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检索条件"主题词=Signal Processing Algorithms"

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E2NGD: A Graph Layout Algorithm Based on Edge-Node Message Passing*

E2NGD: A Graph Layout Algorithm Based on Edge-Node Message P...

引用

International Congress on Image and signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Huan Wang Ao Liu Junjie Zhang Qingming Zhang School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Many existing studies employ graph neural networks (GNNs) to aggregate information from neighboring nodes and learn node embeddings in a low-dimensional space, aiming to produce layouts that satisfy established aesthetic criteria. However, current GNN approaches for graph layout tasks primarily focus on node features, often neglecting the complex relationships between edge features. To address this limitation and enhance the model's ability to capture complex graph structures, we propose a plug-and-play edge message passing enhancement module that can be easily integrated into existing layout frameworks. This module incorporates both edge distance features and angular relationships between edges, enabling more effective information exchange. We evaluate the usability and effectiveness of this module through quantitative and qualitative experiments.

关键词： Measurement Message passing Layout signal processing algorithms signal processing Graph neural networks Robustness Reflection Usability Stress

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A Deep Residual Network-Based Model for Image Steganography Analysis

A Deep Residual Network-Based Model for Image Steganography ...

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International Congress on Image and signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Fei He Renchao Qin Keqiang Tan Lan Zhang Jianlin Long Jialin Duan Suonan Li School of Computer Science and Technology Southwest University of Science and Technology Mianyang Sichuan China Sichuan ShuTian Information Technology Co. Ltd. Yaan Sichuan China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Steganalysis is the process of detecting and analyzing steganography, which is widely utilized in fields such as network security, copyright protection, military, and intelligence. To address the challenges of existing steganalysis models, particularly their difficulty in capturing subtle steganographic information and the low detection rates for adaptive steganography algorithms, this paper proposes an image steganalysis model based on deep residual networks. In the preprocessing stage, the model captures residual features by combining fixed high-pass filters with learnable filters. During feature extraction, feature enhancement is performed between two sets of depthwise separable convolutions to improve the signal-to-noise ratio of the steganographic image. Four improved residual blocks are employed as the backbone network, with the inclusion of the ECA attention mechanism to further enhance the accuracy of steganalysis. The model was tested on the BOSSBase-v1.01 and BOWS2 datasets. Experimental results demonstrate that the model exhibits strong detection performance across different embedding rates of various steganography algorithms, particularly in the detection of the WOW algorithm. For the merged dataset of BOSSBase-v1.01 and BOWS2 with an embedding rate of 0.4bpp, the detection rate reached 95.0%, verifying the effectiveness of the proposed model.

关键词： Adaptation models Steganography Analytical models Filters Attention mechanisms Accuracy Convolution signal processing algorithms Feature extraction Residual neural networks

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A Cross-Talk Robust Multichannel VAD Model For Multiparty Agent Interactions Trained Using Synthetic Re-Recordings

A Cross-Talk Robust Multichannel VAD Model For Multiparty Ag...

引用

Acoustics, Speech, and signal processing Workshops (ICASSPW), IEEE International Conference on

作者： Hyewon Han Naveen Kumar Dept. of Electrical and Electronic Engineering Yonsei University Seoul South Korea Disney Research Imagineering Los Angeles USA

ISBN: (数字)9798350374513

ISBN: (纸本)9798350374520

In this work, we propose a novel cross-talk rejection framework for a multi-channel multi-talker setup for a live multiparty interactive show. Our far-field audio setup is required to be hands-free during live interaction and comprises four adjacent talkers with directional microphones in the same space. Such setups often introduce heavy cross-talk between channels, resulting in reduced automatic speech recognition (ASR) and natural language understanding (NLU) performance. To address this problem, we propose voice activity detection (VAD) model for all talkers using multichannel information, which is then used to filter audio for downstream tasks. We adopt a synthetic training data generation approach through playback and rerecording for such scenarios, simulating challenging speech overlap conditions. We train our models on this synthetic data and demonstrate that our approach outperforms single-channel VAD models and energy-based multi-channel VAD algorithm in various acoustic environments. In addition to VAD results, we also present multiparty ASR evaluation results to highlight the impact of using our VAD model for filtering audio in downstream tasks by significantly reducing the insertion error.

关键词： Voice activity detection Spatial audio Training data signal processing algorithms signal processing Acoustics Data models

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Machine Learning based approach for Myocardial Infarction Prediction using real time ECG signals

Machine Learning based approach for Myocardial Infarction Pr...

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Intelligent Systems and Machine Learning Conference (ISML)

作者： Shilpa Sondkar Sharv Apte Aneesh Deshmukh Paritosh Chavan Dept. Of Instrumentation and Control Engineering VIT Pune India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

This project focuses on utilizing machine learning techniques to identify and detect abnormal cardiac conditions through the analysis of Electrocardiogram (ECG) signals. With cardiovascular diseases being a significant global health concern, the need for precise diagnostic tools is paramount. The primary objectives of this study involve identifying specific cardiac conditions, including Myocardial Infarction, Arrhythmias and discerning normal cardiac conditions. Real time ECG signals were taken from Symbiosis hospital and research center pvt. Limited hospital in Satara. The methodology encompasses the acquisition of diverse ECG datasets, preprocessing to enhance signal quality, and feature extraction to characterize different cardiac conditions effectively. Support vector machines (SVMs), logistic regression, kNNs, and XGBoost these machine learning techniques are employed to develop robust classification models. The anticipated outcomes of this research aim to provide accurate models capable of early detection, enabling prompt interventions and, ultimately, enhancing patient outcomes within the domain of cardiovascular health.

关键词： Support vector machines Machine learning algorithms Accuracy signal processing algorithms Machine learning Cardiac arrest Electrocardiography Myocardium Prediction algorithms Real-time systems

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RD Imaging Algorithm Based on the Waveform with Costas-FSK and 4-PSK for Synthetic Aperture Radar

RD Imaging Algorithm Based on the Waveform with Costas-FSK a...

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Data Science and Computer Application (ICDSCA), IEEE International Conference on

作者： Ni Yang School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen China

ISBN: (数字)9798350368239

ISBN: (纸本)9798350368246

Synthetic aperture radar (SAR) is an important representative of modern radar technology, which are widely used in the field of target detection, location and imaging. In order to prevent the signal transmitted by active radar from being detected and intercepted by the enemy, the radar waveform needs to have the characteristics of low interception. In this paper, the imaging performance of a complex encoded waveform in range doppler (RD) imaging algorithm is studied. By adding frequency symbol keying (FSK) based on Costas encoding and phase symbol keying (PSK) to the single frequency signal, the waveform has the characteristics of low interception with frequency hopping and high randomness. At the same time, due to the characteristic that RD algorithm's azimuth matching filter is not affected by signal waveform, it still maintains good target detection and imaging performance based on this waveform.

关键词： signal processing algorithms Imaging Radar detection Symbols Frequency shift keying Object detection Radar imaging Encoding Doppler radar Synthetic aperture radar

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Optimized Liver Tumor Segmentation: Integrating U-Net with Graph Cut algorithms

Optimized Liver Tumor Segmentation: Integrating U-Net with G...

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International Conference on Communications and signal processing

作者： K. Kishore Anthuvan Sahayaraj Aniket Laad Abhinav Mishra S Prithika Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

This paper presents an innovative approach to liver tumor segmentation, combining the strengths of U-Net architecture with Graph Cut refinement. This paper focuses on enhancing the precision of segmentation in computed tomography (CT) scans, crucial for accurate diagnosis and treatment planning in liver cancer cases. Our methodology utilized the U-Net model for initial segmentation, owing to its efficiency in handling complex biomedical images. To further refine these results, we integrated the Graph Cut algorithm, which significantly improved the accuracy of tumor boundary delineation. We validated our approach using the LiTS17 dataset, comprising diverse CT scans with varying tumor characteristics. Our results demonstrated a Dice Coefficient of 96.4 and an Accuracy of 99.4, outperforming several existing segmentation methods. This fusion of deep learning and optimization algorithms not only provided superior segmentation results but also highlighted the potential of combining different computational techniques for enhanced performance in medical image analysis.

关键词： Deep learning Image segmentation Image analysis Computed tomography Computational modeling signal processing algorithms Liver

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Implications of Virtual Reality on Environmental Sustainability in Restaurants based on AI

Implications of Virtual Reality on Environmental Sustainabil...

引用

International Conference on Communications and signal processing

作者： C Adithya Narayanan E Bharath R Vijayakumar Department of Computer Science Engineering Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

This talks about the various ways that augmented reality (AR) affects environmental sustainability in the restaurant business. When combined with artificial intelligence (AI), augmented reality has the power to completely change the restaurant industry by improving eco-friendly operations and changing many facets of their operations. This article examines how AR and AI might help restaurants become more environmentally sustainable. It focuses on important topics including menu design, waste management, customer interaction, and operational efficiency. Easy access to menus and simplicity are two important elements that make restaurant food ordering simpler. The eating experience for customers is entirely revolutionized by the HOLOGRAM menu. Current programs offer an application that eateries can utilize to include their menus into tablets with CONTROL & HOLOGRAM functionality, making it simpler for customers to swipe, swipe, and tap through menus. The objective of this project is to give the restaurants a tablet menu that suggests items using a novel recommendation algorithm. We store the database on a cloud-based server, which is safe & affordable.

关键词： Industries Waste management Automation Green products signal processing algorithms signal processing Servers

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Hardware Implementation of Area-Time Efficient Modular Multiplication over NIST Prime Fields

Hardware Implementation of Area-Time Efficient Modular Multi...

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IEEE International Conference on signal processing, Information, Communication & Systems (SPICSCON)

作者： Md. Sazedur Rahman Ibnul Zarrah Kalyan Kumar Halder Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna Bangladesh Department of Electrical and Electronic Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh

ISBN: (数字)9798331510213

ISBN: (纸本)9798331510220

This research presents an efficient modular multipli-cation for elliptic curve cryptography (ECC) over NIST prime fields. The development of ECC processors that can perform modular multiplication while minimizing hardware utilization is a crucial need in cryptography and cybersecurity. The efficiency of modular multiplication is a key factor that directly impacts the overall performance of ECC processors. Achieving fast modular multiplication with low hardware usage involves optimizing the design of ECPs to balance computational speed, resource utilization, and resistance to side-channel attacks. Our proposed design aims to achieve higher performance with reduced power consumption. For performance evaluation, we synthesized our design on various FPGA platforms, including Virtex-7 and Virtex-6, using different NIST primes. The mostly used 256-bit modular multiplication device utilization is reduced greatly here, and we extend this work to higher bits, i.e., 384-bit and 521-bit NIST primes.

关键词： Performance evaluation Program processors signal processing algorithms Computer architecture Side-channel attacks NIST signal processing Elliptic curve cryptography Hardware Field programmable gate arrays

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Multichannel Sparse Recovery for Constant Modulus signals via ℓ∞,1 Minimization

Multichannel Sparse Recovery for Constant Modulus Signals vi...

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signal, Information and Data processing (ICSIDP), IEEE International Conference on

作者： Yi-Lin Mo Zai Yang Wenlong Wang Xunmeng Wu School of Mathematics & Statistics Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Compressed sensing techniques have been extensively studied over recent decades, finding broad applications in signal processing. Convex optimization approaches for compressed sensing, such as l 2,1 -minimization, are employed for recovering sparse signals in multichannel scenarios. However, when joint sparse signals also exhibit the constant modulus (CM) property, l 2,1 -minimization fails to exploit this prior information. In this paper, we focus on utilizing l ∞,1 -minimization to recover sparse signals with the CM property. For this convex optimization method, a fast algorithm based on the alternating direction method of multipliers is proposed. Extensive numerical simulations are carried out to demonstrate the effectiveness of l ∞,1 -minimization for CM signal recovery.

关键词： signal processing algorithms signal processing Numerical simulation Minimization Data processing Convex functions Sensors Compressed sensing

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Mittag-Leffler Filter and Its Application in Bio-signals processing*

Mittag-Leffler Filter and Its Application in Bio-signals Pro...

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signal processing algorithms, Architectures, Arrangements and Applications (SPA)

作者： Ivo Petráš Faculty of BERG st. Letná 1/9 Technical University of Košice Košice Slovak Republic

ISBN: (数字)9788362065486

ISBN: (纸本)9798350373806

This contribution introduces a novel variant of the well-known low-pass Gaussian filter, the Mittag-Leffler filter, applicable in 1D and 2D contexts. This new filter incorporates a Mittag-Leffler function within its probability density function. It features three tunable parameters that adjust the curve shape and the filter’s forgetting factor. Illustrative examples involving two real ECG signals (one male and one female) and a real MRI image (female) highlight the main advantages of the proposed filter in comparison to the traditional Gaussian filtering (denoising) technique. Implementation details and the Matlab functions for the proposed 1D and 2D filters are provided too.

关键词： Filtering Shape Magnetic resonance imaging Noise reduction Low-pass filters signal processing algorithms Filtering algorithms Electrocardiography Probability density function MATLAB

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