Interdisciplinary Projects (1) Modelling of Air Quality in Hong Kong - By E-Business Technology Institute The E-Business Technology Institute (ETI) is collaborating with the Environmental Protection Department, HKSAR, in a project that aims at studying the air quality in Hong Kong. The computationally-intensive modelling process is executed across the campus-wide grid testbed, which is mainly managed by the Computer Centre and the Department of Computer Science. The project intends to harness the power and flexibility offered by grid computing, whereas the tasks are partitioned and submitted to the distributed computing resources via the Globus Toolkit and standard grid protocols. (2) Whole Genome Alignment via Mutation-Sensitive Sequence Similarity - By H.L. Chan, N. Lu, and Dr. T.W. Lam Given the genomes (DNA) of two related species, the whole genome alignment problem is to locate regions on the genomes that possibly contain genes conserved over the two species. We study the optimization problems that attempt to uncover conserved genes with a global concern and also with the tolerance of noise. The clusters are used for analyzing data gathered from human and mouse chromosomes. (3) Approximate String Matching on DNA Sequences - By L.L. Cheng Searching similar patterns in DNA sequences is very important in bioinformatics. Since the sizes of DNA sequences are very large, using PC clusters is a practical solution for the problem. In our research, we proposed two parallel algorithms for searching similar patterns in DNA sequences. Both algorithms can improve the searching time well. (4) Simulation for the DNA Shuffling Experiment - By W.H. Hon and Dr. T.W. Lam DNA shuffling is a developed technique that allows accelerated and directed protein evolution in vitro. Some parameters in the DNA shuffling experiment (e.g. annealing temperature, fragments size, sequence identity, number of rounds etc.) greatly influence the efficiency of the experiment. However, the DNA shuffling experiment is time consuming and expensive, therefore it is not practical to find out the optimal setting by doing a lot of experiments. Hence, the demand for developing computer simulation program to predict the possible outcome of the experiment is increasing. (5) Robust Speech Recognition - By J. Wu and Dr. Q. Huo Robust Automatic Speech Recognition is a relatively new area which became a concern when technology began to be transferred from laboratory to field applications. Increasingly, as they are applied in real world applications, speech recognition systems must operate in situations where it is not possible to control the acoustic environment. This may result in a serious mismatch between the training and test conditions, which often causes a dramatic degradation in performance of these systems. Therefore, robustness is the most important factor limiting the application of speech recognition in real-life situations. Our research project is aiming to develop more advanced algorithms as well as the practical solutions to make the speech recognition performance insensitive to the environment change. (6) Parallel Simulation of Turbulent Flow Model - By Dr. C.H. Liu, Department of Mechanical Engineering To test and improve the numerical performance and parallel efficiency of a numerical turbulent flow model.