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YOLO-Ant: A Lightweight Detector via Depthwise Separable Convolutional and Large Kernel Design for Antenna Interference Source Detection

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arXiv 2024年

作者： Tang, Xiaoyu Chen, Xingming Cheng, Jintao Wu, Jin Fan, Rui Zhang, Chengxi Zhou, Zebo The School of Electronic and Information Engineering Faculty of Engineering South China Normal University Guangdong Foshan528225 China The School of Physics South China Normal University Guangdong Guangzhou510000 China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The School of Internet of Things Engineering Jiangnan University Wuxi214122 China The School of Aeronautics Astronautics University of Electronic Science and Technology of China and Aircraft swarm intelligent sensing and cooperative control Key Laboratory of Sichuan Province Chengdu610097 China The National Laboratory on Adaptive Optics Chengdu610209 China

In the era of 5G communication, removing interference sources that affect communication is a resource-intensive task. The rapid development of computer vision has enabled unmanned aerial vehicles to perform various high-altitude detection tasks. Because the field of object detection for antenna interference sources has not been fully explored, this industry lacks dedicated learning samples and detection models for this specific task. In this article, an antenna dataset is created to address important antenna interference source detection issues and serves as the basis for subsequent research. We introduce YOLO-Ant, a lightweight CNN and transformer hybrid detector specifically designed for antenna interference source detection. Specifically, we initially formulated a lightweight design for the network depth and width, ensuring that subsequent investigations were conducted within a lightweight framework. Then, we propose a DSLK-Block module based on depthwise separable convolution and large convolution kernels to enhance the network’s feature extraction ability, effectively improving small object detection. To address challenges such as complex backgrounds and large interclass differences in antenna detection, we construct DSLKVit-Block, a powerful feature extraction module that combines DSLK-Block and transformer structures. Considering both its lightweight design and accuracy, our method not only achieves optimal performance on the antenna dataset but also yields competitive results on public datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Antennas

Adaptive aggregation of Monte Carlo augmented decomposed filters for efficient group-equivariant convolutional neural network

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arXiv 2023年

作者： Zhao, Wenzhao Wichtmann, Barbara D. Albert, Steffen Maurer, Angelika Zöllner, Frank G. Attenberger, Ulrike Hesser, Jürgen Interdisciplinary Center for Scientific Computing Mannheim Institute for Intelligent Systems in Medicine Medical Faculty Mannheim Heidelberg University Germany Department of Diagnostic and Interventional Radiology University Hospital Bonn Germany Computer Assisted Clinical Medicine Mannheim Institute for Intelligent Systems in Medicine Medical Faculty Mannheim Heidelberg University Germany Interdisciplinary Center for Scientific Computing Central Institute for Computer Engineering CSZ Heidelberg Center for Model-Based AI Data Analysis and Modeling in Medicine Mannheim Institute for Intelligent Systems in Medicine Medical Faculty Mannheim Heidelberg University Germany

Group-equivariant convolutional neural networks (G-CNN) heavily rely on parameter sharing to increase CNN’s data efficiency and performance. However, the parameter-sharing strategy greatly increases the computational burden for each added parameter, which hampers its application to deep neural network models. In this paper, we address these problems by proposing a non-parameter-sharing approach for group equivariant neural networks. The proposed methods adaptively aggregate a diverse range of filters by a weighted sum of stochastically augmented decomposed filters. We give theoretical proof about how the continuous group convolution can be approximated by our methods. Our method applies to both continuous and discrete groups, where the augmentation is implemented using Monte Carlo sampling and bootstrap resampling, respectively. We demonstrate that our methods serve as an efficient extension of standard CNN. Experiments on group equivariance tests show how our methods can achieve superior performance to parameter-sharing group equivariant networks. Experiments on image classification and image denoising tasks show that in certain scenarios, with a suitable set of filter bases, our method helps improve the performance of standard CNNs and build efficient lightweight image denoising networks. The code will be available at https://***/ZhaoWenzhao/MCG_CNN. Copyright © 2023, The Authors. All rights reserved.

关键词： Image denoising

Controlling Circularly Polarized Luminescence Using Helically Structured Chiral Silica as a Nanosized Fused Quartz Cell

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JACS Au 2023年第10期3卷 2698-2702页

作者： Sakai, Hinari Yung, Tsz-Ming Mure, Tomoki Kurono, Naoki Fujii, Syuji Nakamura, Yoshinobu Hayakawa, Teruaki Li, Ming-Chia Hirai, Tomoyasu Department of Applied Chemistry Faculty of Engineering and Graduate School of Engineering Osaka Institute of Technology 5-16-1 Omiya Asahi-ku Osaka 535-8585 Japan Department of Biological Science and Technology Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan Department of Materials Science and Engineering School of Materials and Chemical Technology Tokyo Institute of Technology 2-12-1-S8-36 Ookayama Tokyo Meguro-ku 152-8552 Japan

Circularly polarized luminescence (CPL) is typically achieved with a chiral luminophore. However, using a helical nanosized fused quartz cell consisting of chiral silica, we could control the wavelength and helical sense of the CPL of an achiral luminophore. Chiral silica with a helical nanostructure was prepared by calcining a mixture of polyhedral oligomeric silsesquioxane (POSS)-functionalized isotactic poly(methacrylate) (it-PMAPOSS) and a small amount of chiral dopant. The chiral silica encapsulated functional molecules, including luminophores, along the helical nanocavity, leading to induced circular dichroism (ICD) and induced circularly polarized luminescence (iCPL). Because chiral silica can act as a helical nanosized fused quartz cell, it can encapsulate not only the luminophore but also solvent molecules. By changing the solvent in the luminophore-containing nanosized fused quartz cell, the wavelength of the CPL was controlled. This method provides an effective strategy for designing novel CPL-active materials. © 2023 The Authors. Published by American Chemical Society.

关键词： circularly polarized luminescence helical structure polyhedral oligomeric silsesquioxane quartz cell silica stereoregularity

Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 402-411页

作者： Xin Luo Wen Qin Ani Dong Khaled Sedraoui MengChu Zhou the School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808 Hengrui(Chongqing)Artificial Intelligence Research Center Department of Big Data Analyses TechniquesCloudwalkChongqing 401331China the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China the Department of Computer and Information Science City College of Dongguan University of TechnologyDongguan 523419China the Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia the Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21481Saudi Arabia

A recommender system(RS)relying on latent factor analysis usually adopts stochastic gradient descent(SGD)as its learning ***,owing to its serial mechanism,an SGD algorithm suffers from low efficiency and scalability when handling large-scale industrial *** at addressing this issue,this study proposes a momentum-incorporated parallel stochastic gradient descent(MPSGD)algorithm,whose main idea is two-fold:a)implementing parallelization via a novel datasplitting strategy,and b)accelerating convergence rate by integrating momentum effects into its training *** it,an MPSGD-based latent factor(MLF)model is achieved,which is capable of performing efficient and high-quality *** results on four high-dimensional and sparse matrices generated by industrial RS indicate that owing to an MPSGD algorithm,an MLF model outperforms the existing state-of-the-art ones in both computational efficiency and scalability.

关键词： Big data industrial application industrial data latent factor analysis machine learning parallel algorithm recommender system(RS) stochastic gradient descent(SGD)

Multimodal Joint Prediction of Traffic Spatial-Temporal data with Graph Sparse Attention Mechanism and Bidirectional Temporal Convolutional Network

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arXiv 2024年

作者： Zhang, Dongran Yan, Jiangnan Polat, Kemal Alhudhaif, Adi Li, Jun Guangdong Provincial Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518107 China Department of Electrical and Electronics Engineering Faculty of Engineering Bolu Abant Izzet Baysal University Bolu Turkey Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia

Traffic flow prediction plays a crucial role in the management and operation of urban transportation systems. While extensive research has been conducted on predictions for individual transportation modes, there is relatively limited research on joint prediction across different transportation modes. Furthermore, existing multimodal traffic joint modeling methods often lack flexibility in spatial-temporal feature extraction. To address these issues, we propose a method called Graph Sparse Attention Mechanism with Bidirectional Temporal Convolutional Network (GSABT) for multimodal traffic spatial-temporal joint prediction. First, we use a multimodal graph multiplied by self-attention weights to capture spatial local features, and then employ the Top-U sparse attention mechanism to obtain spatial global features. Second, we utilize a bidirectional temporal convolutional network to enhance the temporal feature correlation between the output and input data, and extract inter-modal and intra-modal temporal features through the share-unique module. Finally, we have designed a multimodal joint prediction framework that can be flexibly extended to both spatial and temporal dimensions. Extensive experiments conducted on three real datasets indicate that the proposed model consistently achieves state-of-the-art predictive performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Urban transportation

AR/VR Digital Twin for Simulation and data Collection of Robotic Environments

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AR/VR Digital Twin for Simulation and Data Collection of Rob...

IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： João G. Martins Karle Nutonen Paulo Costa Vladimir Kuts Tauno Otto Armando Sousa Marcelo R. Petry CRIIS - Centre for Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal ViruTech - Virumaa innovation center of digitalization and green technologies Taltech Virumaa College Kohtla-Jarve Estonia Department of Mechanical and Indusrial Taltech Tallinn Estonia

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Digital twins enable real-time modeling, simulation, and monitoring of complex systems, driving advancements in automation, robotics, and industrial applications. This study presents a large-scale digital twin-testing facility for evaluating mobile robots and pilot robotic systems in a research laboratory environment. The platform integrates high-fidelity physical and environmental models, providing a controlled yet dynamic setting for analyzing robotic behavior. A key feature of the system is its comprehensive data collection framework, capturing critical parameters such as position, orientation, and velocity, which can be leveraged for machine learning, performance optimization, and decision-making. The facility also supports the simulation of discrete operational systems, using predictive modeling to bridge informational gaps when real-time data updates are unavailable. The digital twin was validated through a matrix manufacturing system simulation, with an Augmented Reality (AR) interface on the HoloLens 2 to overlay digital information onto mobile platform controllers, enhancing situational awareness. The main contributions include a digital twin framework for deploying data-driven robotic systems and three key AR/VR integration optimization methods. Demonstrated in a laboratory setting, the system is a versatile tool for research and industrial applications, fostering insights into robotic automation and digital twin scalability while reducing costs and risks associated with real-world testing.

关键词： Service robots Navigation Metaverse databases Scalability Digital representation Real-time systems Digital twins Mobile robots Robots

Geometry-aided Underwater 3D Mapping Using Side-scan Sonar

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Geometry-aided Underwater 3D Mapping Using Side-scan Sonar

IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Yiqiao Yang Chenglin Pang Chengdong Wu Zheng Fang Faculty of Robot Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In recent years, the interest in underwater exploration with Autonomous Underwater Vehicles (AUVs) equipped with side-scan sonars (SSS) has grown considerably. However, state-of-the-art SSS Simultaneous Localization and Mapping (SLAM) systems encounter challenges in data association across large viewpoint changes. Additionally, these systems assume that the seabed is a flat surface, leading to significant mapping error in uneven underwater terrains. To address these challenges, we propose a framework that leverages the side-scan sonar geometry to facilitate data association and improve mapping accuracy. The framework begins with a preprocessing module that extracts feature points and provides initial estimates of the elevation angles of the landmarks. Then, a non-consecutive data association module applies epipolar line search to establish correspondences between the current and historical frames. Finally, the mapping module uses side-scan sonar bundle adjustment to recover the positions of the landmarks. The proposed method is evaluated using an underwater terraced fields dataset. Our method achieves over 90% matching rate and reduces the average mapping error from 3.799 to 0.134.

关键词： Geometry Bundle adjustment Autonomous underwater vehicles Simultaneous localization and mapping Accuracy Geometric modeling Feature extraction Side-scan sonar Optimization intelligent robots

EverySync: An Open Hardware Time Synchronization Sensor Suite for Common Sensors in SLAM

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EverySync: An Open Hardware Time Synchronization Sensor Suit...

IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Xuankang Wu Haoxiang Sun Rongguang Wu Zheng Fang Faculty of Robot Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Multi-sensor fusion systems have been widely applied in various fields, including mobile robot, simultaneous localization and mapping (SLAM), and autonomous driving. For a tightly coupled multi-sensor fusion system, strict time synchronization between sensors will improve the accuracy of the system. However, there is currently a lack of open-source and general-purpose hardware synchronization systems for Cameras, IMUs, LiDARs, GNSS/RTK in the academic community. Therefore, we propose EverySync, an open hardware time synchronization system to address this gap. The synchronization accuracy of the system was evaluated through multiple experiments, achieving an accuracy of less than 1 ms. And, real-world experiments proved that hardware time synchronization improves the accuracy of the SLAM system. This open-source system is available on GitHub.

关键词： Simultaneous localization and mapping Accuracy Laser radar Robot vision systems Sensor fusion Cameras Hardware Sensor systems Synchronization Software development management

Tackling Noisy Labels with Network Parameter Additive Decomposition

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arXiv 2024年

作者： Wang, Jingyi Xia, Xiaobo Lan, Long Wu, Xinghao Yu, Jun Yang, Wenjing Han, Bo Liu, Tongliang The Department of Intelligent Data Science College of Computer Science and Technology National University of Defense Technology Changsha410073 China The Trustworthy Machine Learning Lab School of Computer Science Faculty of Engineering University of Sydney DarlingtonNSW2008 Australia The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China The Department of Automation University of Science and Technology of China Hefei230026 China The Department of Computer Science Hong Kong Baptist University Hong Kong

Given data with noisy labels, over-parameterized deep networks suffer overfitting mislabeled data, resulting in poor generalization. The memorization effect of deep networks shows that although the networks have the ability to memorize all noisy data, they would first memorize clean training data, and then gradually memorize mislabeled training data. A simple and effective method that exploits the memorization effect to combat noisy labels is early stopping. However, early stopping cannot distinguish the memorization of clean data and mislabeled data, resulting in the network still inevitably overfitting mislabeled data in the early training stage. In this paper, to decouple the memorization of clean data and mislabeled data, and further reduce the side effect of mislabeled data, we perform additive decomposition on network parameters. Namely, all parameters are additively decomposed into two groups, i.e. , parameters w are decomposed as w = σ + γ. Afterward, the parameters σ are considered to memorize clean data, while the parameters γ are considered to memorize mislabeled data. Benefiting from the memorization effect, the updates of the parameters σ are encouraged to fully memorize clean data in early training, and then discouraged with the increase of training epochs to reduce interference of mislabeled data. The updates of the parameters γ are the opposite. In testing, only the parameters σ are employed to enhance generalization. Extensive experiments on both simulated and real-world benchmarks confirm the superior performance of our method. The implementation is available at: https://***/wangjingyi9924/TNLPAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking