## omnet simulation in Territories

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- October 16, 2014
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** Omnet simulation in Territories:**

**Omnet simulation in Territories **The objective is then to estimate a new basis that accurately representsthe underlying sources. A filtered output signal can thenTABLE omnet simulation in Territories ICOMPARISON OF DFT- AND PCA-DERIVED BASIS FUNCTIONSbe formed by retaining the projection of the original signal alonga weighted subset of the new basis functions.

The basic principles of linear omnet simulation in Territories signal decomposition can bemost easily described by first considering an observed signal,represented discretely as rowvector (dim ),

which can bedecomposed into a weighted sum of orthonormal basis functionswhere are weighting omnet simulation in Territories coefficients for each of orthonormalbasis functions . Coefficients can be expressed as the dotproduct between the observed signal and each orthonormal basisvector where is the conjugate transpose of .

The basis functions can be any set of vectors provided that they are mutually orthonormal orthonormal omnet simulation in Territories basis functions used for linear transformation of the data can be determined either *a priori *or adaptively from the signal itself.

The DFT is an example of linear signal decomposition where basis functions are defined *a priori *such omnet simulation in Territories that are a set of complex exponentials of different frequencies.

This method is efficacious in separating signal components when the underlying sources exhibit distinct frequency characteristics. However, in many applications, including ltrasound omnet simulation in Territories clutter filtering, the underlying source signals often overlap significantly in the frequency domain making source separation. using the DFT unreliable.

Thus, in many instances it is desirable to form the set of basis functions adaptively as arbitrary polynomials. omnet simulation in Territories A common method for signal decomposition with adaptive basis selection is PCA. A comparison between basis functions found using the DFT and the PCA methods are illustrated in Table I.