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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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Unsupervised Learning-Based Fast Beamforming Design for Downlink MIMO

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IEEE ACCESS 2019年 7卷 7599-7605页

作者： Huang, Hao Xia, Wenchao Xiong, Jian Yang, Jie Zheng, Gan Zhu, Xiaomei Nanjing Univ Posts & Telecommun Key Lab Broadband Wireless Commun & Sensor Networ Minist Educ Nanjing 210003 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Natl Engn Res Ctr Commun & Network Technol Nanjing 210003 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Key Lab Wireless Commun Nanjing 210003 Jiangsu Peoples R China Loughborough Univ Wolfson Sch Mech Elect & Mfg Engn Loughborough LE11 3TU Leics England Nanjing Tech Univ Coll Comp Sci & Technol Nanjing 210003 Jiangsu Peoples R China

In the downlink transmission scenario, power allocation and beamforming design at the transmitter are essential when using multiple antenna arrays. This paper considers a multiple input-multiple output broadcast channel to maximize the weighted sum-rate under the total power constraint. The classical weighted minimum mean-square error (WMMSE) algorithm can obtain suboptimal solutions but involves high computational complexity. To reduce this complexity, we propose a fast beamforming design method using unsupervised learning, which trains the deep neural network (DNN) offline and provides real-time service online only with simple neural network operations. The training process is based on an end-to-end method without labeled samples avoiding the complicated process of obtaining labels. Moreover, we use the "APoZ" -based pruning algorithm to compress the network volume, which further reduces the computational complexity and volume of the DNN, making it more suitable for low computation-capacity devices. Finally, the experimental results demonstrate that the proposed method improves computational speed significantly with performance close to the WMMSE algorithm.

关键词： MIMO beamforming deep learning unsupervised learning network pruning

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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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Multi-dimensional speaker information recognition with multi-task neural network 4

Multi-dimensional speaker information recognition with multi...

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4th IEEE International Conference on Computer and communications, ICCC 2018

作者： Chen, Haixia Xu, Longting Yang, Zhen Broadband Wireless Communication and Sensor Network Technology Key Lab. Nanjing University of Posts and Telecommunications China Department of Information and Communication Engineering Donghua University Shanghai201620 China

ISBN: (纸本)9781538683392

This paper proposes a novel approach to simultaneously estimate speaker identity and the other two traits of the speaker, specifically, emotion and gender. This simultaneous process is named as multi-dimensional speaker information recognition. We choose i-vector to represent the utterance with different duration as fixed length vector. In our paper, we first build individual neural networks (NN) for each single recognition task. In this single task, the input is i-vector of one speaker and the output is the corresponding one-hot vector. We then design a multi-task learning (MTL), the difference with the single task is the NN output, which contains three parallel one-hot vectors for MTL. The one-hot vectors denote the identity, emotion and gender information, respectively. The proposed approach is conducted on the KSU-Emotions corpus, which was recorded mainly for emotional recognition. Experimental results show that the best multi-task mechanism recognition accuracy is 4.6% better than the single task. © 2018 IEEE.

关键词： Speech recognition

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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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Joint DBF and SAO Parallel Filtering Based on Multithread Load Balancing

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Hao Ma Dong Hu Yi Li Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing 210003 China Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing 210003 China Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing 210003 China

This paper presents a joint parallel loop filtering algorithm based on multi-thread load balancing in HEVC decoding, which implements the parallel processing of deblocking filtering (DBF) and sample adaptive compensation (SAO). Because of the diversity of video, the texture of different regions in an image is also different, which leads to various CTU partition methods. Therefore, the number of the boundary to be filtered is greatly different, resulting the computation load among multiple threads unbalanced in parallel processing. To solve this problem, an area division scheme is proposed, which divides the image into multiple areas, and the number of boundaries to be filtered in each area is similar. Then, the mapping relationship table is used to allocate these areas to multiple threads for parallel processing, so as to achieve the load balancing among the filtering threads. Finally, the cache technology is used to combine DBF and SAO to reduce the delay between them and improve the overall parallelism of the loop filter. Experimental results show that the performance of the proposed load balancing joint filtering algorithm is 8.15% higher than the previous scheme.

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Scale Adaptive Block Target Tracking Based on Multi-layer Convolution Features and Kernel Correlation Filter

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Ting Zhang Dong Hu Jing Zhang Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing 210003 China Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing 210003 China Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing 210003 China

Target tracking is currently a hot research topic in Computer Vision and has a wide range of use in many research fields. However, due to factors such as occlusion, fast motion, blur and scale variation, tracking method still needs to be deeply studied. In this paper, we propose a block target tracking method based on multi-convolutional layer features and Kernel correlation filter. Our method divides the tracking process into two parts: target position estimation and target scale estimation. First, we block the target frame based on the condition number. Second, we extract the features by the convolutional layer and apply it to the kernel correlation filter to get the center position of different block targets. With the reliability of different blocks measured by the Barker coefficient, the overall target position center is obtained. Then, the affine transformation is adopted to achieve the scale adaptation. The algorithm in this paper is evaluated by the public video sequences in OTB-2013. Numerous experimental results demonstrate that the proposed tracking method can achieve target scale adaptation and effectively improve the tracking accuracy.

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Experimental generation of entanglement-assisted quantum random access code

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Physical Review A 2019年第5期99卷 052313-052313页

作者： Xiao-Run Wang Lu-Yan Wu Chen-Xi Liu Tong-Jun Liu Jian Li Qin Wang Institute of Quantum Information and Technology Nanjing University of Posts and Telecommunications Nanjing 210003 China Broadband Wireless Communication and Sensor Network Technology Key Lab of Ministry of Education NUPT Nanjing 210003 China Telecommunication and Networks National Engineering Research Center NUPT Nanjing 210003 China

n→m random access code (RAC) is an information task to encode an n-bit random message into a shorter m-bit string. Resulting from the compression loss from n to m bits, there exists success probabilities for the string receiver to recover any bits of the initial message. It has been shown that success probabilities of bit recovery can be enhanced by employing quantum resources. Here we present experimental realizations of 2→1 and 3→1 distributed entanglement-assisted random access codes (EARACs) with a two-photon entangled source. The average success probabilities obtained in our experiment are 0.8491(41) for a 2→1 EARAC and 0.7724(34) for a 3→1 EARAC, exceeding the classical counterparts, respectively. Moreover, through the existing experimental data, success probabilities for n varying from 4 to 10 (m=1) are estimated.

关键词： Quantum channels Quantum entanglement

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Asymmetric sending or not sending twin-field quantum key distribution in practice

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Physical Review A 2019年第6期99卷 062316-062316页

作者： Xing-Yu Zhou Chun-Hui Zhang Chun-Mei Zhang Qin Wang Institute of Quantum Information and Technology Nanjing University of Posts and Telecommunications Nanjing 210003 China Broadband Wireless Communication and Sensor Network Technology Key Lab of Ministry of Education NUPT Nanjing 210003 China Telecommunication and Networks National Engineering Research Center NUPT Nanjing 210003 China

Quantum key distribution (QKD) offers a secret way to share keys between legitimate users which is guaranteed by the law of quantum mechanics. Most recently, the limitation of transmission distance without quantum repeaters was broken through by twin-field QKD [M. Lucamarini et al., Nature (London) 557, 400 (2018)]. Based on its main idea, sending or not-sending (SNS) QKD protocol was proposed [X. B. Wang et al. Phys. Rev. A 98, 062323 (2018)], which filled the remaining security loopholes and can tolerate large misalignment errors. In this paper, we give a more general model for SNS QKD, where two legitimate users, Alice and Bob, can possess asymmetric quantum channels. By applying the method present in the paper, the legitimate users can achieve dramatically increased key generation rates and transmission distances compared with compensating the channel asymmetry and utilizing the original symmetric protocol. Therefore, our present paper represents a further step along the progress of practical QKD.

关键词： Quantum cryptography Quantum information processing

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Practical Phase-Modulation Stabilization in Quantum key Distribution via Machine Learning

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Physical Review Applied 2019年第1期12卷 014059-014059页

作者： Jing-Yang Liu Hua-Jian Ding Chun-Mei Zhang Shi-Peng Xie Qin Wang Institute of Quantum Information and Technology Nanjing University of Posts and Telecommunications Nanjing 210003 China Broadband Wireless Communication and Sensor Network Technology Key Lab of Ministry of Education Ministry of Education Nanjing 210003 China Telecommunication and Networks National Engineering Research Center NUPT Nanjing 210003 China

Practical implementation of quantum key distributions (QKD) requires efficient, real-time feedback control to maintain system stability when facing disturbance from either external environment or imperfect internal components. Usually, a “scanning-and-transmitting” program is adopted to compensate physical parameter variations of devices, which can provide accurate compensation but may cost plenty of time in stopping and calibrating processes, resulting in reduced efficiency in key transmissions. Here, we propose to employ a well-known machine learning model, i.e., the long short-term memory network (LSTM), to predict those physical parameter variations in advance and actively perform real-time control on corresponding QKD devices. Experimentally, we take the phase-coding scheme as an example and run the LSTM-model-based QKD system for more than 10 days. Experimental results show that we can keep the same level of quantum-bit error rate as the traditional scanning-and-transmitting program by employing our machine learning method, but dramatically reducing the scanning time and resulting in significantly enhanced key transmission efficiency. Furthermore, our current machine learning model should also be applicable to other QKD systems using any coding scheme or QKD protocols and thus seems a very promising candidate in large-scale application of quantum communication networks in the near future.

关键词： Optical quantum information processing Quantum cryptography Quantum networks Quantum optics Artificial neural networks communication networks Machine learning Optical interferometry

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