| Proj No. | A2151-251 |
| Title | Explore RF signal classifications with Software Defined Radios (SDR) |
| Summary | This project investigates the classification of RF signals using Software Defined Radios (SDR). By leveraging SDR platforms like ADALM-Pluto or USRP, real-world RF signals are captured and analyzed. Machine learning and deep learning techniques, implemented in MATLAB or Python, are used to classify modulated signals such as AM, FM, BPSK, and QPSK. The study explores feature extraction methods like spectrograms, constellation diagrams, and wavelet transforms to enhance classification accuracy. The outcome aims to improve RF spectrum monitoring, cognitive radio applications, and interference detection. |
| Supervisor | Dr Loo Xi Sung (Loc:S1 > S1 B1B > S1 B1B 43, Ext: +65 67905429) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Electronics I (Loc: S1-B3c-28) |
| Single/Group: | Single |
| Area: | Smart Electronics and IC design |
| ISP/RI/SMP/SCP?: |