期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到4篇文章
< 1 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Target Tracking and Classification Using Compressive Measurements of MWIR and LWIR Coded Aperture Cameras 认领 引用 被引量：4
1
作者 Chiman Kwan Bryan Chou +4 位作者 Jonathan Yang Akshay Rangamani Trac Tran Jack Zhang Ralph Etienne-Cummings 《Journal of Signal and Information Processing》 2019年第3期73-95,共23页
Pixel-wise Code Exposure (PCE) camera is one type of compressive sensing camera that has low power consumption and high compression ratio. Moreover, a PCE camera can control individual pixel exposure time that can ena... Pixel-wise Code Exposure (PCE) camera is one type of compressive sensing camera that has low power consumption and high compression ratio. Moreover, a PCE camera can control individual pixel exposure time that can enable high dynamic range. Conventional approaches of using PCE camera involve a time consuming and lossy process to reconstruct the original frames and then use those frames for target tracking and classification. In this paper, we present a deep learning approach that directly performs target tracking and classification in the compressive measurement domain without any frame reconstruction. Our approach has two parts: tracking and classification. The tracking has been done using YOLO (You Only Look Once) and the classification is achieved using Residual Network (ResNet). Extensive experiments using mid-wave infrared (MWIR) and long-wave infrared (LWIR) videos demonstrated the efficacy of our proposed approach. 展开更多
关键词 Target Tracking Classification Compressive Sensing MWIR LWIR YOLO ResNet Infrared Videos
暂未订购 下载PDF
Digital staining in optical microscopy using deep learning-a review 认领 引用 被引量：6
2
作者 Lucas Kreiss Shaowei Jiang +13 位作者 Xiang Li Shiqi Xu Kevin C.Zhou Kyung Chul Lee Alexander Mühlberg Kanghyun Kim Amey Chaware Michael Ando Laura Barisoni Seung Ah Lee Guoan Zheng Kyle J.Lafata Oliver Friedrich Roarke Horstmeyer 《PhotoniX》 SCIE EI 2023年第1期64-95,共32页
Until recently,conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics,fundamental research and biotechnology.Despite this... Until recently,conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics,fundamental research and biotechnology.Despite this role as gold-standard,staining protocols face several challenges,such as a need for extensive,manual processing of samples,substantial time delays,altered tissue homeostasis,limited choice of contrast agents,2D imaging instead of 3D tomography and many more.Label-free optical technologies,on the other hand,do not rely on exogenous and artificial markers,by exploiting intrinsic optical contrast mechanisms,where the specificity is typically less obvious to the human observer.Over the past few years,digital staining has emerged as a promising concept to use modern deep learning for the translation from optical contrast to established biochemical contrast of actual stainings.In this review article,we provide an in-depth analysis of the current state-of-the-art in this field,suggest methods of good practice,identify pitfalls and challenges and postulate promising advances towards potential future implementations and applications. 展开更多
关键词 Deep learning Digital staining Optical microscopy Virtual staining In-silica Pseudo-H&E Virtual fluorescence Generative models Image translation
Improving risk stratification for 2022 European LeukemiaNet favorablerisk patients with acute myeloid leukemia 认领 引用 被引量：2
3
作者 Kellie J.Archer Han Fu +17 位作者 Krzysztof Mrózek Deedra Nicolet Alice S.Mims Geoffrey L.Uy Wendy Stock John C.Byrd Wolfgang Hiddemann Klaus H.Metzeler Christian Rausch Utz Krug Cristina Sauerland Dennis Görlich Wolfgang E.Berdel Bernhard J.Woermann Jan Braess Karsten Spiekermann Tobias Herold Ann-Kathrin Eisfeld 《The Innovation》 EI 2024年第6期89-99,共11页
Assignment of patients diagnosed with acute myeloid leukemia(AML)to the 2022 European LeukemiaNet(ELN)favorable genetic risk group has important clinical implications,as allogeneic stem cell transplantation in first c... Assignment of patients diagnosed with acute myeloid leukemia(AML)to the 2022 European LeukemiaNet(ELN)favorable genetic risk group has important clinical implications,as allogeneic stem cell transplantation in first complete remission(CR)is not advised due to a relatively good outcome of patients receiving chemotherapy alone and transplant-associated mortality.However,not all favorable genetic risk patients experience long-term relapse-free survival(RFS),making recognition of patients who would most likely be cured of high importance. 展开更多
关键词 patients myeloid Leukemia
Quantum computing in intelligent transportation systems:A survey 认领 引用
4
作者 Yifan Zhuang Talha Azfar +4 位作者 Yinhai Wang Wei Sun Xiaokun Wang Qianwen Guo Ruimin Ke 《Chain》 2024年第2期138-149,共12页
Quantum computing,a field utilizing the principles of quantum mechanics,promises great advancements across various industries.This survey paper is focused on the burgeoning intersection of quantum computing and intell... Quantum computing,a field utilizing the principles of quantum mechanics,promises great advancements across various industries.This survey paper is focused on the burgeoning intersection of quantum computing and intelligent transportation systems,exploring its potential to transform areas such as traffic optimization,logistics,routing,and autonomous vehicles.By examining current research efforts,challenges,and future directions,this survey aims to provide a comprehensive overview of how quantum computing could affect the future of transportation. 展开更多
关键词 quantum computing quantum machine learning intelligent transportation systems vehicle routing autonomous driving
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈