Engineering Science - Undergraduate Theses

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Deep Learning for Satellite Image Analysis

Author: 
Peer reviewed: 
No, item is not peer reviewed.
Date created: 
2017-12-18
Abstract: 

Deep learning architectures have the potential of saving the world from losing football fieldsized forest areas each second. These architectures possess large learning capacities when compared to conventional machine learning architectures, and thus are trained on sizable data-sets to efficiently extract both coarse and fine features from various image scenes. As a result, they can provide crucial information that is needed to manage the deforestation process and its consequences on the environment and ecosystem more effectively. This thesis outlines the two deep learning based systems designed for satellite image analysis. The first system analyzed satellite images of the Amazon, and the goal was to interpret the image content by providing a set of labels that best describe it. The highest performing architecture was able to achieve a score of 92.886% while a combination of several high performance, yet uncorrelated, architectures increased the overall score to 93.070%. This result is only 0.248% lower than what current state of the art algorithms achieved on the same task. The second system was designed to detect the presence of clouds in Landsat 8 images by analyzing small chips within each large image. This system produced cloud masks, which were then compared to the corresponding ground truth cloud masks obtained from the provided images. The predicted cloud masks were able to achieve an average score of 92.931%, which is very high for the given accuracy measure.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Ivan V. Bajić
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
Honours Bachelor of Applied Science

Implementation of Active Noise Cancellation in a Duct

Author: 
Peer reviewed: 
No, item is not peer reviewed.
Date created: 
2013-11-15
Abstract: 

An Active Noise Cancellation (ANC) system is implemented in real time using both feed forward LFXLMS (Leaky filtered-X least-mean-square) and feedback LFXLMS approaches for adaptive filtering. ANC algorithms are implemented on a ADAU1446 evaluation board and tested in terms of sound cancellation in a duct. The hardware and software interfaces required for the system are explained in detail. A test bed is developed to measure the performance of sound cancellation. Results are analysed in detail and recommendations are made for future research work to improve the performance of the system and to realize noise cancellation in 3D space.

Document type: 
Thesis
File(s):