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arXiv 2024年

作者： Nigatu, Hassen Jihao, Li Shi, Gaokun Lu, Guodong Dong, Huixu Robot Perception and Grasp Lab School of Mechanical Engineering Zhejiang University Hangzhou310027 China Yuyao Technology Innovation Center No.479 Ye Shan Road Yuyao Zhejiang Province Ningbo Shi China The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering The Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China

This study presents a systematic enumeration of spherical (SO(3)) type parallel robots’ variants using an analytical velocity-level approach. These robots are known for their ability to perform arbitrary rotations around a fixed point, making them suitable for numerous applications. Despite their architectural diversity, existing research has predominantly approached them on a case-by-case basis. This approach hinders the exploration of all possible variants, thereby limiting the benefits derived from architectural diversity. By employing a generalized analytical approach through the reciprocal screw method, we systematically explore all the kinematic conditions for limbs yielding SO(3) ***, all 73 possible types of non-redundant limbs suitable for generating the target SO(3) motion are identified. The approach involves performing an in-depth algebraic motion-constraint analysis and identifying common characteristics among different variants. This leads us to systematically explore all 73 symmetric and 5256 asymmetric variants, which in turn become a total of 5329, each potentially having different workspace capability, stiffness performance, and dynamics. Hence, having all these variants can facilitate the innovation of novel spherical robots and help us easily find the best and optimal ones for our specific applications. © 2024, CC BY.

关键词： robots

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Multilevel Perception Boundary-guided Network for Breast Lesion Segmentation in Ultrasound Images

arXiv

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arXiv 2023年

作者： Yang, Xing Zhang, Jian Chen, Qijian Wang, Li Wang, Lihui Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China

Automatic segmentation of breast tumors from the ultrasound images is essential for the subsequent clinical diagnosis and treatment plan. Although the existing deep learning-based methods have achieved significant progress in automatic segmentation of breast tumor, their performance on tumors with similar intensity to the normal tissues is still not pleasant, especially for the tumor boundaries. To address this issue, we propose a PBNet composed by a multilevel global perception module (MGPM) and a boundary guided module (BGM) to segment breast tumors from ultrasound images. Specifically, in MGPM, the long-range spatial dependence between the voxels in a single level feature maps are modeled, and then the multilevel semantic information is fused to promote the recognition ability of the model for non-enhanced tumors. In BGM, the tumor boundaries are extracted from the high-level semantic maps using the dilation and erosion effects of max pooling, such boundaries are then used to guide the fusion of low and high-level features. Moreover, to improve the segmentation performance for tumor boundaries, a multi-level boundary-enhanced segmentation (BS) loss is proposed. The extensive comparison experiments on both publicly available dataset and in-house dataset demonstrate that the proposed PBNet outperforms the state-of-the-art methods in terms of both qualitative visualization results and quantitative evaluation metrics, with the Dice score, Jaccard coefficient, Specificity and HD95 improved by 0.70%, 1.1%, 0.1% and 2.5% respectively. In addition, the ablation experiments validate that the proposed MGPM is indeed beneficial for distinguishing the non-enhanced tumors and the BGM as well as the BS loss are also helpful for refining the segmentation contours of the tumor. Copyright © 2023, The Authors. All rights reserved.

关键词： Tumors

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Semantic-guided compositional scene representation framework

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Neural Networks 2025年 190卷 107598页

作者： Liu, Qiang Dong, Qiulei Zhang, Yangyong Lu, Xiao Zhang, Zhiguo Chen, Yuqin Shu, Huanzhou Wang, Haixia Key Laboratory for Robot and Intelligent Technology of Shandong Province Shandong University of Science and Technology Qingdao266590 China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao266590 China College of Energy Storage Technology Shandong University of Science and Technology Qingdao266590 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Beijing100190 China

Neural scene representation methods play a key role in computer vision and graphics, but it is difficult for them to generalize to new invisible scenes. Addressing this problem, we propose a semantic-guided compositional scene representation framework in this paper, consisting of a baseline module for scene representation, a semantic mapping module, and a compositional representation strategy. In the proposed framework, the semantic mapping module learns an embedding correlation by training on visible scenes, where the embedding correlation maps explicit semantic attributes to implicit scene representations. By utilizing the embedding correlation, the framework can represent invisible scenes using only semantic attributes. Besides, the compositional representation strategy is designed to fuse the decomposed 1-object scene representations into multi-object scene representation, yielding a higher training efficiency for multi-object scenes. Extensive experimental results on three datasets demonstrate that the proposed framework can achieve high-accuracy representations for visible and invisible multi-object scenes. © 2025 Elsevier Ltd

关键词： Mapping

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Multi-scale Adaptive Semantic Segmentation Network for Remote Sensing Images based on Attention Gating Mechanism

Multi-scale Adaptive Semantic Segmentation Network for Remot...

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Advanced Electronic Materials, Computers and Software engineering (AEMCSE), International Conference on

作者： Yiguo Lu Yurong Qian Lu Bai Peng Liu Yinyin Fan Weijun Gong Key Laboratory of Software Engineering Xinjiang University Urumqi Xinjiang China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Xinjiang University Urumqi Xinjiang China School of Computer Science and Technology Xinjiang University Urumqi Xinjiang China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Xinjiang University Urumqi Xinjiang China School of Information Technology and Communication Hexi University Zhangye Gansu China

ISBN: (数字)9798331510916

ISBN: (纸本)9798331510923

In this paper, a multi-scale adaptive semantic segmentation model for remote sensing images—AG-MSASNet—is proposed to address the imprecise utilization of feature information in multi-scale feature extraction. The model introduces a novel attention-gated multi-scale feature fusion mechanism, which selectively integrates multi-scale features to enhance useful information while suppressing noise, thereby improving segmentation accuracy. To further enhance semantic understanding in complex remote sensing scenes, a Global Context Block (GC Block) is designed to capture long-range dependencies and aggregate global context. Additionally, a joint loss optimization strategy is proposed to balance learning across different scales and classes, contributing to more precise and robust segmentation. Experimental results on the LoveDA dataset demonstrate that the proposed model outperforms the baseline, achieving a $\mathbf{2 . 2 \%}$ improvement in mean intersection over union (mIoU) and a $4.1 \%$ increase in overall accuracy (OA).

关键词： Adaptation models Accuracy Attention mechanisms Adaptive systems Sensitivity Semantic segmentation Semantics Feature extraction Remote sensing Software engineering

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Enhancing robot Teleoperation in Remote Automation Production through an Event-Triggered Control Strategy

Enhancing Robot Teleoperation in Remote Automation Productio...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Mengke Wang Honghao Lv Ruohan Wang Haiteng Wu Lipeng Chen Yi Chen Haihui Yuan Geng Yang State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China Zhejiang Key Laboratory of Intelligent Operation and Maintenance Robot Hangzhou Shenhao Technology Hangzhou China Robotics X Tencent Shenzhen China Intelligent Manufacturing Center DongFang Electric Corporation Academy of Science and Technology Co Chengdu China China South Industries Group Corporation Hangzhou Automation Research Insititute Co. Ltd. China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Zhejiang Key Laboratory of Intelligent Operation and Maintenance Robot Hangzhou China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

With the advancement of intelligent manufacturing, an increasing number of industrial production processes are transforming unmanned operations. This trend is particularly evident in factories that involve potential safety hazards, where the demand for unmanned production is becoming increasingly urgent. Teleoperation, as a core technology for achieving unmanned factories, holds tremendous importance in keeping workers safe from danger. However, traditional teleoperation strategies encounter challenges due to mismatches between master and slave workspaces, making it difficult to achieve both high precision and operation across large workspaces simultaneously. To address this issue, this paper proposes an event-triggered control strategy for a teleoperated robot system. By introducing a foot pedal as the event-triggered switch, the enablement and termination of teleoperation are associated with the activation of the pedal. Through resetting the start position, fine-grained teleoperation is achieved throughout the entire workspace of the robot. To demonstrate the advantages of the proposed control strategy, a mimicked wiring operation task scenario is constructed for experimental verification, and the teleoperation tasks are completed successfully and efficiently.

关键词： Wiring Accuracy Service robots Switches Market research Production facilities Main-secondary

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Deep Multi-kernel Clustering Network

Deep Multi-kernel Clustering Network

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IEEE International Conference on Data Mining (ICDM)

作者： Lina Ren Ruizhang Huang Shengwei Ma Yongbin Qin Yanping Chen Chuan Lin State Key Laboratory of Public Big Data Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University Guiyang China Department of Information Engineering Guizhou Light Industry Technical College Guiyang China

In this paper, a deep multi-kernel clustering network, named DMKCN, is proposed to learn a high-quality and structurally separable kernel representation for the clustering task. Specifically, a multi-kernel learner is proposed to choose a suitable kernel function by learning a suitable combination of kernel functions automatically. A kernel-aid encoder module, consisting of a series of multi-kernel learners, is proposed to learn the structurally separable kernel representation. Besides, a dual self-supervised mechanism, consisting of a kernel self-supervised strategy and a representation self-supervised strategy, is designed to uniformly optimize the kernel representation learning and structural partition. The kernel self-supervised strategy is developed to supervise the multi-kernel learners with the consideration of an objective of clustering task, the representation self-supervised strategy is developed to guide the optimization of kernel representation learning by reconstructing the raw data. Extensive experiments on six real-world datasets demonstrate the outstanding performance of our proposed DMKCN.

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PanoVpr: Towards Unified Perspective-to-Equirectangular Visual Place Recognition via Sliding Windows Across the Panoramic View

PanoVpr: Towards Unified Perspective-to-Equirectangular Visu...

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International Conference on Intelligent Transportation

作者： Ze Shi Hao Shi Kailun Yang Zhe Yin Yining Lin Kaiwei Wang State Key Laboratory of Modern Optical Instrumentation Zhejiang University Hangzhou China Shanghai SUPREMIND Technology Co. Ltd Shanghai China National Engineering Research Center of Robot Visual Perception and Control Technology School of Robotics Hunan University Changsha China

Visual place recognition has gained significant attention in recent years as a crucial technology in autonomous driving and robotics. Currently, the two main approaches are the perspective view retrieval (P2P) paradigm and the equirectangular image retrieval (E2E) paradigm. However, it is practical and natural to assume that users only have consumer-grade pinhole cameras to obtain query perspective images and retrieve them in panoramic database images from map providers. To address this, we propose PanoVPR, a perspective-to-equirectangular (P2E) visual place recognition framework that employs sliding windows to eliminate feature truncation caused by hard cropping. Specifically, PanoVPR slides windows over the entire equirectangular image and computes feature descriptors for each window, which are then compared to determine place similarity. Notably, our unified framework enables direct transfer of the backbone from P2P methods without any modification, supporting not only CNNs but also Transformers. To facilitate training and evaluation, we derive the Pitts250k-P2E dataset from the Pitts250k and establish YQ360, latter is the first P2E visual place recognition dataset collected by a mobile robot platform aiming to simulate real-world task scenarios better. Extensive experiments demonstrate that PanoVPR achieves state-of-the-art performance and obtains 3.8% and 8.0% performance gain on Pitts250k-P2E and YQ360 compared to the previous best method, respectively. Code and datasets will be publicly available at PanoVPR.

关键词：

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SAM+nnUNet: Deep-learning-based Head and Neck Tumor Segmentation on FDG PET

SAM+nnUNet: Deep-learning-based Head and Neck Tumor Segmenta...

引用

IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Z. Fang Z. Lu H. Liu Y. Liu G. S.P. Mok Department of Electrical and Computer Engineering Faculty of Science and Technology Biomedical Imaging Laboratory (BIG) University of Macau Macau China Department of Nuclear Medicine The Affiliated Hospital Southwest Medical University Luzhou China Center for Cognitive and Brain Sciences Institute of Collaborative Innovation University of Macau Macau China

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

FDG PET is a useful tool for diagnosis, staging and treatment monitoring on Head and Neck (H&N) tumors. Segmentation is usually performed for the tumors while the deep learning technique is emerging as an efficient yet precise alternate to manual segmentation. The performance of the conventional CNN-based segmentation networks is limited by insufficient training data. The Segment Anything Model (SAM) has been released by Meta, initiating a paradigm shift to a “smart” interactive segmentation that provides competitive performance when properly prompted. In this study, we proposed a SAM+nnUNet for H&N tumor segmentation on FDG PET to enhance the original SAM, in which the prompting process can be time-consuming and require a high degree of expertise. The HECKTOR2022 PET/CT dataset was used, with 420 patient images for training, 50 cases for validation, and 54 cases for testing. The training dataset of PET was used to fine-tune the mask decoder of pre-trained SAM, while both PET and CT were used to train an nnUNet. The trained nnUNet was first used to perform a preliminary segmentation of the tumors on the testing dataset, which was used to generate bounding box prompts for the SAM input after visual inspection and manual adjustment if necessary. Thresholding segmentation based on 42% of maximum intensity and various U-Net variants are implemented for comparison. Dice Similarity Coefficient (DSC), Hausdorff Distance at 95th percentile (HD95), and SUVmean of segmented tumors compared to the ground truth were analyzed. Our results indicate that 5 out of 54 cases need manual adjustment for the nnUNet-based prompts for SAM. SAM+nnUNet performs best in all quantitative indices. We conclude that our proposed method is promising for H&N tumor segmentation on PET.

关键词： Training Image segmentation Visualization Head Training data Manuals Artificial neural networks

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Convenient connectorization technique between single mode polymer optical fiber and single mode silica optical fiber 8

Convenient connectorization technique between single mode po...

引用

8th Symposium on Novel Photoelectronic Detection technology and Applications

作者： Yue, Xiaoyu Qu, Hang Min, Rui Woyessa, Getinet Bang, Ole Hu, Xuehao Research Center for Advanced Optics and Photoelectronics Department of Physics College of Science Shantou University Guangdong Shantou515063 China Key Laboratory of Intelligent Manufacturing Technology of MOE Shantou University Guangdong Shantou515063 China Center for Cognition and Neuroergonomics State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University at Zhuhai Zhuhai519087 China DTU Fotonik Department of Photonics Engineering Technical University of Denmark Kgs. Lyngby 2800 Denmark

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

Bragg gratings can now be produced in various types of polymer optical fibers. However, compared to silica optical fibers, it is still difficult to obtain highly reflective gratings in polymer optical fibers because of material swelling resulting from larger UV absorption. Thus, real-time grating evolution monitoring in whole inscription process is highly demanded. In this paper, a convenient method is systematically introduced using an objective imaging system for permanent fiber alignment between a polymer optical fiber and a silica optical fiber before inscription process allowing analyzing spectral evolution for further grating improvement. Finally, the performance of fiber connectorization is evaluated with a core coupling loss of ~ -6.33 dB. © 2022 SPIE

关键词： Fiber Bragg gratings

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Salient Object Detection in Traffic Scene through the TSOD10K Dataset

arXiv

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arXiv 2025年

作者： Qiu, Yu Sun, Yuhang Mei, Jie Xiao, Lin Xu, Jing College of Information Science and Engineering Hunan Normal University Changsha410081 China National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases College of Artificial Intelligence Nankai University Tianjin300350 China National Engineering Research Center of Robot Visual Perception and Control Technology School of Robotics Hunan University Changsha410082 China

Traffic Salient Object Detection (TSOD) aims to segment the objects critical to driving safety by combining semantic (e.g., collision risks) and visual saliency. Unlike SOD in natural scene images (NSI-SOD), which prioritizes visually distinctive regions, TSOD emphasizes the objects that demand immediate driver attention due to their semantic impact, even with low visual contrast. This dual criterion, i.e., bridging perception and contextual risk, re-defines saliency for autonomous and assisted driving systems. To address the lack of task-specific benchmarks, we collect the first large-scale TSOD dataset with pixel-wise saliency annotations, named TSOD10K. TSOD10K covers the diverse object categories in various real-world traffic scenes under various challenging weather/illumination variations (e.g., fog, snowstorms, low-contrast, and low-light). Methodologically, we propose a Mamba-based TSOD model, termed Tramba. Considering the challenge of distinguishing inconspicuous visual information from complex traffic backgrounds, Tramba introduces a novel Dual-Frequency Visual State Space module equipped with shifted window partitioning and dilated scanning to enhance the perception of fine details and global structure by hierarchically decomposing high/low-frequency components. To emphasize critical regions in traffic scenes, we propose a traffic-oriented Helix 2D-Selective-Scan (Helix-SS2D) mechanism that injects driving attention priors while effectively capturing global multi-direction spatial dependencies. We establish a comprehensive benchmark by evaluating Tramba and 22 existing NSI-SOD models on TSOD10K, demonstrating Tramba’s superiority. Our research establishes the first foundation for safety-aware saliency analysis in intelligent transportation systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk perception

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