Behavioural Competency Management with special reference to Commercial Banks headquartered in Kerala

This study aims to analyze, compare and contrast the behavioral competency of officials in commercial banks headquartered in Kerala. This is done by analyzing the soft skills/behavioral skills possessed by an individual employee in both clerical and managerial levels and the means adopted to enhance their said skills in near future. The study was conducted with the objective of analyzing the behavioral competency of the managers and clerical staff in the commercial banks headquartered in Kerala. The researcher has gone through the available literature with respect to employee competency, job satisfaction and employee performance evaluation to formulate the problem and conceptualize the framework of the study. The study concluded that the competency of the employees differs from one bank to the other but strengthening the employees’ competency is the only possible solution by which the banks can determine their future growth prospects. Only through competency, banks can achieve high level of performance especially under the globalised situation.

Effect of Gender-role Orientations and Communication Styles on Executive Performance

Biosocial profile can produce variations in Gender-role Orientation of executives. Biosocial variables are not responsible for the development of Communication Style except in cases of number of children, dual career family and fathers occupation. Gender-role orientation is a function of Communication Style. Executive performance is a function of Communication Style.Gender- role orientation can have a decisive influence on executive performance. The cumulative effect of Communication Style and gender role orientation can produce variations in executive performance. Open Communication Style is predominantly responsible for the creation of a higher level executive performance than other Communication Styles.

Development and Evaluation of Blind Identification Techniques for Nonlinear Systems

Identification and Control of Non‐linear dynamical systems are challenging problems to the control engineers.The topic is equally relevant in communication,weather prediction ,bio medical systems and even in social systems,where nonlinearity is an integral part of the system behavior.Most of the real world systems are nonlinear in nature and wide applications are there for nonlinear system identification/modeling.The basic approach in analyzing the nonlinear systems is to build a model from known behavior manifest in the form of system output.The problem of modeling boils down to computing a suitably parameterized model,representing the process.The parameters of the model are adjusted to optimize a performanace function,based on error between the given process output and identified process/model output.While the linear system identification is well established with many classical approaches,most of those methods cannot be directly applied for nonlinear system identification.The problem becomes more complex if the system is completely unknown but only the output time series is available.Blind recognition problem is the direct consequence of such a situation.The thesis concentrates on such problems.Capability of Artificial Neural Networks to approximate many nonlinear input-output maps makes it predominantly suitable for building a function for the identification of nonlinear systems,where only the time series is available.The literature is rich with a variety of algorithms to train the Neural Network model.A comprehensive study of the computation of the model parameters,using the different algorithms and the comparison among them to choose the best technique is still a demanding requirement from practical system designers,which is not available in a concise form in the literature.The thesis is thus an attempt to develop and evaluate some of the well known algorithms and propose some new techniques,in the context of Blind recognition of nonlinear systems.It also attempts to establish the relative merits and demerits of the different approaches.comprehensiveness is achieved in utilizing the benefits of well known evaluation techniques from statistics. The study concludes by providing the results of implementation of the currently available and modified versions and newly introduced techniques for nonlinear blind system modeling followed by a comparison of their performance.It is expected that,such comprehensive study and the comparison process can be of great relevance in many fields including chemical,electrical,biological,financial and weather data analysis.Further the results reported would be of immense help for practical system designers and analysts in selecting the most appropriate method based on the goodness of the model for the particular context.

An efficient scheme for the evaluation of coding performance of high speed links incorporating joint error behaviour based on Monte Carlo Method

While channel coding is a standard method of improving a system’s energy efficiency in digital communications, its practice does not extend to high-speed links. Increasing demands in network speeds are placing a large burden on the energy efficiency of high-speed links and render the benefit of channel coding for these systems a timely subject. The low error rates of interest and the presence of residual intersymbol interference (ISI) caused by hardware constraints impede the analysis and simulation of coded high-speed links. Focusing on the residual ISI and combined noise as the dominant error mechanisms, this paper analyses error correlation through concepts of error region, channel signature, and correlation distance. This framework provides a deeper insight into joint error behaviours in high-speed links, extends the range of statistical simulation for coded high-speed links, and provides a case against the use of biased Monte Carlo methods in this setting