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VAMP-based Channel Estimation for Wideband Beamspace Massive MIMO systems 16

VAMP-Based Channel Estimation for Wideband Beamspace Massive...

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16th international conference on signal Processing systems, ICSPS 2024

作者： Nie, Yang Jing, Lili School of Physics and Electronic Information Engineering Jining Normal University Ulanqab012000 China Institute of Intelligent Signal Processing Jining Normal University Ulanqab012000 China

ISBN: (纸本)9781510689251

Millimeter-wave (mmWave) Massive multiple-input multiple-output (Massive MIMO) communication can provide high-speed network services for emerging application scenarios due to the abundant spectrum resources in the high-frequency band, which has emerged as a key technology for future wireless networks. Beamspace Massive MIMO systems equipped with lensed antenna arrays (LAA) have attracted considerable attention from industry and academic since it is an effective solution with low power and low cost. However, the beam squint effect causes beamspace channel estimation to be significantly complicated in wideband beamspace Massive MIMO systems. To address this problem, we investigate a channel estimator based on the vector approximate message passing (VAMP) algorithm to improve the estimation performance. Specifically, the wideband beamspace channel estimation is firstly considered as the two-dimensional (2D) image reconstruction problem. Subsequently, by the VAMP-based scheme, the 2D natural image is accurately sparse reconstructed from noisy linear measurements, which effectively solve the channel estimation problem. Simulation results verify the effectiveness of the proposed method and highlight its excellent performance in terms of the channel estimation. © 2025 SPIE.

关键词： Channel estimation

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Attack detection on multi-architecture internet of Vehicles honeypots and response mechanism research 24

Attack detection on multi-architecture Internet of Vehicles ...

引用

3rd international conference on signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Ning, Yuqiao Huo, Quanrui Mu, Shiyu Xue, Shihao China Automobile Data of Tianjin Co. Ltd. Tianjin China CATARC Intelligent and Connected Technology Co. Ltd. Tianjin China

ISBN: (纸本)9798400710834

Intelligent connected vehicles (ICVs) refer to a new vehicle type that utilizes advanced information and communication technology to enable information exchange among vehicles, between vehicles and roads, and between vehicles and the internet. Malicious actors may exploit vulnerabilities in ICVs to gain malicious control, steal data, or launch denial-of-service attacks, resulting in significant economic losses and personal harm. Therefore, it is crucial to research attack detection and response mechanisms for ICVs and build effective network defense systems to ensure the safe operation of these vehicles. However, different manufacturers may adopt different architectures, posing significant challenges to network security defense for these vehicles. To address this issue, this paper proposes an attack detection and response mechanism based on Docker container technology for Multi-Architecture Vehicle Honeypots (MAV-honeypots). The aim is to simulate ICVs under various architectures using honeypot technology, attracting and inducing attackers to carry out attacks, thus achieving identification, analysis, and response to attackers. © 2024 Copyright held by the owner/author(s).

关键词： Network security

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Affine Registration for Medical images via Coarse-to-fine Swin Transformer 16

Affine Registration for Medical Images via Coarse-to-fine Sw...

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16th international conference on signal Processing systems, ICSPS 2024

作者： Wang, Zixu Lei, Lei Xi, Feng School of Electronic and Optical Engineering Nanjing University of Science and Technology China College of Information Science and Engineering Jiaxing University China

ISBN: (纸本)9781510689251

Affine registration is a pivotal task in medical image processing, especially when automated methods are needed to handle large-scale datasets. Despite its importance, there remains a notable gap in assessing the standalone performance of affine registration networks. Traditional optimization-based methods often suffer from inefficiency and limited generalizability. CNN-based approaches, while useful, struggle with receptive field limitations in managing long-range dependencies. Similarly, pure Vision Transformer (ViT)-based methods grapple with high computational demands due to their quadratic scaling of time complexity with the number of image patches, resulting in significant training and inference time. This paper introduces the Coarse-to-Fine Swin Vision Transformer (C2FSwinT), a model designed for fast and robust image affine registration. Central to our model is the Swin Transformer Block, which confines self-attention to local windows, markedly reducing computational complexity. Our architecture further incorporates a multi-stage progressive approach to enhance feature extraction. Additionally, the model integrates techniques such as pre-processing for centroid registration, non-overlapped patch embedding, and decoupled atomic transformation to refine the learning of affine transformation matrices. Experimental evaluations demonstrate that C2FSwinT surpasses other state-of-the-art methods, including conventional optimization-based and ViT-based approaches, in terms of registration accuracy, generalizability, and efficiency in training and inference times. © 2025 SPIE.

关键词： image registration

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Application of Smart Terminals in the Power Industrial internet

Application of Smart Terminals in the Power Industrial Inter...

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international Hydropower Development conference, IHDC 2024

作者： Fuqiang, Huang Zhengpu, Chen Jiangtao, Xiao Zhaoan, Jin Jun, Chen Hai, Yang Xiluodu Hydropower Station China Yangtze Power Co. Ltd. Yunnan Province Yongshan County657300 China State Grid International Development Co. Ltd. Hong Kong Shanghai Liqian Power Technology Co. Ltd. Shenzhen China College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China

ISBN: (纸本)9789819791835

The Power Industrial internet serves as an essential platform for the digital transformation of the electric power industry, during its implementation, the integration of Information technology (IT) and Operational technology (OT) in power production and maintenance has posed significant challenges, necessitating the development of intelligent equipment based on Cyber-Physical systems (CPS) to bridge this gap. Microcomputer platforms are well-established and reliable systems capable of running various complex algorithms. However, traditional microcomputer-based protection devices face limitations such as firmware programming, limited types of signal sampling, and insufficient data transmission capabilities. In the current era of digital transformation, there is an urgent need for intelligent devices and smart terminals that can overcome these shortcomings through cloud-edge collaboration to assist the digital transformation of the power industry, continuously creating effective applications that enhance quality and efficiency, and fostering new productive forces. This article starts from the strategic orientation of industrial development, the problem orientation of digital transformation, and the demand orientation for quality improvement and efficiency enhancement. It focuses on the key issues in the advancement of the industrial internet, proposes solutions based on microcomputer platforms, and explores new directions in the development of intelligent and digital transformation in the field of relay protection specialization. © The Author(s) 2025.

关键词： Cloud-Edge Collaboration Cyber-Physical systems Industrial internet Microcomputer Platform Power Industry Smart Terminal

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Hand Gesture Recognition Method based on mmWave Radar with MobileViT and Knowledge Distillation 16

Hand Gesture Recognition Method based on mmWave Radar with M...

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16th international conference on signal Processing systems, ICSPS 2024

作者： Zhang, Xiangqun Ge, Zhizhou Lu, Kai Du, Genyuan Shen, Jiawen Gao, Xiangqian School of Information Engineering Xuchang University Xuchang461000 China Henan International Joint Laboratory of Polarization Sensing and Intelligent Signal Processing Xuchang461000 China Henan Shengshi Hengxin Technology Co. Ltd Xuchang461111 China

ISBN: (纸本)9781510689251

Human-hand gesture recognition using millimetre wave radar is attractive in human-computer interfaces, industrial internet of Things, and smart home. However, the existing CNN or RNN model is so complex and large that it is hard to apply to mobile vision tasks and embedded devices. This paper proposes dual lightweight convolutional neural networks, MobileViT and knowledge distillation, to solve this problem. Firstly, we acquire the original radar echoes of frequency-modulated continuous wave (FWCW) signal and reshape the three-dimensional matrix by Chirps * Samples * Frames. Then, we perform the signal processing method to eliminate the noise and static background. Secondly, we employ the Fast Fourier Transform to extract the hand gestures feature map of distance and Doppler information. Finally, the feature map is input into the modified MobileViT to recognize dynamic hand gestures, and knowledge distillation models is used to simplify the model structure. The experimental results show that the parameter space complexity of the constructed model is reduced to 0.018 M, and the computational complexity is 0.082 GFLOPs after knowledge distillation *** method is verified in 12 complex gesture. It has the potential to be applied in mobile tasks and embedded devices. © 2025 SPIE.

关键词： Fast Fourier transforms

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Medical image Authentication and Self-Recovery Using Fragile Watermarking in the Frequency Domain 6

Medical Image Authentication and Self-Recovery Using Fragile...

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6th IEEE international conference on image Processing, Applications and systems, IPAS 2025

作者： Bouarroudj, Riadh Harrou, Fouzi Zerrouki, Nabil Souami, Feryel Bellala, Fatma Zohra Sun, Ying University of Sciences and Technology Houari Boumedienne Department of Computer Science Algiers Algeria King Abdullah University of Science and Technology CEMSE Division Thuwal Saudi Arabia Center for Development of Advanced Technology DI2M Laboratory Algiers Algeria University of Algiers Benyoucef Benkhedda Department of Computer Science Algiers Algeria

ISBN: (纸本)9798331506520

The rapid growth in digital image sharing, driven by advancements in internet and communication technologies, has raised concerns about image integrity, especially in sensitive fields like healthcare. This paper presents a fully blind fragile watermarking technique for authenticating and self-recovering color and grayscale medical images. The approach applies the Discrete Wavelet Transform (DWT) to the cover image, and the resulting subbands are divided into 3×3 blocks. One authentication and four recovery watermarks are then embedded into the least significant bits (LSBs) of each block. During the extraction phase, if tampering is detected, the model accurately localizes the altered areas, and a three-level recovery process, including a new inpainting technique, is used to recover the original image. Results based on two publically available datasets demonstrate that this method delivers high-quality watermarked images, achieves optimal watermark extraction accuracy, and maintains high sensitivity to various attacks. Additionally, it provides precise tamper localization and delivers high-quality recovered images, even with tampering rates as high as 60%. © 2025 IEEE.

关键词： Frequency domain analysis

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Flickr30K-CFQ: A Compact and Fragmented Query Dataset for Text-image Retrieval 29th

Flickr30K-CFQ: A Compact and Fragmented Query Dataset for Te...

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29th international conference on Database systems for Advanced Applications (DASFAA)

作者： Liu, Haoyu Song, Yaoxian Wang, Xuwu Zhu, Xiangru Li, Zhixu Song, Wei Lie, Tiefeng Zhejiang Lab Res Ctr Intelligent Robot Hangzhou Peoples R China Zhejiang Univ Ctr X Mech Dept Engn Mech Hangzhou Peoples R China Fudan Univ Sch Comp Sci Shanghai Key Lab Data Sci Shanghai Peoples R China

ISBN: (纸本)9789819755547;9789819755554

With the explosive growth of multi-modal information on the internet, unimodal search cannot satisfy the requirement of internet applications. Text-image retrieval research is needed to realize high-quality and efficient retrieval between different modalities. Existing text-image retrieval research is mostly based on general vision-language datasets (e.g. MS-COCO, Flickr30K), in which the query utterance is rigid and unnatural (i.e. verbosity and formality). To overcome the shortcoming, we construct a new Compact and Fragmented Query challenge dataset (named Flickr30K-CFQ) to model text-image retrieval task considering multiple query content and style, including compact and fine-grained entity-relation corpus. We propose a novel LLM-based Query-enhanced method using prompt engineering based on LLM. Experiments show that our proposed Flickr30-CFQ reveals the insufficiency of existing vision-language datasets in realistic text-image tasks. Our LLM-based Query-enhanced method applied on different existing text-image retrieval models improves query understanding performance both on public dataset and our challenge set Flickr30-CFQ with over 0.9% and 2.4% respectively. Our project can be available anonymously in https://***/view/Flickr30K-cfq.

关键词： Text-image Retrieval Natural Query Compact and Fragmented Challenge Set Prompt-enhanced Method

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Enhanced Edge Attention-based SRCNN for Retinal image Super-Resolution 1

Enhanced Edge Attention-Based SRCNN for Retinal Image Super-...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Koshika, Manoj Kandukuri, Prabhakar Moppey, Shyam Kumar Chaitanya Bharathi Institute of Technology Department of Artificial Intelligence & Machine Learning Gandipet Hyderabad India

ISBN: (纸本)9798331529246

The early recognition of retinal disorders such as cataracts, glaucoma, and retinal problems is highly dependent on high-resolution retinal imaging, which is a complex issue because of poor image quality and the lack of important features. The proposed Enhanced Edge Attention Super Resolution Convolutional Neural Network (EEASRCNN) is designed to improve the quality of retinal images high precisely. The model preserves important details like blood vessels and the optic disc by using a custom edge loss function, sharpening filters, and combined attention mechanisms. With a high structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) score and an accuracy of 97.16% in preserving structural details, EEASRCNN outperforms traditional super-resolution methods after being trained on a dataset of diverse retinal images. EEASRCNN offers effective image super resolution in real-time medical image diagnosis. © 2025 IEEE.

关键词： Ophthalmology

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5th international Symposium on signal and image Processing, ISSIP 2024

5th International Symposium on Signal and Image Processing, ...

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5th international Symposium on signal and image Processing, ISSIP 2024

ISBN: (纸本)9789819795147

The proceedings contain 31 papers. The special focus in this conference is on signal and image Processing. The topics include: A Blockchain-based Secure Framework for Storage and Access of Surveillance Video Data;encountering the Challenges and Awareness of internet Security;Data-Driven Strategies for Twitter Engagement: Hashtag Recommendations and API Insights;Analyzing and Visualizing AI Decision-Making for Human-Centered Interaction and Trust;AI-ML-based Handover Techniques in Next-Generation Wireless Networks;castle Defender: Design and Implementation of Game based on Pygame;optimizing Healthcare: Enhancing Disease Management with Recommendation systems;optimizing Diabetes Prediction Models for Enhanced Health Data Processing;comparative Analysis of Classification Models Using Various Feature Sets;An Early Detection of Tomato Plant Disease with Deep Reinforcement Learning and CNN;ioT-Powered Health Monitoring System for Protecting Vital Organs Through Cloud-based Diagnosis;LASKV: Lightweight Authenticated Session Key Agreement for Video Surveillance System;Data Analyses of Factors Influencing Sustainable Adoption of Online Public Services: An Extended UTAUT Analysis in North Macedonia;a Comprehensive Review on Real-Time Campus Compass;deep Learning Approaches for Early Detection of Bovine Respiratory Diseases in Cattle;identifying Suitable Cloud Storage Services from Cross Cloud Platforms based on the Requirements;storage-Savvy Frame Recorder: Enhancing Storage Efficiency and Inspection Speed*;lossless Medical image Compression Using Block-Wise Burrows–Wheeler Transform and Modified Run Length Encoding;advancing Software Defect Detection and Prevention: Bridging Gaps in Early-Stage and Evolving Software systems;a New Way to Communicate: Deep Learning for Deaf and Dumb People;dependent Binomial: A Family of Distributions Derivable by Modifying a Base.

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River-GEM: Generating and Enhancing Muddy Water images

River-GEM: Generating and Enhancing Muddy Water Images

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

作者： Pramanick, Alik Satwik, Chivukula Sairam Kumar, Sonal Daydar, Akshay Sur, Arijit Indian Institute of Technology Guwahati India

ISBN: (纸本)9798350368741

Underwater image enhancement is crucial for marine engineering and aquatic robotics. However, most recent methods have focused on ocean environments, where they trained and tested on oceanic images. As a result, these methods are less effective in river water, where relatively blurry images are produced due to extensive muddy environments. In river water-based image restoration, we have observed two main limitations: (1) the lack of datasets that accurately represent the muddy water conditions found in river environments and (2) the limited effectiveness of current enhancement models in dealing with the unique challenges of muddy water images. To address these limitations, we introduce the "River-GEM" framework, which includes (1) a dataset of highly degraded muddy water images generated using a principle component analysis-based fusion technique and (2) a novel enhancement model utilizing the depth-map guidance to learn complex contextual details of muddy water images. The proposed model captures the intricate details of the objects in degraded images by analyzing feature correlation at multiple scales. Our model shows improvements of 3.42% in SSIM and 5.04% in PSNR over existing methods on the muddy-UIEB dataset. This dataset and enhancement network marks a significant step forward in this area of research. The dataset and code will be available at: https://***/Alik033/River-GEM. © 2025 IEEE.

关键词： image enhancement Muddy image generation principle component analysis transformer

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