Computer and Control Engineering

Educational objectives

General objectives.
Learning the basic notions of differential and integral calculus.

Specific objectives.
Knowledge and understanding:
Learn the theory and the tools of the mathematical analysis of real functions of one variable.
In particular, the concepts of limits, convergence, and differential and integral calculus.

Applying Knowledge and understanding:
To be able to solve classical problems in analysis, such as, making a qualitative plot of the graph of a function,
finding maxima and minima of a function in one variable, computing the area under the graph of a function
via the solution of definite integrals, solving simple ordinary differential equations.

Making judgments:
To be able to carry out a mathematical deduction and to write it in the form of a proof.

Communication skills:
To be able to appropriately use the mathematical language and the related notions, as well as to autonomously develop
and express a mathematical reasoning.

Learning skills:
Learning the method for approaching a scientific topic, recognizing the critical aspects and
trying to give an appropriate explanation in an autonomous manner as well as in collaboration with the professor and other students.

Educational objectives

Dublin descriptor 1.
Upon completion of the course the student will be familiar with the basics of probability calculus, the fundamental results of the theory and the models relevant in engineering applications. He will also know the fundamental concepts of the frequentist approach to statistical inference.

Dublin descriptor 2. Upon completion of the course, the student should be able to select the models to be applied in simple problems from the engineering practice and select the appropriate statistical tools for the estimation of parameters and the verification of hypotheses about the model, using the most common statistical software.

Dublin descriptor 5. Even If the concepts are always exposed in the simplest situations, any technical difficulties related to the extension of these concepts to more general situations are underlined through the distribution of ad hoc material. In the same way, the problems related to the foundations of statistical inference following the frequentist approach will be reported.

Educational objectives

General Objectives:

The goal of the course Introduction to Programming is to provide students with some fundamental techniques of functional and imperative programming through the Python programming language, as well as the study of models for computing. At the end of the course, students will be able to write Python programs involving the use of the programming techniques and data structures introduced during the course. Acquiring the course contents, especially the programming skills, requires the use of a computer.

Specific Objectives:
Knowledge and Understanding: The student will gain a better understanding of programming concepts and data structures by developing Python programs that solve real-world problems

Applying knowledge and understanding: The student will be able to analyze the problem, design its solution and then implement it through the use of the Python language.

Making judgements: The student will be able to identify and critically evaluate the salient aspects of both the analysis and implementation of Python programs, as well as the evaluation of results.

Communication skills: The student will be able to present issues concerning the analysis and solution of computational problems in engineering fields of interest.

Learning skills: The course aims to foster autonomous analytical and learning attitudes oriented towards problem solving.

Educational objectives

Give students the essential linguistic competences needed to deal with written scientific communication

Educational objectives

General Objectives:

The course aims at providing the students with the ability to understand and design programs
that require a deep knowledge of their execution model, specifically, of the
organization and management of the memory. To this end, the course adopts the
Von Neumann architecture as a reference model and the procedural C/C++ language as the programming
tool. The use of the programming language will be focused on memory management techniques (stack, heap), realization of data structures (arrays, matrices, linked lists, stacks, queues, trees, graphs), recursive programming techniques, and implementation of algorithms on such data structures.
The course has a strong bias towards program design and implementation and thus,
includes practicals at the laboratory, developed on a Linux platform.

Specific Objectives:

Knowledge and understanding:
Providing a wide overview of the analysis and design of programs in languages requiring a deep knowledge of the execution model, and in particular of the memory management mechanisms.
The tackled problems are formally defined and both the theoretical and practical bases for their solution are provided.

Applying knowledge and understanding:
Solving specific programming problems, through a proper application of the studied techniques. Lab activity will allow the students to apply the acquired knowledge.

Making judgements:
Being able to assess the correctness of a program and its adequacy wrt to the requirements.

Communication skills:
Ability to describe the decisions made when solving problems and explain the execution mechanisms of programs under the adopted model.

Learning skills:
Self-study of some topics introduced during the course, through the solution at home of exercises proposed during lab activity.

Educational objectives

- Conoscenza e comprensione
Metodo scientifico, fisica classica, cinematica, dinamica, fluidi, termodinamica, elettromagnetismo, onde elettromagnetiche

- Applicare conoscenza e comprensione
Impostare lo svolgimento di un problema di fisica classica e risolverlo, saper descrivere il mondo fisico classico con le grandezze fisiche opportune e le loro relazioni, saper prevedere correttamente e quantitativamente, lo svolgersi di un processo fisico

- Capacità critiche e di giudizio
Capacità di individuare, in forma scritta, per un problema, le grandezze fisiche coinvolte, le loro relazioni e i rapporti numerici esatti o approssimati, tramite esempi in aula relazionati alla parte teorica svolta, esercitazioni scritte durante il corso, aiuto del tutor allo svolgimento degli esercizi e prova scritta finale.

- Capacità comunicative
Tramite domande specifiche su previsioni riguardanti la teoria fisica spiegata, si incoraggiano gli studenti a descrivere il quadro fisico e gli sviluppi della situazione proposta. Prova orale finale dove lo studente è in grado di descrivere a parole e in formule i principali argomenti della materia e le loro implicazioni

- Capacità di apprendimento
In maniera autonoma lo studente è in grado di riconoscere i termini essenziali di un fenomeno fisico classico, quali sono le grandezze fisiche in gioco, le loro relazioni e l’evoluzione nel tempo del sistema, di ipotizzare eventualmente le modifiche per ottenere un diverso risultato desiderato, impostare e risolvere quantitativamente i problemi fisici che si trova di fronte o che vuole impostare. Relazionarsi costruttivamente agli apparati di misura necessari per lo studio quantitativo del fenomeno stesso.

Educational objectives

General objectives.
The class is aimed to provide the first tools in linear Algebra which are necessary to the study of Matrix Algebra and Affine and Euclidean Geometry.

Specific objectives.
Knowledge and understanding:
Once acquired the basic linear algebra, the student should be able to understand the connections between endomorphism and matrices and to solve diagonalization problems. Moreover he should know the geometry in an affine space and particularly the Cartesian Geometry in dimension 2 and 3.

Application skills:
Geometry has also the aim to make the student able to apply these tools. In particular, the student will have to be able to use matrices, to solve linear equations, problems concerning vector spaces and linear functions, to solve exercises about the diagonalization for operators and matrices. Moreover the student should be able to solve problems about affine spaces.
Briefly, he should be able to apply the acquired knowledge to translate a problem (in terms of vectors) in a simpler numerical one by using matrices representations.

Communication skills:
The student will have to learn to present in a clear and rigorous way both theoretical and applicative acquired knowledge.

Judgement autonomy:
Students will be guided to learn in a critical and responsible way all what will be dealt with in class, and to enrich their judgement through the study of the didactic material indicated by the professor.

Educational objectives

General outcomes:

Knowledge of the Java programming language and the UML design language. In particular: objects, methods, classes, interfaces, inheritance, polymorphism, generics, packages, iterators and exceptions. Understanding of methodologic aspecs of the design of programming applications of medium to large size: modularity, robustness, reusability, maintainability, all achieved via abstractions, encapsulation, information hiding, generalization and specialization.

Specific outcomes:

Knowledge and understanding:
Object-oriented programming and its design methodology for large-scale projects. The UML design language and the Java programming language.

Applying knowledge and understanding:
Being able to design and realize an application comprising several classes and associations, and performing a number of activities involving them.

Making judgements:
Being able to evaluate the quality of an application, discriminating the data modeling aspects from those related to processing modeling.

Communication:
The projects and lab activities empower the students with the ability to communicate and share the requirements of an application of medium complexity, its design choices and development methodologies.

Lifelong learning skills:
Besides the usual skills of learning from theoretical descriptions, the course stimulates the students at autonomously learning some of its topics; especially the lab activities encourages working in groups and applying the ideas and tecniques learned.

Educational objectives

General outcomes:
The course aims to provide advanced knowledge of mathematics more directly connected to the learning of basic optimization techniques. In particular, optimization topics concern the mathematical modeling of decision problems and solution algorithms for specific classes of optimization problems.

A) knowledge and understanding: Acquire basic knowledge in the filed of mathematical analysis especially in connection with the study of properties of functions of many variables, with the definition of simple decision models, with the solution of simple minimum problems of for functions of many variables.

B) applying knowledge and understanding: Ability to study the continuity and differentiability of a function of many variables and to solve some exercises connected with the determination of minimum points of linear or non linear problems.

D) E) communication and learning skills: Ability of understanding the nature of some decisional problems by studying the properties of a function of many variables; ability to find the most suitable solution method to solve linear or nonlinear problems

Educational objectives

GENERAL GOALS:
The course on systems theory is focused on methodologies for the mathematical representation of physical and artificial phenomena.The main objective of the course is to provide the student with the main tools for the quantitive analysis of the behaviour of a process in the engineering context or a natural phenomenon, as well as to highlight the problems connected to the non istantaneous dependence of the cause-effect relations in the representation.

SPECIFIC OUTCOMES:
The course provides the methodologies for the comprehension and investigation of the properties of linear continuous time and discrete time systems.

KNOWLEDGE AND UNDERSTANDING:
The comprehension of the generality of the mathematical model with respect to the behaviour of systems in different contexts (mechanical, electrical, demographic,...) will allow the student to study, starting from the model, the physical properties of the particular process under investigation

CAPABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING:
At the end of the course the student will be able to associate a mathematical model to a continuous time or discrete time process and investigate its properties

COMMUNICATION SKILLS:
At the end of the course the student will be able to motivate his/her own design choices

MAKING AUTONOMOUS JUDGEMENTS:
The student will be able to choose between different methodologies, in order to solve the given problem in the best way.

Educational objectives

GENERAL

The course provides an overview of the organization and main functions of a telecommunications system, dealing with both aspects of digital representation of information, signals (in continuous and discrete time) and operations on signals, as well as aspects of networks and related protocols. At the end of the course the student will have fundamental knowledge on the functioning of a telecommunications system, networks and the Internet.

SPECIFIC
• Knowledge and understanding: Know the concepts underlying digital signal processing, their transmission in current telecommunications networks, access and error control protocols and network and transport protocols based on the TPC/IP suite.

• Ability to apply knowledge and understanding: being able to understand how operations on continuous and discrete time signals work, how a protocol works, which are its characterizing functions and how performance can be evaluated. Know how to carry out simple dimensioning of protocols at the various levels of a telecommunication architecture.

• Making judgements: knowing how to analyze the benefits and limitations of signal processing and operations and dimensioning of protocols or TPC/IP network configurations.

• Communication skills: knowing how to present the functionality of signal processing (correlation, sampling, analysis in the time or frequency domain) and how a network protocol works and discuss its performance.

• Learning skills: In addition to the classic learning skills provided by the theoretical study of the teaching material, the way of teaching the course, in particular the exercises (also carried out on simulators and with processing software), stimulate the student towards procedural analysis and quantitative understanding of the functioning of a network and its protocols, facilitating the concrete application of the notions and techniques learned during the course.

Educational objectives

The course provides a programmer's view of how computing systems
execute programs, store information, and communicate, addressing
general issues such as performance, portability, and robustness.
Students are introduced to the basic operation principles of modern
computers, showing how compilers map C programs to assembly code and
how programs interact with the operating system to manage computing
resources.

Educational objectives

General objectives:
The course provides the methodological tools to solve control problems of dynamic systems. The studied concepts are illustrated through examples from various application contexts.

Specific objectives:
Knowledge and understanding:
Design methodologies of feedback control systems using transfer functions or representations in the state space.
The concepts studied are illustrated by examples taken from various application contexts.

Apply knowledge and understanding:
At the end of the course, the student will be able to design controllers that ensure the satisfaction of specifications concerning stability, response precision and disturbance rejection, using techniques that operate in the time or frequency domain.

Critical and judgment skills:
Students will be able to choose the most suitable control methodologies for specific problems and to evaluate the complexity of the proposed solutions.

Communication skills:
The course activities allow the student to be able to communicate/share the design specifications of a feedback control scheme, as well as the design choices and design methodologies of the relavant controllers.

Learning ability:
In addition to the classic learning skills provided by the theoretical study of the teaching material, the course development methods aim to create a mindset of the student oriented to the design of controllers capable of satisfying a series of design specifications.

Educational objectives

General objectives:
Knowledge of the fundamental algorithms and data structures. Ability to both implement them in a modern programming language (Java or C) and to make design choices to solve real problems in application domains. Knowledge of aspects related to the computational cost of algorithms. Ability tto design algorithms for the solution of new problems, using or modifying algorithms and data structures seen during lessons.

Specific objectives:
Ability to:
- design/implement algorithmic solutions based on known techniques or simple variants;
- to approximately evaluate the computational resources necessary for an algorithmic solution;
- make informed design choices for problem solving.

Knowledge and understanding:
Knowing the fundamental data structures and algorithms. Understanding the concepts of correctness and computational cost of an algorithm. Knowing the main paradigms for designing algorithms.

Apply knowledge and understanding:
Being able to design an algorithm that solves a problem and implement it in modern programming language.

Critical and judgment skills:
Being able to assess the correctness, adequacy and efficiency of the algorithmic solution of a problem.

Communication skills:
Being able to effectively describe the requirements of a problem and provide to third parties the relative specifications, design choices and
the reasons underlying these choices.

Learning ability:
The course will allow the development of skills for the independent study of topics related to the course. It will also allow the student to
easily consult advanced and/or specific manuals for autonomous learning of ad-hoc algorithmic solutions.

Educational objectives

General Objectives:
The course aims to provide an overview of corporate investment and financing choices and an assessment of the trade-offs associated with such decisions. More specifically, the course aims to provide students with tools and knowledge useful in understanding and analyzing corporate and shareholder financial objectives, investment evaluation, capital management, financial statements, and budgeting.

Specific objectives:
Knowledge and understanding:
To learn basic concepts related to corporate investment and financing decisions, the separation of ownership and control, and the objectives of corporate governance. To learn basic accounting principles: balance sheet and income statement formation, civil accounting balance sheet and reclassification, accounting systems and accounting methods, revenues and monetary assets, inventories and cost of sales, fixed assets and depreciation, liabilities and equity, and financial statement analysis. To learn basic concepts of analytical accounting: direct and indirect costs, budgeting, and management control.

Apply knowledge and understanding:
To be able to apply methodologies for investment analysis, to read and analyze a financial statement, to create a sales, production, and cash budget.

Making judgements:
To be able to interpret corporate financial data, including indicators of profitability, liquidity, solvency, and operational efficiency. To develop skills for assessing the financial health of companies, identifying strengths and weaknesses through balance sheet analysis.

Communication skills:
The course involves presentations by guest speakers who present case studies or in-depth analyses of specific topics. These seminars allow students to interact with industry professionals or company stakeholders, enhancing their communication skills with people outside the academic environment.

Learning skills:
The fundamental notions acquired in the course enhance the student’s learning abilities, allowing them to further explore topics in business economics with a certain degree of autonomy.

Educational objectives

General objectives
- Understanding the fundamental principles of modeling.
- Learning to build mathematical models of physical processes.
- Learning to perform analysis and numerical simulations of the behaviour of physical processes.

Specific objectives
Descriptor 1 - - Knowledge and understanding
- Understanding the principles of modeling and its role in the analysis, simulation and design of control systems.
- Knowing the classes of mathematical models of physical processes and the methodologies used for determining them.
- Knowing the main techniques used for the discretization of continuous-time systems.
- Knowing examples of mechanical, electrical, hydraulic, thermal and environmental models.

Descriptor 2 - Application skills
- Being able to determine mathematical models of physical processes.
- Being able to discretize models of time-continuous systems.

Descriptor 3 - Autonomy of judgment
- Being able to evaluate the approximations and the validity operational limits of models.
- Being able to identify the level of approximation needed to correctly model, analyze and design the control of the physical process under investigation.
- Being able to deduce nontrivial information about the physical process by analyzing the related mathematical model.
(The objectives that characterize the descriptor are pursued during the classes through the continuous discussion between the professor and the students, as well as among the students themselves under the supervision of the professor, whenever the professor presents a new model)

Descriptor 4 - Communication skills
- Being able to explain the process of buiding a mathematical model of a physical process using an appropriate engineering language.
(The objective that characterizes the descriptor is pursued through specific classes in which the student, upon prior notice, is required to present the professor and the remaining students with a model previously presented by the professor, receiving a feedback about the level of comprehensibility of the above)

Descriptor 5 - Ability to learn
- Being able to identify a physical process that could be of potential interest in the student carrier.
(The objective that characterizes the descriptor is pursued through the individual reading of specific references, indicated by the professor based on the interest shown by the student regarding a particular field of application)

Educational objectives

General objectives
The course provides an overview on supervision architectures and control methodologies for the operation of machines,
physical devices, and industrial processes controlled in real time using distributed computers and a communication network.

Specific objectives
Knowledge and understanding: Students will learn principles, mathematical models and methods for analysis, design, simulation, control and supervision of automation systems.

Apply knowledge and understanding: Students will be able to understand the general problems of automation systems, with focus on their time-driven or event-driven dynamic components, and to design and implement real-time control and supervision techniques.

Critical and judgment skills: Students will be able to analyse the organization and operation of complex automation processes and individual components,
characterizing their properties, performance, and weaknesses.

Communication skills: The course will allow students to be able to present the main problems and technical solutions for the automation of processes and systems of industrial nature and beyond.

Learning ability: The course aims at developing autonomous learning abilities in the students, oriented to the analysis and solution of automation problems.

Educational objectives

The student selects 12 CFU credits from the courses available at the university. This selection must be indicated through the submission of a study plan and must be approved by the program’s academic committee

Educational objectives

The course aims to provide a basic knowledge of an electronic system as
system for data elaboration focusing on gain for the different types of
amplifiers and on the limitations due to band width, power dissipation
and noise for both analog and digital circuits.Risultati di apprendimento attesi
(Inglese):
The student will be able to
analize simple electronic systems identifying the behavior with and
without capacitive elements in both analog and digital circuits

Educational objectives

General Objectives:
The student must be able to use the basic methodologies of the Control Engineering in particular applications, working on a specific project.

Specific Objectives:
They are focused on the improvement of personal skills in reviewing and adapting all the knowledge on analysis and synthesis in Control Engineering acquired in previous courses working on a real practical problem, stimulating the individual and autonomous working abilities.

Knowledge and understanding:
Improvements on the knowledge of Control Engineering methodologies specifically devoted to the development of the project, and additional knowledge in the field of the project.

Applying knowledge and understanding:
The student has to be able to handle a project on a specific Control Engineering problem working autonomously.

Making judgements:
The student has to be able to evaluate the different methodologies and approaches for a specific application and to be able to choose the most effective solution, with a suitable motivation.

Communication skills:
The student must be able to explain the choices adopted in the development of the project and the results attained, in an intelligible language.

Learning skills:
They are stimulated by the necessity of improving the knowledge on a specific problem in a particular application and on the specific methodologies to be adopted.

Educational objectives

The final examination consists of presenting a project activity selected within the student’s academic program. The project may involve extending work already completed during the course of study or developing a new project. The examination includes the preparation of a written report on the project activity, which is submitted for evaluation to a specially appointed committee.

Through the completion of the activities required for the final examination, students develop skills in describing and presenting their work, as well as the ability to communicate effectively with experts in the relevant fields.

Educational objectives

General objectives.
Learning the basic notions of differential and integral calculus.

Specific objectives.
Knowledge and understanding:
Learn the theory and the tools of the mathematical analysis of real functions of one variable.
In particular, the concepts of limits, convergence, and differential and integral calculus.

Applying Knowledge and understanding:
To be able to solve classical problems in analysis, such as, making a qualitative plot of the graph of a function,
finding maxima and minima of a function in one variable, computing the area under the graph of a function
via the solution of definite integrals, solving simple ordinary differential equations.

Making judgments:
To be able to carry out a mathematical deduction and to write it in the form of a proof.

Communication skills:
To be able to appropriately use the mathematical language and the related notions, as well as to autonomously develop
and express a mathematical reasoning.

Learning skills:
Learning the method for approaching a scientific topic, recognizing the critical aspects and
trying to give an appropriate explanation in an autonomous manner as well as in collaboration with the professor and other students.

Educational objectives

Dublin descriptor 1.
Upon completion of the course the student will be familiar with the basics of probability calculus, the fundamental results of the theory and the models relevant in engineering applications. He will also know the fundamental concepts of the frequentist approach to statistical inference.

Dublin descriptor 2. Upon completion of the course, the student should be able to select the models to be applied in simple problems from the engineering practice and select the appropriate statistical tools for the estimation of parameters and the verification of hypotheses about the model, using the most common statistical software.

Dublin descriptor 5. Even If the concepts are always exposed in the simplest situations, any technical difficulties related to the extension of these concepts to more general situations are underlined through the distribution of ad hoc material. In the same way, the problems related to the foundations of statistical inference following the frequentist approach will be reported.

Educational objectives

General Objectives:

The goal of the course Introduction to Programming is to provide students with some fundamental techniques of functional and imperative programming through the Python programming language, as well as the study of models for computing. At the end of the course, students will be able to write Python programs involving the use of the programming techniques and data structures introduced during the course. Acquiring the course contents, especially the programming skills, requires the use of a computer.

Specific Objectives:
Knowledge and Understanding: The student will gain a better understanding of programming concepts and data structures by developing Python programs that solve real-world problems

Applying knowledge and understanding: The student will be able to analyze the problem, design its solution and then implement it through the use of the Python language.

Making judgements: The student will be able to identify and critically evaluate the salient aspects of both the analysis and implementation of Python programs, as well as the evaluation of results.

Communication skills: The student will be able to present issues concerning the analysis and solution of computational problems in engineering fields of interest.

Learning skills: The course aims to foster autonomous analytical and learning attitudes oriented towards problem solving.

Educational objectives

Give students the essential linguistic competences needed to deal with written scientific communication

Educational objectives

General Objectives:

The course aims at providing the students with the ability to understand and design programs
that require a deep knowledge of their execution model, specifically, of the
organization and management of the memory. To this end, the course adopts the
Von Neumann architecture as a reference model and the procedural C/C++ language as the programming
tool. The use of the programming language will be focused on memory management techniques (stack, heap), realization of data structures (arrays, matrices, linked lists, stacks, queues, trees, graphs), recursive programming techniques, and implementation of algorithms on such data structures.
The course has a strong bias towards program design and implementation and thus,
includes practicals at the laboratory, developed on a Linux platform.

Specific Objectives:

Knowledge and understanding:
Providing a wide overview of the analysis and design of programs in languages requiring a deep knowledge of the execution model, and in particular of the memory management mechanisms.
The tackled problems are formally defined and both the theoretical and practical bases for their solution are provided.

Applying knowledge and understanding:
Solving specific programming problems, through a proper application of the studied techniques. Lab activity will allow the students to apply the acquired knowledge.

Making judgements:
Being able to assess the correctness of a program and its adequacy wrt to the requirements.

Communication skills:
Ability to describe the decisions made when solving problems and explain the execution mechanisms of programs under the adopted model.

Learning skills:
Self-study of some topics introduced during the course, through the solution at home of exercises proposed during lab activity.

Educational objectives

General objectives.
The class is aimed to provide the first tools in linear Algebra which are necessary to the study of Matrix Algebra and Affine and Euclidean Geometry.

Specific objectives.
Knowledge and understanding:
Once acquired the basic linear algebra, the student should be able to understand the connections between endomorphism and matrices and to solve diagonalization problems. Moreover he should know the geometry in an affine space and particularly the Cartesian Geometry in dimension 2 and 3.

Application skills:
Geometry has also the aim to make the student able to apply these tools. In particular, the student will have to be able to use matrices, to solve linear equations, problems concerning vector spaces and linear functions, to solve exercises about the diagonalization for operators and matrices. Moreover the student should be able to solve problems about affine spaces.
Briefly, he should be able to apply the acquired knowledge to translate a problem (in terms of vectors) in a simpler numerical one by using matrices representations.

Communication skills:
The student will have to learn to present in a clear and rigorous way both theoretical and applicative acquired knowledge.

Judgement autonomy:
Students will be guided to learn in a critical and responsible way all what will be dealt with in class, and to enrich their judgement through the study of the didactic material indicated by the professor.

Educational objectives

- Conoscenza e comprensione
Metodo scientifico, fisica classica, cinematica, dinamica, fluidi, termodinamica, elettromagnetismo, onde elettromagnetiche

- Applicare conoscenza e comprensione
Impostare lo svolgimento di un problema di fisica classica e risolverlo, saper descrivere il mondo fisico classico con le grandezze fisiche opportune e le loro relazioni, saper prevedere correttamente e quantitativamente, lo svolgersi di un processo fisico

- Capacità critiche e di giudizio
Capacità di individuare, in forma scritta, per un problema, le grandezze fisiche coinvolte, le loro relazioni e i rapporti numerici esatti o approssimati, tramite esempi in aula relazionati alla parte teorica svolta, esercitazioni scritte durante il corso, aiuto del tutor allo svolgimento degli esercizi e prova scritta finale.

- Capacità comunicative
Tramite domande specifiche su previsioni riguardanti la teoria fisica spiegata, si incoraggiano gli studenti a descrivere il quadro fisico e gli sviluppi della situazione proposta. Prova orale finale dove lo studente è in grado di descrivere a parole e in formule i principali argomenti della materia e le loro implicazioni

- Capacità di apprendimento
In maniera autonoma lo studente è in grado di riconoscere i termini essenziali di un fenomeno fisico classico, quali sono le grandezze fisiche in gioco, le loro relazioni e l’evoluzione nel tempo del sistema, di ipotizzare eventualmente le modifiche per ottenere un diverso risultato desiderato, impostare e risolvere quantitativamente i problemi fisici che si trova di fronte o che vuole impostare. Relazionarsi costruttivamente agli apparati di misura necessari per lo studio quantitativo del fenomeno stesso.

Educational objectives

General outcomes:

Knowledge of the Java programming language and the UML design language. In particular: objects, methods, classes, interfaces, inheritance, polymorphism, generics, packages, iterators and exceptions. Understanding of methodologic aspecs of the design of programming applications of medium to large size: modularity, robustness, reusability, maintainability, all achieved via abstractions, encapsulation, information hiding, generalization and specialization.

Specific outcomes:

Knowledge and understanding:
Object-oriented programming and its design methodology for large-scale projects. The UML design language and the Java programming language.

Applying knowledge and understanding:
Being able to design and realize an application comprising several classes and associations, and performing a number of activities involving them.

Making judgements:
Being able to evaluate the quality of an application, discriminating the data modeling aspects from those related to processing modeling.

Communication:
The projects and lab activities empower the students with the ability to communicate and share the requirements of an application of medium complexity, its design choices and development methodologies.

Lifelong learning skills:
Besides the usual skills of learning from theoretical descriptions, the course stimulates the students at autonomously learning some of its topics; especially the lab activities encourages working in groups and applying the ideas and tecniques learned.

Educational objectives

General outcomes:
The course aims to provide advanced knowledge of mathematics more directly connected to the learning of basic optimization techniques. In particular, optimization topics concern the mathematical modeling of decision problems and solution algorithms for specific classes of optimization problems.

A) knowledge and understanding: Acquire basic knowledge in the filed of mathematical analysis especially in connection with the study of properties of functions of many variables, with the definition of simple decision models, with the solution of simple minimum problems of for functions of many variables.

B) applying knowledge and understanding: Ability to study the continuity and differentiability of a function of many variables and to solve some exercises connected with the determination of minimum points of linear or non linear problems.

D) E) communication and learning skills: Ability of understanding the nature of some decisional problems by studying the properties of a function of many variables; ability to find the most suitable solution method to solve linear or nonlinear problems

Educational objectives

GENERAL GOALS:
The course on systems theory is focused on methodologies for the mathematical representation of physical and artificial phenomena.The main objective of the course is to provide the student with the main tools for the quantitive analysis of the behaviour of a process in the engineering context or a natural phenomenon, as well as to highlight the problems connected to the non istantaneous dependence of the cause-effect relations in the representation.

SPECIFIC OUTCOMES:
The course provides the methodologies for the comprehension and investigation of the properties of linear continuous time and discrete time systems.

KNOWLEDGE AND UNDERSTANDING:
The comprehension of the generality of the mathematical model with respect to the behaviour of systems in different contexts (mechanical, electrical, demographic,...) will allow the student to study, starting from the model, the physical properties of the particular process under investigation

CAPABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING:
At the end of the course the student will be able to associate a mathematical model to a continuous time or discrete time process and investigate its properties

COMMUNICATION SKILLS:
At the end of the course the student will be able to motivate his/her own design choices

MAKING AUTONOMOUS JUDGEMENTS:
The student will be able to choose between different methodologies, in order to solve the given problem in the best way.

Educational objectives

GENERAL

The course provides an overview of the organization and main functions of a telecommunications system, dealing with both aspects of digital representation of information, signals (in continuous and discrete time) and operations on signals, as well as aspects of networks and related protocols. At the end of the course the student will have fundamental knowledge on the functioning of a telecommunications system, networks and the Internet.

SPECIFIC
• Knowledge and understanding: Know the concepts underlying digital signal processing, their transmission in current telecommunications networks, access and error control protocols and network and transport protocols based on the TPC/IP suite.

• Ability to apply knowledge and understanding: being able to understand how operations on continuous and discrete time signals work, how a protocol works, which are its characterizing functions and how performance can be evaluated. Know how to carry out simple dimensioning of protocols at the various levels of a telecommunication architecture.

• Making judgements: knowing how to analyze the benefits and limitations of signal processing and operations and dimensioning of protocols or TPC/IP network configurations.

• Communication skills: knowing how to present the functionality of signal processing (correlation, sampling, analysis in the time or frequency domain) and how a network protocol works and discuss its performance.

• Learning skills: In addition to the classic learning skills provided by the theoretical study of the teaching material, the way of teaching the course, in particular the exercises (also carried out on simulators and with processing software), stimulate the student towards procedural analysis and quantitative understanding of the functioning of a network and its protocols, facilitating the concrete application of the notions and techniques learned during the course.

Educational objectives

The course provides a programmer's view of how computing systems
execute programs, store information, and communicate, addressing
general issues such as performance, portability, and robustness.
Students are introduced to the basic operation principles of modern
computers, showing how compilers map C programs to assembly code and
how programs interact with the operating system to manage computing
resources.

Educational objectives

General objectives:
The course provides the methodological tools to solve control problems of dynamic systems. The studied concepts are illustrated through examples from various application contexts.

Specific objectives:
Knowledge and understanding:
Design methodologies of feedback control systems using transfer functions or representations in the state space.
The concepts studied are illustrated by examples taken from various application contexts.

Apply knowledge and understanding:
At the end of the course, the student will be able to design controllers that ensure the satisfaction of specifications concerning stability, response precision and disturbance rejection, using techniques that operate in the time or frequency domain.

Critical and judgment skills:
Students will be able to choose the most suitable control methodologies for specific problems and to evaluate the complexity of the proposed solutions.

Communication skills:
The course activities allow the student to be able to communicate/share the design specifications of a feedback control scheme, as well as the design choices and design methodologies of the relavant controllers.

Learning ability:
In addition to the classic learning skills provided by the theoretical study of the teaching material, the course development methods aim to create a mindset of the student oriented to the design of controllers capable of satisfying a series of design specifications.

Educational objectives

General objectives:
Knowledge of the fundamental algorithms and data structures. Ability to both implement them in a modern programming language (Java or C) and to make design choices to solve real problems in application domains. Knowledge of aspects related to the computational cost of algorithms. Ability tto design algorithms for the solution of new problems, using or modifying algorithms and data structures seen during lessons.

Specific objectives:
Ability to:
- design/implement algorithmic solutions based on known techniques or simple variants;
- to approximately evaluate the computational resources necessary for an algorithmic solution;
- make informed design choices for problem solving.

Knowledge and understanding:
Knowing the fundamental data structures and algorithms. Understanding the concepts of correctness and computational cost of an algorithm. Knowing the main paradigms for designing algorithms.

Apply knowledge and understanding:
Being able to design an algorithm that solves a problem and implement it in modern programming language.

Critical and judgment skills:
Being able to assess the correctness, adequacy and efficiency of the algorithmic solution of a problem.

Communication skills:
Being able to effectively describe the requirements of a problem and provide to third parties the relative specifications, design choices and
the reasons underlying these choices.

Learning ability:
The course will allow the development of skills for the independent study of topics related to the course. It will also allow the student to
easily consult advanced and/or specific manuals for autonomous learning of ad-hoc algorithmic solutions.

Educational objectives

General Objectives:
The course aims to provide an overview of corporate investment and financing choices and an assessment of the trade-offs associated with such decisions. More specifically, the course aims to provide students with tools and knowledge useful in understanding and analyzing corporate and shareholder financial objectives, investment evaluation, capital management, financial statements, and budgeting.

Specific objectives:
Knowledge and understanding:
To learn basic concepts related to corporate investment and financing decisions, the separation of ownership and control, and the objectives of corporate governance. To learn basic accounting principles: balance sheet and income statement formation, civil accounting balance sheet and reclassification, accounting systems and accounting methods, revenues and monetary assets, inventories and cost of sales, fixed assets and depreciation, liabilities and equity, and financial statement analysis. To learn basic concepts of analytical accounting: direct and indirect costs, budgeting, and management control.

Apply knowledge and understanding:
To be able to apply methodologies for investment analysis, to read and analyze a financial statement, to create a sales, production, and cash budget.

Making judgements:
To be able to interpret corporate financial data, including indicators of profitability, liquidity, solvency, and operational efficiency. To develop skills for assessing the financial health of companies, identifying strengths and weaknesses through balance sheet analysis.

Communication skills:
The course involves presentations by guest speakers who present case studies or in-depth analyses of specific topics. These seminars allow students to interact with industry professionals or company stakeholders, enhancing their communication skills with people outside the academic environment.

Learning skills:
The fundamental notions acquired in the course enhance the student’s learning abilities, allowing them to further explore topics in business economics with a certain degree of autonomy.

Educational objectives

General objectives.
The course focuses on concepts of operating systems and computer networks, as well as their structure and mechanisms. It analyzes an operating system's elements and the network protocol stack levels.

Specific objectives.
Study of the elements of an operating system. Concurrency, mutual exclusion and deadlock. Semaphores. Process scheduling. Communication mechanisms. Introduction to the security of operating systems and computer networks. Distributed applications that use Internet network services, both following the client-server and peer-to-peer paradigms. In-depth analysis of the TCP/IP protocol stack with reference to the application level, transport and network layer evolutions, software-defined networking, quality of service support, protocols and techniques for creating multimedia applications on the Internet and wireless access.

Implementation of projects using the tools that operating systems and computer networks offer in order to allow cooperation and concurrency between processes and threads.
Socket programming. Development of distributed applications using the services offered by the Internet.

Knowledge and understanding:
Knowledge of the elements of the operating system and computer networks. In-depth understanding of the motivations behind the design choices adopted in creating the protocol stack. Knowledge of the design of networked systems. Understanding multiprogramming and the problems inherent in the communication and synchronization of multiple processes or threads, even on different devices.

Apply knowledge and understanding:
Knowledge of creating first distributed applications on the network, using socket programming. Knowledge of network protocol implementation. Developing reliable and efficient multi-process and multi-threaded applications through the use of system calls that the operating system makes available and thread libraries.

Critical and judgment skills:
Understanding the mutual exclusion and deadlock problems that an algorithm can have. Be able to understand the most appropriate implementation solution depending on the specific requests. Understanding how to design a protocol stack or how to configure it according to the different quality of service needs of the application to be created. Designing and creating distributed applications on the network, defining their functionality, selecting the application paradigm, the most suitable transport level, and the best techniques for implementing the application.

Communication skills:
Describe the elements of the operating system and a protocol stack and communicate the choices made in developing concurrent applications and network protocols and applications.

Learning ability:
Using the knowledge acquired in advanced courses on operating systems, computer networks, distributed systems and cybersecurity courses. Ability to read and understand industry standards.

Educational objectives

General Objectives

The general aim of the course is to introduce the theoretical foundations of computer science, particularly focusing on the basics of computation theory and complexity theory, and their implications on two fundamental aspects of a computer engineer's preparation: mathematical logic and algorithms. Mathematical logic will be introduced as a powerful tool for modeling and formally reasoning on different aspects of information technology, in particular data management, database querying, program specification and reasoning on the properties of programs and automata. Additionally, the course will cover basic notions related to analysis and design of probabilistic algorithms, algorithms used in dynamic optimization and methods and techniques for classification and machine learning.

Specific Objectives

Knowledge and Understanding:
Students will grasp fundamental concepts of computation models and computational complexity, as well as mathematical logic, the principles according to which the validity of arguments is judged, the relationships between arguments are analyzed and inferences such as deduction, induction and abduction, are evaluated. Students will also learn the basic notions of probabilistic methods and dynamic optimization and acquire the basics to apply these notions to the analysis and design of fundamental algorithms in computer science, including sorting algorithms, algorithms on networks and graphs, classification algorithms, clustering and machine learning.

Applying Knowledge and Understanding:
Students will gain deep understanding of problem decidability/undecidability and tractability/intractability, and of the role of logic in various aspects of a computer engineer's activities. They will acquire basic knowledge to formalize a problem in logic, analyze logical theories and reason about their inferences, construct logical theories for modeling requirements of a moderately complex information system, specify database queries in logic, specify properties of automata in logic, and translate simple computational specifications into logic programs. Additionally, students will gain a deep understanding of the role of the analysis and design of algorithms in various aspects of the activities of a computer engineer and acquire basic knowledge to carry out analysis of probabilistic algorithms, define probabilistic algorithms for problems of medium complexity, apply fundamental methods such as the Monte Carlo method, Markov chains, dynamic programming and Bayesian models to different contexts, such as sequences, graphs, networks, machine learning, classification and clustering.

Critical and judgment skills:
At the end of the course, students are able to evaluate the validity of statements and arguments, the consistency of a set of requirements for an information system, the adequacy of the formulation of a computation that extracts data from a database, the correctness of a program with respect to the specification of certain properties. Students are also able to establish the decidability/complexity of a problem, to analyze probabilistic algorithms, to evaluate the effectiveness of probabilistic and dynamic optimization methods for algorithmic problems and to judge the quality of the application of machine learning, classification and clustering algorithms.

Communication skills:
The design activities and exercises of the course allow the student to acquire crucial tools to communicate and share the critical evaluation of logical tools and languages and their role in different fields of computer science and algorithmic methods and their role in different important contexts of computer engineering.

Learning ability:
In addition to the classic learning skills provided by the theoretical study of the basic topics covered in the course, the methods of carrying out the course itself, in particular the design activities, stimulate the student to autonomously study some topics, to work in groups and concrete application of the concepts and techniques learned during the course.

Educational objectives

The student selects 12 CFU credits from the courses available at the university. This selection must be indicated through the submission of a study plan and must be approved by the program’s academic committee

Educational objectives

The course aims to provide a basic knowledge of an electronic system as
system for data elaboration focusing on gain for the different types of
amplifiers and on the limitations due to band width, power dissipation
and noise for both analog and digital circuits.Risultati di apprendimento attesi
(Inglese):
The student will be able to
analize simple electronic systems identifying the behavior with and
without capacitive elements in both analog and digital circuits

Educational objectives

The final examination consists of presenting a project activity selected within the student’s academic program. The project may involve extending work already completed during the course of study or developing a new project. The examination includes the preparation of a written report on the project activity, which is submitted for evaluation to a specially appointed committee.

Through the completion of the activities required for the final examination, students develop skills in describing and presenting their work, as well as the ability to communicate effectively with experts in the relevant fields.