Andrea Petreti
I got my bachelor degree in Applied Informatics at Urbino University; later I decided to deepen my studies with a master degree in Engineering and Computer Science at the University of Bologna (at Cesena). During this last path I decided to focus on the world of software engineering; I love the quality of the code. The course of study also allowed me to acquire skills related to Computer Vision both through traditional techniques and through machine learning. I’m particularly passionate about the world related to IoT, robotics and artificial intelligence. I enjoy any challenge related to the IT world.
Work Experience
Android and Flutter Developer
Android and Flutter developer. I have experience in both native and hybrid development with Flutter. The main technologies and languages I use are Kotlin with the new Compose framework and Flutter in mobile.
Full Stack Developer
Full stack developer for industry related embedded devices 4.0. Technologies based mainly on C#/Java/C++. Management of small projects in collaboration with Loccioni
Projects
IntelliSerra
Framework developed in Scala which allows managing smart greenhouse. It allows defining smart greenhouse through sensors and actuators and supports an event-based actuation rules system. The main technologies used in this project are Scala, Akka and Prolog, and it developed with Marta Luffarelli, Simone Letizi and Ylenia Battistini.
Scanbage
A powerful web app to recognize types of garbage by photo or barcode through convolutional network (CNN Machine Learning). It is a kind of social based on rewards unlocked through the correct differentiation of garbage. The project has been realized in a university context with Gianluca Aguzzi, Marta Luffarelli and Simone Letizi.
Fluvium
A full stack system for monitoring river rise levels. The system has been developed starting from embedded components (ESP32) up to the web/cloud layer based on AWS. The project is realized in university context with Gianluca Aguzzi.
Face Sketch Recognition - CBIR
University project aimed at finding faces based on the similarity of sketches obtained manually or through identikit software.
Home Assistant Tapo P100 Integration
Integration for controlling smart plugs and smart lights of the Tapo line through the well-known home automation assistant Home Assistant. Made mainly in Python, this is the main integration used in the Home Assistant community.
Subspedia
Mobile application for the subtitling website Subspedia (now discontinued).
Publications
Encouraging users in waste sorting using deep neural networks and gamification
This paper presents ScanBage, a web application designed and developed to support users in separating waste collection. It exploits two machine learning algorithms to automatically classify garbage categories and it employs Gamification elements with the aim of increasing user involvement. https://dl.acm.org/doi/abs/10.1145/3462203.3477056