DCU Top of the Table for Research in Sunday Times University Guide

DCU has been ranked as the top Irish University for Research in the recent Sunday Times University Guide. The guide, which was published on Sunday September 30th stated "Ireland’s youngest university, DCU, overtook Trinity, the oldest, to head the research category." DCU scored 100 points out of a possible 100 for research. The next highest University for research scored 93 points.

The Sunday Times noted that DCU's "350 academics attracted €91,429 each in research ". DCU's recent success in PRTLI 4, where the University attracted €23m in research funding made a major contribution to this. Under PRTLI 4 DCU were granted support for five specific research projects - three science and engineering projects and two humanities and social science based projects. These projects are carried out as part of national collaborative research programmes involving other third-level institudes.

DCU has consistently been rated highly in the Sunday Times University Guide. It won University of the Year in 2004 and was runner up in 2003 and 2006.

Click here for the Sunday Times University League Table 2007

Congratulations to Kealan McCusker

Congratulations to Kealan McCusker who successfully defended his thesis and will be awarded the degree of PhD.

The title of Kealan's thesis is "Cryptographic key distribution in wireless sensor networks: a hardware perspective".

He completed his PhD in the Centre for Digital Video Processing (CDVP), Adaptive Information Cluster (AIC) and the School of Electronic Engineering, DCU under the supervision of Dr. Noel E. O’Connor.

Kealan is currently working as a post-doctoral researcher with the CDVP.

Brief description of Project:
In this work the suitability of different methods of symmetric key distribution for application in wireless sensor networks are discussed. Each method is considered in terms of its security implications for the network. It is concluded that an asymmetric scheme is the optimum choice for key distribution. In particular, Identity-Based Cryptography (IBC) is proposed as the most suitable of the various asymmetric approaches. A protocol for key distribution using identity based Non-Interactive Key Distribution Scheme (NIKDS) and Identity-Based Signature scheme is presented. The protocol is analysed on the ARM920T processor and measurements were taken for the run time and energy of its components parts. It was found that the Tate pairing component of the NIKDS consumes significant amounts of energy, and so it should be ported to hardware. An accelerator was implemented in 65nm CMOS technology and area, timing and energy figures have been obtained for the design. Initial results indicate that a hardware implementation of IBC would meet the strict energy constraint of a wireless sensor network node.

This project was generously funded by Enterprise Ireland (EI) and Science Foundation Ireland (SFI).

Embark Postgraduate Scholarship Success

Congratulations to Huang Jing and Houman Zahedmanesh who have been awarded Embark Postgraduate Research Scholarships. We look forward to working with the new students over the coming years.

Huang Jing will be working with Dr. Xiaojun Wang (School of Electronic Engineering). Houman Zahedmanesh will be working with Dr Caitriona Lally (School of Mechanical and Manufacturing Engineering).

Huang and Houman are two of only 7 non-EU citizens to be awarded the scholarship.

Other Embark successes for DCU are Xi Jiang (Physics), Deirdre Fox (Chemistry) and Ruth Larragy (Mol/Bio).

Background:
The Embark Postgraduate Research Scholarship Scheme is administered by the Irish Research Council for Science, Engineering and Technology (IRCSET). It applies to Science, Engineering and Technology related research by individuals pursuing Masters or Doctorate qualifications in recognised third level establishments.

All students graduating with higher honours in science, engineering and technology are entitled to apply for funding. A decision to grant funding is based on the student’s academic record, research preparation and a personal statement.

PhD funding will be available for up to three years to outstanding students though, exceptionally, this time period may be revised to cater for students whose research demands longer time periods.

DCU was also successful in the the first round of the Embark Postgraduate Research Scholarship Scheme, the results of which were announced in March. The following successful applicants to this scheme will be hosted in the Faculty of Engineering and Computing, (supervisor and School in parentheses):
Oisín Mac Fhearaí (Dr. Mark Humphrys - School of Computing)
Didier Roche (Prof Heather Ruskin - School of Computing)
Robert Ryan (Dr Markus Helfert - School of Computing)
Georgiana Dinu (Prof Josef van Genabith - School of Computing)
Lorna Fitzsimons (Dr Brian Corcoran - School of Mechanical & Manufacturing Engineering)
Stephen O'Brien (Prof Paul F Whelan - School of Electronic Engineering)

Congratulations to George Mitchell

Congratulations to George Mitchell who successfully defended his thesis and will be awarded the degree of PhD.

The title of George's thesis is "Evolutionary Computation Applied to Combinatorial Optimisation Problems ".

He completed his PhD at the school of Electronic Engineering's Artificial Life research group while under the supervision of Professor Barry McMullin. The Artificial Life research group forms part of the Research Institute for Networks and Communications Engineering (RINCE), a national centre for excellence in Information and Communications Technology (ICT) based at Dublin City University.


Brief description of Project:
George's thesis addresses the issues associated with conventional genetic algorithms (GA) when applied to hard optimisation problems. In particular it examines the problem of selecting and implementing appropriate genetic operators in order to meet the validity constraints for constrained optimisation problems. The problem selected is the travelling salesman problem (TSP), a well known NP-hard problem.

Following a review of conventional genetic algorithms, this thesis advocates the use of a repair technique for genetic algorithms: GeneRepair. George evaluated the effectiveness of this operator against a wide range of benchmark problems and compare these results with conventional genetic algorithm approaches. A comparison between GeneRepair and the conventional GA approaches was made in two forms: firstly a handcrafted approach compared GAs without repair against those using GeneRepair. A second automated approach was then presented which utilises a meta-genetic algorithm to examine different configurations of operators and parameters. Through the use of a cost/benefit (Quality-Time Tradeoff) function, the user can balance the computational effort against the quality of the solution and thus allow the user to specify exactly what the cost benefit point should be for the search.

Results identified the optimal configuration settings for solving selected TSP problems. These results show that GeneRepair when used consistently generates very good TSP solutions in an extremely efficient manner, in both time and number of evaluations required.

There are many areas in which the finding of this work could be applied. The findings have definite applications in permutation based problems found in mathematics, engineering and computer science contexts. There are many other areas of high complexity ( e.g. Synthetic Biology) which at present are in the early stages of research. One factor which inhibits research in these areas is the cost of computation, this factor could be addressed through the use of the techniques discovered in the course of this work.

Congratulations to Paul Ferguson

Congratulations to Paul Ferguson who successfully defended his thesis and will be awarded the degree of PhD.

The title of Paul's thesis is "Index Ordering by Query-Independent Measures".
He completed his PhD in the Centre for Digital Video Processing (CDVP), Adaptive Information Cluster (AIC) and the School of Computing, DCU under the supervision of Prof. Alan Smeaton.

Brief description of Project:
Rather than simply utilising more computing resources in order to perform retrieval on large text collections, we investigated ways in which to only search a limited amount of the collection at query-time, in order to speed up this retrieval process (as well as allowing retrieval to be carried out with limited computing resources). Although, in doing this we aimed to limit the loss in retrieval efficacy (in terms of accuracy of results).

In order to do this we identified a number of different query-independent measures that can approximate the documents' query-independent quality in the collection, rather than relying solely on a term-weighting approach to determine the importance of a document. In this way we can choose to limit the amount of information to search through, by eliminating the documents of lesser importance, which not only makes the search more efficient, but should also limit any loss in retrieval accuracy.

In our work we applied a number of traditional combination techniques, as well as machine learning approaches to the task of combining these measures together, to provide a more accurate overall measure. We also provided techniques to allow documents to be effectively eliminated from a sorted inverted index while retaining the same level of performance. This research should allow fast and effective retrieval of large collections of documents be carried out much more efficiently than using traditional retrieval techniques, while also retaining the same level of accuracy for a typical web user.

This project was generously funded by Science Foundation Ireland (SFI).

Congratulations to Ciarán Ó Conaire

Congratulations to Ciarán Ó Conaire who successfully defended his thesis and will be awarded the degree of PhD.

The title of Ciarán's thesis is "Adaptive detection and tracking using Multimodal Information".

He completed his PhD in the Centre for Digital Video Processing (CDVP), Adaptive Information Cluster (AIC) and the School of Electronic Engineering, DCU under the supervision of Dr. Noel E. O’Connor.

Brief description of Project:
The difficulty of fusing data from multiple sources of information for automated visual analysis was addressed in two areas of computer vision: adaptive detection and adaptive object tracking. The work on adaptive object detection explores a new paradigm in dynamic parameter selection, by selecting thresholds for object detection to maximise agreement between pairs of sources.

Object tracking, a complementary technique to object detection, was also explored in a multi-source context and an efficient framework for robust tracking, termed the Spatiogram Bank tracker, was proposed as a means to overcome the difficulties of traditional histogram-based tracking. While general frameworks for data fusion were developed, the fusion of thermal infrared video with standard visual video was specifically targeted. Potential applications include: pervasive human-computer interaction and automated surveillance.

This project was generously funded by the Irish Research Council for Science, Engineering and Technology (IRCSET) and Science Foundation Ireland (SFI).

Congratulations to Neil O'Hare

Congratulations to Neil O'Hare who successfully defended his thesis and will be awarded the degree of PhD.

The title of Neil's thesis is "Semi-Automatic Person-Annotation in Context-Aware Personal Photo Collections".

He completed his PhD in the Centre for Digital Video Processing (CDVP), Adaptive Information Cluster (AIC) and the School of Computing, DCU under the supervision of Prof. Alan Smeaton.

Brief description of Project:

The MediAssist system is a prototype context-aware photo management system that facilitates browsing, searching and semi-automatic annotation of personal photos. Within this system, we developed an approach to semi-automatic person-annotation in personal photo collections that facilitates the annotation of people in a batch manner by suggesting person names for photos as users interact with the system.

We developed person classification and retrieval techniques based on analysis of the context of photo capture, such as the time and location, in addition to analysis of the image content of the photo. We used classification techniques to suggest names for faces detected in photos, and retrieval techniques to suggest faces for a query name. We implemented the proposed techniques and integrated them into the interface of the MediAssist prototype photo management system.

We successfully evaluated our person classification and retrieval techniques using the real photo collections of a number of users, and we also successfully evaluated the interactive annotation system with a number of users.

This project was generously funded by Enterprise Ireland.