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21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Zhang, Hefan Wang, Zhiyuan Zhang, Shan Meng, Qingkai Luo, Hongbin School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Institute of Artificial Intelligence Beihang University Beijing China State Key Laboratory of Software Development Environment China State Key Laboratory of Virtual Reality Technology and Systems China

ISBN: (纸本)9783903176553

Low earth orbit (LEO) satellite networks have the potential to provide low-latency communication with global coverage. To unleash this potential, it is crucial to achieve efficient data delivery. In this paper, we analyze the topology characteristics of LEO satellite networks, and propose a source-route-style forwarding framework. Specifically, we leverage the deterministic neighbor relationship and identify all the unidirectional inter-satellite links (ISLs). Moreover, our framework utilizes the in-packet bloom filter (BF) to store the source-route-style forwarding information. This way, the source satellite could encode multiple ISL identifiers into the BF, which actually specifies the forwarding path. The intermediate satellites only need to check whether the outgoing ISLs are encoded and forward packets accordingly. Due to false positives caused by BF, the more ISLs are encoded at a time, the more redundant forwardings emerge. To reduce forwarding overhead, we take into account segment encoding, allowing the source and intermediate satellites to encode part of ISLs towards the destination. Overall, segment encoding seeks the right balance between forwarding overhead and encoding delay. We characterize a wide range of segment encoding policy in a unified framework, and derive the expected forwarding overhead in a closed-form. The segment encoding design is formulated as a binary non-linear programming, which is NP-hard. To overcome the challenge, we leverage its decomposable structure, and propose an efficient algorithm to solve it optimally. Finally, we validate our analytical results via packet-level experiments. Results also show that our proposed segment encoding policy significantly reduces the queuing delay compared to source encoding. © 2023 IFIP.

关键词： Satellites

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A Spatial-Spectral Decoupling Fusion Framework for Visible and Near-Infrared Images 5

A Spatial-Spectral Decoupling Fusion Framework for Visible a...

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5th ACM International Conference on Multimedia in Asia, MMAsia 2023

作者： Tang, Zhenglin Hu, Hai-Miao Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute of Beihang University Hangzhou China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China

ISBN: (纸本)9798400702051

The visible and near-infrared image fusion aims to generate an image that integrates complementary information from images captured in different spectral bands. However, existing fusion methods either only focus on the fusion of spatial information, or fuse spatial and spectral information without decoupling, resulting in undesirable effects such as halo artifacts, information loss, and inferior visual quality. To address these issues, we propose a Spatial-Spectral Decoupling Fusion (SSDF) framework that can effectively fuse the spatial and spectral information of visible and near-infrared images. The SSDF framework decomposes the image pairs into two main branches: the Spatial Feature Enhancement (SFE) branch and the Spectral Characteristic Preservation (SCP) branch. The SFE branch enhances the salient details in the fused image by exploiting the contrast between spatial features and generating region-based fusion weights, while the SCP branch preserves the intrinsic spectral characteristics of the scene by fusing the reflectance characteristics of visible and near-infrared images. The final image is obtained by combining the spatial and spectral information. We conduct extensive experiments to show that our SSDF method can achieve superior fusion performance in subjective visual quality and objective metrics compared with state-of-the-art methods. © 2023 Copyright held by the owner/author(s).

关键词： Image fusion

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Robust Adaptive Filter for Time-Varying Parameters Estimation in Integrated Navigation of Fixed-Wing Aerial Robot

Robust Adaptive Filter for Time-Varying Parameters Estimatio...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Zhang, Zhaoyu Duan, Haibin Beihang University School of Automation Science and Electrical Engineering Beijing100083 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Automation Science and Electrical Engineering Beijing100083 China Peng Cheng Laboratory Virtual Reality Fundamental Research Laboratory Department of Mathematics and Theories Shenzhen518000 China

ISBN: (纸本)9798350325706

This paper focuses on the autonomous navigation problem of the fixed-wing aerial robot, of which the inertial measurement unit (IMU) has time-varying error characteristic parameters. A robust variational Bayesian adaptive Kalman filter (RVBAKF) is proposed to tackle the noise outliers in integrated navigation brought by sensor uncertainties. The proposed method formulates the joint probability distribution of the system state vector and the measurement noise covariance matrix. A fading factor matrix with strong tracking quality is introduced to enhance the prediction on the prior distribution of the process noise covariance. Then a weighted sliding window mechanism has been constructed to obtain the posterior distribution of the measurement noise outliers. Therefore, the proposed approach is impressive in approximating both the process and measurement noise. The RVBAKF algorithm is implemented in an integrated navigation system which is composed of the strapdown inertial navigation system (SINS) and the global navigation satellite system (GNSS). The integration framework based on RVBAKF is conducted on two typical verification scenarios, which is proven to be exceptional in coping with the time-varying process and measurement noise by comparing the average root mean square error in misalignment angle, velocity and position with the strong tracking filter and the variational Bayesian filter. © 2023 IEEE.

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From Global to Local: An Adaptive Environmental Illumination Estimation for Non-uniform Scattering 5

From Global to Local: An Adaptive Environmental Illumination...

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5th ACM International Conference on Multimedia in Asia, MMAsia 2023

作者： Liu, Huaizhuo Hu, Hai-Miao Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute of Beihang University Hangzhou China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China

ISBN: (纸本)9798400702051

The atmospheric scattering model is one of the most widely used model to describe the optical imaging processing of hazy images. However, the global atmospheric light used in the traditional atmospheric scattering model has limitations in describing images with varying local environmental illumination. In this paper, by extending the global atmospheric light to the local illumination, a non-uniform scattering model is proposed, which can better describe real scenes under non-uniform environmental illumination. Based on this model, an adaptive local illumination estimation for hazy image is proposed, which can adapt to the local differences of environment illumination. The experimental results demonstrate that the proposed algorithm can outperform the state-of-the-art algorithms in terms of not only the non-uniform scattering removal but also the adaptability. © 2023 Copyright held by the owner/author(s).

关键词： atmospheric scattering model global atmospheric light local environment illumination non-uniform scattering removal

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Control Design Based on EKF via Pigeon-Inspired Optimization with Pattern Search for UAV Close Formation Flight

Control Design Based on EKF via Pigeon-Inspired Optimization...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Yuan, Guangsong Duan, Haibin Luo, Delin State Key Laboratory of Virtual Reality Technology and Systems School of Automation Science and Electrical Engineering Beijing100083 China Xiamen University School of Aerospace Engineering Xiamen361102 China

ISBN: (纸本)9798350325706

An unmanned aerial vehicle (UAV) close formation controller based on extended kalman filter (EKF) has been designed in this paper. The Newton-Raphson iterative method is designed for extremum location estimation, namely extremum seeking control (ESC). To facilitate extremum position estimation, the performance function is approximated using first and second-order Taylor expansions. The EKF is still utilized to estimate the gradient and Hessian matrix of the parameterized performance function. Compared with traditional estimation strategy, this allows a faster convergence and smooth command reference for close formation controller. The pigeon-inspired optimization (PIO) with pattern search (PS) is proposed to optimize these free parameters of the EKF. The simulation results show that the proposed PIO with pattern search has strong optimization performance. The EKF optimized by the algorithm enables the controller to converge well to the optimal relative position. © 2023 IEEE.

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SSCCPC-Net: Simultaneously Learning 2D and 3D Features with CLIP for Semantic Scene Completion on Point Cloud 41st

SSCCPC-Net: Simultaneously Learning 2D and 3D Features with...

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41st Computer Graphics International Conference, CGI 2024

作者： Duan, Wantong Bao, Yongtang Qi, Yue State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Shandong University of Science and Technology Qingdao266590 China Jingdezhen Research Institute of Beihang University Jingdezhen333000 China Qingdao Research Institute of Beihang University Qingdao266000 China

ISBN: (纸本)9783031820236

Compare to traditional Scene Completion (SC), Semantic Scene Completion (SSC) is a challenging task that aims to generate complete and semantically consistent 3D scene from partial and sparse input data, which is fundamental to fully understanding the scene and being able to interact with it. Consequently, the SSC task has received much attention in recent years. Most of the methods are voxel-based approaches, but they have high computational and memory requirements. A few works based on point cloud do not sufficiently exploit the correlation between semantic segmentation and geometric completion subtasks, while focusing too much on point cloud shape features and ignoring the rich texture information that RGB images can provide. In this paper, we present SSCCPC-Net (Semantic Scene Completion with CLIP on Point Cloud-Net), a novel network architecture for point cloud semantic scene completion using a combination of 2D and 3D features. Inspired by recent works of large pretrained vision-language models in semantic segmentation, we explore to accomplish SSC task with the help of Contrastive Language-Image Pre-Training (CLIP) model. Specifically, we use the CLIP features for guidance to fuse the 2D features extracted from the RGB image and the 3D features extracted from the point cloud. The fused features are then fed into our designed Semantic-Completion Decoder for per-point semantic prediction and semantic labeling-assisted point cloud completion. Finally, we obtain the complete semantically point cloud. Numerous experiments have demonstrated that our method has higher effectiveness and generalizability compared to state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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DSD-DA: Distillation-based source debiasing for domain adaptive object detection 24

DSD-DA: Distillation-based source debiasing for domain adapt...

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Proceedings of the 41st International Conference on Machine Learning

作者： Yongchao Feng Shiwei Li Yingjie Gao Ziyue Huang Yanan Zhang Qingjie Liu Yunhong Wang State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China and Zhongguancun Laboratory Beijing China and Hangzhou Innovation Institute Beihang University Hangzhou China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China and Hangzhou Innovation Institute Beihang University Hangzhou China

Though feature-alignment based Domain Adaptive Object Detection (DAOD) methods have achieved remarkable progress, they ignore the source bias issue, i.e., the detector tends to acquire more source-specific knowledge, impeding its generalization capabilities in the target domain. Furthermore, these methods face a more formidable challenge in achieving consistent classification and localization in the target domain compared to the source domain. To overcome these challenges, we propose a novel Distillation-based Source Debiasing (DSD) framework for DAOD, which can distill domain-agnostic knowledge from a pre-trained teacher model, improving the detector's performance on both domains. In addition, we design a Target-Relevant Object Localization Network (TROLN), which can mine target-related localization information from source and target-style mixed data. Accordingly, we present a Domain-aware Consistency Enhancing (DCE) strategy, in which these information are formulated into a new localization representation to further refine classification scores in the testing stage, achieving a harmonization between classification and localization. Extensive experiments have been conducted to manifest the effectiveness of this method, which consistently improves the strong baseline by large margins, outperforming existing alignment-based works.

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From Human Oral Instructions to General Representations of Knowledge: A New Paradigm for Industrial Robots Skill Teaching 6th

From Human Oral Instructions to General Representations of K...

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6th International Conference on Cognitive systems and Signal Processing, ICCSIP 2021

作者： Chen, Shiyu Zhao, Yongjia Lei, Xiaoyong Qi, Tao Liu, Kan State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Jiangxi Research Institute Beihang University Jiangxi Beijing China

ISBN: (纸本)9789811692468

Converting human oral instructions into general representations of knowledge which robots can understand, can realize more advanced behaviors for unmanned driving, drones, robots and other fields. This paper presents a novel paradigm based on two-stage structure that transforms human oral instructions into general representations of operating knowledge. Firstly, a Speech-to-Text module is used to realize automatic speech recognition, which results in a text expression for the input oral instruction. In the second stage, a Text-to-Knowledge module is used to realize natural language understanding, which introduces and visualizes task-related knowledge graphs converted from the text expression. To validate this paradigm, the task of computer motherboard assembly was chosen as an example, and a low-cost monophonic speech corpus named PC-CORPUS was built. This PC-CORPUS, is 3 h 44 min long and has 2278 wave audios. 14 speakers from different accent areas in China were invited in the recording. Then experiments were designed and carried out on this paradigm with PC-CORPUS. The average edit distance of this paradigm is 10.87%, comparing the triples of final visual knowledge representations with the ones were labeled by the experts. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Knowledge graph

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When Does Sora Show:The Beginning of TAO to Imaginative Intelligence and Scenarios Engineering

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 809-815页

作者： Fei-Yue Wang Qinghai Miao Lingxi Li Qinghua Ni Xuan Li Juanjuan Li Lili Fan Yonglin Tian Qing-Long Han School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Department of Electrical and Computer Engineering Indiana University-Purdue University IndianapolisIndianapolisIN 46202USA Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.

关键词： something Intelligence replace

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MvEchoSeg: One-shot In-context Learning for Multi-view Echocardiography Segmentation

MvEchoSeg: One-shot In-context Learning for Multi-view Echoc...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Guo, Yuting Duan, Ya Song, Wenfeng Li, Nannan Li, Aili Li, Shuai Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China University of Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Beijing Information Science and Technology University School of Computer Science Beijing100192 China Dalian Maritime University School of Information Science and Technology Dalian116000 China China-Japan Friendship Hospital Beijing100029 China

ISBN: (纸本)9798350386226

Echocardiography is the clinical standard for evaluation of cardiac morphology, and function, and providing hemodynamic parameters in patients with known or suspected heart disease. Due to the diversity and complexity of diagnostic tasks, comprehensive interpretation of echocardiography often requires multi-view imaging for integrated metric analysis. Deep learning methods have become the mainstream approach for echocardiography segmentation. However, achieving segmentation of multi-view echocardiography still requires a substantial amount of annotation. To remedy this, we propose a one-shot in-context learning network mvEchoSeg for multi-view echocardiography segmentation. This network requires only one annotated image for each view. Specifically, we propose a Task Prompt Identifier (TPI) module to identify the task type of the image and allocate the most precise task prompt for it, with minimal adaptation of CLIP and few-shot strategies. Additionally, we leverage a unified In-Context Model (ICM) capable of performing a diverse set of echocardiography segmentation tasks automatically. Using fine-tuning and low-rank adapters improved the performance of the pre-trained model, achieving significant results with minimal training cost. Furthermore, we collect a multi-view echocardiography dataset (MVECD) with 8 views to evaluate our method. The results show an improvement of more than 10% in the DICE score compared to the SOTA foundational medical image segmentation models. To our knowledge, this is the first exploration of a one-shot model for multi-view echocardiography segmentation. Our codes and models are available at https://***/stellating/mvEchoSeg. © 2024 IEEE.

关键词： Image segmentation

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