检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Zhou, Yirong Wu, Yanhuang Su, Yuhan Li, Jing Cai, Jianyun You, Yongfu Guo, Di Qu, Xiaobo The Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361104 China The Department of Electronic Science Key Laboratory of Digital Fujian on IoT Communication Architecture and Safety Technology Xiamen University Xiamen361104 China Shanghai Electric Group CO. LTD Shanghai200001 China China Telecom Group Quanzhou362000 China China Mobile Group Xiamen361005 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Magnetic Resonance Imaging (MRI) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure. Additionally, local data processing demands substantial manpower and hardware investments. Data isolation across different healthcare institutions hinders cross-institutional collaboration in clinics and research. In this work, we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing, 6G bandwidth, edge computing, federated learning, and blockchain technology. This system is called Cloud-MRI, aiming at solving the problems of MRI data storage security, transmission speed, AI algorithm maintenance, hardware upgrading, and collaborative work. The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data (ISMRMRD) format. Then, the data are uploaded to the cloud or edge nodes for fast image reconstruction, neural network training, and automatic analysis. Then, the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services. The Cloud-MRI system will save the raw imaging data, reduce the risk of data loss, facilitate inter-institutional medical collaboration, and finally improve diagnostic accuracy and work efficiency. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Contract-based Cooperative Computation and Communication Resources Sharing in Mobile Edge Computing

引用

Journal of Grid Computing 2023年第1期21卷 14-14页

作者： Zheng, Yifeng Zou, Lushan Zhang, Wenjie Yang, Jingmin Yang, Liwei Lin, Ziqiong School of Computer Sciences Minnan Normal University No.36 Xianqian Street Fujian Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Minnan Normal University No.36 Xianqian Street Fujian Zhangzhou363000 China Department of Electronic Engineering National Taipei University of Technology No.1 Section 3 Zhongxiao East Road Taibei610101 Taiwan College of Information and Electrical Engineering China Agricultural University No.17 Qinghua East Road Haidian District Beijing100000 China

In this paper, we consider a Mobile Edge Computing (MEC) system with a source transmitter (ST) and multiple relay nodes (RNs). ST has sufficient computing resources, but lacks communication ability, while the situation of RN is the opposite. To tackle this resource imbalance problem, we propose a cooperative computation and communication resources sharing strategy using contract theory under complete and incomplete information scenarios. The ST offers a contract combining computation time and relay power, and the RNs forward the computational results for ST in exchange for the usage of computation capacity. For complete information scenario, we derive the optimal contract to maximize the ST’s utility and we show the existence and uniqueness of optimal contract. We further explore the properties of a feasible contract for incomplete information scenario where the channel gain and relay cost are the private information of RNs and not known by ST. We analyze the necessary and sufficient conditions for a contract to be feasible and propose a sequence optimization algorithm to obtain the optimal contract. Finally, we show the feasibility and optimality of the contract through simulation results. © 2023, The Author(s), under exclusive licence to Springer Nature B.V.

关键词： Mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

Spectrograms Fusion-based End-to-end Robust Automatic Speech Recognition

Spectrograms Fusion-based End-to-end Robust Automatic Speech...

引用

Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Hao Shi Longbiao Wang Sheng Li Cunhang Fan Jianwu Dang Tatsuya Kawahara Graduate School of Informatics Kyoto University Kyoto Japan Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China National Institute of Information and Communications Technology (NICT) Kyoto Japan Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Japan Advanced Institute of Science and Technology Ishikawa Japan

ISBN: (纸本)9781665441629

To improve the robustness of automatic speech recognition (ASR), speech enhancement (SE) is often used as a front-end noise-removal process. Although there is complemen-tarity between the mapping-based and the mask-based SE system, one of the SE systems has been conventionally used as the front-end of ASR. We propose a spectrogram fusion (SF)-based end-to-end (E2E) robust ASR system, in which the mapping-based and masking-based SE are used as the front-end simultaneously. We adopt SF to combine the advantages of mapping-based and masking-based SE systems. SF and ASR modules are connected in an E2E manner, and joint training is conducted to finetune the front-end and the back-end. We compared the performance of different front-ends after joint training. From the experiments using Aishell and PNL 100 Nonspeech Sounds datasets, we found that the fusion of two SEs are beneficial for ASR, especially under low signal-to-noise ratio, where a relative improvement of more than 7% is achieved.

关键词： Training Information processing Speech enhancement Robustness Spectrogram Automatic speech recognition Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

On scattering characteristics of winter wheat at different phenological period based on Sentinel-1A SAR images 5

On scattering characteristics of winter wheat at different p...

引用

IET International Radar Conference 2020, IET IRC 2020

作者： Xu, Dongya Zhao, Jianhui Li, Ning College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

Phenophase period refers to the periodic natural phenomena of plants under the influence of environmental factors and human activities. In recent years, synthetic aperture radar (SAR) has been used to growth monitoring of crops. Due to complex morphological changes at different growth stage of crops, single-phase SAR data unable provide sufficient information to analyze scattering characteristics at different phenological period. In this paper, the scattering characteristics of winter wheat are studied, using time series dual-polarization Sentinel-1A SAR images. And winter wheat planting region in Kaifeng city Henan Province is selected as the study area. Time series C-band radar backscatter coefficient is extracted, then analyze correlation between numerical evolution and phenological change with different polarization data. The variation trend is verified by field measured data. The results show that dual-polarization SAR image is great practical significance for winter wheat phenological monitoring. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Three-Dimensional Reconstruction of Moving Hdr Object Based on Psp

SSRN

引用

SSRN 2022年

作者： Wu, Ke Xie, Yinao Lu, Lei Yin, Yongkai Xi, Jiangtao Faculty of Artificial Intelligence in Education Central China Normal University Wuhan430079 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Shandong Provincial Key Laboratory of Laser Technology and Application Department of Optics School of Information Science and Engineering Shandong University Jinan250100 China School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2522 Australia

High quality 3D reconstruction of HDR object can be achieved by projecting extra fringe patterns with different intensity. However, it requires the object to be kept static. The object motion not only introduces phase shift among the fringe patterns, but also changes the position of the overexposure pixels. This paper proposes a new method to reconstruct the moving HDR object by utilizing the features of motion. For the specific point on the object, overexposure will not happen among all the fringe patterns as the overexposure position is changed with the motion. The unsaturation fringe patterns are selected from the captured fringe patterns and the correct phase information is retrieved based on unequal phase shift algorithm. The effectiveness of the proposed algorithm is verified by the experiments. © 2022, The Authors. All rights reserved.

关键词： Interferometry

来源：评论

学校读者我要写书评

暂无评论

Research on Crop Disaster Stress Risk Mapping System Based on Agriculture Big Data

Research on Crop Disaster Stress Risk Mapping System Based o...

引用

Electronic Information technology and Smart Agriculture (ICEITSA), International Conference on

作者： Chunqiao Mi Xiaoning Peng Ling Peng Changhua Zhao Xiaowu Deng School of Computer Science and Engineering Huaihua University Huaihua Hunan China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture Huaihua University Huaihua Hunan China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application Hunan Province Universities Huaihua University Huaihua Hunan China

ISBN: (纸本)9781665413015

At present, the research on crop disaster risk is mostly carried out from the perspective of numerical simulation in one factor aspect, and there is a lack of information system practice that can integrate disaster spatio-temporal analysis technologies such as environment characteristic information extraction, geographic data scale transformation, spatial analysis and map visualization. Based on agriculture big data and highlighting the spatial characteristics of crop disasters, in this study a method of crop disaster risk mapping is designed and a prototype system is implemented, which function is composed of agriculture big data cleaning, scale conversion, multi-source data fusion, feature information extraction, risk probability prediction and map visualization. A specific application of the system is also demonstrated by taking the production of thematic map of wind-induced lodging risk probability distribution during maize jointing and tasseling stage as an example, which shows great help for avoiding maize lodging risk in agriculture practice. The research results have a good role in improving the technical level of agriculture disaster mapping and analysis, and improving the efficiency and effect of disaster prevention and reduction in agriculture production. It has important academic significance for promoting the application of big data in agriculture disaster management and establishing a new perspective of crop disaster mapping, and has important practice application value for optimizing the fine zoning of crop planting area and strengthening the accurate management of agriculture disaster risk.

关键词： Crops Data visualization Prototypes Production Big Data Information retrieval Agriculture

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Assisted Raman Spectroscopy for Rapid Identification of 2D Materials

arXiv

引用

arXiv 2023年

作者： Qi, Yaping Hu, Dan Wu, Zhenping Zheng, Ming Cheng, Guanghui Jiang, Yucheng Chen, Yong P. Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Av. Wai Long 999078 China Tohoku University Sendai980-8577 Japan State Key Laboratory of Information Photonics and Optical Communications School of Science Beijing University of Posts and Telecommunications Beijing100876 China School of Materials Science and Physics China University of Mining and Technology Xuzhou221116 China Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Jiangsu Suzhou215009 China Department of Physics and Astronomy Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Institute of Physics and Astronomy Villum Center for Hybrid Quantum Materials and Devices Aarhus University AarhusC 8000 Denmark

Two-dimensional (2D) materials have attracted extensive attention due to their unique characteristics and application potentials. Raman spectroscopy, as a rapid and non-destructive probe, exhibits distinct features and holds notable advantages in the structural characterization of 2D materials. However, traditional data analysis of Raman spectra relies on manual interpretation and feature extraction, which are both time-consuming and subjective. In this work, we employ deep learning techniques, including classificatory and generative deep learning, to assist the analysis of Raman spectra of typical 2D materials. For the limited and unevenly distributed Raman spectral data, we propose a data augmentation approach based on Denoising Diffusion Probabilistic Models (DDPM) to augment the training dataset and construct a four-layer Convolutional Neural Network (CNN) for 2D material classification. Experimental results illustrate the effectiveness of DDPM in addressing data limitations and significantly improved classification model performance. The proposed DDPM-CNN method shows high reliability, with 100%classification accuracy. Our work demonstrates the practicality of deep learning-assisted Raman spectroscopy for high-precision recognition and classification of 2D materials, offering a promising avenue for rapid and automated spectral analysis. © 2023, CC BY.

关键词： Raman spectroscopy

来源：评论

学校读者我要写书评

暂无评论

Research on cancer rapid diagnosis technology based on adaptive channel attention mechanism and two-dimensional spectral fusion

引用

Microchemical Journal 2025年 213卷

作者： Liu, Wei Chang, Chenjie Li, Jiahe Lv, Xiaoyi Liu, Yang Chen, Chen Chen, Cheng College of Software Xinjiang University Urumqi 830046 China School of Computer Science and Technology Xinjiang University Urumqi 830046 China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi 830046 China Xinjiang Cloud Computing Application Laboratory Xinjiang Cloud Computing Engineering Technology Research Center Karamay 834000 China

With the widespread use of Artificial Intelligence (AI) in biomedicine, integrating infrared and Raman spectra with biological information offers new prospects for enhanced disease diagnosis. For these two types of data, more information can be extracted using multimodal data fusion compared to single modality, but existing studies tend to only fuse one-dimensional spectral data, ignoring the richer information of the spectra when they are converted to two-dimensional images. In this study, we propose a rapid diagnostic method for cancer, which first converts one-dimensional (1D) Raman and Infrared spectra into two-dimensional (2D) spectral images using Short-Time Fourier Transform (STFT), respectively. Then the features are fused by our designed Adaptive Graph Channel Attention Spacial Cross-Attention Fusion Module (ASCFM). This fusion module innovatively uses the adaptive graph channel attention mechanism to strengthen the model's ability to capture the key information of different spectral features, and utilizes the spatial attention fusion module and the cross-attention module for spatial information extraction and fusion. The output fused features effectively avoid feature information loss through this multimodal feature extraction and fusion, which greatly improves the performance of the model. In this study, Gradient-weighted Class Activation Mapping is also used to make the decision-making process of the neural network more interpretable. The results show that the accuracy of 2D Raman spectral images fused with 2D infrared spectral images by ASCFM followed by ResidualNetwork (ResNet) for diagnosis of lung cancer and glioma datasets is 98.88 % and 96.38 %, which suggests that it is a highly promising adjunctive technique for cancer diagnosis. © 2025

关键词： Auxiliary diagnostic technology Deep learning Infrared spectroscopy Lung cancer Multimodal fusion Raman spectroscopy

来源：评论

学校读者我要写书评

暂无评论

GLFAFormer: DeepFake forgery detection with adaptive feature extract and align

引用

Digital Signal Processing: A Review Journal 2025年 165卷

作者： Chen, Long Zhao, Huihuang He, Jiaxin Meng, Weiliang College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

With the rapid development of generative models, there is an increasing demand for universal fake image detectors. In this paper, we investigate the problem of fake image detection for the synthesis of generative models to detect fake images from multiple generative methods. Recent research methods explore the benefits of pre-trained models and mainly adopt a fixed paradigm of training additional classifiers separately, but we find that the fixed paradigm hinders the full learning of forgery features, leading to insufficient representation learning in the detector. The main reason is that the fixed paradigm pays too much attention to global features and neglects local features, which limits the ability of the model to perceive image details and leads to some information loss or confusion. In this regard, based on the pre-trained visual language space, our method introduces two core designs. First, we designed a Deep Window Triple Attention (DWTA) module. A similar dense sliding window strategy is adopted to capture multi-scale local abnormal patterns, and the sensitivity to generated artifacts is enhanced through the triple attention mechanism. Secondly, we proposed a Cross-Space Feature Alignment (CSFA) module. A two-way interactive channel between global features and local features is established, and the alignment loss function is used to achieve semantic alignment of cross-modal feature spaces. The aligned features are adaptively fused through a gating mechanism to obtain the final adaptive forged features. Experiments demonstrate that our method, when trained solely on ProGAN data, achieves superior cross-generator generalization: it attains an average accuracy of 94.7% on unseen GANs and generalizes to unseen diffusion models with 94% accuracy, surpassing existing methods by 2.1%. The source code is available at https://***/long2580h/GLFAFormer. © 2025 Elsevier Inc.

关键词： Visual languages

来源：评论

学校读者我要写书评

暂无评论

StarGAT: Star-Shaped Hierarchical Graph Attentional Network for Heterogeneous Network Representation Learning

StarGAT: Star-Shaped Hierarchical Graph Attentional Network ...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Wen-Zhi Li Ling Huang Chang-Dong Wang Yu-Xin Ye School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Computational Science Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China College of Mathematics and Informatics South China Agricultural University Guangzhou China Guangdong Provincial Key Laboratory of Public Finance and Taxation with Big Data Application Guangzhou China College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9781665423991

Many real-world graphs can be viewed as Heterogeneous Networks or Heterogeneous Information Networks (HINs) for that they comprise a diversity of node types and relation types. Due to the efficient representation ability of Graph Neural Network and the idea of random walk, many recent studies apply graph representation learning to HINs and achieve satisfactory results. However, these works either treat different node types in a metapath equally, which is inconsistent with the original graph semantic information for that different node types should have different statuses, or only consider the first-order (node-level) and second-order (metapath-level, a.k.a. link-level) information aggregation while ignoring the higher-order relations. To tackle these two problems, we propose a novel Star-Shaped Hierarchical Graph Attentional Network (StarGAT) model to boost representation learning in HINs. Specifically, we assume nodes in HINs can be categorized into a star-shaped structure including one center node type and a bunch of auxiliary node types in a specific task; and we encode node-level, link-level and motif-level attentions in a hierarchical manner to capture richer semantic information. Extensive experiments on three datasets illustrate the model effectiveness.

关键词： Representation learning Conferences Semantics Heterogeneous networks Graph neural networks Data mining Task analysis

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：