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Volume 23
Issue 11
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Sensors, Volume 23, Issue 11 (June-1 2023) – 372 articles

Cover Story (view full-size image): The next generation of mobile broadband communication, 5G, is a key driver for the Industrial Internet of Things (IIoT). Its enhanced performance, network customization, and secure data isolation allow for the deployment of 5G Non-Public Networks (NPN), a flexible alternative to the well-known Ethernet wired connections commonly used in industrial settings. This paper presents a practical implementation of IIoT over 5G, featuring a 5G IoT End Device that collects sensor data from shop floor assets, an industrial 5G NPN that acts as communication infrastructure, and an Intelligent Assistant that leverages the data for valuable insights, enabling sustainable asset operations. These infrastructure and application components have been tested and validated on the shop floor, showcasing 5G’s potential towards smarter and greener factories. View this paper
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18 pages, 3609 KiB  
Article
3D Road Lane Classification with Improved Texture Patterns and Optimized Deep Classifier
by Bhavithra Janakiraman, Sathiyapriya Shanmugam, Rocío Pérez de Prado and Marcin Wozniak
Sensors 2023, 23(11), 5358; https://doi.org/10.3390/s23115358 - 5 Jun 2023
Cited by 10 | Viewed by 2069
Abstract
The understanding of roads and lanes incorporates identifying the level of the road, the position and count of lanes, and ending, splitting, and merging roads and lanes in highway, rural, and urban scenarios. Even though a large amount of progress has been made [...] Read more.
The understanding of roads and lanes incorporates identifying the level of the road, the position and count of lanes, and ending, splitting, and merging roads and lanes in highway, rural, and urban scenarios. Even though a large amount of progress has been made recently, this kind of understanding is ahead of the accomplishments of the present perceptual methods. Nowadays, 3D lane detection has become the trending research in autonomous vehicles, which shows an exact estimation of the 3D position of the drivable lanes. This work mainly aims at proposing a new technique with Phase I (road or non-road classification) and Phase II (lane or non-lane classification) with 3D images. Phase I: Initially, the features, such as the proposed local texton XOR pattern (LTXOR), local Gabor binary pattern histogram sequence (LGBPHS), and median ternary pattern (MTP), are derived. These features are subjected to the bidirectional gated recurrent unit (BI-GRU) that detects whether the object is road or non-road. Phase II: Similar features in Phase I are further classified using the optimized BI-GRU, where the weights are chosen optimally via self-improved honey badger optimization (SI-HBO). As a result, the system can be identified, and whether it is lane-related or not. Particularly, the proposed BI-GRU + SI-HBO obtained a higher precision of 0.946 for db 1. Furthermore, the best-case accuracy for the BI-GRU + SI-HBO was 0.928, which was better compared with honey badger optimization. Finally, the development of SI-HBO was proven to be better than the others. Full article
(This article belongs to the Section Intelligent Sensors)
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18 pages, 3777 KiB  
Article
Broadband Measurements of Soil Complex Permittivity
by Justin Stellini, Lourdes Farrugia, Iman Farhat, Julian Bonello, Raffaele Persico, Anthony Sacco, Kyle Spiteri and Charles V. Sammut
Sensors 2023, 23(11), 5357; https://doi.org/10.3390/s23115357 - 5 Jun 2023
Cited by 4 | Viewed by 2033
Abstract
Agriculture is a major consumer of freshwater and is often associated with low water productivity. To prevent drought, farmers tend to over-irrigate, putting a strain on the ever-depleting groundwater resources. To improve modern agricultural techniques and conserve water, quick and accurate estimates of [...] Read more.
Agriculture is a major consumer of freshwater and is often associated with low water productivity. To prevent drought, farmers tend to over-irrigate, putting a strain on the ever-depleting groundwater resources. To improve modern agricultural techniques and conserve water, quick and accurate estimates of soil water content (SWC) should be made, and irrigation timed correctly in order to optimize crop yield and water use. In this study, soil samples common to the Maltese Islands having different clay, sand, and silt contents were, primarily, investigated to: (a) deduce whether the dielectric constant can be considered as a viable indicator of the SWC for the soils of Malta; (b) determine how soil compaction affects the dielectric constant measurements; and (c) to create calibration curves to directly relate the dielectric constant and the SWC for two different soil types of low and high density. The measurements, which were carried out in the X-band, were facilitated by an experimental setup comprising a two-port Vector Network Analyzer (VNA) connected to a rectangular waveguide system. From data analysis, it was found that for each soil investigated, the dielectric constant increases notably with an increase in both density and SWC. Our findings are expected to aid in future numerical analysis and simulations aimed at developing low-cost, minimally invasive Microwave (MW) systems for localized SWC sensing, and hence, in agricultural water conservation. However, it should be noted that a statistically significant relationship between soil texture and the dielectric constant could not be determined at this stage. Full article
(This article belongs to the Collection Dielectric Sensing-Based Systems and Applications)
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16 pages, 900 KiB  
Article
Characterizing Periodic Variations of Atomic Frequency Standards via Their Frequency Stability Estimates
by Weiwei Cheng, Guigen Nie and Jian Zhu
Sensors 2023, 23(11), 5356; https://doi.org/10.3390/s23115356 - 5 Jun 2023
Viewed by 1219
Abstract
The onboard atomic frequency standard (AFS) is a crucial element of Global Navigation Satellite System (GNSS) satellites. However, it is widely accepted that periodic variations can influence the onboard AFS. The presence of non-stationary random processes in AFS signals can lead to inaccurate [...] Read more.
The onboard atomic frequency standard (AFS) is a crucial element of Global Navigation Satellite System (GNSS) satellites. However, it is widely accepted that periodic variations can influence the onboard AFS. The presence of non-stationary random processes in AFS signals can lead to inaccurate separation of the periodic and stochastic components of satellite AFS clock data when using least squares and Fourier transform methods. In this paper, we characterize the periodic variations of AFS using Allan and Hadamard variances and demonstrate that the Allan and Hadamard variances of the periodics are independent of the variances of the stochastic component. The proposed model is tested against simulated and real clock data, revealing that our approach provides more precise characterization of periodic variations compared to the least squares method. Additionally, we observe that overfitting periodic variations can improve the precision of GPS clock bias prediction, as indicated by a comparison of fitting and prediction errors of satellite clock bias. Full article
(This article belongs to the Special Issue GNSS Signals and Precise Point Positioning)
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16 pages, 3400 KiB  
Article
Vision-Based Recognition of Human Motion Intent during Staircase Approaching
by Md Rafi Islam, Md Rejwanul Haque, Masudul H. Imtiaz, Xiangrong Shen and Edward Sazonov
Sensors 2023, 23(11), 5355; https://doi.org/10.3390/s23115355 - 5 Jun 2023
Cited by 3 | Viewed by 2010
Abstract
Walking in real-world environments involves constant decision-making, e.g., when approaching a staircase, an individual decides whether to engage (climbing the stairs) or avoid. For the control of assistive robots (e.g., robotic lower-limb prostheses), recognizing such motion intent is an important but challenging task, [...] Read more.
Walking in real-world environments involves constant decision-making, e.g., when approaching a staircase, an individual decides whether to engage (climbing the stairs) or avoid. For the control of assistive robots (e.g., robotic lower-limb prostheses), recognizing such motion intent is an important but challenging task, primarily due to the lack of available information. This paper presents a novel vision-based method to recognize an individual’s motion intent when approaching a staircase before the potential transition of motion mode (walking to stair climbing) occurs. Leveraging the egocentric images from a head-mounted camera, the authors trained a YOLOv5 object detection model to detect staircases. Subsequently, an AdaBoost and gradient boost (GB) classifier was developed to recognize the individual’s intention of engaging or avoiding the upcoming stairway. This novel method has been demonstrated to provide reliable (97.69%) recognition at least 2 steps before the potential mode transition, which is expected to provide ample time for the controller mode transition in an assistive robot in real-world use. Full article
(This article belongs to the Special Issue Human Activity Recognition in Smart Sensing Environment)
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33 pages, 4823 KiB  
Article
NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio
by Panagiotis T. Karfakis, Micael S. Couceiro and David Portugal
Sensors 2023, 23(11), 5354; https://doi.org/10.3390/s23115354 - 5 Jun 2023
Cited by 8 | Viewed by 4091
Abstract
Robot localization is a crucial task in robotic systems and is a pre-requisite for navigation. In outdoor environments, Global Navigation Satellite Systems (GNSS) have aided towards this direction, alongside laser and visual sensing. Despite their application in the field, GNSS suffers from limited [...] Read more.
Robot localization is a crucial task in robotic systems and is a pre-requisite for navigation. In outdoor environments, Global Navigation Satellite Systems (GNSS) have aided towards this direction, alongside laser and visual sensing. Despite their application in the field, GNSS suffers from limited availability in dense urban and rural environments. Light Detection and Ranging (LiDAR), inertial and visual methods are also prone to drift and can be susceptible to outliers due to environmental changes and illumination conditions. In this work, we propose a cellular Simultaneous Localization and Mapping (SLAM) framework based on 5G New Radio (NR) signals and inertial measurements for mobile robot localization with several gNodeB stations. The method outputs the pose of the robot along with a radio signal map based on the Received Signal Strength Indicator (RSSI) measurements for correction purposes. We then perform benchmarking against LiDAR-Inertial Odometry Smoothing and Mapping (LIO-SAM), a state-of-the-art LiDAR SLAM method, comparing performance via a simulator ground truth reference. Two experimental setups are presented and discussed using the sub-6 GHz and mmWave frequency bands for communication, while the transmission is based on down-link (DL) signals. Our results show that 5G positioning can be utilized for radio SLAM, providing increased robustness in outdoor environments and demonstrating its potential to assist in robot localization, as an additional absolute source of information when LiDAR methods fail and GNSS data is unreliable. Full article
(This article belongs to the Special Issue Sensor Based Perception for Field Robotics)
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18 pages, 7198 KiB  
Article
Classification and Recognition of Building Appearance Based on Optimized Gradient-Boosted Decision Tree Algorithm
by Mengting Hu, Lingxiang Guo, Jing Liu and Yuxuan Song
Sensors 2023, 23(11), 5353; https://doi.org/10.3390/s23115353 - 5 Jun 2023
Cited by 2 | Viewed by 1673
Abstract
There are high concentrations of urban spaces and increasingly complex land use types. Providing an efficient and scientific identification of building types has become a major challenge in urban architectural planning. This study used an optimized gradient-boosted decision tree algorithm to enhance a [...] Read more.
There are high concentrations of urban spaces and increasingly complex land use types. Providing an efficient and scientific identification of building types has become a major challenge in urban architectural planning. This study used an optimized gradient-boosted decision tree algorithm to enhance a decision tree model for building classification. Through supervised classification learning, machine learning training was conducted using a business-type weighted database. We innovatively established a form database to store input items. During parameter optimization, parameters such as the number of nodes, maximum depth, and learning rate were gradually adjusted based on the performance of the verification set to achieve optimal performance on the verification set under the same conditions. Simultaneously, a k-fold cross-validation method was used to avoid overfitting. The model clusters trained in the machine learning training corresponded to various city sizes. By setting the parameters to determine the size of the area of land for a target city, the corresponding classification model could be invoked. The experimental results show that this algorithm has high accuracy in building recognition. Especially in R, S, and U-class buildings, the overall accuracy rate of recognition reaches over 94%. Full article
(This article belongs to the Special Issue Deep Learning-Based Neural Networks for Sensing and Imaging)
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27 pages, 10559 KiB  
Article
Buildings’ Biaxial Tilt Assessment Using Inertial Wireless Sensors and a Parallel Training Model
by Luis Pastor Sánchez-Fernández, Luis Alejandro Sánchez-Pérez, José Juan Carbajal-Hernández, Mario Alberto Hernández-Guerrero and Lucrecia Pérez-Echazabal
Sensors 2023, 23(11), 5352; https://doi.org/10.3390/s23115352 - 5 Jun 2023
Cited by 1 | Viewed by 1310
Abstract
Applications of MEMS-based sensing technology are beneficial and versatile. If these electronic sensors integrate efficient processing methods, and if supervisory control and data acquisition (SCADA) software is also required, then mass networked real-time monitoring will be limited by cost, revealing a research gap [...] Read more.
Applications of MEMS-based sensing technology are beneficial and versatile. If these electronic sensors integrate efficient processing methods, and if supervisory control and data acquisition (SCADA) software is also required, then mass networked real-time monitoring will be limited by cost, revealing a research gap related to the specific processing of signals. Static and dynamic accelerations are very noisy, and small variations of correctly processed static accelerations can be used as measurements and patterns of the biaxial inclination of many structures. This paper presents a biaxial tilt assessment for buildings based on a parallel training model and real-time measurements using inertial sensors, Wi-Fi Xbee, and Internet connectivity. The specific structural inclinations of the four exterior walls and their severity of rectangular buildings in urban areas with differential soil settlements can be supervised simultaneously in a control center. Two algorithms, combined with a new procedure using successive numeric repetitions designed especially for this work, process the gravitational acceleration signals, improving the final result remarkably. Subsequently, the inclination patterns based on biaxial angles are generated computationally, considering differential settlements and seismic events. The two neural models recognize 18 inclination patterns and their severity using an approach in cascade with a parallel training model for the severity classification. Lastly, the algorithms are integrated into monitoring software with 0.1° resolution, and their performance is verified on a small-scale physical model for laboratory tests. The classifiers had a precision, recall, F1-score, and accuracy greater than 95%. Full article
(This article belongs to the Special Issue Application of MEMS/NEMS-Based Sensing Technology)
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16 pages, 3162 KiB  
Article
Monitoring of Cardiorespiratory Parameters during Sleep Using a Special Holder for the Accelerometer Sensor
by Andrei Boiko, Maksym Gaiduk, Wilhelm Daniel Scherz, Andrea Gentili, Massimo Conti, Simone Orcioni, Natividad Martínez Madrid and Ralf Seepold
Sensors 2023, 23(11), 5351; https://doi.org/10.3390/s23115351 - 5 Jun 2023
Cited by 5 | Viewed by 2403
Abstract
Sleep is extremely important for physical and mental health. Although polysomnography is an established approach in sleep analysis, it is quite intrusive and expensive. Consequently, developing a non-invasive and non-intrusive home sleep monitoring system with minimal influence on patients, that can reliably and [...] Read more.
Sleep is extremely important for physical and mental health. Although polysomnography is an established approach in sleep analysis, it is quite intrusive and expensive. Consequently, developing a non-invasive and non-intrusive home sleep monitoring system with minimal influence on patients, that can reliably and accurately measure cardiorespiratory parameters, is of great interest. The aim of this study is to validate a non-invasive and unobtrusive cardiorespiratory parameter monitoring system based on an accelerometer sensor. This system includes a special holder to install the system under the bed mattress. The additional aim is to determine the optimum relative system position (in relation to the subject) at which the most accurate and precise values of measured parameters could be achieved. The data were collected from 23 subjects (13 males and 10 females). The obtained ballistocardiogram signal was sequentially processed using a sixth-order Butterworth bandpass filter and a moving average filter. As a result, an average error (compared to reference values) of 2.24 beats per minute for heart rate and 1.52 breaths per minute for respiratory rate was achieved, regardless of the subject’s sleep position. For males and females, the errors were 2.28 bpm and 2.19 bpm for heart rate and 1.41 rpm and 1.30 rpm for respiratory rate. We determined that placing the sensor and system at chest level is the preferred configuration for cardiorespiratory measurement. Further studies of the system’s performance in larger groups of subjects are required, despite the promising results of the current tests in healthy subjects. Full article
(This article belongs to the Special Issue Selected Papers from 2022 and 2023 IEEE International Conference on e-Health and Bioengineering)
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22 pages, 4072 KiB  
Article
A Low-Carbon and Economic Dispatch Strategy for a Multi-Microgrid Based on a Meteorological Classification to Handle the Uncertainty of Wind Power
by Yang Liu, Xueling Li and Yamei Liu
Sensors 2023, 23(11), 5350; https://doi.org/10.3390/s23115350 - 5 Jun 2023
Cited by 3 | Viewed by 1377
Abstract
In a modern power system, reducing carbon emissions has become a significant goal in mitigating the impact of global warming. Therefore, renewable energy sources, particularly wind-power generation, have been extensively implemented in the system. Despite the advantages of wind power, its uncertainty and [...] Read more.
In a modern power system, reducing carbon emissions has become a significant goal in mitigating the impact of global warming. Therefore, renewable energy sources, particularly wind-power generation, have been extensively implemented in the system. Despite the advantages of wind power, its uncertainty and randomness lead to critical security, stability, and economic issues in the power system. Recently, multi-microgrid systems (MMGSs) have been considered as a suitable wind-power deployment candidate. Although wind power can be efficiently utilized by MMGSs, uncertainty and randomness still have a significant impact on the dispatching and operation of the system. Therefore, to address the wind power uncertainty issue and achieve an optimal dispatching strategy for MMGSs, this paper presents an adjustable robust optimization (ARO) model based on meteorological clustering. Firstly, the maximum relevance minimum redundancy (MRMR) method and the CURE clustering algorithm are employed for meteorological classification in order to better identify wind patterns. Secondly, a conditional generative adversarial network (CGAN) is adopted to enrich the wind-power datasets with different meteorological patterns, resulting in the construction of ambiguity sets. Thirdly, the uncertainty sets that are finally employed by the ARO framework to establish a two-stage cooperative dispatching model for MMGS can be derived from the ambiguity sets. Additionally, stepped carbon trading is introduced to control the carbon emissions of MMGSs. Finally, the alternative direction method of multipliers (ADMM) and the column and constraint generation (C&CG) algorithm are adopted to achieve a decentralized solution for the dispatching model of MMGSs. Case studies indicate that the presented model has a great performance in improving the wind-power description accuracy, increasing cost efficiency, and reducing system carbon emissions. However, the case studies also report that the approach consumes a relative long running time. Therefore, in future research, the solution algorithm will be further improved for the purpose of raising the efficiency of the solution. Full article
(This article belongs to the Special Issue Emerging Machine Learning Techniques in Industrial Internet of Things)
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25 pages, 3947 KiB  
Article
Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain
by Ibrahim Aqeel, Ibrahim Mohsen Khormi, Surbhi Bhatia Khan, Mohammed Shuaib, Ahlam Almusharraf, Shadab Alam and Nora A. Alkhaldi
Sensors 2023, 23(11), 5349; https://doi.org/10.3390/s23115349 - 5 Jun 2023
Cited by 15 | Viewed by 4025
Abstract
The emergence of the Internet of Things (IoT) and its subsequent evolution into the Internet of Everything (IoE) is a result of the rapid growth of information and communication technologies (ICT). However, implementing these technologies comes with certain obstacles, such as the limited [...] Read more.
The emergence of the Internet of Things (IoT) and its subsequent evolution into the Internet of Everything (IoE) is a result of the rapid growth of information and communication technologies (ICT). However, implementing these technologies comes with certain obstacles, such as the limited availability of energy resources and processing power. Consequently, there is a need for energy-efficient and intelligent load-balancing models, particularly in healthcare, where real-time applications generate large volumes of data. This paper proposes a novel, energy-aware artificial intelligence (AI)-based load balancing model that employs the Chaotic Horse Ride Optimization Algorithm (CHROA) and big data analytics (BDA) for cloud-enabled IoT environments. The CHROA technique enhances the optimization capacity of the Horse Ride Optimization Algorithm (HROA) using chaotic principles. The proposed CHROA model balances the load, optimizes available energy resources using AI techniques, and is evaluated using various metrics. Experimental results show that the CHROA model outperforms existing models. For instance, while the Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Whale Defense Algorithm with Firefly Algorithm (WD-FA) techniques attain average throughputs of 58.247 Kbps, 59.957 Kbps, and 60.819 Kbps, respectively, the CHROA model achieves an average throughput of 70.122 Kbps. The proposed CHROA-based model presents an innovative approach to intelligent load balancing and energy optimization in cloud-enabled IoT environments. The results highlight its potential to address critical challenges and contribute to developing efficient and sustainable IoT/IoE solutions. Full article
(This article belongs to the Special Issue Trust in the Internet of Things)
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18 pages, 366 KiB  
Article
Obfuscated Memory Malware Detection in Resource-Constrained IoT Devices for Smart City Applications
by Sakib Shahriar Shafin, Gour Karmakar and Iven Mareels
Sensors 2023, 23(11), 5348; https://doi.org/10.3390/s23115348 - 5 Jun 2023
Cited by 19 | Viewed by 3482
Abstract
Obfuscated Memory Malware (OMM) presents significant threats to interconnected systems, including smart city applications, for its ability to evade detection through concealment tactics. Existing OMM detection methods primarily focus on binary detection. Their multiclass versions consider a few families only and, thereby, fail [...] Read more.
Obfuscated Memory Malware (OMM) presents significant threats to interconnected systems, including smart city applications, for its ability to evade detection through concealment tactics. Existing OMM detection methods primarily focus on binary detection. Their multiclass versions consider a few families only and, thereby, fail to detect much existing and emerging malware. Moreover, their large memory size makes them unsuitable to be executed in resource-constrained embedded/IoT devices. To address this problem, in this paper, we propose a multiclass but lightweight malware detection method capable of identifying recent malware and is suitable to execute in embedded devices. For this, the method considers a hybrid model by combining the feature-learning capabilities of convolutional neural networks with the temporal modeling advantage of bidirectional long short-term memory. The proposed architecture exhibits compact size and fast processing speed, making it suitable for deployment in IoT devices that constitute the major components of smart city systems. Extensive experiments with the recent CIC-Malmem-2022 OMM dataset demonstrate that our method outperforms other machine learning-based models proposed in the literature in both detecting OMM and identifying specific attack types. Our proposed method thus offers a robust yet compact model executable in IoT devices for defending against obfuscated malware. Full article
(This article belongs to the Special Issue Wireless Sensor Network, Smart Grid & Analytic Considerations for Smart Cities and Environmental Sustainability)
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17 pages, 6579 KiB  
Article
Improving APT Systems’ Performance in Air via Impedance Matching and 3D-Printed Clamp
by Liu Liu and Waleed Abdulla
Sensors 2023, 23(11), 5347; https://doi.org/10.3390/s23115347 - 5 Jun 2023
Cited by 1 | Viewed by 1283
Abstract
This paper presents a study on improving the performance of the acoustic piezoelectric transducer system in air, as the low acoustic impedance of air leads to suboptimal system performance. Impedance matching techniques can enhance the acoustic power transfer (APT) system’s performance in air. [...] Read more.
This paper presents a study on improving the performance of the acoustic piezoelectric transducer system in air, as the low acoustic impedance of air leads to suboptimal system performance. Impedance matching techniques can enhance the acoustic power transfer (APT) system’s performance in air. This study integrates an impedance matching circuit into the Mason circuit and investigates the impact of fixed constraints on the piezoelectric transducer’s sound pressure and output voltage. Additionally, this paper proposes a novel equilateral triangular peripheral clamp that is entirely 3D-printable and cost-effective. This study analyses the peripheral clamp’s impedance and distance characteristics and confirms its effectiveness through consistent experimental and simulation results. The findings of this study can aid researchers and practitioners in various fields that employ APT systems to improve their performance in air. Full article
(This article belongs to the Special Issue Acoustic Sensors and Their Applications)
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16 pages, 1802 KiB  
Article
Questionnaires for the Assessment of Cognitive Function Secondary to Intake Interviews in In-Hospital Work and Development and Evaluation of a Classification Model Using Acoustic Features
by Toshiharu Igarashi, Yumi Umeda-Kameyama, Taro Kojima, Masahiro Akishita and Misato Nihei
Sensors 2023, 23(11), 5346; https://doi.org/10.3390/s23115346 - 5 Jun 2023
Cited by 2 | Viewed by 1823
Abstract
The number of people with dementia is increasing each year, and early detection allows for early intervention and treatment. Since conventional screening methods are time-consuming and expensive, a simple and inexpensive screening is expected. We created a standardized intake questionnaire with thirty questions [...] Read more.
The number of people with dementia is increasing each year, and early detection allows for early intervention and treatment. Since conventional screening methods are time-consuming and expensive, a simple and inexpensive screening is expected. We created a standardized intake questionnaire with thirty questions in five categories and used machine learning to categorize older adults with moderate and mild dementia and mild cognitive impairment, based on speech patterns. To evaluate the feasibility of the developed interview items and the accuracy of the classification model based on acoustic features, 29 participants (7 males and 22 females) aged 72 to 91 years were recruited with the approval of the University of Tokyo Hospital. The MMSE results showed that 12 participants had moderate dementia with MMSE scores of 20 or less, 8 participants had mild dementia with MMSE scores between 21 and 23, and 9 participants had MCI with MMSE scores between 24 and 27. As a result, Mel-spectrogram generally outperformed MFCC in terms of accuracy, precision, recall, and F1-score in all classification tasks. The multi-classification using Mel-spectrogram achieved the highest accuracy of 0.932, while the binary classification of moderate dementia and MCI group using MFCC achieved the lowest accuracy of 0.502. The FDR was generally low for all classification tasks, indicating a low rate of false positives. However, the FNR was relatively high in some cases, indicating a higher rate of false negatives. Full article
(This article belongs to the Section Biomedical Sensors)
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16 pages, 5168 KiB  
Article
A Support Vector Machine-Based Approach for Bolt Loosening Monitoring in Industrial Customized Vehicles
by Simone Carone, Giovanni Pappalettera, Caterina Casavola, Simone De Carolis and Leonardo Soria
Sensors 2023, 23(11), 5345; https://doi.org/10.3390/s23115345 - 5 Jun 2023
Cited by 8 | Viewed by 2071
Abstract
Machine learning techniques have progressively emerged as important and reliable tools that, when combined with machine condition monitoring, can diagnose faults with even superior performance than other condition-based monitoring approaches. Furthermore, statistical or model-based approaches are often not applicable in industrial environments with [...] Read more.
Machine learning techniques have progressively emerged as important and reliable tools that, when combined with machine condition monitoring, can diagnose faults with even superior performance than other condition-based monitoring approaches. Furthermore, statistical or model-based approaches are often not applicable in industrial environments with a high degree of customization of equipment and machines. Structures such as bolted joints are a key part of the industry; therefore, monitoring their health is critical to maintaining structural integrity. Despite this, there has been little research on the detection of bolt loosening in rotating joints. In this study, vibration-based detection of bolt loosening in a rotating joint of a custom sewer cleaning vehicle transmission was performed using support vector machines (SVM). Different failures were analyzed for various vehicle operating conditions. Several classifiers were trained to evaluate the influence of the number and location of accelerometers used and to determine the best approach between specific models for each operating condition or a single model for all cases. The results showed that using a single SVM model with data from four accelerometers mounted both upstream and downstream of the bolted joint resulted in more reliable fault detection, with an overall accuracy of 92.4%. Full article
(This article belongs to the Special Issue Sensors and Methods for Diagnostics and Early Fault Detection)
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27 pages, 20456 KiB  
Article
(MARGOT) Monocular Camera-Based Robot Grasping Strategy for Metallic Objects
by Carlos Veiga Almagro, Renato Andrés Muñoz Orrego, Álvaro García González, Eloise Matheson, Raúl Marín Prades, Mario Di Castro and Manuel Ferre Pérez
Sensors 2023, 23(11), 5344; https://doi.org/10.3390/s23115344 - 5 Jun 2023
Cited by 1 | Viewed by 1749
Abstract
Robotic handling of objects is not always a trivial assignment, even in teleoperation where, in most cases, this might lead to stressful labor for operators. To reduce the task difficulty, supervised motions could be performed in safe scenarios to reduce the workload in [...] Read more.
Robotic handling of objects is not always a trivial assignment, even in teleoperation where, in most cases, this might lead to stressful labor for operators. To reduce the task difficulty, supervised motions could be performed in safe scenarios to reduce the workload in these non-critical steps by using machine learning and computer vision techniques. This paper describes a novel grasping strategy based on a groundbreaking geometrical analysis which extracts diametrically opposite points taking into account surface smoothing (even those target objects that might conform highly complex shapes) to guarantee the uniformity of the grasping. It uses a monocular camera, as we are often facing space restrictions that generate the need to use laparoscopic cameras integrated in the tools, to recognize and isolate targets from the background, estimating their spatial coordinates and providing the best possible stable grasping points for both feature and featureless objects. It copes with reflections and shadows produced by light sources (which require extra effort to extract their geometrical properties) in unstructured facilities such as nuclear power plants or particle accelerators on scientific equipment. Based on the experimental results, utilizing a specialized dataset improved the detection of metallic objects in low-contrast environments, resulting in the successful application of the algorithm with error rates in the scale of millimeters in the majority of repeatability and accuracy tests. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sensing and Robotic Systems)
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18 pages, 6958 KiB  
Article
Target Detection-Based Control Method for Archive Management Robot
by Cheng Yan, Jieqi Ren, Rui Wang, Yaowei Chen and Jie Zhang
Sensors 2023, 23(11), 5343; https://doi.org/10.3390/s23115343 - 5 Jun 2023
Viewed by 1602
Abstract
With increasing demand for efficient archive management, robots have been employed in paper-based archive management for large, unmanned archives. However, the reliability requirements of such systems are high due to their unmanned nature. To address this, this study proposes a paper archive access [...] Read more.
With increasing demand for efficient archive management, robots have been employed in paper-based archive management for large, unmanned archives. However, the reliability requirements of such systems are high due to their unmanned nature. To address this, this study proposes a paper archive access system with adaptive recognition for handling complex archive box access scenarios. The system comprises a vision component that employs the YOLOV5 algorithm to identify feature regions, sort and filter data, and to estimate the target center position, as well as a servo control component. This study proposes a servo-controlled robotic arm system with adaptive recognition for efficient paper-based archive management in unmanned archives. The vision part of the system employs the YOLOV5 algorithm to identify feature regions and to estimate the target center position, while the servo control part uses closed-loop control to adjust posture. The proposed feature region-based sorting and matching algorithm enhances accuracy and reduces the probability of shaking by 1.27% in restricted viewing scenarios. The system is a reliable and cost-effective solution for paper archive access in complex scenarios, and the integration of the proposed system with a lifting device enables the effective storage and retrieval of archive boxes of varying heights. However, further research is necessary to evaluate its scalability and generalizability. The experimental results demonstrate the effectiveness of the proposed adaptive box access system for unmanned archival storage. The system exhibits a higher storage success rate than existing commercial archival management robotic systems. The integration of the proposed system with a lifting device provides a promising solution for efficient archive management in unmanned archival storage. Future research should focus on evaluating the system’s performance and scalability. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sensing and Robotic Systems)
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20 pages, 1084 KiB  
Review
The Role of Blockchain Technology in Promoting Traceability Systems in Agri-Food Production and Supply Chains
by Techane Bosona and Girma Gebresenbet
Sensors 2023, 23(11), 5342; https://doi.org/10.3390/s23115342 - 5 Jun 2023
Cited by 26 | Viewed by 6896
Abstract
Due to recurring food quality and safety issues, growing segments of consumers, especially in developed markets, and regulators in agri-food supply chains (AFSCs) require a fast and trustworthy system to retrieve necessary information on their food products. With the existing centralized traceability systems [...] Read more.
Due to recurring food quality and safety issues, growing segments of consumers, especially in developed markets, and regulators in agri-food supply chains (AFSCs) require a fast and trustworthy system to retrieve necessary information on their food products. With the existing centralized traceability systems used in AFSCs, it is difficult to acquire full traceability information, and there are risks of information loss and data tampering. To address these challenges, research on the application of blockchain technology (BCT) for traceability systems in the agri-food sector is increasing, and startup companies have emerged in recent years. However, there have been only a limited number of reviews on the application of BCT in the agriculture sector, especially those that focus on the BCT-based traceability of agricultural goods. To bridge this knowledge gap, we reviewed 78 studies that integrated BCT into traceability systems in AFSCs and additional relevant papers, mapping out the main types of food traceability information. The findings indicated that the existing BCT-based traceability systems focus more on fruit and vegetables, meat, dairy, and milk. A BCT-based traceability system enables one to develop and implement a decentralized, immutable, transparent, and reliable system in which process automation facilitates the monitoring of real-time data and decision-making activities. We also mapped out the main traceability information, key information providers, and challenges and benefits of the BCT-based traceability systems in AFSCs. These helped to design, develop, and implement BCT-based traceability systems, which, in turn, will contribute to the transition to smart AFSC systems. This study comprehensively illustrated that implementing BCT-based traceability systems also has important, positive implications for improving AFSC management, e.g., reductions in food loss and food recall incidents and the achievement of the United Nations SDGs (1, 3, 5, 9, 12). This will contribute to existing knowledge and be useful for academicians, managers, and practitioners in AFSCs, as well as policymakers. Full article
(This article belongs to the Section Sensor Networks)
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12 pages, 2927 KiB  
Article
CVCC Model: Learning-Based Computer Vision Color Constancy with RiR-DSN Architecture
by Ho-Hyoung Choi
Sensors 2023, 23(11), 5341; https://doi.org/10.3390/s23115341 - 5 Jun 2023
Cited by 3 | Viewed by 1591
Abstract
To achieve computer vision color constancy (CVCC), it is vital but challenging to estimate scene illumination from a digital image, which distorts the true color of an object. Estimating illumination as accurately as possible is fundamental to improving the quality of the image [...] Read more.
To achieve computer vision color constancy (CVCC), it is vital but challenging to estimate scene illumination from a digital image, which distorts the true color of an object. Estimating illumination as accurately as possible is fundamental to improving the quality of the image processing pipeline. CVCC has a long history of research and has significantly advanced, but it has yet to overcome some limitations such as algorithm failure or accuracy decreasing under unusual circumstances. To cope with some of the bottlenecks, this article presents a novel CVCC approach that introduces a residual-in-residual dense selective kernel network (RiR-DSN). As its name implies, it has a residual network in a residual network (RiR) and the RiR houses a dense selective kernel network (DSN). A DSN is composed of selective kernel convolutional blocks (SKCBs). The SKCBs, or neurons herein, are interconnected in a feed-forward fashion. Every neuron receives input from all its preceding neurons and feeds the feature maps into all its subsequent neurons, which is how information flows in the proposed architecture. In addition, the architecture has incorporated a dynamic selection mechanism into each neuron to ensure that the neuron can modulate filter kernel sizes depending on varying intensities of stimuli. In a nutshell, the proposed RiR-DSN architecture features neurons called SKCBs and a residual block in a residual block, which brings several benefits such as alleviation of the vanishing gradients, enhancement of feature propagation, promotion of the reuse of features, modulation of receptive filter sizes depending on varying intensities of stimuli, and a dramatic drop in the number of parameters. Experimental results highlight that the RiR-DSN architecture performs well above its state-of-the-art counterparts, as well as proving to be camera- and illuminant-invariant. Full article
(This article belongs to the Collection Computational Imaging and Sensing)
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26 pages, 743 KiB  
Review
Machine Learning-Based Anomaly Detection in NFV: A Comprehensive Survey
by Sehar Zehra, Ummay Faseeha, Hassan Jamil Syed, Fahad Samad, Ashraf Osman Ibrahim, Anas W. Abulfaraj and Wamda Nagmeldin
Sensors 2023, 23(11), 5340; https://doi.org/10.3390/s23115340 - 5 Jun 2023
Cited by 11 | Viewed by 6099
Abstract
Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring [...] Read more.
Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems. Full article
(This article belongs to the Special Issue Knowledge-Defined Cloud-Native Networks: Applying AI and Cloud-Native Principles to Next-Generation Wireless Networks)
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18 pages, 4300 KiB  
Article
A Hardware-Based Configurable Algorithm for Eye Blink Signal Detection Using a Single-Channel BCI Headset
by Rafael López-Ahumada, Raúl Jiménez-Naharro and Fernando Gómez-Bravo
Sensors 2023, 23(11), 5339; https://doi.org/10.3390/s23115339 - 5 Jun 2023
Cited by 1 | Viewed by 2421
Abstract
Eye blink artifacts in electroencephalographic (EEG) signals have been used in multiple applications as an effective method for human–computer interaction. Hence, an effective and low-cost blinking detection method would be an invaluable aid for the development of this technology. A configurable hardware algorithm, [...] Read more.
Eye blink artifacts in electroencephalographic (EEG) signals have been used in multiple applications as an effective method for human–computer interaction. Hence, an effective and low-cost blinking detection method would be an invaluable aid for the development of this technology. A configurable hardware algorithm, described using hardware description language, for eye blink detection based on EEG signals from a one-channel brain–computer interface (BCI) headset was developed and implemented, showing better performance in terms of effectiveness and detection time than manufacturer-provided software. Full article
(This article belongs to the Section Intelligent Sensors)
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16 pages, 3781 KiB  
Article
Cascaded Degradation-Aware Blind Super-Resolution
by Ding Zhang, Ni Tang, Dongxiao Zhang and Yanyun Qu
Sensors 2023, 23(11), 5338; https://doi.org/10.3390/s23115338 - 5 Jun 2023
Cited by 2 | Viewed by 1693
Abstract
Image super-resolution (SR) usually synthesizes degraded low-resolution images with a predefined degradation model for training. Existing SR methods inevitably perform poorly when the true degradation does not follow the predefined degradation, especially in the case of the real world. To tackle this robustness [...] Read more.
Image super-resolution (SR) usually synthesizes degraded low-resolution images with a predefined degradation model for training. Existing SR methods inevitably perform poorly when the true degradation does not follow the predefined degradation, especially in the case of the real world. To tackle this robustness issue, we propose a cascaded degradation-aware blind super-resolution network (CDASRN), which not only eliminates the influence of noise on blur kernel estimation but also can estimate the spatially varying blur kernel. With the addition of contrastive learning, our CDASRN can further distinguish the differences between local blur kernels, greatly improving its practicality. Experiments in various settings show that CDASRN outperforms state-of-the-art methods on both heavily degraded synthetic datasets and real-world datasets. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensing and Image Processing)
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11 pages, 1030 KiB  
Article
Cascading Robustness Analysis of Wireless Sensor Networks with Varying Multisink Placement
by Lin Ding, Dan Sheng, Minsheng Tan and Juan Wen
Sensors 2023, 23(11), 5337; https://doi.org/10.3390/s23115337 - 5 Jun 2023
Cited by 1 | Viewed by 1291
Abstract
In practical wireless sensor networks (WSNs), cascading failures are closely related to network load distribution, which in turn strongly relies on the locations of multiple sink nodes. For such a network, understanding how the multisink placement affects its cascading robustness is essential but [...] Read more.
In practical wireless sensor networks (WSNs), cascading failures are closely related to network load distribution, which in turn strongly relies on the locations of multiple sink nodes. For such a network, understanding how the multisink placement affects its cascading robustness is essential but still largely missing in the field of complex networks. To this end, this paper puts forward an actual cascading model for WSNs based on the multisink-oriented load distribution characteristics, in which two load redistribution mechanisms (i.e., global routing and local routing) are designed to imitate the most commonly used routing schemes. On this basis, a number of topological parameters are considered to quantify the sinks’ locations, and then, the relationship between these quantities with network robustness is investigated on two typical WSN topologies. Moreover, by employing the simulated annealing approach, we find the optimal multisink placement for maximizing network robustness and compare the topological quantities before and after the optimization to validate our findings. The results indicate that for the sake of enhancing the cascading robustness of a WSN, it is better to place its sinks as hubs and decentralize these sinks, which is independent of network structure and routing scheme. Full article
(This article belongs to the Topic Complex Systems and Artificial Intelligence)
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13 pages, 2118 KiB  
Article
Preparation of Nanocomposites for Antibacterial Orthodontic Invisible Appliance Based on Piezoelectric Catalysis
by Yingying Shi, Ningning Zhang, Jiajie Liu, Junbin Wang, Shuhui Shen, Jingxiang Zhang, Xiaoli An and Qingzong Si
Sensors 2023, 23(11), 5336; https://doi.org/10.3390/s23115336 - 5 Jun 2023
Cited by 4 | Viewed by 1882
Abstract
Compared to fixed orthodontic appliances with brackets, thermoplastic invisible orthodontic aligners offer several advantages, such as high aesthetic performance, good comfort, and convenient oral health maintenance, and are widely used in orthodontic fields. However, prolonged use of thermoplastic invisible aligners may lead to [...] Read more.
Compared to fixed orthodontic appliances with brackets, thermoplastic invisible orthodontic aligners offer several advantages, such as high aesthetic performance, good comfort, and convenient oral health maintenance, and are widely used in orthodontic fields. However, prolonged use of thermoplastic invisible aligners may lead to demineralization and even caries in most patients’ teeth, as they enclose the tooth surface for an extended period. To address this issue, we have created PETG composites that contain piezoelectric barium titanate nanoparticles (BaTiO3NPs) to obtain antibacterial properties. First, we prepared piezoelectric composites by incorporating varying amounts of BaTiO3NPs into PETG matrix material. The composites were then characterized using techniques such as SEM, XRD, and Raman spectroscopy, which confirmed the successful synthesis of the composites. We cultivated biofilms of Streptococcus mutans (S. mutans) on the surface of the nanocomposites under both polarized and unpolarized conditions. We then activated piezoelectric charges by subjecting the nanocomposites to 10 Hz cyclic mechanical vibration. The interactions between the biofilms and materials were evaluated by measuring the biofilm biomass. The addition of piezoelectric nanoparticles had a noticeable antibacterial effect on both the unpolarized and polarized conditions. Under polarized conditions, nanocomposites demonstrated a greater antibacterial effect than under unpolarized conditions. Additionally, as the concentration of BaTiO3NPs increased, the antibacterial rate also increased, with the surface antibacterial rate reaching 67.39% (30 wt% BaTiO3NPs). These findings have the potential for application in wearable, invisible appliances to improve clinical services and reduce the need for cleaning methods. Full article
(This article belongs to the Special Issue Advances in Biosensor Technologies for Clinical Applications)
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21 pages, 2687 KiB  
Review
Detecting the Unseen: Understanding the Mechanisms and Working Principles of Earthquake Sensors
by Bingwei Tian, Wenrui Liu, Haozhou Mo, Wang Li, Yuting Wang and Basanta Raj Adhikari
Sensors 2023, 23(11), 5335; https://doi.org/10.3390/s23115335 - 5 Jun 2023
Cited by 2 | Viewed by 8746
Abstract
The application of movement-detection sensors is crucial for understanding surface movement and tectonic activities. The development of modern sensors has been instrumental in earthquake monitoring, prediction, early warning, emergency commanding and communication, search and rescue, and life detection. There are numerous sensors currently [...] Read more.
The application of movement-detection sensors is crucial for understanding surface movement and tectonic activities. The development of modern sensors has been instrumental in earthquake monitoring, prediction, early warning, emergency commanding and communication, search and rescue, and life detection. There are numerous sensors currently being utilized in earthquake engineering and science. It is essential to review their mechanisms and working principles thoroughly. Hence, we have attempted to review the development and application of these sensors by classifying them based on the timeline of earthquakes, the physical or chemical mechanisms of sensors, and the location of sensor platforms. In this study, we analyzed available sensor platforms that have been widely used in recent years, with satellites and UAVs being among the most used. The findings of our study will be useful for future earthquake response and relief efforts, as well as research aimed at reducing earthquake disaster risks. Full article
(This article belongs to the Section Remote Sensors)
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22 pages, 7605 KiB  
Article
Research on Rolling Bearing Fault Diagnosis Based on Digital Twin Data and Improved ConvNext
by Chao Zhang, Feifan Qin, Wentao Zhao, Jianjun Li and Tongtong Liu
Sensors 2023, 23(11), 5334; https://doi.org/10.3390/s23115334 - 5 Jun 2023
Cited by 8 | Viewed by 3243
Abstract
This article introduces a novel framework for diagnosing faults in rolling bearings. The framework combines digital twin data, transfer learning theory, and an enhanced ConvNext deep learning network model. Its purpose is to address the challenges posed by the limited actual fault data [...] Read more.
This article introduces a novel framework for diagnosing faults in rolling bearings. The framework combines digital twin data, transfer learning theory, and an enhanced ConvNext deep learning network model. Its purpose is to address the challenges posed by the limited actual fault data density and inadequate result accuracy in existing research on the detection of rolling bearing faults in rotating mechanical equipment. To begin with, the operational rolling bearing is represented in the digital realm through the utilization of a digital twin model. The simulation data produced by this twin model replace traditional experimental data, effectively creating a substantial volume of well-balanced simulated datasets. Next, improvements are made to the ConvNext network by incorporating an unparameterized attention module called the Similarity Attention Module (SimAM) and an efficient channel attention feature referred to as the Efficient Channel Attention Network (ECA). These enhancements serve to augment the network’s capability for extracting features. Subsequently, the enhanced network model is trained using the source domain dataset. Simultaneously, the trained model is transferred to the target domain bearing using transfer learning techniques. This transfer learning process enables the accurate fault diagnosis of the main bearing to be achieved. Finally, the proposed method’s feasibility is validated, and a comparative analysis is conducted in comparison with similar approaches. The comparative study demonstrates that the proposed method effectively addresses the issue of low mechanical equipment fault data density, leading to improved accuracy in fault detection and classification, along with a certain level of robustness. Full article
(This article belongs to the Special Issue Sensors for Fault Detection and Condition Monitoring)
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16 pages, 7159 KiB  
Article
A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis
by Mingyu Sun, Ben Gabrielson, Mohammad Abu Baker Siddique Akhonda, Hanlu Yang, Francisco Laport, Vince Calhoun and Tülay Adali
Sensors 2023, 23(11), 5333; https://doi.org/10.3390/s23115333 - 5 Jun 2023
Cited by 4 | Viewed by 1584
Abstract
Joint blind source separation (JBSS) has wide applications in modeling latent structures across multiple related datasets. However, JBSS is computationally prohibitive with high-dimensional data, limiting the number of datasets that can be included in a tractable analysis. Furthermore, JBSS may not be effective [...] Read more.
Joint blind source separation (JBSS) has wide applications in modeling latent structures across multiple related datasets. However, JBSS is computationally prohibitive with high-dimensional data, limiting the number of datasets that can be included in a tractable analysis. Furthermore, JBSS may not be effective if the data’s true latent dimensionality is not adequately modeled, where severe overparameterization may lead to poor separation and time performance. In this paper, we propose a scalable JBSS method by modeling and separating the “shared” subspace from the data. The shared subspace is defined as the subset of latent sources that exists across all datasets, represented by groups of sources that collectively form a low-rank structure. Our method first provides the efficient initialization of the independent vector analysis (IVA) with a multivariate Gaussian source prior (IVA-G) specifically designed to estimate the shared sources. Estimated sources are then evaluated regarding whether they are shared, upon which further JBSS is applied separately to the shared and non-shared sources. This provides an effective means to reduce the dimensionality of the problem, improving analyses with larger numbers of datasets. We apply our method to resting-state fMRI datasets, demonstrating that our method can achieve an excellent estimation performance with significantly reduced computational costs. Full article
(This article belongs to the Special Issue Recent Advancements in Sensor Technologies for Healthcare and Biomedical Applications (Volume II))
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14 pages, 5948 KiB  
Article
A Design of a Novel Silicon Photonics Sensor with Ultra-Large Free Spectral Range Based on a Directional Coupler-Assisted Racetrack Resonator (DCARR)
by Osamah Alsalman and Iain Crowe
Sensors 2023, 23(11), 5332; https://doi.org/10.3390/s23115332 - 4 Jun 2023
Viewed by 2271
Abstract
A novel refractive index-based sensor implemented within a silicon photonic integrated circuit (PIC) is reported. The design is based on a double-directional coupler (DC) integrated with a racetrack-type resonator (RR) to enhance the optical response to changes in the near-surface refractive index via [...] Read more.
A novel refractive index-based sensor implemented within a silicon photonic integrated circuit (PIC) is reported. The design is based on a double-directional coupler (DC) integrated with a racetrack-type resonator (RR) to enhance the optical response to changes in the near-surface refractive index via the optical Vernier effect. Although this approach can give rise to an extremely large ‘envelope’ free spectral range (FSRVernier), we restrict the design geometry to ensure this is within the traditional silicon PIC operating wavelength range of 1400–1700 nm. As a result, the exemplar double DC-assisted RR (DCARR) device demonstrated here, with FSRVernier = 246 nm, has a spectral sensitivity SVernier = 5 × 104 nm/RIU. Full article
(This article belongs to the Section Physical Sensors)
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21 pages, 2371 KiB  
Review
Review of Shoreline Extraction Methods from Aerial Laser Scanning
by Andrzej Stateczny, Armin Halicki, Mariusz Specht, Cezary Specht and Oktawia Lewicka
Sensors 2023, 23(11), 5331; https://doi.org/10.3390/s23115331 - 4 Jun 2023
Cited by 3 | Viewed by 1866
Abstract
Autonomous technologies are increasingly used in various areas of science. The use of unmanned vehicles for hydrographic surveys in shallow coastal areas requires accurate estimation of shoreline position. This is a nontrivial task, which can be performed using a wide range of sensors [...] Read more.
Autonomous technologies are increasingly used in various areas of science. The use of unmanned vehicles for hydrographic surveys in shallow coastal areas requires accurate estimation of shoreline position. This is a nontrivial task, which can be performed using a wide range of sensors and methods. The aim of the publication is to review shoreline extraction methods based solely on data from aerial laser scanning (ALS). This narrative review discusses and critically analyses seven publications drawn up in the last ten years. The discussed papers employed nine different shoreline extraction methods based on aerial light detection and ranging (LiDAR) data. It should be noted that unambiguous evaluation of shoreline extraction methods is difficult or impossible. This is because not all of the methods reported achieved accuracy, the methods were assessed on different datasets, the measurements were conducted using different devices, the water areas differed in geometrical and optical properties, the shorelines had different geometries, and the extent of anthropogenic transformation. The methods proposed by the authors were compared with a wide range of reference methods. Full article
(This article belongs to the Collection Navigation Systems and Sensors)
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13 pages, 446 KiB  
Article
Major Depressive Disorder and Chronic Fatigue Syndrome Show Characteristic Heart Rate Variability Profiles Reflecting Autonomic Dysregulations: Differentiation by Linear Discriminant Analysis
by Toshikazu Shinba, Daisuke Kuratsune, Shuntaro Shinba, Yujiro Shinba, Guanghao Sun, Takemi Matsui and Hirohiko Kuratsune
Sensors 2023, 23(11), 5330; https://doi.org/10.3390/s23115330 - 4 Jun 2023
Cited by 4 | Viewed by 5939
Abstract
Major depressive disorder (MDD) and chronic fatigue syndrome (CFS) have overlapping symptoms, and differentiation is important to administer the proper treatment. The present study aimed to assess the usefulness of heart rate variability (HRV) indices. Frequency-domain HRV indices, including high-frequency (HF) and low-frequency [...] Read more.
Major depressive disorder (MDD) and chronic fatigue syndrome (CFS) have overlapping symptoms, and differentiation is important to administer the proper treatment. The present study aimed to assess the usefulness of heart rate variability (HRV) indices. Frequency-domain HRV indices, including high-frequency (HF) and low-frequency (LF) components, their sum (LF+HF), and their ratio (LF/HF), were measured in a three-behavioral-state paradigm composed of initial rest (Rest), task load (Task), and post-task rest (After) periods to examine autonomic regulation. It was found that HF was low at Rest in both disorders, but was lower in MDD than in CFS. LF and LF+HF at Rest were low only in MDD. Attenuated responses of LF, HF, LF+HF, and LF/HF to task load and an excessive increase in HF at After were found in both disorders. The results indicate that an overall HRV reduction at Rest may support a diagnosis of MDD. HF reduction was found in CFS, but with a lesser severity. Response disturbances of HRV to Task were observed in both disorders, and would suggest the presence of CFS when the baseline HRV has not been reduced. Linear discriminant analysis using HRV indices was able to differentiate MDD from CFS, with a sensitivity and specificity of 91.8% and 100%, respectively. HRV indices in MDD and CFS show both common and different profiles, and can be useful for the differential diagnosis. Full article
(This article belongs to the Special Issue Physiological Signal Sensing for Mental Health Monitoring and Management)
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18 pages, 5988 KiB  
Article
Unsupervised Monocular Depth and Camera Pose Estimation with Multiple Masks and Geometric Consistency Constraints
by Xudong Zhang, Baigan Zhao, Jiannan Yao and Guoqing Wu
Sensors 2023, 23(11), 5329; https://doi.org/10.3390/s23115329 - 4 Jun 2023
Cited by 1 | Viewed by 2254
Abstract
This paper presents a novel unsupervised learning framework for estimating scene depth and camera pose from video sequences, fundamental to many high-level tasks such as 3D reconstruction, visual navigation, and augmented reality. Although existing unsupervised methods have achieved promising results, their performance suffers [...] Read more.
This paper presents a novel unsupervised learning framework for estimating scene depth and camera pose from video sequences, fundamental to many high-level tasks such as 3D reconstruction, visual navigation, and augmented reality. Although existing unsupervised methods have achieved promising results, their performance suffers in challenging scenes such as those with dynamic objects and occluded regions. As a result, multiple mask technologies and geometric consistency constraints are adopted in this research to mitigate their negative impacts. Firstly, multiple mask technologies are used to identify numerous outliers in the scene, which are excluded from the loss computation. In addition, the identified outliers are employed as a supervised signal to train a mask estimation network. The estimated mask is then utilized to preprocess the input to the pose estimation network, mitigating the potential adverse effects of challenging scenes on pose estimation. Furthermore, we propose geometric consistency constraints to reduce the sensitivity of illumination changes, which act as additional supervised signals to train the network. Experimental results on the KITTI dataset demonstrate that our proposed strategies can effectively enhance the model’s performance, outperforming other unsupervised methods. Full article
(This article belongs to the Section Intelligent Sensors)
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