Dsp U Lec01 Real Time Dsp Systems

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Real Time Dsp Systems

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Dsp U Lec01 Real Time Dsp Systems

  1. 1. EC533: Digital Signal Processing Lecture 1 • Introduction • Real-Time DSP Systems
  2. 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. 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. 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. 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. 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. 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. 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. 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. 10. What is DSP Used For? …And much more!
  11. 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. 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. 13. 2.1 – Typical Real-Time DSP System
  14. 14. 2.2 – Sampling Theorem & Aliasing 1st Image Frequency Time Domain Frequency Domain
  15. 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. 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. 17. 2.3.1 – Butterworth(LPF)
  18. 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.

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