Blogs

Kalman filter is a powerful algorithm used for estimating the state of a dynamic system from noisy measurements. It was developed by Rudolf Kalman in 1960 and has found applications in various fields including navigation, robotics, and signal processing. The Kalman filter works by combining two sources of information: Predictions based on a mathematical model of the system Measurements from sensors The key features of a Kalman filter include: Recursive nature: It doesn't need to store all past data, making it computationally efficient Optimal estimation: Under certain conditions, it provides the best possible estimate (Can be mathematically proven) Handling uncertainty: It accounts for both measurement and process noise Kalman filters are particularly useful in situations where the system state cannot be directly observed, but indirect and potentially imperfect measurements are available....

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....

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....

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....

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....