelioration of the conversational speeds. The recommendations for prospective advances in coming high-tech AAC are addressed in terms of developments to support mobile health communicative applications. Full article
(This article belongs to the Section Intelligent Sensors)
Open AccessArticle Data-Driven Interaction Review of an Ed-Tech Application
Sensors 2019, 19(8), 1910; https://doi.org/10.3390/s19081910
Received: 12 February 2019 / Revised: 18 April 2019 / Accepted: 18 April 2019 / Published: 22 April 2019
Viewed by 119 | PDF Full-text (2699 KB)
Abstract
Smile and Learn is an Ed-Tech company that runs a smart library with more that
100 applications, games and interactive stories, aimed at children aged two to 10 and their families.
The platform gathers thousands of data points from the interaction with the [...] Read more.
Smile and Learn is an Ed-Tech company that runs a smart library with more that
100 applications, games and interactive stories, aimed at children aged two to 10 and their families.
The platform gathers thousands of data points from the interaction with the system to subsequently
offer reports and recommendations. Given the complexity of navigating all the content, the library
implements a recommender system. The purpose of this paper is to evaluate two aspects of such system
focused on children: the influence of the order of recommendations on user exploratory behavior, and
the impact of the choice of the recommendation algorithm on engagement. The assessment, based on
data collected between 15 October 2018 and 1 December 2018, required the analysis of the number of
clicks performed on the recommendations depending on their ordering, and an A/B/C testing where
two standard recommendation algorithmswere comparedwith a randomrecommendation that served
as baseline. The results suggest a direct connection between the order of the recommendation and the
interest raised, and the superiority of recommendations based on popularity against other alternatives. Full article
(This article belongs to the Special Issue Advanced Sensors Technology in Education)
Open AccessArticle Simple and Effective Secure Group Communications in Dynamic Wireless Sensor Networks
Sensors 2019, 19(8), 1909; https://doi.org/10.3390/s19081909
Received: 25 February 2019 / Revised: 31 March 2019 / Accepted: 17 April 2019 / Published: 22 April 2019
Viewed by 122 | PDF Full-text (1299 KB)
Abstract
Wireless Sensor Network (WSN) is a growing area of research in terms of applications, life enhancement and security. Research interests vary from enhancing network performance and decreasing overhead computation to solving security flaws. Secure Group Communication (SGC) is gaining traction in the world [...] Read more.
Wireless Sensor Network (WSN) is a growing area of research in terms of applications, life enhancement and security. Research interests vary from enhancing network performance and decreasing overhead computation to solving security flaws. Secure Group Communication (SGC) is gaining traction in the world of network security. Proposed solutions in this area focus on generating, sharing and distributing a group key among all group members in a timely manner to secure their communication and reduce the computation overhead. This method of security is called SGC-Shared Key. In this paper, we introduce a simple and effective way to secure the network through Hashed IDs (SGC-HIDs). In our proposed method, we distribute a shared key among the group of nodes in the network. Each node would have the ability to compute the group key each time it needs to. We provide a security analysis for our method as well as a performance evaluation. Moreover, to the best of our knowledge, we present for the first time a definition of joining or leaving attack. Furthermore, we describe several types of such an attack as well as the potential security impacts that occur when a network is being attacked. Full article
(This article belongs to the Section Sensor Networks)
Open AccessArticle Resource Allocation in Unmanned Aerial Vehicle (UAV)-Assisted Wireless-Powered Internet of Things
Sensors 2019, 19(8), 1908; https://doi.org/10.3390/s19081908
Received: 13 February 2019 / Revised: 18 April 2019 / Accepted: 19 April 2019 / Published: 22 April 2019
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Abstract
Most of the wireless nodes in the Internet of Things (IoT) environment face the limited energy problem and the way to provide a sustainable energy for these nodes has become an urgent challenge. In this paper, we present an unmanned aerial vehicle (UAV) [...] Read more.
Most of the wireless nodes in the Internet of Things (IoT) environment face the limited energy problem and the way to provide a sustainable energy for these nodes has become an urgent challenge. In this paper, we present an unmanned aerial vehicle (UAV) to power the wireless nodes in the IoT and an investigation on the optimal resource allocation approach based on dynamic game theory. This IoT system consists of one UAV as the power source and information receiver. The wireless nodes can be powered and collected by the UAV. In order to distinguish the wireless nodes, the wireless nodes are divided into two categories based on the energy consumption. The UAV tries to power these two categories of nodes using a different power level based on the proposed approach, where the wireless nodes control the resources for information transmission. Based on Bellman dynamic programming, the optimal allocated resources for power transfer and information transmission are obtained for both the UAV and wireless nodes, respectively. In order to show the effectiveness of the proposed model and approach, we present numerical simulations. Full article
(This article belongs to the Special Issue UAV-Based Applications in the Internet of Things (IoT))
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Open AccessArticle InAs/InAsSb Strain-Balanced Superlattices for Longwave Infrared Detectors
Sensors 2019, 19(8), 1907; https://doi.org/10.3390/s19081907
Received: 19 March 2019 / Revised: 19 April 2019 / Accepted: 20 April 2019 / Published: 22 April 2019
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Abstract
The InAs/InAsSb type-II superlattices (T2SLs) grown on a GaSb buffer layer and GaAs substrates were theoretically investigated. Due to the stability at high operating temperatures, T2SLs could be used for detectors operating in the longwave infrared (LWIR) range for different sensors to include, [...] Read more.
The InAs/InAsSb type-II superlattices (T2SLs) grown on a GaSb buffer layer and GaAs substrates were theoretically investigated. Due to the stability at high operating temperatures, T2SLs could be used for detectors operating in the longwave infrared (LWIR) range for different sensors to include, e.g., CH4 and C2H6 detection, which is very relevant for health condition monitoring. The theoretical calculations were carried out by the 8 × 8 k·p method. The estimated electrons and heavy holes probability distribution in a InAs/InAsSb superlattice (SL) shows that the wave function overlap increases while the thickness of the SL period decreases. The change in the effective masses for electrons and holes versus the SL period thickness for the kz-direction of the Brillouin zone is shown. The structures with a period lower than 15 nm are more optimal for the construction of LWIR detectors based on InAs/InAsSb SLs. The experimental results of InAs/InAsSb T2SLs energy bandgap were found to be comparable with the theoretical one. The proper fitting of theoretically calculated and experimentally measured spectral response characteristics in terms of a strain-balanced and unbalanced structures is shown. Full article
(This article belongs to the Section Sensor Materials)
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Open AccessArticle Looseness Monitoring of Bolted Spherical Joint Connection Using Electro-Mechanical Impedance Technique and BP Neural Networks
Sensors 2019, 19(8), 1906; https://doi.org/10.3390/s19081906
Received: 19 March 2019 / Revised: 12 April 2019 / Accepted: 14 April 2019 / Published: 22 April 2019
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Abstract
The bolted spherical joint (BSJ) has wide applications in various space grid structures. The bar and the bolted sphere are connected by the high-strength bolt inside the joint. High-strength bolt is invisible outside the joint, which causes the difficulty in monitoring the bolt [...] Read more.
The bolted spherical joint (BSJ) has wide applications in various space grid structures. The bar and the bolted sphere are connected by the high-strength bolt inside the joint. High-strength bolt is invisible outside the joint, which causes the difficulty in monitoring the bolt looseness. Moreover, the bolt looseness leads to the reduction of the local stiffness and bearing capacity for the structure. In this regard, this study used the electro-mechanical impedance (EMI) technique and back propagation neural networks (BPNNs) to monitor the bolt looseness inside the BSJ. Therefore, a space grid specimen having bolted spherical joints and tubular bars was considered for experimental evaluation. Different torques levels were applied on the sleeve to represent different looseness degrees of joint connection. As the torque levels increased, the looseness degrees of joint connection increased correspondingly. The lead zirconate titanate (PZT) patch was used and integrated with the tubular bar due to its strong piezoelectric effect. The root-mean-square deviation (RMSD) of the conductance signatures for the PZT patch were used as the looseness-monitoring indexes. Taking RMSD values of sub-frequency bands and the looseness degrees as inputs and outputs respectively, the BPNNs were trained and tested in twenty repeated experiments. The experimental results show that the formation of the bolt looseness can be detected according to the changes of looseness-monitoring indexes, and the degree of bolt looseness by the trained BPNNs. Overall, this research demonstrates that the proposed structural health monitoring (SHM) technique is feasible for monitoring the looseness of bolted spherical connection in space grid structures. Full article
(This article belongs to the Special Issue Smart Sensors and Smart Structures)
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Open AccessArticle Comparative Study of Relative-Pose Estimations from a Monocular Image Sequence in Computer Vision and Photogrammetry
Sensors 2019, 19(8), 1905; https://doi.org/10.3390/s19081905
Received: 18 February 2019 / Revised: 15 April 2019 / Accepted: 17 April 2019 / Published: 22 April 2019
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Abstract
Techniques for measuring the position and orientation of an object from corresponding images are based on the principles of epipolar geometry in the computer vision and photogrammetric fields. Contributing to their importance, many different approaches have been developed in computer vision, increasing the [...] Read more.
Techniques for measuring the position and orientation of an object from corresponding images are based on the principles of epipolar geometry in the computer vision and photogrammetric fields. Contributing to their importance, many different approaches have been developed in computer vision, increasing the automation of the pure photogrammetric processes. The aim of this paper is to evaluate the main differences between photogrammetric and computer vision approaches for the pose estimation of an object from image sequences, and how these have to be considered in the choice of processing technique when using a single camera. The use of a single camera in consumer electronics has enormously increased, even though most 3D user interfaces require additional devices to sense 3D motion for their input. In this regard, using a monocular camera to determine 3D motion is unique. However, we argue that relative pose estimations from monocular image sequences have not been studied thoroughly by comparing both photogrammetry and computer vision methods. To estimate motion parameters characterized by 3D rotation and 3D translations, estimation methods developed in the computer vision and photogrammetric fields are implemented. This paper describes a mathematical motion model for the proposed approaches, by differentiating their geometric properties and estimations of the motion parameters. A precision analysis is conducted to investigate the main characteristics of the methods in both fields. The results of the comparison indicate the differences between the estimations in both fields, in terms of accuracy and the test dataset. We show that homography-based approaches are more accurate than essential-matrix or relative orientation–based approaches under noisy conditions. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Software and Hardware Requirements and Trade-Offs in Operating Systems for Wearables: A Tool to Improve Devices’ Performance
Sensors 2019, 19(8), 1904; https://doi.org/10.3390/s19081904
Received: 20 March 2019 / Revised: 8 April 2019 / Accepted: 18 April 2019 / Published: 22 April 2019
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Abstract
Wearable device requirements currently vary from soft to hard real-time constraints. Frequently, hardware improvements are a way to speed-up the global performance of a solution. However, changing some parts or the whole hardware may increase device complexity, raising the costs and leading to [...] Read more.
Wearable device requirements currently vary from soft to hard real-time constraints. Frequently, hardware improvements are a way to speed-up the global performance of a solution. However, changing some parts or the whole hardware may increase device complexity, raising the costs and leading to development delays of products or research prototypes. This paper focuses on software improvements, presenting a tool designed to create different versions of operating systems (OSs) fitting the specifications of wearable devices projects. Authors have developed a software tool allowing the end-user to craft a new OS in just a few steps. In order to validate the generated OS, an original wearable prototype for mining environments is outlined. Resulting data presented here allows for measuring the actual impact an OS has in different variables of a solution. Finally, the analysis also allows for evaluating the performance impact associated with each hardware part. Results suggest the viability of using the proposed solution when searching for performance improvements on wearables. Full article
(This article belongs to the collection Wearable and Unobtrusive Monitoring Systems)
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Open AccessArticle Improved Sensitivity of α-Fe2O3 Nanoparticle-Decorated ZnO Nanowire Gas Sensor for CO
Sensors 2019, 19(8), 1903; https://doi.org/10.3390/s19081903
Received: 12 March 2019 / Revised: 5 April 2019 / Accepted: 20 April 2019 / Published: 22 April 2019
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Abstract
A strategy for improving the sensitivity of a sensor for detecting CO and NH3 gases is presented herein. The gas sensor was fabricated from ZnO metal oxide semiconductor nanostructures grown via a vapor–liquid–solid process and decorated with α-Fe2O3 nanoparticles [...] Read more.
A strategy for improving the sensitivity of a sensor for detecting CO and NH3 gases is presented herein. The gas sensor was fabricated from ZnO metal oxide semiconductor nanostructures grown via a vapor–liquid–solid process and decorated with α-Fe2O3 nanoparticles via a sol–gel process. The response was enhanced by the formation of an α-Fe2O3/ZnO n–n heterojunction and the growth of thinner wires. ZnO nanowires were grown on indium–tin–oxide glass electrodes using Sn as a catalyst for growth instead of Au. The structure and elemental composition were investigated using field-emission scanning electron microscopy, energy dispersive X-ray spectroscopy, and X-ray diffraction. The gas sensing results indicate that the response value to 100 ppm CO was 18.8 at the optimum operating temperature of 300 °C. Full article
(This article belongs to the Special Issue Recent Advances in Gas Nanosensors)
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Open AccessArticle A 3D Coverage Algorithm Based on Complex Surfaces for UAVs in Wireless Multimedia Sensor Networks
Sensors 2019, 19(8), 1902; https://doi.org/10.3390/s19081902
Received: 29 March 2019 / Revised: 15 April 2019 / Accepted: 19 April 2019 / Published: 22 April 2019
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Abstract
Following the development of wireless multimedia sensor networks (WMSN), the coverage of the sensors in the network constitutes one of the key technologies that have a significant influence on the monitoring ability, quality of service, and network lifetime. The application environment of WMSN [...] Read more.
Following the development of wireless multimedia sensor networks (WMSN), the coverage of the sensors in the network constitutes one of the key technologies that have a significant influence on the monitoring ability, quality of service, and network lifetime. The application environment of WMSN is always a complex surface, such as a hilly surface, that would likely cause monitoring shadowing problems. In this study, a new coverage-enhancing algorithm is presented to achieve an optimal coverage ratio of WMSN based on three-dimensional (3D) complex surfaces. By aiming at the complex surface, the use of a 3D sensing model, including a sensor monitoring model and a surface map calculation algorithm, is proposed to calculate the WMSN coverage information in an accurate manner. The coverage base map allowed the efficient estimation of the degree of monitoring occlusion efficiently and improved the system’s accuracy. To meet the requests of complex 3D surface monitoring tasks for multiple sensors, we propose a modified cuckoo search algorithm that considers the features of the WMSN coverage problem and combines the survival of the fittest, dynamic discovery probability, and the self-adaptation strategy of rotation. The evaluation outcomes demonstrate that the proposed algorithm can describe the 3D covering field but also improve both the coverage quality and efficiency of the WMSN on a complex surface. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Measurement and Analysis of Near-Ground Propagation Models under Different Terrains for Wireless Sensor Networks
Sensors 2019, 19(8), 1901; https://doi.org/10.3390/s19081901
Received: 7 March 2019 / Revised: 16 April 2019 / Accepted: 18 April 2019 / Published: 22 April 2019
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Abstract
The propagation model is an essential component in the design and deployment of a wireless sensor network (WSN). Although much attention has been given to near-ground propagation models, few studies place the transceiver directly on the ground with the height of antennas at [...] Read more.
The propagation model is an essential component in the design and deployment of a wireless sensor network (WSN). Although much attention has been given to near-ground propagation models, few studies place the transceiver directly on the ground with the height of antennas at the level of a few centimeters, which is a more realistic deployment scenario for WSNs. We measured the Received Signal Strength Indication (RSSI) of these truly near-ground WSNs at 470 MHz under four different terrains, namely flat concrete road, flat grass and two derived scenarios, and obtained the corresponding path loss models. By comprehensive analysis of the influence of different antenna heights and terrain factors, we showed the limit of existing theoretical models and proposed a propagation model selection strategy to more accurately reflect the true characteristics of the near-ground wireless channels for WSNs. In addition, we implemented these models on Cooja simulator and showed that simplistic theoretical models would induce great inaccuracy of network connectivity estimation. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle Intercomparison of Soil Moisture Retrieved from GNSS-R and from Passive L-Band Radiometry at the Valencia Anchor Station
Sensors 2019, 19(8), 1900; https://doi.org/10.3390/s19081900
Received: 28 February 2019 / Revised: 11 April 2019 / Accepted: 18 April 2019 / Published: 22 April 2019
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Abstract
In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on the intercomparison of soil moisture monitoring from Global Navigation Satellite System Reflectometry (GNSS-R) signals and passive L-band microwave radiometer observations at the Valencia Anchor Station is introduced. The GNSS-R instrument has an [...] Read more.
In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on the intercomparison of soil moisture monitoring from Global Navigation Satellite System Reflectometry (GNSS-R) signals and passive L-band microwave radiometer observations at the Valencia Anchor Station is introduced. The GNSS-R instrument has an up-looking antenna for receiving direct signals from satellites, and a dual-pol down-looking antenna for receiving LHCP (left-hand circular polarization) and RHCP (right-hand circular polarization) reflected signals from the soil surface. Data were collected from the three different antennas through the two channels of Oceanpal GNSS-R receiver and, in addition, calibration was performed to reduce the impact from the differing channels. Reflectivity was thus measured, and soil moisture could be retrieved. The ESA (European Space Agency)-funded ELBARA-II (ESA L Band Radiometer II) is an L-band radiometer with two channels with 11 MHz bandwidth and respective center frequencies of 1407.5 MHz and 1419.5 MHz. The ELBARAII antenna is a large dual-mode Picket horn that is 1.4 m wide, with a length of 2.7 m with −3 dB full beam width of 12° (±6° around the antenna main direction) and a gain of 23.5 dB. By comparing GNSS-R and ELBARA-II radiometer data, a high correlation was found between the LHCP reflectivity measured by GNSS-R and the horizontal/vertical reflectivity from the radiometer (with correlation coefficients ranging from 0.83 to 0.91). Neural net fitting was used for GNSS-R soil moisture inversion, and the RMSE (Root Mean Square Error) was 0.014 m3/m3. The determination coefficient between the retrieved soil moisture and in situ measurements was R2 = 0.90 for Oceanpal and R2 = 0.65 for Elbara II, and the ubRMSE (Unbiased RMSE) were 0.0128 and 0.0734 respectively. The soil moisture retrievals by both L-band remote sensing methods show good agreement with each other, and their mutual correspondence with in-situ measurements and with rainfall was also good. Full article
(This article belongs to the Special Issue Satellite Remotely Sensed Soil Moisture)
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Open AccessArticle Improved Real-Time Facial Expression Recognition Based on a Novel Balanced and Symmetric Local Gradient Coding
Sensors 2019, 19(8), 1899; https://doi.org/10.3390/s19081899
Received: 27 February 2019 / Revised: 17 April 2019 / Accepted: 17 April 2019 / Published: 22 April 2019
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Abstract
In the field of Facial Expression Recognition (FER), traditional local texture coding methods have a low computational complexity, while providing a robust solution with respect to occlusion, illumination, and other factors. However, there is still need for improving the accuracy of these methods [...] Read more.
In the field of Facial Expression Recognition (FER), traditional local texture coding methods have a low computational complexity, while providing a robust solution with respect to occlusion, illumination, and other factors. However, there is still need for improving the accuracy of these methods while maintaining their real-time nature and low computational complexity. In this paper, we propose a feature-based FER system with a novel local texture coding operator, named central symmetric local gradient coding (CS-LGC), to enhance the performance of real-time systems. It uses four different directional gradients on 5 × 5 grids, and the gradient is computed in the center-symmetric way. The averages of the gradients are used to reduce the sensitivity to noise. These characteristics lead to symmetric of features by the CS-LGC operator, thus providing a better generalization capability in comparison to existing local gradient coding (LGC) variants. The proposed system further transforms the extracted features into an eigen-space using a principal component analysis (PCA) for better representation and less computation; it estimates the intended classes by training an extreme learning machine. The recognition rate for the JAFFE database is 95.24%, whereas that for the CK+ database is 98.33%. The results show that the system has advantages over the existing local texture coding methods. Full article
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Open AccessArticle A Robust Method for Automatic Panoramic UAV Image Mosaic
Sensors 2019, 19(8), 1898; https://doi.org/10.3390/s19081898
Received: 1 February 2019 / Revised: 2 April 2019 / Accepted: 16 April 2019 / Published: 22 April 2019
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Abstract
This paper introduces a robust method for panoramic unmanned aerial vehicle (UAV) image mosaic. In the traditional automatic panoramic image stitching method (Autostitch), it assumes that the camera rotates about its optical centre and the group of transformations the source images may undergo [...] Read more.
This paper introduces a robust method for panoramic unmanned aerial vehicle (UAV) image mosaic. In the traditional automatic panoramic image stitching method (Autostitch), it assumes that the camera rotates about its optical centre and the group of transformations the source images may undergo is a special group of homographies. It is rare to get such ideal data in reality. In particular, remote sensing images obtained by UAV do not satisfy such an ideal situation, where the images may not be on a plane yet and even may suffer from nonrigid changes, leading to poor mosaic results. To overcome the above mentioned challenges, in this paper a nonrigid matching algorithm is introduced to the mosaic system to generate accurate feature matching on remote sensing images. We also propose a new strategy for bundle adjustment to make the mosaic system suitable for the UAV image panoramic mosaic effect. Experimental results show that our method outperforms the traditional method and some of the latest methods in terms of visual effect. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Recognition of Emotion Intensities Using Machine Learning Algorithms: A Comparative Study
Sensors 2019, 19(8), 1897; https://doi.org/10.3390/s19081897
Received: 24 March 2019 / Revised: 18 April 2019 / Accepted: 18 April 2019 / Published: 21 April 2019
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Abstract
Over the past two decades, automatic facial emotion recognition has received enormous attention. This is due to the increase in the need for behavioral biometric systems and human–machine interaction where the facial emotion recognition and the intensity of emotion play vital roles. The [...] Read more.
Over the past two decades, automatic facial emotion recognition has received enormous attention. This is due to the increase in the need for behavioral biometric systems and human–machine interaction where the facial emotion recognition and the intensity of emotion play vital roles. The existing works usually do not encode the intensity of the observed facial emotion and even less involve modeling the multi-class facial behavior data jointly. Our work involves recognizing the emotion along with the respective intensities of those emotions. The algorithms used in this comparative study are Gabor filters, a Histogram of Oriented Gradients (HOG), and Local Binary Pattern (LBP) for feature extraction. For classification, we have used Support Vector Machine (SVM), Random Forest (RF), and Nearest Neighbor Algorithm (kNN). This attains emotion recognition and intensity estimation of each recognized emotion. This is a comparative study of classifiers used for facial emotion recognition along with the intensity estimation of those emotions for databases. The results verified that the comparative study could be further used in real-time behavioral facial emotion and intensity of emotion recognition. Full article
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Open AccessArticle Potential Application of h-BNC Structures in SERS and SEHRS Spectroscopies: A Theoretical Perspective
Sensors 2019, 19(8), 1896; https://doi.org/10.3390/s19081896
Received: 13 March 2019 / Revised: 5 April 2019 / Accepted: 18 April 2019 / Published: 21 April 2019
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Abstract
In this work, the electronic and optical properties of hybrid boron-nitrogen-carbon structures (h-BNCs) with embedded graphene nanodisks are investigated. Their molecular affinity is explored using pyridine as model system and comparing the results with the corresponding isolated graphene nanodisks. Time-dependent density functional theory [...] Read more.
In this work, the electronic and optical properties of hybrid boron-nitrogen-carbon structures (h-BNCs) with embedded graphene nanodisks are investigated. Their molecular affinity is explored using pyridine as model system and comparing the results with the corresponding isolated graphene nanodisks. Time-dependent density functional theory (TDDFT) analysis of the electronic excited states was performed in the complexes in order to characterize possible surface and charge transfer resonances in the UV region. Static and dynamic (hyper)polarizabilities were calculated with coupled-perturbed Kohn-Sham theory (CPKS) and the linear and nonlinear optical responses of the complexes were analyzed in detail using laser excitation wavelengths available for (Hyper)Raman experiments and near-to-resonance excitation wavelengths. Enhancement factors around 103 and 108 were found for the polarizability and first order hyperpolarizability, respectively. The quantum chemical simulations performed in this work point out that nanographenes embedded within hybrid h-BNC structures may serve as good platforms for enhancing the (Hyper)Raman activity of organic molecules immobilized on their surfaces and for being employed as substrates in surface enhanced (Hyper)Raman scattering (SERS and SEHRS). Besides the better selectivity and improved signal-to-noise ratio of pristine graphene with respect to metallic surfaces, the confinement of the optical response in these hybrid h-BNC systems leads to strong localized surface resonances in the UV region. Matching these resonances with laser excitation wavelengths would solve the problem of the small enhancement factors reported in Raman experiments using pristine graphene. This may be achieved by tuning the size/shape of the embedded nanographene structure. Full article
(This article belongs to the Special Issue Biosensors Incorporating Nano-particles)
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Open AccessArticle Optimization of a Piezoelectric Energy Harvester and Design of a Charge Pump Converter for CMOS-MEMS Monolithic Integration
Sensors 2019, 19(8), 1895; https://doi.org/10.3390/s19081895
Received: 11 February 2019 / Revised: 12 April 2019 / Accepted: 19 April 2019 / Published: 21 April 2019
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Abstract
The increasing interest in the Internet of Things (IoT) has led to the rapid development of low-power sensors and wireless networks. However, there are still several barriers that make a global deployment of the IoT difficult. One of these issues is the energy [...] Read more.
The increasing interest in the Internet of Things (IoT) has led to the rapid development of low-power sensors and wireless networks. However, there are still several barriers that make a global deployment of the IoT difficult. One of these issues is the energy dependence, normally limited by the capacitance of the batteries. A promising solution to provide energy autonomy to the IoT nodes is to harvest residual energy from ambient sources, such as motion, vibrations, light, or heat. Mechanical energy can be converted into electrical energy by using piezoelectric transducers. The piezoelectric generators provide an alternating electrical signal that must be rectified and, therefore, needs a power management circuit to adapt the output to the operating voltage of the IoT devices. The bonding and packaging of the different components constitute a large part of the cost of the manufacturing process of microelectromechanical systems (MEMS) and integrated circuits. This could be reduced by using a monolithic integration of the generator together with the circuitry in a single chip. In this work, we report the optimization, fabrication, and characterization of a vibration-driven piezoelectric MEMS energy harvester, and the design and simulation of a charge-pump converter based on a standard complementary metal–oxide–semiconductor (CMOS) technology. Finally, we propose combining MEMS and CMOS technologies to obtain a fully integrated system that includes the piezoelectric generator device and the charge-pump converter circuit without the need of external components. This solution opens new doors to the development of low-cost autonomous smart dust devices. Full article
(This article belongs to the Special Issue Eurosensors 2018 Selected Papers)
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Open AccessArticle Sensitivity and Stability Enhancement of Surface Plasmon Resonance Biosensors based on a Large-Area Ag/MoS2 Substrate
Sensors 2019, 19(8), 1894; https://doi.org/10.3390/s19081894
Received: 8 March 2019 / Revised: 10 April 2019 / Accepted: 16 April 2019 / Published: 21 April 2019
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Abstract
Surface plasmon resonance (SPR) sensors based on a silver film suffer from signal degradation due to silver oxidation in aqueous sensing environments. To overcome this limitation, we fabricated the planar plasmonic substrate employing an atomic MoS2 layer on a silver surface. Successful [...] Read more.
Surface plasmon resonance (SPR) sensors based on a silver film suffer from signal degradation due to silver oxidation in aqueous sensing environments. To overcome this limitation, we fabricated the planar plasmonic substrate employing an atomic MoS2 layer on a silver surface. Successful production of a large-area MoS2 monolayer blocks the penetration of oxygen and water molecules. In addition, we theoretically and experimentally found that MoS2 layer on the silver film can improve the SPR sensitivity and stability significantly. In this study, the proposed SPR substrate has the potential to provide highly enhanced sensor platforms for surface-limited molecular detections. Full article
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Open AccessArticle An EKF-Based Fixed-Point Iterative Filter for Nonlinear Systems
Sensors 2019, 19(8), 1893; https://doi.org/10.3390/s19081893
Received: 13 March 2019 / Revised: 9 April 2019 / Accepted: 15 April 2019 / Published: 21 April 2019
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Abstract
In this paper, a fixed-point iterative filter developed from the classical extended Kalman filter (EKF) was proposed for general nonlinear systems. As a nonlinear filter developed from EKF, the state estimate was obtained by applying the Kalman filter to the linearized system by [...] Read more.
In this paper, a fixed-point iterative filter developed from the classical extended Kalman filter (EKF) was proposed for general nonlinear systems. As a nonlinear filter developed from EKF, the state estimate was obtained by applying the Kalman filter to the linearized system by discarding the higher-order Taylor series items of the original nonlinear system. In order to reduce the influence of the discarded higher-order Taylor series items and improve the filtering accuracy of the obtained state estimate of the steady-state EKF, a fixed-point function was solved though a nested iterative method, which resulted in a fixed-point iterative filter. The convergence of the fixed-point function is also discussed, which provided the existing conditions of the fixed-point iterative filter. Then, Steffensen’s iterative method is presented to accelerate the solution of the fixed-point function. The final simulation is provided to illustrate the feasibility and the effectiveness of the proposed nonlinear filtering method. Full article
(This article belongs to the collection Multi-Sensor Information Fusion)
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