OP: Collaborative Research: Novel Feature-Based, Randomized Methods for Large-Scale Inversion

OP:协作研究:用于大规模反演的基于特征的新颖随机方法

基本信息

  • 批准号:
    1720291
  • 负责人:
  • 金额:
    $ 28.41万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2017
  • 资助国家:
    美国
  • 起止时间:
    2017-09-01 至 2021-08-31
  • 项目状态:
    已结题

项目摘要

The desire to form an image of a region of space from externally collected data arises in applications ranging from detecting and characterizing cancers in the body, to quantifying the distribution of water, oil, or subsurface pollutants, and to the timely accurate identification of explosives in crowded venues. The physics associated with signal propagation and sensing in these problems creates substantial computational challenges for transforming raw data into useful information. The research team in this project aims to develop computational methods that greatly reduce the cost of real time imaging by providing improvements in statistical inverse theory, numerical inversion methods, simulation models, and hybrid imaging models. The main thrusts of the project will be tested on imaging applications in medical tomography, environmental remediation, and airport security imaging. The techniques form the basis for addressing analogous problems associated with inversion of optical signals across a wide range of spatial and temporal scales. As part of the project, a modular course will be developed to teach these new methods at the graduate level. The course materials will be made available over the internet.The large-scale imaging, or inverse, problems addressed by this collaborative team require the minimization of a parameter-dependent function that expresses the misfit of predicted measurements for a candidate image and actual measurement data. The potentially large number of parameters must be minimized over an ever-increasing huge number of measurements, while concurrently some unknown set of the data may be redundant. Detailed images, however, are not always needed for addressing relevant, practical questions and decision making. A combination of computational techniques will be developed to make large-scale parameter-dependent minimization computationally feasible. Furthermore, novel efficient approaches for inferring critical image features will be developed, obviating need for complete reconstruction of an image. The research builds on recent methods that exploit randomization to compute accurate estimates of solutions at greatly reduced computational cost, and on the efficient construction of smaller, approximate, reduced order numerical models that are accurate for relevant sets of parameters, and thus reduce the cost of full simulation of the sensing physics. Probabilistic approaches for inference of critical image features that guide image interpretation and decision making will be developed. The mathematics associated with this approach requires these methods to capitalize on other new tools also under development in this project.
从外部收集的数据形成空间区域的图像的期望出现在从检测和表征体内癌症到量化水、油或地下污染物的分布以及在拥挤场所中及时准确识别爆炸物的应用中。在这些问题中,与信号传播和传感相关的物理学为将原始数据转换为有用信息带来了巨大的计算挑战。该项目的研究小组旨在通过提供统计反演理论、数值反演方法、模拟模型和混合成像模型的改进,开发大大降低真实的时间成像成本的计算方法。 该项目的主要目标将在医疗断层扫描、环境修复和机场安全成像中的成像应用上进行测试。这些技术形成了解决与跨越宽范围的空间和时间尺度的光信号的反转相关联的类似问题的基础。作为该项目的一部分,将开发一个模块课程,在研究生一级教授这些新方法。课程材料将通过互联网提供。这个合作团队解决的大规模成像或逆问题需要最小化参数依赖函数,该函数表示候选图像和实际测量数据的预测测量的失配。潜在的大量参数必须在不断增加的大量测量中被最小化,而同时一些未知的数据集可能是冗余的。 然而,详细的图像并不总是需要解决相关的实际问题和决策。将开发一种计算技术的组合,使大规模的参数依赖的最小化计算可行。 此外,将开发用于推断关键图像特征的新的有效方法,从而避免对图像的完全重建的需要。该研究建立在最近的方法,利用随机化计算的解决方案的准确估计,大大降低了计算成本,并有效地构建更小的,近似的,降阶的数值模型,是准确的相关参数集,从而降低成本的传感物理的全面模拟。将开发用于推断指导图像解释和决策的关键图像特征的概率方法。与这种方法相关的数学要求这些方法利用本项目正在开发的其他新工具。

项目成果

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Eric Miller其他文献

Dynamic Modeling of Vapor Compression Cycle Systems
蒸汽压缩循环系统的动态建模
  • DOI:
  • 发表时间:
    2012
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Eric Miller
  • 通讯作者:
    Eric Miller
Symptom Severity in Youths with Attention Deficit Hyperactivity Disorder Associated with Normalizing Effects of Treatment on fMRI Response during a Stop Signal Task
患有注意力缺陷多动障碍的青少年的症状严重程度与停止信号任务期间功能磁共振成像反应正常化治疗效果相关
  • DOI:
    10.1101/599803
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Catherine E. Hegarty;Mohan W. Gupta;Eric Miller;Kevin H Terashima;S. Loo;J. McCracken;J. McGough;R. Bilder;R. Poldrack;A. Galván;S. Bookheimer
  • 通讯作者:
    S. Bookheimer
Testing on novel neutron detectors as alternative to <sup>3</sup>He for security applications
  • DOI:
    10.1016/j.nima.2012.07.025
  • 发表时间:
    2012-12-22
  • 期刊:
  • 影响因子:
  • 作者:
    Paolo Peerani;Alice Tomanin;Sara Pozzi;Jennifer Dolan;Eric Miller;Marek Flaska;Marco Battaglieri;Raffaella De Vita;Luisa Ficini;Giacomo Ottonello;Giovanni Ricco;Geraint Dermody;Calvin Giles
  • 通讯作者:
    Calvin Giles
Estimating the Breakeven Cost of Delivered Electricity To Charge Class 8 Electric Tractors
估算 8 级电动拖拉机充电的电力收支平衡成本
  • DOI:
    10.2172/1894645
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Jesse Bennett;Partha P. Mishra;Eric Miller;Brennan Borlaug;A. Meintz;A. Birky
  • 通讯作者:
    A. Birky
sup68/supGa-DOTATATE PET-Based Radiation Contouring Creates More Precise Radiation Volumes for Patients With Meningioma
68Ga-DOTATATE 正电子发射断层扫描(PET)基于辐射轮廓描绘为脑膜瘤患者创建更精确的辐射体积
  • DOI:
    10.1016/j.ijrobp.2022.04.009
  • 发表时间:
    2022-07-15
  • 期刊:
  • 影响因子:
    6.500
  • 作者:
    Haley K. Perlow;Michael Siedow;Yevgeniya Gokun;Joseph McElroy;Jennifer Matsui;Wesley Zoller;Sasha Beyer;Andrea Arnett;Dukagjin Blakaj;Daniel Boulter;Joel Fritz;Eric Miller;Raju Raval;Christopher Kleefisch;Joseph Bovi;Joshua D. Palmer
  • 通讯作者:
    Joshua D. Palmer

Eric Miller的其他文献

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{{ truncateString('Eric Miller', 18)}}的其他基金

Collaborative Research: EAGER-DynamicData: Probabilistic Analysis of Dynamic X-ray Diffraction Data: Toward Validated Computational Models for Polycrystalline Plasticity
合作研究:EAGER-DynamicData:动态 X 射线衍射数据的概率分析:建立经过验证的多晶塑性计算模型
  • 批准号:
    1462387
  • 财政年份:
    2015
  • 资助金额:
    $ 28.41万
  • 项目类别:
    Standard Grant
Collaborative Research: CI-P: Computationally-enhanced optical imaging infrastructure
合作研究:CI-P:计算增强光学成像基础设施
  • 批准号:
    1059314
  • 财政年份:
    2011
  • 资助金额:
    $ 28.41万
  • 项目类别:
    Standard Grant
Revitalization of the SJC Chemistry Instrumentation Laboratory
SJC化学仪器实验室的振兴
  • 批准号:
    0963485
  • 财政年份:
    2010
  • 资助金额:
    $ 28.41万
  • 项目类别:
    Standard Grant
Multi-modal, shape-based Inverse Methods for the Characterization of DNAPL Source Zone Architecture
用于表征 DNAPL 源区架构的多模态、基于形状的反演方法
  • 批准号:
    0838313
  • 财政年份:
    2009
  • 资助金额:
    $ 28.41万
  • 项目类别:
    Continuing Grant
Toward a Unified Approach to Diffuse Wave Inverse Problems
漫波反问题的统一方法
  • 批准号:
    0208548
  • 财政年份:
    2002
  • 资助金额:
    $ 28.41万
  • 项目类别:
    Continuing Grant
Inverse Scattering Models and Algorithms for Functional Brain Imaging with Diffuse Optical Wavefields
漫射光波场功能性脑成像的逆散射模型和算法
  • 批准号:
    0139968
  • 财政年份:
    2002
  • 资助金额:
    $ 28.41万
  • 项目类别:
    Continuing Grant
Symposium on RNA Biology III to be held at the North Carolina Research Center, Triangle Park, N.C., October 15-19, 1999
RNA 生物学研讨会 III 将于 1999 年 10 月 15 日至 19 日在北卡罗来纳州三角公园北卡罗来纳研究中心举行
  • 批准号:
    9900513
  • 财政年份:
    1999
  • 资助金额:
    $ 28.41万
  • 项目类别:
    Standard Grant
CAREER: An Integrated Approach to the Study of Inverse Methods in Electrical Engineering
职业:电气工程逆向方法研究的综合方法
  • 批准号:
    9623721
  • 财政年份:
    1996
  • 资助金额:
    $ 28.41万
  • 项目类别:
    Standard Grant
U.S.-Industrialized Countries Exchange of Scientists and Engineers, 1986/1987 Competition
美国与工业化国家科学家和工程师交流,1986/1987 年竞赛
  • 批准号:
    8603374
  • 财政年份:
    1986
  • 资助金额:
    $ 28.41万
  • 项目类别:
    Standard Grant
U.S.-Industrialized Countries Exchange of Scientists and Engineers, 1986/1987 Competition
美国与工业化国家科学家和工程师交流,1986/1987 年竞赛
  • 批准号:
    8696100
  • 财政年份:
    1986
  • 资助金额:
    $ 28.41万
  • 项目类别:
    Standard Grant

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    $ 28.41万
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  • 批准号:
    1709275
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  • 批准号:
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