bluuebunny/crossref_metadata_2025 huggingface dataset:
Understanding Human Activities in Response to Typhoon Hato from Multi-Source Geospatial Big Data: A Case Study in Guangdong, China
Authors: Huang Sheng , Du Yunyan , Yi Jiawei , Liang Fuyuan , Qian Jiale , Wang Nan , Tu Wenna
Published: March 2022

Every year typhoons severely disrupt the normal rhythms of human activities and pose serious threats to China’s coast. Previous studies have shown that the impact extent and degree of a typhoon can be inferred from various geolocation datasets. However, it remains a challenge to unravel how dwellers respond to a typhoon disaster and what they concern most in the places with significant human activity changes. In this study, we integrated the geotagged microblogs with the Tencent’s location request data to advance our understanding of dweller’s collective response to typhoon Hato and the changes in their concerns over the typhoon process. Our results show that Hato induces both negative and positive anomalies in humans’ location request activities and such anomalies could be utilized to characterize the impacts of wind and rainfall brought by Hato to our study area, respectively. Topic analysis of Hato-related geotagged microblogs reveals that the negative location request anomalies are closely related to damage-related topics, whereas the positive anomalies to traffic-related topics. The negative anomalies are significantly correlated with economic loss and population affected at city level as suggested by an over 0.7 adjusted R2. The changes in the anomalies can be used to portray the response and recovery processes of the cities impacted.
Granularity of Digital Elevation Model and Optimal Level of Detail in Small-Scale Cartographic Relief Presentation
Authors: Samsonov Timofey
Published: March 2022

One of the key applications of digital elevation models (DEMs) is cartographic relief presentation. DEMs are widely used in mapping, most commonly in the form of contours, hypsometric tints, and hill shading. Recent advancements in the coverage, quality, and resolution of global DEMs facilitate the overall improvement of the detail and reliability of terrain-related research. At the same time, geographic problem solving is conducted in a wide variety of scales, and the data used for mapping should have the corresponding level of detail. Specifically, at small scales, intensive generalization is needed, which is also true for elevation data. With the widespread accessibility of detailed DEMs, this principle is often violated, and the data are used for mapping at scales far smaller than what is appropriate. Small-scale relief shading obtained from fine-resolution DEMs is excessively detailed and brings an unclear representation of the Earth’s surface instead of emphasizing what is important at the scale of visualization. Existing coarse-resolution global DEMs do not resolve the issue, since they accumulate the maximum possible information in every pixel, and therefore also require reduction in detail to obtain a high-quality cartographic image. It is clear that guidelines and effective principles for DEM generalization at small scales are needed. Numerous algorithms have been developed for the generalization of elevation data represented either in gridded, contoured, or pointwise form. However, the answer to the most important question—When should we stop surface simplification?—remains unclear. Primitive error-based measures such as vertical distance are not effective for cartography, since they do not account for the landform structure of the surface perceived by the map reader. The current paper approached the problem by elaborating the granularity—a newly developed property of DEMs, which characterizes the typical size of a landform represented on the DEM surface. A methodology of estimating the granularity through a landform width measure was conceptualized and implemented as software. Using the developed program tools, the optimal granularity was statistically learned from DEMs reconstructed for multiple fragments of manually drawn 1:200,000, 1:500,000, and 1:1,000,000 topographic maps covering different relief types. It was shown that the relative granularity should be 5–6 mm at the mapping scale to achieve the clearness of relief presentation typical for manually drawn maps. We then demonstrate how the granularity measure can be used effectively as a constraint during DEM generalization. Experimental results on a combination of contours, hypsometric tints, and hill shading indicated clearly that the optimal level of detail in small-scale cartographic relief presentation can be achieved by DEM generalization constrained by granularity in combination with fine DEM resolution, which facilitates high-quality rendering.
Development of a Phenology-Based Method for Identifying Sugarcane Plantation Areas in China Using High-Resolution Satellite Datasets
Authors: Zheng Yi , Li Zhuoting , Pan Baihong , Lin Shangrong , Dong Jie , Li Xiangqian , Yuan Wenping
Published: March 2022

Sugarcane is an important sugar and biofuel crop with high socio-economic importance, and its planted area has increased rapidly in recent years. China is the world’s third or fourth sugarcane producer. However, to our knowledge, no study has investigated the mapping of sugarcane cultivation areas across entire China. In this study, we developed a phenology-based method to identify sugarcane plantations in China at 30-m spatial resolution from 2016–2020 using the time-series of Landsat and Sentinel-1/2 images derived from Google Earth Engine (GEE) platform. The method worked by comparing the phenological similarity in normalized difference vegetation index (NDVI) series between unknown pixels and sugarcane samples. The phenological similarity was assessed using the time-weighted dynamic time warping method (TWDTW), which has less sensitivity to training samples than machine learning methods and therefore can be easily applied to large areas with limited samples. More importantly, our method introduced multiple and moving time standard phenological curves of sugarcane to the TWDTW by fully considering the variable crop life-cycle of sugarcane, particularly its long harvest season spanning from December to March of the following year. Validations showed the method performed well in 2019, with overall accuracies of 93.47% and 92.74% for surface reflectance (SR) and top of atmosphere reflectance (TOA) data, respectively. The sugarcane maps agreed well with the agricultural statistical areas from 2016–2020. The mapping accuracies using TOA data were comparable to SR data in 2019–2020, but outperformed SR data in 2016–2018 when SR data had lower availability on GEE. The sugarcane maps produced in this study can be used to monitor growing conditions and production of sugarcane and, therefore, can benefit sugarcane management, sustainable sugarcane production, and national food security.
Effects of Land Use/Cover on Regional Habitat Quality under Different Geomorphic Types Based on InVEST Model
Authors: Wang Baixue , Cheng Weiming
Published: March 2022

Research on habitat quality change is of great significance for regional ecological security. Analysis of spatiotemporal change of habitat quality based on different geomorphic types can restore the background of ecological environment in historical periods and provide scientific support for revealing the evolution law of regional ecological environment quality and ecological restoration. This study aimed to identify the change in habitat quality under different geomorphic types from 1995 to 2018. Based on DEM data, geomorphic types of different scales were divided. The InVEST habitat quality model was used to analyze the spatiotemporal change in habitat quality in individual land use types in the Altay region. The spatiotemporal changes and main influencing factors of habitat quality under the background of different geomorphic types were explored. Remote sensing data was used to analyze the land use/cover changes. Sixteen threat sources, their maximum distance of impact, mode of decay, and sensitivity to threats were also estimated for each land use type. The results showed that habitat quality decreased significantly in 2015, which was related to the rapid expansion of cultivated and construction land as threat sources, as well as the decrease of forestland and grassland as sensitive factors. However, habitat quality improved significantly in 2018, because of the implementation of ecological restoration policy in 2015. Affected by elevation and topographic relief, the geomorphic type with the best habitat quality index was the large undulating middle mountain (0.927) and the worst was the medium altitude platform (0.351). Woodland contributed the most to habitat quality in large undulating middle mountain (35.07), and bare rock gravel land contributed the most to medium altitude platform (127.68). Habitat quality of different geomorphic types showed obvious spatial aggregation, and from high altitude to low altitude showed a banded ladder-like distribution. Changes in habitat quality during the past three decades suggested that the conservation and restoration strategies applied in regional ecosystem were effective. On the basis of the analysis results, four types of zoning management schemes were divided, and the ecological management and conservation measures were put forward. Therefore, this study can help decision makers, especially regarding the lack of data on biodiversity.
Identification of the Potential Critical Slip Surface for Fractured Rock Slope Using the Floyd Algorithm
Authors: Song Shengyuan , Zhao Mingyu , Zhu Chun , Wang Fengyan , Cao Chen , Li Haojie , Ma Muye
Published: March 2022

A rock slope can be characterized by tens of persistent discontinuities. A slope can be massive. The slip surface of the slope is usually easier to expand along with the discontinuities because the shear strength of the discontinuities is substantially lower than that of the rock blocks. Based on this idea, this paper takes a jointed rock slope in Hengqin Island, Zhuhai as an example, and establishes a three-dimensional (3D) model of the studied slope by digital close-range photogrammetry to rapidly interpret 222 fracture parameters. Meanwhile, a new Floyd algorithm for finding the shortest path is developed to realize the critical slip surface identification of the studied slope. Within the 3D fracture network model created using the Monte Carlo method, a sequence of cross-sections is placed. These cross-sections containing fractures are used to search for the shortest paths between the designated shear entrances and exits. For anyone combination of entry point and exit point, the shortest paths corresponding to different cross-sections are different and cluttered. For the sake of safety and convenience, these shortest paths are simplified as a circular arc that is regarded as a potential slip surface. The fracture frequency is used to determine the probability of sliding along a prospective critical slip surface. The potential slip surface through the entrance point (0, 80) and exit point (120, 0) is identified as the final critical slip surface of the slope due to the maximum fracture frequency.
Parsimonious Gap-Filling Models for Sub-Daily Actual Evapotranspiration Observations from Eddy-Covariance Systems
Authors: Guo Danlu , Parehkar Arash , Ryu Dongryeol , Wang Quan J. , Western Andrew W.
Published: March 2022

Missing data and low data quality are common issues in field observations of actual evapotranspiration (ETa) from eddy-covariance systems, which necessitates the need for gap-filling techniques to improve data quality and utility for further analyses. A number of models have been proposed to fill temporal gaps in ETa or latent heat flux observations. However, existing gap-filling approaches often use multi-variate models that rely on relationships between ETa and other meteorological and flux variables, highlighting a critical lack of parsimonious gap-filling models. This study aims to develop and evaluate parsimonious approaches to fill gaps in ETa observations. We adapted three gap-filling models previously used for other meteorological variables but never applied to infill sub-daily ETa or flux observations from eddy-covariance systems before. All three models are solely based on the observed diurnal patterns in the ETa data, which infill gaps in sub-daily data with sinusoidal functions (Sinusoidal), smoothing functions (Smoothing) and pattern matching (MaxCor) approaches, respectively. We presented a systematic approach for model evaluation, considering multiple patterns of data gaps during different times of the day. The three gap-filling models were evaluated together with another benchmarking gap-filling model, mean diurnal variation (MDV) that has been commonly used and has similar data requirement. We used a case study with field measurements from an EC system over summer 2020–2021, at a maize field in southeastern Australia. We identified the MaxCor model as the best gap-filling model, which informs the diurnal pattern of the day to infill by using another day with similar temporal patterns and complete data. Following the MaxCor model, the MDV and the Sinusoidal models show comparable performances. We further discussed the infilling models in terms of their dependence on data availability and their suitability for different practical situations. The MaxCor model relies on high data availability for both days with complete data and the available records within each day to infill. The Sinusoidal model does not rely on any day with complete data, which makes it the ideal choice in situations where days with complete records are limited.
ISAR Resolution Enhancement Method Exploiting Generative Adversarial Network
Authors: Wang Haobo , Li Kaiming , Lu Xiaofei , Zhang Qun , Luo Ying , Kang Le
Published: March 2022

Deep learning has been used in inverse synthetic aperture radar (ISAR) imaging to improve resolution performance, but there still exist some problems: the loss of weak scattering points, over-smoothed imaging results, and the universality and generalization. To address these problems, an ISAR resolution enhancement method of exploiting a generative adversarial network (GAN) is proposed in this paper. We adopt a relativistic average discriminator (RaD) to enhance the ability of the network to describe target details. The proposed loss function is composed of feature loss, adversarial loss, and absolute loss. The feature loss is used to get the main characteristics of the target. The adversarial loss ensures that the proposed GAN recovers more target details. The absolute loss is adopted to make the imaging results not over-smoothed. Experiments based on simulated and measured data under different conditions demonstrate that the proposed method has good imaging performance. In addition, the universality and generalization of the proposed GAN are also well verified.
A Multipopulation Dynamic Adaptive Coevolutionary Strategy for Large-Scale Complex Optimization Problems
Authors: Yin Yanlei , Wang Lihua , Zhang Litong
Published: March 2022

In this paper, a multipopulation dynamic adaptive coevolutionary strategy is proposed for large-scale optimization problems, which can dynamically and adaptively adjust the connection between population particles according to the optimization problem characteristics. Based on analysis of the network evolution characteristics of collaborative search between particles, a dynamic adaptive evolutionary network (DAEN) model with multiple interconnection couplings is established in this algorithm. In the model, the swarm type is divided according to the judgment threshold of particle types, and the dynamic evolution of collaborative topology in the evolutionary process is adaptively completed according to the coupling connection strength between different particle types, which enhances the algorithm’s global and local searching capability and optimization accuracy. Based on that, the evolution rules of the particle swarm dynamic cooperative search network were established, the search algorithm was designed, and the adaptive coevolution between particles in different optimization environments was achieved. Simulation results revealed that the proposed algorithm exhibited a high optimization accuracy and converging rate for high-dimensional and large-scale complex optimization problems.
A Low-Cost Metamaterial Sensor Based on DS-CSRR for Material Characterization Applications
Authors: Shahzad Waseem , Hu Weidong , Ali Qasim , Raza Hamid , Abbas Syed Muzahir , Ligthart Leo P.
Published: March 2022

This paper presents a metamaterial sensor using a double slit complementary square ring resonator (DS-CSRR) that has been utilized for the measurement of dielectric materials, especially coal powder. The design is optimized for best performance of deep notch depth in transmission coefficient (Magnitude of S21). Sensitivity analysis of transmission coefficient with respect to structure dimensions has been carried out. Metamaterial properties of double negative permitivity and permeability were extracted from the S–parameters of this sensor. The optimized structure is fabricated using low cost FR-4 PCB board. Measured result shows resonance frequency of 4.75 GHz with a deep notch up to −41 dB. Simulated and measured results show good agreement in desired frequency band. For material characterization, first, two known materials are characterized using this metamaterial sensor. Their respective resonances and dielectric constants are known, so the transcendental equation of the sensor is formulated. Afterwards, the proposed sensor is used for dielectric measurement of two types of coal powder, i.e., Anthracite and Bituminous. The measured value of dielectric constant of Anthracite coal is 3.5 and of Bituminous coal is 2.52. This is a simple and effective nondestructive measurement technique for material testing applications.
A Grip Strength Estimation Method Using a Novel Flexible Sensor under Different Wrist Angles
Authors: Wang Yina , Zheng Liwei , Yang Junyou , Wang Shuoyu
Published: March 2022

It is a considerable challenge to realize the accurate, continuous detection of handgrip strength due to its complexity and uncertainty. To address this issue, a novel grip strength estimation method oriented toward the multi-wrist angle based on the development of a flexible deformation sensor is proposed. The flexible deformation sensor consists of a foaming sponge, a Hall sensor, an LED, and photoresistors (PRs), which can measure the deformation of muscles with grip strength. When the external deformation squeezes the foaming sponge, its density and light intensity change, which is detected by a light-sensitive resistor. The light-sensitive resistor extended to the internal foaming sponge with illuminance complies with the extrusion of muscle deformation to enable relative muscle deformation measurement. Furthermore, to achieve the speed, accuracy, and continuous detection of grip strength with different wrist angles, a new grip strength-arm muscle model is adopted and a one-dimensional convolutional neural network based on the dynamic window is proposed to recognize wrist joints. Finally, all the experimental results demonstrate that our proposed flexible deformation sensor can accurately detect the muscle deformation of the arm, and the designed muscle model and convolutional neural network can continuously predict hand grip at different wrist angles in real-time.
Edge Computing for Vision-Based, Urban-Insects Traps in the Context of Smart Cities
Authors: Saradopoulos Ioannis , Potamitis Ilyas , Ntalampiras Stavros , Konstantaras Antonios I. , Antonidakis Emmanuel N.
Published: March 2022

Our aim is to promote the widespread use of electronic insect traps that report captured pests to a human-controlled agency. This work reports on edge-computing as applied to camera-based insect traps. We present a low-cost device with high power autonomy and an adequate picture quality that reports an internal image of the trap to a server and counts the insects it contains based on quantized and embedded deep-learning models. The paper compares different aspects of performance of three different edge devices, namely ESP32, Raspberry Pi Model 4 (RPi), and Google Coral, running a deep learning framework (TensorFlow Lite). All edge devices were able to process images and report accuracy in counting exceeding 95%, but at different rates and power consumption. Our findings suggest that ESP32 appears to be the best choice in the context of this application according to our policy for low-cost devices.
The Microsoft HoloLens 2 Provides Accurate Measures of Gait, Turning, and Functional Mobility in Healthy Adults
Authors: Miller Koop Mandy , Rosenfeldt Anson B. , Owen Kelsey , Penko Amanda L. , Streicher Matthew C. , Albright Alec , Alberts Jay L.
Published: March 2022

Augmented-reality (AR) headsets, such as the Microsoft HoloLens 2 (HL2), have the potential to be the next generation of wearable technology as they provide interactive digital stimuli in the context of ecologically-valid daily activities while containing inertial measurement units (IMUs) to objectively quantify the movements of the user. A necessary precursor to the widespread utilization of the HL2 in the fields of movement science and rehabilitation is the rigorous validation of its capacity to generate biomechanical outcomes comparable to gold standard outcomes. This project sought to determine equivalency of kinematic outcomes characterizing lower-extremity function derived from the HL2 and three-dimensional (3D) motion capture systems (MoCap). Sixty-six healthy adults completed two lower-extremity tasks while kinematic data were collected from the HL2 and MoCap: (1) continuous walking and (2) timed up-and-go (TUG). For all the continuous walking metrics (cumulative distance, time, number of steps, step and stride length, and velocity), equivalence testing indicated that the HL2 and MoCap were statistically equivalent (error ≤ 5%). The TUG metrics, including turn duration and turn velocity, were also statistically equivalent between the two systems. The accurate quantification of gait and turning using a wearable such as the HL2 provides initial evidence for its use as a platform for the development and delivery of gait and mobility assessments, including the in-person and remote delivery of highly salient digital movement assessments and rehabilitation protocols.
A Vision-Based System for In-Sleep Upper-Body and Head Pose Classification
Authors: Li Yan-Ying , Wang Shoue-Jen , Hung Yi-Ping
Published: March 2022

Sleep quality is known to have a considerable impact on human health. Recent research shows that head and body pose play a vital role in affecting sleep quality. This paper presents a deep multi-task learning network to perform head and upper-body detection and pose classification during sleep. The proposed system has two major advantages: first, it detects and predicts upper-body pose and head pose simultaneously during sleep, and second, it is a contact-free home security camera-based monitoring system that can work on remote subjects, as it uses images captured by a home security camera. In addition, a synopsis of sleep postures is provided for analysis and diagnosis of sleep patterns. Experimental results show that our multi-task model achieves an average of 92.5% accuracy on challenging datasets, yields the best performance compared to the other methods, and obtains 91.7% accuracy on the real-life overnight sleep data. The proposed system can be applied reliably to extensive public sleep data with various covering conditions and is robust to real-life overnight sleep data.
A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks
Authors: Gupta Chaitanya , Johri Ishita , Srinivasan Kathiravan , Hu Yuh-Chung , Qaisar Saeed Mian , Huang Kuo-Yi
Published: March 2022

Today’s advancements in wireless communication technologies have resulted in a tremendous volume of data being generated. Most of our information is part of a widespread network that connects various devices across the globe. The capabilities of electronic devices are also increasing day by day, which leads to more generation and sharing of information. Similarly, as mobile network topologies become more diverse and complicated, the incidence of security breaches has increased. It has hampered the uptake of smart mobile apps and services, which has been accentuated by the large variety of platforms that provide data, storage, computation, and application services to end-users. It becomes necessary in such scenarios to protect data and check its use and misuse. According to the research, an artificial intelligence-based security model should assure the secrecy, integrity, and authenticity of the system, its equipment, and the protocols that control the network, independent of its generation, in order to deal with such a complicated network. The open difficulties that mobile networks still face, such as unauthorised network scanning, fraud links, and so on, have been thoroughly examined. Numerous ML and DL techniques that can be utilised to create a secure environment, as well as various cyber security threats, are discussed. We address the necessity to develop new approaches to provide high security of electronic data in mobile networks because the possibilities for increasing mobile network security are inexhaustible.
A High-Robust Sensor Activity Control Algorithm for Wireless Sensor Networks
Authors: Tsai Rong-Guei , Lv Xiaoyan , Shen Lin , Tsai Pei-Hsuan
Published: March 2022

In wireless sensor networks, it is important to use the right number of sensors to optimize the network and consider the key design and cost. Due to the limited power of sensors, important issues include how to control the state of the sensor through an automatic control algorithm and how to power-save and efficiently distribute work. However, sensor nodes are usually deployed in dangerous or inaccessible locations. Therefore, it is difficult and impractical to supply power to sensors through humans. In this study, we propose a high reliability control algorithm with fast convergence and strong self-organization ability called the sensor activity control algorithm (SACA), which can efficiently control the number of sensors in the active state and extend their use time. In the next round, SACA considers the relationship between the total number of active sensors and the target value and determines the state of the sensor. The data transmission technology of random access is used between the sensor and the base station. Therefore, the sensor in the sleep state does not need to receive the feedback packet from the base station. The sensor can achieve true dormancy and power-saving effects. The experimental results show that SACA has fast convergence, strong self-organization capabilities, and power-saving advantages.
Intelligent Defect Diagnosis of Rolling Element Bearings under Variable Operating Conditions Using Convolutional Neural Network and Order Maps
Authors: Tayyab Syed Muhammad , Chatterton Steven , Pennacchi Paolo
Published: March 2022

Vibration analysis is an established method for fault detection and diagnosis of rolling element bearings. However, it is an expert oriented exercise. To relieve the experts, the use of Artificial Intelligence (AI) techniques such as deep neural networks, especially convolutional neural networks (CNN) have gained the attention of researchers because of their image classification and recognition capability. Most researchers convert the vibration signal into representative time frequency vibration images such as spectrograms and scalograms. These images are used as inputs to train the CNN model for fault diagnosis. Commonly, fault diagnosis is performed under same operating conditions, where models are trained and deployed for prediction under the same operating conditions. However, outside the laboratory environment, in real world applications, different operating conditions, such as variable speed, may be encountered. With the change in speed, the characteristic frequencies of the vibration signal will also change, which will result in changing the vibration image. Consequently, the performance of the CNN model may drop significantly for prediction under different operating conditions. Accessing the training data from all potential operating conditions may not be feasible for most real-world applications. Therefore, there is a need to find some signal properties which are invariant to change in operating conditions and only change due to change in health state so that models trained under one set of operating conditions may predict correctly under different operating conditions. This paper proposes a defect diagnosis method for rolling element bearings, under variable operating conditions (speed and load) based on CNN and order maps. These maps exhibit consistent properties under varying speed; therefore, they can be used to train the CNN model for fault diagnosis under variable speed. The effect of load change on these order maps is experimentally studied and it is found that the proposed method can undertake fault diagnosis on rolling element bearings under variable speeds and loads with high accuracy.
Design of a Supraharmonic Monitoring System Based on an FPGA Device
Authors: Barkas Dimitris A. , Ioannidis George Ch. , Kaminaris Stavros D. , Psomopoulos Constantinos S.
Published: March 2022

During the last few decades, the poor quality of produced electric power is a key factor that has affected the operation of critical electrical infrastructure such as high-voltage equipment. This type of equipment exhibits multiple different failures, which originate from the poor electric power quality. This phenomenon is basically due to the utilization of high-frequency switching devices that operate over modern electrical generation systems, such as PV inverters. The conduction of significant values of electric currents at high frequencies in the range of 2 to 150 kHz can be destructive for electrical and electronic equipment and should be measured. However, the measuring devices that have the ability of analyzing a signal in the frequency domain present the ability of analyzing up to 2.5 kHz–3 kHz, which are frequencies too low in comparison to the high switching frequencies that inverters, for example, work. Electric currents at 16 kHz were successfully measured on an 8 kWp roof PV generator. This paper presents a fast-developed modern measuring system, using a field programmable gate array, aiming to detect electric currents at high frequencies, with a capability for working up to 150 kHz. The system was tested in the laboratory, and the results are satisfactory.
Continuous Glucose Monitoring in Healthy Adults—Possible Applications in Health Care, Wellness, and Sports
Authors: Holzer Roman , Bloch Wilhelm , Brinkmann Christian
Published: March 2022

Introduction: Continuous glucose monitoring (CGM) systems were primarily developed for patients with diabetes mellitus. However, these systems are increasingly being used by individuals who do not have diabetes mellitus. This mini review describes possible applications of CGM systems in healthy adults in health care, wellness, and sports. Results: CGM systems can be used for early detection of abnormal glucose regulation. Learning from CGM data how the intake of foods with different glycemic loads and physical activity affect glucose responses can be helpful in improving nutritional and/or physical activity behavior. Furthermore, states of stress that affect glucose dynamics could be made visible. Physical performance and/or regeneration can be improved as CGM systems can provide information on glucose values and dynamics that may help optimize nutritional strategies pre-, during, and post-exercise. Conclusions: CGM has a high potential for health benefits and self-optimization. More scientific studies are needed to improve the interpretation of CGM data. The interaction with other wearables and combined data collection and analysis in one single device would contribute to developing more precise recommendations for users.
Direction of Arrival Estimation Based on Received Signal Strength Using Two-Row Electronically Steerable Parasitic Array Radiator Antenna
Authors: Rzymowski Mateusz , Nyka Krzysztof , Kulas Lukasz
Published: March 2022

In this paper, we present a novel approach to direction-of-arrival (DoA) estimation using two-row electronically steerable parasitic array radiator (ESPAR) antenna which has 12 passive elements and allows for elevation and azimuth beam switching using a simple microcontroller, relying solely on received signal strength (RSS) values measured at the antenna output port. To this end, we thoroughly investigate all 18 available 3D antenna radiation patterns of the antenna measured in an anechoic chamber with respect to radiation coverage in the horizontal and vertical direction and propose a generalization of the power-pattern cross-correlation (PPCC) algorithm involving a high number of multiple calibration planes (MCP) as well as specific combinations of radiation pattern sets. Additionally, a new way of RSS-based DoA estimation accuracy assessment, which involves thorough testing conducted along the elevation direction when RF signals impinging on the antenna arrive from arbitrary θ angles, has been reported in this paper to verify the overall algorithm’s performance. The results obtained for different signal-to-noise ratio (SNR) levels indicate that two-row ESPAR antenna can produce, even for low SNR values, accurate DoA estimation in the horizontal plane without prior knowledge about the elevation direction of the unknown RF signals by using appropriate combinations of only 12 3D antenna radiation patterns.
Simulation of 3D Body Shapes for Pregnant and Postpartum Women
Authors: Sinthanayothin Chanjira , Xuto Piyanut , Bholsithi Wisarut , Gansawat Duangrat , Wongwaen Nonlapas , Ratisoontorn Nantaporn , Bunporn Parut , Charoensiriwath Supiya
Published: March 2022

Several studies have reported that pre-pregnant women’s body mass index (BMI) affects women’s weight gain with complications during pregnancy and the postpartum weight retention. It is important to control the BMI before, during and after pregnancy. Our objectives are to develop a technique that can compute and visualize 3D body shapes of women during pregnancy and postpartum in various gestational ages, BMI, and postpartum durations. Body changes data from 98 pregnant and 83 postpartum women were collected, tracked for six months, and analyzed to create 3D model shapes. This study allows users to simulate their 3D body shapes in real-time and online, based on weight, height, and gestational age, using multiple linear regression and morphing techniques. To evaluate the results, precision tests were performed on simulated 3D pregnant and postpartum women’s shapes. Additionally, a satisfaction test on the application was conducted on new 149 mothers. The accuracy of the simulation was tested on 75 pregnant and 74 postpartum volunteers in terms of relationships between statistical calculation, simulated 3D models and actual tape measurement of chest, waist, hip, and inseam. Our results can predict accurately the body proportions of pregnant and postpartum women.
Improving Haptic Response for Contextual Human Robot Interaction
Authors: Mugisha Stanley , Guda Vamsi Krisha , Chevallereau Christine , Zoppi Matteo , Molfino Rezia , Chablat Damien
Published: March 2022

For haptic interaction, a user in a virtual environment needs to interact with proxies attached to a robot. The device must be at the exact location defined in the virtual environment in time. However, due to device limitations, delays are always unavoidable. One of the solutions to improve the device response is to infer human intended motion and move the robot at the earliest time possible to the desired goal. This paper presents an experimental study to improve the prediction time and reduce the robot time taken to reach the desired position. We developed motion strategies based on the hand motion and eye-gaze direction to determine the point of user interaction in a virtual environment. To assess the performance of the strategies, we conducted a subject-based experiment using an exergame for reach and grab tasks designed for upper limb rehabilitation training. The experimental results in this study revealed that eye-gaze-based prediction significantly improved the detection time by 37% and the robot time taken to reach the target by 27%. Further analysis provided more insight on the effect of the eye-gaze window and the hand threshold on the device response for the experimental task.
DC Power Line Communication (PLC) on 868 MHz and 2.4 GHz Wired RF Transceivers
Authors: Marsic Vlad , Amietszajew Tazdin , Igic Petar , Faramehr Soroush , Fleming Joe
Published: March 2022

Efficient management through monitoring of Li-ion batteries is critical to the progress of electro-mobility and energy storage globally, since the technology can be hazardous if pushed beyond its safety boundaries. Battery management systems (BMSs) are being actively improved to reduce size, weight, and cost while increasing their capabilities. Using power line communication, wireless monitoring, or hybrid data links are one of the most advanced research directions today. In this work, we propose the use of radio frequency (RF) transceivers as a communication unit that can deliver both wired and wireless services, through their superior analog and digital signal processing capability compared to PLC technology. To validate our approach computational simulation and empirical evaluation was conducted to examine the possibility of using RF transceivers on a direct current (DC) bus for wired BMS. A key advantage of this study is that it proposes a flexible and tested system for communication across a variety of network scenarios, where wireless data links over disrupted connections may be enabled by using this technology in short-range wired modes. This investigation demonstrates that the IEEE 802.15.4-compliant transceivers with operating frequencies of 868 MHz and 2.4 GHz can establish stable data links on a DC bus via capacitive coupling at high data rates.
Electromagnetic Encoders Screen-Printed on Rubber Belts for Absolute Measurement of Position and Velocity
Authors: Paredes Ferran , Herrojo Cristian , Moya Ana , Berenguel Alonso Miguel , Gonzalez David , Bruguera Pep , Delgado Simao Claudia , Martín Ferran
Published: March 2022

This paper presents, for the first time, an absolute linear electromagnetic encoder consisting of a rubber belt with two chains of screen-printed metallic inclusions (rectangular patches). The position, velocity, and direction of the belt (the moving part) is determined by detecting the inclusions when they cross the stator (the static part). The stator is a microstrip line loaded with three complementary split ring resonators (CSRRs), resonant elements exhibiting a resonance frequency perturbed by the presence of inclusions on top of them (contactless). The line is fed by three harmonic signals tuned to the resonance frequencies of the CSRRs. Such signals are generated by a voltage-controlled oscillator (VCO) managed by a microcontroller. The sensed data are retrieved from the pulses contained in the envelope functions of the respective amplitude modulated (AM) signals (caused by the belt motion) generated at the output port of the line. One of the signals provides the absolute belt position, determined by one of the chains, the encoded one. The information relative to the velocity and motion direction is contained in the other AM signals generated by the motion of the other chain, periodic, and thereby, uncoded. The spatial resolution of the system, a figure of merit, is 4 mm. Special emphasis is devoted to the printing process of the belt inclusions.
Vehicle-Assisted UAV Delivery Scheme Considering Energy Consumption for Instant Delivery
Authors: Deng Xudong , Guan Mingke , Ma Yunfeng , Yang Xijie , Xiang Ting
Published: March 2022

Unmanned aerial vehicles (UAVs) are increasingly used in instant delivery scenarios. The combined delivery of vehicles and UAVs has many advantages compared to their respective separate delivery, which can greatly improve delivery efficiency. Although a few studies in the literature have explored the issue of vehicle-assisted UAV delivery, we did not find any studies on the scenario of an UAV serving several customers. This study aims to design a new vehicle-assisted UAV delivery solution that allows UAVs to serve multiple customers in a single take-off and takes energy consumption into account. A multi-UAV task allocation model and a vehicle path planning model were established to determine the task allocation of the UAVs as well as the path of UAVs and the vehicle, respectively. The model also considered the impact of changing the payload of the UAV on energy consumption, bringing the results closer to reality. Finally, a hybrid heuristic algorithm based on an improved K-means algorithm and ant colony optimization (ACO) was proposed to solve the problem, and the effectiveness of the scheme was proven by multi-scale experimental instances and comparative experiments.
Long Range Raman-Amplified Distributed Acoustic Sensor Based on Spontaneous Brillouin Scattering for Large Strain Sensing
Authors: Bakhtiari Gorajoobi Shahab , Masoudi Ali , Brambilla Gilberto
Published: March 2022

A Brillouin distributed acoustic sensor (DAS) based on optical time-domain refractometry exhibiting a maximum detectible strain of 8.7 mε and a low signal fading is developed. Strain waves with frequencies of up to 120 Hz are measured with an accuracy of 12 με at a sampling rate of 1.2 kHz and a spatial resolution of 4 m over a sensing range of 8.5 km. The sensing range is further extended by using a modified inline Raman amplifier configuration. Using 80 ns Raman pump pulses, the signal-to-noise ratio is improved by 3.5 dB, while the accuracy of the measurement is enhanced by a factor of 2.5 to 62 με at the far-end of a 20 km fiber.
Improvement of Schottky Contacts of Gallium Oxide (Ga2O3) Nanowires for UV Applications
Authors: Alhalaili Badriyah , Al-Duweesh Ahmad , Popescu Ileana Nicoleta , Vidu Ruxandra , Vladareanu Luige , Islam M. Saif
Published: March 2022

Interest in the synthesis and fabrication of gallium oxide (Ga2O3) nanostructures as wide bandgap semiconductor-based ultraviolet (UV) photodetectors has recently increased due to their importance in cases of deep-UV photodetectors operating in high power/temperature conditions. Due to their unique properties, i.e., higher surface-to-volume ratio and quantum effects, these nanostructures can significantly enhance the sensitivity of detection. In this work, two Ga2O3 nanostructured films with different nanowire densities and sizes obtained by thermal oxidation of Ga on quartz, in the presence and absence of Ag catalyst, were investigated. The electrical properties influenced by the density of Ga2O3 nanowires (NWs) were analyzed to define the configuration of UV detection. The electrical measurements were performed on two different electric contacts and were located at distances of 1 and 3 mm. Factors affecting the detection performance of Ga2O3 NWs film, such as the distance between metal contacts (1 and 3 mm apart), voltages (5–20 V) and transient photocurrents were discussed in relation to the composition and nanostructure of the Ga2O3 NWs film.
Temperature Optimization by Electrochemical Method for Improving Antioxidant Compound Extraction Efficiency from Origanum vulgare L. and Its Application in a Bread Production
Authors: Mikołajczak Anna , Ligaj Marta , Kobus-Cisowska Joanna
Published: March 2022

This study aims to evaluate the effect of extraction temperature on the electrochemical activity of antioxidant compounds in oregano extract and its application in a bread production. Temperature optimisation was performed by determining the electrochemical index (EI), calculated on the parameters of individual peaks observed on the square wave voltammograms (SWV). The highest value of EI (2.5758 µA/V) was observed at 85 °C for the oregano extract. The composition of several types of bread with oregano extract or dried oregano leaves was then proposed. To specify bread samples, both newly prepared and during their storage, their antioxidant properties were determined using FRAP (Ferric Reducing Antioxidant Power) and CUPRAC (Cupric Reducing Antioxidant Capacity) methods. The study revealed that the addition of extract from oregano or oregano leaves increased the antioxidant compounds content in the bread from 30% to more than 138% compared to the control bread samples. The performed sensory evaluation of the bread samples revealed their high acceptability. It was found that the stored bread with oregano leaves changed sensory qualities to a lesser extent compared to the bread with oregano extract.
Layout Aware Semantic Element Extraction for Sustainable Science & Technology Decision Support
Authors: Kim Hyuntae , Choi Jongyun , Park Soyoung , Jung Yuchul
Published: March 2022

New scientific and technological (S&T) knowledge is being introduced rapidly, and hence, analysis efforts to understand and analyze new published S&T documents are increasing daily. Automated text mining and vision recognition techniques alleviate the burden somewhat, but the various document layout formats and knowledge content granularities across the S&T field make it challenging. Therefore, this paper proposes LA-SEE (LAME and Vi-SEE), a knowledge graph construction framework that simultaneously extracts meta-information and useful image objects from S&T documents in various layout formats. We adopt Layout-aware Metadata Extraction (LAME), which can accurately extract metadata from various layout formats, and implement a transformer-based instance segmentation (i.e., Vision based Semantic Elements Extraction (Vi-SEE)) to maximize the vision-based semantic element recognition. Moreover, to constructing a scientific knowledge graph consisting of multiple S&T documents, we newly defined an extensible Semantic Elements Knowledge Graph (SEKG) structure. For now, we succeeded in extracting about 6 million semantic elements from 49,649 PDFs. In addition, to illustrate the potential power of our SEKG, we provide two promising application scenarios, such as a scientific knowledge guide across multiple S&T documents and questions and answering over scientific tables.
Immunity-Enhancing Effect of Extracts Extracted from Leaves of Rubia hexaphylla, Cymbopogon citratus, and Dioscorea japonica for Sustainable Healthy Life
Authors: Choi Mijeong
Published: March 2022

The COVID-19 era requires research and development into eco-friendly immune technologies in human life. However, such research is limited because of a lack of interest. Therefore, in this study, sub-critical extracts extracted from Rubia hexaphylla leaves, Cymbopogon citratus, and Dioscorea japonica with physiological effects were tested for their ability to enhance immunitye and antioxidant activity in the body. Cell viability experiments, cytokine secretion induction experiments, nitrogen oxide induction experiments, and cytokine content measurements in mouse serum and NK activation experiments were conducted to verify the effects of improving immunitye and antioxidant activity in the body. First, the cell survival rate of all samples was found to be over 95%. Second, it was confirmed that the contents of IL-6 and TNF-α in the macrophages of all samples were increased, especially for RCD 3, at concentrations of 1000 μg/mL. Third, the nitrogen oxide content of all samples was increased in the nitrogen oxide induction evaluation, especially for RCD 3, at the highest concentration of 1000 μg/mL. Fourth, for RCD 3, IL-2 and TNF-γ were the highest measured in the measurement of the cytokine content in the mouse serum. Fifth, in the NK activation experiments, the positive control group of the samples was all found to have higher activity than the negative control group, with more than 60% cell activity in RCD 3.