Implementing a Moving Average Filter on the CH32V003: My Experience in the VSDSquadron mini RISC-V Internship

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As part of my journey in the VSDSquadron mini RISC-V Internship, I recently had the chance to dive deep into signal processing by implementing a Moving Average Filter on the CH32V003 microcontroller. This project was both challenging and rewarding, offering me valuable insights into the practical applications of filtering techniques in embedded systems.

Understanding the Moving Average Filter

The Moving Average Filter is a fundamental tool in signal processing, widely appreciated for its simplicity and effectiveness. The core idea behind this filter is to smooth out short-term fluctuations in data by averaging a fixed number of consecutive data points. This results in a more stable and reliable signal, making it easier to identify trends and make informed decisions based on the data.

In essence, the Moving Average Filter can be thought of as a rolling window that slides across your data. At each step, it sums up the values within this window and divides by the number of data points, producing a single averaged value. This process is continuously repeated as new data comes in, allowing the filter to adapt to changes in the data stream.

Why the Moving Average Filter Matters

In real-world scenarios, data is rarely clean or perfect. Sensors, for instance, are prone to noise and interference, leading to fluctuating and sometimes misleading signals. These variations can obscure the true nature of the data, making it difficult to extract meaningful insights.

This is where the Moving Average Filter shines. By averaging out the noise, it reveals the underlying trends in the data. Whether you're working with sensor data in an IoT device, analyzing financial trends, or processing any kind of time-series data, this filter can make a significant difference. It enhances the accuracy of your measurements and ensures that your data-driven decisions are based on a clearer picture.

My Approach to Implementing the Filter

For this project, I chose to implement the Moving Average Filter on the CH32V003, a RISC-V based microcontroller known for its efficiency and versatility in embedded applications. The goal was to process data from a current sensor, apply the filter, and output a smoothed signal that could be used for further analysis or control purposes.

The filter was designed to take in 35 readings at a time. Each reading represents the analog input from the sensor, and these values are stored in an array. The filter then continuously calculates the average of these readings, updating the result with each new data point.

One of the key challenges was optimizing the code to run efficiently on the CH32V003, which, like many microcontrollers, has limited computational resources. The Moving Average Filter, being computationally light, proved to be an ideal choice for this platform. Its straightforward implementation allowed for quick processing without putting a strain on the microcontroller’s performance.

Practical Impact and Key Learnings

The implementation of this filter on the CH32V003 was more than just an exercise in coding; it was a practical demonstration of how such a simple technique can have a profound impact on data quality in embedded systems.

• Simplicity and Efficiency: The Moving Average Filter is incredibly simple to implement, yet it provides a powerful means of noise reduction. Its low computational requirements make it perfect for microcontroller-based applications, where resources are often limited.
• Improved Data Quality: By smoothing out the fluctuations in the sensor data, the filter produced a much clearer and more stable output. This is crucial in applications like IoT devices, where accurate real-time data is essential for proper operation.
• Real-World Relevance: This project reinforced the importance of filtering techniques in practical applications. Whether in industrial automation, robotics, or consumer electronics, the ability to process and clean data in real-time is invaluable.

Conclusion

Working on this project as part of the VSDSquadron mini RISC-V Internship was an enlightening experience. It allowed me to bridge the gap between theoretical knowledge and practical application, enhancing both my technical skills and my understanding of signal processing in embedded systems.

The Moving Average Filter may be simple, but its impact on data quality and system reliability cannot be overstated. It’s a tool that I’ll continue to use and refine in future projects, confident in its ability to improve the performance and accuracy of any system that relies on real-time data.

A big thank you to the VSDSquadron mini team for providing such a rich learning environment and for the opportunity to work on such meaningful projects!