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检索条件"任意字段=IEEE Information Visualization Conference/IEEE Visualization Conference"

共 54092 条记录，以下是321-330 订阅

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Enhanced Transformers with Dilated Convolutional Attention for Predicting Lung Cancer Outcomes

Enhanced Transformers with Dilated Convolutional Attention f...

引用

2025 International conference on Visual Analytics and Data visualization, ICVADV 2025

作者： Chidambaranathan, Senthilnathan Sundaramoorthy, Pandian Puli, Balaram Daruvuri, Rajesh Vamsilala, Purushothapatnapu Naga Venkata Sathya, R. Corporate Pl S PiscatawayNJ08854 United States El CIC-1W-AMI Ibm 6303 Barfield Rd NE Sandy Springs GA30328 United States Everest Computers Inc Department of Engineering and Data Science 875 Old Roswell Road Suite RoswellGA30076 United States University of Cumberlands 1105 peachtree st atlanta United States Vignan's Lara Institute of Technology and Science Vignan's Lara Institute of Technology and Science Andhra Pradesh Tenali road near Vignan's University Guntur district 522213 India Kongunadu College of Engineering and Technology Department of Information Technology Trichy India

ISBN: (纸本)9798331521394

The study in investigation of the usage of dilated convolutional attention functions with transformers to improve the accuracy of the prognosis of lung cancer. The suggested model exploits the benefits of the dilated convolutions to seize multi-scale spatial features in medical related data while it is integrating the human attention of the transformers that are focused on the most important information. The predicted model needs to cover collected clinical data patterns from which the best results for lung cancer will be achieved. These patterns are including early detection and tracking of tumor progression. this alternative the most effective way to improve feature extraction, but it also ensures presenting the most important trends in medical data to achieve an exact prognosis. The proposed methodology is an structured methodology that health experts use to diagnose tumor at variable stages of patient's testing while also giving hints of the possibility of Lyme Infections. © 2025 ieee.

关键词： Personalized medicine

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Retention Enhanced Cross-modal Attention for Multi-Hop VQA

Retention Enhanced Cross-modal Attention for Multi-Hop VQA

引用

International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zijie Zhu Feng Ding Chenglong Chu Fangming Zhong School of Software Technology Dalian University of Technology Dalian China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Exploring multimodal information from external knowledge bases in Visual Question Answering (VQA) reasoning tasks presents a significant challenge. Current methods, such as attention-based and graph-based approaches, have limitations in effectively capturing contextual key information. It is essential to enhance the model’s cognitive understanding of the associations between the question and the corresponding external knowledge to improve accuracy. To overcome these challenges, this paper introduces RECA, a Retention Enhanced Cross-modal Attention method for multi-hop VQA inspired by the hypergraph. Specifically, the contextual information after cross-modal attention fusion are further enhanced by the proposed retention module based on Retentive Network. Finally, we introduced a mimicry loss through model distillation. This enabled our model to enhance collaborative learning and improve generalisation performance. Our method is evaluated on KVQA, FVQA, PQ, and PQL datasets, demonstrating state-of-the-art performance.

关键词： visualization Accuracy Federated learning Knowledge based systems Signal processing Cognition Question answering (information retrieval) Acoustics Speech processing

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XRXL: A System for Immersive visualization in Large Lectures

XRXL: A System for Immersive Visualization in Large Lectures

引用

ieee Annual International Symposium Virtual Reality

作者： Kabir Batra Zirui Zhang Shuwen Yang Anima Agrawal Yiyin Gu Bedrich Benes Alejandra Magana Voicu Popescu Purdue University

ISBN: (数字)9798331536459

ISBN: (纸本)9798331536466

This paper describes XRXL, an extended-reality system for increasing student engagement in large lectures. Students wear XR headsets to see 3D visualizations controlled by the instructor. The instructor can virtually retract the roof and walls of the classroom to allow for large-scale visualizations that extend beyond the physical boundaries of the classroom, or to turn the classroom into a 360° theater. The instructor can also partition the classroom into small groups of students and to assist individual groups as needed. XRXL was tested in an IRB-approved user study with 82 students in the context of a mock-lecture on neural networks. To the best of our knowledge, the study is the largest deployment of a co-located collaborative XR application to date. The study shows that students had a favorable opinion of XRXL, that XRXL had a low task load, an acceptable usability level, and that it did not cause cybersickness.

关键词： Headphones visualization Three-dimensional displays Cybersickness Neural networks Collaboration User interfaces Usability Augmented reality

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High-Frequency Vision-Based Tactile Sensor with Embedded Transparent Piezoelectric Module for Humanoid Robotic Perception

High-Frequency Vision-Based Tactile Sensor with Embedded Tra...

引用

ieee International conference on Micro Electro Mechanical Systems

作者： Zhengyi Xie Chunpeng Jiang Haoxiang Jiang Yimeng Sun Bin Yang Jingquan Liu National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong University Shanghai China Department of Micro/Nano Electronics School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Zhangjiang Laboratory China Shanghai University of Electric Power China

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

This paper reports a novel composite high-frequency vision-based tactile sensor with an embedded transparent MEMS-based piezoelectric module. Taking advantage of the transparency of the piezoelectric module, the sensor can simultaneously capture high-quality image signals and piezoelectric signals. By combining high-frequency piezoelectric signals with the interpolation algorithm, the sensor enhances the frame rate of image signals, enabling the acquisition of high-frequency, high-quality image signals. Additionally, this integrated approach offers advantages in terms of miniaturization and low cost. This research provides a new direction for the development of humanoid robotic perception and has broad application potential.

关键词： Micromechanical devices Human computer interaction Interpolation visualization Image resolution Heuristic algorithms Multimodal sensors Tactile sensors Humanoid robots Signal resolution

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Unpaved Road Damage Detection via Semantic Segmentation: Enabling Smarter ADAS

Unpaved Road Damage Detection via Semantic Segmentation: Ena...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), ieee conference on

作者： Ruchi Tripathi S. Indu Rohit Kumar Department of ECE Delhi Technological University Delhi India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The visual assessment of damages on unpaved roads, which lack the structured features of paved roads, is one of the primary issues facing the Advanced Driver Assistance System (ADAS). Most existing solutions focus on well-maintained roads with high-quality data and neglect the vehicles operating on unpaved or unstructured roads. This paper proposes a novel deep-learning approach for damage detection on unpaved roads using a UNet architecture with a ResNet-18 backbone. The encoder is ResNet-18, a lightweight network that uses residual connections to capture hierarchical information and alleviate the vanishing gradient issue effectively. Experiments are conducted on the Road Transverse Knowledge (RTK) dataset, which contains low-resolution images and challenging road conditions. The experiments are performed using three architectures—U-Net, LinkNet, and Feature Pyramid Network (FPN)—each tested with ResNet-18, InceptionV3, and VGG16 as the backbone. The results demonstrate that the UNet with ResNet 18 backbone achieves an IoU value of 73.92(%), and F1 Score value of 82.98(%). The U-Net with ResNet-18 backbone model outperforms other state-of-the-art (SOTA) models in segmenting damages on unpaved roads.

关键词： Deep learning visualization Technological innovation Roads Semantic segmentation Advanced driver assistance systems Vehicles

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Cloud-Enabled Regression Model for Enhanced Patient Authentication Systems

Cloud-Enabled Regression Model for Enhanced Patient Authenti...

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2025 International conference on Visual Analytics and Data visualization, ICVADV 2025

作者： Sindhuja, K. Thanalakshmi, S. Jai Ganesh, B. Muthumarilakshmi, S. Ganeshbabu, T.R. Sujatha, S. J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Gojan School of Business and Technology Department of Management Studies Tamil Nadu Chennai India R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Electronics and Communication Tamil Nadu Namakkal India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331521394

Protecting private health information and facilitating easy access to healthcare services depends on reliable patient authentication. Improving patient authentication systems via cloud computing and Artificial Intelligence (AI) approaches is the main topic of this paper. It uses linear regression (LR) models to provide a scalable solution that uses big datasets to enhance authentication efficiency and accuracy by using cloud-based platforms. LR examines several authentication elements to predict and validate patient identities, such as user behavior patterns and biometric data. The proposed solution aims to reduce authentication failures and mitigate possible security concerns while preserving a user-friendly experience. With better performance in terms of authentication accuracy and system responsiveness, experimental findings reveal that the cloud enabled strategy is beneficial. This system provides valuable information for future research on strong patient authentication systems and shows how cloud computing and AI may work together to improve healthcare security. © 2025 ieee.

关键词： Sensitive data

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Mixed Reality 6D Object Pose Estimation using Deep Learning for Visual Markerless Surgical Navigation

Mixed Reality 6D Object Pose Estimation using Deep Learning ...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), ieee conference on

作者： Michał Trojak Artur Jurgas Maciej Stanuch Łukasz Kownacki Andrzej Skalski AGH University of Krakow MedApp S.A. MedApp S.A. Department of Diagnostic Imaging European Health Center Otwock

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

Modern surgical navigation systems usually use external trackers to estimate tool pose. They recognize and track markers such as ArUco/QR codes or reflective spheres. As a consequence, there is additional equipment in the operating room that must be calibrated prior to each procedure. This paper introduces a novel approach that uses only visual information from a Microsoft HoloLens 2 mixed reality headset to estimate the pose of the object. The proposed method is based on PVNet to determine the position and orientation of objects in RGB images and transforms the parameters into the headset coordinate system by incorporating depth data from the built-in ToF camera. The experiments demonstrated the angle estimation error equal to 1.91 ◦ ±1.16 ◦ for orientation angles between 0 ◦ and 90 ◦ and the translation estimation error equal to 0.64±0.19 cm for translations between 0 cm and 45 cm. Those errors meet clinical requirements in terms of accuracy and allow to use the system in practical conditions.

关键词： Headphones Deep learning visualization Estimation error Translation Accuracy Navigation Pose estimation Surgery Mixed reality

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Dual-Environment Dehazing Using Enhanced U-Net for Indoor and Outdoor Scenarios

Dual-Environment Dehazing Using Enhanced U-Net for Indoor an...

引用

ieee Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Swaroop Bhowmik Ajay Sharma Ramraj Dangi School of Computing Science and Engineering VIT Bhopal University Sehore M.P. India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

In this paper, we propose an Enhanced U-Net architecture designed to achieve 99 percent dehazing efficiency for both indoor and outdoor images. The core enhancement involves replacing standard convolution layers in the encoder with dilated convolution layers, which expand the receptive field without increasing the number of parameters. This modification allows the model to capture broader contextual information, crucial for dehazing images with complex environmental variations. Additionally, we replace the conventional ReLU activation function with Leaky ReLU, which prevents neuron inactivity by allowing a small gradient for negative inputs. This choice enhances the network’s learning capability, especially in scenarios involving low-intensity regions or darker images, which are common in dehazing tasks. Experimental results demonstrate that our architecture outperforms traditional U-Net models, achieving superior performance in terms of PSNR, SSIM, and visual clarity, particularly for challenging haze conditions in diverse lighting and scene settings. The architecture efficiently balances model complexity and dehazing precision, making it a robust solution for real-world applications.

关键词： Training Adaptation models visualization Convolution Computational modeling Computer architecture Time measurement Convolutional neural networks Usability Standards

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A Novel Online Path Planning Method for UAV-Based 3D Spectrum Mapping

A Novel Online Path Planning Method for UAV-Based 3D Spectru...

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ieee conference on Wireless Communications and Networking

作者： Yiran Chen Qiuming Zhu Jie Wang Zhipeng Lin Qihui Wu Yang Huang Qiancheng Ye College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Constructing three-dimensional (3D) radio environment maps (REMs) has emerged as a promising solution to visualize the spectrum information over the geographical map. In this paper, we propose a novel online path planning method for unmanned aerial vehicle (UAV)-based 3D spectrum mapping in unknown environments. The UAV can effectively collect spectrum data along dynamically planned paths while adhering to budget constraints. We formulate the path planning problem by a surrogate objective that maximizes the information gain along the sampling path. Specifically, a Gaussian process (GP) is adopted to estimate the spatial distribution of received signal strength (RSS) based on observations. A goal location decision algorithm based on the negative integrated posterior variance (NIPV) criterion is developed, which identifies high-value locations by maximizing the reduction in uncertainty. Besides, an uncertainty-aware local path planner is introduced to optimize sampling paths during flight. Simulation results demonstrate that it achieves at least a 66.49% improvement in REM construction accuracy and 42.74% reduction in mapping uncertainty compared to traditional methods.

关键词： Three-dimensional displays Uncertainty Accuracy Graphical models Simulation Data visualization Gaussian processes Autonomous aerial vehicles Path planning Distribution functions

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LARACROFT: Pilot test of Laparoscopy with Augmented Reality Adaptations for a Collaborative Framework

LARACROFT: Pilot test of Laparoscopy with Augmented Reality ...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), ieee conference on

作者： António Salgueiro Mona Zavichi Anderson Maciel Catarina Moreira Francisca Pais Leite Cátia Botelho Costa Joaquim Jorge INESC-ID / Instituto Superior Técnico Universidade de Lisboa Portugal Data Science Institute / INESC-ID University of Technology Sydney Hospital Da Luz Learning Health Luz Saúde Lisboa Hospital Da Luz Learning Health Luz Saúde Lisboa Portugal

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

Augmented reality (AR) enables laparoscopy surgeons to access a wealth of information and real-time visualizations, enhancing their understanding of anatomical structures and surgical procedures. While numerous studies focused on using AR for individual surgeons, we explore telementoring and cooperation as key factors to enable experienced surgeons to guide better and mentor their counterparts in real-time. Building upon the foundations laid by the "Laparoscopy with augmented reality adaptations" platform, the introduction of telecollaboration in a virtual shared environment allowed us to study the impact of this new operational scenario on surgeons’ performance, assessing how the virtual world adds value to guided surgery.

关键词： Laparoscopes Training visualization Three-dimensional displays Surgery User interfaces Needles Motors Real-time systems Augmented reality

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