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Characterization of State-Preparation Uncertainty in Quantum Key Distribution

Physical Review Applied 2023年第1期19卷 014048-014048页

作者： Anqi Huang Akihiro Mizutani Hoi-Kwong Lo Vadim Makarov Kiyoshi Tamaki Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 People’s Republic of China Graduate School of Engineering Science Osaka University Toyonaka Osaka 560-8531 Japan Department of Electrical & Computer Engineering and Department of Physics Centre for Quantum Information and Quantum Control (CQIQC) University of Toronto Toronto Ontario M5S 3G4 Canada Department of Physics University of Hong Kong Pokfulam Hong Kong Quantum Bridge Technologies Inc. 100 College Street Toronto Ontario M5G 1L5 Canada Russian Quantum Center Skolkovo Moscow 121205 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai 201315 People’s Republic of China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow 119049 Russia Faculty of Engineering University of Toyama Gofuku 3190 Toyama 930-8555 Japan

To achieve secure quantum key distribution, all imperfections in the source unit must be incorporated in a security proof and measured in the lab. Here we perform a proof-of-principle demonstration of the experimental techniques for characterizing the source phase and intensity fluctuation in commercial quantum key distribution systems. When we apply the measured phase-fluctuation intervals to the security proof that takes into account fluctuations in the state preparation, it predicts a key distribution distance of over 100km of fiber. The measured intensity fluctuation intervals are, however, so large that the proof predicts zero key, indicating a source improvement may be needed. Our characterization methods pave the way for a future certification standard.

关键词： Fluctuations & noise Optical quantum information processing Quantum cryptography Quantum engineering Quantum state engineering

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Video stabilization algorithm for tunnel robots based on improved Kalman filter

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Journal of Physics: Conference Series 2021年第2期1748卷

作者： He Liu Shiyi Cui School of Control and Computer Engineering North China Electric Power University Beijing China

The application of tunnel robots in power tunnels is becoming more and more extensive. Due to various reasons, video jitter will inevitably occur during the inspection process, which will affect the real-time processing of subsequent images. In order to reduce the impact of video jitter and meet the requirements of real-time image processing, it is necessary to study fast video image stabilization methods. The traditional Kalman filtering algorithm has less computational cost, but it will cause a large estimation error when the system motion state changes. In this regard, the paper proposes an improved Kalman filtering algorithm, which changes the corresponding filter parameters by real-time estimation of a system in motion to reduce the estimation error. And this algorithm performs Harris corner extraction in the set feature dense area, and combines the PyrLK optical flow algorithm with the homography matrix to calculate the motion trajectory, which improves the calculation speed and accuracy of the algorithm. Experimental results show that this algorithm has better video stabilization effect and faster calculation speed compared with the traditional Kalman filtering algorithm, and can better meet the real-time image processing requirements in the inspection process.

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Adaptive control of pH Neutralization Process based on Feedback Linearization Technique

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Journal of Physics: Conference Series 2021年第1期1905卷

作者： Mohanad Hamad Eljack Elameen Mona Bahaeldin Musa Hago Wen Tan School of Control and Computer Engineering North China Electric Power University Beijing China

This paper presents a nonlinear adaptive controller for pH neutralization process, which is difficult to control due to its nonlinear dynamics, sensibility to small disturbance and time-varying characteristics. Special attention is paid to the effect of internal perturbations and external disturbances typically present in the chemical process. In this work, the performance of the adaptive nonlinear controller based on feedback linearization (FL) is compared with the performance of a conventional PID controller for pH control. It is shown that conventional PID controller rejects input disturbances with poor performance, and the proposed control technique can reject the input disturbances from the control signal and then recovers linear reference tracking very well.

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Short-term Traffic Flow Prediction by Graph Deep Learning with Spatial Temporal Modeling

Short-term Traffic Flow Prediction by Graph Deep Learning wi...

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Electronic Information Technology and Smart Agriculture (ICEITSA), International Conference on

作者： Lilian Yuan Weilin Fang Hongbo Xiao Jianhua Xiao Yuanquan Shi Yujun Yang School of Computer Science and Engineering Huaihua University Huaihua Hunan P. R. China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua Hunan P. R. China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan P. R. China

ISBN: (纸本)9781665464024

An extensive amount of research applications on traffic flow prediction models taking into account spatio-temporal modeling have been undertaken in recent years. Relative to traditional spatio-temporal modeling approaches, good application results have been achieved in predicting traffic flow. However, in real life, traffic flow proves to be time- and space-dependent, which poses difficulties for accurate modeling of traffic flow prediction. To address these problems, we propose the ST-GNN-GRU model, which mainly contains S-GNN and T-GRU modules. The model uses a combination of spatio-temporal modeling and deep learning, which can not only cope with the spatio-temporal modeling problem, but also improve the traffic flow prediction accuracy. The experiments are conducted using the METR-LA dataset, and the outcomes show that the proposed method performs more effectively than other baseline approaches.

关键词： Deep learning Smart agriculture Predictive models Prediction algorithms Information technology Testing

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Comparison of 3D object detection based on LiDAR point cloud 8

Comparison of 3D object detection based on LiDAR point cloud

引用

8th IEEE Data Driven control and Learning Systems Conference, DDCLS 2019

作者： Li, Haoran Zhou, Xiaolei Chen, Yaran Zhang, Qichao Zhao, Dongbin Qian, Dianwei University of Chinese Academy of Sciences Beijing100049 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9781728114545

3D object detection and scene understanding are the key technologies for autonomous driving scenarios. Due to the differences in configuration and datasets used by each 3D object detection algorithm, it is difficult to evaluate the performance of each method. In this work, we provide a comparison of the advanced 3D object detection networks based on LiDAR point cloud in recent two years and analyze each network structure in detail. For the open-sourced networks, we reproduce them on KITTI dataset benchmark with following their original algorithms. Meanwhile, in order to provide more powerful results, we also utilize nuScenes dataset to retrain the networks as mentioned above. The experimental results show that the performance of the networks with point cloud and images as input is better than that of a single input network. © 2019 IEEE.

关键词： Object detection

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Hypergraph optimization for multi-structural geometric model fitting

arXiv

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arXiv 2020年

作者： Lin, Shuyuan Xiao, Guobao Yan, Yan Suter, David Wang, Hanzi Fujian Key Laboratory of Sensing and Computing for Smart City School of Information Science and Engineering Xiamen University China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University China School of Science Edith Cowan University Australia

Recently, some hypergraph-based methods have been proposed to deal with the problem of model fitting in computer vision, mainly due to the superior capability of hypergraph to represent the complex relationship between data points. However, a hypergraph becomes extremely complicated when the input data include a large number of data points (usually contaminated with noises and outliers), which will significantly increase the computational burden. In order to overcome the above problem, we propose a novel hypergraph optimization based model fitting (HOMF) method to construct a simple but effective hypergraph. Specifically, HOMF includes two main parts: an adaptive inlier estimation algorithm for vertex optimization and an iterative hyperedge optimization algorithm for hyperedge optimization. The proposed method is highly efficient, and it can obtain accurate model fitting results within a few iterations. Moreover, HOMF can then directly apply spectral clustering, to achieve good fitting performance. Extensive experimental results show that HOMF outperforms several state-of-the-art model fitting methods on both synthetic data and real images, especially in sampling efficiency and in handling data with severe outliers. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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V-Edge: Virtual edge computing as an enabler for novel microservices and cooperative computing

arXiv

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arXiv 2021年

作者： Dressler, Falko Chiasserini, Carla Fabiana Fitzek, Frank H.P. Karl, Holger Cigno, Renato Lo Capone, Antonio Casetti, Claudio Malandrino, Francesco Mancuso, Vincenzo Klingler, Florian Rizzo, Gianluca The School of Electrical Engineering and Computer Science TU Berlin Berlin Germany The Dept. of Electronics and Telecommunications Politecnico di Torino Torino Italy The School of Engineering Sciences as well as the Excellence Cluster CeTI TU Dresden Dresden Germany The Hasso-Plattner-Institute Potsdam Germany The Dept. of Information Engineering University of Brescia Brescia Italy The Dept. of Electronics Information and Bioengineering Politecnico di Milano Milano Italy The Dept. of Control and Computer Engineering Politecnico di Torino Torino Italy The Institute of Electronics Computer and Communication Engineering of the National Research Council of Italy Torino Italy IMDEA Networks Madrid Spain The Dept. of Computer Science Paderborn University Paderborn Germany The Research Institute of Information Systems HES-SO Valais-Wallis Switzerland University of Foggia Italy

As we move from 5G to 6G, edge computing is one of the concepts that needs revisiting. Its core idea is still intriguing: instead of sending all data and tasks from an end user's device to the cloud, possibly covering thousands of kilometers and introducing delays that are just owed to limited propagation speed, edge servers deployed in close proximity to the user, e.g., at some 5G gNB, serve as proxy for the cloud. Yet this promising idea is hampered by the limited availability of such edge servers. In this paper, we discuss a way forward, namely the virtual edge computing (V-Edge) concept. V-Edge bridges the gap between cloud, edge, and fog by virtualizing all available resources including the end users' devices and making these resources widely available using well-defined interfaces. V-Edge also acts as an enabler for novel microservices as well as cooperative computing solutions. We introduce the general V-Edge architecture and we characterize some of the key research challenges to overcome, in order to enable wide-spread and even more powerful edge services. Copyright © 2021, The Authors. All rights reserved.

关键词： Edge computing

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CVM-Cervix: A Hybrid Cervical Pap-Smear Image Classification Framework Using CNN, Visual Transformer and Multilayer Perceptron

arXiv

引用

arXiv 2022年

作者： Liu, Wanli Li, Chen Xu, Ning Jiang, Tao Rahaman, Md Mamunur Sun, Hongzan Wu, Xiangchen Hu, Weiming Chen, Haoyuan Sun, Changhao Yao, Yudong Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Liaoning Petrochemical University Fushun China School of Control Engineering Chengdu University of Information Technology Chengdu China Shengjing Hospital China Medical University Shenyang China Suzhou Ruiqian Technology Company Ltd. Suzhou China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken United States Institute of Medical Informatics University of Luebeck Luebeck Germany

Cervical cancer is the seventh most common cancer among all the cancers worldwide and the fourth most common cancer among women. Cervical cytopathology image classification is an important method to diagnose cervical cancer. Manual screening of cytopathology images is time-consuming and error-prone. The emergence of the automatic computer-aided diagnosis system solves this problem. This paper proposes a framework called CVM-Cervix based on deep learning to perform cervical cell classification tasks. It can analyze pap slides quickly and accurately. CVM-Cervix first proposes a Convolutional Neural Network module and a Visual Transformer module for local and global feature extraction respectively, then a Multilayer Perceptron module is designed to fuse the local and global features for the final classification. Experimental results show the effectiveness and potential of the proposed CVM-Cervix in the field of cervical Pap smear image classification. In addition, according to the practical needs of clinical work, we perform a lightweight post-processing to compress the model. Copyright © 2022, The Authors. All rights reserved.

关键词： Image classification

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On fuzzy alternate control systems

On fuzzy alternate control systems

引用

International Workshop on Advanced Computational Intelligence (IWACI)

作者： Babatunde Oluwaseun Onasanya Yuming Feng Wei Zhang Shiping Wen Ning Tang Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Wanzhou Chongqing China University of Ibadan Ibadan Nigeria Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology Chongqing Three Gorges University Wanzhou Chongqing China School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing China Australian AI Institute Faculty of Engineering and Information Technology University of Technology Sydney NSW Australia School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Wanzhou Chongqing China

Some researchers have worked on intermittent control and alternate control. In both cases, the choice of the control was a constant quantity. But, in real life, this situation is hard to come by for the reason that control may vary with time and circumstances owing to machine or human errors or both. Hence is the need to employ a method in which the control matrix is rather uncertain as is obtainable in real life. In this paper, we consider a more general model of both intermittent and alternate control which allows the control matrix to be fuzzy (uncertain). It turns out that both the classical intermittent and classical alternate controls are recovered in this mode.

关键词： Adaptation models Computational modeling control systems Computational intelligence

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A Differentially Private Classification Algorithm With High Utility for Wireless Body Area Networks

A Differentially Private Classification Algorithm With High ...

引用

IEEE Conference on Wireless Communications and Networking

作者： Xianwen Sun Lingyun Shi Longfei Wu Zhitao Guan Xiaojiang Du Mohsen Guizani School of Control and Computer Engineering North China Electric Power University China Department of Mathematics and Computer Science Fayetteville State University Fayetteville NC USA Department of Computer and Information Science Temple University Philadelphia PA USA Department of Computer Science and Engineering Qatar University Doha Qatar

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131078

The advancement of the wireless body area networks (WBAN) and sensor technologies allows us to collect a variety of physiological and behavioral data from human body. And appropriate application of machine learning methods can greatly promote the development of e-health. Nevertheless, the collected data contains personal privacy information. When using the machine learning methods to analyze the collected data, some information of the training data will be stored in the learning models unconsciously. To handle such information disclosure problem, we propose a differentially private classification algorithm based on ensemble decision tree with high utility for wireless body area networks. In order to improve the accuracy and stableness of classification, the bagging framework of ensemble learning is used in our algorithm. We aggregate the results of multiple private decision trees as the final classification in a weight-based voting way. For each private decision tree trained on the bootstrap samples, we offer a novel privacy budget allocation strategy that allows the nodes in larger depth to get more privacy budget, which can mitigate the problem of excessive noise introduced to leaf nodes to some extent. The better classification accuracy and stableness of this new algorithm, especially on small dataset, are demonstrated by simulation experiments.

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