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

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

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

1.3 Key DSP Operations Convolution Correlation Filtering Discrete transform Modulation

Key DSP Operations 1.3.1 Convolution

Key DSP Operations Ex a=[0 -0.02 0 0.17 0.5 0.85 1.0 0.85 0.5 0.17 0 -0.02 0]; b=[0 1 1 1 1]; c=conv(a,b) c = Columns 1 through 7 0 0 -0.0200 -0.0200 0.1500 0.6500 1.5200 Columns 8 through 14 2.5200 3.2000 3.2000 2.5200 1.5200 0.6500 0.1500 Columns 15 through 17 -0.0200 -0.0200 0

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

Key DSP Operations 1.3.3 Digital Filtering

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;

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.

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

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;

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)

Overview of Real-Time Signal Processing

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

Application Example: CD Audio System Recoder

Application Example: CD Audio System Player

Homework and Next Lecture Read text book pp. 104-157 Next Lecture Chapter 3. Discrete transform