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Sensors, Volume 22, Issue 16 (August-2 2022) – 387 articles

Cover Story (view full-size image): Concrete constructions need widespread monitoring for the control of their state of integrity during their service life, in particular after critical events such as earthquakes. The aim of the research is to find a solution to this issue, by analyzing the potential of smart concrete with the addition of carbon microfibers to be utilized in real-scale structures. The study presents experimental results on full-scale elements toward the use of embedded smart concrete sensors in real constructions and discusses the strengths and the limitations of such monitoring systems. The tests, at different scales, demonstrate variations in sensitivity, permit the identification of the most promising filler percentage for monitoring applications, and prove the effectiveness of different types of electrical setup and load applications, up to real-scale elements. View this paper
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Article
A Validation of Six Wearable Devices for Estimating Sleep, Heart Rate and Heart Rate Variability in Healthy Adults
Sensors 2022, 22(16), 6317; https://doi.org/10.3390/s22166317 - 22 Aug 2022
Viewed by 1938
Abstract
The primary aim of this study was to examine the validity of six commonly used wearable devices, i.e., Apple Watch S6, Garmin Forerunner 245 Music, Polar Vantage V, Oura Ring Generation 2, WHOOP 3.0 and Somfit, for assessing sleep. The secondary aim was [...] Read more.
The primary aim of this study was to examine the validity of six commonly used wearable devices, i.e., Apple Watch S6, Garmin Forerunner 245 Music, Polar Vantage V, Oura Ring Generation 2, WHOOP 3.0 and Somfit, for assessing sleep. The secondary aim was to examine the validity of the six devices for assessing heart rate and heart rate variability during, or just prior to, night-time sleep. Fifty-three adults (26 F, 27 M, aged 25.4 ± 5.9 years) spent a single night in a sleep laboratory with 9 h in bed (23:00–08:00 h). Participants were fitted with all six wearable devices—and with polysomnography and electrocardiography for gold-standard assessment of sleep and heart rate, respectively. Compared with polysomnography, agreement (and Cohen’s kappa) for two-state categorisation of sleep periods (as sleep or wake) was 88% (κ = 0.30) for Apple Watch; 89% (κ = 0.35) for Garmin; 87% (κ = 0.44) for Polar; 89% (κ = 0.51) for Oura; 86% (κ = 0.44) for WHOOP and 87% (κ = 0.48) for Somfit. Compared with polysomnography, agreement (and Cohen’s kappa) for multi-state categorisation of sleep periods (as a specific sleep stage or wake) was 53% (κ = 0.20) for Apple Watch; 50% (κ = 0.25) for Garmin; 51% (κ = 0.28) for Polar; 61% (κ = 0.43) for Oura; 60% (κ = 0.44) for WHOOP and 65% (κ = 0.52) for Somfit. Analyses regarding the two-state categorisation of sleep indicate that all six devices are valid for the field-based assessment of the timing and duration of sleep. However, analyses regarding the multi-state categorisation of sleep indicate that all six devices require improvement for the assessment of specific sleep stages. As the use of wearable devices that are valid for the assessment of sleep increases in the general community, so too does the potential to answer research questions that were previously impractical or impossible to address—in some way, we could consider that the whole world is becoming a sleep laboratory. Full article
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Article
Intelligent Diagnosis Based on Double-Optimized Artificial Hydrocarbon Networks for Mechanical Faults of In-Wheel Motor
Sensors 2022, 22(16), 6316; https://doi.org/10.3390/s22166316 - 22 Aug 2022
Viewed by 370
Abstract
To avoid the potential safety hazards of electric vehicles caused by the mechanical fault deterioration of the in-wheel motor (IWM), this paper proposes an intelligent diagnosis based on double-optimized artificial hydrocarbon networks (AHNs) to identify the mechanical faults of IWM, which employs a [...] Read more.
To avoid the potential safety hazards of electric vehicles caused by the mechanical fault deterioration of the in-wheel motor (IWM), this paper proposes an intelligent diagnosis based on double-optimized artificial hydrocarbon networks (AHNs) to identify the mechanical faults of IWM, which employs a K-means clustering and AdaBoost algorithm to solve the lower accuracy and poorer stability of traditional AHNs. Firstly, K-means clustering is used to improve the interval updating method of any adjacent AHNs molecules, and then simplify the complexity of the AHNs model. Secondly, the AdaBoost algorithm is utilized to adaptively distribute the weights for multiple weak models, then reconstitute the network structure of the AHNs. Finally, double-optimized AHNs are used to build an intelligent diagnosis system, where two cases of bearing datasets from Paderborn University and a self-made IWM test stand are processed to validate the better performance of the proposed method, especially in multiple rotating speeds and the load conditions of the IWM. The double-optimized AHNs provide a higher accuracy for identifying the mechanical faults of the IWM than the traditional AHNs, K-means-based AHNs (K-AHNs), support vector machine (SVM), and particle swarm optimization-based SVM (PSO-SVM). Full article
(This article belongs to the Special Issue Sensors for Machinery Condition Monitoring and Diagnosis)
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Article
Improving Measurement Range of a Swellable Polymer-Clad Plastic Fiber Optic Humidity Sensor by Dye Addition
Sensors 2022, 22(16), 6315; https://doi.org/10.3390/s22166315 - 22 Aug 2022
Viewed by 332
Abstract
Humidity measurement is required in various fields. We previously developed a sensor that leverages the sudden change in the transmitted light intensity when switching from leakage mode to waveguide mode. By adjusting the low-refractive-index polymer of the cladding, we achieved measurements at 60% [...] Read more.
Humidity measurement is required in various fields. We previously developed a sensor that leverages the sudden change in the transmitted light intensity when switching from leakage mode to waveguide mode. By adjusting the low-refractive-index polymer of the cladding, we achieved measurements at 60% RH. However, for practical use, measurements at low humidity are essential. Therefore, in this study, we developed a sensor using a leakage mode that enables measurements at low humidity. To measure the leakage mode, it is necessary to increase the absorbance of the cladding and the incident angle at the core–cladding interface. Therefore, we developed a sensor in which the core was stretched, and the cladding was doped with a high concentration of dye. The experimental results confirmed that a sensor with a polymer concentration of 4% and a dye concentration of 3% could measure from 0% RH to 95% RH. The sensitivity was 0.1 dB/% RH from 0% RH to 70% RH and 0.32 dB/% RH from 70% RH to 95% RH. The estimated response time for a change from 10% to 90% light transmission for a sensor with 4% polymer concentration and 0.5% dye concentration was 22 s from 45% RH to 0% RH and 50 s from 0% RH to 45% RH. Full article
(This article belongs to the Special Issue Polymer Optical Fiber Sensors and Sensing Technologies)
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Article
Evaluation of Different LiDAR Technologies for the Documentation of Forgotten Cultural Heritage under Forest Environments
Sensors 2022, 22(16), 6314; https://doi.org/10.3390/s22166314 - 22 Aug 2022
Viewed by 358
Abstract
In the present work, three LiDAR technologies (Faro Focus 3D X130—Terrestrial Laser Scanner, TLS-, Kaarta Stencil 2–16—Mobile mapping system, MMS-, and DJI Zenmuse L1—Airborne LiDAR sensor, ALS-) have been tested and compared in order to assess the performances in surveying built heritage in [...] Read more.
In the present work, three LiDAR technologies (Faro Focus 3D X130—Terrestrial Laser Scanner, TLS-, Kaarta Stencil 2–16—Mobile mapping system, MMS-, and DJI Zenmuse L1—Airborne LiDAR sensor, ALS-) have been tested and compared in order to assess the performances in surveying built heritage in vegetated areas. Each of the mentioned devices has their limits of usability, and different methods to capture and generate 3D point clouds need to be applied. In addition, it has been necessary to apply a methodology to be able to position all the point clouds in the same reference system. While the TLS scans and the MMS data have been geo-referenced using a set of vertical markers and sphere measured by a GNSS receiver in RTK mode, the ALS model has been geo-referenced by the GNSS receiver integrated in the unmanned aerial system (UAS), which presents different characteristics and accuracies. The resulting point clouds have been analyzed and compared, focusing attention on the number of points acquired by the different systems, the density, and the nearest neighbor distance. Full article
(This article belongs to the Special Issue UAV Lidar System: Performance Assessment and Application)
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Review
A Survey on 5G and LPWAN-IoT for Improved Smart Cities and Remote Area Applications: From the Aspect of Architecture and Security
Sensors 2022, 22(16), 6313; https://doi.org/10.3390/s22166313 - 22 Aug 2022
Cited by 1 | Viewed by 457
Abstract
Addressing the recent trend of the massive demand for resources and ubiquitous use for all citizens has led to the conceptualization of technologies such as the Internet of Things (IoT) and smart cities. Ubiquitous IoT connectivity can be achieved to serve both urban [...] Read more.
Addressing the recent trend of the massive demand for resources and ubiquitous use for all citizens has led to the conceptualization of technologies such as the Internet of Things (IoT) and smart cities. Ubiquitous IoT connectivity can be achieved to serve both urban and underserved remote areas such as rural communities by deploying 5G mobile networks with Low Power Wide Area Network (LPWAN). The current architectures will not offer flexible connectivity to many IoT applications due to high service demand, data exchange, emerging technologies, and security challenges. Hence, this paper explores various architectures that consider a hybrid 5G-LPWAN-IoT and Smart Cities. This includes security challenges as well as endogenous security and solutions in 5G and LPWAN-IoT. The slicing of virtual networks using software-defined network (SDN)/network function virtualization (NFV) based on the different quality of service (QoS) to satisfy different services and quality of experience (QoE) is presented. Also, a strategy that considers the implementation of 5G jointly with Weightless-N (TVWS) technologies to reduce the cell edge interference is considered. Discussions on the need for ubiquity connectivity leveraging 5G and LPWAN-IoT are presented. In addition, future research directions are presented, including a unified 5G network and LPWAN-IoT architecture that will holistically support integration with emerging technologies and endogenous security for improved/secured smart cities and remote areas IoT applications. Finally, the use of LPWAN jointly with low earth orbit (LEO) satellites for ubiquitous IoT connectivity is advocated in this paper. Full article
(This article belongs to the Special Issue Advances in Wireless Sensor Networks for Smart City)
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Review
Database and AI Diagnostic Tools Improve Understanding of Lung Damage, Correlation of Pulmonary Disease and Brain Damage in COVID-19
Sensors 2022, 22(16), 6312; https://doi.org/10.3390/s22166312 - 22 Aug 2022
Viewed by 498
Abstract
The COVID-19 pandemic caused a sharp increase in the interest in artificial intelligence (AI) as a tool supporting the work of doctors in difficult conditions and providing early detection of the implications of the disease. Recent studies have shown that AI has been [...] Read more.
The COVID-19 pandemic caused a sharp increase in the interest in artificial intelligence (AI) as a tool supporting the work of doctors in difficult conditions and providing early detection of the implications of the disease. Recent studies have shown that AI has been successfully applied in the healthcare sector. The objective of this paper is to perform a systematic review to summarize the electroencephalogram (EEG) findings in patients with coronavirus disease (COVID-19) and databases and tools used in artificial intelligence algorithms, supporting the diagnosis and correlation between lung disease and brain damage, and lung damage. Available search tools containing scientific publications, such as PubMed and Google Scholar, were comprehensively evaluated and searched with open databases and tools used in AI algorithms. This work aimed to collect papers from the period of January 2019–May 2022 including in their resources the database from which data necessary for further development of algorithms supporting the diagnosis of the respiratory system can be downloaded and the correlation between lung disease and brain damage can be evaluated. The 10 articles which show the most interesting AI algorithms, trained by using open databases and associated with lung diseases, were included for review with 12 articles related to EEGs, which have/or may be related with lung diseases. Full article
Article
Rapid Seismic Evaluation of Continuous Girder Bridges with Localized Plastic Hinges
Sensors 2022, 22(16), 6311; https://doi.org/10.3390/s22166311 - 22 Aug 2022
Viewed by 301
Abstract
In seismic assessment of continuous girder bridges, plastic hinges form in bridge piers to dissipate seismic energy through nonlinear restoring forces. Considering temporal and spatial variations of ground motions, seismic evaluation of the bridges involves nonlinear stochastic vibration and expensive computation. This paper [...] Read more.
In seismic assessment of continuous girder bridges, plastic hinges form in bridge piers to dissipate seismic energy through nonlinear restoring forces. Considering temporal and spatial variations of ground motions, seismic evaluation of the bridges involves nonlinear stochastic vibration and expensive computation. This paper presents an approach to significantly increase the efficiency of seismic evaluation for continuous girder bridges with plastic hinges. The proposed approach converts nonlinear motion equations into quasi-linear state equations, solves the equations using an explicit time-domain dimension-reduced iterative method, and incorporates a stochastic sampling method to statistically analyze the seismic response of bridges under earthquake excitation. Taking a 3 × 30 m continuous girder bridge as an example, fiber beam-column elements are used to simulate the elastic–plastic components of the continuous girder bridge, and the elastic–plastic time history analysis of the continuous girder bridge under non-uniform seismic excitation is carried out. Results show that the computation time is only 5% of the time of the nonlinear time history approach while retaining the accuracy. This study advances the capability of rapid seismic assessment and design for bridges with localized nonlinear behaviors such as plastic hinges. Full article
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Article
A Signal Processing Method for Assessing Ankle Torque with a Custom-Made Electronic Dynamometer in Participants Affected by Diabetic Peripheral Neuropathy
Sensors 2022, 22(16), 6310; https://doi.org/10.3390/s22166310 - 22 Aug 2022
Viewed by 336
Abstract
Portable, custom-made electronic dynamometry for the foot and ankle is a promising assessment method that enables foot and ankle muscle function to be established in healthy participants and those affected by chronic conditions. Diabetic peripheral neuropathy (DPN) can alter foot and ankle muscle [...] Read more.
Portable, custom-made electronic dynamometry for the foot and ankle is a promising assessment method that enables foot and ankle muscle function to be established in healthy participants and those affected by chronic conditions. Diabetic peripheral neuropathy (DPN) can alter foot and ankle muscle function. This study assessed ankle toque in participants with diabetic peripheral neuropathy and healthy participants, with the aim of developing an algorithm for optimizing the precision of data processing and interpretation of the results and to define a reference frame for ankle torque measurement in both healthy participants and those affected by DPN. This paper discloses the software chain and the signal processing methods used for voltage—torque conversion, filtering, offset detection and the muscle effort type identification, which further allowed for a primary statistical report. The full description of the signal processing methods will make our research reproducible. The applied algorithm for signal processing is proposed as a reference frame for ankle torque assessment when using a custom-made electronic dynamometer. While evaluating multiple measurements, our algorithm permits for a more detailed parametrization of the ankle torque results in healthy participants and those affected by DPN. Full article
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Communication
Visual Sensing System to Investigate Self-Propelled Motion and Internal Color of Multiple Aqueous Droplets
Sensors 2022, 22(16), 6309; https://doi.org/10.3390/s22166309 - 22 Aug 2022
Viewed by 341
Abstract
This study proposes a visual sensing system to investigate the self-propelled motions of droplets. In the visual sensing of self-propelled droplets, large field-of-view and high-resolution images are both required to investigate the behaviors of multiple droplets as well as chemical reactions in the [...] Read more.
This study proposes a visual sensing system to investigate the self-propelled motions of droplets. In the visual sensing of self-propelled droplets, large field-of-view and high-resolution images are both required to investigate the behaviors of multiple droplets as well as chemical reactions in the droplets. Therefore, we developed a view-expansive microscope system using a color camera head to investigate these chemical reactions; in the system, we implemented an image processing algorithm to detect the behaviors of droplets over a large field of view. We conducted motion tracking and color identification experiments on the self-propelled droplets to verify the effectiveness of the proposed system. The experimental results demonstrate that the proposed system is able to detect the location and color of each self-propelled droplet in a large-area image. Full article
(This article belongs to the Section Chemical Sensors)
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Article
Localization in Structured Environments with UWB Devices without Acceleration Measurements, and Velocity Estimation Using a Kalman–Bucy Filter
Sensors 2022, 22(16), 6308; https://doi.org/10.3390/s22166308 - 22 Aug 2022
Viewed by 315
Abstract
In this work, a novel scheme for velocity and position estimation in a UWB range-based localization system is proposed. The suggested estimation strategy allows to overcome two main problems typically encountered in the localization systems. The first one is that it can be [...] Read more.
In this work, a novel scheme for velocity and position estimation in a UWB range-based localization system is proposed. The suggested estimation strategy allows to overcome two main problems typically encountered in the localization systems. The first one is that it can be suitable for use in environments where the GPS signal is not present or where it might fail. The second one is that no accelerometer measurements are needed for the localization task. Moreover, to deal with the velocity estimation problem, a suitable Kalman–Bucy filter is designed and it is compared, experimentally, with a particle filter by showing the features of the two algorithms in order to be used in a localization context. Additionally, further experimental tests are carried out on a suitable developed test setup in order to confirm the goodness of the proposed approach. Full article
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Article
Dynamic Maritime Traffic Pattern Recognition with Online Cleaning, Compression, Partition, and Clustering of AIS Data
Sensors 2022, 22(16), 6307; https://doi.org/10.3390/s22166307 - 22 Aug 2022
Cited by 1 | Viewed by 347
Abstract
Maritime traffic pattern recognition plays a major role in intelligent transportation services, ship monitoring, route planning, and other fields. Facilitated by the establishment of terrestrial networks and satellite constellations of the automatic identification system (AIS), large quantities of spatial and temporal information make [...] Read more.
Maritime traffic pattern recognition plays a major role in intelligent transportation services, ship monitoring, route planning, and other fields. Facilitated by the establishment of terrestrial networks and satellite constellations of the automatic identification system (AIS), large quantities of spatial and temporal information make ships’ paths trackable and are useful in maritime traffic pattern research. The maritime traffic pattern may vary with changes in the traffic environment, so the recognition method of the maritime traffic pattern should be adaptable to changes in the traffic environment. To achieve this goal, a dynamic maritime traffic pattern recognition method is presented using AIS data, which are cleaned, compressed, partitioned, and clustered online. Old patterns are removed as expired trajectories are deleted, and new patterns are created as new trajectories are added. This method is suitable for processing massive stream data. Experiments show that when the marine traffic route changes due to the navigation environment, the maritime traffic pattern adjusts automatically. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Marine Intelligent Systems)
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Article
Generating Alerts from Breathing Pattern Outliers
Sensors 2022, 22(16), 6306; https://doi.org/10.3390/s22166306 - 22 Aug 2022
Viewed by 397
Abstract
Analysing human physiological data allows access to the health state and the state of mind of the subject individual. Whenever a person is sick, having a panic attack, happy or scared, physiological signals will be different. In terms of physiological signals, we focus, [...] Read more.
Analysing human physiological data allows access to the health state and the state of mind of the subject individual. Whenever a person is sick, having a panic attack, happy or scared, physiological signals will be different. In terms of physiological signals, we focus, in this manuscript, on monitoring breathing patterns. The scope can be extended to also address heart rate and other variables. We describe an analysis of breathing rate patterns during activities including resting, walking, running and watching a movie. We model normal breathing behaviours by statistically analysing signals, processed to represent quantities of interest. We consider moving maximum/minimum, the amplitude and the Fourier transform of the respiration signal, working with different window sizes. We then learn a statistical model for the basal behaviour, per individual, and detect outliers. When outliers are detected, a system that incorporates our approach would send a visible signal through a smart garment or through other means. We describe alert generation performance in two datasets—one literature dataset and one collected as a field study for this work. In particular, when learning personal rest distributions for the breathing signals of 14 subjects, we see alerts generated more often when the same individual is running than when they are tested in rest conditions. Full article
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Article
Optimized Distributed Generalized Reed-Solomon Coding with Space-Time Block Coded Spatial Modulation
Sensors 2022, 22(16), 6305; https://doi.org/10.3390/s22166305 - 22 Aug 2022
Viewed by 305
Abstract
We present a well-known generalized Reed–Solomon (GRS) code incorporated with space-time block coded spatial modulation (STBC-SM) for wireless networks, which is capable of enjoying coded cooperation between the source and the relay. In the proposed distributed GRS-coded STBC-SM (DGRSC-STBC-SM) scheme, the source and [...] Read more.
We present a well-known generalized Reed–Solomon (GRS) code incorporated with space-time block coded spatial modulation (STBC-SM) for wireless networks, which is capable of enjoying coded cooperation between the source and the relay. In the proposed distributed GRS-coded STBC-SM (DGRSC-STBC-SM) scheme, the source and relay nodes use distinct GRS codes. At the relay, we employ the concept of information selection to choose the message symbols from the source for further encoding. Thus, the codewords jointly constructed by the source and relay are generated at the destination. For achieving the best codeword set at the destination, we propose an optimal algorithm at the relay to select partial symbols from the source. To reduce the computational complexity, we propose a more practical algorithm with low complexity. Monte Carlo simulation results show that the proposed scheme using the low-complexity algorithm can achieve near-optimal error performance. Furthermore, our proposed scheme provides better error performance than its corresponding coded non-cooperative counterpart and the existing Reed–Solomon coded cooperative SM (RSCC-SM) scheme under identical conditions. Full article
(This article belongs to the Section Electronic Sensors)
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Article
Frequency, Time, Representation and Modeling Aspects for Major Speech and Audio Processing Applications
Sensors 2022, 22(16), 6304; https://doi.org/10.3390/s22166304 - 22 Aug 2022
Viewed by 335
Abstract
There are many speech and audio processing applications and their number is growing. They may cover a wide range of tasks, each having different requirements on the processed speech or audio signals and, therefore, indirectly, on the audio sensors as well. This article [...] Read more.
There are many speech and audio processing applications and their number is growing. They may cover a wide range of tasks, each having different requirements on the processed speech or audio signals and, therefore, indirectly, on the audio sensors as well. This article reports on tests and evaluation of the effect of basic physical properties of speech and audio signals on the recognition accuracy of major speech/audio processing applications, i.e., speech recognition, speaker recognition, speech emotion recognition, and audio event recognition. A particular focus is on frequency ranges, time intervals, a precision of representation (quantization), and complexities of models suitable for each class of applications. Using domain-specific datasets, eligible feature extraction methods and complex neural network models, it was possible to test and evaluate the effect of basic speech and audio signal properties on the achieved accuracies for each group of applications. The tests confirmed that the basic parameters do affect the overall performance and, moreover, this effect is domain-dependent. Therefore, accurate knowledge of the extent of these effects can be valuable for system designers when selecting appropriate hardware, sensors, architecture, and software for a particular application, especially in the case of limited resources. Full article
(This article belongs to the Special Issue Development, Investigation and Application of Acoustic Sensors)
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Article
State-of-Charge Estimation for Lithium-Ion Batteries Using Residual Convolutional Neural Networks
Sensors 2022, 22(16), 6303; https://doi.org/10.3390/s22166303 - 22 Aug 2022
Viewed by 381
Abstract
State-of-charge (SOC) is a relative quantity that describes the ratio of the remaining capacity to the present maximum available capacity. Accurate SOC estimation is essential for a battery-management system. In addition to informing the user of the expected usage until the next recharge, [...] Read more.
State-of-charge (SOC) is a relative quantity that describes the ratio of the remaining capacity to the present maximum available capacity. Accurate SOC estimation is essential for a battery-management system. In addition to informing the user of the expected usage until the next recharge, it is crucial for improving the utilization efficiency and service life of the battery. This study focuses on applying deep-learning techniques, and specifically convolutional residual networks, to estimate the SOC of lithium-ion batteries. By stacking the values of multiple measurable variables taken at many time instants as the model inputs, the process information for the voltage or current generation, and their interrelations, can be effectively extracted using the proposed convolutional residual blocks, and can simultaneously be exploited to regress for accurate SOCs. The performance of the proposed network model was evaluated using the data obtained from a lithium-ion battery (Panasonic NCR18650PF) under nine different driving schedules at five ambient temperatures. The experimental results demonstrated an average mean absolute error of 1.260%, and an average root-mean-square error of 0.998%. The number of floating-point operations required to complete one SOC estimation was 2.24 × 106. These results indicate the efficacy and performance of the proposed approach. Full article
(This article belongs to the Topic Battery Design and Management)
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Article
Optimal Progressive Pitch for OneWeb Constellation with Seamless Coverage
Sensors 2022, 22(16), 6302; https://doi.org/10.3390/s22166302 - 22 Aug 2022
Viewed by 387
Abstract
Large-scale broadband low earth orbit (LEO) satellite systems have become a possibility due to decreased launch costs and rapidly evolving technology. Preventing huge LEO satellite constellations from interfering with the geostationary earth orbit (GSO) satellite system, progressive pitch is a technique to avoid [...] Read more.
Large-scale broadband low earth orbit (LEO) satellite systems have become a possibility due to decreased launch costs and rapidly evolving technology. Preventing huge LEO satellite constellations from interfering with the geostationary earth orbit (GSO) satellite system, progressive pitch is a technique to avoid interference with the GSO satellite system that allows the LEO satellite system to maintain a certain angle of separation from the GSO satellite system. Aside from interference avoidance, there is also a need to ensure seamless coverage of the LEO constellation and to optimize the overall transmission capacity of the LEO satellite as much as possible, making it extremely complex to design an effective progressive pitch plan. This paper models an inline interference event and seamless coverage and builds an optimization problem by maximizing transmission capacity. This paper reformulates the problem and designs a genetic algorithm to solve it. From the simulation results, the strategy can avoid harmful interference to the GSO satellite system and ensure the seamless coverage of the LEO constellation, and the satellite transmission capacity is also maximized. Full article
(This article belongs to the Special Issue Satellite Based IoT Networks for Emerging Applications)
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Article
Model-Based Reinforcement Learning with Automated Planning for Network Management
Sensors 2022, 22(16), 6301; https://doi.org/10.3390/s22166301 - 22 Aug 2022
Viewed by 329
Abstract
Reinforcement Learning (RL) comes with the promise of automating network management. However, due to its trial-and-error learning approach, model-based RL (MBRL) is not applicable in some network management scenarios. This paper explores the potential of using Automated Planning (AP) to achieve this MBRL [...] Read more.
Reinforcement Learning (RL) comes with the promise of automating network management. However, due to its trial-and-error learning approach, model-based RL (MBRL) is not applicable in some network management scenarios. This paper explores the potential of using Automated Planning (AP) to achieve this MBRL in the functional areas of network management. In addition, a comparison of several integration strategies of AP and RL is depicted. We also describe an architecture that realizes a cognitive management control loop by combining AP and RL. Our experiments evaluate on a simulated environment evidence that the combination proposed improves model-free RL but demonstrates lower performance than Deep RL regarding the reward and convergence time metrics. Nonetheless, AP-based MBRL is useful when the prediction model needs to be understood and when the high computational complexity of Deep RL can not be used. Full article
(This article belongs to the Section Sensor Networks)
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Article
Study of the Steady-State Operation of a Dual-Longitudinal-Mode and Self-Biasing Laser Gyroscope
Sensors 2022, 22(16), 6300; https://doi.org/10.3390/s22166300 - 22 Aug 2022
Viewed by 295
Abstract
In order to stabilize the self-biasing state of a laser gyroscope, a dual-longitudinal-mode asymmetric frequency stabilization technique was studied. The special frequency stabilization is based on the accurate control of the intensity tuning curve in the prism ring laser. In this study, the [...] Read more.
In order to stabilize the self-biasing state of a laser gyroscope, a dual-longitudinal-mode asymmetric frequency stabilization technique was studied. The special frequency stabilization is based on the accurate control of the intensity tuning curve in the prism ring laser. In this study, the effects of the ratio of the Ne isotopes, the inflation pressure, and the frequencies coupling on the intensity tuning curve in a laser gyro were examined. The profiles of the intensity tuning curve were simulated under the mixing ratios of Ne20 and Ne22 of 1:1 and 7:3, and the inflation pressures were 350 Pa, 400 Pa, and 450 Pa. The mixing ratio of Ne20 and Ne27 was dealt with similarly. The method for precisely adjusting the profiles of the intensity tuning curve was analyzed. The profiles were verified by experiments under different isotope ratios and pressures. Finally, based on a prism ring laser with an optical length of 0.47 m, the proposed frequency stabilization method was preliminarily verified. Full article
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Article
A Cloud Enabled Crop Recommendation Platform for Machine Learning-Driven Precision Farming
Sensors 2022, 22(16), 6299; https://doi.org/10.3390/s22166299 - 22 Aug 2022
Viewed by 481
Abstract
Modern agriculture incorporated a portfolio of technologies to meet the current demand for agricultural food production, in terms of both quality and quantity. In this technology-driven farming era, this portfolio of technologies has aided farmers to overcome many of the challenges associated with [...] Read more.
Modern agriculture incorporated a portfolio of technologies to meet the current demand for agricultural food production, in terms of both quality and quantity. In this technology-driven farming era, this portfolio of technologies has aided farmers to overcome many of the challenges associated with their farming activities by enabling precise and timely decision making on the basis of data that are observed and subsequently converged. In this regard, Artificial Intelligence (AI) holds a key place, whereby it can assist key stakeholders in making precise decisions regarding the conditions on their farms. Machine Learning (ML), which is a branch of AI, enables systems to learn and improve from their experience without explicitly being programmed, by imitating intelligent behavior in solving tasks in a manner that requires low computational power. For the time being, ML is involved in a variety of aspects of farming, assisting ranchers in making smarter decisions on the basis of the observed data. In this study, we provide an overview of AI-driven precision farming/agriculture with related work and then propose a novel cloud-based ML-powered crop recommendation platform to assist farmers in deciding which crops need to be harvested based on a variety of known parameters. Moreover, in this paper, we compare five predictive ML algorithms—K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM)—to identify the best-performing ML algorithm on which to build our recommendation platform as a cloud-based service with the intention of offering precision farming solutions that are free and open source, as will lead to the growth and adoption of precision farming solutions in the long run. Full article
(This article belongs to the Special Issue IoT for Smart Agriculture)
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Article
Authenticated Timing Protocol Based on Galileo ACAS
Sensors 2022, 22(16), 6298; https://doi.org/10.3390/s22166298 - 21 Aug 2022
Viewed by 506
Abstract
Global navigation satellite systems (GNSSs) provide accurate positioning and timing services in a large gamut of sectors, including financial institutions, Industry 4.0, and Internet of things (IoT). Any industrial system involving multiple devices interacting and/or coordinating their functionalities needs accurate, dependable, and trustworthy [...] Read more.
Global navigation satellite systems (GNSSs) provide accurate positioning and timing services in a large gamut of sectors, including financial institutions, Industry 4.0, and Internet of things (IoT). Any industrial system involving multiple devices interacting and/or coordinating their functionalities needs accurate, dependable, and trustworthy time synchronization, which can be obtained by using authenticated GNSS signals. However, GNSS vulnerabilities to time-spoofing attacks may cause security issues for their applications. Galileo is currently developing new services aimed at providing increased security and robustness against attacks, such as the open service navigation message authentication (OS-NMA) and commercial authentication service (CAS). In this paper, we propose a robust and secure timing protocol that is independent of external time sources, and solely relies on assisted commercial authentication service (ACAS) and OS-NMA features. We analyze the performance of the proposed timing protocol and discuss its security level in relation to malicious attacks. Lastly, experimental tests were conducted to validate the proposed protocol. Full article
(This article belongs to the Special Issue 800 Years of Research at Padova University)
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Article
Directivity Dependence of a Distributed Fiber Optic Hydrophone on Array Structure
Sensors 2022, 22(16), 6297; https://doi.org/10.3390/s22166297 - 21 Aug 2022
Viewed by 458
Abstract
A distributed fiber optic hydrophone (DFOH) is a new type of fiber optic hydrophone (FOH) with adjustable structure. The dependence of the directivity of a DFOH on array structure is theoretically and experimentally studied. The directivity function of a sensing channel and that [...] Read more.
A distributed fiber optic hydrophone (DFOH) is a new type of fiber optic hydrophone (FOH) with adjustable structure. The dependence of the directivity of a DFOH on array structure is theoretically and experimentally studied. The directivity function of a sensing channel and that of a DFOH are derived. Based on the directivity function, the simulations are performed. Finally, the theoretical analysis is demonstrated by the experiments performed on Qingyang lake, and the results reveal that the longer sensing channel length guarantees the lower first-order side lobe and the narrower main lobe. As the channel length increased from 1 to 3, the main lobe width and first-order side lobe height decreased by 4.9° and 6 dB, respectively. In addition, channel spacing is irrelevant to directivity as the spacing is shorter than the wavelength. As the channel spacing increased from 0 to 1, the variations of the main lobe width and first-order side lobe height are lower than 0.5° and 0.94 dB, respectively. This study would provide guidance for the structure design of a distributed fiber optic hydrophone in signal processing. Full article
(This article belongs to the Special Issue Recent Trends in Distributed Optical Fiber Sensing Technology)
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Article
Characterization of Tensile Stress-Dependent Directional Magnetic Incremental Permeability in Iron-Cobalt Magnetic Sheet: Towards Internal Stress Estimation through Non-Destructive Testing
Sensors 2022, 22(16), 6296; https://doi.org/10.3390/s22166296 - 21 Aug 2022
Viewed by 406
Abstract
Iron-Cobalt ferromagnetic alloys are promoted for electrical energy conversion in aeronautic applications, but their high magnetostrictive coefficients may result in undesired behaviors. Internal stresses can be tuned to limit magnetostriction but must be adequately assessed in a non-destructive way during production. For this, [...] Read more.
Iron-Cobalt ferromagnetic alloys are promoted for electrical energy conversion in aeronautic applications, but their high magnetostrictive coefficients may result in undesired behaviors. Internal stresses can be tuned to limit magnetostriction but must be adequately assessed in a non-destructive way during production. For this, directional magnetic incremental permeability is proposed in this work. For academic purposes, internal stresses have been replaced by homogenous external stress, which is easier to control using traction/compression testbench and results in similar effects. Tests have been limited to tensile stress stimuli, the worst-case scenario for magnetic stress observation on positive magnetostriction coefficient materials. Hysteresis cycles have been reconstructed from the incremental permeability measurement for stability and reproducibility of the measured quantities. The directionality of the sensor provides an additional degree of freedom in the magnetic response observation. The study reveals that an angle of π/2 between the DC (Hsurf DC) and the AC (Hsurf AC) magnetic excitations with a flux density Ba at HsurfDC = 10 kA·m−1 constitute the ideal experimental situation and the highest correlated parameter to a homogeneous imposed tensile stress. Magnetic incremental permeability is linked to the magnetic domain wall bulging magnetization mechanism; this study thus provides insights for understanding such a mechanism. Full article
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Article
A Low Complexity Sensing Algorithm for Non-Sparse Wideband Spectrum
Sensors 2022, 22(16), 6295; https://doi.org/10.3390/s22166295 - 21 Aug 2022
Viewed by 321
Abstract
The vast majority of existing sub-Nyquist sampling wideband spectrum sensing (WSS) methods default to a sparse spectrum. However, research data suggests that in the near future, the wideband spectrum will no longer be sparse. This article proposes a sub-Nyquist sampling WSS algorithm that [...] Read more.
The vast majority of existing sub-Nyquist sampling wideband spectrum sensing (WSS) methods default to a sparse spectrum. However, research data suggests that in the near future, the wideband spectrum will no longer be sparse. This article proposes a sub-Nyquist sampling WSS algorithm that can adapt well to non-sparse spectrum scenarios. The algorithm continues to implement the idea of our previously proposed “no reconstruction (NoR) of spectrum” algorithm, thus having low computational complexity. The new one is actually an advanced version of the NoR algorithm, so it is called AdNoR. The key to its advancement lies in the establishment of a folded time-frequency (TF) spectrum model with the same special structure as in the fold spectrum model of the NoR algorithm. For this purpose, we have designed a comprehensive sampling technique which consists of multicoset sampling, digital fractional delay, and TF transform. It is verified by simulation that the AdNoR algorithm maintains a good sensing performance with low computational complexity in the non-sparse scenario. Full article
(This article belongs to the Section Communications)
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Article
A Novel Wearable Soft Glove for Hand Rehabilitation and Assistive Grasping
Sensors 2022, 22(16), 6294; https://doi.org/10.3390/s22166294 - 21 Aug 2022
Viewed by 414
Abstract
In order to assist patients with finger rehabilitation training and grasping objects, we propose a new type of soft rehabilitation gloves (SRGs), which has both flexion/extension and abduction/adduction movement function for every finger. This paper describes the structure design of the bending actuator [...] Read more.
In order to assist patients with finger rehabilitation training and grasping objects, we propose a new type of soft rehabilitation gloves (SRGs), which has both flexion/extension and abduction/adduction movement function for every finger. This paper describes the structure design of the bending actuator and rotating actuator, the fabrication process of the soft actuator, and the implementation of the soft wearable gloves based on a fabric glove. FEM simulation analysis and experiments were conducted to characterize the mechanical behavior and performance of the soft glove in terms of the angle output and force output upon pressurization. To operate this soft wearable glove, we designed the hardware system for SRGs with a flexible strain sensor and force sensor in the loop and introduced a force/position hybrid PID control algorithm to regulate the pressure inputted. Experiment evaluation focused on rehabilitation training gestures; motions and the precise grasping assistance function were executed. The rotating actuator between each finger can supply abduction/adduction motion manner for patients, which will improve rehabilitation effect. The experimental results demonstrated that the developed SRGs have the potential to improve hand movement freedom and the range of grasping successfully. Full article
(This article belongs to the Section Wearables)
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Article
Integration of Multi-Head Self-Attention and Convolution for Person Re-Identification
Sensors 2022, 22(16), 6293; https://doi.org/10.3390/s22166293 - 21 Aug 2022
Viewed by 343
Abstract
Person re-identification is essential to intelligent video analytics, whose results affect downstream tasks such as behavior and event analysis. However, most existing models only consider the accuracy, rather than the computational complexity, which is also an aspect to consider in practical deployment. We [...] Read more.
Person re-identification is essential to intelligent video analytics, whose results affect downstream tasks such as behavior and event analysis. However, most existing models only consider the accuracy, rather than the computational complexity, which is also an aspect to consider in practical deployment. We note that self-attention is a powerful technique for representation learning. It can work with convolution to learn more discriminative feature representations for re-identification. We propose an improved multi-scale feature learning structure, DM-OSNet, with better performance than the original OSNet. Our DM-OSNet replaces the 9×9 convolutional stream in OSNet with multi-head self-attention. To maintain model efficiency, we use double-layer multi-head self-attention to reduce the computational complexity of the original multi-head self-attention. The computational complexity is reduced from the original O((H×W)2) to O(H×W×G2). To further improve the model performance, we use SpCL to perform unsupervised pre-training on the large-scale unlabeled pedestrian dataset LUPerson. Finally, our DM-OSNet achieves an mAP of 87.36%, 78.26%, 72.96%, and 57.13% on the Market1501, DukeMTMC-reID, CUHK03, and MSMT17 datasets. Full article
(This article belongs to the Section Intelligent Sensors)
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Article
Automatic Stones Classification through a CNN-Based Approach
Sensors 2022, 22(16), 6292; https://doi.org/10.3390/s22166292 - 21 Aug 2022
Viewed by 407
Abstract
This paper presents an automatic recognition system for classifying stones belonging to different Calabrian quarries (Southern Italy). The tool for stone recognition has been developed in the SILPI project (acronym of “Sistema per l’Identificazione di Lapidei Per Immagini”), financed by POR [...] Read more.
This paper presents an automatic recognition system for classifying stones belonging to different Calabrian quarries (Southern Italy). The tool for stone recognition has been developed in the SILPI project (acronym of “Sistema per l’Identificazione di Lapidei Per Immagini”), financed by POR Calabria FESR-FSE 2014-2020. Our study is based on the Convolutional Neural Network (CNNs) that is used in literature for many different tasks such as speech recognition, neural language processing, bioinformatics, image classification and much more. In particular, we propose a two-stage hybrid approach based on the use of a model of Deep Learning (DL), in our case the CNN, in the first stage and a model of Machine Learning (ML) in the second one. In this work, we discuss a possible solution to stones classification which uses a CNN for the feature extraction phase and the Softmax or Multinomial Logistic Regression (MLR), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Random Forest (RF) and Gaussian Naive Bayes (GNB) ML techniques in order to perform the classification phase basing our study on the approach called Transfer Learning (TL). We show the image acquisition process in order to collect adequate information for creating an opportune database of the stone typologies present in the Calabrian quarries, also performing the identification of quarries in the considered region. Finally, we show a comparison of different DL and ML combinations in our Two-Stage Hybrid Model solution. Full article
(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors II)
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Article
EPI Light Field Depth Estimation Based on a Directional Relationship Model and Multiviewpoint Attention Mechanism
Sensors 2022, 22(16), 6291; https://doi.org/10.3390/s22166291 - 21 Aug 2022
Viewed by 375
Abstract
Light field (LF) image depth estimation is a critical technique for LF-related applications such as 3D reconstruction, target detection, and tracking. The refocusing property of LF images provide rich information for depth estimations; however, it is still challenging in cases of occlusion regions, [...] Read more.
Light field (LF) image depth estimation is a critical technique for LF-related applications such as 3D reconstruction, target detection, and tracking. The refocusing property of LF images provide rich information for depth estimations; however, it is still challenging in cases of occlusion regions, edge regions, noise interference, etc. The epipolar plane image (EPI) of LF can effectively deal with the depth estimation because of its characteristics of multidirectionality and pixel consistency—in which the LF depth estimations are converted to calculate the EPI slope. This paper proposed an EPI LF depth estimation algorithm based on a directional relationship model and attention mechanism. Unlike the subaperture LF depth estimation method, the proposed method takes EPIs as input images. Specifically, a directional relationship model was used to extract direction features of the horizontal and vertical EPIs, respectively. Then, a multiviewpoint attention mechanism combining channel attention and spatial attention is used to give more weight to the EPI slope information. Subsequently, multiple residual modules are used to eliminate the redundant features that interfere with the EPI slope information—in which a small stride convolution operation is used to avoid losing key EPI slope information. The experimental results revealed that the proposed algorithm outperformed the compared algorithms in terms of accuracy. Full article
(This article belongs to the Special Issue Advances in 3D Measurement Technology and Sensors)
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Article
Scaled Sea Surface Design and RCS Measurement Based on Rough Film Medium
Sensors 2022, 22(16), 6290; https://doi.org/10.3390/s22166290 - 21 Aug 2022
Viewed by 365
Abstract
The electromagnetic (EM) scattering characteristics of the rough sea surface is very important for target surveying and detection in a sea environment. This work proposes a scaled sea surface designing method based on a rough thin-film medium. For the prototype sea surface, the [...] Read more.
The electromagnetic (EM) scattering characteristics of the rough sea surface is very important for target surveying and detection in a sea environment. This work proposes a scaled sea surface designing method based on a rough thin-film medium. For the prototype sea surface, the permittivity is calculated with the seawater temperature, salinity, and EM wave frequency according to the Debye model. The scale film material is mixed with carbon black and epoxy, whose volume ratio is optimized with the genetic algorithm through the existing electromagnetic parameter library. This method can overcome the previous difficulties of adjusting the same permittivity of the prototype sea water. According to the EM scaled theory, the scaled geometric sample is numerically generated with the D-V spectrum for the given wind speed, and is fabricated using 3D printing to keep the similar seawater shape. Then, the sample is sprayed with a layer of film material for EM scattering measurement. The simulated and measured radar cross-section (RCS) results show good consistency for the prototype seawater and scaled materials, which indicates the proposed scaled method is a more efficient method to get the seawater scattering characteristics. Full article
(This article belongs to the Special Issue Recent Trends and Advances in Microwave Sea Remote Sensing)
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Article
A Fast Point Cloud Recognition Algorithm Based on Keypoint Pair Feature
Sensors 2022, 22(16), 6289; https://doi.org/10.3390/s22166289 - 21 Aug 2022
Viewed by 362
Abstract
At present, PPF-based point cloud recognition algorithms can perform better matching than competitors and be verified in the case of severe occlusion and stacking. However, including certain superfluous feature point pairs in the global model description would significantly lower the algorithm’s efficiency. As [...] Read more.
At present, PPF-based point cloud recognition algorithms can perform better matching than competitors and be verified in the case of severe occlusion and stacking. However, including certain superfluous feature point pairs in the global model description would significantly lower the algorithm’s efficiency. As a result, this paper delves into the Point Pair Feature (PPF) algorithm and proposes a 6D pose estimation method based on Keypoint Pair Feature (K-PPF) voting. The K-PPF algorithm is based on the PPF algorithm and proposes an improved algorithm for the sampling point part. The sample points are retrieved using a combination of curvature-adaptive and grid ISS, and the angle-adaptive judgment is performed on the sampling points to extract the keypoints, therefore improving the point pair feature difference and matching accuracy. To verify the effectiveness of the method, we analyze the experimental results in scenes with different occlusion and complexity levels under the evaluation metrics of ADD-S, Recall, Precision, and Overlap rate. The results show that the algorithm in this paper reduces redundant point pairs and improves recognition efficiency and robustness compared with PPF. Compared with FPFH, CSHOT, SHOT and SI algorithms, this paper improves the recall rate by more than 12.5%. Full article
(This article belongs to the Section Sensing and Imaging)
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Article
Spectral Reflectance Recovery from the Quadcolor Camera Signals Using the Interpolation and Weighted Principal Component Analysis Methods
Sensors 2022, 22(16), 6288; https://doi.org/10.3390/s22166288 - 21 Aug 2022
Viewed by 272
Abstract
The recovery of surface spectral reflectance using the quadcolor camera was numerically studied. Assume that the RGB channels of the quadcolor camera are the same as the Nikon D5100 tricolor camera. The spectral sensitivity of the fourth signal channel was tailored using a [...] Read more.
The recovery of surface spectral reflectance using the quadcolor camera was numerically studied. Assume that the RGB channels of the quadcolor camera are the same as the Nikon D5100 tricolor camera. The spectral sensitivity of the fourth signal channel was tailored using a color filter. Munsell color chips were used as reflective surfaces. When the interpolation method or the weighted principal component analysis (wPCA) method is used to reconstruct spectra, using the quadcolor camera can effectively reduce the mean spectral error of the test samples compared to using the tricolor camera. Except for computation time, the interpolation method outperforms the wPCA method in spectrum reconstruction. A long-pass optical filter can be applied to the fourth channel for reducing the mean spectral error. A short-pass optical filter can be applied to the fourth channel for reducing the mean color difference, but the mean spectral error will be larger. Due to the small color difference, the quadcolor camera using an optimized short-pass filter may be suitable as an imaging colorimeter. It was found that an empirical design rule to keep the color difference small is to reduce the error in fitting the color-matching functions using the camera spectral sensitivity functions. Full article
(This article belongs to the Section Optical Sensors)
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