Electronics Engineering

Educational objectives

GENERAL
The course analyses architecture, basic disciplines and technologies that enable the handling of engineering knowledge needed for planning, managing, and operating large systems dedicated to operations that take place over a territory of any real size. Furthermore, the course aims to examine detection systems by using of distributed sensors on the territory. Their connection will be preferably wireless, and they need show low power and low voltage characteristic, in order to use design based on energy harvesting.

SPECIFIC
• Knowledge and understanding: to know techniques and technologies for monitoring, operation and management of complex scenarios on the territory.
• Applying knowledge and understanding: to apply design methods of detection systems. To apply monitoring techniques by using of distributed sensors forming WSN, by using of prototypal systems (e.g. Arduino) and energy harvesting.
• Critical and judgmental skills: basic elements of systems system architecture. Critical capabilities of electronic design of energy self-sufficient WSN systems. Laboratory tests with the usage of prototypal boards (Arduino / Genuino,…), transceivers, sensors (GPS receivers, IMU,...), DC-DC converters, energy Harvesting components, combined with firmware programming and data processing (MathWorks, Python, Sketch Arduino, ...).
• Communication skills: to know how to describe the architectural and circuit solutions adopted to solve the monitoring by using of WSN and IoT.
• Learning skills: valid learning for insert in working contexts specialized in designing electronic systems such as WSN, sensor node units, firmware programming, management of detection systems, and design of sensor nodes.

Educational objectives

KNOWLEDGE AND UNDERSTANDING. The course provides the basic principles for designing automatic systems for machine learning, addressing classification, clustering, functional approximation, and prediction problems, based on Computational Intelligence techniques, including neural networks, fuzzy logic, and evolutionary algorithms. Students who pass the final examination will be able to read and understand texts and articles on advanced topics in Soft Computing and Computational Intelligence, such as neural networks, optimization metaheuristics, and fuzzy systems.
APPLYING KNOWLEDGE AND UNDERSTANDING. Students who pass the final examination will be able to apply the methodological principles and algorithms studied in the course to the design of innovative machine learning systems in multidisciplinary contexts.
MAKING JUDGEMENTS. Students who pass the final examination will be able to analyse design requirements and select the machine learning system that best fits the case study under consideration.
COMMUNICATION SKILLS. Students who pass the final examination will be able to prepare a technical report and deliver an appropriate presentation aimed at documenting any work involving the design, development, and performance evaluation of a machine learning system.
LEARNING SKILLS. Students who pass the final examination will be able to independently continue studying the topics covered in the course, carrying out the continuous learning process required for professional competence in the ICT field.

Educational objectives

At the end of the course, students will be able to analyze the impact of the Earth’s atmosphere on the propagation of electromagnetic signals and to assess suitable mitigation techniques to support the design and optimization of wireless communication systems. They will acquire a detailed and in-depth understanding of electromagnetic propagation theory in complex environments, with particular focus on applications in information and communication engineering, as well as on key propagation phenomena such as diffraction, geometrical optics, tropospheric and ionospheric propagation, and on propagation conditions typical of terrestrial and satellite links in urban environments.

The integration of electromagnetic modeling and systems engineering aspects, with reference to telecommunication and remote sensing systems, will enable students to critically interpret radiowave propagation phenomena and apply the acquired models to the design and performance assessment of communication and Earth observation systems, thus developing cross-disciplinary skills useful in both academic and professional contexts.

Educational objectives

GENERAL
The course analyses architecture, basic disciplines and technologies that enable the handling of engineering knowledge needed for planning, managing, and operating large systems dedicated to operations that take place over a territory of any real size. Furthermore, the course aims to examine detection systems by using of distributed sensors on the territory. Their connection will be preferably wireless, and they need show low power and low voltage characteristic, in order to use design based on energy harvesting.

SPECIFIC
• Knowledge and understanding: to know techniques and technologies for monitoring, operation and management of complex scenarios on the territory.
• Applying knowledge and understanding: to apply design methods of detection systems. To apply monitoring techniques by using of distributed sensors forming WSN, by using of prototypal systems (e.g. Arduino) and energy harvesting.
• Critical and judgmental skills: basic elements of systems system architecture. Critical capabilities of electronic design of energy self-sufficient WSN systems. Laboratory tests with the usage of prototypal boards (Arduino / Genuino,…), transceivers, sensors (GPS receivers, IMU,...), DC-DC converters, energy Harvesting components, combined with firmware programming and data processing (MathWorks, Python, Sketch Arduino, ...).
• Communication skills: to know how to describe the architectural and circuit solutions adopted to solve the monitoring by using of WSN and IoT.
• Learning skills: valid learning for insert in working contexts specialized in designing electronic systems such as WSN, sensor node units, firmware programming, management of detection systems, and design of sensor nodes.

Educational objectives

At the end of the course, students will be able to analyze the impact of the Earth’s atmosphere on the propagation of electromagnetic signals and to assess suitable mitigation techniques to support the design and optimization of wireless communication systems. They will acquire a detailed and in-depth understanding of electromagnetic propagation theory in complex environments, with particular focus on applications in information and communication engineering, as well as on key propagation phenomena such as diffraction, geometrical optics, tropospheric and ionospheric propagation, and on propagation conditions typical of terrestrial and satellite links in urban environments.

The integration of electromagnetic modeling and systems engineering aspects, with reference to telecommunication and remote sensing systems, will enable students to critically interpret radiowave propagation phenomena and apply the acquired models to the design and performance assessment of communication and Earth observation systems, thus developing cross-disciplinary skills useful in both academic and professional contexts.

Educational objectives

KNOWLEDGE AND UNDERSTANDING. The course provides the basic principles for designing automatic systems for machine learning, addressing classification, clustering, functional approximation, and prediction problems, based on Computational Intelligence techniques, including neural networks, fuzzy logic, and evolutionary algorithms. Students who pass the final examination will be able to read and understand texts and articles on advanced topics in Soft Computing and Computational Intelligence, such as neural networks, optimization metaheuristics, and fuzzy systems.
APPLYING KNOWLEDGE AND UNDERSTANDING. Students who pass the final examination will be able to apply the methodological principles and algorithms studied in the course to the design of innovative machine learning systems in multidisciplinary contexts.
MAKING JUDGEMENTS. Students who pass the final examination will be able to analyse design requirements and select the machine learning system that best fits the case study under consideration.
COMMUNICATION SKILLS. Students who pass the final examination will be able to prepare a technical report and deliver an appropriate presentation aimed at documenting any work involving the design, development, and performance evaluation of a machine learning system.
LEARNING SKILLS. Students who pass the final examination will be able to independently continue studying the topics covered in the course, carrying out the continuous learning process required for professional competence in the ICT field.