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753 results on '"Li, Yuanhao"'

208. Identification of early invisible acute ischemic stroke in non-contrast computed tomography using two-stage deep-learning model

Author

Lu, Jun, primary, Zhou, Yiran, additional, Lv, Wenzhi, additional, Zhu, Hongquan, additional, Tian, Tian, additional, Yan, Su, additional, Xie, Yan, additional, Wu, Di, additional, Li, Yuanhao, additional, Liu, Yufei, additional, Gao, Luyue, additional, Fan, Wei, additional, Nan, Yan, additional, Zhang, Shun, additional, Peng, Xiaolong, additional, Zhang, Guiling, additional, and Zhu, Wenzhen, additional

Published
2022
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218. Individual functional parcellation revealed compensation of dynamic limbic network organization in healthy ageing.

Author

Liu, Tiantian, Shi, Zhongyan, Zhang, Jian, Wang, Kexin, Li, Yuanhao, Pei, Guangying, Wang, Li, Wu, Jinglong, and Yan, Tianyi

Subjects
FUNCTIONAL connectivity, AGE factors in disease, AGING, STATISTICAL correlation
Abstract

Using group‐level functional parcellations and constant‐length sliding window analysis, dynamic functional connectivity studies have revealed network‐specific impairment and compensation in healthy ageing. However, functional parcellation and dynamic time windows vary across individuals; individual‐level ageing‐related brain dynamics are uncertain. Here, we performed individual parcellation and individual‐length sliding window clustering to characterize ageing‐related dynamic network changes. Healthy participants (n = 637, 18–88 years) from the Cambridge Centre for Ageing and Neuroscience dataset were included. An individual seven‐network parcellation, varied from group‐level parcellation, was mapped for each participant. For each network, strong and weak cognitive brain states were revealed by individual‐length sliding window clustering and canonical correlation analysis. The results showed negative linear correlations between age and change ratios of sizes in the default mode, frontoparietal, and salience networks and a positive linear correlation between age and change ratios of size in the limbic network (LN). With increasing age, the occurrence and dwell time of strong states showed inverted U‐shaped patterns or a linear decreasing pattern in most networks but showed a linear increasing pattern in the LN. Overall, this study reveals a compensative increase in emotional networks (i.e., the LN) and a decline in cognitive and primary sensory networks in healthy ageing. These findings may provide insights into network‐specific and individual‐level targeting during neuromodulation in ageing and ageing‐related diseases. [ABSTRACT FROM AUTHOR]

Published
2023
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220. The role of comorbid hypertriglyceridemia and abdominal obesity in the severity of acute pancreatitis: a retrospective study

Author

Yang, Xiaoxi, primary, He, Jiajun, additional, Ma, Shuli, additional, Wang, Tingting, additional, Zhu, Quping, additional, Cao, Fei, additional, Li, Yuanhao, additional, Yang, Chuting, additional, Chen, Chaowu, additional, Lu, Guotao, additional, Hu, Lianghao, additional, Liu, Jun, additional, and Chen, Weiwei, additional

Published
2021
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228. Intelligent Chinese calligraphy beautification from handwritten characters for robotic writing

Author

Li Yuanhao, Zhiyi Zhang, Xinyue Zhang, Shaojun Hu, and Kouichi Konno

Subjects
Focus (computing), Engineering drawing, Computer science, Character (computing), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Touchpad, Computer Graphics and Computer-Aided Design, Sketch, Computer graphics, Calligraphy, Handwriting, Computer Vision and Pattern Recognition, Robotic arm, Software
Abstract

Chinese calligraphy is the artistic expression of character writing and is highly valued in East Asia. However, it is a challenge for non-expert users to write visually pleasing calligraphy with his or her own unique style. In this paper, we develop an intelligent system that beautifies Chinese handwriting characters and physically writes them in a certain calligraphy style using a robotic arm. First, we sketch the handwriting characters using a mouse or a touch pad. Then, we employ a convolutional neural network to identify each stroke from the skeletons, and the corresponding standard stroke is retrieved from a pre-built calligraphy stroke library for robotic arm writing. To output aesthetically beautiful calligraphy with the user’s style, we propose a global optimization approach to solve the minimization problem between the handwritten strokes and standard calligraphy strokes, in which a shape character vector is presented to describe the shape of standard strokes. Unlike existing systems that focus on the generation of digital calligraphy from handwritten characters, our system has the advantage of converting the user-input handwriting into physical calligraphy written by a robotic arm. We take the regular script (Kai) style as an example and perform a user study to evaluate the effectiveness of the system. The writing results show that our system can achieve visually pleasing calligraphy from various input handwriting while retaining the user’s style.

Published
2019

235. A Novel PF-Based Method for Height Reconstruction in Distributed Geosynchronous Repeat-Pass InSAR

Author

Li, Yuanhao, Zhang, Yiran, Chen, Zhiyang, Fu, Tingting, Zhao, Xingzhe, Hu, Cheng, and Monti-Guarnieri, Andrea Virgilio

Abstract

With the advantages of high spatial resolution, short repeat-pass cycle, and large observation area in Ka-band distributed geosynchronous (GEO) synthetic aperture radar (SAR) systems, its interferometry (InSAR) measurement can fast retrieve high-resolution and high-accuracy digital elevation model (DEM). However, compared to low-orbit systems, it is more difficult and costly for distributed GEO SAR systems to perform tight formation flying at such a high orbit altitude, and, therefore, atmospheric effects, which bring nonstationary and non-Gaussian phase errors, should be taken into account in its repeat-pass InSAR. To address the problems earlier, a spatial–temporal joint particle filter-based method (ST-PF) for DEM generation by distributed GEO InSAR is proposed in this article. The proposed ST-PF method is validated under several stationary and nonstationary atmospheric conditions through simulation experiments, and high-accuracy and high-resolution DEMs are obtained. Moreover, the ST-PF method can withstand severe nonlinearity in interferometric phases, which results from a relatively small ambiguity height in the Ka-band. It is also tested that the mean square error (MSE) of the retrieved DEM is consistent with the posterior Cramer–Rao bound (pCRB) of the estimation problem, showing the validity and the accuracy of the proposed ST-PF method. With various processing parameters, errors, and scene types tested, the method shows good robustness in different conditions.

Published
2023
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240. Repeat-pass space-surface bistatic SAR tomography: accurate imaging and first experiment

Author

Chen, Zhiyang, Li, Yuanhao, Hu, Cheng, Wang, Shenglei, Chen, Xinpeng, Datcu, Mihai, and Monti-Guarnieri, Andrea Virgilio

Abstract

Space-surface bistatic synthetic aperture radar (SS-BiSAR) offers an additional observation angle for monostatic spaceborne SAR, making it a promising technology for high-accuracy deformation retrieval technology in local regions. Repeat-pass SS-BiSAR tomography can accurately estimate the surfaces of buildings and steep areas, effectively removing terrain phases during deformation retrieving. However, inaccuracies in the orbital ephemeris can lead to image geometry distortion, reducing image pair coherence, introducing interferometric phase errors, and consequently deteriorating tomographic precision. This paper precisely models the image geometry distortion and interferometric phase error caused by repeat-pass ephemeris error. We propose an ephemeris correction method based on the chirp-Z transform to address these issues. Furthermore, we introduce an accurate tomography model to improve 3D reconstruction accuracy. Our first SS-BiSAR tomography experiment, conducted using the Chinese Lutan-1 satellite, demonstrates that the correlation coefficient is improved by 0.16 after ephemeris error correction. Moreover, the density and precision of the tomographic point cloud are improved by 13.7% and 12.1%, respectively.

Published
2024
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241. A Gesture Recognition Framework Based on Multi-frame Super-resolution Image Sequence

Author

Gangqi Dong, Li Yuanhao, Wang Xiang, Panfeng Huang, and Zhiqiang Ma

Subjects
Computer science, business.industry, 010401 analytical chemistry, Feature extraction, Motion blur, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, Iterative reconstruction, 021001 nanoscience & nanotechnology, 01 natural sciences, Field (computer science), 0104 chemical sciences, Transformation (function), Gesture recognition, Feature (computer vision), Computer vision, Artificial intelligence, 0210 nano-technology, business, Gesture
Abstract

This paper presents a gesture recognition framework based on multi-frame super-resolution image sequence, aiming at solving the low recognition efficiency problem caused by the motion blur during fast gesture transformation and the complex variable visual backgrounds in military operation scenarios. The framework includes three modules: the gesture detection and alignment, the features extraction and fusion, as well as the gesture reconstruction and recognition. Through these three modules, the gesture within each image of the image sequence can be quickly located and aligned, and then the feature information of multiple images can be fused in pixel-level. Finally, the fused features are used to build a gesture reconstruction module to perform high-resolution gesture reconstruction and recognition. On top of that, we build a new video hand dataset named "PHV1000", which covers more than a dozen military operation gestures and fills the gap in this field, and we demonstrate the superior performance of our method in this dataset.

Published
2020

242. Autonomous Obstacle Avoidance for UAV based on Fusion of Radar and Monocular Camera

Author

Panfeng Huang, Fan Zhang, Chen Wang, Hang Yu, and Li Yuanhao

Subjects
0209 industrial biotechnology, Computer science, Real-time computing, 02 engineering and technology, Sensor fusion, Visualization, law.invention, Computer Science::Robotics, Extended Kalman filter, 020901 industrial engineering & automation, law, Obstacle avoidance, Path (graph theory), Motion planning, Radar, Collision avoidance
Abstract

UAVs face many challenges in autonomous obstacle avoidance in large outdoor scenarios, specifically the long communication distance from ground stations. The computing power of onboard computers is limited, and the unknown obstacles cannot be accurately detected. In this paper, an autonomous obstacle avoidance scheme based on the fusion of millimeter wave radar and monocular camera is proposed. The visual detection is designed to detect unknown obstacles which is more robust than traditional algorithms. Then extended Kalman filter (EKF) data fusion is used to build exact real 3D coordinates of the obstacles. Finally, an efficient path planning algorithm is used to obtain the path to avoid obstacles. Based on the theoretical design, an experimental platform is built to verify the UAV autonomous obstacle avoidance scheme proposed in this paper. The experiment results show the proposed scheme cannot only detect different kinds of unknown obstacles, but can also take up very little computing resources to run on an onboard computer. The outdoor flight experiment shows the feasibility of the proposed scheme.

Published
2020

243. Atmospheric Effects on the Performance of Geosynchronous Orbit SAR Systems

Author

Hu Cheng, Dong Xichao, and Li Yuanhao

Subjects
Tropospheric disturbance, Ionospheric disturbance, lcsh:Technology (General), lcsh:T1-995, GEOsynchronous orbit Synthetic Aperture Radar (GEO SAR), SAR imaging, Physics::Atmospheric and Oceanic Physics
Abstract

GEOsynchronous orbit Synthetic Aperture Radar (GEO SAR) has a long synthetic aperture and large observation region; therefore, it is easily affected by atmospheric spatial-temporal changes, which results in a serious degradation in the focusing quality and performance of differential interference processing. In this paper, a high-precision spatial-temporal hybrid GEO SAR signal model is established for the slowly disturbed parts of the atmosphere such as the background troposphere and the ionosphere. The effects of the time rate of different atmospheric parameters on the image quality and accuracy of differential interference processing are analyzed. Considering the influence of random disturbances such as tropospheric turbulence and ionospheric scintillation in atmosphere, a quantitative analysis model is established based on a power law power spectrum model using the atmospheric disturbance and imaging quality parameters. The relationships of the random disturbances intensity in the atmosphere and the imaging evaluation index are obtained. Finally, the model is verified by simulations, and the effects of slow-varying atmospheric errors in a long aperture time on the quality of imaging and differential interference processing are analyzed. Simulation results show that spatial-temporal variable ionospheric disturbances seriously affect L-band GEO SAR imaging and differential interference processing, which must be compensated, and they are slightly affected by tropospheric disturbances. Moreover, it is necessary to consider the tropospheric effects on imaging performance only when the integration time reaches several hundred seconds.

Published
2018

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