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2024 IEEE International Symposium on Circuits and Systems, ISCAS 2024

作者： Zhao, Xin Hu, Zhicheng Chang, Liang University of Electronic Science And Technology of China School of Information And Communication Engineering Chengdu611731 China

ISBN: (纸本)9798350330991

Super-resolution (SR) can promote medical diagnosis efficiency by enriching the details of captured images, such as gastroscopy and colonoscopy. However, the wireless capsule detector used for diagnosis is constrained by the camera's low resolution and limited computing resources, making it difficult to deploy computation- and memory access-intensive SR models. In this paper, we propose an efficient universal SR accelerator based on lookup tables, namely USR-LUT, which can support various SR algorithms. We design a LUT-based computing unit (LCU) with higher efficiency and lower area overhead. By utilizing the sparsity of deconvolution, we propose an efficient data mapping scheme that can flexibly support convolution and deconvolution with different kernel sizes, achieving a 3.24× acceleration for deconvolution. Tile-based computing is adopted to reduce memory resources and external memory access (EMA) overhead. Through experimental evaluation, compared with LUT-based SR algorithms, the USR-LUT achieves the least LUT storage entries of 82k and the smallest LUT resource overhead of 0.078MB, respectively. The USR-LUT achieves the highest area efficiency of 175.7GOPS/mm2 and throughput area ratio (TAR) of 39.9fps/mm2 under the 8-bit precision compared with the state-of-the-art works. To the best of our knowledge, this is the first work of SR accelerators adopting LUT-based computing, which is suitable for tiny mobile devices. © 2024 IEEE.

关键词： Table lookup

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Integrate-and-Fire Neural Network Based Microrings and Van Der Waals Heterostructures for Supervised Learning

Integrate-and-Fire Neural Network Based Microrings and Van D...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Zhang, Qiang Jiang, Ning Hu, Gang Li, Anran Zhang, Yiqun Cao, Yongsheng Qiu, Kun School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China

We propose an integrate-and-fire neural network employing microrings and van der Waals heterostructures. The network facilitates multi-wavelength weighting operations through microrings-based arrays and incorporates nonlinear activation via non-volatile flash memory, subsequently completing supervised learning tasks. © Optica Publishing Group 2024

关键词： Van der Waals forces

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OCDM with Index Modulation for Autonomous Underwater Vehicles communication

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-16页

作者： Jia, Zhenyu Zhang, Rongxin Chen, Zhong Yuan, Fei Key Laboratory of Underwater Acoustic Communication and Marine Information Technology Xiamen University Xiamen China School of Electronic Science and Engineering Xiamen University Xiamen China

Autonomous underwater vehicles (AUVs) equipped with acoustic modems are currently one of the important means of obtaining underwater environment data such as sounds and images. They rely on high-speed underwater acoustic (UWA) communication technology for data transmission tasks. Orthogonal chirp division multiplexing (OCDM) is a multicarrier method based on chirp spread spectrum (CSS) which has better reliability in frequency selective fading channels than orthogonal frequency division multiplexing (OFDM). In this work, we combine index modulation (IM) and propose an OCDM-IM system that provides faster data rates when it activates the same number of subchirps as OCDM. On the other hand, it activates fewer subchirps when the data rate is the same, so it has better anti-interference capability. The simulation results under a measured UWA channel and random channels and the experimental results show that OCDM-IM has a better bit-error rate (BER) performance than that of OCDM and OFDM-IM systems when the subchirps are not fully loaded. In particular, when a small number of subchirps are activated, OCDM-IM is able to provide up to 2x bit rate with more than 5 dB improvement in BER performance. Our simulation and experimental results also show that IM has much better reliability than constellation mapping when Doppler effect is considered. Therefore, OCDM-IM is more suitable for the communication of AUVs. IEEE

关键词： Peak to average power ratio

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AE-DENet: Enhancement for Deep Learning-based Channel Estimation in OFDM Systems

AE-DENet: Enhancement for Deep Learning-based Channel Estima...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Fola, Ephrem Luo, Yang Luo, Chunbo University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China

ISBN: (纸本)9798350351255

Deep learning (DL)-based methods have demonstrated remarkable achievements in addressing orthogonal frequency division multiplexing (OFDM) channel estimation challenges. However, existing DL-based methods mainly rely on separate real and imaginary inputs while ignoring the inherent correlation between the two streams, such as amplitude and phase information that are fundamental in communication signal processing. This paper proposes AE-DENet, a novel autoencoder(AE)-based data enhancement network to improve the performance of existing DL-based channel estimation methods. AE-DENet focuses on enriching the classic least square (LS) estimation input commonly used in DL-based methods by employing a learning-based data enhancement method, which extracts interaction features from the real and imaginary components and fuses them with the original real/imaginary streams to generate an enhanced input for better channel inference. Experimental findings in terms of the mean square error (MSE) results demonstrate that the proposed method enhances the performance of all state-of-the-art DL-based channel estimators with negligible added complexity. Furthermore, the proposed approach is shown to be robust to channel variations and high user mobility. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Structure-performance relationship between denitration performance and catalytic interface morphologies of MnCeO_(x)/P84 catalytic filters

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Journal of Rare Earths 2025年第3期43卷 516-525,I0004页

作者： Bo Yang Yujie Lei Maosen Ni Liuying Wang Qiong Huang Mindong Chen Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution ControlSchool of Environmental Science and EngineeringNanjing University of Information Science&TechnologyNanjing210044China

MnCeO_(x)/P84 catalytic filters with spherical,flower-like,cubic and rod-like catalytic interfaces were synthesized respectively,and their catalytic activities in the NH_(3)-SCR reaction were *** MnCeO_(x)/P84 catalytic filter with spherical catalytic interfaces(recorded as S-MnCeO_(x)/P84)exhibits the best catalytic denitration *** NO_(x)removal efficiency of S-MnCeO_(x)/P84 reaches the highest value of 98.6%at 160℃when the catalyst loading is 100 g/m^(2).At the same time,S-MnCeO_(x)/P84 exhibits good SO_(2)resistance and stability,achieving a NO_(x)removal rate of 83%at 190℃with 30 ppm SO_(2).The characterization results illustrate that the MnCeO_x active component in S-MnCeO_(x)/P84 is present in weak crystalline states,tightly wrapped around the surface of the filter fiber,and uniformly dispersed,and the mesopore is the main pore structure of the S-MnCeO_(x)/P84,which can provide a channel for the catalytic reaction to *** the same time,transmission electron microscopy(TEM)characterization shows that y-MnO_(2)is the main form of MnO_(2)in the S-MnCeO_(x)/*** analysis of H_(2)temperature programmed reduction(H_(2)-TPR).NH_(3)temperature programmed desorption(NH_(3)-TPD)and in-situ diffuse reflectance infrared spectra(DRIFTS)show that S-MnCeO_(x)/P84 has good redox ability at 100-200℃and has abundant Lewis acid sites and Bronsteds acid sites,which provides an important guarantee for its superior low-temperature NH_(3)-SCR denitration performance.

关键词： Catalytic filter Nitrogen oxides(NO_x) Denitration Catalytic interface Rare earths

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An Improve mmW Automotive Radar DOA Estimation Approach: Joint Matrix Completion and Spectrum Extrapolation 17

An Improve mmW Automotive Radar DOA Estimation Approach: Joi...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Yi, Wei Wei, Shunjun Xu, Yanqin University of Electronic Science and Technology of China School of Information and Communication Engineering Sichuan Chendu China

ISBN: (纸本)9798350387384

Millimeter wave (mmW) automotive radar with limited array aperture resulting in low resolution of direction of arrival (DOA) estimation. The sparse linear array (SLA) can effectively improve the resolution of DOA estimation, which can also reduce automotive radar system complexity than uniform linear array (ULA). However, compared with ULA, there are data missing in SLA causing the high sidelobe or grid-lobe interference in DOA estimation. Therefore, we joint matrix completion and spectrum extrapolation to improve DOA estimation in automotive radar. Firstly, we construct an incomplete low-rank Hankel matrix by single-snapshot radar echo data in SLA. Then, to reduce high sidelobe or grid-lobe problem, we use the low rank prior to recover the missing data to reconstruct a complete Hankel matrix. Finally, to further improve the resolution, we use the completed Hankel matrix to expand into a larger virtual array by spectrum extrapolation. The simulation results show that the proposed method not only can effectively suppress sidelobe or grid-lobe of SLA, but also can further improve the resolution of DOA estimation. © 2024 IEEE.

关键词： Automotive radar

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Design of a Novel Accelerometer Based on Cavity Optomechanical Systems

Design of a Novel Accelerometer Based on Cavity Optomechanic...

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2024 International Conference on Intelligent communication, Sensing and Electromagnetics, ICSE 2024

作者： Zhang, Yang Zhu, Weiye Tan, Cheng University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China

ISBN: (纸本)9798350354935

An accelerometer is an inertial sensor used to measure the magnitude of acceleration and vibration frequency of a moving object. It finds wide applications in fields such as aerospace, autonomous driving, and unmanned equipment to enable inertial navigation or inertial measurement. In traditional accelerometers, thermal noise has the greatest impact on detection accuracy. To effectively reduce thermal noise, this paper designs a novel accelerometer structure based on a cavity optomechanical system. The design was optimized through simulations using COMSOL software, which confirmed the structural validity of the proposed accelerometer. Additionally, the first-order modal frequency and linearity simulation results from COMSOL were verified using Simulink software, demonstrating the reasonableness of the results. © 2024 IEEE.

关键词： Accelerometers

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Wideband Photonic Compressive Sensing System Based on Bipolar Optical Chaos

Wideband Photonic Compressive Sensing System Based on Bipola...

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2023 Opto-Electronics and communications Conference, OECC 2023

作者： Li, Anran Jiang, Ning Hu, Gang Cao, Yongsheng Zhang, Qianwu Qiu, Kun University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China Shanghai University School of Communication Engineering and Information Shanghai China

We propose a wideband photonic compressive sensing system based on bipolar wideband optical chaos, which supports simultaneous reconstruction for multiple frequency components in the range of 0-40GHz with only a 2GS/s... 详细信息

ISBN: (纸本)9781665462136

关键词： Compressed sensing

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Deep learning-based software engineering: progress,challenges, and opportunities

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Science China(information Sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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Compressive Sensing Chaos Radar Based on Self-Phase-modulated Feedback Semiconductor Laser Cascaded with Dispersive Component

Compressive Sensing Chaos Radar Based on Self-Phase-modulate...

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2023 Opto-Electronics and communications Conference, OECC 2023

作者： Tang, Chuanjie Jiang, Ning Cao, Yongsheng Zhang, Qianwu Hu, Gang Liu, Jiaming Qiu, Kun University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China Shanghai University School of Communication and Information Engineering Shanghai China

We propose a compressive sensing chaos radar based on a self-phase-modulated feedback semiconductor laser cascaded with a dispersive component, and numerically demonstrate that the chaos radar achieves high imaging qu... 详细信息

ISBN: (纸本)9781665462136

关键词： Radar imaging

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