bluuebunny/crossref_metadata_2025 huggingface dataset:
Remote-Sensing Cross-Domain Scene Classification: A Dataset and Benchmark
Authors: Liu Kang , Yang Jian , Li Shengyang
Published: September 2022

Domain adaptation for classification has achieved significant progress in natural images but not in remote-sensing images due to huge differences in data-imaging mechanisms between different modalities and inconsistencies in class labels among existing datasets. More importantly, the lack of cross-domain benchmark datasets has become a major obstacle to the development of scene classification in multimodal remote-sensing images. In this paper, we present a cross-domain dataset of multimodal remote-sensing scene classification (MRSSC). The proposed MRSSC dataset contains 26,710 images of 7 typical scene categories with 4 distinct domains that are collected from Tiangong-2, a Chinese manned spacecraft. Based on this dataset, we evaluate several representative domain adaptation algorithms on three cross-domain tasks to build baselines for future research. The results demonstrate that the domain adaptation algorithm can reduce the differences in data distribution between different domains and improve the accuracy of the three tasks to varying degrees. Furthermore, MRSSC also achieved fairly results in three applications: cross-domain data annotation, weakly supervised object detection and data retrieval. This dataset is believed to stimulate innovative research ideas and methods in remote-sensing cross-domain scene classification and remote-sensing intelligent interpretation.
Basin-Scale Sea Level Budget from Satellite Altimetry, Satellite Gravimetry, and Argo Data over 2005 to 2019
Authors: Yang Yuanyuan , Feng Wei , Zhong Min , Mu Dapeng , Yao Yanli
Published: September 2022

Monitoring sea level changes and exploring their causes are of great significance for future climate change predictions and the sustainable development of mankind. This study uses multiple sets of satellite altimetry, satellite gravity, and ocean temperature and salinity data to study the basin-scale sea level budget (SLB) from 2005 to 2019. The basin-scale sea level rises significantly at a rate of 2.48–4.31 mm/yr, for which the ocean mass component is a main and stable contributing factor, with a rate of 1.77–2.39 mm/yr, while the steric component explains a ~1 mm/yr sea level rise in most ocean basins, except for the Southern Ocean. Due to the drift in Argo salinity since 2016, the residuals of basin-scale SLB are significant from 2016 to 2019. The worst-affected ocean is the Atlantic Ocean, where the SLB is no longer closed from 2005 to 2019. If halosteric sea level change trends from 2005 to 2015 are used to revise salinity data after 2016, the SLB on the ocean basin scale can be kept closed. However, the SLB on the global scale is still not closed and requires further study. Therefore, we recommend that Argo salinity products after 2016 should be used with caution.
Unsupervised Domain Adaptation with Adversarial Self-Training for Crop Classification Using Remote Sensing Images
Authors: Kwak Geun-Ho , Park No-Wook
Published: September 2022

Crop type mapping is regarded as an essential part of effective agricultural management. Automated crop type mapping using remote sensing images is preferred for the consistent monitoring of crop types. However, the main obstacle to generating annual crop type maps is the collection of sufficient training data for supervised classification. Classification based on unsupervised domain adaptation, which uses prior information from the source domain for target domain classification, can solve the impractical problem of collecting sufficient training data. This study presents self-training with domain adversarial network (STDAN), a novel unsupervised domain adaptation framework for crop type classification. The core purpose of STDAN is to combine adversarial training to alleviate spectral discrepancy problems with self-training to automatically generate new training data in the target domain using an existing thematic map or ground truth data. STDAN consists of three analysis stages: (1) initial classification using domain adversarial neural networks; (2) the self-training-based updating of training candidates using constraints specific to crop classification; and (3) the refinement of training candidates using iterative classification and final classification. The potential of STDAN was evaluated by conducting six experiments reflecting various domain discrepancy conditions in unmanned aerial vehicle images acquired at different regions and times. In most cases, the classification performance of STDAN was found to be compatible with the classification using training data collected from the target domain. In particular, the superiority of STDAN was shown to be prominent when the domain discrepancy was substantial. Based on these results, STDAN can be effectively applied to automated cross-domain crop type mapping without analyst intervention when prior information is available in the target domain.
Analyses of GLONASS and GPS+GLONASS Precise Positioning Performance in Different Latitude Regions
Authors: Zheng Yanli , Zheng Fu , Yang Cheng , Nie Guigen , Li Shuhui
Published: September 2022

The orbital inclination angle of the GLONASS constellation is about 10° larger than that of GPS, Galileo, and BDS. Theoretically, the higher orbital inclination angle could provide better observation geometry in high latitude regions. A wealth of research has investigated the positioning accuracy of GLONASS and its impact on multi-GNSS, but rarely considered the contribution of the GLONASS constellation’s large orbit inclination angle. The performance of GLONASS in different latitude regions is evaluated in both stand-alone mode and integration with GPS in this paper. The performance of GPS is also presented for comparison. Three international GNSS service (IGS) networks located in high, middle, and low latitudes are selected for the current study. Multi-GNSS data between January 2021 and June 2021 are used for the assessment. The data quality check shows that the GLONASS data integrity is significantly lower than that of GPS. The constellation visibility analysis indicates that GLONASS has a much better elevation distribution than GPS in high latitude regions. Both daily double-difference network solutions and daily static Precise Point Positioning (PPP) solutions are evaluated. The statistical analysis of coordinate estimates indicates that, in high latitude regions, GLONASS has a comparable or even better accuracy than that of GPS, and GPS+GLONASS presents the best estimate accuracy; in middle latitude regions, GPS stand-alone constellation provides the best positioning accuracy; in low latitude regions, GLONASS offers the worst accuracy, but the positioning accuracy of GPS+GLONASS is better than that of GPS. The tropospheric estimates of GLONASS do not present a resemblance regional advantage as coordinate estimates, which is worse than that of GPS in all three networks. The PPP processing with combined GPS and GLONASS observations reduces the convergence time and improves the accuracy of tropospheric estimates in all three networks.
Nondestructive Evaluation of Localized Rebar Corrosion in Concrete Using Vibro-Radar Based on Pulse Doppler Imaging
Authors: Miwa Takashi , Nakazawa Yuri
Published: September 2022

Early nondestructive inspection of rebar corrosion in reinforced concrete structures is important, but a practical, accurate, high-speed, and high-resolution method has not yet been proposed. We have proposed a vibro-Doppler radar (VDR) method for quantitative evaluation of rebar corrosion based on vibration displacement of a rebar sinusoidally vibrated by an excitation coil. However, this method is not practical because it is not quick enough, requiring two minutes for a measurement at one point. In this paper, a VDR system based on a pulse radar, which is 100 times faster than the conventional system, was developed, and its effectiveness was verified using a concrete specimen. As a result, it was found that a 30-cm section could be scanned in about 2 min. Furthermore, the vibration displacements spatially distributed on the rebar were monitored while the rebar was corroded by electrolytic corrosion tests. As a result, it was found that the vibration displacement increased locally with a width of a few centimeters, and their positions corresponded to the positions of sectional loss of rebar due to corrosion, indicating that this method can be used for nondestructive evaluation of localized rebar corrosion.
An Efficient Feature Extraction Network for Unsupervised Hyperspectral Change Detection
Authors: Zhao Hongyu , Feng Kaiyuan , Wu Yue , Gong Maoguo
Published: September 2022

Change detection (CD) in hyperspectral images has become a research hotspot in the field of remote sensing due to the extremely wide spectral range of hyperspectral images compared to traditional remote sensing images. It is challenging to effectively extract features from redundant high-dimensional data for hyperspectral change detection tasks due to the fact that hyperspectral data contain abundant spectral information. In this paper, a novel feature extraction network is proposed, which uses a Recurrent Neural Network (RNN) to mine the spectral information of the input image and combines this with a Convolutional Neural Network (CNN) to fuse the spatial information of hyperspectral data. Finally, the feature extraction structure of hybrid RNN and CNN is used as a building block to complete the change detection task. In addition, we use an unsupervised sample generation strategy to produce high-quality samples for network training. The experimental results demonstrate that the proposed method yields reliable detection results. Moreover, the proposed method has fewer noise regions than the pixel-based method.
Geospatial Technology-Based Analysis of Air Quality in India during the COVID-19 Pandemic
Authors: Taloor Ajay Kumar , Singh Anil Kumar , Kumar Pankaj , Kumar Amit , Tripathi Jayant Nath , Kumari Maya , Kotlia Bahadur Singh , Kothyari Girish Ch , Tiwari Surya Prakash , Johnson Brian Alan
Published: September 2022

The study evaluates the impacts of India’s COVID-19 lockdown and unlocking periods on the country’s ambient air quality. India experienced three strictly enforced lockdowns followed by unlocking periods where economic and social restrictions were gradually lifted. We have examined the in situ and satellite data of NO2 emissions for several Indian cities to assess the impacts of the lockdowns in India. Additionally, we analyzed NO2 data acquired from the Sentinel-5P TROPOMI sensor over a few districts of the Punjab state, as well as the National Capital Region. The comparisons between the in situ and satellite NO2 emissions were performed for the years 2019, 2020 and up to July 2021. Further analysis was conducted on the satellite data to map the NO2 emissions over India during March to July for the years of 2019, 2020 and 2021. Based on the in situ and satellite observations, we observed that the NO2 emissions significantly decreased by 45–55% in the first wave and 30% in the second wave, especially over the Northern Indian cities during the lockdown periods. The improved air quality over India is indicative of reduced pollution in the atmosphere due to the lockdown process, which slowed down the industrial and commercial activities, including the migration of humans from one place to another. Overall, the present study contributes to the understanding of the trends of the ambient air quality over large geographical areas using the Sentinel-5P satellite data and provides valuable information for regulatory bodies to design a better decision support system to improve air quality.
3D Sea Surface Electromagnetic Scattering Prediction Model Based on IPSO-SVR
Authors: Dong Chunlei , Meng Xiao , Guo Lixin , Hu Jiamin
Published: September 2022

An Improved Particle Swarm Optimization Algorithm-Support Vector Regression Machine (IPSO-SVR) prediction model is developed in this paper to predict the electromagnetic (EM) scattering coefficients of the three-dimensional (3D) sea surface for large scenes in real-time. At first, the EM scattering model of the 3D sea surface is established based on the Semi-Deterministic Facet Scattering Model (SDFSM), and the validity of SDFSM is verified by comparing with the measured data. Using the SDFSM, the data set of backscattering coefficients from 3D sea surface is generated for different polarizations as the training samples. Secondly, an improved particle swarm optimization algorithm is proposed by combining the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The combined algorithm is utilized to optimize the parameters and train the SVR to build a regression prediction model. In the end, the extrapolated prediction for backscattering coefficients of the 3D sea surface is performed. The Root Mean Square Error (RMSE) of the IPSO-SVR-based prediction model is less than 1.2 dB, and the correlation coefficients are higher than 91%. And the prediction accuracy of the PSO-SVR-based, GA-SVR-based and IPSO-SVR-based prediction models is compared. The average RMSE of the PSO-SVR-based and GA-SVR-based prediction models is 1.4241 dB and 1.6289 dB, respectively. While the average RMSE of the IPSO-SVR-based prediction model is reduced to 1.1006 dB. Besides, the average correlation coefficient of the PSO-SVR-based and GA-SVR-based prediction models is 94.36% and 93.93%, respectively. While the average correlation coefficient of the IPSO-SVR-based prediction model reached 95.12%. It demonstrated that the IPSO-SVR-based prediction model can effectively improve the prediction accuracy compared with the PSO-SVR-based and GA-SVR-based prediction models. Moreover, the simulation time of IPSO-SVR-based prediction model is significantly decreased compared with the SDFSM, and the speedup ratio is greater than 15.0. Therefore, the prediction model in this paper has practical application in the real-time computation of sea surface scattering coefficients in large scenes.
Propagation Measurements for IQRF Network in an Urban Environment
Authors: Bouzidi Mohammed , Mohamed Marshed , Dalveren Yaser , Moldsvor Arild , Cheikh Faouzi Alaya , Derawi Mohammad
Published: September 2022

Recently, IQRF has emerged as a promising technology for the Internet of Things (IoT), owing to its ability to support short- and medium-range low-power communications. However, real world deployment of IQRF-based wireless sensor networks (WSNs) requires accurate path loss modelling to estimate network coverage and other performances. In the existing literature, extensive research on propagation modelling for IQRF network deployment in urban environments has not been provided yet. Therefore, this study proposes an empirical path loss model for the deployment of IQRF networks in a peer-to-peer configured system where the IQRF sensor nodes operate in the 868 MHz band. For this purpose, extensive measurement campaigns are conducted outdoor in an urban environment for Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) links. Furthermore, in order to evaluate the prediction accuracy of well-known empirical path loss models for urban environments, the measurements are compared with the predicted path loss values. The results show that the COST-231 Walfisch–Ikegami model has higher prediction accuracy and can be used for IQRF network planning in LoS links, while the COST-231 Hata model has better accuracy in NLoS links. On the other hand, the effects of antennas on the performance of IQRF transceivers (TRs) for LoS and NLoS links are also scrutinized. The use of IQRF TRs with a Straight-Line Dipole Antenna (SLDA) antenna is found to offer more stable results when compared to IQRF (TRs) with Meander Line Antenna (MLA) antenna. Therefore, it is believed that the findings presented in this article could offer useful insights for researchers interested in the development of IoT-based smart city applications.
Rapid Assessment of Microbial Quality in Edible Seaweeds Using Sensor Techniques Based on Spectroscopy, Imaging Analysis and Sensors Mimicking Human Senses
Authors: Lytou Anastasia E. , Tsakanikas Panagiotis , Lymperi Dimitra , Nychas George-John E.
Published: September 2022

The expansion of the seaweed aquaculture sector along with the rapid deterioration of these products escalates the importance of implementing rapid, real-time techniques for their quality assessment. Seaweed samples originating from Scotland and Ireland were stored under various temperature conditions for specific time intervals. Microbiological analysis was performed throughout storage to assess the total viable counts (TVC), while in parallel FT-IR spectroscopy, multispectral imaging (MSI) and electronic nose (e-nose) analyses were conducted. Machine learning models (partial least square regression (PLS-R)) were developed to assess any correlations between sensor and microbiological data. Microbial counts ranged from 1.8 to 9.5 log CFU/g, while the microbial growth rate was affected by origin, harvest year and storage temperature. The models developed using FT-IR data indicated a good prediction performance on the external test dataset. The model developed by combining data from both origins resulted in satisfactory prediction performance, exhibiting enhanced robustness from being origin unaware towards microbiological population prediction. The results of the model developed with the MSI data indicated a relatively good prediction performance on the external test dataset in spite of the high RMSE values, whereas while using e-nose data from both MI and SAMS, a poor prediction performance of the model was reported.
Rainfall Map from Attenuation Data Fusion of Satellite Broadcast and Commercial Microwave Links
Authors: Saggese Fabio , Lottici Vincenzo , Giannetti Filippo
Published: September 2022

The demand for accurate rainfall rate maps is growing ever more. This paper proposes a novel algorithm to estimate the rainfall rate map from the attenuation measurements coming from both broadcast satellite links (BSLs) and commercial microwave links (CMLs). The approach we pursue is based on an iterative procedure which extends the well-known GMZ algorithm to fuse the attenuation data coming from different links in a three-dimensional scenario, while also accounting for the virga phenomenon as a rain vertical attenuation model. We experimentally prove the convergence of the procedures, showing how the estimation error decreases for every iteration. The numerical results show that adding the BSL links to a pre-existent CML network boosts the accuracy performance of the estimated rainfall map, improving up to 50% the correlation metrics. Moreover, our algorithm is shown to be robust to errors concerning the virga parametrization, proving the possibility of obtaining good estimation performance without the need for precise and real-time estimation of the virga parameters.
Detecting Human Actions in Drone Images Using YoloV5 and Stochastic Gradient Boosting
Authors: Ahmad Tasweer , Cavazza Marc , Matsuo Yutaka , Prendinger Helmut
Published: September 2022

Human action recognition and detection from unmanned aerial vehicles (UAVs), or drones, has emerged as a popular technical challenge in recent years, since it is related to many use case scenarios from environmental monitoring to search and rescue. It faces a number of difficulties mainly due to image acquisition and contents, and processing constraints. Since drones’ flying conditions constrain image acquisition, human subjects may appear in images at variable scales, orientations, and occlusion, which makes action recognition more difficult. We explore low-resource methods for ML (machine learning)-based action recognition using a previously collected real-world dataset (the “Okutama-Action” dataset). This dataset contains representative situations for action recognition, yet is controlled for image acquisition parameters such as camera angle or flight altitude. We investigate a combination of object recognition and classifier techniques to support single-image action identification. Our architecture integrates YoloV5 with a gradient boosting classifier; the rationale is to use a scalable and efficient object recognition system coupled with a classifier that is able to incorporate samples of variable difficulty. In an ablation study, we test different architectures of YoloV5 and evaluate the performance of our method on Okutama-Action dataset. Our approach outperformed previous architectures applied to the Okutama dataset, which differed by their object identification and classification pipeline: we hypothesize that this is a consequence of both YoloV5 performance and the overall adequacy of our pipeline to the specificities of the Okutama dataset in terms of bias–variance tradeoff.
Thermal Error Prediction and Compensation of Digital Twin Laser Cutting Based on T-XGBoost
Authors: Lu Chang , Fei Jiyou , Meng Xiangzhong , Li Yanshu , Liu Zhibo
Published: September 2022

Laser cutting belongs to non-contact processing, which is different from traditional turning and milling. In order to improve the machining accuracy of laser cutting, a thermal error prediction and dynamic compensation strategy for laser cutting is proposed. Based on the time-varying characteristics of the digital twin technology, a hybrid model combining the thermal elastic–plastic finite element (TEP-FEM) and T-XGBoost algorithms is established. The temperature field and thermal deformation under 12 common working conditions are simulated and analyzed with TEP-FEM. Real-time machining data obtained from TEP-FEM simulation is used in intelligent algorithms. Based on the XGBoost algorithm and the simulation data set as the training data set, a time-series-based segmentation algorithm (T-XGBoost) is proposed. This algorithm can reduce the maximum deformation at the slit by more than 45%. At the same time, by reducing the average volume strain under most working conditions, the lifting rate can reach 63% at the highest, and the machining result is obviously better than XGBoost. The strategy resolves the uncontrollable thermal deformation during cutting and provides theoretical solutions to the implementation of the intelligent operation strategies such as predictive machining and quality monitoring.
An Efficient CS-Based Spectral Peak Search Method
Authors: Chen Bingbing , Sun Yufa
Published: September 2022

Spectral peak search is an essential part of the frequency domain parametric method. In this paper, a spectral peak search algorithm employing the principle of compressed sensing (CS) is proposed to rapidly estimate the spectral peaks. The algorithm adopts fast Fourier transform (FFT) with a few points to obtain the coarsely estimated spectral peak positions, and then only three small-scale inner products are iteratively calculated by increasing the input sequence length to rapidly refine the estimated positions. Compared with the conventional methods, this algorithm can directly capture the exact locations of spectral peaks without acquiring the entire spectrum. In addition, the proposed algorithm can be easily integrated into the existing frequency domain interpolation methods to accurately determine the spectral peak positions, and if so, only 30% of inner product operations of the original algorithms are required. Theoretical analysis and numerical results show that this algorithm yields accurate results with low complexity for analyzing both one-dimensional and two-dimensional signals.
Exploiting On-Chip Voltage Regulators for Leakage Reduction in Hardware Masking
Authors: Seçkiner Soner , Köse Selçuk
Published: September 2022

A design space exploration of the countermeasures for hardware masking is proposed in this paper. The assumption of independence among shares used in hardware masking can be violated in practical designs. Recently, the security impact of noise coupling among multiple masking shares has been demonstrated both in practical FPGA implementations and with extensive transistor level simulations. Due to the highly sophisticated interactions in modern VLSI circuits, the interactions among multiple masking shares are quite challenging to model and thus information leakage from one share to another through noise coupling is difficult to mitigate. In this paper, the implications of utilizing on-chip voltage regulators to minimize the coupling among multiple masking shares through a shared power delivery network (PDN) are investigated. Specifically, different voltage regulator configurations where the power is delivered to different shares through various configurations are investigated. The placement of a voltage regulator relative to the masking shares is demonstrated to a have a significant impact on the coupling between masking shares. A PDN consisting of two shares is simulated with an ideal voltage regulator, strong DLDO, normal DLDO, weak DLDO, two DLDOs, and two DLDOs with 180∘ phase shift. An 18 × 18 grid PDN with a normal DLDO is simulated to demonstrate the effect of PDN impedance on security. The security analysis is performed using correlation and t-test analyses where a low correlation between shares can be inferred as security improvement and a t-test value below 4.5 means that the shares have negligible coupling, and thus the proposed method is secure. In certain cases, the proposed techniques achieve up to an 80% reduction in the correlation between masking shares. The PDN with two DLDOs and two-phase DLDO with 180∘ phase shift achieve satisfactory security levels since t-test values remain under 4.5 with 100,000 traces of simulations. The security of the PDN improves if DLDO is placed closer to any one of the masking shares.
Active Electric Dipole Energy Sources: Transduction via Electric Scalar and Vector Potentials
Authors: Tobar Michael E. , Chiao Raymond Y. , Goryachev Maxim
Published: September 2022

The creation of electromagnetic energy may be realised by engineering a device with a method of transduction, which allows an external energy source, such as mechanical, chemical, nuclear, etc., to be impressed into the electromagnetic system through a mechanism that enables the separation of opposite polarity charges. For example, a voltage generator, such as a triboelectric nanogenerator, enables the separation of charges through the transduction of mechanical energy, creating an active physical dipole in the static case, or an active Hertzian dipole in the time-dependent case. The net result is the creation of a static or time-dependent permanent polarisation, respectively, without an applied electric field and with a non-zero vector curl. This system is the dual of a magnetic solenoid or permanent magnet excited by a circulating electrical current or fictitious bound current, respectively, which supplies a magnetomotive force described by a magnetic vector potential and a magnetic geometric phase proportional to the enclosed magnetic flux. Thus, the active electric dipole voltage generator has been described macroscopically by a circulating fictitious magnetic current boundary source and exhibits an electric vector potential with an electric geometric phase proportional to the enclosed electric flux density. This macroscopic description of an active dipole is a semi-classical average description of some underlying microscopic physics, which exhibits emergent nonconservative behaviour not found in classical closed-system laws of electrodynamics. We show that the electromotive force produced by an active dipole in general has both electric scalar and vector potential components to account for the magnitude of the electromotive force it produces. Independent of the electromagnetic gauge, we show that Faraday’s and Ampere’s law may be derived from the time rate of change of the magnetic and dual electric geometric phases. Finally, we analyse an active cylindrical dipole in terms of scalar and vector potential and confirm that the electromotive force produced, and hence potential difference across the terminals is a combination of vector and scalar potential difference depending on the aspect ratio (AR) of the dipole. For long thin active dipoles (AR approaches 0), the electric field is suppressed inside, and the voltage is determined mainly by the electric vector potential. For large flat active dipoles (AR approaches infinity), the electric flux density is suppressed inside, and the voltage is mainly determined by the scalar potential.
Application of a Wireless and Contactless Ultrasonic System to Evaluate Optimal Sawcut Time for Concrete Pavements
Authors: Song Homin , Hong Jinyoung , Yoon Young-Geun , Choi Hajin , Oh Taekeun
Published: September 2022

A recently developed contactless ultrasonic testing scheme is applied to define the optimal saw-cutting time for concrete pavement. The ultrasonic system is improved using wireless data transfer for field applications, and the signal processing and data analysis are proposed to evaluate the modulus of elasticity of early-age concrete. Numerical simulation of leaky Rayleigh wave in joint-half space including air and concrete is performed to demonstrate the proposed data analysis procedure. The hardware and algorithms developed for the ultrasonic system are experimentally validated with a comparison of saw-cutting procedures. In addition, conventional methods for the characterization of early-age concrete, including pin penetration and maturity methods, are applied. The results demonstrated that the developed wireless system presents identical results to the wired system, and the initiation time of leaky Rayleigh wave possibly represents 5% of raveling damage compared to the optimal saw cutting. Further data analysis implies that saw-cutting would be optimally performed at approximately 11.5 GPa elastic modulus of concrete obtained by the wireless and contactless ultrasonic system.
Edge-Computing Meshed Wireless Acoustic Sensor Network for Indoor Sound Monitoring
Authors: Caro-Via Selene , Vidaña-Vila Ester , Ginovart-Panisello Gerardo José , Martínez-Suquía Carme , Freixes Marc , Alsina-Pagès Rosa Ma
Published: September 2022

This work presents the design of a wireless acoustic sensor network (WASN) that monitors indoor spaces. The proposed network would enable the acquisition of valuable information on the behavior of the inhabitants of the space. This WASN has been conceived to work in any type of indoor environment, including houses, hospitals, universities or even libraries, where the tracking of people can give relevant insight, with a focus on ambient assisted living environments. The proposed WASN has several priorities and differences compared to the literature: (i) presenting a low-cost flexible sensor able to monitor wide indoor areas; (ii) balance between acoustic quality and microphone cost; and (iii) good communication between nodes to increase the connectivity coverage. A potential application of the proposed network could be the generation of a sound map of a certain location (house, university, offices, etc.) or, in the future, the acoustic detection of events, giving information about the behavior of the inhabitants of the place under study. Each node of the network comprises an omnidirectional microphone and a computation unit, which processes acoustic information locally following the edge-computing paradigm to avoid sending raw data to a cloud server, mainly for privacy and connectivity purposes. Moreover, this work explores the placement of acoustic sensors in a real scenario, following acoustic coverage criteria. The proposed network aims to encourage the use of real-time non-invasive devices to obtain behavioral and environmental information, in order to take decisions in real-time with the minimum intrusiveness in the location under study.
Utility of the Full ECG Waveform for Stress Classification
Authors: Arquilla Katya , Webb Andrea K. , Anderson Allison P.
Published: September 2022

The detection of psychological stress using the electrocardiogram (ECG) signal is most commonly based on the detection of the R peak—the most prominent part of the ECG waveform—and the heart rate variability (HRV) measurements derived from it. For stress detection algorithms focused on short-duration time windows, there is potential benefit in including HRV features derived from the detection of smaller peaks within the ECG waveform: the P, Q, S, and T waves. However, the potential drawback of using these small peaks is their smaller magnitude and subsequent susceptibility to noise, making them more difficult to reliably detect. In this work, we demonstrate the potential benefits of including smaller waves within binary stress classification using a pre-existing data set of ECG recordings from 57 participants (aged 18–40) with a self-reported fear of spiders during exposure to videos of spiders. We also present an analysis of the performance of an automated peak detection algorithm and the reliability of detection for each of the smaller parts of the ECG waveform. We compared two models, one with only R peak features and one with small peak features. They were similar in precision, recall, F1, area under ROC curve (AUC), and accuracy, with the greatest differences less than the standard deviations of each metric. There was a significant difference in the Akaike Information Criterion (AIC), which represented the information loss of the model. The inclusion of novel small peak features made the model 4.29×1028 times more probable to minimize the information loss, and the small peak features showed higher regression coefficients than the R peak features, indicating a stronger relationship with acute psychological stress. This difference and further analysis of the novel features suggest that small peak intervals could be indicative of independent processes within the heart, reflecting a psychophysiological response to stress that has not yet been leveraged in stress detection algorithms.
Can the Supido Radar Be Used for Measuring Ball Speed during Soccer Kicking? A Reliability and Concurrent Validity Study of a New Low-Cost Device
Authors: Díez-Fernández David M. , Rodríguez-Rosell David , Gazzo Federico , Giráldez Julián , Villaseca-Vicuña Rodrigo , Gonzalez-Jurado Jose A.
Published: September 2022

The aim was to analyze the reliability and validity of a low-cost instrument, based on a radar system, to quantify the kicking ball speed in soccer. A group of 153 male soccer players (under-13, n = 53; under-15, n = 54; under-18, n = 46) participated in this study. Each player performed three kicks on the goal in a standardized condition while the ball speed was measured with three different devices: one Radar Stalker ATS II® (reference criterion) and two Supido Radar® (Supido-front of the goal and Supido-back of the goal). The standard error of measurement (SEM) expressed as a coefficient of variation (CV) and the intraclass correlation coefficient (ICC) were employed for assessing the reliability of each instrument. Stalker and Supido-back showed very high absolute (CV = 4.0–5.4%) and relative (ICC = 0.945–0.958) reliability, whereas Supido-front resulted in moderate to low reliability scores (CV = 7.4–15%, ICC = 0.134–0.693). In addition, Lin’s concordance correlation coefficient (CCC) values revealed an ‘almost perfect’ agreement between Stalker and Supido-back for the average (r = 0.99) and maximal (r = 0.98) ball speed, regardless of the ball speed range analyzed. However, Supido-front resulted in a poor degree of concordance (CCC = 0.688) and a high magnitude of error (17.0–37.5 km·h−1) with the reference Stalker radar gun. The Supido Radar® placed behind the goal could be considered a reliable and valid device for measuring ball speed in soccer.
A High SNR Improvement CMOS Analog Accumulator with Charge Compensation Technique
Authors: Guo Zhongjie , Li Chen , Xu Ruiming , Cheng Xinqi , Su Changxu , Wu Longsheng
Published: September 2022

In this paper, a 7.75 kHz line rate analog domain time delay integration (TDI) CMOS analog accumulator with 128-stage is proposed. An adaptive compensation for the charge loss due to parasitic effects is adopted. Based on the influence mechanism of parasitic effects, alternately charging the top and bottom plates of the storage capacitor while cooperate positive feedback capacitor dynamically compensates for the charge loss of the sampling phase and the holding phase. Using the proposed circuit, after the post-layout simulation verification, the SNR of 128 stage accumulation can be improved by as much as 20.9 dB.
A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things
Authors: Muzammal Syeda Mariam , Murugesan Raja Kumar , Jhanjhi Noor Zaman , Humayun Mamoona , Ibrahim Ashraf Osman , Abdelmaboud Abdelzahir
Published: September 2022

In IoT networks, the de facto Routing Protocol for Low Power and Lossy Networks (RPL) is vulnerable to various attacks. Routing attacks in RPL-based IoT are becoming critical with the increase in the number of IoT applications and devices globally. To address routing attacks in RPL-based IoT, several security solutions have been proposed in literature, such as machine learning techniques, intrusion detection systems, and trust-based approaches. Studies show that trust-based security for IoT is feasible due to its simple integration and resource-constrained nature of smart devices. Existing trust-based solutions have insufficient consideration of nodes’ mobility and are not evaluated for dynamic scenarios to satisfy the requirements of smart applications. This research work addresses the Rank and Blackhole attacks in RPL considering the static as well as mobile nodes in IoT. The proposed Security, Mobility, and Trust-based model (SMTrust) relies on carefully chosen trust factors and metrics, including mobility-based metrics. The evaluation of the proposed model through simulation experiments shows that SMTrust performs better than the existing trust-based methods for securing RPL. The improvisation in terms of topology stability is 46%, reduction in packet loss rate is 45%, and 35% increase in throughput, with only 2.3% increase in average power consumption.
Photoelectric Target Detection Algorithm Based on NVIDIA Jeston Nano
Authors: Zhang Shicheng , Zhang Laixian , Sun Huayan , Guo Huichao
Published: September 2022

This paper proposes a photoelectric target detection algorithm for NVIDIA Jeston Nano embedded devices, exploiting the characteristics of active and passive differential images of lasers after denoising. An adaptive threshold segmentation method was developed based on the statistical characteristics of photoelectric target echo light intensity, which effectively improves detection of the target area. The proposed method’s effectiveness is compared and analyzed against a typical lightweight network that was knowledge-distilled by ResNet18 on target region detection tasks. Furthermore, TensorRT technology was applied to accelerate inference and deploy on hardware platforms the lightweight network Shuffv2_x0_5. The experimental results demonstrate that the developed method’s accuracy rate reaches 97.15%, the false alarm rate is 4.87%, and the detection rate can reach 29 frames per second for an image resolution of 640 × 480 pixels.
Tutorial on High-Definition Map Generation for Automated Driving in Urban Environments
Authors: Jeong Jinseop , Yoon Jun Yong , Lee Hwanhong , Darweesh Hatem , Sung Woosuk
Published: September 2022

High-definition (HD) mapping is a promising approach to realize highly automated driving (AD). Although HD maps can be applied to all levels of autonomy, their use is particularly beneficial for autonomy levels 4 or higher. HD maps enable AD systems to see beyond the field of view of conventional sensors, thereby providing accurate and detailed information regarding a driving environment. An HD map is typically separated into a pointcloud map for localization and a vector map for path planning. In this paper, we introduce two separate but successive HD map generation workflows. Of the several stages involved, the registration and mapping processes are essential for creating the pointcloud and vector maps, respectively. To facilitate the readers’ understanding, the processes of these two stages have been recorded and uploaded online. HD maps are typically generated using open-source software (OSS) tools. CloudCompare and ASSURE, as representative tools, are used in this study. The generated HD maps are validated with localization and path-planning modules in Autoware, which is also an OSS stack for AD systems. The generated HD maps enable environmental-monitoring vehicles to successfully operate at level 4 autonomy.
The Hybrid Stylus: A Multi-Surface Active Stylus for Interacting with and Handwriting on Paper, Tabletop Display or Both
Authors: Campos Cuauhtli , Sandak Jakub , Kljun Matjaž , Čopič Pucihar Klen
Published: September 2022

The distinct properties and affordances of paper provide benefits that enabled paper to maintain an important role in the digital age. This is so much so, that some pen–paper interaction has been imitated in the digital world with touchscreens and stylus pens. Because digital medium also provides several advantages not available to physical paper, there is a clear benefit to merge the two mediums. Despite the plethora of concepts, prototypes and systems to digitise handwritten information on paper, these systems require specially prepared paper, complex setups and software, which can be used solely in combination with paper, and, most importantly, do not support the concurrent precise interaction with both mediums (paper and touchscreen) using one pen only. In this paper, we present the design, fabrication and evaluation of the Hybrid Stylus. The Hybrid Stylus is assembled with the infinity pencil tip (nib) made of graphite and a specially designed shielded tip holder that is attached to an active stylus. The stylus can be used for writing on a physical paper, while it still maintains all the features needed for tablet interaction. Moreover, the stylus also allows simultaneous digitisation of handwritten information on the paper when the paper is placed on the tablet screen. In order to evaluate the concept, we also add a user-friendly manual alignment of paper position on the underlying tablet computer The evaluation demonstrates that the system achieves almost perfect digitisation of strokes (98.6% of strokes were correctly registered with only 1.2% of ghost strokes) whilst maintaining excellent user experience of writing with a pencil on the paper.
Modelling Causal Factors of Unintentional Electromagnetic Emanations Compromising Information Technology Equipment Security
Authors: Martin Maxwell , Sunmola Funlade , Lauder David
Published: September 2022

Information technology equipment (ITE) processing sensitive information can have its security compromised by unintentional electromagnetic radiation. Appropriately assessing likelihood of a potential compromise relies on radio frequency (RF) engineering expertise—specifically, requiring knowledge of the associated causal factors and their interrelationships. Several factors that can cause unintentional electromagnetic emanations that can lead to the compromise of ITE have been found in the literature. This paper confirms the list of causal factors reported in previous work, categorizes the factors as belonging to threat, vulnerability, or impact, and develops an interpretive structural model of the vulnerability factors. A participatory modelling approach was used consisting of focus groups of RF engineers. The resulting hierarchical structural model shows the relationships between factors and illustrates their relative significance. The paper concludes that the resulting model can motivate a deeper understanding of the structural relationship of the factors that can be incorporated in the RF engineers’ assessment process. Areas of future work are suggested.
Employee Involvement and Commitment in Internal Communication
Authors: Meirinhos Galvão , Cardoso António , Silva Rui , Rêgo Reiville , Oliveira Márcio
Published: September 2022

This research project aims to analyze the importance of internal communication in organizations in Benguela (Angola) and to determine its impact on employee engagement and commitment to the organization. To this end, an exploratory study was conducted using a quantitative methodology. In this scope, a questionnaire was applied to 250 employees of the organizations, seeking to evaluate employees as internal consumers; internal communication in terms of tools, means and communicative effectiveness, as well as internal communication management and employee satisfaction. The results demonstrate the need for internal communication to engage and commit the company’s employees, where organizations are increasingly concerned with the management of their human resources.
ESG Investing Issues in Food Industry Enterprises: Focusing on On-the-Job Training in Waste Management
Authors: Grinberga-Zalite Gunta , Zvirbule Andra
Published: September 2022

Recently, there is a growing interest in investing in ways that might eliminate global warming; therefore, a number of studies promote the idea of ESG investing. The current study presents the latest discourses on the interpretation of investment and the role of social aspects in terms of investing in ESG. The topicality of the particular study is justified by the fact that food sector investors and other market participants use ESG information through ESG ratings, which, among social factors, include on-the-job training as an important indicator of a company’s sustainability. This study was based on a mixed-methods methodology that combines qualitative and quantitative research methods in consistent methodological steps. Based on the research of a wide range of scientific literature and the results of focus group interviews with industry practitioners, the authors have explored ESG implementation issues in European food sector enterprises to identify how food sector companies can strengthen their ESG performance by developing practical on-the-job training in waste management. This study has posed a research question: What skills should be integrated in on-the-job training programs in contemporary waste management in food industry enterprises? The research results present a systematized structure that integrates explicit and tacit knowledge, skills and competence that were acknowledged as topical in developing on-the-job training programs for food industry enterprises.