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Volume 22
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Sensors, Volume 22, Issue 4 (February-2 2022) – 393 articles

Cover Story (view full-size image): To obtain the vital signs of the planet and global climate change, constant information from different locations is crucial, and for that, low-cost sensors with high energy autonomy are essential in the ocean. Additionally, submerged sensors are extremely important to acquire and analyze real-time data and in this way help to make the most appropriate decisions and choices without comprising the environment and humankind.
The autonomy of submerged sensors located in the oceans should be increased, as it is imperative to minimize battery replacement due to the undesirable logistics and high cost associated.
The minimum amount of energy needed for the sensors and the correct use of energy management make it essential to develop and optimize submersible energy-harvesting devices based on a linear electromagnetic generator. View this paper
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16 pages, 5131 KiB  
Article
Methodology for Detecting Progressive Damage in Structures Using Ultrasound-Guided Waves
by Gerardo Aranguren, Javier Bilbao, Josu Etxaniz, José Miguel Gil-García and Carolina Rebollar
Sensors 2022, 22(4), 1692; https://doi.org/10.3390/s22041692 - 21 Feb 2022
Cited by 4 | Viewed by 2628
Abstract
Damage detection in structural health monitoring of metallic or composite structures depends on several factors, including the sensor technology and the type of defect that is under the spotlight. Commercial devices generally used to obtain these data neither allow for their installation on [...] Read more.
Damage detection in structural health monitoring of metallic or composite structures depends on several factors, including the sensor technology and the type of defect that is under the spotlight. Commercial devices generally used to obtain these data neither allow for their installation on board nor permit their scalability when several structures or sensors need to be monitored. This paper introduces self-developed equipment designed to create ultrasonic guided waves and a methodology for the detection of progressive damage, such as corrosion damage in aircraft structures, i.e., algorithms for monitoring such damage. To create slowly changing conditions, aluminum- and carbon-reinforced polymer plates were placed together with seawater to speed up the corrosion process. The setup was completed by an array of 10 piezoelectric transducers driven and sensed by a structural health monitoring ultrasonic system, which generated 100 waveforms per test. The hardware was able to pre-process the raw acquisition to minimize the transmitted data. The experiment was conducted over eight weeks. Three different processing stages were followed to extract information on the degree of corrosion: hardware algorithm, pattern matching, and pattern recognition. The proposed methodology allows for the detection of trends in the progressive degradation of structures. 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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6 pages, 6524 KiB  
Communication
Determination of Binary Gas Mixtures by Measuring the Resonance Frequency in a Piezoelectric Tube
by Kanchalar Keeratirawee and Peter C. Hauser
Sensors 2022, 22(4), 1691; https://doi.org/10.3390/s22041691 - 21 Feb 2022
Cited by 4 | Viewed by 2104
Abstract
The composition of gas mixtures may be determined via changes of the speed of sound. As this affects the resonance frequency of the gas inside a tube, indirect measurements through a frequency analysis are also possible. It is demonstrated that this may be [...] Read more.
The composition of gas mixtures may be determined via changes of the speed of sound. As this affects the resonance frequency of the gas inside a tube, indirect measurements through a frequency analysis are also possible. It is demonstrated that this may be carried out with unprecedented simplicity by the novel employment of a piezoelectric tube which serves at the same time as a resonance tube and as transducer into the electronic domain. Experiments were run using a simple diecast aluminum box as the measuring cell, inside which the piezoelectric tube made from lead zirconium titanate with 30-mm length and 5.35-mm inner diameter was suspended. A small loudspeaker placed into the cell served for excitation of the resonance. Peak frequencies between 3910 and 14,590 Hz (for pure CO2 and He, respectively) were obtained. Two component mixtures of O2/N2, CO2/N2, and He/N2 at various composition were tested. A linear frequency change from 4790 to 5100 Hz was observed when going from pure O2 to pure N2. Full article
(This article belongs to the Special Issue Sensors and Sensor-Based Measurement Systems: Design, Development and Challenges)
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12 pages, 2457 KiB  
Article
Evaluation of a New Simplified Inertial Sensor Method against Electrogoniometer for Measuring Wrist Motion in Occupational Studies
by Karnica Manivasagam and Liyun Yang
Sensors 2022, 22(4), 1690; https://doi.org/10.3390/s22041690 - 21 Feb 2022
Cited by 14 | Viewed by 3280
Abstract
Wrist velocity is an important risk factor for work-related musculoskeletal disorders in the elbow/hand, which is also difficult to assess by observation or self-reports. This study aimed to evaluate a new convenient and low-cost inertial measurement unit (IMU)-based method using gyroscope signals against [...] Read more.
Wrist velocity is an important risk factor for work-related musculoskeletal disorders in the elbow/hand, which is also difficult to assess by observation or self-reports. This study aimed to evaluate a new convenient and low-cost inertial measurement unit (IMU)-based method using gyroscope signals against an electrogoniometer for measuring wrist flexion velocity. Twelve participants performed standard wrist movements and simulated work tasks while equipped with both systems. Two computational algorithms for the IMU-based system, i.e., IMUnorm and IMUflex, were used. For wrist flexion/extension, the mean absolute errors (MAEs) of median wrist flexion velocity compared to the goniometer were <10.1°/s for IMUnorm and <4.1°/s for IMUflex. During wrist deviation and pronation/supination, all methods showed errors, where the IMUnorm method had the largest overestimations. For simulated work tasks, the IMUflex method had small bias and better accuracy than the IMUnorm method compared to the goniometer, with the MAEs of median wrist flexion velocity <5.8°/s. The results suggest that the IMU-based method can be considered as a convenient method to assess wrist motion for occupational studies or ergonomic evaluations for the design of workstations and tools by both researchers and practitioners, and the IMUflex method is preferred. Future studies need to examine algorithms to further improve the accuracy of the IMU-based method in tasks of larger variations, as well as easy calibration procedures. Full article
(This article belongs to the Special Issue Advances in Design and Integration of Wearable Sensors for Ergonomics)
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30 pages, 19861 KiB  
Article
Exploration-Based SLAM (e-SLAM) for the Indoor Mobile Robot Using Lidar
by Hasan Ismail, Rohit Roy, Long-Jye Sheu, Wei-Hua Chieng and Li-Chuan Tang
Sensors 2022, 22(4), 1689; https://doi.org/10.3390/s22041689 - 21 Feb 2022
Cited by 19 | Viewed by 6802
Abstract
This paper attempts to uncover one possible method for the IMR (indoor mobile robot) to perform indoor exploration associated with SLAM (simultaneous localization and mapping) using LiDAR. Specifically, the IMR is required to construct a map when it has landed on an unexplored [...] Read more.
This paper attempts to uncover one possible method for the IMR (indoor mobile robot) to perform indoor exploration associated with SLAM (simultaneous localization and mapping) using LiDAR. Specifically, the IMR is required to construct a map when it has landed on an unexplored floor of a building. We had implemented the e-SLAM (exploration-based SLAM) using the coordinate transformation and the navigation prediction techniques to achieve that purpose in the engineering school building which consists of many 100-m2 labs, corridors, elevator waiting space and the lobby. We first derive the LiDAR mesh for the orthogonal walls and filter out the static furniture and dynamic humans in the same space as the IMR. Then, we define the LiDAR pose frame including the translation and rotation from the orthogonal walls. According to the MSC (most significant corner) obtained from the intersection of the orthogonal walls, we calculate the displacement of the IMR. The orientation of the IMR is calculated from the alignment of orthogonal walls in the consecutive LiDAR pose frames, which is also assisted by the LQE (linear quadratic estimation) method. All the computation can be done in a single processor machine in real-time. The e-SLAM technique leads to a potential for the in-house service robot to start operation without having pre-scan LiDAR maps, which can save the installation time of the service robot. In this study, we use only the LiDAR and compared our result with the IMU to verify the consistency between the two navigation sensors in the experiments. The scenario of the experiment consists of rooms, corridors, elevators, and the lobby, which is common to most office buildings. Full article
(This article belongs to the Special Issue Advanced Sensing Techniques for Autonomous Vehicles and Advanced Driver Assistance Systems (ADAS))
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20 pages, 2390 KiB  
Article
Computer Aided Written Character Feature Extraction in Progressive Supranuclear Palsy and Parkinson’s Disease
by Paula Stępień, Jacek Kawa, Emilia J. Sitek, Dariusz Wieczorek, Rafał Sikorski, Magda Dąbrowska, Jarosław Sławek and Ewa Pietka
Sensors 2022, 22(4), 1688; https://doi.org/10.3390/s22041688 - 21 Feb 2022
Viewed by 2522
Abstract
Parkinson’s disease (PD) and progressive supranuclear palsy (PSP) are neurodegenerative movement disorders associated with cognitive dysfunction. The Luria’s Alternating Series Test (LAST) is a clinical tool sensitive to both graphomotor problems and perseverative tendencies that may suggest the dysfunction of prefrontal and/or frontostriatal [...] Read more.
Parkinson’s disease (PD) and progressive supranuclear palsy (PSP) are neurodegenerative movement disorders associated with cognitive dysfunction. The Luria’s Alternating Series Test (LAST) is a clinical tool sensitive to both graphomotor problems and perseverative tendencies that may suggest the dysfunction of prefrontal and/or frontostriatal areas and may be used in PD and PSP assessment. It requires the participant to draw a series of alternating triangles and rectangles. In the study, two clinical groups—51 patients with PD and 22 patients with PSP—were compared to 32 neurologically intact seniors. Participants underwent neuropsychological assessment. The LAST was administered in a paper and pencil version, then scanned and preprocessed. The series was automatically divided into characters, and the shapes were recognized as rectangles or triangles. In the feature extraction step, each rectangle and triangle was regarded both as an image and a two-dimensional signal, separately and as a part of the series. Standard and novel features were extracted and normalized using characters written by the examiner. Out of 71 proposed features, 51 differentiated the groups (p < 0.05). A classifier showed an accuracy of 70.5% for distinguishing three groups. Full article
(This article belongs to the Special Issue Innovations in Biomedical Imaging)
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20 pages, 3250 KiB  
Article
A Machine Learning Approach for an Improved Inertial Navigation System Solution
by Ahmed E. Mahdi, Ahmed Azouz, Ahmed E. Abdalla and Ashraf Abosekeen
Sensors 2022, 22(4), 1687; https://doi.org/10.3390/s22041687 - 21 Feb 2022
Cited by 29 | Viewed by 8093
Abstract
The inertial navigation system (INS) is a basic component to obtain a continuous navigation solution in various applications. The INS suffers from a growing error over time. In particular, its navigation solution depends mainly on the quality and grade of the inertial measurement [...] Read more.
The inertial navigation system (INS) is a basic component to obtain a continuous navigation solution in various applications. The INS suffers from a growing error over time. In particular, its navigation solution depends mainly on the quality and grade of the inertial measurement unit (IMU), which provides the INS with both accelerations and angular rates. However, low-cost small micro-electro-mechanical systems (MEMSs) suffer from huge error sources such as bias, the scale factor, scale factor instability, and highly non-linear noise. Therefore, MEMS-IMU measurements lead to drifts in the solutions when used as a control input to the INS. Accordingly, several approaches have been introduced to model and mitigate the errors associated with the IMU. In this paper, a machine-learning-based adaptive neuro-fuzzy inference system (ML-based-ANFIS) is proposed to leverage the performance of low-grade IMUs in two phases. The first phase was training 50% of the low-grade IMU measurements with a high-end IMU to generate a suitable error model. The second phase involved testing the developed model on the remaining low-grade IMU measurements. A real road trajectory was used to evaluate the performance of the proposed algorithm. The results showed the effectiveness of utilizing the proposed ML-ANFIS algorithm to remove the errors and improve the INS solution compared to the traditional one. An improvement of 70% in the 2D positioning and of 92% in the 2D velocity of the INS solution were attained when the proposed algorithm was applied compared to the traditional INS solution. Full article
(This article belongs to the Section Navigation and Positioning)
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22 pages, 15808 KiB  
Article
Mood State Detection in Handwritten Tasks Using PCA–mFCBF and Automated Machine Learning
by Juan Arturo Nolazco-Flores, Marcos Faundez-Zanuy, Oliver Alejandro Velázquez-Flores, Carolina Del-Valle-Soto, Gennaro Cordasco and Anna Esposito
Sensors 2022, 22(4), 1686; https://doi.org/10.3390/s22041686 - 21 Feb 2022
Cited by 11 | Viewed by 3539
Abstract
In this research, we analyse data obtained from sensors when a user handwrites or draws on a tablet to detect whether the user is in a specific mood state. First, we calculated the features based on the temporal, kinematic, statistical, spectral and cepstral [...] Read more.
In this research, we analyse data obtained from sensors when a user handwrites or draws on a tablet to detect whether the user is in a specific mood state. First, we calculated the features based on the temporal, kinematic, statistical, spectral and cepstral domains for the tablet pressure, the horizontal and vertical pen displacements and the azimuth of the pen’s position. Next, we selected features using a principal component analysis (PCA) pipeline, followed by modified fast correlation–based filtering (mFCBF). PCA was used to calculate the orthogonal transformation of the features, and mFCBF was used to select the best PCA features. The EMOTHAW database was used for depression, anxiety and stress scale (DASS) assessment. The process involved the augmentation of the training data by first augmenting the mood states such that all the data were the same size. Then, 80% of the training data was randomly selected, and a small random Gaussian noise was added to the extracted features. Automated machine learning was employed to train and test more than ten plain and ensembled classifiers. For all three moods, we obtained 100% accuracy results when detecting two possible grades of mood severities using this architecture. The results obtained were superior to the results obtained by using state-of-the-art methods, which enabled us to define the three mood states and provide precise information to the clinical psychologist. The accuracy results obtained when detecting these three possible mood states using this architecture were 82.5%, 72.8% and 74.56% for depression, anxiety and stress, respectively. Full article
(This article belongs to the Section Intelligent Sensors)
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16 pages, 10968 KiB  
Article
Experimental Investigation of Vibration Analysis on Implant Stability for a Novel Implant Design
by Shouxun Lu, Benjamin Steven Vien, Matthias Russ, Mark Fitzgerald and Wing Kong Chiu
Sensors 2022, 22(4), 1685; https://doi.org/10.3390/s22041685 - 21 Feb 2022
Cited by 5 | Viewed by 2447
Abstract
Osseointegrated prostheses are widely used following transfemoral amputation. However, this technique requires sufficient implant stability before and during the rehabilitation period to mitigate the risk of implant breakage and loosening. Hence, reliable assessment methods for the osseointegration process are essential to ensure initial [...] Read more.
Osseointegrated prostheses are widely used following transfemoral amputation. However, this technique requires sufficient implant stability before and during the rehabilitation period to mitigate the risk of implant breakage and loosening. Hence, reliable assessment methods for the osseointegration process are essential to ensure initial and long–term implant stability. This paper researches the feasibility of a vibration analysis technique for the osseointegration (OI) process by investigating the change in the dynamic response of the residual femur with a novel implant design during a simulated OI process. The paper also proposes a concept of an energy index (the E–index), which is formulated based on the normalized magnitude. To illustrate the potential of the E–index, this paper reports on changes in the vibrational behaviors of a 133 mm long amputated artificial femur model and implant system, with epoxy adhesives applied at the interface to simulate the OI process. The results show a significant variation in the magnitude of the colormap against curing time. The study also shows that the E–index was sensitive to the interface stiffness change, especially during the early curing process. These findings highlight the feasibility of using the vibration analysis technique and the E–index to quantitatively monitor the osseointegration process for future improvement on the efficiency of human health monitoring and patient rehabilitation. Full article
(This article belongs to the Special Issue Medical Sensors)
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20 pages, 1456 KiB  
Article
A Multi-Frequency Tomographic Inverse Scattering Using Beam Basis Functions
by Ram Tuvi
Sensors 2022, 22(4), 1684; https://doi.org/10.3390/s22041684 - 21 Feb 2022
Viewed by 1737
Abstract
We present an overview of a beam-based approach to ultra-wide band (UWB) tomographic inverse scattering, where beam-waves are used for local data-processing and local imaging, as an alternative to the conventional plane-wave and Green’s function approaches. Specifically, the method utilizes a phase–space set [...] Read more.
We present an overview of a beam-based approach to ultra-wide band (UWB) tomographic inverse scattering, where beam-waves are used for local data-processing and local imaging, as an alternative to the conventional plane-wave and Green’s function approaches. Specifically, the method utilizes a phase–space set of iso-diffracting beam-waves that emerge from a discrete set of points and directions in the source domain. It is shown that with a proper choice of parameters, this set constitutes a frame (an overcomplete generalization of a basis), termed “beam frame”, over the entire propagation domain. An important feature of these beam frames is that they need to be calculated once and then used for all frequencies, hence the method can be implemented either in the multi-frequency domain (FD), or directly in the time domain (TD). The algorithm consists of two phases: in the processing phase, the scattering data is transformed to the beam domain using windowed phase–space transformations, while in the imaging phase, the beams are backpropagated to the target domain to form the image. The beam-domain data is not only localized and compressed, but it is also physically related to the local Radon transform (RT) of the scatterer via a local Snell’s reflection of the beam-waves. This expresses the imaging as an inverse local RT that can be applied to any local domain of interest (DoI). In previous publications, the emphasis has been set on TD data processing using a special class of localized space–time beam-waves (wave-packets). The goal of the present paper is to present the imaging scheme in the UWB FD, utilizing simpler Fourier-based data-processing tools in the space and time domains. Full article
(This article belongs to the Special Issue Microwave Sensing and Imaging)
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18 pages, 420 KiB  
Article
Student’s t-Kernel-Based Maximum Correntropy Kalman Filter
by Hongliang Huang and Hai Zhang
Sensors 2022, 22(4), 1683; https://doi.org/10.3390/s22041683 - 21 Feb 2022
Cited by 10 | Viewed by 2695
Abstract
The state estimation problem is ubiquitous in many fields, and the common state estimation method is the Kalman filter. However, the Kalman filter is based on the mean square error criterion, which can only capture the second-order statistics of the noise and is [...] Read more.
The state estimation problem is ubiquitous in many fields, and the common state estimation method is the Kalman filter. However, the Kalman filter is based on the mean square error criterion, which can only capture the second-order statistics of the noise and is sensitive to large outliers. In many areas of engineering, the noise may be non-Gaussian and outliers may arise naturally. Therefore, the performance of the Kalman filter may deteriorate significantly in non-Gaussian noise environments. To improve the accuracy of the state estimation in this case, a novel filter named Student’s t kernel-based maximum correntropy Kalman filter is proposed in this paper. In addition, considering that the fixed-point iteration method is used to solve the optimal estimated state in the filtering algorithm, the convergence of the algorithm is also analyzed. Finally, comparative simulations are conducted and the results demonstrate that with the proper parameters of the kernel function, the proposed filter outperforms the other conventional filters, such as the Kalman filter, Huber-based filter, and maximum correntropy Kalman filter. Full article
(This article belongs to the Special Issue Vehicle Localization Based on GNSS and In-Vehicle Sensors)
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15 pages, 1271 KiB  
Article
TraceBERT—A Feasibility Study on Reconstructing Spatial–Temporal Gaps from Incomplete Motion Trajectories via BERT Training Process on Discrete Location Sequences
by Alessandro Crivellari, Bernd Resch and Yuhui Shi
Sensors 2022, 22(4), 1682; https://doi.org/10.3390/s22041682 - 21 Feb 2022
Cited by 5 | Viewed by 2956
Abstract
Trajectory data represent an essential source of information on travel behaviors and human mobility patterns, assuming a central role in a wide range of services related to transportation planning, personalized recommendation strategies, and resource management plans. The main issue when dealing with trajectory [...] Read more.
Trajectory data represent an essential source of information on travel behaviors and human mobility patterns, assuming a central role in a wide range of services related to transportation planning, personalized recommendation strategies, and resource management plans. The main issue when dealing with trajectory recordings, however, is characterized by temporary losses in the data collection, causing possible spatial–temporal gaps and missing trajectory segments. This is especially critical in those use cases based on non-repetitive individual motion traces, when the user’s missing information cannot be directly reconstructed due to the absence of historical individual repetitive routes. Inserted in the context of location-based trajectory modeling, we tackle the problem by proposing a technical parallelism with the natural language processing domain. Specifically, we introduce the use of the Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art language representation model, into the trajectory processing research field. By training deep bidirectional representations from unlabeled location sequences, jointly conditioned on both left and right context, we derive an explicit predicted estimation of the missing locations along the trace. The proposed framework, named TraceBERT, was tested on a real-world large-scale trajectory dataset of short-term tourists, exploring an effective attempt of adapting advanced language modeling approaches into mobility-based applications and demonstrating a prominent potential on trajectory reconstruction over traditional statistical approaches. Full article
(This article belongs to the Special Issue Internet of Things, Big Data and Smart Systems)
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18 pages, 7800 KiB  
Article
An Image Registration Method Based on Correlation Matching of Dominant Scatters for Distributed Array ISAR
by Liqi Zhang and Yanlei Li
Sensors 2022, 22(4), 1681; https://doi.org/10.3390/s22041681 - 21 Feb 2022
Cited by 2 | Viewed by 2010
Abstract
Distributed array radar provides new prospects for three-dimensional (3D) inverse synthetic aperture radar (ISAR) imaging. The accuracy of image registration, as an essential part of 3D ISAR imaging, affects the performance of 3D reconstruction. In this paper, the imaging process of distributed array [...] Read more.
Distributed array radar provides new prospects for three-dimensional (3D) inverse synthetic aperture radar (ISAR) imaging. The accuracy of image registration, as an essential part of 3D ISAR imaging, affects the performance of 3D reconstruction. In this paper, the imaging process of distributed array ISAR is proposed according to the imaging model. The ISAR images of distributed array radar at different APCs have different distribution of scatters. When the local distribution of scatters for the same target are quite different, the performance of the existing ISAR image registration methods may not be optimal. Therefore, an image registration method is proposed by integrating the feature-based method and the area-based method. The proposed method consists of two stages: coarse registration and fine registration. In the first stage, a dominant scatters model is established based on scale-invariant feature transform (SIFT). In the second stage, sub-pixel precision registration is achieved using the local correlation matching method. The effectiveness of the proposed method is verified by comparison with other image registration methods. The 3D reconstruction of the registered experimental data is carried out to assess the practicability of the proposed method. Full article
(This article belongs to the Section Sensing and Imaging)
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13 pages, 1522 KiB  
Article
A Novel Physical Fatigue Assessment Method Utilizing Heart Rate Variability and Pulse Arrival Time towards Personalized Feedback with Wearable Sensors
by Ardo Allik, Kristjan Pilt, Moonika Viigimäe, Ivo Fridolin and Gert Jervan
Sensors 2022, 22(4), 1680; https://doi.org/10.3390/s22041680 - 21 Feb 2022
Cited by 2 | Viewed by 3401
Abstract
This paper proposes a novel method for physical fatigue assessment that can be applied in wearable systems, by utilizing a set of real-time measurable cardiovascular parameters. Daylength measurements, including a morning test set, physical exercise during the day, and an afternoon test set [...] Read more.
This paper proposes a novel method for physical fatigue assessment that can be applied in wearable systems, by utilizing a set of real-time measurable cardiovascular parameters. Daylength measurements, including a morning test set, physical exercise during the day, and an afternoon test set were conducted on 16 healthy subjects (8 female and 8 male). To analyze cardiovascular parameters for physical fatigue assessment, electrocardiography, pulse wave and blood pressure were measured during the test sets. The fatigue assessment questionnaire score, reaction time, countermovement jump height and hand grip strength were also measured and used as reference parameters. This study demonstrates that (i) the compiled test battery can selectively assess the rested vs. physically-fatigued states; (ii) the obtained linear support-vector machine, trained using the heart rate variability based parameter (F-score 0.842, accuracy 0.813) and pulse arrival time based parameter (F-score 0.875, accuracy 0.875) shows a promising ability to classify between the physically mildly fatigued and significantly fatigued states. Despite the somewhat limited study group size, the results of the study are unique and provide a significant advancement on the existing physical fatigue assessment methods towards a personalized and continuous real-time fatigue monitoring system with wearable sensors. Full article
(This article belongs to the Special Issue Applications, Wearables and Sensing Technology in Sports and Physical Activity)
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21 pages, 25555 KiB  
Article
Water Cloud Detection with Circular Polarization Lidar: A Semianalytic Monte Carlo Simulation Approach
by Wiqas Ahmad, Kai Zhang, Yicheng Tong, Da Xiao, Lingyun Wu and Dong Liu
Sensors 2022, 22(4), 1679; https://doi.org/10.3390/s22041679 - 21 Feb 2022
Cited by 5 | Viewed by 3071
Abstract
This work presents polarization property studies of water clouds using a circular polarization lidar through a simulation approach. The simulation approach is based on a polarized, semianalytic Monte Carlo method under multiple-scattering conditions and considers three types of water clouds (namely homogeneous, inhomogeneous [...] Read more.
This work presents polarization property studies of water clouds using a circular polarization lidar through a simulation approach. The simulation approach is based on a polarized, semianalytic Monte Carlo method under multiple-scattering conditions and considers three types of water clouds (namely homogeneous, inhomogeneous and partially inhomogeneous). The simulation results indicate that the layer-integrated circular depolarization ratios show similar variation trends as those of layer-integrated linear depolarization ratios. The Mishchenko–Hovenier relationship is validated to correlate the simulated layer-integrated circular and linear depolarization ratios. In addition, the cloud droplet effective radius, extinction coefficient, lidar field-of-view (FOV) and height of the cloud bottom are all found to affect the layer-integrated depolarization ratio. The current work theoretically indicates that a circular polarization lidar can efficiently perform measurements of water clouds, enjoying the advantage of higher sensitivity compared to a traditional linear polarization lidar. Hence, it should be of interest to researchers in fields of polarization lidar applications. Full article
(This article belongs to the Special Issue Sensors for Environment Monitoring)
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25 pages, 4266 KiB  
Article
The Diverse Gait Dataset: Gait Segmentation Using Inertial Sensors for Pedestrian Localization with Different Genders, Heights and Walking Speeds
by Chao Huang, Fuping Zhang, Zhengyi Xu and Jianming Wei
Sensors 2022, 22(4), 1678; https://doi.org/10.3390/s22041678 - 21 Feb 2022
Cited by 14 | Viewed by 4821
Abstract
Stride length estimation is one of the most crucial aspects of Pedestrian Dead Reckoning (PDR). Due to the measurement noise of inertial sensors, individual variances of pedestrians, and the uncertainty in pedestrians walking, there is a substantial error in the assessment of stride [...] Read more.
Stride length estimation is one of the most crucial aspects of Pedestrian Dead Reckoning (PDR). Due to the measurement noise of inertial sensors, individual variances of pedestrians, and the uncertainty in pedestrians walking, there is a substantial error in the assessment of stride length, which causes the accumulated deviation of Pedestrian Dead Reckoning (PDR). With the help of multi-gait analysis, which decomposes strides in time and space with greater detail and accuracy, a novel and revolutionary stride estimating model or scheme could improve the performance of PDR on different users. This paper presents a diverse stride gait dataset by using inertial sensors that collect foot movement data from people of different genders, heights, and walking speeds. The dataset contains 4690 walking strides data and 19,083 gait labels. Based on the dataset, we propose a threshold-independent stride segmentation algorithm called SDATW and achieve an F-measure of 0.835. We also provide the detailed results of recognizing four gaits under different walking speeds, demonstrating the utility of our dataset for helping train stride segmentation algorithms and gait detection algorithms. Full article
(This article belongs to the Special Issue Instrument and Measurement Based on Sensing Technology in China)
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18 pages, 6187 KiB  
Article
Application of Filters to Improve Flight Stability of Rotary Unmanned Aerial Objects
by Maciej Salwa and Izabela Krzysztofik
Sensors 2022, 22(4), 1677; https://doi.org/10.3390/s22041677 - 21 Feb 2022
Cited by 2 | Viewed by 3696
Abstract
The most common filters used to determine the angular position of quadrotors are the Kalman filter and the complementary filter. The problem of angular position estimation consist is a result of the absence of direct data. The most common sensors on board UAVs [...] Read more.
The most common filters used to determine the angular position of quadrotors are the Kalman filter and the complementary filter. The problem of angular position estimation consist is a result of the absence of direct data. The most common sensors on board UAVs are micro electro mechanical system (MEMS) type sensors. The data acquired from the sensors are processed using digital filters. In the literature, the results of research conducted on the effectiveness of Kalman and complementary filters are known. A significant problem in evaluating the performance of the studied filters was the lack of an arbitrarily determined UAV position. The authors of this paper undertook the task of determining the best filter for a real object. The main objective of this research was to improve the stability of the physical quadrotor. For this purpose, we developed a research method using a laboratory station for testing quadrotor drones. Moreover, using the MATLAB environment, they determined the optimal parameters for the real filter applied using the PX4 software, which is new and has not been considered before in the available scientific literature. It should be mentioned that the authors of this work focused on the analysis of filters most commonly used for flight stabilization, without modifying the structure of these filters. By not modifying the filter structure, it is possible to optimize the existing flight controllers. The main contribution of this study lies in finding the most optimal filter, among those available in flight controllers, for angular position estimation. The special emphasis of our work was to develop a procedure for selecting the filter coefficients for a real object. The algorithm was designed so that other researchers could use it, provided they collected arbitrary data for their objects. Selected results of the research are presented in graphical form. The proposed procedure for improving the embedded filter can be used by other researchers on their subjects. Full article
(This article belongs to the Topic Design, Simulation and New Applications of Unmanned Aerial Vehicles)
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14 pages, 2160 KiB  
Article
Single-Trial Classification of Error-Related Potentials in People with Motor Disabilities: A Study in Cerebral Palsy, Stroke, and Amputees
by Nayab Usama, Imran Khan Niazi, Kim Dremstrup and Mads Jochumsen
Sensors 2022, 22(4), 1676; https://doi.org/10.3390/s22041676 - 21 Feb 2022
Cited by 4 | Viewed by 3172
Abstract
Brain-computer interface performance may be reduced over time, but adapting the classifier could reduce this problem. Error-related potentials (ErrPs) could label data for continuous adaptation. However, this has scarcely been investigated in populations with severe motor impairments. The aim of this study was [...] Read more.
Brain-computer interface performance may be reduced over time, but adapting the classifier could reduce this problem. Error-related potentials (ErrPs) could label data for continuous adaptation. However, this has scarcely been investigated in populations with severe motor impairments. The aim of this study was to detect ErrPs from single-trial EEG in offline analysis in participants with cerebral palsy, an amputation, or stroke, and determine how much discriminative information different brain regions hold. Ten participants with cerebral palsy, eight with an amputation, and 25 with a stroke attempted to perform 300–400 wrist and ankle movements while a sham BCI provided feedback on their performance for eliciting ErrPs. Pre-processed EEG epochs were inputted in a multi-layer perceptron artificial neural network. Each brain region was used as input individually (Frontal, Central, Temporal Right, Temporal Left, Parietal, and Occipital), the combination of the Central region with each of the adjacent regions, and all regions combined. The Frontal and Central regions were most important, and adding additional regions only improved performance slightly. The average classification accuracies were 84 ± 4%, 87± 4%, and 85 ± 3% for cerebral palsy, amputation, and stroke participants. In conclusion, ErrPs can be detected in participants with motor impairments; this may have implications for developing adaptive BCIs or automatic error correction. Full article
(This article belongs to the Special Issue Signal Processing for Brain–Computer Interfaces)
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13 pages, 2752 KiB  
Article
Translational Applications of Wearable Sensors in Education: Implementation and Efficacy
by Brendon Ferrier, Jim Lee, Alex Mbuli and Daniel A. James
Sensors 2022, 22(4), 1675; https://doi.org/10.3390/s22041675 - 21 Feb 2022
Cited by 5 | Viewed by 2658
Abstract
Background: Adding new approaches to teaching curriculums can be both expensive and complex to learn. The aim of this research was to gain insight into students’ literacy and confidence in learning sports science with new wearable technologies, specifically a novel program known as [...] Read more.
Background: Adding new approaches to teaching curriculums can be both expensive and complex to learn. The aim of this research was to gain insight into students’ literacy and confidence in learning sports science with new wearable technologies, specifically a novel program known as STEMfit. Methods: A three-phase design was carried out, with 36 students participating and exposed to wearable devices and associated software. This was to determine whether the technology hardware (phase one) and associated software (phase two) were used in a positive way that demonstrated user confidence. Results: Hardware included choosing a scalable wearable device that worked in conjunction with familiar and readily available software (Microsoft Excel) that extracted data through VBA coding. This allowed for students to experience and provide survey feedback on the usability and confidence gained when interacting with the STEMfit program. Outcomes indicated strong acceptance of the program, with high levels of motivation, resulting in a positive uptake of wearable technology as a teaching tool by students. The initial finding of this study offers an opportunity to further test the STEMfit program on other student cohorts as well as testing the scalability of the system into other year groups at the university level. Full article
(This article belongs to the Special Issue Feature Papers in Wearables Section 2021)
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21 pages, 1890 KiB  
Article
A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm (AI) for Optimal Task Scheduling
by Ajoze Abdulraheem Zubair, Shukor Abd Razak, Md. Asri Ngadi, Arafat Al-Dhaqm, Wael M. S. Yafooz, Abdel-Hamid M. Emara, Aldosary Saad and Hussain Al-Aqrabi
Sensors 2022, 22(4), 1674; https://doi.org/10.3390/s22041674 - 21 Feb 2022
Cited by 22 | Viewed by 3172
Abstract
The search algorithm based on symbiotic organisms’ interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in [...] Read more.
The search algorithm based on symbiotic organisms’ interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The task scheduling problem is NP complete, which makes it hard to obtain a correct solution, especially for large-scale tasks. This paper proposes a modified symbiotic organisms search-based scheduling algorithm for the efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm’s mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aims to minimize the task execution time (makespan), cost, response time, and degree of imbalance, and improve the convergence speed for an optimal solution in an IaaS cloud. The performance of the proposed technique was evaluated using a CloudSim toolkit simulator, and the percentage of improvement of the proposed G_SOS over classical SOS and PSO-SA in terms of makespan minimization ranges between 0.61–20.08% and 1.92–25.68% over a large-scale task that spans between 100 to 1000 Million Instructions (MI). The solutions are found to be better than the existing standard (SOS) technique and PSO. Full article
(This article belongs to the Special Issue Emerging Sensor Communication Network-Based AI/ML Driven Intelligent IoT)
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21 pages, 5778 KiB  
Article
Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks
by Wooseong Kim, Muhammad Muneer Umar, Shafiullah Khan and Muhammad Altaf Khan
Sensors 2022, 22(4), 1673; https://doi.org/10.3390/s22041673 - 21 Feb 2022
Cited by 11 | Viewed by 2641
Abstract
The seamless operation of inter-connected smart devices in Internet of Things (IoT) wireless sensor networks (WSNs) requires consistently available end-to-end routes. However, the sensor nodes that rely on a very limited power source tend to cause disconnection in multi-hop routes due to power [...] Read more.
The seamless operation of inter-connected smart devices in Internet of Things (IoT) wireless sensor networks (WSNs) requires consistently available end-to-end routes. However, the sensor nodes that rely on a very limited power source tend to cause disconnection in multi-hop routes due to power shortages in the WSNs, which eventually results in the inefficiency of the overall IoT network. In addition, the density of the available sensor nodes affects the existence of feasible routes and the level of path multiplicity in the WSNs. Therefore, an efficient routing mechanism is expected to extend the lifetime of the WSNs by adaptively selecting the best routes for the data transfer between interconnected IoT devices. In this work, we propose a novel routing mechanism to balance the energy consumption among all the nodes and elongate the WSN lifetime, which introduces a score value assigned to each node along a path as the combination of evaluation metrics. Specifically, the scoring scheme considers the information of the node density at a certain area and the node energy levels in order to represent the importance of individual nodes in the routes. Furthermore, our routing mechanism allows for incorporating non-cooperative nodes. The simulation results show that the proposed work gives comparatively better results than some other experimented protocols. Full article
(This article belongs to the Special Issue IoT Multi Sensors)
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18 pages, 2851 KiB  
Article
Asynchronous Federated Learning System Based on Permissioned Blockchains
by Rong Wang and Wei-Tek Tsai
Sensors 2022, 22(4), 1672; https://doi.org/10.3390/s22041672 - 21 Feb 2022
Cited by 29 | Viewed by 5385
Abstract
The existing federated learning framework is based on the centralized model coordinator, which still faces serious security challenges such as device differentiated computing power, single point of failure, poor privacy, and lack of Byzantine fault tolerance. In this paper, we propose an asynchronous [...] Read more.
The existing federated learning framework is based on the centralized model coordinator, which still faces serious security challenges such as device differentiated computing power, single point of failure, poor privacy, and lack of Byzantine fault tolerance. In this paper, we propose an asynchronous federated learning system based on permissioned blockchains, using permissioned blockchains as the federated learning server, which is composed of a main-blockchain and multiple sub-blockchains, with each sub-blockchain responsible for partial model parameter updates and the main-blockchain responsible for global model parameter updates. Based on this architecture, a federated learning asynchronous aggregation protocol based on permissioned blockchain is proposed that can effectively alleviate the synchronous federated learning algorithm by integrating the learned model into the blockchain and performing two-order aggregation calculations. Therefore, the overhead of synchronization problems and the reliability of shared data is also guaranteed. We conducted some simulation experiments and the experimental results showed that the proposed architecture could maintain good training performances when dealing with a small number of malicious nodes and differentiated data quality, which has good fault tolerance, and can be applied to edge computing scenarios. Full article
(This article belongs to the Section Sensor Networks)
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18 pages, 17555 KiB  
Article
Numerical Study on Death of Squamous Cell Carcinoma Based on Various Shapes of Gold Nanoparticles Using Photothermal Therapy
by Donghyuk Kim and Hyunjung Kim
Sensors 2022, 22(4), 1671; https://doi.org/10.3390/s22041671 - 21 Feb 2022
Cited by 6 | Viewed by 2555
Abstract
Due to increased exposure to ultraviolet radiation caused by increased outdoor activities, the incidence of skin cancer is increasing. Incision is the most typical method for treating skin cancer, and various treatments that can minimize the risks of incision surgery are being investigated. [...] Read more.
Due to increased exposure to ultraviolet radiation caused by increased outdoor activities, the incidence of skin cancer is increasing. Incision is the most typical method for treating skin cancer, and various treatments that can minimize the risks of incision surgery are being investigated. Among them, photothermal therapy is garnering attention because it does not cause bleeding and affords rapid recovery. In photothermal therapy, tumor death is induced via temperature increase. In this study, a numerical study based on heat transfer theory was conducted to investigate the death of squamous cell carcinoma located in the skin layer based on various shapes of gold nanoparticles (AuNPs) used in photothermal therapy. The quantitative correlation between the conditions of various AuNPs and the laser intensity that yields the optimal photothermal treatment effect was derived using the effective apoptosis ratio. It was confirmed that optimal conditions exist for maximizing apoptosis within a tumor tissue and minimizing the thermal damage to surrounding normal tissues when using AuNPs under various conditions. Furthermore, it is envisioned that research result will be utilized as a standard for photothermal treatment in the future. Full article
(This article belongs to the Special Issue Advanced Laser Phototherapy: Sensing and Applications)
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17 pages, 9397 KiB  
Article
Signal Activity Detection for Fiber Optic Distributed Acoustic Sensing with Adaptive-Calculated Threshold
by Lilong Ma, Tuanwei Xu, Kai Cao, Yinghao Jiang, Dimin Deng and Fang Li
Sensors 2022, 22(4), 1670; https://doi.org/10.3390/s22041670 - 21 Feb 2022
Cited by 1 | Viewed by 2492
Abstract
The key point on analyzing the data stream measured by fiber optic distributed acoustic sensing (DAS) is signal activity detection separating measured signals from environmental noise. The inability to calculate the threshold for signal activity detection accurately and efficiently without affecting the measured [...] Read more.
The key point on analyzing the data stream measured by fiber optic distributed acoustic sensing (DAS) is signal activity detection separating measured signals from environmental noise. The inability to calculate the threshold for signal activity detection accurately and efficiently without affecting the measured signals is a bottleneck problem for current methods. In this article, a novel signal activity detection method with the adaptive-calculated threshold is proposed to solve the problem. With the analysis of the time-varying random noise’s statistical commonality and the short-term energy (STE) of real-time data stream, the top range of the total STE distribution of the noise is found accurately for real-time data stream’s ascending STE, thus the adaptive dividing level of signals and noise is obtained as the threshold. Experiments are implemented with simulated database and urban field database with complex noise. The average detection accuracies of the two databases are 97.34% and 90.94% only consuming 0.0057 s for a data stream of 10 s, which demonstrates the proposed method is accurate and high efficiency for signal activity detection. Full article
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20 pages, 11910 KiB  
Article
Improving Depth Estimation by Embedding Semantic Segmentation: A Hybrid CNN Model
by José E. Valdez-Rodríguez, Hiram Calvo, Edgardo Felipe-Riverón and Marco A. Moreno-Armendáriz
Sensors 2022, 22(4), 1669; https://doi.org/10.3390/s22041669 - 21 Feb 2022
Cited by 14 | Viewed by 3723
Abstract
Single image depth estimation works fail to separate foreground elements because they can easily be confounded with the background. To alleviate this problem, we propose the use of a semantic segmentation procedure that adds information to a depth estimator, in this case, a [...] Read more.
Single image depth estimation works fail to separate foreground elements because they can easily be confounded with the background. To alleviate this problem, we propose the use of a semantic segmentation procedure that adds information to a depth estimator, in this case, a 3D Convolutional Neural Network (CNN)—segmentation is coded as one-hot planes representing categories of objects. We explore 2D and 3D models. Particularly, we propose a hybrid 2D–3D CNN architecture capable of obtaining semantic segmentation and depth estimation at the same time. We tested our procedure on the SYNTHIA-AL dataset and obtained σ3=0.95, which is an improvement of 0.14 points (compared with the state of the art of σ3=0.81) by using manual segmentation, and σ3=0.89 using automatic semantic segmentation, proving that depth estimation is improved when the shape and position of objects in a scene are known. Full article
(This article belongs to the Section Intelligent Sensors)
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23 pages, 39907 KiB  
Review
Recent Progress of Switching Power Management for Triboelectric Nanogenerators
by Han Zhou, Guoxu Liu, Jianhua Zeng, Yiming Dai, Weilin Zhou, Chongyong Xiao, Tianrui Dang, Wenbo Yu, Yuanfen Chen and Chi Zhang
Sensors 2022, 22(4), 1668; https://doi.org/10.3390/s22041668 - 21 Feb 2022
Cited by 22 | Viewed by 5674
Abstract
Based on the coupling effect of contact electrification and electrostatic induction, the triboelectric nanogenerator (TENG) as an emerging energy technology can effectively harvest mechanical energy from the ambient environment. However, due to its inherent property of large impedance, the TENG shows high voltage, [...] Read more.
Based on the coupling effect of contact electrification and electrostatic induction, the triboelectric nanogenerator (TENG) as an emerging energy technology can effectively harvest mechanical energy from the ambient environment. However, due to its inherent property of large impedance, the TENG shows high voltage, low current and limited output power, which cannot satisfy the stable power supply requirements of conventional electronics. As the interface unit between the TENG and load devices, the power management circuit can perform significant functions of voltage and impedance conversion for efficient energy supply and storage. Here, a review of the recent progress of switching power management for TENGs is introduced. Firstly, the fundamentals of the TENG are briefly introduced. Secondly, according to the switch types, the existing power management methods are summarized and divided into four categories: travel switch, voltage trigger switch, transistor switch of discrete components and integrated circuit switch. The switch structure and power management principle of each type are reviewed in detail. Finally, the advantages and drawbacks of various switching power management circuits for TENGs are systematically summarized, and the challenges and development of further research are prospected. Full article
(This article belongs to the Special Issue Micro/Nano Energy and Flexible Sensors)
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19 pages, 6507 KiB  
Article
Continual Learning Objective for Analyzing Complex Knowledge Representations
by Asad Mansoor Khan, Taimur Hassan, Muhammad Usman Akram, Norah Saleh Alghamdi and Naoufel Werghi
Sensors 2022, 22(4), 1667; https://doi.org/10.3390/s22041667 - 21 Feb 2022
Cited by 19 | Viewed by 3197
Abstract
Human beings tend to incrementally learn from the rapidly changing environment without comprising or forgetting the already learned representations. Although deep learning also has the potential to mimic such human behaviors to some extent, it suffers from catastrophic forgetting due to which its [...] Read more.
Human beings tend to incrementally learn from the rapidly changing environment without comprising or forgetting the already learned representations. Although deep learning also has the potential to mimic such human behaviors to some extent, it suffers from catastrophic forgetting due to which its performance on already learned tasks drastically decreases while learning about newer knowledge. Many researchers have proposed promising solutions to eliminate such catastrophic forgetting during the knowledge distillation process. However, to our best knowledge, there is no literature available to date that exploits the complex relationships between these solutions and utilizes them for the effective learning that spans over multiple datasets and even multiple domains. In this paper, we propose a continual learning objective that encompasses mutual distillation loss to understand such complex relationships and allows deep learning models to effectively retain the prior knowledge while adapting to the new classes, new datasets, and even new applications. The proposed objective was rigorously tested on nine publicly available, multi-vendor, and multimodal datasets that span over three applications, and it achieved the top-1 accuracy of 0.9863% and an F1-score of 0.9930. Full article
(This article belongs to the Collection Biomedical Imaging & Instrumentation)
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13 pages, 3599 KiB  
Article
An Optical Fiber Sensor for Axial Strain, Curvature, and Temperature Measurement Based on Single-Core Six-Hole Optical Fiber
by Yujian Li, Changyuan Yu and Ping Lu
Sensors 2022, 22(4), 1666; https://doi.org/10.3390/s22041666 - 21 Feb 2022
Cited by 11 | Viewed by 2754
Abstract
In this paper, the field distribution and effective refractive index of transmission modes in single-core six-hole optical fiber were researched by modeling and simulation experiments. Based on the simulation results, a new type of sensor for axial strain, curvature, and temperature applications measurement [...] Read more.
In this paper, the field distribution and effective refractive index of transmission modes in single-core six-hole optical fiber were researched by modeling and simulation experiments. Based on the simulation results, a new type of sensor for axial strain, curvature, and temperature applications measurement was designed and fabricated. The experimental results showed that the axial strain sensitivities at different dips were −0.97 pm/με and −1.05 pm/με in the range from 0 to 2000 με, and the temperature sensitivities were 35.17 pm/°C and 47.27 pm/°C in the range from 25 to 75 °C. In addition, the proposed sensor also detected the curvature change with sensitivities of 7.36 dB/m−1 and 20.08 dB/m−1 from −2.582 m−1 to −1.826 m−1, respectively. Finally, through theoretical analysis, it can be deduced that this has potential application in the field of simultaneous measurement of strain and temperature. Full article
(This article belongs to the Special Issue Optical Imaging, Optical Sensing and Devices)
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14 pages, 1988 KiB  
Article
A Machine Learning Approach to Minimize Nocturnal Hypoglycemic Events in Type 1 Diabetic Patients under Multiple Doses of Insulin
by Adrià Parcerisas, Ivan Contreras, Alexia Delecourt, Arthur Bertachi, Aleix Beneyto, Ignacio Conget, Clara Viñals, Marga Giménez and Josep Vehi
Sensors 2022, 22(4), 1665; https://doi.org/10.3390/s22041665 - 21 Feb 2022
Cited by 21 | Viewed by 3275
Abstract
Nocturnal hypoglycemia (NH) is one of the most challenging events for multiple dose insulin therapy (MDI) in people with type 1 diabetes (T1D). The goal of this study is to design a method to reduce the incidence of NH in people with T1D [...] Read more.
Nocturnal hypoglycemia (NH) is one of the most challenging events for multiple dose insulin therapy (MDI) in people with type 1 diabetes (T1D). The goal of this study is to design a method to reduce the incidence of NH in people with T1D under MDI therapy, providing a decision-support system and improving confidence toward self-management of the disease considering the dataset used by Bertachi et al. Different machine learning (ML) algorithms, data sources, optimization metrics and mitigation measures to predict and avoid NH events have been studied. In addition, we have designed population and personalized models and studied the generalizability of the models and the influence of physical activity (PA) on them. Obtaining 30 g of rescue carbohydrates (CHO) is the optimal value for preventing NH, so it can be asserted that this is the value with which the time under 70 mg/dL decreases the most, with almost a 35% reduction, while increasing the time in the target range by 1.3%. This study supports the feasibility of using ML techniques to address the prediction of NH in patients with T1D under MDI therapy, using continuous glucose monitoring (CGM) and a PA tracker. The results obtained prove that BG predictions can not only be critical in achieving safer diabetes management, but also assist physicians and patients to make better and safer decisions regarding insulin therapy and their day-to-day lives. Full article
(This article belongs to the Special Issue Sensor Technologies: Artificial Intelligence for Diabetes Management II)
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14 pages, 4608 KiB  
Article
Selectivity of Relative Humidity Using a CP Based on S-Block Metal Ions
by Amalia García-García, Víctor Toral, José F. Quílez del Moral, Alberto Galisteo Pretel, Diego P. Morales, Alfonso Salinas-Castillo, Javier Cepeda, Duane Choquesillo-Lazarte, Marco Bobinger, José F. Salmerón, Almudena Rivadeneyra and Antonio Rodríguez-Diéguez
Sensors 2022, 22(4), 1664; https://doi.org/10.3390/s22041664 - 21 Feb 2022
Cited by 2 | Viewed by 3580
Abstract
Herein, we present the syntheses of a novel coordination polymer (CP) based on the perylene-3,4,9,10-tetracarboxylate (pery) linkers and sodium metal ions. We have chosen sodium metal center with the aim of surmising the effect that the modification of the metal ion may have [...] Read more.
Herein, we present the syntheses of a novel coordination polymer (CP) based on the perylene-3,4,9,10-tetracarboxylate (pery) linkers and sodium metal ions. We have chosen sodium metal center with the aim of surmising the effect that the modification of the metal ion may have on the relative humidity (RH) experimental measurements of the material. We confirm the role of the ions in the functionalization of the deposited layer by modifying their selectivity towards moisture content, paving the way to the generation of sensitive and selective chemical sensors. Full article
(This article belongs to the Special Issue 2D/3D Printed Sensors and Electronics)
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13 pages, 18473 KiB  
Article
Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles
by Esraa Khatab, Ahmed Onsy and Ahmed Abouelfarag
Sensors 2022, 22(4), 1663; https://doi.org/10.3390/s22041663 - 21 Feb 2022
Cited by 12 | Viewed by 4143
Abstract
One of the primary tasks undertaken by autonomous vehicles (AVs) is object detection, which comes ahead of object tracking, trajectory estimation, and collision avoidance. Vulnerable road objects (e.g., pedestrians, cyclists, etc.) pose a greater challenge to the reliability of object detection operations due [...] Read more.
One of the primary tasks undertaken by autonomous vehicles (AVs) is object detection, which comes ahead of object tracking, trajectory estimation, and collision avoidance. Vulnerable road objects (e.g., pedestrians, cyclists, etc.) pose a greater challenge to the reliability of object detection operations due to their continuously changing behavior. The majority of commercially available AVs, and research into them, depends on employing expensive sensors. However, this hinders the development of further research on the operations of AVs. In this paper, therefore, we focus on the use of a lower-cost single-beam LiDAR in addition to a monocular camera to achieve multiple 3D vulnerable object detection in real driving scenarios, all the while maintaining real-time performance. This research also addresses the problems faced during object detection, such as the complex interaction between objects where occlusion and truncation occur, and the dynamic changes in the perspective and scale of bounding boxes. The video-processing module works upon a deep-learning detector (YOLOv3), while the LiDAR measurements are pre-processed and grouped into clusters. The output of the proposed system is objects classification and localization by having bounding boxes accompanied by a third depth dimension acquired by the LiDAR. Real-time tests show that the system can efficiently detect the 3D location of vulnerable objects in real-time scenarios. Full article
(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Autonomous Vehicles)
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