System Identification Toolbox

This toolbox is a MATLAB-based software package for the estimation of dynamic systems.

A wide range of standard estimation approaches are supported. These include the use of non-parametric, subspace-based and prediction-error algorithms coupled (in the latter case) with either MIMO state space or MISO polynomial model structures.

Additionally, some new approaches are included. These include the support for bilinear and other Hammerstein-Wiener non-linear structures, and the use of the expectation-maximisation (EM) algorithm for time and frequency domain estimation of state space structures.

MIMO Communications Testbed

This is a hardware device designed to be used in the design and testing of wireless MIMO communications systems. It is connected to a PC via USB 2.0 or ethernet and uses an on-board FPGA to allow implementation of algorithms in logic, together with provision for multiple radio modules.

Model Predictive Control

This project is concerned with development of algorithms and hardware for high-speed model predictive control (MPC) solutions. Both linear and nonlinear MPC systems are considered.


Future Wireless

Orthgonal Frequency Division Multiplexing (OFDM) is core to emerging and future wireless systems. Of note, 802.16, 802.20 and 3GPP LTE all depend upon OFDM. The goal of this project is to generate core expertise in this area, publish in leading conferences and journals while securing valuable IP for the project participants. Numerous ASIC prototypes will result.


QPC - Quadratic Programming in C

This project offers a collection of software routines for solving quadratic programming problems that can be written in this form

x* = arg min 0.5x'Hx + f'x convex cost
s.t. Ax = b, linear equality constraints,
Lx <= k, general linear inequality constraints,
l <= x <= u, bound constraints.
The routines are written in C and callable from Matlab using the standard syntax. State-of-the-art solvers are available.

System Identification

Theoretical and empirical study of various problems in system identification. Particular attention is paid to robust estimation of Multivariable and Nonlinear systems, and to error quantification.


Error Correction Coding, LDPC codes

This project investigates the design and decoding of low-density parity-check (LDPC) codes and a related class of codes called repeat- accumulate (RA) codes. This project includes an online implementation of density evolution and offers resources related to LDPC and RA codes.

Algorithms to ASICs

These projects are focussed on providing software solutions to assist the mapping of algorithmic solutions to actual implementations in hardware devices. This includes bit accurate modelling of numerical systems in limited precision, analysis of those simulations, and generation of test data for verification with hardware implementations in ASICs and FPGAs.


Filtering and Smoothing

This project offers a suite of software routines that run under Matlab, which perform various signal filtering and smoothing operations. This includes standard Kalman filtering and Kalman smoothing routines.

Variance Quantification

This project develops quantifications for the frequency domain variance of prediction error system estimates. A key theme is to derive new approximations offering improved accuracy via the principles of reproducing kernel principles and orthonormal parametrizations.
Team Members: Prof. Brett Ninness

Wiener Hammerstein Benchmark

Details of our attempt at the Wiener-Hammerstein Benchmark problem

Network Information Theory

This project derives information-theoretic bounds to the capacity (in the Shannon sense) of multiterminal networks, and develops new coding schemes to increase transmission rates.

Resource Allocation for Small-Cell Wireless Heterogeneous Networks

This project investigates and develops advanced radio resource management techniques to support the deployment of small cells in next-generation wireless access networks. Drawing upon the foundation of optimization and game theories, this project will provide distributed algorithms that effectively manage the severe and highly random signal interference. It will also provide fundamental insights into the design and analysis of wireless protocols in large heterogeneous networks.
Team Members: Dr Duy T. Ngo

Maintained by Prof. Brett Ninness
University of Newcastle
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