Accession Number : AD1048429


Title :   Signal Processing for Time-Series Functions on a Graph


Descriptive Note : Technical Report,01 Jun 2017,30 Sep 2017


Corporate Author : US Army Research Laboratory Aberdeen Proving Ground United States


Personal Author(s) : Muoz-Barona,Humberto ; Vettel,Jean ; Bohannon,Addison


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1048429.pdf


Report Date : 01 Feb 2018


Pagination or Media Count : 28


Abstract : Previous research introduced signal processing on graphs, an approach to generalize signal processing tools such as filtering to functions supported on graphs. These methods can be applied to scalar functions with a domain that can be described by a fixed weighted undirected graph. We consider here time-series functions supported on a fixed, weighted, undirected graph and show that an extension to the approach of Shuman et al. does not generalize to this problem, but rather suffers from a catastrophic loss of temporal information in the signal during convolution operations. Finally, we propose alternative signal processing approaches to time-series functions on a fixed graph.


Descriptors :   signal processing , MACHINE LEARNING , graph theory , network science , scalar functions , time series analysis , fourier analysis , weighting functions


Subject Categories : Theoretical Mathematics


Distribution Statement : APPROVED FOR PUBLIC RELEASE