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dc.contributor.authorSrivastava, Madhur
dc.date.accessioned2018-10-23T13:33:34Z
dc.date.available2020-08-22T06:01:03Z
dc.date.issued2018-08-30
dc.identifier.otherSrivastava_cornellgrad_0058F_11042
dc.identifier.otherhttp://dissertations.umi.com/cornellgrad:11042
dc.identifier.otherbibid: 10489660
dc.identifier.urihttps://hdl.handle.net/1813/59564
dc.description.abstractElectron Spin Resonance (ESR) Spectroscopy is a powerful method to study structure and dynamics of biomolecules. It detects magnetic resonance signals from the spin labels (or paramagnetic tags) attached at specific locations of a biological sample such as proteins, DNA, RNA, and lipids, among others. Despite many improvements in ESR instrument sensitivity, experimental signals lack sufficient strength that is needed to study complex biomolecules. We introduce signal processing methods as an alternate approach to remove noise present in the experimental ESR signals. We developed new methods based on wavelet transforms that are effectively able to retrieve signals, including weak signals. The methods are developed for one-dimensional continuous wave electron spin resonance (cw-ESR) signals and pulsed dipolar signals. The denoising methods achieve more than two orders-of-magnitude improvement in Signal-to-Noise Ratio (SNR) as well as can be used to reduce signal acquisition time by similar orders. In cw-ESR, the experimental signals obtained from the spectrometer are directly analyzed. Hence, there is no need for further data processing. In pulsed dipolar spectroscopy, the acquired signal needs to be processed to obtain desired information, i.e. distance distribution between a pair of spin labels. In addition to wavelet denoising, we developed a singular value decomposition (SVD) method to obtain well-resolved accurate distance distributions from the dipolar signals. The SVD method overcomes the limitations of standard Tikhonov regularization method that yields are compromise between a good resolution and a stable solution, as well as the limitation of model fitting methods that require a priori information. Further, we conducted the uncertainty analysis of the SVD solution. This research is first step towards utilizing the power of data processing methods to improve signal analysis in ESR spectroscopy.
dc.language.isoen_US
dc.subjectelectron spin resonance
dc.subjectElectron Paramagnetic Resonance
dc.subjectBiomedical engineering
dc.subjectSingular Value Decomposition
dc.subjectWavelet Denoising
dc.subjectSignal Denoising
dc.titleIMPROVING SIGNAL RESOLUTION AND REDUCING EXPERIMENT TIME IN ELECTRON SPIN RESONANCE SPECTROSCOPY VIA DATA PROCESSING METHODS
dc.typedissertation or thesis
thesis.degree.disciplineBiomedical Engineering
thesis.degree.grantorCornell University
thesis.degree.levelDoctor of Philosophy
thesis.degree.namePh. D., Biomedical Engineering
dc.contributor.chairFreed, Jack H.
dc.contributor.committeeMemberFeigenson, Gerald W.
dc.contributor.committeeMemberAnderson, Catherine Lindsay
dcterms.licensehttps://hdl.handle.net/1813/59810
dc.identifier.doihttps://doi.org/10.7298/X4W957F2


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