Computational Biology and Language

Current scientific research is characterized by increasing specialization, accumulating knowledge at a high speed due to parallel advances in a multitude of sub-disciplines. Recent estimates suggest that human knowledge doubles every two to three years – and with the advances in information and communication technologies, this wide body of scientific knowledge is available to anyone, anywhere, anytime. This may also be referred to as ambient intelligence – an environment characterized by plentiful and available knowledge. The bottleneck in utilizing this knowledge for specific applications is not accessing but assimilating the information and transforming it to suit the needs for a specific application. The increasingly specialized areas of scientific research often have the common goal of converting data into insight allowing the identification of solutions to scientific problems. Due to this common goal, there are strong parallels between different areas of applications that can be exploited and used to cross-fertilize different disciplines. For example, the same fundamental statistical methods are used extensively in speech and language processing, in materials science applications, in visual processing and in biomedicine. Each sub-discipline has found its own specialized methodologies making these statistical methods successful to the given application. The unification of specialized areas is possible because many different problems can share strong analogies, making the theories developed for one problem applicable to other areas of research. It is the goal of this paper to demonstrate the utility of merging two disparate areas of applications to advance scientific research. The merging process requires cross-disciplinary collaboration to allow maximal exploitation of advances in one sub-discipline for that of another. We will demonstrate this general concept with the specific example of merging language technologies and computational biology.

Urban Sprawl Pattern Analysis Using GIS

Urbanisation has evinced interest from a wide section of the society including experts, amateurs, and novices. The multidisciplinary scope of the subject invokes the interest from ecologists, to urban planners and civil engineers, to sociologists, to administrators and policy makers, students and finally the common man. With the development and infrastructure initiatives mostly around the urban centres, the impacts of urbanisation and sprawl would be on the environment and the natural resources. The wisdom lies in how effectively we plan the urban growth without - hampering the environment, excessively harnessing the natural resources and eventually disturbing the natural set-up. The research on these help urban residents and policymakers make informed decisions and take action to restore these resources before they are lost. Ultimately the power to balance the urban ecosystems rests with regional awareness, policies, administration practices, management issues and operational problems. This publication on urban systems is aimed at helping scientists, policy makers, engineers, urban planners and ultimately the common man to visualise how towns and cities grow over a period of time based on investigations in the regions around the highway and cities. Two important highways in Karnataka, South India, viz., Bangalore - Mysore highway and the Mangalore - Udupi highway, in Karnataka and the Tiruchirapalli - Tanjavore - Kumbakonam triangular road network in Tamil Nadu, South India, were considered in this investigation. Geographic Information System and Remote Sensing data were used to analyse the pattern of urbanisation. This was coupled with the spatial and temporal data from the Survey of India toposheets (for 1972), satellite imageries procured from National Remote Sensing Agency (NRSA) (LANDSAT TM for 1987 and IRS LISS III for 1999), demographic details from the Census of India (1971, 1981, 1991 and 2001) and the village maps from the Directorate of Survey Settlements and Land Records, Government of Karnataka. All this enabled in quantifying the increase in the built-up area for nearly three decades. With intent of identifying the potential sprawl zones, this could be modelled and projected for the future decades. Apart from these the study could quantify some of the metrics that could be used in the study of urban sprawl.