bluuebunny/crossref_metadata_2025 huggingface dataset:
Identifying Causes of Urban Differential Subsidence in the Vietnamese Mekong Delta by Combining InSAR and Field Observations
Authors: de Wit Kim , Lexmond Bente R. , Stouthamer Esther , Neussner Olaf , Dörr Nils , Schenk Andreas , Minderhoud Philip S. J.
Published: January 2021

The Mekong delta, like many deltas around the world, is subsiding at a relatively high rate, predominately due to natural compaction and groundwater overexploitation. Land subsidence influences many urbanized areas in the delta. Loading, differences in infrastructural foundation depths, land-use history, and subsurface heterogeneity cause a high spatial variability in subsidence rates. While overall subsidence of a city increases its exposure to flooding and reduces the ability to drain excess surface water, differential subsidence results in damage to buildings and above-ground and underground infrastructure. However, the exact contribution of different processes driving differential subsidence within cities in the Mekong delta has not been quantified yet. In this study we aim to identify and quantify drivers of processes causing differential subsidence within three major cities in the Vietnamese Mekong delta: Can Tho, Ca Mau and Long Xuyen. Satellite-based PS-InSAR (Persistent Scatterer Interferometric Synthetic Aperture Radar) vertical velocity datasets were used to identify structures that moved at vertical velocities different from their surroundings. The selected buildings were surveyed in the field to measure vertical offsets between their foundation and the surface level of their surroundings. Additionally, building specific information, such as construction year and piling depth, were collected to investigate the effect of piling depth and time since construction on differential vertical subsidence. Analysis of the PS-InSAR-based velocities from the individual buildings revealed that most buildings in this survey showed less vertical movement compared to their surroundings. Most of these buildings have a piled foundation, which seems to give them more stability. The difference in subsidence rate can be up to 30 mm/year, revealing the contribution of shallow compaction processes above the piled foundation level (up to 20 m depth). This way, piling depths can be used to quantify depth-dependent subsidence. Other local factors such as previous land use, loading of structures without a piled foundation and variation in piling depth, i.e., which subsurface layer the structures are founded on, are proposed as important factors determining urban differential subsidence. PS-InSAR data, in combination with field observations and site-specific information (e.g., piling depths, land use, loading), provides an excellent opportunity to study urban differential subsidence and quantify depth-dependent subsidence rates. Knowing the magnitude of differential subsidence in urban areas helps to differentiate between local and delta wide subsidence patterns in InSAR-based velocity data and to further improve estimates of future subsidence.
Sub-Pixel Mapping Model Based on Total Variation Regularization and Learned Spatial Dictionary
Authors: Msellmi Bouthayna , Picone Daniele , Ben Rabah Zouhaier , Dalla Mura Mauro , Farah Imed Riadh
Published: January 2021

In this research study, we deal with remote sensing data analysis over high dimensional space formed by hyperspectral images. This task is generally complex due to the large spectral, spatial richness, and mixed pixels. Thus, several spectral un-mixing methods have been proposed to discriminate mixing spectra by estimating the classes and their presence rates. However, information related to mixed pixel composition is very interesting for some applications, but it is insufficient for many others. Thus, it is necessary to have much more data about the spatial localization of the classes detected during the spectral un-mixing process. To solve the above-mentioned problem and specify the spatial location of the different land cover classes in the mixed pixel, sub-pixel mapping techniques were introduced. This manuscript presents a novel sub-pixel mapping process relying on K-SVD (K-singular value decomposition) learning and total variation as a spatial regularization parameter (SMKSVD-TV: Sub-pixel Mapping based on K-SVD dictionary learning and Total Variation). The proposed approach adopts total variation as a spatial regularization parameter, to make edges smooth, and a pre-constructed spatial dictionary with the K-SVD dictionary training algorithm to have more spatial configurations at the sub-pixel level. It was tested and validated with three real hyperspectral data. The experimental results reveal that the attributes obtained by utilizing a learned spatial dictionary with isotropic total variation allowed improving the classes sub-pixel spatial localization, while taking into account pre-learned spatial patterns. It is also clear that the K-SVD dictionary learning algorithm can be applied to construct a spatial dictionary, particularly for each data set.
Galileo Ionospheric Correction Algorithm Integration into the Open-Source GNSS Laboratory Tool Suite (gLAB)
Authors: Aragon-Angel Angela , Rovira-Garcia Adria , Arcediano-Garrido Enrique , Ibáñez-Segura Deimos
Published: January 2021

Users of the global navigation satellite system (GNSS) operating with a single-frequency receiver must use an ionospheric correction algorithm (ICA) to account for the delay introduced on radio waves by the upper atmosphere. Galileo, the European GNSS, uses an ICA named NeQuick-G. In an effort to foster the adoption of NeQuick-G by final users, two implementations in C language have been recently made available to the public by the European Space Agency (ESA) and the Joint Research Centre (JRC) of the European Commission (EC), respectively. The aim of the present contribution is to compare the slant total electron content (STEC) predictions of the two aforementioned implementations of NeQuick-G. For this purpose, we have used actual multi-constellation and multi-frequency data for several hundreds of stations distributed worldwide belonging to the Multi GNSS Experiment (MGEX) network of the International GNSS Service (IGS). For each first day of the month during year 2019, the STECs of the two NeQuick-G versions were compared in terms of accuracy, consistency, availability, and execution time. Our study concludes that both implementations of NeQuick-G perform equivalently. Indeed, in over 99.998% of the 2125 million STECs computed, the output is exactly coincident. In contrast, 0.002% of the whole set of STECs for those rays are tangent to the Earth, the behavior of both implementations differs. We confirmed the discrepancy by processing radio-occultation actual measurements from a COSMIC-2 low Earth orbit satellite. We selected the JRC version of the Galileo ICA to be integrated into the GNSS LABoratory (gLAB) tool suite, because its open license and its processing speed (it is 13.88% faster than the ESA version). NeQuick-G outperforms the GPS ICA in STEC residuals up to 12.15 TECUs (percentile 96.23th) and in the 3D position errors, up to 5.76 m (percentile 99.18th) for code-pseudorange positioning.
A Novel Method for Automated Supraglacial Lake Mapping in Antarctica Using Sentinel-1 SAR Imagery and Deep Learning
Authors: Dirscherl Mariel , Dietz Andreas J. , Kneisel Christof , Kuenzer Claudia
Published: January 2021

Supraglacial meltwater accumulation on ice sheets can be a main driver for accelerated ice discharge, mass loss, and global sea-level-rise. With further increasing surface air temperatures, meltwater-induced hydrofracturing, basal sliding, or surface thinning will cumulate and most likely trigger unprecedented ice mass loss on the Greenland and Antarctic ice sheets. While the Greenland surface hydrological network as well as its impacts on ice dynamics and mass balance has been studied in much detail, Antarctic supraglacial lakes remain understudied with a circum-Antarctic record of their spatio-temporal development entirely lacking. This study provides the first automated supraglacial lake extent mapping method using Sentinel-1 synthetic aperture radar (SAR) imagery over Antarctica and complements the developed optical Sentinel-2 supraglacial lake detection algorithm presented in our companion paper. In detail, we propose the use of a modified U-Net for semantic segmentation of supraglacial lakes in single-polarized Sentinel-1 imagery. The convolutional neural network (CNN) is implemented with residual connections for optimized performance as well as an Atrous Spatial Pyramid Pooling (ASPP) module for multiscale feature extraction. The algorithm is trained on 21,200 Sentinel-1 image patches and evaluated in ten spatially or temporally independent test acquisitions. In addition, George VI Ice Shelf is analyzed for intra-annual lake dynamics throughout austral summer 2019/2020 and a decision-level fused Sentinel-1 and Sentinel-2 maximum lake extent mapping product is presented for January 2020 revealing a more complete supraglacial lake coverage (~770 km2) than the individual single-sensor products. Classification results confirm the reliability of the proposed workflow with an average Kappa coefficient of 0.925 and a F1-score of 93.0% for the supraglacial water class across all test regions. Furthermore, the algorithm is applied in an additional test region covering supraglacial lakes on the Greenland ice sheet which further highlights the potential for spatio-temporal transferability. Future work involves the integration of more training data as well as intra-annual analyses of supraglacial lake occurrence across the whole continent and with focus on supraglacial lake development throughout a summer melt season and into Antarctic winter.
Adaptive Weighting Feature Fusion Approach Based on Generative Adversarial Network for Hyperspectral Image Classification
Authors: Liang Hongbo , Bao Wenxing , Shen Xiangfei
Published: January 2021

Recently, generative adversarial network (GAN)-based methods for hyperspectral image (HSI) classification have attracted research attention due to their ability to alleviate the challenges brought by having limited labeled samples. However, several studies have demonstrated that existing GAN-based HSI classification methods are limited in redundant spectral knowledge and cannot extract discriminative characteristics, thus affecting classification performance. In addition, GAN-based methods always suffer from the model collapse, which seriously hinders their development. In this study, we proposed a semi-supervised adaptive weighting feature fusion generative adversarial network (AWF2-GAN) to alleviate these problems. We introduced unlabeled data to address the issue of having a small number of samples. First, to build valid spectral–spatial feature engineering, the discriminator learns both the dense global spectrum and neighboring separable spatial context via well-designed extractors. Second, a lightweight adaptive feature weighting component is proposed for feature fusion; it considers four predictive fusion options, that is, adding or concatenating feature maps with similar or adaptive weights. Finally, for the mode collapse, the proposed AWF2-GAN combines supervised central loss and unsupervised mean minimization loss for optimization. Quantitative results on two HSI datasets show that our AWF2-GAN achieves superior performance over state-of-the-art GAN-based methods.
Multi-Exposure Fusion of Gray Images Under Low Illumination Based on Low-Rank Decomposition
Authors: Nie Ting , Huang Liang , Liu Hongxing , Li Xiansheng , Zhao Yuchen , Yuan Hangfei , Song Xiangyu , He Bin
Published: January 2021

Existing multi-exposure fusion (MEF) algorithms for gray images under low-illumination cannot preserve details in dark and highlighted regions very well, and the fusion image noise is large. To address these problems, an MEF method is proposed. First, the latent low-rank representation (LatLRR) is used on low-dynamic images to generate low-rank parts and saliency parts to reduce noise after fusion. Then, two components are fused separately in Laplace multi-scale space. Two different weight maps are constructed according to features of gray images under low illumination. At the same time, an energy equation is designed to obtain the optimal ratio of different weight factors. An improved guided filtering based on an adaptive regularization factor is proposed to refine the weight maps to maintain spatial consistency and avoid artifacts. Finally, a high dynamic image is obtained by the inverse transform of low-rank part and saliency part. The experimental results show that the proposed method has advantages both in subjective and objective evaluation over state-of-the-art multi-exposure fusion methods for gray images under low-illumination imaging.
Fully Automated Detection of Supraglacial Lake Area for Northeast Greenland Using Sentinel-2 Time-Series
Authors: Hochreuther Philipp , Neckel Niklas , Reimann Nathalie , Humbert Angelika , Braun Matthias
Published: January 2021

The usability of multispectral satellite data for detecting and monitoring supraglacial meltwater ponds has been demonstrated for western Greenland. For a multitemporal analysis of large regions or entire Greenland, largely automated processing routines are required. Here, we present a sequence of algorithms that allow for an automated Sentinel-2 data search, download, processing, and generation of a consistent and dense melt pond area time-series based on open-source software. We test our approach for a ~82,000 km2 area at the 79 °N Glacier (Nioghalvfjerdsbrae) in northeast Greenland, covering the years 2016, 2017, 2018 and 2019. Our lake detection is based on the ratio of the blue and red visible bands using a minimum threshold. To remove false classification caused by the similar spectra of shadow and water on ice, we implement a shadow model to mask out topographically induced artifacts. We identified 880 individual lakes, traceable over 479 time-steps throughout 2016–2019, with an average size of 64,212 m2. Of the four years, 2019 had the most extensive lake area coverage with a maximum of 333 km2 and a maximum individual lake size of 30 km2. With 1.5 days average observation interval, our time-series allows for a comparison with climate data of daily resolution, enabling a better understanding of short-term climate-glacier feedbacks.
Estimation of Urban Ecosystem Services Value: A Case Study of Chengdu, Southwestern China
Authors: Dai Xiaoai , Johnson Brian Alan , Luo Penglan , Yang Kai , Dong Linxin , Wang Qiang , Liu Chao , Li Naiwen , Lu Heng , Ma Lei , Yang Zhengli , Yao Yuanzhi
Published: January 2021

Research on the service values of urban ecosystems is a hot topic of ecological studies in the current era of rapid urbanization. To quantitatively estimate the ecosystem service value in Chengdu, China from the perspectives of natural ecology and social ecology, the technologies of remote sensing (RS) and geographic information system (GIS) are utilized in this study to extract the land use type information from RS images of Chengdu in 2003, 2007, 2013 and 2018. Subsequently, a driver analysis of the ecosystem services of Chengdu was performed based on socioeconomic data from the last 16 years. The results indicated that: (1) from 2003 to 2018, the land utilization in Chengdu changed significantly, with the area of cultivated lands, forest lands and water decreasing remarkably, while the area of construction lands dramatically increased. (2) The ecosystem services value (ESV) of Chengdu decreased by 30.92% in the last 16 years, from CNY 2.4078 × 1010 in 2003 to CNY 1.6632 × 1010 in 2018. Based on a future simulation, the ESV is further predicted to be reduced to CNY 1.4261 × 1010 by 2033. (3) The ESV of Chengdu showed a negative correlation with the total population, the urbanization rate and the per capita GDP of the region, indicating that the ESV of the studied region was inter-coupled with the socioeconomic development and can be maintained at a high level through rationally regulating the socioeconomic structure.
Distinguishing between Warm and Stratiform Rain Using Polarimetric Radar Measurements
Authors: Matrosov Sergey Y.
Published: January 2021

Modeled statistical differential reflectivity–reflectivity (i.e., ZDR–Ze) correspondences for no bright-band warm rain and stratiform bright-band rain are evaluated using measurements from an operational polarimetric weather radar and independent information about rain types from a vertically pointing profiler. It is shown that these relations generally fit observational data satisfactorily. Due to a relative abundance of smaller drops, ZDR values for warm rain are, on average, smaller than those for stratiform rain of the same reflectivity by a factor of about two (in the logarithmic scale). A ZDR–Ze relation, representing a mean of such relations for warm and stratiform rains, can be utilized to distinguish between warm and stratiform rain types using polarimetric radar measurements. When a mean offset of observational ZDR data is accounted for and reflectivities are greater than 16 dBZ, about 70% of stratiform rains and approximately similar amounts of warm rains are classified correctly using the mean ZDR–Ze relation when applied to averaged data. Since rain rate estimators for warm rain are quite different from other common rain types, identifying and treating warm rain as a separate precipitation category can lead to better quantitative precipitation estimations.
Classification of Street Tree Species Using UAV Tilt Photogrammetry
Authors: Wang Yutang , Wang Jia , Chang Shuping , Sun Lu , An Likun , Chen Yuhan , Xu Jiangqi
Published: January 2021

As an important component of the urban ecosystem, street trees have made an outstanding contribution to alleviating urban environmental pollution. Accurately extracting tree characteristics and species information can facilitate the monitoring and management of street trees, as well as aiding landscaping and studies of urban ecology. In this study, we selected the suburban areas of Beijing and Zhangjiakou and investigated six representative street tree species using unmanned aerial vehicle (UAV) tilt photogrammetry. We extracted five tree attributes and four combined attribute parameters and used four types of commonly-used machine learning classification algorithms as classifiers for tree species classification. The results show that random forest (RF), support vector machine (SVM), and back propagation (BP) neural network provide better classification results when using combined parameters for tree species classification, compared with those using individual tree attributes alone; however, the K-nearest neighbor (KNN) algorithm produced the opposite results. The best combination for classification is the BP neural network using combined attributes, with a classification precision of 89.1% and F-measure of 0.872, and we conclude that this approach best meets the requirements of street tree surveys. The results also demonstrate that optical UAV tilt photogrammetry combined with a machine learning classification algorithm is a low-cost, high-efficiency, and high-precision method for tree species classification.
Deep Localization of Static Scans in Mobile Mapping Point Clouds
Authors: Zang Yufu , Meng Fancong , Lindenbergh Roderik , Truong-Hong Linh , Li Bijun
Published: January 2021

Mobile laser scanning (MLS) systems are often used to efficiently acquire reference data covering a large-scale scene. The terrestrial laser scanner (TLS) can easily collect high point density data of local scene. Localization of static TLS scans in mobile mapping point clouds can afford detailed geographic information for many specific tasks especially in autonomous driving and robotics. However, large-scale MLS reference data often have a huge amount of data and many similar scene data; significant differences may exist between MLS and TLS data. To overcome these challenges, this paper presents a novel deep neural network-based localization method in urban environment, divided by place recognition and pose refinement. Firstly, simple, reliable primitives, cylinder-like features were extracted to describe the global features of a local urban scene. Then, a probabilistic framework is applied to estimate a similarity between TLS and MLS data, under a stable decision-making strategy. Based on the results of a place recognition, we design a patch-based convolution neural network (CNN) (point-based CNN is used as kernel) for pose refinement. The input data unit is the batch consisting of several patches. One patch goes through three main blocks: feature extraction block (FEB), the patch correspondence search block and the pose estimation block. Finally, a global refinement was proposed to tune the predicted transformation parameters to realize localization. The research aim is to find the most similar scene of MLS reference data compared with the local TLS scan, and accurately estimate the transformation matrix between them. To evaluate the performance, comprehensive experiments were carried out. The experiments demonstrate that the proposed method has good performance in terms of efficiency, i.e., the runtime of processing a million points is 5 s, robustness, i.e., the success rate of place recognition is 100% in the experiments, accuracy, i.e., the mean rotation and translation error is (0.24 deg, 0.88 m) and (0.03 deg, 0.06 m) on TU Delft campus and Shanghai urban datasets, respectively, and outperformed some commonly used methods (e.g., iterative closest point (ICP), coherent point drift (CPD), random sample consensus (RANSAC)-based method).
Applying Deep Learning to Clear-Sky Radiance Simulation for VIIRS with Community Radiative Transfer Model—Part 1: Develop AI-Based Clear-Sky Mask
Authors: Liang Xingming , Liu Quanhua
Published: January 2021

A fully connected deep neural network (FCDN) clear-sky mask (CSM) algorithm (FCDN_CSM) was developed to assist the FCDN-based Community Radiative Transfer Model (FCDN_CRTM) to reproduce the Visible Infrared Imaging Radiometer Suite (VIIRS) clear-sky radiances in five thermal emission M (TEB/M) bands. The model design was referenced and enhanced from its earlier version (version 1), and was trained and tested in the global ocean clear-sky domain using six dispersion days’ data from 2019 to 2020 as inputs and a modified NOAA Advanced Clear-Sky Processor over Ocean (ACSPO) CSM product as reference labels. The improved FCDN_CSM (version 2) was further enhanced by including daytime data, which was not collected in version 1. The trained model was then employed to predict VIIRS CSM over multiple days in 2020 as an accuracy and stability check. The results were validated against the biases between the sensor observations and CRTM calculations (O-M). The objectives were to (1) enhance FCDN_CSM performance to include daytime analysis, and improve model stability, accuracy, and efficiency; and (2) further understand the model performance based on a combination of the statistics and physical interpretation. According to the analyses of the F-score, the prediction result showed ~96% and ~97% accuracy for day and night, respectively. The type Cloud was the most accurate, followed by Clear-Sky. The O-M mean biases are comparable to the ACSPO CSM for all bands, both day and night. The standard deviations (STD) were slightly degraded in long wave IRs (M14, M15, and M16), mainly due to contamination by a 3% misclassification of the type Cloud, which may require the model to be further fine-tuned to improve prediction accuracy in the future. However, the consistent O-M means and STDs persist throughout the prediction period, suggesting that FCDN_CSM version 2 is robust and does not have significant overfitting. Given its high F-scores, spatial and long-term stability for both day and night, high efficiency, and acceptable O-M means and STDs, FCDN_CSM version 2 is deemed to be ready for use in the FCDN_CRTM.
Individual Tree Extraction from Terrestrial LiDAR Point Clouds Based on Transfer Learning and Gaussian Mixture Model Separation
Authors: Hui Zhenyang , Jin Shuanggen , Li Dajun , Ziggah Yao Yevenyo , Liu Bo
Published: January 2021

Individual tree extraction is an important process for forest resource surveying and monitoring. To obtain more accurate individual tree extraction results, this paper proposed an individual tree extraction method based on transfer learning and Gaussian mixture model separation. In this study, transfer learning is first adopted in classifying trunk points, which can be used as clustering centers for tree initial segmentation. Subsequently, principal component analysis (PCA) transformation and kernel density estimation are proposed to determine the number of mixed components in the initial segmentation. Based on the number of mixed components, the Gaussian mixture model separation is proposed to separate canopies for each individual tree. Finally, the trunk stems corresponding to each canopy are extracted based on the vertical continuity principle. Six tree plots with different forest environments were used to test the performance of the proposed method. Experimental results show that the proposed method can achieve 87.68% average correctness, which is much higher than that of other two classical methods. In terms of completeness and mean accuracy, the proposed method also outperforms the other two methods.
Consequences of the 2019 Greenland Ice Sheet Melt Episode on Albedo
Authors: Elmes Arthur , Levy Charlotte , Erb Angela , Hall Dorothy K. , Scambos Ted A. , DiGirolamo Nicolo , Schaaf Crystal
Published: January 2021

In mid-June 2019, the Greenland ice sheet (GrIS) experienced an extreme early-season melt event. This, coupled with an earlier-than-average melt onset and low prior winter snowfall over western Greenland, led to a rapid decrease in surface albedo and greater solar energy absorption over the melt season. The 2019 melt season resulted in significantly more melt than other recent years, even compared to exceptional melt years previously identified in the moderate-resolution imaging spectroradiometer (MODIS) record. The increased solar radiation absorbance in 2019 warmed the surface and increased the rate of meltwater production. We use two decades of satellite-derived albedo from the MODIS MCD43 record to show a significant and extended decrease in albedo in Greenland during 2019. This decrease, early in the melt season and continuing during peak summer insolation, caused increased radiative forcing of the ice sheet of 2.33 Wm−2 for 2019. Radiative forcing is strongly influenced by the dramatic seasonal differences in surface albedo experienced by any location experiencing persistent and seasonal snow-cover. We also illustrate the utility of the newly developed Landsat-8 albedo product for better capturing the detailed spatial heterogeneity of the landscape, leading to a more refined representation of the surface energy budget. While the MCD43 data accurately capture the albedo for a given 500 m pixel, the higher spatial resolution 30 m Landsat-8 albedos more fully represent the detailed landscape variations.
Links between Phenology of Large Phytoplankton and Fisheries in the Northern and Central Red Sea
Authors: Gittings John A. , Raitsos Dionysios E. , Brewin Robert J. W. , Hoteit Ibrahim
Published: January 2021

Phytoplankton phenology and size structure are key ecological indicators that influence the survival and recruitment of higher trophic levels, marine food web structure, and biogeochemical cycling. For example, the presence of larger phytoplankton cells supports food chains that ultimately contribute to fisheries resources. Monitoring these indicators can thus provide important information to help understand the response of marine ecosystems to environmental change. In this study, we apply the phytoplankton size model of Gittings et al. (2019b) to 20-years of satellite-derived ocean colour observations in the northern and central Red Sea, and investigate interannual variability in phenology metrics for large phytoplankton (>2 µm in cell diameter). Large phytoplankton consistently bloom in the winter. However, the timing of bloom initiation and termination (in autumn and spring, respectively) varies between years. In the autumn/winter of 2002/2003, we detected a phytoplankton bloom, which initiated ~8 weeks earlier and lasted ~11 weeks longer than average. The event was linked with an eddy dipole in the central Red Sea, which increased nutrient availability and enhanced the growth of large phytoplankton. The earlier timing of food availability directly impacted the recruitment success of higher trophic levels, as represented by the maximum catch of two commercially important fisheries (Sardinella spp. and Teuthida) in the following year. The results of our analysis are essential for understanding trophic linkages between phytoplankton and fisheries and for marine management strategies in the Red Sea.
Hyperledger Fabric Blockchain for Securing the Edge Internet of Things
Authors: Honar Pajooh Houshyar , Rashid Mohammad , Alam Fakhrul , Demidenko Serge
Published: January 2021

Providing security and privacy to the Internet of Things (IoT) networks while achieving it with minimum performance requirements is an open research challenge. Blockchain technology, as a distributed and decentralized ledger, is a potential solution to tackle the limitations of the current peer-to-peer IoT networks. This paper presents the development of an integrated IoT system implementing the permissioned blockchain Hyperledger Fabric (HLF) to secure the edge computing devices by employing a local authentication process. In addition, the proposed model provides traceability for the data generated by the IoT devices. The presented solution also addresses the IoT systems’ scalability challenges, the processing power and storage issues of the IoT edge devices in the blockchain network. A set of built-in queries is leveraged by smart-contracts technology to define the rules and conditions. The paper validates the performance of the proposed model with practical implementation by measuring performance metrics such as transaction throughput and latency, resource consumption, and network use. The results show that the proposed platform with the HLF implementation is promising for the security of resource-constrained IoT devices and is scalable for deployment in various IoT scenarios.
Thermal and Geometric Error Compensation Approach for an Optical Linear Encoder
Authors: Gurauskis Donatas , Kilikevičius Artūras , Kasparaitis Albinas
Published: January 2021

Linear displacement measuring systems, like optical encoders, are widely used in various precise positioning applications to form a full closed-loop control system. Thus, the performance of the machine and the quality of its technological process are highly dependent on the accuracy of the linear encoder used. Thermoelastic deformation caused by a various thermal sources and the changing ambient temperature are important factors that introduce errors in an encoder reading. This work presents an experimental realization of the real-time geometric and thermal error compensation of the optical linear encoder. The implemented compensation model is based on the approximation of the tested encoder error by a simple parametric function and calculation of a linear nature error component according to an ambient temperature variation. The calculation of a two-dimensional compensation function and the real-time correction of the investigated linear encoder position readings are realized by using a field programmable gate array (FPGA) computing platform. The results of the performed experimental research verified that the final positioning error could be reduced up to 98%.
Wind Tunnel Testing of Plasma Actuator with Two Mesh Electrodes to Boundary Layer Control at High Angle of Attack
Authors: Gnapowski Ernest , Pytka Jarosław , Józwik Jerzy , Laskowski Jan , Michałowska Joanna
Published: January 2021

The manuscript presents experimental research carried out on the wing model with the SD 7003 profile. A plasma actuator with DBD (Dielectric Barrier Discharge) discharges was placed on the wing surface to control boundary layer. The experimental tests were carried out in the AeroLab wind tunnel where the forces acting on the wing during the tests were measured. The conducted experimental research concerns the analysis of the phenomena that take place on the surface of the wing with the DBD plasma actuator turned off and on. The plasma actuator used during the experimental tests has a different structure compared to the classic plasma actuator. The commonly tested plasma actuator uses solid/impermeable electrodes, while in the research, the plasma actuator uses a new type of electrodes, two mesh electrodes separated by an impermeable Kapton dielectric. The experimental research was carried out for the angle of attack α = 15° and several air velocities V = 5–15 m/s with a step of 5 m/s for the Reynolds number Re = 87,500–262,500. The critical angle of attack at which the SD 7003 profile has the maximum lift coefficient is about 11°; during the experimental research, the angle was 15°. Despite the high angle of attack, it was possible to increase the lift coefficient. The use of a plasma actuator with two mesh electrodes allowed to increase the lift by 5%, even at a high angle of attack. During experimental research used high voltage power supply for powering the DBD plasma actuator in the voltage range from 7.5 to 15 kV.
Physician-Friendly Tool Center Point Calibration Method for Robot-Assisted Puncture Surgery
Authors: Zhang Leifeng , Li Changle , Fan Yilun , Zhang Xuehe , Zhao Jie
Published: January 2021

After each robot end tool replacement, tool center point (TCP) calibration must be performed to achieve precise control of the end tool. This process is also essential for robot-assisted puncture surgery. The purpose of this article is to solve the problems of poor accuracy stability and strong operational dependence in traditional TCP calibration methods and to propose a TCP calibration method that is more suitable for a physician. This paper designs a special binocular vision system and proposes a vision-based TCP calibration algorithm that simultaneously identifies tool center point position (TCPP) and tool center point frame (TCPF). An accuracy test experiment proves that the designed special binocular system has a positioning accuracy of ±0.05 mm. Experimental research shows that the magnitude of the robot configuration set is a key factor affecting the accuracy of TCPP. Accuracy of TCPF is not sensitive to the robot configuration set. Comparison experiments show that the proposed TCP calibration method reduces the time consumption by 82%, improves the accuracy of TCPP by 65% and improves the accuracy of TCPF by 52% compared to the traditional method. Therefore, the method proposed in this article has higher accuracy, better stability, less time consumption and less dependence on the operations than traditional methods, which has a positive effect on the clinical application of high-precision robot-assisted puncture surgery.
Increase in Fast Response Time of the Resistance-to-Voltage Converter When Monitoring the Cable Products’ Insulation Resistance
Authors: Yermoshin Nikolay I. , Yakimov Evgeny V. , Goldshtein Aleksandr E. , Sednev Dmitry A.
Published: January 2021

Theoretical and experimental studies were conducted to investigate the impact of the cable capacitance during measurements of insulation resistance on the fast response time of a resistance-to-voltage converter. From a comparison of the results of simulation with the data obtained during the experiments, it was determined that the dependence characteristics of the settling time of resistance under measurement on the capacitance are identical to the analogous characteristics of electronic components of the resistance-to-voltage converter. It was experimentally proven that using T-shaped feedback in the resistance-to-voltage converter during the cable insulation resistance measurements reduces the settling time of the data by 1–3 times in comparison with a classical feedback system. Furthermore, when using the optimal parameters, the settling time of the resistance-to-voltage converter with T-shaped feedback depends to a lesser degree on the capacitance of the object under control.
A Predictive Multimodal Framework to Alert Caregivers of Problem Behaviors for Children with ASD (PreMAC)
Authors: Zheng Zhaobo K. , Staubitz John E. , Weitlauf Amy S. , Staubitz Johanna , Pollack Marney , Shibley Lauren , Hopton Michelle , Martin William , Swanson Amy , Juárez Pablo , Warren Zachary E. , Sarkar Nilanjan
Published: January 2021

Autism Spectrum Disorder (ASD) impacts 1 in 54 children in the US. Two-thirds of children with ASD display problem behavior. If a caregiver can predict that a child is likely to engage in problem behavior, they may be able to take action to minimize that risk. Although experts in Applied Behavior Analysis can offer caregivers recognition and remediation strategies, there are limitations to the extent to which human prediction of problem behavior is possible without the assistance of technology. In this paper, we propose a machine learning-based predictive framework, PreMAC, that uses multimodal signals from precursors of problem behaviors to alert caregivers of impending problem behavior for children with ASD. A multimodal data capture platform, M2P3, was designed to collect multimodal training data for PreMAC. The development of PreMAC integrated a rapid functional analysis, the interview-informed synthesized contingency analysis (IISCA), for collection of training data. A feasibility study with seven 4 to 15-year-old children with ASD was conducted to investigate the tolerability and feasibility of the M2P3 platform and the accuracy of PreMAC. Results indicate that the M2P3 platform was well tolerated by the children and PreMAC could predict precursors of problem behaviors with high prediction accuracies.
Evaluation Model of Autonomous Vehicles’ Speed Suitability Based on Overtaking Frequency
Authors: Li Shiwu , Huang Mengyuan , Guo Mengzhu , Yu Miao
Published: January 2021

Speed judgment is a vital component of autonomous driving perception systems. Automobile drivers were able to evaluate their speed as a result of their driving experience. However, driverless automobiles cannot autonomously evaluate their speed suitability through external environmental factors such as the surrounding conditions and traffic flows. This study introduced the parameter of overtaking frequency (OTF) based on the state of the traffic flow on both sides of the lane to reflect the difference between the speed of a driverless automobile and its surrounding traffic to solve the above problem. In addition, a speed evaluation algorithm was proposed based on the long short-term memory (LSTM) model. To train the LSTM model, we extracted OTF as the first observation variable, and the characteristic parameters of the vehicle’s longitudinal motion and the comparison parameters with the leading vehicle were used as the second observation variables. The algorithm judged the velocity using a hierarchical method. We conducted a road test by using real vehicles and the algorithms verified the data, which showed the accuracy rate of the model is 93%. As a result, OTF is introduced as one of the observed variables that can support the accuracy of the algorithm used to judge speed.
Online Extraction of Pose Information of 3D Zigzag-Line Welding Seams for Welding Seam Tracking
Authors: Hong Bo , Jia Aiting , Hong Yuxiang , Li Xiangwen , Gao Jiapeng , Qu Yuanyuan
Published: January 2021

Three-dimensional (3D) zigzag-line welding seams are found extensively in the manufacturing of marine engineering equipment, heavy lifting equipment, and logistics transportation equipment. Currently, due to the large amount of calculation and poor real-time performance of 3D welding seam detection algorithms, real-time tracking of 3D zigzag-line welding seams is still a challenge especially in high-speed welding. For the abovementioned problems, we proposed a method for the extraction of the pose information of 3D zigzag-line welding seams based on laser displacement sensing and density-based clustering point cloud segmentation during robotic welding. after thee point cloud data of the 3D zigzag-line welding seams was obtained online by the laser displacement sensor, it was segmented using theρ-Approximate DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. In the experiment, high-speed welding was performed on typical low-carbon steel 3D zigzag-line welding seams using gas metal arc welding. The results showed that when the welding velocity was 1000 mm/min, the proposed method obtained a welding seam position detection error of less than 0.35 mm, a welding seam attitude estimation error of less than two degrees, and the running time of the main algorithm was within 120 ms. Thus, the online extraction of the pose information of 3D zigzag-line welding seams was achieved and the requirements of welding seam tracking were met.
Integrated, Automated, Fast PCR System for Point-Of-Care Molecular Diagnosis of Bacterial Infection
Authors: Lee Dongkyu , Kim Deawook , Han Jounghyuk , Yun Jongsu , Lee Kang-Ho , Kim Gyu Man , Kwon Ohwon , Lee Jaejong
Published: January 2021

We developed an integrated PCR system that performs automated sample preparation and fast polymerase chain reaction (PCR) for application in point-of care (POC) testing. This system is assembled from inexpensive 3D-printing parts, off-the-shelf electronics and motors. Molecular detection requires a series of procedures including sample preparation, amplification, and fluorescence intensity analysis. The system can perform automated DNA sample preparation (extraction, separation and purification) in ≤5 min. The variance of the automated sample preparation was clearly lower than that achieved using manual DNA extraction. Fast thermal ramp cycles were generated by a customized thermocycler designed to automatically transport samples between heating and cooling blocks. Despite the large sample volume (50 μL), rapid two-step PCR amplification completed 40 cycles in ≤13.8 min. Variations in fluorescence intensity were measured by analyzing fluorescence images. As proof of concept of this system, we demonstrated the rapid DNA detection of pathogenic bacteria. We also compared the sensitivity of this system with that of a commercial device during the automated extraction and fast PCR of Salmonella bacteria.
Joint Fiber Nonlinear Noise Estimation, OSNR Estimation and Modulation Format Identification Based on Asynchronous Complex Histograms and Deep Learning for Digital Coherent Receivers
Authors: Yang Shuailong , Yang Liu , Luo Fengguang , Li Bin , Wang Xiaobo , Du Yuting , Liu Deming
Published: January 2021

In this paper, asynchronous complex histogram (ACH)-based multi-task artificial neural networks (MT-ANNs), are proposed to realize modulation format identification (MFI), optical signal-to-noise ratio (OSNR) estimation and fiber nonlinear (NL) noise power estimation simultaneously for coherent optical communication. Optical performance monitoring (OPM) is demonstrated with polarization mode multiplexing (PDM), 16 quadrature amplitude modulation (QAM), PDM-32QAM, as well as PDM-star 16QAM (S-16QAM) for the first time. The range of launched power is −3 to −2 dBm with a fiber link of 160–1600 km. Then, the accuracy of MFI reaches 100%. The average root mean square error (RMSE) of OSNR estimation can reach 0.37 dB. The average RMSE of NL noise power estimation can reach 0.25 dB. The results show that the monitoring scheme is robust to the increase of fiber length, and the solution can monitor more optical network parameters with better performance and fewer training data, simultaneously. The proposed ACH MT-ANN has certain reference significance for the future long-haul coherent OPM system.
Temporal Convolutional Neural Networks for Radar Micro-Doppler Based Gait Recognition
Authors: Addabbo Pia , Bernardi Mario Luca , Biondi Filippo , Cimitile Marta , Clemente Carmine , Orlando Danilo
Published: January 2021

The capability of sensors to identify individuals in a specific scenario is a topic of high relevance for sensitive sectors such as public security. A traditional approach involves cameras; however, camera-based surveillance systems lack discretion and have high computational and storing requirements in order to perform human identification. Moreover, they are strongly influenced by external factors (e.g., light and weather). This paper proposes an approach based on a temporal convolutional deep neural networks classifier applied to radar micro-Doppler signatures in order to identify individuals. Both sensor and processing requirements ensure a low size weight and power profile, enabling large scale deployment of discrete human identification systems. The proposed approach is assessed on real data concerning 106 individuals. The results show good accuracy of the classifier (the best obtained accuracy is 0.89 with an F1-score of 0.885) and improved performance when compared to other standard approaches.
Effect of the Affordances of the FM New Media Communication Interface Design for Smartphones
Authors: Li Hongyu , Chen Chien-Hsiung
Published: January 2021

Smartphone equipment has promoted the widespread use of new media communication, and users have changed from being passive to actively receiving information. This has changed people’s lifestyles, and has enriched the convenience and entertainment value of knowledge acquisition. However, the new media communication systems can be too complex, and the interface design for users to interact with the new media may not be sufficiently intuitive, which causes interface usability problems. Therefore, this research study focuses on the concept of affordance and the impact of user perception pertinent to smartphone applications on frequency modulation (FM) new media interface design. The experiment is a between-subjects design using one-way ANOVA to examine three different operation modes, namely the Litchi, Himalayas and Archimedes types. The experimental data were obtained through task performance and subjective evaluation. The results indicate that: (1) Visual information presentation methods, such as viewing and deleting, affect user perception. The three operating modes revealed significant differences, with the Himalayas type taking the least amount of task performance time. (2) There were significant differences among the different operation modes, with the Himalayas type the best in terms of the users’ subjective evaluation. (3) The overall analysis of task performance and satisfaction consistently showed that the Himalayas type was better than the Litchi and Archimedes types in all aspects. (4) Smartphone user interface applications provide users with cognitive support, objects, functions, and sensory affordance, which enhance the user’s interactive experience of FM new media.
Fast-Response Colorimetric UVC Sensor Made of a Ga2O3 Photocatalyst with a Hole Scavenger
Authors: Ryou Heejoong , Kim Sunjae , Shin Myunghun , Cho Junsang , Hwang Wan Sik
Published: January 2021

A fast-response colorimetric ultraviolet-C (UVC) sensor was demonstrated using a gallium oxide (Ga2O3) photocatalyst with small amounts of triethanolamine (TEOA) in methylene blue (MB) solutions and a conventional RGB photodetector. The color of the MB solution changed upon UVC exposure, which was observed using an in situ RGB photodetector. Thereby, the UVC exposure was numerically quantified as an MB reduction rate with the R value of the photodetector, which was linearly correlated with the measured spectral absorbance using a UV-Vis spectrophotometer. Small amount of TEOA in the MB solution served as a hole scavenger, which resulted in fast MB color changes due to the enhanced charge separation. However, excessive TEOA over 5 wt.% started to block the catalytical active site on the surface of Ga2O3, prohibiting the chemical reaction between the MB molecules and catalytic sites. The proposed colorimetric UVC sensor could monitor the detrimental UVC radiation with high responsivity at a low cost.