bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantifying Temperate Forest Diversity by Integrating GEDI LiDAR and Multi-Temporal Sentinel-2 Imagery
Authors: Ren Chunying , Jiang Hailing , Xi Yanbiao , Liu Pan , Li Huiying
Published: January 2023

Remotely sensed estimates of forest diversity have become increasingly important in assessing anthropogenic and natural disturbances and their effects on biodiversity under limited resources. Whereas field inventories and optical images are generally used to estimate forest diversity, studies that combine vertical structure information and multi-temporal phenological characteristics to accurately quantify diversity in large, heterogeneous forest areas are still lacking. In this study, combined with regression models, three different diversity indices, namely Simpson (λ), Shannon (H′), and Pielou (J′), were applied to characterize forest tree species diversity by using GEDI LiDAR data and Sentinel-2 imagery in temperate natural forest, northeast China. We used Mean Decrease Gini (MDG) and Boosted Regression Tree (BRT) to assess the importance of certain variables including monthly spectral bands, vegetation indices, foliage height diversity (FHD), and plant area index (PAI) of growing season and non-growing seasons (68 variables in total). We produced 12 forest diversity maps on three different diversity indices using four regression algorithms: Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Lasso Regression (LR). Our study concluded that the most important variables are FHD, NDVI, NDWI, EVI, short-wave infrared (SWIR) and red-edge (RE) bands, especially in the growing season (May and June). In terms of algorithms, the estimation accuracies of the RF (averaged R2 = 0.79) and SVM (averaged R2 = 0.76) models outperformed the other models (R2 of KNN and LR are 0.68 and 0.57, respectively). The study demonstrates the accuracy of GEDI LiDAR data and multi-temporal Sentinel-2 images in estimating forest diversity over large areas, advancing the capacity to monitor and manage forest ecosystems.
Performance Comparison of Deterministic and Stochastic Modifications in Stokes’s and Hotine’s Formulas: The Case of Jilin Province, China
Authors: Wu Qiong , Zhang Guoyu , Wang Bin , Zhong Linshan , Xiao Feng
Published: January 2023

The high-precision regional geoid model provides important fundamental geospatial information for developing and applying many disciplines. Deterministic and stochastic modifications are applied to Stokes’s and Hotine’s formulas of geoid modeling to reduce errors. Based on the Experimental Geopotential Model 2019 (XGM2019), this paper used Stokes’s and Hotine’s formulas to analyze the variation of global root mean square error (RMSE) with modification parameters for two deterministic (Wong and Gore; and Vaníček and Kleusberg) and three stochastic modifications (biased, unbiased, and optimum). Taking the quasigeoid refinement of Jilin Province as an example, the global RMSE, approximate geoid undulation, and additive corrections were calculated. The parameter analysis and the global RMSE calculation showed that the variation of the modification limits and the terrestrial gravity data error variance had a centimeter-level effect on the global RMSE. In contrast, the impact of the integration radius was relatively small. The stochastic modifications were better than the deterministic ones in calculating the global RMSE. The global RMSE of Hotine’s formula was smaller than that of Stokes’s, and its unbiased and optimum modifications reached the minimum value of 12.9 mm. The validation of XGM2019 and the refined quasigeoid based on the high accuracy GPS/leveling points showed that the standard deviation (STD) of XGM2019 was 5.8 cm in Jilin Province, and the refined optimal quasigeoid model was 2.9 cm. Stokes’s and Hotine’s formulas provided the same accuracy in the study area. In the western plain area, the accuracy of the deterministic modifications was 2.0 cm, which was about 0.4 cm higher than that of the stochastic modifications. In the eastern mountainous area, the stochastic modifications were better than the deterministic ones, and the accuracy was about 3.2 cm. Stokes’s and Hotine’s formulas based on deterministic and stochastic modifications significantly improve the accuracy of the XGM2019. The deterministic and stochastic modifications show millimeter-level differences in plain and mountainous areas.
On-Orbit Relative Radiometric Calibration of the Bayer Pattern Push-Broom Sensor for Zhuhai-1 Video Satellites
Authors: Li Litao , Li Zhen , Wang Zhixin , Jiang Yonghua , Shen Xin , Wu Jiaqi
Published: January 2023

The two video satellites of the second and third batch of Zhuhai-1 microsatellites (referred to as OVS-2A/3A) are operational with their hyperspectral satellites, which improves the data acquisi-tion capability of the Zhuhai-1 remote sensing satellite constellation. Contrary to the linear array push-broom hyperspectral satellites and plane array CCD video satellites, the OVS satellite is equipped with a planar array Bayer pattern sensor, which can obtain single-band grayscale images by push-broom imaging. Additionally, the Bayer color reconstruction algorithm can interpolate sensor data to provide RGB color band information. Therefore, for the Bayer pattern push-broom sensor, the relative calibration method of linear push-broom or array cameras cannot be directly applied. The radiometric calibration of the Bayer pattern push-broom imaging mode has become a matter of concern; therefore, this study developed a radiometric calibration method for the Bayer pattern push-broom sensor of the OVS satellite and verified its effectiveness and accuracy. OVS images were used to perform on-orbit relative radiometric calibration, and the calibration accu-racy, including streaking metrics and root-mean-square error, was better than 1%, meeting the specification requirements for the OVS satellite. Visually, after calibration correction, the streaking and striping noise of the Bayer images was removed, and the radiometric quality of the image was considerably improved, providing a good data basis for subsequent research in remote sensing applications.
RCCT-ASPPNet: Dual-Encoder Remote Image Segmentation Based on Transformer and ASPP
Authors: Li Yazhou , Cheng Zhiyou , Wang Chuanjian , Zhao Jinling , Huang Linsheng
Published: January 2023

Remote image semantic segmentation technology is one of the core research elements in the field of computer vision and has a wide range of applications in production life. Most remote image semantic segmentation methods are based on CNN. Recently, Transformer provided a view of long-distance dependencies in images. In this paper, we propose RCCT-ASPPNet, which includes the dual-encoder structure of Residual Multiscale Channel Cross-Fusion with Transformer (RCCT) and Atrous Spatial Pyramid Pooling (ASPP). RCCT uses Transformer to cross fuse global multiscale semantic information; the residual structure is then used to connect the inputs and outputs. ASPP based on CNN extracts contextual information of high-level semantics from different perspectives and uses Convolutional Block Attention Module (CBAM) to extract spatial and channel information, which will further improve the model segmentation ability. The experimental results show that the mIoU of our method is 94.14% and 61.30% on the datasets Farmland and AeroScapes, respectively, and that the mPA is 97.12% and 84.36%, respectively, both outperforming DeepLabV3+ and UCTransNet.
Multi-Dimensional Evaluation of Ecosystem Health in China’s Loess Plateau Based on Function-Oriented Metrics and BFAST Algorithm
Authors: Li Xiaoyue , Liu Xiangnan , Hou Bowen , Tian Lingwen , Yang Qin , Zhu Lihong , Meng Yuanyuan
Published: January 2023

China’s Loess Plateau (CLP) is a typical semi-arid region and is very sensitive to climate and human activity. Under the ecological restoration project, vegetation coverage increased significantly, but the limitation of climate and other factors has meant that vegetation mortality was relatively high. Therefore, it is of great significance to evaluate the ecosystem health in the CLP in terms of the sustainability of ecological restoration projects. The aim of this study is to propose a multi-dimensional assessment method to investigate vegetation health changes in the CLP based on BFAST and BFAST01 algorithms. To achieve this, we constructed local dimension health indexes (the number of disturbances and recovery rate) and overall dimension health indexes (trend types) based on the gross primary productivity (GPP) and vegetation evapotranspiration (Ec) data of the study area from 2001 to 2020 which was collected from the Google Earth Engine (GEE) platform. The result revealed the following. More than 90% of disturbance pixels of GPP and Ec in the short-term change only once and more than 60% of pixels recover after disturbance. However, the recovery rate after disturbance is slow, and the interval with the largest proportion is 0–0.00015. The long-term trend mostly exhibited a monotonic increasing trend. These results indicate that the function of the ecosystem on the CLP has been improved, but the resilience of vegetation is weak. In conclusion, the combination of the local dimension and overall dimension analysis can comprehensively reveal information about the CLP’s vegetation health in the past two decades, and that the method will open new perspectives and generate new knowledge about vegetation health in the CLP.
Aerosol Retrieval Study from a Particulate Observing Scanning Polarimeter Onboard Gao-Fen 5B without Prior Surface Knowledge, Based on the Optimal Estimation Method
Authors: Fan Yizhe , Sun Xiaobing , Ti Rufang , Huang Honglian , Liu Xiao , Yu Haixiao
Published: January 2023

To meet the demand for the aerosol detection of single-angle and multi-band polarization instrument containing short-wave infrared bands, an inversion algorithm that makes full use of multi-band intensity and polarization information is proposed based on optimal estimation theory. This method uses the polarization information in the short-wave infrared band to perform surface and atmosphere decoupling without a prior information on the surface. This obtains the initial value of the aerosol, and then it uses the scalar information to obtain the final result. Moreover, the multi-band information of the instrument is used for decoupling the surface and atmospheric information, which avoids the inversion error caused by the untimely update of the surface reflectance database and the error of spatio-temporal matching. The measured data of the Particulate Observing Scanning Polarimeter (POSP) are used to test the proposed algorithm. Firstly, to verify the effectiveness of the algorithm under different surface conditions, four regions with large geographical differences (Beijing, Hefei, Baotou, and Taiwan) are selected for aerosol optical depth (AOD) inversion, and they are compared with the aerosol robotic network (AERONET) products of the nearby stations. The validation against the AERONET products produces high correlation coefficients of 0.982, 0.986, 0.718, and 0.989, respectively, which verifies the effectiveness of the algorithm in different regions. Further, we analyzed the effectiveness of the proposed algorithm under different pollution conditions. Regions with AOD >0.7 and AOD < 0.7 are screened by using the AOD products of the Moderate-Resolution Imaging Spectroradiomete (MODIS), and the AOD of the corresponding region is inverted using POSP data. It was found to be spatially consistent with the MODIS products. The correlation coefficient and root mean square error (RMSE) in the AOD high region were 0.802 and 0.217, respectively, and 0.944 and 0.022 in the AOD low region, respectively, which verified the effectiveness of the proposed algorithm under different pollution conditions.
HY-1C/D CZI Image Atmospheric Correction and Quantifying Suspended Particulate Matter
Authors: Luo Wei , Li Renhu , Shen Fang , Liu Jianqiang
Published: January 2023

HY-1C/D both carry a coastal zone imager (CZI) with a spatial resolution of 50 m and a swath width of 950 km, two observations can be achieved in three days when two satellites operating in a network. Accurate atmospheric correction is the basis for quantitative inversion of ocean color parameters using CZI However, atmospheric correction in estuarine and coastal waters with complex optical properties is a challenge due to the band setting of CZI. This paper proposed a novel atmospheric correction algorithm for CZI images applicable to turbid waters in estuarine and coastal zone. The Rayleigh scattering reflectance of CZI was calculated based on a vector radiative transfer model. Next, a semi-empirical radiative transfer model with suspended particle concentration as the parameter is used to model the water-atmosphere coupling. Finally, the parameters of the coupling model are solved by combining a global optimization method based on a genetic algorithm. The results indicate that the CZI-derived remote-sensing reflectance (Rrs) are in good agreement with the quasi-synchronous Landsat-8/9 operational land imager (OLI) derived Rrs in the green and red bands (R2 > 0.96). Validation using in situ data revealed that the RMSE of the CZI-derived Rrs in the green and red bands was 0.0036 sr−1 and 0.0035 sr−1. More importantly, the values and spatial distributions of suspended particulate matter (SPM) estimated by CZI and those estimated by OLI in the Subei Shoal and the Yangtze River Estuary are basically consistent, and the validation using in situ data revealed that the inversion of SPM concentration by CZI was effective (R2 = 0.86, RMSE = 0.0362 g/L), indicating that CZI has great potential and broad application prospects for monitoring the spatial and temporal dynamics of SPM in estuarine and coastal waters. The study results will lay the foundation for further estimating suspended sediment fluxes and carbon fluxes, thus providing data support and scientific basis for promoting resource development, utilization and conservation strategies in estuarine and coastal areas.
Estimation of Moist Atmospheric Profiles from Refraction and Attenuation Measurements by Using Centimeter and Millimeter Wave Links between LEO Satellites
Authors: Zhang Zhihua , Wang Xin , Lyu Daren
Published: January 2023

Microwave occultation using centimeter and millimeter wave links between low Earth orbit (LEO) satellites provide a potential way to estimate the moist atmospheric profiles based on refraction and attenuation measurements, which is called the LEO-LEO microwave occultation (LMO) technique. It has not yet been implemented in orbit. In this paper, we analyzed the attenuation properties at different heights of centimeter and millimeter waves based on simulations. The observing capabilities with different frequency combinations at the X, K, and M bands were analyzed. The results show that LMO may improve the retrieval accuracy of bending angles above 35 km. By using several appropriate frequencies at the X+K+M band, water vapor profiles from the near-surface to the lower stratosphere (~24 km) can be obtained. When the M-band frequencies were added, the temperature retrieval accuracy does not change obviously, but the accuracy of water vapor retrieval can significantly improve above 15 km, especially at about 17–24 km, and the RMS errors decrease from over 20% to less 10%. For promoting the LMO mission in the real world, a frequency combination at the X+K band is proposed, which can provide the potential to observe the temperature profiles at about 2.5–50 km and water vapor profiles at about 2.5–15 km accurately under clear and cloudy conditions. This study demonstrates that LMO can greatly extend the capabilities of the radio occultation technique and improve our ability to measure the moist atmospheric profiles globally.
FERA-Net: A Building Change Detection Method for High-Resolution Remote Sensing Imagery Based on Residual Attention and High-Frequency Features
Authors: Xu Xuwei , Zhou Yuan , Lu Xiechun , Chen Zhanlong
Published: January 2023

Buildings can represent the process of urban development, and building change detection can support land use management and urban planning. However, existing building change detection models are unable to extract multi-scale building features effectively or fully utilize the local and global information of the feature maps, such as building edges. These defections affect the detection accuracy and may restrict further applications of the models. In this paper, we propose the feature-enhanced residual attention network (FERA-Net) to improve the performance of the ultrahigh-resolution remote sensing image change detection task. The FERA-Net is an end-to-end network with a U-shaped encoder–decoder structure. The Siamese network is used as the encoder with an attention-guided high-frequency feature extraction module (AGFM) extracting building features and enriching detail information, and the decoder applies a feature-enhanced skip connection module (FESCM) to aggregate the enhanced multi-level differential feature maps and gradually recover the change feature maps in this structure. The FERA-Net can generate predicted building change maps by the joint supervision of building change information and building edge information. The performance of the proposed model is tested on the WHU-CD dataset and the LEVIR-CD dataset. The experimental results show that our model outperforms the state-of-the-art models, with 93.51% precision and a 92.48% F1 score on the WHU-CD dataset, and 91.57% precision and an 89.58% F1 score on the LEVIR-CD dataset.
An In-Vehicle Smartphone RTK/DR Positioning Method Combined with OSM Road Network
Authors: Wang Fuyou , Gao Chengfa , Shang Rui , Zhang Ruicheng , Gan Lu , Liu Qi , Wang Jianchao
Published: January 2023

In vehicle navigation scenarios, the RTK positioning results of smartphones are prone to jumps due to the interference of complex urban environments, and the heading angle of dead reckoning (DR) is prone to divergence. In order to obtain more stable and high-precision smartphone positioning results, this paper proposes an RTK/DR positioning method combined with the OpenStreetMap road network. The OpenStreetMap road network data are used to correct the heading angle during the linear motion phase to improve heading angle accuracy. In order to reduce the impact of RTK results jumping on subsequent DR, it is possible to set up a measurement update switch, which combines the RTK covariance matrix, vehicle motion state, and RTK heading angle change information to determine whether to perform a measurement update. The research uses two smartphones to carry out four vehicle positioning tests. The eight sets of test results show that the heading angle correction method based on the OpenStreetMap road network can effectively control the accumulation of heading angle errors and allow DR trajectory to be more consistent with the benchmark. Compared with RTK, the forward accuracy of RTK/DR positioning method is almost unchanged, even though the direction accuracy and lateral positioning accuracy are significantly improved. The RTK/DR horizontal positioning accuracy of both smartphones is approximately 1.3 m, which is better rather than the RTK results. The proposed RTK/DR positioning method can obtain more reliable orientation and position information than RTK.
Flood Frequency Analysis Using Mixture Distributions in Light of Prior Flood Type Classification in Norway
Authors: Yan Lei , Zhang Liying , Xiong Lihua , Yan Pengtao , Jiang Cong , Xu Wentao , Xiong Bin , Yu Kunxia , Ma Qiumei , Xu Chong-Yu
Published: January 2023

The fundamental assumption of flood frequency analysis is that flood samples are generated by the same flood generation mechanism (FGM). However, flood events are usually triggered by the interaction of meteorological factors and watershed properties, which results in different FMGs. To solve this problem, researchers have put forward traditional two-component mixture distributions (TCMD-T) without clearly linking each component distribution to an explicit FGM. In order to improve the physical meaning of mixture distributions in seasonal snow-covered areas, the ratio of rainfall to flood volume (referred to as rainfall–flood ratio, RF) method was used to classify distinct FGMs. Thus, the weighting coefficient of each component distribution was determined in advance in the rainfall–flood ratio based TCMD (TCMD-RF). TCMD-RF model was applied to 34 basins in Norway. The results showed that flood types can be clearly divided into rain-on-snow-induced flood, snowmelt-induced flood and rainfall-induced flood. Moreover, the design flood and associated uncertainties were also estimated. It is found that TCMD-RF model can reduce the uncertainties of design flood by 20% compared with TCMD-T. The superiority of TCMD-RF is attributed to its clear classification of FGMs, thus determining the weighting coefficients without optimization and simplifying the parameter estimation procedure of mixture distributions.
Differential Spatiotemporal Patterns of CO2 Emissions in Eastern China’s Urban Agglomerations from NPP/VIIRS Nighttime Light Data Based on a Neural Network Algorithm
Authors: Zhou Lei , Song Jun , Chi Yonggang , Yu Quanzhou
Published: January 2023

Urban agglomerations, such as Beijing-Tianjin-Hebei Region, Yangtze River Delta and Pearl River Delta, are the key regions for energy conservation, carbon emission reduction and low-carbon development in China. However, spatiotemporal patterns of CO2 emissions at fine scale in these major urban agglomerations are not well documented. In this study, a back propagation neural network based on genetic algorithm optimization (GABP) coupled with NPP/VIIRS nighttime light datasets was established to estimate the CO2 emissions of China’s three major urban agglomerations at 500 m resolution from 2014 to 2019. The results showed that spatial patterns of CO2 emissions presented three-core distribution in the Beijing-Tianjin-Hebei Region, multiple-core distribution in the Yangtze River Delta, and null-core distribution in the Pearl River Delta. Temporal patterns of CO2 emissions showed upward trends in 28.74–43.99% of the total areas while downward trends were shown in 13.47–15.43% of the total areas in three urban agglomerations. The total amount of CO2 emissions in urban areas was largest among urban circles, followed by first-level urban circles and second-level urban circles. The profiles of CO2 emissions along urbanization gradients featured high peaks and wide ranges in large cities, and low peaks and narrow ranges in small cities. Population density primarily impacted the spatial pattern of CO2 emissions among urban agglomerations, followed by terrain slope. These findings suggested that differences in urban agglomerations should be taken into consideration in formulating emission reduction policies.
A CNN-LSTM Car-Following Model Considering Generalization Ability
Authors: Qin Pinpin , Li Hao , Li Ziming , Guan Weilai , He Yuxin
Published: January 2023

To explore the potential relationship between the leading vehicle and the following vehicle during car-following, we proposed a novel car-following model combining a convolutional neural network (CNN) with a long short-term memory (LSTM) network. Firstly, 400 car-following periods were extracted from the natural driving database and the OpenACC car-following experiment database. Then, we developed a CNN-LSTM car-following model, and the CNN is employed to analyze the potential relationship between the vehicle’s dynamic parameters and to extract the features of car-following behavior to generate the feature vector. The LSTM network is adopted to save the feature vector and predict the speed of the following vehicle. Finally, the CNN-LSTM model is trained and tested with the extracted car-following trajectories data and compared with the classical car-following models (LSTM model, intelligent driver model). The results show that the accuracy and the ability to learn the heterogeneity of the proposed model are better than the other two. Furthermore, the CNN-LSTM model can accurately reproduce the hysteresis phenomenon of congested traffic flow and apply to heterogeneous traffic flow mixed with adaptive cruise control vehicles on the freeway, which indicates that it has strong generalization ability.
VisdaNet: Visual Distillation and Attention Network for Multimodal Sentiment Classification
Authors: Hou Shangwu , Tuerhong Gulanbaier , Wushouer Mairidan
Published: January 2023

Sentiment classification is a key task in exploring people’s opinions; improved sentiment classification can help individuals make better decisions. Social media users are increasingly using both images and text to express their opinions and share their experiences, instead of only using text in conventional social media. As a result, understanding how to fully utilize them is critical in a variety of activities, including sentiment classification. In this work, we provide a fresh multimodal sentiment classification approach: visual distillation and attention network or VisdaNet. First, this method proposes a knowledge augmentation module, which overcomes the lack of information in short text by integrating the information of image captions and short text; secondly, aimed at the information control problem in the multi-modal fusion process in the product review scene, this paper proposes a knowledge distillation based on the CLIP module to reduce the noise information of the original modalities and improve the quality of the original modal information. Finally, regarding the single-text multi-image fusion problem in the product review scene, this paper proposes visual aspect attention based on the CLIP module, which correctly models the text-image interaction relationship in special scenes and realizes feature-level fusion across modalities. The results of the experiment on the Yelp multimodal dataset reveal that our model outperforms the previous SOTA model. Furthermore, the ablation experiment results demonstrate the efficacy of various tactics in the suggested model.
Inter-Satellite Cooperative Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite–Terrestrial Networks
Authors: Tong Minglei , Li Song , Wang Xiaoxiang , Wei Peng
Published: January 2023

Mobile edge computing (MEC)-enabled satellite–terrestrial networks (STNs) can provide task computing services for Internet of Things (IoT) devices. However, since some applications’ tasks require huge amounts of computing resources, sometimes the computing resources of a local satellite’s MEC server are insufficient, but the computing resources of neighboring satellites’ MEC servers are redundant. Therefore, we investigated inter-satellite cooperation in MEC-enabled STNs. First, we designed a system model of the MEC-enabled STN architecture, where the local satellite and the neighboring satellites assist IoT devices in computing tasks through inter-satellite cooperation. The local satellite migrates some tasks to the neighboring satellites to utilize their idle resources. Next, the task completion delay minimization problem for all IoT devices is formulated and decomposed. Then, we propose an inter-satellite cooperative joint offloading decision and resource allocation optimization scheme, which consists of a task offloading decision algorithm based on the Grey Wolf Optimizer (GWO) algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method. The optimal solution is obtained by continuous iterations. Finally, simulation results demonstrate that the proposed scheme achieves relatively better performance than other baseline schemes.
Performance Evaluation of Zone-Based In-Vehicle Network Architecture for Autonomous Vehicles
Authors: Park Chulsun , Park Sungkwon
Published: January 2023

In recent years, various functions such as advanced driver assistance systems (ADAS) and infotainment systems are being mounted in vehicles for safety and convenience to drivers. Among the various functions, autonomous driving-related technologies are being added to all vehicles, from low options to high options. For autonomous driving, hundreds of new electronic control units (ECUs) including various advanced sensors would be needed. Adding more ECUs would enhance safety and convenience for the driver. On the other hand, wiring between these ECUs would be more complex and heavier. The wiring harness is essential for communication and power supply. Currently, the in-vehicle network (IVN) uses the domain-based IVN architecture (DIA) that separates ECUs into domains based on their functions. Recently, in order to minimize the complexity of wiring harness and IVN, zone-based IVN architecture (ZIA) that groups ECUs according to their physical locations is attracting attention. In this paper, we propose a new DIA and ZIA for autonomous driving in the context of time-sensitive networking (TSN). These two new IVN architectures are simulated using the OMNeT++ network simulator. In the simulation process, a mid-size vehicle is assumed. It is shown in this paper that ZIA not only reduces wiring harnesses in both lengths and weights by approximately 24.6% compared to the DIAs, but also reduces data transmission delay.
Planning of Medical Flexible Needle Motion in Effective Area of Clinical Puncture
Authors: Feng Shuai , Wang Shigang , Jiang Wanxiong , Gao Xueshan
Published: January 2023

Lung cancer is the leading cause of cancer deaths worldwide. Although several lung cancer diagnostic methods are available for lung nodule biopsy, there are limitations in terms of accuracy, safety, and invasiveness. Transbronchial needle aspiration (TBNA) is a common method for diagnosing and treating lung cancer that involves a robot-assisted medical flexible needle moving along a curved three-dimensional trajectory, avoiding anatomical barriers to achieve clinically meaningful goals in humans. Inspired by the puncture angle between the needle tip and the vessel in venipuncture, we suggest that different orientations of the medical flexible needle puncture path affect the cost of the puncture trajectory and propose an effective puncture region based on the optimal puncture direction, which is a strategy based on imposing geometric constraints on the search space of the puncture direction, and based on this, we focused on the improved implementation of RCS*. Planning within the TBNA-based lung environment was performed using the rapidly exploring random tree (RRT), resolution-complete search (RCS), and RCS* (a resolution-optimal version of RCS) within an effective puncture region. The experimental results show that the optimal puncture direction corresponding to the lowest cost puncture trajectory is consistent among the three algorithms and RCS* is more efficient for planning. The experiments verified the feasibility and practicality of our proposed minimum puncture angle and puncture effective region and facilitated the study of the puncture direction of flexible needle puncture.
Specular Surface Shape Measurement with Orthogonal Dual-Frequency Fourier Transform Deflectometry
Authors: Li Zhiming , Yin Dayi , Yang Yuanyu , Zhang Quan , Gong Huixing
Published: January 2023

Three-dimensional (3D) shape measurement for specular surfaces is becoming increasingly important in various applications. A novel orthogonal dual-frequency fringe is proposed in the specular surface shape measurement to overcome the phase jumping and discontinuities in spatial phase unwrapping. The fringe recalibrated high-accuracy phase information from its high-frequency fringe component with low-ambiguity phase information from its low-frequency fringe component. An improved Fourier transform deflectometry method based on the orthogonal dual-frequency fringe is proposed to measure 3D specular surface shapes. Simulation results showed that the orthogonal dual-frequency Fourier transform deflectometry (ODD) method could precisely reconstruct flat surfaces with an error of 2.16 nm rms, and concave surfaces with an error of 1.86 μm rms. Experimental results showed that the reconstructed shapes of both the flat mirror and the concave mirror measured by the ODD measurement system were highly comparable to those obtained by the phase-measuring deflectometry (PMD) method. This new fringe provides a distinctive approach to structured pattern construction and reduces the phase unwrapping ambiguities in specular surface shape measurement. The ODD method can achieve accurate 3D shape measurement for specular surfaces by sampling only one fringe, providing a possible basis for future real-time measurement of specular surfaces.
A New Method of Identifying Characteristic Points in the Impedance Cardiography Signal Based on Empirical Mode Decomposition
Authors: Trybek Paulina , Sobotnicka Ewelina , Wawrzkiewicz-Jałowiecka Agata , Machura Łukasz , Feige Daniel , Sobotnicki Aleksander , Richter-Laskowska Monika
Published: January 2023

The accurate detection of fiducial points in the impedance cardiography signal (ICG) has a decisive impact on the proper estimation of diagnostic parameters such as stroke volume or cardiac output. It is, therefore, necessary to find an algorithm that is able to assess their positions with great precision. The solution to this problem is, however, quite challenging with regard to the high sensitivity of the ICG technique to the noise and varying morphology of the acquired signals. The aim of this study is to propose a novel method that allows us to overcome these limitations. The developed algorithm is based on Empirical Mode Decomposition (EMD)—an effective technique for processing and analyzing various types of non-stationary signals. We find high correlations between the results obtained from the algorithm and annotated by an expert. This, in turn, implies that the difference in estimation of the diagnostic-relevant parameters is small, which suggests that the method can automatically provide precise clinical information.
Spherical Wrist Manipulator Local Planner for Redundant Tasks in Collaborative Environments
Authors: Chiurazzi Marcello , Alcaide Joan Ortega , Diodato Alessandro , Menciassi Arianna , Ciuti Gastone
Published: January 2023

Standard industrial robotic manipulators use well-established high performing technologies. However, such manipulators do not guarantee a safe Human–Robot Interaction (HRI), limiting their usage in industrial and medical applications. This paper proposes a novel local path planner for spherical wrist manipulators to control the execution of tasks where the manipulator number of joints is redundant. Such redundancy is used to optimize robot motion and dexterity. We present an intuitive parametrization of the end-effector (EE) angular motion, which decouples the rotation of the third joint of the wrist from the rest of the angular motions. Manipulator EE motion is controlled through a decentralized linear system with closed-loop architecture. The local planner integrates a novel collision avoidance strategy based on a potential repulsive vector applied to the EE. Contrary to classic potential field approaches, the collision avoidance algorithm considers the entire manipulator surface, enhancing human safety. The local path planner is simulated in three generic scenarios: (i) following a periodic reference, (ii) a random sequence of step signal references, and (iii) avoiding instantly introduced obstacles. Time and frequency domain analysis demonstrated that the developed planner, aside from better parametrizing redundant tasks, is capable of successfully executing the simulated paths (max error = 0.25°) and avoiding obstacles.
High-Accuracy Three-Dimensional Deformation Measurement System Based on Fringe Projection and Speckle Correlation
Authors: Zhang Chuang , Liu Cong , Xu Zhihong
Published: January 2023

Fringe projection profilometry (FPP) and digital image correlation (DIC) are widely applied in three-dimensional (3D) measurements. The combination of DIC and FPP can effectively overcome their respective shortcomings. However, the speckle on the surface of an object seriously affects the quality and modulation of fringe images captured by cameras, which will lead to non-negligible errors in the measurement results. In this paper, we propose a fringe image extraction method based on deep learning technology, which transforms speckle-embedded fringe images into speckle-free fringe images. The principle of the proposed method, 3D coordinate calculation, and deformation measurements are introduced. Compared with the traditional 3D-DIC method, the experimental results show that this method is effective and precise.
A Knowledge-Grounded Task-Oriented Dialogue System with Hierarchical Structure for Enhancing Knowledge Selection
Authors: Lee Hayoung , Jeong Okran
Published: January 2023

For a task-oriented dialogue system to provide appropriate answers to and services for users’ questions, it is necessary for it to be able to utilize knowledge related to the topic of the conversation. Therefore, the system should be able to select the most appropriate knowledge snippet from the knowledge base, where external unstructured knowledge is used to respond to user requests that cannot be solved by the internal knowledge addressed by the database or application programming interface. Therefore, this paper constructs a three-step knowledge-grounded task-oriented dialogue system with knowledge-seeking-turn detection, knowledge selection, and knowledge-grounded generation. In particular, we propose a hierarchical structure of domain-classification, entity-extraction, and snippet-ranking tasks by subdividing the knowledge selection step. Each task is performed through the pre-trained language model with advanced techniques to finally determine the knowledge snippet to be used to generate a response. Furthermore, the domain and entity information obtained because of the previous task is used as knowledge to reduce the search range of candidates, thereby improving the performance and efficiency of knowledge selection and proving it through experiments.
Robotic Technology in Foot and Ankle Surgery: A Comprehensive Review
Authors: Stauffer Taylor P. , Kim Billy I. , Grant Caitlin , Adams Samuel B. , Anastasio Albert T.
Published: January 2023

Recent developments in robotic technologies in the field of orthopaedic surgery have largely been focused on higher volume arthroplasty procedures, with a paucity of attention paid to robotic potential for foot and ankle surgery. The aim of this paper is to summarize past and present developments foot and ankle robotics and describe outcomes associated with these interventions, with specific emphasis on the following topics: translational and preclinical utilization of robotics, deep learning and artificial intelligence modeling in foot and ankle, current applications for robotics in foot and ankle surgery, and therapeutic and orthotic-related utilizations of robotics related to the foot and ankle. Herein, we describe numerous recent robotic advancements across foot and ankle surgery, geared towards optimizing intra-operative performance, improving detection of foot and ankle pathology, understanding ankle kinematics, and rehabilitating post-surgically. Future research should work to incorporate robotics specifically into surgical procedures as other specialties within orthopaedics have done, and to further individualize machinery to patients, with the ultimate goal to improve perioperative and post-operative outcomes.
0.5 V Versatile Voltage- and Transconductance-Mode Analog Filter Using Differential Difference Transconductance Amplifier
Authors: Kulej Tomasz , Kumngern Montree , Khateb Fabian , Arbet Daniel
Published: January 2023

In this work, a new versatile voltage- and transconductance-mode analog filter is proposed. The filter, without requiring resistors, employs three differential-difference transconductance amplifiers (DDTAs) and two grounded capacitors, which is suitable for integrated circuit implementation. Unlike previous works, the proposed filter topology provides: (1) high-input and low-output impedances for a voltage-mode (VM) analog filter, that is desirable in a cascade method of realizing higher order filters, and (2) high-input and high-output impedances for a transconductance-mode (TM) analog filter without any circuit modification. Moreover, a quadrature oscillator is obtained by simply adding a feedback connection. Both VM and TM filters provide five standard filtering responses such as low-pass, high-pass, band-pass, band-stop and all-pass responses into single topology. The natural frequency and the condition of oscillation can be electronically controlled. The circuit operates with 0.5 V supply voltage. It was designed and simulated in the Cadence program using 0.18 µm CMOS technology from TSMC.
Improving Generalizability of Spectral Reflectance Reconstruction Using L1-Norm Penalization
Authors: Yao Pengpeng , Wu Hochung , Xin John H.
Published: January 2023

Spectral reflectance reconstruction for multispectral images (such as Weiner estimation) may perform sub-optimally when the object being measured has a texture that is not in the training set. The accuracy of the reconstruction is significantly lower without training samples. We propose an improved reflectance reconstruction method based on L1-norm penalization to solve this issue. Using L1-norm, our method can provide the transformation matrix with the favorable sparse property, which can help to achieve better results when measuring the unseen samples. We verify the proposed method by reconstructing spectral reflection for four types of materials (cotton, paper, polyester, and nylon) captured by a multispectral imaging system. Each of the materials has its texture and there are 204 samples in each of the materials/textures in the experiments. The experimental results show that when the texture is not included in the training dataset, L1-norm can achieve better results compared with existing methods using colorimetric measure (i.e., color difference) and shows consistent accuracy across four kinds of materials.
Cardiac Magnetic Resonance Image Segmentation Method Based on Multi-Scale Feature Fusion and Sequence Relationship Learning
Authors: Qi Yushi , Hu Chunhu , Zuo Liling , Yang Bo , Lv Youlong
Published: January 2023

Accurate segmentation of the left atrial structure using magnetic resonance images provides an important basis for the diagnosis of atrial fibrillation (AF) and its treatment using robotic surgery. In this study, an image segmentation method based on sequence relationship learning and multi-scale feature fusion is proposed for 3D to 2D sequence conversion in cardiac magnetic resonance images and the varying scales of left atrial structures within different slices. Firstly, a convolutional neural network layer with an attention module was designed to extract and fuse contextual information at different scales in the image, to strengthen the target features using the correlation between features in different regions within the image, and to improve the network’s ability to distinguish the left atrial structure. Secondly, a recurrent neural network layer oriented to two-dimensional images was designed to capture the correlation of left atrial structures in adjacent slices by simulating the continuous relationship between sequential image slices. Finally, a combined loss function was constructed to reduce the effect of positive and negative sample imbalance and improve model stability. The Dice, IoU, and Hausdorff distance values reached 90.73%, 89.37%, and 4.803 mm, respectively, based on the LASC2013 (left atrial segmentation challenge in 2013) dataset; the corresponding values reached 92.05%, 89.41% and 9.056 mm, respectively, based on the ASC2018 (atrial segmentation challenge at 2018) dataset.
Application of Optical Communication for an Enhanced Health and Safety System in Underground Mine
Authors: Stoicuta Olimpiu , Riurean Simona , Burian Sorin , Leba Monica , Ionica Andreea
Published: January 2023

The continuous monitoring systems developed for health and safety provisions in underground mines have always been a deep concern in mining activity, since this is one of the most dangerous work activities in the world. All mining activities must follow strict regulations that pose an increased responsibility for the design and implementation of novel technologies aiming to enhance both the workers’ and equipment’s safety in underground mines. Coal mines are some of the most dangerous, with high risk of explosion due to pit gas; therefore, we propose the use of visible light communication for local wireless transmission and optical fiber for remote-cabled transmission. We address, in this article, a complete system comprising real-time personnel tracking and environmental methane measurement, as well as the transmission of data, with a detailed explanation of the complete system and technical description with practical solutions applicable for industrial use.
Simulation of Layer Thickness Measurement in Thin Multi-Layered Material by Variable-Focus Laser Ultrasonic Testing
Authors: Qiu Jinxing , Li Zhengying , Pei Cuixiang , Luo Guoqiang
Published: January 2023

Thin multi-layered materials are widely used in key structures of many high technology industries. To ensure the quality and safety of structures, layer thickness measurement by non-destructive testing (NDT) techniques is essential. In this paper, a novel approach for the measurement of each layer’s thickness in thin multi-layered material is proposed by using ring-shaped laser generated focused ultrasonic bulk waves. The proposed method uses a ring-shaped laser with a variable radius to generate shear waves with variable focus inside the structure. By analyzing the signal characteristics at the ring center when the laser radius varies from zero to maximum, the direct measurement of layer thickness can be realized, considering that only when the focal depth and the layer thickness satisfy the specific relationship, the reflected shear waves converge and form a peak at the ring center. This straightforward approach can increase the pulse-echo SNR and prevent the processing of aliasing signals, and therefore provides higher efficiency and accuracy for the layer thickness measurement. In order to investigate the feasibility of this method, finite element simulations were conducted to simulate the ring-shaped laser generated ultrasonic waves in multi-layered structure in detail. Following the principle of the proposed method, the layer thickness of a bi-layer and 3-layer structure were respectively measured using simulation data. The results confirm that the proposed method can accurately and efficiently measure the layer thickness of thin multi-layered material.
Wearable Noninvasive Glucose Sensor Based on CuxO NFs/Cu NPs Nanocomposites
Authors: Yu Zhipeng , Wu Huan , Xu Zhongshuang , Yang Zhimao , Lv Jian , Kong Chuncai
Published: January 2023

Designing highly active material to fabricate a high-performance noninvasive wearable glucose sensor was of great importance for diabetes monitoring. In this work, we developed CuxO nanoflakes (NFs)/Cu nanoparticles (NPs) nanocomposites to serve as the sensing materials for noninvasive sweat-based wearable glucose sensors. We involve CuCl2 to enhance the oxidation of Cu NPs to generate Cu2O/CuO NFs on the surface. Due to more active sites endowed by the CuxO NFs, the as-prepared sample exhibited high sensitivity (779 μA mM−1 cm−2) for noninvasive wearable sweat sensing. Combined with a low detection limit (79.1 nM), high selectivity and the durability of bending and twisting, the CuxO NFs/Cu NPs-based sensor can detect the glucose level change of sweat in daily life. Such a high-performance wearable sensor fabricated by a convenient method provides a facile way to design copper oxide nanomaterials for noninvasive wearable glucose sensors.