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arXiv 2021年

作者： Mekdad, Yassine Aris, Ahmet Babun, Leonardo Fergougui, Abdeslam E.L. Conti, Mauro Lazzeretti, Riccardo Uluagac, A. Selcuk Laboratory of Computer Networks and Systems Moulay Ismail University of Meknes Zitoune Meknes11201 Morocco Cyber-Physical Systems Security Lab Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States Department of Mathematics University of Padua Padua35121 Italy Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Rome00185 Italy

In the 21st century, the industry of drones, also known as Unmanned Aerial Vehicles (UAVs), has witnessed a rapid increase with its large number of airspace users. The tremendous benefits of this technology in civilian applications such as hostage rescue and parcel delivery will integrate smart cities in the future. Nowadays, the affordability of commercial drones expands its usage at a large scale. However, the development of drone technology is associated with vulnerabilities and threats due to the lack of efficient security implementations. Moreover, the complexity of UAVs in software and hardware triggers potential security and privacy issues. Thus, posing significant challenges for the industry, academia, and governments. In this paper, we extensively survey the security and privacy issues of UAVs by providing a systematic classification at four levels: Hardware-level, Software-level, Communication-level, and Sensor-level. In particular, for each level, we thoroughly investigate (1) common vulnerabilities affecting UAVs for potential attacks from malicious actors, (2) existing threats that are jeopardizing the civilian application of UAVs, (3) active and passive attacks performed by the adversaries to compromise the security and privacy of UAVs, (4) possible countermeasures and mitigation techniques to protect UAVs from such malicious activities. In addition, we summarize the takeaways that highlight lessons learned about UAVs’ security and privacy issues. Finally, we conclude our survey by presenting the critical pitfalls and suggesting promising future research directions for security and privacy of UAVs. © 2021, CC BY.

关键词： Drones

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Smart Resource Allocation Model via Artificial Intelligence in Software Defined 6G networks

Smart Resource Allocation Model via Artificial Intelligence ...

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IEEE International Conference on Communications (ICC)

作者： Ali Nouruzi Atefeh Rezaei Ata Khalili Nader Mokari Mohammad Reza Javan Eduard A. Jorswieck Halim Yanikomeroglu Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Technical University of Berlin Germany Friedrich-Alexander-University Erlangen-Nurnberg Germany Department of Electrical Engineering Shahrood University of Technology Shahrood Iran Institute of Communications Technology Technische Universität Braunschweig Braunschweig Germany Department of Systems and Computer Engineering Non-Terrestrial Networks (NTN) Lab Carleton University

In this paper, we design a new flexible smart software-defined radio access network (Soft-RAN) architecture with traffic awareness for sixth generation (6G) wireless networks. In particular, we consider a hierarchical resource allocation model for the proposed smart soft-RAN model where the software-defined network (SDN) controller is the first and foremost layer of the framework. This unit dynamically monitors the network to select a network operation type on the basis of distributed or centralized resource allocation procedures to intelligently perform decision-making. In this paper, our aim is to make the network more scalable and more flexible in terms of conflicting performance indicators such as achievable data rate, overhead, and complexity indicators. To this end, we introduce a new metric, i.e, throughput-overhead-complexity (TOC), for the proposed machine learning-based algorithm, which supports a trade-off between these performance indicators. In particular, the decision making based on TOC is solved via deep reinforcement learning (DRL) which determines an appropriate resource allocation policy. Furthermore, for the selected algorithm, we employ the soft actor-critic (SAC) method which is more accurate, scalable, and robust than other learning methods. Simulation results demonstrate that the proposed smart network achieves better performance in terms of TOC compared to fixed centralized or distributed resource management schemes that lack dynamism. Moreover, our proposed algorithm outperforms conventional learning methods employed in recent state-of-the-art network designs.

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Continuous-time robust dynamic programming

arXiv

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arXiv 2018年

作者： Bian, Tao Jiang, Zhong-Ping Bank of America Merrill Lynch One Bryant Park New YorkNY10036 United States Control and Networks Lab Department of Electrical and Computer Engineering Tandon School of Engineering New York University 5 Metrotech Center BrooklynNY11201 United States

This paper presents a new theory, known as robust dynamic programming, for a class of continuous-time dynamical systems. Different from traditional dynamic programming (DP) methods, this new theory serves as a fundamental tool to analyze the robustness of DP algorithms, and in particular, to develop novel adaptive optimal control and reinforcement learning methods. In order to demonstrate the potential of this new framework, four illustrative applications in the fields of stochastic optimal control and adaptive DP are presented. Three numerical examples arising from both finance and engineering industries are also given, along with several possible extensions of the proposed framework. Copyright © 2018, The Authors. All rights reserved.

关键词： Dynamic programming

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RT_Vivaldi: A Symmetric & Equality RTT Prediction Algorithm in networks

RT_Vivaldi: A Symmetric & Equality RTT Prediction Algorithm ...

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International Conference on Control Systems and computer Science (CSCS)

作者： Huijun Dai Zhaosheng Dai Xiaolin Gui Min Zhu Kuncai Zhong Shuming Liu Shaanxi Key lab of Computer Networks Xi'an Jiaotong University Shaanxi Shaanxi P R.Chian Suzhou Academy Xi'an Jiaotong University Shaanxi Shaanxi P R.Chian School of Electronics and Communication Engineering

ISBN: (纸本)9781538618400

Predicting network round-trip times (RTT) between nodes is difficult because of the asymmetric and unequal network features. The phenomenon of Triangle Inequality Violation (TIV) can seriously affect the results in prediction. The traditional Vivaldi algorithm has a low accuracy and an unsteady convergence rate. In this paper, a novel Restrained TIV Vivaldi (RT_Vivaldi) algorithm is proposed. Firstly, the TIV is analyzed to set a triangle detection and suppression coefficient. The coefficient is defined to correct the asymmetric and unequal influence on the TIV. Multiple reference neighbor nodes are set based on the distances between them and then dynamically update the parameters to eliminate the effects of the links with the various lengths. Finally, simulations have proven that the proposed RT_Vivaldi algorithm has a higher accuracy and a greater stability than those traditional algorithms.

关键词： Prediction algorithms Delays Heuristic algorithms Internet Springs Convergence Benchmark testing

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Automated characterization of SiP MZI-based switches

Automated characterization of SiP MZI-based switches

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IEEE Optical Interconnects Conference

作者： Mohammed Shafiqul Hai Monireh Moayedi Pour Fard Dong An Fabrizio Gambini Stefano Faralli Giovan Battista Preve Gordon W. Roberts Odile Liboiron-Ladouceur Department of Electrical and Computer Engineering McGill University Montreal QC Canada McGill University Montreal QC CA Scuola Superiore Sant'Anna Instituto TeCIP Pisa Italy CNIT Photonic Networks National Lab Pisa Italy

A testing methodology for commercial automated test equipment (ATE) conventionally used for electrical integrated circuits is developed for photonic integrated circuits. The method is assessing by efficiently characte... 详细信息

关键词： Optical switches Ports (computers) Photonics Optical crosstalk Transmission line matrix methods Testing

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Variational Zero-shot Multispectral Pansharpening

arXiv

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arXiv 2024年

作者： Rui, Xiangyu Cao, Xiangyong Li, Yining Meng, Deyu School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China School of Computer Science and Technology Ministry of Education Key Lab For Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Macao Institute of Systems Engineering Macau University of Science and Technology Taipa China

Pansharpening aims to generate a high spatial resolution multispectral image (HRMS) by fusing a low spatial resolution multispectral image (LRMS) and a panchromatic image (PAN). The most challenging issue for this task is that only the to-be-fused LRMS and PAN are available, and the existing deep learning-based methods are unsuitable since they rely on many training pairs. Traditional variational optimization (VO) based methods are well-suited for addressing such a problem. They focus on carefully designing explicit fusion rules as well as regularizations for an optimization problem, which are based on the researcher’s discovery of the image relationships and image structures. Unlike previous VO-based methods, in this work, we explore such complex relationships by a parameterized term rather than a manually designed one. Specifically, we propose a zero-shot pansharpening method by introducing a neural network into the optimization objective. This network estimates a representation component of HRMS, which mainly describes the relationship between HRMS and PAN. In this way, the network achieves a similar goal to the so-called deep image prior because it implicitly regulates the relationship between the HRMS and PAN images through its inherent structure. We directly minimize this optimization objective via network parameters and the expected HRMS image through alternating minimization. Extensive experiments on various benchmark datasets demonstrate that our proposed method can achieve better performance compared with other state-of-the-art methods. The codes are available at https://***/xyrui/PSDip. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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Impact of estimated CSI quantization on secrecy rate loss in pilot-aided MIMO systems

Impact of estimated CSI quantization on secrecy rate loss in...

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IEEE Globecom Workshops

作者： Zhangjie Peng Jun Zhu Wei Xu Hua Zhang Chunming Zhao National Mobile Communications Research Lab. Southeast University Nanjing China State Key Laboratory of Integrated Services Networks Xidian University China Department of Electrical and Computer Engineering The University of British Columbia Vancouver BC Canada

ISBN: (纸本)9781479974719

In this paper, we investigate the system performance from the physical layer security provision under imperfect channel state information (CSI). In a classical transmitter (Alice)-legitimate receiver (Bob)- eavesdropper (Eve) model, we introduce artificial noise (AN) to disturb Eve's reception, as Eve's CSI is unknown to Alice. For designing the transmit beamforming vector of the information signal and precoding matrix of AN, Bob feeds back the quantized CSI estimation to Alice. Due to the effects of imperfect CSI at Alice, the secrecy system performance is upper bounded at high SNRs. In order to overcome the problem, by utilizing our derived upper bound on the secrecy rate loss, we put forward a scaled feedback strategy for the secrecy system. By employing the proposed strategy, the secrecy rate increases with transmit power despite that Alice can only obtain imperfect Bob's CSI, and the secrecy rate loss between perfect CSI and imperfect CSI can be controlled within a certain gap. computer simulations are provided to verify our derived results.

关键词： Upper bound Quantization (signal) Receivers Vectors Channel estimation Security Signal to noise ratio

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Secure Edge Caching Placement and Delivery for Ultra-Reliable and Low-Latency Vehicular networks

Secure Edge Caching Placement and Delivery for Ultra-Reliabl...

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IEEE Globecom Workshops

作者： Haojun Yang Kuan Zhang Kan Zheng Yi Qian Intelligent Computing and Communications (IC) Lab Wireless Signal Processing and Networks (WSPN) Lab Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering University of Nebraska-Lincoln Omaha NE USA

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

To meet the urgent requirements of ultra-reliable and low-latency communications (URLLCs) in vehicular networks, vehicular edge caching (VEC) becomes a promising solution to reduce backhaul costs and mitigate network congestion. However, the data requested by vehicular users may be maliciously tampered by attackers during the caching delivery phase. In this paper, we investigate the problem of secure caching placement and delivery for a VEC URLLC system. Specifically, we first analyze the lower bound of spectral efficiency for URLLCs and security metric, i.e., vulnerability. Then, an optimization problem of minimizing the maximum vulnerability among all vehicular users is formulated based on the analysis. Subsequently, we propose a secure caching placement and delivery algorithm to obtain the solutions of our formulated problem by reasonable complexity. Simulation results show that the proposed algorithm can significantly improve the performance of security for the VEC URLLC system.

关键词： Security Optimization Roads Reliability Fading channels Signal to noise ratio Antennas

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Heterogeneously Integrated Multi-band Radar Transceiver Based on Micro-Transfer-Printing

Heterogeneously Integrated Multi-band Radar Transceiver Base...

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International Topical Meeting on Microwave Photonics (MWP)

作者： Federico Camponeschi Filippo Scotti Luca Rinaldi Claudio Porzi Jing Zhang Gunther Roelkens Paolo Ghelfi Antonella Bogoni Mirco Scaffardi Telecommunications Computer Engineering and Photonics Institute Pisa Italy Photonic Networks and Technologies National Lab (PNTLab) National Inter-University Consortium for Telecommunications (CNIT) Pisa Italy Department of Information Technology Ghent University-imec Ghent Belgium

ISBN: (数字)9798350375398

ISBN: (纸本)9798350375404

An integrated and fully packaged photonic-based radar transceiver has been proposed and, for the first time, demonstrated. It utilizes micro-transfer-printing technique to incorporate an indium phosphide semiconductor optical amplifier into a silicon-on-insulator photonic integrated circuit. Experimental characterization shows down-conversion of RF signals up to 20 GHz to intermediate frequency.

关键词： Semiconductor optical amplifiers Optical fiber amplifiers Semiconductor device measurement RF signals Silicon-on-insulator Transceivers Loss measurement Radar applications Assembly Indium phosphide

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SHaRPose: Sparse High-Resolution Representation for Human Pose Estimation

arXiv

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arXiv 2023年

作者： An, Xiaoqi Zhao, Lin Gong, Chen Wang, Nannan Wang, Di Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Integrated Services Networks Xidian University China

High-resolution representation is essential for achieving good performance in human pose estimation models. To obtain such features, existing works utilize high-resolution input images or fine-grained image tokens. However, this dense high-resolution representation brings a significant computational burden. In this paper, we address the following question: "Only sparse human keypoint locations are detected for human pose estimation, is it really necessary to describe the whole image in a dense, high-resolution manner?" Based on dynamic transformer models, we propose a framework that only uses Sparse High-resolution Representations for human Pose estimation (SHaRPose). In detail, SHaRPose consists of two stages. At the coarse stage, the relations between image regions and keypoints are dynamically mined while a coarse estimation is generated. Then, a quality predictor is applied to decide whether the coarse estimation results should be refined. At the fine stage, SHaRPose builds sparse high-resolution representations only on the regions related to the keypoints and provides refined high-precision human pose estimations. Extensive experiments demonstrate the outstanding performance of the proposed method. Specifically, compared to the state-of-the-art method ViTPose, our model SHaRPose-Base achieves 77.4 AP (+0.5 AP) on the COCO validation set and 76.7 AP (+0.5 AP) on the COCO test-dev set, and infers at a speed of 1.4× faster than ViTPose-Base. Code is available at https://***/AnxQ/sharpose. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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