The document provides an overview of the course EC533: Digital Signal Processing including the instructor and TA contact details, textbook references, assessment system, course outline covering topics such as real-time DSP systems, discrete-time signals and systems, Z-transform, digital filter design, DFT and FFT. It also gives introductions to digital signal processing, real-time DSP systems, sampling theorem, anti-aliasing filtering, and considerations in selecting the sampling frequency and filter design.

1. EC533: Digital Signal Processing
Lecture 1
• Introduction
• Real-Time DSP Systems

2. EC533: Digital Signal Processing
• Instructor: Dr. Mohamed El‐Mahallawy
Contact info. : E‐mail: mahallawy@ieee.org
Room: 309
Office Hours: Wednesday 3rd
• Teaching Assistant: Eng. El‐Nasser S. Yusef
Contact info. : E‐mail: nasseryousef6@yahoo.com
Room: 314
Office Hours: Tuesday 3rd, 4th.
• References:
– Text Book: Digital Signal Processing, A Practical Approach, E. C.
Ifeachor & B. W. Jervis, 2nd Edition, Prentice Hall, 2002.
– Reference: Applied Signal Processing, Concepts, Circuits, and Systems,
N. Hamdy, CRC Press, 2009.

3. Assessment System
Assessment Tool Marks
4th week quiz 2.5
6th week quiz 2.5
Lab reports (up to 7th week ) 5
7th week Lab quiz 5
7th week exam 15
11th week quiz 5
Lab reports (up to 12th week ) 5
12th week exam 10
Lab project 5
Final Lab exam 5
Final exam 40
Total 100

4. Course Outline
• Introduction.
• Real‐Time DSP Systems.
• Discrete‐Time Signals & Systems.
• Characteristics of Discrete‐Time Signals.
• Z‐Transform.
• Digital Filter Structure.
• Finite Impulse Response (FIR) Filter Design.
• Infinite Impulse Response (IIR) Filter Design.
• Discrete Fourier Transform (DFT) & Fast
Fourier Transform (FFT).

5. 1- Introduction
Text Book : Chapter 1, Sections: 1.1, 1.2.
1.1 – What is Digital Signal Processing ?
A)Digital : Signals are either Analogue, Discrete, or Digital signals.
• Analogue Signal : • Discrete Signal : • Digital Signal :
Continuous in both time Discrete in time (sampled Discrete in time (sampled
and amplitude, any value signal) & Continuous in signal) & Discrete in
at any time can be found. amplitude. amplitude (Quantized
Samples).
2
1.27
1.24
1.24
1.5
1.2
1.2
1.11
1.11
0.98
0.98
0.82
0.82
1
0.64
0.64
0.44
0.44
0.5
0.22
0.22
0
0
0
0
11
13
15
17
19
-0.44 21
23
25
27
29
31
33
-0.44 35
37
1
3
5
7
9
-0.22
-0.22
-0.5
-0.64
-0.64
-0.82
-0.82
-1
-0.98
-0.98
-1.11
-1.11
-1.2
-1.2
-1.26
-1.26
-1.28
-1.5
-2

6. B) Signal : It is an information-bearing function, It is either:
1-D signal as speech.
2-D signal as grey-scale image {i(x,y)}.
3-D signal as video {r(x,y,t),g(x,y,t),b(x,y,t)}.

7. C) Processing :
Signal Processing refers to the work of manipulating signals so that
information carried can be expressed, transmitted, restored,… etc in a
more efficient & reliable way by the system (hardware software).
Least
Least • General Purpose Processors (GPP), Micro‐Controllers.
resource
error • Digital Signal Processors (DSP); Dedicated Integrated
usage
Circuits. Fast Real‐
time
• Programmable Logic (PLD, FPGA). Faster DSP’ing
• Programming Languages: Pascal, C, C++,...
• High‐Level Languages: Matlab, MathCad,…
• Dedicated Tools (e.g. Filter design s/w packages).

8. 1.2 – Why DSP ?
• Greater Flexibility
The same DSP hardware can be programmed and reprogrammed to perform a variety of functions.
• Guaranteed Precision
Accuracy is only determined by the number of bits used. (not on resistors,…etc; analogue parameters).
• No drift in performance with temperature or age.
• Perfect Reproducibility
Identical Performance from unit to unit is obtained since there are no variations due to component
tolerance. e.g. a digital recording can be copied or reproduced several times with the same quality.
• Superior Performance
Performing tasks that are not possible with ASP, e.g. linear phase response and complex adaptive
filtering algorithms.
• DSP benefits from the tremendous advances in semiconductor
technology.
Achieving greater reliability, lower cost, smaller size, lower power consumption, and higher speed.

9. 1.3 – DSP LIMITATIONS
• Speed & Cost Limitations of ADC & DAC
Either too expensive or don’t have sufficient resolution for large-bandwidth DSP
applications.
• Finite Word-Length Problems
Degradation in system performance may result due to the usage of a limited number of
bits for economic considerations.
• Design Time
DSP system design requires a knowledgeable DSP engineer possessing necessary
software resources to accomplish a design in a reasonable time.

10. What is DSP Used For?
…And much more!

11. Application Areas
Image Processing Instrumentation/Control Speech/Audio Military
Pattern recognition spectrum analysis speech recognition secure communications
Robotic vision noise reduction speech synthesis radar processing
Image enhancement data compression text to speech sonar processing
Facsimile position and rate control digital audio missile guidance
animation equalization
Telecommunications Biomedical Consumer applications
Echo cancellation patient monitoring cellular mobile phones
Adaptive equalization scanners UMTS (universal Mobile Telec. Sys.)
ADPCM trans‐coders EEG brain mappers digital television
Spread spectrum ECG Analysis digital cameras
Video conferencing X‐Ray storage/enhancement internet phone
etc.

12. DSP Devices & Architectures
• Selecting a DSP – several choices:
– Fixed‐point;
– Floating point;
– Application‐specific devices
(e.g. FFT processors, speech recognizers,etc.).
• Main DSP Manufacturers:
– Texas Instruments (http://www.ti.com)
– Motorola (http://www.motorola.com)
– Analog Devices (http://www.analog.com)

13. 2.1 – Typical Real-Time DSP System

14. 2.2 – Sampling Theorem & Aliasing
1st Image Frequency
Time Domain Frequency Domain

15. 2.2 – Sampling Theorem & Aliasing - continued
• In practice, aliasing is always present because of noise & the existence of signal outside the
band of interest.
• The problem then is deciding the level of aliasing that is acceptable and then designing a
suitable anti-aliasing filter & choosing an appropriate sampling frequency to achieve this.

16. 2.3 – Anti-aliasing Filtering
To reduce the effect of aliasing:
a)Sharp Cut-off anti-aliasing filters are normally used to band-limit the signal.
b)Increasing the sampling frequency to widen the separation between the signal & the image
spectra.
c)Practical LPF provides sufficient attenuation at f > fN ; f > fstop to a level not detectable be ADC,
Amin = 20 log( 1.5 × 2n )
= 6.02n + 1.76 dB
where n is the no of bits used by ADC

17. 2.3.1 – Butterworth(LPF)

18. 2.3.1 – Butterworth(LPF) - continued
Higher N
narrower transition width (steeper roll-off).
more phase distortion.
allows the use of low sampling rate.
slower, cheaper ADC
Higher fs
fast, expensive ADC. (real-time signal
processing trend).
usage of a simple anti-aliasing filter which
minimizes phase distortion.
Improved SNR.
See illustrative examples in book, P. 45 54 on how to choose the sampling frequency
& aliasing control.