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IEEE International Conference on Signal processing, Communications and Computing

作者： Yige Cheng Zhaocheng Yang Dongcai Hong Jiongzhang Chen Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University

ISBN: (纸本)9781728117096

Detection of sleep anomaly events, such as get up, sit up and turn over, is one of the most important tasks in human sleep monitoring. In this paper, we propose a novel sleep anomaly events detection algorithm based on an adaptive double threshold constant-false-alarm-rate (ADT-CFAR) detector using ultra-wideband (UWB) radar. The proposed algorithm firstly removes the static clutter, detects the human and performs the time-frequency transform for the received signals. By extracting the power burst curve (PBC) from the obtained time-frequency maps, we can formulate the sleep anomaly events detection by a binary hypothesis test and then develop an ADT-CFAR detector, which is based on log-normal distribution. A series of experiments are conducted and the results show the effectiveness of the proposed detector.

关键词： Sleep anomaly events detection UWB radar CFAR detector Sleep monitoring ultra wideband radar Polysomnography Event detection dual threshold anomaly detector Sleep

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Learning and thinking strategy for training sequence generation models 29

Learning and thinking strategy for training sequence generat...

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29th British Machine Vision Conference, BMVC 2018

作者： Li, Yu Tang, Sheng Lin, Min Guo, Junbo Li, Jintao Yan, Shuicheng Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of the Chinese Academy of Sciences Beijing China AI institute Qihoo 360 Beijing China

In order to alleviate the exposure bias problem caused by the discrepancy between training and testing strategies, we propose an Inverse Reinforcement Learning (IRL) based learning and thinking strategy for sequence generation. First, a task-agnostic reward is learned to evaluate the appropriateness of the generated tokens at each time step with the knowledge of ground truth token and current RNN models. With this reward, a deep SARSA network is then designed to meditate among the whole space. Therefore, it can fill in the space that has not been exposed during training with a better policy than the original RNN model. Sequence generation experiments on various text corpus show significant improvements over strong baseline and demonstrate the effectiveness of our method. © 2018. The copyright of this document resides with its authors.

关键词： Inverse problems

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Parallel architecture design of genetic algorithms on HPC platform 19

Parallel architecture design of genetic algorithms on HPC pl...

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2019 ACM Turing Celebration Conference - China, ACM TURC 2019

作者： Yan, Liu Hu, Wenxin Han, Longzhe College of Computer and Software Engineering East China Normal University Shanghai China JiangXi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang China

ISBN: (纸本)9781450371582

In the genetic algorithm for compute-intensive task, the execution time of fitness function increases rapidly. As a result, when the population size or evolution population increases, the convergence speed of the algorithm is very slow. Based on the supercomputer platform, this paper designs and implements a parallel genetic algorithm architecture with dynamic migration strategy. A hybrid parallel genetic algorithm based on "coarse-grained and master-slave" two-level parallel architecture is proposed. The proposed architecture is composed of hybrid parallel programming models. Compared with the traditional genetic algorithm implemented on the simple MPI architecture, the proposed structure achieves better performance. Experiments show that the convergence speed of the algorithm is significantly improved. © 2019 Association for Computing Machinery.

关键词： Genetic algorithms

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Multiple residual dense networks for reconfigurable intelligent surfaces cascaded channel estimation

arXiv

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arXiv 2021年

作者： Jin, Yu Zhang, Jiayi Huang, Chongwen Yang, Liang Xiao, Huahua Ai, Bo Wang, Zhiqin The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Zhejiang Provincial Key Lab of Information Processing Communication and Networking Zhejiang University Hangzhou310007 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China ZTE Corporation State Key Laboratory of Mobile Network Mobile Multimedia Technology Shenzhen518057 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China China Academy of Information and Communications Technology Beijing100191 China

Reconfigurable intelligent surface (RIS) constitutes an essential and promising paradigm that relies programmable wireless environment and provides capability for space-intensive communications, due to the use of low-cost massive reflecting elements over the entire surfaces of man-made structures. However, accurate channel estimation is a fundamental technical prerequisite to achieve the huge performance gains from RIS. By leveraging the low rank structure of RIS channels, three practical residual neural networks, named convolutional blind denoising network, convolutional denoising generative adversarial networks and multiple residual dense network, are proposed to obtain accurate channel state information, which can reflect the impact of different methods on the estimation performance. Simulation results reveal the evolution direction of these three methods and reveal their superior performance compared with existing benchmark schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Channel estimation

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Corrigendum to Intrusion detection based on improved density peak clustering for imbalanced data on sensor-cloud systems Journal of Systems Architecture volume 118 (2021) 102212

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Journal of Systems Architecture 2021年 119卷

作者： Ming Yan Yewang Chen Xiaoliang Hu Dongdong Cheng Yi Chen Jixiang Du The College of Computer Science and Technology Huaqiao University Xiamen China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen China College of Big Data and Intelligent Engineering Yangtze Normal University Chongqing China

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Multifractal temporally weighted detrended partial cross-correlation analysis to quantify intrinsic power-law cross-correlation of two non-stationary time series affected by common external factors

arXiv

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arXiv 2020年

作者： Li, Bao-Gen Ling, Dian-Yi Yu, Zu-Guo Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan Hunan411105 China School of Electrical Engineering and Computer Science Queensland University of Technology GPO Box 2434 BrisbaneQ4001 Australia

When common factors strongly influence two cross-correlated time series recorded in complex natural and social systems, the results will be biased if we use multifractal detrended cross-correlation analysis (MF-DXA) without considering these common factors. Based on multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA) proposed by our group and multifractal partial cross-correlation analysis (MF-DPXA) proposed by Qian et al., we propose a new method-multifractal temporally weighted detrended partial cross-correlation analysis (MF-TWDPCCA) to quantify intrinsic power-law cross-correlation of two non-stationary time series affected by common external factors in this paper. We use MF-TWDPCCA to characterize the intrinsic cross-correlations between the two simultaneously recorded time series by removing the effects of other potential time series. To test the performance of MF-TWDPCCA, we apply it, MF-TWXDFA and MF-DPXA on simulated series. Numerical tests on artificially simulated series demonstrate that MF-TWDPCCA can accurately detect the intrinsic cross-correlations for two simultaneously recorded series. To further show the utility of MF-TWDPCCA, we apply it on time series from stock markets and find that there exists significantly multifractal power-law cross-correlation between stock returns. A new partial cross-correlation coefficient is defined to quantify the level of intrinsic cross-correlation between two time series. Copyright © 2020, The Authors. All rights reserved.

关键词： Fractals

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Multi-Scale Recurrent Network for Single Image Deraining 8

Multi-Scale Recurrent Network for Single Image Deraining

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8th International Conference on Digital Home, ICDH 2020

作者： Li, Youxing Lan, Rushi Sun, Long Li, Ji Luo, Xiaonan Pang, Cheng Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China Guilin University of Electronic Technology National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin541004 China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin541004 China

ISBN: (纸本)9781728192345

Rain streaks noise is one of the greatest influence of outdoor scene tasks. In order to improve the effect of the outdoor scene tasks, we need to reduce the impact of the rain streaks noise while ensuring that other important details are preserved. To handle this issue, we proposed a multi-scale recurrent network (MSReNet) for single image rain removal. By divided MSReNet into two stages: (i) removal rain streaks in the first stage and (ii) restoring background-truth details in the second stage, our network can availably remove huge rain streaks and restore some important background details. Extensive experiments on both synthetic and real images demonstrated that the proposed MSReNet prominently exceeds many recent state-of-the-art methods. Taking the proposed method effectiveness, it is also attractive as a preprocessing process for some visual tasks. © 2020 IEEE.

关键词： Rain

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Moiré cavity quantum electrodynamics

引用

Science Advances 2025年第21期11卷 eadv8115页

作者： Wang, Yu-Tong Ye, Qi-Hang Yan, Jun-Yong Qiao, Yufei Liu, Yu-Xin Ye, Yong-Zheng Chen, Chen Cheng, Xiao-Tian Li, Chen-Hui Zhang, Zi-Jian Huang, Cheng-Nian Meng, Yun Zou, Kai Zhan, Wen-Kang Zhao, Chao Hu, Xiaolong Tee, Clarence Augustine T.H. Sha, Wei E.I. Huang, Zhixiang Liu, Huiyun Jin, Chao-Yuan Ying, Lei Liu, Feng State Key Laboratory of Extreme Photonics and Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Physics Zhejiang Key Laboratory of Micro-nano Quantum Chips and Quantum Control Zhejiang University Hangzhou310027 China International Joint Innovation Center Zhejiang University Haining314400 China School of Precision Instrument and Optoelectronic Engineering Tianjin University Tianjin300072 China Key Laboratory of Optoelectronic Information Science and Technology Ministry of Education Tianjin300072 China Laboratory of Solid State Optoelectronics Information Technology Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Science Beijing101804 China College of Physics and Electrical Information Engineering Zhejiang Normal University Hangzhou310058 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Zhejiang Hangzhou311200 China

Quantum emitters are a key component in photonic quantum technologies. Enhancing single-photon emission by engineering their photonic environment is essential for improving overall efficiency in quantum information processing. However, this enhancement is often limited by the need for ultraprecise emitter placement within conventional photonic cavities. Inspired by the fascinating physics of moiré pattern, we propose a multilayer moiré photonic crystal with a robust isolated flatband. Theoretical analysis reveals that, with nearly infinite photonic density of states, the moiré cavity simultaneously has a high Purcell factor and large tolerance over the emitter’s position, breaking the constraints of conventional cavities. We then experimentally demonstrate various cavity quantum electrodynamic phenomena with a quantum dot in moiré cavity. A large tuning range (up to 40-fold) of quantum dot’s radiative lifetime is achieved through strong Purcell enhancement and inhibition effects. Our findings open the door for moiré flatband cavity–enhanced quantum light sources and quantum nodes for the quantum internet. Copyright © 2025 The Authors, some rights reserved.

关键词： Quantum electronics

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Research on Named Entity recognition for information Extraction

Research on Named Entity Recognition for Information Extract...

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International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)

作者： Qi Guo Shuang Wang Fucheng Wan Key Laboratory of China's Ethnic Languages and Intelligent Processing of Gansu Province Northwest Minzu University Lanzhou China China's Ethnic Information Technology Research Institute Northwest Minzu University Lanzhou China

In natural language processing, named entity recognition, as a basic task used in natural language processing, aims at identifying entities with specific meaning in the text, mainly including personal names, place names, institutional names, proper nouns, etc. Named entity recognition technology is an indispensable part of many natural language processing methods, such as information extraction, information retrieval, machine translation and question answering systems. This paper first briefly introduces the developing process of named entity recognition, and describes the basic concept, objectives and difficulties of named entity recognition. Then, it summarizes the methods of named entity recognition from based on rules and dictionary method, based on statistical method, to the deep learning method and migration of learning, and looks forward to the future development of named entity recognition.

关键词： Deep learning Vocabulary Text recognition Transfer learning Supervised learning Training data Learning (artificial intelligence)

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Bayesian random Fourier filters for Gaussian noises

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Science China(information Sciences) 2018年第12期61卷 214-216页

作者： Shiyuan WANG Wanli WANG Shukai DUAN Lidan WANG Chi K.TSE College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Department of Electronic and Information Engineering Hong Kong Polytechnic University

Dear editor,Signal processing algorithms are generally used to identify an unknown system, and can be classified as batch algorithms and online learning algorithms(OLAs). Batch algorithms are applied to offline scenar... 详细信息

关键词： Bayesian random Fourier filters for Gaussian noises

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