Search

Your search keyword '"Wu, Tongning"' showing total 204 results
Start Over Author "Wu, Tongning"
204 results on '"Wu, Tongning"'

3. A Deep Learning Framework for Evaluating the Over-the-Air Performance of the Antenna in Mobile Terminals.

Author

Chen, Yuming, Qi, Dianyuan, Yang, Lei, Wu, Tongning, and Li, Congsheng

Subjects
MOBILE antennas, CONVOLUTIONAL neural networks, CELL phones, ANTENNAS (Electronics), ELECTROMAGNETIC fields, DEEP learning
Abstract

This study introduces RTEEMF (Real-Time Evaluation Electromagnetic Field)-PhoneAnts, a novel Deep Learning (DL) framework for the efficient evaluation of mobile phone antenna performance, addressing the time-consuming nature of traditional full-wave numerical simulations. The DL model, built on convolutional neural networks, uses the Near-field Electromagnetic Field (NEMF) distribution of a mobile phone antenna in free space to predict the Effective Isotropic Radiated Power (EIRP), Total Radiated Power (TRP), and Specific Absorption Rate (SAR) across various configurations. By converting antenna features and internal mobile phone components into near-field EMF distributions within a Huygens' box, the model simplifies its input. A dataset of 7000 mobile phone models was used for training and evaluation. The model's accuracy is validated using the Wilcoxon Signed Rank Test (WSR) for SAR and TRP, and the Feature Selection Validation Method (FSV) for EIRP. The proposed model achieves remarkable computational efficiency, approximately 2000-fold faster than full-wave simulations, and demonstrates generalization capabilities for different antenna types, various frequencies, and antenna positions. This makes it a valuable tool for practical research and development (R&D), offering a promising alternative to traditional electromagnetic field simulations. The study is publicly available on GitHub for further development and customization. Engineers can customize the model using their own datasets. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

4. Cover Image

Author

Wu, Jindong, primary, Mi, Qunzheng, additional, Zhang, Yi, additional, and Wu, Tongning, additional

Published
2024
Full Text
View/download PDF

6. Spatially Averaged Epithelial/Absorbed Power Density for Nonplanar Skin Models Exposed to Antenna at 10–90 GHz

Author

Li, Kun, primary, Kodera, Sachiko, additional, Poljak, Dragan, additional, Diao, Yinliang, additional, Sasaki, Kensuke, additional, Zhang, Shuai, additional, Yao, Ming, additional, Kapetanovic, Ante, additional, Li, Congsheng, additional, Wu, Tongning, additional, Liebig, Thorsten, additional, Simon, Winfried, additional, and Hirata, Akimasa, additional

Published
2024
Full Text
View/download PDF

8. SVTNet: Automatic bone age assessment network based on TW3 method and vision transformer.

Author

Wu, Jindong, Mi, Qunzheng, Zhang, Yi, and Wu, Tongning

Subjects
TRANSFORMER models, ORTHOPEDISTS, VISUAL training, CARPAL bones, AGE, TERTIARY care
Abstract

This study aims to develop a proficient and clinically applicable algorithm that can accurately assess bone age. This algorithm is based on the principles of the Tanner‐Whitehouse 3 (TW3) integral approach, and aims to achieve efficiency, scalability, and interpretability. We developed a model for bone age prediction in children. The model was tested on a pediatric dataset from a tertiary care hospital consisting of left‐hand radiographs of children between the age of 0 and 18. Our model consists of removing the arm portion using a pre‐trained YOLO network, localizing 37 key points in the hand bone portion using a spatial configuration network, and segmenting the original image through 20 of these points to obtain 20 fixed‐size patches. Finally, each of the 20 bone images is classified by training a visual transformer (ViT) model. In this study, a hybrid network, SVTNet, was developed that incorporates visual transformers to obtain estimates of bone age in the carpal (C series) and metacarpal (RUS series) bones. The sum of the clinical TW3 scoring region scores and bone maturity scores were utilized to determine the bone age for each corresponding region. The performance of the algorithm was evaluated in terms of both training and testing by evaluating 3871 left hand X‐ray micrographs obtained from a tertiary hospital in China. The results showed that the average absolute error of bone age estimation was 0.50 years for the RUS series of bones and 0.47 years for the C series of bones. The main contribution of this study is to propose, for the first time, a ViT‐based bone age assessment method that automates the entire process of the TW3 algorithm and is clinically interpretable, with predictive accuracy comparable to that of an experienced orthopedic surgeon. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

19. Attention Mask R‐CNN with edge refinement algorithm for identifying circulating genetically abnormal cells.

Author

Xu, Xu, Li, Congsheng, Fan, Xianjun, Lan, Xinjie, Lu, Xing, Ye, Xin, and Wu, Tongning

Abstract

Recent studies have suggested that circulating tumor cells with abnormalities in gene copy numbers in mononuclear cell‐enriched peripheral blood samples, such as circulating genetically abnormal cells (CACs), can be used as a non‐invasive tool to detect patients with benign pulmonary nodules. These cells are identified through fluorescence signals counting by using 4‐color fluorescence in situ hybridization (FISH) technology that exhibits high stability, sensitivity, and specificity. When FISH data are analyzed, the overlapping cells and fluorescence noise is a great challenge for identifying of CACs, thereby seriously affecting the efficiency of clinical diagnosis. To address this problem, in this study, we proposed an end‐to‐end FISH‐based method (CACNET) for CAC identification. CACNET achieved nuclear segmentation and counted 4‐color staining signals through improved Mask region‐based convolutional neural network (R‐CNN), followed by cell category (normal cell, deletion cell, gain cell, or CAC) according to pathological criteria. Firstly, the segmentation accuracy of overlapping nuclei was improved by adding an edge constraint head during training. Then, the interference of fluorescence noise was reduced by fusing non‐local module to reconstruct the feature extraction network of Mask R‐CNN. We trained and tested the proposed model on a dataset comprising 700 frames with 58,083 nuclei. The Accuracy, Sensitivity, and Specificity (overall performance metric for the algorithm) of CAC identification with CACNET were 94.06%, 92.1%, and 99.8%, respectively. Moreover, the developed method exhibited approximately identification speed of approximately 0.22 s per frames. The results showed that the proposed method outperformed the existing CAC identification methods, making it a promising approach for early screening of lung cancer. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

35. Cover Image

Author

Cao, Shaomeng, primary, Chen, Zhiye, additional, Li, Congsheng, additional, Lv, Chuanfeng, additional, Wu, Tongning, additional, and Lv, Bin, additional

Published
2019
Full Text
View/download PDF

45. Deformable torso phantoms of Chinese adults for personalized anatomy modelling

Author

Wang, Hongkai, primary, Sun, Xiaobang, additional, Wu, Tongning, additional, Li, Congsheng, additional, Chen, Zhonghua, additional, Liao, Meiying, additional, Li, Mengci, additional, Yan, Wen, additional, Huang, Hui, additional, Yang, Jia, additional, Tan, Ziyu, additional, Hui, Libo, additional, Liu, Yue, additional, Pan, Hang, additional, Qu, Yue, additional, Chen, Zhaofeng, additional, Tan, Liwen, additional, Yu, Lijuan, additional, Shi, Hongcheng, additional, Huo, Li, additional, Zhang, Yanjun, additional, Tang, Xin, additional, Zhang, Shaoxiang, additional, and Liu, Changjian, additional

Published
2018
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results