Online tracking of the degree of nonlinearity within complex signals

Danilo P. Mandic; Phebe Vayanos; Soroush Javidi; Beth Jelfs; Kazuyuki Aihara

doi:10.1109/ICASSP.2008.4518046

Online tracking of the degree of nonlinearity within complex signals

Danilo P. Mandic, Phebe Vayanos, Soroush Javidi, Beth Jelfs, Kazuyuki Aihara

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

Abstract

A novel method for online tracking of the changes in the non-linearity within complex-valued signals is introduced. This is achieved by a collaborative adaptive signal processing approach by means of a hybrid filter. By tracking the dynamics of the adaptive mixing parameter within the employed hybrid filtering architecture, we show that it is possible to quantify the degree of nonlinearity within complex-valued data. Simulations on both benchmark and real world data support the approach.

Original language	English
Title of host publication	2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages	2061-2064
Number of pages	4
DOIs	https://doi.org/10.1109/ICASSP.2008.4518046
Publication status	Published - 2008
Externally published	Yes

Keywords

Adaptive signal processing
Complex LMS
Convex optimisation
Machine learning
Wind modelling

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

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10.1109/ICASSP.2008.4518046

Cite this

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title = "Online tracking of the degree of nonlinearity within complex signals",

abstract = "A novel method for online tracking of the changes in the non-linearity within complex-valued signals is introduced. This is achieved by a collaborative adaptive signal processing approach by means of a hybrid filter. By tracking the dynamics of the adaptive mixing parameter within the employed hybrid filtering architecture, we show that it is possible to quantify the degree of nonlinearity within complex-valued data. Simulations on both benchmark and real world data support the approach.",

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AU - Mandic, Danilo P.

AU - Vayanos, Phebe

AU - Javidi, Soroush

AU - Jelfs, Beth

AU - Aihara, Kazuyuki

PY - 2008

Y1 - 2008

N2 - A novel method for online tracking of the changes in the non-linearity within complex-valued signals is introduced. This is achieved by a collaborative adaptive signal processing approach by means of a hybrid filter. By tracking the dynamics of the adaptive mixing parameter within the employed hybrid filtering architecture, we show that it is possible to quantify the degree of nonlinearity within complex-valued data. Simulations on both benchmark and real world data support the approach.

AB - A novel method for online tracking of the changes in the non-linearity within complex-valued signals is introduced. This is achieved by a collaborative adaptive signal processing approach by means of a hybrid filter. By tracking the dynamics of the adaptive mixing parameter within the employed hybrid filtering architecture, we show that it is possible to quantify the degree of nonlinearity within complex-valued data. Simulations on both benchmark and real world data support the approach.

KW - Adaptive signal processing

KW - Complex LMS

KW - Convex optimisation

KW - Machine learning

KW - Wind modelling

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U2 - 10.1109/ICASSP.2008.4518046

DO - 10.1109/ICASSP.2008.4518046

M3 - Conference contribution

AN - SCOPUS:51449113379

SN - 1424414849

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SP - 2061

EP - 2064

BT - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP

ER -

Online tracking of the degree of nonlinearity within complex signals

Abstract

Keywords

ASJC Scopus subject areas

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