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474 results on '"Zhang, Yingjian"'

4. The specification and use of 18F-FES PET in breast cancer

Author

YANG Zhongyi, XU Xiaoping, WANG Mingwei, ZHANG Yongping, YANG Jianwei, CHEN Yue, XU Wengui, LI Yaming, ZHANG Yingjian, SONG Shaoli

Subjects
breast cancer, positron emission tomography, 16α-18f-17β-fluoroestradiol, specification, application, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Estrogen receptor (ER) is overexpressed in approximately 2/3 of breast cancer patients’ lesions. Noninvasive detection of ER in vivo and dynamic monitoring of ER are crucial for individualized treatment decision-making. 16α-18F-17β-fluoroestradiol (18F-FES) positron emission tomography (PET) has been used in a variety of preclinical and clinical studies to detect ER expression. However, there is still a lack of technical specifications in China. This technical specification was jointly written by domestic experts who had experience of 18F-FES PET imaging and was formed through consultation based on their own experience and research progress in this field both domestically and internationally. This technical specification introduced the synthesis method and quality control requirements of 18F-FES, recommended its clinical application scenarios, and classified them. In addition, experts’ suggestions were provided throughout the entire process systemically and detailly, including pre-imaging preparation, imaging process, image analysis (normal biological distribution, determination of ER positive and negative, lesion detection, influencing factors, false negative and false positive, report requirements), and the limitations of this imaging technique were proposed. The future application prospects were also discussed. This specification aimed to promote the standardized application of 18F-FES PET in China, achieve repeatability and comparability, and provide important molecular imaging technical support for accurate diagnosis and treatment of breast cancer.

Published
2023
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8. FPGA Implementation of Variable Step Power Inversion Array for BeiDou Receiver

Author

Jia Zhuoya, Ni Shuyan, Luo Yalun, Zhang Yingjian, and Mao Wenxuan

Subjects
Power inversion array, variable step size (VSS), navigation anti-jamming, FPGA, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Given the susceptibility of satellite signals to intentional or unintentional interference; it is imperative to incorporate hardware-based navigation anti-jamming algorithms. Currently, spatial domain filtering is widely applied. While numerous variable step size (VSS) algorithms were used to improve the performance, most of these are either computationally complex or depend on many parameters that are hard to tune manually. Some algorithms might improve one index but worsen overall performance, making them unreliable in practical scenarios. To address these challenges, we propose a novel variable step power inversion (PI) that involves just one adaptive parameter. By establishing a new non-linear function model between step size and system output, the parameter can be adapted based on the output. Meanwhile, a variable step PI that can be easily implemented by field programmable gate array (FPGA) is proposed, thereby achieving a balance between accuracy and iteration speed. The proposed algorithm can converge within 10 iterations, effectively suppressing individual interference below −300dB. In comparison to existing algorithms, the proposed algorithm is characterized by swift convergence, high convergence accuracy, low complexity, and robust anti-jamming capabilities. Finally, FPGA hardware testing is conducted, and the experimental and simulation results are in good agreement, affirming anti-jamming capabilities.

Published
2023
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10. Online Identification of Lithium Battery Equivalent Circuit Model Parameters Based on a Variable Forgetting Factor Recursive Least Square Method

Author

Yu, Chuanxiang, Huang, Rui, Zhang, Yingjian, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, He, Jinghan, editor, Li, Yaohua, editor, Yang, Qingxin, editor, and Liang, Xidong, editor

Published
2022
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33. SiamCAF: Complementary Attention Fusion-Based Siamese Network for RGBT Tracking

Author

Zhang, Yingjian Xue, Jianwei Zhang, Zhoujin Lin, Chenglong Li, Bihan Huo, and Yan

Subjects
multi-modal object tracking, RGBT tracking, attention mechanism, deep learning
Abstract

The tracking community is increasingly focused on RGBT tracking, which leverages the complementary strengths of corresponding visible light and thermal infrared images. The most well-known RGBT trackers, however, are unable to balance performance and speed at the same time for UAV tracking. In this paper, an innovative RGBT Siamese tracker named SiamCAF is proposed, which utilizes multi-modal features with a beyond-real-time running speed. Specifically, we used a dual-modal Siamese subnetwork to extract features. In addition, to extract similar features and reduce the modality differences for fusing features efficiently, we designed the Complementary Coupling Feature fusion module (CCF). Simultaneously, the Residual Channel Attention Enhanced module (RCAE) was designed to enhance the extracted features and representational power. Furthermore, the Maximum Fusion Prediction module (MFP) was constructed to boost performance in the response map fusion stage. Finally, comprehensive experiments on three real RGBT tracking datasets and one visible–thermal UAV tracking dataset showed that SiamCAF outperforms other tracking methods, with a remarkable tracking speed of over 105 frames per second.

Published
2023
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37. Supplementary Data from A Prospective Trial of 68Ga-PSMA and 18F-FDG PET/CT in Nonmetastatic Prostate Cancer Patients with an Early PSA Progression During Castration

Author

Wang, Beihe, primary, Liu, Chang, primary, Wei, Yu, primary, Meng, Jin, primary, Zhang, Yingjian, primary, Gan, Hualei, primary, Xu, Xiaoping, primary, Wan, Fangning, primary, Pan, Jian, primary, Ma, Xuejun, primary, Hu, Silong, primary, Freedland, Stephen J., primary, Song, Shaoli, primary, Ye, DingWei, primary, and Zhu, Yao, primary

Published
2023
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50. Scene Overlap Prediction for LiDAR-Based Place Recognition.

Author

Zhang, Yingjian, Dai, Chenguang, Zhou, Ruqin, Zhang, Zhenchao, Ji, Hongliang, Fan, Huixin, Zhang, Yongsheng, and Wang, Hanyun

Abstract

Recently, light detection and ranging (LiDAR)-based place recognition has been widely concerned because of its robustness to light conditions, seasonal changes, and viewpoint variations. Unlike most of the existing methods that represent the whole point cloud scenes with global descriptors, we treat the LiDAR-based place recognition problem as a scene overlap prediction task and propose an end-to-end overlap prediction network, which consists of a feature learning backbone, a feature enhancement module, and an overlap prediction module. Based on the prediction result for each point, the overlapping ratios between two point clouds are computed and used to predict whether these two point clouds are at the same place. In addition, to promote the computational efficiency and reduce the model complexity, a lightweight feature learning backbone is adopted. The experiments conducted on the KITTI Odometry dataset demonstrate that the proposed method achieves superior performance compared with the state-of-the-art methods. The lightweight method also obtains $2\times $ inference speed with little performance degradation compared with the vanilla method. [ABSTRACT FROM AUTHOR]

Published
2023
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