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Volume 22
Issue 13
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Sensors, Volume 22, Issue 13 (July-1 2022) – 410 articles

Cover Story (view full-size image): Multi-port RFID systems are widely used to enable multiple independent measurands to be gathered, or to overcome channel variations. Here, a dual-port/dual-integrated circuit (IC) RFID sensing tag is presented based on a shared aperture microstrip antenna. The tag can be loaded with different sensors where the received signal strength indicator (RSSI) of one IC is modulated using a sensor, and the other acts as a reference. The tag maintains an unloaded read range of over 14 m insensitive to deployment on metals or lossy objects, showing the longest reported range of a multi-port RFID sensing tag. The tag is loaded with a light-dependent resistor (LDR) showing that light intensity variations can be detected in the RF domain at the sensing port, while not affecting the reference port. View this paper
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16 pages, 2166 KiB  
Article
LPWAN Key Exchange: A Centralised Lightweight Approach
by Gaurav Pathak, Jairo Gutierrez, Akbar Ghobakhlou and Saeed Ur Rehman
Sensors 2022, 22(13), 5065; https://doi.org/10.3390/s22135065 - 05 Jul 2022
Cited by 3 | Viewed by 1722
Abstract
The Internet of Things (IoT) is one of the fastest emerging technologies in the industry. It includes diverse applications with different requirements to provide services to users. Secure, low-powered, and long-range transmissions are some of the most vital requirements in developing IoT applications. [...] Read more.
The Internet of Things (IoT) is one of the fastest emerging technologies in the industry. It includes diverse applications with different requirements to provide services to users. Secure, low-powered, and long-range transmissions are some of the most vital requirements in developing IoT applications. IoT uses several communication technologies to fulfill transmission requirements. However, Low Powered Wide Area Networks (LPWAN) transmission standards have been gaining attention because of their exceptional low-powered and long-distance transmission capabilities. The features of LPWAN transmission standards make them a perfect candidate for IoT applications. However, the current LPWAN standards lack state-of-the-art security mechanism s because of the limitations of the IoT devices in energy and computational capacity. Most of the LPWAN standards, such as Sigfox, NB-IoT, and Weightless, use static keys for node authentication and encryption. LoRaWAN is the only LPWAN technology providing session key mechanisms for better security. However, the session key mechanism is vulnerable to replay attacks. In this paper, we propose a centralized lightweight session key mechanism for LPWAN standards using the Blom–Yang key agreement (BYka) mechanism. The security of the session key mechanism is tested using the security verification tool Scyther. In addition, an energy consumption model is implemented on the LoRaWAN protocol using the NS3 simulator to verify the energy depletion in a LoRaWAN node because of the proposed session key mechanisms. The proposed session key is also verified on the Mininet-WiFi emulator for its correctness. The analysis demonstrates that the proposed session key mechanism uses a fewer number of transmissions than the existing session key mechanisms in LPWAN and provides mechanisms against replay attacks that are possible in current LPWAN session key schemes. Full article
(This article belongs to the Special Issue Lightweight Security Integrity and Confidentiality for Internet of Things (IoT))
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16 pages, 17730 KiB  
Communication
A System for Autonomous Seaweed Farm Inspection with an Underwater Robot
by Ivan Stenius, John Folkesson, Sriharsha Bhat, Christopher Iliffe Sprague, Li Ling, Özer Özkahraman, Nils Bore, Zheng Cong, Josefine Severholt, Carl Ljung, Anna Arnwald, Ignacio Torroba, Fredrik Gröndahl and Jean-Baptiste Thomas
Sensors 2022, 22(13), 5064; https://doi.org/10.3390/s22135064 - 05 Jul 2022
Cited by 10 | Viewed by 4741
Abstract
This paper outlines challenges and opportunities in operating underwater robots (so-called AUVs) on a seaweed farm. The need is driven by an emerging aquaculture industry on the Swedish west coast where large-scale seaweed farms are being developed. In this paper, the operational challenges [...] Read more.
This paper outlines challenges and opportunities in operating underwater robots (so-called AUVs) on a seaweed farm. The need is driven by an emerging aquaculture industry on the Swedish west coast where large-scale seaweed farms are being developed. In this paper, the operational challenges are described and key technologies in using autonomous systems as a core part of the operation are developed and demonstrated. The paper presents a system and methods for operating an AUV in the seaweed farm, including initial localization of the farm based on a prior estimate and dead-reckoning navigation, and the subsequent scanning of the entire farm. Critical data from sidescan sonars for algorithm development are collected from real environments at a test site in the ocean, and the results are demonstrated in a simulated seaweed farm setup. Full article
(This article belongs to the Special Issue Underwater Robotics in 2022-2023)
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34 pages, 899 KiB  
Article
Security and Privacy Analysis of Smartphone-Based Driver Monitoring Systems from the Developer’s Point of View
by Dmitry Levshun, Andrey Chechulin and Igor Kotenko
Sensors 2022, 22(13), 5063; https://doi.org/10.3390/s22135063 - 05 Jul 2022
Cited by 1 | Viewed by 2533
Abstract
Nowadays, the whole driver monitoring system can be placed inside the vehicle driver’s smartphone, which introduces new security and privacy risks to the system. Because of the nature of the modern transportation systems, the consequences of the security issues in such systems can [...] Read more.
Nowadays, the whole driver monitoring system can be placed inside the vehicle driver’s smartphone, which introduces new security and privacy risks to the system. Because of the nature of the modern transportation systems, the consequences of the security issues in such systems can be crucial, leading to threat to human life and health. Moreover, despite the large number of security and privacy issues discovered in smartphone applications on a daily basis, there is no general approach for their automated analysis that can work in conditions that lack data and take into account specifics of the application area. Thus, this paper describes an original approach for a security and privacy analysis of driver monitoring systems based on smartphone sensors. This analysis uses white-box testing principles and aims to help developers evaluate and improve their products. The novelty of the proposed approach lies in combining various security and privacy analysis algorithms into a single automated approach for a specific area of application. Moreover, the suggested approach is modular and extensible, takes into account specific features of smartphone-based driver monitoring systems and works in conditions of lack or inaccessibility of data. The practical significance of the approach lies in the suggestions that are provided based on the conducted analysis. Those suggestions contain detected security and privacy issues and ways of their mitigation, together with limitations of the analysis due to the absence of data. It is assumed that such an approach would help developers take into account important aspects of security and privacy, thus reducing related issues in the developed products. An experimental evaluation of the approach is conducted on a car driver monitoring use case. In addition, the advantages and disadvantages of the proposed approach as well as future work directions are indicated. Full article
(This article belongs to the Special Issue Smartphone Sensors for Driver Behavior Monitoring Systems)
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15 pages, 1098 KiB  
Article
Multi-Agent Dynamic Resource Allocation in 6G in-X Subnetworks with Limited Sensing Information
by Ramoni Adeogun and Gilberto Berardinelli
Sensors 2022, 22(13), 5062; https://doi.org/10.3390/s22135062 - 05 Jul 2022
Cited by 2 | Viewed by 1939
Abstract
In this paper, we investigate dynamic resource selection in dense deployments of the recent 6G mobile in-X subnetworks (inXSs). We cast resource selection in inXSs as a multi-objective optimization problem involving maximization of the minimum capacity per inXS while minimizing overhead from intra-subnetwork [...] Read more.
In this paper, we investigate dynamic resource selection in dense deployments of the recent 6G mobile in-X subnetworks (inXSs). We cast resource selection in inXSs as a multi-objective optimization problem involving maximization of the minimum capacity per inXS while minimizing overhead from intra-subnetwork signaling. Since inXSs are expected to be autonomous, selection decisions are made by each inXS based on its local information without signaling from other inXSs. A multi-agent Q-learning (MAQL) method based on limited sensing information (SI) is then developed, resulting in low intra-subnetwork SI signaling. We further propose a rule-based algorithm termed Q-Heuristics for performing resource selection based on similar limited information as the MAQL method. We perform simulations with a focus on joint channel and transmit power selection. The results indicate that: (1) appropriate settings of Q-learning parameters lead to fast convergence of the MAQL method even with two-level quantization of the SI, and (2) the proposed MAQL approach has significantly better performance and is more robust to sensing and switching delays than the best baseline heuristic. The proposed Q-Heuristic shows similar performance to the baseline greedy method at the 50th percentile of the per-user capacity and slightly better at lower percentiles. The Q-Heuristic method shows high robustness to sensing interval, quantization threshold and switching delay. Full article
(This article belongs to the Special Issue 6G Wireless Communication and Its Applications)
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22 pages, 49989 KiB  
Article
Enhanced Perception for Autonomous Driving Using Semantic and Geometric Data Fusion
by Horatiu Florea, Andra Petrovai, Ion Giosan, Florin Oniga, Robert Varga and Sergiu Nedevschi
Sensors 2022, 22(13), 5061; https://doi.org/10.3390/s22135061 - 05 Jul 2022
Cited by 11 | Viewed by 3463
Abstract
Environment perception remains one of the key tasks in autonomous driving for which solutions have yet to reach maturity. Multi-modal approaches benefit from the complementary physical properties specific to each sensor technology used, boosting overall performance. The added complexity brought on by data [...] Read more.
Environment perception remains one of the key tasks in autonomous driving for which solutions have yet to reach maturity. Multi-modal approaches benefit from the complementary physical properties specific to each sensor technology used, boosting overall performance. The added complexity brought on by data fusion processes is not trivial to solve, with design decisions heavily influencing the balance between quality and latency of the results. In this paper we present our novel real-time, 360∘ enhanced perception component based on low-level fusion between geometry provided by the LiDAR-based 3D point clouds and semantic scene information obtained from multiple RGB cameras, of multiple types. This multi-modal, multi-sensor scheme enables better range coverage, improved detection and classification quality with increased robustness. Semantic, instance and panoptic segmentations of 2D data are computed using efficient deep-learning-based algorithms, while 3D point clouds are segmented using a fast, traditional voxel-based solution. Finally, the fusion obtained through point-to-image projection yields a semantically enhanced 3D point cloud that allows enhanced perception through 3D detection refinement and 3D object classification. The planning and control systems of the vehicle receives the individual sensors’ perception together with the enhanced one, as well as the semantically enhanced 3D points. The developed perception solutions are successfully integrated onto an autonomous vehicle software stack, as part of the UP-Drive project. Full article
(This article belongs to the Special Issue Robust Multimodal Sensing for Automated Driving Systems)
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20 pages, 1764 KiB  
Article
RESEKRA: Remote Enrollment Using SEaled Keys for Remote Attestation
by Ernesto Gómez-Marín, Luis Parrilla, Gianfranco Mauro, Antonio Escobar-Molero, Diego P. Morales and Encarnación Castillo
Sensors 2022, 22(13), 5060; https://doi.org/10.3390/s22135060 - 05 Jul 2022
Cited by 2 | Viewed by 2151
Abstract
This paper presents and implements a novel remote attestation method to ensure the integrity of a device applicable to decentralized infrastructures, such as those found in common edge computing scenarios. Edge computing can be considered as a framework where multiple unsupervised devices communicate [...] Read more.
This paper presents and implements a novel remote attestation method to ensure the integrity of a device applicable to decentralized infrastructures, such as those found in common edge computing scenarios. Edge computing can be considered as a framework where multiple unsupervised devices communicate with each other with lack of hierarchy, requesting and offering services without a central server to orchestrate them. Because of these characteristics, there are many security threats, and detecting attacks is essential. Many remote attestation systems have been developed to alleviate this problem, but none of them can satisfy the requirements of edge computing: accepting dynamic enrollment and removal of devices to the system, respecting the interrupted activity of devices, and last but not least, providing a decentralized architecture for not trusting in just one Verifier. This security flaw has a negative impact on the development and implementation of edge computing-based technologies because of the impossibility of secure implementation. In this work, we propose a remote attestation system that, through using a Trusted Platform Module (TPM), enables the dynamic enrollment and an efficient and decentralized attestation. We demonstrate and evaluate our work in two use cases, attaining acceptance of intermittent activity by IoT devices, deletion of the dependency of centralized verifiers, and the probation of continuous integrity between unknown devices just by one signature verification. Full article
(This article belongs to the Section Internet of Things)
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18 pages, 1590 KiB  
Review
Multisensory and Sensorimotor Integration in the Embodied Self: Relationship between Self-Body Recognition and the Mirror Neuron System
by Sotaro Shimada
Sensors 2022, 22(13), 5059; https://doi.org/10.3390/s22135059 - 05 Jul 2022
Cited by 4 | Viewed by 2875
Abstract
The embodied self is rooted in the self-body in the “here and now”. The senses of self-ownership and self-agency have been proposed as the basis of the sense of embodied self, and many experimental studies have been conducted on this subject. This review [...] Read more.
The embodied self is rooted in the self-body in the “here and now”. The senses of self-ownership and self-agency have been proposed as the basis of the sense of embodied self, and many experimental studies have been conducted on this subject. This review summarizes the experimental research on the embodied self that has been conducted over the past 20 years, mainly from the perspective of multisensory integration and sensorimotor integration regarding the self-body. Furthermore, the phenomenon of back projection, in which changes in an external object (e.g., a rubber hand) with which one has a sense of ownership have an inverse influence on the sensation and movement of one’s own body, is discussed. This postulates that the self-body illusion is not merely an illusion caused by multisensory and/or sensorimotor integration, but is the incorporation of an external object into the self-body representation in the brain. As an extension of this fact, we will also review research on the mirror neuron system, which is considered to be the neural basis of recognition of others, and discuss how the neural basis of self-body recognition and the mirror neuron system can be regarded as essentially the same. Full article
(This article belongs to the Special Issue Embodied Minds: From Cognition to Artificial Intelligence)
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12 pages, 4318 KiB  
Communication
Innovative Image Processing Method to Improve Autofocusing Accuracy
by Chien-Sheng Liu and Ho-Da Tu
Sensors 2022, 22(13), 5058; https://doi.org/10.3390/s22135058 - 05 Jul 2022
Cited by 3 | Viewed by 1970
Abstract
For automated optical inspection, autofocusing microscopes play an important role in capturing clear images of the measured object. At present, the image processing part of optics-based autofocusing microscopes often has various factors, which makes it impossible to describe the image information of the [...] Read more.
For automated optical inspection, autofocusing microscopes play an important role in capturing clear images of the measured object. At present, the image processing part of optics-based autofocusing microscopes often has various factors, which makes it impossible to describe the image information of the semicircular (or elliptical) spot with a simple circle-finding method. Accordingly, this study has developed a novel algorithm that can quickly calculate the ideal center of the elliptical spot and effectively compensate the linearity of the focusing characteristic curve. A prototype model was used to characterize and verify the proposed algorithm. The experimental results show that by using the proposed algorithm, the autofocusing accuracy can be effectively improved to less than 1.5 μm. Full article
(This article belongs to the Special Issue Sensor-Based Precision Dimensional Measurement)
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31 pages, 13532 KiB  
Article
Modeling Method to Abstract Collective Behavior of Smart IoT Systems in CPS
by Junsup Song, Dimitris Karagiannis and Moonkun Lee
Sensors 2022, 22(13), 5057; https://doi.org/10.3390/s22135057 - 05 Jul 2022
Cited by 3 | Viewed by 1787
Abstract
This paper presents a new modeling method to abstract the collective behavior of Smart IoT Systems in CPS, based on process algebra and a lattice structure. In general, process algebra is known to be one of the best formal methods to model IoTs, [...] Read more.
This paper presents a new modeling method to abstract the collective behavior of Smart IoT Systems in CPS, based on process algebra and a lattice structure. In general, process algebra is known to be one of the best formal methods to model IoTs, since each IoT can be represented as a process; a lattice can also be considered one of the best mathematical structures to abstract the collective behavior of IoTs since it has the hierarchical structure to represent multi-dimensional aspects of the interactions of IoTs. The dual approach using two mathematical structures is very challenging since the process algebra have to provide an expressive power to describe the smart behavior of IoTs, and the lattice has to provide an operational capability to handle the state-explosion problem generated from the interactions of IoTs. For these purposes, this paper presents a process algebra, called dTP-Calculus, which represents the smart behavior of IoTs with non-deterministic choice operation based on probability, and a lattice, called n:2-Lattice, which has special join and meet operations to handle the state explosion problem. The main advantage of the method is that the lattice can represent all the possible behavior of the IoT systems, and the patterns of behavior can be elaborated by finding the traces of the behavior in the lattice. Another main advantage is that the new notion of equivalences can be defined within n:2-Lattice, which can be used to solve the classical problem of exponential and non-deterministic complexity in the equivalences of Norm Chomsky and Robin Milner by abstracting them into polynomial and static complexity in the lattice. In order to prove the concept of the method, two tools are developed based on the ADOxx Meta-Modeling Platform: SAVE for the dTP-Calculus and PRISM for the n:2-Lattice. The method and tools can be considered one of the most challenging research topics in the area of modeling to represent the collective behavior of Smart IoT Systems. Full article
(This article belongs to the Special Issue Machine Learning for IoT Applications and Digital Twins II)
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15 pages, 6915 KiB  
Article
A Compact Dual Band MIMO Dielectric Resonator Antenna with Improved Performance for mm-Wave Applications
by Meshari D. Alanazi and Salam K. Khamas
Sensors 2022, 22(13), 5056; https://doi.org/10.3390/s22135056 - 05 Jul 2022
Cited by 20 | Viewed by 2468
Abstract
A compact multiple-input-multiple-output (MIMO) dielectric resonator antenna (DRA) that is suitable for internet of things (IoT) sensor networks is proposed with reduced coupling between elements. Two rectangular-shaped DRAs have been placed on the opposite sides of a Rogers substrate and each is fed [...] Read more.
A compact multiple-input-multiple-output (MIMO) dielectric resonator antenna (DRA) that is suitable for internet of things (IoT) sensor networks is proposed with reduced coupling between elements. Two rectangular-shaped DRAs have been placed on the opposite sides of a Rogers substrate and each is fed using a coplanar waveguide (CPW) feed with slots etched in a dedicated metal ground plane that is located under the DRA. Moreover, locating the elements at the opposite sides of the substrate has improved the isolation by 27 dB without the need to incorporate additional complex structures, which has reduced the overall antenna size. Furthermore, a dual band operation is achieved since each antenna resonates at two frequencies: 28 GHz and 38 GHz with respective impedance matching bandwidths of 18% and 13%. As a result, the corresponding data rates are also increased independently. In addition to the advantages of improved isolation, compact size and dual band operation, the proposed configuration offers a diversity gain (DG), envelope correlation coefficient (ECC) and channel capacity loss (CCL) of 9.98 dB, 0.007, 0.06 bits/s/Hz over the desired bands, respectively. A prototype has been built with good agreement between simulated and measured results. Full article
(This article belongs to the Special Issue Antenna Technologies for Millimeter and Terahertz Sensing)
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15 pages, 3300 KiB  
Article
Pilot Study for Correlation of Heart Rate Variability and Dopamine Transporter Brain Imaging in Patients with Parkinsonian Syndrome
by Devdutta S. Warhadpande, Jiayan Huo, William A. Libling, Carol Stuehm, Bijan Najafi, Scott Sherman, Hong Lei, Janet Meiling Roveda and Phillip H. Kuo
Sensors 2022, 22(13), 5055; https://doi.org/10.3390/s22135055 - 05 Jul 2022
Cited by 1 | Viewed by 2483
Abstract
Background: Parkinsonian syndrome (PS) is a broad category of neurodegenerative movement disorders that includes Parkinson disease, multiple system atrophy (MSA), progressive supranuclear palsy, and corticobasal degeneration. Parkinson disease (PD) is the second most common neurodegenerative disorder with loss of dopaminergic neurons of the [...] Read more.
Background: Parkinsonian syndrome (PS) is a broad category of neurodegenerative movement disorders that includes Parkinson disease, multiple system atrophy (MSA), progressive supranuclear palsy, and corticobasal degeneration. Parkinson disease (PD) is the second most common neurodegenerative disorder with loss of dopaminergic neurons of the substantia nigra and, thus, dysfunction of the nigrostriatal pathway. In addition to the motor symptoms of bradykinesia, rigidity, tremors, and postural instability, nonmotor symptoms such as autonomic dysregulation (AutD) can also occur. Heart rate variability (HRV) has been used as a measure of AutD and has shown to be prognostic in diseases such as diabetes mellitus and cirrhosis, as well as PD. I-123 ioflupane, a gamma ray-emitting radiopharmaceutical used in single-photon emission computed tomography (SPECT), is used to measure the loss of dopaminergic neurons in PD. Through the combination of SPECT and HRV, we tested the hypothesis that asymmetrically worse left-sided neuronal loss would cause greater AutD. Methods: 51 patients were enrolled on the day of their standard of care I-123 ioflupane scan for the work-up of possible Parkinsonian syndrome. Demographic information, medical and medication history, and ECG data were collected. HRV metrics were extracted from the ECG data. I-123 ioflupane scans were interpreted by a board-certified nuclear radiologist and quantified by automated software to generate striatal binding ratios (SBRs). Statistical analyses were performed to find correlations between the HRV and SPECT parameters. Results: 32 patients were excluded from the final analysis because of normal scans, prior strokes, cardiac disorders and procedures, or cancer. Abnormal I-123 ioflupane scans were clustered using T-SNE, and one-way ANOVA was performed to compare HRV and SBR parameters. The analysis was repeated after the exclusion of patients taking angiotensin-converting enzyme inhibitors, given the known mechanism on autonomic function. Subsequent analysis showed a significant difference between the high-frequency domains of heart rate variability, asymmetry of the caudate SBR, and putamen-to-caudate SBR. Conclusion: Our results support the hypothesis that more imbalanced (specifically worse left-sided) neuronal loss results in greater AutD. Full article
(This article belongs to the Special Issue Applications of Body Worn Sensors and Wearables)
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27 pages, 4490 KiB  
Article
Evaluation of Simple Algorithms for Proportional Control of Prosthetic Hands Using Intramuscular Electromyography
by Nebojsa Malesevic, Anders Björkman, Gert S. Andersson, Christian Cipriani and Christian Antfolk
Sensors 2022, 22(13), 5054; https://doi.org/10.3390/s22135054 - 05 Jul 2022
Cited by 2 | Viewed by 2078
Abstract
Although seemingly effortless, the control of the human hand is backed by an elaborate neuro-muscular mechanism. The end result is typically a smooth action with the precise positioning of the joints of the hand and an exerted force that can be modulated to [...] Read more.
Although seemingly effortless, the control of the human hand is backed by an elaborate neuro-muscular mechanism. The end result is typically a smooth action with the precise positioning of the joints of the hand and an exerted force that can be modulated to enable precise interaction with the surroundings. Unfortunately, even the most sophisticated technology cannot replace such a comprehensive role but can offer only basic hand functionalities. This issue arises from the drawbacks of the prosthetic hand control strategies that commonly rely on surface EMG signals that contain a high level of noise, thus limiting accurate and robust multi-joint movement estimation. The use of intramuscular EMG results in higher quality signals which, in turn, lead to an improvement in prosthetic control performance. Here, we present the evaluation of fourteen common/well-known algorithms (mean absolute value, variance, slope sign change, zero crossing, Willison amplitude, waveform length, signal envelope, total signal energy, Teager energy in the time domain, Teager energy in the frequency domain, modified Teager energy, mean of signal frequencies, median of signal frequencies, and firing rate) for the direct and proportional control of a prosthetic hand. The method involves the estimation of the forces generated in the hand by using different algorithms applied to iEMG signals from our recently published database, and comparing them to the measured forces (ground truth). The results presented in this paper are intended to be used as a baseline performance metric for more advanced algorithms that will be made and tested using the same database. Full article
(This article belongs to the Special Issue Electromyography (EMG) Signal Acquisition and Processing)
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15 pages, 7173 KiB  
Article
CAFS: An Attention-Based Co-Segmentation Semi-Supervised Method for Nasopharyngeal Carcinoma Segmentation
by Yitong Chen, Guanghui Han, Tianyu Lin and Xiujian Liu
Sensors 2022, 22(13), 5053; https://doi.org/10.3390/s22135053 - 05 Jul 2022
Cited by 1 | Viewed by 2035
Abstract
Accurate segmentation of nasopharyngeal carcinoma is essential to its treatment effect. However, there are several challenges in existing deep learning-based segmentation methods. First, the acquisition of labeled data are challenging. Second, the nasopharyngeal carcinoma is similar to the surrounding tissues. Third, the shape [...] Read more.
Accurate segmentation of nasopharyngeal carcinoma is essential to its treatment effect. However, there are several challenges in existing deep learning-based segmentation methods. First, the acquisition of labeled data are challenging. Second, the nasopharyngeal carcinoma is similar to the surrounding tissues. Third, the shape of nasopharyngeal carcinoma is complex. These challenges make the segmentation of nasopharyngeal carcinoma difficult. This paper proposes a novel semi-supervised method named CAFS for automatic segmentation of nasopharyngeal carcinoma. CAFS addresses the above challenges through three mechanisms: the teacher–student cooperative segmentation mechanism, the attention mechanism, and the feedback mechanism. CAFS can use only a small amount of labeled nasopharyngeal carcinoma data to segment the cancer region accurately. The average DSC value of CAFS is 0.8723 on the nasopharyngeal carcinoma segmentation task. Moreover, CAFS has outperformed the state-of-the-art nasopharyngeal carcinoma segmentation methods in the comparison experiment. Among the compared state-of-the-art methods, CAFS achieved the highest values of DSC, Jaccard, and precision. In particular, the DSC value of CAFS is 7.42% higher than the highest DSC value in the state-of-the-art methods. Full article
(This article belongs to the Special Issue Computational Imaging, Sensing and Analysis for Biomedical Applications)
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19 pages, 3645 KiB  
Article
Eco-Environmental Effect Evaluation of Tamarix chinesis Forest on Coastal Saline-Alkali Land Based on RSEI Model
by Jin Wang, Guangxue Li and Feiyong Chen
Sensors 2022, 22(13), 5052; https://doi.org/10.3390/s22135052 - 05 Jul 2022
Cited by 8 | Viewed by 1742
Abstract
Taking representative Tamarix chinensis forest in the national-level special protection zone for ocean ecology of Changyi city in Shandong province of China as the objective, this research studied how to use remote sensing technology to evaluate natural eco-environment and analyze spatiotemporal variation. In [...] Read more.
Taking representative Tamarix chinensis forest in the national-level special protection zone for ocean ecology of Changyi city in Shandong province of China as the objective, this research studied how to use remote sensing technology to evaluate natural eco-environment and analyze spatiotemporal variation. In the process of constructing the index system of ecological environment effect evaluation based on RSEI (Remote Sensing Ecological Index) model, AOD (Aerosol Optical Depth), Salinity, Greenness, Wetness, Heat and Dryness, which can represent the ecological environment of the reserve, were selected as the corresponding indexes. In order to accurately obtain the value of the RSEI of the study area and to retain the information of the original indexes to the greatest extent, the SPCA (spatial principal components analysis) method was applied in this research. Finally, the RSEI was applied to evaluate the ecological and environmental effects and to analyze the spatial characteristics and spatiotemporal evolution of the study area. The results not only provide scientific evidence and technical guidance for the protection, transformation and management of the Tamarix chinensis forest in the protection zone but also push the development of the universal model of the ecological environment quality with a remote sensing evaluation index system at a regional scale. Full article
(This article belongs to the Special Issue Ocean Observation)
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16 pages, 1390 KiB  
Article
Wi-Fi Fingerprint-Based Indoor Localization Method via Standard Particle Swarm Optimization
by Jin Zheng, Kailong Li and Xing Zhang
Sensors 2022, 22(13), 5051; https://doi.org/10.3390/s22135051 - 05 Jul 2022
Cited by 14 | Viewed by 2782
Abstract
With the continuous development and improvement in Internet-of-Things (IoT) technology, indoor localization has received considerable attention. Particularly, owing to its unique advantages, the Wi-Fi fingerprint-based indoor-localization method has been widely investigated. However, achieving high-accuracy localization remains a challenge. This study proposes an application [...] Read more.
With the continuous development and improvement in Internet-of-Things (IoT) technology, indoor localization has received considerable attention. Particularly, owing to its unique advantages, the Wi-Fi fingerprint-based indoor-localization method has been widely investigated. However, achieving high-accuracy localization remains a challenge. This study proposes an application of the standard particle swarm optimization algorithm to Wi-Fi fingerprint-based indoor localization, wherein a new two-panel fingerprint homogeneity model is adopted to characterize fingerprint similarity to achieve better performance. In addition, the performance of the localization method is experimentally verified. The proposed localization method outperforms conventional algorithms, with improvements in the localization accuracy of 15.32%, 15.91%, 32.38%, and 36.64%, compared to those of KNN, SVM, LR, and RF, respectively. Full article
(This article belongs to the Section Internet of Things)
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15 pages, 5913 KiB  
Article
A Drifter-Based Self-Powered Piezoelectric Sensor for Ocean Wave Measurements
by Seyyed Masoud Kargar and Guangbo Hao
Sensors 2022, 22(13), 5050; https://doi.org/10.3390/s22135050 - 05 Jul 2022
Cited by 4 | Viewed by 2198
Abstract
Recently, piezoelectric materials have received remarkable attention in marine applications for energy harvesting from the ocean, which is a harsh environment with powerful and impactful waves and currents. However, to the best of the authors’ knowledge, although there are various designs of piezoelectric [...] Read more.
Recently, piezoelectric materials have received remarkable attention in marine applications for energy harvesting from the ocean, which is a harsh environment with powerful and impactful waves and currents. However, to the best of the authors’ knowledge, although there are various designs of piezoelectric energy harvesters for marine applications, piezoelectric materials have not been employed for sensory and measurement applications in marine environment. In the present research, a drifter-based piezoelectric sensor is proposed to measure ocean waves’ height and period. To analyze the motion principle and the working performance of the proposed drifter-based piezoelectric sensor, a dynamic model was developed. The developed dynamic model investigated the system’s response to an input of ocean waves and provides design insights into the geometrical and material parameters. Next, finite element analysis (FEA) simulations using the commercial software COMSOL-Multiphysics were carried out with the help of a coupled physics analysis of Solid Mechanics and Electrostatics Modules to achieve the output voltages. An experimental prototype was fabricated and tested to validate the results of the dynamic model and the FEA simulation. A slider-crank mechanism was used to mimic ocean waves throughout the experiment, and the results showed a close match between the proposed dynamic modeling, FEA simulations, and experimental testing. In the end, a short discussion is devoted to interpreting the output results, comparing the results of the simulations with those of the experimental testing, sensor’s resolution, and the self-powering functionality of the proposed drifter-based piezoelectric sensor. Full article
(This article belongs to the Special Issue Sensors in 2022)
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15 pages, 4375 KiB  
Article
Ultrasensitive Electrochemical Detection and Plasmon-Enhanced Photocatalytic Degradation of Rhodamine B Based on Dual-Functional, 3D, Hierarchical Ag/ZnO Nanoflowers
by Neethu Sebastian, Wan-Chin Yu and Deepak Balram
Sensors 2022, 22(13), 5049; https://doi.org/10.3390/s22135049 - 05 Jul 2022
Cited by 5 | Viewed by 2040
Abstract
The sensitive detection and degradation of synthetic dyes are pivotal to maintain safety owing to the adverse side effects they impart on living beings. In this work, we developed a sensitive electrochemical sensor for the nanomolar-level detection of rhodamine B (RhB) using a [...] Read more.
The sensitive detection and degradation of synthetic dyes are pivotal to maintain safety owing to the adverse side effects they impart on living beings. In this work, we developed a sensitive electrochemical sensor for the nanomolar-level detection of rhodamine B (RhB) using a dual-functional, silver-decorated zinc oxide (Ag/ZnO) composite-modified, screen-printed carbon electrode. The plasmon-enhanced photocatalytic degradation of organic pollutant RhB was also performed using this nanocomposite prepared by embedding different weight percentages (1, 3, and 5 wt%) of Ag nanoparticles on the surface of a three-dimensional (3D), hierarchical ZnO nanostructure based on the photoreduction approach. The structure and morphology of an Ag/ZnO nanocomposite were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), elemental mapping, ultraviolet-visible (UV-vis) spectroscopy, and X-ray diffraction (XRD). The electrochemical sensor exhibited a very high sensitivity of 151.44 µAµM−1cm−2 and low detection limit of 0.8 nM towards RhB detection. The selectivity, stability, repeatability, reproducibility, and practical feasibility were also analyzed to prove their reliability. Furthermore, the photocatalysis results revealed that 3 wt% of the Ag/ZnO hybrid nanostructure acquired immense photostability, reusability, and 90.5% degradation efficiency under visible light. Additionally, the pseudo-first-order rate constant of Ag-3/ZnO is 2.186 min−1 suggested promising activity in visible light photocatalysis. Full article
(This article belongs to the Special Issue Sensors Young Investigators’ Contributions Collection)
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18 pages, 4386 KiB  
Article
Estimation of Leaf Area Index with a Multi-Channel Spectral Micro-Sensor for Wireless Sensing Networks
by Laura Maria Comella, Florian Bregler, Eiko Hager, Markus Anys, Johannes Klueppel, Stefan J. Rupitsch, Christiane Werner and Peter Woias
Sensors 2022, 22(13), 5048; https://doi.org/10.3390/s22135048 - 05 Jul 2022
Cited by 1 | Viewed by 2379
Abstract
The leaf area index (LAI) is a key parameter in the context of monitoring the development of tree crowns and plants in general. As parameters such as carbon assimilation, environmental stress on carbon, and the water fluxes within tree canopies are correlated to [...] Read more.
The leaf area index (LAI) is a key parameter in the context of monitoring the development of tree crowns and plants in general. As parameters such as carbon assimilation, environmental stress on carbon, and the water fluxes within tree canopies are correlated to the leaves surface, this parameter is essential for understanding and modeling ecological processes. However, its continuous monitoring using manual state-of-the-art measurement instruments is still challenging. To address this challenge, we present an innovative sensor concept to obtain the LAI based on the cheap and easy to integrate multi-channel spectral sensor AS7341. Additionally, we present a method for processing and filtering the gathered data, which enables very high accuracy measurements with an nRMSE of only 0.098, compared to the manually-operated state-of-the-art instrument LAI-2200C (LiCor). The sensor that is embedded on a sensor node has been tested in long-term experiments, proving its suitability for continuous deployment over an entire season. It permits the estimation of both the plant area index (PAI) and leaf area index (LAI) and provides the first wireless system that obtains the LAI solely powered by solar cells. Its energy autonomy and wireless connectivity make it suitable for a massive deployment over large areas and at different levels of the tree crown. It may be upgraded to allow the parallel measurement of photosynthetic active radiation (PAR) and light quality, relevant parameters for monitoring processes within tree canopies. Full article
(This article belongs to the Section Environmental Sensing)
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10 pages, 1254 KiB  
Article
Human Pulse Detection by a Soft Tactile Actuator
by Zixin Huang, Xinpeng Li, Jiarun Wang, Yi Zhang and Jingfu Mei
Sensors 2022, 22(13), 5047; https://doi.org/10.3390/s22135047 - 05 Jul 2022
Cited by 5 | Viewed by 1940
Abstract
Soft sensing technologies offer promising prospects in the fields of soft robots, wearable devices, and biomedical instruments. However, the structural design, fabrication process, and sensing algorithm design of the soft devices confront great difficulties. In this paper, a soft tactile actuator (STA) with [...] Read more.
Soft sensing technologies offer promising prospects in the fields of soft robots, wearable devices, and biomedical instruments. However, the structural design, fabrication process, and sensing algorithm design of the soft devices confront great difficulties. In this paper, a soft tactile actuator (STA) with both the actuation function and sensing function is presented. The tactile physiotherapy finger of the STA was fabricated by a fluid silica gel material. Before pulse detection, the tactile physiotherapy finger was actuated to the detection position by injecting compressed air into its chamber. The pulse detecting algorithm, which realized the pulse detection function of the STA, is presented. Finally, in actual pulse detection experiments, the pulse values of the volunteers detected by using the STA and by employing a professional pulse meter were close, which illustrates the effectiveness of the pulse detecting algorithm of the STA. Full article
(This article belongs to the Special Issue Sensing, Estimating, and Analyzing Human Movements for Human–Robot Interaction)
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13 pages, 2230 KiB  
Article
Impact of Wedge Parameters on Ultrasonic Lamb Wave Liquid-Level Sensor
by Weizhao Xue, Wanjia Gao, Wenyi Liu, Huixin Zhang and Ruiqing Guo
Sensors 2022, 22(13), 5046; https://doi.org/10.3390/s22135046 - 04 Jul 2022
Cited by 2 | Viewed by 1944
Abstract
The ultrasonic Lamb wave detection principle can realize the noncontact measurement of liquid level in closed containers. When designing an ultrasonic Lamb wave sensor, it is vital to thoroughly study and select the optimal wedge size at the front of the sensor. In [...] Read more.
The ultrasonic Lamb wave detection principle can realize the noncontact measurement of liquid level in closed containers. When designing an ultrasonic Lamb wave sensor, it is vital to thoroughly study and select the optimal wedge size at the front of the sensor. In this paper, firstly, we select the best working mode of Lamb waves according to their propagation dispersion curve in aluminum alloy, and we obtain the best angle of wedge through experiments. Secondly, we study the impact of the size of the wedge block on the results, and we obtain the selection method of wedge block parameters. The evaluations show that, when the frequency–thickness product is 3 MHz·mm, the Lamb waves work in the A1 mode, and the experimental effect is the best. At this time, the incident angle of the ultrasonic wave is 27.39°. The wedge thickness should be designed to avoid the near-field area of the ultrasonic field, and we should choose the length as odd multiples of 1/4 wavelength. The rules obtained from the experiment can effectively select the best working mode for ultrasonic Lamb waves, while also providing a basis for the design of the wedge block size in a Lamb wave sensor. Full article
(This article belongs to the Special Issue Applications of Acoustic and Ultrasonic Waves for Non-Destructive Testing and Structural Health Monitoring)
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21 pages, 8098 KiB  
Article
Assistive Mobility Control of a Robotic Hip-Knee Exoskeleton for Gait Training
by Chuan Changcheng, Yi-Rong Li and Chun-Ta Chen
Sensors 2022, 22(13), 5045; https://doi.org/10.3390/s22135045 - 04 Jul 2022
Cited by 1 | Viewed by 2543
Abstract
In this paper, we present an assistive mobility control for a robotic hip-knee exoskeleton intended for gait training. The robotic hip-knee exoskeleton is designed with an active flexion/extension and a passive abduction/adduction at each hip joint and an active flexion/extension at each knee [...] Read more.
In this paper, we present an assistive mobility control for a robotic hip-knee exoskeleton intended for gait training. The robotic hip-knee exoskeleton is designed with an active flexion/extension and a passive abduction/adduction at each hip joint and an active flexion/extension at each knee joint to comply with the movement of lower limbs. While facilitating walking with the robotic exoskeleton, model-free linear extended state observer (LESO)-based controllers are proposed for gait control, in which the LESO is used to deal with each user’s different lower limb parameters and unknown exerted torques. Walking and ascending experiments were conducted to evaluate the performance of the proposed methods, and the results are shown with respect to walking parameters. Moreover, a preliminary study for an extended application to the recovery of normal gaits that relieves the freezing of gait (FOG) in Parkinson’s disease (PD) patients is also investigated in the paper. Full article
(This article belongs to the Special Issue Challenges and Future Trends of Wearable Robotics)
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16 pages, 2251 KiB  
Article
Form Factors as Potential Imaging Biomarkers to Differentiate Benign vs. Malignant Lung Lesions on CT Scans
by Francesco Bianconi, Isabella Palumbo, Mario Luca Fravolini, Maria Rondini, Matteo Minestrini, Giulia Pascoletti, Susanna Nuvoli, Angela Spanu, Michele Scialpi, Cynthia Aristei and Barbara Palumbo
Sensors 2022, 22(13), 5044; https://doi.org/10.3390/s22135044 - 04 Jul 2022
Cited by 7 | Viewed by 2107
Abstract
Indeterminate lung nodules detected on CT scans are common findings in clinical practice. Their correct assessment is critical, as early diagnosis of malignancy is crucial to maximise the treatment outcome. In this work, we evaluated the role of form factors as imaging biomarkers [...] Read more.
Indeterminate lung nodules detected on CT scans are common findings in clinical practice. Their correct assessment is critical, as early diagnosis of malignancy is crucial to maximise the treatment outcome. In this work, we evaluated the role of form factors as imaging biomarkers to differentiate benign vs. malignant lung lesions on CT scans. We tested a total of three conventional imaging features, six form factors, and two shape features for significant differences between benign and malignant lung lesions on CT scans. The study population consisted of 192 lung nodules from two independent datasets, containing 109 (38 benign, 71 malignant) and 83 (42 benign, 41 malignant) lung lesions, respectively. The standard of reference was either histological evaluation or stability on radiological followup. The statistical significance was determined via the Mann–Whitney U nonparametric test, and the ability of the form factors to discriminate a benign vs. a malignant lesion was assessed through multivariate prediction models based on Support Vector Machines. The univariate analysis returned four form factors (Angelidakis compactness and flatness, Kong flatness, and maximum projection sphericity) that were significantly different between the benign and malignant group in both datasets. In particular, we found that the benign lesions were on average flatter than the malignant ones; conversely, the malignant ones were on average more compact (isotropic) than the benign ones. The multivariate prediction models showed that adding form factors to conventional imaging features improved the prediction accuracy by up to 14.5 pp. We conclude that form factors evaluated on lung nodules on CT scans can improve the differential diagnosis between benign and malignant lesions. Full article
(This article belongs to the Collection Biomedical Imaging & Instrumentation)
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17 pages, 1119 KiB  
Article
One Model is Not Enough: Ensembles for Isolated Sign Language Recognition
by Marek Hrúz, Ivan Gruber, Jakub Kanis, Matyáš Boháček, Miroslav Hlaváč and Zdeněk Krňoul
Sensors 2022, 22(13), 5043; https://doi.org/10.3390/s22135043 - 04 Jul 2022
Cited by 14 | Viewed by 2385
Abstract
In this paper, we dive into sign language recognition, focusing on the recognition of isolated signs. The task is defined as a classification problem, where a sequence of frames (i.e., images) is recognized as one of the given sign language glosses. We analyze [...] Read more.
In this paper, we dive into sign language recognition, focusing on the recognition of isolated signs. The task is defined as a classification problem, where a sequence of frames (i.e., images) is recognized as one of the given sign language glosses. We analyze two appearance-based approaches, I3D and TimeSformer, and one pose-based approach, SPOTER. The appearance-based approaches are trained on a few different data modalities, whereas the performance of SPOTER is evaluated on different types of preprocessing. All the methods are tested on two publicly available datasets: AUTSL and WLASL300. We experiment with ensemble techniques to achieve new state-of-the-art results of 73.84% accuracy on the WLASL300 dataset by using the CMA-ES optimization method to find the best ensemble weight parameters. Furthermore, we present an ensembling technique based on the Transformer model, which we call Neural Ensembler. Full article
(This article belongs to the Special Issue Sensing Systems for Sign Language Recognition)
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14 pages, 3213 KiB  
Article
Accurate Location in Dynamic Traffic Environment Using Semantic Information and Probabilistic Data Association
by Kaixin Yang, Weiwei Zhang, Chuanchang Li and Xiaolan Wang
Sensors 2022, 22(13), 5042; https://doi.org/10.3390/s22135042 - 04 Jul 2022
Cited by 4 | Viewed by 2051
Abstract
High-accurate and real-time localization is the fundamental and challenging task for autonomous driving in a dynamic traffic environment. This paper presents a coordinated positioning strategy that is composed of semantic information and probabilistic data association, which improves the accuracy of SLAM in dynamic [...] Read more.
High-accurate and real-time localization is the fundamental and challenging task for autonomous driving in a dynamic traffic environment. This paper presents a coordinated positioning strategy that is composed of semantic information and probabilistic data association, which improves the accuracy of SLAM in dynamic traffic settings. First, the improved semantic segmentation network, building on Fast-SCNN, uses the Res2net module instead of the Bottleneck in the global feature extraction to further explore the multi-scale granular features. It achieves the balance between segmentation accuracy and inference speed, leading to consistent performance gains on the coordinated localization task of this paper. Second, a novel scene descriptor combining geometric, semantic, and distributional information is proposed. These descriptors are made up of significant features and their surroundings, which may be unique to a traffic scene, and are used to improve data association quality. Finally, a probabilistic data association is created to find the best estimate using a maximum measurement expectation model. This approach assigns semantic labels to landmarks observed in the environment and is used to correct false negatives in data association. We have evaluated our system with ORB-SLAM2 and DynaSLAM, the most advanced algorithms, to demonstrate its advantages. On the KITTI dataset, the results reveal that our approach outperforms other methods in dynamic traffic situations, especially in highly dynamic scenes, with sub-meter average accuracy. Full article
(This article belongs to the Section Sensing and Imaging)
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11 pages, 1121 KiB  
Article
Sleep Measurement Using Wrist-Worn Accelerometer Data Compared with Polysomnography
by John D. Chase, Michael A. Busa, John W. Staudenmayer and John R. Sirard
Sensors 2022, 22(13), 5041; https://doi.org/10.3390/s22135041 - 04 Jul 2022
Cited by 8 | Viewed by 2786
Abstract
This study determined if using alternative sleep onset (SO) definitions impacted accelerometer-derived sleep estimates compared with polysomnography (PSG). Nineteen participants (48%F) completed a 48 h visit in a home simulation laboratory. Sleep characteristics were calculated from the second night by PSG and a [...] Read more.
This study determined if using alternative sleep onset (SO) definitions impacted accelerometer-derived sleep estimates compared with polysomnography (PSG). Nineteen participants (48%F) completed a 48 h visit in a home simulation laboratory. Sleep characteristics were calculated from the second night by PSG and a wrist-worn ActiGraph GT3X+ (AG). Criterion sleep measures included PSG-derived Total Sleep Time (TST), Sleep Onset Latency (SOL), Wake After Sleep Onset (WASO), Sleep Efficiency (SE), and Efficiency Once Asleep (SE_ASLEEP). Analogous variables were derived from temporally aligned AG data using the Cole–Kripke algorithm. For PSG, SO was defined as the first score of ‘sleep’. For AG, SO was defined three ways: 1-, 5-, and 10-consecutive minutes of ‘sleep’. Agreement statistics and linear mixed effects regression models were used to analyze ‘Device’ and ‘Sleep Onset Rule’ main effects and interactions. Sleep–wake agreement and sensitivity for all AG methods were high (89.0–89.5% and 97.2%, respectively); specificity was low (23.6–25.1%). There were no significant interactions or main effects of ‘Sleep Onset Rule’ for any variable. The AG underestimated SOL (19.7 min) and WASO (6.5 min), and overestimated TST (26.2 min), SE (6.5%), and SE_ASLEEP (1.9%). Future research should focus on developing sleep–wake detection algorithms and incorporating biometric signals (e.g., heart rate). Full article
(This article belongs to the Special Issue Advances of Wearables in Health Monitoring)
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17 pages, 29995 KiB  
Article
E-Skin Using Fringing Field Electrical Impedance Tomography with an Ionic Liquid Domain
by Manuchehr Soleimani and Myron Friedrich
Sensors 2022, 22(13), 5040; https://doi.org/10.3390/s22135040 - 04 Jul 2022
Cited by 9 | Viewed by 2780
Abstract
Electrical impedance tomography (EIT) is a promising technique for large area tactile sensing for robotic skin. This study presents a novel EIT-based force and touch sensor that features a latex membrane acting as soft skin and an ionic liquid domain. The sensor works [...] Read more.
Electrical impedance tomography (EIT) is a promising technique for large area tactile sensing for robotic skin. This study presents a novel EIT-based force and touch sensor that features a latex membrane acting as soft skin and an ionic liquid domain. The sensor works based on fringing field EIT where the touch or force leads to a deformation in the latex membrane causing detectable changes in EIT data. This article analyses the performance of this electronic skin in terms of its dynamical behaviour, position accuracy and quantitative force sensing. Investigation into the sensor’s performance showed it to be hypersensitive, in that it can reliably detect forces as small as 64 mN. Furthermore, multi-touch discrimination and annular force sensing is displayed. The hysteresis in force sensing is investigated showing a very negligible hysteresis. This is a direct result of the latex membrane and the ionic liquid-based domain design compared to more traditional fabric-based touch sensors due to the reduction in electromechanical coupling. A novel test is devised that displayed the dynamic performance of the sensor by showing its ability to record a 1 Hz frequency, which was applied to the membrane in a tapping fashion. Overall, the results show a considerable progress in ionic liquid EIT-based sensors. These findings place the EIT-based sensors that comprise a liquid domain, at the forefront of research into tactile robotic skin. Full article
(This article belongs to the Special Issue Advanced Electromagnetic Sensors in Environmental, Industrial and Medical Applications II)
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12 pages, 4930 KiB  
Communication
Carbon-Based Nanomaterials Thin Film Deposited on a Flexible Substrate for Strain Sensing Application
by Shiuh-Chuan Her and Yuan-Ming Liang
Sensors 2022, 22(13), 5039; https://doi.org/10.3390/s22135039 - 04 Jul 2022
Cited by 4 | Viewed by 1603
Abstract
Hybrid nanomaterial film consisting of multi-walled carbon nanotubes (MWCNT) and graphene nanoplatelet (GNP) were deposited on a highly flexible polyimide (PI) substrate using spray gun. The hybridization between 2-D GNP and 1-D MWCNT reduces stacking among the nanomaterials and produces a thin film [...] Read more.
Hybrid nanomaterial film consisting of multi-walled carbon nanotubes (MWCNT) and graphene nanoplatelet (GNP) were deposited on a highly flexible polyimide (PI) substrate using spray gun. The hybridization between 2-D GNP and 1-D MWCNT reduces stacking among the nanomaterials and produces a thin film with a porous structure. Carbon-based nanomaterials of MWCNT and GNP with high electrical conductivity can be employed to detect the deformation and damage for structural health monitoring. The strain sensing capability of carbon-based hybrid nanomaterial film was evaluated by its piezoresistive behavior, which correlates the change of electrical resistance with the applied strain through a tensile test. The effects of weight ratio between MWCNT and GNP and the total amount of hybrid nanomaterials on the strain sensitivity of the nanomaterial thin film were investigated. Experimental results showed that both the electrical conductivity and strain sensitivity of the hybrid nanomaterial film increased with the increase of the GNP contents. The gauge factor used to characterize the strain sensitivity of the nanomaterial film increased from 7.75 to 24 as the GNP weight ratio increased from 0 wt.% to 100 wt.%. In this work, a simple, low cost, and easy to implement deposition process was proposed to prepare a highly flexible nanomaterial film. A high strain sensitivity with gauge factor of 24 was achieved for the nanomaterial thin film. Full article
(This article belongs to the Special Issue Thin Film Materials and Nanostructure Devices for Sensing Applications)
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19 pages, 4962 KiB  
Article
Cooperative Control of Underwater Vehicle–Manipulator Systems Based on the SDC Method
by Aleksey Kabanov, Vadim Kramar, Ivan Lipko and Kirill Dementiev
Sensors 2022, 22(13), 5038; https://doi.org/10.3390/s22135038 - 04 Jul 2022
Cited by 4 | Viewed by 1844
Abstract
The paper considers the problem of cooperative control synthesis for a complex of N underwater vehicle–manipulator systems (UVMS) to perform the work of moving a cargo along a given trajectory. Here, we used the approach based on the representation of nonlinear dynamics models [...] Read more.
The paper considers the problem of cooperative control synthesis for a complex of N underwater vehicle–manipulator systems (UVMS) to perform the work of moving a cargo along a given trajectory. Here, we used the approach based on the representation of nonlinear dynamics models in the form of state space with state-dependent coefficients (SDC-form). That allowed us to apply methods of suboptimal control with feedback based on the state-dependent differential Riccati equation (SDDRE) solution at a finite time interval, providing the change in control intensity with the transient effect of the system matrices in SDC form. The paper reveals two approaches to system implementation: a general controller for the whole system and a set of N independent subcontrollers for UVMSs. The results of both approaches are similar; however, for the systems with a small number of manipulators, the common structure is recommended, and for the systems with a large number of manipulators, the approach with independent subcontrollers may be more acceptable. The proposed method of cooperative control was tested on the task of cooperative control for two UVMSs with six-link manipulators Orion 7R. The simulation results are presented in the article and show the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Underwater Robotics in 2022-2023)
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12 pages, 6959 KiB  
Communication
A Study on the Design of Isolator and the Mounting Method for Reducing the Pyro-Shock of a MEMS IMU
by Kyungdon Ryu, ByungSu Park, Hyungsub Lee, Kyungjun Han and Sangwoo Lee
Sensors 2022, 22(13), 5037; https://doi.org/10.3390/s22135037 - 04 Jul 2022
Viewed by 3060
Abstract
In this paper, we proposed two methods for reducing the pyro-shock of the MEMS Inertial Measurement Unit (IMU). First, we designed the vibration isolator for reducing the pyro-shock inside the IMU. However, it turned out that there is a limit to reducing the [...] Read more.
In this paper, we proposed two methods for reducing the pyro-shock of the MEMS Inertial Measurement Unit (IMU). First, we designed the vibration isolator for reducing the pyro-shock inside the IMU. However, it turned out that there is a limit to reducing the pyro-shock with only the vibration isolator. Therefore, we improved the pyro-shock reduction performance by changing the method of mounting on the flight vehicle. Four mounting options were tested and one of them was adopted. The results showed the best reduction performance when we designed the vibration isolator with an aluminum integrated structure. When mounting, two methods were applied. One was to insert a bracket with a different material between the mounting surface and IMU and the other was to insert a set of three washers that was stacked in a PEEK-metal-PEEK order at each part of the screw connections. Full article
(This article belongs to the Topic MEMS Sensors and Resonators)
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17 pages, 2147 KiB  
Article
An Improved Online Fast Self-Calibration Method for Dual-Axis RINS Based on Backtracking Scheme
by Jing Li, Lichen Su, Fang Wang, Kailong Li and Lili Zhang
Sensors 2022, 22(13), 5036; https://doi.org/10.3390/s22135036 - 04 Jul 2022
Cited by 7 | Viewed by 1434
Abstract
In the field of high accuracy dual-axis rotational inertial navigation system (RINS), the calibration accuracy of the gyroscopes and accelerometers is of great importance. Although rotation modulation can suppress the navigation error caused by scale factor error and bias error in a static [...] Read more.
In the field of high accuracy dual-axis rotational inertial navigation system (RINS), the calibration accuracy of the gyroscopes and accelerometers is of great importance. Although rotation modulation can suppress the navigation error caused by scale factor error and bias error in a static condition, it cannot suppress the scale factor errors thoroughly during the maneuvering process of the vehicle due to the two degrees of rotation freedom. The self-calibration method has been studied by many researchers. However, traditional calibration methods need several hours to converge, which is unable to meet the demand for quick response to positioning and orientation. To solve the above problems, we do the following work in this study: (1) we propose a 39-dimensional online calibration Kalman filtering (KF) model to estimate all calibration parameters; (2) Error relationship between calibration parameters error and navigation error are derived; (3) A backtracking filtering scheme is proposed to shorten the calibration process. Experimental results indicate that the proposed method can shorten the calibration process and improve the calibration accuracy simultaneously. Full article
(This article belongs to the Section Electronic Sensors)
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