MSc thesis project
Neural Networks for Enhanced Analog-to-Digital Converter (ADC)
Pair low-power ADC front-ends with task-specific neural signal enhancement.
This project investigates the integration of neural networks into the signal acquisition pipeline to enhance the performance of low-resolution, low-power analog-to-digital converters (ADCs). Traditional ADCs are designed to maximize signal fidelity across a wide range of applications, but this project takes a domain-adaptive approach by pairing simplified ADC front-ends with task-specific neural post-processing to reconstruct or enhance the acquired signals.
The student will work on a prototype system built around a commercial off-the-shelf ADC (e.g., from Texas Instruments) on a PCB, and implement neural enhancement models in PyTorch to process the raw ADC outputs. The project will include hardware-in-the-loop training and inference, with optional deployment on embedded platforms such as Raspberry Pi or FPGAs. Emphasis will be placed on evaluating power, latency, and accuracy trade-offs in an end-to-end pipeline.
Preferred Skills: PyTorch, Signal Processing Fundamentals, Embedded Systems, PCB Prototyping, Verilog (optional for hardware acceleration)
Contact Person: Dr. Chang Gao (Chang.Gao@tudelft.nl)