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.

Sub-Projects

• c4HDL - C++ for bit-accurate modelling of HDL
• c4Hardware - limited precision modelling in C++
• SBAM - SPM Bit Accurate Modelling

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.

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.

Sub-Projects

• c4Hardware - limited precision modelling in C++
• c4HDL - C++ for bit-accurate modelling of HDL

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.

SBAM - SPM Bit Accurate Modelling

SBAM is a series of libraries that enable high level algorithms to be easily simulated in various reduced precision number formats. Support is provided to enable porting of Matlab and IT++ simulations with minimal changes to code.

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.

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.

Sub-Projects

• MCMC System Identification
• System Identification Toolbox
• Variance Quantification
• Wiener Hammerstein Benchmark

Wiener Hammerstein Benchmark

Details of our attempt at the Wiener-Hammerstein Benchmark problem