1 Digital Signal Processing_ Introduction

04/11/23 Somchai Jitapunkul, DSPRL 1

2102576 Digital Signal Processing2102576 Digital Signal Processing

Dr.Somchai JitapunkulDigital Signal Processing Research LaboratoryDepartment of Electrical EngineeringChulalongkorn University


Course OutlinesCourse Outlines

• Introduction• Discrete-Time Signals and

Systems• Sampling of Continuous-

Time Signals• The z-Transform• Transform Analysis of Linear

Time-Invariant Systems• Structures for Discrete-Time

Systems

• Filter Design Techniques• The Discrete Fourier

Transform• Computation of the Discrete

Fourier Transform• Discrete Hilbert Transform• Fourier Analysis of Signals

Using the Discrete Fourier Transform

• Introduction to Cepstrum Analysis and Homomorphic Deconvolution


BibliographyBibliography

• Text: – A.V. Oppenheim, and R.W. Schafer, Discrete-Time Signal

Processing, Prentice Hall, 1989.• References:

– J.G. Proakis, and D.G. Manolakis, Digital Signal Processing, 3rd ed., Prentice Hall, 1996.

– E.C. Ifeachor, and B.W. Jervis, Digital Signal Processing, Addison Wesley, 1993.

– S.K. Mitra, Digital Signal Processing, 2nd ed., McGraw-HILL, 2001.

– Etc.


Examination and GradingExamination and Grading

• Final Examination: – Date: Monday 25 February 2001– Time: 08:00 am ~ 12:00 am Paper Examination– Time: 12:00 am ~ 17:00 pm (tentatively) Design Project– Room: DSP Laboratory and neighbor room– Building: Engineering 4 (Charoen Vissawakam)

• Grading:– Depend mainly on each individual achievement


10 Outstanding Achievements Between 1964 and 198910 Outstanding Achievements Between 1964 and 1989 Selected by National Academy of Engineering, Washington D.C.

• Moon Landing• Application Satellites• Microprocessors• Computer-Aided Design and Manufacturing• Computerized Axial Tomography Scanner• Advanced Composite Materials• Jumbo Jets• Lasers Fiber-Optics Communications• Genetically Engineered Products


Grand Challenges in Science and Engineering Grand Challenges in Science and Engineering need high-performance computingneed high-performance computing Office of Science and Technology Policy, Washington D.C.

• Prediction of Weather, Climate, and Global Change• Speech Recognition and Understanding• Machine Vision• Vehicle Performance• Superconductivity• Enhanced Oil and Gas Recovery• Nuclear Fusion


DSP ApplicationsDSP Applications• Image Processing

– Pattern recognition– Robotic vision– Image enhancement– Facsimile– Satellite weather map– Animation

• Instrumentation/Control– Spectrum analysis– Position and rate control– Noise reduction– Data compression

• Speech/audio– Speech recognition/synthesis– Text to speech– Digital audio– equalization

• Military– Secure communication– Radar processing– Sonar processing– Missile guidance

• Telecommunications– Echo cancellation– Adaptive equalization– ADPCM transcoders– Spread spectrum– Video conferencing– Data communication

• Biomedical– Patient monitoring– Scanners– EEG brain mappers– ECG analysis– X-ray storage/enhancement


Reasons of Using DSPReasons of Using DSP

• Signals and data of many types are increasingly stored in digital computers, and transmitted in digital form from place to place. In many cases it makes sense to process them digitally as well.

• Digital processing is inherently stable and reliable. It also offers certain technical possibilities not available with analog methods.

• Rapid advances in IC design and manufacture are producing ever more powerful DSP devices at decreasing cost.

• Flexibility in reconfiguring• Better control of accuracy requirement• Easily transportable and possible

offline processing• Cheaper hardware in some case• In many case DSP is used to process a

number of signals simultaneously. This may be done by interlacing samples (technique known as TDM) obtained from the various signal channels.

• Limitation in speed & Requirement in larger bandwidth


DSP in ASIC (Application Specific Integrated DSP in ASIC (Application Specific Integrated Circuit)Circuit)

Advantages• High throughput• Lower silicon area• Lower power consumption• Improved reliability• Reduction in system noise• Low overall system cost

Disadvantages• High investment cost• Less flexibility• Long time from design to

market


Discrete-Time Signals and SystemsDiscrete-Time Signals and Systems• One-dimensional signal (time)• Multidimensional signal (spatial coordinate)

• Real-valued function• Complex-valued function

• Dependent variable• Independent variable

• Analog signal/system• Continuous-time signal/system• Continuous-amplitude signal/system• Discrete-amplitude signal/system• Quantized boxcar signal/system• Discrete-time signal/system• Sampled-data signal/system• Digital signal/system

• Stationary signal• Cyclostationary• Non-stationary signal

• Time-invariant system• Time-varying system

• Causal• Non-causal

• Deterministic signal– Periodic– Non-periodic

• Random signal– Stochastic signal– Noise or interference

• Analytical signal• Distribution


Discrete-time Signals and SystemsDiscrete-time Signals and Systems

• Continuous-time signals are defined along a continuum of times and thus are represented by a continuous independent variable.

• Discrete-time signals are defined at discrete times and thus the independent variable has discrete values.

• Analog signals are those for which both time and amplitude are continuous.

• Digital signals are those for which both time and amplitude are discrete.


Discrete-Time Signals : SequencesDiscrete-Time Signals : Sequences

• Continuous-time signal will be sampled into set of its values at definite time.

• If duration of each sampling time is fixed in equal period called sampling period, T. Thus nth value of sampled signal is equal to the value of the continuous-time signal xc(t) at time nT.

• Representation of sampled values will be shown in a sequence of numbers x = {x[n]}.

• 1/T is called the sampling frequency.


Signal Processing OperationSignal Processing Operation

• Time-domain/spatial-domain operation• Frequency-domain operation

• Real-time operation• Off-line operation


Time-Domain Signal OperationTime-Domain Signal Operation

Basic operations• Scaling : gain

– Amplification– Attenuation

• Delay / Advance• Addition

Elementary operations• Integration / Summation• Differentiation /

Difference• Production• Convolution


FilteringFiltering

Filter Parameters• Passband• Stopband• Transition band• Cutoff frequency• Band Edge Frequency

• Filter Types• Basic Types

– Lowpass– Highpass– Bandpass– Bandstop/band eject

• Other Types– Notch filter– Comb filter– Single band– Multiband


Mathematical ToolsMathematical Tools

• Differential Equation• Difference Equation• Laplace Transform• z-Transform• Fourier Transform• Hilbert Transform• Discrete-Time Fourier Transform• Discrete Fourier Transform


DefinitionsDefinitions• Modulation• Coding• Multiplexing

• Modulated signal• Carrier signal

• Single Sideband (SSB)• Double Sideband (DSB)• Coding

– Source Coding– Channel Coding

• Fading– Rayleigh Fading– Ricean Fading– Nakagami Fading

• Interference• Noise

– White Noise– Gaussian Noise– White Gaussian Noise (WGN)

• Equalization• Quantization

– Scalar Quantization– Vector Quantization

• Spread Spectrum– Direct Sequence or Pseudo-noise– Frequency Hopping– Time Hopping– Combination

• Hidden Markov Model• Kalman (Recursive Least Square) Algorithm• Pseudo-noise (PN) Sequence

– M sequences– Gold Sequences– Kasami Sequences


Modulation/Coding MethodsModulation/Coding Methods• Pulse Amplitude Modulation (PAM)• Digital PAM or Amplitude-Shift Keying (ASK)• Phase Modulation• Digital Phase Modulation or Phase-Shift Keying

(PSK)– Binary PSK (BPSK)– Quadrature PSK (QPSK)– Differential PSK (DPSK)– Staggered Quadrature PSK (SQPSK)

• Quadrature Amplitude Modulation (QAM)• Frequency-Shift Keying (FSK)

– Continuous-Phase FSK (CPFSK)• Amplitude Modulation (AM)• Frequency Modulation (FM)• Pulse Width Modulation (PWM)• Pulse Position Modulation (PPM)• Continuous-Phase Modulation (CPM)• Minimum-Shift Keying (MSK)

• Fixed-Length Code Word • Variable-Length Code Word

– Entropy Coding Huffman Coding • Variable-to-Fixed Length Code Word

– Lempel-Ziv Algorithm• Temporal Waveform Coding

– Pulse coded modulation (PCM)• Adaptive PCM (APCM)

– Differential PCM (DPCM)• Adaptive DPCM (ADPCM)

– Open-loop DPCM (D*PCM)– Delta modulation (DM) or 1-bit or 2-level DPCM

• Linear DM (LDM)• Adaptive DM (ADM)• Continuously Variable Slope DM (CVSD)

• Model-Based Source Coding– Linear Predictive Coding (LPC)

• Spectral Waveform Coding– Subband Coding (SBC)– Transform Coding (TC)


MultiplexingMultiplexing

• Time Division Multiplexing (TDM)• Frequency Division Multiplexing (FDM)• Code Division Multiplexing (CDM)

– Code Division Multiple Access (CDMA) or Spread Spectrum Multiple Access (SSMA)

• Orthogonal Frequency Division Multiplexing (OFDM)


Examples of SignalsExamples of Signals

• Electrocardiography (ECG) Signal• Electroencephalogram (EEG) Signal• Seismic Signals• Engine Signal• Speech, Musical Sound, and Audio Signals• Vibration Signal• Time Series Signal (daily stock prices, etc.)• Images and Video Signals


Typical Sampling Rates and System Latencies for Typical Sampling Rates and System Latencies for Selected ApplicationsSelected Applications

Application I/O Sampling Rate Latency (Delay)

Instrumentation 1 Hz System dependent*

Control > 0.1 kHz System dependent*

Voice 8 kHz < 50 ms

Audio 44.1 kHz < 50 ms*

Video 1~14 MHz < 50 ms*

* Many times, a signal may not need to be concerned with latency: for example, a TV signal is more dependent on synchronization with audio than the latency. In each of these cases, the latency is dependent on the application.•Nasser Kehtarnavaz, and Mansour Keramat, DSP System Design: Using the TMS320C6000, Prentice Hall, 2001.


Examples of ApplicationsExamples of Applications

• Sound Recording Applications– Compressors and Limiters– Expanders and Noise Gates– Equalizers and Filters– Noise Reduction Systems– Delay and Reverberation

Systems– Special Effect Circuits

• Speech Processing– Speech Recognition– Speech Communication

• Telephone Dialing Applications

• FM Stereo Applications• Electronic Music Synthesis

– Subtractive Synthesis– Additive Synthesis

• Echo Cancellation in Telephone Networks

• Interference Cancellation in Wireless Telecommunication


Cellular Phone Wireless Communication Cellular Phone Wireless Communication DSP SystemDSP System Nasser Kehtarnavaz, and Mansour Keramat, DSP System Design: Using the TMS320C6000, Prentice Hall, 2001.


ADSL Wired Communication DSP SystemADSL Wired Communication DSP System Nasser Kehtarnavaz, and Mansour Keramat, DSP System Design: Using the TMS320C6000, Prentice Hall, 2001.


PCM Voiceband DSP SystemPCM Voiceband DSP System Nasser Kehtarnavaz, and Mansour Keramat, DSP System Design: Using the TMS320C6000, Prentice Hall, 2001.


Gigabit Ethernet DSP SystemGigabit Ethernet DSP System Nasser Kehtarnavaz, and Mansour Keramat, DSP System Design: Using the TMS320C6000, Prentice Hall, 2001.


Hard Disk Drive DSP SystemHard Disk Drive DSP System Nasser Kehtarnavaz, and Mansour Keramat, DSP System Design: Using the TMS320C6000, Prentice Hall, 2001.


Motor Control DSP SystemMotor Control DSP System Nasser Kehtarnavaz, and Mansour Keramat, DSP System Design: Using the TMS320C6000, Prentice Hall, 2001.

1 Digital Signal Processing_ Introduction

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