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Conference on Lasers and Electro-Optics (CLEO)

作者： Ruixuan Chen Bitao Shen Haowen Shu Lin Chang Weiqiang Xie Wenchao Liao Zihan Tao John Bowers Xingjun Wang State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara CA USA Frontiers Science Center for Nano-Optoelectronics Peking University Beijing China

We introduce a chaotic microcomb based high interference-tolerance parallel LiDAR. Parallel chaos with record-high noise spectra beyond 7 GHz leads to sub-cm-level ranging accuracy. The intrinsic orthogonality permits... 详细信息

ISBN: (纸本)9781957171258

关键词： Chaos Image resolution Lidar Range finding Ring resonators Three dimensional imaging

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RMS: Real-time Motion Segmentation over the Internet of Vehicles

RMS: Real-time Motion Segmentation over the Internet of Vehi...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Lei Zhan Kai Hu Longhao Zou Zhenhui Yuan Gabriel-Miro Muntean Institue of Future Networks Southern University of Science and Technology Shenzhen China Department of Broadband Communication Peng Cheng Laboratory Shenzhen China Department of Computer and Information Sciences Northumbria University Newcastle UK School of Electronic Engineering Dublin City University Dublin Ireland

In the context of autonomous driving, moving objects such as vehicles and pedestrians are of critical importance as they primarily influence the maneuvering and braking of cars. Unfortunately, due to the limited detection range of sensors, some distant and blocked objects cannot be detected, leading to slow responses when some unexpected situations occur during driving. To address this problem, a real-time motion segmentation multi-task model (RMS), running on an individual vehicle, is introduced to provide motion segmentation of moving objects within its field of view. RMS consists of a shared encoder, a multi-modal fusion module, and a dual decoder. An enhanced High Definition (HD) map constructed with the proposed RMS in line with the recommendations of the 3rd Generation Partnership Project (3GPP) Vehicle-to-everything (V2X) communication standard is produced. Extensive experiments demonstrate how RMS outperforms existing state-of-the-art motion segmentation methods in terms of multiple metrics, including mean Intersection over Union (mIoU). Additionally, Internet of Vehicles (IoV) simulation experiments show how the time required to update the map is better than the times achieved when using other methods.

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Rapid Maize Seedling Detection Based on Receptive-Field and Cross-Dimensional Information Interaction

Rapid Maize Seedling Detection Based on Receptive-Field and ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Fengqi Hao Shulei Zhu Dexin Ma Xiangjun Dong Hoiio Kong Xia Liu Chunhua Mu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Data Science City University of Macau Macau China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Communication College Qingdao Agricultural University Qingdao China National Engineering Laboratory of Wheat and Maize/Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture Maize Research Institute Shandong Academy of Agricultural Sciences Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The seedling stage is important in the growth and development of maize, and it is also a critical period that affects maize yield and quality. Accurately recognizing this stage of maize is challenging, as current maize seedling detection methods struggle with small sizes and complex environments. This paper proposes a rapid maize seedling detection method that emphasizes spatial feature extraction in the receptive field and cross-dimensional interaction—receptive field triplet attention YOLO (RFT-YOLO). First, we design a convolutional module for cross-dimensional information interaction within the receptive field. Second, we introduce an advanced selective fusion network, which boosts the multi-scale fusion capability of small object features while reducing the GFLOPs of the model. Additionally, we employ Inner-IoU to diminish sensitivity to positional biases of small objects and expedite the convergence of the loss function. Finally, we create a dataset of maize seedlings to conduct our experiments. Experimental results demonstrate that the RFT-YOLO significantly enhances performance, reducing the number of parameters by 35.3% and GFLOPs by 14.24%, compared to the baseline model. Moreover, the mean average precision (mAP) improves from 89.6% to 91.5%. These improvements confirm the model's effectiveness in detecting maize seedlings.

关键词： YOLO Sensitivity Accuracy Semantics Feature extraction Complexity theory Data mining Cybernetics Convergence

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High-Speed Network Attack Detection Framework Based on Optimized Feature Selection 1st

High-Speed Network Attack Detection Framework Based on Optim...

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1st International Conference on Security and Privacy in Digital Economy, SPDE 2020

作者： Luo, Zhicheng Ding, Weijia Fu, Anmin Zhang, Zhiyi Zhang, Linjie School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Science and Technology on Communication Networks Laboratory Shijiazhuang China

ISBN: (纸本)9789811591280

In the era of high-speed information, network technology facilitates our life. When we enjoy the service with high quality, various new intrusion methods have also emerged. Network attacks are more challenging to be detected by security applications under the cover of large-scale flow, threatening the security of cyberspace constantly. In view of the increasingly severe security situation, this paper proposes a high-speed network attack detection framework based on feature selection optimization to overcome the difficulties. It quickly collects data packets through the design of the DPDK mechanism, combines the data sampling method based on the genetic algorithm, and the improved feature selection algorithm to optimize the training model. The integration of incremental learning increases the autonomous detection capability of the framework, which is more suitable for intricate network environments. Finally, we verified the validity of our work by experiments and simulated the actual attacks to test the detection effect in the real network. © 2020, Springer Nature Singapore Pte Ltd.

关键词： HIgh speed networks

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Broadband and repetition rate tunable frequency comb based on electro-optic time lens with AlGaAsOI waveguide

Broadband and repetition rate tunable frequency comb based o...

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49th European Conference on Optical Communications (ECOC 2023)

作者： C. Ma C. Wang Y. Pi L. Wang Z. He J. E. Bowers L. Chang Z. Cao S. Yu Research Center of Circuits and Systems Peng Cheng Laboratory Shenzhen 518055 Guangdong People's Republic of China Department of Electrical and Computer Engineering University of California Santa Barbara CA 93106 USA State Key Laboratory of Advanced Optical Communications System and Networks Peking University Beijing 100871 People's Republic of China

We present a tunable repetition rate frequency comb realized by cascaded electro-optical modulators with a high nonlinear AlGaAs-on-isolator waveguide. 18-26.5 GHz comb spacing tunability with broadband spectra was achieved. All the devices can be integrated onto a single chip, which makes this work useful for many applications.

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Auction Theory and Game Theory Based Pricing of Edge Computing Resources: A Survey

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IEEE Internet of Things Journal 2025年

作者： Yu, Jiguo Liu, Shun Zou, Yifei Wang, Guijuan Hu, Chunqiang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Qufu Normal University School of Computer Science Shandong Rizhao China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250353 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China

The proliferation of Internet of Things (IoT) devices and edge computing applications has heightened the demand for efficient resource allocation and pricing mechanisms. Effective pricing strategies play a crucial role in optimizing resource utilization, reducing latency, and ensuring fair resource distribution among users. Both auction theory and game theory have emerged as fundamental economic frameworks for designing pricing mechanisms in edge computing. This paper presents a comprehensive survey of pricing strategies that leverage both auction and game theory, systematically categorizing existing research based on auction mechanisms and game-theoretic models. Unlike previous surveys that focus solely on auction-based pricing, this work integrates game-theoretic perspectives, providing a holistic analysis of strategic interactions in edge computing resource allocation. Furthermore, we discuss key challenges, emerging trends, and future research directions aimed at enhancing efficiency, fairness, and privacy protection in cloud-edge collaborative computing environments. © 2014 IEEE.

关键词： Mobile edge computing

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Rate-Overhead Tradeoff in Beam Training for RRS-Assisted Multi-User Communications

Rate-Overhead Tradeoff in Beam Training for RRS-Assisted Mul...

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IEEE Conference on Vehicular Technology (VTC)

作者： Shupei Zhang Yutong Zhang Boya Di Hongliang Zhang School of Electronics Engineering and Computing Science Peking University Beijing China State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Beijing China Department of Electrical and Computer Engineering Princeton University NJ USA

ISBN: (纸本)9781665454698

Holographic multiple-input multiple-output (HMIMO) with a spatially continuous aperture is a promising solution for future radio access to handle the explosively increasing data demands. As a key enabler of HMIMO, the reconfigurable refractive surface (RRS) can serve as an antenna array with numerous programmable radiation elements. In this paper, we consider a multi-user system with an RRS-aided base station (BS) where the transmit signal is refracted by the RRS towards the users. A beamforming scheme is developed via codebook design and beam training. A larger codebook size implies more codewords, each corresponding to a directional beam. When the codebook size increases, the directivity of the refracted beam is enhanced, bringing a higher data rate. However, it also leads to an exponential growth of the training overhead. To achieve the critical tradeoff between the data rate and overhead, we evaluate the system throughput and model the relation between the codebook size of the RRS and the throughput mathematically. The optimal codebook size is then derived given different user distributions. Simulation results verify our theoretical analysis and show the influence of both codebook size and RRS size on the throughput.

关键词： Training Vehicular and wireless technologies Array signal processing Simulation System performance Transmitting antennas Throughput

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Highly integrated reconfigurable radiofrequency filter based on microcomb and a silicon photonic processor

Highly integrated reconfigurable radiofrequency filter based...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Yuansheng Tao Lin Chang Bitao Shen Haowen Shu Weiqiang Xie Ming Jin Zihan Tao Bowen Bai Shaohua Yu Xingjun Wang John E. Bowers Department of Electronics State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Engineering and Computer Science Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara USA Peng Cheng Laboratory Shenzhen China Frontiers Science Center for Nano-Optoelectronics Peking University Beijing China

We demonstrate a novel chip-scale reconfigurable radiofrequency filter by using AlGaAs-on-insulator microcomb and silicon photonics in combination, simultaneously bypassing the employment of bulky pulse shaper and lon... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Radio frequency Employment Lasers and electrooptics Silicon photonics Electrooptical waveguides Optical fiber dispersion Fiber lasers

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Two-timescale Optimization for E2E Network Slicing-aided Cloud-edge Collaborative networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Yunfeng Zhao, Liqiang Chu, Xiaoli Song, Shenghui Deng, Yansha Nallanathan, Arumugam Zhou, Guorong Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Xidian University Guangzhou Institute of Technology Guangzhou510100 China University of Sheffield Department of Electronic and Electrical Engineering SheffieldS10 2TN United Kingdom Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong King's College London Department of Engineering LondonWC2R 2LS United Kingdom Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

To leverage the synergy between cloud computing (CC) and edge computing (EC) to support various services while reducing the CC/EC switching overhead, the two-timescale utility maximization problem for an end-to-end network slicing-aided cloud-edge collaborative network (E2E-CECN) is formulated covering both the high throughput and low delay service requirements. To solve the utility maximization problem while dynamically adjusting the weights of the E2E-CECN utility to accommodate the variation of users' service requests, we proposed a reward comparison double deep Q network algorithm to optimize the large timescale joint virtual base station activation and CC-EC scheduling, and a reward comparison deep deterministic policy gradient algorithm to optimize the small timescale allocation of backhaul link capacity (BLC), CC/EC capability and transmission power. Numerical results show that both the high-throughput and low-delay service requirements can be satisfied simultaneously under the reasonable BLC usage and power consumption. © 1967-2012 IEEE.

关键词： Cloud platforms

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FedForgery: Generalized Face Forgery Detection with Residual Federated Learning

arXiv

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arXiv 2022年

作者： Liu, Decheng Dang, Zhan Peng, Chunlei Zheng, Yu Li, Shuang Wang, Nannan Gao, Xinbo The State Key Laboratory of Integrated Services Networks School of Cyber Engineering Xidian University Shaanxi Xi’an710071 China The School of Cyber Engineering Xidian University Shaanxi Xi’an710071 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China The State Key Laboratory of Integrated Services Networks School of Telecommunications Engineering Xidian University Shaanxi Xi’an710071 China The Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing400065 China

With the continuous development of deep learning in the field of image generation models, a large number of vivid forged faces have been generated and spread on the Internet. These high-authenticity artifacts could grow into a threat to society security. Existing face forgery detection methods directly utilize the obtained public shared or centralized data for training but ignore the personal privacy and security issues when personal data couldn’t be centralizedly shared in real-world scenarios. Additionally, different distributions caused by diverse artifact types would further bring adverse influences on the forgery detection task. To solve the mentioned problems, the paper proposes a novel generalized residual Federated learning for face Forgery detection (FedForgery). The designed variational autoencoder aims to learn robust discriminative residual feature maps to detect forgery faces (with diverse or even unknown artifact types). Furthermore, the general federated learning strategy is introduced to construct distributed detection model trained collaboratively with multiple local decentralized devices, which could further boost the representation generalization. Experiments conducted on publicly available face forgery detection datasets prove the superior performance of the proposed FedForgery. The designed novel generalized face forgery detection protocols and source code would be publicly available at https://***/GANG370/FedForgery. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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