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SSN02 - SIGNALSSergio Cova – SENSORS SIGNALS AND NOISE rv 2017/02/06 1

Sensors, Signals and Noise

COURSE OUTLINE

• Introduction

• Signals and Noise

• Filtering

• Sensors and associated electronics

SSN02 - SIGNALSSergio Cova – SENSORS SIGNALS AND NOISE rv 2017/02/06

SENSOR FILTERING METER

SIGNIFICANTNoise

of Sensor

SIGNIFICANTNoise

of Preamp circuits (or Front-end)

NEGLIGIBLENoise

of Filtering circuits(hopefully!)

NEGLIGIBLENoise

of Meter circuits(hopefully!)

PREAMPLIFIER(or FRONT-END)

Signal

Set-Up for Sensor Measurements

SIGNAL NOISE

Mathematical Description of Signals

Time domain and frequency domain analysis

Energy signals and correlation functions

Energy Spectrum

Power signals, Correlation Functions and Power Spectrum

Note on truncated signals

and for those who trust only analytical demonstrations

Appendix: Crosscorrelation and Convolution

Time domain and frequency domain analysis of signals

Signals: mathematical description• Signals = electric variables x (voltage, current ...) that carry information• In the domain of time t : deterministic functions x = x(t)

In the domain of frequency f (Fourier transform domain) can be considered linear superposition (sum) of elementary sinusoid components

Example: exponential pulse

In the domain of time t can be considered linear superposition (sum) of elementary δ-pulses of amplitude (i.e. area) x(t)dt

Signals: mathematical description

RECALL: since x(t) is real, the transform X(f) has simple and useful properties,

for instance:

X(‐f) = X*(f) , that is |X(‐f)| = |X(f)| and arg[X(‐f)] = ‐ arg [X(f)]

...... and various other properties!

• X(f) = Fourier transform of x(t)

• X(f) is complex : Module and Phase

(or Real and Imaginary parts)

linear superposition (sum) of elementary sinusoid components

ConvolutionConstant-parameter linear filters (NO switches, NO time-variant components!!)

are characterized by

H(f) transfer function in frequency domain

h(t) δ-response in time domain

h(α)x(α) y(t)

H(f) = F[h(t)]

h(t) = F‐1H(f)]

Signal in Signal out

The input x(α) can be described as a linear superposition (sum) of elementary δ-pulses of amplitude x(α)dα

therefore

the output y(t) can be described as a linear superposition (sum) of elementary δ-pulse responses x(α)dα h(t‐α)

Computing the convolution

h(T‐α)

x(α)h(T‐α)

reversed δ-response delayed by T

input signal

δ-response

output signal

h(α)x(α) y(t)

area = y(T)

Energy signals and correlation functions

Signal Energy

The Energy E of a signal x(t) is defined as

lim→

Signals x(t) with finite E are called energy-signals. Typical example: pulse signals

INTUITIVE VIEW OF ENERGY:

Let x(t) be a voltage pulse on a unitary resistance R=1 Ω

then E is the energy dissipated in R by the pulse

Signal Auto-Correlation Function (Energy-type)

lim→

kxx(τ) gives the degree of similarity of x(t) with itself shifted by τ

Energy = Autocorrelation at zero-shift

x(α+T)

Case: single pulse (exponential)

1 Pt Tx t Ae

xxTk A e

x(α+T)

Case: double pulse (exponential)

1 1 R PP t T Tt TRx t Ae t T Ae

R P R P

T T T TP P

k A T eT TA e A e

Signal Cross-Correlation Function (Energy-type)

lim→

• x(t) and y(t) are two different signals of energy-type

• kxy(τ) gives the degree of similarity of x(t)

with y(t) shifted by τ to left (towards earlier time)

• Various denominations for kxy(τ) :

Cross-Correlation function of x and y

Mutual-Correlation function of x and y

Signal Cross-Correlation Function (Energy-type)

y(α+T)

x(α) y(α+T)

Building step-by-step

the Cross-Correlation function

Cross-Correlation obtained by Convolution

x(‐α)

Convolution

is different from Crosscorrelation

However

Convolution with first term reversed

is equal to Crosscorrelation

Cross-Correlation obtained by Convolution∗

y(α+T)

x(α) x(‐α)

x αx(α) y(α+T)

Energy Spectrum

INTUITIVE VIEW OF ENERGY SPECTRUM:

Let x(t) be voltage on a unitary resistance R=1 Ω

power = voltage x(t) multiplied by current x(t)

x(t) = sum of sinusoid components with frequency f and amplitude | |

sinusoids are orthogonal functions

No power from multiplication of voltage and current of different components (different f)

Every component (at frequency f) contributes an energy

Energy signal x(α) with Fourier transform X(f): by Parseval’s theorem

Sx(f) = 2 is called the Energy Spectrum of the signal x(α)

Energy Spectrum

Energy Spectrum• Alternative definition of the Energy Spectrum is

• Knowing that ∗ we see that the two definitions are consistent

and by a basic property of Fourier transforms

∗ ∗

kxx(τ)

Example ofEnergy, Autocorrelation and Energy Spectrum

V TS f X ffT

1exp1 2P P P

tV x t X f V TT j fT

Autocorrelation

function

Energy Spectrum

Energy 202P

xx PTE k V

Energy 2

x PTE S f df V

VPExponential pulse

(0(0 (

• kxx is symmetrical:

• kxx has positive maximum at zero shift:

Signal Cross-Correlation Function (Energy-type)• x(t) and y(t) are two different energy-type signals

• kxy is NOT symmetrical, however ( (

• the maximum of kxy is neither necessarily positive nor at zero shift,however, the absolute maximum value is limited

( [ 0 + 0 ] (linear mean of the maxima of kxx and kyy)

( 0 0 (geometric mean of the maxima of kxx and kyy)

Auto-Correlation of sum-signals

is the sum of their auto- and cross-correlations

The autocorrelation of the sum of two signals x(t) and y(t)

The energy spectrum [ ]=

is the sum of the two SPECTRA (real) and of the two CROSS-SPECTRA (complex conjugate)

∗ ∗

Complex conjugate

Power signals, Correlation Functionsand Power Spectrum

Signal PowerFor signals x(t) that have NOT finite energy E∞ (DC, sinusoids, periodic signals, etc. ) the Power P is defined as the time-average

lim→ 2

Parseval theorem is valid for the entire integralbut NOT for the truncated integral

For computing P in f domain instead of truncated integral we use truncated signal xT(t)for |α| ≤T

0 for |α| > TWe can thus exploit Parseval theorem: with ] we get

lim→

= lim→

The Power Spectrum of the signal x(α) is defined as the integrand

Sx(f) = lim→ and

Signal Auto-Correlation Function (Power-type)

lim→ 2

Just like power P, the autocorrelation of power signals is defined as time‐average

0note that

NB2: xT(α) energy signal with autocorrelation , Therefore:

lim→ 2 lim

NB1: for finite T it is but for lim

→the = is valid

Also here we use truncated signal xT(α) instead of truncated integral

lim→

Signal Auto-Correlation Function and Power Spectrum

lim→

An alternative definition of signal Power Spectrum is

The two definitions are consistent

lim→

, ] = lim→

, = lim→

Signal Cross-Correlation Function (Power-type)

lim→ 2 lim

• x(t) and y(t) are two different signals, both power-type

• Kxy(τ) measures the degree of similarity of x(t) with y(t) shifted by τ to left (towards earlier time)

• If even only one of the two signals x(t) and y(t) is energy-type the energy type autocorrelation kxy(τ) must be employed

(in fact, the power-type crosscorrelation vanishes Kxy(τ) = 0 and the energy-type crosscorrelation kxy(τ) is finite).

(0 0(0 (

• Kxx is symmetrical:

• Kxx has positive maximum at zero shift:

Signal Cross-Correlation Function (Power-type)• x(t) and y(t) are two different signals, both power-type

• Kxy is NOT symmetrical, however ( (

• the maximum of Kxy is neither necessarily positive, nor at zero shift, however, the absolute maximum value is limited

( [ 0 + 0 ] (linear mean of the maxima of kxx and kyy)

( 0 0 (geometric mean of the maxima of kxx and kyy)

Signal Auto-Correlation Function (Power-type)

Energy-signals and power-signals compared

Energy-type (pulses etc.)

Energy

Autocorrelation

Energy spectrum

Power-type (periodic waveforms etc.)

Power lim→

Autocorrelation

lim→

Power spectrum

, = lim→

Noteworthy case: truncated sinusoidal signal

seen in time domain seen in frequency domain

Idealsinusoid

‐T T

Truncatedsinusoid

fm‐fm f

ffm‐fm

tT‐T

rT(t) TimeWindow1

• In reality the signal is always available over a finite time interval: therefore, in reality we always deal with truncated signals

• cropping in time corresponds to convolution of the signal in the f domain with the transform of the rectangle (sinc function)

• the convolution spreads the signal in the f domain; that is, it makes it wider and smoother

• the narrower is the window 2T, the wider is the sincand more significant is the signal spreading in frequency

• Applying correctly the sampling theorem we see that: the sampling frequency fS to be employed for a truncated sinusoid of frequency fmis NOT fS ≈ 2fm ; it must be REMARKABLY HIGHER fS >> 2fm

• NB1: in general the convolution of complex functions is difficult to visualize because a) it is twofold; it implies shifting in positive and in negative sense of the f axis; b) at every frequency f a sum of complex terms must be computed

• NB2: however, the case here considered is much simpler: at every frequency fonly one contribution is significant, there is no sum to be computed.

APPENDIX: Crosscorrelation and Convolution(for those who trust only analytical demonstrations)

change of variable :

shows that

that is

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