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Radio Frequency Fingerprint Identification in Low SNR based on SCUNet

Radio Frequency Fingerprint Identification in Low SNR Based ...

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IEEE/CIC international conference on Communications in China (ICCC)

作者： Zhang, Yali Liu, Nan Pan, Zhiwen You, Xiaohu Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

ISBN: (纸本)9798350378412

Radio Frequency Fingerprint (RFF) identification is a non-password authentication technique, which have been researched in improving the security of the internet of Things (IoT) widely. For traditional access authentication technology, invaders can intercept or steal users' private information by impersonating their identities, which poses a great security risk. Therefore, the unique features of RFF have an important role in securing the network. However, existing methods for RFF identification in IoT systems may be ineffective in low signal-to-Noise Ratio (SNR) will result in mobile devices being unrecognized or misrecognized, leading to security vulnerabilities. Therefore, we propose an RFF identification scheme designed for a low SNR environment, using the Swin-Conv-UNet (SCUNet) for RFF identification. The effect of noise on RFF is reduced by denoising the Wigner-Ville Distribution (WVD) time-frequency images. In addition, we choose to conduct experiments on the WiFi signal (WiSig) Dataset with 10 different transmitters and the recognition of different devices is realized by ResNet18. The results show that the proposed method has significant improvement in the SNR range from -6 dB to 0 dB and achieves 91.5% accuracy at SNR = -4 dB.

关键词： radio frequency fingerprint identification deep learning low SNR Swin-Conv-UNet

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CSI Fingerprint and GCN based Indoor Localization Using Graph Structures Fusion 23

CSI Fingerprint and GCN Based Indoor Localization Using Grap...

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23rd IEEE international conference on Communication technology, ICCT 2023

作者： Fan, Shuting Yan, Jun College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China

ISBN: (纸本)9798350325959

Since the channel state information (CSI) can provide a fine-grained description of signal propagation process, in this paper, a new CSI fingerprint and graph convolutional network (GCN) based indoor localization algorithm is proposed. First, the amplitude and phase information of CSI measurement is extracted for amplitude and phase based CSI gray image construction, respectively. And then, the corresponding CSI RGB images are obtained by the image render techniques. Next, a pixel value comparison based rule is used to form the graph structure of each channel of the amplitude and phase based CSI image. The front-end fusion is used to form the final graph structure. At last, the GCN model is used to training the graph structure. Some Experiments are carried out to verify the performance of the proposed algorithm. © 2023 IEEE.

关键词： Channel State Information Deep Learning Front-end Fusion Graph Convolutional Network Indoor localization

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2023 6th international conference on signal Processing and Information Security, ICSPIS 2023

2023 6th International Conference on Signal Processing and I...

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6th international conference on signal Processing and Information Security, ICSPIS 2023

ISBN: (纸本)9798350329599

The proceedings contain 44 papers. The topics discussed include: blockchain-enabled DNS: enhancing security and mitigating attacks in domain name systems;deep learning-based human tracking, face mask and social distance monitoring, systems using YOLOv5;IMU-based human activity recognition using machine learning and deep learning models;enhancing brain tumor aid diagnosis with augmented reality rendering-based 6 DoF object pose estimation;secure key exchange scheme and blockchain-oriented data protection in the internet of vehicles;an intelligent edge-deployable indoor air quality monitoring and activity recognition approach;DiffT: a novel approach for privacy preserving data analytics;using data integration platform for effective location-aware service development platform;hybrid whale-mud-ring optimization for precise color skin cancer image segmentation;and an extensive evaluation of plant disease detection using diverse machine learning approaches.

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A Secure Watermarking based image Integrity Verification in IoMT 4

A Secure Watermarking Based Image Integrity Verification in ...

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4th IEEE international conference on Computing, Communication, and Intelligent systems, ICCCIS 2023

作者： Sulfi, Saeeda Neena Raj, N.R. College of Engineering Trivandrum Department of Computer Science and Engineering Thiruvananthapuram India Mar Baselios College of Engineering and Technology Department of Computer Science and Engineering Thiruvananthapuram India

ISBN: (纸本)9798350306118

The internet of Medical Things in healthcare necessitates a secure way to transfer medical images due to the rise of telemedicine. Security breaches in public networks lead to falsification of medical images and further incorrect diagnoses. To address this issue, we propose a multiwatermarking scheme in which logo is embedded in region of interest and text data is concealed in regions of non-interest for providing authentication and integrity verification. Fractal encoding technique is used to ensure the secure transmission of watermarked medical image. Experimental results demonstrate that proposed scheme ensures high visual quality, lossless recovery of text data and high security. The proposed scheme provides better watermarked image quality as compared with state-of-The-Art schemes. © 2023 IEEE.

关键词： Authentication

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An Advanced IoT and GPS-GSM based Real-Time Automated Data Monitoring Robot with image Processing and Live Streaming for Human Life and Land Mine Detection systems

An Advanced IoT and GPS-GSM Based Real-Time Automated Data M...

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2024 international conference on Computer, Electrical and Communication Engineering, ICCECE 2024

作者： Islam, Md. Muhaiminul Akter, Farzana Hossain, Md. Rakib Faruk Hossain, Md. Mahedy Hasan, S.M. Yakin Srizon, Md. Azmain Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Department of Electrical & Electronic Engineering Rajshahi6204 Bangladesh Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350386479

An era of automation is currently being experienced, where everything is becoming more automated day by day. Automation technology has been applied everywhere, from smaller to larger scales. Moreover, real-time communication between complicated human-machine interfaces is one of the difficulties of the fourth industrial revolution. That is why robots are essential in this field because they improve human lifestyles, lighten workloads, and carry out several dangerous tasks. This research presents a groundbreaking advancement in the form of an automated real-time IoT-based data monitoring robot in an era characterized by the proliferation of automation and the internet of Things (IoT). This ground-breaking system combines GPS-GSM functionality, human life detection, land mine detection, image processing, and live streaming in an efficient manner. The robot utilizes its IoT framework and a variety of embedded sensors and technology to gather, process, and transmit realtime data from its surroundings. Notably, it supports disaster response, military operations, and safety measures by performing the vital tasks of detecting human life and land mines. The ability to categorize and process visual data is improved by the image processing feature, and situational awareness is provided by live streaming © 2024 IEEE.

关键词： Global positioning system

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Blockchain-based Secure Routing Algorithm with Accumulating Trust in VANETs 18

Blockchain-based Secure Routing Algorithm with Accumulating ...

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18th international conference on Future Networks and Communications (FNC) / 20th international conference on Mobile systems and Pervasive Computing (MobiSPC) / 13th international conference on Sustainable Energy Information technology (SEIT)

作者： Liu, Mengyu Tang, Huizhi Li, Demin Donghua Univ Engn Res Ctr Digitized Textile & Apparel Technol Coll Informat Sci & Technol Minist Educ Shanghai 201620 Peoples R China

The popularity of vehicles and the upgrading of sensors are driving the development of the internet of Vehicles. Therefore, a number of Vehicular Adhoc networks (VANETs) routing algorithms have emerged. However, vehicles in complex road environments are exposed to numerous malicious attacks. Especially in multi-hop communications, these malicious attacks become more dangerous. So it remains a great challenge to transmit the packet safely to the destination through multi-hop routing when a vehicle is in the area outside the Road Side Unit (RSU) signal coverage. Considering the process of past communication between the vehicles benefits our assessment of the vehicle's security. In this paper, we propose a secure multi-hop routing algorithm based on accumulating trust. First, we obtain the number of times the vehicle forwards the packets through neighbor nodes. Then, we calculate the current trust value of the vehicle based on it, and RSUs will put the current trust value into the blockchain to form an accumulating trust value. Finally, the appropriate route is selected for communication based on the accumulating trust value of the vehicular nodes. At the end of the paper, we carry out a simple numerical simulation for our routing algorithm. (c) 2023 The Authors. Published by Elsevier B.V.

关键词： Hash digest Accumulating trust Secure routing Vehicular Adhoc networks (VANETs) Blackchain

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Ray Tracing based RIS-Assisted Corridor Environment Channel Reconstruction at 300GHz 12

Ray Tracing Based RIS-Assisted Corridor Environment Channel ...

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12th IEEE international conference on Information and Communication Networks, ICICN 2024

作者： Zheng, Wenlong Lu, Hao College of Information Science and Engineering Hohai University Nanjing China

ISBN: (纸本)9798350355802

Reconfigurable intelligent surfaces (RIS) have emerged as a crucial technology for sixth-generation (6G) communication systems. Mastering the modeling and analysis methods of RIS is essential for its effective implementation. In this paper, a channel reconstruction model is proposed based on ray tracing (RT) simulations in an indoor corridor environment at 300 GHz. It can reconstruct the extracted multipath components (MPCs) to enhance their accuracy and alignment with real-world measurement environments. By analyzing the power delay profile (PDP), power angular profile (PAP), and path loss (PL) before and after integrating RIS, the accuracy and effectiveness of the proposed method are verified. It has also confirmed that RIS significantly improves the transmission efficiency and extends the signal coverage of communication systems. © 2024 IEEE.

关键词： image reconstruction

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Neural Network based Landing Assist Using Remote Sensing Data 16

Neural Network Based Landing Assist Using Remote Sensing Dat...

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16th international conference on signal-image technology and internet-based systems (SITIS)

作者： Bharti, Dheeraj Kothari, Mangal Venkatesh, K. S. Indian Inst Technol Kanpur Elect Dept Kanpur India Indian Inst Technol Kanpur Aerosp Dept Kanpur India

ISBN: (纸本)9781665464956

This paper presents the application of computer vision and artificial neural networks for autonomous approach and landing and taxiing for an aircraft. In civil aviation and unmanned aircraft system industry, safety has always been the prime concern. We present a system which uses modern pattern recognition algorithm to aid in the landing of all types of aerial vehicles. The auto-land systems used today in aviation sector utilize a radio waves-based system known as Instrument Landing System (ILS) which has been in operation since decades. Although, it is efficient but might sometime be intermittent and is vulnerable to interference. Moreover, the auto-land system works in conjunction with different devices such as radio altimeter, ILS, Global Positioning System (GPS) and others. But, before reaching the Minimum Decision Altitude (MDA), pilots are expected to have the runway threshold marking, aiming point marking, displacement arrows and other touchdown markings/lights in-sight for landing. For this purpose, use of imaging sensors as an augmentation system for pilots during landing can improve the safety manifolds. Our method uses modern artificial neural networks to learn to recognize and localize important visual references during landing and taxiing useful for pilots by utilizing the satellite imagery dataset from Google Earth Engine (GEE) cloud computing.

关键词： Autoland Faster R-CNN Instrument Landing System Neural Networks Runway Markings

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Research on regional signal control algorithm based on multi-agent DDQN 8

Research on regional signal control algorithm based on multi...

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8th international conference on Traffic Engineering and Transportation System, ICTETS 2024

作者： Yu, Yitao Yuan, Tangxiao Xu, Junshan Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9781510686298

ISBN: (纸本)9781510686281

Traditional traffic signal timing control and single-intersection control methods face challenges in addressing the complexity and dynamics of modern traffic systems. Methods such as Discrete Traffic State Code(DSTE) and image-like approaches have been employed to address difficulties in setting the state space. This paper introduces a multi-agent deep reinforcement learning algorithm based on the Double Deep Q-Network (DDQN) and proposes a state space setting approach utilizing one-hot coding to effectively reduce data dimensionality. By leveraging collaborative multi-agent reinforcement learning methods and "local centralization" strategies, issues such as dimensionality explosion and unstable training are successfully mitigated. To assess the algorithm's performance, a road network model was constructed using SUMO, demonstrating a 22% reduction in waiting time compared to the original algorithm. The results confirm that the regional signal control model and methodology presented in this paper can significantly decrease regional traffic delays and enhance the traffic efficiency of the road network. © 2024 SPIE.

关键词： Deep reinforcement learning

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VulSAT: Source Code Vulnerability Detection Scheme based on SAT Structure 8

VulSAT: Source Code Vulnerability Detection Scheme Based on ...

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8th international conference on signal and image Processing, ICSIP 2023

作者： Xue, Jintao Yu, Zihan Song, Yubo Qin, Zhongyuan Sun, Xin Wang, Wen Southeast University School of Cyber Science and Engineering Nanjing211189 China State Grid Zhejiang Electric Power Co. Ltd. Research Institute Internet Technology Center Hangzhou310063 China State Grid Zhejiang Electric Power Co. Ltd Internet Department Hangzhou310063 China

ISBN: (纸本)9798350397932

The number of software vulnerabilities have increased rapidly, and their forms have shown the characteristics of complexity and diversity, which has brought severe challenges to software systems. Deep learning can automatically learn the features of code and is widely used in source code vulnerability detection. Some studies treat codes as text sequences, ignoring the structural relationship between lines of code. Other studies extract the code as a graph structure, but ignore the order relationship of code lines and do not make full use of the dependencies between code lines. This paper proposes a C/C++ source code vulnerability detection scheme based on SAT (Structure-Aware Transformer) structure. It first standardizes the source code, then extracts the PDG (program dependency graph) representation of source code, next convert each node into a vector representation using sentence embedding, which retains the dependencies between nodes. Finally input it into the SAT architecture model for training, and judge whether the input program contains vulnerabilities according to the output results. We compared VulSAT with a variety of vulnerability detection methods (i.e., Devign, SySeVR, VulCNN, Checkmarx, VulDeePecker), and selected C/C++ functions in the SARD vulnerability dataset as experimental objects. The performance of accuracy rate and false positive rate has been improved to a certain extent. © 2023 IEEE.

关键词： Deep learning

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