Browsing by Author "Mosisa, Abdi"

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    Lightweight Neural Networks for Context Aware Autonomous Embedded System Development
    (Addis Ababa University, 2020-02-02) Mosisa, Abdi; Lemma, Dagmawi (PhD)
    An embedded system is a microcontroller or microprocessor-based system which is designed to perform a specific task by collecting, processing and communicating information. While focusing on specific task, it is also desired to make such system for better and efficient result. In due course, one of the challenges is contextualizing the collected information to predict the output and making smart decision to produce the output. The learning system that can contextualize the surrounding environment should have a capability of automatic mechanism of inferring information like humans do. This calls for neural networks that provide an embedded intelligence for smart systems to make decisions at machine speed. The main challenge to develop such system is the constraints in memory size, computational power and other characteristics of embedded system that can significantly restrict developers from implementing learning algorithms to solve the problem. This thesis presents lightweight neural networks so as to show a method for implementing context-aware embedded system in environment where there is resource limitation. A testbed is setup for collecting the data, training and evaluation. Arduino board is investigated as a main experimental device for the proposed algorithms. The algorithms are simulated using C on Arduino. A good result was obtained after deploying the algorithm and knowledgebase on arduino board for sensor reading.
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    Lightweight Neural Networks for Context Aware Autonomous Embedded System Development
    (Addis Ababa University, 2/2/2020) Mosisa, Abdi; Lemma, Dagmawi (PhD)
    An embedded system is a microcontroller or microprocessor-based system which is designed to perform a specific task by collecting, processing and communicating information. While focusing on specific task, it is also desired to make such system for better and efficient result. In due course, one of the challenges is contextualizing the collected information to predict the output and making smart decision to produce the output. The learning system that can contextualize the surrounding environment should have a capability of automatic mechanism of inferring information like humans do. This calls for neural networks that provide an embedded intelligence for smart systems to make decisions at machine speed. The main challenge to develop such system is the constraints in memory size, computational power and other characteristics of embedded system that can significantly restrict developers from implementing learning algorithms to solve the problem. This thesis presents lightweight neural networks so as to show a method for implementing context-aware embedded system in environment where there is resource limitation. A testbed is setup for collecting the data, training and evaluation. Arduino board is investigated as a main experimental device for the proposed algorithms. The algorithms are simulated using C on Arduino. A good result was obtained after deploying the algorithm and knowledgebase on arduino board for sensor reading.