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Durham University

Advanced Research Computing

Advanced Research Computing

Advanced Research Computing (ARC) is a dedicated computing support unit within the Research Division of the University. We support academic researchers in all faculties across the University, where there is a requirement for the use of computers as part of their research. We provide facilities and expertise, connect people across different disciplines, and build on the services provided by Computing and Information Services. Our range of activity extends from simple coding assistance through to supporting computationally intensive research requiring High Performance Computing.

Northern Intensive Computing Environment

The N8 Centre of Excellence in Computationally Intensive Research, N8 CIR, has been awarded £3.1m from the Engineering and Physical Sciences Resources Council to establish a new Tier 2 computing facility in the north of England. This investment will be matched by £5.3m from the eight universities in the N8 Research Partnership which will fund operational costs and dedicated research software engineering support.

The new facility, known as the Northern Intensive Computing Environment or NICE, will be housed at Durham University and co-located with the existing STFC DiRAC Memory Intensive National Supercomputing Facility. NICE will be based on the same technology that is used in current world-leading supercomputers and will extend the capability of accelerated computing. The technology has been chosen to combine experimental, modelling and machine learning approaches and to bring these specialist communities together to address new research challenges.

Click here for more information on NICE19

News & Events

Data Science Seminar: Learning to Segment in the Images

6th March 2020, 16:00 to 17:00, Odgen Centre OC218

A lot of research is focused on object detection and it has achieved
significant advances with deep learning techniques in recent years.
In terms of security, particular interest lies in the automatic
detection and classification of weapons within X-ray security imagery
in the airport, and drone based platform, with camera have been fast
deployed for surveillance purpose. However, these algorithms are not
usually optimal for dealing with images captured by drone-based
platforms and X-ray imagery, due to various challenges such as
limited availability of such images, rapid view point change,
different scales and density of object distribution and occlusion an
so on. In this presentation, we present initial visualisation results
for detection of objects from drone and X-ray space with Faster R-CNN
model as baseline architecture. We hope that this preliminary results
can boost the research and development in visual analysis of X-ray
and drone platforms.