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

点击翻译按钮获取中文摘要

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.