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The Archimedes principle applied to separation of
uniformly distributed sources
Jean Barrère, Gilles
Chabriel-To cite this version:
Jean Barrère, Gilles Chabriel-. The Archimedes principle applied to separation of uniformly
dis-tributed sources. Proceedings of the 2nd Physics in Signal and Image Processing International
Sym-posium (PSIP’2001), 2001, Marseille, France. �hal-01826178�
UNIFORMLY DISTRIBUTED SOURCES
Jean Barrere - Gilles Chabriel
MS/GESSY-ISITV-UniversitedeToulonetduVar
Av. GeorgesPompidou,BP56-83162LAVALETTEDUVARCEDEX(FRANCE)
Fax: 330494142598-chabriel@isitv.univ-tln.fr-barrere@isitv.univ-tln.fr
ABSTRACT
Inthiswork,weareinterestedintheseparationofNsourcesignalsrecordedsimultaneouslybyNreceivers.Wepresentinthis
workamethodbasedonananalogybetweentheresearchofindependentaxesofanhypercube(geometricalrepresentation
ofamixingofuniformsources)andtheresearchofequilibriumstatesofweighingsystemsubmittedtodiscontinuedgravity
elds. Themethodonlyuse oneorderstatisticsand is able totreat alargeamount ofsources. Presentlythis methodis
limitedtouniformlyorsymmetricallydistributedsources.
1. INTRODUCTION
1.1. Position ofthe problem
Let'sconsiderN independentsourceswithuniformprobabilitydistribution,simultaneouslyreceivedonN sensors.
Themixingprocessischaracterizedbythefollowingequation:
y (t)=Mx(t); (1) inwhichx( t)=[x 1 ( t);x 2 (t);::: ;x N (t) ] T
isthevectorofzero-meanstatisticallyindependentunknownsources
andy(t)=[y 1 (t);y 2 (t);:::;y N ( t)] T
istheobservationvector. In(1), MistheNN unknownmixingmatrix,
assumedtobefull columnrank.
The identication problem consists in estimating aseparatingmatrix Ssuch as: SM =DP , where D is an
regulardiagonalmatrix. Pisapermutation matrixwhichhasonenonzeroentryineachrowandcolumn.
TheproductofSwiththeobservationvectorleadsto:
z(t)=DPx(t): (2)
Thevectorz(t) representsthesourcevectorx(t) exceptforone permutationandascalingfactor.
Wewill recallin the main paperhowtheuse ofsecond order statisticspermitsto reduce the problem tothe
researchofanunitarymixing matrix(whiteningoftheobservations). TheFigure1representsatwodimensional
geometricalillustrationof the spacesources(a), the mixing space(b), the actionof thewhitening procedure (c)
andtheestimatedsourcesinthewhiteningspace(d).
Sources space
Observations space
"Whitened"
Observations space
Estimated
Sources space
(a)
(b)
(c)
(d)
x
2
x
1
z
2
z
1
Figure1: 2Ddierentsspaces
Sointhefollowing,withoutanylossofgenerality,wewillonlyconsiderthecasewhereMisanunitarymatrix
and thesourcesare powerunit. Theproblem reduces in ndingthe unitarymatrixSi.e. theunitarytransform
Let'sconsider thefollowingidealtwodimensionalexperiment: anhomogeneoussquare thinplate is immersed in
anhomogeneousliquid. Theplateisrigidand itsdensityishalf thedensityoftheliquid. Theplateissubmitted
to twokindof dierentforces: volumeforces(surface force in thetwodimensional case)dueto thegravityeld,
andsurfaceforces(lineforceinthetwodimensionalcase)duetothepressureofliquidactingonthebound ofthe
immersedsolid. Wedenoteby
! F
g
theresultantofgravityforces,Oitspointofapplicationand
! F
p
theresultant
ofsurfaceforces,Citspointofapplication. Theequilibriumoftheplateisobtainedwhenthesumoftheresultant
is null and when the momentum of forces is null i.e. when
!
OC and
! F
g
are linearly dependent. The Figure 2
illustratessuchexperiment.
F
p
C
(a)
F
g
O
(c)
F
g
F
p
F
p
F
g
O
(b)
C
C
O
Figure2: ArchimedesPrinciple
Wecanconsiderpressureforcesasvolumeforcesactingonanequivalentvolumeofliquidcorrespondingtothe
immersed partofthesolidand C is itsthecenter ofgravity. Thisphenomenon iswell known astheArchimedes
principle. Becauseofhomogeneity,theequilibriumpositionsgiveustheaxisofsymmetryoftheplate. Forattractive
forces,thestableequilibriumstateisobtainedwhenthedistancejOCjisminimum(seegure(2.c))unstablewhen
thedistance jOCj is maximum(see gure(2.b)). Forrepulsiveforces, thestability stateswill beinverted. This
basicapplicationofstaticcanbeeasilyextendtoNdimensionalhomogeneoussystems. Following,wewillinspired
fromittoconstructaniterativemethodextractingthestablestateofequilibriumoftheN-dimensionalhypercube
ofobservationsimmersed inadiscontinuousvectoreld.
2. ALGORITHM
2.1. Brief description
Wedescribehereonestep(fromiterationk toiterationk+1)oftheiterativemethodproposed. Letconsiderthe
mixingspaceofN uniformlydistributedpowerunit centredsourcesasillustratedinFigure3.a. Eachpointofthis
spacey k ( t)= y k 1 (t);y k 2 (t);:::;y k N (t) T
issubmittedtoadiscontinuousvectoreldpsuchas:
p(y k (t))= ( [1;0;:::;0] T if y k 1 (t)0 [0;0;:::;0] T if y k 1 (t)<0 (3)
andacontinuousvectoreldg :
g (y k
(t))=[ 0:5;0;:::;0] T
(4)
Because ofuniformdistributionofsources,theresultantoftwoeldsare: F
p
= F
g
=Efpg.
Thepointsofapplicationoftheseresultantsare: C
k =Efy k = y k 1
0gforthediscontinuouseld,andO =Efy
k g
forthecontinuouseld.
Let'sdenoteu
1
thevectorsuchasu
1 = ! OC k j ! OC k j
. UsinganGram-Schmidtorthogonalizationonu
1
andasetofN 2
anylinearlyindependentvectors,weconstructasetofN 1ortho-normalizedvectoru
i
; i=2;:::N.
ItresultsaNNunitarymatrix:
U k 1 =[u 1 ;:::;u N ]:
Applying thetransform
U k 1 T
to the mixing space y
k
we obtainannew balanced mixing space versusthe eld
forcein presenceasdescribedinFigure 3.b:
y k +1 = U k 1 T y k k +1
y
1
>0
p
1
=1
p
1
=0
F
p
F
g
F
g
F
p
(a)
(b)
Figure3: Onestep
i.e.U 1
isthean identity matrix. Becauseof uniformity, after convergencewehaveseparationofonesource from
theothers: y n (t)=[z 1 (t);y n 2 (t);:::;y n N (t)] T
TheprocedurecanbeappliedagainontheN 1remainingobservations. Theseparationwillbeachievewhen
all sourceshavebeenextracted. A proof ofconvergence will begiven for thetwo-dimensionalcase in the whole
paper.
2.2. Acceleration ofconvergence
ThemethodcanbestronglyaccelerateifweuseintheGram-Schmidtprocedure asetofvectorjointlyevaluated
fromN dierentdiscontinuouseldvectors:
p j (y k (t))= 8 < : 1;:::;j;:::;N indexofsource [0;:::;1;:::;0] T if y k j (t)0 [0;:::;0;:::;0] T if y k j (t)<0 (5) 3. EVALUATION -APPLICATION
Thevaluationofperformanceofseparationismeasuredbyanindexintroducedin[3],constructedonthetheglobal
matrixG=SM,normalizedbythenumberofnon-diagonaltermsinthis matrix:
ind(G)= 1 2N(N 1) 2 4 X i 0 @ X j jG i;j j 2 max l jG i;l j 2 1 1 A + X j 0 @ X i jG i;j j 2 max l jG l;j j 2 1 1 A 3 5
Thisnon-negativeindextakesitsvaluesbetween0and1. Asmallvalueindicatestheproximityofthedesired
solutions.
Thefollowing table gives an ideaof the sensibility of the method versusthe data number for a mixing of 3
sources. Eachlinewas obtainedforaset of1000dierentrealizationsandmixingmatrices.
DataNumber Mean(ind) Std(ind) Numberof
nonseparatedcases
2048 0.00400 0.0030 5
4096 0.00120 0.0010 1
8192 0.00045 0.0034 0
0
50
100
150
200
250
300
350
400
0
0.05
0.1
0.15
0.2
0.25
Iterations
Performance
80
10
50
30
Figure4: Performancesfordierentsourcenumbers
4. CONCLUSIONS-EXTENSIONS
The method proposed can be considered as an extension of geometric ones, see [1], [2], by the addition of an
exterioreld vectoractingonthespaceobservation. Itcan beinterpretedalso asclassicalsecondorder statistics
onesappliedtononlinearlteredobservations. Theseparationhasbeenpresentedonuniformlydistributedsources.
Becausethemethod extractssymmetryaxes,itcanbeextended tosymmetricallydistributed sources. Ofcourse,
many other eld can be tested in order to generalize the separating process. In case of n-valuated sources the
existenceofmeta-stablecongurationsimpliessomemodicationsofthealgorithm.
REFERENCES
[1] C.G. Puntonet, A.Prieto, C.Jutten, M.Rodrigez-Alvarez,andJ. Ortega. Separation of sources: a geometry
basedprocedurefor reconstructionofn-valuedsignals, inSignalProcessing,Vol.46n3,pp267-284,1995.
[2] C.G.Puntonet, C.A.Mansour,andC.Jutten. Geometrical algorithms forblind separation ofsources, in Actes
duXV
eme
colloqueGRETSI,pp273-275,Sept.1995.
[3] E.MorauandO.Macchi. High-ordercontrastsfor self-adaptativesourceseparation, inInternationalJournalof