ULAMs is an attendance management system that can be used to track attendance of lecturers and students inside the classroom. ULAMs is running based on Location-determination technology using Wireless Local Area Network (WLAN) signal coverage to specifically locate the correct classroom and the users located within the classroom. The attendance of students inside the classroom of each courses are automatically recorded by the app and stored in the university academic management portal. The advantage of the system is the removal of paper-based attendance system and data keyed-in into the system.

The invention is the Location Based Attendance tracking system for University teaching and learning. Novelty of the invention is the capability to track the user in indoor location accurately using WLAN technology. Current technology is unable to locate specificly user in indoor location such as classroom acccurately by leveraging the WLAN signal coverage theory. This removes the limitation by current GPS technology which could not be used for indoor location tracking. The technology is enviromental friendly as it leverages the usage of on-the-shelf WiFi access point that is readily available in the many premises for internet connectivity. Additionally, it removes the usage of paper based attendance which reduce tree-cutting for paper production which will preserve the environment

Advantages

• Low-cost compared to other similar technology that could track the people such as video monitoring, and image processing
• Paperless and only involve the usage of smartphone and does not need additional device to use the system

This product is targeted to academic institution initially to track the attendance of students to the classroom. However the technology could be extended to industry to track attendance of employees to the workplace or meeting. Also, for big event organization they could track the attendance to the event using the system. For the end-user, they will able to use it for small event attendance tracking to certain event.

Inventor

Dr. Mohd Amiruddin Bin Abd Rahman
Faculty/Institute:   Faculty of Science
Department/ Laboratory:   Department of Physics
Expertise: Indoor Positioning; Signal Processing; Machine Learning
Email:    mohdamir@upm.edu.my

Date of Input: 18/06/2020 | Updated: 18/06/2020 | asrizam