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Correction to: Underwater image enhancement using an edge-preserving filtering Retinex algorithm

Multimedia Tools and Applications 2020年第25期79卷 17279-17279页

作者： Peixian Zhuang Xinghao Ding Jiangsu Key Laboratory of Meteorological Observation and Information Processing Jiangsu Technology and Engineering Center of Meteorological Sensor Network School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing China Fujian Key Laboratory of Sensing and Computing for Smart City School of Information Science and Engineering Xiamen University Xiamen China

In the original publication, Equations were incorrectly presented. The original article has been corrected.

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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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QoE-Driven Big Data Management in Pervasive Edge Computing Environment

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Big Data Mining and Analytics 2018年第3期1卷 222-233页

作者： Qianyu Meng Kun Wang Xiaoming He Minyi Guo Jiangsu Engineering Research Center of Communication and Network Technology Nanjing University of Posts and Telecommunications Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai Jiao Tong University

In the age of big data, services in the pervasive edge environment are expected to offer end-users better Quality-of-Experience(QoE) than that in a normal edge environment. However, the combined impact of the storage, delivery, and sensors used in various types of edge devices in this environment is producing volumes of high-dimensional big data that are increasingly pervasive and redundant. Therefore, enhancing the QoE has become a major challenge in high-dimensional big data in the pervasive edge computing environment. In this paper, to achieve high QoE, we propose a QoE model for evaluating the qualities of services in the pervasive edge computing environment. The QoE is related to the accuracy of high-dimensional big data and the transmission rate of this accurate data. To realize high accuracy of high-dimensional big data and the transmission of accurate data through out the pervasive edge computing environment, in this study we focused on the following two ***, we formulate the issue as a high-dimensional big data management problem and test different transmission rates to acquire the best QoE. Then, with respect to accuracy, we propose a Tensor-Fast Convolutional Neural network(TF-CNN) algorithm based on deep learning, which is suitable for high-dimensional big data analysis in the pervasive edge computing environment. Our simulation results reveal that our proposed algorithm can achieve high QoE performance.

关键词： Quality-of-Experience(QoE) high-dimensional big data management deep learning pervasive edge computing

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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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Exploiting Meta-Path with Attention Mechanism for Fine-Grained User Location Prediction in LBSNs

Exploiting Meta-Path with Attention Mechanism for Fine-Grain...

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2019 3rd International Conference on Data Mining, Communications and Information technology (DMCIT 2019)

作者： Zhixiao Wang Wenyao Yan Wendong Wang Ang Gao Lei Yu Shaobo Yang GaoYang Nie School of Computer Science and Engineering Xi'an University of Technology Telematics Group the University of Goettingen Shaanxi Key Laboratory of Network Computing and Security School of Electronic and Information Engineering Xi'an Jiaotong University College of Xi'an Innovation Yan'an University Yan'an University National Satellite Meteorological Center China Meteorological Administration

Based on the huge volumes of user check-in data in LBSNs,users’ intrinsic mobility patterns can be well explored,which is fundamental for predicting where a user will visit next given his/her historical check-in *** there are various types of nodes and interactions in LBSNs,they can be treated as Heterogeneous Information network（HIN） where multiple semantic meta-paths can be *** by the recent success of meta-path context based embedding techniques in HIN,in this paper,we design a deep neural network framework leveraging various meta-path contexts for fine-grained user location *** results based on two real-world LBSN datasets demonstrate the best effectiveness of the proposed approach using various evaluation metrics than others.

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Efficient passive measurement-device-independent quantum key distribution

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Physical Review A 2019年第5期99卷 052325-052325页

作者： 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 Laboratory of Ministry of Education Nanjing University of Posts and Telecommunications Nanjing 210003 China Telecommunication and Networks National Engineering Research Center Nanjing University of Posts and Telecommunications Nanjing 210003 China

To date, only active-decoy-state methods have been adopted in current measurement-device-independent (MDI) quantum key distribution (QKD) protocols, causing security loopholes when modulating multiple light intensities with practical devices. To solve this problem, the passive-decoy-state method will be a good choice. Until today practical passive-decoy-state MDI QKD is still missing. In this paper, based on a recently discussed structure of heralded single-photon sources, we propose a passive-decoy-state MDI QKD protocol. In present work, one does not need to modulate light sources into different intensities, but utilizes the built-in local detection events to passively generate different decoy states. Furthermore, through carrying out an appropriate mathematical method, we are able to give very precise estimations for single-photon-pair contributions. As a result, our protocol can exhibit distinct advantages compared with state-of-the-art MDI QKD schemes even when the finite-key-size effect is taken into account. Therefore, our protocol seems a promising candidate to enhance both the security and performance of MDI QKD in practical implementation.

关键词： Quantum cryptography

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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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Adaptive Multi-Attention Convolutional Neural network for Fine-Grained Image Recognition

Adaptive Multi-Attention Convolutional Neural Network for Fi...

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IEEE Globecom Workshops

作者： Ang Li Jianxin Chen Bin Kang Wenqin Zhuang Xuguang Zhang College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China National Engineering Research Center for Communication and Information Technology (NUPT) Nanjing China Key Laboratory of Broadband Wireless Communications and Sensor Network Technology Ministry of Education (NUPT) Nanjing China

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

Fine-grained recognition is still a difficult task in pattern recognition applications due to the challenge of accurate localization of discriminative parts. Recent CNN-based methods generally utilize attention mechanism to produce attention masks without part labels/annotations and extract corresponding image parts from them. However, these methods extract the attention parts by using fixed-size rectangles to crop images regardless of the size of objects to be recognized, which will hinder the feature expression of the following Part-CNNs. In this paper, we propose an adaptive cropping module based on the information of attention masks to adjust size of cropping rectangles. The training process of adaptive cropping module and Part-CNNscan reinforce each other with the proposed rank loss and the classic softmax loss. To further balance and fuse all attention parts, we propose a part weighting module to evaluate part contributions. Under the optimization of sort loss, the part weighting module will produce part weights in the same order as prediction scores learned by attention parts. The backbone of our network is MA-CNN. Different from MA-CNN, the new proposed adaptive cropping module and part weighting module can jointly guide the framework to produce a more discriminative fine-grained feature. Experiments show that the AMA-CNN outperforms MA-CNN by 1.1% on CUB200-2011 bird dataset.

关键词： Feature extraction Agriculture Training Image recognition Shape Fuses Birds

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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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