bluuebunny/crossref_metadata_2025 huggingface dataset:
Multiscale Detection and Assessment of Vegetation Eco-Environmental Restoration following Ecological Water Compensation in the Lower Reaches of the Tarim River, China
Authors: Zhu Changming , Shen Qian , Zhang Kun , Zhang Xin , Li Junli
Published: November 2022

To protect and restore this downstream ecosystem, the Tarim River Basin Administration Bureau (TBAB) initiated the Ecological Water Compensation (EWC) project from 2000 to 2018. Revealing the mechanism of vegetation-hydroecological response processes in the lower reaches of the Tarim River before and after EWC work is conducive to water resource planning, utilization and protection. In this paper, the spatiotemporal responses of vegetation and groundwater to EWC were examined at the points, lines, and area (PLA) scale by coupling remote sensing techniques and field station observation data collected between 2000 and 2017. The findings indicated that (1) In general, the regional fractional vegetation coverage (FVC) increased significantly, and the average FVC growth rate was 3.5%/year from 2000 to 2017 (R2 > 0.84, p < 0.01, 2-tailed). (2) The regional vegetation restoration process showed obvious fluctuations and stage characteristics, but the spatial scope of the significantly increased vegetation area was limited. Plants grew rapidly within 10 km of the river, while 10 km away from the water channel, no obvious change was observed. (3) Strong coupling relationships were identified among the FVC growth, EWC volume and groundwater depth variations (p < 0.05, 2-tailed). The response times of the regional vegetation and groundwater depth to EWC indicated one-year lags. The above results imply that the regional ecological environment was significantly improved over the study period, thus confirming that the EWC project made remarkable accomplishments. However, the effect of ecological restoration is not sufficiently stable at present. Vegetation restoration has mainly been centralized around the river channel and is greatly dependent on the annual EWC volume. In addition, the local conditions begin to degrade soon after an EWC project is terminated, and vice versa; when EWC commences, the FVC immediately begins to improve. Therefore, the current EWC achievements need to be further consolidated and strengthened in the future.
A Machine-Learning Approach to Intertidal Mudflat Mapping Combining Multispectral Reflectance and Geomorphology from UAV-Based Monitoring
Authors: Brunier Guillaume , Oiry Simon , Lachaussée Nicolas , Barillé Laurent , Le Fouest Vincent , Méléder Vona
Published: November 2022

Remote sensing is a relevant method to map inaccessible areas, such as intertidal mudflats. However, image classification is challenging due to spectral similarity between microphytobenthos and oyster reefs. Because these elements are strongly related to local geomorphic features, including biogenic structures, a new mapping method has been developed to overcome the current obstacles. This method is based on unmanned aerial vehicles (UAV), RGB, and multispectral (four bands: green, red, red-edge, and near-infrared) surveys that combine high spatial resolution (e.g., 5 cm pixel), geomorphic mapping, and machine learning random forest (RF) classification. A mudflat on the Atlantic coast of France (Marennes-Oléron bay) was surveyed based on this method and by using the structure from motion (SfM) photogrammetric approach to produce orthophotographs and digital surface models (DSM). Eight classes of mudflat surface based on indexes, such as NDVI and spectral bands normalised to NIR, were identified either on the whole image (i.e., standard RF classification) or after segmentation into five geomorphic units mapped from DSM (i.e., geomorphic-based RF classification). The classification accuracy was higher with the geomorphic-based RF classification (93.12%) than with the standard RF classification (73.45%), showing the added value of combining topographic and radiometric data to map soft-bottom intertidal areas and the user-friendly potential of this method in applications to other ecosystems, such as wetlands or peatlands.
Source Model and Triggered Aseismic Faulting of the 2021 Mw 7.3 Maduo Earthquake Revealed by the UAV-Lidar/Photogrammetry, InSAR, and Field Investigation
Authors: Yang Ying-Hui , Xu Qiang , Hu Jyr-Ching , Wang Yun-Sheng , Dong Xiu-Jun , Chen Qiang , Zhang Yi-Jun , Li Hao-Liang
Published: November 2022

Multi-source datasets, including the UAV-Lidar/Photogrammetry, InSAR, and field investigation data, have been used for revealing the complex surface displacement pattern and focal mechanism of the 2021 Maduo Mw 7.3 earthquake. First, a co-seismic surface deformation field was extracted from the Synthetic Aperture Radar (SAR) images captured by ALOS-2 and Sentinel-1 satellites. Second, the SAR pixel offset tracking results were adopted to detect the initial location of the seismogenic fault. Then, the Lidar digital elevation model with high spatial resolution and field investigation were employed to refine and verify the location of the seismogenic fault. It was found that bifurcated strike-slip rupture should account for the 2021 Maduo earthquake. As indicated by the estimated faulting model based on the InSAR data, the maximal fault slip was ~6.2 m, occurring in the southeast of the main seismogenic fault, and five remarkable slip concentrations controlled the surface displacement of the 2021 Maduo earthquake. Furthermore, the co- and post-seismic InSAR deformation, dilatation, shear strain, Coulomb failure stress, and aftershock sequence suggest that the co-seismic rupture of the two main seismogenic faults have triggered the aseismic slip along the Changmahe fault. Lastly, according to the Coulomb failure stress change due to the historical earthquakes and the 2021 Maduo earthquake, the 1937 M 7.8 earthquake predominantly controlled the Coulomb failure stress change along the Kunlun fault, and the Xidatan-Alake Lake and Maqin segments had a higher risk of future earthquake than the other segments.
A Survey of Computer Vision Techniques for Forest Characterization and Carbon Monitoring Tasks
Authors: Illarionova Svetlana , Shadrin Dmitrii , Tregubova Polina , Ignatiev Vladimir , Efimov Albert , Oseledets Ivan , Burnaev Evgeny
Published: November 2022

Estimation of terrestrial carbon balance is one of the key tasks in the understanding and prognosis of climate change impacts and the development of tools and policies according to carbon mitigation and adaptation strategies. Forest ecosystems are one of the major pools of carbon stocks affected by controversial processes influencing carbon stability. Therefore, monitoring forest ecosystems is a key to proper inventory management of resources and planning their sustainable use. In this survey, we discuss which computer vision techniques are applicable to the most important aspects of forest management actions, considering the wide availability of remote sensing (RS) data of different resolutions based both on satellite and unmanned aerial vehicle (UAV) observations. Our analysis applies to the most occurring tasks such as estimation of forest areas, tree species classification, and estimation of forest resources. Through the survey, we also provide a necessary technical background with a description of suitable data sources, algorithms’ descriptions, and corresponding metrics for their evaluation. The implementation of the provided techniques into routine workflows is a significant step toward the development of systems of continuous actualization of forest data, including real-time monitoring. It is crucial for diverse purposes on both local and global scales. Among the most important are the implementation of improved forest management strategies and actions, carbon offset projects, and enhancement of the prediction accuracy of system changes under different land-use and climate scenarios.
SEG-ESRGAN: A Multi-Task Network for Super-Resolution and Semantic Segmentation of Remote Sensing Images
Authors: Salgueiro Luis , Marcello Javier , Vilaplana Verónica
Published: November 2022

The production of highly accurate land cover maps is one of the primary challenges in remote sensing, which depends on the spatial resolution of the input images. Sometimes, high-resolution imagery is not available or is too expensive to cover large areas or to perform multitemporal analysis. In this context, we propose a multi-task network to take advantage of the freely available Sentinel-2 imagery to produce a super-resolution image, with a scaling factor of 5, and the corresponding high-resolution land cover map. Our proposal, named SEG-ESRGAN, consists of two branches: the super-resolution branch, that produces Sentinel-2 multispectral images at 2 m resolution, and an encoder–decoder architecture for the semantic segmentation branch, that generates the enhanced land cover map. From the super-resolution branch, several skip connections are retrieved and concatenated with features from the different stages of the encoder part of the segmentation branch, promoting the flow of meaningful information to boost the accuracy in the segmentation task. Our model is trained with a multi-loss approach using a novel dataset to train and test the super-resolution stage, which is developed from Sentinel-2 and WorldView-2 image pairs. In addition, we generated a dataset with ground-truth labels for the segmentation task. To assess the super-resolution improvement, the PSNR, SSIM, ERGAS, and SAM metrics were considered, while to measure the classification performance, we used the IoU, confusion matrix and the F1-score. Experimental results demonstrate that the SEG-ESRGAN model outperforms different full segmentation and dual network models (U-Net, DeepLabV3+, HRNet and Dual_DeepLab), allowing the generation of high-resolution land cover maps in challenging scenarios using Sentinel-2 10 m bands.
The Identification and Driving Factor Analysis of Ecological-Economi Spatial Conflict in Nanjing Metropolitan Area Based on Remote Sensing Data
Authors: Cao Ji , Cao Weidong , Fang Xianwei , Ma Jinji , Mok Diana , Xie Yisong
Published: November 2022

The rapid socio-economic development of the metropolitan area has led to the continuous deterioration of the ecological environment. This leads to intense competition and conflict between different spatial use types. Spatial conflict research is essential to achieve ecological-economic coordination and high-quality development. However, existing studies lack comprehensive and direct ecological-economic spatial conflicts, especially those on the spatial-temporal evolution and potential drivers of spatial conflict. In this study, we identified the ecological-economic spatial conflicts in the Nanjing metropolitan area in 2010, 2015, and 2020. This study used the random forest to analyze the factors that influenced the change of spatial conflict. Results show that: (1) From 2010 to 2020, the ecological-economic spatial conflict in the Nanjing metropolitan area changed significantly. (2) Land use change has an important effect on spatial conflicts, which are easily triggered by uncontrolled urban expansion, but ecological land can mitigate spatial conflicts. (3) Relevant driving factors of spatial conflicts show multi-level features, so the development of conflict reconciliation countermeasures needs to be tailored to local conditions. This study provides a significant foundation for the high-quality development of the Nanjing metropolitan area and provides a reference for the planning and management of the territorial space.
Improved Central Attention Network-Based Tensor RX for Hyperspectral Anomaly Detection
Authors: Zhang Lili , Ma Jiachen , Fu Baohong , Lin Fang , Sun Yudan , Wang Fengpin
Published: November 2022

Recently, using spatial–spectral information for hyperspectral anomaly detection (AD) has received extensive attention. However, the test point and its neighborhood points are usually treated equally without highlighting the test point, which is unreasonable. In this paper, improved central attention network-based tensor RX (ICAN-TRX) is designed to extract hyperspectral anomaly targets. The ICAN-TRX algorithm consists of two parts, ICAN and TRX. In ICAN, a test tensor block as a value tensor is first reconstructed by DBN to make the anomaly points more prominent. Then, in the reconstructed tensor block, the central tensor is used as a convolution kernel to perform convolution operation with its tensor block. The result tensor as a key tensor is transformed into a weight matrix. Finally, after the correlation operation between the value tensor and the weight matrix, the new test point is obtained. In ICAN, the spectral information of a test point is emphasized, and the spatial relationships between the test point and its neighborhood points reflect their similarities. TRX is used in the new HSI after ICAN, which allows more abundant spatial information to be used for AD. Five real hyperspectral datasets are selected to estimate the performance of the proposed ICAN-TRX algorithm. The detection results demonstrate that ICAN-TRX achieves superior performance compared with seven other AD algorithms.
Improving Methodology for Tropical Cyclone Seasonal Forecasting in the Australian and the South Pacific Ocean Regions by Selecting and Averaging Models via Metropolis–Gibbs Sampling
Authors: Qian Guoqi , Chen Lizhong , Kuleshov Yuriy
Published: November 2022

A novel model selection and averaging approach is proposed—through integrating the corrected Akaike information criterion (AICc), the Gibbs sampler, and the Poisson regression models, to improve tropical cyclone seasonal forecasting in the Australian and the South Pacific Ocean regions and sub-regions. It has been found by the new approach that indices which describe tropical cyclone inter-annual variability such as the Dipole Mode Index (DMI) and the El Niño Modoki Index (EMI) are among the most important predictors used by the selected models. The core computational method underlying the proposed approach is a new stochastic search algorithm that we have developed, and is named Metropolis–Gibbs random scan (MGRS). By applying MGRS to minimize AICc over all candidate models, a set of the most important predictors are identified which can form a small number of optimal Poisson regression models. These optimal models are then averaged to improve their overall predictability. Results from our case study of tropical cyclone seasonal forecasting show that the MGRS-AICc method performs significantly better than the commonly used step-wise AICc method.
Dual-Frequency Retrieval of Soil Moisture from L- and S-Band Radar Data for Corn and Soybean
Authors: Liao Tien-Hao , Kim Seung-Bum
Published: November 2022

Radar backscattering responds differently to soil moisture due to vegetation effects depending on the microwave frequency. The retrieval of soil moisture using a single frequency has been common. In this paper, we study how soil moisture retrieval performs using dual-frequency radar backscattering (L- and S-bands) compared with using L-band only. The dual-frequency inputs increase the amount of independent information, which is expected to reduce the uncertainty in estimating soil moisture. Forward scattering models for corn and soybean fields were previously generated and validated with the L-band for the SMAPVEX12 campaign: they are inverted as an independent test for the retrieval of soil moisture using the SMEX02 campaign data in this paper. It is demonstrated that L-band modeling of forward scattering processes is scalable at the S-band, in that the physics and parameters behind modeling the vegetation effects remain the same between L- and S-bands. Either L- or S-band single-frequency retrieval has reliable performance for soil moisture retrieval. Furthermore, averaging the retrieved soil moisture from both frequencies further improves the retrieval performance. The averaging avoids the determination of the weights of the L- and S-band sigma0 during the cost function minimization. The dual frequency retrieval is evaluated with the unbiased RMSE of 0.031 and 0.057 m3/m3 for corn and soybean, respectively, which are improvements by up to 0.010 and 0.004 m3/m3, compared with single-frequency cases. The findings here can apply to the upcoming NISAR mission featuring L- and S-bands.
MODIS Evapotranspiration Downscaling Using a Deep Neural Network Trained Using Landsat 8 Reflectance and Temperature Data
Authors: Che Xianghong , Zhang Hankui K. , Sun Qing , Ouyang Zutao , Liu Jiping
Published: November 2022

The MODIS 8-day composite evapotranspiration (ET) product (MOD16A2) is widely used to study large-scale hydrological cycle and energy budgets. However, the MOD16A2 spatial resolution (500 m) is too coarse for local and regional water resource management in agricultural applications. In this study, we propose a Deep Neural Network (DNN)-based MOD16A2 downscaling approach to generate 30 m ET using Landsat 8 surface reflectance and temperature and AgERA5 meteorological variables. The model was trained at a 500 m resolution using the MOD16A2 ET as reference and applied to the Landsat 8 30 m resolution. The approach was tested on 15 Landsat 8 images over three agricultural study sites in the United States and compared with the classical random forest regression model that has been often used for ET downscaling. All evaluation sample sets applied to the DNN regression model had higher R2 and lower root-mean-square deviations (RMSD) and relative RMSD (rRMSD) (the average values: 0.67, 2.63 mm/8d and 14.25%, respectively) than the random forest model (0.64, 2.76 mm/8d and 14.92%, respectively). Spatial improvement was visually evident both in the DNN and the random forest downscaled 30 m ET maps compared with the 500 m MOD16A2, while the DNN-downscaled ET appeared more consistent with land surface cover variations. Comparison with the in situ ET measurements (AmeriFlux) showed that the DNN-downscaled ET had better accuracy, with R2 of 0.73, RMSD of 5.99 mm/8d and rRMSD of 48.65%, than the MOD16A2 ET (0.65, 7.18 and 50.42%, respectively).
Monitoring of Atmospheric Carbon Dioxide over Pakistan Using Satellite Dataset
Authors: An Ning , Mustafa Farhan , Bu Lingbing , Xu Ming , Wang Qin , Shahzaman Muhammad , Bilal Muhammad , Ullah Safi , Feng Zhang
Published: November 2022

Satellites are an effective source of atmospheric carbon dioxide (CO2) monitoring; however, city-scale monitoring of atmospheric CO2 through space-borne observations is still a challenging task due to the trivial change in atmospheric CO2 concentration compared to its natural variability and background concentration. In this study, we attempted to evaluate the potential of space-based observations to monitor atmospheric CO2 changes at the city scale through simple data-driven analyses. We used the column-averaged dry-air mole fraction of CO2 (XCO2) from the Carbon Observatory 2 (OCO-2) and the anthropogenic CO2 emissions provided by the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) product to explain the scenario of CO2 over 120 districts of Pakistan. To study the anthropogenic CO2 through space-borne observations, XCO2 anomalies (MXCO2) were estimated from OCO-2 retrievals within the spatial boundary of each district, and then the overall spatial distribution pattern of the MXCO2 was analyzed with several datasets including the ODIAC emissions, NO2 tropospheric column, fire locations, cropland, nighttime lights and population density. All the datasets showed a similarity in the spatial distribution pattern. The satellite detected higher CO2 concentrations over the cities located along the China–Pakistan Economic Corridor (CPEC) routes. The CPEC is a large-scale trading partnership between Pakistan and China and large-scale development has been carried out along the CPEC routes over the last decade. Furthermore, the cities were ranked based on mean ODIAC emissions and MXCO2 estimates. The satellite-derived estimates showed a good consistency with the ODIAC emissions at higher values; however, deviations between the two datasets were observed at lower values. To further study the relationship of MXCO2 and ODIAC emissions with each other and with some other datasets such as population density and NO2 tropospheric column, statistical analyses were carried out among the datasets. Strong and significant correlations were observed among all the datasets.
WDBSTF: A Weighted Dual-Branch Spatiotemporal Fusion Network Based on Complementarity between Super-Resolution and Change Prediction
Authors: Fang Shuai , Guo Qing , Cao Yang
Published: November 2022

Spatiotemporal fusion (STF) is a solution to generate satellite images with both high-spatial and high-temporal resolutions. The deep learning-based STF algorithms focus on spatial dimensions to build a super-resolution (SR) model or the temporal dimensions to build a change prediction (CP) model, or the task itself to build a data-driven end-to-end model. The multi-source images used for STF usually have large spatial scale gaps and temporal spans. The large spatial scale gaps lead to poor spatial details based on a SR model; the large temporal spans make it difficult to accurately reconstruct changing areas based on a CP model. We propose a weighted dual-branch spatiotemporal fusion network based on complementarity between super-resolution and change prediction (WDBSTF), which includes the SR branch and CP branch, and a weight module representing the complementarity of the two branches. The SR branch makes full use of edge information and high-resolution reference images to obtain high-quality spatial features for image reconstruction. The CP branch decomposes complex problems via a two-layer cascaded network, changes features from the difference image, and selects high-quality spatial features through the attention mechanism. The fusion result of the CP branch has rich image details, but the fusion accuracy in the changing area is low due to the lack of detail. The SR branch has consistent and excellent fusion performances in the changing and no-changing areas, but the image details are not rich enough compared with the CP branch due to the large amplification factor. Next, a weighted network was designed to combine the advantages of the two branches to produce improved fusion results. We evaluated the performance of the WDBSTF in three representative scenarios, and both visual and quantitative evaluations demonstrate the state-of-the-art performance of our algorithm. (On the LGC dataset, our method outperforms the suboptimal method by 2.577% on SSIM. On the AHB dataset, our method outperforms the suboptimal method by 1.684% on SSIM. On the CIA dataset, our method outperforms the suboptimal method by 5.55% on SAM).
Preflight Evaluation of the Environmental Trace Gases Monitoring Instrument with Nadir and Limb Modes (EMI-NL) Based on Measurements of Standard NO2 Sample Gas
Authors: Yang Taiping , Si Fuqi , Zhou Haijin , Zhao Minjie , Lin Fang , Zhu Lei
Published: November 2022

Hyperspectral observations are used to retrieve high-resolution horizontal distribution and vertical profiles of trace gases (O3, NO2, HCHO, and SO2), thereby playing a vital role in monitoring the spatio-temporal distribution and transportation of atmospheric pollutants. These observations reflect air quality changes on global and regional scales, including China, thereby elucidating the impacts of anthropogenic and natural emissions on atmospheric composition and global climate change. The DaQi 02 (DQ02) satellite carries the Environmental Trace Gases Monitoring Instrument with Nadir and Limb modes (EMI-NL) onboard, which will simultaneously perform nadir and limb measurements of high-resolution ultraviolet and visible solar scattered light in the nadir and limb directions. Combined with the absorption of different trace gases in this wavelength band, this information can provide high-resolution horizontal and vertical distributions of trace gases. We examined the spectral measuring ability and instrument characteristics of both modules of EMI-NL by measuring different light sources and concentrations of the NO2 sample gas. In the nadir module test, when the NO2 sample gas concentration was 198 ppm and 513 ppm with scattered sunlight as the light source, the average relative errors of spatial pixels were 4.02% and 3.64%, respectively. At the NO2 sample gas concentration of 198 ppm with the integrating sphere as the light source, the average relative error of spatial pixels was −2.26%. In the limb module test, when the NO2 sample gas concentration was 198 ppm and 1000 ppm with the tungsten halogen lamp as the light source, the average relative errors of spatial pixels were −3.07% and 8.32%, respectively. When the NO2 sample gas concentration was 198 ppm and 1000 ppm with the integrating sphere as the light source, the spatial pixel average errors were −3.5% and 8.06%, respectively. The retrieved NO2 slant column density between different spatial pixels exhibited notable inconsistency in both modules, which could be used to estimate the stripe of spatial dimension. These results confirm the ability of EMI-NL to provide accurate spaceborne monitoring of NO2 globally.
A Prediction Model for the Outbreak Date of Spring Pollen Allergy in Beijing Based on Satellite-Derived Phenological Characteristics of Vegetation Greenness
Authors: Yang Xinyi , Zhu Wenquan , Zhao Cenliang
Published: November 2022

Pollen allergies have a serious impact on people’s physical and mental health. Accurate and efficient prediction of the outbreak date of pollen allergies plays an important role in the conservation of people sensitive to allergenic pollen. It is a frontier research to combine new social media data and satellite data to develop a model to forecast the outbreak date of pollen allergies. This study extracted the real outbreak dates of spring pollen allergies from Sina Weibo records from 2011 to 2021 in Beijing and calculated five vegetation indices of three vegetation types as phenological characteristics within the 30 days before the average outbreak date. The sensitivity coefficients and correlation coefficients were used to screen the phenological characteristics that best reflected the outbreak date of spring pollen allergy. Based on the best characteristic, two kinds of prediction models for the outbreak date of spring pollen allergy in Beijing were established (the linear fit prediction model and the cumulative linear fit prediction model), and the root mean square error (RMSE) was calculated as the prediction accuracy. The results showed that (1) the date of EVI2 (2-band enhanced vegetation index) in evergreen forest first reaching 0.138 can best reflect the outbreak date of pollen allergies in spring, and (2) the cumulative linear fit prediction model based on EVI2 in evergreen forests can obtain a high accuracy with an average RMSE of 3.6 days, which can predict the outbreak date of spring pollen allergies 30 days in advance. Compared with the existing indirect prediction models (which predict pollen concentrations rather than pollen allergies), this model provides a new direct way to predict pollen allergy outbreaks by using only remote sensing time-series data before pollen allergy outbreaks. The new prediction model also has better representativeness and operability and is capable of assisting public health management.
A Recursive Hull and Signal-Based Building Footprint Generation from Airborne LiDAR Data
Authors: Li Xiao , Qiu Fang , Shi Fan , Tang Yunwei
Published: November 2022

Automatically generating a building footprint from an airborne LiDAR point cloud is an active research topic because of its widespread usage in numerous applications. This paper presents an efficient and automated workflow for generating building footprints from pre-classified LiDAR data. In this workflow, LiDAR points that belong to the building category are first segmented into multiple clusters by applying the grid-based DBSCAN clustering algorithm. Each cluster contains the points of an individual building. Then, the outermost points of each building are extracted, on which the recursive convex hull algorithm is applied to generate the initial outline of each building. Since LiDAR points are irregularly distributed, the initial building outline contains irregular zig-zag shapes. In order to achieve a regularized building footprint that is close to the true building boundary, a signal-based regularization algorithm is developed. The initial outline is first transformed into a signal, which can reveal the wholistic geometric structure of the building outline after applying a denoising procedure. By analyzing the denoised signal, the locations of corners are identified, and the regularized building footprint is generated. The performance of the proposed workflow is tested and evaluated using two datasets that have different point densities and building types. The qualitative assessment reveals that the proposed workflow has a satisfying performance in generating building footprints even for building with complex structures. The quantitative assessment compares the performance of signal-based regularization with existing regularization methods using the 149 buildings contained in the test dataset. The experimental result shows the proposed method has achieved superior results based on a number of commonly used accuracy metrics.
Nonlinear and Dotted Defect Detection with CNN for Multi-Vision-Based Mask Inspection
Authors: Woo Jimyeong , Lee Heoncheol
Published: November 2022

This paper addresses the problem of nonlinear and dotted defect detection for multi-vision-based mask inspection systems in mask manufacturing lines. As the mask production amounts increased due to the spread of COVID-19 around the world, the mask inspection systems require more efficient defect detection algorithms. However, the traditional computer vision detection algorithms suffer from various types and very small sizes of the nonlinear and dotted defects on masks. This paper proposes a deep learning-based mask defect detection method, which includes a convolutional neural network (CNN) and efficient preprocessing. The proposed method was developed to be applied to real manufacturing systems, and thus all the training and inference processes were conducted with real data produced by real mask manufacturing systems. Experimental results show that the nonlinear and dotted defects were successfully detected by the proposed method, and its performance was higher than the previous method.
Polarization Domain Spectrum Sensing Algorithm Based on AlexNet
Authors: Ren Shiyu , Wu Hailong , Chen Wantong , Li Dongxia
Published: November 2022

In this paper, we propose a spectrum sensing algorithm based on the Jones vector covariance matrix (JCM) and AlexNet model, i.e., the JCM-AlexNet algorithm, by taking advantage of the different state characteristics of the signal and noise in the polarization domain. We use the AlexNet model, which is good at extracting matrix features, as the classification model and use the Jones vector, which characterizes the polarization state, to calculate its covariance matrix and convert it into an image and then use it as the input to the AlexNet model. Then, we calculate the likelihood ratio test statistic (AlexNet-LRT) based on the output of the model to achieve the classification of the signal and noise. The simulation analysis shows that the JCM-AlexNet algorithm performs better than the conventional polarization detection (PSD) algorithm and the other three (LeNet5, long short-term memory (LSTM), multilayer perceptron (MLP)) excellent deep-learning-based spectrum sensing algorithms for different signal-to-noise ratios and different false alarm probabilities.
An Optimization-Based Motion Planner for Car-like Logistics Robots on Narrow Roads
Authors: Yu Lingli , Wu Hanzhao , Liu Chongliang , Jiao Hao
Published: November 2022

Thanks to their strong maneuverability and high load capacity, car-like robots with non-holonomic constraints are often used in logistics to improve efficiency. However, it is difficult to plan a safe and smooth optimal path in real time on the restricted narrow roads of the logistics park. To solve this problem, an optimization-based motion planning method inspired by the Timed-Elastic-Band algorithm is proposed, called Narrow-Roads-Timed-Elastic-Band (NRTEB). Three optimization modules are added to the inner and outer workflow of the Timed-Elastic-Band framework. The simulation results show that the proposed method achieves safe reversing planning on narrow roads while the jerk of the trajectory is reduced by 72.11% compared to the original method. Real-world experiments reveal that the proposed method safely and smoothly avoids dynamic obstacles in real time when navigating forward and backward. The motion planner provides a safer and smoother trajectory for car-like robots on narrow roads in real time, which greatly enhances the safety, robustness and reliability of the Timed-Elastic-Band planner in logistics parks.
Development of a New Voltammetric Method for Aceclofenac Determination on Glassy Carbon Electrode Modified with Hierarchical Nanocomposite
Authors: Górska Anna , Paczosa-Bator Beata , Gaidukevič Justina , Piech Robert
Published: November 2022

Aceclofenac (ACL) is an anti-inflammatory drug, which is taken by patients who mainly suffer from rheumatoid conditions. In this work, we propose a new voltammetric method that allows the determination of ACL in pharmaceutics, urine, and plasma. As a working electrode, a glassy carbon electrode (GCE) modified with carbon nanofibers, carbon nanotubes, and NiCo nanoparticles (eCNF/CNT/NiCo-GCE) was used. The mentioned sensors are characterized by good repeatability and sensitivity, and their process of preparation is simple, fast, and cost-effective. Instrumental and method parameters were optimized, and the influence of interferences was investigated. To validate the analytical performance of the method, calibration was conducted. Good linearity was obtained (0.05–1.4 µM, r = 0.998), as well as excellent limit of detection (LOD) and limit of quantification (LOQ) values (0.7 nM and 2.1 nM, respectively). Calculated recoveries that were in the range of 98%–105% indicate that this method is accurate and might be used in routine laboratory practice.
Microvalves for Applications in Centrifugal Microfluidics
Authors: Peshin Snehan , Madou Marc , Kulinsky Lawrence
Published: November 2022

Centrifugal microfluidic platforms (CDs) have opened new possibilities for inexpensive point-of-care (POC) diagnostics. They are now widely used in applications requiring polymerase chain reaction steps, blood plasma separation, serial dilutions, and many other diagnostic processes. CD microfluidic devices allow a variety of complex processes to transfer onto the small disc platform that previously were carried out by individual expensive laboratory equipment requiring trained personnel. The portability, ease of operation, integration, and robustness of the CD fluidic platforms requires simple, reliable, and scalable designs to control the flow of fluids. Valves play a vital role in opening/closing of microfluidic channels to enable a precise control of the flow of fluids on a centrifugal platform. Valving systems are also critical in isolating chambers from the rest of a fluidic network at required times, in effectively directing the reagents to the target location, in serial dilutions, and in integration of multiple other processes on a single CD. In this paper, we review the various available fluidic valving systems, discuss their working principles, and evaluate their compatibility with CD fluidic platforms. We categorize the presented valving systems into either “active”, “passive”, or “hybrid”—based on their actuation mechanism that can be mechanical, thermal, hydrophobic/hydrophilic, solubility-based, phase-change, and others. Important topics such as their actuation mechanism, governing physics, variability of performance, necessary disc spin rate for valve actuation, valve response time, and other parameters are discussed. The applicability of some types of valves for specialized functions such as reagent storage, flow control, and other applications is summarized.
Fuzzy Clustering Algorithm Based on Improved Global Best-Guided Artificial Bee Colony with New Search Probability Model for Image Segmentation
Authors: Alomoush Waleed , Khashan Osama A. , Alrosan Ayat , Houssein Essam H. , Attar Hani , Alweshah Mohammed , Alhosban Fuad
Published: November 2022

Clustering using fuzzy C-means (FCM) is a soft segmentation method that has been extensively investigated and successfully implemented in image segmentation. FCM is useful in various aspects, such as the segmentation of grayscale images. However, FCM has some limitations in terms of its selection of the initial cluster center. It can be easily trapped into local optima and is sensitive to noise, which is considered the most challenging issue in the FCM clustering algorithm. This paper proposes an approach to solve FCM problems in two phases. Firstly, to improve the balance between the exploration and exploitation of improved global best-guided artificial bee colony algorithm (IABC). This is achieved using a new search probability model called PIABC that improves the exploration process by choosing the best source of food which directly affects the exploitation process in IABC. Secondly, the fuzzy clustering algorithm based on PIABC, abbreviated as PIABC-FCM, uses the balancing of PIABC to avoid getting stuck into local optima while searching for the best solution having a set of cluster center locations of FCM. The proposed method was evaluated using grayscale images. The performance of the proposed approach shows promising outcomes when compared with other related works.
3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint
Authors: Behjati Mehran , Nordin Rosdiadee , Zulkifley Muhammad Aidiel , Abdullah Nor Fadzilah
Published: November 2022

This paper proposes an effective global path planning technique for cellular-connected UAVs to enhance the reliability of unmanned aerial vehicles’ (UAVs) flights operating beyond the visual line of sight (BVLOS). Cellular networks are considered one of the leading enabler technologies to provide a ubiquitous and reliable communication link for UAVs. First, this paper investigates a reliable aerial zone based on an extensive aerial drive test in a 4G network within a suburban environment. Then, the path planning problem for the cellular-connected UAVs is formulated under communication link reliability and power consumption constraints. To provide a realistic optimization solution, all constraints of the optimization problem are defined based on real-world scenarios; in addition, the presence of static obstacles and no-fly zones is considered in the path planning problem. Two powerful intelligent optimization algorithms, the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, are used to solve the defined optimization problem. Moreover, a combination of both algorithms, referred to as PSO-GA, is used to overcome the inherent shortcomings of the algorithms. The performances of the algorithms are compared under different scenarios in simulation environments. According to the statistical analysis of the aerial drive test, existing 4G base stations are able to provide reliable aerial coverage up to a radius of 500 m and a height of 85 m. The statistical analysis of the optimization results shows that PSO-GA is a more stable and effective algorithm to rapidly converge to a feasible solution for UAV path planning problems, with a far faster execution time compared with PSO and GA, about two times. To validate the performance of the proposed solution, the simulation results are compared with the real-world aerial drive test results. The results comparison proves the effectiveness of the proposed path planning method in suburban environments with 4G coverage. The proposed method can be extended by identifying the aerial link reliability of 5G networks to solve the UAV global path planning problem in the current 5G deployment.
Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features
Authors: Toma Rafia Nishat , Gao Yangde , Piltan Farzin , Im Kichang , Shon Dongkoo , Yoon Tae Hyun , Yoo Dae-Seung , Kim Jong-Myon
Published: November 2022

In the machine learning and data science pipelines, feature extraction is considered the most crucial component according to researchers, where generating a discriminative feature matrix is the utmost challenging task to achieve high classification accuracy. Generally, the classical feature extraction techniques are sensitive to the noisy component of the signal and need more time for training. To deal with these issues, a comparatively new feature extraction technique, referred to as a wavelet scattering transform (WST) is utilized, and incorporated with ML classifiers to design a framework for bearing fault classification in this paper. The WST is a knowledge-based technique, and the structure is similar to the convolution neural network. This technique provides low-variance features of real-valued signals, which are usually necessary for classification tasks. These signals are resistant to signal deformation and preserve information at high frequencies. The current signal data from a publicly available dataset for three different bearing conditions are considered. By combining the scattering path coefficients, the decomposition coefficients from the 0th and 1st layers are considered as features. The experimental results demonstrate that WST-based features, when used with ensemble ML algorithms, could achieve more than 99% classification accuracy. The performance of ANN models with these features is similar. This work exhibits that utilizing WST coefficients for the motor current signal as features can improve the bearing fault classification accuracy when compared to other feature extraction approaches such as empirical wavelet transform (EWT), information fusion (IF), and wavelet packet decomposition (WPD). Thus, our proposed approach can be considered as an effective classification method for the fault diagnosis of rotating machinery.
Methodology for Quantifying Volatile Compounds in a Liquid Mixture Using an Algorithm Combining B-Splines and Artificial Neural Networks to Process Responses of a Thermally Modulated Metal-Oxide Semiconductor Gas Sensor
Authors: Wawrzyniak Jolanta
Published: November 2022

Metal oxide semiconductor (MOS) gas sensors have many advantages, but the main obstacle to their widespread use is the cross-sensitivity observed when using this type of detector to analyze gas mixtures. Thermal modulation of the heater integrated with a MOS gas sensor reduced this problem and is a promising solution for applications requiring the selective detection of volatile compounds. Nevertheless, the interpretation of the sensor output signals, which take the form of complex, unique patterns, is difficult and requires advanced signal processing techniques. The study focuses on the development of a methodology to measure and process the output signal of a thermally modulated MOS gas sensor based on a B-spline curve and artificial neural networks (ANNs), which enable the quantitative analysis of volatile components (ethanol and acetone) coexisting in mixtures. B-spline approximation applied in the first stage allowed for the extraction of relevant information from the gas sensor output voltage and reduced the size of the measurement dataset while maintaining the most vital features contained in it. Then, the determined parameters of the curve were used as the input vector for the ANN model based on the multilayer perceptron structure. The results show great usefulness of the combination of B-spline and ANN modeling techniques to improve response selectivity of a thermally modulated MOS gas sensor.
Atlas-Based Segmentation in Extraction of Knee Joint Bone Structures from CT and MR
Authors: Zarychta Piotr
Published: November 2022

The main goal of the approach proposed in this study, which is dedicated to the extraction of bone structures of the knee joint (femoral head, tibia, and patella), was to show a fully automated method of extracting these structures based on atlas segmentation. In order to realize the above-mentioned goal, an algorithm employed automated image-matching as the first step, followed by the normalization of clinical images and the determination of the 11-element dataset to which all scans in the series were allocated. This allowed for a delineation of the average feature vector for the teaching group in the next step, which automated and streamlined known fuzzy segmentation methods (fuzzy c-means (FCM), fuzzy connectedness (FC)). These averaged features were then transmitted to the FCM and FC methods, which were implemented for the testing group and correspondingly for each scan. In this approach, two features are important: the centroids (which become starting points for the fuzzy methods) and the surface area of the extracted bone structure (protects against over-segmentation). This proposed approach was implemented in MATLAB and tested in 61 clinical CT studies of the lower limb on the transverse plane and in 107 T1-weighted MRI studies of the knee joint on the sagittal plane. The atlas-based segmentation combined with the fuzzy methods achieved a Dice index of 85.52–89.48% for the bone structures of the knee joint.
Virtual Axle Detector Based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network
Authors: Lorenzen Steven Robert , Riedel Henrik , Rupp Maximilian Michael , Schmeiser Leon , Berthold Hagen , Firus Andrei , Schneider Jens
Published: November 2022

In the practical application of the Bridge Weigh-In-Motion (BWIM) methods, the position of the wheels or axles during the passage of a vehicle is a prerequisite in most cases. To avoid the use of conventional axle detectors and bridge type-specific methods, we propose a novel method for axle detection using accelerometers placed arbitrarily on a bridge. In order to develop a model that is as simple and comprehensible as possible, the axle detection task is implemented as a binary classification problem instead of a regression problem. The model is implemented as a Fully Convolutional Network to process signals in the form of Continuous Wavelet Transforms. This allows passages of any length to be processed in a single step with maximum efficiency while utilising multiple scales in a single evaluation. This allows our method to use acceleration signals from any location on the bridge structure and act as Virtual Axle Detectors (VADs) without being limited to specific structural types of bridges. To test the proposed method, we analysed 3787 train passages recorded on a steel trough railway bridge of a long-distance traffic line. Results of the measurement data show that our model detects 95% of the axles, which means that 128,599 out of 134,800 previously unseen axles were correctly detected. In total, 90% of the axles were detected with a maximum spatial error of 20 cm, at a maximum velocity of vmax=56.3m/s. The analysis shows that our developed model can use accelerometers as VADs even under real operating conditions.