As a PhD researcher and a teaching assistant at KU Leuven, I am pursuing my passion for developing innovative solutions using machine learning and signal processing.
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Radar signal processing for target detection and tracking
Frequency Modulated Continuous Wave Radar (FMCW) is a sensor widely used in different applications. However, processing the FMCW might be challenging at first look. In this blogpost, I will try to go over all the steps of radar signal processing. Before that, some concepts will be explained to give an idea how FMCW radar data looks like. Chirp A chirp is the signal emitted by the radar's antennas. It has a frequency that is linearly increasing with time....
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MLFlow: A platform to streamlining Machine Learning Workflows
At BlablaConf, I had the privilege to give a talk all about MLflow, a handy tool that helps make machine learning projects run smoother. I broke down how it keeps track of different experiments you do, how it bundles up your models neatly, and how it makes deploying them a breeze. I stressed how important it is for teams to work well together in data science, and MLflow makes that happen by keeping everything organized and reproducible....
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Radar human activity recognition with machine learning
During my phd years, I made a demo of indoor human activity recognition using FMCW radar and machine learning. The schematic below shows the end to end system from the sensor node to the cloud. I was involved in the different stages of this work from the sensor configuration and the signal processing to building a convolutional neural network for Doppler maps classification. This schematic shows the workflow of the demo from the sensor edge to the cloud....
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Docker for code developement and deployement on a RaspberryPi
Intro Lately, I had to develop a demo working on a raspberrypi and send it to some project partners. However, it was very difficult in the beginning, since whenever I change something and push it to GitHub, they were not aware of that, and also faced problems with dependecies sometimes. So I decided to solve this problem and use a Docker container. The first step was to check if Docker runs on RaspberryPi, which is the case....
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Regularization techniques
Regularization is a widely used technique since it brings a positive impact on the model鈥檚 performance. However, there are many methods of regularization. I will try to go through some of them in this post. First, what is regularization? Regularization is a technique that helps a machine learning model avoid overfitting and enhance it generalization capabilities over unseen data. Regularization techniques 1. L1, L2 regularization L2 and L1 regularization put a constraint on the model鈥檚 weights an biases....