Analytical Studies on Diffusion and lntermittency in Chaotic Maps

The study of simple chaotic maps for non-equilibrium processes in statistical physics has been one of the central themes in the theory of chaotic dynamical systems. Recently, many works have been carried out on deterministic diffusion in spatially extended one-dimensional maps This can be related to real physical systems such as Josephson junctions in the presence of microwave radiation and parametrically driven oscillators. Transport due to chaos is an important problem in Hamiltonian dynamics also. A recent approach is to evaluate the exact diffusion coefficient in terms of the periodic orbits of the system in the form of cycle expansions. But the fact is that the chaotic motion in such spatially extended maps has two complementary aspects- - diffusion and interrnittency. These are related to the time evolution of the probability density function which is approximately Gaussian by central limit theorem. It is noticed that the characteristic function method introduced by Fujisaka and his co-workers is a very powerful tool for analysing both these aspects of chaotic motion. The theory based on characteristic function actually provides a thermodynamic formalism for chaotic systems It can be applied to other types of chaos-induced diffusion also, such as the one arising in statistics of trajectory separation. It was noted that there is a close connection between cycle expansion technique and characteristic function method. It was found that this connection can be exploited to enhance the applicability of the cycle expansion technique. In this way, we found that cycle expansion can be used to analyse the probability density function in chaotic maps. In our research studies we have successfully applied the characteristic function method and cycle expansion technique for analysing some chaotic maps. We introduced in this connection, two classes of chaotic maps with variable shape by generalizing two types of maps well known in literature.

Meteorological aspects of an abnormal cooling event over Iran in April 2009

During the period from 12 to 15 April, 2009 nearly the entire Iran, apart from the southern border, experienced an advective cooling event. While winter freezing concerns are typical, the nature of this freezing event was unusual with respect to its date of occurrence and accompanying synoptic meteorological situation. To analyze the freezing event, the relevant meteorological data at multiple levels of the atmosphere were examined from the NCEP/ NCAR reanalysis dataset. The results showed that a polar vortex was responsible for the freezing event over the country extending southward extraordinarily in such a way that its ridge influenced most parts of Iran. This was recognized as an abnormal extension of a polar vortex in the recent years. The sea-level pressure fields indicated that a ridge of large-scale anticyclone centered over Black Sea extended southward and prevailed over most parts of Iran. This resulted in the formation of a severe cold air advection from high latitudes (Polar region) over Iran. During the study period, moisture pumping was observed from the Arabian Sea and Persian Gulf. The winds at 1000 hPa level blew with a magnitude of 10 m s-1 toward south in the region of convergence (between -2 9 10-6 s-1 and -12 9 10-6 s-1). The vertical profilesof temperature and humidity also indicated that the ICE structural icing occurred at multiple levels of the atmosphere, i.e, from 800 hPa through 400 hPa levels. In addition to the carburetor (or induction), icing occurred between 900 and 700 hPa levels in the selected radiosonde stations during the study period. In addition, the HYSPLIT backward trajectory model outputs were in quite good agreement with the observed synoptic features

Detection of Flock Movement in Spatio-Temporal Database using Clustering Techniques - an Experience

In this paper, moving flock patterns are mined from spatio- temporal datasets by incorporating a clustering algorithm. A flock is defined as the set of data that move together for a certain continuous amount of time. Finding out moving flock patterns using clustering algorithms is a potential method to find out frequent patterns of movement in large trajectory datasets. In this approach, SPatial clusteRing algoRithm thrOugh sWarm intelligence (SPARROW) is the clustering algorithm used. The advantage of using SPARROW algorithm is that it can effectively discover clusters of widely varying sizes and shapes from large databases. Variations of the proposed method are addressed and also the experimental results show that the problem of scalability and duplicate pattern formation is addressed. This method also reduces the number of patterns produced

Integrated Environment for Launch Vehicle Telemetry Data Management

The telemetry data processing operation intended for a given mission are pre-defined by an onboard telemetry configuration, mission trajectory and overall telemetry methodology have stabilized lately for ISRO vehicles. The given problem on telemetry data processing is reduced through hierarchical problem reduction whereby the sequencing of operations evolves as the control task and operations on data as the function task. The function task Input, Output and execution criteria are captured into tables which are examined by the control task and then schedules when the function task when the criteria is being met.