ATD: Blind and Template Assisted Source Separation Algorithms with Applications to Spectroscopic Data

ATD:盲和模板辅助源分离算法及其在光谱数据中的应用

基本信息

  • 批准号:
    1222507
  • 负责人:
  • 金额:
    $ 45.11万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2012
  • 资助国家:
    美国
  • 起止时间:
    2012-09-01 至 2016-08-31
  • 项目状态:
    已结题

项目摘要

Spectroscopic sensing techniques are powerful analytical tools for detecting and identifying chemical and biological substances, and so are widely used in determining molecular structures, stand-off detection of explosives, imaging of air composition to name a few. However, the objects being imaged in the real-world are more often mixtures than pure substances, making difficult direct identification and quantification of chemical constituents from existing lookup tables or templates. A fundamental scientific problem is to unmix or decompose the measured spectral data into a non-redundant and compact combination of basic components (pure or source spectra) facilitating subsequent verification and quantification based on look-up tables. The principal investigator (PI) and his team study three classes of unmixing problems depending on the available knowledge of the source signals (minimal, partial or full knowledge of a template of source signals). The research problems are blind, partially blind and template assisted source separation and identification. The intellectual merit of the proposed project is a combined geometrical and statistical approach with associated computational algorithms incorporating sparsity regularized optimization techniques. The geometric approach is based on the sparseness of the spectra of the source signals while the statistical approach is on decomposing the errors of template based data fitting when partial and statistical knowledge of the source spectra is available. The proposed methods are shown to be applicable to laboratory data from nuclear magnetic rensonance, Raman spectroscopy and differential optical absorption spectroscopy.The data analysis and computational algorithms on unmixing spectroscopic mixtures by the PI and his team can greatly improve the capability of threat reduction and decision making for public health and security. Their proposed line of work is well-positioned to generate broad impact on information technology, biotechnology, safety of civil infrastruture and environment; in particular the structural understanding and threat assessment of mixtures of chemical compounds originating in battle fields, homeland security, air quality monitoring, metabolic fingerprinting and disease diagnosis. The mathematical tools and numerical data produced in their project also benefit researchers and graduate students in data sharing and management, curriculum development and course offerings. The PI actively engages in mentoring postdoctoal fellows in terms of research and career advancement.
光谱传感技术是检测和识别化学和生物物质的强大分析工具,因此广泛用于确定分子结构、爆炸物的远距离检测、空气成分成像等。然而,在现实世界中被成像的对象通常是混合物而不是纯物质,使得难以从现有的查找表或模板直接识别和量化化学成分。一个基本的科学问题是将测量的光谱数据解混或分解为基本成分(纯光谱或源光谱)的非冗余和紧凑组合,以便于随后基于查找表的验证和量化。主要研究员(PI)和他的团队根据源信号的可用知识(源信号模板的最小,部分或全部知识)研究三类解混问题。研究的问题是盲源、部分盲源和模板辅助源分离与识别。所提出的项目的智力价值是一个相结合的几何和统计方法与相关的计算算法,将稀疏正则化优化技术。几何方法是基于源信号的频谱的稀疏性,而统计方法是在源频谱的部分和统计知识可用时分解基于模板的数据拟合的误差。实验结果表明,所提出的方法适用于核磁共振、拉曼光谱和差分吸收光谱的实验室数据,PI及其团队对光谱混合物解混的数据分析和计算算法可以大大提高公共卫生和安全的威胁降低和决策能力。他们拟议的工作领域有能力对信息技术、生物技术、民用基础设施和环境安全产生广泛影响;特别是对战场上产生的化合物混合物的结构了解和威胁评估、国土安全、空气质量监测、代谢指纹和疾病诊断。在他们的项目中产生的数学工具和数值数据也有利于研究人员和研究生在数据共享和管理,课程开发和课程设置。PI积极参与指导博士后研究员的研究和职业发展。

项目成果

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Jack Xin其他文献

A structure-preserving scheme for computing effective diffusivity and anomalous diffusion phenomena of random flows
计算随机流的有效扩散率和反常扩散现象的结构保持方案
  • DOI:
    10.48550/arxiv.2405.19003
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Tan Zhang;Zhongjian Wang;Jack Xin;Zhiwen Zhang
  • 通讯作者:
    Zhiwen Zhang
Finite Element Computation of KPP Front Speeds in Cellular and Cat#39;s Eye Flows
Cellular 和 Cat 中 KPP 前沿速度的有限元计算
Learning Sparse Neural Networks via \ell _0 and T \ell _1 by a Relaxed Variable Splitting Method with Application to Multi-scale Curve Classification
通过松弛变量分裂方法通过 ell _0 和 T ell _1 学习稀疏神经网络并应用于多尺度曲线分类
Design projects motivated and informed by the needs of severely disabled autistic children
设计项目以严重残疾自闭症儿童的需求为动力和信息
Three $$l_1$$ Based Nonconvex Methods in Constructing Sparse Mean Reverting Portfolios
  • DOI:
    10.1007/s10915-017-0578-5
  • 发表时间:
    2017-10-20
  • 期刊:
  • 影响因子:
    3.300
  • 作者:
    Xiaolong Long;Knut Solna;Jack Xin
  • 通讯作者:
    Jack Xin

Jack Xin的其他文献

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{{ truncateString('Jack Xin', 18)}}的其他基金

Deep Particle Algorithms and Advection-Reaction-Diffusion Transport Problems
深层粒子算法与平流反应扩散传输问题
  • 批准号:
    2309520
  • 财政年份:
    2023
  • 资助金额:
    $ 45.11万
  • 项目类别:
    Standard Grant
Collaborative Research: ATD: Fast Algorithms and Novel Continuous-depth Graph Neural Networks for Threat Detection
合作研究:ATD:用于威胁检测的快速算法和新颖的连续深度图神经网络
  • 批准号:
    2219904
  • 财政年份:
    2023
  • 资助金额:
    $ 45.11万
  • 项目类别:
    Standard Grant
Computational and Mathematical Studies of Compression and Distillation Methods for Deep Neural Networks and Applications
深度神经网络压缩和蒸馏方法的计算和数学研究及应用
  • 批准号:
    2151235
  • 财政年份:
    2022
  • 资助金额:
    $ 45.11万
  • 项目类别:
    Continuing Grant
FRG: Collaborative Research: Robust, Efficient, and Private Deep Learning Algorithms
FRG:协作研究:稳健、高效、私密的深度学习算法
  • 批准号:
    1952644
  • 财政年份:
    2020
  • 资助金额:
    $ 45.11万
  • 项目类别:
    Standard Grant
Computational and Mathematical Studies of Complexity Reduction Methods for Deep Neural Networks and Applications
深度神经网络复杂度降低方法的计算和数学研究及应用
  • 批准号:
    1854434
  • 财政年份:
    2019
  • 资助金额:
    $ 45.11万
  • 项目类别:
    Standard Grant
Collaborative Research: ATD: Robust, Accurate and Efficient Graph-Structured RNN for Spatio-Temporal Forecasting and Anomaly Detection
合作研究:ATD:用于时空预测和异常检测的鲁棒、准确和高效的图结构 RNN
  • 批准号:
    1924548
  • 财政年份:
    2019
  • 资助金额:
    $ 45.11万
  • 项目类别:
    Standard Grant
BIGDATA: Collaborative Research: F: Foundations of Nonconvex Problems in BigData Science and Engineering: Models, Algorithms, and Analysis
BIGDATA:协作研究:F:大数据科学与工程中非凸问题的基础:模型、算法和分析
  • 批准号:
    1632935
  • 财政年份:
    2016
  • 资助金额:
    $ 45.11万
  • 项目类别:
    Standard Grant
Theory and Algorithms of Transformed L1 Minimization with Applications in Data Science
变换 L1 最小化的理论和算法及其在数据科学中的应用
  • 批准号:
    1522383
  • 财政年份:
    2015
  • 资助金额:
    $ 45.11万
  • 项目类别:
    Standard Grant
Reaction-Diffusion Front Speeds in Chaotic and Stochastic Flows
混沌和随机流中的反应扩散前沿速度
  • 批准号:
    1211179
  • 财政年份:
    2012
  • 资助金额:
    $ 45.11万
  • 项目类别:
    Continuing Grant
ADT: Sparse Blind Separation Algorithms of Spectral Mixtures and Applications
ADT:混合光谱的稀疏盲分离算法及应用
  • 批准号:
    0911277
  • 财政年份:
    2009
  • 资助金额:
    $ 45.11万
  • 项目类别:
    Continuing Grant

相似国自然基金

Blind-Sterile小鼠雄性不育致病基因的定位克隆及功能研究
  • 批准号:
    81200465
  • 批准年份:
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