Browsing by Author "Ahmad Bilal"

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    Automation of high efficiency irrigation method with data logging
    (UNIVERSITY OF MANAGEMENT AND TECHNOLOGY, 2015) Ahmad Bilal; M Humza Sarfaraz; Nouman Ahmad
    The project is designed to develop an automatic operation of a high efficiency irrigation system which is drip irrigation system. The designed system is configured to drive the watering pump functioning i.e. when to switch on when to switch off. All this is done in accordance to the data values evaluated by our sensor network that comprises of soil moisture sensors, temperature sensor, humidity sensor and rain sensing pad. In the field of agriculture, the use of appropriate irrigation method is very important. The advantage of using this method is to reduce water losses, energy consumption, human intervention and still ensure proper watering.
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    System to Translate English Video or Audio to Equivalent PSL Sentences
    (UMT, Lahore, 2016) Ahmad Bilal
    Population of deaf and hearing impaired personnel is increasing day by day and communication gap between hearing impaired and unimpaired is larger than ever. Very few efforts have been done for Pakistan to bridge this gap, this research is intended to facilitate deaf community of Pakistan by proposing grammar based conversion system for English and Urdu speech from a media file to equivalent Pakistan Sign Language sentences. For this, we have used API’s and services like Google Speech Recognition to recognize words spoken in the media file, Google Translation to translate Urdu to English, Machine Learning based python script to punctuate English transcript, Google Natural Language services to Extract Sentences from Transcript and our English to Pakistan Sign Language translation module for conversion to English Sentences to equivalent Pakistan Sign Language Sentences. Our system can recognize audio from multiple video and audio sources which enables us to use this system on vast scale. We gathered data, audio/video sentences and stories, to validate and improve accuracy of our system. With use of well-developed and intelligent services we were able to achieve 99.30% accuracy in case of sentence recognition and 94.25% accuracy in case to story recognition.