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Computing Science and Maths Seminars, 2020/2021

Autumn 19 image

Seminars will take place via Microsoft Teams, with a meeting link to be shared via the seminar-announce emails. Unless otherwise stated, from 15.00 to 16.00 on Friday afternoons during semester time, followed by informal discussions.

If you would like to give a seminar to the department in future or if you need more information,  
please contact the seminar organisers, Dr. Sandy Brownlee ( and Dr. Wen-shin Lee (

Spring 2021

Date Speaker Title/Abstract
22 January
Andrew Wallace, Heriot-Watt University PervAsive low-TeraHz and optical sensing for Car Autonomy and Driver Assistance (PATHCAD)*

What if it rains?

While media coverage about the future of the autonomous car changes from the naïve acceptance of a disruptive technology to the damning realisation that it may actually have the occasional accident due to adverse weather, adversarial attack, and hardware/software malfunction, the vast majority of trials have taken place in good weather with considerable pre-mapping and detailed route planning.

The PATHCAD project explored alternatives to vehicle sensing for scene mapping and actor recognition in adverse conditions, based on video, LiDAR and radar technologies. I shall present work to develop a higher resolution radar system, to recognise and track actors and predict their behaviour in radar images, and discuss how active LiDAR imaging can penetrate bad weather by use of full waveform processing, potentially aided by concurrent radar sensing.

As time allows, I will present some additional material on how fast, eye-safe, full wave, automotive LiDAR systems can be built from improvements in solid state semiconductor arrays, allied to random sampling, compressed sensing and approximate computing.

* Much of the work was supported by Jaguar Land Rover and EPSRC (EP/N012402/1) as part of the TASCC programme, and was carried out by the Universities of Birmingham, Edinburgh and Heriot=Watt.
29 January
Peter Hinow, University of Wisconsin - Milwaukee, USA Automated Feature Extraction from Large Cardiac Electrophysiological Data Sets

A multi-electrode array-based application for the long-term recording of action potentials from electrogenic cells makes possible exciting cardiac electrophysiology studies in health and disease. With hundreds of simultaneous electrode recordings being acquired over a period of days, the main challenge becomes achieving reliable signal identification and quantification. We set out to develop an algorithm capable of automatically extracting regions of high-quality action potentials from terabyte size experimental results and to map the trains of action potentials into a low-dimensional feature space for analysis. Our automatic segmentation algorithm finds regions of acceptable action potentials in large data sets of electrophysiological readings. We use spectral methods and support vector machines to classify our readings and to extract relevant features. We show that action potentials from the same cell site can be recorded over days without detrimental effects to the cell membrane. The variability between measurements 24 h apart is comparable to the natural variability of the features at a single time point. Our work contributes towards a non-invasive approach for cardiomyocyte functional maturation, as well as developmental, pathological, and pharmacological studies.

This is joint work with Viviana Zlochiver, Stacie Kroboth (Advocate Aurora Research Institute), and John Jurkiewicz (graduate student at UWM).
5 February
No seminar Internal Event

12 February
Tomas Sauer, University of Passau, Germany Continued Fractions: Music, Moments and Hurwitz

19 February

26 February
No seminar Internal Event (Reading Week)

5 March

12 March

19 March

26 March
No seminar Internal Event

2 April
No seminar Easter Holiday

9 April

16 April

23 April
Dr Craig Docherty TBC

30 April
No seminar Internal Event

7 May

14 May

21 May

28 May
No seminar Internal Event

Previous Seminar Series

2020:  Spring   Autumn
2019:  Spring   Autumn
2018:  Spring   Autumn
2017:  Spring   Autumn
2016:  Spring   Autumn
2015:  Spring   Autumn
2014:  Spring   Autumn
2013:  Spring   Autumn
2012:  Spring   Autumn
2011:  Spring   Autumn
2010:  Spring   Autumn
2009:  Spring   Autumn
2008:  Spring   Autumn
2007:  Spring   Autumn
2006:  Spring   Autumn
2005:  Spring   Autumn
2004:  Spring   Autumn
2003:  Spring   Autumn
2002:  Spring   Autumn
2001:  Spring   Autumn
2000:  Spring   Autumn
1999:  Spring   Autumn
1998:  Spring   Autumn
1997:  Spring   Autumn
1996:  Autumn

Top image: Image and vision processing.
Courtesy of Dr. Deepayan Bhowmik.

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