检索结果-内蒙古大学图书馆

IEEE Symposium on Network Operations and Management

作者： Árpád Somogyi Tamás Tettamanti Pál Varga Zsolt Szalay Dániel Baranyai Tamás Lovas Dept. of Photogrammetry and Geoinformatics Fac. of Civil Engineering Budapest University of Technology and Economics Budapest Hungary Systems and Control Laboratory Institute for Computer Science and Control of the Eötvös Loránd Research Network Budapest Hungary Dept. of Telecommunications and Media Informatics Fac. of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Dept. of Automotive Technologies Fac. of Transportation Eng. and Vehicle Eng. Budapest University of Technology and Economics Budapest Hungary

One major goal of digital twin technology applied in the Architecture, Engineering, and Construction (AEC) Industry is the mapping of roads and road environments with their associated information. Such digital twins can support cost-effective testing, simulations, and the production of mapping backgrounds for automated vehicles and generally for Cooperative, Connected and Automated Mobility (CCAM) developments. The efficient workflow that leads from data acquisition to finished products depends on a number of requirements and knowledge sets. The main contribution of the paper is a general methodology of creating HD-map from point cloud and image data set. Accordingly, the most widely used survey techniques are presented and the requirements for the objects to be derived are summarized. The paper then outlines a general implementation procedure as an expert knowledge through a real-life example, where the results obtained for the ZalaZONE Automotive Proving Ground are demonstrated.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Exploring the Potential of DeepSeek-R1 Model in Transforming Healthcare Solutions: An Overview

Exploring the Potential of DeepSeek-R1 Model in Transforming...

引用

International Symposium on Digital Forensic and Security (ISDFS)

作者： Muhammad Raheel Raza Shahbaz Ahmed Fahad Ahmed Khokhar Asaf Varol Peter Faber Business School Australian Catholic University North Sydney Australia Department of Computer Science Universitat Koblenz Koblenz Germany Department of Informatics & Mathematics University of Florence Firenze FI Italy Department of Engineering Management & Technology College of Eng. & Comp. Science Uni. of Tennessee at Chattanooga Chattanooga TN US Dept. of Computer Engineering College of Eng. & Nat. Sciences Maltepe University Istanbul Turkiye

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

Over the past few decades, artificial intelligence (AI) has become more integrated into healthcare, with Large Language Models (LLMs) being a key component in improving healthcare decision-making. These LLMs' capacity to produce and interpret human-like texts has the potential to revolutionize healthcare procedures, where efficient data processing and communication are crucial. DeepSeek-Rl, created by DeepSeek AI, has drawn notice globally for its reasoning-based design that uses reinforcement learning (RL) to enhance problem-solving skills. Keeping this in view, the paper emphasizes the significance of the DeepSeek- Rl model and its application in the healthcare industry. Highlighting some hypothetical potential scenarios, we believe that DeepSeek models can playa crucial role in transforming existing medical approaches and revolutionizing the future of AI in health care.

关键词： Industries Biological system modeling Large language models Digital forensics Decision making Medical services Reinforcement learning Data processing Security Problem-solving

来源：评论

学校读者我要写书评

暂无评论

Wireless sensor network to connect isolated nodes using link assessment technique 3

Wireless sensor network to connect isolated nodes using link...

引用

3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Sundaram, B. Barani Pandey, Amit Abiko, Aschalew Tirulo Vijaykumar, Janga Genale, Adola Haile Karthika, P. Bule Hora University College of Informatics Bule hora Ethiopia College of Informatics CSI Executive Member CSRSCD BHU Ethiopia Wachemo University School of Computing and Informatics Hosana Ethiopia Bule Hora University College of Informatics Dept. of Information Technology Bule Hora Ethiopia Bule Hora University Department of Information Science Bule hora Ethiopia Kalasalingam Academy of Research and Education Department of Computer Applications Krishnankoil India

ISBN: (纸本)9780738111834

A simultaneous geography control effort is implemented for full-reachability systems and a genetic algorithm with dynamic transformation to find the breakthrough in a blaze-scene. The proposed scheme efficiently takes care of the issue of separate node for transmission run. The suggested technique makes it possible to improve the display of the genetic algorithm in order to maintain a strategic distance from the possible arrangements which fall into nearby ideal. Different experimental simulations have to be confirmed the performance quality, reliability and timeliness of designed approaches. In the Wireless Sensor established network, can connect at least two sensors in a network sensors and deliver jointly via the obtained transmission extend to form the network of total accessibility. Accordingly, excellent blaze exhaust system with complete network accessibility the proposed approaches in this paper established topology and real-time routing methods. This device supports the protection and security of individuals in a blaze scene. © 2021 IEEE.

关键词： Network topology

来源：评论

学校读者我要写书评

暂无评论

Evaluation of Synthetically Generated CT for use in Transcranial Focused Ultrasound Procedures

arXiv

引用

arXiv 2022年

作者： Liu, Han Sigona, Michelle K. Manuel, Thomas J. Chen, Li Min Dawant, Benoit M. Caskey, Charles F. Dept. of Computer Science Vanderbilt University NashvilleTN37235 United States Dept. of Biomedical Engineering Vanderbilt University NashvilleTN37235 United States Institute of Imaging Science Vanderbilt University NashvilleTN37235 United States Dept. of Radiology and Radiological Sciences Vanderbilt University NashvilleTN37235 United States Dept. of Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States

Purpose: Transcranial focused ultrasound (tFUS) is a therapeutic ultrasound method that focuses sound through the skull to a small region noninvasively and often under MRI guidance. CT imaging is used to estimate the acoustic properties that vary between individual skulls to enable effective focusing during tFUS procedures, exposing patients to potentially harmful radiation. A method to estimate acoustic parameters in the skull without the need for CT would be desirable. Approach: Here, we synthesized CT images from routinely acquired T1-weighted MRI by using a 3D patch-based conditional generative adversarial network (cGAN) and evaluated the performance of synthesized CT (sCT) images for treatment planning with tFUS. We compared the performance of sCT to real CT (rCT) images for tFUS planning using Kranion and simulations using the acoustic toolbox, k-Wave. Simulations were performed for 3 tFUS scenarios: 1) no aberration correction, 2) correction with phases calculated from Kranion, and 3) phase shifts calculated from time-reversal. Results: From Kranion, skull density ratio, skull thickness, and number of active elements between rCT and sCT had Pearson’s Correlation Coefficients of 0.94, 0.92, and 0.98, respectively. Among 20 targets, differences in simulated peak pressure between rCT and sCT were largest without phase correction (12.4 ± 8.1%) and smallest with Kranion phases (7.3 ± 6.0%). The distance between peak focal locations between rCT and sCT was less than 1.3 mm for all simulation cases. Conclusions: Real and synthetically generated skulls had comparable image similarity, skull measurements, and acoustic simulation metrics. Our work demonstrates the feasibility of replacing real CTs with the MR-synthesized CT for tFUS planning. Source code and a docker image with the trained model are available at © 2022, CC BY.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

CFNet: A Coarse-to-Fine Learning Strategy for Facial Expression Recognition

TechRxiv

引用

TechRxiv 2022年

作者： Zhang, Xiang Gu, Yu Yan, Huan Ren, Fuji Ji, Yusheng The School of Computer and Information Hefei University of Technology China The Dept. of Information Science and Intelligent Systems Univ. of Tokushima Japan The National Institute of Informatics Tokyo101-8430 Japan

Facial expression recognition (FER) is a challenging job in computer Vision due to data uncertainties rooted in the ambiguity of facial expressions. As a complement to current FER studies huddling in data-level or feature-level for suppressing such uncertainties, we propose a simple yet efficient coarse-to-fine learning strategy at task-level inspired by human beings’ emotion cognitive mode. Specifically, a child learns quickly whether his behavior is allowed by reading adults’ facial expressions for coarse attitude like positive or negative, and then adjusts accordingly via further interpreting its fine sentiment meaning like happy or angry. Similarly, we follow the same divide-and-conquer paradigm and decompose FER into two correlated easier sub-tasks: i) coarse classification for attitude analysis and ii) fine recognition for sentiment interpretation, and build a multi-branch deep network (CFNet) to tackle them in a joint manner. The key idea is to aggregate the discrete universal facial expressions into several coarse groups reflecting attitude tendency based on their empirical projections in the continuous Valence-Arousal (VA) emotion space. However, the coarse classification sub-task is inherently tougher due to its significantly larger intra-class variations compared to the fine recognition sub-task. Such discord could lead to immature learning and degrade the overall performance. To overcome this issue, CFNet leverages a synchronization mechanism to control the learning process via knowledge sharing between both sub-tasks. In addition, a novel center loss is introduced to enhance the discriminative power of the network in extracting compact intra-class representations while preserving intrinsic inter-class relationships. Experiments on three benchmark datasets show that our method achieves state-of-the-art performance, which demonstrates its superiority. The code is available at https://***/codpub/CFNet. © 2022, CC BY.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Low-Energy and Reduced-Area Hardware Architecture for the Versatile Video Coding FME

Low-Energy and Reduced-Area Hardware Architecture for the Ve...

引用

Symposium on Integrated Circuits and Systems Design (SBCCI)

作者： Vanio Rodrigues Filho Ismael Seidel Nicole Citadin Marcio Monteiro Mateus Grellert José Luis Güntzel Dept. of Informatics and Statistics (INE) Embedded Computing Lab. (ECL) Computer Science Graduate Program (PPGCC) Federal University of Santa Catarina (UFSC) Florianópolis Brazil Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

The Versatile Video Coding (VVC) standard incorporates a number of new tools to significantly improve the coding efficiency over its predecessor, the High Efficiency Video Coding (HEVC), at the cost of increased complexity. Hence, to meet real-time and energy efficiency requirements, portable devices must adopt techniques to effectively reduce the VVC complexity, including customized hardware accelerators. This work presents a low-energy hardware architecture design for the Fractional Motion Estimation (FME), which is amongst the most critical VVC and HEVC encoder steps. The proposed architecture reduces the FME complexity by operating over a subset of candidates selected to reduce the energy and area demands without compromising too much the coding efficiency. At the average BD-Rate of only 0.34% and 0.28% for the Low Delay with P slices only (LD-P) and Random Access (RA) configurations, respectively, the proposed architecture achieves a 75.5% energy consumption reduction when compared to a baseline FME architecture while occupying only 36.6 % of the area.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Synthetic CT Skull Generation for Transcranial MR Imaging–Guided Focused Ultrasound Interventions with Conditional Adversarial Networks

arXiv

引用

arXiv 2022年

作者： Liu, Han Sigona, Michelle K. Manuel, Thomas J. Chen, Li Min Caskey, Charles F. Dawant, Benoit M. Dept. of Computer Science Vanderbilt University NashvilleTN37235 United States Dept. of Biomedical Engineering Vanderbilt University NashvilleTN37235 United States Institute of Imaging Science Vanderbilt University NashvilleTN37235 United States Dept. of Radiology and Radiological Sciences Vanderbilt University NashvilleTN37235 United States Dept. of Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States

Transcranial MRI-guided focused ultrasound (TcMRgFUS) is a therapeutic ultrasound method that focuses sound through the skull to a small region noninvasively under MRI guidance. It is clinically approved to thermally ablate regions of the thalamus and is being explored for other therapies, such as blood brain barrier opening and neuromodulation. To accurately target ultrasound through the skull, the transmitted waves must constructively interfere at the target region. However, heterogeneity of the sound speed, density, and ultrasound attenuation in different individuals’ skulls requires patient-specific estimates of these parameters for optimal treatment planning. CT imaging is currently the gold standard for estimating acoustic properties of an individual skull during clinical procedures, but CT imaging exposes patients to radiation and increases the overall number of imaging procedures required for therapy. A method to estimate acoustic parameters in the skull without the need for CT would be desirable. Here, we synthesized CT images from routinely acquired T1-weighted MRI by using a 3D patch-based conditional generative adversarial network (cGAN) and evaluated the performance of synthesized CT images for treatment planning with transcranial focused ultrasound. A dataset of 86 paired CT and T1-weighted MR images were randomly split so that 66 images were used for training, 10 for validation and parameter tuning, and 10 for acoustic testing. We compared the performance of synthetic CT (sCT) to real CT (rCT) images using an open-source treatment planning software, Kranion, and found that the number of active elements, skull density ratio, and skull thickness between rCT and sCT had Pearson’s Correlation Coefficients of 0.989, 0.915, and 0.941, respectively, suggesting strong positive linear correlation. Of a total of 990 elements 95.7 ± 1.4% of active and inactive elements overlapped between rCTs and sCTs. Simulations using the acoustic toolbox, k-Wave, resulted in

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Auto Thresholding Sputum Color Image Segmentation For Tuberculosis Diagnosis Base On Intuitionistic Fuzzy

Auto Thresholding Sputum Color Image Segmentation For Tuberc...

引用

International Conference on computer Engineering, Network and Intelligent Multimedia (CENIM)

作者： Sari Ayu Wulandari I Ketut Eddy Purnama Mauridhi Hery Purnomo Dept. of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Biomedical Engineering Faculty of Electrical Engineering Universitas Dian Nuswantoro COE Science and Technology Universitas Dian Nuswantoro Semarang Indonesia

ISBN: (纸本)9781665476515

In this paper, an automatic algorithm for detecting the number of Mycobacterium tuberculosis is presented from the AFB smear image on I and V-shaped colonies, by applying fuzzy Intuitionistic based on the auto-thresholding segmentation method. Acid-fast bacteria, hereinafter referred to as AFB, are a group of bacteria that have unique characteristics, namely that they can prevent acid decolorization during the staining process, so that when sputum preparations are given a blue color, the AFB will retain its red color. One of the main problems in detecting the number of bacteria based on AFB segmentation is due to differences in light intensity and contrast (due to different lighting distributions). This study aims to segment the AFB images data as a whole, without dividing 1 bacterium into several parts. The segmentation process uses the stages of patch preparation, mask preparation and UNet Architecture. In mask preparation process, it is compare 3 color threshold models (grayscale then black and white - Adaptive Histogram then black and white - Fuzzy Intuitionistic- then black and white), all three are then segmented using the UNet method. The novelty of this paper is the creation of an input image mask. In this research, an optimization method is used with a maximal entropy approach. The idea is to find the maximum degree of disorder by calculating the entropy on the modified input image matrix. From the experimental results, it was found that the method that has high accuracy in segmenting AFB on I and V-shaped colonie is the fuzzy intuitionistic method, with an accuracy rate of 94.78%.

关键词： Image segmentation Microorganisms Image recognition Image color analysis Tuberculosis Image databases Optimization methods

来源：评论

学校读者我要写书评

暂无评论

A Benchmark for End-to-End Zero-Shot biomedical Relation Extraction with LLMs: Experiments with OpenAI Models

arXiv

引用

arXiv 2025年

作者： Brokman, Aviv Ai, Xuguang Jiang, Yuhang Gupta, Shashank Kavuluru, Ramakanth Division of Biomedical Informatics Dept. of Internal Medicine University of Kentucky United States Department of Computer Science University of Kentucky United States Department of Statistics University of Kentucky United States

Objective: Zero-shot methodology promises to cut down on costs of dataset annotation and domain expertise needed to make use of NLP. Generative large language models trained to align with human goals have achieved high zero-shot performance across a wide variety of tasks. As of yet, it is unclear how well these models perform on biomedical relation extraction (RE). To address this knowledge gap, we explore patterns in the performance of OpenAI LLMs across a diverse sampling of RE tasks. Methods: We use OpenAI GPT-4-turbo and their reasoning model o1 to conduct end-to-end RE experiments on seven datasets. We use the JSON generation capabilities of GPT models to generate structured output in two ways: (1) by defining an explicit schema describing the structure of relations, and (2) using a setting that infers the structure from the prompt language. Results: Our work is the first to study and compare the performance of the GPT-4 and o1 for the end-to-end zero-shot biomedical RE task across a broad array of datasets. We found the zero-shot performances to be proximal to that of fine-tuned methods. The limitations of this approach are that it performs poorly on instances containing many relations and errs on the boundaries of textual mentions. Conclusion: Recent large language models exhibit promising zero-shot capabilities in complex biomedical RE tasks, offering competitive performance with reduced dataset curation and NLP modeling needs at the cost of increased computing, potentially increasing medical community accessibility. Addressing the limitations we identify could further boost reliability. The code, data, and prompts for all our experiments are publicly available: https://***/bionlproc/ZeroShotRE © 2025, CC BY.

关键词： Large datasets

来源：评论

学校读者我要写书评

暂无评论

Multi-label transfer learning for identifying lung diseases using chest X-rays 2

Multi-label transfer learning for identifying lung diseases ...

引用

2nd IEEE International Conference on Electronic Engineering, ICEEM 2021

作者： El-Fiky, Azza Shouman, Marwa Ahmed Hamada, Salwa El-Sayed, Ayman Karar, Mohamed Esmail Electronics Research Institute Department of Informatics Cairo Egypt Menoufia University Faculty of Electronic Engineering Department of Computer Science and Engineering Minuf Egypt Menoufia University Faculty of Electronic Engineering Dept. Industrial Electronics and Control Engineering Minuf Egypt

ISBN: (纸本)9781665418423

Chest radiography presents one of the main medical imaging modalities for diagnosing lung diseases. To assist radiologists during interventional procedures, this paper aims at proposing a transfer learning-based classifier to automatically identify 14 different thoracic diseases in Chest X-ray (CXR) images. The proposed method is relied on deep residual neural networks with 50 layers (ResNet-50) to accomplish the diagnostic task of many chest diseases. In this study, a public dataset of 112, 120 frontal radiograph images for Chest X-ray has been used for validating the proposed deep learning classifier. It achieved the best performance of multi-label classification of normal and 14 different lung diseases with an average area under curve (AUC) of 0.911 and F1-score of 0.66. This study demonstrated that the proposed ResNet-50 classifier as a transfer learning model outperforms other relevant methods in the previous studies for automatic multi-label classification of chest X-rays. © 2021 IEEE.

关键词： computer aided diagnosis

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：