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Cross-domain intelligent cooperative spectrum sensing algorithm based on Federated Learning and Swin-Transformer neural network

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engineering Applications of Artificial Intelligence 2025年 157卷

作者： Lingwei Xu Zhihe Gao Yufang Li T. Aaron Gulliver Department of Information Science and Technology Qingdao University of Science & Technology Qingdao 266061 China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing 400065 China Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle Hubei University of Arts and Science Xiangyang 441053 Hubei China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis(Guangxi Minzu University) Nanning 530006 China Department of Electrical and Computer Engineering University of Victoria Victoria BC V8W 2Y2 Canada

The deployment of 6th Generation (6G) mobile networks will bring revolutionary changes to organizations and users through higher speeds, lower latency, and more intelligent network management. This will require more efficient spectrum sharing to improve network performance. Cognitive Radio (CR) can be employed to help achieve this goal. However, data collection at the fusion center for cooperative spectrum sensing (CSS) creates a risk of sensing user (SU) data leakage. In this paper, an intelligent CSS algorithm for mobile communication is proposed based on Federated Learning with a Swin-Transformer. First, to achieve the cross-domain process, a Continuous Wavelet Transform (CWT) is used with the normalized time series data to obtain a time-frequency spectrum map with joint time-frequency features. Second, Federated Learning is employed to realize distributed CSS to reduce sensing overhead, improve the security of sensing data, and eliminate the SU data isolation with traditional distributed CSS. Then, a new Swin-Transformer spectrum sensing is proposed for fusion learning of the time-frequency spectrum map. The fusion of Federated Learning and Swin-Transformer neural network provides cooperation with multiple SUs to achieve efficient spectrum sensing using the K -rank fusion criterion. Compared with the Convolutional Neural Network (CNN), ViT-Transformer, Graph Neural Network (GNN) and EfficientNet algorithms, the detection probability of the proposed algorithm has increased by 45 %, and the false alarm probability has decreased by 13 %.

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Non-invasive reverse engineering of finite state machines using power analysis and Boolean satisfiability

arXiv

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arXiv 2019年

作者： Vamja, Harsh Agrawal, Richa Vemuri, Ranga Digital Design Environments Laboratory Department of Electrical Engineering and Computer Science College of Engineering and Applied Science University of Cincinnati CincinnatiOH United States

In this paper, we present a non-invasive reverse engineering attack based on a novel approach that combines functional and power analysis to recover finite state machines from their synchronous sequential circuit implementations. The proposed technique formulates the machine exploration and state identification problem as a Boolean constraint satisfaction problem and solves it using a SMT (Satisfiability Modulo Theories) solver. It uses power measurements to achieve fast convergence. Experimental results using the LGSynth'91 benchmark suite show that the satisfiability-based approach is several times faster compared to existing techniques and can successfully recover 90%-100% of the transitions of a target machine. Copyright © 2019, The Authors. All rights reserved.

关键词： Reverse engineering

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Pre-Distorted ADO-OFDM for Mutual Interference Eliminating with Low Complexity and Low Latency

Pre-Distorted ADO-OFDM for Mutual Interference Eliminating w...

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IEEE International Conference on Communications Workshops, ICC

作者： Xuan Huang Fang Yang Changyong Pan Jian Song Department of Electronic Engineering Tsinghua University Beijing National Research Center for Information Science and Technology (BNRist) Beijing P. R. China Key Laboratory of Digital TV System of Guangdong Province and Shenzhen City Research Institute of Tsinghua University in Shenzhen Shenzhen China

ISBN: (数字)9781728174402

ISBN: (纸本)9781728174419

Although the conventional asymmetrically clipped direct current biased optical OFDM (ADO-OFDM) scheme shows a tradeoff of the optical power efficiency and spectrum efficiency compared with the asymmetrically clipped optical OFDM (ACO-OFDM) and DC biased optical OFDM (DCO-OFDM) schemes, it will face the problem of mutual interference, leading to the high complexity, extra latency, and error propagation issues. Thus, in this paper, a pre-distorted ADO-OFDM (PADO-OFDM) scheme is proposed to eliminate the mutual interference between ACO-OFDM and DCO-OFDM signals, which means the demodulation of ACO-OFDM and DCO-OFDM signals can be processed simultaneously, and the conventional error propagation from the previous subtraction method can be avoided. Moreover, the corresponding characteristic of PADO-OFDM is also derived, while the formula of probability density function is investigated. Through the complexity analysis, the computational complexity of PADO-OFDM at the receiver is much lower than that of the conventional ADO-OFDM. Moreover, simulation results show that compared with the conventional ADO-OFDM scheme, the proposed PADO-OFDM scheme has the advantages of low process latency.

关键词： OFDM Adaptive optics Optical receivers Interference Complexity theory Time-domain analysis

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Harmony-Search and Otsu based system for Coronavirus Disease (COVID-19) Detection using Lung CT Scan Images

arXiv

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arXiv 2020年

作者： Rajinikanth, V. Dey, Nilanjan Raj, Alex Noel Joseph Hassanien, Aboul Ella Santosh, K.C. Raja, N. Sri Madhava Department of Electronics and Instrumentation Engineering St. Joseph’s College of Engineering Chennai600119 India Department of Information Technology Techno India College of Technology KolkataWest Bengal700156 India Key Laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronic Engineering College of Engineering Shantou University Shantou Guangdong China Faculty of Computers and Artificial Intelligence Cairo University Egypt Department of Computer Science University of South Dakota 414 E Clark St VermillionSD57069 United States

Pneumonia is one of the foremost lung diseases and untreated pneumonia will lead to serious threats for all age groups. The proposed work aims to extract and evaluate the Coronavirus disease (COVID-19) caused pneumonia infection in lung using CT scans. We propose an image-assisted system to extract COVID-19 infected sections from lung CT scans (coronal view). It includes following steps: (i) Threshold filter to extract the lung region by eliminating possible artifacts;(ii) Image enhancement using Harmony-Search-Optimization and Otsu thresholding;(iii) Image segmentation to extract infected region(s);and (iv) Region-of-interest (ROI) extraction (features) from binary image to compute level of severity. The features that are extracted from ROI are then employed to identify the pixel ratio between the lung and infection sections to identify infection level of severity. The primary objective of the tool is to assist the pulmonologist not only to detect but also to help plan treatment process. As a consequence, for mass screening processing, it will help prevent diagnostic burden. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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A power flow tracing based load curtailment technique

A power flow tracing based load curtailment technique

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2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019

作者： Wu, Tao Hu, Bo Xie, Kaigui Tai, Heng-Ming Wang, Shuocheng Li, Bo State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing China Department of Electrical and Computer Engineering University of Tulsa Tulsa United States State Grid Chongqing Economic Research Institute Chongqing China

ISBN: (纸本)9781728135205

A reasonable and efficient load curtailment method enables to restore the system quickly as well as reduce the economic losses both for the power providers and customers when the electrical power system is under contingency. This paper proposes a power flow tracing based load curtailment technique. When the outage occurs, the proposed method determines the load curtailment of each bus according to the power flow tracing results. The technical requirements can be satisfied by just curtailing these loads. It takes the distribution in power transfers between units, transmission lines and loads into account. An extended incidence matrix based power flow tracing scheme is developed for efficient load curtailment. Results in the load curtailment and reliability analysis of the RBTS system demonstrate the correctness of the proposed method. © 2019 IEEE.

关键词： Reliability analysis

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Budget constraint roadside units placement for traffic flows monitoring system with reliability in vehicular networks 21

Budget constraint roadside units placement for traffic flows...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and systems, HPCC/SmartCity/DSS 2019

作者： Jiang, Peng Li, Peng Zhang, Tao Huang, Weiyi He, Heng Nie, Lei Liu, Qin College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-time Industrial System China Department of Computer Science New York Institute of Technology United States School of Cyber Science and Engineering Wuhan University China

ISBN: (纸本)9781728120584

In Traffic Flows Monitoring system (TFMS), the key problem is how to install Roadside Units (RSUs) reliably to monitor traffic flows with the constrained budget. The reliability of Traffic Flows Monitoring system(TFMS) is determined by the number of RSUs each traffic flow pass through, and the RSUs placement number is determined by the budget and the placement strategy. In this paper, we investigate the budget constraint RSUs placement problem for traffic flows monitoring system with reliability in vehicular networks. Given a budget constraint, the problem is how to install RSUs to ensure the reliability of TFMS, that is each traffic flow pass through RSUs as many as possible with the limited budget. We prove that this problem is NP-hard. Then, we propose two greedy algorithms to solve the problem according to the pigeonhole principle. Moreover, approximation ratios of proposed algorithms are given. Finally, we verify the feasibility of proposed algorithms through extensive experiments, and the results show that the proposed algorithms are superior to the other algorithms. © 2019 IEEE.

关键词： Reliability

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Fabry-perot lasers as enablers for parallel reservoir computing

arXiv

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arXiv 2020年

作者： Bogris, Adonis Mesaritakis, Charis Deligiannidis, Stavros Li, Pu Department of Informatics and Computer Engineering University of West Attica Aghiou Spiridonos Egaleo Athens12243 Greece Department of Information & Communication Systems Engineering University of the Aegean 2 Palama & Gorgyras St. Karlovassi Samos83200 Greece Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China

We introduce the use of Fabry-Perot (FP) lasers as potential neuromorphic computing machines with parallel processing capabilities. With the use of optical injection between a master FP laser and a slave FP laser under feedback we demonstrate the potential for scaling up the processing power at longitudinal mode granularity and perform real-time processing for signal equalization in 25 Gbaud intensity modulation direct detection optical communication systems. We demonstrate the improvement of classification performance as the number of nodes increases and the capability of simultaneous processing of arbitrary data streams. Extensive numerical simulations show that up to 8 longitudinal modes in typical Fabry-Perot lasers can be leveraged so as to enhance classification performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Mobile data offloading in the vehicular networks: A truthful incentive mechanism 21

Mobile data offloading in the vehicular networks: A truthful...

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作者： Enfang, Zhong Li, Peng Zhang, Tao Huang, Weiyi Liu, Qin College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China Department of Computer Science New York Institute of Technology United States School of Cyber Science and Engineering Wuhan University China

ISBN: (纸本)9781728120584

Mobile data offloading is a cost-effective and promising approach to alleviate the burden of the cellular network. In this paper, we focus on designing a truthful incentive mechanism that the auction cloud platform motivates mobile users with deadline constraints and capacity constraints to offload data. First, we formulate the mobile data offloading problem as a reverse auction. Then, we reduce this social welfare maximization reverse auction for data offloading as an integer linear programming (ILP) problem. To solve this problem, we propose a data offloading auction greedy algorithm to choose the most cost-effective bids. We decompose the data offload auction greedy algorithm into the winner determination problem (WDP) and the threshold payment scheme. Moreover, we propose a delay winner winning algorithm based on the data offloading auction greedy algorithm. Extensive simulation experiments demonstrate that the data offloading auction mechanism dramatically reduces the burden of the cellular network while meeting the offloading tasks deadline constraints and mobile user capacity constraints. © 2019 IEEE.

关键词： Cost effectiveness

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A budget constraint incentive mechanism in spatial-temporal mobile crowdsensing 21

A budget constraint incentive mechanism in spatial-temporal ...

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作者： Kong, Xujiao Li, Peng Zhang, Tao Tu, Meimei Liu, Qin College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China Department of Computer Science New York Institute of Technology United States School of Cyber Science and Engineering Wuhan University China

ISBN: (纸本)9781728120584

Mobile crowdsensing is an effective way to collect sensing data about regions of interest. Effective incentive mechanisms are invaluable in mobile crowd sensing, for recruiting a number of mobile users to fulfill coverage requirement in the interested regions. In this paper, we exploit the potential of mobile users by designing a spatial-temporal crowd sensing incentive mechanism for smartphone users. We first use the logistic regression technique to mine user preferences from the user's historical data. Then, we propose a sub-module approximation algorithm to solve the participant recruitment problem, which is proved to be an NP-hard problem. Moreover, we design the winner auction algorithm (WAA) based on the reverse auction mechanism to solve the critical payment allocation problem. Finally, we demonstrate that our mechanism achieves truthfulness, individual rationality, and high computation efficiency through both theoretical analysis and experimental simulation. © 2019 IEEE.

关键词： Approximation algorithms

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Minimum cost task assignment for vehicle-based crowdsensing with deterministic and nondeterministic trajectory 21

Minimum cost task assignment for vehicle-based crowdsensing ...

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作者： Tu, Meimei Li, Peng Zhang, Tao Zhou, Qiang Liu, Qin College of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-time Industrial System China Department of Computer Science New York Institute of Technology United States School of Cyber Science and Engineering Wuhan University China

ISBN: (纸本)9781728120584

Vehicle-based crowdsensing (VCS) is a special case in crowdsourcing, and task assignment is a basic and important problem. In this paper, we investigate the minimum cost task assignment (MCTA) problem for vehicle-based crowdsensing. The VCS platform hopes to recruit some vehicles to complete given spatial-temporal tasks with the minimum cost. As vehicle trajectories are dynamic, we divide the MCTA problem into two sub-problems, the deterministic trajectory (D-MCTA) problem, and nondeterministic trajectory (N-MCTA) problem. For D-MCTA problem, we prove that the D-MCTA problem is NP-hard, and we propose a greedy algorithm to solve this problem. For N-MCTA problem, firstly, we determine the probability of each vehicle's trajectory through the logistic regression method. Then, we exploit the semi-Markov method to calculate the probability of each vehicle executing the task. Moreover, a greedy algorithm is proposed and the theoretical analysis is given. Finally, extensive simulations have been conducted to show the performance of our proposed algorithms is superior to the other algorithms. © 2019 IEEE.

关键词： Vehicles

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