Customer Contact TeamQinetiQCody Technology ParkIvely Road, FarnboroughHampshire GU14 0LXUnited KingdomTel +44 (0) 8700 100942www.QinetiQ.com
APPLICATION TO SONAR
Experimental sonobuoy array
APPLICATION TO COMMUNICATIONS
HF antenna array
•Co-channel interference•Successful signal separation
Wireless multimedia networks
•Multiple antennas provide spatial diversity•Exploit multipath propagation
GENE EXPRESSION ANALYSIS
DNA Microarray
Microarray signal processing
FOETAL HEARTBEAT ANALYSIS
Non-invasive diagnostic technique•BSS applied to multiple ECG signals•Successful clinical trials
Separation of triplet heartbeat signals
•QinetiQ and Queen Charlotte’s hospital – joint research•World first – results highlighted in The Lancet (editorial)
PREDICTION OF EPILEPTIC FOCAL SEIZURES
Signal processing applied to scalp EEG•BSS used to estimate original sources
•Better prediction from scalp EEG than intracranial EEG
ACKNOWLEDGEMENTSResearch sponsored by the MoD corporate research programme (QinetiQ) and EPSRC (Cardiff) .
SISTER PARTNERSHIP
Signal Separation: Theory & Engineering Relevance
•Cardiff: Prof. Jonathon Chambers •Centre for Digital Signal Processing (CDSP)•School of Engineering
•QinetiQ: Prof. John G McWhirter FRS FREng•Centre for Signal and Information Processing (CSIP)•Malvern Technology Centre•Associate Prof. Cardiff University •Visiting Prof. Queen’s University Belfast
Scope•Advanced algorithms for signal separation
•broadband signals•convolutive mixing•non-stationary environments
•Practical applications•Wireless communications•Phased array radar and sonar•Bio-medical signal processing
Current projects•EPSRC CASE Studentship
• “Video Assisted Audio Source Separation”• Mr Andrew Aubrey • based in Cardiff
•EPSRC ICASE Studentship• “Broadband Blind Signal Separation”• Ms Joanne Foster• based in Malvern
•EPSRC ICASE Studentship•“The SBR2 Algorithm for Communications Signal Processing
•Mr Martin Davies•based in Malvern
•Participants in EPSRC funded network for ICA
BLIND SIGNAL SEPARATION (BSS)
Typical scenario
•Signal model
•Data matrix
•Unknown mixture matrix A•Unknown signals S•Input signal properties
•non-Gaussian • statistically independent
Independent component analysis (ICA)•Higher Order Statistics•QinetiQ “BLISS” algorithm
1. QinetiQ ltd, Malvern
2. School of Engineering, Cardiff University
John G McWhirter1 FRS FREng and Jonathon Chambers2
© Copyright QinetiQ ltd 2005QinetiQ/?/?/PUB??????
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s t1( )
s t2( )
s t3( )
x t1( ) x t2( ) x t3( ) x tp ( )
)()()( ttt nAsx
NASX
Input Data
Separated sources
Amplitude(micro volts)
-200 -100 0 100 200 300-505
10152025
Time (milliseconds)
Application to triplets
-200 -100 0 100 200 300-6-4-202468
1012
Time (milliseconds)
14
Time (milliseconds)-200 -100 0 100 200 300
-505
10152025
Averaged foetal ECG Triplet 1
Triplet 2
Triplet 3
From estimated EEG source: From intracranial EEG: A DNA microarray. Each dot represents the activity level of a gene. One microarray can measure thousands of gene activity levels.
Image processing
Gene 2
Gene P
t1 t2 tN
Data matrixP >> N
Finding underlying structure
Principle component analysis.Independent component
analysis.
Cardiff – QinetiQ SISTER Partnership