bluuebunny/crossref_metadata_2025 huggingface dataset:
Assimilation of GNSS PWV with NCAR-RTFDDA to Improve Prediction of a Landfall Typhoon
Authors: Wang Haishen , Liu Yubao , Liu Yuewei , Cao Yunchang , Liang Hong , Hu Heng , Liang Jingshu , Tu Manhong
Published: January 2022

Precipitable water vapor (PWV) retrieved from ground-based global navigation satellite system (GNSS) stations acquisition signal of a navigation satellite system provides high spatial and temporal resolution atmospheric water vapor. In this paper, an observation-nudging-based real-time four-dimensional data assimilation (RTFDDA) approach was used to assimilate the PWV estimated from GNSS observation into the WRF (Weather Research and Forecasting) modeling system. A landfall typhoon, “Mangkhut”, is chosen to evaluate the impact of GNSS PWV data assimilation on its track, intensity, and precipitation prediction. The results show that RTFDDA can assimilate GNSS PWV data into WRF to improve the water vapor distribution associated with the typhoon. Assimilating the GNSS PWV improved the typhoon track and intensity prediction when and after the typhoon made landfall, correcting a 5–10 hPa overestimation (too deep) of the central pressure of the typhoon at landfall. It also improved the occurrence and the intensity of the major typhoon spiral rainbands.
Ionospheric Disturbances Observed Following the Ridgecrest Earthquake of 4 July 2019 in California, USA
Authors: Sanchez Saul A. , Kherani Esfhan A. , Astafyeva Elvira , de Paula Eurico R.
Published: January 2022

Earthquakes are known to generate disturbances in the ionosphere. Such disturbances, referred to as co-seismic ionospheric disturbances, or ionoquakes, were previously reported for large earthquakes with magnitudes Mw≥ 6.6. This paper reports ionoquakes associated with the Ridgecrest earthquakes of magnitude (Mw=6.4), that occurred on 4 July 2019 in California, USA. The ionoquakes manifested in total electron content (TEC) in the form of traveling ionospheric disturbances (TIDs) within 1 h from the mainshock onset. These seismic-origin TIDs have unique wave characteristics that distinguish them from TIDs of non-seismic origin arising from a moderate geomagnetic activity on the same day. Moreover, in the space-time domain of the detection of seismic-origin TIDs, TIDs are absent on the day before and day after the earthquake day. Their spectral characteristics relate them to the Earth’s normal modes and atmospheric resonance modes. We found the ground velocity associated with the mainshock, rather than the ground displacement, satisfies the threshold criteria for detectable ionoquakes in TEC measurements. Numerical simulation suggested that the coupled seismo–atmosphere–ionosphere (SAI) dynamics energized by the atmospheric waves are responsible for the generation of ionoquakes. This study’s findings demonstrate the potential of using TEC measurement to detect the ionospheric counterparts of moderate earthquakes.
A Multi-Perspective Assessment Method with a Dynamic Benchmark for Human Activity Impacts on Alpine Ecosystem under Climate Change
Authors: Zhang Fuguang , Zeng Biao , Yang Taibao , Zheng Yuxuan , Cao Ying
Published: January 2022

Intense human activities and rapid climate changes both have obvious impacts on alpine ecosystems. However, the magnitudes and directions of the impacts by these two drivers remain uncertain due to a lack of a reasonable assessment method to distinguish between them. The impact of natural resilience is also generally included in the dynamics of a disturbed ecosystem and is liable to be mixed into the impact of human activity. It is urgent that we quantitatively discriminate human activity impacts on the ecosystem under climate change, especially for fast-developing alpine regions. Here, we propose an assessment method to determine human activity impacts under a dynamic climate, taking the potential net primary production (NPP) of an ecosystem as a benchmark. The potential NPP (NPPP) series under the changing climate was retrieved by an improved integrated biosphere simulator based on the initial disturbed ecosystem status of the assessment period. The actual NPP (NPPA) series monitored by remote sensing was considered as the results derived from the joint impacts of climate change, natural resilience and human activity. Then, the impact of human activity was quantified as the difference between the NPPP and NPPA. The contributions of human activity and natural forces to ecosystem NPP dynamics were then calculated separately and employed to explore the dominant driver(s). This assessment method was demonstrated in a typical alpine ecosystem in Northwest China. The results indicate that this method capably revealed the positive impacts of local afforestation and land-use optimization and the negative impacts caused by grazing during the assessment period of 2001–2017. This assessment method provides a quantitative reference for assessing the performances of ecological protections or human damage to alpine ecosystems at the regional scale.
FCAU-Net for the Semantic Segmentation of Fine-Resolution Remotely Sensed Images
Authors: Niu Xuerui , Zeng Qiaolin , Luo Xiaobo , Chen Liangfu
Published: January 2022

The semantic segmentation of fine-resolution remotely sensed images is an urgent issue in satellite image processing. Solving this problem can help overcome various obstacles in urban planning, land cover classification, and environmental protection, paving the way for scene-level landscape pattern analysis and decision making. Encoder-decoder structures based on attention mechanisms have been frequently used for fine-resolution image segmentation. In this paper, we incorporate a coordinate attention (CA) mechanism, adopt an asymmetric convolution block (ACB), and design a refinement fusion block (RFB), forming a network named the fusion coordinate and asymmetry-based U-Net (FCAU-Net). Furthermore, we propose novel convolutional neural network (CNN) architecture to fully capture long-term dependencies and fine-grained details in fine-resolution remotely sensed imagery. This approach has the following advantages: (1) the CA mechanism embeds position information into a channel attention mechanism to enhance the feature representations produced by the network while effectively capturing position information and channel relationships; (2) the ACB enhances the feature representation ability of the standard convolution layer and captures and refines the feature information in each layer of the encoder; and (3) the RFB effectively integrates low-level spatial information and high-level abstract features to eliminate background noise when extracting feature information, reduces the fitting residuals of the fused features, and improves the ability of the network to capture information flows. Extensive experiments conducted on two public datasets (ZY-3 and DeepGlobe) demonstrate the effectiveness of the FCAU-Net. The proposed FCAU-Net transcends U-Net, Attention U-Net, the pyramid scene parsing network (PSPNet), DeepLab v3+, the multistage attention residual U-Net (MAResU-Net), MACU-Net, and the Transformer U-Net (TransUNet). Specifically, the FCAU-Net achieves a 97.97% (95.05%) pixel accuracy (PA), a 98.53% (91.27%) mean PA (mPA), a 95.17% (85.54%) mean intersection over union (mIoU), and a 96.07% (90.74%) frequency-weighted IoU (FWIoU) on the ZY-3 (DeepGlobe) dataset.
A Bidirectional Deep-Learning-Based Spectral Attention Mechanism for Hyperspectral Data Classification
Authors: Praveen Bishwas , Menon Vineetha
Published: January 2022

Hyperspectral remote sensing presents a unique big data research paradigm through its rich information captured across hundreds of spectral bands, which embodies vital spatial and temporal information about the underlying land cover. Deep-learning-based hyperspectral data analysis methodologies have made significant advancements over the past few years. Despite their success, most deep learning frameworks for hyperspectral data classification tend to suffer in terms of computational and classification efficacy as the data size increases. This is largely due to their equal emphasis criteria on the rich spectral information present in the data, albeit all of the spectral information not being essential for hyperspectral data analysis. On the contrary, this redundant information present in the spectral bands can deter the performance of hyperspectral data analysis techniques. Therefore, in this work, we propose a novel bidirectional spectral attention mechanism, which is computationally efficient and capable of adaptive spectral information diversification through selective emphasis on spectral bands that comprise more information and suppress the ones with lesser information. The concept of 3D-convolutions in tandem with bidirectional long short-term memory (LSTM) is used in the proposed architecture as spectral attention mechanism. A feedforward neural network (FNN)-based supervised classification is then performed to validate the performance of our proposed approach. Experimental results reveal that the proposed hyperspectral data analysis model with spectral attention mechanism outperforms other spatial- and spectral-information-extraction-based hyperspectral data analysis techniques compared.
Retrieval of DTM under Complex Forest Stand Based on Spaceborne LiDAR Fusion Photon Correction
Authors: Li Bin , Fan Guangpeng , Zhao Tianzhong , Deng Zhuo , Yu Yonghui
Published: January 2022

The new generation of satellite-borne laser radar Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data has been successfully used for ground information acquisition. However, when dealing with complex terrain and dense vegetation cover, the accuracy of the extracted understory Digital Terrain Model (DTM) is limited. Therefore, this paper proposes a photon correction data processing method based on ICESat-2 to improve the DTM inversion accuracy in complex terrain and high forest coverage areas. The correction value is first extracted based on the ALOS PALSAR DEM reference data to correct the cross-track photon data of ICESat-2. The slope filter threshold is then selected from the reference data, and the extracted possible ground photons are slope filtered to obtain accurate ground photons. Finally, the impacts of cross-track photon and slope filtering on fine ground extraction from the ICESat-2 data are discussed. The results show that the proposed photon correction and slope filtering algorithms help to improve the extraction accuracy of forest DTM in complex terrain areas. Compared with the forest DTM extracted without the photon correction and slope filtering methods, the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) are reduced by 51.90~57.82% and 49.37~53.55%, respectively. To the best of our knowledge, this is the first study demonstrating that photon correction can improve the terrain inversion ability of ICESat-2, while providing a novel method for ground extraction based on ICESat-2 data. It provides a theoretical basis for the accurate inversion of canopy parameters for ICESat-2.
Flood Detection Using Real-Time Image Segmentation from Unmanned Aerial Vehicles on Edge-Computing Platform
Authors: Hernández Daniel , Cecilia José M. , Cano Juan-Carlos , Calafate Carlos T.
Published: January 2022

With the proliferation of unmanned aerial vehicles (UAVs) in different contexts and application areas, efforts are being made to endow these devices with enough intelligence so as to allow them to perform complex tasks with full autonomy. In particular, covering scenarios such as disaster areas may become particularly difficult due to infrastructure shortage in some areas, often impeding a cloud-based analysis of the data in near-real time. Enabling AI techniques at the edge is therefore fundamental so that UAVs themselves can both capture and process information to gain an understanding of their context, and determine the appropriate course of action in an independent manner. Towards this goal, in this paper, we take determined steps towards UAV autonomy in a disaster scenario such as a flood. In particular, we use a dataset of UAV images relative to different floods taking place in Spain, and then use an AI-based approach that relies on three widely used deep neural networks (DNNs) for semantic segmentation of images, to automatically determine the regions more affected by rains (flooded areas). The targeted algorithms are optimized for GPU-based edge computing platforms, so that the classification can be carried out on the UAVs themselves, and only the algorithm output is uploaded to the cloud for real-time tracking of the flooded areas. This way, we are able to reduce dependency on infrastructure, and to reduce network resource consumption, making the overall process greener and more robust to connection disruptions. Experimental results using different types of hardware and different architectures show that it is feasible to perform advanced real-time processing of UAV images using sophisticated DNN-based solutions.
Updated GOES-13 Heliosat-2 Method for Global Horizontal Irradiation in the Americas
Authors: Bechet Jessica , Albarelo Tommy , Macaire Jérémy , Salloum Maha , Zermani Sara , Primerose Antoine , Linguet Laurent
Published: January 2022

Increasing the utilization of renewable energy is at the center of most sustainability policies. Solar energy is the most abundant resource of this type on Earth, and optimizing its use requires the optimal estimation of surface solar irradiation. Heliosat-2 is one of the most popular methods of global horizontal irradiation (GHI) estimation. Originally developed for the Meteosat satellite, Heliosat-2 has been modified in previous work to deal with GOES-13 data and named here GOES_H2. This model has been validated through the computation of indicators and irradiation maps for the Guiana Shield. This article proposes an improved version of GOES_H2, which has been combined with a radiative transfer parameterization (RTP) and the McClear clear-sky model (MC). This new version, hereafter designated RTP_MC_GOES_H2, was tested on eight stations from the Baseline Surface Radiation Network, located in North and South America, and covered by GOES-13. RTP_MC_GOES_H2 improves the hourly GHI estimates independently of the type of sky. This improvement is independent of the climate, no matter the station, the RTP_MC_GOES_H2 gives better results of MBE and RMSE than the original GOES_H2 method. Indeed, the MBE and RMSE values, respectively, change from −11.93% to −2.42% and 23.24% to 18.24% for North America and from −4.35% to 1.79% and 19.97% to 17.37 for South America. Moreover, the flexibility of the method may allow to improve results in the presence of snow cover and rainy/variable weather. Furthermore, RTP_MC_GOES_H2 results outperform or equalize those of other operational models.
Image-Aided LiDAR Mapping Platform and Data Processing Strategy for Stockpile Volume Estimation
Authors: Manish Raja , Hasheminasab Seyyed Meghdad , Liu Jidong , Koshan Yerassyl , Mahlberg Justin Anthony , Lin Yi-Chun , Ravi Radhika , Zhou Tian , McGuffey Jeremy , Wells Timothy , Bullock Darcy , Habib Ayman
Published: January 2022

Stockpile quantity monitoring is vital for agencies and businesses to maintain inventory of bulk material such as salt, sand, aggregate, lime, and many other materials commonly used in agriculture, highways, and industrial applications. Traditional approaches for volumetric assessment of bulk material stockpiles, e.g., truckload counting, are inaccurate and prone to cumulative errors over long time. Modern aerial and terrestrial remote sensing platforms equipped with camera and/or light detection and ranging (LiDAR) units have been increasingly popular for conducting high-fidelity geometric measurements. Current use of these sensing technologies for stockpile volume estimation is impacted by environmental conditions such as lack of global navigation satellite system (GNSS) signals, poor lighting, and/or featureless surfaces. This study addresses these limitations through a new mapping platform denoted as Stockpile Monitoring and Reporting Technology (SMART), which is designed and integrated as a time-efficient, cost-effective stockpile monitoring solution. The novel mapping framework is realized through camera and LiDAR data-fusion that facilitates stockpile volume estimation in challenging environmental conditions. LiDAR point clouds are derived through a sequence of data collections from different scans. In order to handle the sparse nature of the collected data at a given scan, an automated image-aided LiDAR coarse registration technique is developed followed by a new segmentation approach to derive features, which are used for fine registration. The resulting 3D point cloud is subsequently used for accurate volume estimation. Field surveys were conducted on stockpiles of varying size and shape complexity. Independent assessment of stockpile volume using terrestrial laser scanners (TLS) shows that the developed framework had close to 1% relative error.
Validation of Recent Altimeter Missions at Non-Dedicated Tide Gauge Stations in the Southeastern North Sea
Authors: Esselborn Saskia , Schöne Tilo , Illigner Julia , Weiß Robert , Artz Thomas , Huang Xinge
Published: January 2022

Consistent calibration and monitoring is a basic prerequisite for providing a reliable time series of global and regional sea-level variations from altimetry. The precisions of sea-level measurements and regional biases for six altimeter missions (Jason-1/2/3, Envisat, Saral, Sentinel-3A) are assessed in this study at 11 GNSS-controlled tide gauge stations in the German Bight (SE North Sea) for the period 2002 to 2019. The gauges are partly located at the open water, and partly at the coast close to mudflats. The altimetry is extracted at virtual stations with distances from 2 to 24 km from the gauges. The processing is optimized for the region and adjusted for the comparison with instantaneous tide gauge readings. An empirical correction is developed to account for mean height gradients and slight differences of the tidal dynamics between the gauge and altimetry, which improves the agreement between the two data sets by 15–75%. The precision of the altimeters depends on the location and mission and ranges from 1.8 to 3.7 cm if the precision of the gauges is 2 cm. The accuracy of the regional mission biases is strongly dependent on the mean sea surface heights near the stations. The most consistent biases are obtained based on the CLS2011 model with mission-dependent accuracies from 1.3 to 3.4 cm. Hence, the GNSS-controlled tide gauges operated operationally by the German Waterway and Shipping Administration (WSV) might complement the calibration and monitoring activities at dedicated CalVal stations.
Comparison of Crop Evapotranspiration and Water Productivity of Typical Delta Irrigation Areas in Aral Sea Basin
Authors: Liu Zhibin , Liu Tie , Huang Yue , Duan Yangchao , Pan Xiaohui , Wang Wei
Published: January 2022

The intensity of agricultural activities and the characteristics of water consumption affect the hydrological processes of inland river basins in Central Asia. The crop water requirements and water productivity are different between the Amu Darya and Syr Darya river basins due to the different water resource development and utilization policies of Uzbekistan and Kazakhstan, which have resulted in more severe agricultural water consumption of the Amu Darya delta than the Syr Darya delta, and the differences in the surface runoff are injected into the Aral Sea. To reveal the difference in water resource dissipation, water productivity, and its influencing factors between the two basins, this study selected the irrigation areas of Amu Darya delta (IAAD) and Syr Darya delta (IASD) as typical examples; the actual evapotranspiration (ETa) was retrieved by using the modified surface energy balance algorithm for land model (SEBAL) based on high spatial resolution Landsat images from 2000 to 2020. Land use and cover change (LUCC) and streamflow data were obtained to analyze the reasons for the spatio-temporal heterogeneity of regional ETa. The water productivity of typical crops in two irrigation areas was compared and combined with statistical data. The results indicate that: (1) the ETa simulated by the SEBAL model matched the crop evapotranspiration (ETc) calculated by the Penman–Monteith method and ground-measured data well, with all the correlation coefficients higher than 0.7. (2) In IAAD, the average ETa was 1150 mm, and the ETa had shown a decreasing trend; for the IASD, the average ETa was 800 mm. The ETa showed an increasing trend with low stability due to a large amount of developable cultivated land. The change of cultivated land dominated the spatio-temporal characteristics of ETa in the two irrigation areas (3). Combined with high spatial resolution ETa inversion results, the water productivity of cotton and rice in IAAD was significantly lower than in IASD, and wheat was not significantly different, but all were far lower than the international average. This study can provide useful information for agricultural water management in the Aral Sea region.
An eLoran Signal Cycle Identification Method Based on Joint Time–Frequency Domain
Authors: Yan Wenhe , Dong Ming , Li Shifeng , Yang Chaozhong , Yuan Jiangbin , Hu Zhaopeng , Hua Yu
Published: January 2022

The eLoran system is an international standardized positioning, navigation, and timing service system, which can complement global navigation satellite systems to cope with navigation and timing warfare. The eLoran receiver measures time-of-arrival (TOA) through cycle identification, which is key in determining timing and positioning accuracy. However, noise and skywave interference can cause cycle identification errors, resulting in TOA-measurement errors that are integral multiples of 10 μs. Therefore, this article proposes a cycle identification method in the joint time–frequency domain. Based on the spectrum-division method to determine the cycle identification range, the time–domain peak-to-peak ratio and waveform matching are used for accurate cycle identification. The performance of the method is analyzed via simulation. When the signal-to-noise ratio (SNR) ≥ 0 dB and skywave-to-groundwave ratio (SGR) ≤ 23 dB, the success rate of cycle identification is 100%; when SNR ≥ −13 dB and SGR ≤ 23 dB, the success rate exceeds 75%. To verify its practicability, the method was implemented in the eLoran receiver and tested at three test sites within 1000 km using actual signals emitted by an eLoran system. The results show that the method has a high identification probability and can be used in modern eLoran receivers to improve TOA-measurement accuracy.
RCE-GAN: A Rebar Clutter Elimination Network to Improve Tunnel Lining Void Detection from GPR Images
Authors: Wang Yuanzheng , Qin Hui , Tang Yu , Zhang Donghao , Yang Donghui , Qu Chunxu , Geng Tiesuo
Published: January 2022

Ground penetrating radar (GPR) is one of the most recommended tools for routine inspection of tunnel linings. However, the rebars in the reinforced concrete produce a strong shielding effect on the electromagnetic waves, which may hinder the interpretation of GPR data. In this work, we proposed a method to improve the identification of tunnel lining voids by designing a generative adversarial network-based rebar clutter elimination network (RCE-GAN). The designed network has two sets of generators and discriminators, and by introducing the cycle-consistency loss, the network is capable of learning high-level features between unpaired GPR images. In addition, an attention module and a dilation center part were designed in the network to improve the network performance. Validation of the proposed method was conducted on both synthetic and real-world GPR images, collected from the implementation of finite-difference time-domain (FDTD) simulations and a controlled physical model experiment, respectively. The results demonstrate that the proposed method is promising for its lower demand on the training dataset and the improvement in the identification of tunnel lining voids.
Aerosols Direct Radiative Effects Combined Ground-Based Lidar and Sun-Photometer Observations: Cases Comparison between Haze and Dust Events in Beijing
Authors: Liang Yuanxin , Che Huizheng , Wang Hong , Zhang Wenjie , Li Lei , Zheng Yu , Gui Ke , Zhang Peng , Zhang Xiaoye
Published: January 2022

Aerosols can affect vertical thermal structure during heavily polluted episodes (HPEs). Here, we selected four typical HPEs in 2018, which were further subdivided into dust and haze events. The vertical distribution of aerosols extinction coefficient (EC) and variations in columnar optical properties were investigated based on sun-photometer and Lidar observation at an urban site in Beijing. The vertical characteristics in shortwave radiative heating rate (HR) of aerosols were studied using NASA/Goddard radiative transfer model along with observational data. In the haze episode, EC layer is less than 1.5 km and shows strong scattering, with single-scattering albedo (SSA440nm) of ~0.97. The heating effects are observed at the middle and upper atmosphere, and slight heating effects are found at the lower layer. The mean HR within 1.5 km can be up to 16.3 K day−1 with EC of 1.27 km−1, whereas the HR within 0.5 km is only 1.3 K day−1. In the dust episode, dust aerosols present the absorption with SSA440nm of ~0.88, which would heat the lower atmosphere to promote vertical turbulence, and the height of EC layer can be up to 2.0–3.5 km. In addition, the strong heating effects of dust layer produced cooling effects near the surface. Therefore, the accurate measurement of aerosols optical properties in HPEs is of great significance for modeling aerosols direct radiative effects.
Crop Mapping in the Sanjiang Plain Using an Improved Object-Oriented Method Based on Google Earth Engine and Combined Growth Period Attributes
Authors: Li Mengyao , Zhang Rui , Luo Hongxia , Gu Songwei , Qin Zili
Published: January 2022

In recent years, the scale of rural land transfer has gradually expanded, and the phenomenon of non-grain-oriented cultivated land has emerged. Obtaining crop planting information is of the utmost importance to guaranteeing national food security; however, the acquisition of the spatial distribution of crops in large-scale areas often has the disadvantages of excessive calculation and low accuracy. Therefore, the IO-Growth method, which takes the growth stage every 10 days as the index and combines the spectral features of crops to refine the effective interval of conventional wavebands for object-oriented classification, was proposed. The results were as follows: (1) the IO-Growth method obtained classification results with an overall accuracy and F1 score of 0.92, and both values increased by 6.98% compared to the method applied without growth stages; (2) the IO-Growth method reduced 288 features to only 5 features, namely Sentinel-2: Red Edge1, normalized difference vegetation index, Red, short-wave infrared2, and Aerosols, on the 261st to 270th days, which greatly improved the utilization rate of the wavebands; (3) the rise of geographic data processing platforms makes it simple to complete computations with massive data in a short time. The results showed that the IO-Growth method is suitable for large-scale vegetation mapping.
Classification of Daily Crop Phenology in PhenoCams Using Deep Learning and Hidden Markov Models
Authors: Taylor Shawn D. , Browning Dawn M.
Published: January 2022

Near-surface cameras, such as those in the PhenoCam network, are a common source of ground truth data in modelling and remote sensing studies. Despite having locations across numerous agricultural sites, few studies have used near-surface cameras to track the unique phenology of croplands. Due to management activities, crops do not have a natural vegetation cycle which many phenological extraction methods are based on. For example, a field may experience abrupt changes due to harvesting and tillage throughout the year. A single camera can also record several different plants due to crop rotations, fallow fields, and cover crops. Current methods to estimate phenology metrics from image time series compress all image information into a relative greenness metric, which discards a large amount of contextual information. This can include the type of crop present, whether snow or water is present on the field, the crop phenology, or whether a field lacking green plants consists of bare soil, fully senesced plants, or plant residue. Here, we developed a modelling workflow to create a daily time series of crop type and phenology, while also accounting for other factors such as obstructed images and snow covered fields. We used a mainstream deep learning image classification model, VGG16. Deep learning classification models do not have a temporal component, so to account for temporal correlation among images, our workflow incorporates a hidden Markov model in the post-processing. The initial image classification model had out of sample F1 scores of 0.83–0.85, which improved to 0.86–0.91 after all post-processing steps. The resulting time series show the progression of crops from emergence to harvest, and can serve as a daily, local-scale dataset of field states and phenological stages for agricultural research.
Three-Dimensional Sparse SAR Imaging with Generalized Lq Regularization
Authors: Wang Yangyang , He Zhiming , Zhan Xu , Fu Yuanhua , Zhou Liming
Published: January 2022

Three-dimensional (3D) synthetic aperture radar (SAR) imaging provides complete 3D spatial information, which has been used in environmental monitoring in recent years. Compared with matched filtering (MF) algorithms, the regularization technique can improve image quality. However, due to the substantial computational cost, the existing observation-matrix-based sparse imaging algorithm is difficult to apply to large-scene and 3D reconstructions. Therefore, in this paper, novel 3D sparse reconstruction algorithms with generalized Lq-regularization are proposed. First, we combine majorization–minimization (MM) and L1 regularization (MM-L1) to improve SAR image quality. Next, we combine MM and L1/2 regularization (MM-L1/2) to achieve high-quality 3D images. Then, we present the algorithm which combines MM and L0 regularization (MM-L0) to obtain 3D images. Finally, we present a generalized MM-Lq algorithm (GMM-Lq) for sparse SAR imaging problems with arbitrary q0≤q≤1 values. The proposed algorithm can improve the performance of 3D SAR images, compared with existing regularization techniques, and effectively reduce the amount of calculation needed. Additionally, the reconstructed complex image retains the phase information, which makes the reconstructed SAR image still suitable for interferometry applications. Simulation and experimental results verify the effectiveness of the algorithms.
Multi-Task Model for Esophageal Lesion Analysis Using Endoscopic Images: Classification with Image Retrieval and Segmentation with Attention
Authors: Yu Xiaoyuan , Tang Suigu , Cheang Chak Fong , Yu Hon Ho , Choi I Cheong
Published: January 2022

The automatic analysis of endoscopic images to assist endoscopists in accurately identifying the types and locations of esophageal lesions remains a challenge. In this paper, we propose a novel multi-task deep learning model for automatic diagnosis, which does not simply replace the role of endoscopists in decision making, because endoscopists are expected to correct the false results predicted by the diagnosis system if more supporting information is provided. In order to help endoscopists improve the diagnosis accuracy in identifying the types of lesions, an image retrieval module is added in the classification task to provide an additional confidence level of the predicted types of esophageal lesions. In addition, a mutual attention module is added in the segmentation task to improve its performance in determining the locations of esophageal lesions. The proposed model is evaluated and compared with other deep learning models using a dataset of 1003 endoscopic images, including 290 esophageal cancer, 473 esophagitis, and 240 normal. The experimental results show the promising performance of our model with a high accuracy of 96.76% for the classification and a Dice coefficient of 82.47% for the segmentation. Consequently, the proposed multi-task deep learning model can be an effective tool to help endoscopists in judging esophageal lesions.
A Low-Jitter Harmonic-Free All-Digital Delay-Locked Loop for Multi-Channel Vernier TDC
Authors: Tong Jiyun , Wang Sha , Zhang Shuang , Zhang Mengdi , Zhao Ye , Zhao Fazhan
Published: January 2022

This paper presents a low jitter All-Digital Delay-Locked Loop (ADDLL) with fast lock time and process immunity. A coarse locking algorithm is proposed to prevent harmonic locking with just a small increase in hardware resources. In order to effectively solve the dithering phenomenon after locking, a replica delay line and a modified binary search algorithm with two modes were introduced in our ADDLL, which can significantly reduce the peak-to-peak jitter of the replica delay line. In addition, digital codes for a replica delay line can be conveniently applied to the delay line of multi-channel Vernier TDC while maintaining consistency between channels. The proposed ADDLL has been designed in 55 nm CMOS technology. In addition, the post-layout simulation results show that when operated at 1.2 V, the proposed ADDLL locks within 37 cycles and has a closed-loop characteristic, the peak-to-peak and root-mean-square jitter at 800 MHz are 6.5 ps and 1.18 ps, respectively. The active area is 0.024 mm2 and the power consumption at 800 MHz is 6.92 mW. In order to verify the performance of the proposed ADDLL, an architecture of dual ADDLL is applied to Vernier TDC to stabilize the Vernier delay lines against the process, voltage, and temperature (PVT) variations. With a 600 MHz operating frequency, the TDC achieves a 10.7 ps resolution, and the proposed ADDLL can keep the resolution stable even if PVT varies.
A Two-Stage Approach to Important Area Detection in Gathering Place Using a Novel Multi-Input Attention Network
Authors: Xu Jianqiang , Zhao Haoyu , Min Weidong
Published: January 2022

An important area in a gathering place is a region attracting the constant attention of people and has evident visual features, such as a flexible stage or an open-air show. Finding such areas can help security supervisors locate the abnormal regions automatically. The existing related methods lack an efficient means to find important area candidates from a scene and have failed to judge whether or not a candidate attracts people’s attention. To realize the detection of an important area, this study proposes a two-stage method with a novel multi-input attention network (MAN). The first stage, called important area candidate generation, aims to generate candidate important areas with an image-processing algorithm (i.e., K-means++, image dilation, median filtering, and the RLSA algorithm). The candidate areas can be selected automatically for further analysis. The second stage, called important area candidate classification, aims to detect an important area from candidates with MAN. In particular, MAN is designed as a multi-input network structure, which fuses global and local image features to judge whether or not an area attracts people’s attention. To enhance the representation of candidate areas, two modules (i.e., channel attention and spatial attention modules) are proposed on the basis of the attention mechanism. These modules are mainly based on multi-layer perceptron and pooling operation to reconstruct the image feature and provide considerably efficient representation. This study also contributes to a new dataset called gathering place important area detection for testing the proposed two-stage method. Lastly, experimental results show that the proposed method has good performance and can correctly detect an important area.
Preparation and Research of a High-Performance ZnO/SnO2 Humidity Sensor
Authors: Li Fan , Li Peng , Zhang Hongyan
Published: January 2022

A high-performance zinc oxide/tin dioxide (ZnO/SnO2) humidity sensor was developed using a simple solvothermal method. The sensing mechanism of the ZnO/SnO2 humidity sensor was evaluated by analyzing its complex impedance spectra. The experimental results prove that the ZnO/SnO2 composite material has a larger specific surface area than pure SnO2, which allows the composite material surface to adsorb more water to enhance the response of the ZnO/SnO2 humidity sensor. ZnO can also contribute to the generation of oxygen-rich vacancies on the ZnO/SnO2 composite material surface, allowing it to adsorb a large amount of water and rapidly decompose water molecules into conductive ions to increase the response and recovery speed of the ZnO/SnO2 humidity sensor. These characteristics allowed the Z/S-2 humidity sensor to achieve a higher response (1,225,361%), better linearity, smaller hysteresis (6.6%), faster response and recovery speeds (35 and 8 s, respectively), and long-term stability at 11–95% relative humidity. The successful preparation of the ZnO/SnO2 composite material also provides a new direction for the design of SnO2-based resistance sensors with high humidity-sensing performance.
Rapid Determination of the ‘Legal Highs’ 4-MMC and 4-MEC by Spectroelectrochemistry: Simultaneous Cyclic Voltammetry and In Situ Surface-Enhanced Raman Spectroscopy
Authors: González-Hernández Jerson , Ott Colby Edward , Arcos-Martínez María Julia , Colina Álvaro , Heras Aránzazu , Alvarado-Gámez Ana Lorena , Urcuyo Roberto , Arroyo-Mora Luis E.
Published: January 2022

The synthetic cathinones mephedrone (4-MMC) and 4-methylethcathinone (4-MEC) are two designer drugs that represent the rise and fall effect of this drug category within the stimulants market and are still available in several countries around the world. As a result, the qualitative and quantitative determination of ‘legal highs’, and their mixtures, are of great interest. This work explores for the first time the spectroelectrochemical response of these substances by coupling cyclic voltammetry (CV) with Raman spectroscopy in a portable instrument. It was found that the stimulants exhibit a voltammetric response on a gold screen-printed electrode while the surface is simultaneously electro-activated to achieve a periodic surface-enhanced Raman spectroscopy (SERS) substrate with high reproducibility. The proposed method enables a rapid and reliable determination in which both substances can be selectively analyzed through the oxidation waves of the molecules and the characteristic bands of the electrochemical SERS (EC-SERS) spectra. The feasibility and applicability of the method were assessed in simulated seized drug samples and spiked synthetic urine. This time-resolved spectroelectrochemical technique provides a cost-effective and user-friendly tool for onsite screening of synthetic stimulants in matrices with low concentration analytes for forensic applications.
The New Ion-Selective Electrodes Developed for Ferric Cations Determination, Modified with Synthesized Al and Fe−Based Nanoparticles
Authors: Paut Andrea , Prkić Ante , Mitar Ivana , Guć Lucija , Marciuš Marijan , Vrankić Martina , Krehula Stjepko , Tomaško Lara
Published: January 2022

The solid-state ion-selective electrodes presented here are based on the FePO4:Ag2S:polytetrafluoroethylene (PTFE) = 1:1:2 with an addition of (0.25–1)% microwave-synthesized hematite (α-Fe2O3), magnetite (Fe3O4), boehmite [γ-AlO(OH)], and alumina (Al2O3) nanoparticles (NPs) in order to establish ideal membrane composition for iron(III) cations determination. Synthesized NPs are characterized with Fourier-Transform Infrared (FTIR) spectroscopy, Powder X-Ray Diffraction (PXRD), and Scanning Electron Microscopy (SEM) with Energy Dispersive Spectroscopy (EDS). The iron oxides NPs, more specifically, magnetite and hematite, showed a more positive effect on the sensing properties than boehmite and alumina NPs. The hematite NPs had the most significant effect on the linear range for the determination of ferric cations. The membrane containing 0.25% hematite NPs showed a slope of −19.75 mV per decade in the linear range from 1.2∙10−6 to 10−2 mol L−1, with a correlation factor of 0.9925. The recoveries for the determination of ferric cations in standard solutions were 99.4, 106.7, 93.6, and 101.1% for different concentrations.
AI Based Monitoring of Different Risk Levels in COVID-19 Context
Authors: Melo César , Dixe Sandra , Fonseca Jaime C. , Moreira António H. J. , Borges João
Published: January 2022

COVID-19 was responsible for devastating social, economic, and political effects all over the world. Although the health authorities imposed restrictions provided relief and assisted with trying to return society to normal life, it is imperative to monitor people’s behavior and risk factors to keep virus transmission levels as low as possible. This article focuses on the application of deep learning algorithms to detect the presence of masks on people in public spaces (using RGB cameras), as well as the detection of the caruncle in the human eye area to make an accurate measurement of body temperature (using thermal cameras). For this task, synthetic data generation techniques were used to create hybrid datasets from public ones to train state-of-the-art algorithms, such as YOLOv5 object detector and a keypoint detector based on Resnet-50. For RGB mask detection, YOLOv5 achieved an average precision of 82.4%. For thermal masks, glasses, and caruncle detection, YOLOv5 and keypoint detector achieved an average precision of 96.65% and 78.7%, respectively. Moreover, RGB and thermal datasets were made publicly available.
Machine Learning Methods for Automatic Silent Speech Recognition Using a Wearable Graphene Strain Gauge Sensor
Authors: Ravenscroft Dafydd , Prattis Ioannis , Kandukuri Tharun , Samad Yarjan Abdul , Mallia Giorgio , Occhipinti Luigi G.
Published: January 2022

Silent speech recognition is the ability to recognise intended speech without audio information. Useful applications can be found in situations where sound waves are not produced or cannot be heard. Examples include speakers with physical voice impairments or environments in which audio transference is not reliable or secure. Developing a device which can detect non-auditory signals and map them to intended phonation could be used to develop a device to assist in such situations. In this work, we propose a graphene-based strain gauge sensor which can be worn on the throat and detect small muscle movements and vibrations. Machine learning algorithms then decode the non-audio signals and create a prediction on intended speech. The proposed strain gauge sensor is highly wearable, utilising graphene’s unique and beneficial properties including strength, flexibility and high conductivity. A highly flexible and wearable sensor able to pick up small throat movements is fabricated by screen printing graphene onto lycra fabric. A framework for interpreting this information is proposed which explores the use of several machine learning techniques to predict intended words from the signals. A dataset of 15 unique words and four movements, each with 20 repetitions, was developed and used for the training of the machine learning algorithms. The results demonstrate the ability for such sensors to be able to predict spoken words. We produced a word accuracy rate of 55% on the word dataset and 85% on the movements dataset. This work demonstrates a proof-of-concept for the viability of combining a highly wearable graphene strain gauge and machine leaning methods to automate silent speech recognition.
Reversible Room Temperature H2 Gas Sensing Based on Self-Assembled Cobalt Oxysulfide
Authors: Zhou Hui , Xu Kai , Ha Nam , Cheng Yinfen , Ou Rui , Ma Qijie , Hu Yihong , Trinh Vien , Ren Guanghui , Li Zhong , Ou Jian Zhen
Published: January 2022

Reversible H2 gas sensing at room temperature has been highly desirable given the booming of the Internet of Things (IoT), zero-emission vehicles, and fuel cell technologies. Conventional metal oxide-based semiconducting gas sensors have been considered as suitable candidates given their low-cost, high sensitivity, and long stability. However, the dominant sensing mechanism is based on the chemisorption of gas molecules which requires elevated temperatures to activate the catalytic reaction of target gas molecules with chemisorbed O, leaving the drawbacks of high-power consumption and poor selectivity. In this work, we introduce an alternative candidate of cobalt oxysulfide derived from the calcination of self-assembled cobalt sulfide micro-cages. It is found that the majority of S atoms are replaced by O in cobalt oxysulfide, transforming the crystal structure to tetragonal coordination and slightly expanding the optical bandgap energy. The H2 gas sensing performances of cobalt oxysulfide are fully reversible at room temperature, demonstrating peculiar p-type gas responses with a magnitude of 15% for 1% H2 and a high degree of selectivity over CH4, NO2, and CO2. Such excellent performances are possibly ascribed to the physisorption dominating the gas–matter interaction. This work demonstrates the great potentials of transition metal oxysulfide compounds for room-temperature fully reversible gas sensing.
Clustering Users to Determine the Most Suitable Gamification Elements
Authors: Blanco-M Alejandro , Contreras-Espinosa Ruth S. , Solé-Casals Jordi
Published: January 2022

The use of gamification elements has extended from being a complement for a product to being integrated into multiple public services to motivate the user. The first drawback for service designers is choosing which gamification elements are appropriate for the intended audience, in addition to the possible incompatibilities between gamification elements. This work proposes a clustering technique that enables mapping different user profiles in relation to their preferred gamification elements. Additionally, by mapping the best cluster for each gamification element, it is possible to determine the preferred game genre. The article answered the following research questions: What is the relationship between the genre of the game and the element of gamification? Different user groups (profiles) for each gamification element? Results indicate that there are cases where the users are divided between those who agree or disagree. However, other elements present a great heterogeneity in the number of groups and the levels of agreement.
The Design and Development of a Ship Trajectory Data Management and Analysis System Based on AIS
Authors: Feng Chengxu , Fu Bing , Luo Yasong , Li Houpu
Published: January 2022

To address the data storage, management, analysis, and mining of ship targets, the object-oriented method was employed to design the overall structure and functional modules of a ship trajectory data management and analysis system (STDMAS). This paper elaborates the detailed design and technical information of the system’s logical structure, module composition, physical deployment, and main functional modules such as database management, trajectory analysis, trajectory mining, and situation analysis. A ship identification method based on the motion features was put forward. With the method, ship trajectory was first partitioned into sub-trajectories in various behavioral patterns, and effective motion features were then extracted. Machine learning algorithms were utilized for training and testing to identify many types of ships. STDMAS implements such functions as database management, trajectory analysis, historical situation review, and ship identification and outlier detection based on trajectory classification. STDMAS can satisfy the practical needs for the data management, analysis, and mining of maritime targets because it is easy to apply, maintain, and expand.