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Network Performance Analysis of Starlink: A Proliferated Low Earth Orbit Satellite internet System 17

Network Performance Analysis of Starlink: A Proliferated Low...

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17th international conference on signal Processing and Communication systems, ICSPCS 2024

作者： Wong, Aaron Buhl, Victor McEachen, John Kennedy, Sean McEachen, James Naval Postgraduate School Electrical and Computer Engineering Monterey United States U.S. Air Force DaytonOH United States

ISBN: (纸本)9798350389630

This paper presents a comprehensive analysis of the network performance of a proliferated Low Earth Orbit (LEO) satellite internet system, specifically the Starlink constellation. Starlink is known for its global internet access via numerous satellites. Our initial interest was in the number of satellites in view of our own terminal. We used deterministic and analytical methods for calculating the number of Starlink satellites over-head revealing a bimodal distribution with respect to latitude centered around the equator. Over a two-week evaluation, we observed significant variability in system throughput, attributed to factors beyond packet loss during satellite handover. We proceeded to examine the routing of network packets through the Starlink network, from California, Hawaii, and the Common-wealth of the Northern Mariana Islands (CNMI), by examining traceroute results to 155 global locations. Results show all internet routes pass through the same Point of Presence (PoP), regardless of destination, until terminals move beyond a satellite's radio horizon. Additionally, round-trip time analysis indicates packets remain in the satellite network in space until nearing the PoP as opposed to coming to ground immediately and transiting via fiber to the PoP. Our findings suggest that satellite count does not correlate with performance, emphasizing the need for improved terrestrial infrastructure. © 2024 ieee.

关键词： Satellite ground stations

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An IGA Model for Resource Allocation in Heterogeneous Networks for the internet of Things 1

An IGA Model for Resource Allocation in Heterogeneous Networ...

引用

1st ieee international conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Du, Yansong College of Design and Engineering National University of Singapore Singapore

ISBN: (纸本)9798350316469

Dynamic obstructions such as trees in the road environment can affect the quality of images captured by industrial cameras. In order to improve the quality of image acquisition in high dynamic scenes, this study proposes a new imaging algorithm based on convolutional neural networks and combined with single exposure high dynamic range imaging methods. This algorithm is mainly divided into a color camera image signal processing link module and a single exposure high dynamic range image enhancement module. The above two modules can improve the resolution of high dynamic range images. The experimental results show that the algorithm proposed in this study can achieve a response time of 63ms at the fastest, with PSNR, SSIM, and perceptual loss scores reaching 38.67, 0.976, and 0.087, respectively. Research has found that this algorithm has good application value in the field of high dynamic range image imaging. © 2023 ieee.

关键词： bad auto-exposure deep learning algorithm high dynamic image imaging single-exposure HDRI method

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Hydrological image Data Enhancement based on Generative Adversarial Network and image Pyramid Mechanism 7

Hydrological Image Data Enhancement Based on Generative Adve...

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7th ieee international conference on Smart internet of Things, SmartIoT 2023

作者： Chen, Chen Zhang, Qingmin He, Ci Wang, Hao Zhou, Yang School of Communication Engineering Xidian University Xi'an710071 China Guangzhou Institute of Technology Xidian University Guangzhou510700 China The 54th Research Institute of China Electronics Technology Group Corporation Science and Technology on Communication Networks Laboratory Shijiazhuang050000 China Xi'An Molead Technology Co. LTD Xi'an710071 China

ISBN: (纸本)9798350316575

With the continuous development of intelligent water conservancy and the wide application of computer vision technology in the field of hydrology, the problem of lack of hydrological images and poor quality of hydrological images has emerged. This problem limits the performance improvement of deep learning related research models in hydrology, such as semantic segmentation and target detection, etc. In this paper, a deep learning model based on generative adversarial networks and image pyramids, LapSLC-GAN, is proposed for image generation of hydrological images in the hydrological domain. This model accomplishes image data enhancement, improves the quality of the synthetic images, and solves the problem of unclear image details. In this paper, we have improved the generator. Firstly, we propose a new downsampling module based on the semantic segmentation map to fully extract features and exploit them. Secondly, the model integrates an image pyramid mechanism to enhance the emphasis on high-frequency information and improve semantic loss. This is achieved by overlapping the semantic segmentation map and the image pyramid. On the homemade hydrological image dataset, this paper designs experiments to demonstrate the effectiveness of the downsampling module and LapSLC module. It is also compared with other GAN models. The numerical results show that the model proposed in this paper can improve the accuracy of the segmentation model by about 8% compared with the better-performing models at this stage. © 2023 ieee.

关键词： Semantics

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Challenges and Opportunities in Digital image Watermarking 1

Challenges and Opportunities in Digital Image Watermarking

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1st international conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024

作者： Dubey, Aditya Dixit, Nitin Arora, Rakhi Mishra, Jigyasa Shrivastava, Desh Deepak Kinger, Shreshtha Madhav Institute of Technology & Science Centre for Internet of Things Gwalior India Institute of Technology and Management Computer science & Engineering Gwalior India

ISBN: (纸本)9798350365016

image tampering is so simple in this era of digital revolution;digital image authentication presents a big difficulty. Digital image watermarking is a pivotal technique for protecting intellectual property rights and ensuring the authenticity and integrity of digital images. This paper explores the myriad challenges and opportunities associated with digital image watermarking. Despite significant advancements, challenges such as robustness against various attacks, maintaining image quality, and developing efficient and secure algorithms persist. The paper delves into these challenges, highlighting the technical hurdles and the ongoing need for innovation in algorithm design and implementation. On the other hand, it also underscores the opportunities that lie ahead with emerging technologies like artificial intelligence, machine learning, and blockchain, which possess the capacity to transform watermarking techniques. Furthermore, the integration of watermarking with evolving multimedia applications and digital rights management systems opens new avenues for research and development. By addressing these challenges and leveraging the opportunities, digital image watermarking can continue to evolve, providing more robust, efficient, and secure solutions for the digital era. This article provides a thorough analysis of the frameworks of popular watermarking systems, diving into their subtleties. It also offers an assortment of common requirements used in the creation of watermarking techniques for various uses. This paper also examines the latest advancements in digital image watermarking methods, including the benefits and drawbacks of each. The paper also examines many traditional attacks on watermarking systems and talks about possible directions for future study. © 2024 ieee.

关键词： Authentication

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The Art System of Picture Composition based on Computer Video Processing technology 2

The Art System of Picture Composition Based on Computer Vide...

引用

2nd ieee international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Qiu, Shan Wang, Ning Zibo Technician College Shandong China

ISBN: (纸本)9798350347456

With the development of science and technology and the renewal of media means in the era of cultural industry, electronic media develops rapidly, video image technology with digital realization as the carrier develops rapidly, and gradually becomes an independent art form, which indicates the arrival of a new type of art. The purpose of this paper is to study the picture composition art system based on computer video processing technology. From the picture composition art overview to the introduction of basic knowledge of computer science, and then to the main technology of computer video processing, summed up the coordinate space and its transformation, geometric transformation technology of graphics and other knowledge. This paper mainly analyzes the program flow of picture composition art system and modern geometric composition law module, and introduces the application of video frame in image processing. The real-time performance and effect of the system are tested. When processing video art image composition, the CPU usage of real-time processing is about 40%, If you don't process the video, but just play it, the average CPU usage is about 20%. The real-time performance of video processing can be realized effectively through the image synthesis art system. © 2023 ieee.

关键词： Video signal processing

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ERF-FEM: The Feature Extraction Module based on Effective Receptive Field used for image Inpainting 17

ERF-FEM: The Feature Extraction Module based on Effective Re...

引用

17th ieee international conference on signal Processing, ICSP 2024

作者： Feng, Yahui Chen, Li Wang, Xiaofeng Zhao, Shulin Chai, Enhui Northwest University School of Information Science and Technology Xi'an China

ISBN: (纸本)9798350387384

Many image inpainting models that rely on convolutional neural networks (CNN) have been extensively utilized in diverse image inpainting contexts, achieving outstanding results. Regrettably, due to the constraints of convolutional kernels, these approaches lack the capacity to capture multi-scale features. On the contrary, the majority of image inpainting methods regarded corrupted images as common pixel consistency images, disregarding the influence of the corrupted regions, such as shifts in mean and variance. To address the aforementioned challenges, we propose an image inpainting model that leverages a multi-scale feature extraction module and attention-enhanced learnable region normalization. It is capable of obtaining feature maps of different scales without stacking multiple layers of convolutions, alleviating the problems of detail information loss and low local computational efficiency. Additionally, during normalization, the learnable region normalization enhanced by attention can mitigate the offset effect of invalid pixels on mean and variance. Our method was verified on datasets Places2 and CelebA, and it was indicated by the experimental results that the similar methods currently accessible were outperformed by our method. © 2024 ieee.

关键词： Convolutional neural networks

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An Efficient Hybrid Quantum Variational Classifier With Matrix Product State

An Efficient Hybrid Quantum Variational Classifier With Matr...

引用

2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Sun, Wanqi Xu, Jungang Duan, Chenghua School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

Matrix Product States have been extensively explored as a powerful tool for simulating quantum states in image classification task. However, most research has focused on classical simulations or computations involving high-dimensional unitaries, and significant challenges still exist in the practical preparation of Matrix Product States on quantum computers. This paper proposes a novel and practically feasible quantum variational algorithm based on Matrix Product States for image classification task. We design a hardware-efficient quantum circuit with several adjustable entangling operators to prepare the local tensors in Matrix Product States and integrate minimal residuals to ensure computational stability. We demonstrate that our algorithm can reduce the parameter complexity from growing exponentially with the system size to a linear scale. To validate the effectiveness of this quantum variational algorithm, we conducted experiments on the MNIST dataset, achieving accuracies of 99.95% and 95.96% for binary and ten-class classification tasks, which outperforms other related quantum algorithms. This work advances the practical application of quantum machine learning in resource-constrained environments of quantum computing. © 2025 ieee.

关键词： image classification Matrix product states Quantum circuit Variational quantum algorithm

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G-Bot: Revealing Government Programs with Smarted Assistance 2

G-Bot: Revealing Government Programs with Smarted Assistance

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Ramathilagam, A. Gunasekaran, Kavitha Pandian, Elamparithi Kumar, M. Satheesh Ponkumar, D. David Neels Saravanakumar, R. P.S.R. Engineering College Department of Computer Science and Engineering Tamil Nadu Sivakasi India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Rajapalayam India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350366570

Residents have had trouble getting accurate state program information for years. G-bot's innovative technology employs NLP to link customers to state-level policies and activities to bridge this knowledge gap. G-bot's core NLP module is taught to deliver reliable and trustworthy information to help people make healthy and progressing decisions. G-bot provides real-time, reliable data to promote public awareness, transparency, and involvement in government processes. Users may explore the enormous state program and regulatory information using G-bot's simple interface. G-bot's Flask integration helps users locate vital information. G-bot enhances user-centric apps beyond information retrieval. AI-powered language processing ushers in a new era, letting individuals shape their communities. G-bot uses NER to identify government entities. This tool simplifies identifying organizations, programs, laws, agencies, and people, improving search results and customizing insights for better decision-making. © 2024 ieee.

关键词： Bot (internet)

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SFma-Unet: A Mamba-based Spatial-Frequency Fusion Network for Medical image Segmentation

SFma-Unet: A Mamba-Based Spatial-Frequency Fusion Network fo...

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2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Liu, Zhanpeng Zhang, Yuqiang Wang, Bin Yang, Yang Cai, Lin Institute of Artificial Intelligence Xiamen University Xiamen China School of Artificial Intelligence Hubei University Wuhan China School of Life Sciences Hubei University Wuhan China

ISBN: (纸本)9798350368741

Recently, Mamba-based methods have gained popularity in medical image segmentation due to their ability to model long-range dependencies with linear computational complexity. However, current segmentation methods often face challenges such as low contrast, blurred boundaries, and unclear backgrounds in medical images. Considering that perceived objects and features exhibit greater discriminative power in the frequency domain, we propose a novel mamba-based spatial and frequency domain feature fusion network, SFMa-Unet, to address these challenges. Specifically, we designed the Spatial-Frequency Interaction (SFI) module, which leverages the powerful modeling capabilities of Mamba to fuse spatial and frequency domain features, enhancing feature representation. Additionally, we developed a Mamba-based multi-scale feature channel fusion (MFCF) bridge to capture local and global dependencies across different feature scales, further improving the model's representational capacity. We conduct comprehensive experiments on the ISIC17 and ISIC18 public datasets. Experimental results demonstrate the effectiveness and robustness of SFMa-Unet. Codes are available at https://***/RainCh-zyq/SFma-Unet. © 2025 ieee.

关键词： Medical image Segmentation spatial-frequency fusion Vision State Space Models

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A Framework for Segmentation of Thermal images using Unsupervised Fuzzy based Clustering Methods 10

A Framework for Segmentation of Thermal Images using Unsuper...

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10th international conference on Communication and signal Processing, ICCSP 2024

作者： Akilandeswari, A. Menaka, T. Ganesan, P. Rajasekaran, A. Chandrasekaran, B. Sajiv, G. Simats Saveetha School of Engineering Tamilnadu Chennai India Madha Engineering College Dept. of ECE Chennai Kundrathur India Vidya Jyothi Institute of Technology Dept. of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350353068

image segmentation is a primary task in lots of computer vision applications, including thermal image segmentation. Thermal images are typically characterized by high levels of noise and low contrast, which can make segmentation challenging. In this paper, we propose an outline for thermal image segmentation using unsupervised fuzzy-based clustering methods. The proposed framework consists of three main steps: preprocessing, clustering, and post-processing. In the preprocessing step, the thermal image is enhanced to improve the contrast and remove noise. In the clustering step, a fuzzy clustering algorithm is used to cluster the pixels in the image into different regions based on colors. In the post-processing step, the results of the clustering algorithm are refined to improve the segmentation accuracy. The proposed work utilized three fuzzy based methods as Fuzzy C-Means clustering (FCM), Possiblistic Fuzzy C-Means clustering (PFCM) and Modified Fuzzy C-Means clustering (MFCM) for the thermal image segmentation. The proposed framework is evaluated on a number of thermal images. The outcome demonstrates that the projected framework can achieve good segmentation accuracy, even in the presence of noise and low contrast. The proposed framework is also robust to changes in illumination and can be used to segment thermal images with different levels of details. © 2024 ieee.

关键词： image segmentation

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