School of Infocomm (SOI)

AI for Smart Store

Partner Organisation: Defence Science and Technology Agency

In order to help reduce labour-intensive and mundane work, as well as to address the decreasing workforce population and reduce manpower requirements, an automated loan system was developed for servicing equipment loaning purposes.

Innovative AI Smart Store System: 

In addition to leveraging emerging technologies such as Artificial Intelligence (AI), Machine Learning, Facial Recognition, Object Recognition, Anomaly Detection and Computer Vision, the automated loan system was also equipped with an automated inventory management system that oversees the stock level inventory.

Team Members: 
Lee Jun De Kavan, Khoo Meng Xuan, Adib Akmal B Aminuddin, Yee Zhen Wah

Supervisor:
Mr Zack Toh

Innovative AI Smart Store System

Partner Organisation: Defence Science and Technology Agency

Main Aim of the Project: 

This project aims to reduce labour-intensive and mundane work of workers and manpower requirements for inventory management processes.

To address the issues of reducing workforce population, AI technologies, such as Facial Recognition, Anomaly Object Detection, Object Detection and Behavioural Analysis are implemented and integrated into the inventory management system.

Key Features of AI Automated Store: 

• This is done to automate the operations of a store and perform tasks that do not require much human intervention. 

• With this implementation, the probability of human errors can be minimised and higher level of security can be introduced.

• Users of the system will enjoy more convenience and ease of use.
  
  

Team Members: 
Lin Zhiyu, Teren Tan Yong Quan, Khoo Jun Wen, Aaron, Pang Jee Hwee Justin

Supervisor:
Mr Zack Toh

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2019

Custodia

The current situation has seen a slight increase in "Crimes Against Persons". Recent events, such as the Orchard Towers incident on 2 July 2019, where a man was killed after a fight broke out, and the incident at Bugis Cube on 22 June 2019, where a man was beaten up by four other drunken men, have highlighted the need for our solution.

Raspberry Pi with Sensors Solution:

This project utilises Raspberry Pi equipped with a camera module and a sound sensor. The camera module streams video, takes pictures at intervals, and sends them to the Microsoft Cognitive Services Face API for training in recognising people and reading their emotions.

If the anger level and sound level reach a certain threshold, it is identified as an anomalous activity. An alert is then sent to the security guards on duty via a mobile app to investigate.

Technologies:

• Raspberry Pi and related sensors 

• RPi Camera Module 

• Flutter Framework 

• Azure Web Services 

• Visio Studios 2017 

• Azure SQL Database 

• Azure Cognitive Services
   

Team Members: 
Ilyas Yao, Sean Liew, Martin Lee

Supervisor:
Mr Desmond Lee

CamPlus for Smart Classroom

CamPlus is a smart classroom solution to monitor students' condition using IoT and AI. The solution will take snapshots of students' activity and analyse their emotional profiles.

Parents of the student can view these profiles using mobile phones. The solution will also seek to perform automatic attendance taking. 

Key Technologies: 

• ASP.NET Core

• Google Flutter Framework

• Windows IoT (Raspberry Pi)

• Azure Cognitive Services

• Azure Cloud Services
   

Team Members:
Gu YaoChen, Ng Jin Wei, Xiong Wenxi

Supervisor:
Mr Frankie Cha

Shiny Seniority

Shiny Seniority is an application solution that connects the society at large with the elderlies living in nursing homes or community hospitals. 

Key Features:

• The solution allows family members to stay in touch with their elderly relatives.

• It also enables the public to engage with the elderly by organising meaningful activities with rewards for participation and providing a platform to sign up for volunteering events.

• It utilises blockchain to store these records, ensuring the authenticity of the events, and cryptocurrency serves as a wallet and reward system.
  
  

Team Members:
Pan Mingwei, Wee Kaien, Kristi Ellie Jauw

Supervisor:
Mr Frankie Cha

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