The aim of indoor localization is to locate the objects inside a location wirelessly. This paper reports the models that predict the location along with floor and coordinates from the WAPs (Web Access Points) signal strengths of a user who connects to the internet at a specific location which had three locations. Starting with the cleaning of data, then assigning attributes into proper data types, making subset of dataset for each location, examining each column, and normalizing WAPs rows in order to build models. Different algorithms have been used to predict the location, floor, and coordinates of a logged in user. The models that have been used in this paper are k-Nearest Neighbor (k-NN) for location prediction, random forest for floor prediction and regression with k-NN for coordinate prediction.

In this paper, a technique for optical performance monitoring (OPM) using deep learning-based artificial neural network (ANN) is implemented. ANN is trained with parameters derived from eye-diagram for the identification of optical signal to noise ratio (OSNR), chromatic dispersion (CD) and polarisation mode dispersion (PMD) simultaneously and independently in a 10 Gb/s system with non-return-to-zero (NRZ) on-off keying (OOK) data signal. ANN-based OPM confirms that the proposed approach can provide reliable estimated results. The mean squared errors for OSNR, CD and differential group delay (DGD) are found to be 4.6071 dB, 0.0417 ps/nm/km and 0.0016 ps/km, respectively. The proposed technique may be utilized in analyzing the signals of future heterogeneous optical communication networks intelligently.

AC motors are predominant in the field of industrial Drives. They are widely used because of their efficiency, less maintenance, construction. Speed control is a vital requirement of industries today. The parameter which takes the features of the induction motor away from the DC motor is its speed control. The speed control of AC Motors by conventional method of pole changing, voltage and frequency are very complicated and require more time and are less efficient or expensive techniques. So in industrial applications where variable speed is required, an easy and quick speed control method has to be employed. Here we are going to make use of 89C51 microcontrollers along with electronic circuitry to regulate the speed and retrieve the real time speed on the digital display screen. The speed measurement will be employed by means of infrared transmitter and receiver. Depending on rotation a pulse will be generated and measured by the microcontroller. This ensures quick speed regulation as per the requirement of the user the firing angle is changed to regulate the speed. This would give the use of real time data of running motor; this will prevent the operator from reaching the maximum speed. Thus overall technique will provide reliable and flexible control on the motor.

A technique for the estimation of an optical signal-to-noise ratio (OSNR) using machine learning algorithms has been proposed. The algorithms are trained with parameters derived from eye-diagram via simulation in 10 Gb/s On-Off Keying (OOK) nonreturn-to-zero (NRZ) data signal. The performance of different machine learning (ML) techniques namely, multiple linear regression, random forest, and K-nearest neighbor (K-NN) for OSNR estimation in terms of mean square error and R-squared value has been compared. The proposed methods may be useful for intelligent signal analysis in a test instrument and to monitor optical performance.