Sensors

View the calendar of events relating to this topic.

Projects

Chaotic Analysis of Partial Discharge

Paul Lewin (Investigator), Lyuboslav Petrov

The deterministic character of PD pulses predicted by theory has been shown to be existent for certain PD events. Finding characteristic patterns in phase space enables field-data PD detection with high reliability.

Dynamics simulations for quantum feedback to steer a single-particle harmonic oscillator in non-classical states

Hendrik Ulbricht (Investigator), Ashley Setter

This PhD project is about using digital electronics to implement a parametric feedback loop to modulate the intensity of an optical trapping laser in order to stabilise/cool the centre of mass motion of a nanoparticle. It is then intended we use digital parametric feedback to drive the motion of the particle, which is essentially a quantum harmonic oscillator, into non-classical quantum states such as squeezed and number states.

Investigation into the Interfacial Physics of Field Effect Biosensors

Nicolas Green, Chris-Kriton Skylaris (Investigators), Benjamin Lowe

This interdisciplinary research aims to improve understanding of Field Effect Transistor Biosensors (Bio-FETs) and to work towards a multiscale model which can be used to better understand and predict device response.

On the applicability of nonlinear timeseries methods for partial discharge analysis

Paul Lewin (Investigator), Lyuboslav Petrov

The governing processes of Partial Discharge (PD)
phenomena trigger aperiodic chains of events resulting in ’ap-
parently’ stochastic data, for which the widely adopted analysis
methodology is of statistical nature. However, it can be shown,
that nonlinear analysis methods can prove more adequate in
detecting certain trends and patterns in complex PD timeseries.
In this work, the application of nonlinear invariants and phase
space methods for PD analysis are discussed and potential pitfalls
are identified. Unsupervised statistical inference techniques based
on the use of surrogate data sets are proposed and employed for
the purpose of testing the applicability of nonlinear algorithms
and methods. The Generalized Hurst Exponent and Lempel Ziv
Complexity are used for finding the location of the system under
test on the spectrum between determinism and stochasticity. The
algorithms are found to have strong classification abilities at
discerning between surrogates and original point series, giving
motivation for further investigations.

Predicting Available Energy in Energy Harvesting Wireless Sensor Networks

Geoff Merrett (Investigator), Davide Zilli

Is it possible to predict how much energy a sun-light or wind powered wireless sensor node can harvest and tune its sensing pattern accordingly?

Structured low-rank approximation

Ivan Markovsky

Today's state-of-the-art methods for data processing are model based. We propose a fundamentally new approach that does not depend on an explicit model representation and can be used for model-free data processing. From a theoretical point of view, the prime advantage of the newly proposed paradigm is conceptual unification of existing methods. From a practical point of view, the proposed paradigm opens new possibilities for development of computational methods for data processing.