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Sensors, Volume 19, Issue 7 (April-1 2019)

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Cover Story (view full-size image) The combination of Cyber-Physical Systems (CPSs) and the Internet-of-Things (IoT) has enabled the [...] Read more.
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Open AccessArticle Using Twitter Data to Monitor Natural Disaster Social Dynamics: A Recurrent Neural Network Approach with Word Embeddings and Kernel Density Estimation
Sensors 2019, 19(7), 1746; https://doi.org/10.3390/s19071746
Received: 3 March 2019 / Revised: 29 March 2019 / Accepted: 6 April 2019 / Published: 11 April 2019
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Abstract
In recent years, Online Social Networks (OSNs) have received a great deal of attention for their potential use in the spatial and temporal modeling of events owing to the information that can be extracted from these platforms. Within this context, one of the [...] Read more.
In recent years, Online Social Networks (OSNs) have received a great deal of attention for their potential use in the spatial and temporal modeling of events owing to the information that can be extracted from these platforms. Within this context, one of the most latent applications is the monitoring of natural disasters. Vital information posted by OSN users can contribute to relief efforts during and after a catastrophe. Although it is possible to retrieve data from OSNs using embedded geographic information provided by GPS systems, this feature is disabled by default in most cases. An alternative solution is to geoparse specific locations using language models based on Named Entity Recognition (NER) techniques. In this work, a sensor that uses Twitter is proposed to monitor natural disasters. The approach is intended to sense data by detecting toponyms (named places written within the text) in tweets with event-related information, e.g., a collapsed building on a specific avenue or the location at which a person was last seen. The proposed approach is carried out by transforming tokenized tweets into word embeddings: a rich linguistic and contextual vector representation of textual corpora. Pre-labeled word embeddings are employed to train a Recurrent Neural Network variant, known as a Bidirectional Long Short-Term Memory (biLSTM) network, that is capable of dealing with sequential data by analyzing information in both directions of a word (past and future entries). Moreover, a Conditional Random Field (CRF) output layer, which aims to maximize the transition from one NER tag to another, is used to increase the classification accuracy. The resulting labeled words are joined to coherently form a toponym, which is geocoded and scored by a Kernel Density Estimation function. At the end of the process, the scored data are presented graphically to depict areas in which the majority of tweets reporting topics related to a natural disaster are concentrated. A case study on Mexico’s 2017 Earthquake is presented, and the data extracted during and after the event are reported. Full article
(This article belongs to the Special Issue Wireless Body Area Networks: Applications and Technologies)
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Open AccessArticle Self-Sensing Polymer Composite: White-Light-Illuminated Reinforcing Fibreglass Bundle for Deformation Monitoring
Sensors 2019, 19(7), 1745; https://doi.org/10.3390/s19071745
Received: 20 March 2019 / Revised: 3 April 2019 / Accepted: 9 April 2019 / Published: 11 April 2019
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Abstract
The goal of our research was to develop a continuous glass fibre-reinforced epoxy matrix self-sensing composite. A fibre bundle arbitrarily chosen from the reinforcing glass fabric in the composite was prepared to guide white light. The power of the light transmitted by the [...] Read more.
The goal of our research was to develop a continuous glass fibre-reinforced epoxy matrix self-sensing composite. A fibre bundle arbitrarily chosen from the reinforcing glass fabric in the composite was prepared to guide white light. The power of the light transmitted by the fibres changes as a result of tensile loading. In our research, we show that a selected fibre bundle even without any special preparation can be used as a sensor to detect deformation even before the composite structure is damaged (before fibre breaking). Full article
(This article belongs to the Special Issue Polymeric Sensors)
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Open AccessArticle Reliability and Validity of Non-invasive Blood Pressure Measurement System Using Three-Axis Tactile Force Sensor
Sensors 2019, 19(7), 1744; https://doi.org/10.3390/s19071744
Received: 6 February 2019 / Revised: 18 March 2019 / Accepted: 4 April 2019 / Published: 11 April 2019
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Abstract
Blood pressure (BP) is a physiological parameter reflecting hemodynamic factors and is crucial in evaluating cardiovascular disease and its prognosis. In the present study, the reliability of a non-invasive and continuous BP measurement using a three-axis tactile force sensor was verified. All the [...] Read more.
Blood pressure (BP) is a physiological parameter reflecting hemodynamic factors and is crucial in evaluating cardiovascular disease and its prognosis. In the present study, the reliability of a non-invasive and continuous BP measurement using a three-axis tactile force sensor was verified. All the data were collected every 2 min for the short-term experiment, and every 10 min for the long-term experiment. In addition, the effects on the BP measurement of external physical factors such as the tension to the radial artery on applying the device and wrist circumference were evaluated. A high correlation between the measured BP with the proposed system and with the cuff-based non-invasive blood pressure, and reproducibility, were demonstrated. All data satisfied the Association for the Advancement of Medical Instrumentation criteria. The external physical factors did not affect the measurement results. In addition to previous research indicating the high reliability of the arterial pulse waveforms, the present results have demonstrated the reliability of numerical BP values, and this implies that the three-axis force sensor can be used as a patient monitoring device. Full article
(This article belongs to the Special Issue Non-Invasive Biomedical Sensors)
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Open AccessArticle High Spatial Resolution Simulation of Sunshine Duration over the Complex Terrain of Ghana
Sensors 2019, 19(7), 1743; https://doi.org/10.3390/s19071743
Received: 5 March 2019 / Revised: 9 April 2019 / Accepted: 9 April 2019 / Published: 11 April 2019
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Abstract
In this paper, we propose a remote sensing model based on a 1 × 1 km spatial resolution to estimate the spatio-temporal distribution of sunshine percentage (SSP) and sunshine duration (SD), taking into account terrain features and atmospheric factors. To account for the [...] Read more.
In this paper, we propose a remote sensing model based on a 1 × 1 km spatial resolution to estimate the spatio-temporal distribution of sunshine percentage (SSP) and sunshine duration (SD), taking into account terrain features and atmospheric factors. To account for the influence of topography and atmospheric conditions in the model, a digital elevation model (DEM) and cloud products from the moderate-resolution imaging spectroradiometer (MODIS) for 2010 were incorporated into the model and subsequently validated against in situ observation data. The annual and monthly average daily total SSP and SD have been estimated based on the proposed model. The error analysis results indicate that the proposed modelled SD is in good agreement with ground-based observations. The model performance is evaluated against two classical interpolation techniques (kriging and inverse distance weighting (IDW)) based on the mean absolute error (MAE), the mean relative error (MRE) and the root-mean-square error (RMSE). The results reveal that the SD obtained from the proposed model performs better than those obtained from the two classical interpolators. This results indicate that the proposed model can reliably reflect the contribution of terrain and cloud cover in SD estimation in Ghana, and the model performance is expected to perform well in similar environmental conditions. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change)
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Open AccessArticle An Orthogonal Weighted Occupancy Likelihood Map with IMU-Aided Laser Scan Matching for 2D Indoor Mapping
Sensors 2019, 19(7), 1742; https://doi.org/10.3390/s19071742
Received: 12 March 2019 / Revised: 8 April 2019 / Accepted: 9 April 2019 / Published: 11 April 2019
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Abstract
An indoor map is a piece of infrastructure associated with location-based services. Simultaneous Localization and Mapping (SLAM)-based mobile mapping is an efficient method to construct an indoor map. This paper proposes an SLAM algorithm based on a laser scanner and an Inertial Measurement [...] Read more.
An indoor map is a piece of infrastructure associated with location-based services. Simultaneous Localization and Mapping (SLAM)-based mobile mapping is an efficient method to construct an indoor map. This paper proposes an SLAM algorithm based on a laser scanner and an Inertial Measurement Unit (IMU) for 2D indoor mapping. A grid-based occupancy likelihood map is chosen as the map representation method and is built from all previous scans. Scan-to-map matching is utilized to find the optimal rigid-body transformation in order to avoid the accumulation of matching errors. Map generation and update are probabilistically motivated. According to the assumption that the orthogonal is the main feature of indoor environments, we propose a lightweight segment extraction method, based on the orthogonal blurred segments (OBS) method. Instead of calculating the parameters of segments, we give the scan points contained in blurred segments a greater weight during the construction of the grid-based occupancy likelihood map, which we call the orthogonal feature weighted occupancy likelihood map (OWOLM). The OWOLM enhances the occupancy likelihood map by fusing the orthogonal features. It can filter out noise scan points, produced by objects, such as glass cabinets and bookcases. Experiments were carried out in a library, which is a representative indoor environment, consisting of orthogonal features. The experimental result proves that, compared with the general occupancy likelihood map, the OWOLM can effectively reduce accumulated errors and construct a clearer indoor map. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Color Measurement and Analysis of Fruit with a Battery-Less NFC Sensor
Sensors 2019, 19(7), 1741; https://doi.org/10.3390/s19071741
Received: 8 March 2019 / Revised: 3 April 2019 / Accepted: 7 April 2019 / Published: 11 April 2019
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Abstract
This paper presents a color-based classification system for grading the ripeness of fruit using a battery-less Near Field Communication (NFC) tag. The tag consists of a color sensor connected to a low-power microcontroller that is connected to an NFC chip. The tag is [...] Read more.
This paper presents a color-based classification system for grading the ripeness of fruit using a battery-less Near Field Communication (NFC) tag. The tag consists of a color sensor connected to a low-power microcontroller that is connected to an NFC chip. The tag is powered by the energy harvested from the magnetic field generated by a commercial smartphone used as a reader. The raw RGB color data measured by the colorimeter is converted to HSV (hue, saturation, value) color space. The hue angle and saturation are used as features for classification. Different classification algorithms are compared for classifying the ripeness of different fruits in order to show the robustness of the system. The low cost of NFC chips means that tags with sensing capability can be manufactured economically. In addition, nowadays, most commercial smartphones have NFC capability and thus a specific reader is not necessary. The measurement of different samples obtained on different days is used to train the classification algorithms. The results of training the classifiers have been saved to the cloud. A mobile application has been developed for the prediction based on a table-based method, where the boundary decision is downloaded from a cloud service for each product. High accuracy, between 80 and 93%, is obtained depending on the kind of fruit and the algorithm used. Full article
(This article belongs to the Special Issue Near-Field Communication (NFC) Sensors)
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Open AccessArticle Feasible Classified Models for Parkinson Disease from 99mTc-TRODAT-1 SPECT Imaging
Sensors 2019, 19(7), 1740; https://doi.org/10.3390/s19071740
Received: 6 March 2019 / Revised: 8 April 2019 / Accepted: 9 April 2019 / Published: 11 April 2019
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Abstract
The neuroimaging techniques such as dopaminergic imaging using Single Photon Emission Computed Tomography (SPECT) with 99mTc-TRODAT-1 have been employed to detect the stages of Parkinson’s disease (PD). In this retrospective study, a total of 202 99mTc-TRODAT-1 SPECT imaging were collected. All [...] Read more.
The neuroimaging techniques such as dopaminergic imaging using Single Photon Emission Computed Tomography (SPECT) with 99mTc-TRODAT-1 have been employed to detect the stages of Parkinson’s disease (PD). In this retrospective study, a total of 202 99mTc-TRODAT-1 SPECT imaging were collected. All of the PD patient cases were separated into mild (HYS Stage 1 to Stage 3) and severe (HYS Stage 4 and Stage 5) PD, according to the Hoehn and Yahr Scale (HYS) standard. A three-dimensional method was used to estimate six features of activity distribution and striatal activity volume in the images. These features were skewness, kurtosis, Cyhelsky’s skewness coefficient, Pearson’s median skewness, dopamine transporter activity volume, and dopamine transporter activity maximum. Finally, the data were modeled using logistic regression (LR) and support vector machine (SVM) for PD classification. The results showed that SVM classifier method produced a higher accuracy than LR. The sensitivity, specificity, PPV, NPV, accuracy, and AUC with SVM method were 0.82, 1.00, 0.84, 0.67, 0.83, and 0.85, respectively. Additionally, the Kappa value was shown to reach 0.68. This claimed that the SVM-based model could provide further reference for PD stage classification in medical diagnosis. In the future, more healthy cases will be expected to clarify the false positive rate in this classification model. Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle A 1-GHz 64-Channel Cross-Correlation System for Real-Time Interferometric Aperture Synthesis Imaging
Sensors 2019, 19(7), 1739; https://doi.org/10.3390/s19071739
Received: 8 March 2019 / Revised: 4 April 2019 / Accepted: 6 April 2019 / Published: 11 April 2019
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Abstract
We present a 64-channel 1-bit/2-level cross-correlation system for a passive millimeter wave imager used for indoor human body security screening. Sixty-four commercial comparators are used to perform 1-bit analog-to-digital conversion, and a Field Programmable Gate Array (FPGA) is used to perform the cross-correlation [...] Read more.
We present a 64-channel 1-bit/2-level cross-correlation system for a passive millimeter wave imager used for indoor human body security screening. Sixty-four commercial comparators are used to perform 1-bit analog-to-digital conversion, and a Field Programmable Gate Array (FPGA) is used to perform the cross-correlation processing. This system can handle 2016 cross-correlations at the sample frequency of 1GHz, and its power consumption is 48.75 W. The data readout interface makes it possible to read earlier data while simultaneously performing the next correlation when imaging at video rate. The longest integration time is up to 68.7 s, which can satisfy the requirements of video rate imaging and system calibration. The measured crosstalk between neighboring channels is less than 0.068%, and the stability is longer than 10 s. A correlation efficiency greater than 96% is achieved for input signal levels greater than −25 dBm. Full article
(This article belongs to the Special Issue Radar and Radiometric Sensors and Sensing)
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Open AccessArticle Fear Level Classification Based on Emotional Dimensions and Machine Learning Techniques
Sensors 2019, 19(7), 1738; https://doi.org/10.3390/s19071738
Received: 1 March 2019 / Revised: 3 April 2019 / Accepted: 9 April 2019 / Published: 11 April 2019
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Abstract
There has been steady progress in the field of affective computing over the last two decades that has integrated artificial intelligence techniques in the construction of computational models of emotion. Having, as a purpose, the development of a system for treating phobias that [...] Read more.
There has been steady progress in the field of affective computing over the last two decades that has integrated artificial intelligence techniques in the construction of computational models of emotion. Having, as a purpose, the development of a system for treating phobias that would automatically determine fear levels and adapt exposure intensity based on the user’s current affective state, we propose a comparative study between various machine and deep learning techniques (four deep neural network models, a stochastic configuration network, Support Vector Machine, Linear Discriminant Analysis, Random Forest and k-Nearest Neighbors), with and without feature selection, for recognizing and classifying fear levels based on the electroencephalogram (EEG) and peripheral data from the DEAP (Database for Emotion Analysis using Physiological signals) database. Fear was considered an emotion eliciting low valence, high arousal and low dominance. By dividing the ratings of valence/arousal/dominance emotion dimensions, we propose two paradigms for fear level estimation—the two-level (0—no fear and 1—fear) and the four-level (0—no fear, 1—low fear, 2—medium fear, 3—high fear) paradigms. Although all the methods provide good classification accuracies, the highest F scores have been obtained using the Random Forest Classifier—89.96% and 85.33% for the two-level and four-level fear evaluation modality. Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method
Sensors 2019, 19(7), 1737; https://doi.org/10.3390/s19071737
Received: 8 March 2019 / Revised: 8 April 2019 / Accepted: 10 April 2019 / Published: 11 April 2019
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Abstract
Walking is a basic requirement for participating in daily activities. Neurological diseases such as stroke can significantly affect one’s gait and thereby restrict one’s activities that are a part of daily living. Previous studies have demonstrated that gait temporal parameters are useful for [...] Read more.
Walking is a basic requirement for participating in daily activities. Neurological diseases such as stroke can significantly affect one’s gait and thereby restrict one’s activities that are a part of daily living. Previous studies have demonstrated that gait temporal parameters are useful for characterizing post-stroke hemiparetic gait. However, no previous studies have investigated the symmetry, regularity and stability of post-stroke hemiparetic gaits. In this study, the dynamic time warping (DTW) algorithm, sample entropy method and empirical mode decomposition-based stability index were utilized to obtain the three aforementioned types of gait features, respectively. Studies were conducted with 15 healthy control subjects and 15 post-stroke survivors. Experimental results revealed that the proposed features could significantly differentiate hemiparetic patients from healthy control subjects by a Mann–Whitney test (with a p-value of less than 0.05). Finally, four representative classifiers were utilized in order to evaluate the possible capabilities of these features to distinguish patients with hemiparetic gaits from the healthy control subjects. The maximum area under the curve values were shown to be 0.94 by the k-nearest-neighbor (kNN) classifier. These promising results have illustrated that the proposed features have considerable potential to promote the future design of automatic gait analysis systems for clinical practice. Full article
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Open AccessArticle Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods
Sensors 2019, 19(7), 1736; https://doi.org/10.3390/s19071736
Received: 24 February 2019 / Revised: 29 March 2019 / Accepted: 8 April 2019 / Published: 11 April 2019
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Abstract
Single-trial motor imagery classification is a crucial aspect of brain–computer applications. Therefore, it is necessary to extract and discriminate signal features involving motor imagery movements. Riemannian geometry-based feature extraction methods are effective when designing these types of motor-imagery-based brain–computer interface applications. In the [...] Read more.
Single-trial motor imagery classification is a crucial aspect of brain–computer applications. Therefore, it is necessary to extract and discriminate signal features involving motor imagery movements. Riemannian geometry-based feature extraction methods are effective when designing these types of motor-imagery-based brain–computer interface applications. In the field of information theory, Riemannian geometry is mainly used with covariance matrices. Accordingly, investigations showed that if the method is used after the execution of the filterbank approach, the covariance matrix preserves the frequency and spatial information of the signal. Deep-learning methods are superior when the data availability is abundant and while there is a large number of features. The purpose of this study is to a) show how to use a single deep-learning-based classifier in conjunction with BCI (brain–computer interface) applications with the CSP (common spatial features) and the Riemannian geometry feature extraction methods in BCI applications and to b) describe one of the wrapper feature-selection algorithms, referred to as the particle swarm optimization, in combination with a decision tree algorithm. In this work, the CSP method was used for a multiclass case by using only one classifier. Additionally, a combination of power spectrum density features with covariance matrices mapped onto the tangent space of a Riemannian manifold was used. Furthermore, the particle swarm optimization method was implied to ease the training by penalizing bad features, and the moving windows method was used for augmentation. After empirical study, the convolutional neural network was adopted to classify the pre-processed data. Our proposed method improved the classification accuracy for several subjects that comprised the well-known BCI competition IV 2a dataset. Full article
(This article belongs to the Special Issue Wearable and Nearable Biosensors and Systems for Healthcare)
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Open AccessArticle A Fast Binocular Localisation Method for AUV Docking
Sensors 2019, 19(7), 1735; https://doi.org/10.3390/s19071735
Received: 8 March 2019 / Revised: 2 April 2019 / Accepted: 8 April 2019 / Published: 11 April 2019
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Abstract
Docking technology plays a critical role in realising the long-time operation of autonomous underwater vehicles (AUVs). In this study, a binocular localisation method for AUV docking is presented. An adaptively weighted OTSU method is developed for feature extraction. The foreground object is extracted [...] Read more.
Docking technology plays a critical role in realising the long-time operation of autonomous underwater vehicles (AUVs). In this study, a binocular localisation method for AUV docking is presented. An adaptively weighted OTSU method is developed for feature extraction. The foreground object is extracted precisely without mixing or missing lamps, which is independent of the position of the AUV relative to the station. Moreover, this extraction process is more precise compared to other segmentation methods with a low computational load. The mass centre of each lamp on the binary image is used as matching feature for binocular vision. Using this fast feature matching method, the operation frequency of the binocular localisation method exceeds 10 Hz. Meanwhile, a relative pose estimation method is suggested for instances when the two cameras cannot capture all the lamps. The localisation accuracy of the distance in the heading direction as measured by the proposed binocular vision algorithm was tested at fixed points underwater. A simulation experiment using a ship model has been conducted in a laboratory pool to evaluate the feasibility of the algorithm. The test result demonstrates that the average localisation error is approximately 5 cm and the average relative location error is approximately 2% in the range of 3.6 m. As such, the ship model was successfully guided to the docking station for different lateral deviations. Full article
(This article belongs to the collection Positioning and Navigation)
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Open AccessArticle Vibration-Based In-Situ Detection and Quantification of Delamination in Composite Plates
Sensors 2019, 19(7), 1734; https://doi.org/10.3390/s19071734
Received: 18 February 2019 / Revised: 1 April 2019 / Accepted: 8 April 2019 / Published: 11 April 2019
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Abstract
This paper presents a new methodology for detecting and quantifying delamination in composite plates based on the high-frequency local vibration under the excitation of piezoelectric wafer active sensors. Finite-element-method-based numerical simulations and experimental measurements were performed to quantify the size, shape, and depth [...] Read more.
This paper presents a new methodology for detecting and quantifying delamination in composite plates based on the high-frequency local vibration under the excitation of piezoelectric wafer active sensors. Finite-element-method-based numerical simulations and experimental measurements were performed to quantify the size, shape, and depth of the delaminations. Two composite plates with purpose-built delaminations of different sizes and depths were analyzed. In the experiments, ultrasonic C-scan was applied to visualize the simulated delaminations. In this methodology, piezoelectric wafer active sensors were used for the high-frequency excitation with a linear sine wave chirp from 1 to 500 kHz and a scanning laser Doppler vibrometer was used to measure the local vibration response of the composite plates. The local defect resonance frequencies of delaminations were determined from scanning laser Doppler vibrometer measurements and the corresponding operational vibration shapes were measured and utilized to quantify the delaminations. Harmonic analysis of local finite element model at the local defect resonance frequencies demonstrated that the strong vibrations only occurred in the delamination region. It is shown that the effect of delamination depth on the detectability of the delamination was more significant than the size of the delamination. The experimental and finite element modeling results demonstrate a good capability for the assessment of delamination with different sizes and depths in composite structures. Full article
(This article belongs to the Special Issue Sensors and Sensing Networks Based on Smart Materials)
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Open AccessArticle Environment Sound Classification Using a Two-Stream CNN Based on Decision-Level Fusion
Sensors 2019, 19(7), 1733; https://doi.org/10.3390/s19071733
Received: 13 March 2019 / Revised: 9 April 2019 / Accepted: 9 April 2019 / Published: 11 April 2019
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Abstract
With the popularity of using deep learning-based models in various categorization problems and their proven robustness compared to conventional methods, a growing number of researchers have exploited such methods in environment sound classification tasks in recent years. However, the performances of existing models [...] Read more.
With the popularity of using deep learning-based models in various categorization problems and their proven robustness compared to conventional methods, a growing number of researchers have exploited such methods in environment sound classification tasks in recent years. However, the performances of existing models use auditory features like log-mel spectrogram (LM) and mel frequency cepstral coefficient (MFCC), or raw waveform to train deep neural networks for environment sound classification (ESC) are unsatisfactory. In this paper, we first propose two combined features to give a more comprehensive representation of environment sounds Then, a fourfour-layer convolutional neural network (CNN) is presented to improve the performance of ESC with the proposed aggregated features. Finally, the CNN trained with different features are fused using the Dempster–Shafer evidence theory to compose TSCNN-DS model. The experiment results indicate that our combined features with the four-layer CNN are appropriate for environment sound taxonomic problems and dramatically outperform other conventional methods. The proposed TSCNN-DS model achieves a classification accuracy of 97.2%, which is the highest taxonomic accuracy on UrbanSound8K datasets compared to existing models. Full article
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Open AccessArticle A Printed Wearable Dual-Band Antenna for Wireless Power Transfer
Sensors 2019, 19(7), 1732; https://doi.org/10.3390/s19071732
Received: 25 February 2019 / Revised: 4 April 2019 / Accepted: 10 April 2019 / Published: 11 April 2019
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Abstract
In this work, a dual-band printed planar antenna, operating at two ultra-high frequency bands (2.5 GHz/4.5 GHz), is proposed for wireless power transfer for wearable applications. The receiving antenna is printed on a Kapton polyimide-based flexible substrate, and the transmitting antenna is on [...] Read more.
In this work, a dual-band printed planar antenna, operating at two ultra-high frequency bands (2.5 GHz/4.5 GHz), is proposed for wireless power transfer for wearable applications. The receiving antenna is printed on a Kapton polyimide-based flexible substrate, and the transmitting antenna is on FR-4 substrate. The receiver antenna occupies 2.1 cm 2 area. Antennas were simulated using ANSYS HFSS software and the simulation results are compared with the measurement results. Full article
(This article belongs to the Special Issue UHF Wearable Antennas for RFID Applications)
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Open AccessArticle A Novel CNN-Based Framework for Classification of Signal Quality and Sleep Position from a Capacitive ECG Measurement
Sensors 2019, 19(7), 1731; https://doi.org/10.3390/s19071731
Received: 13 March 2019 / Revised: 27 March 2019 / Accepted: 8 April 2019 / Published: 11 April 2019
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Abstract
The further exploration of the capacitive ECG (cECG) is hindered by frequent fluctuations in signal quality from body movement and changes in sleep position. The processing framework must be fundamentally adapted to make full use of this signal. Therefore, we propose a new [...] Read more.
The further exploration of the capacitive ECG (cECG) is hindered by frequent fluctuations in signal quality from body movement and changes in sleep position. The processing framework must be fundamentally adapted to make full use of this signal. Therefore, we propose a new signal-processing framework that determines the signal quality for short signal segments (2 and 4 seconds) using a multi-class classification model (qua_model) based on a convolutional neural network (CNN). We built another independent deep CNN classifier (pos_model) to classify the sleep position. In the validation, 12 subjects were recruited for a 30-minute experiment, which required the subjects to lie on a bed in different sleeping positions. The short segments, classified as clear (C1 class) by the qua_model, were used to determine sleep positions with the pos_model. In 10-fold cross-validation, the qua_model for signals of 4-second length could recognize the signal of the C1 class at a 0.99 precision and a 0.99 recall; the pos_model could recognize the supine sleep position, the left, and right lateral sleep positions at a 0.99 averaged precision and a 0.99 averaged recall. Given the amount of data accumulated per night and the instability in the signal quality, this fully automatic processing framework is indispensable for a personal healthcare system. Therefore, this study could serve as an important step for cECG technique trying to explore the cECG for unconstrained heart monitoring. Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle Possibilities for Groundwater Flow Sensing with Fiber Bragg Grating Sensors
Sensors 2019, 19(7), 1730; https://doi.org/10.3390/s19071730
Received: 15 February 2019 / Revised: 8 April 2019 / Accepted: 8 April 2019 / Published: 11 April 2019
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Abstract
An understanding of groundwater flow near drinking water extraction wells is crucial when it comes to avoiding well clogging and pollution. A promising new approach to groundwater flow monitoring is the deployment of a network of optical fibers with fiber Bragg grating (FBG) [...] Read more.
An understanding of groundwater flow near drinking water extraction wells is crucial when it comes to avoiding well clogging and pollution. A promising new approach to groundwater flow monitoring is the deployment of a network of optical fibers with fiber Bragg grating (FBG) sensors. In preparation for a field experiment, a laboratory scale aquifer was constructed to investigate the feasibility of FBG sensors for this application. Multiparameter FBG sensors were able to detect changes in temperature, pressure, and fiber shape with sensitivities influenced by the packaging. The first results showed that, in a simulated environment with a flow velocity of 2.9 m/d, FBG strain effects were more pronounced than initially expected. FBG sensors of a pressure-induced strain implemented in a spatial array could form a multiplexed sensor for the groundwater flow direction and magnitude. Within the scope of this research, key technical specifications of FBG interrogators for groundwater flow sensing were also identified. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Application of a Waveguide-Mode Sensor to Blood Testing for Hepatitis B Virus, Hepatitis C Virus, Human Immunodeficiency Virus and Treponema pallidum Infection
Sensors 2019, 19(7), 1729; https://doi.org/10.3390/s19071729
Received: 28 February 2019 / Revised: 28 March 2019 / Accepted: 8 April 2019 / Published: 11 April 2019
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Abstract
Testing for blood-transmitted infectious agents is an important aspect of safe medical treatment. During emergencies, such as significant earthquakes, many patients need surgical treatment and/or blood transfusion. Because a waveguide mode (WM) sensor can be used as a portable, on-site blood testing device [...] Read more.
Testing for blood-transmitted infectious agents is an important aspect of safe medical treatment. During emergencies, such as significant earthquakes, many patients need surgical treatment and/or blood transfusion. Because a waveguide mode (WM) sensor can be used as a portable, on-site blood testing device in emergency settings, we have previously developed WM sensors for detection of antibodies against hepatitis B virus and hepatitis C virus and for forward ABO and Rh(D) and reverse ABO blood typing. In this study, we compared signal enhancement methods using secondary antibodies conjugated with peroxidase, a fluorescent dye, and gold nanoparticles, and found that the peroxidase reaction method offers superior sensitivity while gold nanoparticles provide the most rapid detection of anti-HBs antibody. Next, we examined whether we could apply a WM sensor with signal enhancement with peroxidase or gold nanoparticles to detection of antibodies against hepatitis C virus, human immunodeficiency virus and Treponema pallidum, and HBs antigen in plasma. We showed that a WM sensor can detect significant signals of these infectious agents within 30 min. Therefore, a portable device utilizing a WM sensor can be used for on-site blood testing of infectious agents in emergency settings. Full article
(This article belongs to the Special Issue Optical Bio Sensing)
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Open AccessArticle Front-Vehicle Detection in Video Images Based on Temporal and Spatial Characteristics
Sensors 2019, 19(7), 1728; https://doi.org/10.3390/s19071728
Received: 7 March 2019 / Revised: 4 April 2019 / Accepted: 9 April 2019 / Published: 11 April 2019
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Abstract
Assisted driving and unmanned driving have been areas of focus for both industry and academia. Front-vehicle detection technology, a key component of both types of driving, has also attracted great interest from researchers. In this paper, to achieve front-vehicle detection in unmanned or [...] Read more.
Assisted driving and unmanned driving have been areas of focus for both industry and academia. Front-vehicle detection technology, a key component of both types of driving, has also attracted great interest from researchers. In this paper, to achieve front-vehicle detection in unmanned or assisted driving, a vision-based, efficient, and fast front-vehicle detection method based on the spatial and temporal characteristics of the front vehicle is proposed. First, a method to extract the motion vector of the front vehicle is put forward based on Oriented FAST and Rotated BRIEF (ORB) and the spatial position constraint. Then, by analyzing the differences between the motion vectors of the vehicle and those of the background, feature points of the vehicle are extracted. Finally, a feature-point clustering method based on a combination of temporal and spatial characteristics are applied to realize front-vehicle detection. The effectiveness of the proposed algorithm is verified using a large number of videos. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle Univariate and Multivariate Analysis of Phosphorus Element in Fertilizers Using Laser-Induced Breakdown Spectroscopy
Sensors 2019, 19(7), 1727; https://doi.org/10.3390/s19071727
Received: 5 March 2019 / Revised: 29 March 2019 / Accepted: 9 April 2019 / Published: 11 April 2019
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Abstract
Rapid detection of phosphorus (P) element is beneficial to the control of compound fertilizer production process and is of great significance in the fertilizer industry. The aim of this work was to compare the univariate and multivariate analysis of phosphorus element in compound [...] Read more.
Rapid detection of phosphorus (P) element is beneficial to the control of compound fertilizer production process and is of great significance in the fertilizer industry. The aim of this work was to compare the univariate and multivariate analysis of phosphorus element in compound fertilizers and obtain a reliable and accurate method for rapid detection of phosphorus element. A total of 47 fertilizer samples were collected from the production line; 36 samples were used as a calibration set, and 11 samples were used as a prediction set. The univariate calibration curve was constructed by the intensity of characteristic line and the concentration of P. The linear correlation coefficient was 0.854 as the existence of the matrix effect. In order to eliminate the matrix effect, the internal standardization as the appropriate methodology was used to increase the accuracy. Using silicon (Si) element as an internal element, a linear correlation coefficient of 0.932 was obtained. Furthermore, the chemometrics model of partial least-squares regression (PLSR) was used to analysis the concentration of P in fertilizer. The correlation coefficient was 0.977 and 0.976 for the calibration set and prediction set, respectively. The results indicated that the LIBS technique coupled with PLSR could be a reliable and accurate method in the quantitative determination of P element in complex matrices like compound fertilizers. Full article
(This article belongs to the Special Issue Advanced Sensors for Real-Time Monitoring Applications)
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Open AccessArticle A Gold Nanoclusters Film Supported on Polydopamine for Fluorescent Sensing of Free Bilirubin
Sensors 2019, 19(7), 1726; https://doi.org/10.3390/s19071726
Received: 6 March 2019 / Revised: 8 April 2019 / Accepted: 8 April 2019 / Published: 10 April 2019
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Abstract
Serum bilirubin is an important biomarker for the diagnosis of various types of liver diseases and blood disorders. A polydopamine/gold nanoclusters composite film was fabricated for the fluorescent sensing of free bilirubin. Bovine serum albumin (BSA)-stabilized gold nanoclusters (AuNCs) were used as probes [...] Read more.
Serum bilirubin is an important biomarker for the diagnosis of various types of liver diseases and blood disorders. A polydopamine/gold nanoclusters composite film was fabricated for the fluorescent sensing of free bilirubin. Bovine serum albumin (BSA)-stabilized gold nanoclusters (AuNCs) were used as probes for biorecognition. The polydopamine film was utilized as an adhesion layer for immobilization of AuNCs. When the composite film was exposed to free bilirubin, due to the complex that was formed between BSA and free bilirubin, the fluorescence intensity of the composite film was gradually weakened as the bilirubin concentration increased. The fluorescence quenching ratio (F0/F) was linearly proportional to free bilirubin over the concentration range of 0.8~50 μmol/L with a limit of detection of 0.61 ± 0.12 μmol/L (S/N = 3). The response was quick, the film was recyclable, and common ingredients in human serum did not interfere with the detection of free bilirubin. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessCorrection Correction: Tang, K., et al., A Novel Fingerprint Sensing Technology Based on Electrostatic Imaging. Sensors 2018, 18, 3050
Sensors 2019, 19(7), 1725; https://doi.org/10.3390/s19071725
Received: 4 April 2019 / Accepted: 9 April 2019 / Published: 10 April 2019
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Abstract
The authors wish to make the following corrections to this paper [...] Full article
Open AccessArticle Electrophoretic Separation on an Origami Paper-Based Analytical Device Using a Portable Power Bank
Sensors 2019, 19(7), 1724; https://doi.org/10.3390/s19071724
Received: 11 March 2019 / Revised: 8 April 2019 / Accepted: 9 April 2019 / Published: 10 April 2019
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Abstract
The electrophoresis of ampholytes such as amino acids on a paper device is difficult because of the variation of pH distribution in time. On the basis of this observation, we propose a paper-based analytical device (PAD) with origami structure. By folding a filter [...] Read more.
The electrophoresis of ampholytes such as amino acids on a paper device is difficult because of the variation of pH distribution in time. On the basis of this observation, we propose a paper-based analytical device (PAD) with origami structure. By folding a filter paper, a low operation voltage of 5 V was achieved, where the power was supplied by a 5 V 1.5 A portable power bank through the USB type A receptacle. As a demonstration, we carried out the electrophoretic separation of pI markers (pI 5.5 and 8.7). The separation was achieved within 4 min before the pH distribution on the paper varied. Though the separation distance was small, it could be increased by expanding the origami structure. This result indicates that our proposed PAD is useful for electrophoretic separation on a paper device. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessErratum Erratum: Zhao, Y.; Zhang, N.; Si, G.; Li, X. Study on the Optimal Groove Shape and Glue Material for Fiber Bragg Grating Measuring Bolts. Sensors 2018, 18, 1799
Sensors 2019, 19(7), 1723; https://doi.org/10.3390/s19071723
Received: 21 March 2019 / Accepted: 27 March 2019 / Published: 10 April 2019
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Abstract
The authors wish to correct the affiliation of co-author Guangyao Si, due to name changes of which he was unaware during his leave of absence [...] Full article
(This article belongs to the Special Issue Recent Advances in Fiber Bragg Grating Based Sensors)
Open AccessArticle High Sensitivity Refractometer Based on a Tapered-Single Mode-No Core-Single Mode Fiber Structure
Sensors 2019, 19(7), 1722; https://doi.org/10.3390/s19071722
Received: 1 March 2019 / Revised: 8 April 2019 / Accepted: 8 April 2019 / Published: 10 April 2019
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Abstract
We have proposed a novel tapered-single mode-no core-single mode (TSNS) fiber refractometer based on multimode interference. The TSNS structure exhibits a high contrast ratio (>15 dB) and a uniform interference fringe. The influence of different lengths and diameters of the TSNS on the [...] Read more.
We have proposed a novel tapered-single mode-no core-single mode (TSNS) fiber refractometer based on multimode interference. The TSNS structure exhibits a high contrast ratio (>15 dB) and a uniform interference fringe. The influence of different lengths and diameters of the TSNS on the refractive index unit (RIU) sensitivity was investigated. The experimental investigations indicated a maximum sensitivity of 1517.28 nm/RIU for a refractive index of 1.417 and low-temperature sensitivity (<10 pm/°C). The experimental and simulation results are also in good agreement. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle An Analysis of the Attitude Estimation Errors Caused by the Deflections of Vertical in the Integration of Rotational INS and GNSS
Sensors 2019, 19(7), 1721; https://doi.org/10.3390/s19071721
Received: 10 March 2019 / Revised: 30 March 2019 / Accepted: 30 March 2019 / Published: 10 April 2019
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Abstract
This paper investigates the attitude estimation errors caused by the deflections of vertical (DOV) in the case of a rotational inertial navigation system (INS) integrated with a global satellite navigation system (GNSS). It has been proved theoretically and experimentally that the DOV can [...] Read more.
This paper investigates the attitude estimation errors caused by the deflections of vertical (DOV) in the case of a rotational inertial navigation system (INS) integrated with a global satellite navigation system (GNSS). It has been proved theoretically and experimentally that the DOV can introduce a tilt error to the INS/GNSS integration, whereas less attention has been given to its effect to the heading estimation. In fact, due to the intercoupling characteristic of attitude errors, the heading estimation of an INS/GNSS integrated navigation system can also be affected. In this paper, first, the attitude estimation errors caused by DOV were deduced based on the INS’s error propagation functions. Then, the corresponding simulations were conducted and the results were well consistent with the theoretical analysis. Finally, a real shipborne marine test was organized with the aimed to verify the effect of DOV on attitude estimation in the rotational INS/GNSS integration, whereas the global gravity model was used for DOV compensation. The results with DOV compensation were compared with the corresponding results where the compensation was not used and showed that the heading estimation errors caused by DOV could exceed 20 arcsecs, which must be considered in high-precision application cases. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle Analysis of a Hybrid Micro-Electro-Mechanical Sensor Based on Graphene Oxide/Polyvinyl Alcohol for Humidity Measurements
Sensors 2019, 19(7), 1720; https://doi.org/10.3390/s19071720
Received: 31 January 2019 / Revised: 4 March 2019 / Accepted: 5 March 2019 / Published: 10 April 2019
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Abstract
In this paper, we present a redundant microsensor based on the bulk and etch silicon‑on‑insulator (BESOI) process for measuring relative humidity (RH), by using a graphene‑oxide/polyvinyl‑alcohol (GO/PVA) composite. The MEMS is a mechanical oscillator, composed of a proof mass with multilayer of nanomaterials [...] Read more.
In this paper, we present a redundant microsensor based on the bulk and etch silicon‑on‑insulator (BESOI) process for measuring relative humidity (RH), by using a graphene‑oxide/polyvinyl‑alcohol (GO/PVA) composite. The MEMS is a mechanical oscillator, composed of a proof mass with multilayer of nanomaterials (GO/PVA) and suspended by four crab-leg springs. The redundant approach realized concerns the use of different readout strategies in order to estimate the same measurand RH. This is an intriguing solution to realize a robust measurement system with multiple outputs, by using the GO/PVA as functional material. In the presence of RH variation, GO/PVA (1) changes its mass, and as consequence, a variation of the natural frequency of the integrated oscillator can be observed; and (2) varies its conductivity, which can be measured by using two integrated electrodes. The sensor was designed, analyzed and modeled; experimental results are reported here to demonstrate the effectiveness of our implementation. Full article
(This article belongs to the Special Issue Eurosensors 2018 Selected Papers)
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Open AccessArticle Novel Cost-Effective Microfluidic Chip Based on Hybrid Fabrication and Its Comprehensive Characterization
Sensors 2019, 19(7), 1719; https://doi.org/10.3390/s19071719
Received: 27 February 2019 / Revised: 19 March 2019 / Accepted: 21 March 2019 / Published: 10 April 2019
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Abstract
Microfluidics, one of the most attractive and fastest developed areas of modern science and technology, has found a number of applications in medicine, biology and chemistry. To address advanced designing challenges of the microfluidic devices, the research is mainly focused on development of [...] Read more.
Microfluidics, one of the most attractive and fastest developed areas of modern science and technology, has found a number of applications in medicine, biology and chemistry. To address advanced designing challenges of the microfluidic devices, the research is mainly focused on development of efficient, low-cost and rapid fabrication technology with the wide range of applications. For the first time, this paper presents fabrication of microfluidic chips using hybrid fabrication technology—a grouping of the PVC (polyvinyl chloride) foils and the LTCC (Low Temperature Co-fired Ceramics) Ceram Tape using a combination of a cost-effective xurography technique and a laser micromachining process. Optical and dielectric properties were determined for the fabricated microfluidic chips. A mechanical characterization of the Ceram Tape, as a middle layer in its non-baked condition, has been performed and Young’s modulus and hardness were determined. The obtained results confirm a good potential of the proposed technology for rapid fabrication of low-cost microfluidic chips with high reliability and reproducibility. The conducted microfluidic tests demonstrated that presented microfluidic chips can resist 3000 times higher flow rates than the chips manufactured using standard xurography technique. Full article
(This article belongs to the Special Issue Microfluidic Sensors 2018)
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Open AccessArticle Sparse ECG Denoising with Generalized Minimax Concave Penalty
Sensors 2019, 19(7), 1718; https://doi.org/10.3390/s19071718
Received: 12 March 2019 / Revised: 3 April 2019 / Accepted: 8 April 2019 / Published: 10 April 2019
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Abstract
The electrocardiogram (ECG) is an important diagnostic tool for cardiovascular diseases. However, ECG signals are susceptible to noise, which may degenerate waveform and cause misdiagnosis. In this paper, the ECG noise reduction techniques based on sparse recovery are investigated. A novel sparse ECG [...] Read more.
The electrocardiogram (ECG) is an important diagnostic tool for cardiovascular diseases. However, ECG signals are susceptible to noise, which may degenerate waveform and cause misdiagnosis. In this paper, the ECG noise reduction techniques based on sparse recovery are investigated. A novel sparse ECG denoising framework combining low-pass filtering and sparsity recovery is proposed. Two sparsity recovery algorithms are developed based on the traditional 1 -norm penalty and the novel generalized minimax concave (GMC) penalty, respectively. Compared with the 1 -norm penalty, the non-differentiable non-convex GMC penalty has the potential to strongly promote sparsity while maintaining the convexity of the cost function. Moreover, the GMC punishes large values less severely than 1 -norm, which is utilized to overcome the drawback of underestimating the high-amplitude components for the 1 -norm penalty. The proposed methods are evaluated on ECG signals from the MIT-BIH Arrhythmia database. The results show that underestimating problem is overcome by the proposed GMC-based method. The GMC-based method shows significant improvement with respect to the average of output signal-to-noise ratio improvement ( S N R i m p ), the average of root mean square error (RMSE) and the percent root mean square difference (PRD) over almost any given SNR compared with the classical methods, thus providing promising approaches for ECG denoising. Full article
(This article belongs to the Special Issue Machine Learning for Sensing and Healthcare)
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Open AccessArticle UAVs for Structure-From-Motion Coastal Monitoring: A Case Study to Assess the Evolution of Embryo Dunes over a Two-Year Time Frame in the Po River Delta, Italy
Sensors 2019, 19(7), 1717; https://doi.org/10.3390/s19071717
Received: 4 March 2019 / Revised: 27 March 2019 / Accepted: 8 April 2019 / Published: 10 April 2019
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Abstract
Coastal environments are usually characterized by a brittle balance, especially in terms of sediment transportation. The formation of dunes, as well as their sudden destruction as a result of violent storms, affects this balance in a significant way. Moreover, the growth of vegetation [...] Read more.
Coastal environments are usually characterized by a brittle balance, especially in terms of sediment transportation. The formation of dunes, as well as their sudden destruction as a result of violent storms, affects this balance in a significant way. Moreover, the growth of vegetation on the top of the dunes strongly influences the consequent growth of the dunes themselves. This work presents the results obtained through a long-term monitoring of a complex dune system by the use of Unmanned Aerial Vehicles (UAVs). Six different surveys were carried out between November 2015 and December 2017 in the littoral of Rosolina Mare (Italy). Aerial photogrammetric data were acquired during flight repetitions by using a DJI Phantom 3 Professional with the camera in a nadiral arrangement. The processing of the captured images consisted of the reconstruction of a three-dimensional model using the Structure-from-Motion (SfM). Each model was framed in the European Terrestrial Reference System (ETRS) using GNSS geodetic receivers in Network Real Time Kinematic (NRTK). Specific data management was necessary due to the vegetation by filtering the dense cloud. This task was performed by both performing a slope detection and a removal of the residual outliers. The final products of this approach were thus represented by Digital Elevation Models (DEMs) of the sandy coastal section. In addition, DEMs of Difference (DoD) were also computed for the purpose of monitoring over time and detecting variations. The accuracy assessment of the DEMs was carried out by an elevation comparison through especially GNSS-surveyed points. Relevant cross sections were also extracted and compared. The use of the Structure-from-Motion approach by UAVs finally proved to be both reliable and time-saving thanks to quicker in situ operations for the data acquisition and an accurate reconstruction of high-resolution elevation models. The low cost of the system and its flexibility represent additional strengths, making this technique highly competitive with traditional ones. Full article
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