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Pushing AI to wireless network edge: An overview on integrated sensing, communication, and computation towards 6G

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arXiv 2022年

作者： Zhu, Guangxu Lyu, Zhonghao Jiao, Xiang Liu, Peixi Chen, Mingzhe Xu, Jie Cui, Shuguang Zhang, Ping Shenzhen Research Institute of Big Data Shenzhen518172 China Shenzhen518172 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables33146 United States State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Peng Cheng Laboratory Shenzhen518066 China

Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things (AIoT) in the sixth-generation (6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the huge amount of data scattered at wireless network edge. In general, realizing edge intelligence corresponds to the process of sensing, communication, and computation, which are coupled ingredients for data generation, exchanging, and processing, respectively. However, conventional wireless networks design the sensing, communication, and computation separately in a task-agnostic manner, which encounters difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications such as auto-driving. This thus prompts a new design paradigm of seamless integrated sensing, communication, and computation (ISCC) in a task-oriented manner, which comprehensively accounts for the use of the data in the downstream AI applications. In view of its growing interest, this article provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art development, and shedding light on the road ahead. Copyright © 2022, The Authors. All rights reserved.

关键词： Artificial intelligence

Acousto-drag photovoltaic effect by piezoelectric integration of two-dimensional semiconductors

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arXiv 2023年

作者： Gu, Jiaming Mou, Yicheng Ma, Jianwen Chen, Haonan Zhang, Chuanxin Wang, Yuxiang Wang, Jiayu Guo, Hangwen Shi, Wu Yuan, Xiang Jiang, Xue Ta, Dean Shen, Jian Zhang, Cheng State Key Laboratory of Surface Physics Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China Center for Biomedical Engineering School of Information Science and Technology Fudan University Shanghai200433 China Zhangjiang Fudan International Innovation Center Fudan University Shanghai201210 China Shanghai Qi Zhi Institute Shanghai200232 China State Key Laboratory of Precision Spectroscopy East China Normal University Shanghai200241 China School of Physics and Electronic Science East China Normal University Shanghai200241 China Collaborative Innovation Center of Advanced Microstructures Nanjing210093 China

Light-to-electricity conversion is crucial for energy harvesting and photodetection, requesting efficient electron-hole pair separation to prevent recombination. Traditional junction-based mechanisms using built-in electric fields fail in non-barrier regions. Homogeneous material harvesting under photovoltaic effect is appealing but only realized in non-centrosymmetric systems via bulk photovoltaic effect. Here we report the realization of photovoltaic effect by employing surface acoustic waves (SAW) to generate zero-bias photocurrent in a conventional layered semiconductor MoSe2. SAW induces periodic modulation to electronic bands and drags the photoexcited pairs toward the travelling direction. The photocurrent is extracted by a local barrier. The separation of generation and extraction processes suppresses recombination and yields large nonlocal photoresponse. We distinguish acousto-electric drag and electron-hole pair separation effect by fabricating devices of different configurations. The acousto-drag photovoltaic effect, enabled by piezoelectric integration, offers an efficient light-to-electricity conversion method, independent of semiconductor crystal symmetry. © 2023, CC BY.

关键词： Energy harvesting

Spatiotemporal Modulated Solitons in a Quasi-One-Dimensional Spin-1 Bose-Einstein Condensates

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SSRN

SSRN 2024年

作者： Liu, Fei-Yan Xu, Su-Yong Triki, Houria Choudhuri, Amitava Zhou, Qin Research Group of Nonlinear Optical Science and Technology School of Mathematical and Physical Sciences Wuhan Textile University Wuhan430200 China College of Optical Mechanical and Electrical Engineering Zhejiang A&F University Lin’an 311300 China Radiation Physics Laboratory Department of Physics Faculty of Sciences Badji Mokhtar University P. O. Box 12 Annaba23000 Algeria Department of Physics The University of Burdwan West Bengal Golapbag 713104 India State Key Laboratory of New Textile Materials and Advanced Processing Technologies Wuhan Textile University Wuhan430200 China

In this paper, we investigate the nonautonomous bright/dark solitons in a quasi-one-dimensional spin-1 Bose-Einstein condensates through a three coupled Gross-Pitaevskii (GP) system with space-time-dependent external potential and temporally modulated gain/loss distributions. Based on the Hirota bilinear method, we analytically construct the bright soliton solutions when the coupled GP system exhibits attractive interactions, while we obtain the dark soliton solutions when the coupled GP system exhibits repulsive interactions. The influence of spatiotemporal modulated external potentials, such as the gain/loss distribution Γ(t), on bright/dark soliton dynamics is analyzed in detail via the analytical solutions. By taking different Γ(t), we obtain different types of bright solitons, including periodic, dromion-like and parabolic solitons, and also derive dark solitons on different backgrounds, such as periodic, parabolic and kink backgrounds. We analyze the regulatory effects of different wavenumber ratios on the attraction and squeezing of bound-state solitons. Through the asymptotic analysis, we find that the interactions between two solitons are elastic. In addition, we conduct research on the forward and inverse problems of the above results via the parallel hard-constraint physical informed neural network (phPINN) method. The predicted solitons and potential functions are in good agreement with the exact solitons and potential in the system. © 2024, The Authors. All rights reserved.

关键词： Solitons

Factoring integers with sublinear resources on a superconducting quantum processor

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arXiv 2022年

作者： Yan, Bao Tan, Ziqi Wei, Shijie Jiang, Haocong Wang, Weilong Wang, Hong Luo, Lan Duan, Qianheng Liu, Yiting Shi, Wenhao Fei, Yangyang Meng, Xiangdong Han, Yu Shan, Zheng Chen, Jiachen Zhu, Xuhao Zhang, Chuanyu Jin, Feitong Li, Hekang Song, Chao Wang, Zhen Ma, Zhi Wang, H. Long, Gui-Lu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China School of Physics ZJU-Hangzhou Global Scientific and Technological Innovation Center Interdisciplinary Center for Quantum Information Zhejiang Province Key Laboratory of Quantum Technology and Device Zhejiang University Hangzhou310000 China Beijing Academy of Quantum Information Sciences Beijing100193 China Institute of Information Technology Information Engineering University Zhengzhou450001 China Beijing National Research Center for Information Science and Technology School of Information Tsinghua University Beijing100084 China Frontier Science Center for Quantum Information Beijing100084 China

Shor’s algorithm has seriously challenged information security based on public key cryptosystems. However, to break the widely used RSA-2048 scheme, one needs millions of physical qubits, which is far beyond current technical capabilities. Here, we report a universal quantum algorithm for integer factorization by combining the classical lattice reduction with a quantum approximate optimization algorithm (QAOA). The number of qubits required is O(logN/loglogN), which is sublinear in the bit length of the integer N, making it the most qubit-saving factorization algorithm to date. We demonstrate the algorithm experimentally by factoring integers up to 48 bits with 10 superconducting qubits, the largest integer factored on a quantum device. We estimate that a quantum circuit with 372 physical qubits and a depth of thousands is necessary to challenge RSA-2048 using our algorithm. Our study shows great promise in expediting the application of current noisy quantum computers, and paves the way to factor large integers of realistic cryptographic significance. © 2022, CC BY-NC-ND.

关键词： Qubits

Semi-vortex solitons and their excited states in spin-orbit-coupled binary bosonic condensates

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arXiv 2024年

作者： Deng, Haiming Li, Jinqing Chen, Zhaopin Liu, Yaohui Liu, Dong Jiang, Chunzhi Kong, Chao Malomed, Boris A. School of Physics and Electronic-Electrical Engineering Xiangnan University Chenzhou423000 China Microelectronics and Optoelectronics Technology Key Laboratory of Hunan Higher Education Xiangnan University Chenzhou423000 China Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Xiangnan University Chenzhou423000 China Physics Department Solid-State Institute Technion Haifa32000 Israel Department of Physical Electronics School of Electrical Engineering Faculty of Engineering Tel Aviv University Tel Aviv69978 Israel Instituto de Alta Investigación Universidad de Tarapacá Casilla 7D Arica Chile

It is known that two-dimensional two-component fundamental solitons of the semi-vortex (SV) type, with vorticities (s+, s−) = (0, 1) in their components, are stable ground states (GSs) in the spin-orbit-coupled (SOC) binary Bose-Einstein condensate with the contact self-attraction acting in both components, in spite of the possibility of the critical collapse in the system. However, excited states (ESs) of the SV solitons, with the vorticity set (s+, s−) = (S+, S+ + 1) and S+ = 1, 2, 3, ..., are unstable in the same system. We construct ESs of SV solitons in the SOC system with opposite signs of the self-interaction in the two components. The main finding is stability of the ES-SV solitons, with the extra vorticity (at least) up to S+ = 6. The threshold value of the norm for the onset of the critical collapse, Nthr, in these excited states is higher than the commonly known critical value, Nc ≈ 5.85, associated with the single-component Townes solitons, Nthr increasing with the growth of S+. A velocity interval for stable motion of the GS-SV solitons is found too. The results suggest a solution for the challenging problem of the creation of stable vortex solitons with high topological charges. Copyright © 2024, The Authors. All rights reserved.

关键词： Excited states

SURE-based Stopping Strategy for Fine-tunable Supervised PET Image Denoising

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SURE-based Stopping Strategy for Fine-tunable Supervised PET...

2021 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2021

作者： Cui, Jianan Gong, Kuang Guo, Ning Wollenweber, Scott Jansen, Floris Liu, Huafeng Li, Quanzheng Zhejiang University State Key Laboratory of Modern Optical Instrumentation College of Optical Science and Engineering Hangzhou China Massachusetts General Hospital Harvard Medical School Center for Advanced Medical Computing and Analysis BostonMA United States Massachusetts General Hospital Harvard Medical School Gordon Center for Medical Imaging BostonMA United States Ge Healthcare Waukesha United States

ISBN: (纸本)9781665421133

Previously, based on the unsupervised deep learning method, conditional deep image prior (CDIP), we showed the possibility of finetuning during the testing phase after supervised learning. However, one barrier for CDIP finetuning is that it needs early stopping before the network output converges to the noise which is hard to determine. In this work, we proposed to construct the finetuning loss based on Stein's unbiased risk estimator (SURE), which can provide an unbiased estimation of the true mean square error (MSE). We demonstrated that using SURE-based finetuning loss, the network output can be close to the ground truth while not converging to the noise. Quantification results based on seven real patient datasets show that the proposed method is more stable during finetuning than CDIP. © 2021 IEEE.

关键词： Image denoising

Viscous Effects on the Added Mass and Damping Forces During Free Heave Decay of a Floating Cylinder with a Hemispherical Bottom

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SSRN

SSRN 2022年

作者： Chen, Hao Xu, Qianlong Zheng, Xiaobo Bennetts, Luke G. Xie, Bin Lin, Zhiliang Lin, Zaibin Li, Ye School of Engineering University of Glasgow Glasgow United Kingdom Multiple Function Towing Tank Laboratory State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University 200240 China School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University 200240 China School of Mathematical Sciences University of Adelaide AdelaideSA5005 Australia School of Computing Mathematics and Digital Technology Manchester Metropolitan University Manchester United Kingdom Lanzhou University of Technology China

This paper presents an analysis of viscous effects on the added mass and damping forces during free heave decay of a cylinder with a hemispherical bottom. Results from unsteady Reynolds-averaged Navier-Stokes (URANS) simulations and linear potential-flow solutions are compared. Expressions for the time-varying viscous added mass and damping coefficients are derived based on first-order Fourier analysis of the URANS results. Therefore, viscous effects are extracted by comparing the URANS results and linear potential-flow solutions, as nonlinear effects in this case are demonstrated to be negligible. It is observed that viscous effects on the added mass coefficient are minor during heave decay, such that the linear potential-flow model can predict the value of the added mass coefficient to within 5% deviations. In contrast, the viscous damping coefficient exhibits strong time-dependent variations. A parametric study of the effects of initial displacement, cylinder diameter and draft shows that viscous damping forces can play an important role when the cylinder has a relatively small diameter and large draft. Further, the damping coefficient is transformed to the nondimensional drag force coefficient, and its dependence on the Reynolds number Re is investigated, for Keulegan–Carpenter numbers in the range KC =1.5–2.5. It is found that the drag force coefficient is likely to be unrelated to Re and KC during the first cycle. From the second cycle to the fourth cycle, the drag force coefficient rapidly decreases with increasing Re , up to Re ≈ 3 × 10 5 . © 2022, The Authors. All rights reserved.

关键词： Reynolds number

Deep Learning-Based Object Pose Estimation: A Comprehensive Survey

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arXiv 2024年

作者： Liu, Jian Sun, Wei Yang, Hui Zeng, Zhiwen Liu, Chongpei Zheng, Jin Liu, Xingyu Rahmani, Hossein Sebe, Nicu Mian, Ajmal The National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China The School of Architecture and Art Central South University Changsha410082 China The Department of Automation Tsinghua University Beijing100084 China The School of Computing and Communications Lancaster University LA1 4YW United Kingdom The Department of Information Engineering and Computer Science University of Trento Trento38123 Italy The Department of Computer Science The University of Western Australia WA6009 Australia

Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Nevertheless, several challenges persist in contemporary methods, including their dependency on labeled training data, model compactness, robustness under challenging conditions, and their ability to generalize to novel unseen objects. A recent survey discussing the progress made on different aspects of this area, outstanding challenges, and promising future directions, is missing. To fill this gap, we discuss the recent advances in deep learning-based object pose estimation, covering all three formulations of the problem, i.e., instance-level, category-level, and unseen object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks, providing the readers with a holistic understanding of this field. Additionally, it discusses training paradigms of different domains, inference modes, application areas, evaluation metrics, and benchmark datasets, as well as reports the performance of current state-of-the-art methods on these benchmarks, thereby facilitating the readers in selecting the most suitable method for their application. Finally, the survey identifies key challenges, reviews the prevailing trends along with their pros and cons, and identifies promising directions for future research. We also keep tracing the latest works at Awesome-Object-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

CCA-Net: Zero-shot SSVEP classification via an integration of canonical correlation analysis and deep neural network

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Deng, Yang Ji, Zhiwei Wang, Yijun Zhou, S. Kevin Harbin Institute of Technology School of Life Science and Technology Heilongjiang Harbin150001 China University of Science and Technology of China Division of Life Sciences and Medicine School of Biomedical Engineering Anhui Hefei230026 China Suzhou Institute for Advanced Research Jiangsu Suzhou215123 China Nanjing Agricultural University College of Artificial Intelligence Jiangsu Nanjing210095 China Chinese Academy of Sciences State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Beijing100083 China University of Science and Technology of China Key Laboratory of Precision and Intelligent Chemistry Anhui Hefei230026 China Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China

Accurate decoding of electroencephalographic (EEG) signals is a crucial foundation for brain-computer interface (BCI) applications. Among various decoding approaches, those that do not require calibration are particularly significant for advancing BCI technologies into everyday life, as they alleviate user fatigue by eliminating the need for user-specific training. In this study, we integrate a deep neural network with canonical correlation analysis (CCA) to form the CCA-Net approach for decoding steady-state visually evoked potential (SSVEP) based BCI without user-specific calibration, i.e., at the zero-shot scenario. The CCA-Net aims to reduce the cross-subject domain gap for better transfer learning and make full use of the testing signal itself. Specifically, it adapts the cross-subject dual-domain fusion network (CSDuDoFN) by transferring the global multi-reference least-squares transformation coefficient from source subjects to both source and target data to reduce the cross-subject domain gap. In addition, CCA-Net enhances the transfer template-based canonical correlation analysis (tt-CCA) by introducing a subject-specific template to replace the sine-cosine reference template and adding an extra CCA coefficient to make full use of the testing signal itself, resulting in the Modified tt-CCA. Finally, the features extracted by CSDuDoFN and Modified tt-CCA are integrated to produce the final decoding result. Our approach leverages the strengths of both data-driven and model-driven methods and achieves state-of-the-art performance on three publicly available datasets, thus holding the potential to facilitate everyday BCI applications based on SSVEP. The reproducibility code is available at: https://***/Sungden/Zero-shot-SSVEP-classification. © 1963-2012 IEEE.

关键词： Deep neural networks