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共 7158 条记录，以下是411-420 订阅

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Sub-6 GHz to 5G-Advanced: Multifrequency Channel Measurements, Modeling, and Characteristics Analysis 16

Sub-6 GHz to 5G-Advanced: Multifrequency Channel Measurement...

引用

16th international conference on Wireless Communications and signal processing, WCSP 2024

作者： Wang, Yiwen Wang, Xiaolong Wang, Weimin Wu, Yongle Liu, Yuanan Fan, Wei University of Posts and Telecommunications Beijing Key Laboratory of Work Safety Intelligent Monitoring School of Electronic Engineering Beijing Beijing China School of Information Science and Engineering Southeast University National Mobile Communications Research Laboratory Nanjing China

ISBN: (纸本)9798350390643

Multifrequency cooperative wireless networking will become a critical development trend to fulfill the specific requirements in the future of 5G-Advanced and beyond wireless systems. However, the wide frequency range will introduce new propagation properties that have not been fully investigated in multifrequency bands by using the same channel sounding system. In this paper, we conduct wideband channel measurements at four frequency bands from Sub-6 GHz to 5G-Advanced (4.5, 6.5, 11, and 16 GHz) in various typical indoor scenarios via the same vector network analyzer (VNA)-based channel sounder. Channel characteristics, including path loss (PL), shadow fading (SF), and power angular delay profiles (PADP), are extracted and analyzed. Empirical models of large-scale PL and SF are proposed. In addition, the distribution of the angle of departure (AOD) and the frequency dependence of PL characteristics are studied. The result and analysis can provide realistic channel characteristics for the research on 5G-Advanced and beyond multifrequency systems. © 2024 ieee.

关键词： 5G mobile communication systems

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ICCVAE: Item Concept Causal Variational Auto-Encoder for top-n recommendation 8

ICCVAE: Item Concept Causal Variational Auto-Encoder for top...

引用

8th international conference on intelligent Computing and signal processing, ICSP 2023

作者： Feng, Jingyun Wang, Qianqian Huang, Zhejun Yang, Lili Southern University of Science and Technology Department of Statistics and Data Science Shenzhen China

ISBN: (纸本)9798350302455

Recently, recommendation systems, especially interpretable ones, have become increasingly popular. The recommendation system provides personalized recommendations to users based on their previous behaviour data. Existing approaches often transfer methods in disentangle learning, especially Variational Auto-Encoder (VAE) framework, into the recommendation system. However, VAE is proved sub-optimal due to the independence factor assumption. Unfortunately, few research focuses on the VAE framework without an independent assumption for recommendation system. To escape from sub-optimality from independence assumption and provide more interpretability, in this paper, we propose a new method for top-n recommendation tasks called Item Concept Causal Variational Auto-Encoder (ICCVAE), enabling to build up causal structure for factors, i.e., item concepts in recommendation system. We conduct experiments to prove superiority for ICCVAE. Our framework can reach up to 5.3%, 3.4%, and 6.1% definite improvement over baselines in terms of Recall@20, Recall@50 and NDCG@100. © 2023 ieee.

关键词： Recommender systems

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Avoiding Domain Drift and Constant Predictions with Diffusion Enhanced Vector-Quantized Autoencoders for Temperature Predictions

Avoiding Domain Drift and Constant Predictions with Diffusio...

引用

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

作者： Lampl, Nina de Freitas, João Machado Fuchs, Alexander Brezina, Benedikt Klitzsch, Michael Pernkopf, Franz Christian Doppler Laboratory for Dependable Intelligent Systems in Harsh Environments SPSC Laboratory Graz University of Technology Austria RHI Magnesita GmbH Austria

ISBN: (纸本)9798350368741

Accurate temperature prediction in rotary cement kilns is crucial for process stability and equipment longevity. However, the repeated application of the predicted values creates an error accumulation over the length of the forecast, causing a domain drift of the predictions. This issue is exacerbated for image prediction, as more degrees of freedom lead to a higher sensitivity to small errors as local structures are lost. Using a vector-quantized autoencoder can mitigate the problem as it can map predictions back to the source domain, but it leads to almost constant predictions. Thus, we propose the usage of an additional diffusion model to avoid the local minimum of constant predictions. Our method maintains reliable in-domain predictions, preventing localized temperature peaks and ensuring stable kiln operation. Our experiments show, that the proposed vector-quantized diffusion model (VQ-Diff) can forecast much longer time sequences than reference methods with high accuracy, by being limited to the generation of in-domain images. © 2025 ieee.

关键词： cement diffusion model spatio-temporal forecasting vector-quantized autoencoder

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A RIS-Supported DOA Estimation Using Combined Norm Constraint in Non-Gaussian Noise Environments 6

A RIS-Supported DOA Estimation Using Combined Norm Constrain...

引用

6th ieee international conference on Electronic Information and Communication Technology, ICEICT 2023

作者： He, Lei Li, Liping Ying, Run Yu, Canping Li, Qiang Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350399059

Reconfigurable intelligent Surface (RIS) is a revolutionary technology in modern wireless communication systems, since it can adjust the wireless communication environment with passive components. In this paper, we consider the system environment in the presence of Base Station (BS) interference and implement the estimation of the target Direction-of-Arrival (DOA). To improve the estimation performance, we propose a DOA estimation algorithm based on RIS. The proposed algorithm consists of an error term for the lorentzian bound function and a regular term for the atomic norm (termed as LBF-AN), which are implemented to eliminate system noise and signal interference, respectively. The simulation results show that the DOA estimation performance of the proposed algorithm is better than existing algorithms. © 2023 ieee.

关键词： Gaussian noise (electronic)

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PulseSync BP: A Neural Network Framework for Non-Contact Blood Pressure Estimation

PulseSync BP: A Neural Network Framework for Non-Contact Blo...

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2024 international conference on Advances in Data Engineering and intelligent Computing systems, ADICS 2024

作者： Ghosh, Tanumay Singal, Omisha Lodh, Namit Suchitra, S. School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350364828

Using a cutting-edge neural network framework, 'PulseSync BP' estimates blood pressure without contact. Using photoplethysmogram (PPG) signals and other physiological data, this novel model uses advanced signal processing. The suggested framework changes blood pressure monitoring by eliminating cuff-based measures. PulseSync BP's RNN architecture is carefully designed for optimal prediction accuracy. The algorithm effectively estimates blood pressure through extensive experimentation, demonstrating its potential for continuous healthcare applications. This framework's capacity to use various physiological cues shows its adaptability and versatility. PulseSync BP allows real-time, non-intrusive blood pressure monitoring, advancing individualized health tracking solutions. Cuffless blood pressure estimate is advanced here, demonstrating how artificial intelligence could improve healthcare accessibility and patient-centric monitoring. PulseSync BP and other revolutionary technologies can transform blood pressure monitoring in healthcare. © 2024 ieee.

关键词： Blood pressure

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Position-aware Hypergraph Message-Passing Neural Network

Position-aware Hypergraph Message-Passing Neural Network

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

作者： Zhang, Xinyu Jiang, Qize Zhang, Hanyuan Sun, Weiwei School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China

ISBN: (纸本)9798350368741

Hypergraph neural networks can model more flexible connectivity relationships, are used to model higher-order interactions, and have produced strong results in many real-world applications. However, the currently existing hypergraph neural networks need more exploration in capturing the global positional information of nodes in hypergraphs. Although there have been many explorations of the problem in graph neural networks, extending these approaches to hypergraphs is fraught with challenges. The major challenge is that hyperedges in hypergraphs are the other dimensional element of the incidence structure, have more flexible definitions than edges in graphs, and require more attention when learning global positional information. We propose a novel position-aware hypergraph message-passing neural network framework to address the above challenges. Specifically, we propose a global positional embedding learning approach that can separately model global positional information for nodes and hyperedges. At the same time, we also optimize the learning of local structures with hyperedges. Experiments on several publicly available benchmark datasets find that our proposed method outperforms many state-of-the-art methods. © 2025 ieee.

关键词： Hypergraph Learning Hypergraph Neural Network Message Passing Neural Network

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Stimulus Artifact Denoising Technology in Surface Electromyographic signals Under Functional Electrical Stimulation 8th

Stimulus Artifact Denoising Technology in Surface Electromyo...

引用

8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Li, Chuanjiang Wang, Chenming Zhu, Yanfei Qi, Tom Ziming The College of Information Mechanical and Electrical Engineering Shanghai Normal University Shanghai201418 China New Zealand Maritime School Manukau Institute of Technology Auckland New Zealand

ISBN: (纸本)9789819601875

This paper presents a novel design to remove stimulus artifacts from electromyographic signals under functional electrical stimulation (FES). This design combines Kalman filter (KF), Comb filter (COMB), and Generalized Extreme Studentized Deviate (GESD) algorithms, and its effectiveness is verified by filtering synthetic semi-simulated and real data. Experimental results show that in the signal processed by the KF-COMB-G algorithm, the stimulus artifacts caused by FES are well suppressed. The filtered signal has a higher correlation coefficient and signal-to-noise ratio, which highlights the good effect of this design in removing FES interference. In addition, the algorithm is simple to implement and has strong real-time performance, providing a new and effective method for artifact removal processing of electromyographic signals under FES conditions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： signal to noise ratio

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EFFICIENT TWO-STAGE BEAM TRAINING AND CHANNEL ESTIMATION FOR RIS-AIDED MMWAVE systems VIA FAST ALTERNATING LEAST SQUARES 47

EFFICIENT TWO-STAGE BEAM TRAINING AND CHANNEL ESTIMATION FOR...

引用

47th ieee international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Chung, Hyeonjin Kim, Sunwoo Hanyang Univ Dept Elect & Comp Engn Seoul South Korea

ISBN: (纸本)9781665405409

This paper proposes a two-stage beam training and a channel estimation based on fast alternating least squares (FALS) for reconfigurable intelligent surface (RIS)-aided millimeter-wave systems. To reduce the beam training overhead, only selected columns and rows of the channel matrix are observed by two-stage beam training. This beam training produces a partly observed channel matrix with low coherence, which enables the low rank matrix completion technique to recover unobserved entries. Unobserved entries are recovered by FALS, which alternatingly updates the left and the right singular vectors that comprise the channel. Simulation results and analysis show that the proposed algorithm is computationally efficient and has superior accuracy to existing algorithms.

关键词： Reconfigurable intelligent surface fast alternating least squares low rank matrix completion low overhead channel estimation

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Optimizing EEG signal Classification for Motor Imagery BCIs: FilterBank CSP with Riemannian Manifolds and Ensemble Learning Models 9

Optimizing EEG Signal Classification for Motor Imagery BCIs:...

引用

9th international conference on signal processing and intelligent systems, ICSPIS 2023

作者： Esfahani, M. Moein Najafi, M. Hosein Sadati, Hossein K. N. Toosi University of Technology Department of Mechanical Engineering Tehran Iran Islamic Azad University Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350369922

Motor imagery (MI) is a compelling cognitive phenomenon situated at the intersection of cognitive neuroscience and technology, offering insights into human cognition and a spectrum of practical applications. Electroencephalography (EEG) emerges as a technology, offering a non-invasive means to record electrical activity within the brain. Motor imagery-based Brain-Computer Interfaces (MI-BCIs) enables the translation of cerebral activity into implementable commands found in the sensorimotor areas of the brain. This paper offers a comprehensive exploration of MI, delving into its practical implications, the complexities it entails, and the critical role this research focuses on enhancing the classification accuracy of EEG signals with Motor Imagery task. We employ a comprehensive approach that integrates Filter Bank Common Spatial Pattern (CSP) and the Riemannian manifold to extract features, aiming at maintaining critical signal details. Furthermore, we utilize Linear Discriminant Analysis (LDA) alongside advanced classification methods, such as AdaBoost and Total Boost, to elevate classification accuracy. Our results, reveal significant improvements in the classification accuracy. This research contributes to the advancement of MI EEG signal classification and opens new avenues for applications in fields such as brain-computer interfaces and medical diagnostics. © 2023 ieee.

关键词： Filter banks

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Elevating the Future of Mobility: UAV-enabled intelligent Transportation systems 7

Elevating the Future of Mobility: UAV-enabled Intelligent Tr...

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7th international conference on Advanced Communication Technologies and Networking, CommNet 2024

作者： Saboor, Abdul Vinogradov, Evgenii Cuil, Zhuangzhuang Coene, Sander Joseph, Wout Pollin, Sofie Leuven Belgium Technology Innovation Institute Autonomous Robotics Research Center Abu Dhabi United Arab Emirates IMEC-Ghent University Department of Information Technology Ghent9052 Belgium

ISBN: (纸本)9798350367027

intelligent Transportation systems (ITS) increasingly rely on connectivity for efficient traffic management and enhanced user experience. The existing ITS solutions operate mainly within a 2D domain, thus missing the potential benefits of aerial platforms. This paper envisions 3D ITS by integrating aerial platforms, such as Unmanned Aerial Vehicles (UAVs), to simultaneously improve network coverage and support multimodal transportation, including Advanced Air Mobility (AAM). Using stochastic models, we investigate how UAV-based Aerial Base Stations (ABSs) can address the limitations of traditional Terrestrial Base Stations (TBSs) by offering superior coverage, particularly in urban environments. Our results demonstrate that ABSs have 1 0 6. 6 7% more coverage area than TBS, higher signal-To-Noise Ratio (SNR) distribution, and are suitable for high-Throughput ITS applications. © 2024 ieee.

关键词： Stochastic models

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