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Efficient FPGA Implementation of a High- Performance, Low-Complexity LDPC Decoder for Next-Gen Communication systems

Efficient FPGA Implementation of a High- Performance, Low-Co...

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Frontier Technologies and Solutions (ICFTS), international conference on

作者： M. Paramaiyappan S.Palanivel Rajan Department of Electronics and Communication Engineering J.P.College of Engineering Tenkasi Dept. of Electronics and Communication Engineering Velammal College of Engineering and Technology (Autonomous) Viraganoor Madurai

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

Modern communication systems rely on Low- Density Parity-Check (LDPC) decoders to provide reliable error correction for wireless networks of the next generation including 5G as well as satellite communications and internet of Things (IoT). The research brings forth a real- time optimized implementation of a high-performance low-complexity LAPC decoder designed for FPGAs. The proposed architecture combines QC-LDPC code structures with parallel Min-Sum decoding and pipeline processing to reduce power usage and latency while maintaining high decoding precision. The FPGA hardware shows a Binary Error Rate (BER) measurement of 3.2 × 10– 3 when the signal-to-Noise Ratio (SNR) reaches 2.5 dB and achieves overall 98.6% decoding precision. The proposed decoder design reaches 3.85 Gbps throughput which is better than GPU-based systems by 20% while maintaining 7.8 µs latency for real-time applications. The implemented FPGA system operates at a low power level below 4.80W which exceeds the power requirements of GPU-based systems. The successful outcomes prove that FPGA-based LDPC decoders work as both power-efficient and fast communication system solutions. The designed solution demonstrates scalability and adaptability which constitutes a dependable framework to meet requirements of changing wireless communication standards.

关键词： 5G mobile communication Wireless networks Parity check codes Real-time systems Decoding Error correction internet of Things Field programmable gate arrays Satellite communications signal to noise ratio

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Optimal Deblocking Filtering for H.264 Intra-Prediction Mode Decision

Optimal Deblocking Filtering for H.264 Intra-Prediction Mode...

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Software, systems and Information technology (SSITCON), international conference on

作者： R. D Anitha Kumari A Narendranath Udupa School of Electronics and Communication REVA University Bengaluru India Philips Research Bengaluru India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Video coding standard H.264 exhibits blocking artifacts in its conventional operations due to the inherent characteristics of block-based discrete cosine transforms. A significant amount of research has been done over many years to reduce the blocking artifacts in compressed videos. Therefore, deblocking filters are highly recommended for minimizing blocking artifacts in compressed reconstructed video sequences. Additionally, it improves the signal’s visual quality and prediction performance. The prediction performance gain could also be achieved by smoothing the sharp edges between macro-blocks. However, the in-loop structure of deblocking filter design lacks computational efficiency and also does not ensure the detection of true edges from the image’s subjective and objective perspectives. This study presents a novel framework for optimizing H.264 integrated deblocking filters. Customized filtering ensures that the enhanced signal quality can be reconstructed by discriminating between true edges and artifacts during the intra-frame prediction process. The experiments demonstrate that it can achieve better signal quality while reducing encoding time and gaining coding performance.

关键词： Visualization Filters Smoothing methods image edge detection Video sequences Software systems Prediction algorithms Encoding image reconstruction Standards

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LVQE: Laparoscopic Video Quality Enhancement Using GAN-based Smoke Elimination Guided by an Embedded Dark Channel

LVQE: Laparoscopic Video Quality Enhancement Using GAN-Based...

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IEEE international conference on signal Processing, Information, Communication & systems (SPICSCON)

作者： Akib Jayed Islam Sultanus Salehin Sayem UI Alam Kamrul Islam Shreya Paul Prithy Paul Computational Colour and Spectral Imaging Norwegian University of Science and Technology Gjovik Norway Dept. of ECE The University of Alabama Tuscaloosa USA Electrical and Computer Engineering The University of Alabama Tuscaloosa USA Electrical and Electronics Engineering National Institute of Technology Trichy Tamil Nadu India Computer Science and Engineering East Delta University Chattogram Bangladesh

ISBN: (数字)9798331510213

ISBN: (纸本)9798331510220

Inthis study, a technique for effectively removing smoke from laparoscopic video recordings-which are often negatively impacted during surgeries-using an image-to-image generative adversarial network (GAN) with a built-in guide mask derived from the dark channel is presented. Smoke from tissue dissection tools can obstruct the surgeon's view and introduce errors into computer vision-based systems employed for clinical navigation. The GAN addresses this issue by eliminating noise while maintaining perceptually satisfactory image quality. Using the Laparoscopic Video Quality (LVQ) evaluation dataset, the method is trained and assessed. The section on experimental results provides qualitative examples demonstrating the effectiveness of the proposed approach. With a larger, more diverse dataset for training and validation, further improvements are anticipated. The proposed technique generated smoke-free images and videos, surpassing previous methods.

关键词： Laparoscopes Training image quality Navigation Noise Surgery Generative adversarial networks Distortion Quality assessment Video recording

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Arbitrary Array Distributed ISAR Three-Dimensional Imaging of Ship Targets

Arbitrary Array Distributed ISAR Three-Dimensional Imaging o...

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international conference on Vision, image and signal Processing (ICVISP)

作者： Junqiu Zhang Yong Wang Research Institute of Electronic Engineering Technology Harbin Institute of Technology Harbin China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

In the distributed radar systems, the high array alignment accuracy is often required to enable the coherent processing, however achieving this is challenging to achieve in practical applications. This study proposes a three-dimensional (3-D) radar imaging method for ships based on an arbitrary array configuration within a distributed interferometric inverse synthetic aperture radar (ISAR) system. And each base station consists of an interferometric with three orthogonal channels. Firstly, the separated base stations observe the ship target from arbitrary angles to acquire the echoes of the target from different observation angles. Each station then reconstructs a three-dimensional image of the target through the optimal time selection method and the interferometric technique. These 3-D images, presented as point clouds, contain complementary information due to different observation angles. Finally, a 3-D image fusion technique is applied to obtain the final three-dimensional image. The effectiveness and robustness of the proposed method have been validated through simulation experiments.

关键词： Point cloud compression Base stations Three-dimensional displays Imaging signal processing algorithms Radar imaging Robustness Marine vehicles Inverse synthetic aperture radar image reconstruction

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Secure and High-Fidelity Medical image Generation: A GAN-based Approach for Steganography

Secure and High-Fidelity Medical Image Generation: A GAN-Bas...

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Intelligent Data Communication Technologies and internet of Things (IDCIoT), international conference on

作者： B. Sumitra S. Selvin Ebenezer G. Vallathan B. Sundaramurthy Venkata Rami Reddy Dorsela Department of ECE Geethanjali College of Engineering & Technology Hyderabad India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE Geethanjali College of Engineering and Technology Hyderabad India Department of CSE Vinayaka Missions Research Foundation Vinayaka Missions Kirupananda Variyar Engineering College (Deemed to be University) Salem India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Traditional image steganography methods embed secret data into cover images but often face detection by steganalysis tools due to detectable pixel modifications, which increase the risk of data leakage. These techniques also typically degrade image quality and offer limited capacity. To overcome these challenges, a novel approach for medical imaging is proposed, integrating Generative Adversarial Networks (GANs), PatchGAN, Adaptive LSB steganography, and a dual attention mechanism. PatchGAN improves fine-grained texture details, while Adaptive LSB securely embeds sensitive data within the images. The dual attention mechanism highlights critical regions, enhancing both image clarity and diagnostic accuracy. This method improves image quality and security, benefiting clinical decision-making and patient outcomes. The system is evaluated against traditional methods like GAN and IDGAN using metrics such as embedding capacity, classification accuracy (stego, cover, global), and discriminator loss. Results demonstrate that the proposed system outperforms existing techniques, offering significant improvements in both performance and security for medical imaging applications.

关键词： image quality Steganography Attention mechanisms Adaptive systems Accuracy image synthesis Generative adversarial networks Security Data communication Medical diagnostic imaging

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Prototypical Network for Few-Shot signal Recognition 9

Prototypical Network for Few-Shot Signal Recognition

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9th international conference on Dependable systems and Their Applications, DSA 2022

作者： Wang, Hanhong Qi, Lin Han, Yu Lin, Yun College of Information and Communication Engineering Harbin Engineering University Harbin China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

With the rapid development of 5G and IoT technologies, Wireless Physical Layer Identification (WPLI) is essential for securing wireless communication technology, facing with more complex communication environment and access to heterogeneous networks. How to carry out device authentication in a small sample environment is an urgent and challenging problem. However, most data-driven deep learning (DL)-based WPLI approaches currently are not suitable for scenarios with insufficient samples. To address the issue, we learn from the prototype network in the image domain and use the meta-training method to solve the Few-Shot Learning (FSL) problem in signal recognition. To compare the prototype network performance, we conduct a 1-shot/5-shot test and compare with two baseline models. We further explore the impact of cross-domain on few-shot signal recognition. Simulation results show that prototype network achieves excellent performance on the same domain few-shot classification problem, but when there is distribution drift, the prototype network model adaptability is lower than the baseline model. © 2022 IEEE.

关键词： Heterogeneous networks

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Performance Evaluation of Underwater image Enhancement Techniques

Performance Evaluation of Underwater Image Enhancement Techn...

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Information technology, Electronics and Intelligent Communication systems (ICITEICS), IEEE international conference on

作者： Adagale-Vairagar Shubhangi Praveen Gupta CSMU Navi Mumbai Mumbai India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Absorption, scattering, and colour distortion make underwater photography difficult. Marine biology, underwater archaeology, and surveillance need better underwater photos. This research compares cutting-edge underwater image-enhancing methods to assess their efficacy and usefulness. This paper provides a comparative analysis of the various filtering approaches for underwater image enhancement, such as median filtering, average filtering, Gaussian filtering, Wiener filtering, and wavelet-based soft thresholding. The performance of these techniques is evaluated based on the mean square error, peak signal-to-noise ratio, average gradient, PCQI, and UICQI. The effectiveness of the suggested filtering scheme is validated on some sample images of the public UIEB dataset. It provides the analytical finding, advantages, disadvantages and performance of the different techniques for wide variety of underwater images enhancement. The paper also explores underwater image enhancement issues and future approaches. The promising research directions are integrating domain-specific information, real-time processing, and adaptive algorithms for dynamic underwater scenes. The discoveries help increase underwater photography visibility and interpretability, increasing underwater exploration and monitoring systems.

关键词： Photography Performance evaluation Wiener filters PSNR Filtering Surveillance Scattering

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QH-Bird: A Fine-Grained Bird Visual Dataset and Hierarchical Consistency Denoising Method

QH-Bird: A Fine-Grained Bird Visual Dataset and Hierarchical...

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Artificial Intelligence, internet of Things and Cloud Computing technology (AIoTC), international conference on

作者： Wenjing Song Shengling Geng Dan Zhang School of Computer Science Qinghai Normal University Academy of Plateau Science and Sustainability People's Government of Qinghai Province & Beijing Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal University Xining China

ISBN: (数字)9798331530280

ISBN: (纸本)9798331530297

To address the lack of bird image datasets in the field of fine-grained image recognition, this paper introduces a new fine-grained bird image dataset and a deep learning-based denoising method. The dataset encompasses 50,000 images across 200 species. Initially, we constructed an initial bird image dataset by sourcing and compiling images from specialized books and online resources. Subsequently, we employed image deduplication methods based on difference hashing (dHash) and structural similarity index (SSIM) to clean duplicate images from the dataset. Following this, we applied a deep learning-based hierarchical consistency learning method to identify noisy web images, using classification loss and semantic consistency prediction. To further enhance the dataset's diversity and balance, we expanded the number of images for each bird species using data augmentation techniques such as rotation, mirroring, random cropping, and adjustments in brightness and contrast. Ultimately, we constructed a high-quality, balanced, and diverse bird image dataset, named QH-Bird, which serves as a valuable resource for research and applications in fine-grained image recognition. We evaluated our dataset using four deep learning models: VGG-16, ResNet-50, EfficientNet, and HCLNet, achieving accuracies of 87.17%, 88.59%, 90.28%, and 91.59%, respectively.

关键词： Learning systems Deep learning Visualization Fine-grained image recognition Noise reduction Birds Data augmentation Noise measurement Indexes Biodiversity

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A sapphire fiber Bragg grating strain sensor operating in high temperature environment 14

A sapphire fiber Bragg grating strain sensor operating in hi...

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14th international conference on Information Optics and Photonics, CIOP 2023

作者： Li, Zhuoda He, Jun Xu, Xizhen He, Jia Wu, Jiafeng Wang, Yiping Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education/Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Photonic Devices and Sensing Systems for Internet of Things Guangdong and Hong Kong Joint Research Centre for Optical Fibre Sensors Shenzhen University Shenzhen518060 China

ISBN: (纸本)9781510671744

In situ strain sensing at high-temperature environment is crucial in the aerospace field. Silica fibers will soften at 700 ℃, which can hardly be utilized for strain sensing at higher temperatures. Notably, single crystal sapphire fiber is a promising material for high-temperature sensing due to the high melting point (~2045 ℃). Here, we report the strain sensing at 800 ℃ of sapphire fiber Bragg gratings (SFBG) inscribed by a femtosecond laser line-by-line scanning technique. At first, a line-by-line sapphire fiber grating was inscribed using femtosecond laser direct writing technique. The sapphire fiber ends were polished into bevels to reduce Fresnel reflections, and the signal-to-noise ratio of the SFBG was improved from 9 dB to 17.2 dB. And then, strain characteristics of the SFBG were investigated at room temperature. It was found that the maximum strain of SFBG was decreased to 3600 μΕ (64% reduction) comparing with 9714 μΕ of the pristine sapphire fiber, which is due to the micro-damage introduced by femtosecond laser pulses. In addition, the strain sensitivity of the SFBG is 1.42 pm/μΕ. Subsequently, a strain sensing experiment of the SFBG was carried out at 1100 ℃ using a high-temperature tensile testing system. After annealing at 1100 ℃ for 4 h to improve the high-temperature stability, the SFBG exhibited a strain sensitivity of 1.6 pm/μΕ (R2=0.998) at 1100 ℃. As a result, strain sensing at 1100 ℃ environment was realized based on the SFBG, which indicates a promising application in the aerospace field, especially in strain sensing for structural safety monitoring of hypersonic aircraft at high-temperature. © 2023 SPIE.

关键词： Fiber Bragg gratings

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Reinforcement Learning-based Secure Video Transmission For IOV systems

Reinforcement Learning-Based Secure Video Transmission For I...

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IEEE international conference on image Processing

作者： Lixin Liu Zhibo Liu Xiaozhen Lu Yanling Bu Bin Han Liang Xiao College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China Department of Informatics and Communication Engineering Xiamen University Xiamen China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The rapid growth in the number of vehicles and types of services such as video transmission improves the quality-of-service (QoS) requirements and increases the difficulty in resisting eavesdropping attacks on the internet of Vehicles (IoV). Existing video transmission schemes that either ignore the impact of eavesdropping attacks or have the full knowledge of the attack model have performance degradation in highly dynamic IoV systems. In this paper, we propose a reinforcement learning-based secure video transmission scheme for IoV systems, which jointly optimizes the access control policy for each vehicle (i.e., the selection of access nodes such as the base stations or unmanned aerial vehicles) and the corresponding transmit power level against active eavesdropping. This scheme uses the QoS and eavesdropping rate as the criteria to evaluate the long-term risk of each state-action pair, which is estimated by a designed deep Q-network to avoid the risky access control policies that cause severe data leakage or video transmission failure. Simulation results show that our scheme reduces the energy consumption, transmission latency, and eavesdropping rate compared with the benchmark.

关键词： Access control Simulation image processing Quality of service Performance gain Autonomous aerial vehicles internet

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