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Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
1
Transient Response Analysis Transient Response Analysis for for
Temperature Modulated Temperature Modulated ChemoresistorsChemoresistors
R. Gutierrez-Osuna1,2, A. Gutierrez1,2 and N. Powar2
1Texas A&M University, College Station, TX2Wright State University, Dayton, OH
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
2
Outlineg Introduction
n Research objectivesn Temperature modulation approaches
g Transient Response Analysisn Time-domain analysisn Time-constant or spectral analysis
g Pattern Analysisn Feature extractionn Performance measures
g Resultsn Experimental setupn Sensitivity and selectivity enhancements
g Concluding remarks
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
3
Objectives of this workg Improve information content of COTS gas
sensors by means of n Instrumentation: temperature modulationn Computation: transient-response analysis
gEvaluate various types of transient analysis techniquesn Time-domain techniquesn Time-constant-domain techniques
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
4
Outline
Temperature modulation
Transient analysis
Pattern analysis
Experimental validation
Temperature oscillationTemperature transients
Time domainTime-constant domain
Feature extractionPerformance measures
Experimental setupResults
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
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TEMPERATURE MODULATION
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
6
Temperature modulationgBasic principle
n Selectivity of metal-oxide chemoresistors depends on operating temperature
n Capture the response of the sensor at multiple temperatures
from [Yamazoe and Miura, 1992]
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
7
Temperature modulation approachesgTemperature oscillation
n Sensor heater is driven by a continuous function g e.g., sine wave, ramp
n Information is in the quasi-stationary or dynamic response
gTemperature transientn Sensor heater is driven by a
discontinuous functiong e.g., step function, pulse
n Information is in the transient response
time
VH
time
1/RS
time
VH
time
1/RS
time
VH
time
1/RS
time
VH
time
1/RS
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
8
Temperature modulation examples
100 200 300 400 500 600 700 8000
1
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5x 10
-4 0.125Hz
A
B
D E
C
A AIRB ACETONEC AMMONIAD IPA E VINEGAR
100 200 300 400 500 600 700 8000
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2
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4
5x 10
-4 0.125Hz
A
B
D E
C
A AIRB ACETONEC AMMONIAD IPA E VINEGAR
Temperatureoscillation
Temperaturetransient
S#4, 1-2V
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A
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AI
AM
IM AIM
WS#4, 1-2V
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A
I
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AI
AM
IM AIM
W
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
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TRANSIENTANALYSIS
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
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Transient response analysisgObjective
n Characterize the transient regime of a sensor tog Improve our understanding of the sensor’s behaviorg Extract information to discriminate target analytes
gHow can the transient be characterized?n Time domain
g Extract parameters directly from the time samples
n “Frequency” domaing Convert the transient into a distribution of time constants
( ) ττ τ deGtf t /
0)( −∞
∫=
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
11
Time vs. spectral representations
100 200 300 400
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4
6
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S#2, 4-5V
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S#4, 2-3V
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S#1, 2-3V
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S#4, 1-2V
100
101
-4
-3
-2
-1
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100 200 300 400
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
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Time vs. spectral representations
4
5
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S#4, 1-2V
100
101
-4
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-1
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S#4, 1-2V
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101
-4
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-2
-1
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100 200 300 400100 200 300 400
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
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The Pade-Z Transformg Pade-Z is a system-identification technique that finds
a discrete multi-exponential model of the form
g Pade-Z in a nutshelln Compute the Z-transform of the sampled transient (at z0)n Fit a Pade approximant to the Z-transformn Compute the partial fraction expansion n Convert poles/residues into time-constants/amplitudesn Select a subset of the time-constants/amplitudes
∑=
−=M
m
tm
meGtf1
/)( τ
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
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Multi-Exponential Transient Spectroscopy g METS is a signal-processing technique that yields a
distribution or spectrum of time constants n A differentiation of the sensor transient in logarithmic-time scale
n METS is the convolution product of the true distribution with anasymmetric kernel
( )∫∞ −
==01 )(
)(ln)( ττ
ττ deGttf
tddtMETS
t
)ln())exp(exp()( tywithyyyh =−=
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
15
Ridge Regression Curve Fittingg RRCF is statistical regression technique modified to
produce a spectral representation of the transientn Based on the minimum mean-square error solution
g Problemsn The system of equations is non-linear in the time constantsn M is unknown, meta-search is computationally intensiven Solution is ill-posed due to co-linearity
g Solutionsn Pre-specify a fixed number of time constants
n Stabilize the solution with a regularization term
−= ∑ ∑
−
= =
−21
0 1
/
,,minarg},,{
N
k
M
m
kTmk
GMmm
m
mm
eGfGM τ
ττ
{ }LLLL ,9,,2,1,9.0,,2.0,1.0,09.0,,02.0,01.0=τ
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
16
Validation on synthetic datagSynthetic transient with
three decaysn τ1=0.1sec, τ2=1s, τ3=10sn G1=-10, G2=+10, G3=-10n DC offset G0=10n Gaussian noise N(µ=0,σ=0.1)
0 5 10 15 20 25 30
0
1
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Training dataPade-Z modelResidual error
Time (s)
Tran
sien
t sig
nal
10-1 100 101
-3
-2
-1
0
1
2
3
MET
S 1
Time constant (s)10-2 10-1 100 101 102
-3
-2
-1
0
1
2
3
4
5
6
Time constant (s)
Ampl
itude
s
DC Offset
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
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FEATURE EXTRACTION
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
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Feature extractiongFeatures can be extracted from the sensor
transient through window time slicing (WTS)n WTS can also be used in spectral representations
g Interpretation as filter banks
0 10 20 30 40 50 60time (s)
0
0.2
0.4
0.6
0.8
1
Sens
or re
spon
se
100 101Time constants (s)
0
0.2
0.4
0.6
0.8
1
Spec
trum
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
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Pade-Z feature extractiong Can the model parameters
{Gm,τm} be used as features?
n Partial fraction expansion is an ill-conditioned problem
n Small variations in the transient samples can lead to solutions with different number of exponentials
g Conventional pattern-analysis techniques will expect a fixed dimensionality M for all examples
0 1 2 3 4 5 6
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Log (time constant)
Am
plitu
de
0 1 2 3 4 5 6
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Log (time constant)
Am
plitu
de
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
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Pade-Z feature extractiong Solution
n Consider the Taylor-series expansion of ex:
n Re-grouping terms in the multi-exponential model:
n Yields any desired (and fixed) number of features, regardless ofthe number of exponentials M returned by Pade-Z
∑∞
=
=++++=0
32
!!3!21
n
nx
nxxxxe L
( )∑ ∑∑∞
= ==
− −==
0 11
/
!...
n
M
mnm
mnM
m
tm
GnteG m
ττ
∑ ∑ ∑ ∑= = = =
M
m
M
m
M
m
M
m m
m
m
m
m
mm
GGGG1 1 1 1
22 ,,,, Lτττ
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
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EXPERIMENTAL VALIDATION
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
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Experimental setupgSensor array
n Four TGS Figaro sensors (2600, 2620, 2611, 2610)gOdor delivery
n Static headspace analysisgAnalyte database
n Acetone (A), Isopropyl alcohol (I) and Ammonia (M)gPerformance measures
n Sensitivity and selectivitygTemperature profile
n Staircase temperature modulation (STM)
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
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Staircase temperature modulation
0 1000 2000 30000
5
10TGS 2600
0 1000 2000 30000
5
10TGS 2620
0 1000 2000 30000
5
10TGS 2611
0 1000 2000 30000
5
10TGS 2610
AcetoneIPA
AmmoniaHeater
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
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Feature setsg Transient dataset
n Four sensorsn Six transients per sensorn Four features per transient
g Five feature setsn WTS: 48 dimensionsn Pade-Z: 48 dimensionsn METS: 48 dimensionsn RRCF: 48 dimensionsn DC: 12 dimensions
g Two datasetsn Sensitivity on serial dilutionsn Selectivity on binary/ternary mixtures
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
25
Sensitivity: performance measureg Rationale
n Evaluate misclassification cost as a function of analyte concentration
g Classification based on the nearest neighbor rule
n Misclassification costg Penalize misclassifying
analyte X as analyte Yg Penalize misclassifying
concentration i as concentration j
100 101 102 103 104 105
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10
20
30
40
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80
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100
Analyte Concentration
Cla
ssifi
catio
n ra
te (%
)
Feature set 2
Feature set 1
100 101 102 103 104 105
0
10
20
30
40
50
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100
Analyte Concentration
Cla
ssifi
catio
n ra
te (%
)
Feature set 2
Feature set 1
( ) ( ) ( )
( ) ( )
≠=
=−=
+=
YX1YX0
YX,dandjiji,dwhere
ji,dβ1YX,d
α1YXCost ji
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
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Sensitivity: resultsgSerial dilutions of
individual analytesn Base concentration (v/v)
g A:10-4%, I:10-1%, M:1.0%n These are near the DC-
isothermal detection threshold
n Dilutionsg 5 levels with a 1/10 dilution
factorg Distilled water is treated as
a 6th dilution leveln Data collection
g 17 samples per dayg 4 days 1 2 3 4 5
55
60
65
70
75
80
85
90SENSITIVITY
Concentration level
Cla
ssifi
catio
n ra
te
SSTMWTS
PADE-ZMETSRRCF (a)
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
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Selectivity: performance measureg Rationale
n A good feature set will partition feature space into linearly separable odor-specific regions
n For each analyte A, compute a linear discriminant function
g Examplesn gA(AB)=1n gA(BC)=0
n Discriminant functions are found with the Ho-Kashyap procedureg Guaranteed solution in the linearly separable caseg Guaranteed convergence otherwise
-2 -1.5 -1 -0.5 0 0.5-1
-0.5
0
0.5
1
AAAA AAAA A
A
B BB
BBBB
BBB
CCCCCCC
C CC
ABABABABABAB ABAB AB
AB
ACACACACACACACAC
ACAC
BCBCBCBCBCBCBCBCBC
BCABC
ABC
ABCABCABCABCABCABCABC
ABCBC
BC
ABC
gB(x)gA(x)
gC(x)
Feature 1
Feat
ure
2
-2 -1.5 -1 -0.5 0 0.5-1
-0.5
0
0.5
1
AAAA AAAA A
A
B BB
BBBB
BBB
CCCCCCC
C CC
ABABABABABAB ABAB AB
AB
ACACACACACACACAC
ACAC
BCBCBCBCBCBCBCBCBC
BCABC
ABC
ABCABCABCABCABCABCABC
ABCBC
BC
ABC
gB(x)gA(x)
gC(x)
Feature 1
Feat
ure
2
( ) ⊃
=otherwise0
Axif1xgA
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
28
Selectivity: resultsgBinary/ternary
mixtures of analytesn Base concentration (v/v)
g A:0.3%, I:1.0%, M:33%n Equivalent analyte
intensity on isothermal sensor response
n Dilutionsg 3 levels with a 1/3 dilution
factor
n Data collectiong 24 samples per dayg 3 days
1 3 5 7 9 11 13 15 17 1955
60
65
70
75
80
85
90
95
100
Principal components
Cla
ssifi
catio
n ra
te
SSTMWTS
PADE-ZMETSRRCF
SELECTIVITY
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
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CONCLUDING REMARKS
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
30
ConclusionsgTransient analysis
n Time-domain vs. spectral characterizationn Ridge Regression Curve Fitting
gPattern analysisn Feature extractionn Performance measures
gExperimental resultsn Thermal transients provide improved sensitivity and
selectivityn Thermal transients are faster; no need to reach S-Sn Time-domain characterization provides a more
compact code
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
31
Directions for future workgData-driven placement of WTS kernels
n Kernels should be placed to maximize class separability
g Improvements to Pade-Z characterizationn Regularization of partial fraction expansion n Development of classifiers for multi-modal densities
gExperimental improvementsn Optimizing the duration of thermal transientsn Comparison of ON/OFF and OFF/ON transientsn Evaluation with micro hot-plates
Multi-frequency Temperature Modulation for Metal-oxide Gas SensorsRicardo Gutierrez-OsunaTexas A&M University
32
QUESTIONS …