Genetic algorithms(GA) is optimization technique used in the equivalent load test of the induction motors to select the values of the factors(modulation indicators) that effect on the performance properties in terms of the values of the currents and the total loss within the machine. One way to choose these parameters is by trial and error while this paper based on GA method to improve the parameters selection. A model is designed to simulate the loading of the induction motor and obtain its own results by the MATLAB program 2017a. There are different methods used to achieve this task such as PWM inverter with different modulation techniques, Constant Voltage Variable Frequency (CVVF) method, Variable Voltage Constant Frequency (VVCF) method and Variable Voltage Variable Frequency (VVVF) method.

This project is aimed at developing a prototype wireless charging system that allows for charging convenience using wireless charging also called inductive charging. This allows charging to be made easier and free from connecting leads between charger and handset.
In order to successfully achieve this task, the design of the prototype was developed in two parts, a transmitter with resonator and a receiver with resonator. The transmitter converts DC voltage to AC, and transfers energy wirelessly through an LC resonator to the receiver through mutual induction, and hence induces EMF in the receiver. The EMF is rectified, filtered and regulated and output is used to charge mobile phones.
Testing results and measurements taken on this prototype shows that significant amount of EMF can be induced at receiver through air coupled transformers due to EM waves at high frequencies. The charging distance is 3cm maximum with end-to-end coupling.

Proposed Solar tracker minimizing the angle of incidence and solar panel position towards the sun to achieve the maximum solar irradiation and strength so that it can produced the maximum output (electricity).

Agriculture is the backbone of several countries like India and most of the economy depends directly or indirectly on agriculture for a living. But these days several crops are significantly low and farmers are difficult to analyze the variations of soil moisture on yield in every day. The solution of the mentioned problem is to place the sensors in the yield and to collect the readings of moisture in every day by implementing the technologies: Internet of Things (IoT), Cloud Computing technology and GSM (Global Service Message) communication. In this paper, explain how these technologies and GSM will help the farmers in areas where there is an acute water problem or where water conservation is essential. The system is used Wi-Fi and GSM-based communications to send the sensor data to Cloud and mobile phones. The hardware is developed using TI’s First Wi-Fi on-chip microcontroller: CC3200 LaunchPad, SIM900A GSM module, soil moisture sensor, motor and mobile phone. The software is developed using Energia IDE. Thing Speak Cloud Computing technology is used in this work for storing the sensor values in. CSV format and also shows the graphical representation of the real-time variation of moisture on mobile using mobile application and Laptop, SIM 900A module is used sent the moisture levels to the farmer’s registered mobile numbers. The system also automatically turns ON/OFF the motor based on the variation of moisture on yields. The whole system is easily implemented on yields and gets the moisture information even in remote areas in the world.

There is paucity of information on the possibility of ethnicity identification through fingerprint biometric characteristics and so, this work is set to combine two soft biometric traits (Gender and Ethnicity) in order to ascertain if individual of different ethnicity and gender bias can be identified through their fingerprint. Live scan mechanism was used in order to minimize human errors and as well speed up the rate of fingerprint acquisition which unequivocally ensure good quality capturing of the fingerprint image.
In this work, fingerprints of over a thousand people from three different ethnic groups of both male and female gender in Nigeria were captured and subjected to training, testing and classification using Gabor filter and K-NN respectively. Histogram equalization was used for image enhancement and the system performance was evaluated on the basis of some selected metrics such as Recognition Accuracy, Average Recognition Time, Specificity and Sensitivity. Result of this work indicated over 96% accuracy in predicting person’s ethnicity and gender with an average recognition time of less than 2secs.