This week, I collected more training data from corroded samples. We now have 100+ samples of healthy and corroded samples combined. I also did an analysis of the data we collected and chose a couple new features to include in the processing. These features work together to showcase the differences between the healthy and corroded data. The graphs I created to analyze these features are available in the team Github. Nobody should stare at these graphs for too long because they are too confusing. I simply used these graphs, as well as other graphs of other features from the data I collected, to choose which features were the most different between the defect data and the non-defect data. They are histograms of the feature values from each data input (red is for defect data and blue is for non defect). The feature graphs that had the least overlap between red and blue bars were chosen to be included in the processing pipeline. I wrote a new Python file for extracting these features. This file named extract_features can be found in the team GitHub. Then, I trained an SVM using the data I collected and processed into these features. The file for training this SVM as well as the trained SVM file we downloaded onto the Jetson are in the team Github linked below. The SVM achieves .94 accuracy. Mahati and I worked on debugging the data collection on the robot. The sampling rate was too low, so we increased it. Then, we tested how the data collection system and the SVM classification model works on the robot.
Throughout the design, implementation, and debugging of this project, I developed both technical and communication skills by learning how to select and integrate components based on specific application requirements. For example, I learned how to choose appropriate ultrasonic sensors by considering use-case constraints, such as detecting the acoustic signature of galvanized steel. To support these decisions, I reviewed research papers and analyzed the reasoning behind prior designs, which helped me establish a more structured approach to my own design choices.
In addition to component selection, I had to learn how to effectively use these sensors and integrate them with other system elements. I relied on a variety of informal learning strategies, including watching instructional videos and reading technical documentation and articles from sources such as MATLAB and Adafruit. These resources allowed me to quickly gain practical knowledge and troubleshoot implementation challenges.
Beyond technical skills, I also worked on improving my ability to communicate complex concepts to individuals with much less technical experience. I incorporated feedback from our advising professor to refine how I present and explain my work, which has helped me become more effective in conveying technical ideas clearly and concisely.
My work for this week can be found here: https://github.com/aphadke234/ece_capstone_C7/blob/master/extract_features.py