ABSTRACT:

The interaction of optical fields with disordered and complex media has been an area of considerable interest for many researchers. Such media commonly induces intensity and phase distortions that can be disruptive for optical communications and imaging systems. However, these optical distortions are directly dependent on the environment that the complex media is situated within and therefore measuring these distortions be used a novel way to sense changes in the world around us. Orbital Angular Momentum (OAM) beams are laser beams that twist as they propagate. Their twisted nature means they experience unique distortions during propagation. One such notable behaviour is called vortex splitting, where a beam that carries an OAM m = n, will break up into n vortices of m = 1 that are spatially separated. This behaviour is like the conservation of momentum that would occur if you hit a spinning top with a hammer, where several parts of the spinning top would keep spinning but spatially separated from one another. For optical systems, the equivalent to the hammer is phase aberrations that occur as a beam propagates over turbulent free-space channels or in multimode fibres. If one can decipher the changes or exact origins of the observed aberrations, enhanced environmental sensing technologies could be developed. 

I will discuss our recent work utilising mode decomposition assisted machine learning approaches to identify trainable features in the distortions of OAM beams for environmental monitoring and shape sensing in optical fibres. Our techniques can reveal features that can be used reliably with regression models to predict temperature variations of 0.4C and wind speed variations of 0.03 m/s over km equivalent turbulent free-space channels with controllable and verifiable temperature or locate the presence of a fibre bends to precision of better than 1mm at a remote distance of up to 1km. This approach could be used to create low-cost fibre shape sensors or path dependent weather measurement for a wide range environmental monitoring application.

BIO:

Prof Martin Lavery holds a Professorial Chair in Optics and is the leader of the Structured Photonics Research Group the James Watt School of Engineering University of Glasgow (UofG). His dynamic research group has a track record in investigating fundamental developments in Physics, and successfully applying them to industry inspired engineering challenges. Since 2014, he has successfully attracted almost £5m in research funding as PI, is coordinator of the H2020 Future and Emerging Technologies (FET-Open) consortium project named Super-pixels and lead the EPSRC Project PON-HD (EP/T009012/1). His team have successfully led a range of research programs that have both resulted in self aligning prototype free-space optical (FSO) communication systems that incorporate Space Division Multiplexing (SDM), advanced passive multiplexers for SDM, new findings in the propagation dynamics of structured light in turbulent environments, novel ultra-wide field of view solar collection optics, and bespoke acoustic array antennas for dynamic beamforming. Further, he has active research projects with British Telecom and Huawei focusing on FSO communications use in the access network. Working with the University of the Witwatersrand and others, a road map for deploying high speed sustainable network provision for the African continent that was developed and published in Nature Photonics. Prof Lavery has a H-index is 40, with over 110 publications in peer-reviewed journals and conference manuscripts that have collectively attracted more than 10,000 Citations (Google Scholar August 2022). He has been awarded the 2013 Scopus Young Scientist of the Year for Physical Sciences, 2018 Mobile World Scholar Gold Medal and the 2019 Royal Society of Edinburgh Sir Thomas Makdougall Brisbane Medals for his accomplishments in optical communications.