bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Rotor Position Detection Performance According to the Frequency of Square Waveform Voltage in the Harmonic Injection Sensorless Method through HILS
Authors: Moon Kyeong-Rok , Lee Dong-Myung
Published: December 2021

In this paper, the rotor position estimation performance of the sensorless scheme for permanent magnet synchronous motors (PMSMs) implemented through the injection of high-frequency square-wave voltage according to the frequency of the square-wave voltage is presented through HILS (Hardware In the Loop Simulation) experiments. An inverter using an IGBT device usually has a switching frequency of around 15 kHz. On the other hand, GaN devices that can be switched on and off at frequencies higher than 100 kHz have been recently developed, and research is being actively conducted to apply GaNs to a variable speed system. The purpose of this study is to conduct HILS experiments to analysis the rotor position estimation ability of the sensorless technique in cases where a high switching frequency was applied, such as GaN devices, with that of a system having a usual switching frequency, such as IGBT. In the HILS system used in this study, an inverter and motor model implemented with Simulink are located in a real-time simulator. A sensorless motor control method was implemented with an FPGA control board, which includes a PWM interrupt service routine of 100 kHz frequency and a harmonic injection and position detection algorithm. The HILS experiments show rotor position detection errors according to the various frequency of the harmonic voltage injected for estimating the rotor position with a PWM frequency of 100 kHz cases. According to the experimental results, good position estimation was possible not only when the harmonic of 10 kHz corresponding to 1/10 of the PWM frequency was injected, but also when the harmonic of 1 kHz corresponding to 1/100 of the PWM frequency was injected. The experiments suggest that position estimation errors decrease as the frequency of the harmonic voltage increases, and, based on the foregoing, it is thought that the application of a GaN device capable of realizing a high switching frequency in a variable speed drive system can be another advantage.
Abraham Bids Farewell to Hagar and Ishmael: Continuity and Variation of the Iconographic Type
Authors: García Mahíques Rafael
Published: December 2021

In traditional Christian artistic visualization, the episode of Hagar and Ishmael in the desert has given rise to various iconographic types: “The feast for the weaning of Isaac and Sara’s protests,” “Abraham bids farewell to Hagar and Ishmael,” “Hagar and Ishmael in the desert” and “Divine salvation for Hagar and Ishmael”. This study looks into the continuity and variation over time of the second of these types: “Abraham bids farewell to Hagar and Ishmael,” the one most depicted out of this entire biblical topic or episode. Since the Byzantine Octateuch in the East (11th century.) and the Canterbury Hexateuch (ca. 1025–1049) in the West, this iconographic type has remained into the Late Modern period, with some variations over time. This study is exclusively iconographic or descriptive; it only verifies the codification of the type in order to set out an analytical basis prior to future hermeneutic or iconological studies.
Contextualizing Caves within an Animate Maya Landscape: Caves as Living Agents in the Past and Present
Authors: Woodfill Brent K. S.
Published: December 2021

After groundbreaking work by multiple archaeologists in the latter half of the 20th century, caves in the Maya world are currently acknowledged as fundamentally ritual rather than domestic spaces. However, a more nuanced read of the anthropological literature and conversations with Indigenous collaborators in the past and present pushes us to move still farther and see caves not as passive contexts to contain ceremonies directed elsewhere but animate beings with unique identities and personalities in their own right. This article combines archaeological, ethnohistoric, and ethnographic documentation of Maya cave use in central Guatemala to build a foundation for examining caves as living beings, with particular attention played to the role they play as active agents in local politics and quotidian life. Through ritual offerings, neighboring residents and travelers maintain tight reciprocal relationships with specific caves and other geographic idiosyncrasies dotting the landscape to ensure the success of multiple important activities and the continued well-being of families and communities.
Economic Indicators in Water and Wastewater Sector Contributing to a Circular Economy (CE)
Authors: Smol Marzena , Koneczna Renata
Published: December 2021

Protection and sustainable management of water was indicated as one of the strategic tasks in the process of transformation towards a circular economy (CE) in the European Union (EU), therefore, the water and wastewater sector plays an important role in this process. At the same time, the European Commission (EC) strongly underlined the importance of the possibility to assess the transformation process toward the CE, and developed a set of CE indicators that are available on the Eurostat website. However, these indicators have limited ability to assess the transformation progress in the water and wastewater sector. This paper presents a set of indicators for assessing the economic progress of transformation towards the CE in this sector. The proposed economic CE indicators were grouped into the following actions of the CE model in the water and wastewater sector: reduction, reclamation (removal), reuse, recycling, recovery and landfilling. The selection of specific indicators was based on a systematic review of the literature presenting economic indicators developed by international organisations and researchers (covering different thematic areas, scopes and potential applications). The selected economic CE indicators were assigned to three groups of the cash flow: income (revenues, expenses), costs, and investment financing. The proposed CE indicators can be used by water supply and sewage companies (i.e., supplying water to the public and wastewater treatment plants, and companies that use water in their production processes) to assess the level of the transformation toward the CE at a microeconomic level. An important aspect of future application and usage of the proposed set of CE economic indicators is the collection and processing of data needed for their reporting. The proposed set of CE economic indicators refers to information that are reported by the companies to prove its revenues, costs and investment outlays, and are collected by companies anyway. The proposed set of economic CE indicators is flexible, allowing the adaptation of indicators and areas of interest to maintain effectiveness throughout the transition period from linear to the CE model.
Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones
Authors: Ricciardulli Lucrezia , Mears Carl , Manaster Andrew , Meissner Thomas
Published: December 2021

The NASA CYGNSS satellite constellation measures ocean surface winds using the existing network of the Global Navigation Satellite System (GNSS) and was designed for measurements in tropical cyclones (TCs). Here, we focus on using a consistent methodology to validate multiple CYGNSS wind data records currently available to the public, some focusing on low to moderate wind speeds, others for high winds, a storm-centric product for TC analyses, and a wind dataset from NOAA that applies a track-wise bias correction. Our goal is to document their differences and provide guidance to users. The assessment of CYGNSS winds (2017–2020) is performed here at global scales and for all wind regimes, with particular focus on TCs, using measurements from radiometers that are specifically developed for high winds: SMAP, WindSat, and AMSR2 TC-winds. The CYGNSS high-wind products display significant biases in TCs and very large uncertainties. Similar biases and large uncertainties were found with the storm-centric wind product. On the other hand, the NOAA winds show promising skill in TCs, approaching a level suitable for tropical meteorology studies. At the global level, the NOAA winds are overall unbiased at wind regimes from 0–30 m/s and were selected for a test assimilation into a global wind analysis, CCMP, also presented here.
Retrieval of Phytoplankton Pigment Composition from Their In Vivo Absorption Spectra
Authors: Zhang Yinxue , Wang Guifen , Sathyendranath Shubha , Xu Wenlong , Xiao Yizhe , Jiang Long
Published: December 2021

Algal pigment composition is an indicator of phytoplankton community structure that can be estimated from optical observations. Assessing the potential capability to retrieve different types of pigments from phytoplankton absorption is critical for further applications. This study investigated the performance of three models and the utility of hyperspectral in vivo phytoplankton absorption spectra for retrieving pigment composition using a large database (n = 1392). Models based on chlorophyll-a (Chl-a model), Gaussian decomposition (Gaussian model), and partial least squares (PLS) regression (PLS model) were compared. Both the Gaussian model and the PLS model were applied to hyperspectral phytoplankton absorption data. Statistical analysis revealed the advantages and limitations of each model. The Chl-a model performed well for chlorophyll-c (Chl-c), diadinoxanthin, fucoxanthin, photosynthetic carotenoids (PSC), and photoprotective carotenoids (PPC), with a median absolute percent difference for cross-validation (MAPDCV) < 58%. The Gaussian model yielded good results for predicting Chl-a, Chl-c, PSC, and PPC (MAPDCV < 43%). The performance of the PLS model was comparable to that of the Chl-a model, and it exhibited improved retrievals of chlorophyll-b, alloxanthin, peridinin, and zeaxanthin. Additional work undertaken with the PLS model revealed the prospects of hyperspectral-resolution data and spectral derivative analyses for retrieving marker pigment concentrations. This study demonstrated the applicability of in situ hyperspectral phytoplankton absorption data for retrieving pigment composition and provided useful insights regarding the development of bio-optical algorithms from hyperspectral and satellite-based ocean-colour observations.
Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Inventories for the Complex, Mixed-Species Forests of the Eastern United States
Authors: Ayrey Elias , Hayes Daniel J. , Kilbride John B. , Fraver Shawn , Kershaw John A. , Cook Bruce D. , Weiskittel Aaron R.
Published: December 2021

Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional scale due to varying parameters among LiDAR data acquisitions and the availability of sufficient calibration data. Here, we present a model using a 3-D convolutional neural network (CNN), a form of deep learning capable of scanning a LiDAR point cloud, combined with coincident satellite data (spectral, phenology, and disturbance history). We compared this approach to traditional modeling used for making forest predictions from LiDAR data (height metrics and random forest) and found that the CNN had consistently lower uncertainty. We then applied the CNN to public data over six New England states in the USA, generating maps of 14 forest attributes at a 10 m resolution over 85% of the region. Aboveground biomass estimates produced a root mean square error of 36 Mg ha−1 (44%) and were within the 97.5% confidence of independent county-level estimates for 33 of 38 or 86.8% of the counties examined. CNN predictions for stem density and percentage of conifer attributes were moderately successful, while predictions for detailed species groupings were less successful. The approach shows promise for improving the prediction of forest attributes from regional LiDAR data and for combining disparate LiDAR datasets into a common framework for large-scale estimation.
Using Sentinel-2 Images for Soil Organic Carbon Content Mapping in Croplands of Southwestern France. The Usefulness of Sentinel-1/2 Derived Moisture Maps and Mismatches between Sentinel Images and Sampling Dates
Authors: Urbina-Salazar Diego , Vaudour Emmanuelle , Baghdadi Nicolas , Ceschia Eric , Richer-de-Forges Anne C. , Lehmann Sébastien , Arrouays Dominique
Published: December 2021

In agronomy, soil organic carbon (SOC) content is important for the development and growth of crops. From an environmental monitoring viewpoint, SOC sequestration is essential for mitigating the emission of greenhouse gases into the atmosphere. SOC dynamics in cropland soils should be further studied through various approaches including remote sensing. In order to predict SOC content over croplands in southwestern France (area of 22,177 km²), this study addresses (i) the influence of the dates on which Sentinel-2 (S2) images were acquired in the springs of 2017–2018 as well as the influence of the soil sampling period of a set of samples collected between 2005 and 2018, (ii) the use of soil moisture products (SMPs) derived from Sentinel-1/2 satellites to analyze the influence of surface soil moisture on model performance when included as a covariate, and (iii) whether the spatial distribution of SOC as mapped using S2 is related to terrain-derived attributes. The influences of S2 image dates and soil sampling periods were analyzed for bare topsoil. The dates of the S2 images with the best performance (RPD ≥ 1.7) were 6 April and 26 May 2017, using soil samples collected between 2016 and 2018. The soil sampling dates were also analyzed using SMP values. Soil moisture values were extracted for each sample and integrated into partial least squares regression (PLSR) models. The use of soil moisture as a covariate had no effect on the prediction performance of the models; however, SMP values were used to select the driest dates, effectively mapping topsoil organic carbon. S2 was able to predict high SOC contents in the specific soil types located on the old terraces (mesas) shaped by rivers flowing from the southwestern Pyrénées.
Landslide Extraction from High-Resolution Remote Sensing Imagery Using Fully Convolutional Spectral–Topographic Fusion Network
Authors: Xia Wei , Chen Jun , Liu Jianbo , Ma Caihong , Liu Wei
Published: December 2021

Considering the complexity of landslide hazards, their manual investigation lacks efficiency and is time-consuming, especially in high-altitude plateau areas. Therefore, extracting landslide information using remote sensing technology has great advantages. In this study, comprehensive research was carried out on the landslide features of high-resolution remote sensing images on the Mangkam dataset. Based on the idea of feature-driven classification, the landslide extraction model of a fully convolutional spectral–topographic fusion network (FSTF-Net) based on a deep convolutional neural network of multi-source data fusion is proposed, which takes into account the topographic factor (slope and aspect) and the normalized difference vegetation index (NDVI) as multi-source data input by which to train the model. In this paper, a high-resolution remote sensing image classification method based on a fully convolutional network was used to extract the landslide information, thereby realizing the accurate extraction of the landslide and surrounding ground-object information. With Mangkam County in the southeast of the Qinghai–Tibet Plateau China as the study area, the proposed method was evaluated based on the high-precision digital elevation model (DEM) generated from stereoscopic images of Resources Satellite-3 and multi-source high-resolution remote sensing image data (Beijing-2, Worldview-3, and SuperView-1). Results show that our method had a landslide detection precision of 0.85 and an overall classification accuracy of 0.89. Compared with the latest DeepLab_v3+, our model increases the landslide detection precision by 5%. Thus, the proposed FSTF-Net model has high reliability and robustness.
Integrating Satellite, UAV, and Ground-Based Remote Sensing in Archaeology: An Exploration of Pre-Modern Land Use in Northeastern Iraq
Authors: Laugier Elise Jakoby , Casana Jesse
Published: December 2021

Satellite remote sensing is well demonstrated to be a powerful tool for investigating ancient land use in Southwest Asia. However, few regional studies have systematically integrated satellite-based observations with more intensive remote sensing technologies, such as drone-deployed multispectral sensors and ground-based geophysics, to explore off-site areas. Here, we integrate remote sensing data from a variety of sources and scales including historic aerial photographs, modern satellite imagery, drone-deployed sensors, and ground-based geophysics to explore pre-modern land use along the Upper Diyala/Sirwan River in the Kurdistan Region of Iraq. Our analysis reveals an incredible diversity of land use features, including canals, qanats, trackways, and field systems, most of which likely date to the first millennium CE, and demonstrate the potential of more intensive remote sensing methods to resolve land use features. Our results align with broader trends across ancient Southwest Asia that document the most intensive land use in the first millennium BCE through the first millennium CE. Land use features dating to the earlier Bronze Age (fourth through second millennium BCE) remain elusive and will likely require other investigative approaches.
Study on Mechanical Characteristics of Segmental Joints of a Large-Diameter Shield Tunnel under Ultrahigh Water Pressure
Authors: Kou Lei , Xiong Zhihui , Cui Hao , Zhao Jinjie
Published: December 2021

At present, there is no clear design standard for segmental joints of large-diameter shield tunnels under high water pressure. In this paper, a theoretical calculation model for the bending stiffness of segmental joints under high water pressure is proposed. The numerical simulation method is used to investigate the failure and crack formation processes of single-layer and double-layer lining segments under large axial forces. The effects of axial force, bolt strength, and concrete strength on the bending stiffness of joints are then studied using a theoretical calculation model of segmental joints. The results show that under extremely high water pressure, the influence of double lining on joint stiffness is limited. It is more rational and safe to compute the bending stiffness of segmental joints using this theoretical model rather than the numerical simulation method. The parameter analysis reveals that increasing the bolt strength has a minor impact on bending stiffness and deformation, whereas increasing the concrete strength has the opposite effect. The influence of ultimate bearing capacity and deformation decreases non-linearly as the axial force increases.
Uncertainty Analysis of an Optoelectronic Strain Measurement System for Flywheel Rotors
Authors: Rath Matthias Franz , Schweighofer Bernhard , Wegleiter Hannes
Published: December 2021

The strain in a fast spinning carbon fiber flywheel rotor is of great interest for condition monitoring, as well as for studying long-term aging effects in the carbon fiber matrix. Optoelectronic strain measurement is a contactless measurement principle where a special reflective pattern is applied to the rotor which is scanned by a stationary optical setup. It does not require any active electronic components on the rotor and is suited for operation in a vacuum. In this paper, the influences of the key parts comprising the optoelectronic strain measurement are analyzed. The influence of each part on the measurement result including the uncertainty is modeled. The total uncertainty, as well as each part’s contribution is calculated. This provides a valuable assessment of requirements for component selection, as well as tolerances of mechanical parts and processes to reach a final target measurement uncertainty or to estimate the uncertainty of a given setup. We have shown that the edge quality of the special reflective pattern has the strongest influence, and how to improve it. Considering all influences, it is possible to measure strain with an uncertainty of less than 1% at a rotation speed of 500Hz.
Validation of a Novel Boxing Monitoring System to Detect and Analyse the Centre of Pressure Movement on the Boxer’s Fist
Authors: Menzel Tobias , Potthast Wolfgang
Published: December 2021

The examination of force distribution and centre of pressure (CoP) displacement is a common method to analyse motion, load, and load distribution in biomechanical research. In contrast to gait analysis, the force progression in boxing punches is a new field of investigation. The centre of pressure displacement and distribution of forces on the surface of the fist during a boxing punch is of great interest and crucial to understanding the effect of the punch on the biological structures of the hand as well as the technical biomechanical aspects of the punching action. This paper presents a new method to display the CoP progression on the boxer’s fist Therefore, this study presents the validation of the developed novel boxing monitoring system in terms of CoP displacement. In addition, the CoP progression of different punching techniques in boxing is analysed on the athlete’s fist. The accuracy of the examination method of the CoP course was validated against the gold standard of a Kistler force plate. High correlations were detected between the developed sensor system and the force plate CoP with a Pearson correlation coefficient ranging from 0.93 to 0.97. The information obtained throughout the experimental study is of great importance in order to gain further knowledge into the technical execution of boxing punches as well as to provide a novel measuring method for determining CoP on the surface of the fist, to improve the understanding of the etiology of boxing-related hand injuries.
Human Segmentation and Tracking Survey on Masks for MADS Dataset
Authors: Le Van-Hung , Scherer Rafal
Published: December 2021

Human segmentation and tracking often use the outcome of person detection in the video. Thus, the results of segmentation and tracking depend heavily on human detection results in the video. With the advent of Convolutional Neural Networks (CNNs), there are excellent results in this field. Segmentation and tracking of the person in the video have significant applications in monitoring and estimating human pose in 2D images and 3D space. In this paper, we performed a survey of many studies, methods, datasets, and results for human segmentation and tracking in video. We also touch upon detecting persons as it affects the results of human segmentation and human tracking. The survey is performed in great detail up to source code paths. The MADS (Martial Arts, Dancing and Sports) dataset comprises fast and complex activities. It has been published for the task of estimating human posture. However, before determining the human pose, the person needs to be detected as a segment in the video. Moreover, in the paper, we publish a mask dataset to evaluate the segmentation and tracking of people in the video. In our MASK MADS dataset, we have prepared 28 k mask images. We also evaluated the MADS dataset for segmenting and tracking people in the video with many recently published CNNs methods.
Research on UAV Three-Phase Transmission Line Tracking and Localization Method Based on Electric Field Sensor Array
Authors: Suo Chunguang , Zhao Jiawen , Zhang Wenbin , Li Peng , Huang Rujin , Zhu Junyu , Tan Xiangyu
Published: December 2021

The tracking and positioning of transmission lines is a key element for UAVs (Unmanned Aerial Vehicles) to achieve autonomous inspection of transmission lines. Current methods are vulnerable to weather and environmental factors, have high costs, and have difficulties in data processing. Therefore, this paper proposes a transmission line tracking and localization method based on the electric field sensor array, which calculates the current UAV’s heading angle deflection angle, the distance between the transmission line and the UAV, and the elevation angle, providing a new idea to solve the problem of UAV inspection of transmission lines. At the same time, the electric field distribution of different arrangements of three-phase transmission lines was analyzed using COMSOL to determine the flight area of the UAV. By comparing the electric field distribution of the UAV flight area and single-phase transmission lines, it was verified that the current method is also applicable in the three-phase transmission line scenario, and it was further verified that the sensor array used can sense the change of the UAV position in the flight area, indicating that the electric field sensor array can realize the transmission line tracking and localization of transmission lines. The experimental results showed that, in the three-phase transmission line scenario, when the sensor array moves along the transmission straight wire, the maximum absolute error of the heading angle deflection angle calculated according to this method was 8.2°, the maximum absolute error of the distance between the array and the transmission line was 19.3 cm, and the maximum absolute error of the elevation angle was 11.37°; the error was within a reasonable range and can be used for the UAV to realize autonomous inspection.
A Bibliometric Analysis and Benchmark of Machine Learning and AutoML in Crash Severity Prediction: The Case Study of Three Colombian Cities
Authors: Angarita-Zapata Juan S. , Maestre-Gongora Gina , Calderín Jenny Fajardo
Published: December 2021

Traffic accidents are of worldwide concern, as they are one of the leading causes of death globally. One policy designed to cope with them is the design and deployment of road safety systems. These aim to predict crashes based on historical records, provided by new Internet of Things (IoT) technologies, to enhance traffic flow management and promote safer roads. Increasing data availability has helped machine learning (ML) to address the prediction of crashes and their severity. The literature reports numerous contributions regarding survey papers, experimental comparisons of various techniques, and the design of new methods at the point where crash severity prediction (CSP) and ML converge. Despite such progress, and as far as we know, there are no comprehensive research articles that theoretically and practically approach the model selection problem (MSP) in CSP. Thus, this paper introduces a bibliometric analysis and experimental benchmark of ML and automated machine learning (AutoML) as a suitable approach to automatically address the MSP in CSP. Firstly, 2318 bibliographic references were consulted to identify relevant authors, trending topics, keywords evolution, and the most common ML methods used in related-case studies, which revealed an opportunity for the use AutoML in the transportation field. Then, we compared AutoML (AutoGluon, Auto-sklearn, TPOT) and ML (CatBoost, Decision Tree, Extra Trees, Gradient Boosting, Gaussian Naive Bayes, Light Gradient Boosting Machine, Random Forest) methods in three case studies using open data portals belonging to the cities of Medellín, Bogotá, and Bucaramanga in Colombia. Our experimentation reveals that AutoGluon and CatBoost are competitive and robust ML approaches to deal with various CSP problems. In addition, we concluded that general-purpose AutoML effectively supports the MSP in CSP without developing domain-focused AutoML methods for this supervised learning problem. Finally, based on the results obtained, we introduce challenges and research opportunities that the community should explore to enhance the contributions that ML and AutoML can bring to CSP and other transportation areas.
Short Time Correlation Analysis of Melt Pool Behavior in Laser Metal Deposition Using Coaxial Optical Monitoring
Authors: Zavalov Yury N. , Dubrov Alexander V.
Published: December 2021

The development and improvement of monitoring and process control systems is one of the important ways of advancing laser metal deposition (LMD). The control of hydrodynamic, heat and mass transfer processes in LMD is extremely important, since these processes directly affect the crystallization of the melt and, accordingly, the microstructural properties and the overall quality of the synthesized part. In this article, the data of coaxial video monitoring of the LMD process were used to assess the features of melt dynamics. The obtained images were used to calculate the time dependences of the characteristics of the melt pool (MP) (temperature, width, length and area), which were further processed using the short-time correlation (STC) method. This approach made it possible to reveal local features of the joint behavior of the MP characteristics, and to analyze the nature of the melt dynamics. It was found that the behavior of the melt in the LMD is characterized by the presence of many time periods (patterns), during which it retains a certain ordered character. The features of behavior that are important from the point of view of process control systems design are noted. The approach used for the analysis of melt dynamics based on STC distributions of MP characteristics, as well as the method for determining the moments of pattern termination through the calculation of the correlation power, can be used in processing the results of online LMD diagnostics, as well as in process control systems.
The Use of Smartphone Photogrammetry to Digitise Transtibial Sockets: Optimisation of Method and Quantitative Evaluation of Suitability
Authors: Cullen Sean , Mackay Ruth , Mohagheghi Amir , Du Xinli
Published: December 2021

The fit of a lower limb prosthetic socket is critical for user comfort and the quality of life of lower limb amputees. Sockets are conventionally produced using hand-crafted patient-based casting techniques. Modern digital techniques offer a host of advantages to the process and ultimately lead to improving the lives of amputees. However, commercially available scanning equipment required is often expensive and proprietary. Smartphone photogrammetry could offer a low cost alternative, but there is no widely accepted imaging technique for prosthetic socket digitisation. Therefore, this paper aims to determine an optimal imaging technique for whole socket photogrammetry and evaluate the resultant scan measurement accuracy. A 3D printed transtibial socket was produced to create digital and physical twins, as reference models. The printed socket was photographed from 360 positions and simplified genetic algorithms were used to design a series of experiments, whereby a collection of photos were processed using Autodesk ReCap. The most fit technique was used to assess accuracy. The accuracy of the socket wall volume, surface area and height were 61.63%, 99.61% and 99.90%, respectively, when compared to the digital reference model. The scanned model had a wall thickness ranging from 2.075 mm at the top to 7.758 mm towards the base of the socket, compared to a consistent thickness of 2.025 mm in the control model. The technique selected did not show sufficient accuracy for clinical application due to the degradation of accuracy nearer to the base of the socket interior. However, using an internal wall thickness estimation, scans may be of sufficient accuracy for clinical use; assuming a uniform wall thickness.
Deep Learning Based Air-Writing Recognition with the Choice of Proper Interpolation Technique
Authors: Abir Fuad Al , Siam Md. Al , Sayeed Abu , Hasan Md. Al Mehedi , Shin Jungpil
Published: December 2021

The act of writing letters or words in free space with body movements is known as air-writing. Air-writing recognition is a special case of gesture recognition in which gestures correspond to characters and digits written in the air. Air-writing, unlike general gestures, does not require the memorization of predefined special gesture patterns. Rather, it is sensitive to the subject and language of interest. Traditional air-writing requires an extra device containing sensor(s), while the wide adoption of smart-bands eliminates the requirement of the extra device. Therefore, air-writing recognition systems are becoming more flexible day by day. However, the variability of signal duration is a key problem in developing an air-writing recognition model. Inconsistent signal duration is obvious due to the nature of the writing and data-recording process. To make the signals consistent in length, researchers attempted various strategies including padding and truncating, but these procedures result in significant data loss. Interpolation is a statistical technique that can be employed for time-series signals to ensure minimum data loss. In this paper, we extensively investigated different interpolation techniques on seven publicly available air-writing datasets and developed a method to recognize air-written characters using a 2D-CNN model. In both user-dependent and user-independent principles, our method outperformed all the state-of-the-art methods by a clear margin for all datasets.
Fusion Models for Generalized Classification of Multi-Axial Human Movement: Validation in Sport Performance
Authors: Amerineni Rajesh , Gupta Lalit , Steadman Nathan , Annauth Keshwyn , Burr Charles , Wilson Samuel , Barnaghi Payam , Vaidyanathan Ravi
Published: December 2021

We introduce a set of input models for fusing information from ensembles of wearable sensors supporting human performance and telemedicine. Veracity is demonstrated in action classification related to sport, specifically strikes in boxing and taekwondo. Four input models, formulated to be compatible with a broad range of classifiers, are introduced and two diverse classifiers, dynamic time warping (DTW) and convolutional neural networks (CNNs) are implemented in conjunction with the input models. Seven classification models fusing information at the input-level, output-level, and a combination of both are formulated. Action classification for 18 boxing punches and 24 taekwondo kicks demonstrate our fusion classifiers outperform the best DTW and CNN uni-axial classifiers. Furthermore, although DTW is ostensibly an ideal choice for human movements experiencing non-linear variations, our results demonstrate deep learning fusion classifiers outperform DTW. This is a novel finding given that CNNs are normally designed for multi-dimensional data and do not specifically compensate for non-linear variations within signal classes. The generalized formulation enables subject-specific movement classification in a feature-blind fashion with trivial computational expense for trained CNNs. A commercial boxing system, ‘Corner’, has been produced for real-world mass-market use based on this investigation providing a basis for future telemedicine translation.
Detection of Motion on a Trampoline with a Smartwatch
Authors: Kobayashi Satoshi , Hasegawa Tatsuhito
Published: December 2021

In this study, we develop a method for detecting the motions performed on a trampoline using an accelerometer mounted on a smartwatch. This method will lead to a system that can be used to promote trampoline exercise using a home trampoline by detecting motions on the trampoline using a smartwatch. We proposed a method based on the convolutional neural network to detect the motions on a trampoline. As a result of the performance evaluation by leave-one-subject-out cross-validation on eight subjects, our method achieves 78.8% estimation accuracy, which is the best estimation accuracy compared to the baseline methods. We also evaluate the inference time and the battery consumption when the model is actually running on a smartwatch. Our method is effective for on-device prediction.
Security Analysis of Machine Learning-Based PUF Enrollment Protocols: A Review
Authors: Khalfaoui Sameh , Leneutre Jean , Villard Arthur , Gazeau Ivan , Ma Jingxuan , Urien Pascal
Published: December 2021

The demand for Internet of Things services is increasing exponentially, and consequently a large number of devices are being deployed. To efficiently authenticate these objects, the use of physical unclonable functions (PUFs) has been introduced as a promising solution for the resource-constrained nature of these devices. The use of machine learning PUF models has been recently proposed to authenticate the IoT objects while reducing the storage space requirement for each device. Nonetheless, the use of a mathematically clonable PUFs requires careful design of the enrollment process. Furthermore, the secrecy of the machine learning models used for PUFs and the scenario of leakage of sensitive information to an adversary due to an insider threat within the organization have not been discussed. In this paper, we review the state-of-the-art model-based PUF enrollment protocols. We identity two architectures of enrollment protocols based on the participating entities and the building blocks that are relevant to the security of the authentication procedure. In addition, we discuss their respective weaknesses with respect to insider and outsider threats. Our work serves as a comprehensive overview of the ML PUF-based methods and provides design guidelines for future enrollment protocol designers.
Mitigating Cold Start Problem in Serverless Computing with Function Fusion
Authors: Lee Seungjun , Yoon Daegun , Yeo Sangho , Oh Sangyoon
Published: December 2021

As Artificial Intelligence (AI) is becoming ubiquitous in many applications, serverless computing is also emerging as a building block for developing cloud-based AI services. Serverless computing has received much interest because of its simplicity, scalability, and resource efficiency. However, due to the trade-off with resource efficiency, serverless computing suffers from the cold start problem, that is, a latency between a request arrival and function execution. The cold start problem significantly influences the overall response time of workflow that consists of functions because the cold start may occur in every function within the workflow. Function fusion can be one of the solutions to mitigate the cold start latency of a workflow. If two functions are fused into a single function, the cold start of the second function is removed; however, if parallel functions are fused, the workflow response time can be increased because the parallel functions run sequentially even if the cold start latency is reduced. This study presents an approach to mitigate the cold start latency of a workflow using function fusion while considering a parallel run. First, we identify three latencies that affect response time, present a workflow response time model considering the latency, and efficiently find a fusion solution that can optimize the response time on the cold start. Our method shows a response time of 28–86% of the response time of the original workflow in five workflows.
Towards Environmental RF-EMF Assessment of mmWave High-Node Density Complex Heterogeneous Environments
Authors: Celaya-Echarri Mikel , Azpilicueta Leyre , Rodríguez-Corbo Fidel Alejandro , Lopez-Iturri Peio , Ramos Victoria , Alibakhshikenari Mohammad , Shubair Raed M. , Falcone Francisco
Published: December 2021

The densification of multiple wireless communication systems that coexist nowadays, as well as the 5G new generation cellular systems advent towards the millimeter wave (mmWave) frequency range, give rise to complex context-aware scenarios with high-node density heterogeneous networks. In this work, a radiofrequency electromagnetic field (RF-EMF) exposure assessment from an empirical and modeling approach for a large, complex indoor setting with high node density and traffic is presented. For that purpose, an intensive and comprehensive in-depth RF-EMF E-field characterization study is provided in a public library study case, considering dense personal mobile communications (5G FR2 @28 GHz) and wireless 802.11ay (@60 GHz) data access services on the mmWave frequency range. By means of an enhanced in-house deterministic 3D ray launching (3D-RL) simulation tool for RF-EMF exposure assessment, different complex heterogenous scenarios of high complexity are assessed in realistic operation conditions, considering different user distributions and densities. The use of directive antennas and MIMO beamforming techniques, as well as all the corresponding features in terms of radio wave propagation, such as the body shielding effect, dispersive material properties of obstacles, the impact of the distribution of scatterers and the associated electromagnetic propagation phenomena, are considered for simulation. Discussion regarding the contribution and impact of the coexistence of multiple heterogeneous networks and services is presented, verifying compliance with the current established international regulation limits with exposure levels far below the aforementioned limits. Finally, the proposed simulation technique is validated with a complete empirical campaign of measurements, showing good agreement. In consequence, the obtained datasets and simulation estimations, along with the proposed RF-EMF simulation tool, could be a reference approach for the design, deployment and exposure assessment of the current and future wireless communication technologies on the mmWave spectrum, where massive high-node density heterogeneous networks are expected.
Development of a Novel Methodology for Remaining Useful Life Prediction of Industrial Slurry Pumps in the Absence of Run to Failure Data
Authors: Khan Muhammad Mohsin , Tse Peter W. , Trappey Amy J. C.
Published: December 2021

Smart remaining useful life (RUL) prognosis methods for condition-based maintenance (CBM) of engineering equipment are getting high popularity nowadays. Current RUL prediction models in the literature are developed with an ideal database, i.e., a combination of a huge “run to failure” and “run to prior failure” data. However, in real-world, run to failure data for rotary machines is difficult to exist since periodic maintenance is continuously practiced to the running machines in industry, to save any production downtime. In such a situation, the maintenance staff only have run to prior failure data of an in operation machine for implementing CBM. In this study, a unique strategy for the RUL prediction of two identical and in-process slurry pumps, having only real-time run to prior failure data, is proposed. The obtained vibration signals from slurry pumps were utilized for generating degradation trends while a hybrid nonlinear autoregressive (NAR)-LSTM-BiLSTM model was developed for RUL prediction. The core of the developed strategy was the usage of the NAR prediction results as the “path to be followed” for the designed LSTM-BiLSTM model. The proposed methodology was also applied on publically available NASA’s C-MAPSS dataset for validating its applicability, and in return, satisfactory results were achieved.
Effects of Contact Pressure in Reflectance Photoplethysmography in an In Vitro Tissue-Vessel Phantom
Authors: May James M. , Mejía-Mejía Elisa , Nomoni Michelle , Budidha Karthik , Choi Changmok , Kyriacou Panicos A.
Published: December 2021

With the continued development and rapid growth of wearable technologies, PPG has become increasingly common in everyday consumer devices such as smartphones and watches. There is, however, minimal knowledge on the effect of the contact pressure exerted by the sensor device on the PPG signal and how it might affect its morphology and the parameters being calculated. This study explores a controlled in vitro study to investigate the effect of continually applied contact pressure on PPG signals (signal-to-noise ratio (SNR) and 17 morphological PPG features) from an artificial tissue-vessel phantom across a range of simulated blood pressure values. This experiment confirmed that for reflectance PPG signal measurements for a given anatomical model, there exists an optimum sensor contact pressure (between 35.1 mmHg and 48.1 mmHg). Statistical analysis shows that temporal morphological features are less affected by contact pressure, lending credit to the hypothesis that for some physiological parameters, such as heart rate and respiration rate, the contact pressure of the sensor is of little significance, whereas the amplitude and geometric features can show significant change, and care must be taken when using morphological analysis for parameters such as SpO2 and assessing autonomic responses.
Digital Parenting of Emerging Adults in the 21st Century
Authors: Jensen Michaeline , Hussong Andrea M. , Haston Emily
Published: December 2021

In emerging adulthood, when many young people are away from their families for the first time, mobile phones become an important conduit for maintaining relationships with parents. Yet, objective assessment of the content and frequency of text messaging between emerging adults and their parents is lacking in much of the research to date. We collected two weeks of text messages exchanged between U.S. college students (N = 238) and their parents, which yielded nearly 30,000 parent-emerging adult text messages. We coded these text message exchanges for traditional features of parent-emerging adult communication indexing positive connection, monitoring and disclosures. Emerging adults texted more with mothers than with fathers and many messages constitute parental check-ins and emerging adult sharing regarding youth behavior and well-being. Findings highlight that both the frequency and content of parent-emerging adult text messages can be linked with positive (perceived text message support) and negative (perceived digital pressure) aspects of the parent-emerging adult relationship. The content of parent-emerging adult text messages offers a valuable, objective window into the nature of the parent-emerging adult relationships in the digital age of the 21st century.
Air Quality and Active Transportation Modes: A Spatiotemporal Concurrence Analysis in Guadalajara, Mexico
Authors: Ochoa-Covarrubias Gabriela , González-Figueredo Carlos , DeAlba-Martínez Hugo , Grindlay Alejandro L.
Published: December 2021

The protection of pedestrians, cyclists, and public transportation passengers from environmental pollution is a global concern. This study fills the gap in the existing knowledge of temporal exposure to air pollution in Latin American metropolises. The paper proposes a methodology addressing the relationship between two objects of study, i.e., the users of active modes of transport and air quality. This new methodology assesses the spatiotemporal concurrence of both objects with statistical analysis of large open-access databases, to promote healthy and sustainable urban mobility. The application of the empirical methodology estimated the number of users of active transportation modes exposed to poor air quality episodes in the Guadalajara metropolitan area (Mexico) in 2019. The study considered two pollutants, ozone (O3) and particulate matter (PM10), and two active modes, cycling and bus rapid transit (BRT). Spatiotemporal analyses were carried out with geographic information systems, as well as with numeric computing platforms. First, big data were used to count the number of users for each mode within the area of influence of the air quality monitoring stations. Second, the number of air pollution episodes was obtained using the air quality index proposed by the Environmental Protection Agency (USA) on an hourly basis. Third, the spatiotemporal concurrence between air quality episodes and active mode users was calculated. In particular, the air quality monitoring data from the Jalisco Atmospheric Monitoring System were compared to users of the public bicycle share system, known as MiBici, and of a bus rapid transit line, known as Mi Macro Calzada. The results showed that the number of cyclists and BRT passengers exposed to poor air quality episodes was considerable in absolute terms, that is, 208,660 users, while it was marginal when compared to the total number of users exposed to better air quality categories in the study area, who represented only 10%. To apply the results at the metropolitan scale, the spatial distribution of the air quality monitoring system should be improved, as well as the availability of data on pedestrians and conventional bus passengers.