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作者： Zhen Zhu Cheng Chi Chunhua Zhang University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences The Institute of Acoustics Beijing China Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665427883

Compressive beamforming has been successfully applied to the estimation of the direction of arrival (DOA) of array signals, and has higher angular resolution than traditional high-resolution beamforming methods. However, most of the existing compressive beamforming methods are based on narrow signal models. Wideband signal processing using these existing compressive beamforming methods is to divide the frequency band into several narrow-bands and add up the beamforming results of each narrow-band. However, for sonar application, signals usually consist of continuous spectrum and line spectrum, and the line spectrum is usually more than 10dB higher than the continuous spectrum. Due to the large difference of signal-to-noise ratio (SNR) of each narrow-band, different regularization parameters should be used, otherwise it is difficult to get an ideal result, which makes compressive beamforming highly complicated. In this paper, a compressive beamforming method based on spatial resampling for uniform linear arrays is proposed. The signals are converted into narrow-band signals by spatial resampling technique, and compressive beamforming is then performed to estimate the DOA of the sound source. Experimental results show the superiority of the proposed method, which avoids the problem of using different parameters in the existing compressive beamforming methods, and the resolution is comparable to the existing methods using different parameters for wideband models. The spatial-resampling compressive beamforming has a better robustness when the regularization parameter is fixed, and exhibits lower levels of background interference than the existing methods.

关键词： Direction-of-arrival estimation Array signal processing Sonar applications Interference Robustness Broadband communication Spatial resolution

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Off-Grid Compressive Time-Delay Estimation for Passive Sonars

Off-Grid Compressive Time-Delay Estimation for Passive Sonar...

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作者： Feilong Ding Cheng Chi Shuo Wang Yu Li Haining Huang University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences The Institute of Acoustics Beijing China Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665427883

In passive sonars, the time delay estimation methods based on phase information are robust and accurate. However, these methods face a serious decrease in the accuracy of time delay estimation when phase wrapping arises. This paper proposes an off-grid time delay estimation (OGTDE) method suitable for passive sonars. This proposed method provides high-precision time delay estimation results, while avoiding the phase wrapping problem. The simulations show that the accuracy of time delay estimation of our proposed method is superior to that of the reference method under the same conditions.

关键词： Delay effects Simulation Oceans Estimation Sonar Bandwidth Delays

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Cavity-Quantum Electrodynamics with Moiré Flatband Photonic Crystals

arXiv

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arXiv 2024年

作者： Wang, Yu-Tong Ye, Qi-Hang Yan, Jun-Yong Qiao, Yufei 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 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 International Joint Innovation Center Zhejiang University Haining314400 China

Quantum emitters are a key component in photonic quantum technologies. Enhancing their single-photon emission by engineering the photonic environment using cavities can improve the overall efficiency in quantum information processing. However, this enhancement is often limited by the need for precise nanoscale control over emitter’s position within micro/nano-cavities. Inspired by the fascinated physics of moiré pattern, we present an approach to strongly modify the spontaneous emission rate of a quantum emitter using fine-designed multilayer moiré photonic crystal with a robust isolate-flatband dispersion. Theoretical analysis reveals that due to its nearly infinite photonic density of states, the moiré cavity can simultaneously possess high Purcell factor and large tolerance over emitter’s position. We experimentally demonstrate the coupling between this moiré cavity and a quantum dot via the cavity-determined polarization of the dot’s emission. The radiative lifetime of the quantum dot can be tuned by a factor of 40 from 42 ps to 1692 ps, attributed to strong Purcell enhancement and Purcell inhibition effects. Our findings pave the way for moiré flatband cavity-enhanced quantum light sources, quantum optical switches and quantum nodes for quantum internet. Copyright © 2024, The Authors. All rights reserved.

关键词： Particle beams

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High-Resolution Underwater 3D Acoustical Imaging via Deconvolved Conventional Beamforming

High-Resolution Underwater 3D Acoustical Imaging via Deconvo...

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OCEANS

作者： Peng Wang Cheng Chi Pengfei Zhang Linzhe Wei Jiyuan Liu Haining Huang Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing Institute of Acoustics Chinese Academy of Sciences

ISBN: (数字)9781728154466

ISBN: (纸本)9781728184098

Conventional beamforming is widely used in underwater three-dimensional (3D) acoustical imaging. However, the imaging quality of the conventional beamforming is limited, because of the limited capability of angular resolution and high sidelobe levels of conventional beamforming. This paper proposes to apply a deconvolution method based on the Fourier-based nonnegative least-squares algorithm to underwater 3D imaging, to improve the imaging quality. Experiment shows that the deconvolution method improves the angular resolution by two times and decreases sidelobe levels by more than 10dB for the 3D imaging sonar, compared to the conventional beamforming.

关键词： Three-dimensional displays image resolution Deconvolution Array signal processing Oceans Imaging Sonar

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Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach

arXiv

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arXiv 2022年

作者： Li, Yuanzheng He, Shangyang Li, Yang Ge, Leijiao Lou, Suhua Zeng, Zhigang School of Artificial Intelligence and Automation Key Laboratory on Image Information Processing and Intelligent Control of Ministry of Education Huazhong University of Science and Technology Wuhan430074 China China-Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering Northeast Electric Power University Jilin 132012 China The Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin 300072 China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China

The electric vehicle (EV) and electric vehicle charging station (EVCS) have been widely deployed with the development of large-scale transportation electrifications. However, since charging behaviors of EVs show large uncertainties, the forecasting of EVCS charging power is non-trivial. This paper tackles this issue by proposing a reinforcement learning assisted deep learning framework for the probabilistic EVCS charging power forecasting to capture its uncertainties. Since the EVCS charging power data are not standard time-series data like electricity load, they are first converted to the time-series format. On this basis, one of the most popular deep learning models, the long short-term memory (LSTM) is used and trained to obtain the point forecast of EVCS charging power. To further capture the forecast uncertainty, a Markov decision process (MDP) is employed to model the change of LSTM cell states, which is solved by our proposed adaptive exploration proximal policy optimization (AePPO) algorithm based on reinforcement learning. Finally, experiments are carried out on the real EVCSs charging data from Caltech, and Jet Propulsion laboratory, USA, respectively. The results and comparative analysis verify the effectiveness and outperformance of our proposed framework. Copyright © 2022, The Authors. All rights reserved.

关键词： Markov processes

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signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks

arXiv

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arXiv 2022年

作者： Gao, Dawei Guo, Qinghua Liao, Guisheng Eldar, Yonina C. Li, Yonghui Yu, Yanguang Vucetic, Branka The Hangzhou Institute of Technology Xidian University Hangzhou311200 China The National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China The School of Electrical Computer and Telecommunications Engineering University of Wollongong NSW2522 Australia The Faculty of Math and CS Weizmann Institute of Science Rehovot7610001 Israel The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia

In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in-phase/quadrature imbalance. To deal with the complex combined effects of hardware imperfections, neural network (NN) techniques, in particular deep neural networks (DNNs), have been studied to directly compensate for the impact of hardware impairments. However, it is difficult to train a DNN with limited pilot signals, hindering its practical applications. In this work, we investigate how to achieve efficient Bayesian signal detection in MIMO systems with hardware imperfections. Characterizing combined hardware imperfections often leads to complicated signal models, making Bayesian signal detection challenging. To address this issue, we first train an NN to ‘model’ the MIMO system with hardware imperfections and then perform Bayesian inference based on the trained NN. Modelling the MIMO system with NN enables the design of NN architectures based on the signal flow of the MIMO system, minimizing the number of NN layers and parameters, which is crucial to achieving efficient training with limited pilot signals. We then represent the trained NN with a factor graph, and design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm. The implementation of a turbo receiver with the proposed Bayesian detector is also investigated. Extensive simulation results demonstrate that the proposed technique delivers remarkably better performance than state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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An UKF-based Extremum Seeking Control of Two-Stage Anaerobic Digestion Process

An UKF-based Extremum Seeking Control of Two-Stage Anaerobic...

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Automatics and Informatics (ICAI), International Conference

作者： Dandan Xu Haoping Wang Yang Tian Nicolai Christov Lyudmila Kabaivanova Ivan Simeonov School of Automation Nanjing University of Science and Technology Nanjing China Sino-French Laboratory of Automatic Control and Signal Processing (LaFCAS) Lille University France The Stephan Angeloff Institute of Microbology Bulgarian Academy of Sciences Sofia Bulgaria

In this paper, the Unscented Kalman Filter (UKF) is used in the Extremum Seeking Control (ESC) framework to deal with nonlinear system optimization problem. Compared with the classical ESC algorithm, the UKF-based ESC algorithm can reduce the amplitude of the disturbance signal and eliminate the steady-state oscillation. Applying the novel ESC algorithm to two-stage anaerobic digestion model, more accurate real-time gradient estimation and faster control convergence have been obtained in comparison with use of the classical ESC algorithm.

关键词： Simulation Process control Estimation Prediction algorithms Approximation algorithms Real-time systems Steady-state

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CTCNet: A CNN-Transformer Cooperation Network for Face image Super-Resolution

arXiv

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arXiv 2022年

作者： Gao, Guangwei Xu, Zixiang Li, Juncheng Yang, Jian Zeng, Tieyong Qi, Guo-Jun The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China The School of Communication & Information Engineering Shanghai University Shanghai China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing China The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China The Center for Mathematical Artificial Intelligence Department of Mathematics The Chinese University of Hong Kong Hong Kong Research Center for Industries of the Future School of Engineering Westlake University OPPO Research Seattle United States

Recently, deep convolution neural networks (CNNs) steered face super-resolution methods have achieved great progress in restoring degraded facial details by joint training with facial priors. However, these methods have some obvious limitations. On the one hand, multi-task joint learning requires additional marking on the dataset, and the introduced prior network will significantly increase the computational cost of the model. On the other hand, the limited receptive field of CNN will reduce the fidelity and naturalness of the reconstructed facial images, resulting in suboptimal reconstructed images. In this work, we propose an efficient CNN-Transformer Cooperation Network (CTCNet) for face super-resolution tasks, which uses the multi-scale connected encoder-decoder architecture as the backbone. Specifically, we first devise a novel Local-Global Feature Cooperation Module (LGCM), which is composed of a Facial Structure Attention Unit (FSAU) and a Transformer block, to promote the consistency of local facial detail and global facial structure restoration simultaneously. Then, we design an efficient Feature Refinement Module (FRM) to enhance the encoded features. Finally, to further improve the restoration of fine facial details, we present a Multi-scale Feature Fusion Unit (MFFU) to adaptively fuse the features from different stages in the encoder procedure. Extensive evaluations on various datasets have assessed that the proposed CTCNet can outperform other state-of-the-art methods significantly. Source code will be available at https://***/IVIPLab/CTCNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Restoration

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Scaling properties of scale-free networks in degree-thresholding renormalization flows

arXiv

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arXiv 2021年

作者： Chen, Dan Cai, Defu Su, Housheng The School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan430074 China The State Grid Hubei Electric Power Research Institute Wuhan China

We study the statistical properties of observables of scale-free networks in the degree-thresholding renormalization (DTR) flows. For BA scale-free networks with different sizes, we find that their structural and dynamical observables have similar scaling behavior in the DTR flow. The finite-size scaling analysis confirms this view and reveals a scaling function with a single scaling exponent that collectively captures the changes of these observables. Furthermore, for the scale-free network with a single initial size, we use its DTR snapshots as the original networks in the DTR flows, then perform a similar finite-size scaling analysis. Interestingly, the initial network and its snapshots share the same scaling exponent as the BA synthetic network. Our findings have important guiding significance for analyzing the structure and dynamic behavior of large-scale networks. Such as, in large-scale simulation scenarios with high time complexity, the DTR snapshot could serve as a substitute or guide for the initial network and then quickly explore the scaling behavior of initial networks. © 2021, CC BY.

关键词： Scaling laws

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Editorial: Introduction to the special issue on advances in radar technology

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Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University 2021年第2期48卷 1-6页

作者： Liu, Hongwei Shui, Penglang Kong, Lingjiang Wei, Yinsheng Liu, Yongxiang Wang, Yu Li, Gang Chen, Bo National Laboratory of Radar Signal Processing Xidian University Xi'an710071 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150001 China College of Intelligence Science National University of Defense Technology Changsha410073 China Institute of Aerospace Information Innovation Chinese Academy of Sciences Beijing100864 China Department of Electronic Engineering Tsinghua University Beijing100084 China

雷达历经了80多年的发展,如今已成为重要的信息传感器之一,在国防领域具有战略性地位,在民用领域也得到越来越广泛的应用。随着软硬件相关技术的快速发展,现代雷达已经不仅仅局限于高功率、高分辨、多波段等传统的性能提升和功能扩展,... 详细信息

雷达历经了80多年的发展,如今已成为重要的信息传感器之一,在国防领域具有战略性地位,在民用领域也得到越来越广泛的应用。随着软硬件相关技术的快速发展,现代雷达已经不仅仅局限于高功率、高分辨、多波段等传统的性能提升和功能扩展,且更多聚焦于信号处理的精细化、信息处理的智能化、单一平台功能的多样化和集成化、多平台的网络化与协同化、器件发展的小型化和片上化、服务界面的便利化和人性化等,使其应用日益广泛。为此,《西安电子科技大学学报》组织了"雷达技术进展"专题,共录用23篇论文,包括目标识别、目标检测与跟踪、雷达成像、电子对抗、协同组网等方向,分别介绍如下。

关键词：雷达信息传感器国防领域

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