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OdCFU-Net for Brain Tumor Segmentation Based on MRI

OdCFU-Net for Brain Tumor Segmentation Based on MRI

IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Shang Wu Sijing Yang Boyue Li Shimin Yin Jianming Zhu Shiyong Li Zhencheng Chen Guilin University of Electronic Technology School of Life and Environmental Science Guilin China School of Computer and Information Security Guilin University of Electronic Science and Technology Guilin University of Electronic Technology School of Electronic Engineering and Automation Guilin China Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments Guilin China Guangxi Engineering Technology Research Center of Human-Physiological Information Noninvasive Detection Guilin China

ISBN: (数字)9798350322996

ISBN: (纸本)9798350323009

In the field of medical image analysis, the accurate segmentation of brain tumors plays a crucial role in the diagnosis and treatment of brain-related diseases. This study aims to address key challenges in this domain, with a particular focus on improving feature extraction, information integration, and model generalization capabilities. By concurrently introducing multi-dimensional attention mechanisms in convolutional operations, a method has been developed to dynamically generate convolution kernels in spatial, input channel, and output channel dimensions. This approach optimizes the limitations of traditional static convolutional kernels, maintaining high-performance efficiency while reducing the model’s parameter count. To overcome the deficiencies of traditional convolutional networks in capturing global information, channel and spatial attention mechanisms have been integrated between convolutional layers. This enhances adaptability and global information retrieval capabilities, allowing the model to dynamically adjust the weight of feature maps based on different task requirements. Leveraging the advantages of Vision Transformer in handling global information and addressing its limitations in capturing details and training on small datasets, the algorithm aligns and learns feature representations effectively by incorporating channel attention and normalizing attention weights within its multi-head attention. When applied to the BraTs2021 multi-modal brain tumor image database, the proposed algorithm accurately delineates brain tumor regions, demonstrating excellent performance.

关键词： Training Adaptation models Attention mechanisms Semantic segmentation Virtual environments Transformers Kernel

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Design and Implementation of a Two-Limbed 3T1R Haptic Device

Design and Implementation of a Two-Limbed 3T1R Haptic Device

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Long Kang Yang Yang Byung-Ju Yi PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Xi'an Modern Control Technology Research Institute Xi'an China School of Automation Nanjing University of Information Science and Technology Nanjing China Department of the Electronic Systems Engineering Hanyang University Ansan South Korea

This paper presents a haptic device with a simple architecture of only two limbs that can provide translational motion in three degrees of freedom (DOF) and one-DOF rotational motion. Actuation redundancy eliminates all forward-kinematic singularities and improves the motion-force transmission property. Thanks to the special structure of the kinematic chains, all actuators are close to the base and full gravity compensation is achieved passively by using springs. Force producibility analysis shows that this haptic device is able to produce long-term continuous force feedback of 15–30 N in each direction. By developing a prototype of the haptic device and a virtual three-dimensional simulator, a preliminary performance evaluation of the haptic device was conducted. In addition, a torque distribution algorithm considering a relaxed form of actuator-torque saturation was experimentally evaluated, and a comparison with other algorithms reveals that this algorithm offers several advantages.

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RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete

arXiv

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arXiv 2025年

作者： Ji, Yuheng Tan, Huajie Shi, Jiayu Hao, Xiaoshuai Zhang, Yuan Zhang, Hengyuan Wang, Pengwei Zhao, Mengdi Mu, Yao An, Pengju Xue, Xinda Su, Qinghang Lyu, Huaihai Zheng, Xiaolong Liu, Jiaming Wang, Zhongyuan Zhang, Shanghang State Key Laboratory of Multimedia Information Processing School of Computer Science Peking University China Beijing Academy of Artificial Intelligence China Institute of Automation Chinese Academy of Sciences China Institute of Information Engineering Chinese Academy of Sciences China The University of Hong Kong Hong Kong School of Artificial Intelligence University of Chinese Academy of Sciences China

Recent advancements in Multimodal Large Language Models (MLLMs) have shown remarkable capabilities across various multimodal contexts. However, their application in robotic scenarios, particularly for long-horizon manipulation tasks, reveals significant limitations. These limitations arise from the current MLLMs lacking three essential robotic brain capabilities: Planning Capability, which involves decomposing complex manipulation instructions into manageable sub-tasks;Affordance Perception, the ability to recognize and interpret the affordances of interactive objects;and Trajectory Prediction, the foresight to anticipate the complete manipulation trajectory necessary for successful execution. To enhance the robotic brain’s core capabilities from abstract to concrete, we introduce ShareRobot, a high-quality heterogeneous dataset that labels multi-dimensional information such as task planning, object affordance, and end-effector trajectory. ShareRobot’s diversity and accuracy have been meticulously refined by three human annotators. Building on this dataset, we developed RoboBrain, an MLLM-based model that combines robotic and general multi-modal data, utilizes a multi-stage training strategy, and incorporates long videos and high-resolution images to improve its robotic manipulation capabilities. Extensive experiments demonstrate that RoboBrain achieves state-of-the-art performance across various robotic tasks, highlighting its potential to advance robotic brain capabilities. Project website: RoboBrain. Copyright © 2025, The Authors. All rights reserved.

关键词： End effectors

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Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey

arXiv

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arXiv 2024年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Fang, Yuguang Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Department of Computer Science City University of Hong Kong Kowloon Hong Kong The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets, completing sensor data, and making sequential decisions. In addition, the mixture of experts (MoE) can enable the distributed and collaborative execution of AI models without performance degradation between connected vehicles. In this survey, we explore the integration of MoE and GAI to enable Artificial General Intelligence in IoV, which can enable the realization of full autonomy for IoV with minimal human supervision and applicability in a wide range of mobility scenarios, including environment monitoring, traffic management, and autonomous driving. In particular, we present the fundamentals of GAI, MoE, and their interplay applications in IoV. Furthermore, we discuss the potential integration of MoE and GAI in IoV, including distributed perception and monitoring, collaborative decision-making and planning, and generative modeling and simulation. Finally, we present several potential research directions for facilitating the integration. © 2024, CC BY.

关键词： Integration

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Self-Correcting Robot Manipulation via Gaussian-Splatted Foresight 39

Self-Correcting Robot Manipulation via Gaussian-Splatted For...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Pan, Shaohui Xu, Yong Xu, Ruotao Zhou, Zihan Wu, Si Yu, Zhuliang School of Computer Science and Engineering South China University of Technology Guangzhou510006 China Guangzhou510000 China Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis Guangzhou510000 China Peng Cheng Laboratory of Shenzhen Shenzhen518120 China Key Laboratory of Large-Model Embodied-Intelligent Humanoid Robot 510000 China School of College of Mathematics and Informatics South China Agricultural University Guangzhou510640 China Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou510006 China School of Automation Science and Engineering South China University of Technology Guangzhou510006 China

ISBN: (纸本)157735897X

Language-conditioned robotic manipulation in unstructured environments presents significant challenges for intelligent robotic systems. However, due to partial observation or imprecise action prediction, failure may be unavoidable for learned policies. Moreover, operational failures can lead to the robotic arm entering an untrained state, potentially causing destructive results. Consequently, the ability to detect and self-correct failures is crucial for the development of practical robotic systems. To address this challenge, we propose a foresight-driven failure detection and self-correction module for robot manipulation. By leveraging 3D Gaussian Splatting, we represent the current scene with multiple Gaussians. Subsequently, we train a prediction network to forecast the Gaussian representation of future scenes conditioned on planned actions. Failure is detected when the predicted future significantly deviates from the real observation after action execution. In such cases, the end-effector rolls back to the previous action to avoid an untrained state. Integrating this approach with the PerAct framework, we develop a self-correcting robot manipulation policy. Evaluations on ten RL-Bench tasks with 166 variations demonstrate the superior performance of the proposed method, which outperforms state-of-the-art methods by 12.0% success rate on average. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Intelligent robots

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CEBRA Method: Decoding Brain Activity for Advanced Brain-computer Interface Technology 46

CEBRA Method: Decoding Brain Activity for Advanced Brain-Com...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Yang, Jingcheng Kulwa, Frank Liu, Xuanwei Lu, Yiqing Fu, Yufa Li, Guanglin Huai, Yaping Zhang, Xin Li, Yongcheng Shenzhen Dapeng New District Nanao People's Hospital Guangdong Shenzhen518000 China Shenzhen Longhua District Central Hospital Department of Rehabilitation Medicine Guangdong Shenzhen518000 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Brain Cognition and Brain-Computer Intelligence Integration Group Kunming650500 China Cas Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Shandong Zhongke Advanced Technology Co. Ltd. Jinan250014 China

ISBN: (纸本)9798350371499

The emerging neurorehabilitation technology, Brain-computer Interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector Machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p © 2024 IEEE.

关键词： Patient rehabilitation

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TSOM: Small Object Motion Detection Neural Network Inspired by Avian Visual Circuit

arXiv

引用

arXiv 2024年

作者： Hu, Pingge Zhang, Xiaoteng Li, Mengmeng Zhu, Yingjie Shi, Li Department of Automation Tsinghua University Beijing100084 China School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou450001 China Shenzhen Key Laboratory of Drug Addiction Shenzhen Neher Neural Plasticity Laboratory Brain Cognition and Brain Disease Institute Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

Detecting small moving objects in complex backgrounds from an overhead perspective is a highly challenging task for machine vision systems. As an inspiration from nature, the avian visual system is capable of processing motion information in various complex aerial scenes, and its Retina-OTRt visual circuit is highly sensitive to capturing the motion information of small objects from high altitudes. However, more needs to be done on small object motion detection algorithms based on the avian visual system. In this paper, we conducted mathematical modeling based on extensive studies of the biological mechanisms of the Retina-OT-Rt visual circuit. Based on this, we proposed a novel tectum small object motion detection neural network (TSOM). The neural network includes the retina, SGC dendritic, SGC Soma, and Rt layers, each layer corresponding to neurons in the visual pathway. The Retina layer is responsible for accurately projecting input content, the SGC dendritic layer perceives and encodes spatial-temporal information, the SGC Soma layer computes complex motion information and extracts small objects, and the Rt layer integrates and decodes motion information from multiple directions to determine the position of small objects. Extensive experiments on pigeon neurophysiological experiments and image sequence data showed that the TSOM is biologically interpretable and effective in extracting reliable small object motion features from complex high-altitude backgrounds. Copyright © 2024, The Authors. All rights reserved.

关键词： Timing circuits

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Adaptive Control of the Structural Balance for a Class of Complex Dynamical Networks

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Journal of Systems science & Complexity 2020年第3期33卷 725-742页

作者： GAO Zilin WANG Yinhe PENG Yi LIU Lizhi CHEN Haoguang Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges UniversityChongqing 404100China School of Computer Science and Engineering Chongqing Three Gorges UniversityChongqing 404100China School of Automation Guangdong University of TechnologyGuangzhou 510006China School of Computer Science Zhongshan Institute of Electronic Science and Technology University of ChinaZhongshan 528402China

In social networks,the structural balance is a state of a group of individuals(nodes)with established mutual relationships(connection relationships)between *** is easy to see that a social network can be described by a complex dynamical network model composed of the nodes subsystem(NS)and the connection relationships subsystem(CS),where the two subsystems are usually coupled with each *** implies that the dynamic changes of nodes'states may cause the structural balance in ***,few papers have discussed the relationship between the structural balance and the specific dynamic changes of the nodes'*** paper proposes a model of complex dynamical networks,and mainly focuses on the dynamic changes of states in NS which can lead to the structural balance in *** is proved that if each state in NS is doing a specific dynamic motion via the controller with the parameter adaptive law,then the CS can track a given structural balance matrix via the effective coupling and the structural balance can be *** a result can be regarded as an explanation of the relationship between the structural balance and the specific dynamic changes of the nodes'***,the simulations verify the effectiveness of the proposed method.

关键词： Complex dynamical networks connection relationships effective coupling structural balance

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End-to-End Streaming Customizable Keyword Spotting Based on Text-Adaptive Neural Search 1

引用

18th National Conference on Man-Machine Speech Communication, NCMMSC 2023

作者： Yang, Baochen Guo, Jiaqi Li, Haoyu Xi, Yu Zhuo, Qing Yu, Kai X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China State Key Laboratory of Media Convergence Production Technology and Systems Beijing China AISpeech Ltd. Suzhou China Department of Automation Tsinghua University Beijing China

ISBN: (数字)9789819706013

ISBN: (纸本)9789819706006

Streaming keyword spotting (KWS) is an important technique for voice assistant wake-up. While KWS with a preset fixed keyword has been well studied, test-time customizable keyword spotting in streaming mode remains a great challenge due to the lack of pre-collected keyword-specific training data and the requirement of streaming detection output. In this paper, we propose a novel end-to-end text-adaptive neural search architecture with a multi-label trigger mechanism to allow any pre-trained ASR acoustic model to be effectively used for fast streaming customizable keyword spotting. Evaluation results on various datasets show that our approach significantly outperforms both traditional post-processing baseline and the neural search baseline, meanwhile achieving a 44x search speedup compared to the traditional post-processing method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Customizable keyword Keyword spotting Streaming Text-adaptive search

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Contrastive Learning Based Human Activity Recognition Using Body Sensors

Contrastive Learning Based Human Activity Recognition Using ...

引用

International Conference on Big Data Computing and Communications (BIGCOM)

作者： Chuanshi Xie Yu Zhou Dandan Yu Shilong Sun Xiao Zhang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Information Center The First Affiliated Hospital of Dalian Medical University Dalian China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Department of Computer Science South-Central Minzu University State Ethnic Affairs Commission Key Laboratory of Cyber-Physical Fusion Intelligent Computing South-Central Minzu University Wuhan China

Pervasive sensing and wearable sensor techniques have been increasingly employed to monitor and recognize human activities through body sensors in areas of smart healthcare and manufacturing. However, conventional machine learning or deep learning based human activity recognition (HAR) requires a large amount of labeled data, which is cost-expensive in real-world scenarios. To tackle this issue, we propose a contrastive learning framework for HAR (CL-HAR), which utilizes the generated unlabeled data as the input and explores the supervised features of the unlabeled data under the principle of self-supervised learning. A simple yet effective backbone network as a feature extractor for subsequent activity recognition is proposed. By using a small portion of the labeled samples as the training set which is fed into our learned feature extractor, we build a classifier and use the rest of the data to verify the feasibility and effectiveness of our CL method. Extensive experiments on three benchamark datasets and one real-world dataset demonstrate that CL-HAR can achieve better classification accuracy than compared supervised and semi-supervised methods with less labelled samples, which is of practical use.

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