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Signal Decomposition Into Primitive Known Signal Classes

Galati, David G (2002) Signal Decomposition Into Primitive Known Signal Classes. Master's Thesis, University of Pittsburgh. (Unpublished)

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The detection of simple patterns such as impulses, steps, and ramps in signals is a very important problem in many signal processing applications. The human eye is a very effective filter and hence is capable of performing this task very efficiently. In applications where no humans are involved in the signal interpretation process, this task needs to be performed by a computer. In this thesis, we propose and investigate two novel algorithms to automate this task. Our starting point is a discrete signal composed of an unknown number of ramps, steps, and impulses with unknown magnitudes and delays as well as random noise. We propose two different criteria based on "minimum energy" and "minimum complexity" to decompose the signal into these basic simple patterns. The solutions based on these criteria are proposed and examined. Over all, the "minimum complexity" criterion seems to produce results that are more similar to the human eye's interpretation then the "minimum energy" approach.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Galati, David Gdggst6@pitt.eduDGGST6
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairSimaan, Marwansimaan@ee.pitt.eduSIMAAN
Committee MemberLi, Ching-Chungccl@ee.pitt.eduCCL
Committee MemberBoston, J Robertboston@ee.pitt.eduBBN
Date: 19 April 2002
Date Type: Completion
Defense Date: 3 April 2002
Approval Date: 19 April 2002
Submission Date: 9 April 2002
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: minimum complexity; minimum energy; signal decomposition
Other ID:, etd-04092002-160602
Date Deposited: 10 Nov 2011 19:35
Last Modified: 15 Nov 2016 13:39


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