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Holography, Diffractive Optics, and Applications XIII 2023

作者： Liu, H.P. Feng, J.J. Chen, J. Zhou, W.J. Bi, Q.Y. Zeng, H.P. School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Key Lab of Advanced Optical Manufacturing Technologies of Jiangsu Province Soochow University Suzhou215006 China Chongqing Key Laboratory of Precision Optics Chongqing Institute of East China Normal University Chongqing401120 China State Key Laboratory of Precision Spectroscopy East China Normal University Shanghai200241 China

ISBN: (纸本)9781510667853

A low-loss and wideband silicon polarizing beam splitter is demonstrated with the assistance of a nano-bridge waveguide. Transverse magnetic light can be coupled to the cross port through the nano-bridge waveguide, while the transverse electric light comes out mainly from the through port. The designed device has a coupling length of 19.6 µm, which can realize an extinction ratio of 32.43 dB for TM mode or 34.23 dB for TE mode at a 1565-nm wavelength. Compared with the conventional three-waveguide coupler structure, the proposed device is based on a resonant tunneling principle, which can help to effectively improve the fabrication tolerance. The device is fabricated with a commercial CMOS processing facility, which can achieve an extinction ratio of 23.07 dB for TM or 23.46 dB for TE mode with a low excess loss, and the extinction ratio of more than 10 dB can be realized in the wavelength range from 1525 to 1610 nm for both modes. The device performance can be further improved, which would facilitate its practical applications in commercial integrated optical circuits. © 2023 SPIE.

关键词： Silicon photonics

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Sensing With Random Communication Signals

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IEEE Network 2025年

作者： Lu, Shihang Liu, Fan Xiong, Yifeng Du, Zhen Cui, Yuanhao Li, Shuangyang Yuan, Weijie Yang, Jie Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing210044 China Technical University of Berlin Faculty of Electrical Engineering and Computer Science Berlin10587 Germany Shenzhen518055 China Frontiers Science Center for Mobile Information Communication and Security China Southeast University MOE Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing211189 China

Communication-centric Integrated Sensing and Communication (ISAC) has been recognized as a promising methodology to implement wireless sensing functionality over existing network architectures, due to its cost-effectiveness and backward compatibility to legacy cellular systems. However, the inherent randomness of the communication signal may incur huge fluctuations in sensing capabilities, leading to unfavorable detection and estimation performance. To address this issue, we elaborate on random ISAC signal processing methods in this article, aiming at improving the sensing performance without unduly deteriorating the communication functionality. Specifically, we commence by discussing the fundamentals of sensing with random communication signals, including the performance metrics and optimal ranging waveforms. Building on these concepts, we then present a general framework for random ISAC signal transmission, followed by an in-depth exploration of time-domain pulse shaping, frequency-domain constellation shaping, and spatial-domain precoding methods. We provide a comprehensive overview of each of these topics, including models, results, and design guidelines. Finally, we conclude this article by identifying several promising research directions for random ISAC signal transmission. © 2025 IEEE.

关键词： Mobile telecommunication systems

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Online Traffic Prediction in Multi-RAT Heterogeneous Network: A User-Cybertwin Asynchronous Learning Approach

Online Traffic Prediction in Multi-RAT Heterogeneous Network...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Qihao Li Wen Wu Wei Zhang Xuemin Sherman Shen College of Communication Engineering Jilin University Changchun China School of Electrical Engineering and Intelligentization Dongguan University of Technology Dongguan China Peng Cheng Laboratory Frontier Research Center Shenzhen China School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

In this paper, we propose a novel traffic prediction scheme for multiple radio access technology (multi-RAT) heterogeneous network. The scheme is named user-Cybertwin asynchronous learning (UCAL), which aims to extract meaningful patterns from noisy network traffic measurements and mitigate the impact of highly nonstationary measurements for ensuring the prediction accuracy. Specifically, we design a pattern extraction method that minimizes the Frobnius norm between the collected measurements and the expected k-rank approximation of the measurements in order to extract useful information. Then, by transforming the conventional long short term memory (LSTM) model into a nonlinear state space and incorporating Gaussian noise, we develop an online LSTM algorithm to adapt fast to changing environments. As a result, the parameter updating of the new online LSTM model can keep up with data changes while capturing complicated and nonlinear relationships among measurements. We consider both the surrounding environment conditions on the mobile user side and end-to-end link conditions on the Cybertwin side, and iteratively update the model parameters in both Cybertwin and MU. Simulation results demonstrate that the proposed UCAL scheme can achieve high traffic prediction accuracy in comparison to existing schemes. It can also significantly improve the efficiency in maintaining the prediction accuracy even when the dimension of traffic measurements increases.

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Perceptual Quality Assessment of Octree-RAHT Encoded 3D Point Clouds

arXiv

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arXiv 2024年

作者： Duan, Dongshuai Su, Honglei Liu, Qi Yuan, Hui Gao, Wei Song, Jiarun Wang, Zhou College of Electronics and Information Qingdao University Qingdao266071 China School of Control Science and Engineering Shandong University Ji’nan250061 China School of Electronic and Computer Engineering Peking University Shenzhen518055 China Peng Cheng Laboratory Shenzhen518066 China School of Telecommunications Engineering Xidian University Xi’an710071 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

No-reference bitstream-layer point cloud quality assessment (PCQA) can be deployed without full decoding at any network node to achieve real-time quality monitoring. In this work, we focus on the PCQA problem dedicated to Octree-RAHT encoding mode. First, to address the issue that existing PCQA databases have a small scale and limited distortion levels, we establish the WPC5.0 database which is the first one dedicated to Octree-RAHT encoding mode with a scale of 400 distorted point clouds (PCs) including 4 geometric multiplied by 5 attitude distortion levels. Then, we propose the first PCQA model dedicated to Octree-RAHT encoding mode by parsing PC bitstreams without full decoding. The model introduces texture bitrate (TBPP) to predict texture complexity (TC) and further derives the texture distortion factor. In addition, the Geometric Quantization Parameter (PQS) is used to estimate the geometric distortion factor, which is then integrated into the model along with the texture distortion factor to obtain the proposed PCQA model named streamPCQ-OR. The proposed model has been compared with other advanced PCQA methods on the WPC5.0, BASICS and M-PCCD databases, and experimental results show that our model has excellent performance while having very low computational complexity, providing a reliable choice for time-critical applications. To facilitate subsequent research, the database and source code will be publicly released at https://***/qdushl/Waterloo-Point-Cloud-Database-5.0. Copyright © 2024, The Authors. All rights reserved.

关键词： Database systems

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H-GCN: A Graph Convolutional Network Accelerator on Versal ACAP Architecture

arXiv

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arXiv 2022年

作者： Zhang, Chengming Geng, Tong Guo, Anqi Tian, Jiannan Herbordt, Martin Li, Ang Tao, Dingwen School of Electrical Engineering and Computer Science Washington State University PullmanWA United States Mathematics and Computer Science Division Pacific Northwest National Laboratory RichlandWA United States Department of Electrical and Computer Engineering Boston University BostonMA United States Department of Electrical and Computer Engineering University of Rochester RochesterNY United States School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

Graph Neural Networks (GNNs) have drawn tremendous attention due to their unique capability to extend Machine Learning (ML) approaches to applications broadly-defined as having unstructured data, especially graphs. Compared with other Machine Learning (ML) modalities, the acceleration of Graph Neural Networks (GNNs) is more challenging due to the irregularity and heterogeneity derived from graph typologies. Existing efforts, however, have focused mainly on handling graphs’ irregularity and have not studied their heterogeneity. To this end we propose H-GCN, a PL (Programmable Logic) and AIE (AI Engine) based hybrid accelerator that leverages the emerging heterogeneity of Xilinx Versal Adaptive Compute Acceleration Platforms (ACAPs) to achieve high-performance GNN inference. In particular, H-GCN partitions each graph into three subgraphs based on its inherent heterogeneity, and processes them using PL and AIE, respectively. To further improve performance, we explore the sparsity support of AIE and develop an efficient density-aware method to automatically map tiles of sparse matrix-matrix multiplication (SpMM) onto the systolic tensor array. Compared with state-of-the-art GCN accelerators, H-GCN achieves, on average, speedups of 1.1∼2.3×. © 2022, CC BY.

关键词： Graph neural networks

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Integrated Multiscopic CPSS For Physical and Cognitive Measurement in an Independent Block-Design Test

Integrated Multiscopic CPSS For Physical and Cognitive Measu...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Adnan Rachmat Anom Besari Azhar Aulia Saputra Takenori Obo Kurnianingsih Naoyuki Kubota Graduate School of Systems Design Tokyo Metropolitan University Tokyo Japan Department of Information and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Department of Electrical Engineering Politeknik Negeri Semarang Semarang Indonesia

This paper presents an integrated multiscopic cyber-physical-social system (CPSS) for evaluating physical and cognitive aspects in an independent block-design test (BDT). First, we utilize a hand tracker to extract physical features at the microscopic level, such as hand posture during manipulation. Second, an egocentric vision incorporating an eye tracker describes perceptual representation at the mesoscopic level. This system represents hand-eye coordination by measuring the distance between the hand and the center of visual attention during the test. Thirdly, an upper table vision system uses color feature recognition in each block to evaluate cognition at the macroscopic level. This system detects the blocks and recognizes that the design matches the task given. By evaluating eight designs of WAIS-IV BDT, our system has demonstrated its ability to measure physical and cognitive variables during the BDT successfully. Additional research and development are required to support BDT development in the future. Code and datasets are available online at https://***/anom-tmu/bdt-multiscopic.

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Vision-based Semantic Communications for Metaverse Services: A Contest Theoretic Approach

Vision-based Semantic Communications for Metaverse Services:...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Guangyuan Liu Hongyang Du Dusit Niyato Jiawen Kang Zehui Xiong Boon Hee Soong Energy Research Institute@NTU Interdisciplinary Graduate Program School of Computer Science and Engineering Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Electronic Engineering Nanyang Technological University Singapore

The popularity of Metaverse as an entertainment, social, and work platform has led to a great need for seamless avatar integration in the virtual world. In Metaverse, avatars must be updated and rendered to reflect users' behaviour. Achieving real-time synchronization between the virtual bilocation and the user is complex, placing high demands on the Metaverse Service Provider (MSP)'s rendering resource allocation scheme. To tackle this issue, we propose a semantic communication framework that leverages contest theory to model the interactions between users and MSPs and determine optimal resource allocation for each user. To reduce the consumption of network resources in wireless transmission, we use the semantic communication technique to reduce the amount of data to be transmitted. Under our simulation settings, the encoded semantic data only contains 51 bytes of skeleton coordinates instead of the image size of 8.243 megabytes. Moreover, we implement Deep Q-Network to optimize reward settings for maximum performance and efficient resource allocation. With the optimal reward setting, users are incentivized to select their respective suitable uploading frequency, reducing down-sampling loss due to rendering resource constraints by 66.076% compared with the traditional average distribution method. The framework provides a novel solution to resource allocation for avatar association in VR environments, ensuring a smooth and immersive experience for all users.

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arXiv

引用

arXiv 2024年

作者： Lin, Zhenchao He, Li Yang, Hongqiang Sun, Xiaoqun Zhang, Guojin Chen, Weinan Guan, Yisheng Zhang, Hong School of Electromechanical Engineering Guangdong University of Technology Guangzhou China Department of Electronic and Electrical Engineering Southern University of Science and Technology China Shenzhen Key Laboratory of Robotics and Computer Vision China Meituan Technology Co. Ltd Shenzhen China

Large-scale point cloud consists of a multitude of individual objects, thereby encompassing rich structural and underlying semantic contextual information, resulting in a challenging problem in efficiently segmenting a point cloud. Most existing researches mainly focus on capturing intricate local features without giving due consideration to global ones, thus failing to leverage semantic context. In this paper, we propose a Similarity-Weighted Convolution and local-global Fusion Network, named SWCF-Net, which takes into account both local and global features. We propose a Similarity-Weighted Convolution (SWConv) to effectively extract local features, where similarity weights are incorporated into the convolution operation to enhance the generalization capabilities. Then, we employ a downsampling operation on the K and V channels within the attention module, thereby reducing the quadratic complexity to linear, enabling Transformer to deal with large-scale point cloud. At last, orthogonal components are extracted in the global features and then aggregated with local features, thereby eliminating redundant information between local and global features and consequently promoting efficiency. We evaluate SWCF-Net on large-scale outdoor datasets SemanticKITTI and Toronto3D. Our experimental results demonstrate the effectiveness of the proposed network. Our method achieves a competitive result with less computational cost, and is able to handle large-scale point clouds efficiently. The code is available at https://***/Sylva-Lin/SWCF-Net Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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Examining the Use of Non-fungible Tokens (NFTs) as a Trading Mechanism for the Metaverse 1

引用

29th European systems, Software and Services Process Improvement, EuroSPI 2022

作者： Yilmaz, Murat Hacaloğlu, Tuna Clarke, Paul Department of Computer Engineering Engineering Faculty Gazi University Ankara Turkey Metaverse Research and Development Laboratory Gazi University Ankara Turkey Department of Information Systems Engineering Atilim University Ankara Turkey School of Computing Dublin City University Dublin Ireland Lero the Science Foundation Ireland Research Centre for Software Limerick Ireland

ISBN: (数字)9783031155598

ISBN: (纸本)9783031155581

The notion of a metaverse seems hard to define but encourages the impression that it can be considered as a new virtual metaphysical landscape that somehow goes beyond our geographical locations and understanding (i.e., independent of time and space). Based on virtual reality, augmented reality, and blockchain, it is envisioned as an independent but extended world that is planned to be a digital virtuality entrenched not only in our old habits such as gaming and entertainment but also in virtual asset trade. In particular, trading is a pillar of the virtual economy, and auction houses will be crucial for Metaverse trading. This exploratory study examines the possibility of using an auction environment to improve the trading capabilities in a virtual universe. We investigate the cases of creating a virtual auction house with the potential of social trading of virtual assets with crypto coins and bartering. To this end, we built a virtual auction house and tested it initially using a set of scenarios. Our preliminary findings suggest that creating a virtual trading environment would be beneficial as an environment for buying and selling virtual assets and exploring their consequences. © 2022, Springer Nature Switzerland AG.

关键词： Augmented reality

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Load shedding frequency management of microgrids using hierarchical fuzzy control

Load shedding frequency management of microgrids using hiera...

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2021 World Automation Congress, WAC 2021

作者： Keivanimehr, Mehran Chamorro, Harold R. Zareian-Jahromi, Mehdi Segundo-Sevilla, Felix Rafael Guerrero, Josep M. Konstantinou, Charalambos University of Kashan Department of Electrical Engineering Kashan Iran Royal Institute of Technology KTH Stockholm Sweden Shiraz University School of Electrical and Computer Engineering Shiraz Iran Zurich University of Applied Sciences Power Systems and Smart Grid Lab Zurich Switzerland Aalborg University Aalborg Denmark Division of Computer Electrical and Mathematical Sciences and Engineering Thuwal23955 Saudi Arabia

ISBN: (纸本)9781685241117

Frequency control is one important issue in autonomous operating mode of Microgrids (MGs). In stand-alone microgrids, when generation power is not enough, it is inevitable to shed some parts of the load. This paper presents a decentralized fuzzy controller for optimal load shedding in MGs. The proposed controller which is separately installed for each load center uses a hierarchical fuzzy controller approach to make the optimal decision. Three inputs of the hierarchical fuzzy controller are the power of non-critical load, the load energy not supplied and its interruption frequency. From a specific time of the past, these inputs are calculated for all load centers, and updated periodically. The non-critical part of the loads which can be shed to survive network. Each decentralized controller of the loads has a bilateral communication with the microgrid central controller to transact some information, periodically. Simulation results on a typical microgrid with several load buses showing the effectiveness of the proposed method. © 2021 TSI Enterprises.

关键词： Controllers

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