School of Engineering (SEN)

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Browsing School of Engineering (SEN) by Author "Abdul Wahab"

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    Metal detecting robot using RF communication
    (University of Management and Technology, 2012) Farooq khaliq; khawaja Salman Rasheed; Abdul Wahab; Arslan Mahmood
    For many people robot is a machine that imitates a human—like the androids in Star Wars, Terminator and Star Trek: The Next Generation. However much these robots capture our imagination, such robots still only inhabit Science Fiction. People still haven't been able to give a robot enough 'common sense' to reliably interact with a dynamic world. The type of robots that you will encounter most frequently are robots that do work that is too dangerous, boring, onerous, or just plain nasty. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. In fact, there are over a million of these types of robots working for us today. This robot is controlled by a RF r emote. This can be moved forward and reverse direction using geared motors of 60RPM. Also this robot can take sharp turnings towards left and right directions. This project uses PIC18F452 as its controller. Simultaneously the images around the robot will be transmitted to remote place. User can monitor the images and metal detection alarms on Television. The RF modules used here are STT-433 MHz Transmitter, STR-433 MHz Receiver, PT2262 RF Encoder and PT2272 RF Decoder. The three switches are interfaced to the RF transmitter through RF Encoder. The encoder continuously reads the status of the switches, passes the data to the RF transmitter and the transmitter transmits the data. This project uses 9V battery. This project is much useful for detection and surveillance applications.
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    Race Classification through Convolutional Neural Network
    (University of Management & Technology, 2017) Talha Mahboob Alam; Talha Imtiaz Baig; Abdul Wahab; Malik Furqan Zahid
    Neural networks are a powerful technology to classify different images. However, there are impressive number of different types of neural networks that are used in the literature and in industry but we used Convolutional neural network (CNN) to classify the images. The biometric framework can utilize race to distinguish individuals in the world with a precise personality. This research proposes a design to order individuals into two races Asian and Non-Asian that effectively in any face acknowledgment framework can be incorporated. The proposed procedure takes in the assigned essential characteristic of the face, skin color pattern and other secondary feature from training of images in order to effectively classify races. We use CNN to create a system that classifies facial images that are based on a variety of different facial attributes and classify it into two separate classes. We use 3 convolutional layers. We used 3052 training images of 64*64 pixels and we achieve 85% accuracy.