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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wentian Cai Yulin Cheng Ying Gao Weixiao Liu Xinyan Xie Xiongwen Luo Weixian Yang Zaiyi Liu Changhong Liang School of Computer Science and Engineering South China University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou China Department of Radiology The First Affiliated Hospital of Jinan University Guangzhou China

In this paper, we propose a Dual-path Supplemental Information Learning Architecture (DSILA) for predicting breast cancer Ki-67 status based on T2-weighted (T2w) magnetic resonance imaging (MRI). DSILA consists of two components: 1) a transfer network with multi-scale feature selection strategy to obtain generic multi-scale features most relative to target, 2) a supplemental learning network with a large receptive field and channel-level attention to mine scenario-related semantic information. A regulation item – Aspect Overlap Loss (AOL), is further added to force the supplemental learning network to pay more attention to the regions overlooked by the transfer network. The experimental results tested on the collected T2w MRI breast cancer Ki-67 dataset show that DSILA outperforms state-of-the-art techniques among all adopted evaluation metrics, even achieving 0.85 in Area under the Receiver Operating Characteristic Curve (AUC).

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ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection

arXiv

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arXiv 2022年

作者： Yin, Junbo Zhou, Dingfu Zhang, Liangjun Fang, Jin Xu, Cheng-Zhong Shen, Jianbing Wang, Wenguan School of Computer Science Beijing Institute of Technology China Baidu Research China National Engineering Laboratory of Deep Learning Technology and Application China SKL-IOTSC CIS University of Macau China ReLER AAII University of Technology Sydney Australia

Existing approaches for unsupervised point cloud pre-training are constrained to either scene-level or point/voxel-level instance discrimination. Scene-level methods tend to lose local details that are crucial for recognizing the road objects, while point/voxel-level methods inherently suffer from limited receptive field that is incapable of perceiving large objects or context environments. Considering region-level representations are more suitable for 3D object detection, we devise a new unsupervised point cloud pre-training framework, called ProposalContrast, that learns robust 3D representations by contrasting region proposals. Specifically, with an exhaustive set of region proposals sampled from each point cloud, geometric point relations within each proposal are modeled for creating expressive proposal representations. To better accommodate 3D detection properties, ProposalContrast optimizes with both inter-cluster and inter-proposal separation, i.e., sharpening the discriminativeness of proposal representations across semantic classes and object instances. The generalizability and transferability of ProposalContrast are verified on various 3D detectors (i.e., PV-RCNN, CenterPoint, PointPillars and PointRCNN) and datasets (i.e., KITTI, Waymo and ONCE). Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Lightweight Neural Network Based Garbage Image Classification Using a Deep Mutual Learning 1

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11th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2020

作者： Liu, Xiao Wu, Zhi-Ze Wu, Zi-Jun Zou, Le Xu, Li-Xiang Wang, Xiao-Feng Department of Environmental Engineering Hefei University Hefei Anhui230601 China Anhui Provincial Engineering Laboratory of Big Data Technology Application for Urban Infrastructure Department of Computer Science and Technology Hefei University Hefei230601 China Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Hefei Anhui230601 China

ISBN: (数字)9789811600104

ISBN: (纸本)9789811600098

With the construction and development of civilized cities, image based garbage classification has gradually become an important concern in computer vision community. During the algorithms for image classification, the strong ability of Convolution Neural Networks (CNNs) in feature learning makes it the most successful approach at the moment. However, the parameters of CNNs model are very huge, and its training usually depends on a large amount of samples. In this article, we tackle the problem of lightweight neural network based garbage image classification, which aims to learn classifier with a small number of model parameters. Specifically, we utilize the MobileNetV2 for the backbone of feature extraction network and jointly train such two nets in a way of deep mutual learning. It realizes the information distillation between the teacher and the student. With this, we can significantly improve the learning ability of the MobileNetV2 based lightweight neural network. The experimental results on a self-assembled dataset show that our proposal effectively classifies the garbage and achieves a classification effect batter than the state of the arts in terms of testing accuracy, time and model size. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image classification

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Frame Error Rate Restricted AUV Relaying Data Collection in Underwater Acoustic Sensor Networks

Frame Error Rate Restricted AUV Relaying Data Collection in ...

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IEEE International Conference on Communications (ICC)

作者： Mingyue Cheng Qianqian Wang Quansheng Guan Weiqi Chen Tony Q. S. Quek School of Electronics and Information Engineering South China University of Technology Guangzhou China Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resources Guangzhou China College of Computer Science and Engineering Northwest Normal University Lanzhou China School of Internet Finance and Information Eneineering Guangdong University of Finance Guangzhou China Information Systems Technology and Design Singapore University of Technolgy and Design Singapore

In recent years, reliable and timely data collection from underwater acoustic sensor networks (UASNs) has attracted widespread attention in academia. For this proposal, we study the autonomous underwater vehicle (AUV)-based real-time mobile relaying network in UASNs. Relay placement determines the reliability of communication in the relay network. We first formulate the relay positions that can meet a certain frame error rate (FER) requirement as the FER-restricted area (FRA), and approximate the FRA with a three-dimensional ellipsoid mathematical formula. The problem of reliable and timely data collection becomes planning a short AUV relaying trajectory under the different-sized FRA constraints. To this end, we propose a nearest-community (N-C) trajectory planning algorithm and further propose a member grouping method to form communities. Simulation results verify that the approximate FRA is more than 90% consistent with the real FRA and show that the proposed N-C can successfully receive more packets per minute and consume fewer sensors' energy than other algorithms.

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Construction Equipment Performance with Cloud Data Analysis for Predictive Maintenance

Construction Equipment Performance with Cloud Data Analysis ...

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IEEE Recent Advances in Intelligent Computational Systems (RAICS)

作者： Senthil Kumar Seeni P. Maheswari Jothibabu Konidhala M. Raja Suguna M.V. Ishwarya S. Srinivasan Department of Mobile Application Development Cognizant Technology Solutions Buffalo Grove Illinois USA Faculty of Management Studies SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering University of Technology and Applied Sciences- Al Musannah Musanna South Al Batina Oman Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Artificial intelligence and Data science Department Agni College of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381696

The research uses Internet of Things technology for predictive maintenance in the construction sector. The system addresses incorporating Internet of Things sensors into construction machinery to enable real-time monitoring of operating parameters. The study suggests a proactive maintenance strategy that may anticipate equipment breakdowns by examining the data obtained and using sophisticated prediction models. With this strategy, construction teams can arrange maintenance activities systematically, which cuts down on downtime and helps avoid expensive interruptions. The abstract emphasizes the advantages of this practice, which include reducing unexpected downtime as much as possible, extending the life of equipment, and making the most of available resources. The study proves the usefulness of the IoT-based predictive maintenance approach via empirical investigations and the analysis of case studies. This validates the strategy's efficacy, contributing to enhanced equipment performance and increased work efficiency in the construction industry.

关键词： Electric breakdown Predictive models Real-time systems Sensor systems Maintenance Sensors Trajectory Internet of Things Machinery Predictive maintenance

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SeaConvNeXt:A Lightweight Two-Branch Network Architecture for Efficient Prediction of Specific IHC Proteins and Antigens on Hematoxylin and Eosin(H&E)Images

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Big Data Mining and Analytics 2024年第4期7卷 1212-1236页

作者： Yuli Chen Guoping Chen Guoying Shi Yao Zhou Jiayang Bai Germán Corredor Cheng Lu Xiujuan Lei School of Computer Science Shaanxi Normal UniversityXi’an 710119China Department of Pathology Shaanxi Provincial Hospital of Traditional Chinese MedicineXi’an 710003China Wallace H.Coulter Department of Biomedical Engineering Emory University and Georgia Institute of TechnologyAtlanta 30322GAUSAand also with Joseph Maxwell Cleland Atlanta VA Medical CenterDecatur 30033GAUSA Department of Radiology and Medical Research Institute Guangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhou 510080Chinaand also with Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and ApplicationGuangzhou 510080China

Immunohistochemistry(IHC)is a vital technique for detecting specific proteins and antigens in tissue sections using antibodies,aiding in the analysis of tumor growth and ***,IHC is costly and time-consuming,making it challenging to implement on a large *** address this issue,we introduce a method that enables virtual IHC staining directly on Hematoxylin and Eosin(H&E)***,we have developed a novel registration technique,called Bi-stage Registration based on density Clustering(BiReC),to enhance the registration efficiency between H&E and IHC *** method involves automatically generating numerous Regions Of Interest(ROI)labels on the H&E image for model training,with the labels being determined by the intensity of IHC ***,we propose a novel two-branch network architecture,called SeaConvNeXt,which integrates a lightweight Squeeze-Enhanced Axial(SEA)attention mechanism to efficiently extract and fuse multi-level local and global features from H&E images for direct prediction of specific proteins and *** SeaConvNeXt consists of a ConvNeXt branch and a global fusion *** ConvNeXt branch extracts multi-level local features at four stages,while the global fusion branch,including an SEA Transformer module and three global blocks,is designed for global feature extraction and multiple feature *** experiments demonstrate that SeaConvNeXt outperforms current state-of-the-art methods on two public datasets with corresponding IHC and H&E images,achieving an AUC of 90.7%on the HER2SC dataset and 82.5%on the CRC *** results suggest that SeaConvNeXt has great potential for predicting virtual IHC staining on H&E images.

关键词： Immunohistochemistry(IHC) Bi-stage Registration based on density Clustering(BiReC) automatic label generation SeaConvNeXt attention mechanism multi-level local and global features virtual IHC staning prediction

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Learning impact of recent ICT advances based on virtual reality IoT sensors in a metaverse environment

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Measurement: Sensors 2023年 27卷

作者： Brahma, Mahul Rejula, M. Anline Srinivasan, Bhavana Kumar, S.N. Banu, W. Aisha Malarvizhi, K. Priya, S. Sharon Kumar, Abhishek Bath Business School Bath Spa University United Kingdom Department of Computer Application Scott Christian College Tamilnadu Nagercoil India Bangalore India Department of EEE Amal Jyothi College of Engineering Kerala India Department of CSE B.S. Abdur Rahman Crescent Institute of Science and Technology Tamilnadu Chennai India Department of CSE JCT College of Engineering and Technology Tamil Nādu Coimbatore India India

The use of avatars in the Metaverse and virtual reality technology in education is a rapidly developing field that has the potential to revolutionize the way students learn. In the context of the Indian EdTech market, virtual reality could play a significant role in enhancing the learning experience for students, although more research is needed to explore its full *** has already made a significant impact on education, with academic programming, technical instruction, and theoretical concepts being taught through digital platforms. Virtual reality has the potential to take this a step further, allowing students to experience immersive and interactive learning environments that simulate real-world ***, as mentioned in the paragraph, there is still limited research on the use of virtual reality in education, particularly in the Indian context. Further studies are needed to assess the effectiveness of this technology and ensure that it can be integrated effectively into the educational ***, the use of virtual reality in education is an exciting development in the ICT industry and has the potential to make a significant impact on the learning experience for students. Various non-governmental organizations (NGOs) seek to improve women's lives and provide them with self-sufficiency training. The government is likewise concerned about this issue and has established a number of programs to assist women. Information and communication technology (ICT) is significant in this context. ICT has improved the security, knowledge, education, employability, confidence, and popularity of women. The significance of ICT for the empowerment of women is highlighted in this study. To illuminate the various facets of ICT's impact on society, various examples have been addressed. Because of ICT, new generations of women all over the globe have defied all expectations and proven themselves in every aspect of life, even the most complicated and time-consumin

关键词： Virtual reality

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Quaternion Fractional Order Weighted Generalized Laguerre–Fourier Moments and Moment Invariants for Color Image Analysis

SSRN

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SSRN 2022年

作者： He, Bing Liu, Jun Lin, Guancheng Peng, Cheng Xia, Wenqiang School of Physics and Electrical Engineering Weinan Normal University Weinan714000 China School of Computer Science and Technology Weinan Normal University Weinan714000 China Research Center for X-Ray Detection and Application of Shaanxi Province Weinan Normal University Weinan714000 China

Orthogonal moments based on Laguerre polynomials, especially the quaternion representation of fractional-order orthogonal moments have attracted interest in the imaging field. However, the existing quaternion fractional-order color orthogonal moments (QFr-COMs) based on Laguerre polynomials confront two challenges: Most of the existing algorithms are based on Cartesian coordinate space, hindering the direct realization of the geometric invariance of rotation and the scaling of color image moments, and in the few algorithms that directly apply the polynomial in the Cartesian coordinate system to polar coordinate space, the polynomial does not satisfy orthogonality in polar coordinate space;thus, the constructed image moment strictly belongs to nonorthogonal moments. In this work, we introduce a fast and reliable computing method based on quaternion fractional order weighted generalized Laguerre–Fourier moments (QFr-WGLFMs) and the geometric invariance of the moments. Theoretical analyses and experimental results showed that the proposed QFr-WGLFMs offer enhanced image reconstruction and pattern recognition compared with the existing quaternion fractional-order-based orthogonal moments. © 2022, The Authors. All rights reserved.

关键词： Polynomials

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A Novel Unified Conditional Score-based Generative Framework for Multi-modal Medical Image Completion

arXiv

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arXiv 2022年

作者： Meng, Xiangxi Gu, Yuning Pan, Yongsheng Wang, Nizhuan Xue, Peng Lu, Mengkang He, Xuming Zhan, Yiqiang Shen, Dinggang School of Biomedical Engineering ShanghaiTech University Shanghai China School of Information Science and Technology ShanghaiTech University Shanghai China Shanghai United Imaging Intelligence Co. Ltd. Shanghai China National Engineering Lab. for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Shaanxi Xi’an China

Multi-modal medical image completion has been extensively applied to alleviate the missing modality issue in a wealth of multi-modal diagnostic tasks. However, for most existing synthesis methods, their inferences of missing modalities can collapse into a deterministic mapping from the available ones, ignoring the uncertainties inherent in the cross-modal relationships. Here, we propose the Unified Multi-Modal Conditional Score-based Generative Model (UMM-CSGM) to take advantage of Score-based Generative Model (SGM) in modeling and stochastically sampling a target probability distribution, and further extend SGM to cross-modal conditional synthesis for various missing-modality configurations in a unified framework. Specifically, UMM-CSGM employs a novel multi-in multi-out Conditional Score Network (mm-CSN) to learn a comprehensive set of cross-modal conditional distributions via conditional diffusion and reverse generation in the complete modality space. In this way, the generation process can be accurately conditioned by all available information, and can fit all possible configurations of missing modalities in a single network. Experiments on BraTS19 dataset show that the UMM-CSGM can more reliably synthesize the heterogeneous enhancement and irregular area in tumor-induced lesions for any missing modalities. Copyright © 2022, The Authors. All rights reserved.

关键词： Probability distributions

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Ultra-Short-Term Wind Power Prediction Model with Financial Factors Based on the Lightgbm Algorithm

SSRN

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SSRN 2022年

作者： Wang, Yong Sheng Guan, Shi Jie Liu, Li Min Jing, Gao Xu, Zhi Wei Liu, Guang Wen School of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Software Service Engineering Technology Research Center Inner Mongolia Autonomous Region Hohhot010080 China School of Computer and Information Inner Mongolia Agricultural University Hohhot010018 China

This study introduces an ultra-short-term wind power prediction model with financial factors based on the LightGBM algorithm, which innovatively introduces the KDJ and MACD from financial time series data into the wind power time series data to address the challenges of existing prediction models due to the uncertainty factors such as wind speed and wind direction. The experiments show that the model has satisfactory prediction results and a short prediction time, which can contribute to developing grid dispatching plans and adjusting national strategies. © 2022, The Authors. All rights reserved.

关键词： Wind power

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