bluuebunny/crossref_metadata_2025 huggingface dataset:
Modeling Forest Canopy Cover: A Synergistic Use of Sentinel-2, Aerial Photogrammetry Data, and Machine Learning
Authors: Nasiri Vahid , Darvishsefat Ali Asghar , Arefi Hossein , Griess Verena C. , Sadeghi Seyed Mohammad Moein , Borz Stelian Alexandru
Published: March 2022

Forest canopy cover (FCC) is an important ecological parameter of forest ecosystems, and is correlated with forest characteristics, including plant growth, regeneration, biodiversity, light regimes, and hydrological properties. Here, we present an approach of combining Sentinel-2 data, high-resolution aerial images, and machine learning (ML) algorithms to model FCC in the Hyrcanian mixed temperate forest, Northern Iran. Sentinel-2 multispectral bands and vegetation indices were used as variables for modeling and mapping FCC based on UAV ground truth to a wider spatial extent. Random forest (RF), support-vector machine (SVM), elastic net (ENET), and extreme gradient boosting (XGBoost) were the ML algorithms used to learn and generalize on the remotely sensed variables. Evaluation of variable importance indicated that vegetation indices including NDVI, NDVI-A, NDRE, and NDI45 were the dominant predictors in most of the models. Model accuracy estimation results showed that among the tested models, RF (R2 = 0.67, RMSE = 18.87%, MAE = 15.35%) and ENET (R2 = 0.63, RMSE = 20.04%, MAE = 16.44%) showed the best and the worst performance, respectively. In conclusion, it was possible to prove the suitability of integrating UAV-obtained RGB images, Sentinel-2 data, and ML models for the estimation of FCC, intended for precise and fast mapping at landscape-level scale.
Evaluation of Eight High-Resolution Gridded Precipitation Products in the Heihe River Basin, Northwest China
Authors: Wang Yuwei , Zhao Na
Published: March 2022

The acquisition of the precise spatial distribution of precipitation is of great importance and necessity in many fields, such as hydrology, meteorology and ecological environments. However, in the arid and semiarid regions of Northwest China, especially over mountainous areas such as the Heihe River basin (HRB), the scarcity and uneven distribution of rainfall stations have created certain challenges in gathering information that accurately describes the spatial distribution of precipitation for use in applications. In this study, the accuracy of precipitation estimates from eight high-resolution gridded precipitation products (CMORPHv1-CRT, CRU TSv.4.05, ERA5, GSMaP_NRT, IMERG V06B-Final, MSWEPv2.0, PERSIANN-CDR and TRMM 3B42v7) are comprehensively evaluated by referring to the precipitation observations from 23 stations over the HRB using six indices (root mean square error, standard deviation, Pearson correlation coefficient, relative deviation, mean error and Kling–Gupta efficiency) from different spatial and temporal scales. The results show that at an annual scale, MSWEP has the highest accuracy over the entire basin, while PERSIANN, CRU and ERA5 show the most accurate results in the upper, middle and lower reaches of the HRB, respectively. At a seasonal scale, the performance of IMERG, CRU and ERA5 is superior to that of the other products in all seasons in the upper, middle and lower reaches, respectively. Over the entire HRB, PERSIANN displays the smallest deviation in all seasons except for spring. TRMM shows the highest accuracy in spring and autumn, while MSWEP and CRU show the highest accuracy in summer and winter, respectively. At a monthly scale, TRMM is superior to the other products, with a relatively stronger correlation almost every month, while GSMaP is inferior to the other products. Moreover, MSWEP and PERSIANN perform relatively best, with favorable statistical results around almost every station, while GSMaP shows the worse performance. In addition, ERA5 tends to overestimate higher values, while GSMaP tends to overestimate lower values over the entire basin. Moreover, the overestimation of ERA5 tends to appear in the upper reach area, while that of GSMaP tends to appear in the lower reach area. Only CRU and PERSIANN yield underestimations of precipitation, with the strongest tendency appearing in the upper reach area. The results of this study display some findings on the uncertainties of several frequently used precipitation datasets in the high mountains and poorly gauged regions in the HRB and will be helpful to researchers in various fields who need high-resolution gridded precipitation datasets over the HRB, as well as to data producers who want to improve their products.
Correction: Demattê et al. The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication. Remote Sens. 2022, 14, 740
Authors: Demattê José A. M. , Paiva Ariane Francine da Silveira , Poppiel Raul Roberto , Rosin Nícolas Augusto , Ruiz Luis Fernando Chimelo , Mello Fellipe Alcantara de Oliveira , Minasny Budiman , Grunwald Sabine , Ge Yufeng , Dor Eyal Ben , Gholizadeh Asa , Gomez Cecile , Chabrillat Sabine , Francos Nicolas , Ayoubi Shamsollah , Fiantis Dian , Biney James Kobina Mensah , Wang Changkun , Belal Abdelaziz , Naimi Salman , Hafshejani Najmeh Asgari , Bellinaso Henrique , Moura-Bueno Jean Michel , Silvero Nélida E. Q.
Published: March 2022

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Fast Registration of Terrestrial LiDAR Point Clouds Based on Gaussian-Weighting Projected Image Matching
Authors: Xiong Biao , Li Dengke , Zhou Zhize , Li Fashuai
Published: March 2022

Terrestrial point cloud registration plays an important role in 3D reconstruction, heritage restoration and topographic mapping, etc. Unfortunately, current research studies heavily rely on matching the 3D features of overlapped areas between point clouds, which is error-prone and time-consuming. To this end, we propose an automatic point cloud registration method based on Gaussian-weighting projected image matching, which can quickly and robustly register multi-station terrestrial point clouds. Firstly, the point cloud is regularized into a 2D grid, and the point density of each cell in the grid is normalized by our Gaussian-weighting function. A grayscale image is subsequently generated by shifting and scaling the x-y coordinates of the grid to the image coordinates. Secondly, the scale-invariant features (SIFT) algorithm is used to perform image matching, and a line segment endpoint verification method is proposed to filter out negative matches. Thirdly, the transformation matrix between point clouds from two adjacent stations is calculated based on reliable image matching. Finally, a global least-square optimization is conducted to align multi-station point clouds and then obtain a complete model. To test the performance of our framework, we carry out the experiment on six datasets. Compared to previous work, our method achieves the state-of-the-art performance on both efficiency and accuracy. In terms of efficiency, our method is comparable to an existing projection-based methods and 4 times faster on the indoor datasets and 10 times faster on the outdoor datasets than 4PCS-based methods. In terms of accuracy, our framework is ~2 times better than the existing projection-based method and 6 times better than 4PCS-based methods.
A Scalable and Accurate De-Snowing Algorithm for LiDAR Point Clouds in Winter
Authors: Wang Weiqi , You Xiong , Chen Lingyu , Tian Jiangpeng , Tang Fen , Zhang Lantian
Published: March 2022

Accurate and efficient environmental awareness is a fundamental capability of autonomous driving technology and the real-time data collected by sensors offer autonomous vehicles an intuitive impression of their environment. Unfortunately, the ambient noise caused by varying weather conditions immediately affects the ability of autonomous vehicles to accurately understand their environment and its expected impact. In recent years, researchers have improved the environmental perception capabilities of simultaneous localization and mapping (SLAM), object detection and tracking, semantic segmentation and panoptic segmentation, but relatively few studies have focused on enhancing environmental perception capabilities in adverse weather conditions, such as rain, snow and fog. To enhance the environmental perception of autonomous vehicles in adverse weather, we developed a dynamic filtering method called Dynamic Distance–Intensity Outlier Removal (DDIOR), which integrates the distance and intensity of points based on the systematic and accurate analysis of LiDAR point cloud data characteristics in snowy weather. Experiments on the publicly available WADS dataset (Winter Adverse Driving dataSet) showed that our method can efficiently remove snow noise while fully preserving the detailed features of the environment.
An Assessment of Electric Power Consumption Using Random Forest and Transferable Deep Model with Multi-Source Data
Authors: Cheng Luxiao , Feng Ruyi , Wang Lizhe , Yan Jining , Liang Dong
Published: March 2022

Reliable and fine-resolution electric power consumption (EPC) is essential for effective urban electricity allocation and planning. Currently, EPC data exists mainly as statistics with low resolution. Many studies estimate fine-resolution EPC based on the positive correction between stable nighttime light and EPC distribution. However, EPC is related to various factors other than nighttime light and is spatially non-stationary. Yet this has been ignored in current research. This study developed a novel method to estimate EPC at 500 m resolution by considering spatially non-stationary through fusing geospatial data and high-resolution satellite images. Deep transfer learning and statistical methods were used to extract socio-economic, population density, and landscape features to describe EPC distribution from multi-source geospatial data. Finally, a random forest regression (RFR) model with features and EPC statistics is established to estimate fine-resolution EPC. A study area of Shenzhen city, China, is employed to evaluate the proposed method. The R2 between predicted EPC and statistical EPC is 0.82 at sub-district level in 2013, which is higher than an existing EPC product (Shi’s product) with R2=0.46, illustrating the effectiveness of the proposed method. Moreover, the EPC distribution for Shenzhen from 2013 to 2019 was estimated. Furthermore, the spatiotemporal dynamic of EPC was analyzed at the pixel and sub-district levels.
Automated Delineation of Microstands in Hemiboreal Mixed Forests Using Stereo GeoEye-1 Data
Authors: Gulbe Linda , Zarins Juris , Mednieks Ints
Published: March 2022

A microstand is a small forest area with a homogeneous tree species, height, and density composition. High-spatial-resolution GeoEye-1 multispectral (MS) images and GeoEye-1-based canopy height models (CHMs) allow delineating microstands automatically. This paper studied the potential benefits of two microstand segmentation workflows: (1) our modification of JSEG and (2) generic region merging (GRM) of the Orfeo Toolbox, both intended for the microstand border refinement and automated stand volume estimation in hemiboreal forests. Our modification of JSEG uses a CHM as the primary data source for segmentation by refining the results using MS data. Meanwhile, the CHM and multispectral data fusion were achieved as multiband segmentation for the GRM workflow. The accuracy was evaluated using several sets of metrics (unsupervised, supervised direct assessment, and system-level assessment). Metrics were calculated for a regular segment grid to check the benefits compared with the simple image patches. The metrics showed very similar results for both workflows. The most successful combinations in the workflow parameters retrieved over 75 % of the boundaries selected by a human interpreter. However, the impact of data fusion and parameter combinations on stand volume estimation accuracy was minimal, causing variations of the RMSE within approximately 7 m3/ha.
Automated Parts-Based Model for Recognizing Human–Object Interactions from Aerial Imagery with Fully Convolutional Network
Authors: Ghadi Yazeed , Waheed Manahil , al Shloul Tamara , A. Alsuhibany Suliman , Jalal Ahmad , Park Jeongmin
Published: March 2022

Advanced aerial images have led to the development of improved human–object interaction recognition (HOI) methods for usage in surveillance, security, and public monitoring systems. Despite the ever-increasing rate of research being conducted in the field of HOI, the existing challenges of occlusion, scale variation, fast motion, and illumination variation continue to attract more researchers. In particular, accurate identification of human body parts, the involved objects, and robust features is the key to effective HOI recognition systems. However, identifying different human body parts and extracting their features is a tedious and rather ineffective task. Based on the assumption that only a few body parts are usually involved in a particular interaction, this article proposes a novel parts-based model for recognizing complex human–object interactions in videos and images captured using ground and aerial cameras. Gamma correction and non-local means denoising techniques have been used for pre-processing the video frames and Felzenszwalb’s algorithm has been utilized for image segmentation. After segmentation, twelve human body parts have been detected and five of them have been shortlisted based on their involvement in the interactions. Four kinds of features have been extracted and concatenated into a large feature vector, which has been optimized using the t-distributed stochastic neighbor embedding (t-SNE) technique. Finally, the interactions have been classified using a fully convolutional network (FCN). The proposed system has been validated on the ground and aerial videos of the VIRAT Video, YouTube Aerial, and SYSU 3D HOI datasets, achieving average accuracies of 82.55%, 86.63%, and 91.68% on these datasets, respectively.
Spatial Transferability of Random Forest Models for Crop Type Classification Using Sentinel-1 and Sentinel-2
Authors: Orynbaikyzy Aiym , Gessner Ursula , Conrad Christopher
Published: March 2022

Large-scale crop type mapping often requires prediction beyond the environmental settings of the training sites. Shifts in crop phenology, field characteristics, or ecological site conditions in the previously unseen area, may reduce the classification performance of machine learning classifiers that often overfit to the training sites. This study aims to assess the spatial transferability of Random Forest models for crop type classification across Germany. The effects of different input datasets, i.e., only optical, only Synthetic Aperture Radar (SAR), and optical-SAR data combination, and the impact of spatial feature selection were systematically tested to identify the optimal approach that shows the highest accuracy in the transfer region. The spatial feature selection, a feature selection approach combined with spatial cross-validation, should remove features that carry site-specific information in the training data, which in turn can reduce the accuracy of the classification model in previously unseen areas. Seven study sites distributed over Germany were analyzed using reference data for the major 11 crops grown in the year 2018. Sentinel-1 and Sentinel-2 data from October 2017 to October 2018 were used as input. The accuracy estimation was performed using the spatially independent sample sets. The results of the optical-SAR combination outperformed those of single sensors in the training sites (maximum F1-score–0.85), and likewise in the areas not covered by training data (maximum F1-score–0.79). Random forest models based on only SAR features showed the lowest accuracy losses when transferred to unseen regions (average F1loss–0.04). In contrast to using the entire feature set, spatial feature selection substantially reduces the number of input features while preserving good predictive performance on unseen sites. Altogether, applying spatial feature selection to a combination of optical-SAR features or using SAR-only features is beneficial for large-scale crop type classification where training data is not evenly distributed over the complete study region.
MLCRNet: Multi-Level Context Refinement for Semantic Segmentation in Aerial Images
Authors: Huang Zhifeng , Zhang Qian , Zhang Guixu
Published: March 2022

In this paper, we focus on the problem of contextual aggregation in the semantic segmentation of aerial images. Current contextual aggregation methods only aggregate contextual information within specific regions to improve feature representation, which may yield poorly robust contextual information. To address this problem, we propose a novel multi-level context refinement network (MLCRNet) that aggregates three levels of contextual information effectively and efficiently in an adaptive manner. First, we designed a local-level context aggregation module to capture local information around each pixel. Second, we integrate multiple levels of context, namely, local-level, image-level, and semantic-level, to aggregate contextual information from a comprehensive perspective dynamically. Third, we propose an efficient multi-level context transform (EMCT) module to address feature redundancy and to improve the efficiency of our multi-level contexts. Finally, based on the EMCT module and feature pyramid network (FPN) framework, we propose a multi-level context feature refinement (MLCR) module to enhance feature representation by leveraging multi-level contextual information. Extensive empirical evidence demonstrates that our MLCRNet achieves state-of-the-art performance on the ISPRS Potsdam and Vaihingen datasets.
Missing Data Imputation in GNSS Monitoring Time Series Using Temporal and Spatial Hankel Matrix Factorization
Authors: Liu Hanlin , Li Linchao
Published: March 2022

GNSS time series for static reference stations record the deformation of monitored targets. However, missing data are very common in GNSS monitoring time series because of receiver crashes, power failures, etc. In this paper, we propose a Temporal and Spatial Hankel Matrix Factorization (TSHMF) method that can simultaneously consider the temporal correlation of a single time series and the spatial correlation among different stations. Moreover, the method is verified using real-world regional 10-year period monitoring GNSS coordinate time series. The Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE) are calculated to compare the performance of TSHMF with benchmark methods, which include the time-mean, station-mean, K-nearest neighbor, and singular value decomposition methods. The results show that the TSHMF method can reduce the MAE range from 32.03% to 12.98% and the RMSE range from 21.58% to 10.36%, proving the effectiveness of the proposed method.
Effects of UAV-LiDAR and Photogrammetric Point Density on Tea Plucking Area Identification
Authors: Zhang Qingfan , Hu Maosheng , Zhou Yansong , Wan Bo , Jiang Le , Zhang Quanfa , Wang Dezhi
Published: March 2022

High-cost data collection and processing are challenges for UAV LiDAR (light detection and ranging) mounted on unmanned aerial vehicles in crop monitoring. Reducing the point density can lower data collection costs and increase efficiency but may lead to a loss in mapping accuracy. It is necessary to determine the appropriate point cloud density for tea plucking area identification to maximize the cost–benefits. This study evaluated the performance of different LiDAR and photogrammetric point density data when mapping the tea plucking area in the Huashan Tea Garden, Wuhan City, China. The object-based metrics derived from UAV point clouds were used to classify tea plantations with the extreme learning machine (ELM) and random forest (RF) algorithms. The results indicated that the performance of different LiDAR point density data, from 0.25 (1%) to 25.44 pts/m2 (100%), changed obviously (overall classification accuracies: 90.65–94.39% for RF and 89.78–93.44% for ELM). For photogrammetric data, the point density was found to have little effect on the classification accuracy, with 10% of the initial point density (2.46 pts/m2), a similar accuracy level was obtained (difference of approximately 1%). LiDAR point cloud density had a significant influence on the DTM accuracy, with the RMSE for DTMs ranging from 0.060 to 2.253 m, while the photogrammetric point cloud density had a limited effect on the DTM accuracy, with the RMSE ranging from 0.256 to 0.477 m due to the high proportion of ground points in the photogrammetric point clouds. Moreover, important features for identifying the tea plucking area were summarized for the first time using a recursive feature elimination method and a novel hierarchical clustering-correlation method. The resultant architecture diagram can indicate the specific role of each feature/group in identifying the tea plucking area and could be used in other studies to prepare candidate features. This study demonstrates that low UAV point density data, such as 2.55 pts/m2 (10%), as used in this study, might be suitable for conducting finer-scale tea plucking area mapping without compromising the accuracy.
Two-Stream Swin Transformer with Differentiable Sobel Operator for Remote Sensing Image Classification
Authors: Hao Siyuan , Wu Bin , Zhao Kun , Ye Yuanxin , Wang Wei
Published: March 2022

Remote sensing (RS) image classification has attracted much attention recently and is widely used in various fields. Different to natural images, the RS image scenes consist of complex backgrounds and various stochastically arranged objects, thus making it difficult for networks to focus on the target objects in the scene. However, conventional classification methods do not have any special treatment for remote sensing images. In this paper, we propose a two-stream swin transformer network (TSTNet) to address these issues. TSTNet consists of two streams (i.e., original stream and edge stream) which use both the deep features of the original images and the ones from the edges to make predictions. The swin transformer is used as the backbone of each stream given its good performance. In addition, a differentiable edge Sobel operator module (DESOM) is included in the edge stream which can learn the parameters of Sobel operator adaptively and provide more robust edge information that can suppress background noise. Experimental results on three publicly available remote sensing datasets show that our TSTNet achieves superior performance over the state-of-the-art (SOTA) methods.
Glacier Recession in the Altai Mountains after the LIA Maximum
Authors: Ganyushkin Dmitry , Chistyakov Kirill , Derkach Ekaterina , Bantcev Dmitriy , Kunaeva Elena , Terekhov Anton , Rasputina Valeria
Published: March 2022

The study aims to reconstruct the Altai glaciers at the maximum of the LIA, to estimate the reduction of the Altai glaciers from the LIA maximum to the present, and to analyze glacier reduction rates on the example of the Tavan Bogd mountain range. Research was based on remote sensing and field data. The recent glaciation in the southern part of the Altai is estimated (1256 glaciers with the total area of 559.15 ± 31.13 km2), the area of the glaciers of the whole Altai mountains is estimated at 1096.55 km2. In the southern part of Altai, 2276 glaciers with a total area of 1348.43 ± 56.16 km2 were reconstructed, and the first estimate of the LIA glacial area for the entire Altai mountain system was given (2288.04 km2). Since the LIA, the glaciers decrease by 59% in the southern part of Altai and by 47.9% for the whole Altai. The average increase in ELA in the southern part of Altai was 106 m. The larger increase of ELA in the relatively humid areas was probably caused by a decrease in precipitation. Glaciers in the Tavan Bogd glacial center degraded with higher rates after 1968 relative to the interval between 1850–1968. One of the intervals of fast glacier shrinkage in 2000–2010 was caused by a dry and warm interval between 1989 and 2004. However, the fast decrease in glaciers in 2000–2010 was mainly caused by the shrinkage or disappearance of the smaller glaciers, and large valley glaciers started a fast retreat after 2010. The study results present the first evaluation of the glacier recession of the entire Altai after the LIA maximum.
Low-Cost, Real-Time Polymerase Chain Reaction System for Point-of-Care Medical Diagnosis
Authors: Kadja Tchamie , Liu Chengkun , Sun Yvonne , Chodavarapu Vamsy P.
Published: March 2022

Global health crises due to the prevailing Coronavirus Disease 2019 (COVID-19) pandemic have placed significant strain on health care facilities such as hospitals and clinics around the world. Further, foodborne and waterborne diseases are not only spreading faster, but also appear to be emerging more rapidly than ever before and are able to circumvent conventional control measures. The Polymerase Chain Reaction (PCR) system is a well-known diagnostic tool for many applications in medical diagnostics, environmental monitoring, and food and water quality assessment. Here, we describe the design, development, and testing of a portable, low-cost, and real-time PCR system that can be used in emergency health crises and resource-poor situations. The described PCR system incorporates real-time reaction monitoring using fluorescence as an alternative to gel electrophoresis for reaction analysis, further decreasing the need of multiple reagents, reducing sample testing cost, and reducing sample analysis time. The bill of materials cost of the described system is approximately $340. The described PCR system utilizes a novel progressive selective proportional–integral–derivative controller that helps in reducing sample analysis time. In addition, the system employs a novel primer-based approach to quantify the initial target amplicon concentration, making it well-suited for food and water quality assessment. The developed PCR system performed DNA amplification at a level and speed comparable to larger and more expensive commercial table-top systems. The fluorescence detection sensitivity was also tested to be at the same level as commercially available multi-mode optical readers, thus making the PCR system an attractive solution for medical point-of-care and food and water quality assessment.
Using Low-Resolution Non-Invasive Infrared Sensors to Classify Activities and Falls in Older Adults
Authors: Márquez Gastón , Veloz Alejandro , Minonzio Jean-Gabriel , Reyes Claudio , Calvo Esteban , Taramasco Carla
Published: March 2022

The population is aging worldwide, creating new challenges to the quality of life of older adults and their families. Falls are an increasing, but not inevitable, threat to older adults. Information technologies provide several solutions to address falls, but smart homes and the most available solutions require expensive and invasive infrastructures. In this study, we propose a novel approach to classify and detect falls of older adults in their homes through low-resolution infrared sensors that are affordable, non-intrusive, do not disturb privacy, and are more acceptable to older adults. Using data collected between 2019 and 2020 with the eHomeseniors platform, we determine activity scores of older adults moving across two rooms in a house and represent an older adult fall through skeletonization. We find that our twofold approach effectively detects activity patterns and precisely identifies falls. Our study provides insights to physicians about the daily activities of their older adults and could potentially help them make decisions in case of abnormal behavior.
The Effect of Breathing Laterality on Hip Roll Kinematics in Submaximal Front Crawl Swimming
Authors: Barden John M. , Barber Mike V.
Published: March 2022

The purpose of this study was to determine the effect of breathing laterality on hip roll kinematics in submaximal front crawl swimming. Eighteen elite competitive swimmers performed three 100 m front crawl trials at a consistent sub-maximal speed (70% of seasonal best time) in a 25 m pool. Each trial was performed with one of three different breathing conditions: (1) unilateral breathing (preferred side), (2) bilateral breathing (alternating left/right-side every 3 strokes) and (3) simulated non-breathing using a swim snorkel. A waist-mounted triaxial accelerometer was used to determine continuous hip roll angle throughout the trial, from which peak hip roll angles (Ө) and average angular velocities (ω) were calculated. Two-way repeated measures ANOVAs were used to identify significant main effects for laterality (preferred vs. non-preferred breathing sides) and condition (unilateral, bilateral and snorkel breathing) for both Ө and ω. Peak hip roll to the preferred side was significantly greater (p < 0.001) in the unilateral condition, while ω to the non-preferred side was significantly greater in the unilateral (p < 0.01) and bilateral (p < 0.04) conditions. Significant same-side differences were also found between the different breathing conditions. The results demonstrate that breathing laterality affects hip roll kinematics at submaximal speeds, and that unilateral and snorkel breathing are associated with the least and most symmetric hip roll kinematics, respectively. The findings show that a snorkel effectively balances and controls bilateral hip rotation at submaximal speeds that are consistent with training, which may help to minimize and/or correct roll asymmetries that are the result of unilateral breathing.
An Optimal Subspace Deconvolution Algorithm for Robust and High-Resolution Beamforming
Authors: Su Xiruo , Miao Qiuyan , Sun Xinglin , Ren Haoran , Ye Lingyun , Song Kaichen
Published: March 2022

Utilizing the difference in phase and power spectrum between signals and noise, the estimation of direction of arrival (DOA) can be transferred to a spatial sample classification problem. The power ratio, namely signal-to-noise ratio (SNR), is highly required in most high-resolution beamforming methods so that high resolution and robustness are incompatible in a noisy background. Therefore, this paper proposes a Subspaces Deconvolution Vector (SDV) beamforming method to improve the robustness of a high-resolution DOA estimation. In a noisy environment, to handle the difficulty in separating signals from noise, we intend to initial beamforming value presets by incoherent eigenvalue in the frequency domain. The high resolution in the frequency domain guarantees the stability of the beamforming. By combining the robustness of conventional beamforming, the proposed method makes use of the subspace deconvolution vector to build a high-resolution beamforming process. The SDV method is aimed to obtain unitary frequency matrixes more stably and improve the accuracy of signal subspaces. The results of simulations and experiments show that when the input SNR is less than −27 dB, signals of decomposition differ unremarkably in the subspace while the SDV method can still obtain clear angles. In a marine background, this method works well in separating the noise and recruiting the characteristics of the signal into the DOA for subsequent processing.
Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification
Authors: Tutuko Bambang , Rachmatullah Muhammad Naufal , Darmawahyuni Annisa , Nurmaini Siti , Tondas Alexander Edo , Passarella Rossi , Partan Radiyati Umi , Rifai Ahmad , Sapitri Ade Iriani , Firdaus Firdaus
Published: March 2022

Physicians manually interpret an electrocardiogram (ECG) signal morphology in routine clinical practice. This activity is a monotonous and abstract task that relies on the experience of understanding ECG waveform meaning, including P-wave, QRS-complex, and T-wave. Such a manual process depends on signal quality and the number of leads. ECG signal classification based on deep learning (DL) has produced an automatic interpretation; however, the proposed method is used for specific abnormality conditions. When the ECG signal morphology change to other abnormalities, it cannot proceed automatically. To generalize the automatic interpretation, we aim to delineate ECG waveform. However, the output of delineation process only ECG waveform duration classes for P-wave, QRS-complex, and T-wave. It should be combined with a medical knowledge rule to produce the abnormality interpretation. The proposed model is applied for atrial fibrillation (AF) identification. This study meets the AF criteria with RR irregularities and the absence of P-waves in essential oscillations for even more accurate identification. The QT database by Physionet is utilized for developing the delineation model, and it validates with The Lobachevsky University Database. The results show that our delineation model works properly, with 98.91% sensitivity, 99.01% precision, 99.79% specificity, 99.79% accuracy, and a 98.96% F1 score. We use about 4058 normal sinus rhythm records and 1804 AF records from the experiment to identify AF conditions that are taken from three datasets. The comprehensive testing has produced higher negative predictive value and positive predictive value. This means that the proposed model can identify AF conditions from ECG signal delineation. Our approach can considerably contribute to AF diagnosis with these results.
Defect Detection of Subway Tunnels Using Advanced U-Net Network
Authors: Wang An , Togo Ren , Ogawa Takahiro , Haseyama Miki
Published: March 2022

In this paper, we present a novel defect detection model based on an improved U-Net architecture. As a semantic segmentation task, the defect detection task has the problems of background–foreground imbalance, multi-scale targets, and feature similarity between the background and defects in the real-world data. Conventionally, general convolutional neural network (CNN)-based networks mainly focus on natural image tasks, which are insensitive to the problems in our task. The proposed method has a network design for multi-scale segmentation based on the U-Net architecture including an atrous spatial pyramid pooling (ASPP) module and an inception module, and can detect various types of defects compared to conventional simple CNN-based methods. Through the experiments using a real-world subway tunnel image dataset, the proposed method showed a better performance than that of general semantic segmentation including state-of-the-art methods. Additionally, we showed that our method can achieve excellent detection balance among multi-scale defects.
Solving Inverse Electrocardiographic Mapping Using Machine Learning and Deep Learning Frameworks
Authors: Chen Ke-Wei , Bear Laura , Lin Che-Wei
Published: March 2022

Electrocardiographic imaging (ECGi) reconstructs electrograms at the heart’s surface using the potentials recorded at the body’s surface. This is called the inverse problem of electrocardiography. This study aimed to improve on the current solution methods using machine learning and deep learning frameworks. Electrocardiograms were simultaneously recorded from pigs’ ventricles and their body surfaces. The Fully Connected Neural network (FCN), Long Short-term Memory (LSTM), Convolutional Neural Network (CNN) methods were used for constructing the model. A method is developed to align the data across different pigs. We evaluated the method using leave-one-out cross-validation. For the best result, the overall median of the correlation coefficient of the predicted ECG wave was 0.74. This study demonstrated that a neural network can be used to solve the inverse problem of ECGi with relatively small datasets, with an accuracy compatible with current standard methods.
Simulation and Analysis of Mie-Scattering Lidar-Measuring Atmospheric Turbulence Profile
Authors: Lu Yuqing , Mao Jiandong , Zhang Yingnan , Zhao Hu , Zhou Chunyan , Gong Xin , Wang Qiang , Zhang Yi
Published: March 2022

Based on the residual turbulent scintillation theory, the Mie-scattering lidar can measure the intensity of atmospheric turbulence by detecting the light intensity scintillation index of the laser return signal. In order to evaluate and optimize the reliability of the Mie-scattering lidar system for detecting atmospheric turbulence, the appropriate parameters of the Mie-scattering lidar system are selected and optimized using the residual turbulent scintillation theory. Then, the Fourier transform method is employed to perform the numerical simulation of the phase screen of the laser light intensity transformation on the vertical transmission path of atmospheric turbulence. The phase screen simulation, low-frequency optimization, and scintillation index calculation methods are provided in detail, respectively. Based on the phase distribution of the laser beam, the scintillation index is obtained. Through the relationship between the scintillation index and the atmospheric turbulent refractive index structure constant, the atmospheric turbulence profile is inverted. The simulation results show that the atmospheric refractive index structure constant profile obtained by the iterative method is consistent with the input HV5/7 model below 6500 m, which has great guiding significance to carry out actual experiments to measure atmospheric turbulence using the Mie lidar.
Single- and Double-Comb Tilted Fibre Bragg Grating Refractive Index Demodulation Methods with Fourier Transform Pre-Processing
Authors: Cięszczyk Sławomir , Skorupski Krzysztof , Panas Patryk
Published: March 2022

The development of fibre optic sensors for measuring the refractive index is related to the creation of new periodic structures and demodulation algorithms for the measured spectrum. Recently, we proposed a double-comb Tilted fibre Bragg grating (DCTFBG) structure. In this article, we analyse such a structure for measuring the refractive index in comparison to a single classical structure. Increasing the number of modes causes a significant change in the Fourier spectrum of optical spectra. For the purpose of data pre-processing, we propose the Fourier Transform as a filtering method in the frequency domain. Then, we analyse separately the band-filtered optical spectra for several frequency ranges. For quantitative analysis, we use algorithms that use quantitative changes in the transmission, i.e., the method of the envelope and the length of the spectrum contour. We propose the use of the Hilbert transform as the envelope method. The second type of algorithms used are methods determining the shift of spectrum features along the wavelength axis. The method of determining the centre of gravity of the area bounded by the envelope and the maximum of the second derivative of the smoothed cumulative spectrum contour length is proposed here. Using the developed methods, the measurement resolution was achieved at the level of 2 × 10−5 refractive index unit.
Sustainable Solutions for Advanced Energy Management System of Campus Microgrids: Model Opportunities and Future Challenges
Authors: Muqeet Hafiz Abdul , Javed Haseeb , Akhter Muhammad Naveed , Shahzad Muhammad , Munir Hafiz Mudassir , Nadeem Muhammad Usama , Bukhari Syed Sabir Hussain , Huba Mikulas
Published: March 2022

Distributed generation connected with AC, DC, or hybrid loads and energy storage systems is known as a microgrid. Campus microgrids are an important load type. A university campus microgrids, usually, contains distributed generation resources, energy storage, and electric vehicles. The main aim of the microgrid is to provide sustainable, economical energy, and a reliable system. The advanced energy management system (AEMS) provides a smooth energy flow to the microgrid. Over the last few years, many studies were carried out to review various aspects such as energy sustainability, demand response strategies, control systems, energy management systems with different types of optimization techniques that are used to optimize the microgrid system. In this paper, a comprehensive review of the energy management system of campus microgrids is presented. In this survey, the existing literature review of different objective functions, renewable energy resources and solution tools are also reviewed. Furthermore, the research directions and related issues to be considered in future microgrid scheduling studies are also presented.
WiFi Signal-Based Gesture Recognition Using Federated Parameter-Matched Aggregation
Authors: Zhang Weidong , Wang Zexing , Wu Xuangou
Published: March 2022

Gesture recognition plays an important role in smart homes, such as human–computer interaction, identity authentication, etc. Most of the existing WiFi signal-based approaches exploit a large number of channel state information (CSI) datasets to train a gestures classification model; however, these models require a large number of human participants to train, and are not robust to the recognition environment. To address this problem, we propose a WiFi signal-based gesture recognition system with matched averaging federated learning (WiMA). Since there are differences in the distribution of WiFi signal changes caused by the same gesture in different environments, the traditional federated parameter average algorithm seriously affects the recognition accuracy of the model. In WiMA, we exploit the neuron arrangement invariance of neural networks in parameter aggregation, which can improve the robustness of the gesture recognition model with heterogeneous CSI data of different training environments. We carried out experiments with seven participant users in a distributed gesture recognition environment. Experimental results show that the average accuracy of our proposed system is up to 90.4%, which is very close to the accuracy of state-of-the-art approaches with centralized training models.
Multilabel Classification Methods for Human Activity Recognition: A Comparison of Algorithms
Authors: Lentzas Athanasios , Dalagdi Eleana , Vrakas Dimitris
Published: March 2022

As the world’s population is aging, and since access to ambient sensors has become easier over the past years, activity recognition in smart home installations has gained increased scientific interest. The majority of published papers in the literature focus on single-resident activity recognition. While this is an important area, especially when focusing on elderly people living alone, multi-resident activity recognition has potentially more applications in smart homes. Activity recognition for multiple residents acting concurrently can be treated as a multilabel classification problem (MLC). In this study, an experimental comparison between different MLC algorithms is attempted. Three different techniques were implemented: RAkELd, classifier chains, and binary relevance. These methods are evaluated using the ARAS and CASAS public datasets. Results obtained from experiments have shown that using MLC can recognize activities performed by multiple people with high accuracy. While RAkELd had the best performance, the rest of the methods had on-par results.
Bipartite Consensus of Nonlinear Agents in the Presence of Communication Noise
Authors: Mondal Sabyasachi , Tsourdos Antonios
Published: March 2022

In this paper, a Distributed Nonlinear Dynamic Inversion (DNDI)-based consensus protocol is designed to achieve the bipartite consensus of nonlinear agents over a signed graph. DNDI inherits the advantage of nonlinear dynamic inversion theory, and the application to the bipartite problem is a new idea. Moreover, communication noise is considered to make the scenario more realistic. The convergence study provides a solid theoretical base, and a realistic simulation study shows the effectiveness of the proposed protocol.
SDFormer: A Novel Transformer Neural Network for Structural Damage Identification by Segmenting the Strain Field Map
Authors: Li Zhaoyang , Xu Ping , Xing Jie , Yang Chengxing
Published: March 2022

Damage identification is a key problem in the field of structural health monitoring, which is of great significance to improve the reliability and safety of engineering structures. In the past, the structural strain damage identification method based on specific damage index needs the designer to have rich experience and background knowledge, and the designed damage index is hard to apply to different structures. In this paper, a U-shaped efficient structural strain damage identification network SDFormer (structural damage transformer) based on self-attention feature is proposed. SDFormer regards the problem of structural strain damage identification as an image segmentation problem, and introduces advanced image segmentation technology for structural damage identification. This network takes the strain field map of the structure as the input, and then outputs the predicted damage location and level. In the SDFormer, the low-level and high-level features are smoothly fused by skip connection, and the self-attention module is used to obtain damage feature information, to effectively improve the performance of the model. SDFormer can directly construct the mapping between strain field map and damage distribution without complex damage index design. While ensuring the accuracy, it improves the identification efficiency. The effectiveness and accuracy of the model are verified by numerical experiments, and the performance of an advanced convolutional neural network is compared. The results show that SDFormer has better performance than the advanced convolutional neural network. Further, an anti-noise experiment is designed to verify the anti-noise and robustness of the model. The anti-noise performance of SDFormer is better than that of the comparison model in the anti-noise experimental results, which proves that the model has good anti-noise and robustness.
Flying Chameleons: A New Concept for Minimum-Deployment, Multiple-Target Tracking Drones
Authors: Vargas Manuel , Vivas Carlos , Rubio Francisco R. , Ortega Manuel G.
Published: March 2022

In this paper, we aim to open up new perspectives in the field of autonomous aerial surveillance and target tracking systems, by exploring an alternative that, surprisingly, and to the best of the authors’ knowledge, has not been addressed in that context by the research community thus far. It can be summarized by the following two questions. Under the scope of such applications, what are the implications and possibilities offered by mounting several steerable cameras onboard of each aerial agent? Second, how can optimization algorithms benefit from this new framework, in their attempt to provide more efficient and cost-effective solutions on these areas? The paper presents the idea as an additional degree of freedom to be exploited, which can enable more efficient alternatives in the deployment of such applications. As an initial approach, the problem of the optimal positioning with respect to a set of targets of one single agent, equipped with several onboard tracking cameras with different or variable focal lengths, is addressed. As a consequence of this allowed heterogeneity in focal lengths, the notion of distance needs to be adapted into a notion of optical range, as the agent can trade longer Euclidean distances for correspondingly longer focal lengths. Moreover, the proposed optimization indices try to balance, in an optimal way, the verticality of the viewpoints along with the optical range to the targets. Under these premises, several positioning strategies are proposed and comparatively evaluated.