Ch.3 Part A: Embedded System Hardware Input/Output EECE **** Embedded System Design

Ch.3 Part A: Embedded System Hardware Input/Output

EECE **** Embedded System Design

Universität DortmundUniversität Dortmund2

Department of Electrical and Computer EngineeringCollege of Engineering, Technology and Computer

Science

Simplified design flowfor embedded systems



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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



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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.



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Charge-coupled devices (CCD) image sensors

Based on charge transfer to next pixel cellBased on charge transfer to next pixel cell



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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.



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Comparison CCD/CMOS sensors

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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.



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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)



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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



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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



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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:



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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.

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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



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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



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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



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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

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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



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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;



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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.



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Actuators and output

Huge variety of actuators and output devices, impossible to present all of them.Microsystems motors as examples (© MCNC):

(© MCNC)

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Ch.3 Part A: Embedded System Hardware Input/Output EECE **** Embedded System Design