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2021 IEEE International Conference on Space-Air-Ground Computing, SAGC 2021

作者： Qu, Yuben Dong, Chao Wu, Tao Zhuang, Yan Dai, Haipeng Wu, Fan Nanjing University of Aeronautics and Astronautics The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology China National University of Defense Technology China Shanghai Jiao Tong University Department of Computer Science and Engineering China Nanjing University State Key Laboratory for Novel Software Technology China

ISBN: (纸本)9781665400114

Unmanned aerial vehicles (UAVs) are expected to be widely used in many critical applications including monitoring, surveillance, urban target tracking, and delivery of goods, from military to civil fields. To realize that, empowering UAV swarm with edge intelligence is becoming appealing and inevitable. Federated learning (FL) as an emerging distributed collaborative machine learning (ML) without letting raw data out, could be suitable for UAV swarm networks but remains largely unexplored. In this paper, we first investigate the problem of efficient Edge Intelligence under Clustering for UAV swarm networks (e-EIC), i.e., how to jointly optimize the computation resource allocation and UAV clustering to minimize the total energy consumption of the UAV swarm for FL, subject to the training accuracy and latency constraints. We decompose the challenging e-EIC problem into two subproblems: computation resource allocation and UAV clustering, and accordingly propose an iterative algorithm based on the optimal policy of the first subproblem and local-search techniques for the second subproblem. Extensive performance evaluations demonstrate that our proposed algorithm can reduce more energy consumption of UAVs than three baseline algorithms, while satisfying the training accuracy and latency constraints. © 2021 IEEE.

关键词： Energy utilization

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Optical computing for neural ordinary differential equations

Optical computing for neural ordinary differential equations

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2023 Applied Optics and Photonics China: AI in Optics and Photonics, AOPC 2023

作者： Zhao, Yun Chen, Hang Lin, Min Zhang, Haiou Yan, Tao Lin, Xing Huang, Ruqi Dai, Qionghai Department of Automation Tsinghua University Beijing100084 China Department of Electronic Engineering Tsinghua University Beijing100084 China Tsinghua-Berkeley Shenzhen Institute Tsinghua University Shenzhen University Town Guangdong518071 China Sea AI Lab 1 Fusionopolis Place 17-10Galaxis Singapore138522 Singapore Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9781510672383

Increasing the layer number can improve the model performance of on-chip optical neural networks (ONNs). However, this results in larger integrated photonic chip areas due to the successive cascading of network hidden layers. We introduce a novel architecture for optical computing based on neural ordinary differential equations (ODEs) that employing optical ODE solvers to parameterize the continuous dynamics of hidden layers. The architecture comprises ONNs followed by a photonic integrator and an optical feedback loop, which can be configured to represent residual neural networks (ResNets) and implement the function of recurrent neural networks with effectively reduced chip area occupancy. For the interference-based optoelectronic nonlinear hidden layer, we demonstrate that the single hidden layer architecture can achieve approximately the same accuracy as the two-layer optical ResNets in image classification tasks. Furthermore, the architecture improves the model classification accuracy for the diffraction-based all-optical linear hidden layer. We also utilize the time-dependent dynamics property of architecture for trajectory prediction with high accuracy. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Ordinary differential equations

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Compact Wideband Dual-band Bandpass Filter Based on Single Half-Mode Substrate Integrated Waveguide with Coupled Line 13

Compact Wideband Dual-band Bandpass Filter Based on Single H...

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13th International Conference on Microwave and Millimeter Wave technology, ICMMT 2021

作者： Liu, Jiawei Zhang, Yonghong Li, Xuan Xie, Jinxiong University Of Electronic Science And Technology Of China Extremely High Frequency Key Lab Of Fundamental Science School Of Electronic Science And Engineering Chengdu611731 China Information Center Of Shenzhen Customs Shenzhen518045 China Shenzhen Academy Of Inspection And Quarantine Shenzhen518010 China

ISBN: (纸本)9781665434379

A compact wideband dual-band bandpass filter (BPF) based on a single half-mode substrate integrated waveguide (HMSIW) with a pair of coupled lines is proposed in this paper. Benefiting from the integration of SIW and coupled line technologies, two additional modes are introduced, and used to achieve the first passband. The second passband is achieved by the three resonant modes in a single HMSIW resonator which is perturbed by the coupled lines. Compared to the dominant mode in the origin HMSIW cavity, two additional modes operate at lower frequencies, thus the filter size can be obviously reduced. In addition, utilizing the coupled lines, the bandwidth of passbands and transmission zeros can be adjusted flexibly. To verify the availability of this structure, the filter is fabricated and measured. And the measured results agree well with the simulated ones. © 2021 IEEE.

关键词： Substrate integrated waveguides

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Dual Electrical-Behavior Regulation of Α-Moc Quantum Dots and Mo-N-C Sites for High Photoelectric Conversion Performance of Solar Cells

SSRN

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SSRN 2022年

作者： Yao, Jixin Wang, Wen Wang, Jie Meng, Ying Lu, Shibin Wang, Haibo Du, Feng Yang, Li Yang, Bo Tang, Huaibao Xu, Haifeng Li, Guang Anhui Province Key Laboratory of Simulation and Design for Electronic Information System Universities Joint Key Laboratory of Photoelectric Detection Science and Technology in Anhui Province Hefei Normal University Hefei230601 China School of Materials Science and Engineering Anhui Key Laboratory of Information Materials and Devices Key Laboratory of Structure and Functional Regulation of Hybrid Materials Ministry of Education Institute of Physical Science and Information Technology Anhui University Hefei230601 China Institute of Materials Science and Devices School of Materials Science and Engineering Suzhou University of Science and Technology Suzhou215011 China School of Information Engineering Suzhou University Suzhou234000 China School of Physics and Electronic Information Huaibei Normal University Huaibei235000 China

Constructing low-price yet high-efficiency counter electrode (CE) materials for dye-sensitized solar cells (DSSCs) is vital in the field of energy conversion. Herein, α-MoC quantum dots (QDs) decorated with Mo-N-C sites are encapsulated in a unique carbon flower structure (α-MoCQD@NCF) as efficient CE. The α-MoCQD@NCF CE in DSSC exhibits high power conversion efficiency (PCE) with up to 9.12%, which greatly exceeds that of pure Pt (7.53%). Electron microscopy reveals that α-MoCQD@NCF is an ultrathin-hierarchical hollow structure, which facilitates the adequate contact of the electrolyte. Simultaneously, the ultra-small size of the QDs is advantageous for maximizing the utilization of active sites to accelerate the triiodide reduction reaction. Additionally, theoretical calculations indicate that atomically engineered Mo-N-C sites combined with α-MoC quantum dots can complement and reinforce each other to transfer electrons to the carbon matrix. Therefore, through the synergistic effect of the dual regulation of electron transfer rate and pathway by the two structures, the electron transfer on the CE surface is accelerated, the iodide ion coupling is suppressed and the reaction kinetics are optimized. It not only offers a rational approach to develop low-cost yet highly active electrode material, but also opens an avenue for other energy-related fields. © 2022, The Authors. All rights reserved.

关键词： Electrocatalysis

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Anticipation of Parking Vacancy during Peak/Non-peak Hours using Convolutional Neural Network-YOLOv3 in University Campus

Anticipation of Parking Vacancy during Peak/Non-peak Hours u...

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2021 International Conference on Green Energy, Computing and Sustainable technology, GECOST 2021

作者： Davan, Jasvina Mae A.P. Ealn Koh, Tieng Wei Tong, Dong Ling Tseu, Kwan Lee First City University College Artificial Intelligence Lab Faculty of Computing and Engineering Petaling Jaya Malaysia University of Putra Malaysia Faculty of Computer Science and Information Technology Selangor Malaysia Universiti Tunku Abdul Rahman Centre of IoT Big Data Faculty of Information and Communication Technology Perak Malaysia

ISBN: (纸本)9781665438650

Searching for a publicly available parking space has become a nightmare to many drivers. With the constant development of global urbanization, human population has increased drastically in the past decades. Searching for a publicly available parking space in a highly populated area can be daunting and time consuming. No matter how much time is spent to find a vacant parking space, it always causes traffic congestion in the area. To alleviate these problems, it is of utmost importance to have a system that can detect and display the vacant parking spaces in real-time. This paper has conducted a study of anticipation of parking vacancy using convolutional neural network called YOLOv3 in a university campus. Image data is gathered from the video capture of the university's campus open space parking lot. The YOLOv3 algorithm is used to train and predict whether the space is vacant or occupied. Results showed that YOLOv3 has been able to correctly predict the vacant space. The result of the rendering video will then be transformed into an image and is sent to the students via a Telegram group. © 2021 IEEE.

关键词： Convolutional neural networks

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Dual-Regulation of Α-Moc Quantum Dots and Mo-N-C Sites to Achieve High Photoelectric Conversion Performance in Solar Cell

SSRN

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SSRN 2022年

作者： Yao, Jixin Wang, Wen Wang, Jie Meng, Ying Lu, Shibin Tong, Xiaowei Du, Feng Yang, Li Xu, Haifeng Yang, Bo Tang, Huaibao Li, Guang Anhui Province Key Laboratory of simulation and design for Electronic information system Universities Joint Key Laboratory of photoelectric detection Science and Technology in Anhui Province Hefei Normal University Hefei230601 China School of Materials Science and Engineering Anhui Key Laboratory of Information Materials and Devices Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of Education Institute of Physical Science and Information Technology Anhui University Hefei230601 China Institute of Materials Science and Devices School of Materials Science and Engineering Suzhou University of Science and Technology Suzhou215011 China School of Information Engineering Suzhou University Suzhou234000 China School of Physics and Electronic Information Huaibei Normal University Huaibei235000 China

Constructing low-price yet high-efficiency counter electrode (CE) materials for dye-sensitized solar cells (DSSCs) is vital in energy conversion fields. Herein, α-MoC quantum dots (QD) decorated with Mo-N-C site is encapsulated in a unique carbon flower structure (α-MoCQD@NCF) as efficient CE. The α-MoCQD@NCF CE in DSSC exhibits high power conversion efficiency (PCE) with up to 9.12%, which greatly exceeds the pure Pt (7.53%). The electron microscope reveals that α-MoCQD@NCF is an ultrathin-hierarchical hollow structure, which facilitates the adequate contact of the electrolyte. Simultaneously, the ultra-small size of QD is advantageous for maximizing the utilization of active sites to accelerate the tri-iodide reduction reaction. Additionally, theory calculations indicate that atomic engineering Mo-N-C sites coupled with α-MoC quantum dots can complement and reinforce each other to transfer electrons to the carbon matrix. This electronic modulation synergistically accelerates the electron transport on the surface of CE, restraining iodide ion coupling and optimizing reaction kinetics. © 2022, The Authors. All rights reserved.

关键词： Nanocrystals

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Spatial Attention-Guided Generative Adversarial Network for Synthesizing Contrast-enhanced Computed Tomography Images

Spatial Attention-Guided Generative Adversarial Network for ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yulin Yang Yinhao Li Qingqing Chen Xian-Hua Han Jing Liu Lanfen Lin Hongjie Hu Yen-Wei Chen Gradate School of Information Science and Engineering Ritsumeikan University Shiga Japan College of Information Science and Engineering Ritsumeikan University Shiga Japan Department of Radiology Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China Artificial Intelligence Research Center Yamaguchi University Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China

Compared to non-contrast computed tomography (NC-CT) scans, contrast-enhanced (CE) CT scans provide more abundant information about focal liver lesions (FLLs), which play a crucial role in the FLLs diagnosis. However, CE-CT scans require patient to inject contrast agent into the body, which increase the physical and economic burden of the patient. In this paper, we propose a spatial attention-guided generative adversarial network (SAG-GAN), which can directly obtain corresponding CE-CT images from the patient’s NC-CT images. In the SAG-GAN, we devise a spatial attention-guided generator, which utilize a lightweight spatial attention module to highlight synthesis task-related areas in NC-CT image and neglect unrelated areas. To assess the performance of our approach, we test it on two tasks: synthesizing CE-CT images in arterial phase and portal venous phase. Both qualitative and quantitative results demonstrate that SAG-GAN is superior to existing GANs-based image synthesis methods.

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Orbital torque switching of room temperature two-dimensional van der Waals ferromagnet Fe3GaTe2

arXiv

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arXiv 2024年

作者： Zhang, Delin Wei, Heshuang Duan, Jinyu Chen, Jiali Yue, Dongdong Yang, Yuhe Gou, Jinlong Yan, Junxin Zhai, Kun Wang, Ping Hu, Shuai Jia, Zhiyan Jiang, Wei Wang, Wenhong Li, Yue Jiang, Yong Institute of Quantum Materials and Devices School of Electronic and Information Engineering Tiangong University Tianjin300387 China School of Material Science and Engineering State Key Laboratory of Separation Membrane and Membrane Processes Tiangong University Tianjin300387 China School of Physical Science & Technology Tiangong University Tianjin300387 China Beijing Key Lab of Nanophotonics & Ultrafine Optoelectronic Systems School of Physics Beijing Institute of Technology Beijing100081 China Center for High Pressure Science State Key Laboratory of Metastable Materials Science and Technology Yanshan University Qinhuangdao066000 China

Efficiently manipulating the magnetization of van der Waals (vdW) ferromagnets has attracted considerable interest in developing room-temperature two-dimensional (2D) material-based memory and logic devices. Here, taking advantage of the unique properties of the vdW ferromagnet as well as promising characteristics of the orbital Hall effect, we demonstrate the room-temperature magnetization switching of vdW ferromagnet Fe3GaTe2 through the orbital torque generated by the orbital Hall material, Titanium (Ti). The switching current density is estimated to be around 1.6×106 A/cm2, comparable to that achieved in Fe3GaTe2 using spin-orbit torque from spin Hall materials (e.g., WTe2, and TaIrTe4). The efficient magnetization switching arises from the combined effects of the large orbital Hall conductivity of Ti and the strong spin-orbit correlation of the Fe3GaTe2, as confirmed through theoretical calculations. Our findings advance the understanding of orbital torque switching and pave the way for exploring 2D material-based orbitronic devices. © 2024, CC BY.

关键词： Van der Waals forces

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KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation

arXiv

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arXiv 2024年

作者： Tao, Wei Zhou, Yucheng Wang, Yanlin Zhang, Hongyu Wang, Haofen Zhang, Wenqiang Shanghai Engineering Research Center of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China School of Software Engineering Sun Yat-sen University Guangdong Zhuhai519082 China School of Big Data and Software Engineering Chongqing University Chongqing China College of Design and Innovation Tongji University Shanghai China Engineering Research Center of AI and Robotics Ministry of Education Academy for Engineering and Technology Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University 220 Handan Road Shanghai200433 China

Commit messages are natural language descriptions of code changes, which are important for software evolution such as code understanding and maintenance. However, previous methods are trained on the entire dataset without considering the fact that a portion of commit messages adhere to good practice (i.e., good-practice commits), while the rest do not. On the basis of our empirical study, we discover that training on good-practice commits significantly contributes to the commit message generation. Motivated by this finding, we propose a novel knowledge-aware denoising learning method called KADEL. Considering that good-practice commits constitute only a small proportion of the dataset, we align the remaining training samples with these good-practice commits. To achieve this, we propose a model that learns the commit knowledge by training on good-practice commits. This knowledge model enables supplementing more information for training samples that do not conform to good practice. However, since the supplementary information may contain noise or prediction errors, we propose a dynamic denoising training method. This method composes a distribution-aware confidence function and a dynamic distribution list, which enhances the effectiveness of the training process. Experimental results on the whole MCMD dataset demonstrate that our method overall achieves state-of-the-art performance compared with previous methods. © 2024, CC BY.

关键词： Sampling

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Electrically Tunable Non-volatile Reflective Display Pixel Structure Based on Phase Change Material 5

Electrically Tunable Non-volatile Reflective Display Pixel S...

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2020 5th International Conference on Intelligent Computing and Signal Processing, ICSP 2020

作者： Zhou, Tong Gao, Ding Cao, Shiyue Cheng, Zhiyuan College of Information Science and Electronic Engineering Provincial Key Lab of Micro-Nano Electronic and Smart System Zhejiang University Hangzhou China

A multi-layered non-volatile solid-state reflective display pixel structure composed of a Fabry-Perot type resonance cavity and an ultrathin phase change material film has been fabricated and analysed. By electrically switching the phase states of a phase-changing Ge2Sb2Te5 (GST) film using an electric micro-heater under the stack, a pronounced colour change in pixel structures is observed, which is attributed to the change in the resonance condition of the multi-layered stack. Furthermore, the range of attainable colour can be widened by exploiting a spatial colour mixing structure. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Phase change materials

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