Modelling & Scientific Computing @ School of Computing, DCU, Ireland



School of Computing, Dublin City University, Ireland

MODELLING & SCIENTIFIC COMPUTING


  
Home     About the Group     Researchers     Publications     Seminars     Events / Conf. Inputs
Research:    Social Systems // Environmental and Transport // Financial and Socioeconomic Modelling // Assisted Living // Biocomputation // Data Science
  

 

Data Science :
Quality, Analytics, Pattern Matching & Visualisation, HEC

The predominant goal of Pattern Recognition is the classification of objects into a number of categories or classes. Patterns can arise from a wide range of phenomena, e.g. images, signal waveforms, microarray data, financial market movements and so on. To a great extent, the ability to perceive, measure and interpret patterns goes hand-in-hand with the ability to form a mental or physical image of them, so that visualisation (or the visualisation tool in computer terms) is often referred to in the same breath. While attractive, the image is only part of the story, however, and the modelling techniques are the core. Pattern Recognition has a long history, which is rooted in statistical theory, but in the latter part of the 20th century, computational advances enormously increased the demand for practical applications of pattern recognition and these have in turn stimulated new theoretical developments. For example, Pattern Recognition is an integral part in most machine intelligence systems built for decision-making. Similarly, machine vision is an area of key importance and is used for a variety of tasks, such as finding "defects" in automated quality processes, determining "hot spots" in medical diagnosis, interpretation of gesture and speech and so on. Areas of key interest to group members include recognition of hand gestures from sign language for the Deaf such as ISL (Irish Sign Language), Spatio-temporal Gesture recognition and Human-Computer Natural Interfaces. In the work on ISL, for example, images are blurred at different stages in order to make a hierarchical database of shapes and a Hidden Markov Model uses the database information to extract the shapes for recognition. In HCI, and in order to interact with computers with no physical connection, a gesture recognition system is required.
  



Image credit: (1), (2) & (3) - Machine Vision Section, (4) - U.S. Department of Energy Genomes to Life Program, http://doegenomestolife.org

Additionally, and very recently, the successful mapping of the Human Genome has increased the already-rapid rate of advance in molecular biology and has spawned a number of new inter-disciplinary areas. While widely-used misnomers, such as Bioinformatics, suggest that most of the work is in database and information retrieval, the impact on inter-disciplinary science is, in fact, far greater. The field of microarray data analysis, for example, has been around for less than ten years and is the focus of large and growing efforts of statisticians, biologists and others. Large-scale, high throughput assays may involve parallel collection, systematic and random error analysis and of course classification and interpretation of gene expression data. Pattern recognition and visualisation have a major role to play in new biological experimentation methods.

Biocomputation and Models, Microarray, GA etc. - H.J. Ruskin, M. Crane, A. Barat
ISL, Gesture Recognition and MV Section - A. Sutherland, A. Dehghani

Selected Group Publications:

  • Li, N., Crane, M. and Ruskin, H.J. (2013). Visual Experience for Recognising Human Activities, Communications in Computer and Information Science, Evaluating AAL Systems Through Competitive Benchmarking, vol: 362, pp. 173-185, 2013. Springer Berlin Heidelberg, ISBN: 978-3-642-37418-0.

  • Marbach D., Costello, J.C., Kffner, R., Vega, N., Prill, R.J., Camacho, D.M., Allison, K.R., the DREAM5 Consortium, Kellis, M., Collins, J.J., and Stolovitzky, G. Contributors: A.Sibru, Crane M. and Ruskin H.J. (2012). Nature Methods 9, 796804.

  • Srbu, A., Kerr, G., Crane, M. and Ruskin, H. J. (2012). PLoS ONE 7(12): e50986

  • Barat A., Ruskin H.J. (2010) The Open Colorectal Cancer Journal, 3, pp36-46, 2010.

  • Conlon, T., Ruskin, H.J., Crane, M. (2009). Physica A, 388, 705-714, 2009.

    Researchers: Martin Crane, Heather J. Ruskin, Ana Barat, John Burns (Assoc.), Markus Helfert and affiliated Business Informatics/ Analytics Group(BIG) Alistair Sutherland and affiliated 3D Vision Group Andrew McCarren, Marija Bezbradica, Irina Roznovăţ (Assoc.) Na Li, Hourieh Hamrah


  •    

    Contact: +353 1 700 6747 / modsci@computing.dcu.ie