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Volume 23
Issue 9
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Sensors, Volume 23, Issue 9 (May-1 2023) – 396 articles

Cover Story (view full-size image): Reflection-mode holographic sensors visibly change color in response to target physical stressors or chemical analytes, making them widely applicable, e.g., in medical diagnostics, environmental monitoring, and smart packaging. Reflection holograms produced in thick photopolymer films, however, are highly angularly selective, making them challenging to view and interpret. We have developed a speckle reflection holography recording technique in order to counteract this. The full width at half maximum of the speckle grating angular selectivity curve is double that of standard reflection gratings. We show the successful use of speckle reflection gratings as colorimetric MPa range pressure sensors. Finally, we present a prototype reflection hologram viewer. While conceptually simple, the viewer is versatile and described as being easy-to-use by non-experts. View this paper
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14 pages, 7519 KiB  
Communication
Research of a Cross-Interference Suppression Method for Piezoresistive Three-Dimensional Force Sensor
by You Zhao and Yulong Zhao
Sensors 2023, 23(9), 4573; https://doi.org/10.3390/s23094573 - 08 May 2023
Cited by 4 | Viewed by 1432
Abstract
Cross-interference is not only an important factor that affects the measuring accuracy of three-dimensional force sensors, but also a technical difficulty in three-dimensional force sensor design. In this paper, a cross-interference suppression method is proposed, based on the octagonal ring’s structural symmetry as [...] Read more.
Cross-interference is not only an important factor that affects the measuring accuracy of three-dimensional force sensors, but also a technical difficulty in three-dimensional force sensor design. In this paper, a cross-interference suppression method is proposed, based on the octagonal ring’s structural symmetry as well as Wheatstone bridge’s balance principle. Then, three-dimensional force sensors are developed and tested to verify the feasibility of the proposed method. Experimental results show that the proposed method is effective in cross-interference suppression, and the optimal cross-interference error of the developed sensors is 1.03%. By optimizing the positioning error, angle deviation, and bonding process of strain gauges, the cross-interference error of the sensor can be further reduced to −0.36%. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies in Robot Systems)
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19 pages, 5188 KiB  
Article
Automatic Branch–Leaf Segmentation and Leaf Phenotypic Parameter Estimation of Pear Trees Based on Three-Dimensional Point Clouds
by Haitao Li, Gengchen Wu, Shutian Tao, Hao Yin, Kaijie Qi, Shaoling Zhang, Wei Guo, Seishi Ninomiya and Yue Mu
Sensors 2023, 23(9), 4572; https://doi.org/10.3390/s23094572 - 08 May 2023
Cited by 1 | Viewed by 2271
Abstract
The leaf phenotypic traits of plants have a significant impact on the efficiency of canopy photosynthesis. However, traditional methods such as destructive sampling will hinder the continuous monitoring of plant growth, while manual measurements in the field are both time-consuming and laborious. Nondestructive [...] Read more.
The leaf phenotypic traits of plants have a significant impact on the efficiency of canopy photosynthesis. However, traditional methods such as destructive sampling will hinder the continuous monitoring of plant growth, while manual measurements in the field are both time-consuming and laborious. Nondestructive and accurate measurements of leaf phenotypic parameters can be achieved through the use of 3D canopy models and object segmentation techniques. This paper proposed an automatic branch–leaf segmentation pipeline based on lidar point cloud and conducted the automatic measurement of leaf inclination angle, length, width, and area, using pear canopy as an example. Firstly, a three-dimensional model using a lidar point cloud was established using SCENE software. Next, 305 pear tree branches were manually divided into branch points and leaf points, and 45 branch samples were selected as test data. Leaf points were further marked as 572 leaf instances on these test data. The PointNet++ model was used, with 260 point clouds as training input to carry out semantic segmentation of branches and leaves. Using the leaf point clouds in the test dataset as input, a single leaf instance was extracted by means of a mean shift clustering algorithm. Finally, based on the single leaf point cloud, the leaf inclination angle was calculated by plane fitting, while the leaf length, width, and area were calculated by midrib fitting and triangulation. The semantic segmentation model was tested on 45 branches, with a mean Precisionsem, mean Recallsem, mean F1-score, and mean Intersection over Union (IoU) of branches and leaves of 0.93, 0.94, 0.93, and 0.88, respectively. For single leaf extraction, the Precisionins, Recallins, and mean coverage (mCoV) were 0.89, 0.92, and 0.87, respectively. Using the proposed method, the estimated leaf inclination, length, width, and area of pear leaves showed a high correlation with manual measurements, with correlation coefficients of 0.94 (root mean squared error: 4.44°), 0.94 (root mean squared error: 0.43 cm), 0.91 (root mean squared error: 0.39 cm), and 0.93 (root mean squared error: 5.21 cm2), respectively. These results demonstrate that the method can automatically and accurately measure the phenotypic parameters of pear leaves. This has great significance for monitoring pear tree growth, simulating canopy photosynthesis, and optimizing orchard management. Full article
(This article belongs to the Special Issue Sensors and Data-Driven Precision Agriculture)
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17 pages, 1233 KiB  
Article
Willingness of Participation in an Application-Based Digital Data Collection among Different Social Groups and Smartphone User Clusters
by Ákos Máté, Zsófia Rakovics, Szilvia Rudas, Levente Wallis, Bence Ságvári, Ákos Huszár and Júlia Koltai
Sensors 2023, 23(9), 4571; https://doi.org/10.3390/s23094571 - 08 May 2023
Cited by 1 | Viewed by 1740
Abstract
The main question of this paper is what factors influence willingness to participate in a smartphone-application-based data collection where participants both fill out a questionnaire and let the app collect data on their smartphone usage. Passive digital data collection is becoming more common, [...] Read more.
The main question of this paper is what factors influence willingness to participate in a smartphone-application-based data collection where participants both fill out a questionnaire and let the app collect data on their smartphone usage. Passive digital data collection is becoming more common, but it is still a new form of data collection. Due to the novelty factor, it is important to investigate how willingness to participate in such studies is influenced by both socio-economic variables and smartphone usage behaviour. We estimate multilevel models based on a survey experiment with vignettes for different characteristics of data collection (e.g., different incentives, duration of the study). Our results show that of the socio-demographic variables, age has the largest influence, with younger age groups having a higher willingness to participate than older ones. Smartphone use also has an impact on participation. Advanced users are more likely to participate, while users who only use the basic functions of their device are less likely to participate than those who use it mainly for social media. Finally, the explorative analysis with interaction terms between levels has shown that the circumstances of data collection matter differently for different social groups. These findings provide important clues on how to fine-tune circumstances to improve participation rates in this novel passive digital data collection. Full article
(This article belongs to the Special Issue Sensors in 2023)
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14 pages, 987 KiB  
Article
Scenario Generation for Autonomous Vehicles with Deep-Learning-Based Heterogeneous Driver Models: Implementation and Verification
by Li Gao , Rui Zhou and Kai Zhang
Sensors 2023, 23(9), 4570; https://doi.org/10.3390/s23094570 - 08 May 2023
Cited by 1 | Viewed by 2352
Abstract
Virtual testing requires hazardous scenarios to effectively test autonomous vehicles (AVs). Existing studies have obtained rarer events by sampling methods in a fixed scenario space. In reality, heterogeneous drivers behave differently when facing the same situation. To generate more realistic and efficient scenarios, [...] Read more.
Virtual testing requires hazardous scenarios to effectively test autonomous vehicles (AVs). Existing studies have obtained rarer events by sampling methods in a fixed scenario space. In reality, heterogeneous drivers behave differently when facing the same situation. To generate more realistic and efficient scenarios, we propose a two-stage heterogeneous driver model to change the number of dangerous scenarios in the scenario space. We trained the driver model using the HighD dataset, and generated scenarios through simulation. Simulations were conducted in 20 experimental groups with heterogeneous driver models and 5 control groups with the original driver model. The results show that, by adjusting the number and position of aggressive drivers, the percentage of dangerous scenarios was significantly higher compared to that of models not accounting for driver heterogeneity. To further verify the effectiveness of our method, we evaluated two driving strategies: car-following and cut-in scenarios. The results verify the effectiveness of our approach. Cumulatively, the results indicate that our approach could accelerate the testing of AVs. Full article
(This article belongs to the Section Vehicular Sensing)
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20 pages, 4030 KiB  
Article
Spatial Calibration of Humanoid Robot Flexible Tactile Skin for Human–Robot Interaction
by Sélim Chefchaouni Moussaoui, Rafael Cisneros-Limón, Hiroshi Kaminaga, Mehdi Benallegue, Taiki Nobeshima, Shusuke Kanazawa and Fumio Kanehiro
Sensors 2023, 23(9), 4569; https://doi.org/10.3390/s23094569 - 08 May 2023
Cited by 1 | Viewed by 2345
Abstract
Recent developments in robotics have enabled humanoid robots to be used in tasks where they have to physically interact with humans, including robot-supported caregiving. This interaction—referred to as physical human–robot interaction (pHRI)—requires physical contact between the robot and the human body; one way [...] Read more.
Recent developments in robotics have enabled humanoid robots to be used in tasks where they have to physically interact with humans, including robot-supported caregiving. This interaction—referred to as physical human–robot interaction (pHRI)—requires physical contact between the robot and the human body; one way to improve this is to use efficient sensing methods for the physical contact. In this paper, we use a flexible tactile sensing array and integrate it as a tactile skin for the humanoid robot HRP-4C. As the sensor can take any shape due to its flexible property, a particular focus is given on its spatial calibration, i.e., the determination of the locations of the sensor cells and their normals when attached to the robot. For this purpose, a novel method of spatial calibration using B-spline surfaces has been developed. We demonstrate with two methods that this calibration method gives a good approximation of the sensor position and show that our flexible tactile sensor can be fully integrated on a robot and used as input for robot control tasks. These contributions are a first step toward the use of flexible tactile sensors in pHRI applications. Full article
(This article belongs to the Special Issue Robotic Contact with the Human Body in Physical Human–Robot Interaction)
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16 pages, 5028 KiB  
Article
Energy-Optimal Adaptive Control Based on Model Predictive Control
by Yuxi Li and Gang Hao
Sensors 2023, 23(9), 4568; https://doi.org/10.3390/s23094568 - 08 May 2023
Cited by 4 | Viewed by 1416
Abstract
Energy-optimal adaptive cruise control (EACC) is becoming increasingly popular due to its ability to save energy. Considering the negative impacts of system noise on the EACC, an improved modified model predictive control (MPC) is proposed, which combines the Sage-Husaadaptive Kalman filter (SHAKF), the [...] Read more.
Energy-optimal adaptive cruise control (EACC) is becoming increasingly popular due to its ability to save energy. Considering the negative impacts of system noise on the EACC, an improved modified model predictive control (MPC) is proposed, which combines the Sage-Husaadaptive Kalman filter (SHAKF), the cubature Kalman filter (CKF), and the back-propagation neural network (BPNN). The proposed MPC improves safety and tracking performance while further reducing energy consumption. The final simulation results show that the proposed algorithm has a stronger energy-saving capability compared to previous studies and always maintains an appropriate relative distance and relative speed to the vehicle in front, verifying the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue Advanced Sensing Technology in Intelligent Transportation Systems)
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18 pages, 3894 KiB  
Article
Optimized Dynamic Collision Avoidance Algorithm for USV Path Planning
by Hongyang Zhu and Yi Ding
Sensors 2023, 23(9), 4567; https://doi.org/10.3390/s23094567 - 08 May 2023
Cited by 6 | Viewed by 2099
Abstract
Ship collision avoidance is a complex process that is influenced by numerous factors. In this study, we propose a novel method called the Optimal Collision Avoidance Point (OCAP) for unmanned surface vehicles (USVs) to determine when to take appropriate actions to avoid collisions. [...] Read more.
Ship collision avoidance is a complex process that is influenced by numerous factors. In this study, we propose a novel method called the Optimal Collision Avoidance Point (OCAP) for unmanned surface vehicles (USVs) to determine when to take appropriate actions to avoid collisions. The approach combines a model that accounts for the two degrees of freedom in USV dynamics with a velocity obstacle method for obstacle detection and avoidance. The method calculates the change in the USV’s navigation state based on the critical condition of collision avoidance. First, the coordinates of the optimal collision avoidance point in the current ship encounter state are calculated based on the relative velocities and kinematic parameters of the USV and obstacles. Then, the increments of the vessel’s linear velocity and heading angle that can reach the optimal collision avoidance point are set as a constraint for dynamic window sampling. Finally, the algorithm evaluates the probabilities of collision hazards for trajectories that satisfy the critical condition and uses the resulting collision avoidance probability value as a criterion for course assessment. The resulting collision avoidance algorithm is optimized for USV maneuverability and is capable of handling multiple moving obstacles in real-time. Experimental results show that the OCAP algorithm has higher and more robust path-finding efficiency than the other two algorithms when the dynamic obstacle density is higher. Full article
(This article belongs to the Collection Advances in Human-Robot Interaction: Sensing, Cognition and Control)
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18 pages, 364 KiB  
Article
TDOA-Based Target Tracking Filter While Reducing NLOS Errors in Cluttered Environments
by Jonghoek Kim
Sensors 2023, 23(9), 4566; https://doi.org/10.3390/s23094566 - 08 May 2023
Cited by 1 | Viewed by 1388
Abstract
We consider tracking a moving target in a wireless communication system that is based on the radio signal. Considering a bounded workspace with many unknown obstacles, we handle tracking a non-cooperative transmitter using multiple signal receivers. Here, a non-cooperative transmitter is a transmitter [...] Read more.
We consider tracking a moving target in a wireless communication system that is based on the radio signal. Considering a bounded workspace with many unknown obstacles, we handle tracking a non-cooperative transmitter using multiple signal receivers. Here, a non-cooperative transmitter is a transmitter whose signal emission time is not known in advance. We consider a time difference of arrival (TDOA) location problem, which locates the transmitter by processing the signal measurement time at multiple receivers. In tracking a non-cooperative transmitter, non-line-of-sight (NLOS) errors occur if obstacles block the LOS line connecting the receiver and the moving transmitter. Our article addresses how to track a moving transmitter while decreasing the NLOS error in TDOA-only measurements. We propose an algorithm to localize a transmitter while decreasing the NLOS error in TDOA measurements. For tracking a moving transmitter in real time, we integrate the proposed localization algorithm and the interacting multiple model Kalman filter (IMM KF). As far as we know, our article is novel in tracking a moving transmitter based on TDOA-only measurements in an unknown mixed LOS/NLOS workspace. We show that the proposed filter considerably decreases the NLOS errors in TDOA-only measurements while running fast. Therefore, the proposed tracking scheme is suitable for tracking a moving transmitter in real time. Through MATLAB simulations, we show that the proposed filter outperforms other state-of-the-art TDOA filters, considering both time efficiency and tracking accuracy. Full article
(This article belongs to the Section Sensors and Robotics)
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14 pages, 2810 KiB  
Article
Margined Horn-Shaped Air Chamber for Body-Conduction Microphone
by Shun Muramatsu, Yuki Kohata, Emi Hira, Yasuyuki Momoi, Michitaka Yamamoto, Seiichi Takamatsu and Toshihiro Itoh
Sensors 2023, 23(9), 4565; https://doi.org/10.3390/s23094565 - 08 May 2023
Viewed by 1184
Abstract
The sound amplification ratios of sealed air chambers with different shapes were quantitatively compared to design a body-conduction microphone to measure animal scratching sounds. Recently, quantitative monitoring of scratching intensity in dogs has been required. We have already developed a collar with a [...] Read more.
The sound amplification ratios of sealed air chambers with different shapes were quantitatively compared to design a body-conduction microphone to measure animal scratching sounds. Recently, quantitative monitoring of scratching intensity in dogs has been required. We have already developed a collar with a body-conduction microphone to measure body-conducted scratching sounds. However, the air chamber, one of the components of the body-conduction microphone, has not been appropriately designed. This study compared the amplification ratios of air chambers with different shapes through numerical analysis and experiments. According to the results, the horn-shaped air chamber achieved the highest amplification performance, at least for sound frequencies below 3 kHz. The simulated amplification ratio of the horn-shaped air chamber with a 1 mm height and a 15 mm diameter was 52.5 dB. The deformation of the bottom of the air chamber affected the amplification ratio. Adjusting the margin of the margined horn shape could maintain its amplification ratio at any pressing force. The simulated and experimental amplification ratios of the margined horn-shaped air chamber were 53.4 dB and 19.4 dB, respectively. Full article
(This article belongs to the Special Issue Micro/Nano Electromechanical Sensors and Actuators)
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14 pages, 3186 KiB  
Article
Shaping Perpendicular Magnetic Anisotropy of Co2MnGa Heusler Alloy Using Ion Irradiation for Magnetic Sensor Applications
by Anmol Mahendra, Peter P. Murmu, Susant Kumar Acharya, Atif Islam, Holger Fiedler, Prasanth Gupta, Simon Granville and John Kennedy
Sensors 2023, 23(9), 4564; https://doi.org/10.3390/s23094564 - 08 May 2023
Cited by 2 | Viewed by 2265
Abstract
Magnetic sensors are key elements in many industrial, security, military, and biomedical applications. Heusler alloys are promising materials for magnetic sensor applications due to their high spin polarization and tunable magnetic properties. The dynamic field range of magnetic sensors is strongly related to [...] Read more.
Magnetic sensors are key elements in many industrial, security, military, and biomedical applications. Heusler alloys are promising materials for magnetic sensor applications due to their high spin polarization and tunable magnetic properties. The dynamic field range of magnetic sensors is strongly related to the perpendicular magnetic anisotropy (PMA). By tuning the PMA, it is possible to modify the sensing direction, sensitivity and even the accuracy of the magnetic sensors. Here, we report the tuning of PMA in a Co2MnGa Heusler alloy film via argon (Ar) ion irradiation. MgO/Co2MnGa/Pd films with an initial PMA were irradiated with 30 keV 40Ar+ ions with fluences (ions·cm−2) between 1 × 1013 and 1 × 1015 Ar·cm−2, which corresponds to displacement per atom values between 0.17 and 17, estimated from Monte-Carlo-based simulations. The magneto optical and magnetization results showed that the effective anisotropy energy (Keff) decreased from ~153 kJ·m−3 for the un-irradiated film to ~14 kJ·m−3 for the 1 × 1014 Ar·cm−2 irradiated film. The reduced Keff and PMA are attributed to ion-irradiation-induced interface intermixing that decreased the interfacial anisotropy. These results demonstrate that ion irradiation is a promising technique for shaping the PMA of Co2MnGa Heusler alloy for magnetic sensor applications. Full article
(This article belongs to the Collection Magnetic Sensors)
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12 pages, 846 KiB  
Article
Grad-MobileNet: A Gradient-Based Unsupervised Learning Method for Laser Welding Surface Defect Classification
by Sizhe Xiao, Zhenguo Liu, Zhihong Yan and Mingquan Wang
Sensors 2023, 23(9), 4563; https://doi.org/10.3390/s23094563 - 08 May 2023
Cited by 3 | Viewed by 1257
Abstract
Deep learning technology has advanced rapidly and has started to be applied for the detection of welding defects. In the manufacturing process of power batteries for new energy vehicles, welding defects may occur due to the high directivity, convergence, and penetration of the [...] Read more.
Deep learning technology has advanced rapidly and has started to be applied for the detection of welding defects. In the manufacturing process of power batteries for new energy vehicles, welding defects may occur due to the high directivity, convergence, and penetration of the laser beam. The accuracy of deep learning prediction relies heavily on big data, but balanced big data of welding defects is hard to acquire at the battery production site. In this paper, the authors construct a dataset named RIAM, which consists of images captured from an industrial environment for laser welding of power battery modules. RIAM contains four types of images: Normality, Lack of fusion, Surface porosity, and Scaled surface. The characteristics of RIAM are carefully considered in the application scenarios. Moreover, this paper proposes a gradient-based unsupervised model named Grad-MobileNet, which can be trained with only a few normal images and can extract the feature gradients of the input images. Welding defects can then be classified by the gradient distribution. This model is based on MobileNetV3, which is a lightweight convolutional neural network (CNN), and achieves 99% accuracy, which is higher than the accuracy expected from supervised learning. Full article
(This article belongs to the Section Industrial Sensors)
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18 pages, 2035 KiB  
Article
A Novel Wide-Band Directional MUSIC Algorithm Using the Strength Proportion
by Wencong Xu, Bingshu Chen, Yue Hu and Jianxun Li
Sensors 2023, 23(9), 4562; https://doi.org/10.3390/s23094562 - 08 May 2023
Cited by 1 | Viewed by 1310
Abstract
The directional multiple signal classification (Dir-MUSIC) algorithm based on the antenna gain array manifold has been proposed to find the direction of the partial discharge (PD) source in substations. However, PD signals are wideband signals and the antenna gain pattern functions are always [...] Read more.
The directional multiple signal classification (Dir-MUSIC) algorithm based on the antenna gain array manifold has been proposed to find the direction of the partial discharge (PD) source in substations. However, PD signals are wideband signals and the antenna gain pattern functions are always different at different frequencies; therefore, the accuracy can be improved using a wideband Dir-MUSIC algorithm. In this paper, wideband Dir-MUSIC algorithms are discussed and a novel wideband Dir-MUSIC algorithm using the strength proportion (DirSP) is proposed. This algorithm estimates a focusing PD signal at a certain frequency using the strength proportion among different directions, and then the Dir-MUSIC algorithm can process the focusing PD signal at this frequency. In simulations, when the antenna gain functions among different frequency bins are quite different, the Dir-MUSIC algorithm loses accuracy; meanwhile, DirDP performs very well. In the experiments, we deal with six sets of samples, and the mean error and standard deviation are both smaller than 4° better than other methods. Full article
(This article belongs to the Section Internet of Things)
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16 pages, 4522 KiB  
Article
Design and Experimental Validation of a Switchable Frequency Selective Surface with Incorporated Control Network
by Andrei-Marius Silaghi, Farzad Mir, Aldo De Sabata and Ladislau Matekovits
Sensors 2023, 23(9), 4561; https://doi.org/10.3390/s23094561 - 08 May 2023
Cited by 1 | Viewed by 1504
Abstract
Tunable/switchable devices are more and more required in modern communication systems. However, the realization of the tuning requires the presence of active devices, which in turn must be biased. The current paper comes up with a solution for designing and experimentally validating such [...] Read more.
Tunable/switchable devices are more and more required in modern communication systems. However, the realization of the tuning requires the presence of active devices, which in turn must be biased. The current paper comes up with a solution for designing and experimentally validating such a switchable Frequency Selective Surface. Two different metallic structures are simulated and measured, having incorporated the same topology control network (CN). In this scenario, the main innovation of this paper is the presence of the feeding part, namely the control network. In this work, the main FSS structure is flanked by three parallel CN microstrip lines and several via holes that allow biasing the active elements, namely PIN diodes. The switchability of the proposed structure is achieved through PIN diodes, whose bias determines the values of the elements in the equivalent circuit. At different biases, the response of the FSS changes accordingly. From all possible values of the bias, the extreme cases when the diodes act as (almost) short- and open-circuits are considered in the submitted manuscript for the sake of brevity. These cases are modeled by the main and cut-slot structures, respectively. The proposed structures have been evaluated using electromagnetic simulation and implemented on an FR4 substrate having a thickness of 1.58 mm. With the periodicity of the square-shaped unit cell of 18 mm edge length, different filtering bands are obtained below 12 GHz. Another novelty that has received very little consideration in the existing literature is the use of a finite array of unit cells instead of an infinite one. And finally, tests in an anechoic chamber have proved that there is a good agreement between practical and simulation results and also demonstrated the proper performance of the devices for wide angular incidence for both TE and TM polarizations. Full article
(This article belongs to the Special Issue High-Performance Metamaterial Sensors)
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12 pages, 3589 KiB  
Article
Near-Infrared Light-Responsive Hydrogels for Highly Flexible Bionic Photosensors
by Rui Huang, Zhenhua Fan, Bin Xue, Junpeng Ma and Qundong Shen
Sensors 2023, 23(9), 4560; https://doi.org/10.3390/s23094560 - 08 May 2023
Cited by 1 | Viewed by 1613
Abstract
Soft biological tissues perform various functions. Sensory nerves bring sensations of light, voice, touch, pain, or temperature variation to the central nervous system. Animal senses have inspired tremendous sensors for biomedical applications. Following the same principle as photosensitive nerves, we design flexible ionic [...] Read more.
Soft biological tissues perform various functions. Sensory nerves bring sensations of light, voice, touch, pain, or temperature variation to the central nervous system. Animal senses have inspired tremendous sensors for biomedical applications. Following the same principle as photosensitive nerves, we design flexible ionic hydrogels to achieve a biologic photosensor. The photosensor allows responding to near-infrared light, which is converted into a sensory electric signal that can communicate with nerve cells. Furthermore, with adjustable thermal and/or electrical signal outputs, it provides abundant tools for biological regulation. The tunable photosensitive performances, high flexibility, and low cost endow the photosensor with widespread applications ranging from neural prosthetics to human–machine interfacing systems. Full article
(This article belongs to the Special Issue The Advanced Flexible Electronic Devices)
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23 pages, 12597 KiB  
Article
A Systematic Comparison of High-End and Low-Cost EEG Amplifiers for Concealed, Around-the-Ear EEG Recordings
by Michael Thomas Knierim, Martin Georg Bleichner and Pierluigi Reali
Sensors 2023, 23(9), 4559; https://doi.org/10.3390/s23094559 - 08 May 2023
Cited by 4 | Viewed by 2498
Abstract
Wearable electroencephalography (EEG) has the potential to improve everyday life through brain–computer interfaces (BCI) for applications such as sleep improvement, adaptive hearing aids, or thought-based digital device control. To make these innovations more practical for everyday use, researchers are looking to miniaturized, concealed [...] Read more.
Wearable electroencephalography (EEG) has the potential to improve everyday life through brain–computer interfaces (BCI) for applications such as sleep improvement, adaptive hearing aids, or thought-based digital device control. To make these innovations more practical for everyday use, researchers are looking to miniaturized, concealed EEG systems that can still collect neural activity precisely. For example, researchers are using flexible EEG electrode arrays that can be attached around the ear (cEEGrids) to study neural activations in everyday life situations. However, the use of such concealed EEG approaches is limited by measurement challenges such as reduced signal amplitudes and high recording system costs. In this article, we compare the performance of a lower-cost open-source amplification system, the OpenBCI Cyton+Daisy boards, with a benchmark amplifier, the MBrainTrain Smarting Mobi. Our results show that the OpenBCI system is a viable alternative for concealed EEG research, with highly similar noise performance, but slightly lower timing precision. This system can be a great option for researchers with a smaller budget and can, therefore, contribute significantly to advancing concealed EEG research. Full article
(This article belongs to the Section Biomedical Sensors)
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19 pages, 8538 KiB  
Article
Ackerman Unmanned Mobile Vehicle Based on Heterogeneous Sensor in Navigation Control Application
by Chi-Huang Shih, Cheng-Jian Lin and Jyun-Yu Jhang
Sensors 2023, 23(9), 4558; https://doi.org/10.3390/s23094558 - 08 May 2023
Cited by 1 | Viewed by 1593
Abstract
With the advancement of science and technology, the development and application of unmanned mobile vehicles (UMVs) have emerged as topics of crucial concern in the global industry. The development goals and directions of UMVs vary according to their industrial uses, which include navigation, [...] Read more.
With the advancement of science and technology, the development and application of unmanned mobile vehicles (UMVs) have emerged as topics of crucial concern in the global industry. The development goals and directions of UMVs vary according to their industrial uses, which include navigation, autonomous driving, and environmental recognition; these uses have become the priority development goals of researchers in various fields. UMVs employ sensors to collect environmental data for environmental analysis and path planning. However, the analysis function of a single sensor is generally affected by natural environmental factors, resulting in poor identification results. Therefore, this study introduces fusion technology that employs heterogeneous sensors in the Ackerman UMV, leveraging the advantages of each sensor to enhance accuracy and stability in environmental detection and identification. This study proposes a fusion technique involving heterogeneous imaging and LiDAR (laser imaging, detection, and ranging) sensors in an Ackerman UMV. A camera is used to obtain real-time images, and YOLOv4-tiny and simple online real-time tracking are then employed to detect the location of objects and conduct object classification and object tracking. LiDAR is simultaneously used to obtain real-time distance information of detected objects. An inertial measurement unit is used to gather odometry information to determine the position of the Ackerman UMV. Static maps are created using simultaneous localization and mapping. When the user commands the Ackerman UMV to move to the target point, the vehicle control center composed of the robot operating system activates the navigation function through the navigation control module. The Ackerman UMV can reach the destination and instantly identify obstacles and pedestrians when in motion. Full article
(This article belongs to the Topic IOT, Communication and Engineering)
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13 pages, 2981 KiB  
Article
DNA Sensing Platforms: Novel Insights into Molecular Grafting Using Low Perturbative AFM Imaging
by Silvia Maria Cristina Rotondi, Paolo Canepa, Elena Angeli, Maurizio Canepa and Ornella Cavalleri
Sensors 2023, 23(9), 4557; https://doi.org/10.3390/s23094557 - 08 May 2023
Cited by 2 | Viewed by 1407
Abstract
By using AFM as a nanografting tool, we grafted micrometer-sized DNA platforms into inert alkanethiol SAMs. Tuning the grafting conditions (surface density of grafting lines and scan rate) allowed us to tailor the molecular density of the DNA platforms. Following the nanografting process, [...] Read more.
By using AFM as a nanografting tool, we grafted micrometer-sized DNA platforms into inert alkanethiol SAMs. Tuning the grafting conditions (surface density of grafting lines and scan rate) allowed us to tailor the molecular density of the DNA platforms. Following the nanografting process, AFM was operated in the low perturbative Quantitative Imaging (QI) mode. The analysis of QI AFM images showed the coexistence of molecular domains of different heights, and thus different densities, within the grafted areas, which were not previously reported using contact AFM imaging. Thinner domains corresponded to low-density DNA regions characterized by loosely packed, randomly oriented DNA strands, while thicker domains corresponded to regions with more densely grafted DNA. Grafting with densely spaced and slow scans increased the size of the high-density domains, resulting in an overall increase in patch height. The structure of the grafted DNA was compared to self-assembled DNA, which was assessed through nanoshaving experiments. Exposing the DNA patches to the target sequence produced an increase in the patch height, indicating that hybridization was accomplished. The relative height increase of the DNA patches upon hybridization was higher in the case of lower density patches due to hybridization leading to a larger molecular reorganization. Low density DNA patches were therefore the most suitable for targeting oligonucleotide sequences. Full article
(This article belongs to the Special Issue Atomic Force Microscope (AFM) for Sensing, Imaging, and Measurement)
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17 pages, 3829 KiB  
Article
An Investigation about Modern Deep Learning Strategies for Colon Carcinoma Grading
by Pierluigi Carcagnì, Marco Leo, Luca Signore and Cosimo Distante
Sensors 2023, 23(9), 4556; https://doi.org/10.3390/s23094556 - 08 May 2023
Cited by 2 | Viewed by 1809
Abstract
Developing computer-aided approaches for cancer diagnosis and grading is currently receiving an increasing demand: this could take over intra- and inter-observer inconsistency, speed up the screening process, increase early diagnosis, and improve the accuracy and consistency of the treatment-planning processes.The third most common [...] Read more.
Developing computer-aided approaches for cancer diagnosis and grading is currently receiving an increasing demand: this could take over intra- and inter-observer inconsistency, speed up the screening process, increase early diagnosis, and improve the accuracy and consistency of the treatment-planning processes.The third most common cancer worldwide and the second most common in women is colorectal cancer (CRC). Grading CRC is a key task in planning appropriate treatments and estimating the response to them. Unfortunately, it has not yet been fully demonstrated how the most advanced models and methodologies of machine learning can impact this crucial task.This paper systematically investigates the use of advanced deep models (convolutional neural networks and transformer architectures) to improve colon carcinoma detection and grading from histological images. To the best of our knowledge, this is the first attempt at using transformer architectures and ensemble strategies for exploiting deep learning paradigms for automatic colon cancer diagnosis. Results on the largest publicly available dataset demonstrated a substantial improvement with respect to the leading state-of-the-art methods. In particular, by exploiting a transformer architecture, it was possible to observe a 3% increase in accuracy in the detection task (two-class problem) and up to a 4% improvement in the grading task (three-class problem) by also integrating an ensemble strategy. Full article
(This article belongs to the Special Issue Feature Papers in "Sensing and Imaging" Section 2023)
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13 pages, 409 KiB  
Article
Validity and Reliability of the activPAL4TM for Measurement of Body Postures and Stepping Activity in 6–12-Year-Old Children
by Esraa Burahmah, Sivaramkumar Shanmugam, Daniel Williams and Ben Stansfield
Sensors 2023, 23(9), 4555; https://doi.org/10.3390/s23094555 - 08 May 2023
Cited by 1 | Viewed by 1070
Abstract
A link between inappropriate physical behaviour patterns (low physical activity and high sedentary behaviour) and poor health outcomes has been observed. To provide evidence to quantify this link, it is important to have valid and reliable assessment tools. This study aimed to assess [...] Read more.
A link between inappropriate physical behaviour patterns (low physical activity and high sedentary behaviour) and poor health outcomes has been observed. To provide evidence to quantify this link, it is important to have valid and reliable assessment tools. This study aimed to assess the validity and reliability of the activPAL4TM monitor for distinguishing postures and measuring stepping activity of 6–12-year-old children. Thirteen children (8.5 ± 1.8 years) engaged in pre-determined standardised (12 min) and non-standardised (6 min) activities. Agreement, specificity and positive predictive value were assessed between the activPAL4TM and direct observation (DO) (nearest 0.1 s). Between-activPAL4TM (inter-device) and between-observer (inter-rater) reliability were determined. Detection of sitting and stepping time and forward purposeful step count were all within 5% of DO. Standing time was slightly overestimated (+10%) and fast walking/jogging steps underestimated (−20%). For non-standardised activities, activPAL4TM step count matched most closely to combined backward and forward purposeful steps; however, agreement varied widely. The activPAL4TM demonstrated high levels of reliability (ICC(1, 1) > 0.976), which were higher in some instances than could be achieved through direct observation (ICC(2, 1) > 0.851 for non-standardised activities). Overall, the activPAL4TM recorded standardised activities well. However, further work is required to establish the exact nature of steps counted by the activPAL4TM. Full article
(This article belongs to the Section Biomedical Sensors)
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5 pages, 209 KiB  
Editorial
Advanced Field-Effect Sensors
by Antonio Di Bartolomeo
Sensors 2023, 23(9), 4554; https://doi.org/10.3390/s23094554 - 08 May 2023
Cited by 1 | Viewed by 1326
Abstract
Sensors based on the field-effect principle have been used for more than fifty years in a variety of applications ranging from bio-chemical sensing to radiation detection or environmental parameter monitoring [...] Full article
(This article belongs to the Special Issue Advanced Field-Effect Sensors)
18 pages, 5563 KiB  
Article
EOG Signal Classification with Wavelet and Supervised Learning Algorithms KNN, SVM and DT
by Sandy Nohemy Hernández Pérez, Francisco David Pérez Reynoso, Carlos Alberto González Gutiérrez, María De los Ángeles Cosío León and Rocío Ortega Palacios
Sensors 2023, 23(9), 4553; https://doi.org/10.3390/s23094553 - 07 May 2023
Cited by 3 | Viewed by 2447
Abstract
The work carried out in this paper consists of the classification of the physiological signal generated by eye movement called Electrooculography (EOG). The human eye performs simultaneous movements, when focusing on an object, generating a potential change in origin between the retinal epithelium [...] Read more.
The work carried out in this paper consists of the classification of the physiological signal generated by eye movement called Electrooculography (EOG). The human eye performs simultaneous movements, when focusing on an object, generating a potential change in origin between the retinal epithelium and the cornea and modeling the eyeball as a dipole with a positive and negative hemisphere. Supervised learning algorithms were implemented to classify five eye movements; left, right, down, up and blink. Wavelet Transform was used to obtain information in the frequency domain characterizing the EOG signal with a bandwidth of 0.5 to 50 Hz; training results were obtained with the implementation of K-Nearest Neighbor (KNN) 69.4%, a Support Vector Machine (SVM) of 76.9% and Decision Tree (DT) 60.5%, checking the accuracy through the Jaccard index and other metrics such as the confusion matrix and ROC (Receiver Operating Characteristic) curve. As a result, the best classifier for this application was the SVM with Jaccard Index. Full article
(This article belongs to the Section Biosensors)
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18 pages, 28847 KiB  
Article
Hierarchical Multi-Objective Optimization for Dedicated Bus Punctuality and Supply–Demand Balance Control
by Chunlin Shang, Fenghua Zhu, Yancai Xu, Xiaoming Liu and Tianhua Jiang
Sensors 2023, 23(9), 4552; https://doi.org/10.3390/s23094552 - 07 May 2023
Cited by 1 | Viewed by 1420
Abstract
Public transportation is a crucial component of urban transportation systems, and improving passenger sharing rates can help alleviate traffic congestion. To enhance the punctuality and supply–demand balance of dedicated buses, we propose a hierarchical multi-objective optimization model to optimize bus guidance speeds and [...] Read more.
Public transportation is a crucial component of urban transportation systems, and improving passenger sharing rates can help alleviate traffic congestion. To enhance the punctuality and supply–demand balance of dedicated buses, we propose a hierarchical multi-objective optimization model to optimize bus guidance speeds and bus operation schedules. Firstly, we present an intelligent decision-making method for bus driving speed based on the mathematical description of bus operation states and the application of the Lagrange multiplier method, which improves the overall punctuality rate of the bus line. Secondly, we propose an optimization method for bus operation schedules that respond to passenger needs by optimizing departure time intervals and station schedules for supply–demand balance. The experiments were conducted in Future Science City, Beijing, China. The results show that the bus line’s punctuality rate has increased to 90.53%, while the retention rate for platform passengers and the intersection stop rate have decreased by 36.22% and 60.93%, respectively. These findings verify the effectiveness and practicality of the proposed hierarchical multi-objective optimization model. Full article
(This article belongs to the Special Issue Sensors and Sensor Fusion in Autonomous Vehicles)
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15 pages, 18105 KiB  
Article
Effect of a Patient-Specific Structural Prior Mask on Electrical Impedance Tomography Image Reconstructions
by Rongqing Chen, Sabine Krueger-Ziolek, Alberto Battistel, Stefan J. Rupitsch and Knut Moeller
Sensors 2023, 23(9), 4551; https://doi.org/10.3390/s23094551 - 07 May 2023
Cited by 2 | Viewed by 1267
Abstract
Electrical Impedance Tomography (EIT) is a low-cost imaging method which reconstructs two-dimensional cross-sectional images, visualising the impedance change within the thorax. However, the reconstruction of an EIT image is an ill-posed inverse problem. In addition, blurring, anatomical alignment, and reconstruction artefacts can hinder [...] Read more.
Electrical Impedance Tomography (EIT) is a low-cost imaging method which reconstructs two-dimensional cross-sectional images, visualising the impedance change within the thorax. However, the reconstruction of an EIT image is an ill-posed inverse problem. In addition, blurring, anatomical alignment, and reconstruction artefacts can hinder the interpretation of EIT images. In this contribution, we introduce a patient-specific structural prior mask into the EIT reconstruction process, with the aim of improving image interpretability. Such a prior mask ensures that only conductivity changes within the lung regions are reconstructed. To evaluate the influence of the introduced structural prior mask, we conducted numerical simulations with two scopes in terms of their different ventilation statuses and varying atelectasis scales. Quantitative analysis, including the reconstruction error and figures of merit, was applied in the evaluation procedure. The results show that the morphological structures of the lungs introduced by the mask are preserved in the EIT reconstructions and the reconstruction artefacts are decreased, reducing the reconstruction error by 25.9% and 17.7%, respectively, in the two EIT algorithms included in this contribution. The use of the structural prior mask conclusively improves the interpretability of the EIT images, which could facilitate better diagnosis and decision-making in clinical settings. Full article
(This article belongs to the Special Issue Advances in Sensors for Intelligent Personalized Monitoring and Therapy)
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17 pages, 2158 KiB  
Article
Contactless Camera-Based Heart Rate and Respiratory Rate Monitoring Using AI on Hardware
by Dimitrios Kolosov, Vasilios Kelefouras, Pandelis Kourtessis and Iosif Mporas
Sensors 2023, 23(9), 4550; https://doi.org/10.3390/s23094550 - 07 May 2023
Viewed by 3529
Abstract
Detecting vital signs by using a contactless camera-based approach can provide several advantages over traditional clinical methods, such as lower financial costs, reduced visit times, increased comfort, and enhanced safety for healthcare professionals. Specifically, Eulerian Video Magnification (EVM) or Remote Photoplethysmography (rPPG) methods [...] Read more.
Detecting vital signs by using a contactless camera-based approach can provide several advantages over traditional clinical methods, such as lower financial costs, reduced visit times, increased comfort, and enhanced safety for healthcare professionals. Specifically, Eulerian Video Magnification (EVM) or Remote Photoplethysmography (rPPG) methods can be utilised to remotely estimate heart rate and respiratory rate biomarkers. In this paper two contactless camera-based health monitoring architectures are developed using EVM and rPPG, respectively; to this end, two different CNNs, (Mediapipe’s BlazeFace and FaceMesh) are used to extract suitable regions of interest from incoming video frames. These two methods are implemented and deployed on four off-the-shelf edge devices as well as on a PC and evaluated in terms of latency (in each stage of the application’s pipeline), throughput (FPS), power consumption (Watt), efficiency (throughput/Watt), and value (throughput/cost). This work provides important insights about the computational costs and bottlenecks of each method on each hardware platform, as well as which platform to use depending on the target metric. One of our insights shows that the Jetson Xavier NX platform is the best platform in terms of throughput and efficiency, while Raspberry Pi 4 8 GB is the best platform in terms of value. Full article
(This article belongs to the Special Issue Embedded Systems for AI-Based Health Monitoring in Cyber Physical Systems)
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25 pages, 1679 KiB  
Article
A Deep Learning-Driven Self-Conscious Distributed Cyber-Physical System for Renewable Energy Communities
by Giovanni Cicceri, Giuseppe Tricomi, Luca D’Agati, Francesco Longo, Giovanni Merlino and Antonio Puliafito
Sensors 2023, 23(9), 4549; https://doi.org/10.3390/s23094549 - 07 May 2023
Cited by 2 | Viewed by 1626
Abstract
The Internet of Things (IoT) is transforming various domains, including smart energy management, by enabling the integration of complex digital and physical components in distributed cyber-physical systems (DCPSs). The design of DCPSs has so far been focused on performance-related, non-functional requirements. However, with [...] Read more.
The Internet of Things (IoT) is transforming various domains, including smart energy management, by enabling the integration of complex digital and physical components in distributed cyber-physical systems (DCPSs). The design of DCPSs has so far been focused on performance-related, non-functional requirements. However, with the growing power consumption and computation expenses, sustainability is becoming an important aspect to consider. This has led to the concept of energy-aware DCPSs, which integrate conventional non-functional requirements with additional attributes for sustainability, such as energy consumption. This research activity aimed to investigate and develop energy-aware architectural models and edge/cloud computing technologies to design next-generation, AI-enabled (and, specifically, deep-learning-enhanced), self-conscious IoT-extended DCPSs. Our key contributions include energy-aware edge-to-cloud architectural models and technologies, the orchestration of a (possibly federated) edge-to-cloud infrastructure, abstractions and unified models for distributed heterogeneous virtualized resources, innovative machine learning algorithms for the dynamic reallocation and reconfiguration of energy resources, and the management of energy communities. The proposed solution was validated through case studies on optimizing renewable energy communities (RECs), or energy-aware DCPSs, which are particularly challenging due to their unique requirements and constraints; in more detail, in this work, we aim to define the optimal implementation of an energy-aware DCPS. Moreover, smart grids play a crucial role in developing energy-aware DCPSs, providing a flexible and efficient power system integrating renewable energy sources, microgrids, and other distributed energy resources. The proposed energy-aware DCPSs contribute to the development of smart grids by providing a sustainable, self-consistent, and efficient way to manage energy distribution and consumption. The performance demonstrates our approach’s effectiveness for consumption and production (based on RMSE and MAE metrics). Our research supports the transition towards a more sustainable future, where communities adopting REC principles become key players in the energy landscape. Full article
(This article belongs to the Special Issue Sensors Technology and Data Analytics Applied in Smart Grid)
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15 pages, 4798 KiB  
Article
Propagation Constant Measurement Based on a Single Transmission Line Standard Using a Two-Port VNA
by Ziad Hatab, Arezoo Abdi, Gregor Steinbauer, Michael Ernst Gadringer and Wolfgang Bösch
Sensors 2023, 23(9), 4548; https://doi.org/10.3390/s23094548 - 07 May 2023
Cited by 1 | Viewed by 1337
Abstract
This study presents a new method for measuring the propagation constant of transmission lines using a single line standard and without prior calibration of a two-port vector network analyzer (VNA). The method provides accurate results by emulating multiple line standards of the multiline [...] Read more.
This study presents a new method for measuring the propagation constant of transmission lines using a single line standard and without prior calibration of a two-port vector network analyzer (VNA). The method provides accurate results by emulating multiple line standards of the multiline calibration method. Each line standard was realized by sweeping an unknown network along a transmission line. The network need not be symmetric or reciprocal, but must exhibit both transmission and reflection. We performed measurements using a slab coaxial airline and repeated the measurements on three different VNAs. The measured propagation constant of the slab coaxial airline from all VNAs was nearly identical. By avoiding disconnecting or moving the cables, the proposed method eliminates errors related to the repeatability of connectors, resulting in improved broadband traceability to SI units. Full article
(This article belongs to the Special Issue Microwave Sensing Systems)
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19 pages, 7070 KiB  
Article
Vision and Vibration Data Fusion-Based Structural Dynamic Displacement Measurement with Test Validation
by Cheng Xiu, Yufeng Weng and Weixing Shi
Sensors 2023, 23(9), 4547; https://doi.org/10.3390/s23094547 - 07 May 2023
Cited by 4 | Viewed by 1840
Abstract
The dynamic measurement and identification of structural deformation are essential for structural health monitoring. Traditional contact-type displacement monitoring inevitably requires the arrangement of measurement points on physical structures and the setting of stable reference systems, which limits the application of dynamic displacement measurement [...] Read more.
The dynamic measurement and identification of structural deformation are essential for structural health monitoring. Traditional contact-type displacement monitoring inevitably requires the arrangement of measurement points on physical structures and the setting of stable reference systems, which limits the application of dynamic displacement measurement of structures in practice. Computer vision-based structural displacement monitoring has the characteristics of non-contact measurement, simple installation, and relatively low cost. However, the existing displacement identification methods are still influenced by lighting conditions, image resolution, and shooting-rate, which limits engineering applications. This paper presents a data fusion method for contact acceleration monitoring and non-contact displacement recognition, utilizing the high dynamic sampling rate of traditional contact acceleration sensors. It establishes and validates an accurate estimation method for dynamic deformation states. The structural displacement is obtained by combining an improved KLT algorithm and asynchronous multi-rate Kalman filtering. The results show that the presented method can help improve the displacement sampling rate and collect high-frequency vibration information compared with only the vision measurement technique. The normalized root mean square error is less than 2% for the proposed method. Full article
(This article belongs to the Section Sensing and Imaging)
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21 pages, 5534 KiB  
Article
Petrochemical Equipment Tracking by Improved Yolov7 Network and Hybrid Matching in Moving Scenes
by Zhenqiang Wei, Shaohua Dong and Xuchu Wang
Sensors 2023, 23(9), 4546; https://doi.org/10.3390/s23094546 - 07 May 2023
Cited by 1 | Viewed by 1619
Abstract
Petrochemical equipment tracking is a fundamental and important technology in petrochemical industry security monitoring, equipment working risk analysis, and other applications. In complex scenes where the multiple pipelines present different directions and many kinds of equipment have huge scale and shape variation in [...] Read more.
Petrochemical equipment tracking is a fundamental and important technology in petrochemical industry security monitoring, equipment working risk analysis, and other applications. In complex scenes where the multiple pipelines present different directions and many kinds of equipment have huge scale and shape variation in seriously mutual occlusions captured by moving cameras, the accuracy and speed of petrochemical equipment tracking would be limited because of the false and missed tracking of equipment with extreme sizes and severe occlusion, due to image quality, equipment scale, light, and other factors. In this paper, a new multiple petrochemical equipment tracking method is proposed by combining an improved Yolov7 network with attention mechanism and small target perceive layer and a hybrid matching that incorporates deep feature and traditional texture and location feature. The model incorporates the advantages of channel and spatial attention module into the improved Yolov7 detector and Siamese neural network for similarity matching. The proposed model is validated on the self-built petrochemical equipment video data set and the experimental results show it achieves a competitive performance in comparison with the related state-of-the-art tracking algorithms. Full article
(This article belongs to the Special Issue Sensors in Intelligent Industrial Applications)
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38 pages, 5115 KiB  
Article
A Distributed Supervisor Architecture for a General Wafer Production System
by Fotis N. Koumboulis, Dimitrios G. Fragkoulis and Panteleimon Georgakopoulos
Sensors 2023, 23(9), 4545; https://doi.org/10.3390/s23094545 - 07 May 2023
Cited by 3 | Viewed by 1340
Abstract
The current trend in the wafer production industry is to expand the production chain with more production stations, more buffers, and robots. The goal of the present paper is to develop a distributed control architecture to face this challenge by controlling wafer industrial [...] Read more.
The current trend in the wafer production industry is to expand the production chain with more production stations, more buffers, and robots. The goal of the present paper is to develop a distributed control architecture to face this challenge by controlling wafer industrial units in a general production chain, with a parametric number of production stations, one robot per two stations where each robot serves its two adjacent production stations, and one additional robot serving a parametric number of stations. The control architecture is analyzed for individual control units, one per robot, monitoring appropriate event signals from the control units of the adjacent robots. Each control unit is further analyzed to individual supervisors. In the present paper, a modular parametric discrete event model with respect to the number of production stations, the number of buffers, and the number of robotic manipulators is developed. A set of specifications for the total system is proposed in the form of rules. The specifications are translated and decomposed to a set of local regular languages for each robotic manipulator. The distributed supervisory control architecture is developed based on the local regular languages, where a set of local supervisors are designed for each robotic manipulator. The desired performance of the total manufacturing system, the realizability, and the nonblocking property of the proposed architecture is guaranteed. Finally, implementation issues are tackled, and the complexity of the distributed architecture is determined in a parametric formula. Overall, the contribution of the present paper is the development of a parametric model of the wafer manufacturing systems and the development of a parametric distributed supervisory control architecture. The present results provide a ready-to-hand solution for the continuously expanding wafer production industry. Full article
(This article belongs to the Special Issue Sensors in 2023)
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21 pages, 5013 KiB  
Article
Celestial Bodies Far-Range Detection with Deep-Space CubeSats
by Vittorio Franzese and Francesco Topputo
Sensors 2023, 23(9), 4544; https://doi.org/10.3390/s23094544 - 07 May 2023
Cited by 4 | Viewed by 1592
Abstract
Detecting celestial bodies while in deep-space travel is a critical task for the correct execution of space missions. Major bodies such as planets are bright and therefore easy to observe, while small bodies can be faint and therefore difficult to observe. A critical [...] Read more.
Detecting celestial bodies while in deep-space travel is a critical task for the correct execution of space missions. Major bodies such as planets are bright and therefore easy to observe, while small bodies can be faint and therefore difficult to observe. A critical task for both rendezvous and fly-by missions is to detect asteroid targets, either for relative navigation or for opportunistic observations. Traditional, large spacecraft missions can detect small bodies from far away, owing to the large aperture of the onboard optical cameras. This is not the case for deep-space miniaturized satellites, whose small-aperture cameras pose new challenges in detecting and tracking the line-of-sight directions to small bodies. This paper investigates the celestial bodies far-range detection limits for deep-space CubeSats, suggesting active measures for small bodies detection. The M–ARGO CubeSat mission is considered as the study case for this activity. The analyses show that the detection of small asteroids (with absolute magnitude fainter than 24) is expected to be in the range of 30,000–50,000 km, exploiting typical miniaturized cameras for deep-space CubeSats. Given the limited detection range, this paper recommends to include a zero-phase-angle way point at close range in the mission design phase of asteroid rendezvous missions exploiting deep-space CubeSats to allow detection. Full article
(This article belongs to the Section Sensor Networks)
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