Final Year Projects (FYPs) - Undergraduates

Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/43

This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.

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Modeling, Controlling Of Automatic Washing Machine
(COMSATS University Islambad Lahore Campus, 2020) Salman Chaudhary , Syed Ahsan Raza Shamsi,; SP15-BTE-006 , SP15-BTE-044; Dr. Muhammad Yaqoob Javed, Assistant Profesor [Supervisor]; LHR TP 5908
This project presents a design of automatic washing machine which is controlled using programmable logic controller (PLC). PLC is used to control the system which have specific functions. Basic PLC functioning like timing, sequence, controlling and relaying were implemented. The hardware contains one agitator and shell tub. A motor is used to run the agitator in either direction required as per sequence programming. solenoid valves are used for water inlet and drain out. Operation of these devices is completely automated using PLC
Neural Network Based Gesture Identification System
(COMSATS University Islamabad, Lahore Campus Library Information Services, CUI Lahore, 2020) By: Zoolnurain , Anas Idrees , Ali Raza, Contributor; FA15-EEE-033 , FA15-EEE-032 , FA15-EEE-002; Muhammad Usman Rafique,; LHR TP 5864
Physically disabled individuals like deaf, mute and patients suffering from various disabilities require an effective communication device to make them independent. Traditionally flex sensors-based gloves have been used to identify the gestures of Sign languages. Current work is limited to only one glove used for capturing the gesture. ,Gesture recognition in the 3D environment has been a challenging task. The problem can be solved using Machine Learning techniques to separate the true gesture from the false gesture. Therefore, the aim of this project is to develop a portable universal communication device to assist patients with disabilities and provide them with better standards of living. One of the goals of this project is to implement and compare the performance of Neural networks to identify the true gesture. The system will use sign language (gestures identification from flex sensors) to communicate with people around them. The data will then be analysed in Matlab based machine learning environment to identify the performance of Machine learning algorithms. This proposal will also look into the feasibility of implementing machine learning algorithms in Android phones for gesture recognition.