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

2. Universität Dortmund 2 Department of Electrical and Computer Engineering College of Engineering, Technology and Computer Science Simplified design flow for embedded systems

3. Universität Dortmund 3 Department of Electrical and Computer Engineering College of Engineering, Technology and Computer Science Embedded System Hardware 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

4. Universität Dortmund 4 Department of Electrical and Computer Engineering College 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.

5. Universität Dortmund 5 Department of Electrical and Computer Engineering College of Engineering, Technology and Computer Science Charge-coupled devices (CCD) image sensors Based on charge transfer to next pixel cell

6. Universität Dortmund 6 Department of Electrical and Computer Engineering College of Engineering, Technology and Computer Science CMOS image sensors Based on standard production process for CMOS chips, allows integration with other components.

7. Universität Dortmund 7 Department of Electrical and Computer Engineering College of Engineering, Technology and Computer Science Comparison CCD/CMOS sensors Source: B. Diericks: CMOS image sensor concepts. Photonics West 2000 Short course (Web)

8. Universität Dortmund 8 Department of Electrical and Computer Engineering College of Engineering, Technology and Computer Science Example: Biometrical Sensors 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.

9. Universität Dortmund 9 Department of Electrical and Computer Engineering College 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)

10. Universität Dortmund 10 Department of Electrical and Computer Engineering College 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

11. Universität Dortmund 11 Department of Electrical and Computer Engineering College of Engineering, Technology and Computer Science Discretization of time V x 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 V e is a mapping ℝ  ℝ

12. Universität Dortmund 12 Department of Electrical and Computer Engineering College of Engineering, Technology and Computer Science Discretization of values: A/D-converters 1. 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 values  A/D-conversion; many methods with different speeds. Example: 1. Flash A/D converter: Digital computers require digital form of physical values  A/D-conversion; many methods with different speeds. Example: 1. Flash A/D converter:

13. Universität Dortmund 13 Department of Electrical and Computer Engineering College 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 MSB Set MSB-1='1' if too large: reset MSB-1 Key idea: binary search: Set MSB='1' if too large: reset MSB Set MSB-1='1' if too large: reset MSB-1 Speed: O(ld(n)) Hardware complexity: O(ld(n)) with n= # of distinguished voltage levels; slow, but high precision possible. Speed: O(ld(n)) Hardware complexity: O(ld(n)) with n= # of distinguished voltage levels; slow, but high precision possible. 1100 1000 1010 1011 t V VxVx V-V-

14. Universität Dortmund 14 Department of Electrical and Computer Engineering College of Engineering, Technology and Computer Science Quantization Noise N = (approximated - real signal) called quantization noise. Example: quantization noise for sine wave

15. Universität Dortmund 15 Department of Electrical and Computer Engineering College 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 Additional noise for non-ideal converters e.g.: 20 log(2)=6.02 decibels

16. Universität Dortmund 16 Department of Electrical and Computer Engineering College of Engineering, Technology and Computer Science 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

17. Universität Dortmund 17 Department of Electrical and Computer Engineering College of Engineering, Technology and Computer Science Possible to reconstruct analog value from digitized value? Let f s be the sampling frequency Input signals with frequency components > f s /2 cannot be distinguished from signals with frequency components < f s /2. Example: Signal: 5.6 Hz; Sampling: 9 Hz Let f s be the sampling frequency Input signals with frequency components > f s /2 cannot be distinguished from signals with frequency components < f s /2. Example: Signal: 5.6 Hz; Sampling: 9 Hz

18. Universität Dortmund 18 Department of Electrical and Computer Engineering College of Engineering, Technology and Computer Science Frequency spectrum of sampled signal Let X c (): frequency spectrum of the continuous signal, cut-off frequency Ω N =2π f N Let Ω s =2π f s : sampling frequency Let X s : frequency spectrum of the sampled signal X s consists of multiple copies of X c, separated by Ω s Let X c (): frequency spectrum of the continuous signal, cut-off frequency Ω N =2π f N Let Ω s =2π f s : sampling frequency Let X s : frequency spectrum of the sampled signal X s consists of multiple copies of X c, separated by Ω s Formally: X s = X c folded with S, with S: frequency spectrum of clock Cannot be distinguished in sampled signal

19. Universität Dortmund 19 Department of Electrical and Computer Engineering College of Engineering, Technology and Computer Science Aliasing If Ω s < 2  Ω N copies of spectrum will overlap (we don’t know the original frequencies any more) If Ω s < 2  Ω N copies of spectrum will overlap (we don’t know the original frequencies any more) No problem for signal reconstruction if this is avoided. Impossible to reconstruct fast signals after slow sampling: multiple fast signals share same sampled sequence;

20. Universität Dortmund 20 Department of Electrical and Computer Engineering College 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] S/H A/D-converterD/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.

21. Universität Dortmund 21 Department of Electrical and Computer Engineering College of Engineering, Technology and Computer Science Actuators and output Huge variety of actuators and output devices, impossible to present all of them. Microsystems motors as examples (© MCNC): (© MCNC)