检索结果-内蒙古大学图书馆

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wu, Xuening Shen, Yiqing Zhao, Qing Kang, Yanlan Hu, Ruiqi Zhang, Wenqiang Fudan University Shanghai Engineering Research Center of Ai & Robotics Academy for Engineering & Technology Shanghai China Johns Hopkins University Department of Computer Science BaltimoreMD United States Hong Kong Polytechnic University Department of Applied Mathematics Hong Kong Fudan University Engineering Research Center of Ai & Robotics Ministry of Education Academy for Engineering & Technology School of Computer Science Shanghai China

ISBN: (纸本)9798350386226

Accurate nuclei segmentation is fundamental in histology image analysis, playing an essential role in cancer grading and diagnosis. However, this task remains challenging due to variations in staining protocols, heterogeneity among nuclei types, and the densely clustered nature of nuclei. While SAM exhibits zero-shot generalization capabilities in natural image segmentation, its performance degrades when applied to nuclei segmentation in histology images. Existing adaptations of SAM for medical imaging primarily focus on organ or lesion segmentation, which differs substantially from nuclei segmentation due to the unique characteristics of nuclei - specifically, their sparse distribution combined with dense clustering. To address these challenges, we propose Edge-SAN (Segment Any Nuclei with Edge Prompting), an interactive segmentation foundation model specifically designed for nuclei segmentation. Edge-SAN introduces a novel edge prompting method that enhances the delineation of nuclei boundaries, particularly among densely clustered nuclei, by leveraging edge information to improve segmentation accuracy. We evaluate Edge-SAN on 12 diverse datasets in both few-shot and zero-shot scenarios, demonstrating its effectiveness as a foundation model for nuclei segmentation, achieving 66.81% AJI and 73.13% DSC - improvements of 16.33% and 15.48% over SAM-Med2D, respectively. The code is available at https://***/deep-geo/Edge-SAN. © 2024 IEEE.

关键词： Deep Learning Digital Pathology Foundation Model Nuclei Segmentation Segment Anything Model

来源：评论

学校读者我要写书评

暂无评论

Target Detection and Tracking of Survey Robots Using Multimodal Internet of Things Sensor data Fusion Using YOLOv5 2

Target Detection and Tracking of Survey Robots Using Multimo...

引用

2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Ye, Chengjing Wu, Miaoyong Yang, Yefen Zhang, Ke Bu, Xinhua Huang, Miaomiao Guangdong Polytechnic of Science and Technology Faculty of Robotics Guangdong Zhuhai China Guangdong Polytechnic of Science and Technology Computer Engineering Technical College Guangdong Zhuhai China Faculty of Architectural Engineering Guangdong Polytechnic of Science and Technology Guangdong Zhuhai China

ISBN: (纸本)9798350382952

In recent years, due to the rapid development of computer vision technology, real-time detection and tracking of objects using computer vision has become a hot topic. Object detection mainly consists of two parts: object segmentation and object recognition, that is, object acquisition. Accurate detection of targets is the key to achieving high-precision and high-efficiency tracking. Based on the above discussion, this article explored the target detection and tracking of survey robots using the multimodal Internet of Things (IoT) sensor data fusion of YOLOv5, and compared and analyzed the performance of direct frame difference method, traditional ORB method, and the algorithm in this paper through experiments. The results indicated that the accuracy and recall of the robot image object detection algorithm proposed in this paper were both above 90%, and higher than other algorithms, indicating that the algorithm has extremely high accuracy in object detection. © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Mixed Reality Application in Robot Navigation and Control 16

Mixed Reality Application in Robot Navigation and Control

引用

16th International Conference on Applied and Theoretical Electricity, ICATE 2024

作者： Cismaru, Stefan-Irinel Petcu, Florina-Luminita Resceanu, Ionut-Cristian Roibu, Horatiu Trasculescu, Andrei-Costin Pana, Cristina-Floriana Bizdoaca, Nicu-George University of Craiova Faculty of Automation Computers and Electronics Mechatronics and Robotics Department Craiova Romania University of Craiova Faculty of Automation Computers and Electronics Computer Science Department Craiova Romania

ISBN: (纸本)9798350388107

Among the emerging technologies, Mixed Reality (MR) has provided the means to interact with holograms. A very distant future is now near and accessible, which allows for the replacement of classic controllers for robots and is driven by MR. Holograms involve dynamic 3D elements that are integrated into the environment, enhancing control means for the user. These elements may be visually attended to and interacted with via eye concentration or hand gestures, and they have the capability to execute a variety of activities. This emerging technology enables interactions that are otherwise unattainable in alternative settings or contexts, such as the presence of an avatar inside the program. Furthermore, it has the potential to be expanded to govern other mechatronic devices, including robots. The purpose of this work is to offer an improved and interactive method of controlling a mechatronic platform by using holograms, such that the adaptability of this technology may be shown in the context of operating real systems. Important for the authors of this paper is the broadening of the spectrum in which the application lies, thus contributing to the process followed for the development and applicability of the solution in key areas such as industry, training, and medicine. © 2024 IEEE.

关键词： Holograms

来源：评论

学校读者我要写书评

暂无评论

Metrics to Estimate Model Comprehension: Towards a Reliable Quantification Framework 19

Metrics to Estimate Model Comprehension: Towards a Reliable ...

引用

19th International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2024

作者： Tenbergen, Bastian Daun, Marian Department of Computer Science State University of New York at Oswego Oswego United States Center of Robotics Technical University of Applied Sciences Würzburg-Schweinfurt Schweinfurt Germany

ISBN: (纸本)9789897586965

Model-driven development has established itself as one of the core practices in software engineering. Increases in quality demands paired with shorter times to market and increased mission-criticality of software systems have sensitized software engineering practitioners to make use of not only formal, but also semi-formal models, particularly graphical diagrams to express the system under development in ways that facilitate collaboration, validation & verification, as well as configuration and runtime monitoring. However, what does and does not constitute a "good" model, i.e., a model that is fit for a practical purpose? While some model quality frameworks exist, the trouble with most of these is that they often lack the ability to concretely quantify and thereby objectively differentiate a "good" from a "poor" model, i.e., models that can be easily understood by the model reader. Without being able to reliably produce easily comprehensible models, training new team members during on-boarding or educating software engineering students is dramatically hindered. In this paper, we report on a research trajectory towards reliably measuring the comprehensibility of graphical diagrams. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Verification

来源：评论

学校读者我要写书评

暂无评论

SEMG-Based Gesture Recognition in Multiple Scenarios Using a Phase Locked Value-Based Deep Learning Method

SEMG-Based Gesture Recognition in Multiple Scenarios Using a...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Xueze Wang, Minjie Chen, Zihua Wang, Lu Wang, Hongbo Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of Ai & Robotics Ministry of Education Shanghai Engineering Research Center of Ai & Robotics Moe Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of Ai & Robotics Institude of Meta-Medical Academy for Engineering & Technology Fudan University Shanghai China

ISBN: (纸本)9798350386226

Surface electromyography (sEMG) signals-based gesture recognition method is widely employed in human-computer interaction task. In this paper, we proposed a phase locked value (PLV)-based feature extraction method for sEMG-based gesture recognition. We conducted validation experiments on public datasets by integrating the previous proposed gesture recognition algorithms, STCN-GR and ConSSL. PLV-STCN-GR and PLV-ConSSL were utilized to test the performance of proposed feature extraction method in intra-session, inter-session and inter-subject scenarios. In general, the proposed PLV-based sEMG decoding framework has achieved good recognition results on public datasets and demonstrated the possibility of its application in practical scenarios. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Quality Ranking for Synthetically Generated Images

Quality Ranking for Synthetically Generated Images

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Sekmen, Ali Bilgin, Bahadir Sereff, Guy Tennessee State University Department of Computer Science NashvilleTN37209 United States Amazon Robotics Ai Computer Vision Software Development NashvilleTN37209 United States

ISBN: (纸本)9798350324457

This paper proposes a novel metric for evaluating the quality of images of the same category generated by a synthetic image generator. The proposed method assumes that the points representing synthetic images in a feature space lie on a manifold, and it aims to determine the curvatures of the manifold at those points. The lower the angle, the higher the quality of the generated images is considered to be. Let F be the set of data points in a D-dimensional feature space where each point represents one synthetically generated image. Let M be the corresponding manifold to match F. For each data point y inRD on M, a number of neighboring points are determined. A local subspace S is matched for y and a local subspace is also matched for each of its neighboring points. Then, a set of weighted angles between S and each neighboring local subspace are computed. The average of those weighted angles is used as a measure of curvature of M at y. Experimental results demonstrate the effectiveness of the proposed metric in evaluating the quality of synthetically generated images. © 2023 IEEE.

关键词： synthetic image generation Synthetic image quality ranking

来源：评论

学校读者我要写书评

暂无评论

Exploring Children’s Strategies in Response to Robot’s Advice During a Group Task with iCub and Nao 16th

Exploring Children’s Strategies in Response to Robot’s A...

引用

16th International Conference on Social robotics, ICSR + AI 2024

作者： Pusceddu, Giulia Sangineto, Mariapia Cocchella, Francesca Bogliolo, Michela Belgiovine, Giulia Lastrico, Linda Casadio, Maura Rea, Francesco Gena, Cristina Sciutti, Alessandra Robotics Brain and Cognitive Science Department Italian Institute of Technology Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Department of Computer Science University of Turin Turin Italy Cognitive Architecture for Collaborative Technologies Unit Italian Institute of Technology Genoa Italy Scuola di Robotica Genoa Italy

ISBN: (纸本)9789819635184

This study investigates children’s trust in two humanoid robots, Nao and iCub, through a cooperative game designed to elicit spontaneous behaviors and group dynamics. We investigate whether participants change their choices after hearing advice from a robot, considering rounds where the advice is correct or incorrect, with the robot apologizing for its mistakes. Results reveal that, in most cases, participants prefer not to change their moves after the robot’s advice. Additionally, after the robots made mistakes, participants are less likely to actively follow their advice and more likely to reject it. No significant differences in strategies among the iCub and Nao samples are evidenced. Furthermore, we examine the influence of group dynamics, identifying "Proactive Players" who consistently initiated moves. A significant association between the strategies of this player and their teammates is found, suggesting potential influence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Scanning the Issue – Sept 2024 Additional Papers

引用

IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 10457-10461页

作者： Simona Sacone Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genova Genova Italy

Summary form only: Abstracts of articles that will be presented in future issues of this publication.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Interpreting Deep Convolutional Neural Networks for Medical Diagnosis Using Perceptual Encoding and Retrieval 15

Interpreting Deep Convolutional Neural Networks for Medical ...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kasture, Shreyas Maurya, Sudhanshu Verma, Rohan Yadav, Ajay Kumar Shukla, Amit Nagpur Campus Pune India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India United Institute of Management Department of Computer Application Prayagraj India Centre of Artificial Intelligence & Robotics Mandi India

ISBN: (纸本)9798350370249

The robust performance of deep neural networks (DNNs) in many areas, including medical diagnosis, is not accessible from the problem of interpretability. Even though DNNs have high accuracy, they tend to operate as black boxes, usually without any explanation of which basis their predictions are made. This interpretability issue has become important, especially for high-stakes applications, such as medical image analysis and disease diagnosis, in which people's lives are at risk. The study was structured to raise novel approaches for DNNs, internal representation translation and provide visualizations to assist medical diagnosis tasks and ensure reliable and transparent decision-making procedures. Such techniques have been developed to improve the interpretability of DNN without sacrificing the performance. The models for proposed models succeeded in the clinical tasks of medical diagnosis. They reached competitive accuracy with the best model, obtaining an accuracy of 95.7% and an F1-score of 0.914 on lung abnormality detection from chest X-rays. AUC-ROC high sensitivity and specificity of 0.963 were observed when diagnosing brain tumors based on MRI scans. These models also provided improved faithfulness perceived through saliency maps, which had an average score of 0.82, and concept activation vectors, with a consistency of 0.77 from the evaluation done by medical experts. The suggested methods offer a chance for a better patient outcome via high-precision and safe diagnostics and treatment options with an expected change in misdiagnosis rate of 10-15%. © 2024 IEEE.

关键词： Active Learning Deep Neural Networks Interpretation Synthesis Medical Diagnosis Network Traffic Forecasting Saliency Map Aggregation and Saliency Maps

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Reinforcement Learning Techniques in Simulated robotics Environments 7

Comparative Analysis of Reinforcement Learning Techniques in...

引用

7th International Conference on Intelligent robotics and Control Engineering, IRCE 2024

作者： Yoseph, Abey Tanveer, M. Hassan Voicu, Razvan Cristian Shahid, M. Farrukh Kennesaw State University Department of Robotics & Mechatronics Engineering MariettaGA United States National University of Computer and Emerging Sciences Department of Ai and Data Science Karachi Pakistan

ISBN: (纸本)9798350352399

Machine learning is a critical component of artificial intelligence and computer science that uses data and algorithms to emulate the way that humans learn and predict. Reinforcement learning, one of the many forms of machine learning, trains an agent through trial and error, with successful outcomes being rewarded and unsuccessful outcomes being penalized. The implementation of three reinforcement learning algorithms, Qlearning, Deep Q-Learning, and Double Deep Q-Learning, was tested using a series of ROS (Robot Operating System) packages in the Gazebo simulation environment. The Python openai-ros package and Gym libraries were used to input a robot into a configurable environment and test out the performance of a specific reinforcement learning algorithm. The agent's action space was discretized into three specific actions, move forward, turn left, or turn right, that the robot could take. The reward or punishment was given after each step based on if the current action moved the robot towards the goal state or away from it. For each algorithm, the agent was first pre-trained on an obstacle free environment for 200 episodes and then was placed in an environment with two box-shaped obstacles to avoid. The algorithm/neural network parameters from the pre-training environment were loaded prior to the agent being tested in the new environment. The goal state at each episode was randomly generated and the robot's performance was evaluated using the cumulative reward achieved per episode and the number of times it reached the goal. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：