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作者： Zhang, Ming Chen, Jianxin Wei, Xin Zhang, Dezhou Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Nanjing China

The ability to realize human-motion imitation using robots is closely related to developments in the field of artificial intelligence. However, it is not easy to imitate human motions entirely owing to the physical differences between the human body and robots. In this paper, we propose a work chain-based inverse kinematics to enable a robot to imitate the human motion of upper limbs in real time. Two work chains are built on each arm to ensure that there is motion similarity, such as the end effector trajectory and the joint-angle configuration. In addition, a two-phase filter is used to remove the interference and noise, together with a self-collision avoidance scheme to maintain the stability of the robot during the imitation. Experimental results verify the effectiveness of our solution on the humanoid robot Nao-H25 in terms of accuracy and real-time performance. © 2018 ETRI

关键词： Anthropomorphic robots

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Dynamic Mapping and Quality of Service Driven Re-Embedding in Virtualization Environment

Dynamic Mapping and Quality of Service Driven Re-Embedding i...

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IEEE Global communications Conference

作者： Haotong Cao Shengchen Wu Qin Wang Longxiang Yang Key Lab of Broadband Wireless Communication and Sensor Network Technique Nanjing University of Posts and Telecommunications Ministry of Education Nanjing China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Embedding customized virtual networks (VNs) onto the underlying physical network (PN) is a key issue in virtualization research. This issue is virtual network embedding (VNE). Since the issue inception, multiple mapping algorithms have been studied. However, prior mapping algorithms are mostly static. Existing dynamic mapping algorithms just focus on accepting as many VNs as possible. No existing dynamic algorithm considers optimizing the quality of service (QoS) performance of each accepted VN. On these backgrounds, we jointly investigate the dynamic VN embedding and optimize the QoS performance of each accepted VN. A dynamic heuristic algorithm is proposed. When one VN service is requested, the VN will be mapped by the dynamic heuristic algorithm. If the QoS demand of the VN is not guaranteed, the re-embedding scheme of the heuristic algorithm will be driven. Certain virtual elements of the VN will be re-embedded. The dynamic embedding algorithm ensures flexible VN assignment and improves the PN resource utilization. Finally, simulation results are illustrated in order to highlight our proposed dynamic algorithm. For instance, VN acceptance ratio of our dynamic heuristic algorithm improves at least 13%.

关键词： Heuristic algorithms Quality of service Delays Bandwidth Propagation delay Virtualization

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Practical Reference-Frame-Independent Measurement-Device-Independent Quantum key Distribution Systems Against the Worst Relative Rotation of Reference Frames

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communications in Theoretical Physics 2018年第10期70卷 379-383页

作者： Chun-Mei Zhang Jian-Rong Zhu Qin Wang Institute of Quantum Information and Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China

Reference-frame-independent measurement-device-independent QKD(RFI-MDI-QKD), immune to both the slow unknown drift of reference frames and detector side channel attacks, can generate information-theoretic secure keys. Despite its natural resistance to the slow drift of reference frames, the performance of practical RFI-MDI-QKD systems descends with the increasing drift of reference frames. In this paper, we demonstrate the worst relative rotation of reference frames for practical RFI-MDI-QKD systems, and investigate their performance against the worst-case scenario,both of which are unclear to date. Simulation results show that, practical RFI-MDI-QKD systems can achieve quite good performance even against the worst-case scenario, which clearly demonstrates that it is possible to implement practical MDI-QKD systems with freely drifting reference frames.

关键词： reference-frame-independent measurement-device-independent quantum key distribution relative rotation

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A GA-Optimized Weighted Mixed Kernel Function of SVM Based on Information Entropy 1

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5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Zhao, Xuejian Zhang, Xinhui Xiang, Feng Ye, Feng Sun, Zhixin Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Nanjing China National Engineering Laboratory for Logistics Information Technology YuanTong Express Co. Ltd. Shanghai China Department of Electrical and Computer Engineering University of Dayton DaytonOH United States

ISBN: (数字)9783030242657

ISBN: (纸本)9783030242640

The parameters selection and optimization of kernel function is the core of support vector machine (SVM), which is closely related to the distribution of datasets. It can be obtained a series of different group index, either mapped by different kernel functions on the same dataset or did by same kernel function on the different subsets. We analyze the impact of the choice of kernel functions and parameters on the performance of SVM, and propose a GA-optimized weighted mixed kernel function of SVM based on information entropy (GA-IE-RBF-SVM). The algorithm uses the information entropy to improve the contribution of the features that are conducive to classification firstly to mitigate falling into a local optimum, then learn from the idea of multi-core learning to enhance the adaptability of the algorithm. The optimal genetic algorithm (GA) is used to select the type of mixed kernel function, kernel function parameters and error penalty factor. The experimental results show that compared with other similar algorithms, this algorithm has a higher classification accuracy rate and faster convergence speed. © 2019, Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Twin-field quantum digital signatures

arXiv

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arXiv 2020年

作者： Zhang, Chun-Hui Fan, Yu-Teng Zhang, Chun-Mei Guo, Guang-Can Wang, Qin Institute of quantum information and technology Nanjing University of Posts and Telecommunications Nanjing210003 China Broadband Wireless Communication and Sensor Network Technology Key Lab of Ministry of Education NUPT Nanjing210003 China Telecommunication and Networks National Engineering Research Center NUPT Nanjing210003 China Key Laboratory of Quantum Information CAS University of Science and Technology of China Hefei Anhui230026 China

Digital signature is a key technique in information security, especially for identity authentications. Compared with classical correspondence, quantum digital signatures (QDSs) provide a considerably higher level of security, i.e., information-theoretic security. At present, its performance is limited by key generation protocols (e.g., BB84 or measurement-device-independent protocols), which are fundamentally limited in terms of channel capacity. Fortunately, the recently proposed twin-field quantum key distribution can overcome this limit. This paper presents a twin-field QDS protocol and details a corresponding security analysis. In its distribution stage, a specific key generation protocol, the sending-or-not-sending twin-field protocol, has been adopted and full parameter optimization method has been implemented. Numerical simulation results show that the new protocol exhibits outstanding security and practicality compared with all other existing protocols. Therefore, the new protocol paves the way toward real-world applications of QDSs. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum cryptography

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Spectrum Occupancy Prediction for Internet of Things via Long Short-Term Memory

Spectrum Occupancy Prediction for Internet of Things via Lon...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Haoyu Li Xiaojin Ding Yiguang Yang Xiaogu Huang Genxin Zhang Key Laboratory of Broadband Wireless Communication and Sensor Network Technology NUPT National Mobile Communications Research Laboratory Southeast University Nanjing China

With the development of Internet of things (IoT), the demand on spectrum is increasing rapidly. Moreover, due to lack of power and the feature of short burst, the signals of IoT may be transmitted relying on accessing the idle spectrum, leading to a higher successful transmitting probability. Thus, the spectrum should be allocated in advance for the ongoing terminals of IoT. In this paper, a long short-term memory aided spectrum-prediction (LSTMSP) scheme has been conceived by analyzing the relationships between time and frequency of historical spectrum data. Performance evaluations on realworld spectrum data show that the accuracy of the spectrum occupancy prediction is above 0.7, demonstrating the benefits of the conceived LSTMSP method.

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Deep Reinforcement Learning for User Association in Heterogeneous networks with Dual Connectivity

Deep Reinforcement Learning for User Association in Heteroge...

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IEEE Conference on wireless communications and networking

作者： Mengjie Yi Yan Zhang Xijun Wang Chao Xu Xiao Ma State Key Lab of Integrated Service Networks Information Science Institue Xidian University Xi’an Shaanxi China School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences 865 Changning Road Shanghai China School of Information Engineering Northwest A&F University Yangling Shaanxi China Shaanxi Normal University Xi’an P. R. China

The dual connectivity is emerging as a promising solution to boost capacity in heterogeneous networks. However, it is challenging to obtain an optimal user association in heterogeneous networks with dual connectivity, due to its non-convex and combinatorial nature. In this paper, we propose a user association scheme based on deep reinforcement learning to maximize the overall network utility, which takes both throughput and user fairness into account, in the downlink of a heterogeneous network. Particularly, each user associates with the macro base station (BS) and a micro BS. We apply a deep Q-network (DQN) to obtain the nearly optimal policy to associate the users and micro BSs. Simulation results demonstrate that DQN-based user association performs better compared to the conventional user association schemes in heterogeneous networks with dual connectivity, and it behaves good scalability when the environment changes.

关键词： Base stations Simulation Scalability Conferences Reinforcement learning Markov processes Throughput

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Quantum-enhanced interferometry with asymmetric beam splitters

arXiv

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arXiv 2020年

作者： Zhong, Wei Wang, Fan Zhou, Lan Xu, Peng Sheng, Yu-Bo Institute of Quantum Information and Technology Nanjing University of Posts and Telecommunications Nanjing210003 China National Laboratory of Solid State Microstructures Nanjing University Nanjing210093 China School of Science Nanjing University of Posts and Telecommunications Nanjing210003 China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Nanjing210003 China

In this paper, we investigate the phase sensitivities in two-path optical interferometry with asymmetric beam splitters. Here, we present the optimal conditions for the transmission ratio and the phase of the beam splitter to gain the highest sensitivities for a general class of non-classical states with parity symmetry. Additionally, we address the controversial question of whether the scheme with a combination of coherent state and photon-added or photon-subtracted squeezed vacuum state is better or worse than the most celebrated one using a combination of coherent state and squeezed vacuum state. Copyright © 2020, The Authors. All rights reserved.

关键词： Fisher information matrix

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Deep Reinforcement Learning for Fresh Data Collection in UAV-assisted IoT networks

Deep Reinforcement Learning for Fresh Data Collection in UAV...

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IEEE Conference on Computer communications Workshops, INFOCOM Wksps

作者： Mengjie Yi Xijun Wang Juan Liu Yan Zhang Bo Bai Science and Technology on Communication Network Laboratory Shijiazhuang Hebei China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences 865 Changning Road Shanghai China School of Electrical Engineering and Computer Science Ningbo University Zhejiang China Theory Lab (FKA Future Network Theory Lab) 2012 Labs Huawei Technologies Co. Ltd. Hong Kong

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186962

Due to the flexibility and low operational cost, dispatching unmanned aerial vehicles (UAVs) to collect information from distributed sensors is expected to be a promising solution in Internet of Things (IoT), especially for time-critical applications. How to maintain the information freshness is a challenging issue. In this paper, we investigate the fresh data collection problem in UAV-assisted IoT networks. Particularly, the UAV flies towards the sensors to collect status update packets within a given duration while maintaining a non-negative residual energy. We formulate a Markov Decision Process (MDP) to find the optimal flight trajectory of the UAV and transmission scheduling of the sensors that minimizes the weighted sum of the age of information (AoI). A UAV-assisted data collection algorithm based on deep reinforcement learning (DRL) is further proposed to overcome the curse of dimensionality. Extensive simulation results demonstrate that the proposed DRL-based algorithm can significantly reduce the weighted sum of the AoI compared to other baseline algorithms.

关键词： sensors Trajectory Data collection Unmanned aerial vehicles Internet of Things wireless sensor networks Energy consumption

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Homomorphic Encryption technology for Cloud Computing

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Procedia Computer Science 2019年 154卷 73-83页

作者： Min Zhao E Yang Geng School of Computer Nanjing University of Post and Telecommunications Nanjing 210003 China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education Nanjing University of Post and Telecommunications Nanjing 210003 China

Four kinds of single homomorphic encryption algorithm were summarized for the advantages of homomorphic encryption technology in the cloud environment. It analyzed the security characteristics of four kinds of encryption algorithms. On the basis of experiments, the paper compared the efficiency of four single homomorphic encryption algorithms, gave the application scenarios of various algorithms, and introduced the research and application of single homomorphic encryption algorithm in the cloud environment. We described five kinds of fully homomorphic encryption algorithms, summarized the research situation and its application in the cloud environment and the performance of some fully homomorphic encryption algorithms were analyzed. Finally, we proposed the research direction for the subsequent studies.

关键词： Single homomorphic encryption Fully homomorphic encryption (FHE) Cloud computing Ciphertext computing

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