This study aims to create a sorting system with high accuracy that can classify various beverage containers based on types and separate them accordingly. This reverse vending machine (RVM) provides an image classification method and allows for recycling three types of beverage containers: drink carton boxes, polyethylene terephthalate (PET) bottles, and aluminium cans. The image classification method used in this project is transfer learning with convolutional neural networks (CNN). AlexNet, GoogLeNet, DenseNet201, InceptionResNetV2, InceptionV3, MobileNetV2, XceptionNet, ShuffleNet, ResNet 18, ResNet 50, and ResNet 101 are the neural networks that used in this project. This project will compare the F1-score and computational time among the eleven networks. The F1-score and computational time of image classification differs for each neural network. In this project, the AlexNet network gave the best F1-score, 97.50% with the shortest computational time, 2229.235 s among the eleven neural networks.

This project aims to develop a vision system that can detect traffic lightcounter and to recognise the numbers shown on it. The system used you onlylook once version 3 (YOLOv3) algorithm because of its robust performanceand reliability and able to be implemented in Nvidia Jetson nano kit. A totalof 2204 images consisting of numbers from 0-9 green and 0-9 red. Another80% (1764) from the images are used for training and 20% (440) are used fortesting. The results obtained from the training demonstrated Totalprecision=89%, Recall=99.2%, F1 score=70%, intersection over union(IoU)=70.49%, mean average precision (mAp)=87.89%, Accuracy=99.2%and the estimate total confidence rate for red and green are 98.4% and 99.3%respectively. The results were compared with the previous YOLOv5algorithm, and the results are substantially close to each other as the YOLOv5accuracy and recall at 97.5% and 97.5% respectively.

Recently, Vector Control also known as Field Oriented Control used in AC induction motor drive provides us of a way to control AC induction motor similar to that of a DC motor. This objective is achieved by transforming the time-varying, difficult to control stator currents into a simple time-invariant system by means of coordinate transformations. This in turn provides us with a systematical way towards designing a controller using classical control or modern state-space design methodologies. Purpose of this research is to use the latter in designing a controller towards regulating current responsible for torque response. A non-linear model of the AC Induction Motor is modeled in the rotating (d,q) reference frame for the control purposes. Then, a state feedback linearization controller was design based on the idea of “exact linearization” to transform the non-linear model into linear state-space model, thus enabling controller design using modern state-space approach. A Linear Quadratic optimal controller and Feedback+Feedforward controller is then designed and applied to the linearized induction motor model. For comparison purposes a classical P/PI controller was also designed. Simulation is then carried out using MATLAB/SIMULINK software and results shows good current regulation by controller design using modern state –space methodologies.