Project by Tereza Almášiová: Programming of a neural network on embedded systems

The rapid advancement of artificial intelligence (AI) and machine learning (ML) has significantly transformed various fields, particularly process control. Traditionally reliant on human capabilities, these areas now benefit from technologies enabling machines to perform tasks autonomously, thereby enhancing the efficiency and effectiveness of control systems. This study explores the feasibility Read more…

Project by Simona Pokorná: Participation in the Development of a Novel Dynamic Process for a Remote Laboratory

We’re excited to announce our collaboration with the University of Edinburgh on an innovative project called Remote Laboratories. This initiative promises to revolutionize the way students engage with laboratory experiments and learning. Remote Labs allow students to connect to real devices from the comfort of their homes using just a Read more…

Project by Ema Radačovská: Development and Optimization of a Neural Network Model for Prediction of Docking Scores of Molecules

In modern medicine and pharmaceutical research, molecular docking plays a key role in the identification of potential drugs. This process allows scientists to simulate interactions between small molecules (ligands) and target proteins, which helps predict how effective these molecules might be as drugs. Traditional docking methods often involve complicated and Read more…

Project by Branislav Daráš: Advanced Model Predictive Control Design For a Smart Greenhouse

In the realm of modern agriculture, ensuring optimal growing conditions is crucial for maximizing yield and quality. This challenge is particularly pronounced in greenhouse environments where factors such as temperature, humidity, and CO2 levels must be meticulously controlled. Traditional control systems like Proportional-Integral-Derivative (PID) controllers have been the go-to for Read more…

Project by Viktória Koncserová: Anomaly Detection with Real-Time Tracking of Fault Origin

In today’s data-driven world, precise decision-making hinges on accurate data analysis. However, anomalies—unusual data points that differ significantly from the rest of the dataset—can still arise, posing challenges to this process. Anomaly detection, which identifies these outliers, and root cause analysis, which investigates their origins, are essential for maintaining data Read more…