Introduction to Digital Signal Processing (Discrete-time Signal Processing) Prof. Ja-Ling Wu Dept. CSIE & GINM National Taiwan University
Overview Introduction to DSP Information Theory and Coding Tech. C&C Digital Speech Processing Multi-D DSP Digital Image Processing Multi-rate DSP VLSI DSP Advanced topics in DSP Data Compression Error Control coding Digital Modulation Information Security Multimedia Security Recognition Understanding DSP-processor Application-specific system efficient communication system Reliable communication system Secure communication system
Introduction DSP theory has been applied to a variety of problems such as: Biomedical data processing Digital Audio Sonar and Radar processing Speech processing Data Communication Reliable data storage of computerized information Seismic Signal Processing Image Processing and Vision Error Control Coding Information Security : EEG, ECG(EKG), CT : CE-Disk, Audio-CD : homeland security, video surveillance, military applications : recognition : ISDN, Digital Communications : digital storage with error correction capability : oil exploration, underwater mapping : Data Compression, Computer Vision : Reliable communication : steganograph / data hiding / watermarking / forensics
Signal Classification Continuous time Discrete time x(t) x(t) Continuous amplitude Analog t Discrete t x(t) x(t) Discrete amplitude Sample-data t Digital t
Block Diagram of digital processing for analog waveforms Band-limited signal Analog Signal input Low-pass filter Analog / Digital Conversion Sampling Quantization digital DSP double- period sequence Digital Processes Analog Signal output Low-pass filter Digital / Analog Conversion Sample-and-hold digital
Digital Devices (VLSI) Digital Computers DSP Advantages of digital processing Reliable easy to be stored and/or transmitted Flexible Accurate Faster Challenges Disadvantage: Easy to make Optical processing Exact copy!! Good forgery!! Bio-computing Serious IPR threats
What does digital mean? x(t) Analog Waveform z Quantization step Discrete in amplitude t t : Sampling period Discrete in time
Remarks 1. What is DSP? keep what we want and eliminate what don t as as possible! f(t) Much Precisely Soon 2. Δt (Sampling period) Sampling Theorem Fourier Analysis / Transform Interpolation / Extrapolation trend prediction? T T+n video motion estimation t
3. Δz (Quantization step) Finite-Wordlength Effects Available Hardware Support Precision Requirement 4. What kind of signals can we really process? Bandlimited finite-dynamic-range uniform vs. non-uniform scalar vs. vector quantization If the signal is stochastic, some statistic properties must be known; say, mean, variance, acf, psd, etc. (random/stochastic process) long-duration signal (such as: voice/speech signal) short-time analysis. (sliding window)
Course outline 1. Introduction 2. Signals and Systems Z-transform System function LTI-system digital convolution Sampling Theorem System stability 3. Fourier Response of a System Fourier Transform DFT Fast Fourier Transform Convolution Theorem
4. Digital Filters FIR filter IIR filter 5. Quantization and Finite Wordlength Effects 6. Specific topics in DSP References 1. Digital Signal Processing by Roberts & Mullis (Addison Wesley) 2. Discrete time Signal Processing by Oppenheim (Prentice-Hall) 3. Signal Processing First by James H. McClellan, Ronald W. Schafer, Mark A. Yoder
Course Information Lecturer: Ja-Ling Wu (wjl@cmlab.csie.edu.tw) TA: Yin-Tzu Lin (known@cmlab.cie.ntu.edu.tw) Yun-Chung Shen (cazindo@cmlab.csie.ntu.edu.tw) Lecture Notes http://www.cmlab.csie.ntu.edu.tw/~dsp/dsp2010 Grades 40% Homework 15% Quiz 1 : in-class + take home(extra 15% of Quiz 1) 15% Quiz 2 : in-class + take home(extra 15% of Quiz 2) 30% Final Writtten Quiz (60% of final) Monograph (40% of final) MATLAB will be used in homework