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作者： Shaofeng Lu Cheng Li Xinyi Feng Yuefeng Lu Yulong Hu Wenxi Li School of Computer Science and Engineering Northeastern University China Transport Telecommunications & Information Center School of Civil and Architectural Engineering Shandong University of Technology State Key Laboratory of Resources and Environmental Information System Institute of Geographical Sciences and Natural Resources ResearchChinese Academy of Sciences School of geography and Information Engineering China University of Geosciences

Hamming distance is one of the core problems in similarity *** is a new problem in cryptography to calculate Hamming distance between two objects on the premise of privacy *** has important theoretical significance and application value in graph recognition,machine learning,artificial intelligence,bioinformatics and other aspects which are related to data privacy *** this paper,a new coding method is designed,which encodes the information of both sides to be calculated,transforms the problem into the one of finding the number of the same numbers between the two *** with the idea of XOR,we also solves the privacy protection problem of Hamming distance calculation with the help of homomorphic encryption *** solution in this paper is *** simulation results show that the protocol in this paper is *** analysis shows that the protocol can calculate the Hamming distance between two objects efficiently and safely.

关键词： Privacy-preserving secure multi-party computation Hamming distance similarity measurement

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PM₂.₅ Monitoring: Use Information Abundance Measurement and Wide and Deep Learning

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IEEE transactions on neural networks and learning systems 2021年第10期32卷 4278-4290页

作者： Ke Gu Hongyan Liu Zhifang Xia Junfei Qiao Weisi Lin Daniel Thalmann Faculty of Information Technology Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing University of Technology Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore EPFL CH Lausanne Switzerland

This article devises a photograph-based monitoring model to estimate the real-time PM concentrations, overcoming currently popular electrochemical sensor-based PM monitoring methods' shortcomings such as low-density spatial distribution and time delay. Combining the proposed monitoring model, the photographs taken by various camera devices (e.g., surveillance camera, automobile data recorder, and mobile phone) can widely monitor PM concentration in megacities. This is beneficial to offering helpful decision-making information for atmospheric forecast and control, thus reducing the epidemic of COVID-19. To specify, the proposed model fuses Information Abundance measurement and Wide and Deep learning, dubbed as IAWD, for PM monitoring. First, our model extracts two categories of features in a newly proposed DS transform space to measure the information abundance (IA) of a given photograph since the growth of PM concentration decreases its IA. Second, to simultaneously possess the advantages of memorization and generalization, a new wide and deep neural network is devised to learn a nonlinear mapping between the above-mentioned extracted features and the groundtruth PM concentration. Experiments on two recently established datasets totally including more than 100 000 photographs demonstrate the effectiveness of our extracted features and the superiority of our proposed IAWD model as compared to state-of-the-art relevant computing techniques.

关键词： Feature extraction Monitoring Atmospheric modeling COVID-19 Atmospheric measurements Transforms Temperature measurement

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Efficient Gridless DoA Estimation Method of Non-uniform Linear Arrays with Applications in Automotive Radars

arXiv

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arXiv 2023年

作者： Gao, Silin Zhang, Zhe Wang, Muhan Zhang, Yan Zhao, Jie Zhang, Bingchen Wang, Yue Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute Jiangsu Suzhou China The Beijing Autoroad Tech Co. Ltd China Electrical and Computer Engineering Department George Mason University FairfaxVA United States

This paper focuses on the gridless direction-of-arrival (DoA) estimation for data acquired by non-uniform linear arrays (NLAs) in automotive applications. Atomic norm minimization (ANM) is a promising gridless sparse recovery algorithm under the Toeplitz model and solved by convex relaxation, thus it is only applicable to uniform linear arrays (ULAs) with array manifolds having a Vandermonde structure. In automotive applications, it is essential to apply the gridless DoA estimation to NLAs with arbitrary geometry with efficiency. In this paper, a fast ANM-based gridless DoA estimation algorithm for NLAs is proposed, which employs the array manifold separation technique and the accelerated proximal gradient (APG) technique, making it applicable to NLAs without losing of efficiency. Simulation and measurement experiments on automotive multiple-input multiple-outpt (MIMO) radars demonstrate the superiority of the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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MARVEL: Raster Manga Vectorization via Primitive-wise Deep Reinforcement Learning

arXiv

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arXiv 2021年

作者： Su, Hao Niu, Jianwei Liu, Xuefeng Cui, Jiahe Wan, Ji Wu, Xinghao Wang, Nana State Key Lab of VR Technology and System School of Computer Science and Engineering Beihang University Beijing100000 China Industrial Technology Research Institute School of Information Engineering Zhengzhou University Henan450000 China Hangzhou Innovation Institute Beihang University Zhejiang310000 China

Manga is a fashionable Japanese-style comic form that is composed of black-and-white strokes and is generally displayed as raster images on digital devices. Typical mangas have simple textures, wide lines, and few color gradients, which are vectorizable natures to enjoy the merits of vector graphics, e.g., adaptive resolutions and small file sizes. In this paper, we propose MARVEL (MAnga’s Raster to VEctor Learning), a primitive-wise approach for vectorizing raster mangas by Deep Reinforcement Learning (DRL). Unlike previous learning-based methods which predict vector parameters for an entire image, MARVEL introduces a new perspective that regards an entire manga as a collection of basic primitives—stroke lines, and designs a DRL model to decompose the target image into a primitive sequence for achieving accurate vectorization. To improve vectorization accuracies and decrease file sizes, we further propose a stroke accuracy reward to predict accurate stroke lines, and a pruning mechanism to avoid generating erroneous and repeated strokes. Extensive subjective and objective experiments show that our MARVEL can generate impressive results and reaches the state-of-the-art level. Our code is open-source at: https://***/SwordHolderSH/Mang2Vec. Copyright © 2021, The Authors. All rights reserved.

关键词： Textures

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Discretely modulated continuous-variable quantum key distribution with an untrusted entanglement source

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Physical Review A 2020年第3期102卷 032604-032604页

作者： Qin Liao Gang Xiao Chu-Gui Xu Yang Xu Ying Guo College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China Institute of Advanced Photoelectric Detection and Quantum System Central South University Changsha 410075 China

The discretely modulated continuous-variable quantum key distribution (DM CVQKD) has been proven to be secure, and a basic assumption for the current theoretical security proof of the DM CVQKD is that the signal source cannot be compromised. However, this assumption is quite unpractical in real quantum communication systems. In this paper, we investigate the DM CVQKD with a special configuration in which the entanglement source is placed at the untrusted channel. With this configuration, the source is no longer protected by the sender but is exposed to the vulnerable environment. In particular, we consider the configuration for two typical DM CVQKD protocols, which are the four-state protocol and the eight-state protocol. Security analysis based on linear bosonic channel shows that the DM CVQKD with an untrusted entanglement source is able to defend itself against the most powerful quantum collective attack in a certain distance range and it can still generate positive secret key rate when considering finite-size effect and composable security, thereby providing a theoretical proof for applying the DM CVQKD system to a realistic environment.

关键词： Quantum cryptography

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Intrusion Detection using Deep Neural Network Algorithm on the Internet of Things

Intrusion Detection using Deep Neural Network Algorithm on t...

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IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Syariful Ikhwan Adi Wibowo Budi Warsito Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia D3 Telecommunication Engineering Institut Teknologi Telkom Purwokerto Banyumas Indonesia Computer Science Informatics Department of Science and Mathematics Faculty Diponegoro University Semarang Indonesia Statistics Department of Science and Mathematics Faculty Diponegoro University Semarang Indonesia

ISBN: (数字)9781665460309

ISBN: (纸本)9781665460316

The increasing use of IoT devices on future networks is very helpful for humans in their lives. However, the increase in devices connected to IoT networks also increases the potential for attacks against those networks. Vulnerabilities in Internet of Things (IoT) networks can be exposed at any time. Artificial intelligence can be used to protect the IoT network by being able to detect attacks on the network so that they can be prevented. In this study, network detection was carried out using the Deep Neural Network (DNN) algorithm. The test was carried out using the UNSW Bot-IoT dataset with a comparison of training data of 75% of the overall data. The results obtained show the ability of the algorithm to detect attacks on average with 99.999% accuracy. The validation loss and training loss look very small. In this study, there is a validation loss that still occurs in overfitting, but the difference is very small.

关键词： Deep learning Training Satellites Neural networks Intrusion detection Training data Feature extraction

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A New Reference Governor Strategy For Union of Linear Constraints

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IFAC-PapersOnLine 2020年第2期53卷 5499-5504页

作者： Raffaele Romagnoli Luis D. Couto Emanuele Garone Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA 15213-3815 USA Department of Control Engineering and System Analysis Université libre de Bruxelles B-1050 Brussels Belgium

Classical scalar Reference Governor (RG) schemes require a convex admissible region. Recently, a novel scalar RG approach has been proposed for the case of nonconvex constraints that can be approximated as union of polyhedral sets. This new method, specifically developed for the charge control of lithium-ion batteries, shows good performance and the capability of handling these kind of constraints while keeping a very low computational footprint. However, this method can guarantee that the system will reach the desired set point only under very specific properties of the constraints. In this paper, we analyze these limitations and propose a solution that ensures convergence of the RG scheme under much milder conditions on the topology of the constraints.

关键词： Constrained Control Nonconvex constraints Reference Governor Tracking UAVs

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Highly efficient difference-frequency generation for mid-infrared pulses by passively synchronous seeding

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High Power Laser Science and engineering 2021年第1期9卷 42-47页

作者： Kun Huang Yinqi Wang Jianan Fang Huaixi Chen Minghang Xu Qiang Hao Ming Yan Heping Zeng State Key Laboratory of Precision Spectroscopy East China Normal UniversityShanghai 200062China Key Laboratory of Optoelectronic Materials Chemistry and Physics Fujian Institute of Research on the Structure of MatterChinese Academy of SciencesFuzhou 350002China University of Chinese Academy of Sciences Beijing 100049China Shanghai Key Laboratory of Modern Optical System and Engineering Research Center of Optical Instrument and SystemMinistry of EducationSchool of Optical Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Jinan Institute of Quantum Technology Jinan 250101China CAS Center for Excellence in Ultra-intense Laser Science Shanghai 201800China Shanghai Research Center for Quantum Sciences Shanghai 201315China

We have proposed and experimentally demonstrated a novel scheme for efficient mid-infrared difference-frequency generation based on passively synchronized fiber lasers. The adoption of coincident seeding pulses in the nonlinear conversion process could substantially lower the pumping threshold for mid-infrared parametric emission. Consequently,a picosecond mid-infrared source at 3.1 μm was prepared with watt-level average power, and a maximum power conversion efficiency of 77% was realized from pump to down-converted light. Additionally, the long-term stability of generated power was manifested with a relative fluctuation as low as 0.17% over one hour. Thanks to the all-optical passive synchronization and all-polarization-maintaining fiber architecture, the implemented laser system was also featured with simplicity, compactness and robustness, which would favor subsequent applications beyond laboratory operation.

关键词： mid-infrared fiber laser nonlinear optics difference-frequency generation

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Capacity Building Among European Stakeholders In the Areas of Cyber-Physical systems, IoT & Embedded systems: The SMART4ALL Digital Innovation Hub Perspective

Capacity Building Among European Stakeholders In the Areas o...

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IEEE computer Society Annual Symposium on VLSI

作者： Christos P. Antonopoulos Georgios Keramidas Vassilis Tsakanikas Evi Faliagka Christos Panagiotou Nikolaos Voros Electrical and Computer Engineering Department Embedded System Design & Applications Laboratory University of Peloponnese University of Peloponnese Tripolis Greece

ISBN: (数字)9781728157757

ISBN: (纸本)9781728157764

The goal of the SMART4ALL EU funded project is to build capacity amongst European stakeholders via the development of self-sustained, cross-border experiments that transfer knowledge and technology between academia and industry. It targets Customized Low Energy Computing (CLEC) in the Cyber-Physical (CPS) and the Internet of Things (IoT) domains by combining a set of unique characteristics that join together different cultures, different policies, different geographical areas, and different application domains. The SMART4ALL vision will be realized mainly through funded (via open calls) Pathfinder Application Experiments (PAEs) that will enable the transformation of academic knowledge into industry especially targeting South and Eastern European countries that are currently underrepresented in European funding opportunities. In this way community building, strategy development, and ecosystem learning are envisioned for boosting high-quality research and development in South Eastern Europe (SEE), through an efficient combination of regional, national and European policies. Furthermore SMART4ALL sets forward the concept of marketplace which is offered as a service (Marketplace-as-a-Service or MaaS) that acts as one-stop-smart-stop by offering tools, services, platforms based mainly on open sources technologies as well as technology suppliers - adopter matchmaking capabilities customized to the targeted thematic pillars of the project.

关键词： Europe Technological innovation Tools Companies Ecosystems

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Disassociation of Visual-proprioception Feedback to Enhance Endotracheal Intubation

Disassociation of Visual-proprioception Feedback to Enhance ...

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Future Trends in Smart Communities (ICFTSC), International Conference on

作者： Mahdiyeh Sadat Moosavi Jacob Williams Christophe Guillet Frédéric Merienne J. Cecil Michael Pickett Arts et Metiers Institute of Technology LISPEN HESAM Université Chalon-Sur-Saone France Department of Computer Science Oklahoma State University Stillwater Oklahoma United States of America LISPEN UBFC Université de Bourgogne Chalon-Sur-Saone Department of Computer Science Center for Cyber-Physical system Oklahoma State University Stillwater Oklahoma United States of America Department of Mechanical Engineering Oklahoma State University Stillwater Oklahoma United States of America

ISBN: (纸本)9798350334555

This paper discusses the key elements of a research study that focused on training an important procedure called “Endotracheal intubation” to novice students. Such a procedure is a virtual part of treating patients who are infected with the covid-19 virus. A virtual reality environment was created to facilitate the training of novice nurses (or nurse trainees) using the HTC Vive platform. The primary interaction with the virtual objects inside this simulation-based training environment was using the hand controller. However, the small mouth of the virtual patient and the necessity of utilizing both hands to pick up the laryngoscope and endotracheal tube at the same time (during training), led to collisions involving the hand controllers and hampered the immersive experience of the participants. A multi-sensory conflict notion-based approach was proposed to address this problem. We used “Haptic retargeting” method to solve this issue. And we compared the result of the haptic retargeting method with reference condtion. Initial Results (through a questionnaire) suggest that this Haptic retargeting approach increases the participants’ sense of presence in the virtual environment.

关键词： Training Smart cities Virtual environments Mouth Immersive experience Robot sensing systems Market research

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