Sensors

18 pages, 28421 KiB

Open AccessArticle

The Event Detection System in the NEXT-White Detector

by Raúl Esteve Bosch, José F. Toledo Alarcón, Vicente Herrero Bosch, Ander Simón Estévez, Francesc Monrabal Capilla, Vicente Álvarez Puerta, Javier Rodríguez Samaniego, Marc Querol Segura and Francisco Ballester Merelo

Sensors 2021, 21(2), 673; https://doi.org/10.3390/s21020673 - 19 Jan 2021

Cited by 4 | Viewed by 3661

Abstract

This article describes the event detection system of the NEXT-White detector, a 5 kg high pressure xenon TPC with electroluminescent amplification, located in the Laboratorio Subterráneo de Canfranc (LSC), Spain. The detector is based on a plane of photomultipliers (PMTs) for energy measurements [...] Read more.

This article describes the event detection system of the NEXT-White detector, a 5 kg high pressure xenon TPC with electroluminescent amplification, located in the Laboratorio Subterráneo de Canfranc (LSC), Spain. The detector is based on a plane of photomultipliers (PMTs) for energy measurements and a silicon photomultiplier (SiPM) tracking plane for offline topological event filtering. The event detection system, based on the SRS-ATCA data acquisition system developed in the framework of the CERN RD51 collaboration, has been designed to detect multiple events based on online PMT signal energy measurements and a coincidence-detection algorithm. Implemented on FPGA, the system has been successfully running and evolving during NEXT-White operation. The event detection system brings some relevant and new functionalities in the field. A distributed double event processor has been implemented to detect simultaneously two different types of events thus allowing simultaneous calibration and physics runs. This special feature provides constant monitoring of the detector conditions, being especially relevant to the lifetime and geometrical map computations which are needed to correct high-energy physics events. Other features, like primary scintillation event rejection, or a double buffer associated with the type of event being searched, help reduce the unnecessary data throughput thus minimizing dead time and improving trigger efficiency. Full article

(This article belongs to the Special Issue Electronics for Sensors)

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19 pages, 4292 KiB

Open AccessArticle

A Service Discovery Solution for Edge Choreography-Based Distributed Embedded Systems

by Sara Blanc, José-Luis Bayo-Montón, Senén Palanca-Barrio and Néstor X. Arreaga-Alvarado

Sensors 2021, 21(2), 672; https://doi.org/10.3390/s21020672 - 19 Jan 2021

Cited by 4 | Viewed by 2822

Abstract

This paper presents a solution to support service discovery for edge choreography based distributed embedded systems. The Internet of Things (IoT) edge architectural layer is composed of Raspberry Pi machines. Each machine hosts different services organized based on the choreography collaborative paradigm. The [...] Read more.

This paper presents a solution to support service discovery for edge choreography based distributed embedded systems. The Internet of Things (IoT) edge architectural layer is composed of Raspberry Pi machines. Each machine hosts different services organized based on the choreography collaborative paradigm. The solution adds to the choreography middleware three messages passing models to be coherent and compatible with current IoT messaging protocols. It is aimed to support blind hot plugging of new machines and help with service load balance. The discovery mechanism is implemented as a broker service and supports regular expressions (Regex) in message scope to discern both publishing patterns offered by data providers and client services necessities. Results compare Control Process Unit (CPU) usage in a request–response and datacentric configuration and analyze both regex interpreter latency times compared with a traditional message structure as well as its impact on CPU and memory consumption. Full article

(This article belongs to the Section Internet of Things)

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25 pages, 13734 KiB

Open AccessArticle

Optimal Consensus with Dual Abnormality Mode of Cellular IoT Based on Edge Computing

by Shin-Hung Pan and Shu-Ching Wang

Sensors 2021, 21(2), 671; https://doi.org/10.3390/s21020671 - 19 Jan 2021

Viewed by 2504

Abstract

The continuous development of fifth-generation (5G) networks is the main driving force for the growth of Internet of Things (IoT) applications. It is expected that the 5G network will greatly expand the applications of the IoT, thereby promoting the operation of cellular networks, [...] Read more.

The continuous development of fifth-generation (5G) networks is the main driving force for the growth of Internet of Things (IoT) applications. It is expected that the 5G network will greatly expand the applications of the IoT, thereby promoting the operation of cellular networks, the security and network challenges of the IoT, and pushing the future of the Internet to the edge. Because the IoT can make anything in anyplace be connected together at any time, it can provide ubiquitous services. With the establishment and use of 5G wireless networks, the cellular IoT (CIoT) will be developed and applied. In order to provide more reliable CIoT applications, a reliable network topology is very important. Reaching a consensus is one of the most important issues in providing a highly reliable CIoT design. Therefore, it is necessary to reach a consensus so that even if some components in the system is abnormal, the application in the system can still execute correctly in CIoT. In this study, a protocol of consensus is discussed in CIoT with dual abnormality mode that combines dormant abnormality and malicious abnormality. The protocol proposed in this research not only allows all normal components in CIoT to reach a consensus with the minimum times of data exchange, but also allows the maximum number of dormant and malicious abnormal components in CIoT. In the meantime, the protocol can make all normal components in CIoT satisfy the constraints of reaching consensus: Termination, Agreement, and Integrity. Full article

(This article belongs to the Special Issue Cloud and Edge Computing for the Next Generation of Internet of Things Applications)

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14 pages, 3418 KiB

Open AccessCommunication

An Optical Frequency Domain Angle Measurement Method Based on Second Harmonic Generation

by Wijayanti Dwi Astuti, Hiraku Matsukuma, Masaru Nakao, Kuangyi Li, Yuki Shimizu and Wei Gao

Sensors 2021, 21(2), 670; https://doi.org/10.3390/s21020670 - 19 Jan 2021

Cited by 16 | Viewed by 4119

Abstract

This paper proposes a new optical angle measurement method in the optical frequency domain based on second harmonic generation with a mode-locked femtosecond laser source by making use of the unique characteristic of the high peak power and wide spectral range of the [...] Read more.

This paper proposes a new optical angle measurement method in the optical frequency domain based on second harmonic generation with a mode-locked femtosecond laser source by making use of the unique characteristic of the high peak power and wide spectral range of the femtosecond laser pulses. To get a wide measurable range of angle measurement, a theoretical calculation for several nonlinear optical crystals is performed. As a result, LiNbO₃ crystal is employed in the proposed method. In the experiment, the validity of the use of a parabolic mirror is also demonstrated, where the chromatic aberration of the focusing beam caused the localization of second harmonic generation in our previous research. Moreover, an experimental demonstration is also carried out for the proposed angle measurement method. The measurable range of 10,000 arc-seconds is achieved. Full article

(This article belongs to the Collection Position Sensor)

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12 pages, 851 KiB

Open AccessLetter

Ramie Yield Estimation Based on UAV RGB Images

by Hongyu Fu, Chufeng Wang, Guoxian Cui, Wei She and Liang Zhao

Sensors 2021, 21(2), 669; https://doi.org/10.3390/s21020669 - 19 Jan 2021

Cited by 16 | Viewed by 4289

Abstract

Timely and accurate crop growth monitoring and yield estimation are important for field management. The traditional sampling method used for estimation of ramie yield is destructive. Thus, this study proposed a new method for estimating ramie yield based on field phenotypic data obtained [...] Read more.

Timely and accurate crop growth monitoring and yield estimation are important for field management. The traditional sampling method used for estimation of ramie yield is destructive. Thus, this study proposed a new method for estimating ramie yield based on field phenotypic data obtained from unmanned aerial vehicle (UAV) images. A UAV platform carrying RGB cameras was employed to collect ramie canopy images during the whole growth period. The vegetation indices (VIs), plant number, and plant height were extracted from UAV-based images, and then, these data were incorporated to establish yield estimation model. Among all of the UAV-based image data, we found that the structure features (plant number and plant height) could better reflect the ramie yield than the spectral features, and in structure features, the plant number was found to be the most useful index to monitor the yield, with a correlation coefficient of 0.6. By fusing multiple characteristic parameters, the yield estimation model based on the multiple linear regression was obviously more accurate than the stepwise linear regression model, with a determination coefficient of 0.66 and a relative root mean square error of 1.592 kg. Our study reveals that it is feasible to monitor crop growth based on UAV images and that the fusion of phenotypic data can improve the accuracy of yield estimations. Full article

(This article belongs to the Section Remote Sensors)

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10 pages, 4335 KiB

Open AccessLetter

Serial MTJ-Based TMR Sensors in Bridge Configuration for Detection of Fractured Steel Bar in Magnetic Flux Leakage Testing

by Zhenhu Jin, Muhamad Arif Ihsan Mohd Noor Sam, Mikihiko Oogane and Yasuo Ando

Sensors 2021, 21(2), 668; https://doi.org/10.3390/s21020668 - 19 Jan 2021

Cited by 50 | Viewed by 6771

Abstract

Thanks to high sensitivity, excellent scalability, and low power consumption, magnetic tunnel junction (MTJ)-based tunnel magnetoresistance (TMR) sensors have been widely implemented in various industrial fields. In nondestructive magnetic flux leakage testing, the magnetic sensor plays a significant role in the detection results. [...] Read more.

Thanks to high sensitivity, excellent scalability, and low power consumption, magnetic tunnel junction (MTJ)-based tunnel magnetoresistance (TMR) sensors have been widely implemented in various industrial fields. In nondestructive magnetic flux leakage testing, the magnetic sensor plays a significant role in the detection results. As highly sensitive sensors, integrated MTJs can suppress frequency-dependent noise and thereby decrease detectivity; therefore, serial MTJ-based sensors allow for the design of high-performance sensors to measure variations in magnetic fields. In the present work, we fabricated serial MTJ-based TMR sensors and connected them to a full Wheatstone bridge circuit. Because noise power can be suppressed by using bridge configuration, the TMR sensor with Wheatstone bridge configuration showed low noise spectral density (0.19 μV/Hz^0.5) and excellent detectivity (5.29 × 10⁻⁸ Oe/Hz^0.5) at a frequency of 1 Hz. Furthermore, in magnetic flux leakage testing, compared with one TMR sensor, the Wheatstone bridge TMR sensors provided a higher signal-to-noise ratio for inspection of a steel bar. The one TMR sensor system could provide a high defect signal due to its high sensitivity at low lift-off (4 cm). However, as a result of its excellent detectivity, the full Wheatstone bridge-based TMR sensor detected the defect even at high lift-off (20 cm). This suggests that the developed TMR sensor provides excellent detectivity, detecting weak field changes in magnetic flux leakage testing. Full article

(This article belongs to the Special Issue Magnetic Sensing/Functionalized Devices and Applications)

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20 pages, 617 KiB

Open AccessArticle

Measurement-Based Modelling of Material Moisture and Particle Classification for Control of Copper Ore Dry Grinding Process

by Oliwia Krauze, Dariusz Buchczik and Sebastian Budzan

Sensors 2021, 21(2), 667; https://doi.org/10.3390/s21020667 - 19 Jan 2021

Cited by 6 | Viewed by 3155

Abstract

Moisture of bulk material has a significant impact on energetic efficiency of dry grinding, resultant particle size distribution and particle shape, and conditions of powder transport. As a consequence, moisture needs to be measured or estimated (modelled) in many points. This research investigates [...] Read more.

Moisture of bulk material has a significant impact on energetic efficiency of dry grinding, resultant particle size distribution and particle shape, and conditions of powder transport. As a consequence, moisture needs to be measured or estimated (modelled) in many points. This research investigates mutual relations between material moisture and particle classification process in a grinding installation. The experimental setup involves an inertial-impingement classifier and cyclone being part of dry grinding circuit with electromagnetic mill and recycle of coarse particles. The tested granular material is copper ore of particle size 0–1.25 mm and relative moisture content 0.5–5%, fed to the installation at various rates. Higher moisture of input material is found to change the operation of the classifier. Computed correlation coefficients show increased content of fine particles in lower product of classification. Additionally, drying of lower and upper classification products with respect to moisture of input material is modelled. Straight line models with and without saturation are estimated with recursive least squares method accounting for measurement errors in both predictor and response variables. These simple models are intended for use in automatic control system of the grinding installation. Full article

(This article belongs to the Special Issue Humidity Sensors for Industrial and Agricultural Applications)

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19 pages, 8741 KiB

Open AccessArticle

Double Ghost Convolution Attention Mechanism Network: A Framework for Hyperspectral Reconstruction of a Single RGB Image

by Wenju Wang and Jiangwei Wang

Sensors 2021, 21(2), 666; https://doi.org/10.3390/s21020666 - 19 Jan 2021

Cited by 12 | Viewed by 4678

Abstract

Current research on the reconstruction of hyperspectral images from RGB images using deep learning mainly focuses on learning complex mappings through deeper and wider convolutional neural networks (CNNs). However, the reconstruction accuracy of the hyperspectral image is not high and among other issues [...] Read more.

Current research on the reconstruction of hyperspectral images from RGB images using deep learning mainly focuses on learning complex mappings through deeper and wider convolutional neural networks (CNNs). However, the reconstruction accuracy of the hyperspectral image is not high and among other issues the model for generating these images takes up too much storage space. In this study, we propose the double ghost convolution attention mechanism network (DGCAMN) framework for the reconstruction of a single RGB image to improve the accuracy of spectral reconstruction and reduce the storage occupied by the model. The proposed DGCAMN consists of a double ghost residual attention block (DGRAB) module and optimal nonlocal block (ONB). DGRAB module uses GhostNet and PRELU activation functions to reduce the calculation parameters of the data and reduce the storage size of the generative model. At the same time, the proposed double output feature Convolutional Block Attention Module (DOFCBAM) is used to capture the texture details on the feature map to maximize the content of the reconstructed hyperspectral image. In the proposed ONB, the Argmax activation function is used to obtain the region with the most abundant feature information and maximize the most useful feature parameters. This helps to improve the accuracy of spectral reconstruction. These contributions enable the DGCAMN framework to achieve the highest spectral accuracy with minimal storage consumption. The proposed method has been applied to the NTIRE 2020 dataset. Experimental results show that the proposed DGCAMN method outperforms the spectral accuracy reconstructed by advanced deep learning methods and greatly reduces storage consumption. Full article

(This article belongs to the Special Issue Computational Spectral Imaging)

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30 pages, 7999 KiB

Open AccessArticle

Multi-Zone Authentication and Privacy-Preserving Protocol (MAPP) Based on the Bilinear Pairing Cryptography for 5G-V2X

by Shimaa A. Abdel Hakeem and HyungWon Kim

Sensors 2021, 21(2), 665; https://doi.org/10.3390/s21020665 - 19 Jan 2021

Cited by 18 | Viewed by 3159

Abstract

5G-Vehicle-to-Everything (5G-V2X) supports high-reliability and low latency autonomous services and applications. Proposing an efficient security solution that supports multi-zone broadcast authentication and satisfies the 5G requirement is a critical challenge. In The 3rd Generation Partnership Project (3GPP) Release 16 standard, for Cellular- Vehicle-to-Everything [...] Read more.

5G-Vehicle-to-Everything (5G-V2X) supports high-reliability and low latency autonomous services and applications. Proposing an efficient security solution that supports multi-zone broadcast authentication and satisfies the 5G requirement is a critical challenge. In The 3rd Generation Partnership Project (3GPP) Release 16 standard, for Cellular- Vehicle-to-Everything (C-V2X) single-cell communication is suggested to reuse the IEEE1609.2 security standard that utilizes the Public Key Infrastructure (PKI) cryptography. PKI-based solutions provide a high-security level, however, it suffers from high communication and computation overhead, due to the large size of the attached certificate and signature. In this study, we propose a light-weight Multi-Zone Authentication and Privacy-Preserving Protocol (MAPP) based on the bilinear pairing cryptography and short-size signature. MAPP protocol provides three different authentication methods that enable a secure broadcast authentication over multiple zones of large-scale base stations, using a single message and a single short signature. We also propose a centralized dynamic key generation method for multiple zones. We implemented and analyzed the proposed key generation and authentication methods using an authentication simulator and a bilinear pairing library. The proposed methods significantly reduce the signature generation time by 16 times–80 times, as compared to the previous methods. Additionally, the proposed methods significantly reduced the signature verification time by 10 times–16 times, as compared to the two previous methods. The three proposed authentication methods achieved substantial speed-up in the signature generation time and verification time, using a short bilinear pairing signature. Full article

(This article belongs to the Section Communications)

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17 pages, 7359 KiB

Open AccessArticle

Optimization of 3D Point Clouds of Oilseed Rape Plants Based on Time-of-Flight Cameras

by Zhihong Ma, Dawei Sun, Haixia Xu, Yueming Zhu, Yong He and Haiyan Cen

Sensors 2021, 21(2), 664; https://doi.org/10.3390/s21020664 - 19 Jan 2021

Cited by 9 | Viewed by 4148

Abstract

Three-dimensional (3D) structure is an important morphological trait of plants for describing their growth and biotic/abiotic stress responses. Various methods have been developed for obtaining 3D plant data, but the data quality and equipment costs are the main factors limiting their development. Here, [...] Read more.

Three-dimensional (3D) structure is an important morphological trait of plants for describing their growth and biotic/abiotic stress responses. Various methods have been developed for obtaining 3D plant data, but the data quality and equipment costs are the main factors limiting their development. Here, we propose a method to improve the quality of 3D plant data using the time-of-flight (TOF) camera Kinect V2. A K-dimension (k-d) tree was applied to spatial topological relationships for searching points. Background noise points were then removed with a minimum oriented bounding box (MOBB) with a pass-through filter, while outliers and flying pixel points were removed based on viewpoints and surface normals. After being smoothed with the bilateral filter, the 3D plant data were registered and meshed. We adjusted the mesh patches to eliminate layered points. The results showed that the patches were closer. The average distance between the patches was 1.88 × 10⁻³ m, and the average angle was 17.64°, which were 54.97% and 48.33% of those values before optimization. The proposed method performed better in reducing noise and the local layered-points phenomenon, and it could help to more accurately determine 3D structure parameters from point clouds and mesh models. Full article

(This article belongs to the Special Issue Sensing Technologies for Agricultural Automation and Robotics)

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25 pages, 7511 KiB

Open AccessArticle

Development of a Real-Time Human-Robot Collaborative System Based on 1 kHz Visual Feedback Control and Its Application to a Peg-in-Hole Task

by Yuji Yamakawa, Yutaro Matsui and Masatoshi Ishikawa

Sensors 2021, 21(2), 663; https://doi.org/10.3390/s21020663 - 19 Jan 2021

Cited by 9 | Viewed by 3850

Abstract

In this research, we focused on Human-Robot collaboration. There were two goals: (1) to develop and evaluate a real-time Human-Robot collaborative system, and (2) to achieve concrete tasks such as collaborative peg-in-hole using the developed system. We proposed an algorithm for visual sensing [...] Read more.

In this research, we focused on Human-Robot collaboration. There were two goals: (1) to develop and evaluate a real-time Human-Robot collaborative system, and (2) to achieve concrete tasks such as collaborative peg-in-hole using the developed system. We proposed an algorithm for visual sensing and robot hand control to perform collaborative motion, and we analyzed the stability of the collaborative system and a so-called collaborative error caused by image processing and latency. We achieved collaborative motion using this developed system and evaluated the collaborative error on the basis of the analysis results. Moreover, we aimed to realize a collaborative peg-in-hole task that required a system with high speed and high accuracy. To achieve this goal, we analyzed the conditions required for performing the collaborative peg-in-hole task from the viewpoints of geometric, force and posture conditions. Finally, in this work, we show the experimental results and data of the collaborative peg-in-hole task, and we examine the effectiveness of our collaborative system. Full article

(This article belongs to the Section Sensors and Robotics)

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16 pages, 836 KiB

Open AccessArticle

Supervised SVM Transfer Learning for Modality-Specific Artefact Detection in ECG

by Jonathan Moeyersons, John Morales, Amalia Villa, Ivan Castro, Dries Testelmans, Bertien Buyse, Chris Van Hoof, Rik Willems, Sabine Van Huffel and Carolina Varon

Sensors 2021, 21(2), 662; https://doi.org/10.3390/s21020662 - 19 Jan 2021

Cited by 4 | Viewed by 2885

Abstract

The electrocardiogram (ECG) is an important diagnostic tool for identifying cardiac problems. Nowadays, new ways to record ECG signals outside of the hospital are being investigated. A promising technique is capacitively coupled ECG (ccECG), which allows ECG signals to be recorded through insulating [...] Read more.

The electrocardiogram (ECG) is an important diagnostic tool for identifying cardiac problems. Nowadays, new ways to record ECG signals outside of the hospital are being investigated. A promising technique is capacitively coupled ECG (ccECG), which allows ECG signals to be recorded through insulating materials. However, as the ECG is no longer recorded in a controlled environment, this inevitably implies the presence of more artefacts. Artefact detection algorithms are used to detect and remove these. Typically, the training of a new algorithm requires a lot of ground truth data, which is costly to obtain. As many labelled contact ECG datasets exist, we could avoid the use of labelling new ccECG signals by making use of previous knowledge. Transfer learning can be used for this purpose. Here, we applied transfer learning to optimise the performance of an artefact detection model, trained on contact ECG, towards ccECG. We used ECG recordings from three different datasets, recorded with three recording devices. We showed that the accuracy of a contact-ECG classifier improved between 5 and 8% by means of transfer learning when tested on a ccECG dataset. Furthermore, we showed that only 20 segments of the ccECG dataset are sufficient to significantly increase the accuracy. Full article

(This article belongs to the Special Issue Recent Advances in ECG Monitoring)

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15 pages, 3273 KiB

Open AccessArticle

A Novel Hybrid Approach for Risk Evaluation of Vehicle Failure Modes

by Wencai Zhou, Zhaowen Qiu, Shun Tian, Yongtao Liu, Lang Wei and Reza Langari

Sensors 2021, 21(2), 661; https://doi.org/10.3390/s21020661 - 19 Jan 2021

Cited by 5 | Viewed by 2558

Abstract

This paper addresses the problem of evaluating vehicle failure modes efficiently during the driving process. Generally, the most critical factors for preventing risk in potential failure modes are identified by the experience of experts through the widely used failure mode and effect analysis [...] Read more.

This paper addresses the problem of evaluating vehicle failure modes efficiently during the driving process. Generally, the most critical factors for preventing risk in potential failure modes are identified by the experience of experts through the widely used failure mode and effect analysis (FMEA). However, it has previously been difficult to evaluate the vehicle failure mode with crisp values. In this paper, we propose a novel hybrid scheme based on a cost-based FMEA, fuzzy analytic hierarchy process (FAHP), and extended fuzzy multi-objective optimization by ratio analysis plus full multiplicative form (EFMULTIMOORA) to evaluate vehicle failure modes efficiently. Specifically, vehicle failure modes are first screened out by cost-based FMEA according to maintenance information, and then the weights of the three criteria of maintenance time (T), maintenance cost (C), and maintenance benefit (B) are calculated using FAHP and the rankings of failure modes are determined by EFMULTIMOORA. Different from existing schemes, the EFMULTIMOORA in our proposed hybrid scheme calculates the ranking of vehicle failure modes based on three new risk factors (T, C, and B) through fuzzy linguistic terms for order preference. Furthermore, the applicability of the proposed hybrid scheme is presented by conducting a case study involving vehicle failure modes of one common vehicle type (Hyundai), and a sensitivity analysis and comparisons are conducted to validate the effectiveness of the obtained results. In summary, our numerical analyses indicate that the proposed method can effectively help enterprises and researchers in the risk evaluation and the identification of critical vehicle failure modes. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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18 pages, 3129 KiB

Open AccessReview

Resonance Energy Transfer-Based Biosensors for Point-of-Need Diagnosis—Progress and Perspectives

by Felix Weihs, Alisha Anderson, Stephen Trowell and Karine Caron

Sensors 2021, 21(2), 660; https://doi.org/10.3390/s21020660 - 19 Jan 2021

Cited by 20 | Viewed by 5058

Abstract

The demand for point-of-need (PON) diagnostics for clinical and other applications is continuing to grow. Much of this demand is currently serviced by biosensors, which combine a bioanalytical sensing element with a transducing device that reports results to the user. Ideally, such devices [...] Read more.

The demand for point-of-need (PON) diagnostics for clinical and other applications is continuing to grow. Much of this demand is currently serviced by biosensors, which combine a bioanalytical sensing element with a transducing device that reports results to the user. Ideally, such devices are easy to use and do not require special skills of the end user. Application-dependent, PON devices may need to be capable of measuring low levels of analytes very rapidly, and it is often helpful if they are also portable. To date, only two transduction modalities, colorimetric lateral flow immunoassays (LFIs) and electrochemical assays, fully meet these requirements and have been widely adopted at the point-of-need. These modalities are either non-quantitative (LFIs) or highly analyte-specific (electrochemical glucose meters), therefore requiring considerable modification if they are to be co-opted for measuring other biomarkers. Förster Resonance Energy Transfer (RET)-based biosensors incorporate a quantitative and highly versatile transduction modality that has been extensively used in biomedical research laboratories. RET-biosensors have not yet been applied at the point-of-need despite its advantages over other established techniques. In this review, we explore and discuss recent developments in the translation of RET-biosensors for PON diagnoses, including their potential benefits and drawbacks. Full article

(This article belongs to the Special Issue Biennial State-of-the-Art Sensors Technology in Australia 2019-2020)

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18 pages, 11655 KiB

Open AccessArticle

Improving the Head Pose Variation Problem in Face Recognition for Mobile Robots

by Samuel-Felipe Baltanas, Jose-Raul Ruiz-Sarmiento and Javier Gonzalez-Jimenez

Sensors 2021, 21(2), 659; https://doi.org/10.3390/s21020659 - 19 Jan 2021

Cited by 4 | Viewed by 3784

Abstract

Face recognition is a technology with great potential in the field of robotics, due to its prominent role in human-robot interaction (HRI). This interaction is a keystone for the successful deployment of robots in areas requiring a customized assistance like education and healthcare, [...] Read more.

Face recognition is a technology with great potential in the field of robotics, due to its prominent role in human-robot interaction (HRI). This interaction is a keystone for the successful deployment of robots in areas requiring a customized assistance like education and healthcare, or assisting humans in everyday tasks. These unconstrained environments present additional difficulties for face recognition, extreme head pose variability being one of the most challenging. In this paper, we address this issue and make a fourfold contribution. First, it has been designed a tool for gathering an uniform distribution of head pose images from a person, which has been used to collect a new dataset of faces, both presented in this work. Then, the dataset has served as a testbed for analyzing the detrimental effects this problem has on a number of state-of-the-art methods, showing their decreased effectiveness outside a limited range of poses. Finally, we propose an optimization method to mitigate said negative effects by considering key pose samples in the recognition system’s set of known faces. The conducted experiments demonstrate that this optimized set of poses significantly improves the performance of a state-of-the-art, cutting-edge system based on Multitask Cascaded Convolutional Neural Networks (MTCNNs) and ArcFace. Full article

(This article belongs to the Special Issue Social Robots in Healthcare)

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19 pages, 5112 KiB

Open AccessArticle

An Accurate Linear Method for 3D Line Reconstruction for Binocular or Multiple View Stereo Vision

by Lijun Zhong, Junyou Qin, Xia Yang, Xiaohu Zhang, Yang Shang, Hongliang Zhang and Qifeng Yu

Sensors 2021, 21(2), 658; https://doi.org/10.3390/s21020658 - 19 Jan 2021

Cited by 12 | Viewed by 3327

Abstract

For the problem of 3D line reconstruction in binocular or multiple view stereo vision, when there are no corresponding points on the line, the method called Direction-then-Point (DtP) can be used, and if there are two pairs of corresponding points on the line, [...] Read more.

For the problem of 3D line reconstruction in binocular or multiple view stereo vision, when there are no corresponding points on the line, the method called Direction-then-Point (DtP) can be used, and if there are two pairs of corresponding points on the line, the method called Two Points 3D coordinates (TPS) can be used. However, when there is only one pair of corresponding points on the line, can we get the better accuracy than DtP for 3D line reconstruction? In this paper, a linear and more accurate method called Point-then-Direction (PtD) is proposed. First, we used the intersection method to obtain the 3D point’s coordinate from its corresponding image points. Then, we used this point as a position on the line to calculate the direction of the line by minimizing the image angle residual. PtD is also suitable for multiple camera systems. The simulation results demonstrate that PtD increases the accuracy of both the direction and the position of the 3D line compared to DtP. At the same time, PtD achieves a better result in direction of the 3D line than TPS, but has a lower accuracy in the position of 3D lines than TPS. Full article

(This article belongs to the Section Physical Sensors)

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18 pages, 14707 KiB

Open AccessArticle

Improvement of Reliability Determination Performance of Real Time Kinematic Solutions Using Height Trajectory

by Aoki Takanose, Yoshiki Atsumi, Kanamu Takikawa and Junichi Meguro

Sensors 2021, 21(2), 657; https://doi.org/10.3390/s21020657 - 19 Jan 2021

Cited by 5 | Viewed by 2861

Abstract

Autonomous driving support systems and self-driving cars require the determination of reliable vehicle positions with high accuracy. The real time kinematic (RTK) algorithm with global navigation satellite system (GNSS) is generally employed to obtain highly accurate position information. Because RTK can estimate the [...] Read more.

Autonomous driving support systems and self-driving cars require the determination of reliable vehicle positions with high accuracy. The real time kinematic (RTK) algorithm with global navigation satellite system (GNSS) is generally employed to obtain highly accurate position information. Because RTK can estimate the fix solution, which is a centimeter-level positioning solution, it is also used as an indicator of the position reliability. However, in urban areas, the degradation of the GNSS signal environment poses a challenge. Multipath noise caused by surrounding tall buildings degrades the positioning accuracy. This leads to large errors in the fix solution, which is used as a measure of reliability. We propose a novel position reliability estimation method by considering two factors; one is that GNSS errors are more likely to occur in the height than in the plane direction; the other is that the height variation of the actual vehicle travel path is small compared to the amount of movement in the horizontal directions. Based on these considerations, we proposed a method to detect a reliable fix solution by estimating the height variation during driving. To verify the effectiveness of the proposed method, an evaluation test was conducted in an urban area of Tokyo. According to the evaluation test, a reliability judgment rate of 99% was achieved in an urban environment, and a plane accuracy of less than 0.3 m in RMS was achieved. The results indicate that the accuracy of the proposed method is higher than that of the conventional fix solution, demonstratingits effectiveness. Full article

(This article belongs to the Special Issue GNSS Data Processing and Navigation in Challenging Environments)

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15 pages, 904 KiB

Open AccessArticle

An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

by Xavier Larriva-Novo, Víctor A. Villagrá, Mario Vega-Barbas, Diego Rivera and Mario Sanz Rodrigo

Sensors 2021, 21(2), 656; https://doi.org/10.3390/s21020656 - 19 Jan 2021

Cited by 68 | Viewed by 5998

Abstract

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have [...] Read more.

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks. Full article

(This article belongs to the Special Issue Cybersecurity and Privacy in Smart Cities)

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13 pages, 2832 KiB

Open AccessArticle

Laser Cut Interruption Detection from Small Images by Using Convolutional Neural Network

by Benedikt Adelmann, Max Schleier and Ralf Hellmann

Sensors 2021, 21(2), 655; https://doi.org/10.3390/s21020655 - 19 Jan 2021

Cited by 9 | Viewed by 3792

Abstract

In this publication, we use a small convolutional neural network to detect cut interruptions during laser cutting from single images of a high-speed camera. A camera takes images without additional illumination at a resolution of 32 × 64 pixels from cutting steel sheets [...] Read more.

In this publication, we use a small convolutional neural network to detect cut interruptions during laser cutting from single images of a high-speed camera. A camera takes images without additional illumination at a resolution of 32 × 64 pixels from cutting steel sheets of varying thicknesses with different laser parameter combinations and classifies them into cuts and cut interruptions. After a short learning period of five epochs on a certain sheet thickness, the images are classified with a low error rate of 0.05%. The use of color images reveals slight advantages with lower error rates over greyscale images, since, during cut interruptions, the image color changes towards blue. A training set on all sheet thicknesses in one network results in tests error rates below 0.1%. This low error rate and the short calculation time of 120 µs on a standard CPU makes the system industrially applicable. Full article

(This article belongs to the Special Issue Advances in Machine Learning for Intelligent Engineering Systems and Applications II)

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14 pages, 2951 KiB

Open AccessArticle

Moving the Lab into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured Environments

by Brian Russell, Andrew McDaid, William Toscano and Patria Hume

Sensors 2021, 21(2), 654; https://doi.org/10.3390/s21020654 - 19 Jan 2021

Cited by 13 | Viewed by 3398

Abstract

Goal: To develop and validate a field-based data collection and assessment method for human activity recognition in the mountains with variations in terrain and fatigue using a single accelerometer and a deep learning model. Methods: The protocol generated an unsupervised labelled dataset of [...] Read more.

Goal: To develop and validate a field-based data collection and assessment method for human activity recognition in the mountains with variations in terrain and fatigue using a single accelerometer and a deep learning model. Methods: The protocol generated an unsupervised labelled dataset of various long-term field-based activities including run, walk, stand, lay and obstacle climb. Activity was voluntary so transitions could not be determined a priori. Terrain variations included slope, crossing rivers, obstacles and surfaces including road, gravel, clay, mud, long grass and rough track. Fatigue levels were modulated between rested to physical exhaustion. The dataset was used to train a deep learning convolutional neural network (CNN) capable of being deployed on battery powered devices. The human activity recognition results were compared to a lab-based dataset with 1,098,204 samples and six features, uniform smooth surfaces, non-fatigued supervised participants and activity labelling defined by the protocol. Results: The trail run dataset had 3,829,759 samples with five features. The repetitive activities and single instance activities required hyper parameter tuning to reach an overall accuracy 0.978 with a minimum class precision for the one-off activity (climbing gate) of 0.802. Conclusion: The experimental results showed that the CNN deep learning model performed well with terrain and fatigue variations compared to the lab equivalents (accuracy 97.8% vs. 97.7% for trail vs. lab). Significance: To the authors knowledge this study demonstrated the first successful human activity recognition (HAR) in a mountain environment. A robust and repeatable protocol was developed to generate a validated trail running dataset when there were no observers present and activity types changed on a voluntary basis across variations in terrain surface and both cognitive and physical fatigue levels. Full article

(This article belongs to the Special Issue Wearable Sensors for Biomechanical Monitoring in Sport)

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17 pages, 3040 KiB

Open AccessArticle

Highly Efficient Lossless Coding for High Dynamic Range Red, Clear, Clear, Clear Image Sensors

by Paweł Pawłowski, Karol Piniarski and Adam Dąbrowski

Sensors 2021, 21(2), 653; https://doi.org/10.3390/s21020653 - 19 Jan 2021

Cited by 5 | Viewed by 4201

Abstract

In this paper we present a highly efficient coding procedure, specially designed and dedicated to operate with high dynamic range (HDR) RCCC (red, clear, clear, clear) image sensors used mainly in advanced driver-assistance systems (ADAS) and autonomous driving systems (ADS). The coding procedure [...] Read more.

In this paper we present a highly efficient coding procedure, specially designed and dedicated to operate with high dynamic range (HDR) RCCC (red, clear, clear, clear) image sensors used mainly in advanced driver-assistance systems (ADAS) and autonomous driving systems (ADS). The coding procedure can be used for a lossless reduction of data volume under developing and testing of video processing algorithms, e.g., in software in-the-loop (SiL) or hardware in-the-loop (HiL) conditions. Therefore, it was designed to achieve both: the state-of-the-art compression ratios and real-time compression feasibility. In tests we utilized FFV1 lossless codec and proved efficiency of up to 81 fps (frames per second) for compression and 87 fps for decompression performed on a single Intel i7 CPU. Full article

(This article belongs to the Special Issue CMOS Image Sensors and Related Applications)

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14 pages, 3078 KiB

Open AccessArticle

Quantifying Coordination and Variability in the Lower Extremities after Anterior Cruciate Ligament Reconstruction

by Sangheon Park and Sukhoon Yoon

Sensors 2021, 21(2), 652; https://doi.org/10.3390/s21020652 - 19 Jan 2021

Cited by 3 | Viewed by 2567

Abstract

Patients experience various biomechanical changes following reconstruction for anterior cruciate ligament (ACL) injury. However, previous studies have focused on lower extremity joints as a single joint rather than simultaneous lower extremity movements. Therefore, this study aimed to determine the movement changes in the [...] Read more.

Patients experience various biomechanical changes following reconstruction for anterior cruciate ligament (ACL) injury. However, previous studies have focused on lower extremity joints as a single joint rather than simultaneous lower extremity movements. Therefore, this study aimed to determine the movement changes in the lower limb coordination patterns according to movement type following ACL reconstruction. Twenty-one post ACL reconstruction patients (AG) and an equal number of healthy adults (CG) participated in this study. They were asked to perform walking, running, and cutting maneuvers. The continuous relative phase and variability were calculated to examine the coordination pattern. During running and cutting at 30 and 60°, the AG demonstrated a lower in-phase hip–knee coordination pattern in the sagittal plane. The AG demonstrated low hip–knee variability in the sagittal plane during cutting at 60°. The low in-phase coordination pattern can burden the knee by generating unnatural movements following muscle contraction in the opposite direction. Based on the results, it would be useful to identify the problem and provide the fundamental evidence for the optimal timing of return-to-sport after ACL reconstruction (ACLR) rehabilitation, if the coordination variable is measured with various sensors promptly in the sports field to evaluate the coordination of human movement. Full article

(This article belongs to the Special Issue Smart Sensors: Applications and Advances in Human Motion Analysis)

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2 pages, 143 KiB

Open AccessEditorial

Special Issue: ECG Monitoring System

by Florent Baty

Sensors 2021, 21(2), 651; https://doi.org/10.3390/s21020651 - 19 Jan 2021

Cited by 1 | Viewed by 2039

Abstract

This editorial of the Special Issue “ECG Monitoring System” provides a short overview of the 13 contributed articles published in this issue [...] Full article

(This article belongs to the Special Issue ECG Monitoring System)

12 pages, 2628 KiB

Open AccessLetter

Combination of Aptamer Amplifier and Antigen-Binding Fragment Probe as a Novel Strategy to Improve Detection Limit of Silicon Nanowire Field-Effect Transistor Immunosensors

by Cao-An Vu, Pin-Hsien Pan, Yuh-Shyong Yang, Hardy Wai-Hong Chan, Yoichi Kumada and Wen-Yih Chen

Sensors 2021, 21(2), 650; https://doi.org/10.3390/s21020650 - 19 Jan 2021

Cited by 5 | Viewed by 3756

Abstract

Detecting proteins at low concentrations in high-ionic-strength conditions by silicon nanowire field-effect transistors (SiNWFETs) is severely hindered due to the weakened signal, primarily caused by screening effects. In this study, aptamer as a signal amplifier, which has already been reported by our group, [...] Read more.

Detecting proteins at low concentrations in high-ionic-strength conditions by silicon nanowire field-effect transistors (SiNWFETs) is severely hindered due to the weakened signal, primarily caused by screening effects. In this study, aptamer as a signal amplifier, which has already been reported by our group, is integrated into SiNWFET immunosensors employing antigen-binding fragments (Fab) as the receptors to improve its detection limit for the first time. The Fab-SiNWFET immunosensors were developed by immobilizing Fab onto Si surfaces modified with either 3-aminopropyltriethoxysilane (APTES) and glutaraldehyde (GA) (Fab/APTES-SiNWFETs), or mixed self-assembled monolayers (mSAMs) of polyethylene glycol (PEG) and GA (Fab/PEG-SiNWFETs), to detect the rabbit IgG at different concentrations in a high-ionic-strength environment (150 mM Bis-Tris Propane) followed by incubation with R18, an aptamer which can specifically target rabbit IgG, for signal enhancement. Empirical results revealed that the signal produced by the sensors with Fab probes was greatly enhanced compared to the ones with whole antibody (Wab) after detecting similar concentrations of rabbit IgG. The Fab/PEG-SiNWFET immunosensors exhibited an especially improved limit of detection to determine the IgG level down to 1 pg/mL, which has not been achieved by the Wab/PEG-SiNWFET immunosensors. Full article

(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Taiwan)

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16 pages, 21196 KiB

Open AccessArticle

3D Hand Pose Estimation Based on Five-Layer Ensemble CNN

by Lili Fan, Hong Rao and Wenji Yang

Sensors 2021, 21(2), 649; https://doi.org/10.3390/s21020649 - 19 Jan 2021

Cited by 9 | Viewed by 4573

Abstract

Estimating accurate 3D hand pose from a single RGB image is a highly challenging problem in pose estimation due to self-geometric ambiguities, self-occlusions, and the absence of depth information. To this end, a novel Five-Layer Ensemble CNN (5LENet) is proposed based on hierarchical [...] Read more.

Estimating accurate 3D hand pose from a single RGB image is a highly challenging problem in pose estimation due to self-geometric ambiguities, self-occlusions, and the absence of depth information. To this end, a novel Five-Layer Ensemble CNN (5LENet) is proposed based on hierarchical thinking, which is designed to decompose the hand pose estimation task into five single-finger pose estimation sub-tasks. Then, the sub-task estimation results are fused to estimate full 3D hand pose. The hierarchical method is of great benefit to extract deeper and better finger feature information, which can effectively improve the estimation accuracy of 3D hand pose. In addition, we also build a hand model with the center of the palm (represented as Palm) connected to the middle finger according to the topological structure of hand, which can further boost the performance of 3D hand pose estimation. Additionally, extensive quantitative and qualitative results on two public datasets demonstrate the effectiveness of 5LENet, yielding new state-of-the-art 3D estimation accuracy, which is superior to most advanced estimation methods. Full article

(This article belongs to the Section Sensing and Imaging)

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16 pages, 5655 KiB

Open AccessArticle

Design and Validation of a Scalable, Reconfigurable and Low-Cost Structural Health Monitoring System

by Juan J. Villacorta, Lara del-Val, Roberto D. Martínez, José-Antonio Balmori, Álvaro Magdaleno, Gamaliel López, Alberto Izquierdo, Antolín Lorenzana and Luis-Alfonso Basterra

Sensors 2021, 21(2), 648; https://doi.org/10.3390/s21020648 - 19 Jan 2021

Cited by 20 | Viewed by 4764

Abstract

This paper presents the design, development and testing of a low-cost Structural Health Monitoring (SHM) system based on MEMS (Micro Electro-Mechanical Systems) triaxial accelerometers. A new control system composed by a myRIO platform, managed by specific LabVIEW software, has been developed. The LabVIEW [...] Read more.

This paper presents the design, development and testing of a low-cost Structural Health Monitoring (SHM) system based on MEMS (Micro Electro-Mechanical Systems) triaxial accelerometers. A new control system composed by a myRIO platform, managed by specific LabVIEW software, has been developed. The LabVIEW software also computes the frequency response functions for the subsequent modal analysis. The proposed SHM system was validated by comparing the data measured by this set-up with a conventional SHM system based on piezoelectric accelerometers. After carrying out some validation tests, a high correlation can be appreciated in the behavior of both systems, being possible to conclude that the proposed system is sufficiently accurate and sensitive for operative purposes, apart from being significantly more affordable than the traditional one. Full article

(This article belongs to the Special Issue Sensors for Cultural Heritage Monitoring)

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13 pages, 7243 KiB

Open AccessArticle

Strength Training Characteristics of Different Loads Based on Acceleration Sensor and Finite Element Simulation

by Bo Pang, Zhongqiu Ji, Zihua Zhang, Yunchuan Sun, Chunmin Ma, Zirong He, Xin Hu and Guiping Jiang

Sensors 2021, 21(2), 647; https://doi.org/10.3390/s21020647 - 19 Jan 2021

Cited by 8 | Viewed by 2914

Abstract

Deep squat, bench press and hard pull are important ways for people to improve their strength. The use of sensors to measure force is rare. Measuring strength with sensors is extremely valuable for people to master the intensity of exercise to scientifically effective [...] Read more.

Deep squat, bench press and hard pull are important ways for people to improve their strength. The use of sensors to measure force is rare. Measuring strength with sensors is extremely valuable for people to master the intensity of exercise to scientifically effective exercise. To this end, in this paper, we used a real-time wireless motion capture and mechanical evaluation system of the wearable sensor to measure the dynamic characteristics of 30 young men performing deep squat, bench press and hard pull maneuvers. The data of tibia were simulated with AnyBody 5.2 and ANSYS 19.2 to verify the authenticity. The result demonstrated that the appropriate force of the deep squat elbow joint, the hip joint and the knee joint is 40% 1RM, the appropriate force of the bench press is 40% 1RM and the appropriate force of the hard pull is 80% 1RM. The external force is the main factor of bone change. The mechanical characteristics of knee joint can be simulated after the Finite Element Analysis and the simulation of AnyBody model are verified. Full article

(This article belongs to the Special Issue Wearable Sensors for Healthcare)

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14 pages, 4515 KiB

Open AccessArticle

Optical Fiber Pyrometer Designs for Temperature Measurements Depending on Object Size

by Arántzazu Núñez-Cascajero, Alberto Tapetado, Salvador Vargas and Carmen Vázquez

Sensors 2021, 21(2), 646; https://doi.org/10.3390/s21020646 - 19 Jan 2021

Cited by 15 | Viewed by 3613

Abstract

The modelling of temperature measurements using optical fiber pyrometers for different hot object sizes with new generalized integration limits is presented. The closed equations for the calculus of the radiated power that is coupled to the optical fiber for two specific scenarios are [...] Read more.

The modelling of temperature measurements using optical fiber pyrometers for different hot object sizes with new generalized integration limits is presented. The closed equations for the calculus of the radiated power that is coupled to the optical fiber for two specific scenarios are proposed. Accurate predictions of critical distance for avoiding errors in the optical fiber end location depending on fiber types and object sizes for guiding good designs are reported. A detailed model for estimating errors depending on target size and distance is provided. Two-color fiber pyrometers as a general solution are also discussed. Full article

(This article belongs to the Section Optical Sensors)

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10 pages, 6415 KiB

Open AccessLetter

Using Geiger Dosimetry EKO-C Device to Detect Ionizing Radiation Emissions from Building Materials

by Maciej Gliniak, Tomasz Dróżdż, Sławomir Kurpaska and Anna Lis

Sensors 2021, 21(2), 645; https://doi.org/10.3390/s21020645 - 18 Jan 2021

Cited by 2 | Viewed by 2528

Abstract

The purpose of the article is to check and assess what radiation is emitted by particular building materials with the passage of time. The analysis was performed with the EKO-C dosimetry device from Polon-Ekolab. The scope of the work included research on sixteen [...] Read more.

The purpose of the article is to check and assess what radiation is emitted by particular building materials with the passage of time. The analysis was performed with the EKO-C dosimetry device from Polon-Ekolab. The scope of the work included research on sixteen selected construction materials, divided into five groups. The analysis of the results showed that samples such as bricks (first group) and hollow blocks (second group) emit the highest radiation in the tested objects. When comparing these materials, the highest value was recorded when measuring the ceramic block of 15.76 mSv·yr⁻¹. Taking into account the bricks, the highest value of radiation was shown by a full clinker brick, 11.3 mSv·yr⁻¹. Insulation materials and finishing boards are two other groups of building materials that have been measured. They are characterised by a low level of radiation. In the case of materials for thermal insulation, the highest condition was demonstrated by graphite polystyrene of 4.463 mSv·yr⁻¹, while among finishing boards, the highest value of radiation was recorded for the measurement of gypsum board of 3.76 mSv·yr⁻¹. Comparing the obtained test results to the requirements of the Regulation of the Council of Ministers on ionizing radiation dose limits applicable in Poland, it can be noted that the samples examined individually do not pose a radiation risk to humans. When working with all types of samples, the radiation doses are added up. According to the guidelines of the regulation, the total radiation dose does not exceed 50 mSv·yr⁻¹ and does not constitute a threat to human health. Full article

(This article belongs to the Special Issue Advanced Electromagnetic Sensors in Environmental, Industrial and Medical Applications)

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15 pages, 6095 KiB

Open AccessArticle

Implementation of Neuro-Memristive Synapse for Long-and Short-Term Bio-Synaptic Plasticity

by Zubaer I. Mannan, Hyongsuk Kim and Leon Chua

Sensors 2021, 21(2), 644; https://doi.org/10.3390/s21020644 - 18 Jan 2021

Cited by 20 | Viewed by 7118

Abstract

In this paper, we propose a complex neuro-memristive synapse that exhibits the physiological acts of synaptic potentiation and depression of the human-brain. Specifically, the proposed neuromorphic synapse efficiently imitates the synaptic plasticity, especially long-term potentiation (LTP) and depression (LTD), and short-term facilitation (STF) [...] Read more.

In this paper, we propose a complex neuro-memristive synapse that exhibits the physiological acts of synaptic potentiation and depression of the human-brain. Specifically, the proposed neuromorphic synapse efficiently imitates the synaptic plasticity, especially long-term potentiation (LTP) and depression (LTD), and short-term facilitation (STF) and depression (STD), phenomena of a biological synapse. Similar to biological synapse, the short- or long-term potentiation (STF and LTP) or depression (STD or LTD) of the memristive synapse are distinguished on the basis of time or repetition of input cycles. The proposed synapse is also designed to exhibit the effect of reuptake and neurotransmitters diffusion processes of a bio-synapse. In addition, it exhibits the distinct bio-realistic attributes, i.e., strong stimulation, exponentially decaying conductance trace of synapse, and voltage dependent synaptic responses, of a neuron. The neuro-memristive synapse is designed in SPICE and its bio-realistic functionalities are demonstrated via various simulations. Full article

(This article belongs to the Section Biomedical Sensors)

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