Soren J. Henriksen

Bayesian system identification via Markov chain Monte Carlo techniques

The work here explores new numerical methods for supporting a Bayesian approach to parameter estimation of dynamic systems. This is primarily motivated by the goal of providing accurate quantification of estimation error that is valid for arbitrary, and hence even very short length data records. The main innovation is the employment of the Metropolis-Hastings algorithm to construct an ergodic Markov chain with invariant density equal to the required posterior density. Monte Carlo analysis of samples from this chain then provides a means for efficiently and accurately computing posteriors for model parameters and arbitrary functions of them.

Digital hardware implementation of a current controller for IM variable speed drives

This paper presents the design of an induction machine current controller that is entirely implemented in digital hardware. A hardware current controller allows high switching frequencies with only modest processing power as well as simplified controller hardware and software. The paper briefly presents the concepts of the algorithm implemented, and then outlines the changes that are made to make the digital implementation even more efficient. It then discusses the architecture used for the hardware design. Experimental results are presented to demonstrate the algorithms performance.

A digital current controller for three phase voltage source inverters

The usual techniques for implementing current control for hard switched inverters usually involve PI control or hysteresis based control. PI strategies suffer from poor transient performance, and the latter requires complex analog circuitry to give a constant switching frequency. This paper describes a new current control algorithm suitable for implementation in software or digital hardware. A novel feature of the algorithm is that it is able to identify the required machine parameters online. Furthermore there is no tuning of control parameters required from the user. The algorithm output is the switching times for the inverter switches. Comprehensive simulation results are presented, and issues related to hardware digital implementation are presented.

Parallel Implementation of Particle MCMC Methods on a GPU

Abstract This paper examines the problem of estimating the parameters describing system models of quite general nonlinear and multi-variable form. The approach is a computational one in which quantities that are intractable to evaluate exactly are approximated by sample averages from randomized algorithms. The main contribution is to illustrate the viability and utility of this approach by examining how high computational loads can be simply managed using commodity hardware. The proposed algorithms and solution architectures are profiled on concrete examples.

Time-Scale Modification of Speech Signals

This paper presents methods for independently modifying the time and pitch scale of acoustic signals, with an emphasis on speech signals. The algorithms developed here use parametric (sinusoidal) modeling techniques introduced by other authors, but new perspectives on the role of vocal tract decomposition and maintaining phase relationships between sinusoidal tracks are derived that achieve improved output quality with decreased computational load. Simulation results are provided to illustrate performance, and the algorithms developed here have been demonstrated capable of implementation on simple DSP hardware.

A 4×4 FPGA-based wireless testbed for LTE applications

We present early results from a 4times4 2.4 GHz ISM band multiple-input multiple-output (MIMO) testbed developed at the University of Newcastle for over-the-air evaluation of MIMO algorithms. To provide maximum flexibility for development of 3 GPP Long Term Evolution (LTE) algorithms, the testbed is designed around a field-programmable gate array (FPGA). The testbed features a simple GUI interface for testbed control as well as a MATLAB interface allowing real channel measurements to be used in algorithm development and evaluation. These features are illustrated here with experimental results from the implementation of Alamouti Space-Time Coding with 64-QAM.

Convergence of Markov-Chain Monte-Carlo Approaches to Multiuser and MIMO Detection

Markov-chain Monte-Carlo methods have been demonstrated to offer an attractive alternative to the design of approximate (near optimal) maximum a-posteriori (MAP) detectors for synchronous direct-sequence code-division multiple access (DS-CDMA) and multi-input, multi-output (MIMO) multiple antenna applications. Central to evaluating these method is understanding their convergence properties. In other works, this has been established via simulation, and the underlying theoretical basis has been identified. The contribution of this paper is to extend the theoretical understanding by rigorously establishing both convergence and convergence rate results for a wide class of Metropolis-Hastings methods.

System identification via a computational Bayesian approach

This paper takes a Bayesian approach to the problem of dynamic system estimation, and illustrates how posterior densities for system parameters, or more abstract and rather arbitrary system properties (such a frequency response, phase margin etc.) may be numerically computed. In achieving this, the key idea of constructing an ergodic Markov chain with invariant distribution equal to the desired posterior is fundamental, and it is inspired by recent developments in the mathematical statistics literature. An essential point of the work here is that via the associated posterior computation from the Markov chain, error bounds on estimates are provided that do not rely on asymptotic in data length arguments, and hence they apply with arbitrary accuracy for arbitrarily short data records.

System identification via a computational bayesian approach

Abstract This paper takes a Bayesian approach to the problem of dynamic system estimation, and illustrates how posterior densities for system parameters, or more abstract and rather arbitrary system properties (such a frequency response, phase margin etc.) may be numerically computed. In achieving this, the key idea of constructing an ergodic Markov chain with invariant distribution equal to the desired posterior is fundamental, and it is inspired by recent developments in the mathematical statistics literature. An essential point of the work here is that via the associated posterior computation from the Markov chain, error bounds on estimates are provided that do not rely on asymptotic in data length arguments, and hence they apply with arbitrary accuracy for arbitrarily short data records.

Time and frequency scale modification of speech signals

This paper presents new and improved methods for independently modifying the time and pitch scale of acoustic signals, with an emphasis on speech signals. The algorithms developed here use parametric (sinusoidal) modelling techniques introduced by other authors, but new ideas are presented here that achieve improved output quality with decreased computational load. In particular, speech quality is improved by using novel ideas to reduce phase dispersion in the scaled signal.