NSF/CBMS Regional Conference in Mathematical Sciences - Imaging in Random Media - Spring 2008

NSF/CBMS 数学科学区域会议 - 随机介质成像 - 2008 年春季

• 批准号: 0735368
• 负责人: Liliana Borcea
• 金额: $ 3.3万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-11-15 至 2008-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0735368&HistoricalAwards=false
• 关键词: NSF; CBMS; Regional; Conference; Mathematical; NSF; CBMS; Regional; Conference; Mathematical

The goal of this conference is to introduce a mathematically oriented audience to sensor array imaging in cluttered media. This is an emerging interdisciplinary area in applied mathematics, with roots in wave propagation, random media, statistics, optimization and numerical analysis. Over the years, our ability to deploy very large sensor arrays and to collect large data sets has increased steadily, but the available imaging methodology is reaching its limits. Its foundations must be reexamined in an interdisciplinary framework, its resolution limits must be reassessed and the robustness to noise and clutter has to be addressed.Some very interesting results have emerged in recent years: Novel adaptive imaging algorithms for cluttered media have been developed and put in a clear mathematical framework. New approaches to resolution theory, such as the concept of statistical stability of the imaging process and quantification of resolution loss caused by clutter have been advanced. Novel image enhancement-techniques have been obtained using tools from statistics, wave propagation in random media and modern optimization. The principal lecturer of theconference, Professor George C. Papanicolaou, has been at the forefront of this research. His lectures will introduce mathematicians from academia, the oil industry and medical imaging to a new and mathematically advanced technology in imaging science. This is a technology with potential for a big impact in crucial applications such as seismic exploration, ultrasonic, non-destructive testing of aging concrete structures, foliage or ground penetrating radar, etc.

这次会议的目的是将以数学为导向的受众介绍到混乱的媒体中的传感器阵列成像。 这是应用数学中新兴的跨学科领域，其根源在波传播，随机介质，统计，优化和数值分析中。 多年来，我们部署非常大的传感器阵列和收集大型数据集的能力稳步增加，但是可用的成像方法已经达到了限制。必须在跨学科的框架中重新审查其基础，必须重新评估其分辨率限制，并且必须解决噪音和混乱的稳健性。近年来，有些非常有趣的结果出现了：新颖的适应性成像算法的杂物媒体已经开发出来，并在一个清晰的数学框架中发展了。解决了分辨率理论的新方法，例如成像过程的统计稳定性概念以及杂物引起的分辨率损失的量化。使用来自统计数据的工具，随机介质中的波传播和现代优化获得了新型图像增强技术。 会议的主要讲师乔治·帕帕尼科洛教授一直处于这项研究的最前沿。他的讲座将介绍学术界，石油行业和医学成像的数学家，以进入成像科学的新技术先进技术。这是一项具有对关键应用产生重大影响的技术，例如对衰老混凝土结构，叶子或地面穿透性雷达的超声探索，超声波，无损的测试，等等。