Paper Info
Title
Wavelet Based Compressed Sensing Sampling And Estimation Of N-States Random Evolution Model Parameters In Microtubule Signal
Abstract
Studies of biological processes such as Microtubules (MTs), often suffer from limited data availability due to physical constraints of the data acquisition process. Typically, the periodic collection of biological data using optical microscopes is prone to the dangers of overexposure and destruction of either specimen or probe, thereby limiting the data collected over a period of time. In addition, the data collected is often a sampled and approximated observation of the analog physical phenomena. Hence, to emulate the non-uniform sampling process that occurs during the physical data acquisition process, compressed sensing (CS) based sampling is used in an effort to reconstruct the MT signal from fewer samples than the Nyquist rate. We also introduce a novel wavelet estimation based N-states random evolution model to study the MT dynamic instability phenomenon. Experimental results demonstrate that our proposed method yielded superior overall performance, effective reconstruction and estimation of MT signal from fewer samples with low error rates. Even at lower sampling rates, the estimated MT transition parameters are shown to closely approximate the original MT signal.
Year
DOI
Venue
2018
10.1109/BIBM.2018.8621251
PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM)
Field
DocType
ISSN
Biological data,Exposure,Computer science,Data acquisition,Algorithm,Artificial intelligence,Sampling (statistics),Periodic graph (geometry),Nyquist rate,Machine learning,Compressed sensing,Wavelet
Conference
2156-1125
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Vineetha Menon101.35
Shantia Yarahmadian2154.37