Dr Andrew Markham
Jan 2015: Our paper on novel magneto-inductive localization of underground animals has been published in Methods in Ecology and Evolution!
Apr 2014: We received the best paper award at IPSN 2014 for the paper "Lightweight map matching for indoor localisation using conditional random fields"!
Feb 2014: We received the best poster award at EWSN 2014 for the poster "A Case for Magneto-Inductive Indoor Localization"!
I am a University Lecturer (Associate Professor) in Software Engineering, looking at sensing and communication in extreme and challenging application. Previously I was an EPSRC Postdoctoral Research Fellow, working on the UnderTracker project. I am investigating how to localize people, animals and objects in environments where technologies like GPS fail, such as underground or indoors. Key to this is the use of magneto-inductive tracking and communication. This has been applied to monitoring animals in their underground habitats, allowing for the first time detailed reconstruction of animal trajectories in their underground burrows.
I also worked on the WildSensing project, which used wireless sensor nodes to monitor badger behaviour. My work is typically cross-disciplinary, and one interesting avenue of research was automatically evolving code for distributed computing. This used a computational analog of a biological process, termed a discrete Gene Regulatory Network (dGRN). I obtained my PhD from the University of Cape Town, South Africa in 2008 researching the design and implementation of a wildlife tracking system, using heterogeneous wireless sensor networks.
PhD in Electrical Engineering, University of Cape Town, South Africa (2008):
"On a wildlife tracking and telemetry system: a wireless network approach"
BSc in Electrical Engineering, First Class Honours, University of Cape Town, South Africa (2004)
Robust Attentional Aggregation of Deep Feature Sets for Multi−view 3D Reconstruction
Bo Yang‚ Sen Wang‚ Andrew Markham and Niki Trigoni
In International Journal of Computer Vision. 2019.
Autonomous Learning of Speaker Identity and WiFi Geofence from Noisy Sensor Data
Chris Xiaoxuan Lu‚ Yuanbo Xiangli‚ Peijun Zhao‚ Changhao Chen‚ Niki Trigoni and Andrew Markham
In IEEE Internet of Things Journal. 2019.
Distilling Knowledge From a Deep Pose Regressor Network
Muhamad Risqi U. Saputra Pedro P. B. de Gusmao Yasin Almalioglu Andrew Markham and Niki Trigoni
In IEEE/CVF International Conference on Computer Vision (ICCV). 2019.
- Deep Learning Based Inertial Tracking
- Intuitive Physics
- Mobile and People-centric Systems and Sensing
- Intelligent Resource Constrained Systems
- Machine Learning Systems
- Distributed Sensing and Coordination
- Positioning in GPS-denied Environments
- Software Engineering