لخّصلي

This document covers analog-to-digital conversion (ADC) in embedded systems, beginning with a review of C programming and logic operations. It introduces AVR microcontroller architecture and programming, focusing on its ADC, interrupt, timing, and serial communication subsystems. The core concept differentiates analog signals (continuous values) from digital signals (discrete values), highlighting the necessity of ADC for digital systems interacting with the analog world (e.g., voice communication). ADC involves sampling (taking signal snapshots), quantization (approximating sample values to discrete levels), and encoding. Optimal sampling, determined by the Nyquist rate (2fm, where fm is the maximum input frequency), prevents aliasing (signal distortion). Quantization introduces error, reduced by increasing the number of quantization levels (determined by the number of bits). Higher resolution (smaller step size between levels) requires more bits, increasing the data rate (bits per second). The example of a telephone system illustrates how increased bit depth for higher resolution significantly impacts data rate and system capacity.

Embedded System
Analog-to-Digital Conversion (ADC)
Part 1
• Review of C programming and Revision of logic operations.
• Overview of Embedded system
• AVR microcontroller Architecture
• AVR microcontroller Programming
• Analog-to-Digital Conversion (ADC)
• AVR (ADC)
• AVR Interrupt Subsystem
• AVR Timing Subsystem
• Serial Communication protocols
• AVR Serial Communication Subsystem
Analog versus Digital Signals
• Analog signals are those whose
physical variable values change
continuously
over
independent variable.
their
• Most physical variables, your
speech, movement of stars, and
the music you hear are analog
signals
• The figure shows an altitude
trajectory of a bird flying over a
short period. The signal shows
how the altitude of the bird
changes continuously.
• Digital signals, on the other hand, have their physical variables
defined only for discrete instances over their independent variables.
Analog versus Digital Signals
• Why Digital ?
✓Guaranteed Precision
✓No drift in performance with temperature or age.
✓Perfect Reproducibility
✓ Digital components are faster, cheaper, more efficient
• All signals represented in digital systems, computers, and microcontrollers
are in digital forms. We need to convert analog signals to digital ones.
• For example, human voices must be converted to corresponding digital
signals before they can be routed by digital switching circuits in telephone
communication systems. Similarly, voice commands to robots must be
converted to a digital form before robots can understand the command.
• we live in an analog world; that is, physical variables are analog signals. It is
precisely this reason why the ADC is so very important in any digital systems
that interact with an analog environment.
Analog
to
digital conversion process
sampling
quantization
encoding
Sampling
• Sampling is the process of taking ‘‘snapshots’’ of a signal over time.
• Naturally, when we sample a signal, we want to sample it in an optimal
fashion such that we can capture the essence of the signal while minimizing
the use of resources.
• The higher the sampling rate, the better the chances to reconstruct a near
replica of the original signal.
• The minimum acceptable sampling rate (Nyquist rate), as given by the
sampling theorem is
where fm is the maximum frequency of the input signal
If this condition is not satisfied, resulting in a sort of unrecoverable distortion
called Aliasing;
i.e., the reconstructed signal has a distortion
Sampling
To simplify the idea:
• If a diver makes a simple routine
dive, you only need to take a few
pictures over the period of the dive.

Two very different cases of motions
generated by a diver is shown in the
Figure. The same time sequence is
used to capture samples for both
motions.
• As can be seen from figure, frame
(a) motion cannot be regenerated
from the samples, whereas the
motion shown in frame (b) can
clearly be reconstructed from the
same number of samples used to
capture both motions.
Sampling
We can represent the sampling process as
multiplying the analog signal x(t) by a train of pulses
p(t), as shown in the next figure.
Remember
: the sampling frequency Fs must be
higher than twice of the maximum signal frequency
fm (Nyquist theory)
For example, if we are dealing with the human voice
signal that contains frequency that span from 20 Hz
to 4 kHz, the Nyquist sample theorem tells us that we
must sample the signal at least at 8 kHz, 8000
‘‘snapshots’’ every second
Sampling
0 1 2 3
−2
−1
0
1
2
x(t),x(0.2n)
(a)
0 1 2 3
−2
−1
0
1
2
x(t),x(0.4n)
(b)
−2
−1
0
1
2
x(t),x(0.5n)
(c)
−2
−1
0
1
2
x(t),x(n)
(d)
0 1 2 3 0 1 2 3
t t
Sampling of x (t) = 2 cos(2πt + π/4):
(a) Ts = 0.2, (b) Ts = 0.4, (c) Ts = 0.5 and (d) Ts = 1 sec/sample
Example of
Aliasing
In the case Ts=1 sec as in (d), Aliasing is occurred, and the original signal x(t) can not
be reconstructed from the sampled signal
Quantization
• The quantizing operation approximates each sample value to the nearest
level in a finite set of discrete levels/values, known as quantization levels.
• Suppose you have 2 bits to represent each sample, you can represent four
different levels, 00, 01, 10, and 11.
• This approximation introduces quantization error. Therefore, once quantized,
the instantaneous values of the signals are lost, and can never be
reconstructed exactly.
Continuous signal values
at discrete times
Discrete signal values
at discrete times
Quantizer
Quantization
Example
Resolution and Data Rate
n levels
m levels (m