Fri, 09 Mar 2018

10:00 - 11:00
L3

1-3 Composite Modelling

Hannah Rose
(Thales)
Abstract

An important and relevant topic at Thales is 1-3 composite modelling capability. In particular, sensitivity enhancement through design.

A simplistic model developed by Smith and Auld1 has grouped the polycrystalline active and filler materials into an effective homogenous medium by using the rule of weighted averages in order to generate “effective” elastic, electric and piezoelectric properties. This method had been further improved by Avellaneda & Swart2. However, these models fail to provide all of the terms necessary to populate a full elasto-electric matrix – such that the remaining terms need to be estimated by some heuristic approach. The derivation of an approach which allowed all of the terms in the elasto-electric matrix to be calculated would allow much more thorough and powerful predictions – for example allowing lateral modes etc. to be traced and allow a more detailed design of a closely-packed array of 1-3 sensors to be conducted with much higher confidence, accounting for inter-elements coupling which partly governs the key field-of-view of the overall array. In addition, the ability to populate the matrix for single crystal material – which features more independent terms in the elasto-electric matrix than conventional polycrystalline material- would complement the increasing interest in single crystals for practical SONAR devices.

1.“Modelling 1-3 Composite Piezoelectrics: Hydrostatic Response” – IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control 40(1):41-

2.“Calculating the performance of 1-3 piezoelectric composites for hydrophone applications: An effective medium approach” The Journal of the Acoustical Society of America 103, 1449, 1998

Fri, 03 Mar 2017

10:00 - 11:00
L4

Predictions for Roads

Steve Hilditch
(Thales)
Abstract

Road travel is taking longer each year in the UK. This has been true for the last four years. Travel times have increased by 4% in the last two years. Applying the principle finding of the Eddington Report 2006, this change over the last two years will cost the UK economy an additional £2bn per year going forward even without further deterioration. Additional travel times are matched by a greater unreliability of travel times.

Knowing demand and road capacity, can we predict travel times?

We will look briefly at previous partial solutions and the abundance of motorway data in the UK. Can we make a breakthrough to achieve real-time predictions?

Fri, 04 Mar 2016

10:00 - 11:00
L4

Fault prediction from time series data

Mike Newman
(Thales)
Abstract

On the railway network, for example, there is a large base of installed equipment with a useful life of many years.  This equipment has condition monitoring that can flag a fault when a measured parameter goes outside the permitted range.  If we can use existing measurements to predict when this would occur, preventative maintenance could be targeted more effectively and faults reduced.  As an example, we will consider the current supplied to a points motor as a function of time in each operational cycle.

Fri, 12 Jun 2015

10:00 - 11:00
L5

A recommendation system for journey planning

Darren Price
(Thales)
Abstract

A recommendation system for multi-modal journey planning could be useful to travellers in making their journeys more efficient and pleasant, and to transport operators in encouraging travellers to make more effective use of infrastructure capacity.

Journeys will have multiple quantifiable attributes (e.g. time, cost, likelihood of getting a seat) and other attributes that we might infer indirectly (e.g. a pleasant view).  Individual travellers will have different preferences that will affect the most appropriate recommendations.  The recommendation system might build profiles for travellers, quantifying their preferences.  These could be inferred indirectly, based on the information they provide, choices they make and feedback they give.  These profiles might then be used to compare and rank different travel options.

Fri, 04 Jun 2010

10:00 - 13:00
DH 1st floor SR

Compressive sampling of radar and electronic warfare data

Andy Stove
(Thales)
Abstract

'Compressive sampling' is a topic of current interest. It relies on data being sparse in some domain, which allows what is apparently 'sub Nyquist' sampling so that the quantities of data which must be handled become more closely related to the information rate. This principal would appear to have (at least) three applications for radar and electronic warfare: \\

The most modest application is to reduce the amount of data which we must handle: radar and electronic warfare receivers generate vast amounts of data (up to 1Gbit/second or even 10Gbit.sec). It is desirable to be able to store this data for future analysis and it is also becoming increasingly important to be able to share it between different sensors, which, prima facie, requires vast communication bandwidths and it would be valuable to be able to find ways to handle this more efficiently. \\

The second advantage is that if suitable data domains can be identified, it may also be possible to pre-process the data before the analogue to digital converters in the receivers, to reduce the demands on these critical components. \\

The most ambitious use of compressive sensing would be to find ways of modifying the radar waveforms, and the electronic warfare receiver sampling strategies, to change the domain in which the information is represented to reduce the data rates at the receiver 'front ends', i.e. make the data at the front end better match the information we really want to acquire.\\

The aim of the presentation will be to describe the issues with which we are faced, and to discuss how compressive sampling might be able to help. A particular issue which will be raised is how we might find domains in which the data is sparse.

Fri, 20 Mar 2009
10:00
DH 1st floor SR

Signal detection, identification, extraction and classification

Edward Stansfield
(Thales)
Abstract

PROBLEM STATEMENT:

Consider a set of measurements made by many sensors placed in a noisy environment, the noise is both temporally and spatially correlated and has time varying statistics. Given this environment, characterised by spatial and temporal scales of correlation, the challenge is to detect the presence of a weak, stationary signal described by smaller scales of temporal and spatial correlation.

Many current and future challenges involve detection of signals in the presence of other, similar, signals. The signal environment is extremely busy and thus the traditional process of detection of a signal buried in noise at reducing signal to noise ratio is no longer sufficient. Signals of interest may be at high SNR but need to be detected, classified, isolated and analysed as close to real time as is possible. All interfering signals are potentially signals of interest and all overlap in time and frequency.

Can the performance of signal detection algorithms be parameterised by some characteristic(s) of the signal environment?

A problem exists to detect and classify multiple signal types, but with a very low duty cycle for the receiver. In certain circumstances, very short windows of opportunity exist where the local signal environment can be sampled and the duty cycle of observation opportunities can be as low as 10%. The signals to be detected may be continuous or intermittent (burst) transmissions. Within these short windows, it is desirable to detect and classify multiple transmissions in terms of signal type (e.g. analogue or digital comms, navigation etc.) and location of transmitters. The low duty cycle of observations for the receiver makes this a challenging prospect.

Again, can the performance of signal detection algorithms be parameterised by some characteristic(s) of the signal environment?

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