Compressed/low SWAP-C sensing

Compressed/low SWAP-C sensing

While most sensing and processing tasks have been traditionally optimized separately, joint development of sensors, computing hardware and algorithms, designed to achieve specific tasks can lead to lower size, weight, power and cost (SWaP-C) systems, without significant degradation of the information recovered. A typical example is compressed sensing, achieving data compression via linear transformations. Although non-linear dimensionality reduction/compression can further reduce data volumes, understanding the trade-offs of such methods while ensuring performance guarantees remains challenging and crucial, especially in a defence and security context. This theme will focus on the development of novel statistical methods for low SWaP-C sensing, targeting existing and next-generation resource constraints hardware. This theme finds direct application in electro-optics (EO) sensing and fusion of heterogeneous sensors.

This theme will consider a range of methodological tools from user-defined to data-driven compression schemes, enabling identification of compact representations from exemplar data. Probabilistic and Bayesian frameworks will be preferred to enable uncertainty quantification and management, as well as simplification of acquisition/processing pipelines. Of particular interest will be algorithms compatible with implementation on fixed-point hardware and neuromorphic processors (spiking architectures).

Theme Lead: Dr Yoann Altmann y.altmann@hw.ac.uk

https://www.hw.ac.uk/study/apply/uk/postgraduate.htm link to the findaphd.com please mention the potential supervisor in your application.

Smart Products Made Smarter, a collaboration with Heriot-Watt University, University of Edinburgh and Leonardo is pleased to invite applications for PhD studentships to work as part of a leading team of experts. These studentships will be supported by an enhanced stipend of £21,400 per year over 3.5 years.

This grant, sponsored by the EPSRC, is a collaboration between academia and Leonardo. There are currently PhD opportunities available to work on diverse topics as part of this collaborative team. The work will involve strong links with industry.

The research addresses a broad range of challenges. These challenges exemplify future product lifecycle management from smart concept, design, development and manufacture to enhanced end-user capability, united by a common digital thread to enable smarter products to be made smarter. Each challenge area has clearly identified initial research themes and associated research challenges to be addressed and these are indicated below:

Challenge 1 (C1) the Making challenge: To create new hybrid manufacturing processes, that combine multiple Additive Manufacturing (AM) process with precision machining and coating processes to create components that disrupt the traditional functional trade-offs of Size, Weight and Power (SWaP) through techniques such as varying the material properties within a part and harnessing the digital production of optical components.

Challenge 2 (C2) the Manipulation challenge: To create new handling processes that fully exploit the digital data flows which define custom components whose shape and functionality is tailored to production by dexterous, highly adaptable robots that are programmed dynamically.

Challenge 3 (C3) the Computation challenge: To create new signal processing & machine learning methodologies that enable intelligent, digital & connected sensor products while mitigating the data deluge from the multiple sensors produced by Leonardo operating across the EM spectrum.

The themes represent areas that could form the basis of your PhD. These PhD positions offer great flexibility and we welcome the opportunity to explore other ideas & themes.

To submit an application please contact the relevant theme lead indicated below.