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.
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....
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....
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....
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....
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....