Graham Kendall
Various Images

Professor Graham Kendall

University of Nottingham, UK

I am a Professor of Computer Science at the University of Nottingham (UK). I am currently the Vice-Provost (Research and Knowledge Transfer) at our campus in Malaysia. I am a member of the Automated Scheduling, Optimisation and Planning (ASAP) Research Group. My interests include Operational Research, Evolutionary Computing, Scheduling (particularly sports scheduling), Cutting and Packing, Timetabling and Games (both games in the usual sense of the word as well as mathematical games such as the Iterated Prisoners Dilemma).

Latest Blog Post

Bibtax parser: Mashup no more

Random Blog Post

Conjuring Trick

Cutting and Packing

I have published a number of papers on Cutting and Packing
http://bit.ly/dQPw7T

Publication

The Cross-domain Heuristic Search Challenge - An International Research Competition
http://bit.ly/gCFZLy

Publication

A Parallel Branch-and-Bound Approach to the Rectangular Guillotine Strip Cutting Problem
http://bit.ly/fVXMjm

Publication

Constructing Initial Neighbourhoods to Identify Critical Constraints
http://bit.ly/h3xfnd

Graham Kendall: Details of Requested Publication


Citation

Oates, R The Suitability of the Dendritic Cell Algorithm for Robotic Security Applications. Ph.D. Thesis, School of Computer Science, University of Nottingham, UK, 2010.

Robert started his PhD in January 2006 and graduated in July 2010. His viva took place on 5th March 2010


Abstract

The implementation and running of physical security systems is costly and potentially hazardous for those employed to patrol areas of interest. From a technial perspective, the physical security problem can be seen as minimising the probability that intruders and other anomalous events will occur unobserved. A robotic solution is proposed using an artificial immune system, traditionally applied to software security, to identify threats and hazards: the dendritic cell algorithm. It is demonstrated that the migration from the software world to the hardware world is achievable for this algorithm and key properties of the resulting system are explored empirically and theoretically. It is found that the algorithm has a hitherto unknown frequency-dependent component, making it ideal for filtering out sensor noise. Weaknesses of the algorithm are also discovered, by mathematically phrasing the signal processing phase as a collection of linear classifiers. It is concluded that traditional machine learning approaches are likely to outperform the implemented system in its current form. However, it is also observed that the algorithm’s inherent filtering characteristics make modification, rather than rejection, the most beneficial course of action. Hybridising the dendritic cell algorithm with more traditional machine learning techniques, through the introduction of a training phase and using a non-linear classification phase is suggested as a possible future direction.


pdf

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Bibtex

@PHDSUPERVISED{o2010, author = {R. Oates},
title = {The Suitability of the Dendritic Cell Algorithm for Robotic Security Applications},
school = {School of Computer Science, University of Nottingham, UK},
year = {2010},
note = {Robert started his PhD in January 2006 and graduated in July 2010. His viva took place on 5th March 2010},
abstract = {The implementation and running of physical security systems is costly and potentially hazardous for those employed to patrol areas of interest. From a technial perspective, the physical security problem can be seen as minimising the probability that intruders and other anomalous events will occur unobserved. A robotic solution is proposed using an artificial immune system, traditionally applied to software security, to identify threats and hazards: the dendritic cell algorithm. It is demonstrated that the migration from the software world to the hardware world is achievable for this algorithm and key properties of the resulting system are explored empirically and theoretically. It is found that the algorithm has a hitherto unknown frequency-dependent component, making it ideal for filtering out sensor noise. Weaknesses of the algorithm are also discovered, by mathematically phrasing the signal processing phase as a collection of linear classifiers. It is concluded that traditional machine learning approaches are likely to outperform the implemented system in its current form. However, it is also observed that the algorithm’s inherent filtering characteristics make modification, rather than rejection, the most beneficial course of action. Hybridising the dendritic cell algorithm with more traditional machine learning techniques, through the introduction of a training phase and using a non-linear classification phase is suggested as a possible future direction.},
owner = {gxk},
timestamp = {2011.02.03},
webpdf = {http://www.graham-kendall.com/papers/o2010.pdf} }