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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Xia, Di Huang, Jianteng Luo, Liyang Li, Yufei Li, Zhaohui Zhang, Bin Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electrical and Information Technology Sun Yat-Sen University Guangzhou510275 China Key Laboratory of Optoelectronic Materials and Technologies Sun Yat-Sen University Guangzhou510275 China Zhuhai519000 China

We present a reconfigurable selective mode splitting based on self-assembled refractive index grating in integrated GeSbS microresonators, enabling an efficient Brillouin laser with a record low threshold power of 0.4... 详细信息

ISBN: (纸本)9781957171258

关键词： Microresonators

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Revisiting Unsupervised Domain Adaptation Models: A Smoothness Perspective 16th

Revisiting Unsupervised Domain Adaptation Models: A Smoothne...

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16th Asian Conference on computer Vision, ACCV 2022

作者： Wang, Xiaodong Zhuo, Junbao Zhang, Mengru Wang, Shuhui Fang, Yuejian School of Software and Microelectronics Peking University Beijing China Key Laboratory of Intelligent Information Processing Indian Institute of Computer Technology CAS Beijing China MEGVII Technology Beijing China

ISBN: (纸本)9783031263507

Unsupervised Domain Adaptation (UDA) aims to leverage the labeled source data and unlabeled target data to generalize better in the target domain. UDA methods utilize better domain alignment or carefully-designed regularizations to increase the discriminability of target features. However, most methods focus on directly increasing the distance between cluster centers of target features, i.e., enlarging inter-class variance, which intuitively increases the discriminability of target features and is easy to implement. However, due to intra-class variance optimization being under-explored, there are still some samples of the same class are prone to be classified into several classes. To handle this problem, we aim to equip UDA methods with the high smoothness constraint. We first define the model’s smoothness as the predictions similarity within each class, and propose a simple yet effective technique LeCo (impLicit smoothness Constraint) to promote the smoothness. We construct the weak and strong "views" of each target sample and enforce the model predictions of these two views to be consistent. Besides, a new uncertainty measure named Instance Class Confusion conditions the consistency is proposed to guarantee the transferability. LeCo implicitly reduces the model sensitivity to perturbations for target samples and guarantees smaller intra-class variance. Extensive experiments show that the proposed technique improves various baseline approaches by a large margin, and helps yield comparable results to the state-of-the-arts on four public datasets. Our codes are publicly available at https://***/Wang-Xiaodong1899/LeCo_UDA. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Large dataset

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Neural Network Based Electrical Dispersion Pre-Compensation for Intensity-modulation and Direct-detection Systems

Neural Network Based Electrical Dispersion Pre-Compensation ...

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2023 Opto-Electronics and Communications Conference, OECC 2023

作者： Rong, Limin Chen, Zhiwei Yin, Mingzhu Tang, Xianfeng Li, Fan Sun Yat-Sen University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Opto-Electronic Information Processing Chips and Systems Guangzhou510275 China Zhuhai519000 China Beijing University of Posts and Telecommunications State Key Laboratory of Information Photonics and Optical Communications Beijing100876 China

Based on deep neural network (DNN), intensity modulation and direct detection (IM/DD) system is demonstrated by simulation and 56-GBaud OOK signal is successfully transmitted over 80-km single mode fiber (SMF) with 3-... 详细信息

ISBN: (纸本)9781665462136

关键词： Chromatic dispersion

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High-quality Integrated Chalcogenide Microring Resonators for Efficient Brillouin lasers

High-quality Integrated Chalcogenide Microring Resonators fo...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Li, Yufei Xia, Di Xiao, Junjie Luo, Liyang Zhao, Xin Li, Zhixin Li, Zhaohui Zhang, Bin Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electrical and Information Technology Sun Yat-sen University Guangzhou510275 China Key Laboratory of Optoelectronic Materials and Technologies Sun Yat-sen University Guangzhou510275 China Zhuhai519000 China

We report an integrated GeSbS microring resonator with intrinsic quality factors of more than 5 million, first demonstrating a Brillouin laser with fundamental linewidth of 143 Hz and an o u t p u t p o w e r u p t o ... 详细信息

ISBN: (纸本)9781957171258

关键词： Optical resonators

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FusionNet: An Effective Network Phishing Website Detection Framework Based on Multi-Modal Fusion 25

FusionNet: An Effective Network Phishing Website Detection F...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Sun, Yu Liu, Guangqi Han, Xiaohui Zuo, Wenbo Liu, Weihua Qilu University of Technology Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Quancheng Provincial Laboratory Jinan China

ISBN: (纸本)9798350330014

Recently, the rapid advancement of communication technology has undoubtedly improved people's lives. Unfortunately, this progress has also given rise to a significant issue: the proliferation of phishing websites. Regrettably, phishing web sites have now emerged as one of the primary threats to cybersecurity. While there have been extensive efforts to develop anti-phishing techniques, many of these approaches focus solely on a single modality of information, such as the website's URL, HTML source code, or visual features. Given the growing sophistication of phishing attacks, relying on just one modality of information is no longer sufficient to accurately identify phishing websites. To tackle this challenge, our research aims to leverage the complementarity of various modalities to enhance phishing website detection. In this paper, we introduce a multi-modal framework called FusionNet to identify phishing web pages. This framework utilizes three distinct information modalities: URLs, HTML source codes, and visual features. Specifically, our approach first designs specific representation learning architectures tailored to each modality's unique properties. Subsequently, we incorpo-rate an attention mechanism to merge these representations, thereby exploiting the synergies between the multi-modal data. The learned feature representations are more discriminative, contributing to significantly improved phishing website detection accuracy. To evaluate the performance of FusionNet, we collect a dataset comprising phishing webpages, each sample contains all three modalities-URLs, HTML source codes, and visual features. Our extensive experiments conducted on this dataset demonstrate that FusionNet outperforms state-of-the-art methods in terms of both robustness and accuracy. © 2023 IEEE.

关键词： Websites

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LGDB-Net: Dual-Branch Path for Building Extraction from Remote Sensing Image 30

LGDB-Net: Dual-Branch Path for Building Extraction from Remo...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Ronghuan Zhao, Jing Li, Ming Zou, Qingzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong University of Traditional Chinese Medicine School of Intelligence and Information Engineering Jinan China

ISBN: (纸本)9798331515966

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural Networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch Network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity. © 2024 IEEE.

关键词： Convolutional neural networks

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Engineered Raman-Kerr Microcomb in integrated chalcogenide microresonators

Engineered Raman-Kerr Microcomb in integrated chalcogenide m...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Luo, Liyang Xia, Di Wang, Zifu Zhao, Xin Li, Yufei Li, Zhaohui Zhang, Bin Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electrical and Information Technology Sun Yat-sen University Guangzhou510275 China Key Laboratory of Optoelectronic Materials and Technologies Sun Yat-sen University Guangzhou510275 China Zhuhai519000 China

We first propose and experimentally demonstrate the engineered Raman-Kerr microcomb by adjusting the gains between Raman and Kerr effects in an integrated chalcogenide microresonator, leading to controllable approache... 详细信息

ISBN: (纸本)9781957171258

关键词： Microresonators

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A memory-augmented multi-task collaborative framework for unsupervised traffic anomaly detection in driving videos

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Pattern Recognition 2025年 168卷

作者： Liang, Rongqin Li, Yuanman Yi, Yingxin Zhou, Jiantao Li, Xia Guangdong Provincial Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China

Identifying traffic anomalies in driving videos is crucial to ensuring the safety of autonomous driving and driver assistance systems. To address the potential danger caused by the long-tailed distribution of driving events, existing traffic anomaly detection (TAD) methods mainly rely on unsupervised learning. However, most previous unsupervised methods rely on a single pretext task, either appearance-based or future object localization-based TAD methods, which struggle to model both global and local motion in driving scenes, resulting in suboptimal traffic anomaly detection performance. In this paper, we propose a novel memory-augmented multi-task collaborative framework for unsupervised traffic anomaly detection. Different from previous approaches, our method can more accurately detect both ego-involved and non-ego anomalies by comprehensively modeling global motion and local motions during driving through the collaboration of optical flow reconstruction and future object localization tasks. Further, we introduce a memory-augmented motion representation mechanism to fully explore the interrelation between different types of motion representations and exploit the high-level features of normal traffic patterns stored in memory to augment motion representations, thus enlarging the difference from anomalies. Experimental results on published datasets demonstrate that our method achieves better performance compared to previous state-of-the-art approaches. © 2025 Elsevier Ltd

关键词： Buffer storage

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Specific Emitter Identification based on CNN and Transformer 5

Specific Emitter Identification based on CNN and Transformer

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5th International Academic Exchange Conference on Science and technology Innovation, IAECST 2023

作者： Wang, Bin Gao, Ning Wang, Feng The 38th Research Institute of China Electronics Technology Group Corporation Key Laboratory of Aperture Array and Space Application Key Laboratory of Intelligent Information Processing Anhui Hefei230088 China Hefei University of Technology School of Computer and Information Hefei230601 China

ISBN: (纸本)9798350357738

Specific emitter identification(SEI) plays an increasingly crucial and potential role in both military and civilian scenarios. It refers to a process to discriminate individual emitters from each other by analyzing extracted characteristics from given radio signals. In this paper, we present a novel radiation source identification method named CNNFormer based on a hybrid architecture of Convolutional Neural Network and Transformer, harnessing the strengths of both architectures to enhance feature extraction and sequence modeling. We demonstrate the effectiveness of our approach on simulation datasets, achieving state-of-the-art accuracy in emitter identification. We showcase outstanding performance of our method on simulation datasets, achieving 98.29% three-class identification accuracy the emitter dataset with three devices and 97.60% ten-class identification accuracy on an RF fingerprint dataset with ten XSPR transmitters. Furthermore, our model allows for insight into the key features contributing to identification. This work contributes to enhancing the security and reliability of wireless communication systems. © 2023 IEEE.

关键词： Convolutional neural networks

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Experimental demonstration of phase-sensitive multimode continuous variable quantum key distribution with improved secure key rate

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Photonics Research 2023年第11期11卷 1861-1869页

作者： ZIKANG SU JINTAO WANG DAJIAN CAI XIAOJIE GUO DAWEI WANG ZHAOHUI LI Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electronics and Information TechnologySun Yat-sen UniversityGuangzhou 510006China Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications Institute of Photonics TechnologyJinan UniversityGuangzhou 510632China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai 519000China

We develop and experimentally demonstrate a phase-sensitive continuous variable quantum key distribution system with improved secure key *** is achieved using multimode coherent states with phase-conjugated subcarrier modulation and phase-sensitive *** local oscillator for phase-sensitive detection is regenerated from a polarization-multiplexed carrier wave via optical injection *** proposed scheme has a higher classical information capacity at a given number of received photons and exhibits a higher secure key rate when applying the security analysis of the GG02 *** results confirm the higher secret key rate and better excess noise tolerance of the new scheme compared to the typical implementation of GG02.

关键词： phase distribution quantum

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