Cellular Network Dataset Analysis: Evaluate a dataset consisting of numerous traces taken
by different vehicles, including bikes, cars, and trains. Each trace includes several fields,
such as GPS position, received signal strength from nearby cellular towers (2G, 4G), and their
GPS locations, among others. The recorded dataset is focused on Umbria. The goal is to analyze
this dataset and create models for data inference. Many models can be used, such as regression,
machine learning, and others. A good knowledge of the Python and C++ languages is required.
Smart Pot Application: Develop an Arduino-based application with the goal of creating an
automated system capable of analyzing soil pH and moisture levels. The application will provide
water to a plant pot if necessary. Machine learning can be utilized for providing feedback.
Required skills include knowledge of Arduino, sensors, and the C programming language.
DJI Mini 3 Pro Flight API: Develop an API tailored to DJI SDK 5.x, focusing specifically
on compatibility with the DJI Mini 3 Pro drone. While the official API does not support
autonomous flight
missions using the Wayline method with these drones, it does support the Virtual Stick method.
Therefore,
the Virtual Stick method needs to be adapted to mimic the functionality of the Wayline method.
Proficiency in Android Studio and the Java programming language is necessary for this task.
Localization Mechanism using UWB PDoA Antennas: Develop a sensor localization system
utilizing Ultra Wide Band (UWB) antennas, specifically
leveraging the PDoA kit from DecaWave. The objective is to create a system that can be utilized
by drones to localize ground sensors. This project involves both theoretical exploration and
practical implementation tasks. Proficiency in Raspberry Pi, Linux commands, C programming
language, and some hardware skills are necessary for successful execution.
Halyomorpha halys (HH) Lens App: Develop a cross-platform app for semi-automatic
annotation of an image dataset within the bug
detection system context. The app takes model predictions as input and should allow users to
confirm correct/incorrect bounding boxes and verify accurate annotations. Additionally, it
should enable the suggestion of bounding boxes missed by the model and facilitate the insertion
and validation of annotations using a certain selection mechanism. Possible environments include
Xamarin (C# or .NET) or Flutter (Java or Kotlin).
"Improve the comparison of weather data from established datasets with that
collected by a custom microclimate station located within an orchard. This requires thorough
analysis of diverse sources to assess bug detection rates in relation to current weather
conditions and geographic location. Additionally, participate in configuring a Jetson Nano on
the DJI Matrice 300 RTK to conduct machine learning-based recognition tasks as part of our
continuous endeavors."
2023
Preliminary comparison of weather data between Arpae dataset and a custom
microclimate station located inside an orchard.
2022
Implementation of drone-based delivery algorithms in a mixed
Euclidean-Manhattan Grids.