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共 3487 条记录，以下是81-90 订阅

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When Sparse Graph Representation Learning Falls into Domain Shift: Feature Augmentation for Cross-Domain Graph Meta-Learning

When Sparse Graph Representation Learning Falls into Domain ...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Simin Niu Xun Liang Sensen Zhang Zhiyu Li Xuan Zhang Wu Bo Hanyu Wang Shichao Song Mengwei Wang Jiawei Yang School of Information Renmin University of China Beijing China Institute for Advanced Algorithms Research Shanghai China Guanghua School of Management Peking University Beijing China Xiangjiang Laboratory Central South University Hunan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Graph Meta-learning methods have improved the performance of few-shot node classification by means of applying meta-learning to the data in non-Euclidean domains. However, most works focus on adopting a single domain, ignoring the fact that tasks in various domains may be distinct, which can cause overfitting problems and thus limit generalizability. To tackle this challenge, we propose a novel Graph Meta-learning framework called Feature-Enhanced Cross-domain Graph Meta-learning that consists of two crucial modules: 1) A feature information extraction module that aims to capture discriminative node importance and simulate various node feature distributions under distinct domains; 2) A heterogeneous graph encoder module that leverages the enhanced node features and topological information to generate task-specific node embeddings with simple fine-tuning. Moreover, we meta-learn the parameters involved to ensure the generalizability in the unseen domains. Results show that our method markedly outperforms the existing state-of-the-art methods.

关键词： Metalearning Representation learning signal processing Feature extraction Information retrieval Acoustics Data mining Speech processing Overfitting

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Learning Diffusion Model from Noisy Measurement using Principled Expectation-Maximization Method

Learning Diffusion Model from Noisy Measurement using Princi...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Weimin Bai Weiheng Tang Enze Ye Siyi Chen Wenzheng Chen He Sun Academy for Advanced Studies Peking University School of Physics Peking University College of Future Technology Peking University Wangxuan Institute of Computer Technology Peking University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Diffusion models have demonstrated exceptional ability in modeling complex image distributions, making them versatile plug-and-play priors for solving imaging inverse problems. However, their reliance on large-scale clean datasets for training limits their applicability in scenarios where acquiring clean data is costly or impractical. Recent approaches have attempted to learn diffusion models directly from corrupted measurements, but these methods either lack theoretical convergence guarantees or are restricted to specific types of data corruption. In this paper, we propose a principled expectation-maximization (EM) framework that iteratively learns diffusion models from noisy data with arbitrary corruption types. Our framework employs a plug-and-play Monte Carlo method to accurately estimate clean images from noisy measurements, followed by training the diffusion model using the reconstructed images. This process alternates between estimation and training until convergence. We evaluate the performance of our method across various imaging tasks, including inpainting, denoising, and deblurring. Experimental results demonstrate that our approach enables the learning of high-fidelity diffusion priors from noisy data, significantly enhancing reconstruction quality in imaging inverse problems.

关键词： Training Inverse problems Imaging signal processing algorithms Diffusion models Acoustic measurements Noise measurement Speech processing Image reconstruction Convergence

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Robust signal processing Approaches for Enhancing Communication Reliability in Ultra-Dense Massive MIMO Networks Facing Fading Challenges

Robust Signal Processing Approaches for Enhancing Communicat...

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Automation and Computation (AUTOCOM), International conference on

作者： Rashmy Moray Savita Kumari Deepak Upadhyay Rashmi Sharma Aakash Singh Ranswal Nookala Venu Symbiosis Institute of Management Studies (SIMS) A constituent of Symbiosis International Deemed University School of Computing Science and Engineering Galgotias University Greater Noida India Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of CSE Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

The goal of this paper is to introduce robust signal processing techniques for realistic communication scenarios with fading in ultra-dense massive MIMO communications. The proposed techniques tackle major challenges like channel estimation accuracy, interference management, and energy efficiency, which are essential for sustaining high performance in highly dense and dynamic scenarios. The proposed system outperforms traditional algorithms through advanced techniques and pilot contamination mitigation, interference-to-signal ratio optimization, and hybrid relaying. It is shown by the simulation results those considerable gains in spectral efficiency, energy efficiency and packet errors are achieved, even in the presence of varying user mobility and hardware impairments. These approaches provide reliable communication and optimize the network performance and thus are desirable for future ultradense 6G networks.

关键词： Fading channels Wireless communication Spectral efficiency Prevention and mitigation Channel estimation Massive MIMO Energy efficiency Hardware Reliability Contamination

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Graph Anomaly Detection via Multi-Scale Reconstruction of Graph Encoder-Decoder Networks

Graph Anomaly Detection via Multi-Scale Reconstruction of Gr...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jiaqi Yu Hong Yang Peng Zhang Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Existing unsupervised graph anomaly detection (GAD) methods can be categorized into reconstruction based methods and contrastive learning based methods. The principle of reconstruction methods is to capture anomalous nodes based on data reconstruction errors. However, existing reconstruction methods are limited to a single scale reconstruction of graph structures, typically predicting edges solely based on direct connections, without considering the rich contextual information within node neighborhoods. Moreover, reconstructing original low-density inputs usually introduce noise, where the reconstruction model may overfit the anomalous data. To this end, we propose a new reconstruction based graph anomaly detection method (GADMR for short). GADMR introduces a multi-scale reconstruction, which contains both structure and feature levels. From structure level, we use a node-subgraph reconstruction method to capture the relationship between a node and their neighboring subgraphs at multiple scales. From feature level, we employ a multi-layer representation reconstruction method to facilitate the evaluation of discrepancies in high-level feature representations. Extensive experiments on six benchmark datasets show that GADMR outperforms popular anomaly detection methods.

关键词： Image edge detection Noise Neural networks Contrastive learning Reconstruction algorithms Data models Decoding Speech processing Anomaly detection Periodic structures

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Using Ear-EEG to Decode Auditory Attention in Multiple-speaker Environment

Using Ear-EEG to Decode Auditory Attention in Multiple-speak...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Haolin Zhu Yujie Yan Xiran Xu Zhongshu Ge Pei Tian Xihong Wu Jing Chen Speech and Hearing Research Center School of Intelligence Science and Technology Peking University National Key Laboratory of General Artificial Intelligence Peking University Center for BioMed-X Research Academy for Advanced Interdisciplinary Studies Peking University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Auditory Attention Decoding (AAD) can help to determine the identity of the attended speaker during an auditory selective attention task, by analyzing and processing measurements of electroencephalography (EEG) data. Most studies on AAD are based on scalp-EEG signals in two-speaker scenarios, which are far from real application. Ear-EEG has recently gained significant attention due to its motion tolerance and invisibility during data acquisition, making it easy to incorporate with other devices for applications. In this work, participants selectively attended to one of the four spatially separated speakers’ speech in an anechoic room. The EEG data were concurrently collected from a scalp-EEG system and an ear-EEG system (cEEGrids). Temporal response functions (TRFs) and stimulus reconstruction (SR) were utilized using ear-EEG data. Results showed that the attended speech TRFs were stronger than each unattended speech and decoding accuracy was 41.3% in the 60s (chance level of 25%). To further investigate the impact of electrode placement and quantity, SR was utilized in both scalp-EEG and ear-EEG, revealing that while the number of electrodes had a minor effect, their positioning had a significant influence on the decoding accuracy. One kind of auditory spatial attention detection (ASAD) method, STAnet, was testified with this ear-EEG database, resulting in 93.1% in 1-second decoding window. The implementation code and database for our work are available on GitHub: https://***/zhl486/Ear_EEG_*** and Zenodo: https://***/records/10803261.

关键词： Electrodes Accuracy Neural activity signal processing algorithms Electroencephalography Spatial databases Decoding Recording Speech processing Software development management

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International conference on signal processing, Communication, Power and Embedded System, SCOPES 2016 - Proceedings

International Conference on Signal Processing, Communication...

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2016 IEEE International conference on signal processing, Communication, Power and Embedded System, SCOPES 2016

ISBN: (纸本)9781509046201

The proceedings contain 377 papers. The topics discussed include: compensation of voltage distribunces in SMIB system using ANN based DPFC controller;image and text steganography with cryptography using MATLAB;adaptive filters using EVSSLMS algorithms based on the communication system;integration of smart grid with renewable energy for energy demand management: Puducherry case study;performance analysis for higher order Alamouti schemes with different modulation formats;identification of vehicles in traffic video;vector codebook design using gravitational search algorithm;design of a X band defected ground circular patch antenna with diamond shaped slot and ring in patch for UWB applications;deciding meaningful number of shares in digital watermarking scheme for secret images;an approach for bitstream video watermarking on mobile device;a review on energy consumption and conservation techniques for sensor node in WSN;design of MAC unit in artificial neural network architecture using Verilog HDL;multimedia for effective communication;novel advanced switching technique ZCS/ZVS fed bidirectional DC-DC topology to EVS;and biometric fusion: combining multimodal and multi algorithmic approach.

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advanced Array signal processing algorithms for DoA Estimation of Satellites and Launchers 7

Advanced Array Signal Processing Algorithms for DoA Estimati...

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7th advanced Satellite Multimedia Systems conference / 13th signal processing for Space Communications Workshop (ASMS/SPSC)

作者： Anton, A. Garcia-Rojo, I. Giron, A. Morales, E. ISDEFE Ground Segment Commun Madrid Spain

ISBN: (纸本)9781479958931

Orbiting satellites and other spatial vehicles have complex trajectories that can usually be precisely approximated with analytical or numerical trajectory estimation algorithms. However, some scenarios, such as LEOP (Launch and Early Orbit Phase) or during critical manoeuvres, present greater angular uncertainty. During these, large dish antennas used for TT&C (Telemetry, Tracking & Command) may have too narrow a beamwidth to perform a reliable and fast acquisition. A novel acquisition aid system based on distributed array elements placed on the rim of the main antenna's reflector has been implemented and successfully tested with real satellite signals. This paper analyses the subjacent digital signal processing algorithms required for DoA (Direction of Arrival) estimation, and shows simulations and field results which validate their performance.

关键词： antenna arrays DoA estimation TT&C ground stations launchers LEOP MUSIC super-resolution digital signal processing

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Physical Layer simulative comparison of DVB-S2X/RCS2 and 3GPP 5G NR-NTN technologies over Geostationary Satellite Scenario

Physical Layer simulative comparison of DVB-S2X/RCS2 and 3GP...

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advanced Satellite Mobile Systems (ASMS)

作者： Baptiste Chamaillard Albekaye Traoré Nathan Borios Stefano Cioni Business Line Telecommunication Thales Alenia Space Toulouse France Telecommunication Systems and Techniques Section European Space Agency Noordwijk Netherlands

ISBN: (数字)9798331522353

ISBN: (纸本)9798331522384

This simulation study aims to compare the physical layer performance of the 5th Generation (5G) New Radio Non-Terrestrial Networks (NR NTN) air interface with Digital Video Broadcasting (DVB) technologies, specifically analyzing the Second Generation Satellite Extensions (S2X) standard on the Downlink (DL) and DVB- Return Channel via Satellite 2nd generation (RCS2) standard on the Uplink (UL). The study assumes a reference geosynchronous satellite telecommunication system operating in the Ka-band frequencies. The comparison analysis of the physical layers demodulation performance on the Forward (FWD) link reveals an average spectral efficiency decrease from 15 to 26% for NR PDSCH (Physical Downlink Shared Channel) in comparison to DVB-S2X. The analysis of the Return (RTN) link indicates comparable performance between NR PUSCH (Physical Uplink Shared Channel) and DVB-RCS2, with a marginal advantage for either technology based on specific study cases. The conclusions drawn from the performance comparison emphasize the potential for optimizing NR waveform configuration to improve spectral efficiency and reduce implementation losses through enhanced channel estimation and phase tracking algorithms. Moreover, this study outcome advocates for the completion of the physical layer comparison with System Level simulations of both technologies. This would make it possible to consider additional implications such as nonlinear amplification effects, control plane overhead, and scheduling efficiency.

关键词： Spectral efficiency Satellite broadcasting Channel estimation signal processing algorithms Physical layer Uplink Ka-band Standards Digital video broadcasting Geostationary satellites

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Vehicle-Mounted Millimeter-Wave Radar Vital Signs Detection and Early Warning Control System

Vehicle-Mounted Millimeter-Wave Radar Vital Signs Detection ...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International conference on

作者： Wenshan Li Hunan Institute of Traffic Engineering Hengyang Hunan China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Traditional methods of monitoring vital signs in vehicles often fall short when it comes to response time, especially in fast-moving situations. To tackle this, our paper introduces a groundbreaking solution: leveraging millimeter-wave radar technology to monitor vital signs in real time within the vehicle, providing timely alerts when necessary. This approach not only enhances the accuracy of readings but also ensures a swift response that is crucial during vehicle operation. By tapping into the unique abilities of millimeter-wave radar to detect subtle human signs, we propose a novel method that processes radar signals to monitor vital signs like heart rate and respiratory rate. This technique is highly effective in meeting the stringent demands of real-time accuracy and speed, especially while driving. Our system includes three main components: radar sensors, a signal processing unit, and an early-warning control module, working together seamlessly to ensure optimal monitoring. The radar sensor continuously transmits and receives millimeter-wave signals, allowing it to track vital signs without interruption. A specially designed feature extraction algorithm filters out noise and extracts critical information, including heart and respiratory rates. Then, advanced algorithms analyze this data in real time, spotting any abnormalities and triggering alerts through the control module. What sets this system apart is its rigorous testing under various vehicle speeds and environmental conditions, confirming its stability, reliability, and fast response times. In real-world applications, the system responds to a high heart rate in 3.7 to 4.0 seconds and to a rapid breathing rate in 4.1 to 4.3 seconds. This millimeter-wave radar-based system offers a fresh approach to vehicle safety, combining practicality with promising future potential.

关键词： Heart rate Accuracy signal processing algorithms Millimeter wave radar Real-time systems Stability analysis Sensor systems Sensors Time factors Monitoring

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Next-Generation Wireless Networks: Advancements in Hybrid Beamforming for Massive MIMO Systems

Next-Generation Wireless Networks: Advancements in Hybrid Be...

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advanced Computing Technologies (ICoACT), International conference on

作者： Sudhakar K Tejnithish S Thirupukal G Arsath Farves T S Electrions and Communication Engineering M. Kumarasamy College of Engineering Karur Tamilnadu

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

Large-scale wireless communication systems are key enablers of next-generation networks, offering substantial improvements in capacity and data rates. However, the complexity and cost associated with these systems, particularly when implementing fully digital beamforming, present significant challenges. Reinforcement Learning (RL), a branch of machine learning, offers a novel and effective solution to address these challenges by dynamically optimizing beamforming strategies. This paper investigates the effectiveness of applying RL to three major beamforming architectures-analog, digital, and hybrid beamforming, with a primary focus on hybrid beamforming. RL frameworks allow the beamforming direction to be dynamically adjusted based on real-time feedback from channel state information (CSI). Through iterative learning, RL agents optimize system performance, adapting to varying channel conditions and minimizing power consumption. In hybrid beamforming, antennas share a single radio frequency (RF) chain, contrasting with digital beamforming where each antenna is connected to an individual RF chain. RL algorithms, such as Q-learning and deep reinforcement learning (DRL), are employed to select optimal beamforming weights, reducing computational complexity and enhancing energy efficiency while maintaining high data rates. This research evaluates the performance of RL-driven large-scale wireless communication systems across key metrics, including bit error rate (BER), signal-to-noise ratio (SNR), achievable sum rate, power consumption, and energy efficiency. By leveraging RL, the system learns to balance performance and complexity, achieving significant improvements over conventional methods. A detailed comparison of the three beamforming techniques is provided, highlighting the enhanced adaptability and efficiency of RL-driven hybrid beamforming. The findings demonstrate that RL-based hybrid beamforming achieves an optimal trade-off between performance and complexity, ma

关键词： Radio frequency Power demand Array signal processing Wireless networks System performance Energy efficiency Real-time systems Next generation networking Wideband signal to noise ratio

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