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Abstract-The advent of smart meters has automated the entire process of billing generation system over commercial energy usage which was previously done using digital meter. Although western countries practice its usage more, it is still unknown to many developing countries along with its power distribution. Hence, this paper reviews the working principle of smart meters along with the brief of basic operation description. It thoroughly investigates the implementation work towards algorithm design and techniques developed that are being carried out in last five years towards smart meters. The paper examines the various significant technology that has evolved to address the problems in smart meter e.g. performance improvement, energy efficiency, security factor, etc. Finally, a set of research gap is explored after scrutinizing the advantages and limitations of existing techniques followed by brief highlights of the feasible line of research to compensate the unaddressed problems associated with research work direction towards smart meters.

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In this paper, an optimal artificial neural network (ANN) controller for load frequency control (LFC) of a four-area interconnected power system with non-linearity is presented. A feed forward neural network with multi-layers and Bayesian regularization backpropagation (BRB) training function is used. This controller is designed on the basis of optimal control theory to overcome the problem of load frequency control as load changes in the power system. The system comprised of transfer function models of twothermal units, one nuclear unit and one hydro unit. The controller model is developed by considering generation rate constraint (GRC) of different units as a non-linearity. The typical system parameters obtained from IEEE press power engineering series and EPRI books. The robustness, effectiveness, and performance of the proposed optimal ANN controller for a step load change and random load change in the system is simulated through using MATLAB-Simulink. The time response characteristics are compared with that obtained from the proportional, integral and derivative (PID) controller and non-linear autoregressive-moving average (NARMA-L2) controller. The results show that the algorithm developed for proposed controller has a superiority in accuracy as compared to other two controllers.

Flexible AC Transmission System (FACTS), under different conditions, are being incorporated in a power system for the improvement of active power flow along with voltage at each bus. Among the many FACTS devices, Interline Power Flow Controller (IPFC) is one of its kind, that has the capability of controlling multi-transmission systems. In this paper, an Improved Power Injection Model (IPIM) of Interline Power Flow Controller (IPFC) has been introduced. This model was included to Newton-Raphson (NR) method of load flow solution. The proposed model is tested on the standard test systems. The results of standard 5-bus system, IEEE-14 bus system is presented for the purpose of demonstration.

Mammography is a specialized medical imaging that uses a low-dose x-ray system to examine the breasts. A mammogram is a mammography exam report that helps in the detection and diagnosis of breast diseases in women at an early stage. This project proposes to classify mammography breast scans into their respective classes and uses attention learning to localize the specific pixels of malignancy using a heat map overlay. The attention learning model is a standard encoder-decoder circuit wherein convolutional neural networks perform the encoding and recurrent neural networks perform the decoding. Convolutional neural networks enable feature extraction from the mammography scans which is thereafter fed into a recurrent neural network that focuses on the region of malignancy based on the weights assigned to the extracted features over a series of iterations during which the weights are continuously adjusted owing to the feedback received from the previous iteration or epoch. Mammography images are equalized, enhanced and augmented before extracting the features and assigning weights to them as a part of the data preprocessing procedures. This procedure would essentially help in tumor localization in case of breast cancers.

In this paper, decentralized control scheme for Load Frequency Control (LFC) problem in a two-area interconnected power system with Fuzzy Logic Controller (FLC) is presented and its performance is compared with that of Optimal Controller (OC) and Proportional-Integral-Derivative (PID) Controller used in the same power system. This control scheme is simulated in MATLAB-Simulink for a two-area interconnected power system consisting of two generating units with non-reheat turbines to highlight the performance in terms of robustness and optimality. The step response of these control schemes against step load change is analysed and compared.