Title: Analysis and Optimization of LoRa protocols for IoT measurement systems, with practical implementation on Raspberry.
Short Abstract
With the advent of the Industry 4.0 paradigm, IoT measurement systems became of fundamental importance, to smarly (and even wirelessly) connect sensors and collect measures. Low Power Wide Area Networks (LP-WANs) are surely valid protocols to handle time-critical industrial traffic. The objective of this work is to develop and optimize a LoRa communication network, specifically designed for industrial measurements. In particular, LoRa modules will be controlled by using raspberry devices, where firmware and software must be implemented (exploiting C and Python languages), pointing to increase the measurement accuracy. Suitable Machine Learning techniques will be also exploited, to optimize the network configuration.
Title: Smart and IoT based measurement systems in the Industry 4.0 era: exploiting Time Sensitive Networking (TSN) protocols to increase measurement accuracy.
Short Abstract
Sensor networks, in the current industrial scenario, must exploit high-performing hybrid wired and wireless architectures, pointing to the so-called Industrial Internet of Things (I-IoT). In this context, the exploitation of TSN protocols is an attractive research challenge. Furthermore, as wide experimental testbeds are costly and difficult to implement, the development of realistic simulation tools is of fundamental importance. This work aims at the implementation of a suitable, realistic simulation tool of a distributed measurement system exploiting TSN protocols, by exploiting the Omnet++ framework and C++ / python languages.
Title: Vision Based Measurements Systems: Exploiting Machine Learning Techniques to Derive Accurate Measures from Ophthalmic Images.
Short Abstract
The application of Machine Vision to the instrumentation and measurement field, namely Vision–Based Measurement (VBM) systems, is nowadays a hot research topic. In the OptoLab laboratory, several ophthalmic instruments are going to be developed, where Artificial Intelligence (in particular Machine Learning Techniques) are going to be exploited. In particular, between others, CNN-based Neural Networks and Generative Adversarial Networks (GANs) may enhance the functionalities of our VBM systems.
Title: Development of a Control System for Light Automotive Applications.
Short Abstract
The main objective of the activity is to develop a hardware controller able to control small internal combustion engines, two stroke or 4 stroke, fueled with gasoline. In order to achieve the best control performances, the controller has to read several analog inputs: ambient pressure, ambient temperature, exhaust gas temperature, throttle position sensor, phonic wheel. The controller has to be designed by exploiting Simulink, because of the flexibility in code generation. The second phase of the work consists in the low level programming of a microcontroller. The final objective is to build up the board prototype (PCB) and to test it in a real engine.
Candidate skills: interest for automotive related topics, in particular for internal combustion engines, knowledge of Simulink, PCB design, Power Electronics, Microcontroller Programming