Chen Qi

• Developed and optimized a Python script to convert Excel-based survey questions to Word format for MS Forms generation.
• Built an automated pipeline using Excel Scripts and Power Automate to update the dashboard every 1 minute, ensuring real-time data visualization and continuous updates.
• Automated course survey report generation using Python and Excel scripts, reducing manual processing time by over 90% through dynamic sheet merging, formatting, and metric calculation across multiple files.
• Designed C programming problems, exams, and reference code for undergraduate students, tailored to varying difficulty levels and specific testing objectives.

Internship Assignment 1 - Single Tone Detection

• Developed a C program utilizing the Goertzel Algorithm and dynamic thresholding to implement single-tone detection on sensor-equipped microphones, aimed at smart home safety applications (e.g., gas leak detection).
• Integrated microphone-captured audio signal data into the system via API for real-time processing.
• Optimized performance through extensive hyperparameter tuning (e.g., sampling frequency, number of samples etc.) to enhance accuracy and reliability. This application enables the sensor to detect the target tone with a resolution of 10 Hz.

Internship Assignment 2 - Lossless Compression on Signal Data

• Analysed and compared various lossless compression methods for LiDAR signal data to determine the most efficient and suitable approach.
• Implemented LiDAR signal data compression using MATLAB and Huffman coding, achieving over 70% reduction in storage requirements, enhancing data archiving and retrieval for deep learning applications.

Internship Assignment 3 - Implementing Vedic Multiplier for Fast Fourier Transform (FFT) Optimization:

• Investigated the impact of using a Vedic multiplier in place of the standard multiplier within FFT, using MATLAB for implementation.
• Developed custom FFT and Vedic multiplier algorithms for various digit lengths (up to 32 digits) in MATLAB, replacing the built-in multiplier to evaluate time and space complexity.
• Despite the built-in multiplier outperforming the Vedic multiplier in MATLAB, findings suggest that the Vedic multiplier could enhance FFT performance on low-level embedded platforms.