Sensors

21 pages, 2592 KiB

Open AccessArticle

Individual Tree Structural Parameter Extraction and Volume Table Creation Based on Near-Field LiDAR Data: A Case Study in a Subtropical Planted Forest

by Sha Gao, Zhengnan Zhang and Lin Cao

Sensors 2021, 21(23), 8162; https://doi.org/10.3390/s21238162 - 6 Dec 2021

Cited by 14 | Viewed by 3953

Abstract

Individual tree structural parameters are vital for precision silviculture in planted forests. This study used near-field LiDAR (light detection and ranging) data (i.e., unmanned aerial vehicle laser scanning (ULS) and ground backpack laser scanning (BLS)) to extract individual tree structural parameters and fit [...] Read more.

Individual tree structural parameters are vital for precision silviculture in planted forests. This study used near-field LiDAR (light detection and ranging) data (i.e., unmanned aerial vehicle laser scanning (ULS) and ground backpack laser scanning (BLS)) to extract individual tree structural parameters and fit volume models in subtropical planted forests in southeastern China. To do this, firstly, the tree height was acquired from ULS data and the diameter at breast height (DBH) was acquired from BLS data by using individual tree segmentation algorithms. Secondly, point clouds of the complete forest canopy were obtained through the combination of ULS and BLS data. Finally, five tree taper models were fitted using the LiDAR-extracted structural parameters of each tree, and then the optimal taper model was selected. Moreover, standard volume models were used to calculate the stand volume; then, standing timber volume tables were created for dawn redwood and poplar. The extraction of individual tree structural parameters exhibited good performance. The volume model had a good performance in calculating the standing volume for dawn redwood and poplar. Our results demonstrate that near-field LiDAR has a strong capability of extracting tree structural parameters and creating volume tables for subtropical planted forests. Full article

(This article belongs to the Section Radar Sensors)

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16 pages, 3497 KiB

Open AccessArticle

Deep Reinforcement Learning-Based Accurate Control of Planetary Soft Landing

by Xibao Xu, Yushen Chen and Chengchao Bai

Sensors 2021, 21(23), 8161; https://doi.org/10.3390/s21238161 - 6 Dec 2021

Cited by 13 | Viewed by 3116

Abstract

Planetary soft landing has been studied extensively due to its promising application prospects. In this paper, a soft landing control algorithm based on deep reinforcement learning (DRL) with good convergence property is proposed. First, the soft landing problem of the powered descent phase [...] Read more.

Planetary soft landing has been studied extensively due to its promising application prospects. In this paper, a soft landing control algorithm based on deep reinforcement learning (DRL) with good convergence property is proposed. First, the soft landing problem of the powered descent phase is formulated and the theoretical basis of Reinforcement Learning (RL) used in this paper is introduced. Second, to make it easier to converge, a reward function is designed to include process rewards like velocity tracking reward, solving the problem of sparse reward. Then, by including the fuel consumption penalty and constraints violation penalty, the lander can learn to achieve velocity tracking goal while saving fuel and keeping attitude angle within safe ranges. Then, simulations of training are carried out under the frameworks of Deep deterministic policy gradient (DDPG), Twin Delayed DDPG (TD3), and Soft Actor Critic (SAC), respectively, which are of the classical RL frameworks, and all converged. Finally, the trained policy is deployed into velocity tracking and soft landing experiments, results of which demonstrate the validity of the algorithm proposed. Full article

(This article belongs to the Section Sensors and Robotics)

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17 pages, 6087 KiB

Open AccessArticle

Real-Time Jellyfish Classification and Detection Based on Improved YOLOv3 Algorithm

by Meijing Gao, Yang Bai, Zhilong Li, Shiyu Li, Bozhi Zhang and Qiuyue Chang

Sensors 2021, 21(23), 8160; https://doi.org/10.3390/s21238160 - 6 Dec 2021

Cited by 11 | Viewed by 4260

Abstract

In recent years, jellyfish outbreaks have frequently occurred in offshore areas worldwide, posing a significant threat to the marine fishery, tourism, coastal industry, and personal safety. Effective monitoring of jellyfish is a vital method to solve the above problems. However, the optical detection [...] Read more.

In recent years, jellyfish outbreaks have frequently occurred in offshore areas worldwide, posing a significant threat to the marine fishery, tourism, coastal industry, and personal safety. Effective monitoring of jellyfish is a vital method to solve the above problems. However, the optical detection method for jellyfish is still in the primary stage. Therefore, this paper studies a jellyfish detection method based on convolution neural network theory and digital image processing technology. This paper studies the underwater image preprocessing algorithm because the quality of underwater images directly affects the detection results. The results show that the image quality is better after applying the three algorithms namely prior defogging, adaptive histogram equalization, and multi-scale retinal enhancement, which is more conducive to detection. We establish a data set containing seven species of jellyfishes and fish. A total of 2141 images are included in the data set. The YOLOv3 algorithm is used to detect jellyfish, and its feature extraction network Darknet53 is optimized to ensure it is conducted in real-time. In addition, we introduce label smoothing and cosine annealing learning rate methods during the training process. The experimental results show that the improved algorithms improve the detection accuracy of jellyfish on the premise of ensuring the detection speed. This paper lays a foundation for the construction of an underwater jellyfish optical imaging real-time monitoring system. Full article

(This article belongs to the Topic Data Analytics and Machine Learning in Artificial Emotional Intelligence)

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15 pages, 1075 KiB

Open AccessCommunication

Triple Estimation of Fractional Variable Order, Parameters, and State Variables Based on the Unscented Fractional Order Kalman Filter

by Dominik Sierociuk and Michal Macias

Sensors 2021, 21(23), 8159; https://doi.org/10.3390/s21238159 - 6 Dec 2021

Cited by 4 | Viewed by 2681

Abstract

In this paper, a method for states, parameters, and fractional order estimation is presented. The proposed method is an extension of the traditional dual estimation method and uses three blocks of filters with appropriate data interconnections. As the main part of the estimation [...] Read more.

In this paper, a method for states, parameters, and fractional order estimation is presented. The proposed method is an extension of the traditional dual estimation method and uses three blocks of filters with appropriate data interconnections. As the main part of the estimation algorithm, the Fractional Unscented Kalman Filter was used. The proposed Triple Estimation algorithm might be treated as a convenient tool for estimation and analysis of a wide range of dynamical systems with fractional constants or variable order nature, especially when knowledge about the identified system is very restricted and both order and system parameters are unknown. In order to show the performance of the proposed algorithm, sets of numerical results are presented. Full article

(This article belongs to the Special Issue Fractional Sensor Fusion and Its Applications)

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14 pages, 15293 KiB

Open AccessArticle

Characterization of Supersonic Compressible Fluid Flow Using High-Speed Interferometry

by Pavel Psota, Gramoz Çubreli, Jindřich Hála, David Šimurda, Petr Šidlof, Jan Kredba, Marek Stašík, Vít Lédl, Michal Jiránek, Martin Luxa and Jan Lepicovsky

Sensors 2021, 21(23), 8158; https://doi.org/10.3390/s21238158 - 6 Dec 2021

Cited by 7 | Viewed by 3410

Abstract

This paper presents a very effective interference technique for the sensing and researching of compressible fluid flow in a wind tunnel facility. The developed technique is very sensitive and accurate, yet easy to use under conditions typical for aerodynamic labs, and will be [...] Read more.

This paper presents a very effective interference technique for the sensing and researching of compressible fluid flow in a wind tunnel facility. The developed technique is very sensitive and accurate, yet easy to use under conditions typical for aerodynamic labs, and will be used for the nonintrusive investigation of flutter in blade cascades. The interferometer employs a high-speed camera, fiber optics, and available “of-the-shelf” optics and optomechanics. The construction of the interferometer together with the fiber optics ensures the high compactness and portability of the system. Moreover, single-shot quantitative data processing based on introducing a spatial carrier frequency and Fourier analysis allows for almost real-time quantitative processing. As a validation case, the interferometric system was successfully applied in the research of supersonic compressible fluid discharge from a narrow channel in a wind tunnel. Density distributions were quantitatively analyzed with the spatial resolution of about

50 μ m

. The results of the measurement revealed important features of the flow pattern. Moreover, the measurement results were compared with Computational Fluid Dynamics (CFD) simulations with a good agreement. Full article

(This article belongs to the Section Sensing and Imaging)

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11 pages, 1939 KiB

Open AccessCommunication

Deep Convolution Neural Network for Laryngeal Cancer Classification on Contact Endoscopy-Narrow Band Imaging

by Nazila Esmaeili, Esam Sharaf, Elmer Jeto Gomes Ataide, Alfredo Illanes, Axel Boese, Nikolaos Davaris, Christoph Arens, Nassir Navab and Michael Friebe

Sensors 2021, 21(23), 8157; https://doi.org/10.3390/s21238157 - 6 Dec 2021

Cited by 24 | Viewed by 3139

Abstract

(1) Background: Contact Endoscopy (CE) and Narrow Band Imaging (NBI) are optical imaging modalities that can provide enhanced and magnified visualization of the superficial vascular networks in the laryngeal mucosa. The similarity of vascular structures between benign and malignant lesions causes a challenge [...] Read more.

(1) Background: Contact Endoscopy (CE) and Narrow Band Imaging (NBI) are optical imaging modalities that can provide enhanced and magnified visualization of the superficial vascular networks in the laryngeal mucosa. The similarity of vascular structures between benign and malignant lesions causes a challenge in the visual assessment of CE-NBI images. The main objective of this study is to use Deep Convolutional Neural Networks (DCNN) for the automatic classification of CE-NBI images into benign and malignant groups with minimal human intervention. (2) Methods: A pretrained Res-Net50 model combined with the cut-off-layer technique was selected as the DCNN architecture. A dataset of 8181 CE-NBI images was used during the fine-tuning process in three experiments where several models were generated and validated. The accuracy, sensitivity, and specificity were calculated as the performance metrics in each validation and testing scenario. (3) Results: Out of a total of 72 trained and tested models in all experiments, Model 5 showed high performance. This model is considerably smaller than the full ResNet50 architecture and achieved the testing accuracy of

0.835

on the unseen data during the last experiment. (4) Conclusion: The proposed fine-tuned ResNet50 model showed a high performance to classify CE-NBI images into the benign and malignant groups and has the potential to be part of an assisted system for automatic laryngeal cancer detection. Full article

(This article belongs to the Special Issue Machine Learning Techniques for Medical Imaging, Sensing, and Analysis)

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16 pages, 4519 KiB

Open AccessArticle

Efficient Chemical Surface Modification Protocol on SiO₂ Transducers Applied to MMP9 Biosensing

by Ana L. Hernandez, Sidharam P. Pujari, María F. Laguna, Beatriz Santamaría, Han Zuilhof and Miguel Holgado

Sensors 2021, 21(23), 8156; https://doi.org/10.3390/s21238156 - 6 Dec 2021

Cited by 3 | Viewed by 3564

Abstract

The bioreceptor immobilization process (biofunctionalization) turns to be one of the bottlenecks when developing a competent and high sensitivity label-free biosensor. Classical approaches seem to be effective but not efficient. Although biosensing capacities are shown in many cases, the performance of the biosensor [...] Read more.

The bioreceptor immobilization process (biofunctionalization) turns to be one of the bottlenecks when developing a competent and high sensitivity label-free biosensor. Classical approaches seem to be effective but not efficient. Although biosensing capacities are shown in many cases, the performance of the biosensor is truncated by the inefficacious biofunctionalization protocol and the lack of reproducibility. In this work, we describe a unique biofunctionalization protocol based on chemical surface modification through silane chemistry on SiO₂ optical sensing transducers. Even though silane chemistry is commonly used for sensing applications, here we present a different mode of operation, applying an unusual silane compound used for this purpose (3-Ethoxydimethylsilyl)propylamine, APDMS, able to create ordered monolayers, and minimizing fouling events. To endorse this protocol as a feasible method for biofunctionalization, we performed multiple surface characterization techniques after all the process steps: Contact angle (CA), X-ray photoelectron spectroscopy (XPS), ellipsometry, and fluorescence microscopy. Finally, to evidence the outputs from the SiO₂ surface characterization, we used those SiO₂ surfaces as optical transducers for the label-free biosensing of matrix metalloproteinase 9 (MMP9). We found and demonstrated that the originally designed protocol is reproducible, stable, and suitable for SiO2-based optical sensing transducers. Full article

(This article belongs to the Section Biosensors)

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14 pages, 526 KiB

Open AccessArticle

WPO-Net: Windowed Pose Optimization Network for Monocular Visual Odometry Estimation

by Nivesh Gadipudi, Irraivan Elamvazuthi, Cheng-Kai Lu, Sivajothi Paramasivam and Steven Su

Sensors 2021, 21(23), 8155; https://doi.org/10.3390/s21238155 - 6 Dec 2021

Cited by 6 | Viewed by 2846

Abstract

Visual odometry is the process of estimating incremental localization of the camera in 3-dimensional space for autonomous driving. There have been new learning-based methods which do not require camera calibration and are robust to external noise. In this work, a new method that [...] Read more.

Visual odometry is the process of estimating incremental localization of the camera in 3-dimensional space for autonomous driving. There have been new learning-based methods which do not require camera calibration and are robust to external noise. In this work, a new method that do not require camera calibration called the “windowed pose optimization network” is proposed to estimate the 6 degrees of freedom pose of a monocular camera. The architecture of the proposed network is based on supervised learning-based methods with feature encoder and pose regressor that takes multiple consecutive two grayscale image stacks at each step for training and enforces the composite pose constraints. The KITTI dataset is used to evaluate the performance of the proposed method. The proposed method yielded rotational error of 3.12 deg/100 m, and the training time is 41.32 ms, while inference time is 7.87 ms. Experiments demonstrate the competitive performance of the proposed method to other state-of-the-art related works which shows the novelty of the proposed technique. Full article

(This article belongs to the Special Issue Advanced Computer Vision Techniques for Autonomous Driving)

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28 pages, 18823 KiB

Open AccessArticle

Design and Analysis of In-Pipe Hydro-Turbine for an Optimized Nearly Zero Energy Building

by Muhammad Shahbaz Aziz, Muhammad Adil Khan, Harun Jamil, Faisal Jamil, Alexander Chursin and Do-Hyeun Kim

Sensors 2021, 21(23), 8154; https://doi.org/10.3390/s21238154 - 6 Dec 2021

Cited by 12 | Viewed by 5584

Abstract

Pakistan receives Direct Normal Irradiation (DNI) exceeding 2000 kWh/m²/annum on approximately 83% of its land, which is very suitable for photovoltaic production. This energy can be easily utilized in conjunction with other renewable energy resources to meet the energy demands and reduce the [...] Read more.

Pakistan receives Direct Normal Irradiation (DNI) exceeding 2000 kWh/m²/annum on approximately 83% of its land, which is very suitable for photovoltaic production. This energy can be easily utilized in conjunction with other renewable energy resources to meet the energy demands and reduce the carbon footprint of the country. In this research, a hybrid renewable energy solution based on a nearly Zero Energy Building (nZEB) model is proposed for a university facility. The building in consideration has a continuous flow of water through its water delivery vertical pipelines. A horizontal-axis spherical helical turbine is designed in SolidWorks and is analyzed through a computational fluid dynamics (CFD) analysis in ANSYS Fluent 18.1 based on the K-epsilon turbulent model. Results obtained from ANSYS Fluent have shown that a 24 feet vertical channel with a water flow of 0.2309 m³/s and velocity of 12.66 m/s can run the designed hydroelectric turbine, delivering 168 W of mechanical power at 250 r.p.m. Based on the turbine, a hybrid renewable energy system (HRES) comprising photovoltaic and hydroelectric power is modelled and analyzed in HOMER Pro software. Among different architectures, it was found that architecture with hydroelectric and photovoltaic energy provided the best COE of $0.09418. Full article

(This article belongs to the Special Issue Energy Harvesting Sensors)

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12 pages, 3154 KiB

Open AccessArticle

Fabrication and Evaluation of a Flexible MEMS-Based Microthermal Flow Sensor

by Myoung-Ock Cho, Woojin Jang and Si-Hyung Lim

Sensors 2021, 21(23), 8153; https://doi.org/10.3390/s21238153 - 6 Dec 2021

Cited by 10 | Viewed by 3897

Abstract

Based on the results of computational fluid dynamics simulations, this study designed and fabricated a flexible thermal-type micro flow sensor comprising one microheater and two thermistors using a micro-electromechanical system (MEMS) process on a flexible polyimide film. The thermistors were connected to a [...] Read more.

Based on the results of computational fluid dynamics simulations, this study designed and fabricated a flexible thermal-type micro flow sensor comprising one microheater and two thermistors using a micro-electromechanical system (MEMS) process on a flexible polyimide film. The thermistors were connected to a Wheatstone bridge circuit, and the resistance difference between the thermistors resulting from the generation of a flow was converted into an output voltage signal using LabVIEW software. A mini tube flow test was conducted to demonstrate the sensor’s detection of fluid velocity in gas and liquid flows. A good correlation was found between the experimental results and the simulation data. However, the results for the gas and liquid flows differed in that for gas, the output voltage increased with the fluid’s velocity and decreased against the liquid’s flow velocity. This study’s MEMS-based flexible microthermal flow sensor achieved a resolution of 1.1 cm/s in a liquid flow and 0.64 cm/s in a gas flow, respectively, within a fluid flow velocity range of 0–40 cm/s. The sensor is suitable for many applications; however, with some adaptations to its electrical packaging, it will be particularly suitable for detecting biosignals in healthcare applications, including measuring respiration and body fluids. Full article

(This article belongs to the Section Biomedical Sensors)

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19 pages, 5191 KiB

Open AccessArticle

Vehicle Trajectory Prediction with Lane Stream Attention-Based LSTMs and Road Geometry Linearization

by Dongyeon Yu, Honggyu Lee, Taehoon Kim and Sung-Ho Hwang

Sensors 2021, 21(23), 8152; https://doi.org/10.3390/s21238152 - 6 Dec 2021

Cited by 14 | Viewed by 4532

Abstract

It is essential for autonomous vehicles at level 3 or higher to have the ability to predict the trajectories of surrounding vehicles to safely and effectively plan and drive along trajectories in complex traffic situations. However, predicting the future behavior of vehicles is [...] Read more.

It is essential for autonomous vehicles at level 3 or higher to have the ability to predict the trajectories of surrounding vehicles to safely and effectively plan and drive along trajectories in complex traffic situations. However, predicting the future behavior of vehicles is a challenging issue because traffic vehicles each have different drivers with different driving tendencies and intentions and they interact with each other. This paper presents a Long Short-Term Memory (LSTM) encoder–decoder model that utilizes an attention mechanism that focuses on certain information to predict vehicles’ trajectories. The proposed model was trained using the Highway Drone (HighD) dataset, which is a high-precision, large-scale traffic dataset. We also compared this model to previous studies. Our model effectively predicted future trajectories by using an attention mechanism to manage the importance of the driving flow of the target and adjacent vehicles and the target vehicle’s dynamics in each driving situation. Furthermore, this study presents a method of linearizing the road geometry such that the trajectory prediction model can be used in a variety of road environments. We verified that the road geometry linearization mechanism can improve the trajectory prediction model’s performance on various road environments in a virtual test-driving simulator constructed based on actual road data. Full article

(This article belongs to the Special Issue Sensor Fusion for Vehicles Navigation and Robotic Systems)

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13 pages, 34365 KiB

Open AccessCommunication

Refractive Index-Based Terahertz Sensor Using Graphene for Material Characterization

by Aruna Veeraselvam, Gulam Nabi Alsath Mohammed, Kirubaveni Savarimuthu, Jaume Anguera, Jessica Constance Paul and Ram Kumar Krishnan

Sensors 2021, 21(23), 8151; https://doi.org/10.3390/s21238151 - 6 Dec 2021

Cited by 27 | Viewed by 3622

Abstract

In this paper, a graphene-based THz metamaterial has been designed and characterized for use in sensing various refractive index profiles. The proposed single-band THz sensor was constructed using a graphene-metal hybridized periodic metamaterial wherein the unit cell had a footprint of 1.395λ_eff [...] Read more.

In this paper, a graphene-based THz metamaterial has been designed and characterized for use in sensing various refractive index profiles. The proposed single-band THz sensor was constructed using a graphene-metal hybridized periodic metamaterial wherein the unit cell had a footprint of 1.395λ_eff × 1.395λ_eff and resonated at 4.4754 THz. The realized peak absorption was 98.88% at 4.4754 THz. The sensitivity of the proposed metamaterial sensor was estimated using the absorption characteristics of the unit cell. The performance of the sensor was analyzed under two different categories, viz. the random dielectric loading and chemical analytes, based on the refractive index. The proposed THz sensor offered a peak sensitivity of 22.75 GHz/Refractive Index Unit (RIU) for the various sample loadings. In addition, the effect of the sample thickness on the sensor performance was analyzed and the results were presented. From the results, it can be inferred that the proposed metamaterial THz sensor that was based on a refractive index is suitable for THz sensing applications. Full article

(This article belongs to the Special Issue Antenna Technologies for Millimeter and Terahertz Sensing)

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20 pages, 7662 KiB

Open AccessArticle

Wireless Torque and Power Transfer Using Multiple Coils with LCC-S Topology for Implantable Medical Drug Pump

by Jaewon Rhee, Yujun Shin, Seongho Woo, Changmin Lee, Dongwook Kim, Jangyong Ahn, Haerim Kim and Seungyoung Ahn

Sensors 2021, 21(23), 8150; https://doi.org/10.3390/s21238150 - 6 Dec 2021

Cited by 10 | Viewed by 3024

Abstract

In this paper, we propose a method of wirelessly torque transfer (WTT) and power (WPT) to a drug pump, one of implantable medical devices. By using the magnetic field generated by the WPT system to transfer torque and power to the receiving coil [...] Read more.

In this paper, we propose a method of wirelessly torque transfer (WTT) and power (WPT) to a drug pump, one of implantable medical devices. By using the magnetic field generated by the WPT system to transfer torque and power to the receiving coil at the same time, applications that previously used power from the battery can be operated without a battery. The proposed method uses a receiving coil with magnetic material as a motor, and can generate torque in a desired direction using the magnetic field from the transmitting coil. The WPT system was analyzed using a topology that generates a constant current for stable torque generation. In addition, a method for detecting the position of the receiving coil without using additional power was proposed. Through simulations and experiments, it was confirmed that WTT and WPT were possible at the same time, and in particular, it was confirmed that WTT was stably possible. Full article

(This article belongs to the Special Issue Selected Papers from 2021 IEEE MTT-S Wireless Power Transfer Conference)

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17 pages, 3644 KiB

Open AccessArticle

Evaluation of Polytyramine Film and 6-Mercaptohexanol Self-Assembled Monolayers as the Immobilization Layers for a Capacitive DNA Sensor Chip: A Comparison

by Ally Mahadhy, Bo Mattiasson, Eva StåhlWernersson and Martin Hedström

Sensors 2021, 21(23), 8149; https://doi.org/10.3390/s21238149 - 6 Dec 2021

Cited by 5 | Viewed by 2554

Abstract

The performance of a biosensor is associated with the properties of an immobilization layer on a sensor chip. In this study, gold sensor chips were modified with two different immobilization layers, polytyramine film and 6-mercaptohexanol self-assembled monolayer. The physical, electrochemical and analytical properties [...] Read more.

The performance of a biosensor is associated with the properties of an immobilization layer on a sensor chip. In this study, gold sensor chips were modified with two different immobilization layers, polytyramine film and 6-mercaptohexanol self-assembled monolayer. The physical, electrochemical and analytical properties of polytyramine film and mercaptohexanol self-assembled monolayer modified gold sensor chips were studied and compared. The study was conducted using atomic force microscopy, cyclic voltammetry and a capacitive DNA-sensor system (CapSenze™ Biosystem). The results obtained by atomic force microscopy and cyclic voltammetry indicate that polytyramine film on the sensor chip surface possesses better insulating properties and provides more spaces for the immobilization of the capture probe than a mercaptohexanol self-assembled monolayer. A capacitive DNA sensor hosting a polytyramine single-stranded DNA-modified sensor chip displayed higher sensitivity and larger signal amplitude than that of a mercaptohexanol single-stranded DNA-modified sensor chip. The linearity responses for polytyramine single-stranded DNA- and mercaptohexanol single-stranded DNA-modified sensor chips were obtained at log concentration ranges, equivalent to 10⁻¹² to 10⁻⁸ M and 10⁻¹⁰ to 10⁻⁸ M, with detection limits of 4.0 × 10⁻¹³ M and 7.0 × 10⁻¹¹ M of target complementary single-stranded DNA, respectively. Mercaptohexanol single-stranded DNA- and polytyramine single-stranded DNA-modified sensor chips exhibited a notable selectivity at an elevated hybridization temperature of 50 °C, albeit the signal amplitudes due to the hybridization of the target complementary single-stranded DNA were reduced by almost 20% and less than 5%, respectively. Full article

(This article belongs to the Special Issue Capacitive and Impedance-Based Biosensors)

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18 pages, 8592 KiB

Open AccessArticle

HeadUp: A Low-Cost Solution for Tracking Head Movement of Children with Cerebral Palsy Using IMU

by Sana Sabah Al-azzawi, Siavash Khaksar, Emad Khdhair Hadi, Himanshu Agrawal and Iain Murray

Sensors 2021, 21(23), 8148; https://doi.org/10.3390/s21238148 - 6 Dec 2021

Cited by 7 | Viewed by 6669

Abstract

Cerebral palsy (CP) is a common reason for human motor ability limitations caused before birth, through infancy or early childhood. Poor head control is one of the most important problems in children with level IV CP and level V CP, which can affect [...] Read more.

Cerebral palsy (CP) is a common reason for human motor ability limitations caused before birth, through infancy or early childhood. Poor head control is one of the most important problems in children with level IV CP and level V CP, which can affect many aspects of children’s lives. The current visual assessment method for measuring head control ability and cervical range of motion (CROM) lacks accuracy and reliability. In this paper, a HeadUp system that is based on a low-cost, 9-axis, inertial measurement unit (IMU) is proposed to capture and evaluate the head control ability for children with CP. The proposed system wirelessly measures CROM in frontal, sagittal, and transverse planes during ordinary life activities. The system is designed to provide real-time, bidirectional communication with an Euler-based, sensor fusion algorithm (SFA) to estimate the head orientation and its control ability tracking. The experimental results for the proposed SFA show high accuracy in noise reduction with faster system response. The system is clinically tested on five typically developing children and five children with CP (age range: 2–5 years). The proposed HeadUp system can be implemented as a head control trainer in an entertaining way to motivate the child with CP to keep their head up. Full article

(This article belongs to the Section Internet of Things)

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21 pages, 10901 KiB

Open AccessArticle

Experimental Validation of LiDAR Sensors Used in Vehicular Applications by Using a Mobile Platform for Distance and Speed Measurements

by Ionuț Vasile, Emil Tudor, Ion-Cătălin Sburlan, Marius-Alin Gheți and Gabriel Popa

Sensors 2021, 21(23), 8147; https://doi.org/10.3390/s21238147 - 6 Dec 2021

Cited by 6 | Viewed by 3843

Abstract

LiDAR sensors are needed for use in vehicular applications, particularly due to their good behavior in low-light environments, as they represent a possible solution for the safety systems of vehicles that have a long braking distance, such as trams. The testing of long-range [...] Read more.

LiDAR sensors are needed for use in vehicular applications, particularly due to their good behavior in low-light environments, as they represent a possible solution for the safety systems of vehicles that have a long braking distance, such as trams. The testing of long-range LiDAR dynamic responses is very important for vehicle applications because of the presence of difficult operation conditions, such as different weather conditions or fake targets between the sensor and the tracked vehicle. The goal of the authors in this paper was to develop an experimental model for indoor testing, using a scaled vehicle that can measure the distances and the speeds relative to a fixed or a moving obstacle. This model, containing a LiDAR sensor, was developed to operate at variable speeds, at which the software functions were validated by repeated tests. Once the software procedures are validated, they can be applied on the full-scale model. The findings of this research include the validation of the frontal distance and relative speed measurement methodology, in addition to the validation of the independence of the measurements to the color of the obstacle and to the ambient light. Full article

(This article belongs to the Special Issue Feature Papers in Vehicular Sensing)

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15 pages, 4581 KiB

Open AccessArticle

DB-YOLO: A Duplicate Bilateral YOLO Network for Multi-Scale Ship Detection in SAR Images

by Haozhen Zhu, Yao Xie, Huihui Huang, Chen Jing, Yingjiao Rong and Changyuan Wang

Sensors 2021, 21(23), 8146; https://doi.org/10.3390/s21238146 - 6 Dec 2021

Cited by 35 | Viewed by 3629

Abstract

With the wide application of convolutional neural networks (CNNs), a variety of ship detection methods based on CNNs in synthetic aperture radar (SAR) images were proposed, but there are still two main challenges: (1) Ship detection requires high real-time performance, and a certain [...] Read more.

With the wide application of convolutional neural networks (CNNs), a variety of ship detection methods based on CNNs in synthetic aperture radar (SAR) images were proposed, but there are still two main challenges: (1) Ship detection requires high real-time performance, and a certain detection speed should be ensured while improving accuracy; (2) The diversity of ships in SAR images requires more powerful multi-scale detectors. To address these issues, a SAR ship detector called Duplicate Bilateral YOLO (DB-YOLO) is proposed in this paper, which is composed of a Feature Extraction Network (FEN), Duplicate Bilateral Feature Pyramid Network (DB-FPN) and Detection Network (DN). Firstly, a single-stage network is used to meet the need of real-time detection, and the cross stage partial (CSP) block is used to reduce the redundant parameters. Secondly, DB-FPN is designed to enhance the fusion of semantic and spatial information. In view of the ships in SAR image are mainly distributed with small-scale targets, the distribution of parameters and computation values between FEN and DB-FPN in different feature layers is redistributed to solve the multi-scale detection. Finally, the bounding boxes and confidence scores are given through the detection head of YOLO. In order to evaluate the effectiveness and robustness of DB-YOLO, comparative experiments with the other six state-of-the-art methods (Faster R-CNN, Cascade R-CNN, Libra R-CNN, FCOS, CenterNet and YOLOv5s) on two SAR ship datasets, i.e., SSDD and HRSID, are performed. The experimental results show that the AP50 of DB-YOLO reaches 97.8% on SSDD and 94.4% on HRSID, respectively. DB-YOLO meets the requirement of real-time detection (48.1 FPS) and is superior to other methods in the experiments. Full article

(This article belongs to the Section Sensing and Imaging)

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26 pages, 5496 KiB

Open AccessArticle

Waymarking in Social Robots: Environment Signaling Using Human–Robot Interaction

by Ana Corrales-Paredes, María Malfaz, Verónica Egido-García and Miguel A. Salichs

Sensors 2021, 21(23), 8145; https://doi.org/10.3390/s21238145 - 6 Dec 2021

Cited by 3 | Viewed by 2706

Abstract

Travellers use the term waymarking to define the action of posting signs, or waymarks, along a route. These marks are intended to be points of reference during navigation for the environment. In this research, we will define waymarking as the skill of a [...] Read more.

Travellers use the term waymarking to define the action of posting signs, or waymarks, along a route. These marks are intended to be points of reference during navigation for the environment. In this research, we will define waymarking as the skill of a robot to signal the environment or generate information to facilitate localization and navigation, both for its own use and for other robots as well. We present an automated environment signaling system using human–robot interaction and radio frequency identification (RFID) technology. The goal is for the robot, through human–robot interaction, to obtain information from the environment and use this information to carry out the signaling or waymarking process. HRI will play a key role in the signaling process since this type of communication makes it possible to exchange more specific and enriching information. The robot uses common phrases such as “Where am I?” and “Where can I go?”, just as we humans do when we ask other people for information about the environment. It is also possible to guide the robot and “show” it the environment to carry out the task of writing the signs. The robot will use the information received to create, update, or improve the navigation data in the RFID signals. In this paper, the signaling process will be described, how the robot acquires the information for signals, writing and updating process and finally, the implementation and integration in a real social robot in a real indoor environment. Full article

(This article belongs to the Special Issue Autonomous Navigation in Robotics: A New Challenge towards Social Robots)

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13 pages, 4027 KiB

Open AccessArticle

Development of a Novel Anesthesia Airway Management Robot

by Xuesong Ma, Bo Pan, Tao Song, Yanwen Sun and Yili Fu

Sensors 2021, 21(23), 8144; https://doi.org/10.3390/s21238144 - 6 Dec 2021

Cited by 2 | Viewed by 2643

Abstract

Non-invasive positive pressure ventilation has attracted increasing attention for air management in general anesthesia. This work proposes a novel robot equipped with two snake arms and a mask-fastening mechanism to facilitate trachea airway management for anesthesia as well as deep sedation and to [...] Read more.

Non-invasive positive pressure ventilation has attracted increasing attention for air management in general anesthesia. This work proposes a novel robot equipped with two snake arms and a mask-fastening mechanism to facilitate trachea airway management for anesthesia as well as deep sedation and to improve surgical outcomes. The two snake arms with supporting terminals have been designed to lift a patient’s jaw with design optimization, and the mask-fastening mechanism has been utilized to fasten the mask onto a patient’s face. The control unit has been developed to implement lifting and fastening force control with safety and robustness. Loading experiments on the snake arm and tension experiments on the mask-fastening mechanism have been performed to investigate and validate the performances of the proposed anesthesia airway management robot. Experiments on a mock person have also been employed to further verify the effectiveness and reliability of the developed robot system. As an early study of an anesthesia airway management robot, it was verified as a valid attempt to perform mask non-invasive positive pressure ventilation technology by taking advantage of a robotic system. Full article

(This article belongs to the Topic Robotics and Automation in Smart Manufacturing Systems)

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17 pages, 3132 KiB

Open AccessArticle

Me-Doped Ti–Me Intermetallic Thin Films Used for Dry Biopotential Electrodes: A Comparative Case Study

by Cláudia Lopes, Patrique Fiedler, Marco Sampaio Rodrigues, Joel Borges, Maurizio Bertollo, Eduardo Alves, Nuno Pessoa Barradas, Silvia Comani, Jens Haueisen and Filipe Vaz

Sensors 2021, 21(23), 8143; https://doi.org/10.3390/s21238143 - 6 Dec 2021

Cited by 5 | Viewed by 3994

Abstract

In a new era for digital health, dry electrodes for biopotential measurement enable the monitoring of essential vital functions outside of specialized healthcare centers. In this paper, a new type of nanostructured titanium-based thin film is proposed, revealing improved biopotential sensing performance and [...] Read more.

In a new era for digital health, dry electrodes for biopotential measurement enable the monitoring of essential vital functions outside of specialized healthcare centers. In this paper, a new type of nanostructured titanium-based thin film is proposed, revealing improved biopotential sensing performance and overcoming several of the limitations of conventional gel-based electrodes such as reusability, durability, biocompatibility, and comfort. The thin films were deposited on stainless steel (SS) discs and polyurethane (PU) substrates to be used as dry electrodes, for non-invasive monitoring of body surface biopotentials. Four different Ti–Me (Me = Al, Cu, Ag, or Au) metallic binary systems were prepared by magnetron sputtering. The morphology of the resulting Ti–Me systems was found to be dependent on the chemical composition of the films, specifically on the type and amount of Me. The existence of crystalline intermetallic phases or glassy amorphous structures also revealed a strong influence on the morphological features developed by the different systems. The electrodes were tested in an in-vivo study on 20 volunteers during sports activity, allowing study of the application-specific characteristics of the dry electrodes, based on Ti–Me intermetallic thin films, and evaluation of the impact of the electrode–skin impedance on biopotential sensing. The electrode–skin impedance results support the reusability and the high degree of reliability of the Ti–Me dry electrodes. The Ti–Al films revealed the least performance as biopotential electrodes, while the Ti–Au system provided excellent results very close to the Ag/AgCl reference electrodes. Full article

(This article belongs to the Special Issue EEG Sensors for Biomedical Applications)

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16 pages, 3974 KiB

Open AccessArticle

Deep Learning-Based Transfer Learning for Classification of Skin Cancer

by Satin Jain, Udit Singhania, Balakrushna Tripathy, Emad Abouel Nasr, Mohamed K. Aboudaif and Ali K. Kamrani

Sensors 2021, 21(23), 8142; https://doi.org/10.3390/s21238142 - 6 Dec 2021

Cited by 74 | Viewed by 10503

Abstract

One of the major health concerns for human society is skin cancer. When the pigments producing skin color turn carcinogenic, this disease gets contracted. A skin cancer diagnosis is a challenging process for dermatologists as many skin cancer pigments may appear similar in [...] Read more.

One of the major health concerns for human society is skin cancer. When the pigments producing skin color turn carcinogenic, this disease gets contracted. A skin cancer diagnosis is a challenging process for dermatologists as many skin cancer pigments may appear similar in appearance. Hence, early detection of lesions (which form the base of skin cancer) is definitely critical and useful to completely cure the patients suffering from skin cancer. Significant progress has been made in developing automated tools for the diagnosis of skin cancer to assist dermatologists. The worldwide acceptance of artificial intelligence-supported tools has permitted usage of the enormous collection of images of lesions and benevolent sores approved by histopathology. This paper performs a comparative analysis of six different transfer learning nets for multi-class skin cancer classification by taking the HAM10000 dataset. We used replication of images of classes with low frequencies to counter the imbalance in the dataset. The transfer learning nets that were used in the analysis were VGG19, InceptionV3, InceptionResNetV2, ResNet50, Xception, and MobileNet. Results demonstrate that replication is suitable for this task, achieving high classification accuracies and F-measures with lower false negatives. It is inferred that Xception Net outperforms the rest of the transfer learning nets used for the study, with an accuracy of 90.48. It also has the highest recall, precision, and F-Measure values. Full article

(This article belongs to the Special Issue Smart Healthcare Systems Based on the Internet of Things and Artificial Intelligence)

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16 pages, 7009 KiB

Open AccessArticle

A Resonant Coupler for Subcutaneous Implant

by Sen Bing, Khengdauliu Chawang and J.-C. Chiao

Sensors 2021, 21(23), 8141; https://doi.org/10.3390/s21238141 - 6 Dec 2021

Cited by 10 | Viewed by 2389

Abstract

A resonator coupler for subcutaneous implants has been developed with a new impedance matching pattern added to the conventional loop antenna. The tuning element of a concentric metal pad contributes distributed capacitance and inductance to the planar inductive loop and improves resonance significantly. [...] Read more.

A resonator coupler for subcutaneous implants has been developed with a new impedance matching pattern added to the conventional loop antenna. The tuning element of a concentric metal pad contributes distributed capacitance and inductance to the planar inductive loop and improves resonance significantly. It provides a better qualify factor for resonant coupling and a much lower reflection coefficient for the implant electronics. Practical constraints are taken into account for designs including the requirement of operation within a regulated frequency band and the limited thickness for a monolithic implant. In this work, two designs targeting to operate in the two industrial, scientific, and medical (ISM) bands at 903 MHz and 2.45 GHz are considered. The tuning metal pad improves their resonances significantly, compared to the conventional loop designs. Since it is difficult to tune the implant antenna after implantation, the effects of tissue depth variations due to the individual’s surgery and the appropriate implant depths are investigated. Simulations conducted with the dielectric properties of human skin documented in the literature are compared to measurements done with hydrated ground pork as phantoms. Experiments and simulations are conducted to explain the discrepancies in frequency shifts due to the uses of pork phantoms. The design method is thus validated for uses on human skin. A noninvasive localization method to identify the implant under the skin has been examined and demonstrated by both simulations and measurements. It can efficiently locate the subcutaneous implant based on the high quality-factor resonance owing to the tuning elements in both implant and transmitter couplers. The planar resonant coupler for wireless power transfer shows good performance and promise in subcutaneous applications for implants. Full article

(This article belongs to the Special Issue Selected Papers from 2021 IEEE MTT-S Wireless Power Transfer Conference)

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18 pages, 4355 KiB

Open AccessArticle

A Non-Invasive Millimetre-Wave Radar Sensor for Automated Behavioural Tracking in Precision Farming—Application to Sheep Husbandry

by Alexandre Dore, Cristian Pasquaretta, Dominique Henry, Edmond Ricard, Jean-François Bompa, Mathieu Bonneau, Alain Boissy, Dominique Hazard, Mathieu Lihoreau and Hervé Aubert

Sensors 2021, 21(23), 8140; https://doi.org/10.3390/s21238140 - 6 Dec 2021

Cited by 2 | Viewed by 3094

Abstract

The automated quantification of the behaviour of freely moving animals is increasingly needed in applied ethology. State-of-the-art approaches often require tags to identify animals, high computational power for data collection and processing, and are sensitive to environmental conditions, which limits their large-scale utilization, [...] Read more.

The automated quantification of the behaviour of freely moving animals is increasingly needed in applied ethology. State-of-the-art approaches often require tags to identify animals, high computational power for data collection and processing, and are sensitive to environmental conditions, which limits their large-scale utilization, for instance in genetic selection programs of animal breeding. Here we introduce a new automated tracking system based on millimetre-wave radars for real time robust and high precision monitoring of untagged animals. In contrast to conventional video tracking systems, radar tracking requires low processing power, is independent on light variations and has more accurate estimations of animal positions due to a lower misdetection rate. To validate our approach, we monitored the movements of 58 sheep in a standard indoor behavioural test used for assessing social motivation. We derived new estimators from the radar data that can be used to improve the behavioural phenotyping of the sheep. We then showed how radars can be used for movement tracking at larger spatial scales, in the field, by adjusting operating frequency and radiated electromagnetic power. Millimetre-wave radars thus hold considerable promises precision farming through high-throughput recording of the behaviour of untagged animals in different types of environments. Full article

(This article belongs to the Collection Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming)

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12 pages, 2903 KiB

Open AccessArticle

Medical Range Radiation Dosimeter Based on Polymer-Embedded Fiber Bragg Gratings

by Marie-Anne Lebel-Cormier, Tommy Boilard, Martin Bernier and Luc Beaulieu

Sensors 2021, 21(23), 8139; https://doi.org/10.3390/s21238139 - 6 Dec 2021

Cited by 4 | Viewed by 2513

Abstract

Fiber Bragg gratings (FBGs) are valuable dosimeters for doses up to 100 kilograys (kGy), but have hardly been used for the low-dose range of a few grays (Gy) required in medical radiation dosimetry. We report that embedding a doped silica fiber FBG in [...] Read more.

Fiber Bragg gratings (FBGs) are valuable dosimeters for doses up to 100 kilograys (kGy), but have hardly been used for the low-dose range of a few grays (Gy) required in medical radiation dosimetry. We report that embedding a doped silica fiber FBG in a polymer material allows a minimum detectable dose of 0.3 Gy for

γ

-radiation. Comparing the detector response for different doped silica fibers with various core doping, we obtain an independent response, in opposition to what is reported for high-dose range. We hypothesized that the sensor detection is based on the radio-induced thermal expansion of the surrounding polymer. Hence, we used a simple physical model based on the thermal and mechanical properties of the surrounding polymer and obtained good accordance between measured and calculated values for different compositions and thicknesses. We report that over the 4 embedding polymers tested, polyether ether ketone and polypropylene have respectively the lowest (0.056 pm/Gy) and largest sensitivity (0.087 pm/Gy). Such FBG-based dosimeters have the potential to be distributed along the fiber to allow multipoint detection while having a sub-millimeter size that could prove very useful for low-dose applications, in particular for radiotherapy dosimetry. Full article

(This article belongs to the Special Issue Optical Fiber Sensors in Radiation Environments)

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16 pages, 5741 KiB

Open AccessArticle

Evaluation of Ride Comfort in a Railway Passenger Car Depending on a Change of Suspension Parameters

by Ján Dižo, Miroslav Blatnický, Juraj Gerlici, Bohuš Leitner, Rafał Melnik, Stanislav Semenov, Evgeny Mikhailov and Mariusz Kostrzewski

Sensors 2021, 21(23), 8138; https://doi.org/10.3390/s21238138 - 6 Dec 2021

Cited by 23 | Viewed by 3820

Abstract

Ride comfort for passengers remains a pressing topic. The level of comfort in a vehicle can influences passengers’ preferences for a particular means of transport. The article aims to evaluate the influence of changes in suspension parameters on the ride comfort for passengers. [...] Read more.

Ride comfort for passengers remains a pressing topic. The level of comfort in a vehicle can influences passengers’ preferences for a particular means of transport. The article aims to evaluate the influence of changes in suspension parameters on the ride comfort for passengers. The theoretical background includes a description of the applied method for a creating the virtual model of an investigated vehicle as well as the method of evaluating the ride comfort. The ride comfort of the vehicle is assessed based on the standard method, which involves calculating the mean comfort method, i.e., ride comfort index N_MV in chosen points on a body floor. The N_MV ride comfort index (Mean Comfort Standard Method) requires the input of acceleration signals in three directions. The rest of the article offers the results of simulation computations. The stiffness–damping parameters of the primary and secondary suspension systems were changed at three levels and the vehicle was run on the real track section. The ride index N_MV was calculated for all three modifications of the suspension system in the chosen fifteen points of the body floor. It was found that lower values in the stiffness of the secondary suspension system lead to lower levels of ride comfort in the investigated railway passenger car; however, lower values in the stiffness–damping parameters of the primary suspension system did not decrease the levels of ride comfort as significantly. Full article

(This article belongs to the Special Issue Feature Papers in Vehicular Sensing)

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14 pages, 3821 KiB

Open AccessArticle

A Highly Sensitive and Miniature Optical Fiber Sensor for Electromagnetic Pulse Fields

by Min Zhao, Xing Zhou and Yazhou Chen

Sensors 2021, 21(23), 8137; https://doi.org/10.3390/s21238137 - 6 Dec 2021

Cited by 8 | Viewed by 2511

Abstract

The detection of an electromagnetic pulse (EMP) field is of great significance in determining the field environment of tested equipment in small spaces. Finger-shaped miniature optical fiber sensors for electromagnetic pulse field measurement were designed. The antenna of a weak field sensor was [...] Read more.

The detection of an electromagnetic pulse (EMP) field is of great significance in determining the field environment of tested equipment in small spaces. Finger-shaped miniature optical fiber sensors for electromagnetic pulse field measurement were designed. The antenna of a weak field sensor was integrated with a shielding shell, and the wire welded at the direct electro-optic converting circuit connected to an optical fiber through special structure and circuit design was taken as the antenna of a strong field sensor. Measurements in the time domain and frequency domain had been carried out for the two sensors. Experiment results demonstrate that the weak field sensor and the strong field sensor have flat responses from 100 kHz to 1 GHz with a variation of 2.3 dB and 2.9 dB, respectively, and the EMP waveform detected by the sensors agrees well with the applied standard square wave. Moreover, the strong field sensor exhibits linear responses from 645 V/m to 83 kV/m. The resolution of the weak field sensor is as low as 13 V/m. The result indicated that the designed sensors had good performance. Full article

(This article belongs to the Section Optical Sensors)

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13 pages, 3516 KiB

Open AccessArticle

DeepLabV3+/Efficientnet Hybrid Network-Based Scene Area Judgment for the Mars Unmanned Vehicle System

by Shuang Hu, Jin Liu and Zhiwei Kang

Sensors 2021, 21(23), 8136; https://doi.org/10.3390/s21238136 - 5 Dec 2021

Cited by 13 | Viewed by 3279

Abstract

Due to the complexity and danger of Mars’s environment, traditional Mars unmanned ground vehicles cannot efficiently perform Mars exploration missions. To solve this problem, the DeepLabV3+/Efficientnet hybrid network is proposed and applied to the scene area judgment for the Mars unmanned vehicle system. [...] Read more.

Due to the complexity and danger of Mars’s environment, traditional Mars unmanned ground vehicles cannot efficiently perform Mars exploration missions. To solve this problem, the DeepLabV3+/Efficientnet hybrid network is proposed and applied to the scene area judgment for the Mars unmanned vehicle system. Firstly, DeepLabV3+ is used to extract the feature information of the Mars image due to its high accuracy. Then, the feature information is used as the input for Efficientnet, and the categories of scene areas are obtained, including safe area, report area, and dangerous area. Finally, according to three categories, the Mars unmanned vehicle system performs three operations: pass, report, and send. Experimental results show the effectiveness of the DeepLabV3+/Efficientnet hybrid network in the scene area judgment. Compared with the Efficientnet network, the accuracy of the DeepLabV3+/Efficientnet hybrid network is improved by approximately 18% and reaches 99.84%, which ensures the safety of the exploration mission for the Mars unmanned vehicle system. Full article

(This article belongs to the Section Navigation and Positioning)

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13 pages, 2949 KiB

Open AccessArticle

Pocketable Labs for Everyone: Synchronized Multi-Sensor Data Streaming and Recording on Smartphones with the Lab Streaming Layer

by Sarah Blum, Daniel Hölle, Martin Georg Bleichner and Stefan Debener

Sensors 2021, 21(23), 8135; https://doi.org/10.3390/s21238135 - 5 Dec 2021

Cited by 13 | Viewed by 8730

Abstract

The streaming and recording of smartphone sensor signals is desirable for mHealth, telemedicine, environmental monitoring and other applications. Time series data gathered in these fields typically benefit from the time-synchronized integration of different sensor signals. However, solutions required for this synchronization are mostly [...] Read more.

The streaming and recording of smartphone sensor signals is desirable for mHealth, telemedicine, environmental monitoring and other applications. Time series data gathered in these fields typically benefit from the time-synchronized integration of different sensor signals. However, solutions required for this synchronization are mostly available for stationary setups. We hope to contribute to the important emerging field of portable data acquisition by presenting open-source Android applications both for the synchronized streaming (Send-a) and recording (Record-a) of multiple sensor data streams. We validate the applications in terms of functionality, flexibility and precision in fully mobile setups and in hybrid setups combining mobile and desktop hardware. Our results show that the fully mobile solution is equivalent to well-established desktop versions. With the streaming application Send-a and the recording application Record-a, purely smartphone-based setups for mobile research and personal health settings can be realized on off-the-shelf Android devices. Full article

(This article belongs to the Section Sensing and Imaging)

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17 pages, 7140 KiB

Open AccessArticle

Fast γ Photon Imaging for Inner Surface Defects Detecting

by Min Yao, Guangdong Luo, Min Zhao, Ruipeng Guo and Jian Liu

Sensors 2021, 21(23), 8134; https://doi.org/10.3390/s21238134 - 5 Dec 2021

Viewed by 2258

Abstract

Only a few effective methods can detect internal defects and monitor the internal state of complex structural parts. On the basis of the principle of PET (positron emission computed tomography), a new measurement method, using γ photon to detect defects of an inner [...] Read more.

Only a few effective methods can detect internal defects and monitor the internal state of complex structural parts. On the basis of the principle of PET (positron emission computed tomography), a new measurement method, using γ photon to detect defects of an inner surface, is proposed. This method has the characteristics of strong penetration, anti-corrosion and anti-interference. With the aim of improving detection accuracy and imaging speed, this study also proposes image reconstruction algorithms, combining the classic FBP (filtered back projection) with MLEM (maximum likelihood expectation Maximization) algorithm. The proposed scheme can reduce the number of iterations required, when imaging, to achieve the same image quality. According to the operational demands of FPGAs (field-programmable gate array), a BPML (back projection maximum likelihood) algorithm is adapted to the structural characteristics of an FPGA, which makes it feasible to test the proposed algorithms therein. Furthermore, edge detection and defect recognition are conducted after reconstructing the inner image. The effectiveness and superiority of the algorithm are verified, and the performance of the FPGA is evaluated by the experiments. Full article

(This article belongs to the Special Issue Recent Advances in Sensing and Data Centric Methods for Structural Health Monitoring and Resilience)

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15 pages, 871 KiB

Open AccessArticle

Design and Development of an AIoT Architecture for Introducing a Vessel ETA Cognitive Service in a Legacy Port Management Solution

by Clara I. Valero, Enrique Ivancos Pla, Rafael Vaño, Eduardo Garro, Fernando Boronat and Carlos E. Palau

Sensors 2021, 21(23), 8133; https://doi.org/10.3390/s21238133 - 5 Dec 2021

Cited by 9 | Viewed by 3229

Abstract

Current Internet of Things (IoT) stacks are frequently focused on handling an increasing volume of data that require a sophisticated interpretation through analytics to improve decision making and thus generate business value. In this paper, a cognitive IoT architecture based on FIWARE IoT [...] Read more.

Current Internet of Things (IoT) stacks are frequently focused on handling an increasing volume of data that require a sophisticated interpretation through analytics to improve decision making and thus generate business value. In this paper, a cognitive IoT architecture based on FIWARE IoT principles is presented. The architecture incorporates a new cognitive component that enables the incorporation of intelligent services to the FIWARE framework, allowing to modernize IoT infrastructures with Artificial Intelligence (AI) technologies. This allows to extend the effective life of the legacy system, using existing assets and reducing costs. Using the architecture, a cognitive service capable of predicting with high accuracy the vessel port arrival is developed and integrated in a legacy sea traffic management solution. The cognitive service uses automatic identification system (AIS) and maritime oceanographic data to predict time of arrival of ships. The validation has been carried out using the port of Valencia. The results indicate that the incorporation of AI into the legacy system allows to predict the arrival time with higher accuracy, thus improving the efficiency of port operations. Moreover, the architecture is generic, allowing an easy integration of the cognitive services in other domains. Full article

(This article belongs to the Special Issue Artificial Intelligence of Things for Real-Time Data Monitoring and Processing)

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