Publication - IPUS: An architecture for the integrated processing and understanding of signals

Authors: Lesser, V.R., Nawab, S.H., and Klassner, F.I.
Title: IPUS: An architecture for the integrated processing and understanding of signals
Abstract: The Integrated Processing and Understanding of Signals I(PUS) architecture is presented as a framework that exploits formal signal processing models to structure the bidirectional interaction between front-end signal processing and signal understanding processes. This architecture is appropriate for complex environments, which are characterized by variable signal to noise ratios, unpredictable source behaviors, and the simultaneous occurrence of objects whose signal signatures can distort each other. A key aspect of this architecture is that front-end signal processing is dynamically modifiable in response to scenario changes and to the need to re-analyze ambiguous or distorted data. The architecture tightly integrates the search for the appropriate front-end signal processing configuration with the search for plausible interpretations. In our opinion, this dual search, informed by formal signal processing theory, is a necessary component of perceptual systems that must interact with complex environments. To explain this architecture in detail, we discuss examples of its use in an implemented system for acoustic signal interpretation.
Keywords: IPUS
Publication: Artificial Intelligence, Vol: 77, pp. 129 - 171
Publisher: Elsevier Science
Date: January 1995
Sources: PS: ftp://ftp.cs.umass.edu/pub/lesser/lesser-aij-ipus.ps
PDF: /Documents/lesser/lesser-aij-ipus.pdf
Reference: Lesser, V.R., Nawab, S.H., and Klassner, F.I.. IPUS: An architecture for the integrated processing and understanding of signals. Artificial Intelligence, Volume 77, Elsevier Science, pp. 129-171. January 1995.
bibtex: 
@article{Lesser-2,
  author    = "V.R. Lesser and S.H. Nawab and F.I. Klassner",
  title     = "{IPUS: An architecture for the integrated
               processing and understanding of signals}",
  journal   = "Artificial Intelligence",
  volume    = "77",
  publisher = "Elsevier Science",
  pages     = "129-171",
  month     = "January",
  year      = "1995",
  url       = "http://mas.cs.umass.edu/paper/2",
}