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arXiv 2022年

作者： Zeng, Zhi Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications Nanjing210003 China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China

We present an efficient scheme for the complete analysis of hyperentangled Greenberger-Horne-Zeilinger (GHZ) state in polarization and time-bin degrees of freedom with two steps. First, the polarization GHZ state is distinguished completely and nondestructively, resorting to the controlled phase flip (CPF) gate constructed by the cavity-assisted interaction. Subsequently, the time-bin GHZ state is analyzed by using the preserved polarization entanglement. With the help of CPF gate and self-assisted mechanism, our scheme can be directly generalized to the complete Nphoton hyperentangled GHZ state analysis, and it may have potential applications in the hyperentanglement-based quantum communication. © 2022, CC BY.

关键词： Polarization

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Geometry Based LM of Robot to Imitate Human Motion with Kinect 4

Geometry Based LM of Robot to Imitate Human Motion with Kine...

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

作者： Guo, Wei Chen, Jianxin Zhang, Ming Pan, Zhaolai Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing210003 China

ISBN: (纸本)9781728123257

Humanoid robots are easily accepted by people because they have a body shape and appearance close to human. Humanoid robots have many application scenarios and great application potentials. Nevertheless, it is not easy to realize robot simulation due to the different physical structures of human bodies and robots. In this paper, we propose a geometry based LM algorithm for the robot to imitate the human motion of upper limb in real time. This method greatly improves the joint angle similarity under the premise of ensuring the similarity of the end trajectories. We designed a comprehensive and unrestricted motion simulation experiment to verify this method. © 2019 IEEE.

关键词： Inverse kinematics

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Research on DNS Resolution technology based on Blockchain

Research on DNS Resolution Technology based on Blockchain

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Chinese Automation Congress (CAC)

作者： Wang Hu-qing Zhang Ying-jiao Zhang Xu Sun Zhi-xin Post Industry Technology Research and Development Center of the State Posts Bureau(Intemet of Things Technology) Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China School of Modern Posts Nanjing University of Posts and Telecommunications Nanjing China Aahui Yougu Express Intelligent Technology Co. LTD Nanjing China

DNS completes the resolution of domain names to IP addresses, which is an effective means to realize various In-ternet applications, and is also an indispensable part of Internet infrastructure, playing an important role. However, the resolution efficiency and security of DNS have always been a hot spot in the industry. This study deeply discusses the principle of Blockchain-based DNS resolution and conducts extensive re-search on Blockchain-based DNS resolution schemes. Based on compatibility with traditional DNS, these schemes are divided into two categories: compatible and incompatible. For different scenarios, we conduct a detailed analysis and comparison, focusing on the technical characteristics and performance of each so-lution. Finally, the future research direction is looked forward to, and the full text is summarized.

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Exploring MEC Server Strategy in Blockchain networks: Mining for Mobile Users or for Self

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IEEE Transactions on Mobile Computing 2025年

作者： Ling, Xintong Jiang, Rui Cao, Weihang Chen, Mingkai Wang, Jiaheng Ding, Zhi Gao, Xiqi Nanjing University of Posts and Telecommunications Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing210003 China University of Maryland College Park Department of Electrical and Computer Engineering College ParkMD20742 United States Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Purple Mountain Laboratories Nanjing210023 China Southeast University School of Cyber Science and Engineering Nanjing210096 China University of California Department of Electrical and Computer Engineering DavisCA95616 United States

Blockchain-based decentralized applications (DApps) offer enhanced security and decentralization features;nevertheless, their maintenance demands substantial computational resources and poses challenges for deployment in mobile networks. To address this, a number of studies have explored offloading blockchain mining tasks from mobile users to mobile edge computing (MEC) servers. However, the existing literature overlooks the fact that MEC servers can not only mine for mobile users but also mine for themselves, potentially explaining why MEC mining offloading has not gained broad acceptance within the industry. In this work, we exploit a more practical case and rethink the question of whether MEC servers lease computing power to mobile users by taking into account that MEC servers can mine for themselves. We establish a game model and apply backward induction to analytically characterize Nash equilibria for mining strategies adopted by MEC and mobile users. Our findings suggest that, if MEC can mine for self, MEC would mine for mobile users only under specific conditions where mobile users possess superior information gathering capability (at least better than the MEC server) or the whole blockchain system exhibits significant network value. We further provide a series of simulations to verify our conclusion and illustrate the impact of network parameters on the strategies of both sides. © 2025 IEEE.

关键词： Mobile edge computing

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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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Age of Information Minimization in Intelligent Reflecting Surface-Aided Covert communications

Age of Information Minimization in Intelligent Reflecting Su...

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IEEE Globecom Workshops

作者： Chao Wang Zan Li Yue Zhao Derrick Wing Kwan Ng Naofal Al-Dhahir State Key Laboratory of Integrated Service Network Xidian University Xi’an China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia Department of Electrical and Computer Engineering the University of Texas at Dallas Richardson TX USA

ISBN: (纸本)9781665459761

In this work, we investigate the covert information freshness in intelligent reflecting surface (IRS)-aided multi antenna communications, where a public full-duplex user (Alice) and a private full-duplex user (Bob) exchange information in the presence of a watchful warden (Willie). Specifically, the covert transmission from Bob to Alice is shielded from Willie under the mask of Alice’s undisguised signal transmission. To minimize the covert information age, we exploit the channel statistics to jointly design the active beamforming at Alice and Bob, the passive beamforming at the IRS, and the blocklength of the confidential data packet under the constraints of the maximum tolerable age of information (AoI) at Bob and communication covertness. Although the considered joint optimization problem is nonconvex, we employ the successive convex approximation to design an iterative algorithm to obtain an efficient suboptimal solution. Numerical results demonstrate the superiority of our proposed joint optimization algorithm and further show that the enhanced covert channel quality can reduce the required blocklength to improve information freshness.

关键词： Array signal processing Error probability Simulation Full-duplex system Information age Approximation algorithms Reflector antennas

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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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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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