Artificial intelligence monitoring at the edge for smart nation deployment

A key aspect of Smart Nation is the collection of data with the use of live monitoring, which is then sent for analysis using artificial intelligence algorithms to extract the desired information. This project aims to improve the model training process using data augmentation via mixing noise int...

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Bibliographic Details
Main Author: Chua, Jeremy Chin Yew
Other Authors: Gan Woon Seng
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/167564
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1675642023-07-07T17:45:06Z Artificial intelligence monitoring at the edge for smart nation deployment Chua, Jeremy Chin Yew Gan Woon Seng School of Electrical and Electronic Engineering EWSGAN@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence A key aspect of Smart Nation is the collection of data with the use of live monitoring, which is then sent for analysis using artificial intelligence algorithms to extract the desired information. This project aims to improve the model training process using data augmentation via mixing noise into the training data, then implement the model prediction in a Raspberry Pi. This allows for sensors or microphones to be installed in various strategic locations, capturing the audio type of interest such as loud noises like crashes or screams to regular noises such as traffic or footstep noises. With this proof-of-concept system can help to eventually automate and streamline the process of noise and audio-related monitoring. Bachelor of Engineering (Information Engineering and Media) 2023-05-31T04:10:02Z 2023-05-31T04:10:02Z 2023 Final Year Project (FYP) Chua, J. C. Y. (2023). Artificial intelligence monitoring at the edge for smart nation deployment. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167564 https://hdl.handle.net/10356/167564 en A3100-221 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::Computer science and engineering::Computing methodologies::Artificial intelligence
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Chua, Jeremy Chin Yew
Artificial intelligence monitoring at the edge for smart nation deployment
description A key aspect of Smart Nation is the collection of data with the use of live monitoring, which is then sent for analysis using artificial intelligence algorithms to extract the desired information. This project aims to improve the model training process using data augmentation via mixing noise into the training data, then implement the model prediction in a Raspberry Pi. This allows for sensors or microphones to be installed in various strategic locations, capturing the audio type of interest such as loud noises like crashes or screams to regular noises such as traffic or footstep noises. With this proof-of-concept system can help to eventually automate and streamline the process of noise and audio-related monitoring.
author2 Gan Woon Seng
author_facet Gan Woon Seng
Chua, Jeremy Chin Yew
format Final Year Project
author Chua, Jeremy Chin Yew
author_sort Chua, Jeremy Chin Yew
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 2023
url https://hdl.handle.net/10356/167564
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