检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Zheng, Qingping Zheng, Ling Deng, Jiankang Li, Ying Shang, Changjing Shen, Qiang School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northewestern Polytechnical University Shaanxi Xi’an710129 China Fuzhou Institute of Data Technology Fujian350200 China Department of Computing Imperial College London LondonSW7 2AZ United Kingdom

HighlightsTransformer-Based Hierarchical Dynamic Decoders for Salient Object DetectionQingping Zheng, Ling Zheng, Jiankang Deng, Ying Li, Changjing Shang, Qiang Shen• T-HDDNet employs dynamic upsampling and fusion for data-driven salient object detection.• T-HDDNet uses a spatially sensitive loss to learn salient object structures, yielding a high-resolution map.• Our method exceeds benchmarks at 22.19G MACs and 27M Params, reducing costs compared to the top model. © 2023, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Directional Source Localization Based on RSS-AOA Combined Measurements

引用

China Communications 2020年第11期17卷 181-193页

作者： Peiliang Zuo Tao Peng Hao Wu Kangyong You Hanbo Jing Wenbin Guo Wenbo Wang Wireless Signal Processing and Network Laboratory Key Laboratory of Universal Wireless CommunicationMinistry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China Department of Electronic and Communication Engineering Beijing Electronic Science&Technology InstituteBeijing 100070China th Research Institute of China Electronics Technology Group Corporation(CETC) Guangzhou 510310China Beijing Institute of Remote Sensing Equipment Beijing 100854China

Source localization plays an indispensable role in many *** paper addresses the directional source localization problem in a three-dimensional(3D)wireless sensor network using hybrid received-signal-strength(RSS)and angle-of-arrival(AOA)*** the position and transmission orientation of the source are to be *** the considered positioning scenario,the angle and range measurements are respectively corresponding to the AOA model and RSS model that integrates the Gaussian-shaped radiation *** that the localization problem is non-convex and the unknown parameters therein are coupled together,this paper adopts the second-order cone relaxation and alternating optimization techniques in the proposed estimation ***,to provide a performance benchmark for any localization method,the corresponding Cramer-Rao lower bounds(CRLB)of estimating the unknown position and transmission orientation of the source are *** and simulation results demonstrate that the presented algorithm effectively resolves the problem,and its estimation performance is close to the CRLB for the localization with the hybrid measurements.

关键词： localization directional source CRLB RSS AOA wireless sensor network(WSN) second-order cone programming(SOCP)

来源：评论

学校读者我要写书评

暂无评论

Prognostic Prediction for Non-small-Cell Lung Cancer Based on Deep Neural Network and Multimodal Data 17th

Prognostic Prediction for Non-small-Cell Lung Cancer Based o...

引用

17th International Conference on Intelligent Computing, ICIC 2021

作者： Zhang, Zhong-Si Xu, Fei Jiang, Han-Jing Chen, Zhan-Heng Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University No. 4800 Caoan Road Shanghai201804 China

ISBN: (纸本)9783030845315

Non-small-cell lung cancer (NSCLC) is the most common lung cancer with poor prognosis. Prognostic prediction is significant in improving the prognosis of NSCLC patients. Clinical information and multi-omics data including gene expression, miRNA, copy number variations, and DNA methylation are closely related to NSCLC prognosis. In this study, we propose a deep neural network to conduct prognostic prediction for NSCLC patients based on all five types of data. Given the high dimensional features of the omics data, past works reduce the feature dimension by regression analysis or correlation sorting algorithms. A shortcoming of these methods is that only a small number of features are considered. To overcome it, we propose a convolution neural network-based feature transformation method, which considers all features and extracts the abstract representations of the omics data. Based on the representations, we predict the five-year survival status of NSCLC patients. The results show that our method achieves a more precise prediction than previous work, with the areas under the curve improved from 0.8163 to 0.8508 and the accuracy improved from 0.7544 to 0.8096. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

An External Denoising Framework for Magnetic Resonance Imaging: Leveraging Anatomical Similarities Across Subjects with Fast Searches

An External Denoising Framework for Magnetic Resonance Imagi...

引用

IEEE International Conference on signal and image processing (ICSIP)

作者： Lifeng Mei Sixing Liu Chenhui Tang Jiaqia Cai Jingli Wang Yilong Liu Ed X. Wu Mengye Lyu College of Health Science and Environmental Engineering Shenzhen Technology University Shenzhen China College of Big Data and Internet Shenzhen Technology University Shenzhen China Guangdong-Hongkong-Macau Institute of CNS Regeneration Key Laboratory of CNS Regeneration (Ministry of Education) Jinan University Guangzhou China Department of Electrical and Electronic Engineering Laboratory of Biomedical Imaging and Signal Processing The University of Hong Kong Hong Kong China

External denoising, also known as reference-based denoising, utilizes information from clean reference images, yielding more robust results than internal denoising, especially in situations with high noise levels. The effectiveness of reference-based denoising relies on the similarity between reference and noisy images. In this paper, we introduce a novel external denoising framework for magnetic resonance imaging (MRI) that takes advantage of the inherent anatomical structure similarities across subjects. Our framework employs noise-resistant neural networks to extract deep features, which facilitate searches for high-quality reference images from a large external database. This versatile framework is compatible with all reference-based denoising algorithms, obviating the need for acquiring additional reference images from the subject under examination.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Short-Time Variational Mode Decomposition

arXiv

引用

arXiv 2025年

作者： Jia, Hao Cao, Pengfei Liang, Tong Caiafa, Cesar F. Sun, Zhe Kushihashi, Yasuhiro Grau, Antoni Bolea, Yolanda Duan, Feng Solé-Casals, Jordi School of Medicine Nankai University Tianjin China Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Tianjin China Universitat Politècnica de Catalunya Barcelona Spain Mechanical Systems Engineering Nippon Institute of Technology Saitama Japan Villa Elisa Argentina Computational Bioengineering Laboratory Faculty of Health Data Science Juntendo University Tokyo Japan Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Spain

Variational mode decomposition (VMD) and its extensions like Multivariate VMD (MVMD) decompose signals into ensembles of band-limited modes with narrow central frequencies. These methods utilize Fourier transformations to shift signals between time and frequency domains. However, since Fourier transformations span the entire time-domain signal, they are suboptimal for non-stationary time series. We introduce Short-Time Variational Mode Decomposition (STVMD), an innovative extension of the VMD algorithm that incorporates the Short-Time Fourier transform (STFT) to minimize the impact of local disturbances. STVMD segments signals into short time windows, converting these segments into the frequency domain. It then formulates a variational optimization problem to extract band-limited modes representing the windowed data. The optimization aims to minimize the sum of the bandwidths of these modes across the windowed data, extending the cost functions used in VMD and MVMD. Solutions are derived using the alternating direction method of multipliers, ensuring the extraction of modes with narrow bandwidths. STVMD is divided into dynamic and non-dynamic types, depending on whether the central frequencies vary with time. Our experiments show that non-dynamic STVMD is comparable to VMD with properly sized time windows, while dynamic STVMD better accommodates non-stationary signals, evidenced by reduced mode function errors and tracking of dynamic central frequencies. This effectiveness is validated by steady-state visual-evoked potentials in electroencephalogram signals. Copyright © 2025, The Authors. All rights reserved.

关键词： Cost functions

来源：评论

学校读者我要写书评

暂无评论

Memory Analysis for Memristors and Memristive Recurrent Neural Networks

引用

IEEE/CAA Journal of Automatica Sinica 2020年第1期7卷 96-105页

作者： Gang Bao Yide Zhang Zhigang Zeng Key Laboratory of Cascaded Hydropower Stations Operation and Control Electrical Engineering and New EnergyChina Three Gorges UniversityYichang 443002and Hubei Key Laboratory of Applied Mathematics(Hubei University)Wuhan 430074China Department of Medical Engineering California Institute of TechnologyPasadenaCalifornia 91125 USA School of Artificial Intelligence and Automation Huazhong University of Science and Technologyand also with the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of ChinaWuhan 430074China

Traditional recurrent neural networks are composed of capacitors, inductors, resistors, and operational *** neural networks are constructed by replacing resistors with memristors. This paper focuses on the memory analysis,i.e. the initial value computation, of memristors. Firstly, we present the memory analysis for a single memristor based on memristors’ mathematical models with linear and nonlinear ***, we present the memory analysis for two memristors in series and parallel. Thirdly, we point out the difference between traditional neural networks and those that are memristive. Based on the current and voltage relationship of memristors, we use mathematical analysis and SPICE simulations to demonstrate the validity of our methods.

关键词： Dopant drift memory memristive neural networks memristor

来源：评论

学校读者我要写书评

暂无评论

Distributed Online Optimization for Charging and Discharging of Plug-in Electric Vehicles

Distributed Online Optimization for Charging and Discharging...

引用

IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Haiming Xiong Ming Chi Zhi-Wei Liu Dandan Hu Liang-Liang Zhu Cheng Jiang Jun-Ping Gui Yao Rao School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan China School of Management South-Central University for Nationalities Wuhan China Nari Group Corporation State Grid Electric Power Research Institute Nanjing China State Grid Electric Power Research Institute Wuhan Efficiency Evaluation Company Limited Wuhan China

The increasing number of uncoordinated charging and discharging for plug-in electric vehicles (PEVs) poses new challenges to the operation of the distribution network. We introduce a dynamic price mechanism so that charging at peak load will lead to an increase in cost. Then, a distributed online charging and discharging scheduling scheme for PEVs based on the dynamic price mechanism is proposed, considering the minimization of charging cost and battery degradation cost. Unlike most of the existing studies that require the future information of PEVs to schedule charging, the proposed algorithm can solve the scheduling problem for PEVs by taking advantage of a distributed alternating direction method of multipliers (ADMM). Simulation results demonstrate the effectiveness and correctness of the proposed scheduling scheme.

关键词： Plug-in electric vehicles Schedules Costs Heuristic algorithms Simulation Dynamic scheduling Minimization

来源：评论

学校读者我要写书评

暂无评论

Machine learning prediction of network dynamics with privacy protection

arXiv

引用

arXiv 2022年

作者： Xia, Xin Su, Yansen Lü, Linyuan Zhang, Xingyi Lai, Ying-Cheng Zhang, Hai-Feng The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui University Hefei230601 China The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei230601 China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610054 China School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States

Predicting network dynamics based on data, a problem with broad applications, has been studied extensively in the past, but most existing approaches assume that the complete set of historical data from the whole network is available. This requirement presents a great challenge in applications, especially for large, distributed networks in the real world, where data collection is accomplished by many clients in a parallel fashion. Often, each client only has the time series data from a partial set of nodes and the client has access to only partial timestamps of the whole time series data and partial structure of the network. Due to privacy concerns or license related issues, the data collected by different clients cannot be shared. To accurately predict the network dynamics while protecting the privacy of different parties is a critical problem in the modern time. Here, we propose a solution based on federated graph neural networks (FGNNs) that enables the training of a global dynamic model for all parties without data sharing. We validate the working of our FGNN framework through two types of simulations to predict a variety of network dynamics (four discrete and three continuous dynamics). As a significant real-world application, we demonstrate successful prediction of State-wise influenza spreading in the USA. Our FGNN scheme represents a general framework to predict diverse network dynamics through collaborative fusing of the data from different parties without disclosing their privacy. © 2022, CC BY.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

An Enhanced Coarse-to-Fine Framework for the Segmentation of Clinical Target Volume 23rd

An Enhanced Coarse-to-Fine Framework for the Segmentation of...

引用

Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR images Challenge, ABCs 2020, Learn2Reg Challenge, L2R 2020 and Thyroid Nodule Segmentation and Classification in Ultrasound images Challenge, TN-SCUI 2020 held in conjunction with 23rd International Conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Chen, Huai Qian, Dahong Liu, Weiping Li, Hui Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Algorithm and Research Shanghai Aitrox Technology Co. Ltd. Shanghai China National Key Laboratory of Science and Technology on Nano/Micro Fabrication Key Laboratory for Thin Film and Micro Fabrication of the Ministry of Education Institute of Micro-Nano Science and Technology Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783030718268

In radiation therapy, obtaining accurate boundary of the clinical target volume (CTV) is the vital step to decrease the risk of treatment failures. However, it is a time-consuming and laborious task to obtain the delineation by hand. Therefore, an automatic algorithm is urgently needed to realize accurate segmentation. In this paper, we propose an enhanced coarse-to-fine frameworkto automatically fuse the information of CT, T1 and T2 images to get the target region. This framework includes a coarse-segmentation stage to identify the region of interest (ROI) of targets and a fine-segmentation stage to iteratively refine the segmentation. In the coarse-segmentation stage, the F-loss is proposed to keep the high recall rate of the ROI. In the fine segmentation, the ROI of target will be first cropped according to the ROI obtained by coarse-segmentation and be fed into a 3D-Unet to get the initial results. Then, the prediction and medium features will be set as additional information for the next one network to refine the results. When evaluated on the validation dataset of challenge of Anatomical Brain Barriers to Cancer Spread (ABCs), our method won the 3th place in the public leaderboard. © 2021, Springer Nature Switzerland AG.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Secure Optical Wireless Links with Dynamic Beam and Diversity Receiver 7

Secure Optical Wireless Links with Dynamic Beam and Diversit...

引用

7th IEEE International Conference on Network Intelligence and Digital Content, IC-NIDC 2021

作者： DIng, Jupeng Lin, Chih I Wang, Jintao Yang, Hui Wang, Lili School of Information Science and Engineering Xinjiang University Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region China China Mobile Research Institute Beijing China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing China Ludong University School of Information and Electrical Engineering Yantai China

ISBN: (纸本)9781665405829

Optical wireless communication (OWC) has emerged as one promising candidate to mitigate the frequency spectrum crisis. Due to the broadcast nature of OWC channel, physical layer security (PLS) techniques have been considered and explored to improve the transmission confidentiality of OWC links. However, almost all current schemes focus on scenarios including multiple transmitters and fail to match the scenarios with limited transmitters, even single transmitter. For addressing this issue, in this work, the dynamic optical beam based PLS enhancement scheme is proposed. Unlike conventional Lambertian beam based technique paradigm, the above scheme tentatively utilize the commercially available non-Lambertian beam to configure the secure OWC links. Numerical results show that, compared with about 1.10 bps/Hz average secrecy capacity (SC) of the conventional configuration, up to 1.93 bps/Hz average SC gain could be provided by the proposed dynamic beam scheme. Moreover, this potential gain will be increased to about 2.39 bps/Hz when the diversity combing is available for the legitimate user receiver. © 2021 IEEE.

关键词： Dynamics

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：