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International Conference on mobile Ad-hoc and Sensor Networks, MSN

作者： Xue Lin Yushui Geng Jing Zhao Wenfeng Jiang Zhen Yan School of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China

Due to the characteristics of high noise and low resolution in medical images, it is difficult to extract local features, which affects the accuracy of image diagnosis and classification. To exploit the discriminative features of local image regions, we propose a network model method that combines improved residual bilinear and attention mechanism. First, in the ResNeXt model, it performs segmentation and convolution on the original residual unit structure to extract multi-scale features of the image. And it replaces the VGGNet model in bilinear. Then, it uses channel nonlinear attention to obtain expressive features when extracting features, and employs spatial attention for weight region selection to achieve BAP (Bilinear Attention Pooling) fusion. Finally, it implements classification in the SVM classifier and tests our model on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The results show that the model has better accuracy and robustness than other models in AD diagnosis classification.

关键词： Support vector machines Neuroimaging Image segmentation Image resolution Feature extraction Robustness Sensors

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DT-Net: Point Cloud Completion Network with Neighboring Adaptive Denoiser and Splitting-based Upsampling Transformer

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IEEE Transactions on Emerging Topics in computing 2025年

作者： Mao, Aihua Liu, Qing Tang, Yuxuan Ye, Sheng Yi, Ran Yu, Minjing Liu, Yong-Jin South China University of Technology School of Computer Science and Engineering Guangzhou China South China University of Technology School of Software Engineering Guangzhou China Tsinghua University BNRist MOE-Key Laboratory of Pervasive Computing the Department of Computer Science and Technology Beijing China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Tianjin University College of Intelligence and Computing Tianjin 300350 China

Point cloud completion, which involves inferring missing regions of 3D objects from partial observations, remains a challenging problem in 3D vision and robotics. Existing learning-based frameworks typically leverage an encoder-decoder architecture to predict the complete point cloud based on the global shape representation extracted from the incomplete input, or further introduce a refinement network to optimize the obtained complete point cloud in a coarse-to-fine manner, which is unable to capture fine-grained local geometric details and filled with noisy points in the thin or complex structure. In this paper, we propose a novel coarse-to-fine point cloud completion framework called DT-Net, by focusing on coarse point cloud denoising and multi-level upsampling. Specifically, we propose a Neighboring Adaptive Denoiser (NAD) to effectively denoise the coarse point cloud generated by an autoencoder, and reduce noise around the slender structures, making them clear and well represented. Moreover, a novel Splitting-based Upsampling Transformer (SUT), which effectively incorporates spatial and semantic relationships between local neighborhoods in the point cloud, is also proposed for multi-level upsampling. Extensive qualitative and quantitative experiments demonstrate that our method outperforms state-of-the-art methods under widely used benchmarks. © 2013 IEEE.

关键词： 3D vision deep learning denoising Point cloud completion upsampling

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MonoPlane: Exploiting Monocular Geometric Cues for Generalizable 3D Plane Reconstruction

arXiv

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arXiv 2024年

作者： Zhao, Wang Liu, Jiachen Zhang, Sheng Li, Yishu Chen, Sili Huang, Sharon X. Liu, Yong-Jin Guo, Hengkai BNRist MOEKey Laboratory of Pervasive Computing Department of Computer Science and Technology Tsinghua University Beijing China College of Information Sciences and Technology Pennsylvania State University University ParkPA16802 United States Bytedance Inc. Beijing China

This paper presents a generalizable 3D plane detection and reconstruction framework named MonoPlane. Unlike previous robust estimator-based works (which require multiple images or RGB-D input) and learning-based works (which suffer from domain shift), MonoPlane combines the best of two worlds and establishes a plane reconstruction pipeline based on monocular geometric cues, resulting in accurate, robust and scalable 3D plane detection and reconstruction in the wild. Specifically, we first leverage large-scale pre-trained neural networks to obtain the depth and surface normals from a single image. These monocular geometric cues are then incorporated into a proximity-guided RANSAC framework to sequentially fit each plane instance. We exploit effective 3D point proximity and model such proximity via a graph within RANSAC to guide the plane fitting from noisy monocular depths, followed by image-level multi-plane joint optimization to improve the consistency among all plane instances. We further design a simple but effective pipeline to extend this single-view solution to sparse-view 3D plane reconstruction. Extensive experiments on a list of datasets demonstrate our superior zero-shot generalizability over baselines, achieving state-of-the-art plane reconstruction performance in a transferring setting. Our code is available at https://***/thuzhaowang/MonoPlane. © 2024, CC BY.

关键词： 3D reconstruction

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Prediction of Cervical Cancer from Behavior Risk Using Machine Learning Techniques

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SN computer science 2021年第3期2卷 177页

作者： Akter, Laboni Ferdib-Al-Islam Islam, Md. Milon Al-Rakhami, Mabrook S. Haque, Md. Rezwanul Department of Biomedical Engineering Khulna University of Engineering & Technology Khulna 9203 Bangladesh Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna 9203 Bangladesh Research Chair of Pervasive and Mobile Computing Information Systems Department College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia

Cervical cancer growth is the fourth maximum of regular diseases in females. It is one of the sicknesses which is compromising ladies' wellbeing everywhere in the world and it is difficult to notice any sign in the beginning phase. But the screening process of cervical cancer sometimes is being hampered due to some social-behavioral factors. There is still a limited number of researches directed in cervical cancer identification dependent on the behavior and machine learning in the area of gynecology and computer science. In this research, we have proposed three machine learning models such as Decision Tree, Random Forest, and XGBoost to predict cervical cancer from behavior and its variables and we got significantly improved outcomes than the current methods with 93.33% accuracy. Moreover, we have shown the top features from the dataset according to the feature important scores to know their impacts on the development of the classification model. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Behavior risk Cervical cancer Correlation Feature importance Machine learning

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StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning

arXiv

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arXiv 2022年

作者： Huang, Yi-Hua He, Yue Yuan, Yu-Jie Lai, Yu-Kun Gao, Lin Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences China School of Computer and Control Engineering University of Chinese Academy of Sciences China School of Computer Science & Informatics Cardiff University United Kingdom

3D scene stylization aims at generating stylized images of the scene from arbitrary novel views following a given set of style examples, while ensuring consistency when rendered from different views. Directly applying methods for image or video stylization to 3D scenes cannot achieve such consistency. Thanks to recently proposed neural radiance fields (NeRF), we are able to represent a 3D scene in a consistent way. Consistent 3D scene stylization can be effectively achieved by stylizing the corresponding NeRF. However, there is a significant domain gap between style examples which are 2D images and NeRF which is an implicit volumetric representation. To address this problem, we propose a novel mutual learning framework for 3D scene stylization that combines a 2D image stylization network and NeRF to fuse the stylization ability of 2D stylization network with the 3D consistency of NeRF. We first pre-train a standard NeRF of the 3D scene to be stylized and replace its color prediction module with a style network to obtain a stylized NeRF. It is followed by distilling the prior knowledge of spatial consistency from NeRF to the 2D stylization network through an introduced consistency loss. We also introduce a mimic loss to supervise the mutual learning of the NeRF style module and fine-tune the 2D stylization decoder. In order to further make our model handle ambiguities of 2D stylization results, we introduce learnable latent codes that obey the probability distributions conditioned on the style. They are attached to training samples as conditional inputs to better learn the style module in our novel stylized NeRF. Experimental results demonstrate that our method is superior to existing approaches in both visual quality and long-range consistency. Copyright © 2022, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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A Chan–Vese Model Based on the Markov Chain for Unsupervised Medical Image Segmentation

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Tsinghua science and Technology 2021年第6期26卷 833-844页

作者： Quanwei Huang Yuezhi Zhou Linmi Tao Weikang Yu Yaoxue Zhang Li Huo Zuoxiang He Key Laboratory of Pervasive Computing(Ministry of Education)and the Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Department of Nuclear Medicine Peking Union Medical College HospitalBeijing 100730China School of Clinical Medicine Tsinghua Universityand also with Beijing Tsinghua Changgung HospitalBeijing 100084China

The accurate segmentation of medical images is crucial to medical care and research;however, many efficient supervised image segmentation methods require sufficient pixel level labels. Such requirement is difficult to meet in practice and even impossible in some cases, e.g., rare Pathoma images. Inspired by traditional unsupervised methods, we propose a novel Chan–Vese model based on the Markov chain for unsupervised medical image segmentation. It combines local information brought by superpixels with the global difference between the target tissue and the background. Based on the Chan–Vese model, we utilize weight maps generated by the Markov chain to model and solve the segmentation problem iteratively using the min-cut algorithm at the superpixel *** method exploits abundant boundary and local region information in segmentation and thus can handle images with intensity inhomogeneity and object sparsity. In our method, users gain the power of fine-tuning parameters to achieve satisfactory results for each segmentation. By contrast, the result from deep learning based methods is *** performance of our method is assessed by using four computerized Tomography(CT) datasets. Experimental results show that the proposed method outperforms traditional unsupervised segmentation techniques.

关键词： medical image unsupervised segmentation Markov chain

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ReflecTrack: Enabling 3D Acoustic Position Tracking Using Commodity Dual-Microphone Smartphones 21

ReflecTrack: Enabling 3D Acoustic Position Tracking Using Co...

引用

34th Annual ACM Symposium on User Interface Software and Technology, UIST 2021

作者： Zhuang, Yuzhou Wang, Yuntao Yan, Yukang Xu, Xuhai Shi, Yuanchun Department of Computer Science and Technology Global Innovation Exchange Institute Tsinghua University Beijing China Department of Computer Science and Technology Key Laboratory of Pervasive Computing Ministry of Education Tsinghua University Beijing China Information School University of Washington SeattleWashington United States

ISBN: (纸本)9781450386357

3D position tracking on smartphones has the potential to unlock a variety of novel applications, but has not been made widely available due to limitations in smartphone sensors. In this paper, we propose ReflecTrack, a novel 3D acoustic position tracking method for commodity dual-microphone smartphones. A ubiquitous speaker (e.g., smartwatch or earbud) generates inaudible Frequency Modulated Continuous Wave (FMCW) acoustic signals that are picked up by both smartphone microphones. To enable 3D tracking with two microphones, we introduce a reflective surface that can be easily found in everyday objects near the smartphone. Thus, the microphones can receive sound from the speaker and echoes from the surface for FMCW-based acoustic ranging. To simultaneously estimate the distances from the direct and reflective paths, we propose the echo-aware FMCW technique with a new signal pattern and target detection process. Our user study shows that ReflecTrack achieves a median error of 28.4 mm in the 60cm × 60cm × 60cm space and 22.1 mm in the 30cm × 30cm × 30cm space for 3D positioning. We demonstrate the easy accessibility of ReflecTrack using everyday surfaces and objects with several typical applications of 3D position tracking, including 3D input for smartphones, fine-grained gesture recognition, and motion tracking in smartphone-based VR systems. © 2021 ACM.

关键词： Smartphones

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Low-Complexity Joint Radar-Communication Beamforming: From Optimization to Deep Unfolding

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IEEE Journal on Selected Topics in Signal Processing 2025年

作者： Zhang, Jianjun Masouros, Christos Liu, Fan Huang, Yongming Swindlehurst, A. Lee Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China University College London Department of Electronic & Electrical Engineering LondonWC1E7JE United Kingdom Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen518055 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China University of California at Irvine Center for Pervasive Communications and Computing IrvineCA92697 United States

By sharing the same hardware platform, spectral resource as well as transmit waveform, dual-functional radar-communication (DFRC) systems have been envisioned as a key technology for the future wireless networks. However, advanced signal processing algorithms for DFRC, which can achieve better performance, tradeoff or other design goals, often suffer from prohibitive computational complexity. This motivates us to design low-complexity joint radar sensing and communication beamforming algorithms in this paper, so as to achieve better energy efficiency, communication-sensing tradeoff, and so on. First, we formulate the problem of joint radar-communication beamforming based on symbol-level precoding (SLP) by incorporating constructive interference so as to improve the energy efficiency. To address the formulated problem, we tailor highly parallelizable iterative optimization algorithms that are shown to converge to stationary (or locally optimal) points. To achieve better performance, we propose efficient recursive optimizations that monotonically improve the performance metric of interest. Simulation results indicate that the proposed iterative algorithms outperform the previous approaches. Finally, to further reduce the complexity, we employ deep unfolding to design efficient learning-based algorithms. Besides parallelizability, the learning-based algorithms also enjoy appealing advantages of scalability in the number of served users, the number of transmit antennas and the length of the radar pulse. © 2007-2012 IEEE.

关键词： Radar interference

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Adaptive Multi-modal Data Fusion Method for Dermatosis Diagnosis

Adaptive Multi-modal Data Fusion Method for Dermatosis Diagn...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yaning Li Xiaodong Yang Yiqiang Chen Shubai Chen Bixiao Zeng Hong Cai The Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China College of Information Science and Engineering University of Jinan Shandong China Shandong Academy of Intelligent Computing Technology Shandong China Department of Dermatology Air Force Medical Center of the PLA Beijing China

Meta-data, e.g., gender, medical history, etc., is of vital importance for dermatosis diagnosis clinically, while most existing AI-based methods ignore this information and only employ dermoscopy images. Due to the modality disequilibrium of image and meta-data, there would be unequal optimization during the modal fusion, resulting in the weak meta-data modality being suppressed and losing their contributions to the classification. To address this issue, we propose an Adaptive Multi-modal Fusion (AMF) method for dermatosis diagnosis, which reweights the fusion factor of image and meta-data by evaluating their optimization discrepancy through the Euclidean norm. What’s more, the Co-Attention mechanism is employed to align the two modalities implicitly. To evaluate the effectiveness of the proposed method, extensive experiments are conducted on multi-modal dermatosis public datasets including ISIC 2019, PDA-UFES-20, and HAM10000. The results demonstrate that the proposed method can achieve better performance in comparison to the state-of-the-art methods.

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Coordination of networking and computing: toward new information infrastructure and new services mode

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Frontiers of Information Technology & Electronic Engineering 2024年第5期25卷 629-632页

作者： Xiaoyun WANG Tao SUN Yong CUI Rajkumar BUYYA Deke GUO Qun HUANG Hassnaa MOUSTAFA Chen TIAN Shangguang WANG China Mobile Communications Corporation Beijing 100032China China Mobile Research Institute Beijing 100053China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China CLOUDS Lab School of Computing and Information SystemsUniversity of MelbourneMelbourneVIC 3010Australia National Key Laboratory of Information Systems Engineering National University of Defense TechnologyChangsha 410073China School of Computer Science Peking UniversityBeijing 100871China Intel Corporation Santa ClaraCA 95054USA State Key Laboratory for Novel Sofiware Technology Nanjing UniversityNanjing 210023China State Key Laboratory of Networking and Switching Technology Bejing University of Posts and TelecommunicationsBeijing 100876China

computing,serving as the cornerstone of information processing,plays a pivotal role in the digital service ***"network"and"computing,"responsible for information transmission and processing respectively,traditionally belong to different stakeholders and have evolved ***,the recent trend toward the coordination and integration of computing and networks has garnered significant attention from both industry and *** such as"computility network"and"computing force network"have emerged,and the International Telecommunication Union-Telecommunication Standardization(ITU-T)has initiated efforts to develop standards for the coordination of networking and computing(CNC),focusing on the architecture and framework.

关键词： computing services network

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