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Background/objectives: The objective of the study is to increase the resolution and radiate sharper beam towards the user in mobile communication using Smart Antenna. Methodology: The Conventional and Subspace Algorithms from the literature are studied and simulated in MATLAB so that the foundation is laid for better detection of algorithms and radiation formation. The results are explained for varying number of antenna elements and mobiles sources placed close to far. Findings: The classical direction of arrival algorithms namely CAPCON, Maximum Entropy Method, Maximum Likelihood Method are used to find the direction of mobile users based on the computation of the power spectrum. Several methods namely Least Mean Square, Griffiths Method, Variable Step Size Griffiths and Recursive Least Square are used to form the main beam for the user detected by the direction of arrival algorithms. Novelty/improvements: In order to take further the research on enhancing the resolution and having a higher convergence rate with reasonable step size, this paper presents the well-known conventional and modern algorithms from the literature. The results are simulated are well described for performing parameters.

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The classification of remotely sensed data on thematic map is a challenging task from very long time and it is also a goal of today’s remote sensing because of complexity level of earth surface and selection of suitable classification technique. Hence selection of best classification technique in remote sensing will give better result. Classification of remotely sensed data is an important task within the domain of remote sensing and it is outlined as processing technique that uses a systematic approach to group the pixels into different classes. In this study, we have classified the multispectral data of Udupi district, Karnataka, India using different classifier including Support Vector Machine (SVM), Maximum Likelihood, Minimum Distance and Mahalanobis Distance classifier. The data of dimension 3980x3201 pixels are collected from a Landsat-3 satellite. Performance of the each classifier is compared by conducting accuracy assessment test and Kappa analysis. The obtained results shows that SVM will give accuracy of 95.35% and kappa value of 0.9408 respectively when compared other classifier, hence effectiveness of SVM is a good choice for classifying remotely sensed data.

The power generation using solar photovoltaic (PV) system in microgrid requires energy storage system due to their dilute and intermittent nature. The system requires efficient control techniques to ensure the reliable operation of the microgrid. This work presents dynamic power management using a decentralized approach. The control techniques in microgrid including droop controllers in cascade with proportional-integral (PI) controllers for voltage stability and power balance have few limitations. PI controllers alone will not ensure microgrid’s stability. Their parameters cannot be optimized for varying demand and have a slow transient response which increases the settling time. The droop controllers have lower efficiency. The load power variation and steady-state voltage error make the droop control ineffective. This paper presents a control scheme for dynamic power management by incorporating the combined PI and hysteresis controller (CPIHC) technique. The system becomes robust, performs well under varying demand conditions, and shows a faster dynamic response. The proposed DC microgrid has solar PV as an energy source, a lead-acid battery as the energy storage system, constant and dynamic loads. The simulation results show the proposed CPIHC technique efficiently manages the dynamic power, regulates DC link voltage and battery’s state of charge (SoC) compared to conventional combined PI and droop controller (CPIDC).