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Universität DortmundUniversität Dortmund2
Department of Electrical and Computer EngineeringCollege of Engineering, Technology and Computer
Science
Simplified design flowfor embedded systems
Universität DortmundUniversität Dortmund3
Department of Electrical and Computer EngineeringCollege of Engineering, Technology and Computer
Science
Embedded System Hardware
Embedded system hardware is frequently used in a loop(„hardware in a loop“):
Embedded system hardware is frequently used in a loop(„hardware in a loop“):
actuators
Many Example of such loops
Heating; Lights; Engine control; Power supply; …; Robots
Many Example of such loops
Heating; Lights; Engine control; Power supply; …; Robots
Universität DortmundUniversität Dortmund4
Department of Electrical and Computer EngineeringCollege of Engineering, Technology and Computer
Science
Sensors
Processing of physical data starts with capturing this data.
Sensors can be designed for virtually every physical and
chemical quantity• including weight, velocity, acceleration, electrical current,
voltage, temperatures etc.• chemical compounds.
Many physical effects used for constructing sensors.
Examples:• law of induction (generation of voltages in an electric field),• light-electric effects.
Huge amount of sensors designed in recent years.
Processing of physical data starts with capturing this data.
Sensors can be designed for virtually every physical and
chemical quantity• including weight, velocity, acceleration, electrical current,
voltage, temperatures etc.• chemical compounds.
Many physical effects used for constructing sensors.
Examples:• law of induction (generation of voltages in an electric field),• light-electric effects.
Huge amount of sensors designed in recent years.
Universität DortmundUniversität Dortmund5
Department of Electrical and Computer EngineeringCollege of Engineering, Technology and Computer
Science
Charge-coupled devices (CCD) image sensors
Based on charge transfer to next pixel cellBased on charge transfer to next pixel cell
Universität DortmundUniversität Dortmund6
Department of Electrical and Computer EngineeringCollege of Engineering, Technology and Computer
Science
CMOS image sensors
Based on standard production process for CMOS chips, allows integration with other components.
Based on standard production process for CMOS chips, allows integration with other components.
Universität DortmundUniversität Dortmund7
Department of Electrical and Computer EngineeringCollege of Engineering, Technology and Computer
Science
Comparison CCD/CMOS sensors
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Universität DortmundUniversität Dortmund8
Department of Electrical and Computer EngineeringCollege of Engineering, Technology and Computer
Science
Example: Biometrical Sensors
Example: Fingerprint sensor (© Siemens, VDE):Example: Fingerprint sensor (© Siemens, VDE):
Matrix of 256 x 256 elem.Voltage ~ distance. Resistance also computed. No fooling by photos and wax copies.Carbon dust?
Integrated into ID mouse.
Universität DortmundUniversität Dortmund9
Department of Electrical and Computer EngineeringCollege of Engineering, Technology and Computer
Science
Artificial eyes
He looks hale, hearty, and healthy — except for the wires. They run from the laptops into the signal processors, then out again and across the table and up into the air, flanking his face like curtains before disappearing into holes drilled through his skull. Since his hair is dark and the wires are black, it's hard to see the actual points of entry. From a distance the wires look like long ponytails.
© Dobelle Institute(www.dobelle.com)
Universität DortmundUniversität Dortmund10
Department of Electrical and Computer EngineeringCollege of Engineering, Technology and Computer
Science
Other sensors
Rain sensors for wiper control(„Sensors multiply like rabbits“ [IIT automotive])
Pressure sensors
Proximity sensors
Engine control sensors
Hall effect sensors
Rain sensors for wiper control(„Sensors multiply like rabbits“ [IIT automotive])
Pressure sensors
Proximity sensors
Engine control sensors
Hall effect sensors
Universität DortmundUniversität Dortmund11
Department of Electrical and Computer EngineeringCollege of Engineering, Technology and Computer
Science
Discretization of time
Vx is a sequence of values or a mapping ℤ ℝ Vx is a sequence of values or a mapping ℤ ℝ
In this course: restriction to digital information processing; Known digital computers can only process discrete time series. Discrete time; sample and hold-devices.
Ideally: width of clock pulse -> 0
In this course: restriction to digital information processing; Known digital computers can only process discrete time series. Discrete time; sample and hold-devices.
Ideally: width of clock pulse -> 0
Ve is a mapping ℝ ℝVe is a mapping ℝ ℝ
Universität DortmundUniversität Dortmund12
Department of Electrical and Computer EngineeringCollege of Engineering, Technology and Computer
Science
Discretization of values: A/D-converters1. Flash A/D converter
Parallel comparison with reference voltage
Speed: O(1)
Hardware complexity: O(n)
with n= # of distin-guished voltage levels
Parallel comparison with reference voltage
Speed: O(1)
Hardware complexity: O(n)
with n= # of distin-guished voltage levels
Digital computers require digital form of physical valuesA/D-conversion; many methods with different speeds.Example: 1. Flash A/D converter:
Digital computers require digital form of physical valuesA/D-conversion; many methods with different speeds.Example: 1. Flash A/D converter:
Universität DortmundUniversität Dortmund13
Department of Electrical and Computer EngineeringCollege of Engineering, Technology and Computer
Science
Discretization of values : A/D-converters 2. Successive approximation
Key idea: binary search:Set MSB='1'if too large: reset MSBSet MSB-1='1'if too large: reset MSB-1
Key idea: binary search:Set MSB='1'if too large: reset MSBSet MSB-1='1'if too large: reset MSB-1
Speed: O(ld(n))Hardware complexity: O(ld(n))with n= # of distinguishedvoltage levels;slow, but high precision possible.
Speed: O(ld(n))Hardware complexity: O(ld(n))with n= # of distinguishedvoltage levels;slow, but high precision possible.
1100
1000
1010
1011
t
V
Vx
V-
Universität DortmundUniversität Dortmund14
Department of Electrical and Computer EngineeringCollege of Engineering, Technology and Computer
Science
Quantization Noise
N = (approximated - real signal) called quantization noise.Example: quantization noise for sine wave
N = (approximated - real signal) called quantization noise.Example: quantization noise for sine wave
Universität DortmundUniversität Dortmund15
Department of Electrical and Computer EngineeringCollege of Engineering, Technology and Computer
Science
Quantization noise for audio signal
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
voltage noise effective
voltage signal effective log 20 [db] (SNR) ratio noise to signal
Additional noise for non-ideal converters
e.g.: 20 log(2)=6.02 decibels
Universität DortmundUniversität Dortmund16
Department of Electrical and Computer EngineeringCollege of Engineering, Technology and Computer
Science
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Due to Kirchhoff‘s laws:
Due to Kirchhoff‘s laws:
Current into Op-Amp=0:
Hence:
Output voltage no. represented by x
Various types, can be quite simple, e.g.:
Digital-to-Analog (D/A) Converters
Universität DortmundUniversität Dortmund17
Department of Electrical and Computer EngineeringCollege of Engineering, Technology and Computer
Science
Possible to reconstruct analog value from digitized value?
Let fs be the sampling frequency
Input signals with frequency components > fs/2 cannot be distinguished from signals with frequency components < fs/2.
Example: Signal: 5.6 Hz; Sampling: 9 Hz
Let fs be the sampling frequency
Input signals with frequency components > fs/2 cannot be distinguished from signals with frequency components < fs/2.
Example: Signal: 5.6 Hz; Sampling: 9 Hz
-1.5
-1
-0.5
0
0.5
1
1.5
Universität DortmundUniversität Dortmund18
Department of Electrical and Computer EngineeringCollege of Engineering, Technology and Computer
Science
Frequency spectrum of sampled signal
Let Xc(): frequency spectrum of the continuous signal, cut-off frequency ΩN=2π fN
Let Ωs=2π fs: sampling frequency
Let Xs: frequency spectrum of the sampled signal
Xs consists of multiple copies of Xc, separated by Ωs
Let Xc(): frequency spectrum of the continuous signal, cut-off frequency ΩN=2π fN
Let Ωs=2π fs: sampling frequency
Let Xs: frequency spectrum of the sampled signal
Xs consists of multiple copies of Xc, separated by Ωs
Formally: Xs = Xc folded with S,with S: frequency spectrum of clock
Formally: Xs = Xc folded with S,with S: frequency spectrum of clock
Cannot be distinguished in sampled signal
Universität DortmundUniversität Dortmund19
Department of Electrical and Computer EngineeringCollege of Engineering, Technology and Computer
Science
Aliasing
If Ωs < 2 ΩN copies of spectrum will overlap
(we don’t know the original frequencies any more)
If Ωs < 2 ΩN copies of spectrum will overlap
(we don’t know the original frequencies any more)
No problem for signal reconstruction if this is avoided.No problem for signal reconstruction if this is avoided.
Impossible to reconstruct fast signals after slow sampling:multiple fast signals share same sampled sequence;
Universität DortmundUniversität Dortmund20
Department of Electrical and Computer EngineeringCollege of Engineering, Technology and Computer
Science
Nyquist theorem
• Analog input to sample-and-hold can be precisely reconstructed from its output, provided that sampling proceeds at double of the highest frequency found in the input voltage. [Nyquist 1928, Shannon, 1949]
• Analog input to sample-and-hold can be precisely reconstructed from its output, provided that sampling proceeds at double of the highest frequency found in the input voltage. [Nyquist 1928, Shannon, 1949]
S/H A/D-converter D/A-converter
= ?
Inter-polate
Does not capture effect of value quantization:Quantization noise prevents precise reconstruction.
Does not capture effect of value quantization:Quantization noise prevents precise reconstruction.