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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Guanghui Wu Lujiao Liu Jianhao Li Hongxin Pan Hao Shi Beijing Institute of Technology Chongqing Innovation Center Beijing Institute of Technology Chongqing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Synthetic Aperture Radar (SAR) offers high-resolution imaging capabilities that are unaffected by weather and lighting conditions, making it ideal for complex environments. Recent advancements in spaceborne SAR technology have provided diverse high-resolution, multi-temporal SAR data, crucial for applications like airport monitoring. Despite these advancements, there is a notable lack of multi-temporal airport SAR datasets for research. This study addresses this gap by presenting a method to create a multi-temporal airport SAR image dataset. Utilizing known latitude and longitude information, the method converts geographic coordinates to image projection coordinates to locate airports in SAR images, followed by image conversion and alignment to construct the dataset. Additionally, this study introduces a registration method named SAR-SURF for multi-temporal SAR images, employing a SURF-based Feature Points Detector and an improved Fast Sample Consensus (FSC) algorithm. The experimental results demonstrate that this method effectively reduces speckle noise, ensures matching accuracy, and minimizes processing time, enabling rapid and precise registration of multi-temporal airport SAR images.

关键词： Accuracy Spaceborne radar Noise Detectors Speckle Airports Feature extraction Radar polarimetry Reliability Synthetic aperture radar

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Efficient Algorithms for Minimizing the Kirchhoff Index via Adding Edges

arXiv

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arXiv 2025年

作者： Zhou, Xiaotian Zehmakan, Ahad N. Zhang, Zhongzhi Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China School of Computing Australian National University Canberra Australia

The Kirchhoff index, which is the sum of the resistance distance between every pair of nodes in a network, is a key metric for gauging network performance, where lower values signify enhanced performance. In this paper, we study the problem of minimizing the Kirchhoff index by adding edges. We first provide a greedy algorithm for solving this problem and give an analysis of its quality based on the bounds of the submodularity ratio and the curvature. Then, we introduce a gradient-based greedy algorithm as a new paradigm to solve this problem. To accelerate the computation cost, we leverage geometric properties, convex hull approximation, and approximation of the projected coordinate of each point. To further improve this algorithm, we use pre-pruning and fast update techniques, making it particularly suitable for large networks. Our proposed algorithms have nearly-linear time complexity. We provide extensive experiments on ten real networks to evaluate the quality of our algorithms. The results demonstrate that our proposed algorithms outperform the state-of-the-art methods in terms of efficiency and effectiveness. Moreover, our algorithms are scalable to large graphs with over 5 million nodes and 12 million edges. Copyright © 2025, The Authors. All rights reserved.

关键词： Consensus algorithm

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An improved multi-attribute decision-making based network selection algorithm for heterogeneous vehicular network

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Frontiers of Computer Science 2022年第3期16卷 211-213页

作者： Lei NIE Bo LIU Peng LI Heng HE Libing WU School of Computer Science and Technology Wuhan University of Science and TechnologyWuhan 430065China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan 430065China School of Cyber Science and Engineering and School of Computer Science Wuhan UniversityWuhan 430072China Shenzhen Research Institute of Wuhan University Shenzhen 518057China

1 Introduction With sharply rising quantity of urban vehicles over the past few decades,traffic jams and safety have gradually become outstanding *** recent years,Vehicular Ad hoc Network(VANET)is appeared and utilized to solve traffic related *** is a type of self-organized and open-structured network,and provides Vehicle-to-Everything(V2X)*** all kinds of applications in VANET rely on efficient data transmission and interaction[1-3].

关键词： network sharply utilized

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Distributed intelligent Reflecting Surfaces-Aided Device-to-Device Communications System

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Journal of Communications and information Networks 2021年第3期6卷 197-207页

作者： Chiya Zhang Wenyu Chen Qian Chen Chunlong He School of Electronic and Information Engineering Harbin Institute of TechnologyShenzhen 518055China Peng Cheng Laboratory(PCL) Shenzhen 5180523China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen UniversityShenzhen 518060China National Mobile Communications Research Laboratory Southeast UniversityNanjing 211189China

intelligent reflecting surface(IRS)is a revolutionizing and promising technology to improve the high transmission rate of the wireless communication ***,an IRS consists of a great amount of low-cost passive reflecting elements,which reflect the incident signals to the desired user by collaboratively using passive *** paper introduces IRSs into a device-to-device(D2D)underlying cellular system to enhance transmission rate performance of the D2D *** formulate an optimization problem of maximizing the transmission rate of the D2D pairs while satisfying the minimum required rate of the cellular *** address this problem by jointly optimizing the reuse indicator,received beamforming,power allocation,and phase shift *** coordinate descent(BCD)algorithm is adopted to decouple the original problem into four *** form solutions are obtained by solving the sub-problems of optimizing the received beamforming and power ***,Kuhn-Munkres(KM)algorithm and minimization-majorization(MM)algorithm are adopted to solve the sub-problems of optimizing the reuse indicator and phase shift matrices,*** results demonstrate that IRSs can effectively improve the transmission rate of the D2D pairs and our proposed distributed IRSs scheme outperforms the other benchmark schemes.

关键词： intelligent reflecting surface device-to-device communications block coordinate descent

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Advancing Controllable Diffusion Model for Few-Shot Object Detection in Optical Remote Sensing Imagery

Advancing Controllable Diffusion Model for Few-Shot Object D...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Tong Zhang Yin Zhuang Xinyi Zhang Guanqun Wang He Chen Fukun Bi National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China Peking University Beijing China North China University of Technology Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Few-shot object detection (FSOD) from optical remote sensing imagery has to detect rare objects given only a few annotated bounding boxes. The limited training data is hard to represent the data distribution of realistic remote sensing scenes, restricting the performance of FSOD. Recently, learning conditional controls for text-to-image diffusion model has achieved great progress, which is capable of precisely generating the controllable yet imaginational images by text prompt and spatially localized input conditions. Accordingly, in this work, we aim to explore the potential of diffusion model and propose a solution for few-shot object detection by controllable data generation. Firstly, draw upon a few annotated objects, their bounding boxes and categories are respectively used as the spatial conditions and text prompts, then employ them into large text-to-image diffusion models for controlled image generation. Secondly, based the generated images, in order to adapt to the scale and orientation variances of remote sensing objects, a data transformation is devised for boosting the robustness of model training. Finally, some experiments were conducted on public remote sensing dataset DIOR, and the results proved its effectiveness.

关键词： Training Image synthesis Text to image Training data Object detection Diffusion models Optical imaging

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Viterbi Decoding of Directed Acyclic Transformer for Non-Autoregressive Machine Translation

arXiv

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arXiv 2022年

作者： Shao, Chenze Ma, Zhengrui Feng, Yang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Non-autoregressive models achieve significant decoding speedup in neural machine translation but lack the ability to capture sequential dependency. Directed Acyclic Transformer (DA-Transformer) was recently proposed to model sequential dependency with a directed acyclic graph. Consequently, it has to apply a sequential decision process at inference time, which harms the global translation accuracy. In this paper, we present a Viterbi decoding framework for DA-Transformer, which guarantees to find the joint optimal solution for the translation and decoding path under any length constraint. Experimental results demonstrate that our approach consistently improves the performance of DA-Transformer while maintaining a similar decoding speedup. Copyright © 2022, The Authors. All rights reserved.

关键词： Decoding

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Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer

arXiv

引用

arXiv 2022年

作者： Nan, Qiong Wang, Danding Zhu, Yongchun Sheng, Qiang Shi, Yuhui Cao, Juan Li, Jintao Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Both real and fake news in various domains, such as politics, health, and entertainment are spread via online social media every day, necessitating fake news detection for multiple domains. Among them, fake news in specific domains like politics and health has more serious potential negative impacts on the real world (e.g., the infodemic led by COVID-19 misinformation). Previous studies focus on multi-domain fake news detection, by equally mining and modeling the correlation between domains. However, these multi-domain methods suffer from a seesaw problem: the performance of some domains is often improved at the cost of hurting the performance of other domains, which could lead to an unsatisfying performance in specific domains. To address this issue, we propose a Domain- and Instance-level Transfer Framework for Fake News Detection (DITFEND), which could improve the performance of specific target domains. To transfer coarse-grained domain-level knowledge, we train a general model with data of all domains from the meta-learning perspective. To transfer fine-grained instance-level knowledge and adapt the general model to a target domain, we train a language model on the target domain to evaluate the transferability of each data instance in source domains and re-weigh each instance’s contribution. Offline experiments on two datasets demonstrate the effectiveness of DITFEND. Online experiments show that DITFEND brings additional improvements over the base models in a real-world scenario. Copyright © 2022, The Authors. All rights reserved.

关键词： Social networking (online)

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Streaming Media Load Balancing with Improved Genetic Algorithm

Streaming Media Load Balancing with Improved Genetic Algorit...

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Chinese Control and Decision Conference, CCDC

作者： Yang Zhang Xing Yang Yuan Xu Yanlin He Mingqing Zhang Qunxiong Zhu College of Information Science and Technology Beijing University of Chemical Technology Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Hangzhou China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The load balancing algorithm of streaming media server is facing unique challenges. Different from the traditional Web server, the streaming media server needs to consider the characteristics of real-time, bandwidth occupation and multimedia data transmission. Therefore, the traditional load balancing algorithm is not necessarily suitable for streaming media servers, and needs special design and research. Aiming at the network requests of streaming media server clusters, a multi-dimensional matrix evaluation method based on streaming media characteristics is proposed, the genetic algorithm is improved, and it is applied to the dynamic load balancing algorithm scheduling scenario, and analyzed and simulated. Experimental results show that the algorithm can effectively reduce the average execution time of all requests, speed up the average response time and improve throughput.

关键词： Heuristic algorithms Clustering algorithms Streaming media Throughput Load management Dynamic scheduling Web servers

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TMFIF:Transformer-based multi-focus image fusion 5

TMFIF:Transformer-based multi-focus image fusion

引用

5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Li, Rui Geng, Shengling Zhang, Dan Zhou, Mingquan Qinghai Normal University School of Computer Science Xining China Nanyang Institute of Technology School of Computer and Software Nanyang China People's Government of Qinghai Province & Beijing Normal University Academy of Plateau Science and Sustainability Xining China Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application China

ISBN: (纸本)9798350373820

Multi-focus image fusion is a hot topic in the field of image processing, and it is a fundamental problem in the fields of image editing, image synthesis, and target retrieval. In previous fusion methods, although feature-rich datasets, models, and algorithms have been provided, there are still many problems with the effective fusion of distant and near views in complex backgrounds. To solve the challenging multi-focus image fusion problem more accurately, we introduce the Transformer Network based on Encoder Decoder (TMFIF), which can extract more generalized features based on the features of multi-focused images to achieve image fusion in complex backgrounds for better visual effects. In this work, we achieve fusion by inputting two images, near and far view. We compare the performance with other multifocal image fusion algorithms by conducting experiments on publicly available datasets and illustrated using existing evaluation methods and evaluation metrics;the results of the experiments show that our method visualizes the fusion effect better through the encoder and the decoder and the evaluation metrics are also relatively good. © 2024 IEEE.

关键词： Image fusion

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Heterogeneous Graph Convolutional Network for Visual Reinforcement Learning of Action Detection 8

Heterogeneous Graph Convolutional Network for Visual Reinfor...

引用

8th International Conference on Automation, Control and Robotics Engineering, CACRE 2023

作者： Wang, Liangliang Huang, Chengxi Chen, Xinwei Wuhan University of Technology School of Computer Science and Artificial Intelligence Luoshi Road 122 Wuhan430070 China -HIT Research Institute of Engineering and Technology Building 9 Software Park Quanzhou362008 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9798350302776

Existing action detection approaches do not take spatio-temporal structural relationships of action clips into account, which leads to a low applicability in real-world scenarios and can benefit detecting if exploited. To this end, this paper proposes to formulate the action detection problem as a reinforcement learning process which is rewarded by observing both the clip sampling and classification results via adjusting the detection schemes. In particular, our framework consists of a heterogeneous graph convolutional network to represent the spatio-temporal features capturing the inherent relation, a policy network which determines the probabilities of a predefined action sampling spaces, and a classification network for action clip recognition. We accomplish the network joint learning by considering the temporal intersection over union and Euclidean distance between detected clips and ground-truth. Experiments on ActivityNet v1.3 and THUMOS14 demonstrate our method. © 2023 IEEE.

关键词： Reinforcement learning

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