Adaptive Signal Processing (SIP 7024)

Lecturer Prof Doug Gray
The University of Adelaide

Mode of delivery
On-line with possibility of 3-5 day short course or weekly lecture delivery.

Assumed knowledge
Linear algebra (matrices), basic ideas of probability theory and statistics (consider taking Detection, Estimation and Classification course), basic concepts of linear systems, for example, impulse response, transfer functions, Fourier and z-transforms (consider taking Introduction to Discrete Linear Systems course), MATLAB.

Aim/Learning Objectives
This subject is strongly mathematics based and it's principal aim is to teach students the fundamental mathematical principles underlying the subject. Equally important is to impart to students the ability to apply such mathematical principles to data through the processing of actual signals. The subject is specifically structured around these aims.

On successful completion of the course students will
  1. have sufficient grasp of the basic mathematical principles of random processes, optimum and adaptive filtering to enable them to understand research literature on this subject
  2. be able to design optimum and adaptive linear filters for application to noisy time series data.
  3. be able to implement algorithms in MATLAB and to understand and interpret results obtained.
In practice it is not always obvious how a particular filtering problem may be able to be solved using the methods introduced in the lectures. Often very useful techniques can be developed by reinterpreting what is signal and what is noise. This is where your skills and ingenuity can be used -- the better your understanding of the basic principles the better the solutions you will be able to come up with.

Content
After some introductory material the course splits into the four main areas listed below.

Statistical Signal Processing
   Random variables
   Random processes
   Covariance matrices

Optimum Filters
   Wiener filters
   Matched filter
   Minimum mean square error - discrete
   Constrained optimisation
   Method of steepest descent - convergence issues

Adaptive Filters
   Stochastic gradient descent - convergence in mean and misadjustment
   Recursive least squares and vector space approaches

Linear prediction
   Levinson-Durbin
   Lattice filters
   Applications of linear prediction

Finally some case studies and extension topics will briefly be visited and not all of the above topics may be covered.

Assessment
It will generally be 50% assignments (5), 25% examination, 25% quizzes, however these percentages are indicative only and may be varied at the lecturer's discretion. Details of the actual assessment used in a given year can be found in the study guide provided at the start of the semester.

Resources
The material is presented within a rigorous mathematical framework although the notational details have been kept to a minimum as much as possible. A comprehensive set of lecture notes are provided but not all of these will be covered in class and students are expected to do their own reading out of session. Lectures will focus on concepts, applications and practical implementation issues rather than rigorous mathematical proofs although students are expected to master these as well. Many of the assignments and tutorials are focussed on the application of the theory covered in lectures to data and the course standard of MATLAB has been chosen for this.

Most material is available on the web and any additional material will be emailed to students during the course.
Beamforming and Array Processing (SIP 7025)

Lecturers
Prof. Doug Gray (University of Adelaide)

Mode of delivery
Mixed mode: on-line and short course/weekly lectures.

Assumed knowledge
A knowledge of Linear Systems (discrete and continuous), Linear Algebra (matrices), Probability Theory, Fourier and Z Transforms and MATLAB.

Aims/Learning Objectives
This subject is both mathematically and experimentally (computer pracs) based. It's principal aim is to teach students the fundamental mathematical principles underlying the subject and to enable them to understand and apply these principles to real data through problem based learning using simulated data. The subject is specifically structured around these aims.

On successful completion of the course students will

  1. have sufficient grasp of the principles of conventional, optimum and adaptive beamforming, to act as a basis for understanding current research literature on this subject.
  2. be able to design optimum and adaptive beamformers for application to noisy multichannel time series data from arrays of receivers.
  3. be able to implement algorithms in MATLAB and to understand and interpret results obtained.
Content
Introductory Material: Concepts, key issues and motivating array examples; Simple propagating field models.
Deterministic Signals: Conventional beamforming concepts: narrowband beamforming; Beam patterns: beamwidth, sidelobes and grating lobes, Array shading real weights, Array factor theorems; Multiple simultaneous beams; Wavevectors and frequency wavenumber beamforming; Time delay and sum beamforming.
Random Signals: Probability and random processes for arrays; Cross-spectral matrices.
Frequency Domain Beamforming: Frequency domain Approach single and multiple beams; Array Gain; Frequency wavenumber; Array shading and null steering.
Optimum Beamforming in Frequency Domain: Optimisation criteria constrained minimum mean square and Conventional and Optimum Comparisons; Constraints: minbeam and nulls; Sample Matrix Inverse and statistical considerations.
Adaptive Beamforming in Frequency Domain: Sample Matrix Inverse update, Gradient descent and optimisation surfaces with constraints; Convergence requirements;
Stochastic Descent Methods: Least Mean Square; Convergence in the mean and mean square convergence.
Optimum and Adaptive Beamforming in Time Domain: Multichannel tapped delay line approach; Optimum solution; Adaptive solution with passband constraints.

Assessment
Assessment will generally be 50% assignments (5), 25% examinations, 25% quizes, however these percentages are indicative only and may be varied at the lecturer's discretion. Details of the actual assessment used in a given year can be found in the study guide provided at the start of the semester.

Resources
The lecture material is presented within a rigorous mathematical framework although the notational details have been kept to a minimum as much as possible. Students are expected to be able to master but not remember such proofs. Many of the assignments and tutorials are focussed on the application of the theory covered in lectures to data and the course standard of Matlab has been chosen for this.

Students will be provided with a copy of a comprehensive array processing Matlab toolbox and will find that this toolbox is referred to in many of the exercises that are linked to the electronic version of the notes. To be able to run this toolbox Matlab 5.3 or higher wil be needed.

All this material is available on the web or on the course CD. Additional material will be emailed to students during the course.
Detection, Estimation and Classification (SIP 7012)

Lecturer
Assoc. Prof. Anatoli Torokhti
University of South Australia

Mode of delivery
On-line only.

Assumed knowledge
A basic knowledge of probability theory and statistics.

Aims/Learning Objectives
On successful completion of this course, students will be able to:
  1. read the research literature on the subject;
  2. formulate a problem in detection, estimation and classification;
  3. implement algorithms in MATLAB to solve such a problem.
Thus the aim of the course is to understand the theory of this material and to implement that understanding in the formulation and solution problems. The examination at the end of the course will reflect this aim. Like any worthwile course at this level, the underlying aim is to foster a way of thinking about certain kinds of problems -- in this case the handling of signals. This is not a recipe book, though students will be provided with a collection of tools and ideas about where to use those tools.

Content
Basic Ideas: Probability - Probability distributions, expectations, multivariate normals; Random variables; Independence; Conditional probability; Covariance matrix.
Hypothesis testing: Bayes Rule; Likelihood; Applications to detection and classification problems; Priors and MAP; Cost functions and decision rules; Minimum risk;
Composite testing: ROC's; Kernel Estimator method for finding pdf.
Karhunen-Loeve and Linear Discriminate analysis: Review of eigenvalues and eigenvectors, singular value decomposition; Karhunen-Loeve method: reduction of continuous to discrete data; Linear discriminant analysis; Linear detection; Linear classifier.
Parameter estimation: Bias and consistency; Efficiency; Maximum Likelihood; Bayesian Estimates; Linear Mean-Square Estimation.
Advanced parametric methods: Minimax method; Neyman-Pearson method; The EM algorithm; Robust parameter estimation and detection.
Evaluation: Probability of error in hypothesis testing; Chernoff bounds; Probability of error in parameter estimation; Cramer-Rao lower bounds; Dimension and misclassification.

Assessment
50% examination, 50% assignments (5), essay (1), however these percentages are indicative only and may be varied at the lecturer's discretion. Details of the actual assessment used in a given year can be found in the study guide provided at the start of the semester.

Resources
The notes cover all of the material students will be expected to know for the course, but references to extension material will be provided too.
Image Processing (SIP 7007)

Lecturer:
Dr Jimmy Li
Flinders University of South Australia

Mode of delivery
On-line with possibility of weekly lectures.

Assumed knowledge
Linear algebra (matrices), a basic knowledge on differential equations (linear systems) and probability theory, MATLAB.

Aims/Learning Objectives
To provide the student with knowledge of the theories and applications of selected topics in image processing including but not limited to median based detail-preserving filters, image interpolation using MAP and other methods, frequency and spatial domain based approaches for super-resolution image reconstruction, motion estimation, optical flow, edge detection and various image enhancement techniques.

To familiarize the student with some important concepts and analytical techniques for linear and non-linear image processing.

To give the student experience in image processing applications and research using MATLAB.

To familiarize the student with a board range of tools and techniques for image filtering and enhancement, image expansion, reconstruction of super-resolution images, motion estimation, and etc.

Content
Image enhancement: Histogram modification (quantization): image space, Fourier smoothing; Sharpening: highpass filters, differential operators; Model based; Masking.
Image segmentation: thresholding: global, local; Feature detection and representation: point detection, edge detection, boundary detection, line detection; Region growing; Texture analysis; Classification.
Compression: Model based; Quantization, Filter based.
Image registration: many techniques listed above; Geometric transformations.

Assessment
Details of the actual assessment used in a given year can be found in the study guide provided at the start of the semester.

Resources
All the materials necessary for the course will be availabe on-line. The lecture notes also include an extended bibliography for further reading on the subject.
Information Theory (SIP 7001)

Lecturer
Prof Charles Pearce
The University of Adelaide

Mode of delivery
On-line.

Assumed knowledge
A basic understanding of probability theory is required. Knowledge of communication theory would be advantageous. Assignment work will assume knowledge of MATLAB.

Aims/Learning Objectives
The course aims to introduce the basic theoretical techniques of information theory. Illustrative examples are used to show how these techniques are employed and exercises given to help familiarise the student with their use. The various parts of a Shannon communications system are examined, including coding and decoding with or without the presence of noise and with data compression and efficient transmission in mind.

Content
Information Measures: entropy, relative entropy and mutual information.
Source coding: Discrete memoryless sources, Shannon's first (noiseless) coding theorem, Shannon-Fano-Elias coding, Huffman coding. Sources with memory. Universal source coding theorem. Ziv-Lempel Coding.
Channel coding: Discrete memoryless channels, channel capacity, Shannon's second (noisy) coding theorem, error control coding, performance bounds.
Advanced topics: multiple-user information theory, fading channels, multiple-antenna channels.

Assessment
60% assignments and 40% exam, however these percentages are indicative only and may be varied at the lecturer's discretion. Details of the actual assessment used in a given year can be found in the study guide provided at the start of the semester.

Resources
All the materials necessary for the course will be availabe on-line. The lecture notes also include an extended bibliography for further reading on the subject.
Introduction to Discrete Linear Systems (SIP 7013)

Lecturer
Dr Sergey Simakov

Mode of delivery
On-line only.

Assumed knowledge
Linear algebra (matrices), a basic knowledge on differential equations (linear systems) and complex analysis (Laplace transforms), probability theory, MATLAB.

Aims/Learning Objectives
The course considers as two separate components the deterministic linear systems and elements of linear stochastic modelling. Both components have linkages with the linear control theory and signal processing techniques. The students will become familiar with such fundamental concepts as the reachability/controllability and observability of discrete- and continuous-time linear systems in the time-invariant and general time-dependent cases. In the second part of the course the students will be introduced to elements of parametric modelling involving ARMA series and the use of the Yule-Walker equations. Connection between the Autoregressive Modelling and linear prediction (the Wiener filter) will be discussed in the examples.

The course exercises will give the students ample opportunity to review their knowledge of linear systems and algebra, as well as to refine their MATLAB skills and use them in the context of time-series modelling.

Content
Deterministic time-invariant linear systems: discrete-time and continuous-time state vector equations and state variable diagrams; solution of state vector equations, matrix exponentials, state-transition matrices; controllability and observability; solution by Z-transforms and Laplace transforms, transfer functions; stability,asymptotic stability, state feedback and pole placement.
Introduction to stochastic linear systems: stochastic processes, ergodic series, autocorrelation function, the ARMA model, special cases of the ARMA process, Yule-Walker equations and system parameter estimation.

Assessment
40% assignments (2), 20% intermediate exam and 40% final exam, however these percentages are indicative and may be varied at the lecturer's discretion. Details of the actual assessment used in a given year can be found in the study guide provided at the start of the semester.

Resources
All the materials necessary for the course will be availabe on-line. The lecture notes also include a bibliography for further reading on the subject.
Kalman Filtering and Tracking (SIP 7002)

Lecturers
Prof. Iven Mareels and Dr Ying Tan
The University of Melbourne

Mode of delivery
On-line with a possibility of a short course.

Assumed knowledge
Linear algebra (matrices), probability theory, linear systems and MATLAB.

Aim/Learning Objectives
This course focuses on the Kalman filter design and applications. Topics include the basic knowledge of Kalman filtering, Kalman filter design and implementations as well as the applications. The course consists of a series of lectures and tutorials aimed at helping students understand the idea and implementations of the Kalman filter and related topics.

At the completion of the course students will have knowledge of the theory and applications of the Kalman filter in the area of signal processing (and control). The course outcomes are to provide both theoretical and practical skills necessary to design and implement Kalman filter algorithms.

Content
The Kalman Filter: Stochastic state-variable systems, Optimality criteria for the estimation of state variables; The Maximum-likelihood solution for independent Gaussian noise processes; The innovations sequence; The least-squares Kalman filter; Systems with correlated noise processes; Stochastic systems with time-invariant coefficients; The square-root algorithm; The extended Kalman filter, Adaptive system identification.
Tracking Theory: Alpha-beta trackers, Kalman-filter tracking; Probability Data Association Tracking Hidden Markov models and the Viterbi Algorithm.

Assessment
Details of the actual assessment used in a given year can be found in the study guide provided at the start of the semester.

Resources
All the materials necessary for the course will be availabe on-line. The lecture notes are based on the following text books:
  1. A. V. Balakrishnan, (1984) Kalman Filtering Theory. Optimization Software, Inc.
  2. Mohinder S. Grewal and Angus P. Andrews, (2001) Kalman Filtering: Theory and Practice using Matlab. Second edition, John Wiley & Sons, Inc.
Mathematical Coding and Cryptology (SIP 7026)

Lecturers
A/Prof Bob Clarke, Dr Sue Barwick
University of Adelaide

Mode of delivery
On-line delivery with possible twice a week lectures at University of Adelaide North Terrace campus.

Assumed knowledge
To be advised

Aim/Learning Objectives
Aims: This course aims to give students an introduction to the two areas of cryptology and coding theory.

Objectives: At the end of this course students should:
  • have a knowledge of classical cryptosystems and the techniques used to break them;
  • understand the ideas of public key cryptosystems and digital signature schemes, and be able to use the algorithms for RSA and ElGamal;
  • understand the ideas involved in error correcting codes;
  • understand linear codes, syndrome decoding and perfect codes;
  • understand the basic properties of cyclic codes.
Content
The fundamental objective of Cryptology is to enable two people Alice and Bob to communicate over an insecure channel (such as a telephone line or a computer network) in such a way that an eavesdropper cannot understand what is being said. Classical cryptosystems required Alice and Bob to share a key which they could use both to encrypt a message before sending it, and to decrypt a received message. The prior communication of this key is difficult in practice. In 1976, work by Diffie and Hellman revolutionised modern cryptology. They developed a public key system which removed the need for Alice and Bob to share a private key. This course covers classical cryptosystems, public key cryptosystems, digital signature schemes and the cryptosystems DES and AES used as standards by the USA government and many financial institutions.

The birth of Coding Theory was inspired by the classical paper of Shannon in 1948. Since then a great deal of research has been devoted to finding efficient schemes by which digital information can be coded for reliable transmission through a noisy channel. Error correcting codes are widely used in applications, for example returning pictures from deep space, storage of data on CDs and design of identification numbers such as student numbers. Coding theory is also of great mathematical interest, it illustrates the power and beauty of modern algebra and has applications to many other areas of mathematics.

The first part of the subject concentrates on linear codes, with topics including syndrome decoding, perfect codes and cyclic codes. The Hamming and Golay codes, and others, are discussed.
The second part is an introduction to contemporary cryptology, including both symmetric and public key systems. Examples of cryptosystems studied include the Data Encryphon Standard and the RSA algorithm.
The course concludes with a selection of topics from authentication, identification and digital signatures.

Assessment
Details of the actual assessment used in a given year can be found in the study guide provided at the start of the semester.

Resources
All the materials necessary for the course will be availabe on-line. The lecture notes are based on the following books:
Cryptology:
    Douglas R. Stinson, Cryptography: theory and practice, (1995)
Coding Theory:
    Raymond Hill, A first course in coding theory, (1988)

A bibliography for further reading on the subject will be provided.
Mobile Communications (SIP 7004)

Lecturer
Dr Sylvie Perreau
University of South Australia

Mode of delivery
On-line with possibility of once a fortnight tutorials at Mawson Lakes.

Assumed knowledge
To be advised

Aim/Learning Objectives
The aim of this subject is to provide a solid understanding of this topic and also prepare students for further study or research on the subjects which follow. Specifically, in this subject, students will study the mobile communication channel and communication techniques specific to this channel. Students will also be introduced to cellular systems and in particular, how to design such systems and evaluate their performance. We will also discuss the general trends in mobile communications such as third and fourth generation systems and the current issues associated with their implementation. This course is both descriptive and detailed where the mathematical background required to follow the mathematical derivations is provided.

Content
Introduction, mobile radio propagation,channel modelling, modulation, diversity, terminal mobility and teletraffic models, cellular systems, the AMPS cellular system, time division multiple access cellular,personal communications networks and intelligent networks, low earth orbit.

Assessment
Details of the actual assessment used in a given year can be found in the study guide provided at the start of the semester.

Resources
All the materials necessary for the course will be availabe on-line.
The text book which is the main reference in the study material is:
Rappaport T.S., Wireless Communications: principles and practice. Newyork, prentice Hall.
In addition to this major text book, it can be useful to consult from time to time the book:
Yacoub, Michel Daoud, Foundations of mobile radio engineering Boca Raton, Florida, CRC Press.
Multisensor Data Fusion (SIP 7005)

Lecturers
Dr Branko Ristic and Dr Neil Gordon
DSTO

Mode of delivery
On-line with a possibility of weekly lectures.

Assumed knowledge
Linear algebra (matrices), probability theory, estimation theory, Kalman Filtering and Tracking (SIP 7002), MATLAB.

Aim/Learning Objectives
To provide the student with the theoretical and practical knowledge of an emerging field of data fusion, mainly in the context of modern surveillance systems. The emphasis will be on target tracking and identification in a network centric environment, covering techniques used for filtering, measurement association, track association and fusion, sensor registration and fusion based target identification.

Content
Overview: The role of multiple sensor fusion; Typical applications and sensors; Benefits of information fusion; Problems and limitations.

Architectural concepts and network issues: Centralised, distributed and hybrid architectures; Typical network issues (communication bandwidth, data latency, data incest, picture consistency)

Centralised multi-sensor filtering: Alternative track update methods; Filtering out-of-sequence measurements (algorithm A and B); Performance bounds for filtering.

Distributed multi-sensor filtering via track fusion: Bar-Shalom-Campo fusion; Information matrix fusion; Fusion using equivalent measurements.

Data association methods (measurement-to-track association in centarlised architectures and track-to-track association in distributed architectures): Distance measures; Gating; Global nearest neighbour; Joint probabilistic data association.

Sensor Registration: Registration error sources; Least squares method; Recursive methods; Coordinate systems.

Fusion of target ID declarations: Problem description; Heuristic methods; Bayesian inference; Evidential theory approach; A case study.

Assessment
Assignment (60%) and examination (40%), however the percentages are indicative only. Details can be found in the study guide provided at the start of the semester.

Assignment projects:

The topic of assignment project should be one of the following:
  • Track fusion and comparison with the centralised architecture.
  • Sensor registration.
  • Data association.
  • Filtering out-of-sequence measurements.
  • Distributed multiple passive sensor tracking.
  • Fusion of active (position) and passive (angle-only) tracks.
  • Fusion of target ID declarations.
Students can also make their own suggestion of a topic. The assignment topic has to be defined and approved by the lecturer before week 4. The assignment reports must include a literature review, a description of the problem and its algorithmic solution(s), implementation, numerical simulations with performance evaluation. Some comparisons and conclusions are highly desirable.

Resources
All the materials necessary for the course will be availabe on-line. The lecture notes also include an extended bibliography for further reading on the subject.
Satellite Communications (SIP 7023)

Lecturer
Dr Adrian Barbulescu
University of South Australia

Mode of delivery
On-line and once a week at Mawson Lakes.

Assumed knowledge
To be advised

Aim/Learning Objectives
The course aims to provide the student with knowledge of system design and performance analysis of satellite communication systems. On completion of this course the students should be able to describe the properties of satellite and earth station sub-systems, carry out link-budget performance calculations, understand design issues for earth station, payload and platform, describe typical multiple access techniques used in satellite communications and understand current satellite applications.

Content
Satellite link models. Link budget calaculations. Space segment.Propagation and interference. Modulation for non-linear satellite channels. Combined modulation and coding. Multiple access techniques. Case studies.

Assessment
Details of the actual assessment used in a given year can be found in the study guide provided at the start of the semester.

Resources
All the materials necessary for the course will be availabe on-line or emailed to students by the lecturer.
Signal Synthesis and Analysis (SIP 7015)

Lecturer
Dr John van der Hoek
University of Adelaide

Mode of delivery
On-line with possibility of weekly lectures.

Assumed knowledge
Basic knowledge of Fourier transforms and Z-transforms, Linear Algebra.

Aim/Learning Objectives
The course is a basic course for other sudies in signal processing, providing in a coherent and reasonably comprehensive way the L2 theory of signals

Signals can be analysed into component signals which we will do through Fourier related ideas (Fourier Transforms, Fourier Series, Fast Fourier Transforms and so on) and through Wavelet representations. In fact because of its importance, the study of wavelet related ideas will comprise the biggest portion of the course.

Synthesis will refer to the construction of signals -- this could refer to the reconstruction of a signal from its components, from sampling, or reconstruction from a compressed transmitted signal, and so on.

In preparing these lectures, a search was made of the relevant literature and the lecture notes attempt to cover the major tools of this area. However when discussing wavelets, we do not get into a discussion of the most recent topics like ``wavelet packets'', which should be covered in a more advanced course.

Content
Hilbert space: Inner product, completeness, L2, orthogonality and Reisz basis, Parsevaal's theorem, linear operators and resolutions of unity.
Fourier Series: Basis, L2(Rn), Plancherel Theorem, Uncertainty Theorem, Multidimensional Fourier transform, Short Time Fourier transform. Discrete Fourier Transform Properties, DFT Matrix, factorisation, Fast Fourier transform, sampling and Interpolation, Shannon sampling.
Wavelets Multiresolution Analysis: Scaling function and dilation, orthogonal wavelets, compact supported wavelets, Quadrature Mirror filters, Finite discrete wavelet transform, wavelet design. Overview of other transforms.

The Course includes example(s) like how wavelet analysis can be used with coding for data transmission.

Assessment
50% examination (mid-term and final), 50% assignments (5), essay (1), however these percentages are indicative only and may be varied at the lecturer's discretion. Details of the actual assessment used in a given year can be found in the study guide provided at the start of the semester.

Resources
All the materials necessary for the course will be availabe on-line. The lecture notes also include an extended bibliography for further reading on the subject.