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Transcript
NETW 1010
IOT Design:
An Embedded System Overview
Sensors
Dr. Eng. Amr T. Abdel-Hamid
Fall 2016
Embedded System Hardware
Embedded Systems
 Embedded system hardware is frequently use
d
in a loop (“hardware in a loop“):
Dr. Amr Talaat
 cyber-physical systems
What are Sensors?
Embedded Systems
 American National Standards Institute (ANSI) Definition
 A device which provides a usable output in response to
a specified measurand
Input Signal
Output Signal
Sensor
 A sensor acquires a physical parameter and converts it int
o a signal suitable for processing (e.g. optical, electrical,
mechanical)
Dr. Amr Talaat
 A transducer
 Microphone, Loud Speaker, Biological Senses (e.g. touc
h, sight,…ect)
Detectable Phenomenon
Embedded Systems
Stimulus
Acoustic
Biological & Chemical
Electric
Magnetic
Quantity
Wave (amplitude, phase, polarization), Spectrum, Wave
Velocity
Fluid Concentrations (Gas or Liquid)
Charge, Voltage, Current, Electric Field (amplitude,
phase,
polarization), Conductivity, Permittivity
Magnetic Field (amplitude, phase, polarization), Flux,
Permeability
Dr. Amr Talaat
Optical
Refractive Index, Reflectivity, Absorption
Thermal
Temperature, Flux, Specific Heat, Thermal Conductivity
Mechanical
Position, Velocity, Acceleration, Force, Strain, Stress,
Pressure, Torque
Discretization of time
Embedded Systems
 Digital computers require discrete sequences of ph
ysical values
s : DT  DV
Discrete time domain
Dr. Amr Talaat
 Sample-and-hold circuits
Physical Principles
 Amperes’s Law
Embedded Systems
 A current carrying conductor in a magnetic field experiences
a force (e.g. galvanometer)
 Curie-Weiss Law
 There is a transition temperature at which ferromagnetic mat
erials exhibit paramagnetic behavior
 Faraday’s Law of Induction
 A coil resist a change in magnetic field by generating an opp
osing voltage/current (e.g. transformer)
 Photoconductive Effect
Dr. Amr Talaat
 When light strikes certain semiconductor materials, the resist
ance of the material decreases (e.g. photoresistor)
Choosing a Sensor
Embedded Systems
Dr. Amr Talaat
Temperature Sensor
Embedded Systems
 Temperature sensors appear in building, chemic
al process plants, engines, appliances, computer
s, and many other devices that require temperat
ure monitoring
 Many physical phenomena depend on temperatu
re, so we can often measure temperature indire
ctly by measuring pressure, volume, electrical re
sistance, and strain
Dr. Amr Talaat
Temperature Sensor
Embedded Systems
 Bimetallic Strip
Metal A
L  L 0[1   (T - T0)]
δ
Metal B
 Application
Dr. Amr Talaat
 Thermostat (makes
or breaks electrical
connection with defl
ection)
Temperature Sensor
Embedded Systems
 Resistance temper
ature device.
R  R 0[1   (T - T0)]
R  R0 e
1 1 

 T T0 
 
Dr. Amr Talaat
Light Sensor
Embedded Systems
 Light sensors are used
in cameras, infrared d
etectors, and ambient
lighting applications
Dr. Amr Talaat
 Sensor is composed of
photoconductor such a
s a photoresistor, phot
odiode, or phototransi
stor
I
p
+
n
V
-
Magnetic Field Sensor
Embedded Systems
 Magnetic Field sens
ors are used for po
wer steering, secur
ity, and current me
asurements on tra
nsmission lines
Dr. Amr Talaat
 Hall voltage is prop
ortional to magneti
c field
VH 
I B
n  q t
I (protons)
+ + + + + + + + + + + + + + +
x
x
x
x
x
x
x
x
x B x
x
x
x
x
x
x
x
x
- - - - - - - - - - - - - - -
+
VH
-
CO2 Gas Sensor
Embedded Systems
 CO2 sensor measures gas
eous CO2 levels in an env
ironment
 Measures CO2 levels in th
e range of 0-5000 ppm
Dr. Amr Talaat
 Monitors how much infrar
ed radiation is absorbed
by CO2 molecules
Infrared Source
IR Detector
Signals
Embedded Systems
 Sensors generate signals
 Definition: a signal s is a mapping from the time
domain DT to a value domain DV:
 s: DT  DV
 DT : continuous or discrete time domain
 DV : continuous or discrete value domain.
Dr. Amr Talaat
Sample-and-hold circuits
Embedded Systems
Clocked transistor + capacitor;
Capacitor stores sequence values
Dr. Amr Talaat
e(t) is a mapping ℝ  ℝ
 h(t) is a sequence of values or a mappin
gℤℝ
1
6
The Sampling Theorem
Embedded Systems
 Obviously, the more samples we take the better
those samples approximate the original function
 The Nyquist sampling theorem:
A continuous bandlimited function can be completely re
presented by a set of equally spaced samples, if the sa
mples occur at more than twice the frequency of the hi
ghest frequency component of the function
Dr. Amr Talaat
1
7
The Sampling Theorem
Embedded Systems
Dr. Amr Talaat
 In other words, to adequately capture a function
with maximum frequency F, we need to sample i
t at frequency N = 2F.
 N is called the Nyquist limit.
 The Nyquist sampling theorem applied to CDs
 Most humans can hear to 20 kHz
Some to 22 kHz
 CDs are sampled at 44.1 kHz
Although not twice the “highest frequency
component”, it is twice the “highest freque
ncy component” that can be (generally) he
ard
Aliasing
Embedded Systems
 2 t 
 2 t 
e3 (t )  sin 
  0.5 sin 

 8 
 4 
Dr. Amr Talaat
 2 t 
 2 t 
 2 t 
e4 (t )  sin 
  0.5 sin 
  0.5 sin 

 8 
 4 
 1 
 Periods of 8,4,1
 Indistinguishable if sampled at integer times, ps=1
Matlab deo
Aliasing (2)
Embedded Systems
 Reconstruction impossible, if not sampling frequently eno
ugh
How frequently do we have to sample?
Nyquist criterion (sampling theory):
Aliasing can be avoided if we restrict the frequencies
of the incoming signal to less than half of the
sampling rate.
ps < ½ pN where pN is the period of the “fastest” sine wave
or fs > 2 fN where fN is the frequency of the “fastest” sine wave
Dr. Amr Talaat
fN is called the Nyquist frequency, fs is the sampling rate.
Anti-aliasing filter
Embedded Systems
 A filter is needed to remove high frequencies
g (t )
e(t )
e4(t) changed into e3(t)
Ideal filter
Dr. Amr Talaat
Realizable
filter
fs /2
fs
Examples of Aliasing in computer graphics
Embedded Systems
 Original
Sub-sampled, no filtering
Dr. Amr Talaat
http://en.wikipedia.org/wiki/Image:
Moire_pattern_of_bricks_small.jpg
Discretization of values: A/D-converters
Embedded Systems
 Digital computers require digital form of physical
values
s: DT  DV
Discrete value domain
Dr. Amr Talaat
A/D-conversion; many methods with different speeds.
Flash A/D converter
Embedded Systems
*
 Encodes input nu
mber of most sign
ificant ‘1’ as an u
nsigned number,
e.g.
“1111” -> “100”,
“0111” -> “011”,
“0011” -> “010”,
“0001” -> “001”,
“0000” -> “000”
(Priority encoder)
.
Dr. Amr Talaat
* Frequently, the case h(t) > Vref would not be decoded
Assuming 0  h(t)  Vref
Embedded Systems
Encoding of voltage intervals
Dr. Amr Talaat
“11“
“10“
“01“
“00“
Vref /4
Vref /2
3Vref /4
Vref
h(t)
Resolution
 Resolution (in bits): number of bits produced
Embedded Systems
 Resolution Q (in volts): difference between two in
put voltages causing the output to be incremente
d by 1
VFSR
Q
n
with
Dr. Amr Talaat
Q:
resolution in volts per step
VFSR: difference between largest
and smallest voltage
n:
number of voltage intervals
Example:
Q = Vref /4 for the
previous slide,
assuming * to be
absent
Resolution and speed of Flash A/D-converter
Embedded Systems
 Parallel comparison with reference voltage
 Speed:
O(1)
 Hardware complexity:
O(n)
 Applications: e.g. in video processing
Dr. Amr Talaat
Higher resolution: Successive approximation
Embedded Systems
h(t)
V-
Dr. Amr Talaat





Key idea: binary search:
Set MSB='1'
if too large: reset MSB
Set MSB-1='1'
if too large: reset MSB-1
w(t)
Speed:
O(log2(n))
Hardware complexity: O(log2(n))
with n= # of distinguished
voltage levels;
slow, but high precision possible.
Successive approximation (2)
Embedded Systems
V
1100
Vx
h(t)
1011
1010
1000
V-
Dr. Amr Talaat
t
Quantization Noise
Embedded Systems
Assuming
“rounding“
(truncating)
towards 0
 h(
t)
w(t)
Dr. Amr Talaat
w(t)-h(t)
Quantization Noise
Embedded Systems
 h(
t)
w(t)
h(t)-w(t)
Assuming
“rounding“
(truncating)
towards 0
Dr. Amr Talaat
Quantization noise for audio signal
Embedded Systems
e.g.: 20 log(2)=6.02 decibels
Dr. Amr Talaat
 effective signal voltage 

signal to noise ratio (SNR) [db]  20 log 
 effective noise voltage 
Signal to noise for ideal n-bit converter : n * 6.02 + 1.76 [dB]
e.g. 98.1 db for 16-bit converter, ~ 160 db for 24-bit converter
Additional noise for non-ideal converters
Signal to noise ratio
Embedded Systems
 effective signal voltage 

signal to noise ratio (SNR) [db]  20 log10 
 effective noise voltage 
e.g.: 20 log10(2)=6.02 decibels
Dr. Amr Talaat
Signal to noise for ideal n-bit converter : n * 6.02 + 1.76
[dB]
e.g. 98.1 db for 16-bit converter, ~ 160 db for 24-bit
converter
Additional noise for non-ideal converters
Digital-to-Analog (D/A) Converters
Embedded Systems
Various types, can be quite
simple, e.g.:
Dr. Amr Talaat
Current ~ no. represented by x
Embedded Systems
Loop rule:
x0  I 0  8  R  V  Vref  0
I 0  x0 

In general: I i  xi 
Junction rule: I 
I
Vref
8 R
Vref
2 3 i  R
i
i
Dr. Amr Talaat
 I  x3 
Vref
R
 x2 
Vref
2 R
 x1 
Vref
4 R
 x0 
I ~ nat (x), where nat(x): natural number represented by x;
Vref
8 R

Vref
8 R
3
  xi  2i
i 0
Output voltage ~ no. represented by x
Embedded Systems
Loop rule*:
y  R1  I '  0
Junction rule°:
I  I'

y  R1  I  0
°
*
From the previous slide
I
Vref
8 R
3
  xi  2i
i 0
Dr. Amr Talaat
Hence:
3
R1
R1
i
y  Vref 
xi  2  Vref 
 nat ( x)

8  R i 0
8 R
Op-amp turns
current I ~ nat
(x) into a
voltage ~ nat (x)
Output generated from signal e3(t)
Embedded Systems
*
* Assuming
“zero-order
hold”
Dr. Amr Talaat
Possible to
reconstruct
input signal?