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arXiv 2023年

作者： Zhang, Maojun Li, Yang Liu, Dongzhu Jin, Richeng Zhu, Guangxu Zhong, Caijun Quek, Tony Q.S. The College of information Science and Electronic Engineering Zhejiang University Hangzhou China China Academy of Information and Communications Technology Beijing China School of Computing Science University of Glasgow United Kingdom The Department of Information and Communication Engineering Zhejiang University Hangzhou310007 China The Zhejiang–Singapore Innovation and AI Joint Research Lab Hangzhou310007 China Hangzhou310007 China The Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China The Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Guangzhou510555 China The Singapore University of Technology and Design Singapore487372 Singapore

Federated edge learning (FEEL) is a popular distributed learning framework for privacy-preserving at the edge, in which densely distributed edge devices periodically exchange model-updates with the server to complete the global model training. Due to limited bandwidth and uncertain wireless environment, FEEL may impose heavy burden to the current communication system. In addition, under the common FEEL framework, the server needs to wait for the slowest device to complete the update uploading before starting the aggregation process, leading to the straggler issue that causes prolonged communication time. In this paper, we propose to accelerate FEEL from two aspects: i.e., 1) performing data compression on the edge devices and 2) setting a deadline on the edge server to exclude the straggler devices. However, undesired gradient compression errors and transmission outage are introduced by the aforementioned operations respectively, affecting the convergence of FEEL as well. In view of these practical issues, we formulate a training time minimization problem, with the compression ratio and deadline to be optimized. To this end, an asymptotically unbiased aggregation scheme is first proposed to ensure zero optimality gap after convergence, and the impact of compression error and transmission outage on the overall training time are quantified through convergence analysis. Then, the formulated problem is solved in an alternating manner, based on which, the novel joint compression and deadline optimization (JCDO) algorithm is derived. Numerical experiments for different use cases in FEEL including image classification and autonomous driving show that the proposed method is nearly 30X faster than the vanilla FedSGD algorithm, and outperforms the state-of-the-art schemes. © 2023, CC BY.

关键词： Autonomous vehicles

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A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation

A Semi-Supervised Approach with Error Reflection for Echocar...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoxiang Han Yiman Liu Jiang Shang Qingli Li Jiangang Chen Menghan Hu Qi Zhang Yuqi Zhang Yan Wang School of Communication and Information Engineering Shanghai University Shanghai P.R.China The SMART (Smart Medicine and AI-based Radiology Technology) Lab Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai P.R.China Department of Pediatric Cardiology Shanghai Children’s Medical Center School of Medicine Shanghai Jiao Tong University Shanghai P.R.China Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP) Shanghai P.R.China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai P.R.China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Segmenting internal structure from echocardiography is essential for the diagnosis and treatment of various heart diseases. Semi-supervised learning shows its ability in alleviating annotations scarcity. While existing semi-supervised methods have been successful in image segmentation across various medical imaging modalities, few have attempted to design methods specifically addressing the challenges posed by the poor contrast, blurred edge details and noise of echocardiography. These characteristics pose challenges to the generation of high-quality pseudo-labels in semi-supervised segmentation based on Mean Teacher. Inspired by human reflection on erroneous practices, we devise an error reflection strategy for echocardiography semi-supervised segmentation architecture. The process triggers the model to reflect on inaccuracies in unlabeled image segmentation, thereby enhancing the robustness of pseudo-label generation. Specifically, the strategy is divided into two steps. The first step is called reconstruction reflection. The network is tasked with reconstructing authentic proxy images from the semantic masks of unlabeled images and their auxiliary sketches, while maximizing the structural similarity between the original inputs and the proxies. The second step is called guidance correction. Reconstruction error maps decouple unreliable segmentation regions. Then, reliable data that are more likely to occur near high-density areas are leveraged to guide the optimization of unreliable data potentially located around decision boundaries. Additionally, we introduce an effective data augmentation strategy, termed as multi-scale mixing up strategy, to minimize the empirical distribution gap between labeled and unlabeled images and perceive diverse scales of cardiac anatomical structures. Extensive experiments on a public echocardiography dataset CAMUS, and a private clinical echocardiography dataset demonstrate the competitiveness of the proposed method.

关键词： Image segmentation Echocardiography Noise Semantics Anatomical structure Semisupervised learning Data augmentation Reflection Robustness Image reconstruction

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Neural Network-Assisted Hybrid Model Based Message Passing for Parametric Holographic MIMO Near Field Channel Estimation

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IEEE Transactions on Wireless communications 2025年

作者： Yuan, Zhengdao Guo, Yabo Gao, Dawei Guo, Qinghua Wang, Zhongyong Huang, Chongwen Jin, Ming Wong, Kai-Kit Henan Open University Artificial Intelligence Technology Engineering Research Center Zhengzhou450002 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Zhengzhou University School of Information Engineering Zhengzhou450002 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China University of Wollongong School of Electrical Computer and Telecommunications Engineering NSW2522 Australia Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University College London Department of Electronic and Electrical Engineering Torrington Place WC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of

Holographic multiple-input and multiple-output (HMIMO) is a promising technology with the potential to achieve high energy and spectral efficiencies, enhance system capacity and diversity, etc. In this work, we address the challenge of HMIMO near field (NF) channel estimation, which is complicated by the intricate model introduced by the dyadic Green's function. Despite its complexity, the channel model is governed by a limited set of parameters. This makes parametric channel estimation highly attractive, offering substantial performance enhancements and enabling the extraction of valuable sensing parameters, such as user locations, which are particularly beneficial in mobile networks. However, the relationship between these parameters and channel gains is nonlinear and compounded by integration, making the estimation a formidable task. To tackle this problem, we propose a novel neural network (NN) assisted hybrid method. With the assistance of NNs, we first develop a novel hybrid channel model with a significantly simplified expression compared to the original one, thereby enabling parametric channel estimation. Using the readily available training data derived from the original channel model, the NNs in the hybrid channel model can be effectively trained offline. Then, building upon this hybrid channel model, we formulate the parametric channel estimation problem with a probabilistic framework and design a factor graph representation for Bayesian estimation. Leveraging the factor graph representation and unitary approximate message passing (UAMP), we develop an effective message passing-based Bayesian channel estimation algorithm. Extensive simulations demonstrate the superior performance of the proposed method. © 2025 IEEE.

关键词： Green's function

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Measurement-device-independent quantum secure direct communication

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science China(Physics,Mechanics & Astronomy) 2020年第3期63卷 2-7页

作者： ZengRong Zhou Yu Bo Sheng PengHao Niu LiuGuo Yin GuiLu Long Lajos Hanzo State Key Laboratory of Low-dimensional Quantum Physics Beijing 100084China Department of Physics Tsinghua UniversityBeijing 100084China Collaborative Innovation Center of Quantum Matter Beijing 100084China Beijing Academy of Quantum Information Beijing 100084China Institute of Quantum Information and Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China College of Telecommunications&Information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing 210003China Beijing National Research Center for Information Science and Technology Beijing 100084China School of Information and Technology Tsinghua UniversityBeijing 100084China School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK

Quantum secure direct communication(QSDC)is a unique technique,which supports the secure transmission of confidential information directly through a quantum channel without the need for a secret key and for *** this secure communication protocol fundamentally differs from its conventional *** this article,we report the first measurement-deviceindependent(MDI)QSDC protocol relying on sequences of entangled photon pairs and single ***,it eliminates the security loopholes associated with the measurement ***,this MDI technique is capable of doubling the communication distance of its conventional counterpart operating without using our MDI *** also conceive a protocol associated with linear optical Bell-basis measurements,where only two of the four Bell-basis states could be *** the number of qubits in a sequence reduces to 1,the MDI-QSDC protocol degenerates to a deterministic MDI quantum key distribution protocol.

关键词： quantum information secure communication quantum communication

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Aligning Recommendation and Conversation via Dual Imitation

arXiv

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arXiv 2022年

作者： Zhou, Jinfeng Wang, Bo Huang, Minlie Zhao, Dongming Huang, Kun He, Ruifang Hou, Yuexian College of Intelligence and Computing Tianjin University Tianjin China State Key Laboratory of Communication Content Cognition People’s Daily Online Beijing China The CoAI Group DCST Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China AI Lab China Mobile Communication Group Tianjin Co. Ltd. China

Human conversations of recommendation naturally involve the shift of interests which can align the recommendation actions and conversation process to make accurate recommendations with rich explanations. However, existing conversational recommendation systems (CRS) ignore the advantage of user interest shift in connecting recommendation and conversation, which leads to an ineffective loose coupling structure of CRS. To address this issue, by modeling the recommendation actions as recommendation paths in a knowledge graph (KG), we propose DICR (Dual Imitation for Conversational Recommendation), which designs a dual imitation to explicitly align the recommendation paths and user interest shift paths in a recommendation module and a conversation module, respectively. By exchanging alignment signals, DICR achieves bidirectional promotion between recommendation and conversation modules and generates high-quality responses with accurate recommendations and coherent explanations. Experiments demonstrate that DICR outperforms the state-of-the-art models on recommendation and conversation performance with automatic, human, and novel explainability metrics. © 2022, CC BY.

关键词： Knowledge graph

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Donut Modes in Space Wavelength Division Multiplexing: Multimode Optical Fiber Transmission based on Electrical Feedback Equalizer 5

Donut Modes in Space Wavelength Division Multiplexing: Multi...

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5th International Conference on Electronic Design, ICED 2020

作者： Ghazi, Alaan Aljunid, S.A. Idrus, Syed Zulkarnain Syed Endut, R. Ali, N. Amphawan, Angela Fareed, Alaa Al-Dawoodi, Aras Noori, Awab School of Human Development and Techno-communication University Malaysia Perlis Malaysia School of Computer and Communication Engineering University Malaysia Perlis Malaysia Computer Technical Engineering Al-Qalam University College Iraq Centre of Excellence Universiti Malaysia Perlis Malaysia Universiti Malaysia Perlis Perlis Malaysia Lightwave Laboratory School of Engineering and Technology Sunway University Petaling Jaya Selangor47500 Malaysia School of Computing University Utara Malaysia Malaysia Computer Science Department College of Computer Science and Information Technology Kirkuk University Iraq Data Science Research Lab School of Computing Universiti Utara Malaysia Malaysia

This study demonstrates the implementation of multi-input-multi-output (MIMO) transmission donut modes in space wavelength division multiplexing (SWDM) over multi-mode optical fiber (MMOF) and simulating the effect of mode coupling of the transmitted signal. However, during the data transmitted over MMF by using SWDM an issue is encountered, which is the inter symbol interference (ISI) due to mode coupling. ISI has been recognized as a major obstacle to high-speed data transmission. The signal processing in SDM of donut modes over MMOF with electrical feedback equalizer is used to improve the performance for increasing the bandwidth. A data rate 80 Gbit/s up for a distance of 1600 meters is achieved. SWDM topology was designed emitted from a spatial vertical cavity surface-emitting laser (VCSEL) through MMOF in conjunction with the configurations of electrical feedback equalizer. The parameters and values used in Opt-Sim simulation topology were listed. The performance measurement of this study based on bit-error rates (BER) and eye diagram before and after electrical feedback equalization were analysed. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Space division multiple access

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Recent advancements in plasmonic optical biosensors: a review

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ISSS Journal of Micro and Smart Systems 2021年第1期11卷 31-42页

作者： Agrawal, Niteshkumar Saxena, Reshu Singh, Lokendra Saha, Chinmoy Kumar, Santosh Pillai College of Engineering Mumbai University Panvel Navi Mumbai India Immunobiology Lab Translational Health Science and Technology Institute Faridabad India School of Engineering University of Engineering and Technology Roorkee Roorkee Haridwar India Department of Avionics Indian Institute of Space Science and Technology Thiruvananthapuram India Department of Electrical and Electronics & Communication Engineering DIT University Dehradun India Shandong Key Laboratory of Optical Communication Science and Technology School of Physics Science and Information Technology Liaocheng University Liaocheng China

This article presents a review of various recent developments over plasmonic optical sensors for in vivo and in vitro detection. This study also highlights the relative study over detection of various biochemical, cells, bacteria, and viruses, i.e., application of plasmon-enhanced optical sensing in in vivo and in vitro detection. A brief overview of the current biosensing devices, plasmonic-based sensors, their types, comparative study over in vivo and in vitro detection, wearable sensors, packaging of optic-plasmonic sensors, and colloidal nanostructures (NSs) is reported in this study. Plasmonic optical sensors nowadays are employed in a plethora of applications ranging from environmental monitoring to bio (chemical) sensing. In brief, these sensors are applicable for biomedical diagnosis, drug discovery & therapies, material analysis & shaping, (bio) chemical sensing, and environmental monitoring. Plasmonics is a promising field of technology that examines the interaction between light and metallic NSs at the metal–dielectric interface. The commonly employed plasmonic-based methods such as surface plasmonic resonance (SPR) and localized SPR (LSPR), comparative study over the recently reported sensors are well discussed in this review work. This comprehensive study on the extent of biosensor innovation and the respective challenges would open new avenues for exploring the use of plasmonic optical sensors for commercial applications. Overall, we review the major pros and cons of plasmonic-based biosensing with a brief outlook on its advancement and applicability.

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Generative Adversarial Network-based Channel Modeling for Free-Space Optical communication

Generative Adversarial Network-based Channel Modeling for Fr...

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OptoElectronics and communications Conference, OECC

作者： Wenbin Chen Danshi Wang Dongdong Wang Yuchen Song Jin Li Min Zhang State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Science and Technology on Communication Networks Laboratory Shijiazhuang China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang China Purple Mountain Lab Nanjing China

Two deep learning-based differentiable models based on GAN and BiLSTM are proposed for FSO channel modeling. Through contrastive analysis, it shows that GAN is more suitable for learning FSO channel distribution with ... 详细信息

ISBN: (数字)9781943580927

ISBN: (纸本)9781665400060

关键词： Wireless communication Analytical models Free-space optical communication Stochastic processes Generative adversarial networks Channel models

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A Brain-To-Brain Interface with a Flexible Neural Probe for Mouse Turning Control by Human Mind

A Brain-To-Brain Interface with a Flexible Neural Probe for ...

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International Conference on Solid State Sensors and Actuators (TRANSDUCERS)

作者： Yifei Ye Zhenyu Wang Ye Tian Han Wang Cunkai Zhou Honglin Hu Ting Zhou Zhitao Zhou Xiaoling Wei Jianlong Zhao Tiger H. Tao Liuyang Sun State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China Intelligent Communication Lab Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China 2020 X-Lab Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China School of Graduate Study University of Chinese Academy of Sciences Beijing China School of Microelectronics Shanghai University Shanghai China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China School of Physical Science and Technology ShanghaiTech University Shanghai China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Neuroxess Co. Ltd. (Jiangxi) Nanchang China Guangdong Institute of Intelligence Science and Technology Hengqin Zhuhai China Tianqiao and Chrissy Chen Institute for Translational Research Shanghai China

We proposed a brain-to-brain interface (B2BI) that enabled effective control of mouse turning behavior by human mind using an implantable, ultra-flexible neural probe. The neural probe was capable of writing in neural signals to the mouse brain, with effective electrical stimulation observed at a low current of 5 μA. Additionally, a wireless EEG device was developed for the remote transmission of human brain signals, which were decoded by the canonical correlation analysis (CCA) method with an accuracy rate of up to 98.6%. These results demonstrate the potential of B2BI technology to enable communication and control between humans and animals.

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Generation of Scalable Genuine Multipartite Gaussian Entanglement with a Parametric Amplifier Network

arXiv

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arXiv 2024年

作者： Kim, Saesun Onoe, Sho Marino, Alberto M. Homer L. Dodge Department of Physics and Astronomy The University of Oklahoma NormanOK73019 United States Center for Quantum Research and Technology University of Oklahoma NormanOK73019 United States Centre for Quantum Computation and Communication Technology School of Mathematics and Physics The University of Queensland St. LuciaQLD4072 Australia femtoQ Lab Department of Engineering Physics Polytechnique Montréal MontréalQCH3T 1JK Canada Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Quantum Science Center Oak Ridge National Laboratory Oak RidgeTN37381 United States

Genuine multipartite entanglement is a valuable resource in quantum information science, as it exhibits stronger non-locality compared to bipartite entanglement. This non-locality can be exploited in various quantum information protocols, such as teleportation, dense coding, and quantum interferometry. Here, we propose a scheme to generate scalable genuine multipartite continuous-variable entangled states of light using a parametric amplifier network. We verify the presence of genuine quadripartite, hexapartite, and octapartite entanglement through a violation of the positive partial transpose (PPT) criteria. Additionally, we use α-entanglement of formation to demonstrate the scalability of our approach to an arbitrary number of 2N genuinely entangled parties by taking advantage of the symmetries present in our scheme. Copyright © 2024, The Authors. All rights reserved.

关键词： Parametric amplifiers

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