bluuebunny/crossref_metadata_2025 huggingface dataset:
Is the Air Too Polluted for Outdoor Activities? Check by Using Your Photovoltaic System as an Air-Quality Monitoring Device
Authors: Lolli Simone
Published: September 2021

Over the past few decades, the concentrating photovoltaic systems, a source of clean and renewable energy, often fully integrated into the roof structure, have been commonly installed on private houses and public buildings. The purpose of those panels is to transform the incoming solar radiation into electricity thanks to the photovoltaic effect. The produced electric power is affected, in the first instance, by the solar panel efficiency and its technical characteristics, but it is also strictly dependent on site elevation, the meteorological conditions and on the presence of the atmospheric constituents, i.e., clouds, hydrometeors, gas molecules and sub-micron-sized particles suspended in the atmosphere that can scatter and absorb the incoming shortwave solar radiation. The Aerosol Optical Depth (AOD) is an adimensional wavelength-dependent atmospheric column variable that accounts for aerosol concentration. AOD can be used as a proxy to evaluate the concentration of surface particulate matter and atmospheric column turbidity, which in turn affects the solar panel energy production. In this manuscript, a new technique is developed to retrieve the AOD at 550 nm through an iterative process: the atmospheric optical depth, incremented in steps of 0.01, is used as input together with the direct and diffuse radiation fluxes computed by Fu–Liou–Gu Radiative Transfer Model, to forecast the produced electric energy by a photovoltaic panel through a simple model. The process will stop at that AOD value (at 550 nm), for which the forecast electric power will match the real produced electric power by the photovoltaic panel within a previously defined threshold. This proof of concept is the first step of a wider project that aims to develop a user-friendly smartphone application where photovoltaic panel owners, once downloaded it on a voluntary basis, can turn their photovoltaic system into a sunphotometer to continuously retrieve the AOD, and more importantly, to monitor the air quality and detect strong air pollution episodes that pose a threat for population health.
Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part II: Brain Signals
Authors: Martinek Radek , Ladrova Martina , Sidikova Michaela , Jaros Rene , Behbehani Khosrow , Kahankova Radana , Kawala-Sterniuk Aleksandra
Published: September 2021

As it was mentioned in the previous part of this work (Part I)—the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work—various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.
Estimation of Pollutant Emissions in Real Driving Conditions Based on Data from OBD and Machine Learning
Authors: Rivera-Campoverde Néstor Diego , Muñoz-Sanz José Luis , Arenas-Ramirez Blanca del Valle
Published: September 2021

This article proposes a methodology for the estimation of emissions in real driving conditions, based on board diagnostics data and machine learning, since it has been detected that there are no models for estimating pollutants without large measurement campaigns. For this purpose, driving data are obtained by means of a data logger and emissions through a portable emissions measurement system in a real driving emissions test. The data obtained are used to train artificial neural networks that estimate emissions, having previously estimated the relative importance of variables through random forest techniques. Then, by the application of the K-means algorithm, labels are obtained to implement a classification tree and thereby determine the selected gear by the driver. These models were loaded with a data set generated covering 1218.19 km of driving. The results generated were compared to the ones obtained by applying the international vehicle emissions model and with the results of the real driving emissions test, showing evidence of similar results. The main contribution of this article is that the generated model is stronger in different traffic conditions and presents good results at the speed interval with small differences at low average driving speeds because more than half of the vehicle’s trip occurs in urban areas, in completely random driving conditions. These results can be useful for the estimation of emission factors with potential application in vehicular homologation processes and the estimation of vehicular emission inventories.
An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications
Authors: Anand Ankita , Rani Shalli , Anand Divya , Aljahdali Hani Moaiteq , Kerr Dermot
Published: September 2021

The role of 5G-IoT has become indispensable in smart applications and it plays a crucial part in e-health applications. E-health applications require intelligent schemes and architectures to overcome the security threats against the sensitive data of patients. The information in e-healthcare applications is stored in the cloud which is vulnerable to security attacks. However, with deep learning techniques, these attacks can be detected, which needs hybrid models. In this article, a new deep learning model (CNN-DMA) is proposed to detect malware attacks based on a classifier—Convolution Neural Network (CNN). The model uses three layers, i.e., Dense, Dropout, and Flatten. Batch sizes of 64, 20 epoch, and 25 classes are used to train the network. An input image of 32 × 32 × 1 is used for the initial convolutional layer. Results are retrieved on the Malimg dataset where 25 families of malware are fed as input and our model has detected is Alueron.gen!J malware. The proposed model CNN-DMA is 99% accurate and it is validated with state-of-the-art techniques.
Unsupervised Machine Learning Applied to Seismic Interpretation: Towards an Unsupervised Automated Interpretation Tool
Authors: Celecia Alimed , Figueiredo Karla , Rodriguez Carlos , Vellasco Marley , Maldonado Edwin , Silva Marco Aurélio , Rodrigues Anderson , Nascimento Renata , Ourofino Carla
Published: September 2021

Seismic interpretation is a fundamental process for hydrocarbon exploration. This activity comprises identifying geological information through the processing and analysis of seismic data represented by different attributes. The interpretation process presents limitations related to its high data volume, own complexity, time consumption, and uncertainties incorporated by the experts’ work. Unsupervised machine learning models, by discovering underlying patterns in the data, can represent a novel approach to provide an accurate interpretation without any reference or label, eliminating the human bias. Therefore, in this work, we propose exploring multiple methodologies based on unsupervised learning algorithms to interpret seismic data. Specifically, two strategies considering classical clustering algorithms and image segmentation methods, combined with feature selection, were evaluated to select the best possible approach. Additionally, the resultant groups of the seismic data were associated with groups obtained from well logs of the same area, producing an interpretation with aggregated lithologic information. The resultant seismic groups correctly represented the main seismic facies and correlated adequately with the groups obtained from the well logs data.
Reducing Unspecific Protein Adsorption in Microfluidic Papers Using Fiber-Attached Polymer Hydrogels
Authors: von Stockert Alexander Ritter , Luongo Anna , Langhans Markus , Brandstetter Thomas , Rühe Jürgen , Meckel Tobias , Biesalski Markus
Published: September 2021

Microfluidic paper combines pump-free water transport at low cost with a high degree of sustainability, as well as good availability of the paper-forming cellulosic material, thus making it an attractive candidate for point-of-care (POC) analytics and diagnostics. Although a number of interesting demonstrators for such paper devices have been reported to date, a number of challenges still exist, which limit a successful transfer into marketable applications. A strong limitation in this respect is the (unspecific) adsorption of protein analytes to the paper fibers during the lateral flow assay. This interaction may significantly reduce the amount of analyte that reaches the detection zone of the microfluidic paper-based analytical device (µPAD), thereby reducing its overall sensitivity. Here, we introduce a novel approach on reducing the nonspecific adsorption of proteins to lab-made paper sheets for the use in µPADs. To this, cotton linter fibers in lab-formed additive-free paper sheets are modified with a surrounding thin hydrogel layer generated from photo-crosslinked, benzophenone functionalized copolymers based on poly-(oligo-ethylene glycol methacrylate) (POEGMA) and poly-dimethyl acrylamide (PDMAA). This, as we show in tests similar to lateral flow assays, significantly reduces unspecific binding of model proteins. Furthermore, by evaporating the transport fluid during the microfluidic run at the end of the paper strip through local heating, model proteins can almost quantitatively be accumulated in that zone. The possibility of complete, almost quantitative protein transport in a µPAD opens up new opportunities to significantly improve the signal-to-noise (S/N) ratio of paper-based lateral flow assays.
Enhancing 5G Small Cell Selection: A Neural Network and IoV-Based Approach
Authors: Alablani Ibtihal Ahmed , Arafah Mohammed Amer
Published: September 2021

The ultra-dense network (UDN) is one of the key technologies in fifth generation (5G) networks. It is used to enhance the system capacity issue by deploying small cells at high density. In 5G UDNs, the cell selection process requires high computational complexity, so it is considered to be an open NP-hard problem. Internet of Vehicles (IoV) technology has become a new trend that aims to connect vehicles, people, infrastructure and networks to improve a transportation system. In this paper, we propose a machine-learning and IoV-based cell selection scheme called Artificial Neural Network Cell Selection (ANN-CS). It aims to select the small cell that has the longest dwell time. A feed-forward back-propagation ANN (FFBP-ANN) was trained to perform the selection task, based on moving vehicle information. Real datasets of vehicles and base stations (BSs), collected in Los Angeles, were used for training and evaluation purposes. Simulation results show that the trained ANN model has high accuracy, with a very low percentage of errors. In addition, the proposed ANN-CS decreases the handover rate by up to 33.33% and increases the dwell time by up to 15.47%, thereby minimizing the number of unsuccessful and unnecessary handovers (HOs). Furthermore, it led to an enhancement in terms of the downlink throughput achieved by vehicles.
Validity of Peripheral Oxygen Saturation Measurements with the Garmin Fēnix® 5X Plus Wearable Device at 4559 m
Authors: Schiefer Lisa M. , Treff Gunnar , Treff Franziska , Schmidt Peter , Schäfer Larissa , Niebauer Josef , Swenson Kai E. , Swenson Erik R. , Berger Marc M. , Sareban Mahdi
Published: September 2021

Decreased oxygen saturation (SO2) at high altitude is associated with potentially life-threatening diseases, e.g., high-altitude pulmonary edema. Wearable devices that allow continuous monitoring of peripheral oxygen saturation (SpO2), such as the Garmin Fēnix® 5X Plus (GAR), might provide early detection to prevent hypoxia-induced diseases. We therefore aimed to validate GAR-derived SpO2 readings at 4559 m. SpO2 was measured with GAR and the medically certified Covidien Nellcor SpO2 monitor (COV) at six time points in 13 healthy lowlanders after a rapid ascent from 1130 m to 4559 m. Arterial blood gas (ABG) analysis served as the criterion measure and was conducted at four of the six time points with the Radiometer ABL 90 Flex. Validity was assessed by intraclass correlation coefficients (ICCs), mean absolute percentage error (MAPE), and Bland–Altman plots. Mean (±SD) SO2, including all time points at 4559 m, was 85.2 ± 6.2% with GAR, 81.0 ± 9.4% with COV, and 75.0 ± 9.5% with ABG. Validity of GAR was low, as indicated by the ICC (0.549), the MAPE (9.77%), the mean SO2 difference (7.0%), and the wide limits of agreement (−6.5; 20.5%) vs. ABG. Validity of COV was good, as indicated by the ICC (0.883), the MAPE (6.15%), and the mean SO2 difference (0.1%) vs. ABG. The GAR device demonstrated poor validity and cannot be recommended for monitoring SpO2 at high altitude.
Design of a Multi-Point Kinematic Coupling for a High Precision Telescopic Simultaneous Measurement System
Authors: Acero Raquel , Aguilar Juan José , Brosed Francisco Javier , Santolaria Jorge , Aguado Sergio , Pueo Marcos
Published: September 2021

This paper covers the design of a new multi-point kinematic coupling specially developed for a high precision multi-telescopic arm measurement system for the volumetric verification of machine tools with linear and/or rotary axes. The multipoint kinematic coupling allows the simultaneous operation of the three telescopic arms that are registered at the same time to a sphere fixed on the machine tool spindle nose. Every coupling provides an accurate multi-point contact to the sphere, avoiding collisions and interferences with the other two multi-point kinematic couplings, and generating repulsion forces among them to ensure the coupling’s fingers interlacing along the machine tool x/y/z travels in the verification process. Simulation presents minimal deformation of the kinematic coupling under load, assuring the precision of the sphere-to-sphere distance measurement. Experimental results are provided to show that the multi-point kinematic coupling developed has repeatability values below ±1.2 µm in the application.
Multi-Phase Joint-Angle Trajectory Generation Inspired by Dog Motion for Control of Quadruped Robot
Authors: Choi Jungsu
Published: September 2021

Quadruped robots are receiving great attention as a new means of transportation for various purposes, such as military, welfare, and rehabilitation systems. The use of four legs enables a robustly stable gait; compared to the humanoid robots, the quadruped robots are particularly advantageous in improving the locomotion speed, the maximum payload, and the robustness toward disturbances. However, the more demanding conditions robots are exposed to, the more challenging the trajectory generation of robotic legs becomes. Although various trajectory generation methods (e.x., central pattern generator, finite states machine) have been developed for this purpose, these methods have limited degrees of freedom with respect to the gait transition. The conventional methods do not consider the transition of the gait phase (i.e., walk, amble, trot, canter, and gallop) or use a pre-determined fixed gait phase. Additionally, some research teams have developed locomotion algorithms that take into account the transition of the gait phase. Still, the transition of the gait phase is limited (mostly from walking to trot), and the transition according to gait speed is not considered. In this paper, a multi-phase joint-angle trajectory generation algorithm is proposed for the quadruped robot. The joint-angles of an animal are expressed as a cyclic basis function, and an input to the basis function is manipulated to realize the joint-angle trajectories in multiple gait phases as desired. To control the desired input of a cyclic basis function, a synchronization function is formulated, by which the motions of legs are designed to have proper ground contact sequences with each other. In the gait of animals, each gait phase is optimal for a certain speed, and thus transition of the gait phases is necessary for effective increase or decrease in the locomotion speed. The classification of the gait phases, however, is discrete, and thus the resultant joint-angle trajectories may be discontinuous due to the transition. For the smooth and continuous transition of gait phases, fuzzy logic is utilized in the proposed algorithm. The proposed methods are verified by simulation studies.
Dynamic Hand Gesture Recognition in In-Vehicle Environment Based on FMCW Radar and Transformer
Authors: Zheng Lianqing , Bai Jie , Zhu Xichan , Huang Libo , Shan Chewu , Wu Qiong , Zhang Lei
Published: September 2021

Hand gesture recognition technology plays an important role in human-computer interaction and in-vehicle entertainment. Under in-vehicle conditions, it is a great challenge to design gesture recognition systems due to variable driving conditions, complex backgrounds, and diversified gestures. In this paper, we propose a gesture recognition system based on frequency-modulated continuous-wave (FMCW) radar and transformer for an in-vehicle environment. Firstly, the original range-Doppler maps (RDMs), range-azimuth maps (RAMs), and range-elevation maps (REMs) of the time sequence of each gesture are obtained by radar signal processing. Then we preprocess the obtained data frames by region of interest (ROI) extraction, vibration removal algorithm, background removal algorithm, and standardization. We propose a transformer-based radar gesture recognition network named RGTNet. It fully extracts and fuses the spatial-temporal information of radar feature maps to complete the classification of various gestures. The experimental results show that our method can better complete the eight gesture classification tasks in the in-vehicle environment. The recognition accuracy is 97.56%.
UAV-Based Intelligent Transportation System for Emergency Reporting in Coverage Holes of Wireless Networks
Authors: Almasoud Abdullah M.
Published: September 2021

During critical moments, disaster and accident victims may not be able to request help from the emergency response center. This may happen when the victim’s vehicle is located within a coverage hole in a wireless network. In this paper, we adopt an unmanned aerial vehicle (UAV) to work as an automatic emergency dispatcher for a user in a vehicle facing a critical condition. Given that the UAV is located within a coverage hole and a predetermined critical condition is detected, the UAV becomes airborne and dispatches distress messages to a communication network. We propose to use a path loss map for UAV trajectory design, and we formulate our problem mathematically as an Integer Linear Program (ILP). Our goals are to minimize the distress messages delivery time and the UAV’s mission completion time. Due to the difficulty of obtaining the optimal solution when the scale of the problem is large, we proposed an efficient algorithm that reduces the computational time significantly. We simulate our problem under different scenarios and settings, and study the performance of our proposed algorithm.
Co-Density Distribution Maps for Advanced Molecule Colocalization and Co-Distribution Analysis
Authors: De Santis Ilaria , Lorenzini Luca , Moretti Marzia , Martella Elisa , Lucarelli Enrico , Calzà Laura , Bevilacqua Alessandro
Published: September 2021

Cellular and subcellular spatial colocalization of structures and molecules in biological specimens is an important indicator of their co-compartmentalization and interaction. Presently, colocalization in biomedical images is addressed with visual inspection and quantified by co-occurrence and correlation coefficients. However, such measures alone cannot capture the complexity of the interactions, which does not limit itself to signal intensity. On top of the previously developed density distribution maps (DDMs), here, we present a method for advancing current colocalization analysis by introducing co-density distribution maps (cDDMs), which, uniquely, provide information about molecules absolute and relative position and local abundance. We exemplify the benefits of our method by developing cDDMs-integrated pipelines for the analysis of molecules pairs co-distribution in three different real-case image datasets. First, cDDMs are shown to be indicators of colocalization and degree, able to increase the reliability of correlation coefficients currently used to detect the presence of colocalization. In addition, they provide a simultaneously visual and quantitative support, which opens for new investigation paths and biomedical considerations. Finally, thanks to the coDDMaker software we developed, cDDMs become an enabling tool for the quasi real time monitoring of experiments and a potential improvement for a large number of biomedical studies.
SiamMFC: Visual Object Tracking Based on Mainfold Full Convolution Siamese Network
Authors: Chen Jia , Wang Fan , Zhang Yingjie , Ai Yibo , Zhang Weidong
Published: September 2021

Visual tracking task is divided into classification and regression tasks, and manifold features are introduced to improve the performance of the tracker. Although the previous anchor-based tracker has achieved superior tracking performance, the anchor-based tracker not only needs to set parameters manually but also ignores the influence of the geometric characteristics of the object on the tracker performance. In this paper, we propose a novel Siamese network framework with ResNet50 as the backbone, which is an anchor-free tracker based on manifold features. The network design is simple and easy to understand, which not only considers the influence of geometric features on the target tracking performance but also reduces the calculation of parameters and improves the target tracking performance. In the experiment, we compared our tracker with the most advanced public benchmarks and obtained a state-of-the-art performance.
Classifying Goliath Grouper (Epinephelus itajara) Behaviors from a Novel, Multi-Sensor Tag
Authors: Brewster Lauran R. , Ibrahim Ali K. , DeGroot Breanna C. , Ostendorf Thomas J. , Zhuang Hanqi , Chérubin Laurent M. , Ajemian Matthew J.
Published: September 2021

Inertial measurement unit sensors (IMU; i.e., accelerometer, gyroscope and magnetometer combinations) are frequently fitted to animals to better understand their activity patterns and energy expenditure. Capable of recording hundreds of data points a second, these sensors can quickly produce large datasets that require methods to automate behavioral classification. Here, we describe behaviors derived from a custom-built multi-sensor bio-logging tag attached to Atlantic Goliath grouper (Epinephelus itajara) within a simulated ecosystem. We then compared the performance of two commonly applied machine learning approaches (random forest and support vector machine) to a deep learning approach (convolutional neural network, or CNN) for classifying IMU data from this tag. CNNs are frequently used to recognize activities from IMU data obtained from humans but are less commonly considered for other animals. Thirteen behavioral classes were identified during ethogram development, nine of which were classified. For the conventional machine learning approaches, 187 summary statistics were extracted from the data, including time and frequency domain features. The CNN was fed absolute values obtained from fast Fourier transformations of the raw tri-axial accelerometer, gyroscope and magnetometer channels, with a frequency resolution of 512 data points. Five metrics were used to assess classifier performance; the deep learning approach performed better across all metrics (Sensitivity = 0.962; Specificity = 0.996; F1-score = 0.962; Matthew’s Correlation Coefficient = 0.959; Cohen’s Kappa = 0.833) than both conventional machine learning approaches. Generally, the random forest performed better than the support vector machine. In some instances, a conventional learning approach yielded a higher performance metric for particular classes (e.g., the random forest had a F1-score of 0.971 for backward swimming compared to 0.955 for the CNN). Deep learning approaches could potentially improve behavioral classification from IMU data, beyond that obtained from conventional machine learning methods.
Recognition of Fine-Grained Walking Patterns Using a Smartwatch with Deep Attentive Neural Networks
Authors: Kim Hyejoo , Kim Hyeon-Joo , Park Jinyoon , Ryu Jeh-Kwang , Kim Seung-Chan
Published: September 2021

Generally, people do various things while walking. For example, people frequently walk while looking at their smartphones. Sometimes we walk differently than usual; for example, when walking on ice or snow, we tend to waddle. Understanding walking patterns could provide users with contextual information tailored to the current situation. To formulate this as a machine-learning problem, we defined 18 different everyday walking styles. Noting that walking strategies significantly affect the spatiotemporal features of hand motions, e.g., the speed and intensity of the swinging arm, we propose a smartwatch-based wearable system that can recognize these predefined walking styles. We developed a wearable system, suitable for use with a commercial smartwatch, that can capture hand motions in the form of multivariate timeseries (MTS) signals. Then, we employed a set of machine learning algorithms, including feature-based and recent deep learning algorithms, to learn the MTS data in a supervised fashion. Experimental results demonstrated that, with recent deep learning algorithms, the proposed approach successfully recognized a variety of walking patterns, using the smartwatch measurements. We analyzed the results with recent attention-based recurrent neural networks to understand the relative contributions of the MTS signals in the classification process.
Refined Node Energy Consumption Modeling in a LoRaWAN Network
Authors: Maudet Sébastien , Andrieux Guillaume , Chevillon Romain , Diouris Jean-François
Published: September 2021

LPWAN technologies such as LoRa are widely used for the deployment of IoT applications, in particular for use cases requiring wide coverage and low energy consumption. To minimize the maintenance cost, which can become significant when the number of sensors deployed is large, it is essential to optimize the lifetime of nodes, which remains an important research topic. For this reason, it is necessary that it is based on a fine energy consumption model. Unfortunately, many existing consumption models do not take into account the specifications of the LoRaWAN protocol. In this paper, a refined energy consumption model based on in-situ measurements is provided for a LoRaWAN node. This improved model takes into account the number of nodes in the network, the collision probability that depends on the density of sensors, and the number of retransmissions. Results show the influence of the number of nodes in a LoRaWAN network on the energy consumption of a node and demonstrate that the number of sensors that can be integrated into a LoRaWAN network is limited due to the probability of collision.
A Scalable Framework for Map Matching Based Cooperative Localization
Authors: Yang Chizhao , Strader Jared , Gu Yu
Published: September 2021

Localization based on scalar field map matching (e.g., using gravity anomaly, magnetic anomaly, topographics, or olfaction maps) is a potential solution for navigating in Global Navigation Satellite System (GNSS)-denied environments. In this paper, a scalable framework is presented for cooperatively localizing a group of agents based on map matching given a prior map modeling the scalar field. In order to satisfy the communication constraints, each agent in the group is assigned to different subgroups. A locally centralized cooperative localization method is performed in each subgroup to estimate the poses and covariances of all agents inside the subgroup. Each agent in the group, at the same time, could belong to multiple subgroups, which means multiple pose and covariance estimates from different subgroups exist for each agent. The improved pose estimate for each agent at each time step is then solved through an information fusion algorithm. The proposed algorithm is evaluated with two different types of scalar field based simulations. The simulation results show that the proposed algorithm is able to deal with large group sizes (e.g., 128 agents), achieve 10-m level localization performance with 180 km traveling distance, while under restrictive communication constraints.
A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States
Authors: Pinto-Bernal Maria J. , Cifuentes Carlos A. , Perdomo Oscar , Rincón-Roncancio Monica , Múnera Marcela
Published: September 2021

Physical exercise contributes to the success of rehabilitation programs and rehabilitation processes assisted through social robots. However, the amount and intensity of exercise needed to obtain positive results are unknown. Several considerations must be kept in mind for its implementation in rehabilitation, as monitoring of patients’ intensity, which is essential to avoid extreme fatigue conditions, may cause physical and physiological complications. The use of machine learning models has been implemented in fatigue management, but is limited in practice due to the lack of understanding of how an individual’s performance deteriorates with fatigue; this can vary based on physical exercise, environment, and the individual’s characteristics. As a first step, this paper lays the foundation for a data analytic approach to managing fatigue in walking tasks. The proposed framework establishes the criteria for a feature and machine learning algorithm selection for fatigue management, classifying four fatigue diagnoses states. Based on the proposed framework and the classifier implemented, the random forest model presented the best performance with an average accuracy of ≥98% and F-score of ≥93%. This model was comprised of ≤16 features. In addition, the prediction performance was analyzed by limiting the sensors used from four IMUs to two or even one IMU with an overall performance of ≥88%.
Graph Convolutional Network: Traffic Speed Prediction Fused with Traffic Flow Data
Authors: Liu Duanyang , Xu Xinbo , Xu Wei , Zhu Bingqian
Published: September 2021

Traffic speed prediction plays an important role in intelligent transportation systems, and many approaches have been proposed over recent decades. In recent years, methods using graph convolutional networks (GCNs) have been more promising, which can extract the spatiality of traffic networks and achieve a better prediction performance than others. However, these methods only use inaccurate historical data of traffic speed to forecast, which decreases the prediction accuracy to a certain degree. Moreover, they ignore the influence of dynamic traffic on spatial relationships and merely consider the static spatial dependency. In this paper, we present a novel graph convolutional network model called FSTGCN to solve these problems, where the model adopts the full convolutional structure and avoids repeated iterations. Specifically, because traffic flow has a mapping relationship with traffic speed and its values are more exact, we fused historical traffic flow data into the forecasting model in order to reduce the prediction error. Meanwhile, we analyzed the covariance relationship of the traffic flow between road segments and designed the dynamic adjacency matrix, which can capture the dynamic spatial correlation of the traffic network. Lastly, we conducted experiments on two real-world datasets and prove that our model can outperform state-of-the-art traffic speed prediction.
Measurement of Axial Strain of Geogrid by Optical Sensors
Authors: Drusa Marian , Kais Ladislav , Dubovan Jozef , Markovic Miroslav , Bahleda Frantisek , Mecar Martin
Published: September 2021

In recent years, the technology of optical fibers has rapidly gained ground in many areas of science and industry, including the construction industry. In this article, the technology of optical fibers based on a fiber Bragg grating (FBG) was used to determine tensile forces acting in a basal reinforcement of a scaled down physical model, which included piled embankment and basal reinforcement. Installing FBG sensors on the geogrid made monitoring of axial strains possible, thus allowing determination of the behavior of the basal reinforcement of the piled embankment. On the basis of three tests performed on the physical model, numerical model calibration with the physical model was carried out using the software PLAXIS 3D Tunnel 2.4. The results showed accurate predictions, especially for the low and middle part of the measured deformations where the numerical analysis proposed a solution that can be considered as safe. Installing FBG sensors on biaxial geogrids was a bold idea that was not easy to implement. However, other possibilities have been successfully tested, such as high-frequency measurements of the response of reinforced soil structure under dynamic loading.
Reconfigurable Wireless Sensor Node Remote Laboratory Platform with Cloud Connectivity
Authors: Chamunorwa Tinashe , Modran Horia Alexandru , Ursuțiu Doru , Samoilă Cornel , Hedeșiu Horia
Published: September 2021

Thanks to the recent rapid technological advancement in IoT usage, there is a need for students to learn IoT-based concepts using a dedicated experimental platform. Furthermore, being forced into remote learning due to the ongoing COVID-19 pandemic, there is an urgent need for innovative learning methods. From our perspective, a learning platform should be reconfigurable to accommodate multiple applications and remotely accessible at any time, from anywhere, and on any connected device. Considering that many of the university courses are now held online, the reliability and scalability of the system become critical. This paper presents the design and development of a wireless configurable myRIO-based sensor node that connects to SystemLink Cloud. The sensors that were used are for ambient light, temperature, and proximity. A graphical programming environment (G-LabVIEW) and related APIs were used for rapid concept-to-development process. Distinct applications have been developed for the instructor and students, respectively. The students can select which sensor and application to run on the system and observe the measurements on the local student’s application or the cloud platform at a specific moment. They can also read the data on the cloud platform and use them in their LabVIEW application. In the context of remote education, we strongly believe that this platform is and will be suitable for the COVID and Post-COVID eras as well because it creates a much better remote laboratory experience for students. In conclusion, the system that was developed is innovative because it is software reconfigurable from the device, from the instructor’s application and cloud via a web browser, it is intuitive, and it has a user-friendly interface. It meets most of the necessary requirements in the current era, being also highly available and scalable in the cloud.
Comparing Methods of Feature Extraction of Brain Activities for Octave Illusion Classification Using Machine Learning
Authors: Pilyugina Nina , Tsukahara Akihiko , Tanaka Keita
Published: September 2021

The aim of this study was to find an efficient method to determine features that characterize octave illusion data. Specifically, this study compared the efficiency of several automatic feature selection methods for automatic feature extraction of the auditory steady-state responses (ASSR) data in brain activities to distinguish auditory octave illusion and nonillusion groups by the difference in ASSR amplitudes using machine learning. We compared univariate selection, recursive feature elimination, principal component analysis, and feature importance by testifying the results of feature selection methods by using several machine learning algorithms: linear regression, random forest, and support vector machine. The univariate selection with the SVM as the classification method showed the highest accuracy result, 75%, compared to 66.6% without using feature selection. The received results will be used for future work on the explanation of the mechanism behind the octave illusion phenomenon and creating an algorithm for automatic octave illusion classification.
Rainfall Intensity and Quantity Estimation Method Based on Gamma-Dose Rate Monitoring
Authors: Yakovleva Valentina , Yakovlev Grigorii , Parovik Roman , Zelinskiy Aleksey , Kobzev Aleksey
Published: September 2021

The features of the atmospheric γ-background reaction to liquid atmospheric precipitation in the form of bursts is investigated, and various forms of them are analyzed. A method is described for interpreting forms of the measured γ-background response with the determination of the beginning and ending time of precipitation, the distinctive features of changes in the intensity of precipitation and the number of single (separate) events that form one burst. It is revealed that a change in the intensity of precipitation in one event leads to a change in the γ-radiation dose rate increase speed (time derivative). A method of estimating the average value of the intensity and amount of precipitation for one event, reconstructing the intensity spectrum from experimental data on the dynamics of the measured dose rate of γ-radiation, is developed. The method takes into account the radioactive decay of radon daughter products in the atmosphere and on the soil surface during precipitation, as well as the purification of the atmosphere from radionuclides. Recommendations are given for using the developed method to correct for changes (daily variations) in radon flux density from the ground surface, which lead to variations in radon in the atmosphere. Experimental verification of the method shows good agreement between the values of the intensity of liquid atmospheric precipitation, calculated and measured with the help of shuttle and optical rain precipitation gauges.
Validation of the Apple Watch for Estimating Moderate-to-Vigorous Physical Activity and Activity Energy Expenditure in School-Aged Children
Authors: Kwon Sunku , Kim Youngwon , Bai Yang , Burns Ryan D. , Brusseau Timothy A. , Byun Wonwoo
Published: September 2021

The Apple Watch is one of the most popular wearable devices designed to monitor physical activity (PA). However, it is currently unknown whether the Apple Watch accurately estimates children’s free-living PA. Therefore, this study assessed the concurrent validity of the Apple Watch 3 in estimating moderate-to-vigorous physical activity (MVPA) time and active energy expenditure (AEE) for school-aged children under a simulated and a free-living condition. Twenty elementary school students (Girls: 45%, age: 9.7 ± 2.0 years) wore an Apple Watch 3 device on their wrist and performed prescribed free-living activities in a lab setting. A subgroup of participants (N = 5) wore the Apple Watch for seven consecutive days in order to assess the validity in free-living condition. The K5 indirect calorimetry (K5) and GT3X+ were used as the criterion measure under simulated free-living and free-living conditions, respectively. Mean absolute percent errors (MAPE) and Bland-Altman (BA) plots were conducted to assess the validity of the Apple Watch 3 compared to those from the criterion measures. Equivalence testing determined the statistical equivalence between the Apple Watch and K5 for MVPA time and AEE. The Apple Watch provided comparable estimates for MVPA time (mean bias: 0.3 min, p = 0.91, MAPE: 1%) and for AEE (mean bias: 3.8 kcal min, p = 0.75, MAPE: 4%) during the simulated free-living condition. The BA plots indicated no systematic bias for the agreement in MVPA and AEE estimates between the K5 and Apple Watch 3. However, the Apple Watch had a relatively large variability in estimating AEE in children. The Apple Watch was statistically equivalent to the K5 within ±17.7% and ±20.8% for MVPA time and AEE estimates, respectively. Our findings suggest that the Apple Watch 3 has the potential to be used as a PA assessment tool to estimate MVPA in school-aged children.
PINC: Pickup Non-Critical Node Based k-Connectivity Restoration in Wireless Sensor Networks
Authors: Khalilpour Akram Vahid , Akusta Dagdeviren Zuleyha , Dagdeviren Orhan , Challenger Moharram
Published: September 2021

A Wireless Sensor Network (WSN) is connected if a communication path exists among each pair of sensor nodes (motes). Maintaining reliable connectivity in WSNs is a complicated task, since any failure in the nodes can cause the data transmission paths to break. In a k-connected WSN, the connectivity survives after failure in any k-1 nodes; hence, preserving the k-connectivity ensures that the WSN can permit k-1 node failures without wasting the connectivity. Higher k values will increase the reliability of a WSN against node failures. We propose a simple and efficient algorithm (PINC) to accomplish movement-based k-connectivity restoration that divides the nodes into the critical, which are the nodes whose failure reduces k, and non-critical groups. The PINC algorithm pickups and moves the non-critical nodes when a critical node stops working. This algorithm moves a non-critical node with minimum movement cost to the position of the failed mote. The measurements obtained from the testbed of real IRIS motes and Kobuki robots, along with extensive simulations, revealed that the PINC restores the k-connectivity by generating optimum movements faster than its competitors.
BCI-Based Control for Ankle Exoskeleton T-FLEX: Comparison of Visual and Haptic Stimuli with Stroke Survivors
Authors: Barria Patricio , Pino Angie , Tovar Nicolás , Gomez-Vargas Daniel , Baleta Karim , Díaz Camilo A. R. , Múnera Marcela , Cifuentes Carlos A.
Published: September 2021

Brain–computer interface (BCI) remains an emerging tool that seeks to improve the patient interaction with the therapeutic mechanisms and to generate neuroplasticity progressively through neuromotor abilities. Motor imagery (MI) analysis is the most used paradigm based on the motor cortex’s electrical activity to detect movement intention. It has been shown that motor imagery mental practice with movement-associated stimuli may offer an effective strategy to facilitate motor recovery in brain injury patients. In this sense, this study aims to present the BCI associated with visual and haptic stimuli to facilitate MI generation and control the T-FLEX ankle exoskeleton. To achieve this, five post-stroke patients (55–63 years) were subjected to three different strategies using T-FLEX: stationary therapy (ST) without motor imagination, motor imagination with visual stimulation (MIV), and motor imagination with visual-haptic inducement (MIVH). The quantitative characterization of both BCI stimuli strategies was made through the motor imagery accuracy rate, the electroencephalographic (EEG) analysis during the MI active periods, the statistical analysis, and a subjective patient’s perception. The preliminary results demonstrated the viability of the BCI-controlled ankle exoskeleton system with the beta rebound, in terms of patient’s performance during MI active periods and satisfaction outcomes. Accuracy differences employing haptic stimulus were detected with an average of 68% compared with the 50.7% over only visual stimulus. However, the power spectral density (PSD) did not present changes in prominent activation of the MI band but presented significant variations in terms of laterality. In this way, visual and haptic stimuli improved the subject’s MI accuracy but did not generate differential brain activity over the affected hemisphere. Hence, long-term sessions with a more extensive sample and a more robust algorithm should be carried out to evaluate the impact of the proposed system on neuronal and motor evolution after stroke.
Real-Time FPGA Accelerated Stereo Matching for Temporal Statistical Pattern Projector Systems
Authors: Brus Zan , Kos Marko , Erker Matic , Kramberger Iztok
Published: September 2021

The presented paper describes a hardware-accelerated field programmable gate array (FPGA)–based solution capable of real-time stereo matching for temporal statistical pattern projector systems. Modern 3D measurement systems have seen an increased use of temporal statistical pattern projectors as their active illumination source. The use of temporal statistical patterns in stereo vision systems includes the advantage of not requiring information about pattern characteristics, enabling a simplified projector design. Stereo-matching algorithms used in such systems rely on the locally unique temporal changes in brightness to establish a pixel correspondence between the stereo image pair. Finding the temporal correspondence between individual pixels in temporal image pairs is computationally expensive, requiring GPU-based solutions to achieve real-time calculation. By leveraging a high-level synthesis approach, matching cost simplification, and FPGA-specific design optimizations, an energy-efficient, high throughput stereo-matching solution was developed. The design is capable of calculating disparity images on a 1024 × 1024(@291 FPS) input image pair stream at 8.1 W on an embedded FPGA platform (ZC706). Several different design configurations were tested, evaluating device utilization, throughput, power consumption, and performance-per-watt. The average performance-per-watt of the FPGA solution was two times higher than in a GPU-based solution.
Patterns for Visual Management in Industry 4.0
Authors: Fenza Giuseppe , Loia Vincenzo , Nota Giancarlo
Published: September 2021

The technologies of Industry 4.0 provide an opportunity to improve the effectiveness of Visual Management in manufacturing. The opportunity of improvement is twofold. From one side, Visual Management theory and practice can inspire the design of new software tools suitable for Industry 4.0; on the other side, the technology of Industry 4.0 can be used to increase the effectiveness of visual software tools. The paper first explores how the theoretical result on Visual Management can be used as a guideline to improve human-computer interaction, then a methodology is proposed for the design of visual patterns for manufacturing. Four visual patterns are presented that contribute to the solution of problems frequently encountered in discrete manufacturing industries; these patterns help to solve planning and control problems thus providing support to various management functions. Positive implications of this research concern people engagement and empowerment as well as improved problem solving, decision-making and management of manufacturing processes.
Conceptual Design and Image Motion Compensation Rate Analysis of Two-Axis Fast Steering Mirror for Dynamic Scan and Stare Imaging System
Authors: Sun Jianjun , Ding Yalin , Zhang Hongwen , Yuan Guoqin , Zheng Yuquan
Published: September 2021

In order to enable the aerial photoelectric equipment to realize wide-area reconnaissance and target surveillance at the same time, a dual-band dynamic scan and stare imaging system is proposed in this paper. The imaging system performs scanning and pointing through a two-axis gimbal, compensating the image motion caused by the aircraft and gimbal angular velocity and the aircraft liner velocity using two two-axis fast steering mirrors (FSMs). The composition and working principle of the dynamic scan and stare imaging system, the detailed scheme of the two-axis FSM and the image motion compensation (IMC) algorithm are introduced. Both the structure and the mirror of the FSM adopt aluminum alloys, and the flexible support structure is designed based on four cross-axis flexural hinges. The Root-Mean-Square (RMS) error of the mirror reaches 15.8 nm and the total weight of the FSM assembly is 510 g. The IMC rate equations of the two-axis FSM are established based on the coordinate transformation method. The effectiveness of the FSM and IMC algorithm is verified by the dynamic imaging test in the laboratory and flight test.