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Sensors, Volume 20, Issue 1 (January-1 2020) – 325 articles

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Cover Story (view full-size image) Plasmonic biosensing has had a huge impact on both research and industry, particularly thanks to [...] Read more.
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Open AccessArticle
Optimization of Saliva Collection and Immunochromatographic Detection of Salivary Pepsin for Point-of-Care Testing of Laryngopharyngeal Reflux
Sensors 2020, 20(1), 325; https://doi.org/10.3390/s20010325 - 06 Jan 2020
Viewed by 395
Abstract
Salivary pepsin is a promising marker for the non-invasive diagnosis of laryngopharyngeal reflux (LPR). For reliable results regarding pepsin in saliva, it is critical to standardize the collection, storage, and pre-processing methods. In this study, we optimized the saliva collection protocols, including storage [...] Read more.
Salivary pepsin is a promising marker for the non-invasive diagnosis of laryngopharyngeal reflux (LPR). For reliable results regarding pepsin in saliva, it is critical to standardize the collection, storage, and pre-processing methods. In this study, we optimized the saliva collection protocols, including storage conditions, i.e., solution, temperature, and time, and the pre-processing filter for pepsin. Moreover, we prepared a simple immunochromatographic strip for the rapid detection of pepsin and evaluated its sensing performance. As a result, we selected a polypropylene (PP) filter as the pre-processing filter for salivary pepsin in low resource settings, such as those where point of care testing (POCT) is conducted. This filter showed a similar efficiency to the centrifuge (standard method). Finally, we detected the pepsin using gold nanoparticles conjugated with monoclonal pepsin antibody. Under optimized conditions, the lower limit of detection for pepsin test strips was determined as 0.01 μg/mL. Furthermore, we successfully detected the salivary pepsin in real saliva samples of LPR patients, which were pre-processed by the PP filter. Therefore, we expect that our saliva collection protocol and pepsin immunochromatographic strip can be utilized as useful tools for a non-invasive diagnosis/screening of LPR in POCT. Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle
Lane Detection Method with Impulse Radio Ultra-Wideband Radar and Metal Lane Reflectors
Sensors 2020, 20(1), 324; https://doi.org/10.3390/s20010324 - 06 Jan 2020
Viewed by 348
Abstract
An advanced driver-assistance system (ADAS), based on lane detection technology, detects dangerous situations through various sensors and either warns the driver or takes over direct control of the vehicle. At present, cameras are commonly used for lane detection; however, their performance varies widely [...] Read more.
An advanced driver-assistance system (ADAS), based on lane detection technology, detects dangerous situations through various sensors and either warns the driver or takes over direct control of the vehicle. At present, cameras are commonly used for lane detection; however, their performance varies widely depending on the lighting conditions. Consequently, many studies have focused on using radar for lane detection. However, when using radar, it is difficult to distinguish between the plain road surface and painted lane markers, necessitating the use of radar reflectors for guidance. Previous studies have used long-range radars which may receive interference signals from various objects, including other vehicles, pedestrians, and buildings, thereby hampering lane detection. Therefore, we propose a lane detection method that uses an impulse radio ultra-wideband radar with high-range resolution and metal lane markers installed at regular intervals on the road. Lane detection and departure is realized upon using the periodically reflected signals as well as vehicle speed data as inputs. For verification, a field test was conducted by attaching radar to a vehicle and installing metal lane markers on the road. Experimental scenarios were established by varying the position and movement of the vehicle, and it was demonstrated that the proposed method enables lane detection based on the data measured. Full article
(This article belongs to the Section Electronic Sensors)
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Open AccessArticle
High Sensitivity Differential Giant Magnetoresistance (GMR) Based Sensor for Non-Contacting DC/AC Current Measurement
Sensors 2020, 20(1), 323; https://doi.org/10.3390/s20010323 - 06 Jan 2020
Viewed by 366
Abstract
This paper presents the design and implementation of a high sensitivity giant magnetoresistance (GMR) based current sensor with a broad range of applications. The novelty of our approach consists in using a double differential measurement system, based on commercial GMR sensors, with an [...] Read more.
This paper presents the design and implementation of a high sensitivity giant magnetoresistance (GMR) based current sensor with a broad range of applications. The novelty of our approach consists in using a double differential measurement system, based on commercial GMR sensors, with an adjustable biasing system used to linearize the field response of the system. The work aims to act as a fully-operational proof of concept application, with an emphasis on the mode of operation and methods to improve the sensitivity and linearity of the measurement system. The implemented system has a broad current measurement range from as low as 75 mA in DC and 150 mA in AC up to 4 A by using a single setup. The sensor system is also very low power, consuming only 6.4 mW. Due to the way the sensors are polarized and positioned above the U-shaped conductive band through which the current to be measured is flowing, the differential setup offers a sensitivity of about between 0.0272 to 0.0307 V/A (signal from sensors with no amplifications), a high immunity to external magnetic fields, low hysteresis effects of 40 mA, and a temperature drift of the offset of about −2.59×10−4 A/°C. The system provides a high flexibility in designing applications where local fields with very low amplitudes must be detected. This setup can be redesigned for a wide range of applications, thus allowing further specific optimizations, which would provide an even greater accuracy and a significantly extended operation range. Full article
(This article belongs to the Special Issue Recent Advances in Magnetic Sensors)
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Open AccessArticle
A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction
Sensors 2020, 20(1), 322; https://doi.org/10.3390/s20010322 - 06 Jan 2020
Viewed by 415
Abstract
Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL algorithms may vary depending upon the data sets being used, as well as on [...] Read more.
Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL algorithms may vary depending upon the data sets being used, as well as on the suitability of algorithms to the data and the application domain under consideration. Hence, determining which ML/DL algorithm is most suitable for a specific application domain and its related data sets would be a key advantage. To respond to this need, a comparative analysis of well-known ML/DL techniques, including Multilayer Perceptron, K-Nearest Neighbors, Decision Tree, Random Forest, and Voting Classifier (or the Ensemble Learning Approach) for the prediction of parking space availability has been conducted. This comparison utilized Santander’s parking data set, initiated while working on the H2020 WISE-IoT project. The data set was used in order to evaluate the considered algorithms and to determine the one offering the best prediction. The results of this analysis show that, regardless of the data set size, the less complex algorithms like Decision Tree, Random Forest, and KNN outperform complex algorithms such as Multilayer Perceptron, in terms of higher prediction accuracy, while providing comparable information for the prediction of parking space availability. In addition, in this paper, we are providing Top-K parking space recommendations on the basis of distance between current position of vehicles and free parking spots. Full article
(This article belongs to the Special Issue Sensor Systems in Smart Environments)
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Open AccessArticle
Distributed Optical Fiber-Based Approach for Soil–Structure Interaction
Sensors 2020, 20(1), 321; https://doi.org/10.3390/s20010321 - 06 Jan 2020
Viewed by 367
Abstract
Scour is a hydraulic risk threatening the stability of bridges in fluvial and coastal areas. Therefore, developing permanent and real-time monitoring techniques is crucial. Recent advances in strain measurements using fiber optic sensors allow new opportunities for scour monitoring. In this study, the [...] Read more.
Scour is a hydraulic risk threatening the stability of bridges in fluvial and coastal areas. Therefore, developing permanent and real-time monitoring techniques is crucial. Recent advances in strain measurements using fiber optic sensors allow new opportunities for scour monitoring. In this study, the innovative optical frequency domain reflectometry (OFDR) was used to evaluate the effect of scour by performing distributed strain measurements along a rod under static lateral loads. An analytical analysis based on the Winkler model of the soil was carefully established and used to evaluate the accuracy of the fiber optic sensors and helped interpret the measurements results. Dynamic tests were also performed and results from static and dynamic tests were compared using an equivalent cantilever model. Full article
(This article belongs to the Special Issue Optical Sensors for Structural Health Monitoring)
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Open AccessArticle
Triplet Loss Guided Adversarial Domain Adaptation for Bearing Fault Diagnosis
Sensors 2020, 20(1), 320; https://doi.org/10.3390/s20010320 - 06 Jan 2020
Viewed by 329
Abstract
Recently, deep learning methods are becomingincreasingly popular in the field of fault diagnosis and achieve great success. However, since the rotation speeds and load conditions of rotating machines are subject to change during operations, the distribution of labeled training dataset for intelligent fault [...] Read more.
Recently, deep learning methods are becomingincreasingly popular in the field of fault diagnosis and achieve great success. However, since the rotation speeds and load conditions of rotating machines are subject to change during operations, the distribution of labeled training dataset for intelligent fault diagnosis model is different from the distribution of unlabeled testing dataset, where domain shift occurs. The performance of the fault diagnosis may significantly degrade due to this domain shift problem. Unsupervised domain adaptation has been proposed to alleviate this problem by aligning the distribution between labeled source domain and unlabeled target domain. In this paper, we propose triplet loss guided adversarial domain adaptation method (TLADA) for bearing fault diagnosis by jointly aligning the data-level and class-level distribution. Data-level alignment is achieved using Wasserstein distance-based adversarial approach, and the discrepancy of distributions in feature space is further minimized at class level by the triplet loss. Unlike other center loss-based class-level alignment approaches, which hasto compute the class centers for each class and minimize the distance of same class center from different domain, the proposed TLADA method concatenates 2 mini-batches from source and target domain into a single mini-batch and imposes triplet loss to the whole mini-batch ignoring the domains. Therefore, the overhead of updating the class center is eliminated. The effectiveness of the proposed method is validated on CWRU dataset and Paderborn dataset through extensive transfer fault diagnosis experiments. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
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Open AccessArticle
A Machine Condition Monitoring Framework Using Compressed Signal Processing
Sensors 2020, 20(1), 319; https://doi.org/10.3390/s20010319 - 06 Jan 2020
Viewed by 334
Abstract
The vibration monitoring of ball bearings of a rotating machinery is a crucial aspect for smooth functioning and sustainability of plants. The wireless vibration monitoring using conventional Nyquist sampling techniques is costly in terms of power consumption, as it generates lots of data [...] Read more.
The vibration monitoring of ball bearings of a rotating machinery is a crucial aspect for smooth functioning and sustainability of plants. The wireless vibration monitoring using conventional Nyquist sampling techniques is costly in terms of power consumption, as it generates lots of data that need to be processed. To overcome this issue, compressive sensing (CS) can be employed, which directly acquires the signal in compressed form and hence reduces power consumption. The compressive measurements so generated can easily be transmitted to the base station and the original signal can be recovered there using CS reconstruction algorithms to diagnose the faults. However, the CS reconstruction is very costly in terms of computational time and power. Hence, this conventional CS framework is not suitable for diagnosing the machinery faults in real time. In this paper, a bearing condition monitoring framework is presented based on compressed signal processing (CSP). The CSP is a newer research area of CS, in which inference problems are solved without reconstructing the original signal back from compressive measurements. By omitting the reconstruction efforts, the proposed method significantly improves the time and power cost. This leads to faster processing of compressive measurements for solving the required inference problems for machinery condition monitoring. This gives a way to diagnose the machinery faults in real-time. A comparison of proposed scheme with the conventional method shows that the proposed scheme lowers the computational efforts while simultaneously achieving the comparable fault classification accuracy. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
An Evidential Framework for Localization of Sensors in Indoor Environments
Sensors 2020, 20(1), 318; https://doi.org/10.3390/s20010318 - 06 Jan 2020
Viewed by 320
Abstract
Indoor localization has several applications ranging from people tracking and indoor navigation, to autonomous robot navigation and asset tracking. We tackle the problem as a zoning localization where the objective is to determine the zone where the mobile sensor resides at any instant. [...] Read more.
Indoor localization has several applications ranging from people tracking and indoor navigation, to autonomous robot navigation and asset tracking. We tackle the problem as a zoning localization where the objective is to determine the zone where the mobile sensor resides at any instant. The decision-making process in localization systems relies on data coming from multiple sensors. The data retrieved from these sensors require robust fusion approaches to be processed. One of these approaches is the belief functions theory (BFT), also called the Dempster–Shafer theory. This theory deals with uncertainty and imprecision with a theoretically attractive evidential reasoning framework. This paper investigates the usage of the BFT to define an evidence framework for estimating the most probable sensor’s zone. Real experiments demonstrate the effectiveness of this approach and its competence compared to state-of-the-art methods. Full article
(This article belongs to the Special Issue Sensors Localization in Indoor Wireless Networks)
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Open AccessArticle
Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model
Sensors 2020, 20(1), 317; https://doi.org/10.3390/s20010317 - 06 Jan 2020
Viewed by 391
Abstract
Human activity recognition (HAR) techniques are playing a significant role in monitoring the daily activities of human life such as elderly care, investigation activities, healthcare, sports, and smart homes. Smartphones incorporated with varieties of motion sensors like accelerometers and gyroscopes are widely used [...] Read more.
Human activity recognition (HAR) techniques are playing a significant role in monitoring the daily activities of human life such as elderly care, investigation activities, healthcare, sports, and smart homes. Smartphones incorporated with varieties of motion sensors like accelerometers and gyroscopes are widely used inertial sensors that can identify different physical conditions of human. In recent research, many works have been done regarding human activity recognition. Sensor data of smartphone produces high dimensional feature vectors for identifying human activities. However, all the vectors are not contributing equally for identification process. Including all feature vectors create a phenomenon known as ‘curse of dimensionality’. This research has proposed a hybrid method feature selection process, which includes a filter and wrapper method. The process uses a sequential floating forward search (SFFS) to extract desired features for better activity recognition. Features are then fed to a multiclass support vector machine (SVM) to create nonlinear classifiers by adopting the kernel trick for training and testing purpose. We validated our model with a benchmark dataset. Our proposed system works efficiently with limited hardware resource and provides satisfactory activity identification. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICKII 2019)
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Open AccessArticle
Measuring the Water Content in Wood Using Step-Heating Thermography and Speckle Patterns-Preliminary Results
Sensors 2020, 20(1), 316; https://doi.org/10.3390/s20010316 - 06 Jan 2020
Viewed by 301
Abstract
The relationship between wood and its degree of humidity is one of the most important aspects of its use in construction and restoration. The wood presents a behavior similar to a sponge, therefore, moisture is related to its expansion and contraction. The nondestructive [...] Read more.
The relationship between wood and its degree of humidity is one of the most important aspects of its use in construction and restoration. The wood presents a behavior similar to a sponge, therefore, moisture is related to its expansion and contraction. The nondestructive evaluation (NDE) of the amount of moisture in wood materials allows to define, e.g., the restoration procedures of buildings or artworks. In this work, an integrated study of two non-contact techniques is presented. Infrared thermography (IRT) was able to retrieve thermal parameters of the wood related to the amount of water added to the samples, while the interference pattern generated by speckles was used to quantify the expansion and contraction of wood that can be related to the amount of water. In twenty-seven wooded samples, a known quantity of water was added in a controlled manner. By applying advanced image processing to thermograms and specklegrams, it was possible to determine fundamental values controlling both the absorption of water and the main thermophysical parameters that link the samples. On the one hand, results here shown should be considered preliminary because the experimental values obtained by IRT need to be optimized for low water contents introduced into the samples. On the other hand, speckle interferometry by applying an innovative procedure provided robust results for both high and low water contents. Full article
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Open AccessArticle
Integrated Piezoelectric AlN Thin Film with SU-8/PDMS Supporting Layer for Flexible Sensor Array
Sensors 2020, 20(1), 315; https://doi.org/10.3390/s20010315 - 06 Jan 2020
Viewed by 317
Abstract
This research focuses on the development of a flexible tactile sensor array consisting of aluminum nitride (AlN) based on micro-electro-mechanical system (MEMS) technology. A total of 2304 tactile sensors were integrated into a small area of 2.5 × 2.5 cm2. Five [...] Read more.
This research focuses on the development of a flexible tactile sensor array consisting of aluminum nitride (AlN) based on micro-electro-mechanical system (MEMS) technology. A total of 2304 tactile sensors were integrated into a small area of 2.5 × 2.5 cm2. Five hundred nm thick AlN film with strong c-axis texture was sputtered on Cr/Au/Cr (50/50/5 nm) layers as the sacrificial layer coated on a Si wafer. To achieve device flexibility, polydimethylsiloxane (PDMS) polymer and SU-8 photoresist layer were used as the supporting layers after etching away a release layer. Twenty-five mM (3-mercaptopropyl) trimethoxysilane (MPTMS) improves the adhesion between metal and polymers due to formation of a self-assembled monolayer (SAM) on the surface of the top electrode. The flexible tactile sensor has 8 × 8 channels and each channel has 36 sensor elements with nine SU-8 bump blocks. The tactile sensor array was demonstrated to be flexible by bending 90 degrees. The tactile sensor array was demonstrated to show clear spatial resolution through detecting the distinct electrical response of each channel under local mechanical stimulus. Full article
(This article belongs to the Special Issue Development of Piezoelectric Sensors and Actuators)
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Open AccessArticle
Machine Learning Methodology in a System Applying the Adaptive Strategy for Teaching Human Motions
Sensors 2020, 20(1), 314; https://doi.org/10.3390/s20010314 - 06 Jan 2020
Viewed by 265
Abstract
The teaching of motion activities in rehabilitation, sports, and professional work has great social significance. However, the automatic teaching of these activities, particularly those involving fast motions, requires the use of an adaptive system that can adequately react to the changing stages and [...] Read more.
The teaching of motion activities in rehabilitation, sports, and professional work has great social significance. However, the automatic teaching of these activities, particularly those involving fast motions, requires the use of an adaptive system that can adequately react to the changing stages and conditions of the teaching process. This paper describes a prototype of an automatic system that utilizes the online classification of motion signals to select the proper teaching algorithm. The knowledge necessary to perform the classification process is acquired from experts by the use of the machine learning methodology. The system utilizes multidimensional motion signals that are captured using MEMS (Micro-Electro-Mechanical Systems) sensors. Moreover, an array of vibrotactile actuators is used to provide feedback to the learner. The main goal of the presented article is to prove that the effectiveness of the described teaching system is higher than the system that controls the learning process without the use of signal classification. Statistical tests carried out by the use of a prototype system confirmed that thesis. This is the main outcome of the presented study. An important contribution is also a proposal to standardize the system structure. The standardization facilitates the system configuration and implementation of individual, specialized teaching algorithms. Full article
(This article belongs to the Special Issue Intelligent Sensor Signal in Machine Learning)
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Open AccessArticle
Brain and Body Emotional Responses: Multimodal Approximation for Valence Classification
Sensors 2020, 20(1), 313; https://doi.org/10.3390/s20010313 - 06 Jan 2020
Viewed by 289
Abstract
In order to develop more precise and functional affective applications, it is necessary to achieve a balance between the psychology and the engineering applied to emotions. Signals from the central and peripheral nervous systems have been used for emotion recognition purposes, however, their [...] Read more.
In order to develop more precise and functional affective applications, it is necessary to achieve a balance between the psychology and the engineering applied to emotions. Signals from the central and peripheral nervous systems have been used for emotion recognition purposes, however, their operation and the relationship between them remains unknown. In this context, in the present work, we have tried to approach the study of the psychobiology of both systems in order to generate a computational model for the recognition of emotions in the dimension of valence. To this end, the electroencephalography (EEG) signal, electrocardiography (ECG) signal and skin temperature of 24 subjects have been studied. Each methodology has been evaluated individually, finding characteristic patterns of positive and negative emotions in each of them. After feature selection of each methodology, the results of the classification showed that, although the classification of emotions is possible at both central and peripheral levels, the multimodal approach did not improve the results obtained through the EEG alone. In addition, differences have been observed between cerebral and peripheral responses in the processing of emotions by separating the sample by sex; though, the differences between men and women were only notable at the peripheral nervous system level. Full article
(This article belongs to the Special Issue Sensors for Affective Computing and Sentiment Analysis)
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Open AccessArticle
Ammonium Pyrrolidine Dithiocarbamate-Modified CdTe/CdS Quantum Dots as a Turn-on Fluorescent Sensor for Detection of Trace Cadmium Ions
Sensors 2020, 20(1), 312; https://doi.org/10.3390/s20010312 - 06 Jan 2020
Viewed by 258
Abstract
In this work, ammonium pyrrolidine dithiocarbamate (APDC) was used as a surface etchant to modify CdTe/CdS core-shell quantum dots (QDs). The APDC etchant combines with the cadmium ions (Cd2+) on the surface of the QDs, resulting in the formation of surface [...] Read more.
In this work, ammonium pyrrolidine dithiocarbamate (APDC) was used as a surface etchant to modify CdTe/CdS core-shell quantum dots (QDs). The APDC etchant combines with the cadmium ions (Cd2+) on the surface of the QDs, resulting in the formation of surface holes. The formation of these holes changes the QD surface structure, which leads to fluorescence quenching of the QDs. Newly added Cd2+ can selectively recognize and combine with these holes; thus, the fluorescence intensity of the QDs can be restored. The linear response of this turn-on fluorescent sensor was found to be 0–100 μg/L and 100–600 μg/L under the determined optimal conditions, and its limit of detection (LOD) for Cd2+ was 2.642 μg/L (23.5 nmol/L). Full article
(This article belongs to the Special Issue Development and Application of Chemosensors)
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Open AccessArticle
A Power Spectrum Maps Estimation Algorithm Based on Generative Adversarial Networks for Underlay Cognitive Radio Networks
Sensors 2020, 20(1), 311; https://doi.org/10.3390/s20010311 - 06 Jan 2020
Viewed by 274
Abstract
In the underlay cognitive radio networks, the main challenge in detecting the idle radio resources is to estimate the power spectrum maps (PSMs), where the radio propagation characteristics are hard to obtain. For this reason, we propose a novel PSMs estimation algorithm based [...] Read more.
In the underlay cognitive radio networks, the main challenge in detecting the idle radio resources is to estimate the power spectrum maps (PSMs), where the radio propagation characteristics are hard to obtain. For this reason, we propose a novel PSMs estimation algorithm based on the generative adversarial networks (GANs). First, we constructed the PSMs estimation model as a regression model in deep learning. Then, we converted the estimation task into an image reconstruction task by image color mapping. We fulfilled the above task by designing an image generator and an image discriminator in the proposed maps’ estimation GANs (MEGANs). The generator is trained to extract the radio propagation characteristics and generate the PSMs images. However, the discriminator is trained to identify the generated images and help to improve the generator’s performance. With the training process of MEGANs, the abilities of the generator and the discriminator are enhanced continually until reaching a balance, which means a high-accuracy PSMs estimation is achieved. The proposed MEGANs algorithm learns and utilizes accurate radio propagation features from the training process rather than making direct imprecise or biased propagation assumptions as in the traditional methods. Simulation results demonstrate that the MEGANs algorithm provides a more accurate estimation performance than the conventional methods. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessFeature PaperArticle
Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection
Sensors 2020, 20(1), 310; https://doi.org/10.3390/s20010310 - 06 Jan 2020
Viewed by 303
Abstract
Continuous monitoring of complex activities is valuable for understanding human behavior and providing activity-aware services. At the same time, recognizing these activities requires both movement and location information that can quickly drain batteries on wearable devices. In this paper, we introduce Change Point-based [...] Read more.
Continuous monitoring of complex activities is valuable for understanding human behavior and providing activity-aware services. At the same time, recognizing these activities requires both movement and location information that can quickly drain batteries on wearable devices. In this paper, we introduce Change Point-based Activity Monitoring (CPAM), an energy-efficient strategy for recognizing and monitoring a range of simple and complex activities in real time. CPAM employs unsupervised change point detection to detect likely activity transition times. By adapting the sampling rate at each change point, CPAM reduces energy consumption by 74.64% while retaining the activity recognition performance of continuous sampling. We validate our approach using smartwatch data collected and labeled by 66 subjects. Results indicate that change point detection techniques can be effective for reducing the energy footprint of sensor-based mobile applications and that automated activity labels can be used to estimate sensor values between sampling periods. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
A Wrapper Feature Selection Algorithm: An Emotional Assessment Using Physiological Recordings from Wearable Sensors
Sensors 2020, 20(1), 309; https://doi.org/10.3390/s20010309 - 06 Jan 2020
Viewed by 257
Abstract
Assessing emotional state is an emerging application field boosting research activities on the topic of analysis of non-invasive biosignals to find effective markers to accurately determine the emotional state in real-time. Nowadays using wearable sensors, electrocardiogram and thoracic impedance measurements can be recorded, [...] Read more.
Assessing emotional state is an emerging application field boosting research activities on the topic of analysis of non-invasive biosignals to find effective markers to accurately determine the emotional state in real-time. Nowadays using wearable sensors, electrocardiogram and thoracic impedance measurements can be recorded, facilitating analyzing cardiac and respiratory functions directly and autonomic nervous system function indirectly. Such analysis allows distinguishing between different emotional states: neutral, sadness, and disgust. This work was specifically focused on the proposal of a k-fold approach for selecting features while training the classifier that reduces the loss of generalization. The performance of the proposed algorithm used as the selection criterion was compared to the commonly used standard error function. The proposed k-fold approach outperforms the conventional method with 4% hit success rate improvement, reaching an accuracy near to 78%. Moreover, the proposed selection criterion method allows the classifier to produce the best performance using a lower number of features at lower computational cost. A reduced number of features reduces the risk of overfitting while a lower computational cost contributes to implementing real-time systems using wearable electronics. Full article
(This article belongs to the Special Issue Emotion Monitoring System Based on Sensors and Data Analysis)
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Open AccessArticle
Reference-Driven Compressed Sensing MR Image Reconstruction Using Deep Convolutional Neural Networks without Pre-Training
Sensors 2020, 20(1), 308; https://doi.org/10.3390/s20010308 - 06 Jan 2020
Viewed by 293
Abstract
Deep learning has proven itself to be able to reduce the scanning time of Magnetic Resonance Imaging (MRI) and to improve the image reconstruction quality since it was introduced into Compressed Sensing MRI (CS-MRI). However, the requirement of using large, high-quality, and patient-based [...] Read more.
Deep learning has proven itself to be able to reduce the scanning time of Magnetic Resonance Imaging (MRI) and to improve the image reconstruction quality since it was introduced into Compressed Sensing MRI (CS-MRI). However, the requirement of using large, high-quality, and patient-based datasets for network training procedures is always a challenge in clinical applications. In this paper, we propose a novel deep learning based compressed sensing MR image reconstruction method that does not require any pre-training procedure or training dataset, thereby largely reducing clinician dependence on patient-based datasets. The proposed method is based on the Deep Image Prior (DIP) framework and uses a high-resolution reference MR image as the input of the convolutional neural network in order to induce the structural prior in the learning procedure. This reference-driven strategy improves the efficiency and effect of network learning. We then add the k-space data correction step to enforce the consistency of the k-space data with the measurements, which further improve the image reconstruction accuracy. Experiments on in vivo MR datasets showed that the proposed method can achieve more accurate reconstruction results from undersampled k-space data. Full article
(This article belongs to the Special Issue Compressed Sensing in Biomedical Signal and Image Analysis)
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Open AccessFeature PaperArticle
A High-Performance 2.5 μm Charge Domain Global Shutter Pixel and Near Infrared Enhancement with Light Pipe Technology
Sensors 2020, 20(1), 307; https://doi.org/10.3390/s20010307 - 06 Jan 2020
Viewed by 508
Abstract
We developed a new 2.5 μm global shutter (GS) pixel using a 65 nm process with an advanced light pipe (LP) structure. This is the world’s smallest charge domain GS pixel reported so far. This new developed pixel platform is a key enabler [...] Read more.
We developed a new 2.5 μm global shutter (GS) pixel using a 65 nm process with an advanced light pipe (LP) structure. This is the world’s smallest charge domain GS pixel reported so far. This new developed pixel platform is a key enabler for ultra-high resolution sensors, industrial cameras with wide aperture lenses, and low form factors optical modules for mobile applications. The 2.5 μm GS pixel showed excellent optical performances: 68% quantum efficiency (QE) at 530 nm, ±12.5 degrees angular response (AR), and quite low parasitic light sensitivity (PLS)—10,400 1/PLS with the F#2.8 lens. In addition, we achieved an extremely low memory node (MN) dark current 13 e/s at 60 °C by fully pinned MN. Furthermore, we studied how the LP technology contributes to the improvement of the modulation transfer function (MTF) in near infrared (NIR) enhanced GS pixel. The 2.8 μm GS pixel using a p-substrate showed 109 lp/mm [email protected]% at 940 nm, which is 1.6 times better than that without an LP. The MTF can be more enhanced by the combination of the LP and the deep photodiode (PD) electrically isolated from the substrate. We demonstrated the advantage of using LP technology and our advanced stacked deep photodiode (SDP) technology together. This unique combination showed an improvement of more than 100% in NIR QE while maintaining an MTF that is close to the theoretical Nyquist limit (MTF @50% = 156 lp/mm). Full article
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Open AccessArticle
Improvement of Flow Velocity Measurement Algorithms Based on Correlation Function and Twin Plane Electrical Capacitance Tomography
Sensors 2020, 20(1), 306; https://doi.org/10.3390/s20010306 - 06 Jan 2020
Viewed by 304
Abstract
This article discusses the correlation method for time delay estimation, its disadvantages, and drawbacks. It is shown that the correlation method for material velocity measurement based on images of instantaneous changes of the concentration material inside measured by twin planes electrical tomography has [...] Read more.
This article discusses the correlation method for time delay estimation, its disadvantages, and drawbacks. It is shown that the correlation method for material velocity measurement based on images of instantaneous changes of the concentration material inside measured by twin planes electrical tomography has serious limitations, especially in the case of plug regime. The basic problem is the non-stationarity of measured data, therefore the requirement of correlability of input data should be fulfilled. The requirement correlatability of input data imposes limitations on the possibility of continuous velocity measurement. This means that the material velocity can only be calculated when data are correlatable. An original algorithm of automatic extraction of the suitable time intervals to calculate material velocity is proposed. The algorithm allows measuring the flow velocity in a proper and accurate way. The examples of the correct velocity calculation, using the proposed concept for the gas-solid flow regime, are presented. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Real-Time Wood Behaviour: The Use of Strain Gauges for Preventive Conservation Applications
Sensors 2020, 20(1), 305; https://doi.org/10.3390/s20010305 - 06 Jan 2020
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Abstract
Within the heritage field, the application of strain gauges on wood surfaces is a little-explored but inexpensive and effective method to analyse the environmental appropriateness of rooms for the wooden heritage collections they contain. This contribution proposes a wood sensor connected to a [...] Read more.
Within the heritage field, the application of strain gauges on wood surfaces is a little-explored but inexpensive and effective method to analyse the environmental appropriateness of rooms for the wooden heritage collections they contain. This contribution proposes a wood sensor connected to a data logger to identify short moments with an elevated risk of harm. Two experiments were performed to obtain insights pertaining to the applicability of wood sensors to evaluate preservation conditions. (1) The representativeness of strain gauges on dummies was tested for their use in evaluating the preservation conditions of a range of wooden objects exposed to the same environment. For this, three situations were mimicked: a bare wood surface, a wood surface covered with a preparation layer, and a wood surface covered with a preparation and varnish layer. (2) The usability of strain gauges to monitor the wood behaviour in real-time measurements was tested with a monitoring campaign of almost two years in a church where a new heating system was installed. The results of both experiments are promising, and the authors encourage a broader application of strain gauges in the heritage field. Full article
(This article belongs to the Special Issue Sensors for Cultural Heritage Monitoring)
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Open AccessArticle
A Cycle Slip Detection Framework for Reliable Single Frequency RTK Positioning
Sensors 2020, 20(1), 304; https://doi.org/10.3390/s20010304 - 06 Jan 2020
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Abstract
Single frequency real-time kinematic (RTK) positioning is expected to be the leading implementation platform for a variety of emerging GNSS mass-market applications. During RTK positioning, the most common source of measurement errors is carrier-phase cycle slips (CS). The presence of CS in carrier-phase [...] Read more.
Single frequency real-time kinematic (RTK) positioning is expected to be the leading implementation platform for a variety of emerging GNSS mass-market applications. During RTK positioning, the most common source of measurement errors is carrier-phase cycle slips (CS). The presence of CS in carrier-phase measurements is tested by a CS detection technique and correspondingly taken care of. While using CS prone measurement data, positioning reliability is an area of concern for RTK users. Reliability can be linked with the CS detection scheme through a least squares (LS) adjustment process. This paper proposes a CS detection framework for reliable RTK positioning using single-frequency GNSS receivers. The scheme uses double differenced measurements for CS detection via LS adjustment using a detection, identification, and adaptation approach. For reliable positioning, the procedure to link the detection and identification stages is described. Through tests conducted on kinematic data, internal and external reliability are theoretically determined by calculating minimal detectable bias (MDB) and marginally detectable errors, respectively. After introducing CS, the actual values of MDB are found to be four cycles, which are higher than the theoretically obtained values of one and two cycles. Although CS detection for reliable positioning is implemented for single-frequency RTK users, the proposed procedure is generic and can be used whenever CS are detected through statistical tests during LS adjustment. Full article
(This article belongs to the collection Positioning and Navigation)
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Open AccessArticle
Development of an On-Board Measurement System for Railway Vehicle Wheel Flange Wear
Sensors 2020, 20(1), 303; https://doi.org/10.3390/s20010303 - 06 Jan 2020
Viewed by 393
Abstract
The maintenance of railway systems is critical for their safe operation. However some landscape geographical features force the track line to have sharp curves with small radii. Sharp curves are known to be the main source of wheel flange wear. The reduction of [...] Read more.
The maintenance of railway systems is critical for their safe operation. However some landscape geographical features force the track line to have sharp curves with small radii. Sharp curves are known to be the main source of wheel flange wear. The reduction of wheel flange thickness to an extreme level increases the probability of train accidents. To avoid the unsafe operation of a rail vehicle, it is important to stay continuously up to date on the status of the wheel flange thickness dimensions by using precise and accurate measurement tools. The wheel wear measurement tools that are based on laser and vision technology are quite expensive to implement in railway lines of developing countries. Alternatively significant measurement errors can result from using imprecise measurement tools such as the hand tools, which are currently utilized by the railway companies such as Addis Ababa Light Rail Transit Service (AALRTS). Thus, the objective of this research is to propose and test a new measurement tool that uses an inductive displacement sensor. The proposed system works in both static and dynamic state of the railway vehicle and it is able to save the historical records of the wheel flange thickness for further analysis. The measurement system is fixed on the bogie frame. The fixture was designed using dimensions of the bogie and wheelset structure of the trains currently used by AALRTS. Laboratory experiments and computer simulations for of the electronic system were carried out to assess the feasibility of the data acquisition and analysis method. The noises and unwanted signals due to the dynamics of the system are filtered out from the sensor readings. The results show that the implementation of the proposed measurement system can accurately measure the wheel flange wear. Also, the faulty track section can be identified using the system recorded data and the operational control center data. Full article
(This article belongs to the Special Issue Achieving Predictive Maintenance using Sensors: Real or Fantasy?)
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Open AccessArticle
Robust Sparse Bayesian Learning-Based Off-Grid DOA Estimation Method for Vehicle Localization
Sensors 2020, 20(1), 302; https://doi.org/10.3390/s20010302 - 05 Jan 2020
Viewed by 410
Abstract
With the rapid development of the Internet of Things (IoT), autonomous vehicles have been receiving more and more attention because they own many advantages compared with traditional vehicles. A robust and accurate vehicle localization system is critical to the safety and the efficiency [...] Read more.
With the rapid development of the Internet of Things (IoT), autonomous vehicles have been receiving more and more attention because they own many advantages compared with traditional vehicles. A robust and accurate vehicle localization system is critical to the safety and the efficiency of autonomous vehicles. The global positioning system (GPS) has been widely applied to the vehicle localization systems. However, the accuracy and the reliability of GPS have suffered in some scenarios. In this paper, we present a robust and accurate vehicle localization system consisting of a bistatic passive radar, in which the performance of localization is solely dependent on the accuracy of the proposed off-grid direction of arrival (DOA) estimation algorithm. Under the framework of sparse Bayesian learning (SBL), the source powers and the noise variance are estimated by a fast evidence maximization method, and the off-grid gap is effectively handled by an advanced grid refining strategy. Simulation results show that the proposed method exhibits better performance than the existing sparse signal representation-based algorithms, and performs well in the vehicle localization system. Full article
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Open AccessArticle
Comparative Study on Noise Reduction Effect of Fiber Optic Hydrophone Based on LMS and NLMS Algorithm
Sensors 2020, 20(1), 301; https://doi.org/10.3390/s20010301 - 05 Jan 2020
Viewed by 319
Abstract
Adaptive filtering has the advantages of real-time processing, small computational complexity, and good adaptability and robustness. It has been widely used in communication, navigation, signal processing, optical fiber sensing, and other fields. In this paper, by adding an interferometer with the same parameters [...] Read more.
Adaptive filtering has the advantages of real-time processing, small computational complexity, and good adaptability and robustness. It has been widely used in communication, navigation, signal processing, optical fiber sensing, and other fields. In this paper, by adding an interferometer with the same parameters as the signal interferometer as the reference channel, the sensing signal of the interferometric fiber-optic hydrophone is denoised by two adaptive filtering schemes based on the least mean square (LMS) algorithm and the normalized least mean square (NLMS) algorithm respectively. The results show that the LMS algorithm is superior to the NLMS algorithm in reducing total harmonic distortion, improving the signal-to-noise ratio and filtering effect. Full article
(This article belongs to the Special Issue Fiber Optic Sensing Technology)
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Open AccessArticle
Channel Prediction Based on BP Neural Network for Backscatter Communication Networks
Sensors 2020, 20(1), 300; https://doi.org/10.3390/s20010300 - 05 Jan 2020
Viewed by 308
Abstract
Backscatter communication networks are receiving a lot of attention thanks to the application of ultra-low power sensors. Because of the large amount of sensor data, increasing network throughput becomes a key issue, so rate adaption based on channel quality is a novel direction. [...] Read more.
Backscatter communication networks are receiving a lot of attention thanks to the application of ultra-low power sensors. Because of the large amount of sensor data, increasing network throughput becomes a key issue, so rate adaption based on channel quality is a novel direction. Most existing methods share common drawbacks; that is, spatial and frequency diversity cannot be considered at the same time or channel probe is expensive. In this paper, we propose a channel prediction scheme for backscatter networks. The scheme consists of two parts: the monitoring module, which uses the data of the acceleration sensor to monitor the movement of the node itself, and uses the link burstiness metric β to monitor the burstiness caused by the environmental change, thereby determining that new data of channel quality are needed. The prediction module predicts the channel quality at the next moment using a prediction algorithm based on BP (back propagation) neural network. We implemented the scheme on readers. The experimental results show that the accuracy of channel prediction is high and the network goodput is improved. Full article
(This article belongs to the Special Issue Wireless Systems and Networks in the IoT)
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Open AccessArticle
A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model
Sensors 2020, 20(1), 299; https://doi.org/10.3390/s20010299 - 05 Jan 2020
Viewed by 282
Abstract
The control effect of various intelligent terminals is affected by the data sensing precision. The filtering method has been the typical soft computing method used to promote the sensing level. Due to the difficult recognition of the practical system and the empirical parameter [...] Read more.
The control effect of various intelligent terminals is affected by the data sensing precision. The filtering method has been the typical soft computing method used to promote the sensing level. Due to the difficult recognition of the practical system and the empirical parameter estimation in the traditional Kalman filter, a neuron-based Kalman filter was proposed in the paper. Firstly, the framework of the improved Kalman filter was designed, in which the neuro units were introduced. Secondly, the functions of the neuro units were excavated with the nonlinear autoregressive model. The neuro units optimized the filtering process to reduce the effect of the unpractical system model and hypothetical parameters. Thirdly, the adaptive filtering algorithm was proposed based on the new Kalman filter. Finally, the filter was verified with the simulation signals and practical measurements. The results proved that the filter was effective in noise elimination within the soft computing solution. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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Open AccessArticle
Bovine Serum Albumin Protein-Based Liquid Crystal Biosensors for Optical Detection of Toxic Heavy Metals in Water
Sensors 2020, 20(1), 298; https://doi.org/10.3390/s20010298 - 05 Jan 2020
Viewed by 340
Abstract
A new methodology involving the use of Bovine Serum Albumin (BSA) as a probe and liquid crystal (LC) as a signal reporter for the detection of heavy metal ions in water at neutral pH was developed. BSA acted as a multi-dentate ligand for [...] Read more.
A new methodology involving the use of Bovine Serum Albumin (BSA) as a probe and liquid crystal (LC) as a signal reporter for the detection of heavy metal ions in water at neutral pH was developed. BSA acted as a multi-dentate ligand for the detection of multiple metal ions. The LC sensor was fabricated by immobilizing 3 µg mL−1 BSA solution on dimethyloctadecyl-[3-(trimethoxysilyl)propyl]ammonium chloride (DMOAP)-coated glass slides. In the absence of heavy metal ions, a dark optical image was observed, while in their presence, a dark optical image turned to bright. The optical response was characterized by using a polarized optical microscope (POM). The BSA based LC sensor selectively detected toxic metal ions as compared to s block metal ions and ammonium ions in water. Moreover, the limit of detection was found to be very low (i.e., 1 nM) for the developed new biosensor in comparison to reported biosensors. Full article
(This article belongs to the Section Biosensors)
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Open AccessArticle
Study of Metal–Semiconductor–Metal CH3NH3PbBr3 Perovskite Photodetectors Prepared by Inverse Temperature Crystallization Method
Sensors 2020, 20(1), 297; https://doi.org/10.3390/s20010297 - 05 Jan 2020
Viewed by 299
Abstract
Numerous studies have addressed the use of perovskite materials for fabricating a wide range of optoelectronic devices. This study employs the deposition of an electron transport layer of C60 and an Ag electrode on CH3NH3PbBr3 perovskite crystals [...] Read more.
Numerous studies have addressed the use of perovskite materials for fabricating a wide range of optoelectronic devices. This study employs the deposition of an electron transport layer of C60 and an Ag electrode on CH3NH3PbBr3 perovskite crystals to complete a photodetector structure, which exhibits a metal–semiconductor–metal (MSM) type structure. First, CH3NH3PbBr3 perovskite crystals were grown by inverse temperature crystallization (ITC) in a pre-heated circulator oven. This oven was able to supply uniform heat for facilitating the growth of high-quality and large-area crystals. Second, the different growth temperatures for CH3NH3PbBr3 perovskite crystals were investigated. The electrical, optical, and morphological characteristics of the perovskite crystals were analyzed by X-ray diffraction (XRD), scanning electron microscopy (SEM), ultraviolet-visible spectroscopy, and photoluminescence (PL). Finally, the CH3NH3PbBr3 perovskite crystals were observed to form a contact with the Ag/C60 as the photodetector, which revealed a responsivity of 24.5 A/W. Full article
(This article belongs to the Section Sensor Materials)
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Open AccessArticle
Towards Mixed-Initiative Human–Robot Interaction: Assessment of Discriminative Physiological and Behavioral Features for Performance Prediction
Sensors 2020, 20(1), 296; https://doi.org/10.3390/s20010296 - 05 Jan 2020
Viewed by 284
Abstract
The design of human–robot interactions is a key challenge to optimize operational performance. A promising approach is to consider mixed-initiative interactions in which the tasks and authority of each human and artificial agents are dynamically defined according to their current abilities. An important [...] Read more.
The design of human–robot interactions is a key challenge to optimize operational performance. A promising approach is to consider mixed-initiative interactions in which the tasks and authority of each human and artificial agents are dynamically defined according to their current abilities. An important issue for the implementation of mixed-initiative systems is to monitor human performance to dynamically drive task allocation between human and artificial agents (i.e., robots). We, therefore, designed an experimental scenario involving missions whereby participants had to cooperate with a robot to fight fires while facing hazards. Two levels of robot automation (manual vs. autonomous) were randomly manipulated to assess their impact on the participants’ performance across missions. Cardiac activity, eye-tracking, and participants’ actions on the user interface were collected. The participants performed differently to an extent that we could identify high and low score mission groups that also exhibited different behavioral, cardiac and ocular patterns. More specifically, our findings indicated that the higher level of automation could be beneficial to low-scoring participants but detrimental to high-scoring ones, and vice versa. In addition, inter-subject single-trial classification results showed that the studied behavioral and physiological features were relevant to predict mission performance. The highest average balanced accuracy (74%) was reached using the features extracted from all input devices. These results suggest that an adaptive HRI driving system, that would aim at maximizing performance, would be capable of analyzing such physiological and behavior markers online to further change the level of automation when it is relevant for the mission purpose. Full article
(This article belongs to the Special Issue Human-Machine Interaction and Sensors)
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