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Enhanced Peak Separation and Signal Recovery in Overlapped and Noisy Spectral Data.

This is a past event.

Friday, October 6, 2023 at 2:00pm to 3:00pm

DM - Deuxieme Maison, 164
11200 SW 8th ST, Deuxieme Maison, Miami, Florida 33199

Speaker.  Yuanchang Sun, Math & Stats, FIU.

Title. Enhanced Peak Separation and Signal Recovery in Overlapped and Noisy Spectral Data.

Abstract. Substances such as chemical compounds and biological agents are invisible to human eyes, they are usually captured by sensing equipment with their spectral fingerprints.  The work in this talk is motivated by the analysis of Nuclear Magnetic Resonance (NMR) spectra, which presents challenges due to signal overlap and the presence of noise.

In the first part of this talk, we propose a novel approach for enhancing peak separation and signal recovery in overlapped and mixing noisy NMR spectra without the knowledge of the mixing process and the mixing substances (so called blind source separation).  

Moving on to the second part, we consider a problem where the knowledge of spectral references of the substances are available, the task of data fitting is to solve their weights, which usually can be solved by least squares. Complications occur if a shift of the source signal peaks occurs in the mixtures. we formulate mathematical model for such distortions and build them into data fitting algorithms.

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Meeting ID: 930 5388 4755

Passcode: AAM2023

Event Type

Academics, Lectures & conferences


Students, Faculty & Staff, Alumni



Department of Mathematics and Statistics


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