检索结果-内蒙古大学图书馆

IEEE Electron Device Letters 2025年

作者： Kurup, Siddharth Bhattacharya, Swagat Banner, Rebecca K. Yue, Kaifan Ayyappan, Praveen Raj Aziz, Daniel Elly, Hubert N. Barton, Kira L. Hasler, Jennifer Filler, Michael A. Vogel, Eric M. Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States Georgia Institute of Technology School of Materials Science and Engineering AtlantaGA30332 United States University of Michigan Department of Robotics Ann ArborMI48109 United States Georgia Institute of Technology School of Chemical and Biomolecular Engineering AtlantaGA30332 United States University of Michigan Department of Mechanical Engineering Ann ArborMI48109 United States

Micromodular metal-HfOx-metal resistive random-access memories (RRAM) were fabricated, transferred to foreign substrates, and contacted using high-resolution electrohydrodynamic jet (e-jet) printing. The modular RRAM exhibited bipolar switching at low operating voltages and multi-level analog resistance states programmable using variable amplitude voltage pulses. These variable resistance RRAMs were also integrated with an amplifier and a bandpass filter to modulate their gain and corner frequencies. This work enables integration of high-quality RRAM devices in modular circuits and paves the way for microscale heterogeneous integration at the device level. © 1980-2012 IEEE.

关键词： RRAM

来源：评论

学校读者我要写书评

暂无评论

An efcient GP approach to recognizing cognitive tasks from fNIRS neural signals

引用

science China(Information sciences) 2013年第10期56卷 277-283页

作者： AN Jinungn LEE JongHyun AHN ChangWook Robotics Research Division Daegu Gyeongbuk Institute of Science & Technology Department of Computer Engineering Sungkyunkwan University

This paper presents a new genetic programming(GP)approach to accurately classifying cognitive tasks from non-stationary and noisy fNIRS neural *** this end,a new GP that efectively handles multiclass problems is *** accordance with multi-tree structure,GP operators are innovated:crossover exchanges every subtree of parents without sufering from any incongruity problem and mutation fne-tunes candidate solutions by a less destructive *** results verifes the efectiveness of the proposed GP classifer over existing references.

关键词： classifcation cognitive task genetic programming fNIRS multi-tree representation

来源：评论

学校读者我要写书评

暂无评论

Towards Fluorescence-Guided Autonomous Robotic Partial Nephrectomy on Novel Tissue-Mimicking Hydrogel Phantoms

arXiv

引用

arXiv 2025年

作者： Kilmer, Ethan Chen, Joseph Ge, Jiawei Sarda, Preksha Cha, Richard Cleary, Kevin Shepard, Lauren Ghazi, Ahmed Ezzat Scheikl, Paul Maria Krieger, Axel Laboratory of Computational Sensing and Robotics Johns Hopkins University BaltimoreMD21218 United States Department of Computer Science and Software Engineering Rose-Hulman Institute of Technology Terre HauteIN47803 United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC20010 United States Department of Urology Johns Hopkins University BaltimoreMD21218 United States

Autonomous robotic systems hold potential for improving renal tumor resection accuracy and patient outcomes. We present a fluorescence-guided robotic system capable of planning and executing incision paths around exophytic renal tumors with a clinically relevant resection margin. Leveraging point cloud observations, the system handles irregular tumor shapes and distinguishes healthy from tumorous tissue based on near-infrared imaging, akin to indocyanine green staining in partial nephrectomy. Tissue-mimicking phantoms are crucial for the development of autonomous robotic surgical systems for interventions where acquiring ex-vivo animal tissue is infeasible, such as cancer of the kidney and renal pelvis. To this end, we propose novel hydrogel-based kidney phantoms with exophytic tumors that mimic the physical and visual behavior of tissue, and are compatible with electrosurgical instruments, a common limitation of silicone-based phantoms. In contrast to previous hydrogel phantoms, we mix the material with near-infrared dye to enable fluorescence-guided tumor segmentation. Autonomous real-world robotic experiments validate our system and phantoms, achieving an average margin accuracy of 1.44 mm in a completion time of 69 sec. Copyright © 2025, The Authors. All rights reserved.

关键词： Phantoms

来源：评论

学校读者我要写书评

暂无评论

PAR - A CSG-BASED UNIQUE REPRESENTATION SCHEME FOR ROTATIONAL PARTS

引用

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS 1987年第6期17卷 1039-1049页

作者： LEE, YC JEA, KFJ Robotics Research Laboratory Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109

PAR (principal axis representation) for rotational parts (i.e., solids of revolution) is proposed as an internal representation scheme for constructive solid geometry (CSG). They key idea of PAR is to represent an object uniquely by its principal axis and a set of boundary curves. Since PAR is in an evaluated form, geometrical properties of parts can be computed more directly and efficiently from this evaluated representation than from the original CSG tree. The scheme is described, and operations (e.g. union) on PARs are defined. The uniqueness of the scheme is proved. An algorithm that converts a CSG representation into PAR is presented along with examples. The equivalence of PAR to the CSG scheme for rotational parts is proved.

关键词： Feature extraction Solids Geometry Syntactics Task analysis Solid modeling Pattern recognition

来源：评论

学校读者我要写书评

暂无评论

A circuit for high-speed time switching

引用

IEEE TRANSACTIONS ON COMMUNICATIONS 1997年第3期45卷 287-288页

作者： Bucholc, K Faculty of Electrical Engineering Department of Control Robotics and Computer Science Poznan University of Technology Poznan Poland

A method of time switching for time-division communication systems is introduced, A compact shift register-based circuit is used for this purpose in order to achieve high-speed switching.

关键词： time-division communication time switching

来源：评论

学校读者我要写书评

暂无评论

Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

引用

arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

PFS3F: Probabilistic Fusion of Superpixel-wise and Semantic-aware Structural Features for Hyperspectral Image Classification

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zhang, Ying Duan, Puhong Liang, Lianhui Kang, Xudong Li, Jun Plaza, Antonio Hunan University of Technology College of Railway Transportation Zhuzhou412007 China Hunan University School of Robotics Changsha410082 China Guangxi University School of Electrical Engineering Nanning530004 China China University of Geosciences Hubei Key Laboratory of Intelligent Geo-Information Processing the School of Computer Science Wuhan430078 China Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politecnica University of Extremadura CaceresE-10071 Spain

Processing high-dimensional data cubes and developing high-performance classifiers are core objectives in the field of hyperspectral image classification (HSIC). Superpixel-based methods are widely used in HSIC due to their efficacy in reducing redundant information and enhancing local features. However, imprecise segmentation, especially in complex structures and textures of hyperspectral images (HSIs), may lead to inconsistencies in the regions extracted by superpixels and the boundaries between different ground objects. Such inconsistencies significantly degrade the classification performance of HSIs. Alternatively, when parameter settings are inaccurate, edge-aware feature extraction methods often introduce sharpening artifacts at the image boundaries, resulting in a decrease in classification accuracy. To effectively address these challenges, we propose a novel probabilistic fusion method for HSIC. This method consists of the following stages. First, spatial information is extracted by a multiscale superpixel segmentation method and then probabilistically optimized by the extended random walk (ERW) method. Next, semantic-aware structural features (S2Fs) are extracted along with edge information of different objects. Lastly, a probabilistic framework is proposed to fuse the class probabilities of superpixel-based spatial information and semantic-aware structural features. Experimental results on three real datasets show state-of-the-art classification performance, even with limited training sets. ©1991-2012 IEEE.

关键词： Superpixels

来源：评论

学校读者我要写书评

暂无评论

MangoLeafXNet: An Explainable Deep Learning Model for Accurate Mango Leaf Disease Classification

引用

IEEE Access 2025年

作者： Rayed, Md Eshmam Jim, Jamin Rahman Islam, Md Juniadul Mridha, M.F. Kabir, Md Mohsin Hossen, Md. Jakir American International University-Bangladesh Department of Computer Science and Engineering Dhaka1229 Bangladesh Mälardalens University Division of Computer Science and Software Engineering Västerås722 20 Sweden Multimedia University Faculty of Engineering and Technology Department of Robotics and Automation Melaka Malaysia

Addressing the global challenge of ensuring a consistent and abundant supply of fresh fruit, particularly in the context of fruit crops, is hindered by the prevalence of plant diseases. These diseases directly impact the quality of fruits, leading to a decline in overall agricultural production. Mango leaf diseases pose significant threats to global mango production, necessitating accurate and efficient classification techniques for timely disease management. Our study focuses on introducing MangoLeafXNet, a customized Convolutional Neural Network (CNN) architecture specifically tailored for the classification of mango leaf diseases, along with a healthy class. Our proposed model comprises six layers optimized to capture intricate disease patterns, demonstrating superior performance compared with prevalent pre-trained models. The model is trained and evaluated on three publicly available datasets: MangoLeafBD (4000 images across 8 classes), MangoPest (16 pest classes including healthy leaves), and MLDID (3000 high-resolution images across 5 classes). Our model demonstrated exceptional classification performance, attaining 99.8% accuracy, 99.62% recall, 99.5% precision, and an F1-score of 99.56%. Further validation on the MangoPest dataset and the Mango Leaf Disease Identification Dataset (MLDID) resulted in accuracies of 96.31% and 96.33%, respectively, confirming the robustness and adaptability of MangoLeafXNet across different datasets. Additionally, we incorporate Explainable AI techniques, including GRAD-CAM, Saliency Map, and LIME to enhance the interpretability of our model. We deployed Gradio web interface to create an interactive interface that allows users to upload images of mango leaves and get real-time classification and validation results along with confidence scores. This contribution not only advances the state-of-the-art in mango leaf disease classification but also offers promising prospects for real-time disease diagnosis and precision agriculture

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

Solar-based hybrid electric vehicles for low CO2 emission

引用

International Journal of Sustainable engineering 2025年第1期18卷

作者： Islam, Md. Ariful Kar, Pankaj Chandra Deowan, Shamim Ahmed Department of Robotics and Mechatronics Engineering University of Dhaka Dhaka Bangladesh Department of Information and Communication Technology Comilla University Cumilla Bangladesh Department of Computer Science and Engineering Stamford University Bangladesh Dhaka Bangladesh

This study investigates the impact of greenhouse gas and (Formula presented.) emissions from conventional vehicles (CVS) in Bangladesh and proposes a renewable energy and battery storage-based hybrid electric vehicle (HEV) to mitigate these emissions. The proposed HEV focuses on fuel economy, driving range, performance and cost-efficiency. It integrates hybrid batteries with ultra-capacitors for improved acceleration and energy storage, along with fuel cells, flywheels and regenerative braking. The propulsion system, combining electric, mechanical, chemical and electronics engineering, is a key challenge affecting vehicle design and performance. Additionally, the study develops an automatic solar power system with maximum power point tracking (MPPT) to optimise solar energy generation by considering sunrise and sunset times, panel size, charging duration, load profiling and power loss. The solar-based HEV, with an average speed of 60 km/h, can operate for 3.4 h or 204 km solely on solar power. Findings indicate a significant reduction in (Formula presented.) emissions, with solar HEVs emitting approximately 17,775,012.2 g and 13,825,009.5 g less (Formula presented.) than petrol and diesel vehicles, respectively. The study advocates for government adoption to promote a cleaner, more sustainable transportation sector in Bangladesh. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Hybrid vehicles

来源：评论

学校读者我要写书评

暂无评论

Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

引用

IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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：