analysis of signals and characterization of...
TRANSCRIPT
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Analysis of Signals and Characterization of Systems
Chih-Ping LinGeotechnical Engineering GroupNational Chiao Tung University
Outline
1. Signals and Systems
2. Time Domain Analysis
3. Frequency Domain Analysis of Signals
4. Frequency Domain Analysis of Systems
5. Discrete Inverse Theory
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Signals and Systems
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Signals and Systems
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Classification of Systems
Lumped vs. distributed parameter Causal vs. non-causal Linear vs. non-linear Time-invariant vs. time-variant
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Combination of Systems
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Combination of Systems
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Classification of Signals
Multi-channel and multi-dimensional Continuous time vs. discrete time Continuous-valued vs. discrete-valued Deterministic vs. random Periodic vs. aperiodic
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Elementary Signals
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Decomposition Methods
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Applications
Computation of the output response
Identification of similarities
Noise control
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Time Domain Analysis
Convolution: Computing the output signal (in a LTI system)
Cross-Correlation: Identifying similarities
Noise control in time domain
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Convolution
Decomposition in time domain
Convolution sum
k
knkxnx )()()(
kk
k
knhkxknTkx
knkxTnxTny
),()()()(
)()()()(
)(),( knhknh
)(*)()()()( nhnxknhkxnyk
Time invariant
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Properties of Convolution
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Graphical Computation of Convolution
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Convolution sum in matrix form
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Example of convolution sum
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Determination of Impulse Response
Direct solution of the difference equation
Estimated experimentally by excite the system with a signal of short duration
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Cross-Correlation
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n
xy mnynxmr )()()(
n
xy nymnxmr )()()(
)(*)()()()( mymxmnynxmrn
xy
Definition:or
Computation:
)()( mrmr xyyx Properties:
Cross-Correlation
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Cross-CorrelationTypical example
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Application of Cross-Correlation
Identifying similarities Determination of travel time Identifying replicas in noisy signals Biases: Lossy media, dispersive and
multiple paths
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Application of Cross-Correlation
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Noise control in time domain
StackingIf xi's are independent and identical (i.i.d) Gaussian with mean and variance 2 (i.e. N(, 2) ) and y= mean(x), then y = N(, 2/N).
Smoothing with moving average
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2/)1(
2/)1(
)()()(M
Mk
kixkwiy 1)(kwwhere
)(*)()( nwnxny
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Noise control in time domainStacking
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Noise control in time domainMoving average
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Frequency Domain Analysis of Signals
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The concept of frequency in continuous and discrete time
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1
2 tFtAtAtxa ),2cos()cos()(
•Continuous-time sinusoidal signals
a. For every F, xa is periodicb. Xa distinct for distinct Fc. Rate of oscillation increases with F
•Discrete-time sinusoidal signals nfnAnAnx ),2cos()cos()(
a. xa is periodic only when f is a rational numberb. Xa identical when frequencies (f) are separated
by an integer numberc. The highest rate of oscillation is attained when
|f| =1/2
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Effect of sampling on frequency
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F f-Fs/2 Fs/2 -1/2 1/2F1 F3 F2 f1 f3 f2
Fi = fi *Fs
Aliasing
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The sampling theorem
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max2F
Fs
Fs/2 : Nyquist frequency
To avoid aliasing:
F is uniquely defined in –Fs/2 F Fs/2
Types of signal (revisited)
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•Periodic Signal vs. Aperiodic Signal
•Energy Signal vs. Power Signal
dttxE
2)(
n
nxE2
)(
T
TTdttx
TP
2)(
2
1lim
N
NnN
nxN
P2
)(12
1lim
•Energy signal is sufficient to guarantee the existence of Fourier Transform
•Power signal is sufficient to guarantee the existence of Fourier Series expansion
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Complex Number
Complex Exponential
Euler reasoned that he needed something with half the strength of a minus sign (or the −1) so when it was squared it would equal a minus sign.
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Complex Exponential
Spectral Analysis and Synthesis of Signals
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Orthogonal Expansion
Eigenvalue Problems Orthogonal Vectors Eigenvector expansion
Sturm-Liouville Problems Orthogonal Functions Eigenfunction expansion
Fourier Expansion (Spectral Expansion)
•Fourier Series (for power signals)
•Fourier Transform (for energy signals)
are basically orthogonal expansion of a continuous functionusing sinusoidal functions or complex exponential functions
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Fourier Series
Spectral Analysis Sampling in time domain
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Spectral Analysis Sampling in frequency domain
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1
0
/2 1,,2,1,0 ,)()(N
n
Nknj NkenxkX
1
0
/2 1,,2,1,0 ,)(1
)(N
k
Nknj NnekXN
nx
•Discrete Fourier Transform (DFT)
•Inverse Discrete Fourier Transform (IDFT)
Duality relation
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1
2X(nt)
t0 T=1/ft
f0 f
X(nf)
TimeWindow
FrequencyWindow
F=1/t
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Spectral AnalysisSummary
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Sampling of bandlimited signal Aliasing of spectral components
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Symmetry Properties of DTFT
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Properties of DTFT
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Symmetry Properties of DFT
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Properties of DFT
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Computation of DFT
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Fast Fourier Transform (FFT) algorithmPlots:
Practical considerationFrequency resolution – zero padding
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Practical considerationTruncation leakage - windowing
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Frequency Domain Analysis of Systems
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Does the sinusoid offer any advantage in the study of system?
Impulse Inpulse response
Sinusoid Frequency Response
System
System
The sinusoid is an eigenfunction analogous to eigenvector in matrix form•If x is an eigenvector, then y = x•If x is a sinusoid, then y=H() xAnalytical and experimental determination of H(w)Are made easier because of the eigenfunction property
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Convolution
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2
Response of a LTI systemto periodic signals
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5
1
2
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Response of a LTI systemto periodic signals
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2
Response of a LTI systemto aperiodic signals
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1
2
t
x(t)
t
x(t)
Time Periodof Interest
PaddingZeros
Tr
2Tr
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Response of a LTI systemto aperiodic signals
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5
1
2
X(nt)
t0 T=1/ft
f0 f
X(nf)
TimeWindow
FrequencyWindow
F=1/t
tfF
12 max
ftT
12 max
Cross-correlation and cross-spectral density
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4
5
1
2)()()(
)}(*)({))({
fYfXfC
nynxDTFTnrDTFT
xy
xy
)()()( fXfXfCxx
)()()()()()()()( fCfHfHfXfXfYfXfC xxyx
)()()()()()()()()(2
______________
fCfHfXfHfXfHfYfYfC xxyy
Cross-spectral density
Auto-spectral density (power spectral density)
Important relations:
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Noise control in frequency domain
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1
2)()()(' kWkXkX
Filters
Filters
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1
2
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Determination of frequency response
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2
)(
)()(
)(
)()()()(
kX
kRkH
kX
kRkXkHkH noisy
)()()(
)()()]())()([(
)()]()()([
)()()(
kCkHkC
kXkRkHkXkX
kXkRkHkX
kXkZkCC
xrxx
xz
)(
)()(
kC
kCkH
xx
xz
)(
)()(
kX
kYkH
Measure of Noise
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4
5
1
2Coherence:
)(
)()(2
kC
kCk
ZZ
YY
)()(
)()(
22
kCkC
kCk
ZZXX
XZ
)(
)()(
kC
kCkNSR
YY
NN
)(
)(1)(
2
2
k
kkNSR
Noise-to-signal ration (N/S):
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Discrete Inverse Theory
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ModelParameters
mMODEL g(.)
DataMeasurement
d
Methods of solution
Exact inversion Neural network Inversion based on a forward model
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1
2
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Neural network
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1
21
2
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4
1
3
2
1
2
3
Input layer Hidden layer Output layer
Inversion based on a forward model
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1
2Method of Solution
Exact InversionInversion Based on a
Forward ModelNeural Network
StatisticalApproach
Linear AlgebraicApproach
ClassicalInterpretation
BayesianInterpretation
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Least Squares Solution If the problem y=hx is over-determined and inconsistent, there
is no unique solution, and the estimate of the solution x will be a compromise.
One alternative is to minimize the L2-norm of the vector of residuals e=y-hx. The objective function to be minimized:
The solution is found by setting the derivative of Γw.r.t. x equal to zero. The solution is found as
hxhxyhxhxyyy
hxyhxyTTTTTT
T
)()(
yhhhx TTest
1)(
Inversion based on Optimization
Matlab function fmin() or fminsearch() Examples
2cxbxay
)/(1000 bxay
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Bayesian Inversion
Thank You!
Chih-Ping LinGeotechnical Engineering GroupNational Chiao Tung University