| Proj No. | B2168-251 |
| Title | Depth sensing camera with embedded AI features |
| Summary | The project focuses on developing a low-cost depth sensing camera for IoT applications, making advanced spatial sensing more accessible. This innovation benefits applications like robotics, smart surveillance, and gesture recognition, driving advancements in spatial data utilization for enhanced functionality and performance across various fields. However, existing depth sensing solutions are limited in accuracy and speed, limiting their accessibility for high precision manufacturing applications. The system integrates microprocessors for edge processing, enabling real-time AI-driven data analysis and reducing latency. By processing data locally, this system would enhance response times, conserve bandwidth, and improve data security. Project Stages and Tasks: 1. Literature Review: Conduct a brief literature review of the topic, understand the state-of-the-art for existing cameras for depth sensing and/or associated algorithms or image processing methods for this purpose used with normal cameras, and their performance features. 2. System Design: Understand the schematics of the necessary circuit(s), draw them and their PCB layout. 3. System Implementation (Hardware): Help with the purchase of the desired components, mounting them on the PCBs and do first checks of proper functionality of hardware. 4. System Implementation (Software): Perform the microprocessor programming to control the embedded electronic circuits/devices with image processing capabilities, 5. Final Report: Compile and document findings and results in a comprehensive final report. The student may contribute to only one, or a few, or (ideally) all the activities listed above, depending on the available time, his background and skills, and other factors (e.g. collaboration with other parties, time needed for various other elements, etc.) While not mandatory, preliminary knowledge -or even proficiency- in MATLAB, Python, SPICE, Altium, and/or RISC programming is highly desirable and would be a big plus. This project is in collaboration with Singapore Institute of Manufacturing (SIMTech), where the student will carry out most of the work, joining a research team of experienced scientists and engineers, under the guidance of Dr. Seck Hon Luen as co-supervisor. It is preferrable if the student is willing to start the work earlier (BEFORE the school starts), namely to gradually begin as soon as possible after the end of the exam session. Since this is a company-based project the candidate(s) may first be interviewed by the company co-supervisor and the supervising prof. |
| Supervisor | A/P Poenar Daniel Puiu (Loc:S2 > S2 B2A > S2 B2A 27, Ext: +65 67904237) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Machine Learning and Data Analytics Lab (Loc: S2.1, B4-01) |
| Single/Group: | Single |
| Area: | Intelligent Systems and Control Engineering |
| ISP/RI/SMP/SCP?: | ISP: Dr. Seck Hon Luen Senior Scientist II Singapore Institute of Manufacturing (SIMTech) hlseck@SIMTech.a-star.edu.sg |