bluuebunny/crossref_metadata_2025 huggingface dataset:
Smuggled Hinduism—From Dōgen’s Viewpoint
Authors: Hayakawa Atsushi (Shōken)
Published: December 2022

The central question of this paper is what kind of view Dōgen had about Mazu. At first glance, this may seem completely irrelevant to the theme of this issue. In fact, however, Dōgen’s view points to a subtle relationship between Buddhism and Hinduism in an interesting way. Dōgen seems to regard Mazu as an ambiguous figure, standing on the borderline between Buddhism and Hinduism. However, Dōgen’s intention was to save Mazu and keep him on the side of Buddhism. So how can Mazu be saved? To answer this question is to trace the fundamental boundary between Buddhism and Hinduism according to the outstanding Zen master. In this study we adopt the usual method of textual analysis. Our discussion proceeds in the following order. (The steps do not correspond exactly to the section breaks.) (1) First, the argument of a person called Senni is presented from Dōgen’s Bendōwa, where Dōgen severely criticizes him as a non-Buddhist heresy. At this step we will confirm that Senni is a Sāṅkhya theorist (hence, a Hinduist). (2) We take up a parallel to the above passage from Dōgen’s Shōbōgenzō, Chapter “Sokushinzebutsu”. It becomes clear that the true target of Dōgen’s criticism was Mazu, the great Chinese Chan master. (3) The above operation shows that Dōgen was trying to position Mazu as someone on the borderline between Hinduism and Buddhism. (4) We try to reconstruct from the text what in Senni angered Dōgen, or, in other words, from what he wanted to save Mazu. As a result, the borderline as seen by Dōgen will be visible to us. The main findings of this paper are as follows: (1) The mark that distinguishes Buddhism from Hinduism, according to Dōgen, is the presence of the never-ending Bodhi-mind. This is in fact what TSUNODA Tairyū suggested in his 1985 article. Dōgen implemented this idea as an endless loop of Bodhi-mind, which makes the goal unreachable. (2) The implicit object of Dōgen’s criticism is not the Japanese Tendai or the Darumashū, but Mazu, as HE Yansheng indicated in his 2000 book. The so-called Critical Buddhism movement began on the basis of a misunderstanding. The large amount of secondary literature that has resulted is also indirectly based on this error.
Economic Value Added Research: Mapping Thematic Structure and Research Trends
Authors: Tripathi Prasoon Mani , Chotia Varun , Solanki Umesh , Meena Rahul , Khandelwal Vinay
Published: December 2022

The purpose of this article is to examine the academic literature about the function, structure, calculation, and weaknesses of economic value added (EVA). EVA has been used as a measure of economic profit and captures the inadequacies of using traditional rates of return. In addition, this article tackles the main residual earnings (RI) modifications used to calculate EVA. A keyword search for publications was conducted in early 2022. This study couples an inferential analysis with descriptive analyses of the existing research. The articles were sorted into different clusters based on bibliographic coupling analysis. This study identifies the main areas and current dynamics of EVA research while evaluating the quality and impact of the scientific output. Three broad themes emerged from the analysis of the cluster related to the use and application of EVA: residual income and valuation, financial performance, and performance management. In doing so, we hope to enhance the understanding and contributions of EVA research to advance its theory and practice.
Mapping and Predicting Land Cover Changes of Small and Medium Size Cities in Alabama Using Machine Learning Techniques
Authors: Shrestha Megha , Mitra Chandana , Rahman Mahjabin , Marzen Luke
Published: December 2022

In the southeastern US, Atlanta is always the focus of attention, despite the rapid expansion of small and medium-sized cities (SMSCs) in the region. Clearly, larger cities have more people, resulting in more loss during disasters. However, SMSCs also face natural calamities and must be made robust and sustainable. Keeping this in mind, this study chooses to focus on ten SMSCs in Alabama (Population > 40,000) which have encountered at least a 6% increase in population size between 1990 and 2020, out of which two large cities (Population > 180,000) which experienced loss during the same time. This paper examines the change in urban built-up area between 1990 and 2020 using the random forest algorithm in Google Earth Engine (GEE) and estimates future 2050 urban expansion scenarios using the Cellular Automata (CA) Markov model in TerrSet’s Land Change Modeler (LCM). The results revealed urban built-up areas grew rapidly from 1990 to 2020, with some cities doubling or tripling in size due to population growth. The future growth model predicted growth for most cities and urban expansion along transportation networks. The outcome of this research showcases the importance of proper planning and building sustainably in SMSCs for future natural disaster events.
Research on Parameter Optimization Method of Sliding Mode Controller for the Grid-Connected Composite Device Based on IMFO Algorithm
Authors: Sun Ji , Liu Jiajun , Miao Miao , Lin Haokun
Published: December 2022

In order to make the grid-connected composite device (GCCD) controller meet the requirements of different operating modes and complex working conditions of power grid, this paper proposes to introduce sliding mode control (SMC) into GCCD controller. Firstly, the mathematical model of MMC converter is established, and the sliding mode controller is designed based on the SMC principle. Then, aiming at the problems of complex controller structure and difficult parameter tuning in multiple modes of the GCCD, this paper proposes a controller parameter optimization method based on improved Month Flame optimization (IMFO) algorithm. This method improves the MFO algorithm by introducing good point set (GPS) initialization and Levy flight strategy, which accelerates the convergence speed of the algorithm while avoiding falling into local optimization, and realizes the optimization of converter controller parameters. Under a variety of standard test functions, the advantages of the proposed IMFO algorithm are verified by comparing it with the traditional algorithm. Finally, in order to realize the automatic tuning of control parameters, the Python–PSCAD joint simulation method is studied and implemented. Taking the comprehensive integral of time and absolute error (CITAE) index as the objective function, the parameters of the sliding mode controller are optimized. The simulation results show that the controller parameters optimized by the IMFO algorithm can make the GCCD have better dynamic performance.
Research on Indoor 3D Positioning Algorithm Based on WiFi Fingerprint
Authors: Wang Lixing , Shang Shuang , Wu Zhenning
Published: December 2022

Indoor 3D positioning is useful in multistory buildings, such as shopping malls, libraries, and airports. This study focuses on indoor 3D positioning using wireless access points (AP) in an environment without adding additional hardware facilities in large-scale complex places. The integration of a deep learning algorithm into indoor 3D positioning is studied, and a 3D dynamic positioning model based on temporal fingerprints is proposed. In contrast to the traditional positioning models with a single input, the proposed method uses a sliding time window to build a temporal fingerprint chip as the input of the positioning model to provide abundant information for positioning. Temporal information can be used to distinguish locations with similar fingerprint vectors and to improve the accuracy and robustness of positioning. Moreover, deep learning has been applied for the automatic extraction of spatiotemporal features. A temporal convolutional network (TCN) feature extractor is proposed in this paper, which adopts a causal convolution mechanism, dilated convolution mechanism, and residual connection mechanism and is not limited by the size of the convolution kernel. It is capable of learning hidden information and spatiotemporal relationships from the input features and the extracted spatiotemporal features are connected with a deep neural network (DNN) regressor to fit the complex nonlinear mapping relationship between the features and position coordinates to estimate the 3D position coordinates of the target. Finally, an open-source public dataset was used to verify the performance of the localization algorithm. Experimental results demonstrated the effectiveness of the proposed positioning model and a comparison between the proposed model and existing models proved that the proposed model can provide more accurate three-dimensional position coordinates.
Prototype of Instrumented Rock Bolt for Continuous Monitoring of Roof Fall Hazard in Deep Underground Mines
Authors: Fuławka Krzysztof , Pytel Witold , Szumny Marcin , Mertuszka Piotr , Pałac-Walko Bogumiła , Hartlieb Philipp , Jakić Michel , Nöger Michael
Published: December 2022

Roof falls are currently one of the most dangerous threats associated with underground mining at great depth. Every occurrence of such an event poses a significant risk to the mining crew and disturbs the continuity of the mining process, which clearly affects the economy of the exploitation process. The development of a reliable monitoring system may significantly reduce the impact of eventual roof failure and will have a positive effect on the sustainability of the extraction process. Within this research study, a prototype of an instrumented rock bolt developed for continuous stress measurement is presented. The procedure of a 4-groove multilevel instrumented rock bolt is described and the calibration process is shown. Then, preliminary results of long-term in situ monitoring are presented. Based on the continuous monitoring of stress distribution within immediate roof strata, it was concluded that the developed instrumented rock bolt provides reliable results and is a very useful device, ensuring the possibility of early warning for miners about increasing roof fall risk.
Multimodal Warnings Design for In-Vehicle Robots under Driving Safety Scenarios
Authors: Wang Jianmin , Wang Chengji , Liu Yujia , Yue Tianyang , Wang Yuxi , You Fang
Published: December 2022

In case of dangerous driving, the in-vehicle robot can provide multimodal warnings to help the driver correct the wrong operation, so the impact of the warning signal itself on driving safety needs to be reduced. This study investigates the design of multimodal warnings for in-vehicle robots under driving safety warning scenarios. Based on transparency theory, this study addressed the content and timing of visual and auditory modality warning outputs and discussed the effects of different robot speech and facial expressions on driving safety. Two rounds of experiments were conducted on a driving simulator to collect vehicle data, subjective data, and behavioral data. The results showed that driving safety and workload were optimal when the robot was designed to use negative expressions for the visual modality during the comprehension (SAT 2) phase and speech at a rate of 345 words/minute for the auditory modality during the comprehension (SAT 2) and prediction (SAT 3) phases. The design guideline obtained from the study provides a reference for the interaction design of driver assistance systems with robots as the interface.
Generation of Correction Data for Autonomous Driving by Means of Machine Learning and On-Board Diagnostics
Authors: Flores Fernández Alberto , Sánchez Morales Eduardo , Botsch Michael , Facchi Christian , García Higuera Andrés
Published: December 2022

A highly accurate reference vehicle state is a requisite for the evaluation and validation of Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADASs). This highly accurate vehicle state is usually obtained by means of Inertial Navigation Systems (INSs) that obtain position, velocity, and Course Over Ground (COG) correction data from Satellite Navigation (SatNav). However, SatNav is not always available, as is the case of roofed places, such as parking structures, tunnels, or urban canyons. This leads to a degradation over time of the estimated vehicle state. In the present paper, a methodology is proposed that consists on the use of a Machine Learning (ML)-method (Transformer Neural Network—TNN) with the objective of generating highly accurate velocity correction data from On-Board Diagnostics (OBD) data. The TNN obtains OBD data as input and measurements from state-of-the-art reference sensors as a learning target. The results show that the TNN is able to infer the velocity over ground with a Mean Absolute Error (MAE) of 0.167 kmh (0.046 ms) when a database of 3,428,099 OBD measurements is considered. The accuracy decreases to 0.863 kmh (0.24 ms) when only 5000 OBD measurements are used. Given that the obtained accuracy closely resembles that of state-of-the-art reference sensors, it allows INSs to be provided with accurate velocity correction data. An inference time of less than 40 ms for the generation of new correction data is achieved, which suggests the possibility of online implementation. This supports a highly accurate estimation of the vehicle state for the evaluation and validation of AD and ADAS, even in SatNav-deprived environments.
Nanosphere Structures Using Various Materials: A Strategy for Signal Amplification for Virus Sensing
Authors: Muttaqien Sjaikhurrizal El , Khoris Indra Memdi , Pambudi Sabar , Park Enoch Y.
Published: December 2022

Nanomaterials have been explored in the sensing research field in the last decades. Mainly, 3D nanomaterials have played a vital role in advancing biomedical applications, and less attention was given to their application in the field of biosensors for pathogenic virus detection. The versatility and tunability of a wide range of nanomaterials contributed to the development of a rapid, portable biosensor platform. In this review, we discuss 3D nanospheres, one of the classes of nanostructured materials with a homogeneous and dense matrix wherein a guest substance is carried within the matrix or on its surface. This review is segmented based on the type of nanosphere and their elaborative application in various sensing techniques. We emphasize the concept of signal amplification strategies using different nanosphere structures constructed from a polymer, carbon, silica, and metal–organic framework (MOF) for rendering high-level sensitivity of virus detection. We also briefly elaborate on some challenges related to the further development of nanosphere-based biosensors, including the toxicity issue of the used nanomaterial and the commercialization hurdle.
Assessment of the Electromagnetic Radiation Exposure at EV Charging Facilities
Authors: Bae Hongguk , Park Sangwook
Published: December 2022

As the number of electric vehicles (EV) increases, the number of EV chargers also increases. Charging infrastructure will be built into our close environment. Because of this, the assessment of the electromagnetic field exposure generated from the charger is an important issue. This paper valuates the electromagnetic field exposure of six EV chargers. To assess the level of exposure of EV chargers, the electromagnetic fields from six chargers were measured and analyzed. In addition, measured electromagnetic field exposure levels were evaluated against ICNIRP guidelines. Higher electromagnetic fields were measured with standard chargers than with fast chargers. For the fast charger in the charging state, the magnetic field increased with the charging current. Electromagnetic field exposures for all six chargers did not exceed standard limits. The results of the assessment of the electromagnetic field exposure of the six EV chargers will contribute to the establishment of standards for the evaluation of the electromagnetic field exposure of the EV chargers in the future.
High-Discrimination Circular Polarization Detection Based on Dielectric-Metal-Hybrid Chiral Metamirror Integrated Quantum Well Infrared Photodetectors
Authors: Shen Jinyong , Zhu Tianyun , Zhou Jing , Chu Zeshi , Ren Xiansong , Deng Jie , Dai Xu , Li Fangzhe , Wang Bo , Chen Xiaoshuang , Lu Wei
Published: December 2022

Circular polarization detection enables a wide range of applications. With the miniaturization of optoelectronic systems, integrated circular polarization detectors with native sensitivity to the spin state of light have become highly sought after. The key issues with this type of device are its low circular polarization extinction ratios (CPERs) and reduced responsivities. Metallic two-dimensional chiral metamaterials have been integrated with detection materials for filterless circular polarization detection. However, the CPERs of such devices are typically below five, and the light absorption in the detection materials is hardly enhanced and is even sometimes reduced. Here, we propose to sandwich multiple quantum wells between a dielectric two-dimensional chiral metamaterial and a metal grating to obtain both a high CPER and a photoresponse enhancement. The dielectric-metal-hybrid chiral metamirror integrated quantum well infrared photodetector (QWIP) exhibits a CPER as high as 100 in the long wave infrared range, exceeding all reported CPERs for integrated circular polarization detectors. The absorption efficiency of this device reaches 54%, which is 17 times higher than that of a standard 45° edge facet coupled device. The circular polarization discrimination is attributed to the interference between the principle-polarization radiation and the cross-polarization radiation of the chiral structure during multiple reflections and the structure-material double polarization selection. The enhanced absorption efficiency is due to the excitation of a surface plasmon polariton wave. The dielectric-metal-hybrid chiral mirror structure is compatible with QWIP focal plane arrays.
A Deep Learning Based Data Recovery Approach for Missing and Erroneous Data of IoT Nodes
Authors: Vedavalli Perigisetty , Ch Deepak
Published: December 2022

Internet of things (IoT) nodes are deployed in large-scale automated monitoring applications to capture the massive amount of data from various locations in a time-series manner. The captured data are affected due to several factors such as device malfunctioning, unstable communication, environmental factors, synchronization problem, and unreliable nodes, which results in data inconsistency. Data recovery approaches are one of the best solutions to reduce data inconsistency. This research provides a missing data recovery approach based on spatial-temporal (ST) correlation between the IoT nodes in the network. The proposed approach has a clustering phase (CL) and a data recovery (DR) phase. In the CL phase, the nodes can be clustered based on their spatial and temporal relationship, and common neighbors are extracted. In the DR phase, missing data can be recovered with the help of neighbor nodes using the ST-hierarchical long short-term memory (ST-HLSTM) algorithm. The proposed algorithm has been verified on real-world IoT-based hydraulic test rig data sets which are gathered from things speak real-time cloud platform. The algorithm shows approximately 98.5% reliability as compared with the other existing algorithms due to its spatial-temporal features based on deep neural network architecture.
Train–Track–Bridge Dynamic Interaction on a Bowstring-Arch Railway Bridge: Advanced Modeling and Experimental Validation
Authors: Ribeiro Diogo , Calçada Rui , Brehm Maik , Zabel Volkmar
Published: December 2022

This article describes the validation of a 3D dynamic interaction model of the train–track–bridge system on a bowstring-arch railway bridge based on experimental tests. The train, track, and bridge subsystems were modeled on the basis of large-scale and highly complex finite elements models previously calibrated on the basis of experimental modal parameters. The train–bridge dynamic interaction problem, in the vertical direction, was efficiently solved using a dedicated computational application (TBI software). This software resorts to an uncoupled methodology that considers the two subsystems, bridge and train, as two independent structures and uses an iterative procedure to guarantee the compatibility of the forces and displacements at the contact points at each timestep. The bridge subsystem is solved by the mode superposition method, while the train subsystem is solved by a direct integration method. The track irregularities were included in the dynamic problem based on real measurements performed by a track inspection vehicle. A dynamic test under traffic actions allowed measuring the responses in the bridge, track, and vehicles, which were synchronized by GPS systems. The test results demonstrated the occurrence of upward displacements on the deck, which is a characteristic of structures with an arch structural behavior, as well as an alternation of tensile/compressive stresses between the rail and deck due to the deck–track composite effect. Furthermore, the acceleration response of the bridge proved to be significantly influenced by the train operating speed. The validation procedure involved comparing the dynamic responses obtained from the train–bridge interaction model, including track irregularities, and the responses obtained experimentally, through the test under traffic actions. A very good correlation was obtained between numerical and experimental results in terms of accelerations, displacements, and strains. The contributions derived from the parametric excitation of the train, the global/local dynamic behavior of the bridge, and the excitation derived from the track irregularities were decisive to accurately reproduce the complex behavior of the train–track–bridge system.
Three Dimensional Membrane Vibration Measurement Using a Two Dimensional Position Sensitive Device
Authors: Białek Rafał , Białek Kamila
Published: December 2022

This study presents the outcome of research into membrane vibrations in a 3D space performed using a system based on position sensitive device (PSD) sensors. Here, measurements were conducted for harmonic vibrations. The use of such detectors for assessing the movement of objects within a plane or space requires determining the position of more than one marker. The article reviews two methods for detecting the position of several light spots on a sensor’s photosensitive plane: a marker sequential control method, and a method based on Fast Fourier Transform (FFT) that employs a square wave control signal. The authors present an approach to improving measurement accuracy for both methods. They also discuss the advantages and disadvantages of each.
Abdominal Aortic Thrombus Segmentation in Postoperative Computed Tomography Angiography Images Using Bi-Directional Convolutional Long Short-Term Memory Architecture
Authors: Jung Younhyun , Kim Suhyeon , Kim Jihu , Hwang Byunghoon , Lee Sungmin , Kim Eun Young , Kim Jeong Ho , Hwang Hyoseok
Published: December 2022

Abdominal aortic aneurysm (AAA) is a fatal clinical condition with high mortality. Computed tomography angiography (CTA) imaging is the preferred minimally invasive modality for the long-term postoperative observation of AAA. Accurate segmentation of the thrombus region of interest (ROI) in a postoperative CTA image volume is essential for quantitative assessment and rapid clinical decision making by clinicians. Few investigators have proposed the adoption of convolutional neural networks (CNN). Although these methods demonstrated the potential of CNN architectures by automating the thrombus ROI segmentation, the segmentation performance can be further improved. The existing methods performed the segmentation process independently per 2D image and were incapable of using adjacent images, which could be useful for the robust segmentation of thrombus ROIs. In this work, we propose a thrombus ROI segmentation method to utilize not only the spatial features of a target image, but also the volumetric coherence available from adjacent images. We newly adopted a recurrent neural network, bi-directional convolutional long short-term memory (Bi-CLSTM) architecture, which can learn coherence between a sequence of data. This coherence learning capability can be useful for challenging situations, for example, when the target image exhibits inherent postoperative artifacts and noises, the inclusion of adjacent images would facilitate learning more robust features for thrombus ROI segmentation. We demonstrate the segmentation capability of our Bi-CLSTM-based method with a comparison of the existing 2D-based thrombus ROI segmentation counterpart as well as other established 2D- and 3D-based alternatives. Our comparison is based on a large-scale clinical dataset of 60 patient studies (i.e., 60 CTA image volumes). The results suggest the superior segmentation performance of our Bi–CLSTM-based method by achieving the highest scores of the evaluation metrics, e.g., our Bi-CLSTM results were 0.0331 higher on total overlap and 0.0331 lower on false negative when compared to 2D U-net++ as the second-best.
Design and Implementation of a Cloud-IoT-Based Home Energy Management System
Authors: Condon Felipe , Martínez José M. , Eltamaly Ali M. , Kim Young-Chon , Ahmed Mohamed A.
Published: December 2022

The advances in the Internet of Things (IoT) and cloud computing opened new opportunities for developing various smart grid applications and services. The rapidly increasing adoption of IoT devices has enabled the development of applications and solutions to manage energy consumption efficiently. This work presents the design and implementation of a home energy management system (HEMS), which allows collecting and storing energy consumption data from appliances and the main load of the home. Two scenarios are designed and implemented: a local HEMS isolated from the Internet and relies on its processing and storage duties using an edge device and a Cloud HEMS using AWS IoT Core to manage incoming data messages and provide data-driven services and applications. A testbed was carried out in a real house in the city of Valparaiso, Chile, over a one-year period, where four appliances were used to collect energy consumption using smart plugs, as well as collecting the main energy load of the house through a data logger acting as a smart meter. To the best of our knowledge, this is the first electrical energy dataset with a 10-second sampling rate from a real household in Valparaiso, Chile. Results show that both implementations perform the baseline tasks (collecting, storing, and controlling) for a HEMS. This work contributes by providing a detailed technical implementation of HEMS that enables researchers and engineers to develop and implement HEMS solutions to support different smart home applications.
Evaluation of Doppler Effect Error Affecting the Radio Altimeter Altitude Measurements
Authors: Češkovič Marek , Kurdel Pavol , Gecejová Natália , Labun Ján , Laššák Miroslav
Published: December 2022

The measurement of the real altitude of aircraft is usually done using an aviation radio altimeter (ALT). A radio altimeter provides crucial information about the instantaneous (radio) altitude of aircraft, helicopter, or unmanned aerial vehicle, to the pilot or another assistance system, such as an autopilot or an anti-collision system. However, this flight altitude measurement is affected by several errors, methodological errors and the operating frequency and modulation parameters instability, or the Doppler shift error. This article is focused on the evaluation of how the Doppler effect error develops during the operation of an ALT and its potential use as an information carrier concerning a possible loss of radio altitude, leading to dangerous situations. This paper briefly explains in a theoretical and practical way how this error develops and how it can affect the process of creation of height impulses. Practical experiments were conducted and evaluated in this research, and a theoretical design of a simple circuit capable of signalization of radio altitude loss presented. As the Doppler shift error was previously recognized solely as a measurement error, it could be used in a new function as a source of supplemental warning information.
Quantifying the Influence of Surface Texture and Shape on Structure from Motion 3D Reconstructions
Authors: Nielsen Mikkel Schou , Nikolov Ivan , Kruse Emil Krog , Garnæs Jørgen , Madsen Claus Brøndgaard
Published: December 2022

In general, optical methods for geometrical measurements are influenced by the surface properties of the examined object. In Structure from Motion (SfM), local variations in surface color or topography are necessary for detecting feature points for point-cloud triangulation. Thus, the level of contrast or texture is important for an accurate reconstruction. However, quantitative studies of the influence of surface texture on geometrical reconstruction are largely missing. This study tries to remedy that by investigating the influence of object texture levels on reconstruction accuracy using a set of reference artifacts. The artifacts are designed with well-defined surface geometries, and quantitative metrics are introduced to evaluate the lateral resolution, vertical geometric variation, and spatial–frequency information of the reconstructions. The influence of texture level is compared to variations in capturing range. For the SfM measurements, the ContextCapture software solution and a 50 Mpx DSLR camera are used. The findings are compared to results using calibrated optical microscopes. The results show that the proposed pipeline can be used for investigating the influence of texture on SfM reconstructions. The introduced metrics allow for a quantitative comparison of the reconstructions at varying texture levels and ranges. Both range and texture level are seen to affect the reconstructed geometries although in different ways. While an increase in range at a fixed focal length reduces the spatial resolution, an insufficient texture level causes an increased noise level and may introduce errors in the reconstruction. The artifacts are designed to be easily replicable, and by providing a step-by-step procedure of our testing and comparison methodology, we hope that other researchers will make use of the proposed testing pipeline.
A Review of Radio Frequency Based Localisation for Aerial and Ground Robots with 5G Future Perspectives
Authors: Kabiri Meisam , Cimarelli Claudio , Bavle Hriday , Sanchez-Lopez Jose Luis , Voos Holger
Published: December 2022

Efficient localisation plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned Aerial Vehicles (UAVs), which contributes to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities to enhance the localisation of UAVs and UGVs. In this paper, we review radio frequency (RF)-based approaches to localisation. We review the RF features that can be utilized for localisation and investigate the current methods suitable for Unmanned Vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localisation for both UAVs and UGVs is examined, and the envisioned 5G NR for localisation enhancement, and the future research direction are explored.
Relevant Cybersecurity Aspects of IoT Microservices Architectures Deployed over Next-Generation Mobile Networks
Authors: Aldea Constantin Lucian , Bocu Razvan , Vasilescu Anca
Published: December 2022

The design and implementation of secure IoT platforms and software solutions represent both a required functional feature and a performance acceptance factor nowadays. This paper describes relevant cybersecurity problems considered during the proposed microservices architecture development. Service composition mechanisms and their security are affected by the underlying hardware components and networks. The overall speedup of the platforms, which are implemented using the new 5G networks, and the capabilities of new performant IoT devices may be wasted by an inadequate combination of authentication services and security mechanisms, by the architectural misplacing of the encryption services, or by the inappropriate subsystems scaling. Considering the emerging microservices platforms, the Spring Boot alternative is used to implement data generation services, IoT sensor reading services, IoT actuators control services, and authentication services, and ultimately assemble them into a secure microservices architecture. Furthermore, considering the designed architecture, relevant security aspects related to the medical and energy domains are analyzed and discussed. Based on the proposed architectural concept, it is shown that well-designed and orchestrated architectures that consider the proper security aspects and their functional influence can lead to stable and secure implementations of the end user’s software platforms.
Refined Carbon Emission Measurement Based on NPP-VIIRS Nighttime Light Data: A Case Study of the Pearl River Delta Region, China
Authors: Yang Jian , Li Weihong , Chen Jieying , Sun Caige
Published: December 2022

The accurate measurement of CO2 emissions is helpful for realizing the goals of “carbon neutralization” and “carbon peak”. However, most current research on CO2 emission measurements utilizes the traditional energy balance coefficient and top-down methods. The data granularity is large, and most studies are concentrated at the national, provincial, municipal, or district/county administrative unit scale. As an important part of the Guangdong–Hong Kong–Macao Greater Bay Area of China, the Pearl River Delta region has good nighttime light vitality and faces huge carbon emission pressure. Using the Pearl River Delta as the research area, this study constructed an optimized pixel-scale regression model based on NPP-VIIRS (The Visible Infrared Imaging Radiometer Suite on the Suomi National Polar-Orbiting Partnership spacecraft) nighttime light data and CO2 emissions data at the district and county levels for 2017. In addition, the spatial pattern of CO2 emissions in the Pearl River Delta was analyzed based on the predicted CO2 emission status. The results showed that the spatial pattern of CO2 emissions in the Pearl River Delta had the distinct characteristics of the “center-edge” effect, the spatial spillover effect, and high-value aggregation, which should be considered when making related social or public decisions.
Machine Learning Approach to Model Physical Fatigue during Incremental Exercise among Firefighters
Authors: Bustos Denisse , Cardoso Filipa , Rios Manoel , Vaz Mário , Guedes Joana , Torres Costa José , Santos Baptista João , Fernandes Ricardo J.
Published: December 2022

Physical fatigue is a serious threat to the health and safety of firefighters. Its effects include decreased cognitive abilities and a heightened risk of accidents. Subjective scales and, recently, on-body sensors have been used to monitor physical fatigue among firefighters and safety-sensitive professionals. Considering the capabilities (e.g., noninvasiveness and continuous monitoring) and limitations (e.g., assessed fatiguing tasks and models validation procedures) of current approaches, this study aimed to develop a physical fatigue prediction model combining cardiorespiratory and thermoregulatory measures and machine learning algorithms within a firefighters’ sample. Sensory data from heart rate, breathing rate and core temperature were recorded from 24 participants during an incremental running protocol. Various supervised machine learning algorithms were examined using 21 features extracted from the physiological variables and participants’ characteristics to estimate four physical fatigue conditions: low, moderate, heavy and severe. Results showed that the XGBoosted Trees algorithm achieved the best outcomes with an average accuracy of 82% and accuracies of 93% and 86% for recognising the low and severe levels. Furthermore, this study evaluated different methods to assess the models’ performance, concluding that the group cross-validation method presents the most practical results. Overall, this study highlights the advantages of using multiple physiological measures for enhancing physical fatigue modelling. It proposes a promising health and safety management tool and lays the foundation for future studies in field conditions.
Reliable Fast (20 Hz) Acquisition Rate by a TD fNIRS Device: Brain Resting-State Oscillation Studies
Authors: Re Rebecca , Pirovano Ileana , Contini Davide , Amendola Caterina , Contini Letizia , Frabasile Lorenzo , Levoni Pietro , Torricelli Alessandro , Spinelli Lorenzo
Published: December 2022

A high power setup for multichannel time-domain (TD) functional near infrared spectroscopy (fNIRS) measurements with high efficiency detection system was developed. It was fully characterized based on international performance assessment protocols for diffuse optics instruments, showing an improvement of the signal-to-noise ratio (SNR) with respect to previous analogue devices, and allowing acquisition of signals with sampling rate up to 20 Hz and source-detector distance up to 5 cm. A resting-state measurement on the motor cortex of a healthy volunteer was performed with an acquisition rate of 20 Hz at a 4 cm source-detector distance. The power spectrum for the cortical oxy- and deoxyhemoglobin is also provided.
End-to-End Model-Based Detection of Infants with Autism Spectrum Disorder Using a Pretrained Model
Authors: Lee Jung Hyuk , Lee Geon Woo , Bong Guiyoung , Yoo Hee Jeong , Kim Hong Kook
Published: December 2022

In this paper, we propose an end-to-end (E2E) neural network model to detect autism spectrum disorder (ASD) from children’s voices without explicitly extracting the deterministic features. In order to obtain the decisions for discriminating between the voices of children with ASD and those with typical development (TD), we combined two different feature-extraction models and a bidirectional long short-term memory (BLSTM)-based classifier to obtain the ASD/TD classification in the form of probability. We realized one of the feature extractors as the bottleneck feature from an autoencoder using the extended version of the Geneva minimalistic acoustic parameter set (eGeMAPS) input. The other feature extractor is the context vector from a pretrained wav2vec2.0-based model directly applied to the waveform input. In addition, we optimized the E2E models in two different ways: (1) fine-tuning and (2) joint optimization. To evaluate the performance of the proposed E2E models, we prepared two datasets from video recordings of ASD diagnoses collected between 2016 and 2018 at Seoul National University Bundang Hospital (SNUBH), and between 2019 and 2021 at a Living Lab. According to the experimental results, the proposed wav2vec2.0-based E2E model with joint optimization achieved significant improvements in the accuracy and unweighted average recall, from 64.74% to 71.66% and from 65.04% to 70.81%, respectively, compared with a conventional model using autoencoder-based BLSTM and the deterministic features of the eGeMAPS.
Systematic Review on the Applicability of Principal Component Analysis for the Study of Movement in the Older Adult Population
Authors: Moreira Juliana , Silva Bruno , Faria Hugo , Santos Rubim , Sousa Andreia
Published: December 2022

Principal component analysis (PCA) is a dimensionality reduction method that has identified significant differences in older adults’ motion analysis previously not detected by the discrete exploration of biomechanical variables. This systematic review aims to synthesize the current evidence regarding PCA use in the study of movement in older adults (kinematics and kinetics), summarizing the tasks and biomechanical variables studied. From the search results, 1685 studies were retrieved, and 19 studies were included for review. Most of the included studies evaluated gait or quiet standing. The main variables considered included spatiotemporal parameters, range of motion, and ground reaction forces. A limited number of studies analyzed other tasks. Further research should focus on the PCA application in tasks other than gait to understand older adults’ movement characteristics that have not been identified by discrete analysis.
Early Stage Forest Fire Detection from Himawari-8 AHI Images Using a Modified MOD14 Algorithm Combined with Machine Learning
Authors: Maeda Naoto , Tonooka Hideyuki
Published: December 2022

The early detection and rapid extinguishing of forest fires are effective in reducing their spread. Based on the MODIS Thermal Anomaly (MOD14) algorithm, we propose an early stage fire detection method from low-spatial-resolution but high-temporal-resolution images, observed by the Advanced Himawari Imager (AHI) onboard the geostationary meteorological satellite Himawari-8. In order to not miss early stage forest fire pixels with low temperature, we omit the potential fire pixel detection from the MOD14 algorithm and parameterize four contextual conditions included in the MOD14 algorithm as features. The proposed method detects fire pixels from forest areas using a random forest classifier taking these contextual parameters, nine AHI band values, solar zenith angle, and five meteorological values as inputs. To evaluate the proposed method, we trained the random forest classifier using an early stage forest fire data set generated by a time-reversal approach with MOD14 products and time-series AHI images in Australia. The results demonstrate that the proposed method with all parameters can detect fire pixels with about 90% precision and recall, and that the contribution of contextual parameters is particularly significant in the random forest classifier. The proposed method is applicable to other geostationary and polar-orbiting satellite sensors, and it is expected to be used as an effective method for forest fire detection.
Step-Counting Accuracy of a Commercial Smartwatch in Mild-to-Moderate PD Patients and Effect of Spatiotemporal Gait Parameters, Laterality of Symptoms, Pharmacological State, and Clinical Variables
Authors: Bianchini Edoardo , Caliò Bianca , Alborghetti Marika , Rinaldi Domiziana , Hansen Clint , Vuillerme Nicolas , Maetzler Walter , Pontieri Francesco E.
Published: December 2022

Commercial smartwatches could be useful for step counting and monitoring ambulatory activity. However, in Parkinson’s disease (PD) patients, an altered gait, pharmacological condition, and symptoms lateralization may affect their accuracy and potential usefulness in research and clinical routine. Steps were counted during a 6 min walk in 47 patients with PD and 47 healthy subjects (HS) wearing a Garmin Vivosmart 4 (GV4) on each wrist. Manual step counting was used as a reference. An inertial sensor (BTS G-Walk), placed on the lower back, was used to compute spatial-temporal gait parameters. Intraclass correlation coefficient (ICC) and mean absolute percentage error (MAPE) were used for accuracy evaluation and the Spearman test was used to assess the correlations between variables. The GV4 overestimated steps in PD patients with only a poor-to-moderate agreement. The OFF pharmacological state and wearing the device on the most-affected body side led to an unacceptable accuracy. The GV4 showed an excellent agreement and MAPE in HS at a self-selected speed, but an unacceptable performance at a slow speed. In PD patients, MAPE was not associated with gait parameters and clinical variables. The accuracy of commercial smartwatches for monitoring step counting might be reduced in PD patients and further influenced by the pharmacological condition and placement of the device.
BER Reduction and Capacity Enhancement with Novel Guard Sequence Selection for Multi-Carrier Communication
Authors: Ologun Oluwaseun , Wu Shaochuan , Shah Raza Ali , Altaf Khattak Sohaib Bin , Nasralla Moustafa M.
Published: December 2022

Orthogonal frequency division multiplexing (OFDM) is an efficient multicarrier scheme that uses different types of guard intervals such as cyclic prefix (CP) and known symbol padding (KSP) (zero padding (ZP), unique word (UW), etc.) in block formation. Among these guard intervals, CP varies for each block, while other guard intervals remain fixed from block to block. These guard intervals efficiently perform channel estimation, synchronization and remove inter-block interference (IBI); nevertheless, none of the existing schemes develop any relationship between the guard interval (sequence) and the data symbols on different subcarriers of the OFDM block. We present a new idea of selecting the guard interval based on the data symbols of a subset of subcarriers in the block and exploit the high auto-correlation of the selected guard sequence to improve the bit error rate (BER) performance of the system. The results based on a fair comparison show that our enhanced orthogonal frequency division multiplexing (eOFDM) scheme inherits significant improvements in BER and the capacity of a multicarrier system as compared to the existing techniques.
Differential Deformation Identification of High-Speed Railway Substructures Based on Dynamic Inspection of Longitudinal Level
Authors: Ma Shuai , Liu Xiubo , Zhang Bo , Wei Jianmei
Published: December 2022

High-speed railway administrations are particularly concerned about safety and comfort issues, which are sometimes threatened by the differential deformation of substructures. Existing deformation-monitoring techniques are impractical for covering the whole range of a railway line at acceptable costs. Fortunately, the information about differential substructure deformation is contained in the dynamic inspection data of longitudinal level from comprehensive inspections trains. In order to detect potential differential deformations, an identification method, combining digital filtering, a convolutional neural network and infrastructure base information, is proposed. In this method, a low-pass filter is designed to remove short-waveband components of the longitudinal level. Then, a one-dimensional convolutional neural network is constructed to serve as a feature extractor from local longitudinal-level waveforms, and a binary classifier of potential differential deformations in place of the visual judgement of humans with profound expertise. Finally, the infrastructure base information is utilized to further classify the differential deformations into several types, according to the positional distribution of the substructures. The inspection data of four typical high-speed railways are selected to train and test the method. The results show that the convolutional neural network can identify differential substructure-deformations, with the precision, recall, accuracy and F1 score all exceeding 98% on the test data. In addition, four types of deformation can be further classified with the support of infrastructure base information. The proposed method can be used for directly locating adverse substructure deformations, and is also becoming a promising addition to existing deformation monitoring methods.