检索结果-内蒙古大学图书馆

第35届中国控制与决策会议

作者： Xintong Wang Yibo Sun Xuan Wang Enliang Wang Zhixin Sun Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Post Industry Technology Research and Development Center of the State Posts Bureau (Internet of Things Technology) Nanjing Universiry of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing University of Posts and Telecommunications School of Information Technology and Electrical Engineering The University of Queensland Brisbane

Traffic prediction is the basis for dynamic network services and resource *** Defined networking(SDN) provides a global view for network measurement and management,and a reliable way for network information collection and network traffic ***,SDN traffic prediction model enabled by deep learning mainly focuses on mining the temporal correlation of traffic data,while the modeling of its spatial correlation remains to be *** the perspective of the problem,a novel Spatial-Temporal Residual Graph Convolutional network(STRGCN) model for SDN Traffic Matrix(TM) prediction is *** with SDN network topology,TM prediction is defined as time series data prediction in non-Euclidean *** GatedConv Blocks are based on Gated Convolutional Layer,and Spatial Graph-Conv Blocks are based on Graph Convolutional network(GCN).Through identity mapping between SpatialTemporal Conv Blocks,the deep spatial and temporal features extraction is *** by TM data from CERNET and GEANT datasets,the accuracy and the timely performance of our model are *** results suggest that the general performance of STRGCN for TM prediction in SDN is superior to the current deep learning models.

关键词： software defined networking traffic prediction traffic matrix graph convolutional neural network spatialtemporal residual graph network

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Study on the segmentation method of multi-phase CT liver tumor based on dual channel U-Nets

Study on the segmentation method of multi-phase CT liver tum...

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2020 International Symposium on Automation, Information and Computing, ISAIC 2020

作者： Xu, Changchang Hu, Dong Zhang, YaoDong Pang, Yuwei Jiangsu Province’s Key Lab of Image Procession and Image Communications Nanjing University of Posts and Telecommunications Nanjing210003 China Education Ministry’s Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Nanjing210003 China Education Ministry’s Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing210003 China first clinical medical college of Nanjing Medical University Nanjing University of Posts and Telecommunications Nanjing210003 China

As a common malignant tumor disease, hepatocellular carcinoma is the most common cancers in the world. The incidence of hepatocellular carcinoma in China is higher than that in the world. Therefore, it is very important for doctors to separate liver and tumor from CT images by means of computer-aided diagnosis and treatment. In this paper, a multiscale DC-CUNets network liver tumor segmentation method is proposed to enhance the fusion of multi-phase image features in CT, the scale of liver tumors, and the optimization of network training process. (1) A multistage CT liver tumor segmentation method based on two-channel cascaded U-Nets (DC-CUNets) is proposed. The liver was segmented using the first-order U-Net, and then the segmented area of interest of the liver was input into the second-order U-Net network to segment liver tumors. We designed two-channel U-Nets to learn the image characteristics of CT images in arterial and venous phases respectively, and to achieve two-channel feature fusion through feature cascade to improve the overall accuracy of liver tumor segmentation.(2) A multistage CT liver tumor segmentation method based on multiscale DC-CUNets was proposed. For the scale problem of liver tumors, we designed a two-layer multiscale void convolution module to obtain image features at different scales for large, medium and small tumors, and fuse the multiscale features at the output of the module. We have replaced the convolution layer of the fourth module in the second-order two-channel liver tumor segmentation U-Nets by the two-layer multiscale cavity convolution module to implement multiscale DC-CUNets. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Convolution

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Arbitrary-Oriented Ship Detection Based on Rotation Region Locating networks in Large Scale Remote Sensing Images 4

Arbitrary-Oriented Ship Detection Based on Rotation Region L...

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4th IEEE International Conference on Image, Vision and Computing, ICIVC 2019

作者： Liu, Xipeng Chen, Jianxin Kang, Bin Zhang, Xuguang Nanjing University of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing China

ISBN: (纸本)9781728123257

Although deep learning has dominated the ship detection domain, it still has two challenges: arbitrary-oriented densely arranged ships cause detection omissions and large scale image contains redundant areas. This paper proposes an effective convolutional neural network framework for arbitrary-oriented ship detection in large scale and complex scenes. In this framework, we propose Cumulative Feature Pyramid networks for multi-receptive-field feature fusion, which enhances high-level semantic information at all scales. Based on the outputs of Region Proposal networks, Rotation Region Locating network predicts rotation bounding box of arbitrary-oriented ships and adopts rotation intersection over union to avoid the effect of ship dense arrangement. For large scale scenes, No-Ship Area Suppression uses OTSU algorithm to generate binary mask and then filter out non-ship regions to reduce redundant computations. Additionally, we firstly build a remote sensing image dataset for ship detection, which contains 4237 images and over 21200 ships. Experimental comparisons with the state-of-the-art approaches validate the effectiveness, accuracy and robustness of the proposed method. © 2019 IEEE.

关键词： Ships

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A Gridless Wideband DOA Estimation Based On Atomic Norm Minimization

A Gridless Wideband DOA Estimation Based On Atomic Norm Mini...

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sensor Array and Multichannel Signal Processing Workshop

作者： Yuanyuan Jiang Dan Li Xiaohuan Wu Wei-Ping Zhu Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

This paper proposes a new method for wideband directionof-arrival (DOA) estimation. The main idea of the proposed method is focusing the array output on frequency point to the reference frequency point. But different from coherent signalsubspace method (CSSM), it has no requirement of an initial DOA estimation. Atomic norm minimization (ANM) is then applied after the focusing step. Our proposed method is able to achieve better estimation performance than other wideband DOA estimators. Numerical simulations are carried out to demonstrate the performance of the proposed method compared to existing ones.

关键词： Direction-of-arrival estimation Frequency-domain analysis Simulation Conferences Estimation Focusing Minimization

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A Gridless Method for Doa Estimation Under the Coexistence of Mutual Coupling and Unknown Nonuniform Noise

A Gridless Method for Doa Estimation Under the Coexistence o...

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sensor Array and Multichannel Signal Processing Workshop

作者： Dan Li Yuanyuan Jiang Xiaohuan Wu Wei-Ping Zhu Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

This paper addresses the issue of direction-of-arrival (DOA) estimation under the coexistence of mutual coupling and nonuniform noise. We first construct a selection matrix based on the structural characteristics of the mutual coupling matrix (MCM) to form a new manifold matrix which is not affected by the mutual coupling effect. Then, we employ the covariance fitting criteria to form a semidefinite programming for DOA estimation. Compared to most of existing sparse methods, our method does not require discretization of the angle space, hence is able to achieve super-resolution. Simulations are carried out to verify the effectiveness of the propose method.

关键词： Manifolds Mutual coupling Direction-of-arrival estimation Fitting Superresolution Estimation Signal processing algorithms

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Deep learning for the design of non-Hermitian topolectrical circuits

arXiv

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arXiv 2024年

作者： Chen, Xi Sun, Jinyang Wang, Xiumei Jiang, Hengxuan Zhu, Dandan Zhou, Xingping College of Integrated Circuit Science and Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China Portland Institute Nanjing University of Posts and Telecommunications Nanjing210003 China College of Electronic and Optical Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China Institute of AI Education Shanghai East China Normal University Shanghai200333 China Institute of Quantum Information and Technology Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China

Non-Hermitian topological phases can produce some remarkable properties, compared with their Hermitian counterpart, such as the breakdown of conventional bulk-boundary correspondence and the non-Hermitian topological edge mode. Here, we introduce several algorithms with multi-layer perceptron (MLP), and convolutional neural network (CNN) in the field of deep learning, to predict the winding of eigenvalues non-Hermitian Hamiltonians. Subsequently, we use the smallest module of the periodic circuit as one unit to construct high-dimensional circuit data features. Further, we use the Dense Convolutional network (DenseNet), a type of convolutional neural network that utilizes dense connections between layers to design a non-Hermitian topolectrical Chern circuit, as the DenseNet algorithm is more suitable for processing high-dimensional data. Our results demonstrate the effectiveness of the deep learning network in capturing the global topological characteristics of a non-Hermitian system based on training data. © 2024, CC BY.

关键词： Eigenvalues and eigenfunctions

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Photonic Modes Prediction via Multi-Modal Diffusion Model

arXiv

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arXiv 2024年

作者： Sun, Jinyang Chen, Xi Wang, Xiumei Zhu, Dandan Zhou, Xingping Portland Institute Nanjing University of Posts and Telecommunications Nanjing210003 China College of Integrated Circuit Science and Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China College of Electronic and Optical Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China Institute of AI Education Shanghai East China Normal University Shanghai200333 China Institute of Quantum Information and Technology Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China

The concept of photonic modes is the cornerstone in optics and photonics, which can describe the propagation of the light. The Maxwell's equations play the role in calculating the mode field based on the structure information, while this process needs a great deal of computations, especially in the handle with a three-dimensional model. To overcome this obstacle, we introduce the Multi-Modal Diffusion model to predict the photonic modes in one certain structure. The Contrastive Language–Image Pre-training (CLIP) model is used to build the connections between photonic structures and the corresponding modes. Then we exemplify Stable Diffusion (SD) model to realize the function of optical fields generation from structure information. Our work introduces Multi-Modal deep learning to construct complex mapping between structural information and light field as high-dimensional vectors, and generates light field images based on this mapping. © 2024, CC BY.

关键词： Maxwell equations

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Resource Allocation for Outage Probability Minimization in Cognitive wireless Powered communication networks 19

Resource Allocation for Outage Probability Minimization in C...

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19th IEEE International Conference on communication technology, ICCT 2019

作者： Li, Qun Xu, DIng Nanjing University of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing China

ISBN: (纸本)9781728105352

This paper considers a cognitive wireless powered communication network (CWPCN) consisting of a cognitive base station (CBS) and a set of secondary users (SUs) sharing spectrum with a primary user (PU). The transmission time is assumed to be divided into two parts, where the CBS transfers energy in the downlink during the first time part, and the SUs use the harvested energy to transmit in the uplink based on the time-division multiple access (TDMA) during the second time part. We investigate the problem for minimizing the average outage probability of the SUs by jointly optimizing the time allocation and the downlink transmit power allocation under the interference power and the transmit power constraints and propose a heuristic algorithm to solve it. Simulations show that the proposed algorithm greatly outperforms the benchmark algorithms. © 2019 IEEE.

关键词： Cognitive radio

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Secure Transmission in Multicarrier Power-Splitting wireless Powered communication networks with Full-duplex Receivers 19

Secure Transmission in Multicarrier Power-Splitting Wireless...

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19th IEEE International Conference on communication technology, ICCT 2019

作者： Li, Qun Xu, DIng Ministry of Education Nanjing University of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing China

ISBN: (纸本)9781728105352

We consider a three-node multicarrier network consisting of a source node, a wireless powered full-duplex destination node, and an eavesdropper. The destination node is assumed to adopt the power splitting technique to coordinate information decoding and energy harvesting. To improve security performance, the destination node can send jamming signals using the harvested energy. The problem for maximizing the secrecy rate by optimizing transmit power allocation at the source node, the transmit power allocation for jamming at the destination node and the power splitting ratio at the destination node is investigated and solved by a proposed iterative algorithm. Simulation results show that the proposed algorithm outperforms other reference algorithms. © 2019 IEEE.

关键词： Energy harvesting

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Heart Rate Detection with the Off-the-Shelf Camera: Static to Non-Static

Heart Rate Detection with the Off-the-Shelf Camera: Static t...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Rui Chen Jianxin Chen LiMing Cheng XiangJun Huang Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Nanjing China

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

Imaging photo-plethysmography (IPPG) technique has been proven to detect heart rate (HR) with a camera. Currently it might be used in a rigid scenario, such as the static subject, the short measurement distance and good illumination. In this paper, we try to relax these constraints by extending the measurement distance and applying it to the non-static subject with IPPG technique using the off-the-shelf camera. To achieve it, the landmark detection method is used to locate the facial region of interest (ROI) for the subject during movements. On this ROI, R/G/B channel signals are extracted with the spatial average of pixels. Then we use a skin appearance model (CHROME) to remove the noise, and HR is estimated from the output of the CHROME through the power spectrum. Experimental results showed that our method works effectively in both static and non-static scenarios. In static scenario, the measurement distance can extend to 4 meters, while the accuracy can reach 86% compared with the results from a finger detection equipment. In non-static scenario, our method achieve better results than previous studies.

关键词： Heart rate Current measurement Predictive models Cameras Skin Velocity measurement Motion measurement

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