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ICCG:low-cost and efficient consistency with adaptive synchronization for metadata replication

Frontiers of Computer science 2025年第1期19卷 53-70页

作者： Chenhao ZHANG Liang WANG Jing SHANG Zhiwen XIAO Limin XIAO Meng HAN Bing WEI Runnan SHEN Jinquan WANG State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Cyberspace Security Hainan UniversityHaikou 570228China China Mobile Information Technology Center Beijing 100033China

The rapid growth in the storage scale of wide-area distributed file systems (DFS) calls for fast and scalable metadata management. Metadata replication is the widely used technique for improving the performance and scalability of metadata management. Because of the POSIX requirement of file systems, many existing metadata management techniques utilize a costly design for the sake of metadata consistency, leading to unacceptable performance overhead. We propose a new metadata consistency maintenance method (ICCG), which includes an incremental consistency guaranteed directory tree synchronization (ICGDT) and a causal consistency guaranteed replica index synchronization (CCGRI), to ensure system performance without sacrificing metadata consistency. ICGDT uses a flexible consistency scheme based on the state of files and directories maintained through the conflict state tree to provide an incremental consistency for metadata, which satisfies both metadata consistency and performance requirements. CCGRI ensures low latency and consistent access to data by establishing a causal consistency for replica indexes through multi-version extent trees and logical time. Experimental results demonstrate the effectiveness of our methods. Compared with the strong consistency policies widely used in modern DFSes, our methods significantly improve the system performance. For example, in file creation, ICCG can improve the performance of directory tree operations by at least 36.4 times.

关键词： metadata management metadata replication consistency directory tree replica index

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Physical-layer security with frequency diverse array for DF multi-antenna relaying SWIPT system

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International Journal of electronics Letters 2023年第3期11卷 291-299页

作者： Jian, Jiangwei Huang, Bang Wang, Wen-Qin School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

In this letter, we establish physical-layer security based on frequency diverse array (FDA) together with directional modulation (DM) for decode and forward (DF) relaying simultaneous wireless information and power transfer (SWIPT) system. The analytical expressions of the ergodic system secrecy capacity under the time switching (TS) protocol are derived. Moreover, the impacts of system parameters on secure performance are analysed. Numerical results reveal that the proposed method achieves better security performance than conventional PA solutions, especially in the range dimension. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Decode and forward (DF) directional modulation (DM) frequency diverse array (FDA) physical-layer security simultaneous wireless information and power transfer (SWIPT)

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Indoor Localization Method Based on WOA Algorithm Optimization of Xgboost 4

Indoor Localization Method Based on WOA Algorithm Optimizati...

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4th International Conference on Neural Networks, information and Communication engineering, NNICE 2024

作者： Wang, Jinquan Liu, Guangwen Jia, Xinyong Qiao, Shixia Changchun University of Science and Technology School of Electronic and Information Engineering Jilin Changchun130022 China

ISBN: (纸本)9798350394375

Due to the complex and dynamic nature of indoor environments, satellite signals are severely obstructed by indoor structures when propagating indoors. Therefore, indoor positioning technologies are needed to replace satellite signals for obtaining location information. With the rapid advancements in communication technology and artificial intelligence, AI-based indoor positioning techniques can effectively achieve indoor localization. This paper proposes a machine learning-based indoor positioning technique that utilizes the WOA-optimized XGBoost algorithm to predict the target location in a WiFi-based indoor positioning system. For dynamic targets, the Unscented Kalman Filter (UKF) is employed to fuse measurements and achieve precise position estimation. © 2024 IEEE.

关键词： Indoor positioning systems

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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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2024 Conference on Lasers and Electro-Optics, CLEO 2024

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

ISBN: (纸本)9781957171395

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, © 2024 The Author(s)

关键词： Van der Waals forces

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Boosting 3D Object Detection with Semantic-Aware Multi-Branch Framework

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5697-5710页

作者： Jing, Hao Wang, Anhong Zhao, Lijun Yang, Yakun Bu, Donghan Zhang, Jing Zhang, Yifan Hou, Junhui The School of Electronic Information Engineering Taiyuan University of Science and Technology No. 66 Waliu Road Taiyuan030024 China The Department of Computer Science City University of Hong Kong Kowloon Tong Hong Kong

In autonomous driving, LiDAR sensors are vital for acquiring 3D point clouds, providing reliable geometric information. However, traditional sampling methods of preprocessing often ignore semantic features, leading to detail loss and ground point interference in 3D object detection. To address this, we propose a multi-branch two-stage 3D object detection framework using a Semantic-aware Multi-branch Sampling (SMS) module and multi-view consistency constraints. The SMS module includes random sampling, Density Equalization Sampling (DES) for enhancing distant objects, and Ground Abandonment Sampling (GAS) to focus on non-ground points. The sampled multi-view points are processed through a Consistent KeyPoint Selection (CKPS) module to generate consistent keypoint masks for efficient proposal sampling. The first-stage detector uses multi-branch parallel learning with multi-view consistency loss for feature aggregation, while the second-stage detector fuses multi-view data through a Multi-View Fusion Pooling (MVFP) module to precisely predict 3D objects. The experimental results on the KITTI dataset and Waymo Open Dataset show that our method achieves excellent detection performance improvement for a variety of backbones, especially for low-performance backbones with simple network structures. The code will be publicly available at https://***/HaoJing-SX/SMS. © 1991-2012 IEEE.

关键词： Object detection

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A Precise Decompression Method for Intel FPGA Bitstream 3

A Precise Decompression Method for Intel FPGA Bitstream

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3rd International Conference on electronics and information technology, EIT 2024

作者： Ren, Lingrui Zhang, Xingcan Wang, Jian University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China

ISBN: (纸本)9798350369151

FPAG bitstream is a binary file containing chip configuration information. It is often compressed to conserve storage space and reduce configuration time. In this paper, we propose a precise decompression method for commercial FPGA bitstreams, which is a crucial step for assessing the security of FPGAs. In detail, by comparing the structure of compressed and uncompressed bitstream files, we identify the compression region, extract the compressed data, and construct a database with the mapping information. Then, we introduce a decoding algorithm that utilizes a sliding window approach in terms of the characteristics of the compression data. Finally, our decompression framework is validated on 24 different types of Intel FPGAs from 6 families, with a perfect accuracy rate of 100%. © 2024 IEEE.

关键词： Data compression

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Optimization Scheduling for Low-carbon Economy of Distributed Electric-Thermal-Gas Coupling Comprehensive Energy System 9

Optimization Scheduling for Low-carbon Economy of Distribute...

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9th Asia Conference on Power and Electrical engineering, ACPEE 2024

作者： Jing, Suchuan Chen, Zhenping Fan, Songli Zhi, Jiawen Zhu, Tong Suzhou University of Science and Technology School of Electronic and Information Engineering Jiangsu Suzhou215009 China

ISBN: (纸本)9798350309638

As energy systems continue to evolve and improve, the economic and environmental aspects of integrated energy systems have gained increasing attention. To enhance the overall utilization efficiency of social resources and achieve green low-carbon development in the energy industry, this paper proposes a low-carbon economic scheduling strategy for electric-thermal-gas coupling comprehensive energy systems. Firstly, an 'electricthermal-gas' energy conversion model is established, taking into account the influence of energy storage on the economic scheduling of the integrated energy system, as well as the transmission losses during energy transmission, to enhance energy efficiency. Secondly, in terms of environmental benefits, an improved tiered carbon trading mechanism is introduced for management. Then, by utilizing the leader-following consensus algorithm, the incremental cost of energy supply side is transformed into the incremental cost of electricity, heat, and energy storage of the system after energy hub, serving as the consistency variable, achieving distributed scheduling of the comprehensive energy system to achieve optimal output of energy supply units. Finally, the efficacy of the proposed low-carbon economy scheduling strategy is validated by different experimental cases. © 2024 IEEE.

关键词： Energy efficiency

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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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Semi-supervised Modulation Recognition Greatly Improved by Strong Data Augmentation

Semi-supervised Modulation Recognition Greatly Improved by S...

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International Conference on Communications, Signal Processing, and Systems, CSPS 2023

作者： Wang, Weidong Luo, Cheng Gan, Lu School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9789819975396

Modulation recognition plays an important role in modern wireless communications. Recent work has shown that modulation recognition based on deep learning significantly outperforms conventional approaches. However, this superiority relies largely on using plenty of labeled data for supervised learning, whereas training deep neural networks with limited data generally falls into overfitting, resulting in poor performance. In addition, it is also challenging to obtain plenty of labeled data in real-world communication activities, with expensive and time-consuming costs. To this end, we present a semi-supervised method for modulation recognition, which can take advantage of unlabeled samples that are more easily accessible in practice to enhance generalization and thus reduce such demand for labeled data. By introducing strong data augmentation, we improve supervised training and simultaneously perturb unlabeled data for consistency-based regularization, resulting in a remarkable generalization improvement. Experimental results on RadioML 2018.01A dataset demonstrate that our proposed method for semi-supervised modulation recognition is far superior to other competing ones and achieves almost fully supervised performance with a very small number of labeled samples. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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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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