bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Vegetation Vulnerability Dynamics and Driving Forces to Multiple Drought Stresses in a Changing Environment
Authors: Wei Xiaoting , Huang Shengzhi , Huang Qiang , Liu Dong , Leng Guoyong , Yang Haibo , Duan Weili , Li Jianfeng , Bai Qingjun , Peng Jian
Published: August 2022

Quantifying changes in the vulnerability of vegetation to various drought stresses in different seasons is important for rational and effective ecological conservation and restoration. However, the vulnerability of vegetation and its dynamics in a changing environment are still unknown, and quantitative attribution analysis of vulnerability changes has been rarely studied. To this end, this study explored the changes of vegetation vulnerability characteristics under various drought stresses in Xinjiang and conducted quantitative attribution analysis using the random forest method. In addition, the effects of ecological water transport and increased irrigation areas on vegetation vulnerability dynamics were examined. The standardized precipitation index (SPI), standardized precipitation-evapotranspiration index (SPEI), and standardized soil moisture index (SSMI) represent atmospheric water supply stress, water and heat supply stress, and soil water supply stress, respectively. The results showed that: (1) different vegetation types responded differently to water stress, with grasslands being more sensitive than forests and croplands in summer; (2) increased vegetation vulnerability under drought stresses dominated in Xinjiang after 2003, with vegetation growth and near-surface temperature being the main drivers, while increased soil moisture in the root zone was the main driver of decreased vegetation vulnerability; (3) vulnerability of cropland to SPI/SPEI/SSMI-related water stress increased due to the rapid expansion of irrigation areas, which led to increasing water demand in autumn that was difficult to meet; and (4) after ecological water transport of the Tarim River Basin, the vulnerability of its downstream vegetation to drought was reduced.
Performance Evaluation of Random Access Methods for IoT-over-Satellite
Authors: Chan Chiu Chun , Al Homssi Bassel , Al-Hourani Akram
Published: August 2022

Low Earth orbit (LEO) satellite constellations are currently being explored to provide global and seamless coverage for IoT-over-Satellite applications. Random access techniques require low transmission overhead providing a compatible route for IoT-over-Satellite applications, however, coming at the expense of the offered quality-of-service. In this paper, we develop a realistic uplink performance framework that incorporates many practical parameters such as the satellite availability, packet collision and interference, Doppler shift, and impairments experienced in a typical Satellite-to-Ground channel. The framework is capable of assessing multiple key performance indicators of the overall IoT-over-Satellite random access system. The performance is presented in terms of the bit error rate, packet error rate, and the energy wasted per IoT device. To emulate a realistic IoT-over-Satellite network, LoRa modulated traffic is first generated and injected into the Satellite-to-Ground channel. The results indicate high resistance to Doppler shifts even without any Doppler correction and provide some resistance to highly congested environments.
Sentinel-1 Shadows Used to Quantify Canopy Loss from Selective Logging in Gabon
Authors: Carstairs Harry , Mitchard Edward T. A. , McNicol Iain , Aquino Chiara , Chezeaux Eric , Ebanega Médard Obiang , Dikongo Anaick Modinga , Disney Mathias
Published: August 2022

Selective logging is a major cause of forest degradation in the tropics, but its precise scale, location and timing are not known as wide-area, automated remote sensing methods are not yet available at this scale. This limits the abilities of governments to police illegal logging, or monitor (and thus receive payments for) reductions in degradation. Sentinel-1, a C-band Synthetic Aperture Radar satellite mission with a 12-day repeat time across the tropics, is a promising tool for this due to the known appearance of shadows in images where canopy trees are removed. However, previous work has relied on optical satellite data for calibration and validation, which has inherent uncertainties, leaving unanswered questions about the minimum magnitude and area of canopy loss this method can detect. Here, we use a novel bi-temporal LiDAR dataset in a forest degradation experiment in Gabon to show that canopy gaps as small as 0.02 ha (two 10 m × 10 m pixels) can be detected by Sentinel-1. The accuracy of our algorithm was highest when using a timeseries of 50 images over 20 months and no multilooking. With these parameters, canopy gaps in our study site were detected with a false alarm rate of 6.2%, a missed detection rate of 12.2%, and were assigned disturbance dates that were a good qualitative match to logging records. The presence of geolocation errors and false alarms makes this method unsuitable for confirming individual disturbances. However, we found a linear relationship (r2=0.74) between the area of detected Sentinel-1 shadow and LiDAR-based canopy loss at a scale of 1 hectare. By applying our method to three years’ worth of imagery over Gabon, we produce the first national scale map of small-magnitude canopy cover loss. We estimate a total gross canopy cover loss of 0.31 Mha, or 1.3% of Gabon’s forested area, which is a far larger area of change than shown in currently available forest loss alert systems using Landsat (0.022 Mha) and Sentinel-1 (0.019 Mha). Our results, which are made accessible through Google Earth Engine, suggest that this approach could be used to quantify the magnitude and timing of degradation more widely across tropical forests.
Spatiotemporal Dynamics of Ecological Condition in Qinghai-Tibet Plateau Based on Remotely Sensed Ecological Index
Authors: Cao Jiaxi , Wu Entao , Wu Shuhong , Fan Rong , Xu Lei , Ning Ke , Li Ying , Lu Ri , Xu Xixi , Zhang Jian , Yang Junliu , Yang Le , Lei Guangchun
Published: August 2022

The eco-system in the Qinghai-Tibet Plateau (QTP) is extremely fragile, and highly vulnerable to climate change. Knowledge of the changes in the ecological conditions is vital to mitigate the impact of climate change. In this study, we investigated the trend of ecological conditions of the QTP using the remotely sensed ecological index (RSEI), which is the first PCA (principal component analysis) axis of the four indexes derived from the MODIS (Moderate resolution Imaging Spectroradiometer) images captured in the growing season of 2000–2020. The four indexes, i.e., NDVI (normalized difference vegetation index), heat (land surface temperature, LST), wetness (tasseled cap wetness index, WET) and dryness (normalized difference impervious surface index, NDBSI), were calculated on the Google Earth Engine platform. Using land use cover change (LUCC) data, long-term meteorological records and the supplementary annual livestock production, we explored the drivers of spatiotemporal changes in the RSEI. The results show the following points: (1) the ecological conditions of the QTP have remarkable spatiotemporal variations. There were two ecological degradation periods, one of them occurred in the central region during 2005–2010, mainly attributed to the rising temperatures and decreasing precipitation. The other occurred during 2015–2020, driven primarily by overgrazing in the southwest. From 2000 to 2005, it was a period of rapid ecological restoration in the ecologically fragile northeast region. (2) The contribution rate of pc1 was stable at 60%, while the contribution rate of pc2 remained below 40%, indicating that pc1 demonstrated most of the characteristics of the four indexes. The correlation coefficients between NDVI and WET with pc1 are both positive, while LST and NDBSI have negative correlation coefficients, i.e., negative effects. This is consistent with the actual situation. (3) Overgrazing caused grass degradation in the southwest area of the QTP, which might be the main reason for the poor ecological conditions (i.e., low RSEI value) during 2015–2020. (4) Temperature and precipitation showed an increasing trend during the study period. A warmer and wetter climate is expected to have profound impacts on the ecosystems in QTP and practices should be concentrated on identifying climate-sensitive ecosystem components and designating adaptative options.
H2A2Net: A Hybrid Convolution and Hybrid Resolution Network with Double Attention for Hyperspectral Image Classification
Authors: Shi Hao , Cao Guo , Zhang Youqiang , Ge Zixian , Liu Yanbo , Fu Peng
Published: August 2022

Deep learning (DL) has recently been a core ingredient in modern computer vision tasks, triggering a wave of revolutions in various fields. The hyperspectral image (HSI) classification task is no exception. A wide range of DL-based methods have shone brilliantly in HSI classification. However, understanding how to better exploit spectral and spatial information regarding HSI is still an open area of enquiry. In this article, we propose a hybrid convolution and hybrid resolution network with double attention for HSI classification. First, densely connected 3D convolutional layers are employed to extract preliminary spatial–spectral features. Second, these coarse features are fed to the hybrid resolution module, which mines the features at multiple scales to obtain high-level semantic information and low-level local information. Finally, we introduce a novel attention mechanism for further feature adjustment and refinement. Extensive experiments are conducted to evaluate our model in a holistic manner. Compared to several popular methods, our approach yields promising results for four datasets.
Influence of the Solar Spectra Models on PACO Atmospheric Correction
Authors: De Los Reyes Raquel , Richter Rudolf , Bachmann Martin , Alonso Kevin , Pflug Bringfried , Lafrance Bruno , Reinartz Peter
Published: August 2022

The solar irradiance is the source of energy used by passive optical remote sensing to measure the ground reflectance and, from there, derive the ground properties. Therefore, the precise knowledge of the incoming solar irradiance is fundamental for the atmospheric correction (AC) algorithms. These algorithms use the simulation results of a model of the interactions of the atmosphere with the incoming solar irradiance to determine the atmospheric contribution of the remote sensing observations. This study presents the differences in the atmospherically corrected ground reflectance of multi- and hyper-spectral sensors assuming three different solar models: Thuillier 2003, Fontenla 2011 and TSIS-1 HRS. The results show no difference when the solar irradiance model is preserved through the full processing chain. The differences appear when the solar irradiance model used in the atmospheric correction changes, and this difference is larger between some irrradiance models (e.g., TSIS and Thuillier 2003) than for others (e.g., Fontenla 2011 and TSIS).
Determination of Long-Term Soil Apparent Thermal Diffusivity Using Near-Surface Soil Temperature on the Tibetan Plateau
Authors: Tong Bing , Xu Hui , Horton Robert , Bian Lingen , Guo Jianping
Published: August 2022

The knowledge of soil apparent thermal diffusivity (k) is important for investigating soil surface heat transfer and temperature. Long-term k determined using the near-surface soil temperature is limited on the Tibetan Plateau (TP). The main objective of this study is to determine k with a conduction–convection method using the near-surface soil temperature measured at three sites during 2014–2016 on the TP. The hourly, daily, and monthly k values of the 0.0 m to 0.20 m layer were obtained. The hourly and daily k values ranged from 0.3 × 10−6 m2 s−1 to 1.9 × 10−6 m2 s−1 at the wet site, and from 1.0 × 10−7 m2 s−1 to 4.0 × 10−7 m2 s−1 at the two dry sites. For the monthly timescale, k ranged from 0.4 (±0.0) × 10−6 m2 s−1 to 1.1 (±0.2) × 10−6 m2 s−1 at the wet site, and varied between 1.7 (±0.0) × 10−7 m2 s−1 and 3.3 (±0.2) × 10−7 m2 s−1 at the two dry sites. The k was not constant over a day, and it varied seasonally to different degrees at different sites and years. The variation of k with soil moisture (θ) appeared to be roughly similar for unfrozen soil at these sites and years, namely, k increased sharply before reaching the peak as θ increased, and then it tended to be stable or varied slightly with further increases in θ. This variation trend was consistent with previous studies. However, the relationship between k and θ changed when soil temperature was below 0 °C, because ice had higher k than water. The correlation coefficients (r) between k and θ ranged from 0.37 to 0.80, and 0.80 to 0.92 on hourly and monthly timescales, respectively. The monthly and annual k values were significantly correlated (r: 0.73~0.93) to the Normalized Difference Vegetation Index (NDVI). The results broaden our understanding of the relationship between in situ k and θ. The presented values of k at various timescales can be used as soil parameters when modeling land–atmosphere interactions at these TP regions.
Understanding the Linkage between Urban Growth and Land Surface Temperature—A Case Study of Bangalore City, India
Authors: Kanga Shruti , Meraj Gowhar , Johnson Brian Alan , Singh Suraj Kumar , PV Muhammed Naseef , Farooq Majid , Kumar Pankaj , Marazi Asif , Sahu Netrananda
Published: August 2022

Planning for a sustainable future involves understanding the past and present problems associated with urban centers. Rapid urbanization has caused significant adverse impacts on the environment and natural resources. In cities, one such impact is the unsettling urban growth, resulting in the urban heat island (UHI) effect, which causes considerable positive feedback in the climate system. It can be assessed by investigating the relationships between urban Land Use/Land Cover (LULC) changes and changes in land surface temperature. This study links the urban transformations in Bangalore, India, between 2001 and 2021, with the city’s changing average land surface temperatures. LULC classification was performed on Landsat satellite images for the years 2001, 2011, and 2021, using the support vector machine (SVM) classification algorithm. LULC change analysis revealed an increase in the built-up area coinciding with a decreasing trend of water bodies, vegetation, and the area under the others (wasteland/open land/barren land) category. The results show that built-up increased from 462.49 km2 to 867.73 km2, vegetation decreased from 799.4 km2 to 485.72 km2, and waterbody declined from 34.28 km2 to 24.69 km2 in 20 years. The impact of urbanization was evident in Bangalore’s land temperature changes between 2001 and 2021, showing the average temperature increased by 0.34 °C per year between the highest UHI events, contrary to 0.14 °C per year in non-urbanized areas. It is hoped that the results of this study can help the urban planners of Bangalore city identify critical areas where improvement in urban dwelling could be planned sustainably according to the global smart cities concept, an offshoot concept of the Sustainable Development Goal (SDG)-11.
Ground-Based SAR Moving Target Refocusing Based on Relative Speed for Monitoring Mine Slopes
Authors: Shen Wenjie , Wang Shuo , Lin Yun , Li Yang , Ding Fan , Wang Yanping
Published: August 2022

Ground-based synthetic aperture radar (GBSAR) has the advantage of retrieving submillimeter deformation of the mine slope by using the differential interferometry technique, which is important for safe production in mining applications. However, the moving vehicle’s defocus/displaced signal will mask the SAR image of the mining area which affects the accuracy of interference phase extraction and deformation inversion. In order to remove its influence, the moving target can first be refocused and then removed. To our knowledge, there is no GBSAR moving target refocusing method currently. Hence, the refocusing method is necessary. To solve the above problem, this paper proposes a single-channel FMCW-GBSAR moving target refocusing method based on relative speed. Firstly, the FMCW-GBSAR moving target signal model is analyzed, and then the relative speed based signal model is deduced. Based on the model and GBSAR’s feature of incomplete synthetic aperture, the Range Doppler (RD) algorithm is adopted and improved to achieve refocusing using relative speed parameters. The algorithm is controlled by relative speed and squint angle; thus, the refocused target image can be obtained via searching 2D parameters. The proposed method is verified by the synthetic data, which are generated by combining NCUT FMCW GBSAR real data and simulated moving target echo.
Impact of Saline-Alkali Land Greening on the Local Surface Temperature—A Multiscale Assessment Based on Remote Sensing
Authors: Xin Bingxia , Yu Lingxue , Li Guangshuai , Jiao Yue , Liu Tingxiang , Zhang Shuwen , Lei Zhongying
Published: August 2022

In recent years, the conversion of saline-alkali land to rice fields has become the most dominant land use change feature in western Jilin, leading to significant surface greening. Saline–alkali land and paddy fields have distinct surface biophysical properties; however, there is a lack of systematic assessment of the moderating effect of planting rice on saline–alkali land on regional climate by changing surface properties. In this paper, multiscale data on the surface temperature of saline–alkali land and paddy fields were obtained using 1 km MODIS product, 30 m Landsat 8 satellite imagery and centimeter-scale UAV imagery in Da’an City, western Jilin as the study area, and the various characteristics of the surface temperature of saline-alkali land and paddy fields in different months of the year and at different times of the day were analyzed. Furthermore, the effect of rice cultivation in saline–alkali land on the local surface temperature was assessed using a space-for-time approach. The results based on satellite observations including both MODIS and Landsat showed that the surface temperature of saline–alkali land was significantly higher than that of paddy fields during the crop growing season, especially in July and August. The high temporal resolution MODIS LST data also indicated the paddy fields cool the daytime surface temperature, while warming the nighttime surface temperature, which was in contrast for saline–alkali land during the growing season. High-resolution UAV observations in July confirmed that the cooling effect of paddy fields was most significant at the middle of day. From the biophysical perspective, the reclamation of saline–alkali land into paddy fields leads to an increase in leaf area index, followed by a significant increase in evapotranspiration. Meanwhile, rice cultivation in saline–alkali land reduces surface albedo and increases surface net radiation. The trade-off relationship between the two determines the seasonal difference in the surface temperature response of saline–alkali land for rice cultivation. At the same time, the daily cycle of crop evapotranspiration and the thermal insulation effect of paddy fields at night are the main reasons for the intraday difference in surface temperature between saline–alkali land and paddy field. Based on the multiscale assessment of the impact of rice cultivation in saline-alkali land on surface temperature, this study provides a scientific basis for predicting future regional climate change and comprehensively understanding the ecological and environmental benefits of saline–alkali land development.
Deep Pansharpening via 3D Spectral Super-Resolution Network and Discrepancy-Based Gradient Transfer
Authors: Su Haonan , Jin Haiyan , Sun Ce
Published: August 2022

High-resolution (HR) multispectral (MS) images contain sharper detail and structure compared to the ground truth high-resolution hyperspectral (HS) images. In this paper, we propose a novel supervised learning method, which considers pansharpening as the spectral super-resolution of high-resolution multispectral images and generates high-resolution hyperspectral images. The proposed method learns the spectral mapping between high-resolution multispectral images and the ground truth high-resolution hyperspectral images. To consider the spectral correlation between bands, we build a three-dimensional (3D) convolution neural network (CNN). The network consists of three parts using an encoder–decoder framework: spatial/spectral feature extraction from high-resolution multispectral images/low-resolution (LR) hyperspectral images, feature transform, and image reconstruction to generate the results. In the image reconstruction network, we design the spatial–spectral fusion (SSF) blocks to reuse the extracted spatial and spectral features in the reconstructed feature layer. Then, we develop the discrepancy-based deep hybrid gradient (DDHG) losses with the spatial–spectral gradient (SSG) loss and deep gradient transfer (DGT) loss. The spatial–spectral gradient loss and deep gradient transfer loss are developed to preserve the spatial and spectral gradients from the ground truth high-resolution hyperspectral images and high-resolution multispectral images. To overcome the spectral and spatial discrepancy between two images, we design a spectral downsampling (SD) network and a gradient consistency estimation (GCE) network for hybrid gradient losses. In the experiments, it is seen that the proposed method outperforms the state-of-the-art methods in the subjective and objective experiments in terms of the structure and spectral preservation of high-resolution hyperspectral images.
Semi-Supervised Contrastive Learning for Few-Shot Segmentation of Remote Sensing Images
Authors: Chen Yadang , Wei Chenchen , Wang Duolin , Ji Chuanjun , Li Baozhu
Published: August 2022

Deep learning has been widely used in remote sensing image segmentation, while a lack of training data remains a significant issue. The few-shot segmentation of remote sensing images refers to the segmenting of novel classes with a few annotated samples. Although the few-shot segmentation of remote sensing images method based on meta-learning can get rid of the dependence on large data training, the generalization ability of the model is still low. This work presents a few-shot segmentation of remote sensing images with a self-supervised background learner to boost the generalization capacity for unseen categories to handle this challenge. The methodology in this paper is divided into two main modules: a meta learner and a background learner. The background learner supervises the feature extractor to learning latent categories in the image background. The meta learner expands on the classic metric learning framework by optimizing feature representation through contrastive learning between target classes and latent classes acquired from the background learner. Experiments on the Vaihingen dataset and the Zurich Summer dataset show that our model has satisfactory in-domain and cross-domain transferring abilities. In addition, broad experimental evaluations on PASCAL-5i and COCO-20i demonstrate that our model outperforms the prior works of few-shot segmentation. Our approach surpassed previous methods by 1.1% with ResNet-101 in a 1-way 5-shot setting.
Validating Ionospheric Scintillation Indices Extracted from 30s-Sampling-Interval GNSS Geodetic Receivers with Long-Term Ground and In-Situ Observations in High-Latitude Regions
Authors: Zhao Dongsheng , Wang Qianxin , Li Wang , Shi Shuangshuang , Quan Yiming , Hancock Craig M. , Roberts Gethin Wyn , Zhang Kefei , Chen Yu , Liu Xin , Hao Zemin , Cui Shuanglei , Zhang Xueli , Wang Xing
Published: August 2022

As a frequently-occurred phenomenon in the high-latitude region, ionospheric scintillations affect the stable service of the positioning navigation and timing service of the Global Navigation Satellite System (GNSS), calling for an urgent need of monitoring the scintillations accurately. The monitoring of scintillations usually adopts a special type of receiver, called an ionospheric scintillation monitoring receiver (ISMR), which cannot cover the whole high-latitude region due to its loss distribution. Geodetic receivers are densely distributed, but set at a 30s-sampling-interval usually. It is a controversial issue, namely, the accuracy of the scintillation index extracted from 30s-sampling-interval observations. This paper evaluates the accuracy of two 30s-sampling-interval indices in monitoring scintillations from both the time and space aspects using observations collected in the whole year of 2020. The accuracy in the time aspect is assessed with the phase scintillation index from ISMR as the reference through the following three-pronged approaches, i.e., the accuracy of the daily scintillation occurrence rates in the year 2020, the correlation with space weather parameters, and the variation pattern of the scintillation occurrence rate with the local time and day of the year 2020. The accuracy in space is studied based on the scintillation grid model considering the following two aspects, i.e., the scintillation monitoring performance in a Swarm satellite observation arc, and the statistical scintillation occurrence rate in the whole research region throughout the year 2020. The results of this paper reveal the efficiency of the 30s-sampling-interval scintillation indices in monitoring scintillations and detecting the occurrence patterns in the high-latitude region. The outcome of this paper can provide a basic idea for introducing the widely distributed geodetic receivers to monitor and model the scintillations in the high-latitude region.
Three-Dimensional Gridded Radar Echo Extrapolation for Convective Storm Nowcasting Based on 3D-ConvLSTM Model
Authors: Sun Nengli , Zhou Zeming , Li Qian , Jing Jinrui
Published: August 2022

Radar echo extrapolation has been widely developed in previous studies for precipitation and storm nowcasting. However, most studies have focused on two-dimensional radar images, and extrapolation of multi-altitude radar images, which can provide more informative and visual forecasts about weather systems in realistic space, has been less explored. Thus, this paper proposes a 3D-convolutional long short-term memory (ConvLSTM)-based model to perform three-dimensional gridded radar echo extrapolation for severe storm nowcasting. First, a 3D-convolutional neural network (CNN) is used to extract the 3D spatial features of each input grid radar volume. Then, 3D-ConvLSTM layers are leveraged to model the spatial–temporal relationship between the extracted 3D features and recursively generate the 3D hidden states correlated to the future. Nowcasting results are obtained after applying another 3D-CNN to up-sample the generated 3D hidden states. Comparative experiments were conducted on a public National Center for Atmospheric Research Data Archive dataset with a 3D optical flow method and other deep-learning-based models. Quantitative evaluations demonstrate that the proposed 3D-ConvLSTM-based model achieves better overall and longer-term performance for storms with reflectivity values above 35 and 45 dBZ. In addition, case studies qualitatively demonstrate that the proposed model predicts more realistic storm evolution and can facilitate early warning regarding impending severe storms.
VR Multiscale Geovisualization Based on UAS Multitemporal Data: The Case of Geological Monuments
Authors: Papadopoulou Ermioni-Eirini , Papakonstantinou Apostolos , Kapogianni Nikoletta-Anna , Zouros Nikolaos , Soulakellis Nikolaos
Published: August 2022

Technological progress in Virtual Reality (VR) and Unmanned Aerial Systems (UASs) offers great advantages in the field of cartography and particularly in the geovisualization of spatial data. This paper investigates the correlation between UAS flight characteristics for data acquisition and the quality of the derived maps and 3D models of geological monuments for VR geovisualization in different scales and timeframes. In this study, we develop a methodology for mapping geoheritage monuments based on different cartographic scales. Each cartographic scale results in diverse orthophotomaps and 3D models. All orthophotomaps and 3D models provide an optimal geovisualization, combining UAS and VR technologies and thus contributing to the multitemporal 3D geovisualization of geological heritage on different cartographic scales. The study area selected was a fossilite ferrous site located in Lesvos Geopark, UNESCO. The study area contains a fossil site surrounding various findings. The three distinct scales that occur are based on the object depicted: (i) the fossilite ferrous site (1:120), (ii) the fossil root system (1:20), and (iii) individual fossils (≥1:10). The methodology followed in the present research consists of three main sections: (a) scale-variant UAS data acquisition, (b) data processing and results (2D–3D maps and models), and (c) 3D geovisualization to VR integration. Each different mapping scale determines the UAS data acquisition parameters (flight pattern, camera orientation and inclination, height of flight) and defines the resolution of the 3D models to be embedded in the VR environment. Due to the intense excavation of the study area, the location was spatiotemporally monitored on the cartographic scale of 1:120. For the continuous monitoring of the study area, four different UASs were also used. Each of them was programmed to fly and acquire images with a constant ground sampling distance (GSD). The data were processed by image-based 3D modeling and computer vision algorithms from which the 3D models and orthophotomaps were created and used in the VR environment. As a result, a VR application visualizing multitemporal data of geoheritage monuments across three cartographic scales was developed.
An Efficient MUSIC Algorithm Enhanced by Iteratively Estimating Signal Subspace and Its Applications in Spatial Colored Noise
Authors: Zhang Xuejun , Feng Dazheng
Published: August 2022

The classical multiple signal classification (MUSIC) algorithms mainly have two limitations. One is an insufficient number of snapshots, which usually causes an ill-posed sample covariance matrix in many real applications. The other limitation is the intense space-colored and time-white noise, which also breaks the separability between signal and noise subspaces. In the case of the insufficient sample, there are few signal components in the non-zero delay sample covariance matrix (SCM), where the space-colored and time-white noise components are suppressed by the temporal method. A set of non-zero delay sample covariance matrices are constructed, and a nonlinear object function is formulated. Hence, the sufficient non-zero delay SCMs ensure that enough signal components are used for signal subspace estimation. Then, the constrained optimization problem is converted into an unconstrained one by exploiting the Lagrange multiplier method. The nonlinear equation is solved by Newton’s method iteratively. Moreover, a proper initial value of the new algorithm is given, which can improve the convergence of the iterative algorithm. In this paper, the noise subspace is removed by the pre-projection technique in every iteration step. Then, an improved signal subspace is obtained, and a more efficient MUSIC algorithm is proposed. Experimental results show that the proposed algorithm achieves significantly better performance than the existing methods.
Hyperspectral Image Classification Based on a Least Square Bias Constraint Additional Empirical Risk Minimization Nonparallel Support Vector Machine
Authors: Liu Guangxin , Wang Liguo , Liu Danfeng
Published: August 2022

Hyperspectral image classification technology is important for the application of hyperspectral technology. Support vector machines (SVMs) work well in supervised classifications of hyperspectral images; however, they still have some shortcomings, and their use of a parallel decision plane makes it difficult to conform to real hyperspectral data distribution. The improved nonparallel support vector machine based on SVMs, i.e., the bias constraint additional empirical risk minimization nonparallel support vector machine (BC-AERM-NSVM), has improved classification accuracy compared its predecessor. However, BC-AERM-NSVMs have a more complicated solution problem than SVMs, and if the dataset is too large, the training speed is significantly reduced. To solve this problem, this paper proposes a least squares algorithm, i.e., the least square bias constraint additional empirical risk minimization nonparallel support vector machine (LS-BC-AERM-NSVM). The dual problem of the LS-BC-AERM-NSVM is an unconstrained convex quadratic programming problem, so its solution speed is greatly improved. Experiments on hyperspectral image data demonstrate that the LS-BC-AERM-NSVM displays a vast improvement in terms of solution speed compared with the BC-AERM-NSVM and achieves good classification accuracy.
Profiling the Planetary Boundary Layer Wind with a StreamLine XR Doppler LiDAR: Comparison to In-Situ Observations and WRF Model Simulations
Authors: Tzadok Tamir , Ronen Ayala , Rostkier-Edelstein Dorita , Agassi Eyal , Avisar David , Berkovic Sigalit , Manor Alon
Published: August 2022

Halo-Photonics StreamLine XR Doppler LiDAR measurements are performed using several scan configurations (Velocity Azimuth Display-VAD and Doppler Beam Swing-DBS) and elevation angles of 60° and 80°. The measurements are compared to wind observations conducted by various in situ instruments (tethered balloon, meteorological mast, and radiosondes). Good agreement is obtained, with R2 over 0.90 for wind speed and a standard error ≤ 18.6° for wind direction. The best performance was attained for lower elevation scans (60°), which is consistent with the higher spatial horizontal homogeneity exhibited by lower angle scans. VAD and DBS scans performed almost equally well with slight advantage for VAD in higher altitudes and for DBS for lower altitudes. The boundary layer structure along a diurnal cycle is analyzed by utilizing retrieved backscatter data and wind measurements in conjunction with Weather Research and Forecast (WRF) simulations. The presence of multiple inversions which allow the coexistence of different layers within the studied profile is also verified using data acquired by several radiosondes. Synergic use of LiDAR data with WRF simulations for low SNR regions is demonstrated.
Vibration Analysis of a 1-DOF System Coupled with a Nonlinear Energy Sink with a Fractional Order Inerter
Authors: Chen Yandong , Tai Yongpeng , Xu Jun , Xu Xiaomei , Chen Ning
Published: August 2022

The fluid inerter described by the fractional derivative model is introduced into the traditional nonlinear energy sink (NES), which is called fractional-order NES in this paper. The slowly varying dynamic equation (SVDE) of the system coupled with fractional-order NES is obtained by the complex averaging method, in which the fractional derivative term is treated using the fractional Leibniz theorem. Then, the discriminants (Δ, Δ1, and Δ2) of the number of equilibrium points are derived. By using the variable substitution method, the characteristic equation for judging the stability is established. The results show: (1) the approximate SVDE is sufficient to reflect the slowly varying characteristics of the system, which shows that the mathematical treatment of the fractional derivative term is reliable; (2) the discriminant conditions (Δ1, Δ2) can accurately reflect the number of equilibrium points, and the corresponding range of nonlinear parameter κ can be calculated when the system has three equilibrium points. The expressions of Δ1, Δ2 are simpler than Δ, which is suitable for analysis and design parameters; (3) the stability discrimination methods of schemes 1 and 2 are accurate. Compared with scheme 2, scheme 1 is more prone to various responses, especially various strongly and weakly modulated responses. In scheme 2, the inertia effect of mass can be completely replaced by integer order inerter. Compared with integer order inerter, the introduction of fractional order inerter, whether in series or in parallel, means that the amplitude of the equilibrium point on the NES vibrator is smaller, but it is also for this reason that it is not easy to produce a modulated response with scheme 2, and the vibration suppression effect of the main structure is not good.
Minimize Tracking Occlusion in Collaborative Pick-and-Place Tasks: An Analytical Approach for Non-Wrist-Partitioned Manipulators
Authors: Montazer Zohour Hamed , Belzile Bruno , Gomes Braga Rafael , St-Onge David
Published: August 2022

Several industrial pick-and-place applications, such as collaborative assembly lines, rely on visual tracking of the parts. Recurrent occlusions are caused by the manipulator motion decrease line productivity and can provoke failures. This work provides a complete solution for maintaining the occlusion-free line of sight between a variable-pose camera and the object to be picked by a 6R manipulator that is not wrist-partitioned. We consider potential occlusions by the manipulator as well as the operator working at the assembly station. An actuated camera detects the object goal (part to pick) and keeps track of the operator. The approach consists of using the complete set of solutions obtained from the derivation of the univariate polynomial equation solution to the inverse kinematics (IK). Compared to numerical iterative solving methods, our strategy grants us a set of joint positions (posture) for each root of the equation from which we extract the best (minimizing the risks of occlusion). Our analytical-based method, integrating collision and occlusion avoidance optimizations, can contribute to greatly enhancing the efficiency and safety of collaborative assembly workstations. We validate our approach with simulations as well as with physical deployments on commercial hardware.
Cross-Language Speech Emotion Recognition Using Bag-of-Word Representations, Domain Adaptation, and Data Augmentation
Authors: Kshirsagar Shruti , Falk Tiago H.
Published: August 2022

To date, several methods have been explored for the challenging task of cross-language speech emotion recognition, including the bag-of-words (BoW) methodology for feature processing, domain adaptation for feature distribution “normalization”, and data augmentation to make machine learning algorithms more robust across testing conditions. Their combined use, however, has yet to be explored. In this paper, we aim to fill this gap and compare the benefits achieved by combining different domain adaptation strategies with the BoW method, as well as with data augmentation. Moreover, while domain adaptation strategies, such as the correlation alignment (CORAL) method, require knowledge of the test data language, we propose a variant that we term N-CORAL, in which test languages (in our case, Chinese) are mapped to a common distribution in an unsupervised manner. Experiments with German, French, and Hungarian language datasets were performed, and the proposed N-CORAL method, combined with BoW and data augmentation, was shown to achieve the best arousal and valence prediction accuracy, highlighting the usefulness of the proposed method for “in the wild” speech emotion recognition. In fact, N-CORAL combined with BoW was shown to provide robustness across languages, whereas data augmentation provided additional robustness against cross-corpus nuance factors.
Novel Spatio-Temporal Continuous Sign Language Recognition Using an Attentive Multi-Feature Network
Authors: Aditya Wisnu , Shih Timothy K. , Thaipisutikul Tipajin , Fitriajie Arda Satata , Gochoo Munkhjargal , Utaminingrum Fitri , Lin Chih-Yang
Published: August 2022

Given video streams, we aim to correctly detect unsegmented signs related to continuous sign language recognition (CSLR). Despite the increase in proposed deep learning methods in this area, most of them mainly focus on using only an RGB feature, either the full-frame image or details of hands and face. The scarcity of information for the CSLR training process heavily constrains the capability to learn multiple features using the video input frames. Moreover, exploiting all frames in a video for the CSLR task could lead to suboptimal performance since each frame contains a different level of information, including main features in the inferencing of noise. Therefore, we propose novel spatio-temporal continuous sign language recognition using the attentive multi-feature network to enhance CSLR by providing extra keypoint features. In addition, we exploit the attention layer in the spatial and temporal modules to simultaneously emphasize multiple important features. Experimental results from both CSLR datasets demonstrate that the proposed method achieves superior performance in comparison with current state-of-the-art methods by 0.76 and 20.56 for the WER score on CSL and PHOENIX datasets, respectively.
High-Temperature-Resistant Fiber Laser Vector Accelerometer Based on a Self-Compensated Multicore Fiber Bragg Grating
Authors: Xiao Xunzhou , He Jun , Xu Xizhen , Chen Runxiao , Du Bin , Chen Yanping , Liu Shen , Fu Cailing , Wang Yiping
Published: August 2022

We propose and demonstrate a novel high-temperature-resistant vector accelerometer, consisting of a ring cavity laser and sensing probe (i.e., fiber Bragg gratings (FBGs)) inscribed in a seven-core fiber (SCF) by using the femtosecond laser direct writing technique. A ring cavity laser serves as a light source. Three FBGs in the outer cores of SCF, which are not aligned in a straight line, are employed to test the vibration. These three FBGs have 120° angular separation in the SCF, and hence, vibration orientation and acceleration can be measured simultaneously. Moreover, the FBG in the central core was used as a reflector in the ring cavity laser, benefiting to resist external interference factors, such as temperature and strain fluctuation. Such a proposed accelerometer exhibits a working frequency bandwidth ranging from 4 to 68 Hz, a maximum sensitivity of 54.2 mV/g, and the best azimuthal angle accuracy of 0.21° over a range of 0–360°. Furthermore, we investigated the effect of strain and temperature on the performance of this sensor. The signal-to-noise ratio (SNR) only exhibits a fluctuation of ~1 dB in the range (0, 2289 με) and (50 °C, 1050 °C). Hence, such a vector accelerometer can operate in harsh environments, such as in aerospace and a nuclear reactor.
Novel Design of a Multimodal Technology-Based Smart Stethoscope for Personal Cardiovascular Health Monitoring
Authors: Park Heejoon , Wei Qun , Lee Soomin , Lee Miran
Published: August 2022

Heart sounds and heart rate (pulse) are the most common physiological signals used in the diagnosis of cardiovascular diseases. Measuring these signals using a device and analyzing their interrelationships simultaneously can improve the accuracy of existing methods and propose new approaches for the diagnosis of cardiovascular diseases. In this study, we have presented a novel smart stethoscope based on multimodal physiological signal measurement technology for personal cardiovascular health monitoring. The proposed device is designed in the shape of a compact personal computer mouse for easy grasping and attachment to the surface of the chest using only one hand. A digital microphone and photoplehysmogram sensor are installed on the bottom and top surfaces of the device, respectively, to measure heart sound and pulse from the user’s chest and finger simultaneously. In addition, a high-performance Bluetooth Low Energy System-on-Chip ARM microprocessor is used for pre-processing of measured data and communication with the smartphone. The prototype is assembled on a manufactured printed circuit board and 3D-printed shell to conduct an in vivo experiment to test the performance of physiological signal measurement and usability by observing users’ muscle fatigue variation.
Small-Diameter Tube Wall Damage-Detection Method Based on TE01 Mode Microwave
Authors: Shi Meng , Yang Lijian , Gao Songwei , Wang Guoqing
Published: August 2022

Accidents occur frequently in urban gas pipelines, and pipeline damage detection is an important means of ensuring pipeline safety. Aiming at the problem that the small diameter pipeline is difficult to detect, this paper proposes a detection method for the inner wall damage of a small-diameter pipeline based on the TE01 mode microwave and uses the TE01 mode to detect the inner wall damage of the pipeline by the terminal short-circuit reflection method. By analyzing the transition of microwave propagation mode at the defect, based on the Maxwell equation and the field distribution equation of the TE01 mode microwave in the pipe and the pipe wall current equation, the microwave reflection coefficient at the defect is established when the microwave distortion modes at the defect are TE and TM modes. A small-diameter pipeline simulation model is established, and the influence of the electric field, magnetic field, wall current distribution, and reflected wave reflection coefficient in the pipeline when inner wall defects of different widths are analyzed using the finite integral theory during microwave detection of the TE01 mode. An experimental platform for the microwave detection of small-diameter pipes was built to detect defects on the inner walls of pipes with different widths. The results show that the inner wall defect causes the electric field, magnetic field, current propagation period, and energy distribution of the TE01 mode microwave propagated in the pipe to be distorted, and the microwave reflection coefficient and return loss exhibit a significant frequency shift with the change in the defect width. The experimental and simulation results had a good consistency.
A Novel Prediction Model for Malicious Users Detection and Spectrum Sensing Based on Stacking and Deep Learning
Authors: Benazzouza Salma , Ridouani Mohammed , Salahdine Fatima , Hayar Aawatif
Published: August 2022

Cooperative network is a promising concept for achieving a high-accuracy decision of spectrum sensing in cognitive radio networks. It enables a collaborative exchange of the sensing measurements among the network users to monitor the primary spectrum occupancy. However, the presence of malicious users leads to harmful interferences in the system by transmitting incorrect local sensing observations.To overcome this security related problem and to improve the accuracy decision of spectrum sensing in cooperative cognitive radio networks, we proposed a new approach based on two machine learning solutions. For the first solution, a new stacking model-based malicious users detection is proposed, using two innovative techniques, including chaotic compressive sensing technique-based authentication for feature extraction with a minimum of measurements and an ensemble machine learning technique for users classification. For the second solution, a novel deep learning technique is proposed, using scalogram images as inputs for the primary user spectrum’s classification. The simulation results show the high efficiency of both proposed solutions, where the accuracy of the new stacking model reaches 97% in the presence of 50% of malicious users, while the new scalogram technique-based spectrum sensing is fast and achieves a high probability of detection with a lower number of epochs and a low probability of false alarm.