Artificial intelligence monitoring at the edge for smart nation deployment
Machine learning and computer vision have become closely related in the recent years. With the vast amount of data generated in this heavily digitalized century, the advancement of machine learning field is growing exponentially. The ability to utilize data to categories and predict pattern made mac...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2020
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/141069 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-141069 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1410692023-07-07T18:41:23Z Artificial intelligence monitoring at the edge for smart nation deployment Lim, Shi Yong Gan Woon Seng School of Electrical and Electronic Engineering Smart Nation TRANS Lab EWSGAN@ntu.edu.sg Engineering::Electrical and electronic engineering Machine learning and computer vision have become closely related in the recent years. With the vast amount of data generated in this heavily digitalized century, the advancement of machine learning field is growing exponentially. The ability to utilize data to categories and predict pattern made machine learning a powerful tool to solve problems in many fields. In the field of computer vision, machine learning helps to improve recognition and tracking. In this paper, we will discuss the possibility of using machine learning technique to implement Direction of Arrival (DOA) estimation in embedded system to detect the source of audio signal. This project consists of mainly two parts, data acquisition and creating machine learning model for estimation. The performance is evaluated with the accuracy outcome of the model to estimate the angel of audio played in the environment. Bachelor of Engineering (Information Engineering and Media) 2020-06-04T00:16:25Z 2020-06-04T00:16:25Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/141069 en A3092-91 application/pdf Nanyang Technological University |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Engineering::Electrical and electronic engineering |
spellingShingle |
Engineering::Electrical and electronic engineering Lim, Shi Yong Artificial intelligence monitoring at the edge for smart nation deployment |
description |
Machine learning and computer vision have become closely related in the recent years. With the vast amount of data generated in this heavily digitalized century, the advancement of machine learning field is growing exponentially. The ability to utilize data to categories and predict pattern made machine learning a powerful tool to solve problems in many fields. In the field of computer vision, machine learning helps to improve recognition and tracking. In this paper, we will discuss the possibility of using machine learning technique to implement Direction of Arrival (DOA) estimation in embedded system to detect the source of audio signal. This project consists of mainly two parts, data acquisition and creating machine learning model for estimation. The performance is evaluated with the accuracy outcome of the model to estimate the angel of audio played in the environment. |
author2 |
Gan Woon Seng |
author_facet |
Gan Woon Seng Lim, Shi Yong |
format |
Final Year Project |
author |
Lim, Shi Yong |
author_sort |
Lim, Shi Yong |
title |
Artificial intelligence monitoring at the edge for smart nation deployment |
title_short |
Artificial intelligence monitoring at the edge for smart nation deployment |
title_full |
Artificial intelligence monitoring at the edge for smart nation deployment |
title_fullStr |
Artificial intelligence monitoring at the edge for smart nation deployment |
title_full_unstemmed |
Artificial intelligence monitoring at the edge for smart nation deployment |
title_sort |
artificial intelligence monitoring at the edge for smart nation deployment |
publisher |
Nanyang Technological University |
publishDate |
2020 |
url |
https://hdl.handle.net/10356/141069 |
_version_ |
1772827204379475968 |