Stochastic Models and Techniques for Sparse Signals

Real-world data such as multimedia, biomedical, and telecommunication signals are formed of specific structures. However, these structures only determine some general properties of the data while the unknown or unpredictable parts are assumed to be random. This fact suggests that we can use stochastic models to explain real-world signals. Processes such as Gaussian white noise or Gaussian ARMA processes are well-known examples which are extensively used in modeling some components of the natural signals.


Published in:
Proceedings of the 2012 IEICE General Conference (IEICE'12), 岡山市 (Okayama), Japan, SS-37–SS-39
Year:
2012
Publisher:
IEICE
Laboratories:




 Record created 2015-09-18, last modified 2018-03-17

External links:
Download fulltextURL
Download fulltextURL
Download fulltextURL
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)