检索结果-内蒙古大学图书馆

32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Yihao Yang, Meng Cao, Rui School of Computer Science and Engineering Sun Yat-Sen University Guangdong Guangzhou China State Key Laboratory of Integrated Services Networks Xidian University Shaanxi Xi'an China Ministry of Education Guangdong Guangzhou China School of Information Science and Technology State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services Northwest University Shaanxi Xi'an China

ISBN: (纸本)9798400706868

Addressing the disparity in description granularity and information gap between images and text has long been a formidable challenge in text-based person retrieval (TBPR) tasks. Recent researchers tried to solve this problem by random local alignment. However, they failed to capture the fine-grained relationships between images and text, so the information and modality gaps remain on the table. We align image regions and text phrases at the same semantic granularity to address the semantic atomicity gap. Our idea is first to extract and then exploit the relationships between fine-grained locals. We introduce a novel Fine-grained Semantic Alignment with Transferred Person-SAM (SAP-SAM) approach. By distilling and transferring knowledge, we propose a Person-SAM model to extract fine-grained semantic concepts at the same granularity from images and texts of TBPR and its relationships. With the extracted knowledge, we optimize the fine-grained matching via Explicit Local Concept Alignment and Attentive Cross-modal Decoding to discriminate fine-grained image and text features at the same granularity level and represent the important semantic concepts from both modalities, effectively alleviating the granularity and information gaps. We evaluate our proposed approach on three popular TBPR datasets, demonstrating that SAP-SAM achieves state-of-the-art results and underscores the effectiveness of end-to-end fine-grained local alignment in TBPR tasks. © 2024 ACM.

关键词： Visual languages

来源：评论

学校读者我要写书评

暂无评论

Detection Method of River Floating Objects based on Edge Computing 2

Detection Method of River Floating Objects based on Edge Com...

引用

2nd International Conference on Robotics, Automation and Intelligent Control, ICRAIC 2022

作者： Li, He Jia, Huimei Zhang, Rui Yang, Shuaipeng Qi, Qinglei Liu, Tianyang Henan Costar Group Co. Ltd Nanyang China College of Information Engineering Henan University of Science and Technology Luoyang China Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang Normal University Nanyang China Sydney Business School Shanghai University Shanghai China

For the detection of objects floating in the river, most of the traditional intelligent video monitoring methods are used to monitor through manual viewing. During the traditional video data transmission and processing, the massive amount of data will put pressure on the background such as network and computing power. The new effective method is to crop and optimize YOLOV5s, add a specific image pre-processing module, and deploy it by edge computing, embed a SOC (System on Chip) chip in the web camera for real-time processing of video data. Simulation experiments prove that the above method can improve the accuracy and speed of the target detection network. © Published under licence by IOP Publishing Ltd.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

From Non-Expert to Expert: Recurrent Refined Learning for Medical image Segmentation

From Non-Expert to Expert: Recurrent Refined Learning for Me...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jiang, Ruohui Li, Changtai Ban, Xiaojuan Yin, Shihua Yao, Chao Guo, Yu Obaidat, Mohammad S. University of Science and Technology Beijing Beijing Advanced Innovation Center for Materials Genome Engineering Beijing China University of Science and Technology Beijing School of Intelligence Science and Technology Beijing China Liaoning Academy of Materials Institute of Materials Intelligent Technology Shenyang China Second Affiliated Hospital Guangxi Medical University Nanning China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China University of Jordan King Abdullah Ii School of Information Technology Amman Jordan Amity University UP Noida India

ISBN: (纸本)9798350386226

Precise lesion segmentation is essential in computer-aided diagnosis and treatment. Prevalent deep-learning-based approaches need high-quality annotations to achieve satisfying performance. However, blurring effects and infiltration hamper the accurate delineation of lesions. A common solution is to divide the annotation process into initial annotation by non-specialized personnel and subsequent modification by expert physicians, where the latent correction patterns and non-expert labels are seldom utilized effectively. To explore their helpfulness, we propose ReReNet, a recurrent refined network for lesion segmentation that learns from non-expert to expert. It achieves progressively refined segmentation results through multiple iterations with tailored discrepancy-aware supervision. During training, as the iterative process perceives discrepancies between refining and expert labels, the model gradually grasps the knowledge of turning the barely correct into the clinically accurate. We validate ReReNet's capability on three medical image segmentation (MIS) datasets, including magnetic resonance (MR) and computed tomography (CT) modalities. Comparison results indicate that the proposed approach achieves superior performance by introducing the designed recurrent mechanism and outperforms mainstream methods, demonstrating the effectiveness of mining hidden correction patterns by utilizing non-expert information. © 2024 IEEE.

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

advanced eHealth with Explainable AI: Secured by Blockchain with AI-Empowered Block Sensitivity for Adaptive Authentication 35

Advanced eHealth with Explainable AI: Secured by Blockchain ...

引用

35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Dutta, Joy Eldeeb, Hossien B. Ho, Tu Dac Khalifa University C2PS Research Center Department of Computer Science Abu Dhabi United Arab Emirates University of Cambridge Electrical Engineering Division CambridgeCB3 0FA United Kingdom Faculty of Information Technology and Electrical Engineering Trondheim Norway

ISBN: (纸本)9798350362244

This paper presents an innovative, yet secure, eHealth framework that leverages Explainable Artificial Intelligence (XAI) and blockchain technology to enhance transparency and security in the IoT-edge-cloud continuum. The framework incorporates SHapley Additive exPlanations (SHAP) to provide real-time, model-agnostic explanations for AI predictions, enabling personalized health monitoring and informed decisionmaking in healthcare. To strengthen data security, a consortium blockchain is employed, and AI is utilized to identify block data sensitivity at the edge within blockchain-integrated IoT architectures using Random Forest (RF) algorithm. This approach achieves high accuracy in validating block sensitivity, enabling efficient selection of authentication mechanisms in the proof of authentication (PoAh) consensus within the consortium blockchain. This ensures heightened protection for sensitive data and contributes to improved overall blockchain performance. The proposed framework is evaluated in an edge computing environment and demonstrates significant potential for advancing security and authentication in eHealth, representing a substantial advancement in healthcare technology. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Data science for oceanography:from small data to big data

引用

Big Earth Data 2022年第2期6卷 236-250页

作者： Chengcheng Qian Baoxiang Huang Xueqing Yang Ge Chen Department of Information and Files North China Sea Marine Forecasting Center of State Oceanic AdministrationQingdaoChina College of Computer Science and Technology Qingdao UniversityQingdaoChina Marine Division 1 Piesat Information Technology Co.LtdBeijingChina College of Information Science and Engineering Ocean University of ChinaQingdaoChina Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and TechnologyQingdaoChina

The rapid development of ocean observation technology has resulted in the accumulation of a large amount of data and this is pushing ocean science towards being *** on the types and distribution of oceanographic data,this paper analyzes the present and makes predictions for the future regarding the use of big and small data in ocean *** ocean science has not fully entered the era of big *** are two ways to expand the amount of oceanographic data to better understanding and man-agement of the *** the data level,fully exploit the potential value of big and small ocean data,and transform the limited,small data into rich,big data,will help to achieve *** the application level,oceanographic data are of great value if realize the federation of the core data owners and the *** oceanographic data will provide not only a reliable scientific basis for climate,ecological,disaster and other scientific research,but also provide an unprecedented rich source of information that can be used to make predictions of the future.

关键词： Ocean science data big data small data

来源：评论

学校读者我要写书评

暂无评论

Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

引用

computer Systems science & engineering 2023年第10期47卷 919-935页

作者： Ramya Nemani G.Jose Moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han Department of Mathematics Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVishakhapatnam CampusGITAM(Deemed to be a University)VishakhapatnamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)*** the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

关键词： Statistical data mining predictive models deep learning rainfall prediction parameter tuning

来源：评论

学校读者我要写书评

暂无评论

High responsivity and fast response 8×8β-Ga_(2)O_(3)solar-blind ultraviolet imaging photodetector array

引用

science China(Technological sciences) 2023年第11期66卷 3259-3266页

作者： SHEN GaoHui LIU Zeng TANG Kai SHA ShuLin LI Lei TAN Chee-Keong GUO YuFeng TANG WeiHua Innovation Center of Gallium Oxide Semiconductor(IC-GAO) College of Integrated Circuit Science and EngineeringNanjing University of Posts and TelecommunicationsNanjing 210023China National and Local Joint Engineering Laboratory for RF Integration and Micro-Assembly Technologies Nanjing University of Posts and TelecommunicationsNanjing 210023China Key Laboratory of Aerospace Information Materials and Physics(NUAA) MIITNanjing 211106China College of Physics MIIT Key Laboratory of Aerospace Information Materials and PhysicsKey Laboratory for Intelligent Nano Materials and DevicesNanjing University of Aeronautics and AstronauticsNanjing 211106China Advanced Materials Thrust Function HubThe Hong Kong University of Science and Technology(Guangzhou)Nansha 511458China Department of Electronic and Computer Engineering School of EngineeringThe Hong Kong University of Science and TechnologyHong Kong 999077China

In this work,an 8×8 Ga_(2)O_(3)solar-blind ultraviolet photodetector array is introduced for image sensing *** 2-in wafer-scaled Ga_(2)O_(3)thin film was grown by metalorganic chemical vapor deposition technique;and the photodetector array was fabricated through ultraviolet photolithography,lift-off,and electron-beam *** addition to the high solar-blind/visible rejection ratio of 104,every photodetector cell in the array has high performance and fast response speed,such as responsivity of 49.4 A W^(-1),specific detectivity of 6.8×10^(14)Jones,external quantum efficiency of 1.9×10^(4)%,linear dynamic range of 117.8 d B,and response time of 41 ms,respectively,indicating the high photo-response performance of the ***,the photodetector array displayed uniform responsivity with a standard deviation of~6%,and presented a sensing image of low chromatic aberration,owing to the high resolution of the photodetector *** a word,this work may contribute to developing Ga_(2)O_(3)-based optoelectronic device applications.

关键词： Ga_(2)O_(3) photodetector array imaging photo-response

来源：评论

学校读者我要写书评

暂无评论

Implementation and Evaluation of 0.6 µm CMOS Complex RCPF for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

引用

IEEJ Transactions on Electronics, information and Systems 2025年第1期145卷 25-37页

作者： Sato, Lina Fukagawa, Kanta Shouno, Kazuhiro Tanimoto, Hiroshi Muto, Cosy Moriyama, Seijiro Takahashi, Chikau Yoshino, Michitaka Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan Anagix Corporation 3-22-27 Numama Kanagawa Zushi249-0004 Japan Takamori Corporation Japan Research Center for Micro-Nano Technology Hosei University 3-11-15 Midori-cho Koganei Tokyo184-0003 Japan

This paper describes the implementation and evaluation of an RC polyphase filter (RCPF) and circuitry for measuring its frequency characteristics. The integrated circuit is fabricated on a 0.6 µm CMOS process and its supply voltage is 5 V. The integrated circuit includes a filter under test (FUT), a four-phase square wave generator, several analog switches and differential amplifiers to amplify the high-impedance signals of the RCPF. This FUT is a third-order complex RCPF whose passband is 8 − 40 MHz. This paper describes the design of these circuits in detail. This integrated circuit can select two measurement methods by means of external control signals. The first method uses a four-phase square wave. The frequency characteristics are measured by using an on-chip four-phase square wave generator driven by an external clock generator and by a spectrum analyzer tuned to the fundamental frequency. The second method uses an external network analyzer. Furthermore, this integrated circuit is equipped with a calibration circuit to measure the frequency characteristics of the circuitry except FUT by passing through the input / output signals of the FUT. As a result, the frequency characteristics of the FUT itself can be accurately measured. The effectiveness is confirmed through evaluation of the fabricated integrated circuit. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Differential amplifiers

来源：评论

学校读者我要写书评

暂无评论

Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

引用

computers, Materials & Continua 2022年第7期72卷 243-259页

作者： S.Karthik Robin Singh Bhadoria Jeong Gon Lee Arun Kumar Sivaraman Sovan Samanta A.Balasundaram Brijesh Kumar Chaurasia S.Ashokkumar Department of ECE College of Engineering and TechnologySRM Institute of Science and TechnologyVadapalaniChennai600026India Department of CSE Birla Institute of Applied Sciences(BIAS)BhimtalUttarakhand263136India Division of Applied Mathematics Wonkwang University460Iksan-DaeroIksan-SiJeonbuk54538Korea School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India Department of Mathematics Tamralipta MahavidyalayaWest Bengal721636India School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of Technology(VIT)Chennai600127India Indian Institute of Information Technology(IIIT) LucknowUttar Pradesh226002India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai602105India

Data is always a crucial issue of concern especially during its prediction and computation in digital *** paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data *** also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the *** paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and *** method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from *** proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown *** scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic *** also demonstrates the generative function forKalman-filer based prediction model and its *** paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration(CPE)for Python.

关键词： Bayesian learning model kalman filter machine learning data accuracy prediction

来源：评论

学校读者我要写书评

暂无评论

Soliton Vortex Comb Generation in an AlGaAs Microresonator for Self-Torque Pulse Synthesis

Soliton Vortex Comb Generation in an AlGaAs Microresonator f...

引用

2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Liu, Y. Chen, B. Zhou, Y. Ye, C. Cao, Q. Huang, P. Kim, C. Zheng, Y. Oxenløwe, L.K. Yvind, K. Li, J. Li, J. Zhang, Y. Dong, C. Fu, S. Zhan, Q. Wang, X. Liu, J. Pu, M. DTU Electro Department of Electrical and Photonics Engineering Technical University of Denmark Kgs. LyngbyDK-2800 Denmark State Key Laboratory of Optoelectronic Materials and Technologies School of Physics School of Electronics and Information Technology Sun Yat-sen University Guangzhou China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China Zhangjiang Laboratory Shanghai China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei China Institute of Advanced Photonics Technology School of Information Engineering Guangdong University of Technology Guangzhou China Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen China

We demonstrate bright soliton generation at room temperature within a grating-dressed AlGaAs-on-insulator microresonator and the simultaneous emission of phase-locked vortices, enabling the synthesis of self-torque pu... 详细信息

ISBN: (纸本)9781957171395

关键词： Microcavities

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：