bluuebunny/crossref_metadata_2025 huggingface dataset:
A Multi-Sensor Satellite Approach to Characterize the Volcanic Deposits Emitted during Etna’s Lava Fountaining: The 2020–2022 Study Case
Authors: Ganci Gaetana , Bilotta Giuseppe , Zuccarello Francesco , Calvari Sonia , Cappello Annalisa
Published: February 2023

Between December 2020 and February 2022, the South East Crater of Etna has been the source of numerous eruptions, mostly characterized by the emission of lava fountains, pyroclastic material and short-lasting lava flows. Here we estimate the volume and distribution of the lava deposits by elaborating multi-source satellite imagery. SEVIRI data have been elaborated using CL-HOTSAT to estimate the lava volume emitted during each event and calculate the cumulative volume; Pléiades and WorldView-1 data have been used to derive Digital Surface Models, whose differences provide thickness distributions and hence volumes of the volcanic deposits. We find a good agreement, with the total average lava volume obtained by SEVIRI reaching 73.2 × 106 m3 and the one from optical data amounting to 67.7 × 106 m3. This proves the robustness of both techniques and the accuracy of the volume estimates, which provide important information on the lava flooding history and evolution of the volcano.
Estimating Afforestation Area Using Landsat Time Series and Photointerpreted Datasets
Authors: Cavalli Alice , Francini Saverio , McRoberts Ronald E. , Falanga Valentina , Congedo Luca , De Fioravante Paolo , Maesano Mauro , Munafò Michele , Chirici Gherardo , Scarascia Mugnozza Giuseppe
Published: February 2023

Afforestation processes, natural and anthropogenic, involve the conversion of other land uses to forest, and they represent one of the most important land use transformations, influencing numerous ecosystem services. Although remotely sensed data are commonly used to monitor forest disturbance, only a few reported studies have used these data to monitor afforestation. The objectives of this study were two fold: (1) to develop and illustrate a method that exploits the 1985–2019 Landsat time series for predicting afforestation areas at 30 m resolution at the national scale, and (2) to estimate afforestation areas statistically rigorously within Italian administrative regions and land elevation classes. We used a Landsat best-available-pixel time series (1985–2019) to calculate a set of temporal predictors that, together with the random forests prediction technique, facilitated construction of a map of afforested areas in Italy. Then, the map was used to guide selection of an estimation sample dataset which, after a complex photointerpretation phase, was used to estimate afforestation areas and associated confidence intervals. The classification approach achieved an accuracy of 87%. At the national level, the afforestation area between 1985 and 2019 covered 2.8 ± 0.2 million ha, corresponding to a potential C-sequestration of 200 million t. The administrative region with the largest afforested area was Sardinia, with 260,670 ± 58,522 ha, while the smallest area of 28,644 ± 12,114 ha was in Valle d’Aosta. Considering elevation classes of 200 m, the greatest afforestation area was between 400 and 600 m above sea level, where it was 549,497 ± 84,979 ha. Our results help to understand the afforestation process in Italy between 1985 and 2019 in relation to geographical location and altitude, and they could be the basis of further studies on the species composition of afforestation areas and land management conditions.
A Novel Dual-Encoder Model for Hyperspectral and LiDAR Joint Classification via Contrastive Learning
Authors: Wu Haibin , Dai Shiyu , Liu Chengyang , Wang Aili , Iwahori Yuji
Published: February 2023

Deep-learning-based multi-sensor hyperspectral image classification algorithms can automatically acquire the advanced features of multiple sensor images, enabling the classification model to better characterize the data and improve the classification accuracy. However, the currently available classification methods for feature representation in multi-sensor remote sensing data in their respective domains do not focus on the existence of bottlenecks in heterogeneous feature fusion due to different sensors. This problem directly limits the final collaborative classification performance. In this paper, to address the bottleneck problem of joint classification due to the difference in heterogeneous features, we innovatively combine self-supervised comparative learning while designing a robust and discriminative feature extraction network for multi-sensor data, using spectral–spatial information from hyperspectral images (HSIs) and elevation information from LiDAR. The advantages of multi-sensor data are realized. The dual encoders of the hyperspectral encoder by the ConvNeXt network (ConvNeXt-HSI) and the LiDAR encoder by Octave Convolution (OctaveConv-LiDAR) are also used. The adequate feature representation of spectral–spatial features and depth information obtained from different sensors is performed for the joint classification of hyperspectral images and LiDAR data. The multi-sensor joint classification performance of both HSI and LiDAR sensors is greatly improved. Finally, on the Houston2013 dataset and the Trento dataset, we demonstrate through a series of experiments that the dual-encoder model for hyperspectral and LiDAR joint classification via contrastive learning achieves state-of-the-art classification performance.
A Multi-Scale Edge Constraint Network for the Fine Extraction of Buildings from Remote Sensing Images
Authors: Wang Zhenqing , Zhou Yi , Wang Futao , Wang Shixin , Qin Gang , Zou Weijie , Zhu Jinfeng
Published: February 2023

Building extraction based on remote sensing images has been widely used in many industries. However, state-of-the-art methods produce an incomplete segmentation of buildings owing to unstable multi-scale context aggregation and a lack of consideration of semantic boundaries, ultimately resulting in large uncertainties in predictions at building boundaries. In this study, efficient fine building extraction methods were explored, which demonstrated that the rational use of edge features can significantly improve building recognition performance. Herein, a fine building extraction network based on a multi-scale edge constraint (MEC-Net) was proposed, which integrates the multi-scale feature fusion advantages of UNet++ and fuses edge features with other learnable multi-scale features to achieve the effect of prior constraints. Attention was paid to the alleviation of noise interference in the edge features. At the data level, according to the improvement of copy-paste according to the characteristics of remote sensing imaging, a data augmentation method for buildings (build-building) was proposed, which increased the number and diversity of positive samples by simulating the construction of buildings to increase the generalization of MEC-Net. MEC-Net achieved 91.13%, 81.05% and 74.13% IoU on the WHU, Massachusetts and Inria datasets, and it has a good inference efficiency. The experimental results show that MEC-Net outperforms the state-of-the-art methods, demonstrating its superiority. MEC-Net improves the accuracy of building boundaries by rationally using previous edge features.
A Lightweight Siamese Neural Network for Building Change Detection Using Remote Sensing Images
Authors: Yang Haiping , Chen Yuanyuan , Wu Wei , Pu Shiliang , Wu Xiaoyang , Wan Qiming , Dong Wen
Published: February 2023

Building change detection (BuCD) can offer fundamental data for applications such as urban planning and identifying illegally-built new buildings. With the development of deep neural network-based approaches, BuCD using high-spatial-resolution remote sensing images (RSIs) has significantly advanced. These deep neural network-based methods, nevertheless, typically demand a considerable number of computational resources. Additionally, the accuracy of these algorithms can be improved. Hence, LightCDNet, a lightweight Siamese neural network for BuCD, is introduced in this paper. Specifically, LightCDNet comprises three components: a Siamese encoder, a multi-temporal feature fusion module (MultiTFFM), and a decoder. In the Siamese encoder, MobileNetV2 is chosen as the feature extractor to decrease computational costs. Afterward, the multi-temporal features from dual branches are independently concatenated based on the layer level. Subsequently, multiscale features computed from higher levels are up-sampled and fused with the lower-level ones. In the decoder, deconvolutional layers are adopted to gradually recover the changed buildings. The proposed network LightCDNet was assessed using two public datasets: namely, the LEVIR BuCD dataset (LEVIRCD) and the WHU BuCD dataset (WHUCD). The F1 scores on the LEVIRCD and WHUCD datasets of LightCDNet were 89.6% and 91.5%, respectively. The results of the comparative experiments demonstrate that LightCDNet outperforms several state-of-the-art methods in accuracy and efficiency.
Correction: Tian et al. Multiscale Superpixel-Based Fine Classification of Crops in the UAV-Based Hyperspectral Imagery. Remote Sens. 2022, 14, 3292
Authors: Tian Shuang , Lu Qikai , Wei Lifei
Published: February 2023

In the original article [...]
Analysis and Correction of Water Forward-Scattering-Induced Bathymetric Bias for Spaceborne Photon-Counting Lidar
Authors: Yang Jian , Ma Yue , Zheng Huiying , Gu Yuanfei , Zhou Hui , Li Song
Published: February 2023

The new spaceborne photon-counting lidar, i.e., ICESat-2, has shown great advantages in obtaining nearshore bathymetry at a global scale. The forward-scattering effect in the water column is one of the main error sources in airborne lidar bathymetry (ALB). However, the magnitude of the bathymetric bias for spaceborne lidars and how can we effectively correct this bias have not been evaluated and are very worthy of investigation. In this study, the forward-scattering effect on spaceborne photon-counting lidar bathymetry is quantitatively modeled and analyzed based on the semi-analytic Monte Carlo simulation method. Meanwhile, an empirical formula for correcting forward-scattering-induced bathymetric bias specific to ICESat-2 is derived. When the water depth exceeds 20 m, this bias cannot be neglected for ICESat-2 even in clear open ocean waters. In two study areas with local in situ measurements (St. Thomas and Hawaii), the bathymetric bias of ICESat-2 in deep waters (>20 m) is corrected from exceeding 50 cm to less than 13 cm using the proposed empirical formula. This study is valuable to evaluate and correct the forward-scattering-induced bias for the existing ICESat-2 and is also fundamental to optimizing the hardware parameters of a possible future photon-counting bathymetric lidar.
Development of a Biomechanical Device for Parameter Quantification Associated with the Sit-to-Stand Movement
Authors: Oliveira Natacha , Carvalho Filipe , Laíns Jorge , Rasteiro Deolinda , Roseiro Luis
Published: February 2023

The “sit-to-stand” (STS) movement is essential during activities of daily living (ADL). In individuals with physical-motor diseases, its execution and repetition increases activity levels, which is crucial for a good motor rehabilitation process and daily training. Interestingly, there are no sit-to-stand devices that allow a quantitative assessment of the key variables that happen during STS, and there is a need to come up with a new device. This work presents a developed biomechanical support device that measures the force of the upper limbs during the STS movement, aiming to motivate and encourage people undergoing physical therapy in the lower limbs. The device uses two instrumented beams and allows real-time visualization of both arms’ applied force and it records the data for post-processing. The device was tested with a well-defined protocol on a group of 34 healthy young volunteers and an elderly group of 16 volunteers from a continuing care unit. The system showed robust strength and stiffness, good usability, and a user interface that acquired and recorded data effectively, allowing one to observe force-time during the execution of the movement through the application interface developed and in recording data for post-processing. Asymmetries in the applied forces in the STS movement between the upper limbs were identified, particularly in volunteers of the continuing care unit. From the application and the registered data, it can be observed that volunteers with motor problems in the lower limbs performed more strength in their arms to compensate. As expected, the maximum average strength of the healthy volunteers (both arms: force = 105 Newton) was higher than that of the volunteers from the continuing care unit (right arm: force = 54 Newton; left arm: force = 56 Newton). Among others, moderate correlations were observed between weight-applied and height-applied forces and there was a moderately high correlation between the Sequential Clinical Assessment of Respiratory Function (SCAR-F score) and time to perform the movement. Based on the obtained results, the developed device can be a helpful tool for monitoring the evaluation of a patient with limitations in the upper and lower limbs. In addition, the developed system allows for easy evolution, such as including a barometric platform and implementing serious games that can stimulate the execution of the STS movement.
Augmented Reality-Assisted Ultrasound Breast Biopsy
Authors: Costa Nuno , Ferreira Luís , de Araújo Augusto R. V. F. , Oliveira Bruno , Torres Helena R. , Morais Pedro , Alves Victor , Vilaça João L.
Published: February 2023

Breast cancer is the most prevalent cancer in the world and the fifth-leading cause of cancer-related death. Treatment is effective in the early stages. Thus, a need to screen considerable portions of the population is crucial. When the screening procedure uncovers a suspect lesion, a biopsy is performed to assess its potential for malignancy. This procedure is usually performed using real-time Ultrasound (US) imaging. This work proposes a visualization system for US breast biopsy. It consists of an application running on AR glasses that interact with a computer application. The AR glasses track the position of QR codes mounted on an US probe and a biopsy needle. US images are shown in the user’s field of view with enhanced lesion visualization and needle trajectory. To validate the system, latency of the transmission of US images was evaluated. Usability assessment compared our proposed prototype with a traditional approach with different users. It showed that needle alignment was more precise, with 92.67 ± 2.32° in our prototype versus 89.99 ± 37.49° in a traditional system. The users also reached the lesion more accurately. Overall, the proposed solution presents promising results, and the use of AR glasses as a tracking and visualization device exhibited good performance.
Visualization and Object Detection Based on Event Information
Authors: Fang Yinghong , Piao Yongjie , Xie Xiaoguang , Li Miao , Li Xiaodong , Ji Haolin , Xu Wei , Gao Tan
Published: February 2023

A dynamic vision sensor is an optical sensor that focuses on dynamic changes and outputs event information containing only position, time, and polarity. It has the advantages of high temporal resolution, high dynamic range, low data volume, and low power consumption. However, a single event can only indicate that the increase or decrease in light exceeds the threshold at a certain pixel position and a certain moment. In order to further study the ability and characteristics of event information to represent targets, this paper proposes an event information visualization method with adaptive temporal resolution. Compared with methods with constant time intervals and a constant number of events, it can better convert event information into pseudo-frame images. Additionally, in order to explore whether the pseudo-frame image can efficiently complete the task of target detection according to its characteristics, this paper designs a target detection network named YOLOE. Compared with other algorithms, it has a more balanced detection effect. By constructing a dataset and conducting experimental verification, the detection accuracy of the image obtained by the event information visualization method with adaptive temporal resolution was 5.11% and 4.74% higher than that obtained using methods with a constant time interval and number of events, respectively. The average detection accuracy of pseudo-frame images in the YOLOE network designed in this paper is 85.11%, and the number of detection frames per second is 109. Therefore, the effectiveness of the proposed visualization method and the good performance of the designed detection network are verified.
Compact Modeling of Two-Dimensional Field-Effect Biosensors
Authors: Pasadas Francisco , El Grour Tarek , G. Marin Enrique , Medina-Rull Alberto , Toral-Lopez Alejandro , Cuesta-Lopez Juan , G. Ruiz Francisco , El Mir Lassaad , Godoy Andrés
Published: February 2023

A compact model able to predict the electrical read-out of field-effect biosensors based on two-dimensional (2D) semiconductors is introduced. It comprises the analytical description of the electrostatics including the charge density in the 2D semiconductor, the site-binding modeling of the barrier oxide surface charge, and the Stern layer plus an ion-permeable membrane, all coupled with the carrier transport inside the biosensor and solved by making use of the Donnan potential inside the ion-permeable membrane formed by charged macromolecules. This electrostatics and transport description account for the main surface-related physical and chemical processes that impact the biosensor electrical performance, including the transport along the low-dimensional channel in the diffusive regime, electrolyte screening, and the impact of biological charges. The model is implemented in Verilog-A and can be employed on standard circuit design tools. The theoretical predictions obtained with the model are validated against measurements of a MoS2 field-effect biosensor for streptavidin detection showing excellent agreement in all operation regimes and leading the way for the circuit-level simulation of biosensors based on 2D semiconductors.
Novel Speech Recognition Systems Applied to Forensics within Child Exploitation: Wav2vec2.0 vs. Whisper
Authors: Vásquez-Correa Juan Camilo , Álvarez Muniain Aitor
Published: February 2023

The growth in online child exploitation material is a significant challenge for European Law Enforcement Agencies (LEAs). One of the most important sources of such online information corresponds to audio material that needs to be analyzed to find evidence in a timely and practical manner. That is why LEAs require a next-generation AI-powered platform to process audio data from online sources. We propose the use of speech recognition and keyword spotting to transcribe audiovisual data and to detect the presence of keywords related to child abuse. The considered models are based on two of the most accurate neural-based architectures to date: Wav2vec2.0 and Whisper. The systems were tested under an extensive set of scenarios in different languages. Additionally, keeping in mind that obtaining data from LEAs are very sensitive, we explore the use of federated learning to provide more robust systems for the addressed application, while maintaining the privacy of the data from LEAs. The considered models achieved a word error rate between 11% and 25%, depending on the language. In addition, the systems are able to recognize a set of spotted words with true-positive rates between 82% and 98%, depending on the language. Finally, federated learning strategies show that they can maintain and even improve the performance of the systems when compared to centralized trained models. The proposed systems set the basis for an AI-powered platform for automatic analysis of audio in the context of forensic applications of child abuse. The use of federated learning is also promising for the addressed scenario, where data privacy is an important issue to be managed.
A Novel Tactile Function Assessment Using a Miniature Tactile Stimulator
Authors: Sung Chung-Tung , Wang Yung-Jung , Huang Jian-Jia , Pei Yu-Cheng , Lin Lei-Chi , Mai Wen-Hsin , Chang Bao-Luen
Published: February 2023

Several methods for the measurement of tactile acuity have been devised previously, but unexpected nonspatial cues and intensive manual skill requirements compromise measurement accuracy. Therefore, we must urgently develop an automated, accurate, and noninvasive method for assessing tactile acuity. The present study develops a novel method applying a robotic tactile stimulator to automatically measure tactile acuity that comprises eye-opened, eye-closed training, and testing sessions. Healthy participants judge the orientation of a rotating grating ball presented on their index fingerpads in a two-alternative forced-choice task. A variable rotation speed of 5, 10, 40, or 160 mm/s was used for the tactile measurement at a variety of difficulties. All participants met the passing criteria for the training experiment. Performance in orientation identification, quantified by the proportion of trials with correct answers, differed across scanning directions, with the highest rotation speed (160 mm/s) having the worst performance. Accuracy did not differ between vertical and horizontal orientations. Our results demonstrated the utility of the pre-test training protocol and the functionality of the developed procedure for tactile acuity assessment. The novel protocol performed well when applied to the participants. Future studies will be conducted to apply this method to patients with impairment of light touch.
A USV-UAV Cooperative Trajectory Planning Algorithm with Hull Dynamic Constraints
Authors: Huang Tao , Chen Zhe , Gao Wang , Xue Zhenfeng , Liu Yong
Published: February 2023

Efficient trajectory generation in complex dynamic environments remains an open problem in the operation of an unmanned surface vehicle (USV). The perception of a USV is usually interfered by the swing of the hull and the ambient weather, making it challenging to plan optimal USV trajectories. In this paper, a cooperative trajectory planning algorithm for a coupled USV-UAV system is proposed to ensure that a USV can execute a safe and smooth path as it autonomously advances through multi-obstacle maps. Specifically, the unmanned aerial vehicle (UAV) plays the role of a flight sensor, providing real-time global map and obstacle information with a lightweight semantic segmentation network and 3D projection transformation. An initial obstacle avoidance trajectory is generated by a graph-based search method. Concerning the unique under-actuated kinematic characteristics of the USV, a numerical optimization method based on hull dynamic constraints is introduced to make the trajectory easier to be tracked for motion control. Finally, a motion control method based on NMPC with the lowest energy consumption constraint during execution is proposed. Experimental results verify the effectiveness of the whole system, and the generated trajectory is locally optimal for USV with considerable tracking accuracy.
Land Surface Model Calibration Using Satellite Remote Sensing Data
Authors: Khaki Mehdi
Published: February 2023

Satellite remote sensing provides a unique opportunity for calibrating land surface models due to their direct measurements of various hydrological variables as well as extensive spatial and temporal coverage. This study aims to apply terrestrial water storage (TWS) estimated from the gravity recovery and climate experiment (GRACE) mission as well as soil moisture products from advanced microwave scanning radiometer–earth observing system (AMSR-E) to calibrate a land surface model using multi-objective evolutionary algorithms. For this purpose, the non-dominated sorting genetic algorithm (NSGA) is used to improve the model’s parameters. The calibration is carried out for the period of two years 2003 and 2010 (calibration period) in Australia, and the impact is further monitored over 2011 (forecasting period). A new combined objective function based on the observations’ uncertainty is developed to efficiently improve the model parameters for a consistent and reliable forecasting skill. According to the evaluation of the results against independent measurements, it is found that the calibrated model parameters lead to better model simulations both in the calibration and forecasting period.
Indoor 2D Positioning Method for Mobile Robots Based on the Fusion of RSSI and Magnetometer Fingerprints
Authors: Sarcevic Peter , Csik Dominik , Odry Akos
Published: February 2023

Received signal strength indicator (RSSI)-based fingerprinting is a widely used technique for indoor localization, but these methods suffer from high error rates due to various reflections, interferences, and noises. The use of disturbances in the magnetic field in indoor localization methods has gained increasing attention in recent years, since this technology provides stable measurements with low random fluctuations. In this paper, a novel fingerprinting-based indoor 2D positioning method, which utilizes the fusion of RSSI and magnetometer measurements, is proposed for mobile robots. The method applies multilayer perceptron (MLP) feedforward neural networks to determine the 2D position, based on both the magnetometer data and the RSSI values measured between the mobile unit and anchor nodes. The magnetic field strength is measured on the mobile node, and it provides information about the disturbance levels in the given position. The proposed method is validated using data collected in two realistic indoor scenarios with multiple static objects. The magnetic field measurements are examined in three different combinations, i.e., the measurements of the three sensor axes are tested together, the magnetic field magnitude is used alone, and the Z-axis-based measurements are used together with the magnitude in the X-Y plane. The obtained results show that significant improvement can be achieved by fusing the two data types in scenarios where the magnetic field has high variance. The achieved results show that the improvement can be above 35% compared to results obtained by utilizing only RSSI or magnetic sensor data.
Wearable Sensors and Smart Devices to Monitor Rehabilitation Parameters and Sports Performance: An Overview
Authors: De Fazio Roberto , Mastronardi Vincenzo Mariano , De Vittorio Massimo , Visconti Paolo
Published: February 2023

A quantitative evaluation of kinetic parameters, the joint’s range of motion, heart rate, and breathing rate, can be employed in sports performance tracking and rehabilitation monitoring following injuries or surgical operations. However, many of the current detection systems are expensive and designed for clinical use, requiring the presence of a physician and medical staff to assist users in the device’s positioning and measurements. The goal of wearable sensors is to overcome the limitations of current devices, enabling the acquisition of a user’s vital signs directly from the body in an accurate and non–invasive way. In sports activities, wearable sensors allow athletes to monitor performance and body movements objectively, going beyond the coach’s subjective evaluation limits. The main goal of this review paper is to provide a comprehensive overview of wearable technologies and sensing systems to detect and monitor the physiological parameters of patients during post–operative rehabilitation and athletes’ training, and to present evidence that supports the efficacy of this technology for healthcare applications. First, a classification of the human physiological parameters acquired from the human body by sensors attached to sensitive skin locations or worn as a part of garments is introduced, carrying important feedback on the user’s health status. Then, a detailed description of the electromechanical transduction mechanisms allows a comparison of the technologies used in wearable applications to monitor sports and rehabilitation activities. This paves the way for an analysis of wearable technologies, providing a comprehensive comparison of the current state of the art of available sensors and systems. Comparative and statistical analyses are provided to point out useful insights for defining the best technologies and solutions for monitoring body movements. Lastly, the presented review is compared with similar ones reported in the literature to highlight its strengths and novelties.
A Novel Study on a Generalized Model Based on Self-Supervised Learning and Sparse Filtering for Intelligent Bearing Fault Diagnosis
Authors: Nie Guocai , Zhang Zhongwei , Shao Mingyu , Jiao Zonghao , Li Youjia , Li Lei
Published: February 2023

Recently, deep learning has become more and more extensive in the field of fault diagnosis. However, most deep learning methods rely on large amounts of labeled data to train the model, which leads to their poor generalized ability in the application of different scenarios. To overcome this deficiency, this paper proposes a novel generalized model based on self-supervised learning and sparse filtering (GSLSF). The proposed method includes two stages. Firstly (1), considering the representation of samples on fault and working condition information, designing self-supervised learning pretext tasks and pseudo-labels, and establishing a pre-trained model based on sparse filtering. Secondly (2), a knowledge transfer mechanism from the pre-training model to the target task is established, the fault features of the deep representation are extracted based on the sparse filtering model, and softmax regression is applied to distinguish the type of failure. This method can observably enhance the model’s diagnostic performance and generalization ability with limited training data. The validity of the method is proved by the fault diagnosis results of two bearing datasets.
Sensors Network as an Added Value for the Characterization of Spatial and Temporal Air Quality Patterns at the Urban Scale
Authors: Graça Daniel , Reis Johnny , Gama Carla , Monteiro Alexandra , Rodrigues Vera , Rebelo Micael , Borrego Carlos , Lopes Myriam , Miranda Ana Isabel
Published: February 2023

Within the scope of the Aveiro STEAM City project, an air quality monitoring network was installed in the city of Aveiro (Portugal), to evaluate the potential of sensors to characterize spatial and temporal patterns of air quality in the city. The network consists of nine sensors stations with air quality sensors (PM10, PM2.5, NO2, O3 and CO) and two meteorological stations, distributed within selected locations in the city of Aveiro. The analysis of the data was done for a one-year measurement period, from June 2020 to May 2021, using temporal profiles, statistical comparisons with reference stations and Air Quality Indexes (AQI). The analysis of sensors data indicated that air quality variability exists for all pollutants and stations. The majority of the study area is characterized by good air quality, but specific areas—associated with hotspot traffic zones—exhibit medium, poor and bad air quality more frequently. The daily patterns registered are significantly different between the affected and non-affected road traffic sites, mainly for PM and NO2 pollutants. The weekly profile, significative deltas are found between week and weekend: NO2 is reduced on the weekends at traffic sites, but PM10 is higher in specific areas during winter weekends, which is explained by residential combustion sources.
A 21.4 pW Subthreshold Voltage Reference with 0.020 %/V Line Sensitivity Using DIBL Compensation
Authors: Colbach Louis , Jang Taekwang , Ji Youngwoo
Published: February 2023

This paper presents an ultra-low-power voltage reference designed in 180 nm CMOS technology. To achieve near-zero line sensitivity, a two-transistor (2-T) voltage reference is biased with a current source to cancel the drain-induced barrier-lowering (DIBL) effect of the 2-T core, thus improving the line sensitivity. This compensation circuit achieves a Monte-Carlo-simulated line sensitivity of 0.035 %/V in a supply range of 0.6 to 1.8 V, while generating a reference voltage of 307.8 mV, with 21.4 pW power consumption. The simulated power supply rejection ratio (PSRR) is −54 dB at 100 Hz. It also achieves a temperature coefficient of 24.8 ppm/°C in a temperature range of −20 to 80 °C, with a projected area of 0.003 mm2.
Neural Networks Application for Accurate Retina Vessel Segmentation from OCT Fundus Reconstruction
Authors: Marciniak Tomasz , Stankiewicz Agnieszka , Zaradzki Przemyslaw
Published: February 2023

The use of neural networks for retinal vessel segmentation has gained significant attention in recent years. Most of the research related to the segmentation of retinal blood vessels is based on fundus images. In this study, we examine five neural network architectures to accurately segment vessels in fundus images reconstructed from 3D OCT scan data. OCT-based fundus reconstructions are of much lower quality compared to color fundus photographs due to noise and lower and disproportionate resolutions. The fundus image reconstruction process was performed based on the segmentation of the retinal layers in B-scans. Three reconstruction variants were proposed, which were then used in the process of detecting blood vessels using neural networks. We evaluated performance using a custom dataset of 24 3D OCT scans (with manual annotations performed by an ophthalmologist) using 6-fold cross-validation and demonstrated segmentation accuracy up to 98%. Our results indicate that the use of neural networks is a promising approach to segmenting the retinal vessel from a properly reconstructed fundus.
Low-Cost Microcontroller-Based Multiparametric Probe for Coastal Area Monitoring
Authors: Parra Lorena , Viciano-Tudela Sandra , Carrasco David , Sendra Sandra , Lloret Jaime
Published: February 2023

The monitoring of the coastal environment is a crucial factor in ensuring its proper management. Nevertheless, existing monitoring technologies are limited due to their cost, temporal resolution, and maintenance needs. Therefore, limited data are available for coastal environments. In this paper, we present a low-cost multiparametric probe that can be deployed in coastal areas and integrated into a wireless sensor network to send data to a database. The multiparametric probe is composed of physical sensors capable of measuring water temperature, salinity, and total suspended solids (TSS). The node can store the data in an SD card or send them. A real-time clock is used to tag the data and to ensure data gathering every hour, putting the node in deep sleep mode in the meantime. The physical sensors for salinity and TSS are created for this probe and calibrated. The calibration results indicate that no effect of temperature is found for both sensors and no interference of salinity in the measuring of TSS or vice versa. The obtained calibration model for salinity is characterised by a correlation coefficient of 0.9 and a Mean Absolute Error (MAE) of 0.74 g/L. Meanwhile, different calibration models for TSS were obtained based on using different light wavelengths. The best case was using a simple regression model with blue light. The model is characterised by a correlation coefficient of 0.99 and an MAE of 12 mg/L. When both infrared and blue light are used to prevent the effect of different particle sizes, the determination coefficient of 0.98 and an MAE of 57 mg/L characterised the multiple regression model.
Development of Deep Belief Network for Tool Faults Recognition
Authors: Kale Archana P. , Wahul Revati M. , Patange Abhishek D. , Soman Rohan , Ostachowicz Wieslaw
Published: February 2023

The controlled interaction of work material and cutting tool is responsible for the precise outcome of machining activity. Any deviation in cutting parameters such as speed, feed, and depth of cut causes a disturbance to the machining. This leads to the deterioration of a cutting edge and unfinished work material. Recognition and description of tool failure are essential and must be addressed using intelligent techniques. Deep learning is an efficient method that assists in dealing with a large amount of dynamic data. The manufacturing industry generates momentous information every day and has enormous scope for data analysis. Most intelligent systems have been applied toward the prediction of tool conditions; however, they must be explored for descriptive analytics for on-board pattern recognition. In an attempt to recognize the variation in milling operation leading to tool faults, the development of a Deep Belief Network (DBN) is presented. The network intends to classify in total six tool conditions (one healthy and five faulty) through image-based vibration signals acquired in real time. The model was designed, trained, tested, and validated through datasets collected considering diverse input parameters.
Designing an Embedded Feature Selection Algorithm for a Drowsiness Detector Model Based on Electroencephalogram Data
Authors: Bencsik Blanka , Reményi István , Szemenyei Márton , Botzheim János
Published: February 2023

Driver fatigue reduces the safety of traditional driving and limits the widespread adoption of self-driving cars; hence, the monitoring and early detection of drivers’ drowsiness plays a key role in driving automation. When representing the drowsiness indicators as large feature vectors, fitting a machine learning model to the problem becomes challenging, and the problem’s perspicuity decreases, making dimensionality reduction crucial in practice. For this reason, we propose an embedded feature selection algorithm that can be later utilized as a building block in the system development of a neural network-based drowsiness detector. We have adopted a technique: a so-called Feature Prune Layer is placed in front of the first layer in the architecture; as a result, its weights change regarding the importance of the corresponding input features and are deleted iteratively until the desired number is reached. We test the algorithm on EEG data, as it is one of the best indicators of drowsiness based on the literature. The proposed FS algorithm is able to reduce the original feature set by 95% with only 1% degradation in precision, while the precision increases by 1.5% and 2.7% respectively when selecting the top 10% and top 20% of the initial features. Moreover, the proposed method outperforms the widely popular Principal Component Analysis and the Chi-squared test when reducing the original feature set by 95%: it achieves 24.3% and 3.2% higher precision respectively.
Marine Structural Health Monitoring with Optical Fiber Sensors: A Review
Authors: Chen Shimeng , Wang Jiahui , Zhang Chao , Li Mengqi , Li Na , Wu Haojun , Liu Yun , Peng Wei , Song Yongxin
Published: February 2023

Real-time monitoring of large marine structures’ health, including drilling platforms, submarine pipelines, dams, and ship hulls, is greatly needed. Among the various kinds of monitoring methods, optical fiber sensors (OFS) have gained a lot of concerns and showed several distinct advantages, such as small size, high flexibility and durability, anti-electromagnetic interference, and high transmission rate. In this paper, three types of OFS used for marine structural health monitoring (SHM), including point sensing, quasi-distributed sensing, and distributed sensing, are reviewed. Emphases are given to the applicability of each type of the sensors by analyzing the operating principles and characteristics of the OFSs. The merits and demerits of different sensing schemes are discussed, as well as the challenges and future developments in OFSs for the marine SHM field.
Physical-Layer Security with Irregular Reconfigurable Intelligent Surfaces for 6G Networks
Authors: Frimpong Emmanuel Obeng , Oh Bong-Hwan , Kim Taehoon , Bang Inkyu
Published: February 2023

The goal of 6G is to make far-reaching changes in communication systems with stricter demands, such as high throughput, extremely low latency, stronger security, and ubiquitous connectivity. Several promising techniques, such as reconfigurable intelligent surfaces (RISs), have been introduced to achieve these goals. An RIS is a 2D low-cost array of reflecting elements that can adjust the electromagnetic properties of an incident signal. In this paper, we guarantee secrecy by using an irregular RIS (IRIS). The main idea of an IRIS is to irregularly activate reflecting elements for a given number of RIS elements. In this work, we consider a communication scenario in which, with the aid of an IRIS, a multi-antenna base station establishes a secure link with a legitimate single-antenna user in the presence of a single-antenna eavesdropper. To this end, we formulate a topology-and-precoding optimization problem to maximize the secrecy rate. We then propose a Tabu search-based algorithm to jointly optimize the RIS topology and the precoding design. Finally, we present simulation results to validate the proposed algorithm, which highlights the performance gain of the IRIS in improving secure transmissions compared to an RIS. Our results show that exploiting an IRIS can allow additional spatial diversity to be achieved, resulting in secrecy performance improvement and overcoming the limitations of conventional RIS-assisted systems (e.g., a large number of active elements).
Assistive Robots for Healthcare and Human–Robot Interaction
Authors: D’Onofrio Grazia , Sancarlo Daniele
Published: February 2023

Assistive robots are still mostly prototypes that only remotely recall human interactive dynamics [...]
Real-Time Forest Fire Detection by Ensemble Lightweight YOLOX-L and Defogging Method
Authors: Huang Jiarun , He Zhili , Guan Yuwei , Zhang Hongguo
Published: February 2023

Forest fires can destroy forest and inflict great damage to the ecosystem. Fortunately, forest fire detection with video has achieved remarkable results in enabling timely and accurate fire warnings. However, the traditional forest fire detection method relies heavily on artificially designed features; CNN-based methods require a large number of parameters. In addition, forest fire detection is easily disturbed by fog. To solve these issues, a lightweight YOLOX-L and defogging algorithm-based forest fire detection method, GXLD, is proposed. GXLD uses the dark channel prior to defog the image to obtain a fog-free image. After the lightweight improvement of YOLOX-L by GhostNet, depth separable convolution, and SENet, we obtain the YOLOX-L-Light and use it to detect the forest fire in the fog-free image. To evaluate the performance of YOLOX-L-Light and GXLD, mean average precision (mAP) was used to evaluate the detection accuracy, and network parameters were used to evaluate the lightweight effect. Experiments on our forest fire dataset show that the number of the parameters of YOLOX-L-Light decreased by 92.6%, and the mAP increased by 1.96%. The mAP of GXLD is 87.47%, which is 2.46% higher than that of YOLOX-L; and the average fps of GXLD is 26.33 when the input image size is 1280 × 720. Even in a foggy environment, the GXLD can detect a forest fire in real time with a high accuracy, target confidence, and target integrity. This research proposes a lightweight forest fire detection method (GXLD) with fog removal. Therefore, GXLD can detect a forest fire with a high accuracy in real time. The proposed GXLD has the advantages of defogging, a high target confidence, and a high target integrity, which makes it more suitable for the development of a modern forest fire video detection system.
Development of a Frugal, In Situ Sensor Implementing a Ratiometric Method for Continuous Monitoring of Turbidity in Natural Waters
Authors: Sanchez Raul , Groc Michel , Vuillemin Renaud , Pujo-Pay Mireille , Raimbault Vincent
Published: February 2023

Turbidity is a commonly used indicator of water quality in continental and marine waters and is mostly caused by suspended and colloidal particles such as organic and inorganic particles. Many methods are available for the measurement of turbidity, ranging from the Secchi disk to infrared light-based benchtop or in situ turbidimeters as well as acoustic methods. The operational methodologies of the large majority of turbidity instruments involve the physics of light scattering and absorption by suspended particles when light is passed through a sample. As such, in the case of in situ monitoring in water bodies, the measurement of turbidity is highly influenced by external light and biofouling. Our motivation for this project is to propose an open-source, low-cost in situ turbidity sensor with a suitable sensitivity and operating range to operate in low-to-medium-turbidity natural waters. This prototype device combines two angular photodetectors and two infrared light sources with different positions, resulting in two different types of light detection, namely nephelometric (i.e., scattering) and attenuation light, according to the ISO 7027 method. The mechanical design involves 3D-printed parts by stereolithography, which are compatible with commercially available waterproof enclosures, thus ensuring easy integration for future users. An effort was made to rely on mostly off-the-shelf electronic components to encourage replication of the system, with the use of a highly integrated photometric front-end commonly used in portable photoplethysmography systems. The sensor was tested in laboratory conditions against a commercial benchtop turbidimeter with Formazin standards. The monitoring results were analyzed, obtaining a linear trendline from 0 to 50 Nephelometric Turbidity Unit (NTU) and an accuracy of +/−0.4 NTU in the 0 to 10 NTU range with a response time of less than 100 ms.
Clinical Sensor-Based Fall Risk Assessment at an Orthopedic Clinic: A Case Study of the Staff’s Views on Utility and Effectiveness
Authors: Ehn Maria , Kristoffersson Annica
Published: February 2023

In-hospital falls are a serious threat to patient security and fall risk assessment (FRA) is important to identify high-risk patients. Although sensor-based FRA (SFRA) can provide objective FRA, its clinical use is very limited and research to identify meaningful SFRA methods is required. This study aimed to investigate whether examples of SFRA methods might be relevant for FRA at an orthopedic clinic. Situations where SFRA might assist FRA were identified in a focus group interview with clinical staff. Thereafter, SFRA methods were identified in a literature review of SFRA methods developed for older adults. These were screened for potential relevance in the previously identified situations. Ten SFRA methods were considered potentially relevant in the identified FRA situations. The ten SFRA methods were presented to staff at the orthopedic clinic, and they provided their views on the SFRA methods by filling out a questionnaire. Clinical staff saw that several SFRA tasks could be clinically relevant and feasible, but also identified time constraints as a major barrier for clinical use of SFRA. The study indicates that SFRA methods developed for community-dwelling older adults may be relevant also for hospital inpatients and that effectiveness and efficiency are important for clinical use of SFRA.
End-to-End Implementation of a Convolutional Neural Network on a 3D-Integrated Image Sensor with Macropixel Array
Authors: Lepecq Maria , Dalgaty Thomas , Fabre William , Chevobbe Stéphane
Published: February 2023

Three-dimensional-integrated focal-plane array image processor chips offer new opportunities to implement highly parallelised computer vision algorithms directly inside sensors. Neural networks in particular can perform highly complex machine vision tasks, and therefore their efficient implementation in such imagers are of significant interest. However, studies with existing pixel-processor array chips have focused on the implementation of a subset of neural network components—notably convolutional kernels—on pixel processor arrays. In this work, we implement a continuous end-to-end pipeline for a convolutional neural network from the digitisation of incoming photons to the output prediction vector on a macropixel processor array chip (where a single processor acts on group of pixels). Our implementation performs inference at a rate between 265 and 309 frames per second, directly inside of the sensor, by exploiting the different levels of parallelism available.