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arXiv 2023年

作者： Cheng, Xiang Zhang, Haotian Zhang, Jianan Gao, Shijian Li, Sijiang Huang, Ziwei Bai, Lu Yang, Zonghui Zheng, Xinhu Yang, Liuqing The State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China The Samsung Semiconductor Samsung SoC Research and Development Lab San DiegoCA92121 United States Shandong University Jinan250100 China Guangzhou510000 China Guangzhou510000 China The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

In the era of sixth-generation (6G) wireless communications, integrated sensing and communications (ISAC) is recognized as a promising solution to upgrade the physical system by endowing wireless communications with sensing capability. Existing ISAC is mainly oriented to static scenarios with radio-frequency (RF) sensors being the primary participants, thus lacking a comprehensive environment feature characterization and facing a severe performance bottleneck in dynamic environments. To date, extensive surveys on ISAC have been conducted but are limited to summarizing RF-based radar sensing. Currently, some research efforts have been devoted to exploring multi-modal sensing-communication integration but still lack a comprehensive review. To fill the gap, we embark on an initial endeavor with the goal of establishing a unified framework of intelligent multi-modal sensing-communication integration by generalizing the concept of ISAC and providing a comprehensive review under this framework. Inspired by the human synesthesia, the so-termed Synesthesia of Machines (SoM) gives the clearest cognition of such an intelligent integration and details its paradigm for the first time. We commence by justifying the necessity and potential of the new paradigm. Subsequently, we offer a rigorous definition of SoM and zoom into the detailed paradigm, which is summarized as three operational modes realizing the integration. To facilitate SoM research, we overview the prerequisite of SoM research, that is, mixed multi-modal (MMM) datasets, and introduce our work. Built upon the MMM datasets, we introduce the mapping relationships between multi-modal sensing and communications, and discuss how channel modeling can be customized to support the exploration of such relationships. Afterward, aiming at giving a comprehensive survey on the current research status of multi-modal sensing-communication integration, we cover the technological review on SoM-enhance-based and SoM-concert-based app

关键词： Beamforming

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A Survey on Indoor Visible Light Positioning systems: Fundamentals, Applications, and Challenges

arXiv

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arXiv 2024年

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Faculty of Applied Science School of Engineering The University of British Columbia KelownaBCV1V 1V7 Canada The Chinese University of Hong Kong Shenzhen518172 China

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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Resource Allocation in UAV-NOMA communication systems Based on Proportional Fairness

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Journal of communications and Information networks 2020年第2期5卷 111-120页

作者： Hui Zhang Baoji Wang Chen Chen Xiang Cheng Hang Li State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Shanghai Institute of Microsystem and Information Tech-nology Chinese Academy of SciencesShanghai 200050China Data-Driven Information System Laboratory Shenzhen Re-search Institute of Big DataShenzhen 518172China

This paper investigates the power allocation problem in non-orthogonal multiple access(NOMA)integrated unmanned aerial vehicle(UAV)communication *** particular,we propose a novel resource allocation scheme to increase the transmission rate of the users that have relatively worse channel state information,while reducing the sum rate *** solve this problem efficiently,we decouple the optimization problem into three ***,we solve the problem of user pairing and subchannel ***,the optimum power proportional factor is derived to allocate transmit power among different users on the same *** last,different subchannels are allocated with appropriate power to improve the performance of the *** results show that the proposed scheme can enjoy a better performance than the benchmark methods since it can achieve a proper trade-off between the system sum rate and the proportional fairness.

关键词： UAV communications NOMA power allo-cation proportional fairness

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Deep Neural Network Enhanced Chip-on-Board Hybrid-Packaged Photonic-Electrical Silicon Transceiver for High-speed Optical Interconnects

Deep Neural Network Enhanced Chip-on-Board Hybrid-Packaged P...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Jun Qin Qipeng Yang Changhao Han Yan Zhou Yunhao Zhang Yichen WU Yu Sun Junde Lu Siming Liu Yueqin Li Jian Sun Miao Min Weiwei Hu Zhixue He Lei Wang Shaohua Yu Haowen Shu Xingjun Wang Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara CA USA Peking University Yangtze Delta Institute of Optoelectronics Nantong China Peng Cheng Laboratory Shenzhen China State Key Laboratory of Information Photonics and Optical Communications BUPT Beijing China

We explore implementing a multilevel deep neural network to enhance the performance of a 4-channel photonic-electrical hybrid-packaged silicon transceiver. Stable transmission and reception of 150 Gbps/λ PAM4 signals... 详细信息

ISBN: (纸本)9798350369311

关键词： Electric potential Optical interconnections System performance Artificial neural networks Transceivers Silicon Electro-optical waveguides Photonics

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A GCN-assisted deep learning method for peripapillary retinal layer segmentation in OCT images 25

A GCN-assisted deep learning method for peripapillary retina...

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Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV 2021

作者： Li, Jiaxuan Ling, Yuye He, Jiangnan Jin, Peiyao Zhu, Jianfeng Zou, Haidong Xu, Xun Shao, Shuo Gan, Yu Su, Yikai John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai200240 China Department of Preventative Ophthalmology Shanghai Eye Disease Prevention and Treatment Center Shanghai Eye Hospital Shanghai200040 China Department of Ophthalmology Shanghai Jiao Tong University Shanghai General Hospital Shanghai200080 China Department of Electrical and Computer Engineering The University of Alabama AL35487 United States Department of Electronic Engineering State Key Lab of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9781510640962

ISBN: (纸本)9781510640955

Accurate retinal layer segmentation, especially the peripapillary retinal nerve fiber layer (RNFL) is critical for the diagnosis of ophthalmic diseases. However, due to the complex morphologies of the peripapillary region, most of the existing methods focus on segmenting the macular region and could not be directly applied to the peripapillary retinal optical coherence tomography (OCT) images. In this paper, we propose a novel graph convolutional network (GCN)-assisted segmentation framework based on a U-shape neural network for peripapillary retinal layer segmentation in OCT images. We argue that the strictly stratified structure of retina layers in addition to the centered optic disc is an ideal objective for GCN. Specifically, a graph reasoning block is inserted between the encoder and decoder of the U-shape neural network to conduct spatial reasoning. In this way, the peripapillary retina in OCT images is segmented into nine layers including RNFL. The proposed method was trained and tested on our collected dataset of peripapillary retinal OCT images. Experimental results showed that our segmentation method outperformed other state-of-the-art methods. In particular, compared with ReLayNet, the average and RNFL Dice coefficients are improved by 1.2% and 2.6%, respectively. © 2021 SPIE.

关键词： Optical tomography

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Multi-scale Sparse Representation-Based Shadow Inpainting for Retinal OCT Images

arXiv

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arXiv 2022年

作者： Tang, Yaoqi Li, Yufan Liu, Hongshan Li, Jiaxuan Jin, Peiyao Gan, Yu Ling, Yuye Su, Yikai John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Computer Engineering The University of Alabama AL35487 United States Department of Ophthalmology Shanghai General Hospital Shanghai Jiao Tong University Shanghai200080 China State Key Lab of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China

Inpainting shadowed regions cast by superficial blood vessels in retinal optical coherence tomography (OCT) images is critical for accurate and robust machine analysis and clinical diagnosis. Traditional sequence-based approaches such as propagating neighboring information to gradually fill in the missing regions are cost-effective. But they generate less satisfactory outcomes when dealing with larger missing regions and texture-rich structures. Emerging deep learning-based methods such as encoder-decoder networks have shown promising results in natural image inpainting tasks. However, they typically need a long computational time for network training in addition to the high demand on the size of datasets, which makes it difficult to be applied on often small medical datasets. To address these challenges, we propose a novel multi-scale shadow inpainting framework for OCT images by synergically applying sparse representation and deep learning: sparse representation is used to extract features from a small amount of training images for further inpainting and to regularize the image after the multi-scale image fusion, while convolutional neural network (CNN) is employed to enhance the image quality. During the image inpainting, we divide preprocessed input images into different branches based on the shadow width to harvest complementary information from different scales. Finally, a sparse representation-based regularizing module is designed to refine the generated contents after multi-scale feature aggregation. Experiments are conducted to compare our proposal versus both traditional and deep learning-based techniques on synthetic and real-world shadows. Results demonstrate that our proposed method achieves favorable image inpainting in terms of visual quality and quantitative metrics, especially when wide shadows are presented. © 2022, CC BY.

关键词： Image fusion

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Data Computing in Covert Domain 1st

Data Computing in Covert Domain

引用

1st International Conference on Security and Privacy in Digital Economy, SPDE 2020

作者： Qian, Zhenxing Wang, Zichi Zhang, Xinpeng Shanghai Institute of Intelligent Electronics and Systems School of Computer Science Fudan University Shanghai201203 China Shanghai Institute for Advanced Communication and Data Science Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai200444 China

ISBN: (纸本)9789811591280

This paper proposes an idea of data computing in the covert domain (DCCD). We show that with information hiding some data computing tasks can be executed beneath the covers like images, audios, random data, etc. In the proposed framework, a sender hides his source data into two covers and uploads them onto a server. The server executes computation within the stego and returns the covert computing result to a receiver. With the covert result, the receiver can extract the computing result of the source data. During the process, it is imperceptible for the server and the adversaries to obtain the source data as they are hidden in the cover. The transmission can be done over public channels. Meanwhile, since the computation is realized in the covert domain, the cloud cannot obtain the knowledge of the computing result. Therefore, the proposed idea is useful for secure computing. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Covert computation Information hiding

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Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemble

arXiv

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arXiv 2024年

作者： Chen, Chuangtao Zhao, Qinglin Zhou, MengChu He, Zhimin Sun, Zhili Situ, Haozhen Faculty of Innovation Engineering Macau University of Science and Technology Macao999078 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States School of Electronic and Information Engineering Foshan University Foshan528000 China Institute of Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China

Classical diffusion models have shown superior generative results. Exploring them in the quantum domain can advance the field of quantum generative learning. This work introduces Quantum Generative Diffusion Model (QGDM) as their simple and elegant quantum counterpart. Through a non-unitary forward process, any target quantum state can be transformed into a completely mixed state that has the highest entropy and maximum uncertainty about the system. A trainable backward process is used to recover the former from the latter. The design requirements for its backward process includes non-unitarity and small parameter count. We introduce partial trace operations to enforce non-unitary and reduce the number of trainable parameters by using a parameter-sharing strategy and incorporating temporal information as an input in the backward process. We present QGDM’s resource-efficient version to reduce auxiliary qubits while preserving generative capabilities. QGDM exhibits faster convergence than Quantum Generative Adversarial Network (QGAN) because its adopted convex-based optimization can result in better convergence. The results of comparing it with QGAN demonstrate its effectiveness in generating both pure and mixed quantum states. It can achieve 53.02% higher fidelity in mixed-state generation than QGAN. The results highlight its great potential to tackle challenging quantum generation tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

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Offloading safety- And mission-critical tasks via unreliable connections 32

Offloading safety- And mission-critical tasks via unreliable...

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32nd Euromicro Conference on Real-Time systems, ECRTS 2020

作者： Schönberger, Lea Chen, Kuan-Hsun Youssef, Hazem Wietfeld, Christian Chen, Jian-Jia von der Brüggen, Georg Sliwa, Benjamin Venkatapathy, Aswin Karthik Ramachandran ten Hompel, Michael Design Automation for Embedded Systems Group Faculty of Computer Science TU Dortmund University Germany Material Handling and Warehousing Faculty of Mechanical Engineering TU Dortmund University Germany Communication Networks Institute Faculty of Electrical Engineering TU Dortmund University Germany

ISBN: (纸本)9783959771528

For many cyber-physical systems, e.g., IoT systems and autonomous vehicles, offloading workload to auxiliary processing units has become crucial. However, since this approach highly depends on network connectivity and responsiveness, typically only non-critical tasks are offloaded, which have less strict timing requirements than critical tasks. In this work, we provide two protocols allowing to offload critical and non-critical tasks likewise, while providing different service levels for non-critical tasks in the event of an unsuccessful offloading operation, depending on the respective system requirements. We analyze the worst-case timing behavior of the local cyber-physical system and, based on these analyses, we provide a sufficient schedulability test for each of the proposed protocols. In the course of comprehensive experiments, we show that our protocols have reasonable acceptance ratios under the provided schedulability tests. Moreover, we demonstrate that the system behavior under our proposed protocols is strongly dependent on probability of unsuccessful offloading operations, the percentage of critical tasks in the system, and the amount of offloaded workload. © Lea Schönberger, Georg von der Brüggen, Kuan-Hsun Chen, Benjamin Sliwa, Hazem Youssef, Aswin Ramachandran, Christian Wietfeld, Michael ten Hompel, and Jian-Jia Chen.

关键词： Cyber Physical System

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