1. Digital Signal Processing Chapter 1 Introduction
School of Computer Science and Engineering
Pusan National University
Jeong Goo Kim

2. 1. Introduction Objectives
Explain the meaning and benefits of digital signal processing
Introduce key DSP operations
1.1 Digital Signal Processing and Its Benefits
Key advantages with DSP
Guaranteed accuracy, Perfect reproducibility
Utilize the semiconductor technology
Greater flexibility: programmability
Superior performance
Key disadvantages with DSP
Speed & cost
Design time
Finite length problems

3. Application Areas
1.2 Application Areas
Image processing: pattern recognition. Robot vision, image enhancement, facsimile, satellite weather map, animation
Instrumentation/control: spectrum analysis, position and rate control, noise reduction, data compression
Speech/audio: speech recognition, speech synthesis, text to speech, digital audio, equalization
Military: secure communication, radar procssing, sonar processing, missile guidance
Telecommunications: echo cancellation, adaptive equalization, ADPCM trancoders, spread spectrum, data communication
Biomedical: patient monitoring, scanners, EEG brain mappers, ECG analysis, X-ray storage/enchancement

4. 1.3 Key DSP Operations Convolution Correlation Filtering
Discrete transform
Modulation

5. Key DSP Operations
1.3.1 Convolution

6. Key DSP Operations Ex
a=[ ];
b=[ ];
c=conv(a,b)
c =
Columns 1 through 7
Columns 8 through 14
Columns 15 through 17

7. 1.3.2 Correlation Crosscorrelation Autocorrelation Key DSP Operations
n=1:100; x(n)=1; y(n)=0; a=[x y x y x y x y]*1000;
b=(rand(1,800)-0.5)*2000;c=a+b; 1=xcorr(a,'biased');
b1=xcorr(b,'biased');c1=xcorr(a,c,'biased'); M,N]=size(a1); len=(N-1)/2; time_lag=-len:1:len;
subplot(3,2,1);plot(a);grid;
subplot(3,2,2);plot(time_lag,a1);grid; subplot(3,2,3);plot(b);grid;
subplot(3,2,4);plot(time_lag,b1);grid; subplot(3,2,5);plot(c);grid
subplot(3,2,6);plot(time_lag,c1);grid

8. Key DSP Operations
1.3.3 Digital Filtering

9. 1.3.4 Discrete Transformation Time Domain ⇔ Frequency Domain
Key DSP Operations
1.3.4 Discrete Transformation Time Domain ⇔ Frequency Domain
t=-25:25;
b=0.4*sinc(0.4*t);
[H,w]=freqz(b,1,512,2);
subplot(2,1,1);
plot(t,b); grid;
subplot(2,1,2);
plot(w,abs(H)), grid;

10. Key DSP Operations
1.3.5 Modulation
Digital signals are modulated to match their frequency characteristics to those of the transmission and/or storage media to minimize signal distortion, to utilize the available bandwidth efficiency, or to ensure that the signals have some desirable properties.

11. Overview of Real-Time Signal Processing
1.4.1 Typical Real-Time DSP Systems
Analog-to-digital Converter

12. Overview of Real-Time Signal Processing
1.4.2 Sampling Theorem
Aliasing
t=0:0.001:8;f1=7/8;f2=1/8;
x1=sin(2*pi*f1*t);
x2=-sin(2*pi*f2*t);
plot(t,x1);
hold on;
plot(t,x2); grid;

13. Overview of Real-Time Signal Processing(
Nyquist Sampling Theorem
Sampling Theorem
For accurate representation of a signal x(t) by its time samples x(nT), two conditions must be met:
x(t) must be bandlimited. 2.
e.g. (Fmax) Speech (4kHz), Audio (20kHz), Video (4MHz)

14. Overview of Real-Time Signal Processing

15. Overview of Real-Time Signal Processing
1.4.3 Digital-to-analog Conversion Processing: Signal Recovery

16. Application Example: CD Audio System
Recoder

17. Application Example: CD Audio System
Player

18. Homework and Next Lecture
Read text book pp
Next Lecture
Chapter 3. Discrete transform