Open Projects for Master Students

Send me your CV to chang.gao at if you are interested in an open project or if you want me to create a project for you.

This project aims to harness the potential of the OpenDPD framework together with OpenWiFi to build an open-source Wi-Fi demonstrator on an Analog Device ADRV9364-Z7020 FPGA-RF platform. Integrating a digital pre-distortion module developed through advanced machine learning techniques, this project will tackle the power amplifier (PA) non-linearity correction challenge in a practical Wi-Fi system—a critical aspect for next-generation wireless systems. Through the practical application of deep neural networks (DNNs) in digital pre-distortion, the project will elevate the efficiency and reliability of Wi-Fi systems.

Audio denoising is critical in modern voice-controlled devices, digital assistants, and other audio-centric applications. The increasing demand for high-quality audio and low-power consumption necessitates the development of efficient and effective denoising techniques. This master's project aims to design, implement, and evaluate a neuromorphic hardware accelerator optimized for audio denoising tasks, taking advantage of the parallelism, low power consumption, and real-time processing capabilities of neuromorphic computing.

The project will investigate state-of-the-art neural network-based audio denoising techniques and their efficient hardware implementation. The expected delivery will be an application-specific integrated circuit (ASIC) accelerating the audio denoising algorithm to achieve less than 10 millisecond latency.

Automatic Speech Recognition (ASR) leverages Machine Learning or Artificial Intelligence (AI) technologies to convert spoken language into text. A low-latency ASR system is crucial for enabling seamless interactions between humans and machines. In this project, you will employ the Verilog hardware description language to develop an Zynq FPGA-based hardware accelerator for the cutting-edge, open-source ASR system, Whisper, by OpenAI. The objective is to achieve a target latency of 10 milliseconds, ensuring real-time speech recognition capabilities.

This project aims to explore and implement artificial intelligence techniques in the realm of 6G radio frequency (RF) signal processing. Focusing on key areas such as PAPR (Peak-to-Average Power Ratio) compression and digital predistortion, the project seeks to enhance signal efficiency, reduce power consumption, and improve overall transmission quality in the upcoming 6G networks. By integrating advanced AI algorithms into RF signal processing, this research endeavors to address the challenges of 6G technology, offering significant contributions to the field of wireless communication and opening doors to further innovative research in this rapidly evolving domain. (The concept picture is generated by DALL-E 3.)

This project aims to develop a cutting-edge digital VLSI system to enhance the precision and efficiency of eye-tracking technologies. Central to this project is the use of event cameras, which offer high temporal resolution and sparse data patterns, enabling more efficient hardware implementation and real-time processing capabilities. The project promises significant advancements in fields such as augmented reality, medical diagnostics, and user interface design. Additionally, it presents an opportunity for Master's students interested in digital technology innovations and has the potential to lead to a Ph.D. project.