extention of the music algorithm to am-fm signalsusers.rowan.edu/~bouaynaya/nidhal_ug_10.pdf ·...

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EXTENTION OF THE MUSIC ALGORITHM TO AM-FM SIGNALS Christina Dunlap Advisor: Dr. Nidhal Bouaynaya Department of Systems Engineering, University of Arkansas at Little Rock ABSTRACT ABSTRACT The The goal goal of of this this project project is is to to extend extend the the stationary stationary MUltiple MUltiple SIgnal SIgnal Classification Classification (MUSIC) (MUSIC) algorithm algorithm to to non non-stationary stationary Amplitude Amplitude and and Frequency Frequency Modulated Modulated (AM (AM-FM) FM) signals signals. Classical approaches to amplitude and frequency 2 3 4 Noisy Frequency Modulated Signal THE EFFECT OF NOISE THE EFFECT OF NOISE BIOLOGICAL APPLICATION BIOLOGICAL APPLICATION The proposed algorithm will be assessed using the AM- FMN calcium signaling response in lymphocite cells [1]. AM-FM signaling modes can help us detect the deleterious processes in cancerous cells for which there Classical approaches to amplitude and frequency estimation assume a stationary data model, i.e., both the amplitude and frequency of the signal are constant and do not vary with time. Non-stationary harmonic signals are, however, ubiquitous in various applications such as speech, music, seismic, radar, sonar, and biology. We are particularly interested in biological applications. Recent studies in cell biology have shown that a number of cell types THE STATIONARY MUSIC ALGORITHM THE STATIONARY MUSIC ALGORITHM 10 20 30 40 50 60 70 80 90 -4 -3 -2 -1 0 1 time Amplitude deleterious processes in cancerous cells, for which there is a discrepancy in either the amplitude or the period of the gene’s expressions between normal and cancerous cells. biology have shown that a number of cell types respond to external stimulation by encoding cellular information in their amplitude (AM) and frequency (FM). FORMS OF ANALOG FORMS OF ANALOG MODULATION MODULATION The The MUSIC MUSIC algorithm algorithm gives gives a high high-resolution resolution (higher (higher than than the the spectrum) spectrum) estimation estimation of of frequency frequency parameters parameters based based on on the the eigendecomposition eigendecomposition of of a given given stationary stationary signal signal ( ) 1 [] [] i i p j n i i x n Ae wn ω ϕ + = = + 2 -1.5 -1 -0.5 0 0.5 1 1.5 2 Amplitude Modulated Signal Amplitud e AM MUSIC AM MUSIC CONCLUSIONS AND FUTURE CONCLUSIONS AND FUTURE RESEARCH RESEARCH Amplitude Modulated Signal THE PROPOSED AM THE PROPOSED AM-FM MUSIC ALGORITHM FM MUSIC ALGORITHM Our approach relies on a basis decomposition of the time-dependent amplitude and frequency signals. We show that the introduction of a basis representation reduces the non-stationary estimation problem to a stationary one. 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 -2 time 0.5 1 1.5 Frequency Modulated Signal Amplitude AM MUSIC AM MUSIC RESEARCH RESEARCH Similarly to the stationary case, the AM and FM MUSIC have higher resolutions (in terms of frequency separation) than the non-parametric time-frequency decomposition methods. The The next next step step is is to to develop develop the the AM AM-FM FM MUSIC MUSIC algorithm algorithm. W ill b l i i h i i l ( [ ] ) 1 , [ ] [ ] [ ] [ ] i i p j n i i M i ik k x n Ae wn n d f n ω ϕ ω + = = + = 0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 -1.5 -1 -0.5 0 Amplitude and Frequency Modulated Signal . 8 10 12 14 x 10 12 itude We will, subsequently, investigate the statistical properties of the amplitude and frequency estimates by computing their respective Camer-Rao lower bounds (CRLB). 1 k = FM MUSIC FM MUSIC ( [ ] ) p j ACKNOWLEDGEMENT ACKNOWLEDGEMENT We would like to thank the Arkansas 0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01 -1.5 -1 -0.5 0 0.5 1 1.5 time 0 20 40 60 80 100 120 0 20 40 60 80 100 120 0 2 4 6 Ampl ( [ ] ) 1 , 1 [] [] [] [] i i j n i i M i ik k k x n Ae wn n d f n ω ϕ ω + = = = + = We would like to thank the Arkansas Department of Higher Education for their funding through a SURF grant 2010. REFERENCES REFERENCES [1] M.J. Berridge, “The AM and FM of Calcium Signalling” Nature, vol. 386, pp. 759-760, 1997.

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Page 1: EXTENTION OF THE MUSIC ALGORITHM TO AM-FM SIGNALSusers.rowan.edu/~bouaynaya/Nidhal_UG_10.pdf · 2010. 6. 23. · EXTENTION OF THE MUSIC ALGORITHM TO AM-FM SIGNALS Christina Dunlap

EXTENTION OF THE MUSIC ALGORITHM TO AM-FM SIGNALSChristina Dunlap

Advisor: Dr. Nidhal BouaynayaDepartment of Systems Engineering, University of Arkansas at Little Rock

ABSTRACTABSTRACTTheThe goalgoal ofof thisthis projectproject isis toto extendextend thethe stationarystationaryMUltipleMUltiple SIgnalSIgnal ClassificationClassification (MUSIC)(MUSIC) algorithmalgorithmtoto nonnon--stationarystationary AmplitudeAmplitude andand FrequencyFrequencyModulatedModulated (AM(AM--FM)FM) signalssignals..Classical approaches to amplitude and frequency 1

2

3

4

Noisy Frequency Modulated Signal

e

THE EFFECT OF NOISETHE EFFECT OF NOISE BIOLOGICAL APPLICATIONBIOLOGICAL APPLICATIONThe proposed algorithm will be assessed using the AM-FMN calcium signaling response in lymphocite cells [1].AM-FM signaling modes can help us detect thedeleterious processes in cancerous cells for which thereClassical approaches to amplitude and frequency

estimation assume a stationary data model, i.e., boththe amplitude and frequency of the signal areconstant and do not vary with time. Non-stationaryharmonic signals are, however, ubiquitous in variousapplications such as speech, music, seismic, radar,sonar, and biology. We are particularly interested inbiological applications. Recent studies in cellbiology have shown that a number of cell types THE STATIONARY MUSIC ALGORITHMTHE STATIONARY MUSIC ALGORITHM

0 10 20 30 40 50 60 70 80 90 100-4

-3

-2

-1

0

1

time

Ampl

itude deleterious processes in cancerous cells, for which there

is a discrepancy in either the amplitude or the period ofthe gene’s expressions between normal and cancerouscells.

biology have shown that a number of cell typesrespond to external stimulation by encoding cellularinformation in their amplitude (AM) and frequency(FM).

FORMS OF ANALOG FORMS OF ANALOG MODULATIONMODULATION

TheThe MUSICMUSIC algorithmalgorithm givesgives aa highhigh--resolutionresolution (higher(higher thanthan thethe spectrum)spectrum) estimationestimation ofof frequencyfrequency parametersparameters basedbasedonon thethe eigendecompositioneigendecomposition ofof aa givengiven stationarystationary signalsignal

( )

1[ ] [ ]i i

pj n

ii

x n A e w nω ϕ+

=

= +∑

2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Amplitude Modulated Signal

Ampl

itud

e

AM MUSICAM MUSICCONCLUSIONS AND FUTURE CONCLUSIONS AND FUTURE

RESEARCHRESEARCH

Amplitude Modulated Signal THE PROPOSED AMTHE PROPOSED AM--FM MUSIC ALGORITHMFM MUSIC ALGORITHMOur approach relies on a basis decomposition of the time-dependent amplitude and frequencysignals. We show that the introduction of a basis representation reduces the non-stationaryestimation problem to a stationary one.

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1-2

time

0.5

1

1.5

Frequency Modulated Signal

Ampl

itude

AM MUSICAM MUSIC RESEARCHRESEARCH

Similarly to the stationary case, the AM and FMMUSIC have higher resolutions (in terms of frequencyseparation) than the non-parametric time-frequencydecomposition methods.

TheThe nextnext stepstep isis toto developdevelop thethe AMAM--FMFM MUSICMUSICalgorithmalgorithm..

W ill b l i i h i i l

( [ ] )

1

,

[ ] [ ]

[ ] [ ]

i i

pj n

ii

M

i i k k

x n A e w n

n d f n

ω ϕ

ω

+

=

= +

=

0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01-1.5

-1

-0.5

0

Amplitude and Frequency Modulated Signal

..

8

10

12

14

x 1012

litud

e

We will, subsequently, investigate the statisticalproperties of the amplitude and frequency estimates bycomputing their respective Camer-Rao lower bounds(CRLB).

1k =

FM MUSICFM MUSIC

( [ ] )p

j∑

ACKNOWLEDGEMENTACKNOWLEDGEMENTWe would like to thank the Arkansas

0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01-1.5

-1

-0.5

0

0.5

1

1.5 time

020

4060

80100

120

0

20

40

60

80

100

1200

2

4

6

Ampl( [ ] )

1

,1

[ ] [ ]

[ ] [ ]

i ij ni

i

M

i i k kk

x n A e w n

n d f n

ω ϕ

ω

+

=

=

= +

=

We would like to thank the ArkansasDepartment of Higher Education for theirfunding through a SURF grant 2010.

REFERENCESREFERENCES[1] M.J. Berridge, “The AM and FM of CalciumSignalling” Nature, vol. 386, pp. 759-760, 1997.