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GNSS signal Spoofing Detection based on Satellite Oscillator Stability

GNSS Signal Spoofing Detection based on Satellite Oscillator...

引用

2024 international conference on Satellite internet, SAT-NET 2024

作者： Wang, Haiyang Guo, Shichang Liu, Ziqi Zhang, Yuanyu Shen, Yulong Jiang, Xiaohong Xidian University School of Computer Science and Technology Xi'an China Future University Hakodate School of Systems Information Science Hokkaido Hakodate Japan

ISBN: (纸本)9798350375176

Global navigation satellite systems (GNSS) provide all-weather timing, positioning and navigation services, playing a crucial role in both military and civilian applications. Despite their widespread use, GNSS is vulnerable to severe signal spoofing attacks. In this paper, we propose a novel GNSS signal spoofing detection method by exploiting the radio frequency fingerprint (RFF) of GNSS satellites. We introduce a new RFF feature named satellite oscillator stability, which is closely related to the hardware impairments of GNSS satellites and extracted from the GNSS receiver carrier tracking loop. Utilizing this new feature, we propose a GNSS signal spoofing detection method, which takes the extracted satellite oscillator features as input and employs a Residual Network as a classifier to distinguish between legitimate and spoofed signals. For verification, we use the most widely used GPS and Beidou signals, and conducted experiments in two scenarios: GPS generative spoofing and Beidou forwarding spoofing. The experimental results demonstrated that our proposed method achieve a detection accuracy of over 98% in the GPS generative spoofing scenario, and over 93% in the Beidou forwarding spoofing scenario. © 2024 IEEE.

关键词： Global positioning system

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SAR image Despeckling based on Gradient Domain Convolutional Sparse Coding 15

SAR Image Despeckling based on Gradient Domain Convolutional...

引用

15th international conference on signal Processing systems, ICSPS 2023

作者： Chen, Gao Li, Zifeng Zhou, Qingfeng Liu, Chanzi Dongguan University Of Technology 1 University Rd Dongguan China

ISBN: (纸本)9781510675056

Sparse regularization is an effective tool for synthetic aperture radar (SAR) image *** effective sparse regularization terms plays a very important role in this kind of method. Existing sparse regularization despeckling methods use conventional patch-based sparse representation to design regularization term. This patch-based manner will lose some important spatial information along edges between patches, resulting in staircase effect. In this paper, we propose a new Gradient domain Convolutional Sparse Coding-based (GCSC) method for SAR image despeckling, and derive a feasible algorithm to efficiently solve the corresponding nonconvex optimization problem. In contrast to the well-known sparse regularization despeckling methods that divide a SAR image into patches and process patches individually in the spatial domain or the transform domain, GCSC works on the whole SAR image to learn a convolutional sparsifying regularizer in gradient domain. By taking advantage of the gradient domain convolutional sparse coding, GCSC can capture the correlation between local neighborhoods and exploit the gradient image global correlation to produce better edges and sharp features of SAR image. Experiments conducted on real SAR images demonstrate that the proposed GCSC outperforms those state-of-the-art SAR despeckling methods in terms of subjective and objective evaluation. © 2024 SPIE.

关键词： Synthetic aperture radar

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Dehazing images Using Vision Transformer Algorithm

Dehazing Images Using Vision Transformer Algorithm

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Karthik, Anyapu Reddy, B.Siva Chanikya Ranjani, M. Sandhia, G.K. Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798331509828

image dehazing is an important task to obtain clear images from blurry vision in low vision individuals. Although traditional methods and deep learning have made progress in this field, there are still challenges, especially in solving the real-life haze problem. This paper studies the application of Vision Transformer architecture, especially the Dehaze Former model, to improve the image dehazing performance. We present a new dataset that provides a comprehensive evaluation of the capabilities of Dehaze Former for difficult haze conditions (and perform function adjustments), and it outperforms existing models using state- of-the-art techniques when measured with new data. We propose several metrics to validate our findings, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Also a good comparison shows the effectiveness of this model in preserving image details and reducing artifacts. This research contributes to the advancement of image dehazing technology by demonstrating the ability of Vision Transformers to process grassy lawns. The insights gained from this study not only highlight the strengths of the Dehaze Former model, but also pave the way for future explorations into dehazing performance using Transformer-based architectures © 2025 IEEE.

关键词： Dataset Deep Learning image Dehazing Non-Homogeneous Haze Single image Restoration Vision Transformer

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image Segmentation Algorithm of Ischemic Stroke based on Wavelet Pooling Neural Network 2

Image Segmentation Algorithm of Ischemic Stroke based on Wav...

引用

2nd IEEE international conference on signal, Information and Data Processing, ICSIDP 2024

作者： Zhu, Huiyuan Zhai, Yao Wu, Jinghe Lu, Chuwei Zhang, Xianchao Li, Ruide Beijing Institute of Technology School of Cyberspace Science and Technology Beijing China College of Medicine Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Department of Nursing Jiaxing China Beijing Institute of Technology School of Information and Electronics Beijing China Aviation Industry Corporation of China Avic Xi' An Flight Automatic Control Research Institute Xi'an China Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems Jiaxing China

ISBN: (纸本)9798331515669

Ischemic stroke is a high-risk brain disease that causes disability or death in adults worldwide. Rapid diagnosis and treatment, as well as segmentation of ischemic lesions from stroke medical images so that doctors can evaluate and develop treatment plans, are crucial to reducing brain damage. Currently, doctors' manual segmentation of lesions is time-consuming and labor-intensive and prone to missed or misdiagnosed diagnoses, delaying the best time for treatment. A well-trained neural network is considered as an effective aid for image segmentation tasks. However, in the traditional neural network down-sampling process, the pooling layer loses feature information, and the lost information significantly impacts the edge regions. While for medical images, edge regions are crucial features in the segmentation process. Therefore, to address the above issues, in this paper we improve upon the existing stroke lesion segmentation algorithm U-net, we design a segmentation network based on wavelet pooling and attention mechanisms. The network was tested on ISLES 2018 and ISLES 2022 datasets, and the Dice coefficient improved by 2.13% and 1.93% compared to baseline network, respectively. © 2024 IEEE.

关键词： image segmentation

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Face Recognition Implementation based on image Processing Techniques 2

Face Recognition Implementation Based on Image Processing Te...

引用

2nd international conference on signal Processing and Intelligent Computing, SPIC 2024

作者： Yang, Bingcan Chen, Xiai Shenyang Ligong University School of Automation and Electrical Engineering Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China

ISBN: (纸本)9798350368888

image processing is a technique that involves the manipulation and enhancement of digital images. It encompasses various aspects such as image acquisition, image preprocessing, image enhancement, image segmentation, feature extraction, and object detection. The purpose of image processing is to process and enhance images using computer technology, aiming to achieve higher image quality for subsequent processing and applications. In practical applications, image processing techniques employ different algorithms and methods to achieve various processing and enhancement effects. Additionally, image processing techniques can be applied in fields such as medicine, security, autonomous driving, VR/AR, etc. For instance, in the medical field, image processing techniques can be utilized for image segmentation and diagnosis of medical images, thereby improving the accuracy and analytical capabilities of medical imaging. In the security field, image processing techniques can be employed for tasks such as video surveillance and facial recognition, enhancing the efficiency and accuracy of security monitoring. In the domain of autonomous driving, image processing techniques can be used for detecting and recognizing the surrounding environment of vehicles, thereby enhancing the safety and performance of autonomous driving systems. In conclusion, image processing technology is a very important technology. By continuously improving the accuracy and reliability of the algorithm, it is expected to be applied in a wider range of applications. © 2024 IEEE.

关键词： MATLAB

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Transformer based image Dehazing 16

Transformer based Image Dehazing

引用

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

作者： Suarez, Patricia L. Carpio, Dario Sappa, Angel D. Velesaca, Henry O. IESPOL Polytech Univ Guayaquil Ecuador Comp Vis Ctr Barcelona Spain

ISBN: (纸本)9781665464956

This paper presents a novel approach to remove non homogeneous haze from real images. The proposed method consists mainly of image feature extraction, haze removal, and image reconstruction. To accomplish this challenging task, we propose an architecture based on transformers, which have been recently introduced and have shown great potential in different computer vision tasks. Our model is based on the SwinIR an image restoration architecture based on a transformer, but by modifying the deep feature extraction module, the depth level of the model, and by applying a combined loss function that improves styling and adapts the model for the non-homogeneous haze removal present in images. The obtained results prove to be superior to those obtained by state-of-the-art models.

关键词： atmospheric light brightness component computational cost dehazing quality haze-free image

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Proceedings - 2024 international conference on Information technology, Communication Ecosystem and Management, ITCEM 2024

Proceedings - 2024 International Conference on Information T...

引用

2024 international conference on Information technology, Communication Ecosystem and Management, ITCEM 2024

ISBN: (纸本)9798331542696

The proceedings contain 51 papers. The topics discussed include: design and implementation of vehicle collaborative control system under intelligent connected environment for new energy vehicles;research on image signal enhancement and denoising algorithms for intelligent communication systems;optimizing unmanned vessel vision systems for biological outbreak prediction in nuclear power plant waters using machine learning;research on collaborative optimization model of complex system reliability and system availability;optimization of aircraft trajectory design based on intelligent learning methods;research on the construction of robot target recognition systems driven by deep learning algorithms and the optimization of intelligent localization technology performance;research on software defect detection model based on neural network;and research on adaptive optimization model of software system based on reinforcement learning.

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2D-ZasHen Chaotic Map-based Medical image Cryptographic Approach in IoT-based Healthcare Monitoring 20

2D-ZasHen Chaotic Map-Based Medical Image Cryptographic Appr...

引用

20th ACS/IEEE international conference on Computer systems and Applications (AICCSA)

作者： Amarouche, Sylia Talbi, Said Daoui, Mehammed Couchot, Jean-Francois Mouloud Mammeri Univ Dept Comp Sci Lab Comp Sci Res LARI Tizi Ouzou Algeria Univ Bourgogne CNRS Femto ST Inst Belfort France

ISBN: (纸本)9798350319439

The internet of Things (IoT) is an innovative technology that is revolutionizing the global economy and acquired significant recognition across various sectors, notably within the healthcare field. IoT cameras play a crucial role in facilitating real-time monitoring of the human body through video streams. However, this kind of IoT equipment is vulnerable to various threats. Traditional encryption algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest Shamir Adleman) are commonly used to encrypt textual data. However, they are not suitable for digital image encryption due to computational constraints. Consequently, researchers have proposed cryptographic approaches based on chaotic systems, which offer high speed, security, and low computational costs. In this paper, a new 2D-ZasHen map is proposed, resulting from a combination of two existing 2D maps, along with a new secure chaos-based image encryption approach for IoT healthcare monitoring. The proposed approach involves three significant processes which are key generation, permutation and substitution, inspired by the global existing scheme. Moreover, it considers the confusion and diffusion processes as a single step, executed simultaneously in the same round. This approach effectively meets the requirements of both confusion and diffusion processes, optimizing time efficiency, enhancing security, and ensuring robust encryption and decryption phases. Extensive tests have demonstrated the robustness of the proposed chaos-based approach cryptosystem against differential and entropy attacks, the randomness of the encrypted images, and their sensitivity to slight changes.

关键词： Connected Healthcare IoT Security image Encryption Chaotic systems

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Single image Deraining based on Denoising Diffusion Implicit Models 20

Single Image Deraining Based on Denoising Diffusion Implicit...

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20th international conference on Natural Computation, Fuzzy systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Chang, Luyao Zhang, Zhi College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9798350356328

To address the challenges of prolonged sampling time and the limitation to process specific image sizes in current image deraining research, we propose a novel single image deraining approach based on denoising diffusion implicit models. The method adopts a variant of the U-Net fully convolutional network architecture based on SR3, concatenating rainy images of arbi-trary sizes and noise images along the channel dimension to iteratively obtain high-resolution rain-free images. Specifically, we in-corporate residual blocks from BigGAN-deep to replace traditional residual blocks, thereby deepening the network to enhance model generalization. Additionally, we employ smooth noise esti-mation via image overlapping block processing to improve image fidelity. Introducing deterministic acceleration sampling, we expe-dite the sampling process by uniformly selecting time steps. Comprehensive testing on synthetic and real datasets demonstrates that the proposed method achieves deraining for images of any size, yielding significant improvements in peak signal-to-noise ra-tio and structural similarity. The generated images preserve more complete details, showing superior deraining efficacy. © 2024 IEEE.

关键词： image denoising

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international conference on internet of Everything and Quantum Information Processing, IEQIP 2023

International Conference on Internet of Everything and Quant...

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international conference on internet of Everything and Quantum Information Processing, IEQIP 2023

ISBN: (纸本)9783031619281

The proceedings contain 31 papers. The special focus in this conference is on internet of Everything and Quantum Information Processing. The topics include: Revolutionizing Agriculture: A Mobile App for Rapid Plant Disease Prediction and Sustainable Food Security;EMG based Human Machine Integration for IoT based Instruments;medrack: Bridging Trust and technology for Safer Drug Supply Chain Using Ethereum and IoT;a Review on Tuberculosis Pattern Detection based on Various Machine Learning Techniques;sensor based Hand Gesture Identification for Human Machine Interface;an Improved Detection System Using Genetic Algorithm and Decision Tree;a Detailed Analysis of Colorectal Polyp Segmentation with U-Network;a Review on internet of Things (IoT): Parkinson’s Disease Monitoring Device;Machine Learning-based Prediction of Temperature Rise in Squirrel Cage Induction Motor (SCIM);quantum Many-Body Problems: Quantum Machine Learning Applications;Experimental Study on the Impact of Airborne Dust Deposition on PV Modules Using internet of Things;bidirectional Converter with Time Utilization-based Tariff Investigation and IoT Monitoring of Charging Parameters based on G2V and V2G Operations;predictive Analysis of Telecom Customer Churn Using Machine Learning Techniques;baker’s Map based Chaotic image Encryption in Military Surveillance systems;Cyber Security Investigation of GPS-Spoofing Attack in Military UAV Networks;ioT based Enhanced Safety Monitoring System for Underground Coal Mines Using LoRa technology;ioT based Hydroponic System for Sustainable Organic Farming;predicting Stride Length from Acceleration signals Using Lightweight Machine Learning Algorithms;unveiling Hate: Multimodal Perspectives and Knowledge Graphs;vision-based Toddler Activity Recognition: Challenges and Applications;automated W-Sitting Posture Detection in Toddlers.

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