bluuebunny/crossref_metadata_2025 huggingface dataset:
Forest Height Retrieval Based on the Dual PolInSAR Images
Authors: Managhebi Tayebe , Maghsoudi Yasser , Amani Meisam
Published: September 2022

A new algorithm for forest height estimation based on dual polarimetric interferometric SAR data is presented in this study. The main objective is to consider the efficiency of the dual-polarization data compared to the full polarimetric images with respect to forest height retrieval. Accordingly, the forest height estimation based on the random volume over the ground model is examined using a geometrical procedure named the three-stage method. An exhaustive search polarization optimization technique is also applied to improve the results by employing the efficiency of all the polarization bases based on the four-dimensional lexicographic PolInSAR vector. The repeat-pass experimental SAR (ESAR) images, which include both L- and P-band full polarimetric data, are employed for the accuracy assessment of the dual PolInSAR data and the newly proposed method for forest height estimation. The experimental results on the L-band PolInSAR data show the ability of the dual PolInSAR data for forest height estimation with an average root mean square error (RMSE) of 4.97 m against Lidar data based on the conventional three-stage method. Additionally, the proposed method results in an accuracy of 2.95 m for forest height estimation, indicating its high potential for tree height retrieval.
Landslide Identification and Gradation Method Based on Statistical Analysis and Spatial Cluster Analysis
Authors: Dai Huayan , Zhang Hong , Dai Huayang , Wang Chao , Tang Wei , Zou Lichuan , Tang Yixian
Published: September 2022

As a type of earth observation technology, interferometric synthetic aperture radar (InSAR) is increasingly widely used in the field of geological disaster detection. However, the application of InSAR in low-coherence areas, such as alpine canyon areas and vegetation coverage areas, is subject to considerable limitations. How to accurately identify landslides from InSAR measurement data in these areas remains the subject of several challenges and shortcomings. Based on statistical analysis and spatial cluster analysis, in this paper, we propose an automatic landslide identification and gradation method suitable for low-coherence areas. The proposed method combines the small baseline subset InSAR (SBAS-InSAR) method and the interferogram stacking (stacking-InSAR) method to obtain a deformation map in the study area, using statistical analysis and spatial cluster analysis to extract deformation regions and landslide polygons to propose a landslide screening model (LSM) based on multivariate features to screen landslides and reduce the interference of noise in landslide identification, in addition to proposing a landslide gradation model (LGM) based on signum function to grade the identified landslides and provide support to distinguish landslides with different deformation degrees. The method was applied to landslide identification in the upper section of the Jinsha River basin, and 47 potential landslides were identified, including 15 high-risk landslides and 13 landslides endangering villages. The experimental results show that the proposed method can identify landslides accurately and hierarchically in low-coherence areas, providing support for geological hazard investigation agencies and local departments.
What Are Contemporary Mexican Conifers Telling Us? A Perspective Offered from Tree Rings Linked to Climate and the NDVI along a Spatial Gradient
Authors: Pompa-García Marín , Vivar-Vivar Eduardo D. , Sigala-Rodríguez José A. , Padilla-Martínez Jaime R.
Published: September 2022

Forest structure and composition have changed rapidly worldwide, presenting tendencies towards an increasing proportion of younger trees. From chronologies of tree-ring indices (TRI) and the reconstruction of the basal area increment (BAI), a dendroecological study was conducted from the perspective of the radial growth of twelve contemporary conifer species in a highly diverse region of the planet. From an elevational perspective, the TRI were associated with climate and the NDVI, while the BAI was also modeled as a potential proxy for forest productivity. Climate affects the species differently according to elevation: at 1900 m asl, Pinus caribaea, P. oocarpa and P. jeffreyi presented the lowest sensitivities to climate and drought. For their part, species occupying the intermediate part of the gradient (1901–3000 m asl), such as P. engelmannii, P. patula, P. johannis and P. maximartinezii, were very sensitive to maximum temperature (TMax), precipitation (PP) and drought during the winter–spring period. Finally, of the species distributed on the upper part of the gradient (>3000 m asl), only Abies religiosa was associated with TMax and drought; Juniperus deppeana, A. hickelii and P. hartwegii did not seem to be vulnerable to drought. Complementarily, we found significant differences in the BAI as a function of elevation, with the sites at 1001–1500 m asl presenting higher BAI. The results suggest that the growth in these forests is impacted by droughts and follows a distinct spatial pattern, with greater restriction found in mid-elevation forests. Consistent implications are also observed in BAI trends. For its part, the NDVI demonstrated a decreasing tendency in greenness from south to north, although no elevation pattern was evident. The combined proxies utilized here produced parameters that improve our understanding of forest growth and should be considered in vegetation dynamics models in order to reduce their uncertainty in the face of climate vulnerability. These forests must be sustainably managed, and it is therefore crucial to determine the influence of ecological variables on their growth.
Strip Attention Networks for Road Extraction
Authors: Huan Hai , Sheng Yu , Zhang Yi , Liu Yuan
Published: September 2022

In recent years, deep learning methods have been widely used for road extraction in remote sensing images. However, the existing deep learning semantic segmentation networks generally show poor continuity in road segmentation due to the high-class similarity between roads and buildings surrounding roads in remote sensing images, and the existence of shadows and occlusion. To deal with this problem, this paper proposes strip attention networks (SANet) for extracting roads in remote sensing images. Firstly, a strip attention module (SAM) is designed to extract the contextual information and spatial position information of the roads. Secondly, a channel attention fusion module (CAF) is designed to fuse low-level features and high-level features. The network is trained and tested using the CITY-OSM dataset, DeepGlobe road extraction dataset, and CHN6-CUG dataset. The test results indicate that SANet exhibits excellent road segmentation performance and can better solve the problem of poor road segmentation continuity compared with other networks.
Reading Greenness in Urban Areas: Possible Roles of Phenological Metrics from the Copernicus HR-VPP Dataset
Authors: Borgogno-Mondino Enrico , Fissore Vanina
Published: September 2022

Vegetation phenology is that branch of science that describes periodic plant life cycle events across the growing seasons. Remote sensing typically monitors these significant events by means of time series of vegetation indices, permitting to characterize vegetation dynamics. It is well known that vegetation in urban areas, i.e., green spaces in general, may benefit human health mainly by mitigating noise and air pollution, promoting physical or social activities, and improving mental health. Based on the influence that green space exposure seems to exert on Public Health and using a multidisciplinary approach, we mapped phenological behavior of urban green areas to explore yearly persistence of their potential favorable effect, such as heat reduction, air purification, noise mitigation, and promotion of physical/social activities and improvement of mental health. The study area corresponds to the municipality of Torino (about 800,000 inhabitants, NW, Italy). Renouncing to a rigorous at-species level phenological description, this work investigated macro-phenology of vegetated areas for the 2018, 2019 and 2020 years with reference to the new free and open Copernicus HR-VPP dataset. Vegetation type, deduced with reference to the 2019 BDTRE official technical map of the Piemonte Region, was considered and related to the correspondent macro-phenology using a limited number of metrics from the HR-VPP dataset. Investigation was aimed at exploring their capability of providing synthetic and easy-to-use information for urban planners. No validation was achieved about phenological metrics values (assuming their accuracy correspondent to the nominal one reported in the associated manuals). Nevertheless, a spatial validation was operated to investigate the capability of the dataset to properly recognize vegetated areas, thus providing correspondent metrics. Preliminary results showed a spatial inconsistency related to the HR-VPP dataset, that greatly overestimates (about 50%) vegetated areas in the city, assigning metric values to pixels that, if compared with technical maps, do not fall within vegetated areas. The work found out that, among HR-VPP metrics, LOS (Length Of Season) and SPROD (Seasonal Productivity) well characterized vegetation patches, making it possible to clearly read vegetation behavior, which can be effectively exploited to zone the city and make management of green areas and real estate considerations more effective.
Seasonal Flow Forecasting Using Satellite-Driven Precipitation Data for Awash and Omo-Gibe Basins, Ethiopia
Authors: Woldegebrael Surafel M. , Kidanewold Belete B. , Melesse Assefa M.
Published: September 2022

Hydrologic extreme events such as flooding impact people and the environment and delay sustainable development in flood-prone areas when it is excessive. The present study developed a seasonal floodwater forecast system for the Awash and Omo-Gibe basins of Ethiopia using the 2021 rainy season (June to September) as a temporal case study. In Ethiopia, there is no seasonal forecasting system available to cope with the recurrent flooding impacts instead of exercising ineffective and traditional monitoring approaches. The satellite-driven precipitation and temperature forecasts, observed rainfall, discharge, reservoir water levels, land cover, and soil data were used in the hydrologic (HEC-HMS) and hydraulic (HEC-RAS) models, spreadsheet, and GIS applications. The results obtained were forecasts of the runoff, reservoir water levels, and storage. The coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), percent of bias (Pbias), and Kling–Gupta efficiency (KGE) were used to evaluate the model’s performance in addition to plots as presented in the manuscript. The R2 values obtained for the Koka and Gibe-3 reservoirs’ inflows (water levels) were 0.97 (0.95) and 0.93 (0.99), respectively, and the NSE values were 0.90 (0.88) and 0.92 (0.95) for each reservoir. Similarly, the water levels (meter) and storage (Mm3) for the Koka and Gibe-3 reservoirs at the end of the 2021 flood season were 111.0 (1467.58) and 890.8 (13,638.5), respectively. Excess floodwater can be maintained in and released from reservoirs depending on the future water uses and flood monitoring activities downstream. In addition, the flood inundation extents from Earth remote sensing satellite observation and model results were examined and showed agreement.
Building Change Detection Based on an Edge-Guided Convolutional Neural Network Combined with a Transformer
Authors: Xia Liegang , Chen Jun , Luo Jiancheng , Zhang Junxia , Yang Dezhi , Shen Zhanfeng
Published: September 2022

Change detection extracts change areas in bitemporal remote sensing images, and plays an important role in urban construction and coordination. However, due to image offsets and brightness differences in bitemporal remote sensing images, traditional change detection algorithms often have reduced applicability and accuracy. The development of deep learning-based algorithms has improved their applicability and accuracy; however, existing models use either convolutions or transformers in the feature encoding stage. During feature extraction, local fine features and global features in images cannot always be obtained simultaneously. To address these issues, we propose a novel end-to-end change detection network (EGCTNet) with a fusion encoder (FE) that combines convolutional neural network (CNN) and transformer features. An intermediate decoder (IMD) eliminates global noise introduced during the encoding stage. We noted that ground objects have clearer semantic information and improved edge features. Therefore, we propose an edge detection branch (EDB) that uses object edges to guide mask features. We conducted extensive experiments on the LEVIR-CD and WHU-CD datasets, and EGCTNet exhibits good performance in detecting small and large building objects. On the LEVIR-CD dataset, we obtain F1 and IoU scores of 0.9008 and 0.8295. On the WHU-CD dataset, we obtain F1 and IoU scores of 0.9070 and 0.8298. Experimental results show that our model outperforms several previous change detection methods.
Estimation of Biomass and N Uptake in Different Winter Cover Crops from UAV-Based Multispectral Canopy Reflectance Data
Authors: Holzhauser Katja , Räbiger Thomas , Rose Till , Kage Henning , Kühling Insa
Published: September 2022

Cover crops are known to provide beneficial effects to agricultural systems such as a reduction in nitrate leaching, erosion control, and an increase in soil organic matter. The monitoring of cover crops’ growth (e.g., green area index (GAI), nitrogen (N) uptake, or dry matter (DM)) using remote sensing techniques allows us to identify the physiological processes involved and to optimise management decisions. Based on the data of a two-year trial (2018, 2019) in Kiel, Northern Germany, the multispectral sensor Sequoia (Parrot) was calibrated to the selected parameters of the winter cover crops oilseed radish, saia oat, spring vetch, and winter rye as sole cover crops and combined in mixtures. Two simple ratios (SRred, SRred edge) and two normalised difference indices (NDred, NDred edge) were calculated and tested for their predicting power. Furthermore, the advantage of the species/mixture–individual compared to the universal models was analysed. SRred best predicted GAI, DM, and N uptake (R2: 0.60, 0.53, 0.45, respectively) in a universal model approach. The canopy parameters of saia oat and spring vetch were estimated by species–individual models, achieving a higher R2 than with the universal model. Comparing mixture–individual models to the universal model revealed low relative error differences below 3%. The findings of the current study serve as a tool for the rapid and inexpensive estimation of cover crops’ canopy parameters that determine environmental services.
Non-Line-of-Sight Target Detection Based on Dual-View Observation with Single-Channel UWB Radar
Authors: Yang Yiping , Chen Chuan , Jia Yong , Cui Guolong , Guo Shisheng
Published: September 2022

Non-line-of-sight (NLOS) target detection utilizing multipath plays an important role in anti-terrorism, urban warfare, indoor rescue and intelligent driving. In this paper, an imaging method based on single-channel ultra-wideband (UWB) radar and dual-view observation is proposed for detecting NLOS targets. First, the cause of producing ghosts from a single view in a previous study is analyzed in detail. On this basis, with focusing on dual-view observation, the ghost problem is handled at two levels. From the level of the radar echo, path partition is conducted with the utilization of dual-view observation, solving the problem of uneven energy among multipaths. After that, two images are generated by multipath imaging, respectively, which reduces the accumulation of ghosts. From the level of the image, owing to the distinct distribution of ghosts between the imaging result, the ghosts at different locations are eliminated by image fusion. Experimental results demonstrate that compared with single view and direct multipath imaging, the approach based on dual-view observation successfully eliminates most ghosts while retaining the targets in both single-target and double-targets scenarios, which verifies the effectiveness of the method.
Integrating Adversarial Generative Network with Variational Autoencoders towards Cross-Modal Alignment for Zero-Shot Remote Sensing Image Scene Classification
Authors: Ma Suqiang , Liu Chun , Li Zheng , Yang Wei
Published: September 2022

Remote sensing image scene classification takes image blocks as classification units and predicts their semantic descriptors. Because it is difficult to obtain enough labeled samples for all classes of remote sensing image scenes, zero-shot classification methods which can recognize image scenes that are not seen in the training stage are of great significance. By projecting the image visual features and the class semantic features into the latent space and ensuring their alignment, the variational autoencoder (VAE) generative model has been applied to address remote-sensing image scene classification under a zero-shot setting. However, the VAE model takes the element-wise square error as the reconstruction loss, which may not be suitable for measuring the reconstruction quality of the visual and semantic features. Therefore, this paper proposes to augment the VAE models with the generative adversarial network (GAN) to make use of the GAN’s discriminator in order to learn a suitable reconstruction quality metric for VAE. To promote feature alignment in the latent space, we have also proposed cross-modal feature-matching loss to make sure that the visual features of one class are aligned with the semantic features of the class and not those of other classes. Based on a public dataset, our experiments have shown the effects of the proposed improvements. Moreover, taking the ResNet models of ResNet18, extracting 512-dimensional visual features, and ResNet50 and ResNet101, both extracting 2048-dimensional visual features for testing, the impact of the different visual feature extractors has also been investigated. The experimental results show that better performance is achieved by ResNet18. This indicates that more layers of the extractors and larger dimensions of the extracted features may not contribute to the image scene classification under a zero-shot setting.
Impact of Training Set Size and Lead Time on Early Tomato Crop Mapping Accuracy
Authors: Croci Michele , Impollonia Giorgio , Blandinières Henri , Colauzzi Michele , Amaducci Stefano
Published: September 2022

Estimating key crop parameters (e.g., phenology, yield prediction) is a prerequisite for optimizing agrifood supply chains through the use of satellite imagery, but requires timely and accurate crop mapping. The moment in the season and the number of training sites used are two main drivers of crop classification performance. The combined effect of these two parameters was analysed for tomato crop classification, through 125 experiments, using the three main machine learning (ML) classifiers (neural network, random forest, and support vector machine) using a response surface methodology (RSM). Crop classification performance between minority (tomato) and majority (‘other crops’) classes was assessed through two evaluation metrics: Overall Accuracy (OA) and G-Mean (GM), which were calculated on large independent test sets (over 400,000 fields). RSM results demonstrated that lead time and the interaction between the number of majority and minority classes were the two most important drivers for crop classification performance for all three ML classifiers. The results demonstrate the feasibility of preharvest classification of tomato with high performance, and that an RSM-based approach enables the identification of simultaneous effects of several factors on classification performance. SVM achieved the best grading performances across the three ML classifiers, according to both evaluation metrics. SVM reached highest accuracy (0.95 of OA and 0.97 of GM) earlier in the season (low lead time) and with less training sites than the other two classifiers, permitting a reduction in cost and time for ground truth collection through field campaigns.
Dominant Impacting Factors on Water-Use Efficiency Variation in Inner Mongolia from 2001 to 2018: Vegetation or Climate?
Authors: Luo Hao , Bie Xiaojuan , Yi Guihua , Zhou Xiaobing , Zhang Tingbin , Li Jingji , Lai Pingqing
Published: September 2022

Water-use efficiency (WUE) is not only an important indicator to connect the carbon and water cycles of a terrestrial ecosystem, but also a key parameter for an ecosystem to respond to climate change. It is crucial for understanding the mechanism of regional ecosystem response to environmental change by researching the influences of vegetation and climate change on WUE variation and revealing its drivers. Based on trend analysis, grey relational analysis, and ridge-regression analysis, this study analyzed the spatiotemporal variation characteristics of WUE in Inner Mongolia (IM) from 2001 to 2018 and determined the dominant influencing factors of WUE variation. The results showed that the annual mean WUE in IM was 1.39 g C m−2 mm−1 and it generally presented a rising trend, with an increasing rate of 0.0071 g C m−2 mm−1 yr−1. Leaf-area index (LAI) and precipitation were the most important factors influencing WUE in IM, followed by relative humidity and wind speed. Temperature, water vapor pressure and sunshine duration slightly influenced WUE and they were relatively less important. According to the ridge-regression analysis, LAI, precipitation and relative humidity had a positive contribution to WUE variation, while the wind speed had a negative contribution. Regionally, LAI was the dominant cause of WUE variation. The contribution and relative contribution rate of LAI to WUE variation were 0.008 g C m−2 mm−1 yr−1 and 44.57%, which were significantly higher than those of precipitation, relative humidity, and sunshine duration. Thus, vegetation primarily dominated WUE variability during the study period. The relative contribution rate of LAI varied across the different vegetation types and ranged from 25.26% in swamps to 51.29% in meadows. Our results improve the understanding of the effects of driving factors on WUE, which can help policymakers with water resource management and ecological restoration.
SDGSAT-1 TIS Prelaunch Radiometric Calibration and Performance
Authors: Hu Zhuoyue , Zhu Min , Wang Qiyao , Su Xiaofeng , Chen Fansheng
Published: September 2022

SDGSAT-1 was launched in November 2021, and TIS (Thermal infrared sensor) is a major instrument onboard this satellite. An analysis of the radiometric calibration and noise performance of the TIS has been carried out in the thermal vacuum chamber before launch in order to ensure that it meets the requirements. The prelaunch test results show NEdT (noise equivalent temperature difference) is 0.034 K, 0.047 K and 0.076 K@300 K for the three bands, respectively. The maximum fitting residuals are less than 0.5 K at measured blackbody temperatures. In addition, this paper also discusses the dependence between TIS performance and instrument temperature and focal plane array (FPA) temperature. The good radiometric and noise performance of TIS demonstrates it has potential to provide high resolution thermal remote sensing data in urban heat island, and other environmental issues research.
Particle Scattering Induced Orbital Angular Momentum Spectrum Change of Vector Bessel–Gaussian Vortex Beam
Authors: Shi Chenge , Cheng Mingjian , Guo Lixin , Lavery Martin P. J. , Wang Ping , Liu Songhua , Li Renxian , Li Jiangting
Published: September 2022

In this paper, we obtain the intensity and phase distributions of the scattering and external fields of a vector Bessel–Gaussian vortex beam in the far-field region after being scattered by a particle. In our analysis, we use the Generalized Lorenz–Mie theory (GLMT) and the angular spectrum decomposition method (ASDM). The orbital angular momentum (OAM) spectra of the fields are analyzed by using the spiral spectrum expansion method, which is a frequently used tool for studying the propagation of vortex beams in turbulent atmospheres. Both scattered and external fields show a significant difference in spiral spectra for particles with different characteristic parameters, such as the size and complex refractive index. We also examine sampling the phase along with a circle and show that it is unable to fully express the information of the fields. This study can provide a theoretical basis for the inversion of characteristic parameters of the Bessel–Gaussian vortex beam and spherical particle by OAM spectra with applications in remote sensing engineering.
On the Accuracy and Consistency of Quintuple Collocation Analysis of In Situ, Scatterometer, and NWP Winds
Authors: Vogelzang Jur , Stoffelen Ad
Published: September 2022

The accuracy and consistency of a quintuple collocation analysis of ocean surface vector winds from buoys, scatterometers, and NWP forecasts is established. A new solution method is introduced for the general multiple collocation problem formulated in terms of covariance equations. By a logarithmic transformation, the covariance equations reduce to ordinary linear equations that can be handled using standard methods. The method can be applied to each determined or overdetermined subset of the covariance equations. Representativeness errors are estimated from differences in spatial variances. The results are in good agreement with those from quadruple collocation analyses reported elsewhere. The geometric mean of all solutions from determined subsets of the covariance equations equals the least-squares solution of all equations. The accuracy of the solutions is estimated from synthetic data sets with random Gaussian errors that are constructed from the buoy data using the values of the calibration coefficients and error variances from the quintuple collocation analysis. For the calibration coefficients, the spread in the models is smaller than the accuracy, but for the observation error variances, the spread and the accuracy are about equal only for representativeness errors evaluated at a scale of 200 km for u and 100 km for v. Some average error covariances differ significantly from zero, indicating weak inconsistencies in the underlying error model. Possible causes for this are discussed. With a data set of 2454 collocations, the accuracy in the observation error standard deviation is 0.02 to 0.03 m/s at the one-sigma level for all observing systems.
Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images
Authors: Sertel Elif , Ekim Burak , Ettehadi Osgouei Paria , Kabadayi M. Erdem
Published: September 2022

Deep learning-based segmentation of very high-resolution (VHR) satellite images is a significant task providing valuable information for various geospatial applications, specifically for land use/land cover (LULC) mapping. The segmentation task becomes more challenging with the increasing number and complexity of LULC classes. In this research, we generated a new benchmark dataset from VHR Worldview-3 images for twelve distinct LULC classes of two different geographical locations. We evaluated the performance of different segmentation architectures and encoders to find the best design to create highly accurate LULC maps. Our results showed that the DeepLabv3+ architecture with an ResNeXt50 encoder achieved the best performance for different metric values with an IoU of 89.46%, an F-1 score of 94.35%, a precision of 94.25%, and a recall of 94.49%. This design could be used by other researchers for LULC mapping of similar classes from different satellite images or for different geographical regions. Moreover, our benchmark dataset can be used as a reference for implementing new segmentation models via supervised, semi- or weakly-supervised deep learning models. In addition, our model results can be used for transfer learning and generalizability of different methodologies.
High-Sensitivity Optical Fiber-Based Glucose Sensor Using Helical Intermediate-Period Fiber Grating
Authors: Zhong Junlan , Liu Shen , Zou Tao , Yan Wenqi , Chen Peijing , Liu Bonan , Sun Zhongyuan , Wang Yiping
Published: September 2022

An all-fiber glucose sensor is proposed and demonstrated based on a helical intermediate-period fiber grating (HIPFG) produced by using a hydrogen/oxygen flame heating method. The HIPFG, with a grating length of 1.7 cm and a period of 35 μm, presents four sets of double dips with low insertion losses and strong coupling strengths in the transmission spectrum. The HIPFG possesses an averaged refractive index (RI) sensitivity of 213.6 nm/RIU nm/RIU in the RI range of 1.33–1.36 and a highest RI sensitivity of 472 nm/RIU at RI of 1.395. In addition, the HIPFG is demonstrated with a low-temperature sensitivity of 3.67 pm/°C, which promises a self-temperature compensation in glucose detection. In the glucose-sensing test, the HIPFG sensor manifests a detection sensitivity of 0.026 nm/(mg/mL) and a limit of detection (LOD) of 1 mg/mL. Moreover, the HIPFG sensor exhibits good stability in 2 h, indicating its capacity for long-time detection. The properties of easy fabrication, high flexibility, insensitivity to temperature, and good stability of the proposed HIPFG endow it with a promising potential for long-term and compact biosensors.
Acne Detection by Ensemble Neural Networks
Authors: Zhang Hang , Ma Tianyi
Published: September 2022

Acne detection, utilizing prior knowledge to diagnose acne severity, number or position through facial images, plays a very important role in medical diagnoses and treatment for patients with skin problems. Recently, deep learning algorithms were introduced in acne detection to improve detection precision. However, it remains challenging to diagnose acne based on the facial images of patients due to the complex context and special application scenarios. Here, we provide an ensemble neural network composed of two modules: (1) a classification module aiming to calculate the acne severity and number; (2) a localization module aiming to calculate the detection boxes. This ensemble model could precisely predict the acne severity, number, and position simultaneously, and could be an effective tool to help the patient self-test and assist the doctor in the diagnosis.
Control of Brushless Direct-Current Motors Using Bioelectric EMG Signals
Authors: Glowinski Sebastian , Pecolt Sebastian , Błażejewski Andrzej , Młyński Bartłomiej
Published: September 2022

(1) Background: The purpose of this study was to evaluate the analysis of measurements of bioelectric signals obtained from electromyographic sensors. A system that controls the speed and direction of rotation of a brushless DC motor (BLDC) was developed; (2) Methods: The system was designed and constructed for the acquisition and processing of differential muscle signals. Basic information for the development of the EMG signal processing system was also provided. A controller system implementing the algorithm necessary to control the speed and direction of rotation of the drive rotor was proposed; (3) Results: Using two muscle groups (biceps brachii and triceps), it was possible to control the direction and speed of rotation of the drive unit. The control system changed the rotational speed of the brushless motor with a delay of about 0.5 s in relation to the registered EMG signal amplitude change; (4) Conclusions: The prepared system meets all the design assumptions. In addition, it is scalable and allows users to adjust the signal level. Our designed system can be implemented for rehabilitation, and in exoskeletons or prostheses.
3D Simulation, Electrical Characteristics and Customized Manufacturing Method for a Hemispherical Electrode Detector
Authors: Liu Manwen , Cheng Wenzheng , Li Zheng , Zhao Zhenyang , Li Zhihua
Published: September 2022

The theoretical basis of a hypothetical spherical electrode detector was investigated in our previous work. It was found that the proposed detector has very good electrical characteristics, such as greatly reduced full depletion voltage, small capacitance and ultra-fast collection time. However, due to the limitations of current technology, spherical electrode detectors cannot be made. Therefore, in order to use existing CMOS technology to realize the fabrication of the detector, a hemispherical electrode detector is proposed. In this work, 3D modeling and simulation including potential and electric field distribution and hole concentration distribution are carried out using the TCAD simulation tools. In addition, the electrical characteristics, such as I-V, C-V, induced current and charge collection efficiency (CCE) with different radiation fluences, are studied to predict the radiation hardness property of the device. Furthermore, a customized manufacturing method is proposed and simulated with the TCAD-SPROCESS simulation tool. The key is to reasonably set the aspect ratio of the deep trench in the multi-step repetitive process and optimize parameters such as the angle, energy, and dose of ion implantation to realize the connection of the heavily doped region of the near-hemispherical electrode. Finally, the electrical characteristics of the process simulation are compared with the device simulation results to verify its feasibility.
Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET
Authors: Shah Mumtaz Ali , Zeeshan Khan Farrukh , Abbas Ghulam , Abbas Ziaul Haq , Ali Jehad , Aljameel Sumayh S. , Khan Irfan Ullah , Aslam Nida
Published: September 2022

In vehicular ad hoc networks (VANETs), helpful information dissemination establishes the foundation of communication. One of the significant difficulties in developing a successful dissemination system for VANETs is avoiding traffic fatalities. Another essential success metric is the transfer of reliable and secure warning messages through the shortest path, particularly on highways with high mobility. Clustering vehicles is a general solution to these challenges, as it allows warning alerts to be re-broadcast to nearby clusters by fewer vehicles. Hence, trustworthy cluster head (CH) selections are critical to decreasing the number of retransmissions. In this context, we suggest a clustering technique called Optimal Path Routing Protocol for Warning Messages (OPRP) for dissemination in highway VANETs. OPRP relies on mobility measured to reinforce cluster creation, evade transmission overhead, and sustain message authenticity in a high mobility environment. Moreover, we consider communication between the cluster heads to reduce the number of transmissions. Furthermore, the cluster head is chosen using the median technique based on an odd or even number of vehicles for a stable and lengthy cluster life. By altering traffic densities and speeds, OPRP is compared with prominent schemes. Simulation results revealed that OPRP offers enhanced throughput, end-to-end delay, maximizing packet delivery ratio, and message validity.
An Improved Grey Wolf Optimization with Multi-Strategy Ensemble for Robot Path Planning
Authors: Dong Lin , Yuan Xianfeng , Yan Bingshuo , Song Yong , Xu Qingyang , Yang Xiongyan
Published: September 2022

Grey wolf optimization (GWO) is a meta-heuristic algorithm inspired by the hierarchy and hunting behavior of grey wolves. GWO has the superiorities of simpler concept and fewer adjustment parameters, and has been widely used in different fields. However, there are some disadvantages in avoiding prematurity and falling into local optimum. This paper presents an improved grey wolf optimization (IGWO) to ameliorate these drawbacks. Firstly, a modified position update mechanism for pursuing high quality solutions is developed. By designing an ameliorative position update formula, a proper balance between the exploration and exploitation is achieved. Moreover, the leadership hierarchy is strengthened by proposing adaptive weights of α, β and δ. Then, a dynamic local optimum escape strategy is proposed to reinforce the ability of the algorithm to escape from the local stagnations. Finally, some individuals are repositioned with the aid of the positions of the leaders. These individuals are pulled to new positions near the leaders, helping to accelerate the convergence of the algorithm. To verify the effectiveness of IGWO, a series of contrast experiments are conducted. On the one hand, IGWO is compared with some state-of-the-art GWO variants and several promising meta-heuristic algorithms on 20 benchmark functions. Experimental results indicate that IGWO performs better than other competitors. On the other hand, the applicability of IGWO is verified by a robot global path planning problem, and simulation results demonstrate that IGWO can plan shorter and safer paths. Therefore, IGWO is successfully applied to the path planning as a new method.
Phase Target-Based Calibration of Projector Radial Chromatic Aberration for Color Fringe 3D Measurement Systems
Authors: Zhang Yuzhuo , Sun Yaqin , Gao Nan , Meng Zhaozong , Zhang Zonghua
Published: September 2022

The camera and projector are indispensable hardware parts of a color fringe projection 3D measurement system. Chromatic aberration between different color channels of the projector and camera has an impact on the measurement accuracy of the color fringe projection 3D profile measurement. There are many studies on camera calibration, but the chromatic aberration of the projector remains a question deserving of further investigation. In view of the complex system architecture and theoretical derivation of the traditional projector radial chromatic aberration method, a phase target based on projector radial chromatic aberration measurement and the correction method are proposed in this paper. This method uses a liquid crystal display with a holographic projection film as the phase target. The liquid crystal display sequentially displays red, green, and blue horizontal and vertical sinusoidal fringe images. The projector projects red, green, and blue horizontal and vertical sinusoidal fringe images to the phase target in turn, and calculates the absolute phases of the display fringes and reflection fringes, respectively. Taking the green channel as the reference channel, a phase coordinate system is established based on the phases of the vertical and horizontal directions displayed on the display screen, using the phase of the reflection fringes on the display screen as the ideal phase value of the phase point. Then, the phase coordinate system of the red and blue channels is transferred to the green phase coordinate system to calculate the chromatic aberration of the red-green channels and the blue-green channels, and pre-compensation is conducted. Experimental results prove that this method can measure and calibrate the radial chromatic aberration of the projector without being affected by the image quality of the camera. The correction effect of this method is that the maximum chromatic aberration of the red-green channel decreases from 1.9591/pixel to 0.5759/pixel, and the average chromatic aberration decreases from 0.2555/pixel to 0.1865/pixel. In addition, blue-green channel maximum chromatic aberration decreased from 1.8906/pixel to 0.5938/pixel, and the average chromatic aberration decreased from 0.2347/pixel to 0.1907/pixel. This method can improve the projection quality for fringe projection 3D profile measurement technology.
Design and Testing of Real-Time Sensing System Used in Predicting the Leakage of Subsea Pipeline
Authors: Chen Yung-Hsu , Shen Sheng-Chih , Wu Yan-Kuei , Lee Chun-Yen , Chen Yen-Ju
Published: September 2022

This study integrates the array sensing module and the flow leakage algorithm. In this study, a real-time monitoring deep-sea pipeline damage sensing system is designed to provide decision-making parameters such as damage coordinates and damage area. The array sensor module is composed of multiple YF-S201 hall sensors and controllers. YF-S201 hall sensors are arranged inside the pipeline in an array. The flow signal in the deep-sea pipeline can be transmitted to the electronic control interface to analyze the leakage position and leakage flowrate of the pipeline. The theory of this system is based on the conservation of mass. Through the flow of each sensor, it is judged whether the pipeline is damaged. When the pipeline is not damaged, the flowrate of each sensor is almost the same. When the pipeline is damaged, the flowrate will drop significantly. When the actual size of leakage in the pipeline is 5.28 cm2, the size calculated by the flowrate of hall sensors is 2.58 cm2 in average, indicating the error between experimental data and theoretical data is 46%. When the actual size of leakage in the pipeline is 1.98 cm2, the size calculated by the flowrate of hall sensors is 1.31 cm2 in average, indicating the error between experimental data and theoretical data is 21%. This can accurately confirm the location of the broken pipeline, which is between sensor A and sensor B, so that the AUV/ROV can accurately locate and perform pipeline maintenance in real time. It is expected to be able to monitor the flowrate through the array magnetic sensing module designed in this study. It can grasp the status of deep-sea pipelines, improve the quality of deep-sea extraction and pipeline maintenance speed.
Investigating the Dose-Response Relationship between Deep Breathing and Heart Rate Variability in Healthy Participants and Across-Days Reliability in Patients with Rheumatoid Arthritis and Systemic Lupus Erythematosus
Authors: Liboriussen Caroline Hundborg , Andersen Stine Søgaard , Andersen Sally Søgaard , Jensen Mette Kjeldsgaard , Jochumsen Mads , Kristensen Salome
Published: September 2022

Rheumatoid Arthritis (RA) and Systemic Lupus Erythematosus (SLE) are associated with autonomic dysfunction, potentially through reduced vagus nerve tone. Vagus nerve stimulation has been proposed as an anti-inflammatory treatment, and it can be performed through deep breathing (DB) exercises. In this study, the dose-response relationship between DB exercises and heart rate variability (HRV) was investigated in healthy participants and reliability across days in patients with RA and SLE. On three separate days, 41 healthy participants performed DB for: 5, 15, or 30 min. On two separate days, 52 RA or SLE patients performed DB with the dose associated with the highest HRV increase in healthy participants. The HRV was estimated from ECG-recordings recorded prior and post the DB exercises. Increases in dose led to larger HRV-responses. Thirty minutes led to the largest HRV-response. In the RA and SLE patients, this dose increased the HRV-parameters consistently across the two days, indicating reliability. DB increases HRV in healthy participants and RA or SLE patients, which indicates stimulation of the vagus nerve. Of the tested durations, 30 min of DB was the optimal period of stimulation. A potential anti-inflammatory effect of DB exercises should be investigated in future studies.
Evaluation of Various State of the Art Head Pose Estimation Algorithms for Clinical Scenarios
Authors: Hammadi Yassine , Grondin François , Ferland François , Lebel Karina
Published: September 2022

Head pose assessment can reveal important clinical information on human motor control. Quantitative assessment have the potential to objectively evaluate head pose and movements’ specifics, in order to monitor the progression of a disease or the effectiveness of a treatment. Optoelectronic camera-based motion-capture systems, recognized as a gold standard in clinical biomechanics, have been proposed for head pose estimation. However, these systems require markers to be positioned on the person’s face which is impractical for everyday clinical practice. Furthermore, the limited access to this type of equipment and the emerging trend to assess mobility in natural environments support the development of algorithms capable of estimating head orientation using off-the-shelf sensors, such as RGB cameras. Although artificial vision is a popular field of research, limited validation of human pose estimation based on image recognition suitable for clinical applications has been performed. This paper first provides a brief review of available head pose estimation algorithms in the literature. Current state-of-the-art head pose algorithms designed to capture the facial geometry from videos, OpenFace 2.0, MediaPipe and 3DDFA_V2, are then further evaluated and compared. Accuracy is assessed by comparing both approaches to a baseline, measured with an optoelectronic camera-based motion-capture system. Results reveal a mean error lower or equal to 5.6∘ for 3DDFA_V2 depending on the plane of movement, while the mean error reaches 14.1∘ and 11.0∘ for OpenFace 2.0 and MediaPipe, respectively. This demonstrates the superiority of the 3DDFA_V2 algorithm in estimating head pose, in different directions of motion, and suggests that this algorithm can be used in clinical scenarios.
Application of YOLOv4 Algorithm for Foreign Object Detection on a Belt Conveyor in a Low-Illumination Environment
Authors: Chen Yiming , Sun Xu , Xu Liang , Ma Sencai , Li Jun , Pang Yusong , Cheng Gang
Published: September 2022

The most common failures of belt conveyors are runout, coal piles and longitudinal tears. The detection methods for longitudinal tearing are currently not particularly effective. A key study area for minimizing longitudinal belt tears with the advancement of machine learning is how to use machine vision technology to detect foreign items on the belt. In this study, the real-time detection of foreign items on belt conveyors is accomplished using a machine vision method. Firstly, the KinD++ low-light image enhancement algorithm is used to improve the quality of the captured low-quality images through feature processing. Then, the GridMask method partially masks the foreign objects in the training images, thus extending the data set. Finally, the YOLOv4 algorithm with optimized anchor boxes is combined to achieve efficient detection of foreign objects in belt conveyors, and the method is verified as effective.
Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach
Authors: Choi Hongrok , Pack Sangheon
Published: September 2022

In low earth orbit (LEO) satellite-based applications (e.g., remote sensing and surveillance), it is important to efficiently transmit collected data to ground stations (GS). However, LEO satellites’ high mobility and resultant insufficient time for downloading make this challenging. In this paper, we propose a deep-reinforcement-learning (DRL)-based cooperative downloading scheme, which utilizes inter-satellite communication links (ISLs) to fully utilize satellites’ downloading capabilities. To this end, we formulate a Markov decision problem (MDP) with the objective to maximize the amount of downloaded data. To learn the optimal approach to the formulated problem, we adopt a soft-actor-critic (SAC)-based DRL algorithm in discretized action spaces. Moreover, we design a novel neural network consisting of a graph attention network (GAT) layer to extract latent features from the satellite network and parallel fully connected (FC) layers to control individual satellites of the network. Evaluation results demonstrate that the proposed DRL-based cooperative downloading scheme can enhance the average utilization of contact time by up to 17.8% compared with independent downloading and randomly offloading schemes.