MEASUREMENT SYSTEMS FOR MECHATRONICS

MEASUREMENT SYSTEMS FOR MECHATRONICS
2 YEAR II semester  6 CFU
Arianna Mencattini A.Y. 2021-22
A.Y. 2022-23
Code: 8039787
SSD: ING/INF/07

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.

MAKING JUDGEMENTS: :
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.

COMMUNICATION SKILLS:
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.

LEARNING SKILLS:
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.

 

SYLLABUS:

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.

POWERTRAIN TECHNOLOGIES FOR FUTURE MOBILITY (ex Internal Combustion Engines)

POWERTRAIN TECHNOLOGIES FOR FUTURE MOBILITY (ex Internal Combustion Engines)
1 YEAR (Block C)

2 YEAR (Blocks A|B)

II semester  9 CFU
Stefano CORDINER

Lorenzo BARTOLUCCI

A.Y. 2021-22

Internal Combustion Engines

A.Y. 2022-23

POWERTRAIN TECHNOLOGIES FOR FUTURE MOBILITY

Code:
SSD: ING/IND/08

LEARNING OUTCOMES:
The aim of the course is to provide students with in-depth scientific training to properly address the design, selection and management of internal combustion engines and their interaction with the environment, as well as to create the conditions for the development of innovative solutions. To this aim, students will develop in-depth knowledge of the principles of engine operation and learn simulation procedures for testing and sizing an alternative internal combustion engine and its main components. Special attention is also given to the latest technological development of internal combustion engine technology aimed at exceeding current limits in terms of emissions and efficiency and defining innovative scenarios of sustainable mobility.

KNOWLEDGE AND UNDERSTANDING:
The course aim is to provide the students with tools for the analysis of the performances and the evaluation of proper design solutions for internal combustion engines and their core components. At the end of the course, the student will be able to independently understand the functional link between design variables and the performance of internal combustion engines also in case of innovative design,

APPLYING KNOWLEDGE AND UNDERSTANDING:
The course, through the analysis of specific problems and quantitative data, is aimed at providing the tools for analysis and evaluation of the effects of different design choices. The theme of energy efficiency and pollution reduction are at the heart of the teaching organization. The student will be able to interpret and propose design solutions, even innovative ones, adapted to the specificity of the problems that are presented to him.

MAKING JUDGEMENTS:
By studying theoretical and practical aspects of engine design and critically assessing the influence of different design variables, the student will be able to improve his judgment and proposal in relation to design. and the management of internal combustion engines.

COMMUNICATION SKILLS:
The presentation of the theoretical and application profiles underlying the operation of internal combustion engines will be carried out to allow the knowledge of the technical language of the appropriate specialist terminology; The development of communication skills, both oral and written, will also be stimulated through classroom discussion, participation in seminary activities and through final tests.

LEARNING SKILLS:
The learning capacity, even individual, will be stimulated through numerical exercises, the drafting of papers on specialized topics, the discussion in the classroom, also aimed at verifying the actual understanding of the topics treated. The learning capacity will also be stimulated by integrative educational aids (journal articles and economic newspapers) in order to develop autonomous application capabilities.

SYLLABUS:

General information on internal combustion engines: Characteristics and Classification, thermodynamic and performance analysis. Experimental analysis of the performance of an internal combustion engine Air Supply for 4-stroke engines: volumetric efficiency and its evaluation, quasi-stationary effects; valve sizing; the influence of other engine parameters; Variable Valve Actuation systems; non-stationary phenomena in the intake and exhaust: inertia and wave propagation; variable valve geometry systems, computational models; 2-stroke engines: construction schemes; Supercharging; In cylinder charge motion: Turbulence; swirl, squish, tumble, stratified charge engines Traditional and alternative fuels; Fuels general properties: fuel, air stoichiometric; calorific value gaseous fuels: natural gas, hydrogen and mixtures thereof. bioethanol , bio-diesel and DME. Features and their use in engines: technical solutions, performance and emissions Fuel metering. Otto engines: carburetor; injection systems; lambda probe. Diesel engines: fuel injectors and injection systems, dimensioning. Experimental tests on a diesel injection system Common Rail Combustion: Fundamentals of analytical study of combustion, thermodynamics of combustion processes, calculation of the chemical composition and temperature in adiabatic equilibrium transport phenomena (notes), chemical kinetics (notes). Combustion in Otto and Diesel engines. Emissions and their control systems: emissions formation mechanisms, effects on health and environment, measurement of emissions; influence of engine parameters, test cycles and legislation; procedures and systems for the reduction of emissions in engines. Experimental tests. Cooling system: Heat flows, heat transfer in the engine cooling systems, liquid and air: structural layouts and sizing; thermal stress of the mechanical parts. Sustainable mobility. Principles of operation of hybrid vehicles: series and parallel solution; engines there and electrical workers, regenerative braking, lithium batteries, performance and prospects. Plug-in hybrid vehicles, engines c.i. ” Range extender “. Electric vehicles, characteristics and perspectives For all the topics of the course the numerical simulation tools will be presented

 

Digital Signal Processing (block C-choice2)

Digital Signal Processing (block C-choice2)
1 YEAR II semester  6 CFU
Marina RUGGIERI A.Y. 2021-22
Marina RUGGIERI (8cfu)

Tommaso ROSSI (1cfu)

A.Y. 2022-23
Code: 8039514
SSD: ING-INF/03

OBJECTIVES

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.

BACKGROUND

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

PROGRAMME

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; VLAB and applications (Dr. Tommaso Rossi) with design examples and applications of IIR and FIR filters, Matlab-based lab and exercises (optional).

TEXTBOOKS

[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.

ANALOGUE ELECTRONICS (block B)

ANALOGUE ELECTRONICS (block B)
1 YEAR II semester  6 CFU
Rocco Giofre’ A.Y. 2021-22
A.Y. 2022-23
Code: 80300060
SSD: ING-INF/01

LEARNING OUTCOMES:
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.

KNOWLEDGE AND UNDERSTANDING:
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.

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

MAKING JUDGEMENTS:
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.

COMMUNICATION SKILLS:
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.

SYLLABUS:

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

HOW TO ATTEND LESSONS:

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

NANOTECHNOLOGY

NANOTECHNOLOGY
1 YEAR II semester  6 CFU
Antonio Agresti (3cfu)

Francesca De Rossi (3cfu)

A.Y. 2021-22
Antonio Agresti (3cfu)

Fabio Matteocci (3cfu)

A.Y. 2022-23
Code:
SSD: ING-INF/01

LEARNING OBJECTIVES AND EXPECTED LEARNING OUTCOMES:

LEARNING OUTCOMES:
The first part of the Nanotechnology course introduces thin film depositions using both physical and chemical vapour depositions. The main objective is the knowledge of the potential and limits of the different thin film depositions in the nanotechnology field. Particular attention is destinated to the deposition technique used in micro and nanoelectronics based on semiconductors using top-down and bottom-up approaches. The interaction of both approaches has been discussed with the student in order to share the importance of multidisciplinary knowledge (physics, chemistry and engineering) where the nanotechnology field is based. The final part of module 1 is destinated to the introduction of the case study of the course about the thin film fabrication of an emergent photovoltaic technology: the perovskite solar cells. In particular, the study of the optoelectronic properties of the materials and the fabrication of several device architectures is important to understand the important role of the manufacturing design in thin film photovoltaic technologies destinated at the industrial level.

KNOWLEDGE AND UNDERSTANDING:
Regarding the first module, at the end of the course, the student will have a clear overview of the main deposition technique studied and applied in nanotechnology for different application fields.
Regarding the second module, at the end of the course, the student will know the main characterization techniques for nanostructured materials and electronic and optoelectronic devices till nanometric size.

APPLYING KNOWLEDGE AND UNDERSTANDING:
The student will be able to recognize the applicability areas for the various characterization and realization techniques at nanometric scales. She/He will also be able to apply the knowledge and understanding developed during the course to study and understand recent literature.

MAKING JUDGEMENTS:
The transversal preparation provided by the course implies
1) the student’s capability to integrate knowledge and manage complexity
2) the student’s ability to deal with new and emerging areas in nanotechnology application to energy and nanoelectronics.

COMMUNICATION SKILLS:
The student will be able to clearly and unequivocally communicate the course content to specialized interlocutors. He will also be able to communicate the main physico-chemical characteristics of nanostructured materials and to indicate the most appropriate deposition/processing technique of these materials to technical interlocutors (example: other engineers, physicists, chemists) but not specialists in the field of electronics or devices. The student will also have a sufficient background to undertake a thesis/research work in modern nanotechnology laboratories.

LEARNING SKILLS:
The structure of the course contents, characterized by various topics apparently separated but connected by an interdisciplinary and modular vision, will contribute to developing a systemic learning capacity that will allow the student to approach in a self-directed or autonomous way to other frontier problems on nanotechnology application to energy and nanoelectronics. Furthermore, the student will be able to read and understand recent scientific literature.

 

SYLLABUS

 

First Module: 1 Prof. Antonio Agresti (3 cfu)

1) Quantum Mechanics and p-n junction

2 )Solar Cells: main electrical characterization techniques

3) Absorbance and Fluorescence Spectroscopy

4) Electron scanning microscopy (SEM)

5) Electron transmission microscopy (TEM)

6) Scanning tunneling microscopy (STM)

7) Atomic force microscopy (AFM)

8) Kelvin Probe Microscopy (KPFM)

9) Raman spectroscopy

10) Bi-Dimensional Materials

 

Second module – Prof. Fabio Matteocci (3cfu)

 

1) Introduction to nanotechnology and thin film properties;
2) Thin Film Deposition: the importance of vacuum and plasma;
3) Thermal Evaporation: Working mechanism, material properties, deposition parameters and applications;
4) DC and RF Sputtering: Working mechanism, material properties, deposition parameters and applications;
5) Pulsed Laser Deposition: Working mechanism, material properties, deposition parameters and applications;
6) Chemical Vapour Deposition: Working mechanism, material properties, deposition parameters and applications;
7) Atomic Layer Deposition: Working mechanism, material properties, deposition parameters and applications;
8) Solution Processing: Spin coating, Screen Printing, Blade Coating, Slot die coating;
9) Patterning Procedures: Photolithography and Laser Ablation;
10) Introduction to Perovskite Solar Cell: Working mechanism, material properties, deposition techniques, up-scaling process and applications;
11) Building Integration Photovoltaics;