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This paper reports results of the estimation of dynamical invariants, namely Lyapunov exponents, dimension, and metric entropy for speech signals. Two optimality criteria from dynamical systems literature, namely singular value decomposition method and the redundancy method, are used to reconstruct state space trajectories of speech and make observations. The positive values of the largest Lyapunov exponent of speech signals in the form of phoneme articulations show the average exponential divergence of nearby trajectories in the reconstructed state space. The dimension of a time series is a measure of its complexity and gives bounds on the number of state space variables needed to model it. It is found that most speech signals in the form of phoneme articulations are low dimensional. For comparison, a statistical model of a speech time series is also used to estimate the correlation dimension. The second-order dynamical entropy (which is a lower bound of metric entropy) of speech time series is found to be positive. This independently corroborates the interpretation of the largest Lyapunov exponent results and gives an estimate of the predictability time.

In the corrole homologue, 6,11,16-triarylbipyricorrole, the bipyrrole unit is replaced by a 2,2'-bipyridine unit. This modification effectively alters the corrole N4 coordination sphere from the trianionic [(NH)3 N] to the monoanionic [N3NH] state. The newly formed monoanionic core stabilizes Zn(II) ions with enhanced emission properties. The enhanced emission was further utilized for metal ion sensing studies and exploited for the selective detection of Zn(II) ions.

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Annealing of flat cold rolled steel sheets is one of the critical operations, which significantly influences the final product quality and overall productivity of cold rolling mills. An integrated process model, with the capability of predicting spatial and temporal evolution of temperature, microstructure and mechanical properties in cold rolled coils during batch annealing, has been developed. The model is based on fundamental principles of heat transfer, microstructural evolution kinetics and microstructure-property correlation. The parameters for heat transfer are based on in plant experimentation, whereas the coefficients for microstructural kinetics and structure-property correlation were obtained from laboratory studies. The prediction capability of the model has been extensively validated through data collected from industrial operations. Details of the model formulation, its validation with the plant data and possible uses of such an integrated model are presented.

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