bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficient and Robust Feature Matching for High-Resolution Satellite Stereos
Authors: Gong Danchao , Huang Xu , Zhang Jidan , Yao Yongxiang , Han Yilong
Published: November 2022

Feature matching between high-resolution satellite stereos plays an important role in satellite image orientation. However, images of changed regions, weak-textured regions and occluded regions may generate low-quality matches or even mismatches. Furthermore, matching throughout the entire satellite images often has extremely high time cost. To compute good matching results at low time cost, this paper proposes an image block selection method for high-resolution satellite stereos, which processes feature matching in several optimal blocks instead of the entire images. The core of the method is to formulate the block selection into the optimization of an energy function, and a greedy strategy is designed to compute an approximate solution. The experimental comparisons on various satellite stereos show that the proposed method could achieve similar matching accuracy and much lower time cost when compared with some state-of-the-art satellite image matching methods. Thus, the proposed method is a good compromise between matching accuracy and matching time, which has great potential in large-scale satellite applications.
Critical Climate Periods Explain a Large Fraction of the Observed Variability in Vegetation State
Authors: Kern Anikó , Barcza Zoltán , Hollós Roland , Birinyi Edina , Marjanović Hrvoje
Published: November 2022

Previous studies have suggested that a major part of the observed variability in vegetation state might be associated with variability in climatic drivers during relatively short periods within the year. Identification of such critical climate periods, when a particular climate variable most likely has a pronounced influence on the vegetation state of a particular ecosystem, becomes increasingly important in the light of climate change. In this study, we present a method to identify critical climate periods for eight different semi-natural ecosystem categories in Hungary, in Central Europe. The analysis was based on the moving-window correlation between MODIS NDVI/LAI and six climate variables with different time lags during the period 2000–2020. Distinct differences between the important climate variables, critical period lengths, and direction (positive or negative correlations) have been found for different ecosystem categories. Multiple linear models for NDVI and LAI were constructed to quantify the multivariate influence of the environmental conditions on the vegetation state during the late summer. For grasslands, the best models for NDVI explained 65–87% variance, while for broad-leaved forests, the highest explained variance for LAI was up to 50%. The proposed method can be easily implemented in other geographical locations and can provide essential insight into the functioning of different ecosystem types.
Regional Patterns of Vegetation Dynamics and Their Sensitivity to Climate Variability in the Yangtze River Basin
Authors: Wang Qin , Ju Qin , Wang Yueyang , Fu Xiaolei , Zhao Wenjie , Du Yiheng , Jiang Peng , Hao Zhenchun
Published: November 2022

To better understand the mechanisms of the hydro-ecological cycle in the changing environments of the Yangtze River Basin (YZRB), it is valuable to investigate vegetation dynamics and their response to climate change. This study explored the spatial patterns of vegetation dynamics and the essential triggers of regional differences by analyzing vegetation variations in the 1982–2015 period at different time scales and the interannual variability of vegetation sensitivity to climate variability. The results showed that the normalized difference vegetation index (NDVI) increased significantly in the last three decades, but vegetation displayed great spatiotemporal variations at different time scales. The vegetation in the central part of the YZRB dominated by forests and shrublands was more sensitive to climate variability than vegetation in the source region of the YZRB, which was dominated by alpine meadows and tundra (AMT). The contribution of climate variables to the vegetation sensitivity index (VSI) had large spatial differences, but solar radiation and temperature were the dominant factors. Furthermore, 57.9% of the YZRB had increasing VSIs, especially in the south-central part. Consistent with the distributions of elevation and vegetation types, vegetation dynamics in the YZRB were divided into five spatial patterns, with the largest increasing NDVI trend in Region III and the largest VSI in Region IV. Moreover, the VSI exhibited fairly consistent dynamics in all subregions, but the contributions of climate variables to the VSI varied greatly among the different regions.
Early-Season Crop Identification in the Shiyang River Basin Using a Deep Learning Algorithm and Time-Series Sentinel-2 Data
Authors: Yi Zhiwei , Jia Li , Chen Qiting , Jiang Min , Zhou Dingwang , Zeng Yelong
Published: November 2022

Timely and accurate crop identification and mapping are of great significance for crop yield estimation, disaster warning, and food security. Early-season crop identification places higher demands on the quality and mining of time-series information than post-season mapping. In recent years, great strides have been made in the development of deep-learning algorithms, and the emergence of Sentinel-2 data with a higher temporal resolution has provided new opportunities for early-season crop identification. In this study, we aimed to fully exploit the potential of deep-learning algorithms and time-series Sentinel-2 data for early-season crop identification and early-season crop mapping. In this study, four classifiers, i.e., two deep-learning algorithms (one-dimensional convolutional networks and long and short-term memory networks) and two shallow machine-learning algorithms (a random forest algorithm and a support vector machine), were trained using early-season Sentinel-2 images and field samples collected in 2019. Then, these algorithms were applied to images and field samples for 2020 in the Shiyang River Basin. Twelve scenarios with different classifiers and time intervals were compared to determine the optimal combination for the earliest crop identification. The results show that: (1) the two deep-learning algorithms outperformed the two shallow machine-learning algorithms in early-season crop identification; (2) the combination of a one-dimensional convolutional network and 5-day interval time-series Sentinel-2 data outperformed the other schemes in obtaining the early-season crop identification time and achieving early mapping; and (3) the early-season crop identification mapping time in the Shiyang River Basin was identified as the end of July, and the overall classification accuracy reached 0.83. In addition, the early identification time for each crop was as follows: the wheat was in the flowering stage (mid-late June); the alfalfa was in the first harvest (mid-late June); the corn was in the early tassel stage (mid-July); the fennel and sunflower were in the flowering stage (late July); and the melons were in the fruiting stage (around late July). This study demonstrates the potential of using Sentinel-2 time-series data and deep-learning algorithms to achieve early-season crop identification, and this method is expected to provide new solutions and ideas for addressing early-season crop identification monitoring.
Global Soil Salinity Prediction by Open Soil Vis-NIR Spectral Library
Authors: Zhou Yin , Chen Songchao , Hu Bifeng , Ji Wenjun , Li Shuo , Hong Yongsheng , Xu Hanyi , Wang Nan , Xue Jie , Zhang Xianglin , Xiao Yi , Shi Zhou
Published: November 2022

Soil salinization is one of the major degradation processes threatening food security and sustainable development. Detailed soil salinity information is increasingly needed to tackle this global challenge for improving soil management. Soil-visible and near-infrared (Vis-NIR) spectroscopy has been proven to be a potential solution for estimating soil-salinity-related information (i.e., electrical conductivity, EC) rapidly and cost-effectively. However, previous studies were mainly conducted at the field, regional, or national scale, so the potential application of Vis-NIR spectroscopy at a global scale needs further investigation. Based on an extensive open global soil spectral library (61,486 samples with both EC and Vis-NIR spectra), we compared four spectral predictive models (PLSR, Cubist, Random Forests, and XGBoost) in estimating EC. Our results indicated that XGBoost had the best model performance (R2 of 0.59, RMSE of 1.96 dS m−1) in predicting EC at a global scale, whereas PLSR had a relatively limited ability (R2 of 0.39, RMSE of 2.41 dS m−1). The results also showed that auxiliary environmental covariates (i.e., coordinates, elevation, climatic variables) could greatly improve EC prediction accuracy by the four models, and the XGBoost performed best (R2 of 0.71, RMSE of 1.65 dS m−1). The outcomes of this study provide a valuable reference for improving broad-scale soil salinity prediction by the coupling of the spectroscopic technique and easily obtainable environmental covariates.
Developing an Automated Python Surface Energy Balance System (PySEBS) Software for Calculating Actual Evapotranspiration-Software Development and Application Case in Jilin Province, China
Authors: Liu Haipeng , Huang Feng , Li Yingxuan , Ren Pinpin , Marek Gary W. , Ding Beibei , Li Baoguo , Chen Yong
Published: November 2022

In this study, Python Surface Energy Balance System (PySEBS) software was developed in the Python 2.7 programming language for continuous calculation of actual evapotranspiration (ETa) at regional scales. The software is based on the Surface Energy Balance System (SEBS) model, which uses basic meteorological data, MODIS remote sensing data, and Digital Elevation Model (DEM) data as the original input data and finally outputs daily-scale ETa in the form of raster data with a spatial resolution of 1 km × 1 km. To verify the reliability of the PySEBS model, the ETa of spring maize during the growing season in Jilin Province, China, from 2001 to 2020 was calculated and analyzed in this study and compared with the results of similar studies by others. The findings showed that the PySEBS model has a reasonable accuracy in estimating ETa within ±15% and is a robust model that can achieve the continuous calculation of ETa at a regional scale. Therefore, PySEBS software is a useful tool for regional irrigation scheduling and water resources management.
Evolution of Influence Ranges of Neolithic-Bronze Age Cities in the Songshan Mountain Region of Central China Based on GIS Spatial Analysis
Authors: Tian Yan , Chen Panpan , Lu Peng , Yang He , Yang Shugang , Zhang Li , Wei Qingli , Zhao Xiangli , Guo Lanbo , Wang Zhen , Luo Rongji
Published: November 2022

Exploring the influence range of early cities is significant for understanding the mechanisms behind ancient settlement systems and human-environment interactions. Due to a lack of effective research methods, the evolution processes and impact mechanisms of the influence ranges of prehistoric cities are still ambiguous. In this study, we chose the Songshan Mountain region for research, which witnessed the origin and development of Chinese civilization. Using GIS spatial analyses such as the ‘average nearest neighbor’ and ‘Thiessen polygon’, we explored the spatial-temporal distributions and influence ranges of Neolithic-Bronze Age cities in the region. The roles of human culture and the natural environment in the process were also investigated. The results indicated that the spatial distributions of early cities were random during the Yangshao (7000–5000 BP) and Longshan (5000–4000 BP) cultures. During the Erlitou culture (3800–3500 BP) and Shang Dynasty (3600–3046 BP), the spatial distributions changed into dispersed models. During the Zhou Dynasty (3046–2256 BP), the spatial distribution model was random again. Correspondingly, the influence range of early cities during the Erlitou culture is the largest, followed by those of the Longshan culture, Yangshao culture, Shang Dynasty, and Zhou Dynasty. This is different from the conventional view that the ancient city’s influence range continuously expands as time advances. Both the natural environment and human culture are believed to impact this evolutional process. Specifically, the Holocene climate variation and the consequent cyclic river downcutting and silting affect the city site selection and thus the spatial-temporal distribution and influence range of early cities. The enfeoffment system occurring during the Erlitou culture should also have played a vital role in this evolution. In general, the natural environment is more important for the spatial distribution and influence range of early cities during Yangshao, Longshan, and Erlitou cultures, while human culture represented by the enfeoffment system plays a dominant role during Shang and Zhou Dynasties when the natural environment is relatively stable.
Spatial and Temporal Characteristics of Water Use Efficiency in Typical Ecosystems on the Loess Plateau in the Last 20 Years, with Drivers and Implications for Ecological Restoration
Authors: Ma Ruixue , Cui Ximin , Wang Dacheng , Wang Shudong , Wang Hongsen , Yao Xiaojing , Li Shenshen
Published: November 2022

The water use efficiency (WUE) is an essential indicator of carbon–water coupling between terrestrial ecosystems and the atmosphere, and it is an important parameter for studying ecosystem responses to global climate change. A comprehensive understanding of the water–carbon coupling process in the Loess Plateau can reflect the balance between the “carbon absorption” and “water consumption” in vegetation, which drives the ecosystem succession process. In recent years, scholars have gained a more comprehensive understanding of the WUE and the driving factors of the Loess Plateau. However, there is still a need to study the carbon and water coupling mechanisms of different land use types in the Loess Plateau region. In this article, based on the gross primary productivity (GPP), evapotranspiration (ET), surface cover remote sensing products, and meteorological observation data, the trend of WUE changes for different vegetation types in the Loess Plateau from 2001 to 2020 and the correlations with the Normalized Difference Vegetation Index (NDVI), precipitation, and temperature values were analyzed using the Theil–Sen median (SEN) trend analysis method and correlation coefficient analysis method. The spatial distribution patterns of the changes with the drought index showed that the multi-year average WUE value of the Loess Plateau was 1.24 g C mm−1 H2O, and the mean WUE values in different seasons were ranked as follows: summer > autumn > spring. The WUE growth rates of all vegetation types showed a decreasing trend with the increase in drought index, and the size of the WUE response rate for each vegetation type to drought was ranked as follows: grassland > forest > shrub > crop. The annual average WUE increase rate of the Loess Plateau was 0.02 g C mm−1 H2O yr−1, of which 93.36% of the area showed an increasing trend. The NDVI was the dominant factor affecting the spatial and temporal variations in WUE rates in the Loess Plateau, and the correlation between the NDVI and WUE was strongest in summer. In the more arid regional ecosystems, the WUE was negatively correlated with the precipitation and temperature, but in summer the precipitation had a positive effect on the WUE. The correlation of grassland and shrub WUE rates with temperature was more sensitive to the drought index than that of the forest and crop areas, but there was also a threshold effect. Therefore, when vegetation restoration is carried out in arid and semi-arid regions, the carbon and water coupling mechanisms of different vegetation types and the reasonable allocation of regional water resources should be fully considered.
A Meta-Analysis of Remote Sensing Technologies and Methodologies for Crop Characterization
Authors: Bahrami Hazhir , McNairn Heather , Mahdianpari Masoud , Homayouni Saeid
Published: November 2022

Climate change and population growth risk the world’s food supply. Annual crop yield production is one of the most crucial components of the global food supply. Moreover, the COVID-19 pandemic has stressed global food security, production, and supply chains. Using biomass estimation as a reliable yield indicator, space-based monitoring of crops can assist in mitigating these stresses by providing reliable product information. Research has been conducted to estimate crop biophysical parameters by destructive and non-destructive approaches. In particular, researchers have investigated the potential of various analytical methods to determine a range of crop parameters using remote sensing data and methods. To this end, they have investigated diverse sources of Earth observations, including radar and optical images with various spatial, spectral, and temporal resolutions. This paper reviews and analyzes publications from the past 30 years to identify trends in crop monitoring research using remote sensing data and tools. This analysis is accomplished through a systematic review of 277 papers and documents the methods, challenges, and opportunities frequently cited in the scientific literature. The results revealed that research in this field had increased dramatically over this study period. In addition, the analyses confirmed that the normalized difference vegetation index (NDVI) had been the most studied vegetation index to estimate crop parameters. Moreover, this analysis showed that wheat and corn were the most studied crops, globally.
Remote Sensing Monitoring of Vegetation Reclamation in the Antaibao Open-Pit Mine
Authors: Hu Jiameng , Ye Baoying , Bai Zhongke , Feng Yu
Published: November 2022

After the regreening of the open-pit mine dump, vegetation usually needs to be managed and protected manually for several years before it reaches stability. Due to the spontaneous combustion of coal gangue, surface collapse, and other reasons, secondary damage may occur at any time. Regreening monitoring plays a vital role in the restoration and reconstruction of the mining ecosystem and can provide support for the timely replenishment of seedlings in the damaged area. In this study, remote sensing images were collected from 1986 to 2020 to obtain the NDVI distribution of dumps in the Antaibao open-pit coal mine. In order to obtain the overall growth law of regreening vegetation over time, the study adopted the unary regression analysis method and tested the correlation between NDVI and time by the Pearson correlation coefficient. However, through the Sen+Mann–Kendall trend analysis, it was found that there were differences in the trends of NDVI within the same dump. Next, by means of the Mann–Kendall mutation test and interactive interpretation, information, such as stable nodes of different regreening vegetation and vegetation growth patterns in degraded areas, were obtained. Through the above methods, the following conclusions were drawn: (1) The earlier the dumps were regreened, the more the areas were covered by significantly improved vegetation. In this study: 97.31% (the proportion of significantly improved vegetation in the south dump) >95.58% (the proportion in the west dump) >86.56% (the proportion in the inner dump) >79.89% (the proportion in the west expansion dump). (2) Different vegetation types have different time nodes for reaching stability. It takes about three years for wood, shrub, and a mix of grass, shrub, and wood to reach stability, but only one year for grass. (3) The destruction in mining areas is expansive and repeatable. Monitoring the growth patterns of regreening vegetation is conducive to understanding the reclamation effect, and provides a scientific basis for land reclamation planning and land management policies in the mining area. At the same time, the trend analysis method in this study can quickly extract problem areas after dump regreening and is applicable in most dumps.
Impacts of FY-4A Atmospheric Motion Vectors on the Henan 7.20 Rainstorm Forecast in 2021
Authors: Xie Yanhui , Chen Min , Zhang Shuting , Shi Jiancheng , Liu Ruixia
Published: November 2022

Atmospheric motion vectors (AMVs) derived from images of the geostationary satellite, Fengyun-4A (FY-4A), can provide high-spatiotemporal-resolution wind observations in the atmospheric middle and upper levels. To explore the potential benefits of these data for the numerical forecasting of severe weather events, the characteristics of FY-4A AMVs in different channels were analyzed and three groups of assimilation experiments were conducted in this study. The impacts of FY-4A AMVs on the forecasts of the rainstorm that occurred in Henan province in China on 20 July 2021, were investigated based on the Weather Research and Forecasting (WRF) model. The results show that FY-4A AMVs with a higher quality indicator (QI) exhibited a lower error characteristic at the cost of a reduced sample size. The assimilation of FY-4A AMVs reduced the error of the upper-level wind fields in 24 h forecasts. A positive impact could also be obtained for 10 m wind in 24 h forecasts, with an improvement of up to 9.74% for the mean bias and 3.0% for the root-mean-square error due to the inclusion of FY-4A AMVs with a QI > 70. Assimilating the AMVs with a QI > 80, there was an overall positive impact on the CSI score skills of 6 h accumulated precipitation above 1.0 mm in the 24 h forecast. A significant improvement could be found in the forecasting of heavy rainfall above 25.0 mm after 6 h of the forecast. The spatial distribution of the 24 h accumulated heavy rainfall zone was closer to the observations with the assimilation of the FY-4A AMVs. The adjustment of the initial wind fields resulting from the FY-4A AMVs brought a clear benefit to the quantitative precipitation forecasting skills in the event of the Henan 7.20 rainstorm; however, the AMV data assimilation still had difficulty in capturing the hourly maximum rainfall and intensity well.
Ground Deformation in Yuxi Basin Based on Atmosphere-Corrected Time-Series InSAR Integrated with the Latest Meteorological Reanalysis Data
Authors: Guo Shipeng , Zuo Xiaoqing , Wu Wenhao , Li Fang , Li Yongfa , Yang Xu , Zhu Shasha , Zhao Yanxi
Published: November 2022

Time-series interferometric synthetic aperture radar (TS-InSAR) is often affected by tropospheric artifacts caused by temporal and spatial variability in the atmospheric refractive index. Conventional temporal and spatial filtering cannot effectively distinguish topography-related stratified delays, leading to biased estimates of the deformation phases. Here, we propose a TS-InSAR atmospheric delay correction method based on ERA-5; the robustness and accuracy of ERA-5 data under the influence of different atmospheric delays were explored. Notably, (1) wet delay was the main factor affecting tropospheric delay within the interferogram; the higher spatial and temporal resolution of ERA-5 can capture the wet delay signal better than MERRA-2. (2) The proposed method can mitigate the atmospheric delay component in the interferogram; the average standard deviation (STD) reduction for the Radarsat-2 and Sentinel-1A interferograms were 19.68 and 14.75%, respectively. (3) Compared to the empirical linear model, the correlation between the stratified delays estimated by the two methods reached 0.73. We applied this method for the first time to a ground subsidence study in the Yuxi Basin and successfully detected three subsidence centers. We analyzed and discussed ground deformation causes based on rainfall and fault zones. Finally, we verified the accuracy of the proposed method by using leveling monitoring data.
Robust Estimation and Optimized Transmission of 3D Feature Points for Computer Vision on Mobile Communication Network
Authors: Kim Jin-Kyum , Park Byung-Seo , Kim Woosuk , Park Jung-Tak , Lee Sol , Seo Young-Ho
Published: November 2022

Due to the amount of transmitted data and the security of personal or private information in wireless communication, there are cases where the information for a multimedia service should be directly transferred from the user’s device to the cloud server without the captured original images. This paper proposes a new method to generate 3D (dimensional) keypoints based on a user’s mobile device with a commercial RGB camera in a distributed computing environment such as a cloud server. The images are captured with a moving camera and 2D keypoints are extracted from them. After executing feature extraction between continuous frames, disparities are calculated between frames using the relationships between matched keypoints. The physical distance of the baseline is estimated by using the motion information of the camera, and the actual distance is calculated by using the calculated disparity and the estimated baseline. Finally, 3D keypoints are generated by adding the extracted 2D keypoints to the calculated distance. A keypoint-based scene change method is proposed as well. Due to the existing similarity between continuous frames captured from a camera, not all 3D keypoints are transferred and stored, only the new ones. Compared with the ground truth of the TUM dataset, the average error of the estimated 3D keypoints was measured as 5.98 mm, which shows that the proposed method has relatively good performance considering that it uses a commercial RGB camera on a mobile device. Furthermore, the transferred 3D keypoints were decreased to about 73.6%.
A Generic Pixel Pitch Calibration Method for Fundus Camera via Automated ROI Extraction
Authors: Long Tengfei , Xu Yi , Zou Haidong , Lu Lina , Yuan Tianyi , Dong Zhou , Dong Jiqun , Ke Xin , Ling Saiguang , Ma Yingyan
Published: November 2022

Pixel pitch calibration is an essential step to make the fundus structures in the fundus image quantitatively measurable, which is important for the diagnosis and treatment of many diseases, e.g., diabetes, arteriosclerosis, hereditary optic atrophy, etc. The conventional calibration approaches require the specific parameters of the fundus camera or several specially shot images of the chess board, but these are generally not accessible, and the calibration results cannot be generalized to other cameras. Based on automated ROI (region of interest) and optic disc detection, the diameter ratio of ROI and optic disc (ROI–disc ratio) is quantitatively analyzed for a large number of fundus images. With the prior knowledge of the average diameter of an optic disc in fundus, the pixel pitch can be statistically estimated from a large number of fundus images captured by a specific camera without the availability of chess board images or detailed specifics of the fundus camera. Furthermore, for fundus cameras of FOV (fixed field-of-view), the pixel pitch of a fundus image of 45° FOV can be directly estimated according to the automatically measured diameter of ROI in the pixel. The average ROI–disc ratio is approximately constant, i.e., 6.404 ± 0.619 in the pixel, according to 40,600 fundus images, captured by different cameras, of 45° FOV. In consequence, the pixel pitch of a fundus image of 45° FOV can be directly estimated according to the automatically measured diameter of ROI in the pixel, and results show the pixel pitches of Canon CR2, Topcon NW400, Zeiss Visucam 200, and Newvision RetiCam 3100 cameras are 6.825 ± 0.666 μm, 6.625 ± 0.647 μm, 5.793 ± 0.565 μm, and 5.884 ± 0.574 μm, respectively. Compared with the manually measured pixel pitches, based on the method of ISO 10940:2009, i.e., 6.897 μm, 6.807 μm, 5.693 μm, and 6.050 μm, respectively, the bias of the proposed method is less than 5%. Since our method doesn’t require chess board images or detailed specifics, the fundus structures on the fundus image can be measured accurately, according to the pixel pitch obtained by this method, without knowing the type and parameters of the camera.
Rice Crop Counting Using Aerial Imagery and GIS for the Assessment of Soil Health to Increase Crop Yield
Authors: Hassan Syeda Iqra , Alam Muhammad Mansoor , Zia Muhammad Yousuf Irfan , Rashid Muhammad , Illahi Usman , Su’ud Mazliham Mohd
Published: November 2022

Rice is one of the vital foods consumed in most countries throughout the world. To estimate the yield, crop counting is used to indicate improper growth, identification of loam land, and control of weeds. It is becoming necessary to grow crops healthy, precisely, and proficiently as the demand increases for food supplies. Traditional counting methods have numerous disadvantages, such as long delay times and high sensitivity, and they are easily disturbed by noise. In this research, the detection and counting of rice plants using an unmanned aerial vehicle (UAV) and aerial images with a geographic information system (GIS) are used. The technique is implemented in the area of forty acres of rice crop in Tando Adam, Sindh, Pakistan. To validate the performance of the proposed system, the obtained results are compared with the standard plant count techniques as well as approved by the agronomist after testing soil and monitoring the rice crop count in each acre of land of rice crops. From the results, it is found that the proposed system is precise and detects rice crops accurately, differentiates from other objects, and estimates the soil health based on plant counting data; however, in the case of clusters, the counting is performed in semi-automated mode.
Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate Signals
Authors: Dénes-Fazakas Lehel , Siket Máté , Szilágyi László , Kovács Levente , Eigner György
Published: November 2022

Non-coordinated physical activity may lead to hypoglycemia, which is a dangerous condition for diabetic people. Decision support systems related to type 1 diabetes mellitus (T1DM) still lack the capability of automated therapy modification by recognizing and categorizing the physical activity. Further, this desired adaptive therapy should be achieved without increasing the administrative load, which is already high for the diabetic community. These requirements can be satisfied by using artificial intelligence-based solutions, signals collected by wearable devices, and relying on the already available data sources, such as continuous glucose monitoring systems. In this work, we focus on the detection of physical activity by using a continuous glucose monitoring system and a wearable sensor providing the heart rate—the latter is accessible even in the cheapest wearables. Our results show that the detection of physical activity is possible based on these data sources, even if only low-complexity artificial intelligence models are deployed. In general, our models achieved approximately 90% accuracy in the detection of physical activity.
Monopole Antenna with Enhanced Bandwidth and Stable Radiation Patterns Using Metasurface and Cross-Ground Structure
Authors: Danuor Patrick , Anim Kyei , Jung Young-Bae
Published: November 2022

In this paper, a printed monopole antenna with stable omnidirectional radiation patterns is presented for applications in ocean buoy and the marine Internet of Things (IoT). The antenna is composed of a rectangular patch, a cross-ground structure, and two frequency-selective surface (FSS) unit cells. The cross-ground structure is incorporated into the antenna design to maintain consistent monopole-like radiation patterns over the antenna’s operating band, and the FSS unit cells are placed at the backside of the antenna to improve the antenna gain aiming at the L-band. In addition, the FSS unit cells exhibit resonance characteristics that, when incorporated with the cross-ground structure, result in a broader impedance bandwidth compared to the conventional monopole antenna. To validate the structure, a prototype is fabricated and measured. Good agreement between the simulated and measured results show that the proposed antenna exhibits an impedance bandwidth of 83.2% from 1.65 to 4 GHz, compared to the conventional printed monopole antenna. The proposed antenna realizes a peak gain of 4.57 dBi and a total efficiency of 97% at 1.8 GHz.
Research on a Non-Contact Multi-Electrode Voltage Sensor and Signal Processing Algorithm
Authors: Zhang Wenbin , Yang Yonglong , Zhao Jingjing , Huang Rujin , Cheng Kang , He Mingxing
Published: November 2022

Traditional contact voltage measurement requires a direct electrical connection to the system, which is not easy to install and maintain. The voltage measurement based on the electric field coupling plate capacitance structure does not need to be in contact with the measured object or the ground, which can avoid the above problems. However, most of the existing flat-plate structure voltage measurement sensors are not only expensive to manufacture, but also bulky, and when the relative position between the wire under test and the sensor changes, it will bring great measurement errors, making it difficult to meet actual needs. Aiming to address the above problems, this paper proposes a multi-electrode array structure non-contact voltage sensor and signal processing algorithm. The sensor is manufactured by the PCB process, which effectively reduces the manufacturing cost and process difficulty. The experimental and simulation results show that, when the relative position of the wire and the sensor is offset by 10 mm in the 45° direction, the relative error of the traditional single-electrode voltage sensor is 17.62%, while the relative error of the multi-electrode voltage sensor designed in this paper is only 0.38%. In addition, the ratio error of the sensor under the condition of power frequency of 50 Hz is less than ±1% and the phase difference is less than 4°. The experimental results show that the sensor has good accuracy and linearity.
Analysis of Ultrasonic Machining Characteristics under Dynamic Load
Authors: Chen Zhangping , Zhao Xinghong , Chen Shixing , Chen Honghuan , Ni Pengfei , Zhang Fan
Published: November 2022

This research focuses on the load characteristics of piezoelectric transducers in the process of longitudinal vibration ultrasonic welding. We are primarily interested in the impedance characteristics of the piezoelectric transducer during loading, which is studied by leveraging the equivalent circuit theory of piezoelectric transducers. Specifically, we propose a cross-value mapping method. This method can well map the load change in ultrasonic welding to the impedance change, aiming to obtain an equivalent model of impedance and load. The least-squares strategy is used for parameter identification during data fitting. Extensive simulations and physical experiments are conducted to verify the proposed model. As a result, we can empirically find that the result from our model agrees with the impedance characteristics from the real-life data measured by the impedance meter, indicating its potential for real practice in controller research and transducer design.
Attention-Guided Disentangled Feature Aggregation for Video Object Detection
Authors: Muralidhara Shishir , Hashmi Khurram Azeem , Pagani Alain , Liwicki Marcus , Stricker Didier , Afzal Muhammad Zeshan
Published: November 2022

Object detection is a computer vision task that involves localisation and classification of objects in an image. Video data implicitly introduces several challenges, such as blur, occlusion and defocus, making video object detection more challenging in comparison to still image object detection, which is performed on individual and independent images. This paper tackles these challenges by proposing an attention-heavy framework for video object detection that aggregates the disentangled features extracted from individual frames. The proposed framework is a two-stage object detector based on the Faster R-CNN architecture. The disentanglement head integrates scale, spatial and task-aware attention and applies it to the features extracted by the backbone network across all the frames. Subsequently, the aggregation head incorporates temporal attention and improves detection in the target frame by aggregating the features of the support frames. These include the features extracted from the disentanglement network along with the temporal features. We evaluate the proposed framework using the ImageNet VID dataset and achieve a mean Average Precision (mAP) of 49.8 and 52.5 using the backbones of ResNet-50 and ResNet-101, respectively. The improvement in performance over the individual baseline methods validates the efficacy of the proposed approach.
Autonomous Control of the Large-Angle Spacecraft Maneuvers in a Non-Cooperative Mission
Authors: Huang Cheng , Cao Tianzeng , Huang Jinglin
Published: November 2022

Aiming at the large-angle maneuver control problem of tracking spacecraft attitude in non-cooperative target rendezvous and proximity tasks, under the condition that the target spacecraft attitude information is unknown and the actuator output has physical limitations, a limited-time autonomous control method is proposed. First, an end-to-end pose estimation network is designed based on adaptive dual-channel feature extraction and dual attention. The information around the target is obtained through the adaptive dual-channel feature extraction module. The addition of spatial attention and channel attention allows the network to learn the target’s characteristics more accurately. Secondly, based on the improved adaptive update law, a finite-time saturation controller is designed using the hyperbolic tangent function and the auxiliary system. The hyperbolic tangent function can strictly ensure that the control torque of the control system is bounded. Finally, the simulation results show that the proposed autonomous control method can accurately estimate the attitude of the non-cooperative target spacecraft and can maneuver to the target attitude within 20 s under the condition that the actuator’s output is physically limited.
Wearable Devices Combined with Artificial Intelligence—A Future Technology for Atrial Fibrillation Detection?
Authors: Mäkynen Marko , Ng G. , Li Xin , Schlindwein Fernando
Published: November 2022

Atrial fibrillation (AF) is the most common cardiac arrhythmia in the world. The arrhythmia and methods developed to cure it have been studied for several decades. However, professionals worldwide are still working to improve treatment quality. One novel technology that can be useful is a wearable device. The two most used recordings from these devices are photoplethysmogram (PPG) and electrocardiogram (ECG) signals. As the price lowers, these devices will become significant technology to increase sensitivity, for monitoring and for treatment quality support. This is important as AF can be challenging to detect in advance, especially during home monitoring. Modern artificial intelligence (AI) has the potential to respond to this challenge. AI has already achieved state of the art results in many applications, including bioengineering. In this perspective, we discuss wearable devices combined with AI for AF detection, an approach that enables a new era of possibilities for the future.
Extending the Incidence Angle of Shear Vertical Wave Electromagnetic Acoustic Transducer with Horizontal Magnetization
Authors: Qu Zhengyang , Li Zhichao , Yang Runjie , Hu Songtao , Wang Shujuan
Published: November 2022

Angled shear vertical (SV) waves have been successfully employed in the non-destructive testing of welds, pipes, and railways. Non-contact meander-line coil electromagnetic acoustic transducers (EMAT) have many benefits in generating angled SV waves. The most important benefit is that the incidence angle of an SV wave can be controlled by the excitation frequency. However, the incidence angle of a traditional SV-wave EMAT is reported to be under 45 degrees in many cases. In this work, such cases are tested, and the problems of the received signal at large incidence angles are found to be due to wave interference and small signal amplitudes. An equivalent finite element (FE) model is established to analyze the problem, and the main reason is found to be the head wave. An alternative configuration of angled SV-wave EMAT with horizontal magnetization is proposed to reduce the influence of the head wave. Finally, the results from simulations and experiments show that the proposed EMAT has a larger signal amplitude and significantly reduced interference in large-incidence angle scenarios. Moreover, an incidence angle of an SV wave of up to 60 degrees can be achieved, which will help improve the performance and capability of nondestructive testing.
Object Relocation Visual Tracking Based on Histogram Filter and Siamese Network in Intelligent Transportation
Authors: Zhang Jianlong , Liu Yifan , Li Qiao , He Ci , Wang Bin , Wang Tianhong
Published: November 2022

Target detection and tracking algorithms are one of the key technologies in the field of autonomous driving in intelligent transportation, providing important sensing capabilities for vehicle localization and path planning. Siamese network-based trackers formulate the visual tracking mission as an image-matching process by regression and classification branches, which simplifies the network structure and improves the tracking accuracy. However, there remain many problems, as described below. (1) The lightweight neural networks decrease the feature representation ability. It is easy for the tracker to fail under the disturbing distractors (e.g., deformation and similar objects) or large changes in the viewing angle. (2) The tracker cannot adapt to variations of the object. (3) The tracker cannot reposition the object that has failed to track. To address these issues, we first propose a novel match filter arbiter based on the Euclidean distance histogram between the centers of multiple candidate objects to automatically determine whether the tracker fails. Secondly, the Hopcroft–Karp algorithm is introduced to select the winners from the dynamic template set through the backtracking process, and object relocation is achieved by comparing the Gradient Magnitude Similarity Deviation between the template and the winners. The experiments show that our method obtains better performance on several tracking benchmarks, i.e., OTB100, VOT2018, GOT-10k, and LaSOT, compared with state-of-the-art methods.
Packet Loss Characterization Using Cross Layer Information and HMM for Wi-Fi Networks
Authors: da Silva Carlos Alexandre Gouvea , Pedroso Carlos Marcelo
Published: November 2022

Packet loss is a major problem for wireless networks and has significant effects on the perceived quality of many internet services. Packet loss models are used to understand the behavior of packet losses caused by several reasons, e.g., interferences, coexistence, fading, collisions, and insufficient/excessive memory buffers. Among these, the Gilbert-Elliot (GE) model, based on a two-state Markov chain, is the most used model in communication networks. However, research has proven that the GE model is inadequate to represent the real behavior of packet losses in Wi-Fi networks. In this last category, variables of a single network layer are used, usually the physical one. In this article, we propose a new packet loss model for Wi-Fi that simultaneously considers the temporal behavior of losses and the variables that describe the state of the network. In addition, the model uses two important variables, the signal-to-noise ratio and the network occupation, which none of the packet loss models available for Wi-Fi networks simultaneously take into account. The proposed model uses the well-known Hidden Markov Model (HMM), which facilitates training and forecasting. At each state of HMM, the burst-length of losses is characterized using probability distributions. The model was evaluated by comparing computer simulation and real data samples for validation, and using the log-log complementary distribution of burst-length. We compared the proposed model with competing models through the analysis of mean square error (MSE) using a validation sample collected from a real network. Results demonstrated that the proposed model outperforms the currently available models for packet loss in Wi-Fi networks.
The Trade-Off between Airborne Pandemic Control and Energy Consumption Using Air Ventilation Solutions
Authors: Alexi Ariel , Rosenfeld Ariel , Lazebnik Teddy
Published: November 2022

Airborne diseases cause high mortality and adverse socioeconomic consequences. Due to urbanization, more people spend more time indoors. According to recent research, air ventilation reduces long-range airborne transmission in indoor settings. However, air ventilation solutions often incur significant energy costs and ecological footprints. The trade-offs between energy consumption and pandemic control indoors have not yet been thoroughly analyzed. In this work, we use advanced sensors to monitor the energy consumption and pandemic control capabilities of an air-conditioning system, a pedestal fan, and an open window in hospital rooms, classrooms, and conference rooms. A simulation of an indoor airborne pandemic spread of Coronavirus (COVID-19) is used to analyze the Pareto front. For the three examined room types, the Pareto front consists of all three air ventilation solutions, with some ventilation configurations demonstrating significant inefficiencies. Specifically, air-conditioning is found to be efficient only at a very high energy cost and fans seem to pose a reasonable alternative. To conclude, a more informed ventilation policy can bring about a more desirable compromise between energy consumption and pandemic spread control.
How Does a Port Build Influence? Diffusion Patterns in Global Oil Transportation
Authors: Peng Peng , Claramunt Christophe , Cheng Shifen , Lu Feng
Published: November 2022

Ports play a critical role in the global oil trade market, and those with significant influence have an implicit advantage in global oil transportation. In order to offer a thorough understanding of port influences, the research presented in this paper analyzes the evolution of the dominance mechanisms underlying port influence diffusion. Our study introduces a port influence diffusion model to outline global oil transport patterns. It examines the direct and indirect influence of ports using worldwide vessel trajectory data from 2009 to 2016. Port influences are modelled via diffusion patterns and the resulting ports influenced. The results of the case study applied to specific ports show different patterns and influence evolutions. Four main port influence trends are identified. The first one is that ports that have a strong direct influence over their neighboring ports materialize a directly influenced area. Second, geographical distance still plays an important role in the whole port influence patterns. Third, it clearly appears that, the higher the number of directly influenced ports, the higher the probability of having an influence pattern, as revealed by the diffusion process. The peculiarity of this approach is that, in contrast to previous studies, global maritime trade is analyzed in terms of direct and indirect influences and according to oil trade flows.