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Digital Signal Processing &
Digital Filters
An Introductory Course
By
Professor A G Constantinides
MSc, EE4, ISE4, PhD
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Contents
1-Introduction
1) Introduction to Digital Signal Processing
 Review of background DSP
 Review of mathematical methods
 Review of discrete-time random
processes and linear systems
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2) Multirate techniques and wavelets
 Introduction to short-time Fourier analysis
 Filter-banks and overlap-add methods of analysis
and synthesis
 Introduction to generalised time-frequency
representation
 Wavelet analysis
 Multirate signal processing
 Interpolation and decimation
 Efficient filter structures for interpolation and
decimationProfessor A G
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3) Classical spectrum estimation methods
 Power spectrum, power spectral density functions,
random processes and linear systems
 Introduction to statistical estimation and estimators
 Biased and unbiased estimators
 Einstein/Wiener Khintchine Theorem
 Estimation of autocorrelations
 Means and variances of periodograms
 Smoothed spectral estimates, leakage
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4) Modern spectrum estimation methods
 Introduction to modern spectral estimation:
Principles and approaches
 Cramer-Rao Lower Bound (CRLB) and Efficient
estimators
 The Maximum Entropy Method (MEM) or
Autoregressive Power Spectrum Estimation:
Principles.
 The MEM equations and Levinson/Durbin
algorithm
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4) Modern spectrum estimation methods
(continued)
 Introduction to Linear Prediction
 Linear Predictive Coding using covariances
and correlations
 Cholesky decomposition
 Lattice Filters
 Linear Prediction of Speech Signals
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5) Adaptive signal processing
 Introduction to adaptive signal processing
 Objective measures of goodness
 Least squares and consequences
 Steepest descent
 The LMS and RLS algorithms
 Kalman Filters
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6) Applications
 Communications
 Biomedical
 Seismic
 Audio/Music
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Digital Filters
 In this course you will learn:
 How to choose an appropriate filter
response.
 Why Butterworth responses are maximally
flat.
 Why Chebyshev and Elliptic responses are
equiripple.
 When to choose an IIR and when an FIR
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 How do you design FIR and IIR filters from
specifications on amplitude performance?
 What are multirate systems and their
properties? What is interpolation /
Upsampling and Decimation /
Downsampling?
 How do you design efficient Decimation and
Interpolation systems?
 What are frequency transformations and how
do you design these?
 How accurateProfessor A G as a spectrum
is the DFT
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 What are short FFT algorithms?
 How do you choose the required
wordlength?
 What are Fast Convolutions and how
are they realised?
 How do you deal with a DSP problem in
practice?
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Assumed DSP background
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DSP Background folder
 1-Introduction

2-z transform

3-transfer functions
 4-Signal Flow Graphs
 5-digital filters intro
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2-Digital Filter Design
 1-Digital Filters (FIR)
 2-Digital Filters (IIR)
3-Multirate
1-Interpolation_Decimation
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4-Tranforms
 1-DFT
 2-DFT_one2two
 3-general transforms
 4-Wavelets
5-Finite Wordlength
 1-Finite Wordlength
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6-Spectrum Estimation (Assumed
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background in Mathematical
Background folder)
 1-Fourier transform & DFT
 2-FFT-based Power Spectrum Estimation
 3-Modern Spectrum Estimation
 4-Intro-Estimation
 5-Eigen-based methods
 6-A PredictionProfessor A G
Problem
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7-Adaptive Signal Processing
 1-Adaptive Signal Processing
8-Applications
 1-Applications
 2-Applications
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BOOKS
 Main Course text books: Digital Signal
Processing: A computer Based Approach, S
K Mitra, McGraw Hill
 Mathematical Methods and Algorithms for
Signal Processing, Todd Moon, Addison
Wesley
 Other books:
 Digital Signal Processing, Roberts & Mullis,
Addison Wesley
 Digital Filters, Antoniou, McGraw Hill
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Analogue Vs Digital Signal Processing
Reliability:
Analogue system performance degrades due
to:
• Long term drift (ageing)
• Short term drift (temperature?)
• Sensitivity to voltage instability.
• Batch-to-Batch component variation.
• High discrete component count
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Digital Systems:
• No short or long term drifts.
• Relative immunity to minor power supply
variations.
• Virtually identical components.
• IC’s have > 15 year lifetime
• Development costs
• System changes at design/development
stage only software changes.
• Digital system simulation is realistic.
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Power aspects
 Size
 Dissipation
 DSP chips available as well as ASIC/FPGA
realisations
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Applications
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Radar systems & Sonar systems
• Doppler filters.
• Clutter Suppression.
• Matched filters.
• Target tracking.
 Identification
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Image Processing
 Image data compression.
 Image filtering.
 Image enhancement.
 Spectral Analysis.
 Scene Analysis / Pattern recognition.
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Biomedical Signal Analysis
 Spatial image enhancement. (X-rays)
 Spectral Analysis.
 3-D reconstruction from projections.
 Digital filtering and Data compression.
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Music
 Music recording.
 Multi-track “mixing”.
 CD and DAT.
 Filtering / Synthesis / Special effects.
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Seismic Signal Analysis
 Bandpass Filtering for S/N
improvement.
 Predictive deconvolution to extract
reverberation characteristics.
 Optimal filtering. (Wiener and Kalman.)
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Telecommunications and Consumer
Products
These are the largest and most
pervasive applications of DSP
and Digital Filtering
 Mobile Communications
 Digital Recording
 Digital Cameras
 Blue Tooth or similar
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