bluuebunny/crossref_metadata_2025 huggingface dataset:
The Role of Online Schooling, Screen-Based Activities, and Parent Coping in Canadian Children’s COVID-19-Related Trauma and Anxiety Symptoms
Authors: Guimond Fanny-Alexandra , Smith Jonathan , Vitoroulis Irene , Aucoin Philip , St. John Elizabeth , Gardam Olivia , MacLachlan Madison
Published: May 2021

This study investigated the effects of online schooling and screen-based activities on Canadian children’s COVID-19-related trauma and generalized anxiety symptoms and how parents’ coping strategies influenced these associations. The participants were 121 Canadian children aged from 7 to 12. Parents were asked to report on their children’s school attendance, screen-based activities, and trauma and generalized anxiety symptoms, as well as their own coping strategies during the COVID-19 pandemic. Online schooling was associated with less trauma and generalized anxiety symptoms in children than school non-attendance. Screen-based activities were not directly associated with children’s trauma and anxiety symptoms, but the way parents coped with pandemic stressors moderated these associations. Parents’ active and adaptive coping strategies mitigated the effects of school non-attendance and increased screen-based activity use on children’s COVID-19-related symptoms. The findings not only highlight the detrimental effects of complete school closures, but they also underscore the importance of better equipping parents to cope with pandemic stressors. The findings also suggest that virtual school attendance might have similar benefits to in-person attendance, as it appears to protect against adverse mental health outcomes.
Identification of Going-Concern Risks in CSR and Integrated Reports of Polish Companies from the Construction and Property Development Sector
Authors: Szczepankiewicz Elżbieta Izabela
Published: May 2021

The question of non-financial and risk disclosures in corporate annual statements has been discussed globally for over a decade. The stakeholders of socially responsible organisations report a constantly growing demand for financial and non-financial information, including that related to threats and risks connected to the organisation’s activity. The aim of this paper is to determine whether companies from the construction and property development sector disclose financial risk in a CSR or integrated reports, and whether it is possible to assess going-concern risks based on the reports. The author analysed the content of selected CSR and integrated reports to describe the scope and structure of going-concern risk information in Polish companies from the construction and property development sector. The author reached two key empirical findings. Firstly, the results may suggest that companies are at different stages of the process of adopting integrated reporting, depending on the year of issue of the first CSR report. Secondly, less than half of the analysed companies disclose their financial data and risk, as well as describe their risk management systems. The study also shows that the ‘soft’ solutions set out in the regulations give companies considerable freedom in disclosing risk information, which is sometimes counterproductive. Therefore, it is of key importance to develop a single integrated standard for risk disclosures. In this paper, the author demonstrates a logical process of reasoning ensuing from the literature review through empirical research down to the implementation stage of conceptual model for disclosures on financial and going-concern risks in CSR and integrated reports. The present study makes a valuable contribution to CSR and integrated reporting theories and constitutes a breakthrough in identifying risks affecting socially responsible companies in Poland. The study fills a research gap in the area of non-financial (including information on risk) disclosures in annual reports of listed companies and other companies from the construction and property development sector.
Ruin Probability for the Insurer–Reinsurer Model for Exponential Claims: A Probabilistic Approach
Authors: Burnecki Krzysztof , Teuerle Marek A. , Wilkowska Aleksandra
Published: May 2021

In this paper, we consider a two-dimensional risk process in which the companies split each claim and premium in a fixed proportion. It serves as a classical framework of a quota-share reinsurance contract for a given business line. Such a contract reduces the insurer’s exposure to the liabilities created through its underwriting activities. For the analyzed model, we derive a joint infinite-time ruin probability formula for exponentially distributed claims. To this end, we apply a change of measure technique. We illustrate the admissible range of parameters of the risk process. We also justify our result using Monte Carlo simulations and compare it with Theorem 2 in Avram, Palmowski and Pistorius [Insurance: Mathematics and Economics 42 (2008) 227], which was obtained by explicitly inverting a Laplace transform of the ruin probability. Our formula leads to a correction of that result. Finally, we note that the obtained formula leads to efficient approximation of the ruin probability for other claim amount distributions using De Vylder’s idea.
Risk Management and Financial Stability in the Polish Public Hospitals: The Moderating Effect of the Stakeholders’ Engagement in the Decision-Making
Authors: Frączkiewicz-Wronka Aldona , Ingram Tomasz , Szymaniec-Mlicka Karolina , Tworek Piotr
Published: May 2021

Public healthcare organizations usually operate under significant financial strain and frequently strive for survival. Thus, in most cases, financial stability is a “holy grail” of public healthcare organizations in general and hospitals in particular. The financial stability itself is partly dependent upon the ability to manage risk associated with hospital actions. In the paper, we seek to address the question related to the moderating role of stakeholders’ engagement in the relationship between risk management practices and a hospital’s financial stability. To answer this question, we designed and carried out empirical research on a sample of 103 out of 274 Polish public hospitals operating at the first-level (closest to the patient). Results show that risk management practices are positively related to financial stability. Hospitals with well-developed risk management practices are better prepared and find appropriate answers to threats, helping them attain financial stability. We also found that stakeholder engagement acts as a moderator of the relationship between risk management practices and financial stability. Research results indicate that with more sophisticated risk management practices, stakeholder engagement in decision-making leads to statistically lower financial stability. On the other hand, high levels of stakeholders’ engagement help when risk management practices are underdeveloped.
Comparison of Numerical Calculation Methods for Stem Diameter Retrieval Using Terrestrial Laser Data
Authors: You Lei , Wei Jie , Liang Xiaojun , Lou Minghua , Pang Yong , Song Xinyu
Published: May 2021

Terrestrial laser scanning (TLS) can be used as a millimeter-level measurement tool for forest inventories. However, the stem diameter retrieval accuracy in sample plot scanning is not yet convincing. The errors in each step of stem diameter retrieval algorithms must be evaluated. In this study, six numerical calculation methods for the numerical calculation step, i.e., cylinder fitting (CYF), circle fitting (CF), convex hull line fitting (CLF), the proposed caliper simulation method (CSM), closure B-spline curve fitting (SP) and closure Bézier curve fitting with global convexity (SPC), were applied to stem diameter retrieval, and the similarities and differences were evaluated. The ovality, completeness and roughness were used to evaluate the stem slice point cloud quality. A total of 165 stem slice point clouds at breast height collected from three Larix kaempferi plots were used. Compared with the field-measured stem diameters at breast height (DBHs), the root mean square errors (RMSEs) of the CYF, CF, CLF, CSM, SP and SPC methods were 0.30 cm, 0.30 cm, 0.51 cm, 0.51 cm, 0.56 cm and 0.54 cm, respectively. Compared with the SPC method results, the RMSE of the CSM results was 0.05 cm. The results illustrated that the CYF and CF methods performed the same, as did the CLF and CSM methods. Most DBHs retrieved by the CYF and CF methods were smaller than the field-measured DBHs, and most DBHs retrieved by the CLF, CSM, SP and SPC methods were larger than the field-measured DBHs. This study demonstrated that the CYF and CF methods perform the best and are the most robust, and the measurements by a diameter tape and a caliper are similar enough for forestry inventories. Evaluating and preprocessing stem slice point clouds is a potential way to improve stem diameter retrieval accuracy.
Mapping Particle Size and Soil Organic Matter in Tropical Soil Based on Hyperspectral Imaging and Non-Imaging Sensors
Authors: Nanni Marcos Rafael , Demattê José Alexandre Melo , Rodrigues Marlon , Santos Glaucio Leboso Alemparte Abrantes dos , Reis Amanda Silveira , Oliveira Karym Mayara de , Cezar Everson , Furlanetto Renato Herrig , Crusiol Luís Guilherme Teixeira , Sun Liang
Published: May 2021

We evaluated the use of airborne hyperspectral imaging and non-imaging sensors in the Vis—NIR—SWIR spectral region to assess particle size and soil organic matter in the surface layer of tropical soils (Oxisols, Ultisols, Entisols). The study area is near Piracicaba municipality, São Paulo state, Brazil, in a sugarcane cultivation area of 135 hectares. The study area, with bare soil, was imaged in April 2016 by the AisaFENIX aerotransported hyperspectral sensor, with spectral resolution of 3.5 nm between 380 and 970 nm, and 12 nm between 970 and 2500 nm. We collected 66 surface soil samples. The samples were analyzed for particle size and soil organic matter content. Laboratory spectral measurements were performed using a non-imaging spectroradiometer (ASD FieldSpec 3 Jr). Partial Least Square Regression (PLSR) was used to predict clay, silt, sand and soil organic matter (SOM). The PLSR functions developed were applied to the hyperspectral image of the study area, allowing development of a prediction map of clay, sand, and SOM. The developed PLSR models demonstrated the relationship between the predictor variables at the cross-validation step, both for the non-imaging and imaging sensors, when the highest r and R2 values were obtained for clay, sand, and SOM, with R2 over 0.67. We did not obtain a satisfactory model for silt content. For the non-imaging sensor at the prediction step, R2 values for clay and SOM were over 0.7 and sand was lower than 0.54. The imaging sensor yielded models for clay, sand, and SOM with R2 values of 0.62, 0.66, and 0.67, respectively. Pearson correlation between sensors was greater than 0.849 for the prediction of clay, sand, and SOM. Our study successfully generated, from the imaging sensor, a large-scale and detailed predicted soil maps for particle size and SOM, which are important in the management of tropical soils.
Effective Range and Driving Factors of the Urban Ventilation Corridor Effect on Urban Thermal Comfort at Unified Scale with Multisource Data
Authors: Liu Weiling , Zhang Guo , Jiang Yonghua , Wang Jingyin
Published: May 2021

Urban ventilation corridors serve as channels of fresh air flow between the city and suburbs, helping to improve the wind and thermal environments and thermal comfort. However, owing to the limited number of weather stations, it is impossible to quantitatively reveal the effective effect range of urban ventilation corridors on urban thermal comfort at the scale of 100 × 100 m, which is optimal for urban ventilation corridors. In this study, we integrated building data, the European Centre for Medium-Range weather forecast data (ECMWF), MOD13Q1, and other multisource data to analyse the effect of urban ventilation corridors on urban thermal comfort at a unified scale of 100 × 100 m. The results showed that ECMWF and Landsat8 data could be used as substitute factors to improve the universal thermal climate index (UTCI) urban spatial resolution. The effective range of urban ventilation corridor effects on the urban surface temperature and urban comfort was ≤1000 m, with building density and vegetation coverage as the main factors limiting this range. Therefore, attention should be paid to the effective range of urban ventilation corridors, the surrounding building density, vegetation coverage, and the rational use of urban ventilation corridors to reduce the energy consumption of air conditioning in summer.
The Application of UAV for the Analysis of Geological Hazard in Krk Island, Croatia, Mediterranean Sea
Authors: Ružić Igor , Benac Čedomir , Jovančević Sanja Dugonjić , Radišić Maja
Published: May 2021

The coastal area around the settlement of Stara Baška (Krk Island, NE channel zone of Adriatic Sea) is in a delicate geodynamic balance. The main causes are the geological structure, hydrological and hydrogeological conditions and the direct exposure of the coast to the waves and storm surges. In this paper, the effects of the expected sea level rise on the geological vulnerability of the coast are investigated. Detailed field research was conducted. The complex coastal morphology was surveyed using an Uncrewed Aerial Vehicle (UAV), and the UAV-derived data was used as a 3D point cloud and orthophoto for analysis. In the investigated coastal zone, more resistant Quaternary talus breccias predominate over more susceptible flysch rock mass. One major and two smaller landslides are in a creeping condition and pose a hazard to the surrounding homes. During storm surges, the pocket beaches are completely inundated due to the narrow beach width and waves reach the toe of the slopes. According to the expected sea level rise, coastal processes could become more intense and threaten the current coastal equilibrium. The vulnerability of a large part of the Stara Baška settlement will increase significantly. The conducted research showed the advantages of UAV-derived data for the study of complex rocky coasts and emphasized the need for repeatable UAV surveys.
Integrating Spectral and Textural Information for Monitoring the Growth of Pear Trees Using Optical Images from the UAV Platform
Authors: Guo Yahui , Chen Shouzhi , Wu Zhaofei , Wang Shuxin , Robin Bryant Christopher , Senthilnath Jayavelu , Cunha Mario , Fu Yongshuo H.
Published: May 2021

With the recent developments of unmanned aerial vehicle (UAV) remote sensing, it is possible to monitor the growth condition of trees with the high temporal and spatial resolutions of data. In this study, the daily high-throughput RGB images of pear trees were captured from a UAV platform. A new index was generated by integrating the spectral and textural information using the improved adaptive feature weighting method (IAFWM). The inter-relationships of the air climatic variables and the soil’s physical properties (temperature, humidity and conductivity) were firstly assessed using principal component analysis (PCA). The climatic variables were selected to independently build a linear regression model with the new index when the cumulative variance explained reached 99.53%. The coefficient of determination (R2) of humidity (R2 = 0.120, p = 0.205) using linear regression analysis was the dominating influencing factor for the growth of the pear trees, among the air climatic variables tested. The humidity (%) in 40 cm depth of soil (R2 = 0.642, p < 0.001) using a linear regression coefficient was the largest among climatic variables in the soil. The impact of climatic variables on the soil was commonly greater than those in the air, and the R2 grew larger with the increasing depth of soil. The effects of the fluctuation of the soil-climatic variables on the pear trees’ growth could be detected using the sliding window method (SWM), and the maximum absolute value of coefficients with the corresponding day of year (DOY) of air temperature, soil temperature, soil humidity, and soil conductivity were confirmed as 221, 227, 228, and 226 (DOY), respectively. Thus, the impact of the fluctuation of climatic variables on the growth of pear trees can last 14, 8, 7, and 9 days, respectively. Therefore, it is highly recommended that the adoption of the integrated new index to explore the long-time impact of climate on pears growth be undertaken.
A Fast Iterative Procedure for Adjacency Effects Correction on Remote Sensed Data
Authors: Guzzi Donatella , Nardino Vanni , Lastri Cinzia , Raimondi Valentina
Published: May 2021

This paper describes a simple, iterative atmospheric correction procedure based on the MODTRAN®5 radiative transfer code. Such a procedure receives in input a spectrally resolved at-sensor radiance image, evaluates the different contributions to received radiation, and corrects the effect of adjacency from surrounding pixels permitting the retrieval of ground reflectance spectrum for each pixel of the image. The procedure output is a spectral ground reflectance image obtained without the need of any user-provided a priori hypothesis. The novelty of the proposed method relies on its iterative approach for evaluating the contribution of surrounding pixels: a first run of the atmospheric correction procedure is performed by assuming that the spectral reflectance of the surrounding pixels is equal to that of the pixel under investigation. Such information is used in the subsequent iteration steps to estimate the spectral radiance of the surrounding pixels, in order to make a more accurate evaluation of the reflectance image. The results are here presented and discussed for two different cases: synthetic images produced with the hyperspectral simulation tool PRIMUS and real images acquired by CHRIS–PROBA sensor. The retrieved reflectance error drops after a few iterations, providing a quantitative estimate for the number of iterations needed. Relative error after the procedure converges is in the order of few percent, and the causes of remaining uncertainty in retrieved spectra are discussed.
Microwave and Radar Week (MRW 2020): Selected Papers
Authors: Jędrzejewski Konrad , Colantonio Paolo , Abramowicz Adam
Published: May 2021

The 9th Microwave and Radar Week (MRW 2020) was held in Warsaw the capital of Poland, on 5–7 October 2020 [...]
Remote Sensing Time Series Classification Based on Self-Attention Mechanism and Time Sequence Enhancement
Authors: Liu Jingwei , Yan Jining , Wang Lizhe , Huang Liang , He Haixu , Liu Hong
Published: May 2021

Nowadays, in the field of data mining, time series data analysis is a very important and challenging subject. This is especially true for time series remote sensing classification. The classification of remote sensing images is an important source of information for land resource planning and management, rational development, and protection. Many experts and scholars have proposed various methods to classify time series data, but when these methods are applied to real remote sensing time series data, there are some deficiencies in classification accuracy. Based on previous experience and the processing methods of time series in other fields, we propose a neural network model based on a self-attention mechanism and time sequence enhancement to classify real remote sensing time series data. The model is mainly divided into five parts: (1) memory feature extraction in subsequence blocks; (2) self-attention layer among blocks; (3) time sequence enhancement; (4) spectral sequence relationship extraction; and (5) a simplified ResNet neural network. The model can simultaneously consider the three characteristics of time series local information, global information, and spectral series relationship information to realize the classification of remote sensing time series. Good experimental results have been obtained by using our model.
Quantitative Retrieval of Volcanic Sulphate Aerosols from IASI Observations
Authors: Guermazi Henda , Sellitto Pasquale , Cuesta Juan , Eremenko Maxim , Lachatre Mathieu , Mailler Sylvain , Carboni Elisa , Salerno Giuseppe , Caltabiano Tommaso , Menut Laurent , Serbaji Mohamed Moncef , Rekhiss Farhat , Legras Bernard
Published: May 2021

We developed a new retrieval algorithm based on the Infrared Atmospheric Sounding Interferometer (IASI) observations, called AEROIASI-H2SO4, to measure the extinction and mass concentration of sulphate aerosols (binary solution droplets of sulphuric acid and water), with moderate random uncertainties (typically ∼35% total uncertainty for column mass concentration estimations). The algorithm is based on a self-adapting Tikhonov–Phillips regularization method. It is here tested over a moderate-intensity eruption of Mount Etna volcano (18 March 2012), Italy, and is used to characterise this event in terms of the spatial distribution of the retrieved plume. Comparisons with simultaneous and independent aerosol optical depth observations from MODIS (Moderate Resolution Imaging Spectroradiometer), SO2 plume observations from IASI and simulations with the CHIMERE chemistry/transport model show that AEROIASI-H2SO4 correctly identifies the volcanic plume horizontal morphology, thus providing crucial new information towards the study of volcanic emissions, volcanic sulphur cycle in the atmosphere, plume evolution processes, and their impacts. Insights are given on the possible spectroscopic evidence of the presence in the plume of larger-sized particles than previously reported for secondary sulphate aerosols from volcanic eruptions.
Identification of Cotton Leaf Lesions Using Deep Learning Techniques
Authors: Caldeira Rafael Faria , Santiago Wesley Esdras , Teruel Barbara
Published: May 2021

The use of deep learning models to identify lesions on cotton leaves on the basis of images of the crop in the field is proposed in this article. Cultivated in most of the world, cotton is one of the economically most important agricultural crops. Its cultivation in tropical regions has made it the target of a wide spectrum of agricultural pests and diseases, and efficient solutions are required. Moreover, the symptoms of the main pests and diseases cannot be differentiated in the initial stages, and the correct identification of a lesion can be difficult for the producer. To help resolve the problem, the present research provides a solution based on deep learning in the screening of cotton leaves which makes it possible to monitor the health of the cotton crop and make better decisions for its management. With the learning models GoogleNet and Resnet50 using convolutional neural networks, a precision of 86.6% and 89.2%, respectively, was obtained. Compared with traditional approaches for the processing of images such as support vector machines (SVM), Closest k-neighbors (KNN), artificial neural networks (ANN) and neuro-fuzzy (NFC), the convolutional neural networks proved to be up to 25% more precise, suggesting that this method can contribute to a more rapid and reliable inspection of the plants growing in the field.
Performance Test of the Rotational Sensor blueSeis-3A in a Huddle Test in Fürstenfeldbruck
Authors: Izgi Gizem , Eibl Eva P. S. , Donner Stefanie , Bernauer Felix
Published: May 2021

Rotational motions play a key role in measuring seismic wavefield properties. Using newly developed portable rotational instruments, it is now possible to directly measure rotational motions in a broad frequency range. Here, we investigated the instrumental self-noise and data quality in a huddle test in Fürstenfeldbruck, Germany, in August 2019. We compare the data from six rotational and three translational sensors. We studied the recorded signals using correlation, coherence analysis, and probabilistic power spectral densities. We sorted the coherent noise into five groups with respect to the similarities in frequency content and shape of the signals. These coherent noises were most likely caused by electrical devices, the dehumidifier system in the building, humans, and natural sources such as wind. We calculated self-noise levels through probabilistic power spectral densities and by applying the Sleeman method, a three-sensor method. Our results from both methods indicate that self-noise levels are stable between 0.5 and 40 Hz. Furthermore, we recorded the 29 August 2019 ML 3.4 Dettingen earthquake. The calculated source directions are found to be realistic for all sensors in comparison to the real back azimuth. We conclude that the five tested blueSeis-3A rotational sensors, when compared with respect to coherent noise, self-noise, and source direction, provide reliable and consistent results. Hence, field experiments with single rotational sensors can be undertaken.
A Dense Neural Network Approach for Detecting Clone ID Attacks on the RPL Protocol of the IoT
Authors: Morales-Molina Carlos D. , Hernandez-Suarez Aldo , Sanchez-Perez Gabriel , Toscano-Medina Linda K. , Perez-Meana Hector , Olivares-Mercado Jesus , Portillo-Portillo Jose , Sanchez Victor , Garcia-Villalba Luis Javier
Published: May 2021

At present, new data sharing technologies, such as those used in the Internet of Things (IoT) paradigm, are being extensively adopted. For this reason, intelligent security controls have become imperative. According to good practices and security information standards, particularly those regarding security in depth, several defensive layers are required to protect information assets. Within the context of IoT cyber-attacks, it is fundamental to continuously adapt new detection mechanisms for growing IoT threats, specifically for those becoming more sophisticated within mesh networks, such as identity theft and cloning. Therefore, current applications, such as Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS), and Security Information and Event Management Systems (SIEM), are becoming inadequate for accurately handling novel security incidents, due to their signature-based detection procedures using the matching and flagging of anomalous patterns. This project focuses on a seldom-investigated identity attack—the Clone ID attack—directed at the Routing Protocol for Low Power and Lossy Networks (RPL), the underlying technology for most IoT devices. Hence, a robust Artificial Intelligence-based protection framework is proposed, in order to tackle major identity impersonation attacks, which classical applications are prone to misidentifying. On this basis, unsupervised pre-training techniques are employed to select key characteristics from RPL network samples. Then, a Dense Neural Network (DNN) is trained to maximize deep feature engineering, with the aim of improving classification results to protect against malicious counterfeiting attempts.
Fast Star Matching Method Based on Double K-Vector Lookup Tables for Multi-Exposure Star Trackers
Authors: Yu Wenbo , Jiang Jie , Wu Pei , Xuan Chuanzhong , Zhang Chunhui
Published: May 2021

A high update rate is always one of the vital indices of star trackers. By recording star positions at N moments within a single star image, the multi-exposure imaging approach (MEIA) proposed in an earlier study can improve the attitude update rate of star trackers by N times. Unfortunately, when the existing star matching method is adopted to match the observed and predicted stars in MEIA, the matching time is significantly increased with the increase in multi-exposure times, N, or the number of navigation stars, M, which sharply affects the MEIA’s performance. Therefore, a fast star matching method based on double K-vector lookup tables (DKVLUTs) is proposed to address the above issue. In this method, the information of all predicted stars is used to establish the DKVLUT, and thus, the speed of the whole matching process between observed and predicted stars would be increased effectively by means of the DKVLUT. Both simulations and experiments are conducted to verify the performance of the proposed method. The results both show that the matching time of the proposed method is reduced by nearly one order of magnitude compared with that of the existing method, which demonstrates the feasibility and effectiveness of the proposed method.
Luminance-Degradation Compensation Based on Multistream Self-Attention to Address Thin-Film Transistor-Organic Light Emitting Diode Burn-In
Authors: Park Seong-Chel , Park Kwan-Ho , Chang Joon-Hyuk
Published: May 2021

We propose a deep-learning algorithm that directly compensates for luminance degradation because of the deterioration of organic light-emitting diode (OLED) devices to address the burn-in phenomenon of OLED displays. Conventional compensation circuits are encumbered by high cost of the development and manufacturing processes because of their complexity. However, given that deep-learning algorithms are typically mounted onto systems on chip (SoC), the complexity of the circuit design is reduced, and the circuit can be reused by only relearning the changed characteristics of the new pixel device. The proposed approach comprises deep-feature generation and multistream self-attention, which decipher the importance of the variables, and the correlation between burn-in-related variables. It also utilizes a deep neural network that identifies the nonlinear relationship between extracted features and luminance degradation. Thereafter, luminance degradation is estimated from burn-in-related variables, and the burn-in phenomenon can be addressed by compensating for luminance degradation. Experiment results revealed that compensation was successfully achieved within an error range of 4.56%, and demonstrated the potential of a new approach that could mitigate the burn-in phenomenon by directly compensating for pixel-level luminance deviation.
Environment Mapping Using Sensor Fusion of 2D Laser Scanner and 3D Ultrasonic Sensor for a Real Mobile Robot
Authors: Tran Tien Quang , Becker Andreas , Grzechca Damian
Published: May 2021

Mapping the environment is necessary for navigation, planning and manipulation. In this paper, a fusion framework (as data-in-decision-out) is introduced for a 2D LIDAR and a 3D ultrasonic sensor to achieve three-dimensional mapping without expensive 3D LiDAR scanner or visual processing. Two sensor models are proposed for the two sensors used for map updating. Furthermore, 2D/3D map representations are discussed for our fusion approach. We also compare different probabilistic fusion methods and discuss criterias for choosing appropriate methods. Experiments are carried out with a real ground robot platform in an indoor environment. The 2D and 3D map results demonstrate that our approach is able to show the surrounding in more details. Sensor fusion provides a better estimation of the environment and the ego-pose whilst lowering the necessary resources. This gives the robot’s perception of the environment more information by using only one additional low-cost 3D ultrasonic sensor. This is especially important for robust and light-weight robots with limited resources.
Offshore Online Measurements of Total Suspended Solids Using Microscopy Analyzers
Authors: Hansen Dennis Severin , Jespersen Stefan , Bram Mads Valentin , Yang Zhenyu
Published: May 2021

Accurate online water quality measurements have gained attention during the last decades in the oil and gas industry for improving operational performance and protecting the surrounding environment. One potential solution to extend the reservoirs’ economic life and put less strain on the environment is by re-injecting the produced water, but the injected water quality must be high and consistent to prevent injectivity reduction. This paper evaluates two different online microscopy analyzers that utilize a high-resolution video camera for capturing images of the particles passing their view cell. The calibration procedure for both online microscopy analyzers has been thoroughly validated for steady-state and real-time measurements. The real-time measurements were achieved by post-processing the data captured by the microscopes and applying a trailing moving average window. The performance of measuring the oil-in-water concentration was compared with an online fluorescence-based monitor. The paper addresses the statistical considerations when defining the level of accuracy of the predicted particle size distribution within a defined confidence interval. Both microscopes showed promising results for measuring known particle sizes and oil-in-water concentrations, both in steady-state and real-time.
Compact Antenna in 3D Configuration for Rectenna Wireless Power Transmission Applications
Authors: Sidibe Alassane , Takacs Alexandru , Loubet Gaël , Dragomirescu Daniela
Published: May 2021

This work presents methods for miniaturizing and characterizing a modified dipole antenna dedicated to the implementation of wireless power transmission systems. The antenna size should respect the planar dimensions of 60 mm × 30 mm to be integrated with small IoT devices such as a Bluetooth Lower Energy Sensing Node. The provided design is based on a folded short-circuited dipole antenna, also named a T-match antenna. Faced with the difficulty of reducing the physical dimensions of the antenna, we propose a 3D configuration by adding vertical metallic arms on the edges of the antenna. The adopted 3D design has an overall size of 56 mm × 32 mm × 10 mm at 868 MHz. Three antenna-feeding techniques were evaluated to characterize this antenna. They consist of soldering a U.FL connector on the input port; vertically connecting a tapered balun to the antenna; and integrating a microstrip transition to the layer of the antenna. The experimental results of the selected feeding techniques show good agreements and the antenna has a maximum gain of +1.54 dBi in the elevation plane (E-plane). In addition, a final modification was operated to the designed antenna to have a more compact structure with a size of 40 mm × 30 mm × 10 mm at 868 MHz. Such modification reduces the radiation surface of the antenna and so the antenna gain and bandwidth. This antenna can achieve a maximum gain of +1.1 dBi in the E-plane. The two antennas proposed in this paper were then associated with a rectifier to perform energy harvesting for powering Bluetooth Low Energy wireless sensors. The measured RF-DC (radiofrequency to direct current) conversion efficiency is 73.88% (first design) and 60.21% (second design) with an illuminating power density of 3.1 µW/cm2 at 868 MHz with a 10 kΩ load resistor.
Swarm Intelligence Algorithms in Text Document Clustering with Various Benchmarks
Authors: Selvaraj Suganya , Choi Eunmi
Published: May 2021

Text document clustering refers to the unsupervised classification of textual documents into clusters based on content similarity and can be applied in applications such as search optimization and extracting hidden information from data generated by IoT sensors. Swarm intelligence (SI) algorithms use stochastic and heuristic principles that include simple and unintelligent individuals that follow some simple rules to accomplish very complex tasks. By mapping features of problems to parameters of SI algorithms, SI algorithms can achieve solutions in a flexible, robust, decentralized, and self-organized manner. Compared to traditional clustering algorithms, these solving mechanisms make swarm algorithms suitable for resolving complex document clustering problems. However, each SI algorithm shows a different performance based on its own strengths and weaknesses. In this paper, to find the best performing SI algorithm in text document clustering, we performed a comparative study for the PSO, bat, grey wolf optimization (GWO), and K-means algorithms using six data sets of various sizes, which were created from BBC Sport news and 20 newsgroups. Based on our experimental results, we discuss the features of a document clustering problem with the nature of SI algorithms and conclude that the PSO and GWO SI algorithms are better than K-means, and among those algorithms, the PSO performs best in terms of finding the optimal solution.
Advanced Physical-Layer Technologies for Beyond 5G Wireless Communication Networks
Authors: Khalid Waqas , Yu Heejung , Ali Rashid , Ullah Rehmat
Published: May 2021

Fifth-generation (5G) networks will not satisfy the requirements of the latency, bandwidth, and traffic density in 2030 and beyond, and next-generation wireless communication networks with revolutionary enabling technologies will be required. Beyond 5G (B5G)/sixth-generation (6G) networks will achieve superior performance by providing advanced functions such as ultralow latency, ultrahigh reliability, global coverage, massive connectivity, and better intelligence and security levels. Important aspects of B5G/6G networks require the modification and exploitation of promising physical-layer technologies. This Special Issue (SI) presents research efforts to identify and discuss the novel techniques, technical challenges, and promising solution methods of physical-layer technologies with a vision of potential involvement in the B5G/6G era. In particular, this SI presents innovations and concepts, including nonorthogonal multiple access, massive multiple-input multiple-output (MIMO), energy harvesting, hybrid satellite terrestrial relays, Internet of Things-based home automation, millimeter-wave bands, device-to-device communication, and artificial-intelligence or machine-learning techniques. Further, this SI covers the proposed solutions, including MIMO antenna design, modulation detection, interference management, hybrid precoding, and statistical beamforming along with their performance improvements in terms of performance metrics, including bit error rate, outage probability, ergodic sum rate, spectrum efficiency, and energy efficiency.
Model-Based Systems Engineering Applied to Trade-Off Analysis of Wireless Power Transfer Technologies for Implanted Biomedical Microdevices
Authors: Martínez Rojas Juan A. , Fernández José L. , Sánchez Montero Rocío , López Espí Pablo Luis , Diez-Jimenez Efren
Published: May 2021

Decision-making is an important part of human life and particularly in any engineering process related to a complex product. New sensors and actuators based on MEMS technologies are increasingly complex and quickly evolving into products. New biomedical implanted devices may benefit from system engineering approaches, previously reserved to very large projects, and it is expected that this need will increase in the future. Here, we propose the application of Model Based Systems Engineering (MBSE) to systematize and optimize the trade-off analysis process. The criteria, their utility functions and the weighting factors are applied in a systematic way for the selection of the best alternative. Combining trade-off with MBSE allow us to identify the more suitable technology to be implemented to transfer energy to an implanted biomedical micro device.
The Effects of Powered Exoskeleton Gait Training on Cardiovascular Function and Gait Performance: A Systematic Review
Authors: Duddy Damien , Doherty Rónán , Connolly James , McNally Stephen , Loughrey Johnny , Faulkner Maria
Published: May 2021

Patients with neurological impairments often experience physical deconditioning, resulting in reduced fitness and health. Powered exoskeleton training may be a successful method to combat physical deconditioning and its comorbidities, providing patients with a valuable and novel experience. This systematic review aimed to conduct a search of relevant literature, to examine the effects of powered exoskeleton training on cardiovascular function and gait performance. Two electronic database searches were performed (2 April 2020 to 12 February 2021) and manual reference list searches of relevant manuscripts were completed. Studies meeting the inclusion criteria were systematically reviewed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. n = 63 relevant titles were highlighed; two further titles were identified through manual reference list searches. Following analysis n = 23 studies were included. Data extraction details included; sample size, age, gender, injury, the exoskeleton used, intervention duration, weekly sessions, total sessions, session duration and outcome measures. Results indicated that exoskeleton gait training elevated energy expenditure greater than wheelchair propulsion and improved gait function. Patients exercised at a moderate-intensity. Powered exoskeletons may increase energy expenditure to a similar level as non-exoskeleton walking, which may improve cardiovascular function more effectively than wheelchair propulsion alone.
Noise Radar Technology: Waveforms Design and Field Trials
Authors: Galati Gaspare , Pavan Gabriele , Savci Kubilay , Wasserzier Christoph
Published: May 2021

Performance of continuous emission noise radar systems are affected by the sidelobes of the output of the matched filter, with significant effects on detection and dynamic range. Hence, the sidelobe level has to be controlled by a careful design of the transmitted waveform and of the transmit/receive parts of the radar. In this context, the average transmitted power has to be optimized by choosing waveforms with a peak-to-average power ratio as close to the unity as possible. However, after coherent demodulation and acquisition of the received signal and of the reference signal at the transmitting antenna port, the goodness (low sidelobes) of the output from the matched filter can be considerably reduced by the deleterious effects due to the radar hardware, including the analog-to-digital converter (ADC). This paper aims to solve the above problems from both the theoretical and the practical viewpoint and recommends the use of tailored waveforms for mitigating the dynamic range issues. The new findings are corroborated by the results from two noise radar demonstrators operating in Germany (rural environment) and in Turkey (coast and sea environment) and the related lessons learnt.
Plantar Pressure Variability and Asymmetry in Elderly Performing 60-Minute Treadmill Brisk-Walking: Paving the Way towards Fatigue-Induced Instability Assessment Using Wearable In-Shoe Pressure Sensors
Authors: Zhang Guoxin , Wong Duo Wai-Chi , Wong Ivy Kwan-Kei , Chen Tony Lin-Wei , Hong Tommy Tung-Ho , Peng Yinghu , Wang Yan , Tan Qitao , Zhang Ming
Published: May 2021

Evaluation of potential fatigue for the elderly could minimize their risk of injury and thus encourage them to do more physical exercises. Fatigue-related gait instability was often assessed by the changes of joint kinematics, whilst planar pressure variability and asymmetry parameters may complement and provide better estimation. We hypothesized that fatigue condition (induced by the treadmill brisk-walking task) would lead to instability and could be reflected by the variability and asymmetry of plantar pressure. Fifteen elderly adults participated in the 60-min brisk walking trial on a treadmill without a pause, which could ensure that the fatigue-inducing effect is continuous and participants will not recover halfway. The plantar pressure data were extracted at baseline, the 30th minute, and the 60th minute. The median of contact time, peak pressure, and pressure-time integrals in each plantar region was calculated, in addition to their asymmetry and variability. After 60 min of brisk walking, there were significant increases in peak pressure at the medial and lateral arch regions, and central metatarsal regions, in addition to their impulses (p < 0.05). In addition, the variability of plantar pressure at the medial arch was significantly increased (p < 0.05), but their asymmetry was decreased. On the other hand, the contact time was significantly increased at all plantar regions (p < 0.05). The weakened muscle control and shock absorption upon fatigue could be the reason for the increased peak pressure, impulse, and variability, while the improved symmetry and prolonged plantar contact time could be a compensatory mechanism to restore stability. The outcome of this study can facilitate the development of gait instability or fatigue assessment using wearable in-shoe pressure sensors.
Detecting Attention Levels in ADHD Children with a Video Game and the Measurement of Brain Activity with a Single-Channel BCI Headset
Authors: Serrano-Barroso Almudena , Siugzdaite Roma , Guerrero-Cubero Jaime , Molina-Cantero Alberto J. , Gomez-Gonzalez Isabel M. , Lopez Juan Carlos , Vargas Juan Pedro
Published: May 2021

Attentional biomarkers in attention deficit hyperactivity disorder are difficult to detect using only behavioural testing. We explored whether attention measured by a low-cost EEG system might be helpful to detect a possible disorder at its earliest stages. The GokEvolution application was designed to train attention and to provide a measure to identify attentional problems in children early on. Attention changes registered with NeuroSky MindWave in combination with the CARAS-R psychological test were used to characterise the attentional profiles of 52 non-ADHD and 23 ADHD children aged 7 to 12 years old. The analyses revealed that the GokEvolution was valuable in measuring attention through its use of EEG–BCI technology. The ADHD group showed lower levels of attention and more variability in brain attentional responses when compared to the control group. The application was able to map the low attention profiles of the ADHD group when compared to the control group and could distinguish between participants who completed the task and those who did not. Therefore, this system could potentially be used in clinical settings as a screening tool for early detection of attentional traits in order to prevent their development.
Quantification of Blood Flow Velocity in the Human Conjunctival Microvessels Using Deep Learning-Based Stabilization Algorithm
Authors: Jo Hang-Chan , Jeong Hyeonwoo , Lee Junhyuk , Na Kyung-Sun , Kim Dae-Yu
Published: May 2021

The quantification of blood flow velocity in the human conjunctiva is clinically essential for assessing microvascular hemodynamics. Since the conjunctival microvessel is imaged in several seconds, eye motion during image acquisition causes motion artifacts limiting the accuracy of image segmentation performance and measurement of the blood flow velocity. In this paper, we introduce a novel customized optical imaging system for human conjunctiva with deep learning-based segmentation and motion correction. The image segmentation process is performed by the Attention-UNet structure to achieve high-performance segmentation results in conjunctiva images with motion blur. Motion correction processes with two steps—registration and template matching—are used to correct for large displacements and fine movements. The image displacement values decrease to 4–7 μm during registration (first step) and less than 1 μm during template matching (second step). With the corrected images, the blood flow velocity is calculated for selected vessels considering temporal signal variances and vessel lengths. These methods for resolving motion artifacts contribute insights into studies quantifying the hemodynamics of the conjunctiva, as well as other tissues.