Adaptive Systems (block C-opt) –> Identification and Neural Networks (24-25)

Adaptive Systems (block C-opt) –> Identification and Neural Networks (24-25)
2 YEAR II semester  6 CFU
Patrizio Tomei (4cfu)
Eugenio Martinelli (2cfu)
A.Y. 2023-24
SANTOSUOSSO Giovanni Luca A.Y. 2024-25 (new name “Identification and Neural Networks”
Code: 80300088

Pre-requirement: The basics of systems theory and control are required.

LEARNING OUTCOMES: The course aims to provide the basic techniques for the design of predictors, filters, and adaptive controllers.

KNOWLEDGE AND UNDERSTANDING: Students must obtain a detailed understanding of design techniques with the help of MATLAB-SIMULINK to solve industrial problems of adaptive filtering, adaptive prediction, and adaptive control.

APPLYING KNOWLEDGE AND UNDERSTANDING: Students must be able to apply the project techniques learned in the course even in different industrial situations than those examined in the various phases of the course.

MAKING JUDGEMENTS: Students must be able to apply the appropriate design technique to the specific cases examined, choosing the most effective algorithms.

COMMUNICATION SKILLS: Students must be able to communicate using the terminology used for filtering, prediction, and adaptive control. They must also be able to provide logical and progressive exposures starting from the basics, from structural properties, from modeling to the design of algorithms, without requiring particular prerequisites. Students are believed to be able to understand the main results of a technical publication on the course topics. Guided individual projects (which include the use of Matlab-Simulink) require assiduous participation and exchange of ideas.

LEARNING SKILLS: Students must be able to identify the appropriate techniques and algorithms in real cases that arise in industrial applications. Furthermore, it is believed that students have the ability to modify the algorithms learned during the course in order to adapt them to particular situations under consideration.


Adaptive Filtering Prediction and Control, Graham C. Goodwin, Kwai Sang Sin, Dover Publications, 2009.


2 YEAR II semester  6 CFU
Arianna Mencattini A.Y. 2021-22

A.Y. 2022-23

A.Y. 2023-24

Computer Vision A.Y. 24-25

Code: 8039787

LEARNING OUTCOMES: Learning basic concepts in digital image processing and analysis as a novel measurement system in biomedical fields. The main algorithms will be illustrated particularly devoted to the image medical fields.

KNOWLEDGE AND UNDERSTANDING: The student acquires knowledge related to the possibility to use an image analysis platform to monitor the dynamics of a given phenomenon and to extract quantitative information from digital images such as object localization and tracking in digital videos.

APPLYING KNOWLEDGE AND UNDERSTANDING: The student acquires the capability to implement the algorithms in Matlab through dedicated lessons during the course with the aim of being able to autonomously develop new codes for the solution of specific problems in different application fields.

The student must be able to integrate the basic knowledge provided with those deriving from the other courses such as probability, signal theory, and pattern recognition. some fundamentals of measurement systems as well as basic metrological definitions will be provided in support of background knowledge.

The student solves a written test and develops a project in Matlab that illustrates during the oral exam. The project can be done in a group to demonstrate working group capabilities.

Students will be able to read and understand scientific papers and books in English and also to deepen some topics. In some cases, students will develop also experimental tests with time-lapse microscopy acquisition in the department laboratory.



Fundamentals of metrology. Basic definitions: resolution, accuracy, precision, reproducibility, and their impact over an image based measurement system. Image processing introduction. Image representation. Spatial and pixel resolution. Image restoration. Deconvolution. Deblurring. Image quality assessment. Image enhancement. Image filtering for smoothing and sharpening. Image segmentation: pixel based (otsu method), edge based, region based (region growing), model based (active contour, Hough transform), semantic segmentation. Morphological operators. Object recognition and image classification. Case study: defects detection, object tracking in biology, computer assisted diagnosis, facial expression in human computer interface.
Matlab exercises.

Digital Electronics (block B)

Digital Electronics (block B)
1 YEAR I semester  6 CFU
Marco Re
A.Y. 2021-24
A.Y. 2024-25

Code: 80300061
(by Engineering Sciences)





The objective of this course is to provide students with the knowledge for the analysis and synthesis of the electronic systems presented during the course and the means for their resolution. The course has both theoretical and practical character, it is therefore important that the student is able to carry out concrete problems, such as those presented during the exercises.

Students will learn the analysis techniques used in the analysis of electronic systems in different operating regimes, and acquire the necessary knowledge to carry out circuit simulations through different software.

ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING: students will be able to evaluate which of the existing methods has to be used to analyze and synthesize the system under consideration with the aim of simplifying the resolution of the problem. Finally, they will be able to apply the software presented to perform the analysis of electronic systems in different operating regimes.

The verification methods implemented will lead the students to 1) know how to quickly choose the methodology to be adopted for solving the proposed problems, and 2) be able to illustrate in a synthetic and analytical way the topics covered by the course using equations and schemes .

With the didactic material presented during the course (both written and video) and the list of bibliographic references proposed by the teachers, students have the opportunity to autonomously expand their knowledge on the subject by integrating topics not directly addressed in the course.


  Specification of Combinational Systems: definitions and specification level, data representation and coding, binary specification of combinational systems.

     Combinational Integrated Circuits – Characteristics and Capabilities: representation of binary variables, structure and operation of CMOS gates, propagation delays, voltage variations and noise margins, power dissipation and delay-power product, Buses and three-state drivers, circuit characterization of a CMOS-family.

     Description and Analysis of Gate Networks: definition, description and characteristics, sets of gates.

     Design of Combinational Systems – two-level gate networks: minimal two-level networks, Karnaugh maps, minimization of sum of products and product of sums, design of multiple-output two-level gate networks, two-level NAND-NAND and NOR-NOR networks, limitations of two-level networks, programmable modules: PLA and PLA.

     Design of Combinational Systems – Multilevel Gates Networks:

Transformations, alternative implementations, networks with XOR and XNOR gates, and networks with two-input multiplexers.

     Specification of Sequential Systems: synchronous sequential systems, representation of the state transition and output functions, time behavior and finite state machines, finite memory sequential systems, controllers, equivalent sequential systems and minimization of the number of states, binary specification of sequential systems, specification of different types of sequential systems.

     Sequential Networks: canonical form, high-level and binary implementations, gated latch and D flip-flop, timing characteristics, analysis of canonical sequential networks, design of canonical sequential networks, other flip-flop modules: SR, JK, T, analysis of networks with flip-flops, design using special state assignments.

Fundamentals of Mechanisms of Systems (block A) (since 2022-23)

Fundamentals of Mechanisms of Systems (block A) (since 2022-23)
1 semester 6 CFU
Marco Ceccarelli A.Y. 2021-22

A.Y. 2022-23

Code: 803000062
(by Engineering Sciences)


LEARNING OUTCOMES: The course aims to teach students the knowledge and tools that are needed to address the issues that are related to the identification, modeling, analysis, and design of multi-body planar systems in English language and terminology

KNOWLEDGE AND UNDERSTANDING: modeling and procedures to recognize the structure and characteristics of mechanisms and machines

APPLYING KNOWLEDGE AND UNDERSTANDING: acquisition of analysis procedures for the understanding of kinematic and dynamic characteristics of mechanisms and machines

MAKING JUDGEMENTS: possibility of judging the functionality of mechanisms and machines with their own qualitative and quantitative assessments

COMMUNICATION SKILLS: learning technical terminology and procedures for presenting the performance of mechanisms

LEARNING SKILLS: learning technical terminology and procedures for the presentation of the performance of mechanisms

PREREQUISITES: knowledge of basic mechanics of rigid bodies and computation skills


Structure and classification of planar mechanical systems, kinematic modeling, mobility analysis, graphical approaches of kinematics analysis, kinematic analysis with computer-oriented algorithms; dynamics and statics modeling, graphical approaches of dynamics analysis, dynamic analysis with computer-oriented algorithms, performance evaluation; elements of mechanical transmissions.


Lopez-Cajùn C., Ceccarelli M., Mecanismos, Trillas, Città del Messico
Shigley J.E., Pennock G.R., Uicker J.J., “Theory of Machines and Mechanisms”, McGraw-Hill, New York
Handnotes and papers by the teachers

Machine Design (block A)

Machine Design (block A)
2 YEAR II semester  6 CFU
Luciano CANTONE A.Y. 2023-24
(by Engineering Sciences)
Code: 80300065

LEARNING OUTCOMES: Designing mechanical components considering the need to save weight, material and energy while respecting safety, to promote the usefulness and social impact of the designed product.
KNOWLEDGE AND UNDERSTANDING: The design of mechanical systems, in particular, basic knowledge of the design methodologies of important machine components.
APPLYING KNOWLEDGE AND UNDERSTANDING: Know how to recognise, distinguish, and use the main techniques and tools to design mechanical components.
MAKING JUDGEMENTS: Students must assume the missing data of a problem and be able to independently formulate basic hypotheses (such as that on safety coefficients) based on the operational and functional context of the system/component they have to design.
COMMUNICATION SKILLS: Transfer information, ideas and solutions to specialist and non-specialist interlocutors through intensive use of English terminology.
LEARNING SKILLS: Students, by learning the basics of design, acquire the tools to learn the necessary design techniques of systems/components not directly addressed during the course.


Solution of the assignments given during the teaching. The written exam consists of 4 exercises, similar to those solved during the lessons. After passing the written test, there is an oral examination with two questions. The student’s evaluation is related to the understanding and mastery of the principles and methods of design for both written and oral exams.
The oral exam questions aim to ascertain the student’s knowledge and reasoning skills in connecting the different topics covered within the course.
The final vote of the exam is expressed out of thirty and follows the next graduation system:
Not pass, essential deficiencies in the knowledge and understanding of the topics; limited capacity for analysis and synthesis, frequent generalizations and limited critical and judgmental capacity, the topics are set out inconsistently and with inappropriate language
18-21, the student has acquired the basic concepts of the discipline and has an analytical capacity that emerges only with the teacher’s help. The way of speaking and the language used are, on the whole, correct.
22-25, the student has acquired the basic concepts of the discipline discreetly, knows how to orient himself or herself among the various topics covered and has an autonomous analysis capacity that knows how to express with the correct language.
26-29, the student has a well-structured knowledge base. He/She can independently rework the knowledge acquired in the context of the choice of conventional and unconventional materials according to the application; the way of speaking and the technical language are correct.
30 and 30 cum laude, the student has a complete and in-depth knowledge base. The cultural references are rich and up-to-date, expressed with brilliance and properties of technical language.

The midterm tests are optional and allow you to avoid the oral examination with an overall mark of at least 18/30.


Consolidation of basic knowledge to put the student in the right conditions to face a generic machine design problem: Mechanical Engineering design in Broad, Perspective, Load Analysis, Materials, Static Body Stresses, Elastic strain, Deflection, Stability (Eulerian buckling), Vibrations (beam Eigen-modes), Failure Theories,Safety Factors, Reliability, High cycles Fatigue, Low cycles Fatigue, Surface Damage, Contact and impact problems. During the course, several design activities will be demonstrated by exercises and by real life applications.


Machine Component Design, 7th Edition International Student Version Robert C. Juvinall – (University of Michigan), Kurt M. Marshek (University of Texas at Austin)
Teacher’s slides

Digital Signal Processing (block C1-C2-opt)

Digital Signal Processing (block C1-C2-opt)
1 YEAR II semester  6 CFU
ICT and Internet Engineering
Marina RUGGIERI (5cfu)

Tommaso ROSSI (1cfu)

A.Y. 2023-24
A.Y. 2024-25
Code: 8039514


LEARNING OUTCOMES: The course aims at providing to the students the theoretical and practical tools for the development of design capabilities and implementation awareness of Digital Signal Processing (DSP) systems and applications.

KNOWLEDGE AND UNDERSTANDING: Students are envisaged to understand the DSP theoretical, design and algorithm elements and to be able to apply them in design exercises.

APPLYING KNOWLEDGE AND UNDERSTANDING: Students are envisaged to apply broadly and to personalize the design techniques and algorithm approaches taught during the lessons.

MAKING JUDGEMENTS: Students are envisaged to provide a reasoned description of the design and algorithm techniques and tools, with proper integrations and links.

COMMUNICATION SKILLS: Students are envisaged to describe analytically the theoretical elements and to provide a description of the design techniques and the algorithm steps, also providing eventual examples.

LEARNING SKILLS: Students are envisaged to deal with design tools and manuals. The correlation of topics is important, particularly when design trade-offs are concerned.


A good mathematical background (in particular on complex numbers, series, functions of complex variable) is strongly recommended.



PART I – Discrete-time signals and systems; sampling process; Discrete-time Fourier transform (DTFT); Z-transform; Discrete Fourier Series (DFS).
PART II – Processing algorithms: introduction to processing; Discrete Fourier Transform (DFT); finite and long processing; DFT-based Processing; Fast Fourier Transform (FFT); processing with FFT.
PART III – Filter Design: introduction to digital filters: FIR and IIR classification; structures, design and implementation of IIR and FIR filters; analysis of finite word length effects; DSP system design and applications;
PART IV – Random sequences; processing of random sequences with digital filters; introduction to random sequence estimation; estimators of mean, variance and auto-covariance of random sequences with performance analysis; power spectrum estimation; periodogram and performance analysis; smoothed estimators of the power spectrum and performance analysis; use of FFT in power spectrum estimation.

(Dott. Tommaso ROSSI)

PART V – VLAB: applications with design examples and applications of IIR and FIR filters, Matlab-based lab and exercises; use of Matlab in the power spectrum estimation.



a) Combination of: design test (written); deepening on DSP System development (written); oral.
The design test is propedeutic to the oral one.
The course offers a verify in progress (with a related recovery date) that if passed exempts from the design test of the exam session.

b) The written exam includes design exercises.
The oral part envisages questions on the whole program and a discussion on the design test.

c) The written exam is scored from FAIL to EXCELLENT. The design test and the oral concur almost evenly to the final score (x/30).

d) The final score is based on the level of knowledge of the theoretical, design and algorithm elements and tools as well as on their effective use in design exercises; in particular, the final evaluation refers to 70% of the student’s knowledge level and for 30% to her/his capability of expressing the knowledge and providing an autonomous judgment in the design and oral exam phases.

The detailed final evaluation criteria are as follows:

Failed exam: deep lack and/or inaccuracy of knowledge and comprehension of topics and design techniques; limited capabilities in analysis and synthesis, critical ability and judgment; designs and topics are presented with a non-coherent and technically inadequate approach.
18-20: sufficient knowledge and comprehension of topics and design techniques with possible imperfections; sufficient capabilities in analysis, synthesis and autonomous judgment; designs and topics are presented with a not too much coherent and technically appropriate approach.
21-23: flat knowledge and comprehension of topics and design techniques; appropriate capabilities in analysis and synthesis with fair autonomous judgment; designs and topics are presented with sufficient coherency and technically appropriate approach.
24-26: more than fair knowledge and comprehension of topics and design techniques; good capabilities in analysis and synthesis with good autonomous judgment; designs and topics are presented with coherency and technically appropriate approach.
27-29: complete knowledge and very good comprehension of topics and design techniques; remarkable capabilities in analysis and synthesis with very good autonomous judgment; designs and topics are presented with a rigorous and technically very appropriate approach.
30-30L: excellent knowledge and complete comprehension of topics and design techniques; excellent capabilities in analysis and synthesis with excellent autonomous judgment and originality; designs and topics are presented with a rigorous and technically excellent approach.


[1] “Digital Signal Processing Exercises and Applications”, Marina Ruggieri, Michele Luglio, Marco Pratesi. Aracne Editrice, ISBN: 88-7999-907-9.
[2] The River Publishers’ Series in Signal, Image & Speech Processing, “An Introduction to Digital Signal Processing: A Focus on Implementation”, Stanley Henry Mneney. River Publishers, ISBN: 978-87-92329-12-7.
[3] Slides (exercises are also included therein) published on the teaching website.

Mechanics of Materials and Structures (block A-E)

Mechanics of Materials and Structures (block A-E)
1 YEAR II semester  6 CFU
Andrea Micheletti

Edoardo Artioli

A.Y. 2021-22 (9 cfu)
Andrea Micheletti A.Y. 2022-23 (6 cfu)
Code: 80300064
(by Engineering Sciences)


LEARNING OUTCOMES: The goal of this course, composed of two Modules, is to provide the student with basic knowledge of the mechanics of linearly elastic structures and of the strength of materials. By completing this class successfully, the student will be able to compute simple structural elements and reasonably complex structures.

KNOWLEDGE AND UNDERSTANDING: At the end of this course, the student will be able to:
– compute constraint reactions and internal actions in rigid-body systems and beams subjected to point/distributed forces and couples
– compute centroid position and central principal second-order moments of area distributions
– understand the formal structure of the theory of linear elasticity for beams and 3D bodies
– analyze strain and stress states in 3D bodies
– compute the stress state in beams subjected to uniaxial bending, biaxial bending, eccentric axial force
– understand the behaviour of beams subjected to shear with bending and torsion
– understand how to compute displacements/rotations in isostatic beam systems, how to solve statically underdetermined systems, how to apply yield criteria, and how to design beams against buckling

APPLYING KNOWLEDGE AND UNDERSTANDING: The student will apply the knowledge and understanding skills developed during the course to the analysis of practical problems. This includes the analysis of linearly elastic structures and structural members in terms of strength and stiffness.

MAKING JUDGEMENTS: The student will have to demonstrate his awareness of the modeling assumptions useful to describe and calculate structural elements, as well as his critical judgement on the static response of elastic structures under loads, in terms of stresses, strains, and displacements.

COMMUNICATION SKILLS: The student will demonstrate, mostly during the oral test, his capacity of analyzing and computing the static response of linearly elastic structures, as well as his knowledge of the underlying theoretical models.

LEARNING SKILLS: The student will get familiar with the modeling of structures and structural elements in practical problems, mostly during the development of his skills for the written test. This mainly concerns beams and three-dimensional bodies.

PREREQUISITES: The student should have already attended the basic courses of calculus, geometry, and physics.
It is required that the student has good skills with regard to differential and integral calculus, linear algebra and matrix calculations.


Together with the other Module of this course, the following topics are covered.

Review of basic notions of vector and tensor algebra and calculus.
Kinematics and statics of rigid-body systems.
Geometry of area distributions.
Strain and stress in 3D continuous bodies and beam-like bodies.
Virtual power and virtual work equation for beams and 3D bodies.
One-dimensional beam models: Bernoulli-Navier model, Timoshenko model, constitutive equations, governing differential equations.
Constitutive equation for linearly elastic and isotropic bodies, material moduli.
Hypothesis in linear elasticity, equilibrium problem for linearly elastic beams and 3D bodies.
Three-dimensional beam model: the Saint-Venant problem, uniaxial and biaxial bending, eccentric axial force, shear and bending, torsion.
Elastic energy of beams and 3D bodies, work-energy theorem, Betti’s reciprocal theorem, Castigliano’s theorem.
Yield criteria (maximum normal stress, maximum tangential stress, maximum elastic energy, maximum distortion energy).
Buckling instability, bifurcation diagrams, load and geometry imperfections, Euler buckling load, design against buckling.
Basic notions on the finite element method and structural analysis software.

Analogue Electronics (block B-opt)

Analogue Electronics (block B-opt)
1 YEAR II semester  6 CFU + 3 cfu extra
Rocco Giofre’ A.Y. 2021-22

A.Y. 2022-23

Paolo Colantonio A.Y. 2023-24
Code: 80300060
(by Engineering Sciences)

The students who include Analogue Electronics in their study plan are strongly advised to include it in its 9-CFU version, with the last 3 CFUs (out of 9) working as Extra Credits.

Learning the basic concept of analogue electronic systems and circuits and developing the competencies to design electronic circuits.
The educational objectives are pursued through lectures and exercises.

The student acquires the basic conceptual and analytical knowledge, both theoretical and applied, of the main basic electronic components. Subsequently, it acquires knowledge related to the integration of basic electronic components for the development of more complex electronic systems, such as amplifiers, oscillators, rectifiers, etc.

The student will demonstrate to have acquired the methodologies for the analysis and synthesis (design) of simple electronic circuits.

The student must be able to integrate the basic knowledge provided with those deriving from physics, mathematics, and electrical engineering courses, in order to correctly select the most appropriate analytical and circuit synthesis options.

Students must be able to illustrate the basic themes of the course synthetically and analytically, linking together the different concepts that are integrated into more complex electronic systems.

Prerequisite: Knowledge of network analysis in general.


Diode semiconductor devices and circuit applications: clipper, clamper, peak detector, etc. Bipolar Junction and Field Effect Transistors. Biasing techniques for Transistors. Amplifiers classification, analysis, and circuit design. Frequency response of single and cascaded amplifiers. Differential amplifiers and Cascode. Current mirrors. Feedback amplifiers and stability issues. Power amplifiers. Operational amplifiers and related applications. Oscillator circuits. Integrated circuits and voltage waveform generators.

Books for references
“Electronics: a systems approach”, Neil Storey, Prentice Hall
“Elettronica di Millman”, J. Millman, A. Grabel, P. Terreni, McGraw-Hill


Although attendance is optional, given the complexity of the topics covered, it is strongly recommended to follow the lessons.