Symbolic analysis of time series signals using generalized hilbert transform

Soumik Sarkar, Kushal Mukherjee, Asok Ray

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


A recent publication has shown a Hilbert-transform-based partitioning method, called analytic signal space partitioning (ASSP). When used in conjunction with DMarkov machines, also reported in recent literature, ASSP provides a fast tool for pattern recognition. However, Hilbert transform does not specifically address the issue of noise reduction and the usage of D-Markov machines with a small depth D could potentially lead to information loss for noisy signals. On the other hand, a large D tends to make execution of pattern recognition computationally less efficient due to an increased number of machine states. This paper explores generalization of Hilbert transform that addresses symbolic analysis of noisecorrupted dynamical systems. In this context, theoretical results are derived based on the concepts of information theory. These results are validated on time series data, generated from a laboratory apparatus of nonlinear electronic systems.

Original languageEnglish (US)
Title of host publication2009 American Control Conference, ACC 2009
Number of pages6
StatePublished - 2009
Event2009 American Control Conference, ACC 2009 - St. Louis, MO, United States
Duration: Jun 10 2009Jun 12 2009

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2009 American Control Conference, ACC 2009
Country/TerritoryUnited States
CitySt. Louis, MO

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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