Block C1

COMPUTER VISION (2024-25)

Simona Ranieri 2nd year, 6 CFU, a.a. 2024-25, ALL blocks, Block A, Block B, Block C, Block C1, Block C2, II semester, Subjects , ,
COMPUTER VISION (2024-25)
2 YEAR II semester  6 CFU
Arianna Mencattini A.Y. 2023-24 (ex MEASUREMENT SYSTEMS FOR MECHATRONICS)
A.Y. 24-25
Code: 8039787
SSD: ING/INF/07

LEARNING OUTCOMES:
Learning of 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 mechatronics fields.

KNOWLEDGE AND UNDERSTANDING:
The student acquires the 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 to 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 of basic metrological definitions will be provided in support of background knwoledge.

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 group to demonstrate working group capabilities.

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

TEXTS

Digital image processing, Gonzalez and Woods, Prentice Hall, New York, 2002.

BiPM, I. E. C., IFCC, I., IUPAC, I., & ISO, O. (2012). The international vocabulary of metrology— basic and general concepts and associated terms (VIM). JCGM, 200, 2012.

ATTENDANCE

Although attendance is optional, it is strongly recommended to follow the lessons. The professor recommends the students to subscribe the course on the Delphi website. The teams platform will be used as a consequence to communicate with the Professor, ask for doubts, and download the materials used for the lessons.

Deep Learnig and applications (block C1-opt)

Simona Ranieri 2nd year, 6 CFU, a.a. 2025-26, Block C1, c1-choice31, II semester, Subjects , ,
Deep Learning
2 YEAR II semester  6 CFU

Eugenio Martinelli
 A.Y. 2024-25 new
Didatticaweb
Code:
SSD: ING-INF/01

PREREQUISITES

Basic knowledge of probability theory, signal theory, and pattern recognition.

FORMATIVE OBJECTIVES

LEARNING OUTCOMES:
Learning the basic concepts of deep learning algorithms. The main Machine Learning algorithms will be covered, followed by a focus on those related to deep learning, with particular emphasis on their application in the field of mechatronics.

KNOWLEDGE AND UNDERSTANDING:
The student acquires knowledge related to the field of Machine Learning, with particular reference to the ability to extract quantitative and qualitative information from images and videos and multivariate data and their subsequent processing for regression and classification tasks.

APPLYING KNOWLEDGE AND UNDERSTANDING:
The student acquires the capability to implement the algorithms in Matlab through dedicated lessons during the course to 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.

COMMUNICATION SKILLS:
The student develops a project in Matlab that illustrates during the oral exam. The project can be done in groups to demonstrate working group capabilities.

LEARNING SKILLS:
Students will need to be able to read and understand scientific texts and articles in English for in-depth exploration of the topics covered. They should also independently expand their knowledge of the subject to include topics not directly addressed in the course, particularly those connected with the rapid technological developments in the field of Deep Learning and, more generally, in machine learning.

SYLLABUS

Today, deep neural networks surpass traditional hand-crafted algorithms and match human performance in various complex tasks, including image recognition, natural language processing, and prediction models. This course offers a comprehensive introduction to neural networks (NNs), covering traditional feedforward (FFNN) and recurrent (RNN) neural networks, as well as the most advanced deep-learning models like convolutional neural networks (CNN), Variational Autoencoders, and Diffusion models.

The primary objective of the course is to equip students with the theoretical knowledge and practical skills needed to understand and utilize neural networks (NN), while also familiarizing them with deep learning techniques for solving complex engineering challenges.
This goal is pursued in the course by:
• Describing the most important algorithms for NN training (e.g., backpropagation, adaptive gradient algorithms, etc.)
• Illustrating the best practices for successful training and using these models (e.g., dropout, data augmentation, etc.) in a practical session using a phyton environment.
• Providing an overview of the most successful Deep Learning architectures (e.g., convolutional networks, autoencoder for embedding, diffusion models, etc.)
• Providing an overview of the most successful applications with particular emphasis on models for solving visual recognition tasks.

TEXTS

Pattern recognition and machine learning, Christopher Bishop.

Deep Learning, Ian Goodfellow et al.

– slides of the professor

 

 

Multimedia Processing and Communication (block C-opt)

Simona Ranieri 2nd year, 6 CFU, a.a.2022-2023, Block C, Block C1, c-choice3, c1-choice32, I sem, Subjects , , ,
Multimedia Processing and Communication (block C-opt)
2 YEAR I semester  6 CFU
Tommaso Rossi

Cesare Roseti

 ICT and Internet Engineering
A.Y. 2023-24
Code:
SSD: ING-INF/03

FORMATIVE OBJECTIVES

The course module provides an overview of the technologies involved in the multimedia application evolution from analogue to digital, from linear television to video on demand. To this aim, the module addresses the main TV standards, the TCP/IP protocols involved in modern streaming services, the network architectures and the different service modes.

PREREQUISITES: A good background in TCP/IP protocols.

SYLLABUS:

PARTE I – Digital TV standards, MPEG-2  and  Transport Stream, IP encapsulation over  DVB.

PARTE II – IP multicast, IGMP, IP multicast routing

PARTE III –  Transport protocols for IP multimedia applications; Video streaming applications and CDN, the multimedia protocol stack, RTP and RTCP, multimedia signalling protocols: RTSP, SDP and SIP, Key Performance Indicators.

PARTE IV -Adaptive Streaming over HTTP, MPEG-DASH, Support to multimedia applications over 5G.

Control of Electrical Machines (B-C-E) –> CONTROL OF ELECTRICAL MOTORS AND VEHICLES (B-C1-C2-E) (25-26)

Simona Ranieri 2nd year, a.a.2022-2023, b-choice2B, Block B, Block C, Block C1, Block C2, Block E, II semester, Subjects , , , , ,
CEM
2 YEAR II semester 6 CFU
Cristiano M. Verrelli A.Y. 2021-22 to A.Y. 2024-25
 

 

 A.Y. 2025-26 (new name CONTROL OF ELECTRICAL MOTORS AND VEHICLES )
Code:8039782
SSD: ING-INF/04

 

LEARNING OUTCOMES: The course aims to provide a unified exposition of the most important steps and concerns in mathematical modeling and design of estimation and control algorithms for electrical machines such as:
– permanent magnet synchronous motors
– permanent magnet stepper motors
– synchronous motors with damping windings
– induction (asynchronous) motors
– synchronous generators.

KNOWLEDGE AND UNDERSTANDING: Students should be able to gain profound insight into the fundamental mathematical modeling and control design techniques for electrical machines, which are of interest and value not only to engineers engaged in the control of electric machines but also to a broader audience interested in (nonlinear) control design.

APPLYING KNOWLEDGE AND UNDERSTANDING: Students should be able to deeply understand mathematical modeling through nonlinear differential equations, stability and nonlinear control theory concepts, and design of (nonlinear) adaptive controls containing parameter estimation algorithms (important for applications). Students should be able to apply the related knowledge to learning control of robotic manipulators and cruise/yaw rate control of electric vehicles.

MAKING JUDGEMENTS: Students should be able to identify the specific design scenario and apply the most suitable techniques. Students should be able to compare the effectiveness of different controls while analyzing theoretical/experimental advantages and drawbacks.

COMMUNICATION SKILLS: Students should be able to use a single notation and modern (nonlinear) control terminology. Students should be able to exhibit a logical and progressive exposition starting from basic assumptions, structural properties, modeling, control, and estimation algorithms. Students are also expected to be able to read and capture the main results of a technical paper concerning the topics of the course, as well as to effectively communicate in a precise and clear way the content of the course. Tutor-guided individual projects (including Maple and Matlab-Simulink computer simulations and lab visits) invite intensive participation and exchanging ideas.

LEARNING SKILLS: Being enough skilled in the specific field to undertake the following studies characterized by a high degree of autonomy.

TEXTS

R. Marino, P. Tomei, C.M. Verrelli, Induction Motor Control Design, Springer, 2010.
Latest journal papers.

VERIFICATION OF THE KNOWLEDGE

Verify the knowledge and skills acquired by the student on the topics covered by the program. The intermediate exams, the final written tests, and the oral exam will consist of questions related to the topics covered by the program of the course. The questions are aimed at ascertaining the student’s knowledge and his/her reasoning skills in making logical connections between the different topics.

The final vote of the exam is expressed out of thirty and will be obtained through the following graduation system:

Not pass: important deficiencies in the knowledge and in the understanding of the topics; limited capacity for analysis and synthesis, frequent mistakes and limited critical and judgmental capacity, inconsistent reasoning, inappropriate language.

18-21: the student has acquired the basic concepts of the discipline and has an analytical capacity that comes out only with the help of the teacher. The way of speaking and the language used are almost correct, though not precise.

22-25: the student has acquired the basic concepts of the discipline in a discrete way; he/she knows how to discuss the various topics; he/she has an autonomous analysis capacity while adopting a correct language.

26-29: the student has a well-structured knowledge base. He/She is able to independently adopt a correct logical reasoning;  notations and 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 while being expressed by means of brilliant technical language.

ELECTRONICS OF IOT AND EMBEDDED SYSTEMS

Simona Ranieri 12 CFU, 2nd year, a.a. 2023-2024, a.a. 2024-25, a.a.2022-2023, ALL blocks, Block A, Block B, Block C, Block C1, Block C2, Block D, Block E, I sem, Subjects ,
ELECTRONICS OF IOT AND EMBEDDED SYSTEMS

 

2 YEAR 1 semester 12 CFU
Patrick LONGHI (3cfu)
Giancarlo ORENGO (3cfu)
Gian Carlo CARDARILLI (4cfu)
Luca DI NUNZIO (2cfu)		 since A.Y. 2019-20
M-5519 – ELECTRONICS OF IOT (6cfu)
M-5520 – DESIGN OF EMBEDDED SYSTEMS FOR MECHATRONICS (6cfu)
Code: 8039795
SSD: ING-INF/01

EDUCATIONAL OBJECTIVES:
The objectives of the course are:
1) to provide the tools to carry out a radio link assessment in a real application context.
2) learn the fundamental parameters of the antennas used in IoT applications
3) provide the tools to interpret the electrical diagram of the RF front end of a typical trans receiver.

KNOWLEDGE AND UNDERSTANDING:
Provide the fundamental tools to understand the most advanced and updated content from publications, magazines, forums, blogs, etc., to always be updated on the state of the art.

ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING:
Practical radio link budget, electronic noise evaluation on receiver behavior, installation effects of the antennas, understanding of key parameters of commonly used antennas in the targeted scenario, analysis of an RF transceiver block diagram

AUTONOMY OF JUDGMENT:
With the enormous amount of information that is available today to developers of IoT applications, the course seeks to develop the ability of the student to select the highest quality and most validated content.

COMMUNICATION SKILLS:
The final test is based on an oral exam in which the student illustrates a part of the module

LEARNING ABILITY:
The course aims to develop in the student the ability to independently learn new and constantly updated content because the knowledge acquired today soon becomes obsolete.

SYLLABUS:

(Longhi):

Introduction to radiating elements and their key parameters.
Ideal and practical link budget.
The effect of noise in electronic receivers, figures of merit and mathematical modelling. Receiver G/T.
Practical aspects of IoT RF systems
RFID
Radiating elements key parameters, gain, directivity, HPBW, nulls, radiation pattern, polarization, and input impedance. Some practical cases: the mono/di-pole family, microstrip antennas, parabolic reflector, wearables
Introduction to RF transceiver systems and key-components (switches, HPA, LNA, mixers, frequency generators).

(G.Orengo):

Summary of Digital Electronics: digital encoding of information, binary (fixed and floating point), hexadecimal and ASCII; operators and main logic circuits, registers and memories, programmable devices. Prototyping boards for IoT (Arduino, Rasberry), Systems on Chip (SoC), architecture of a microcontroller, description of the Arduino Uno board. Programming languages ​​(assembly, compiled, interpreted), structure of an Arduino sketch (libraries, setups, loops, functions, interrupts), programming elements in C (variables, math and logical operations, cycles, conditional statements). Use of digital and analog I/O ports (A/D conversion, PWM output). Synchronous and asynchronous serial communication modes, wired (USB) and wireless with Bluetooth, RF and WiFi modules. Remote control of electronic modules (sensors, dc stepper and servo motors, LED/LCD displays etc.) from portable devices (Windows, IoS), through applications developed in Processing and Python, and mobile (Android), through Apps developed with the MIT App Inventor platform. Internet protocols for device local/remote control through WiFi modules connected as access points/clients to web platforms or public/private cloud servers controlled by laptops and/or mobile devices.

(G.Cardarilli):

– Introduction to the Internet of Things (IoT) and embedded systems
– Wireless and mobile communications
– The Sensors
– Low power processing
– IoT and machine learning applications
– Future developments in the field of IoT and embedded systems

 

CONTROL OF MECHANICAL SYSTEMS

Simona Ranieri 2nd year, 9 CFU, a.a. 2023-2024, a.a. 2024-25, a.a.2022-2023, ALL blocks, Block A, Block B, Block C, Block C1, Block C2, Block D, Block E, I sem, Subjects ,
CONTROL OF MECHANICAL SYSTEMS
2 YEAR
 1 semester 9 CFU
Riccardo MARINO Since 2019-20
Code: 8039823
SSD: ING-INF/04

LEARNING OUTCOMES:

Ability to understand scientific papers on the control of mechanical systems

KNOWLEDGE AND UNDERSTANDING:

Knowledge of dynamic modeling of mechanical systems. Knowledge of basic feedback control techniques for single input single output systems and of decoupling techniques for multi input multi output nonlinear systems

APPLYING KNOWLEDGE AND UNDERSTANDING:

Ability to simulate using Matlab Simulink complex controlled mechanical systems

MAKING JUDGEMENTS:

Ability to evaluate stability, robustness, and performance of a control system

COMMUNICATION SKILLS: Ability to present and discuss an autonomous design project

LEARNING SKILLS: Ability to fully understand a scientific paper on the control of mechanical systems

SYLLABUS:

BASIC CONTROL TOOLS
Bounded- input bounded- output linear systems. Pole placement theorem for controllable and observable linear systems. Luenberger observers for observable systems. Design of dynamic compensators for linear systems. Integral feedback control to reject constant disturbances. PID control. System inverses for minimum phase linear systems. The combination of feedback and feedforward control actions.
ADVANCED CONTROL TOOLS
Linear approximations of nonlinear control systems about operating conditions. The definition of region of attraction for an operating condition. Output feedback compensators with integral actions to control nonlinear systems about a given operating condition. Liapunov matrix equations to determine quadratic Liapunov functions and assess the region of attraction. The definition of the sensitivity transfer function and its properties. The gang of four: sensitivity, complementary sensitivity, load sensitivity and noise sensitivity functions. How to determine the robustness of a control loop using the gang of four functions. Bode’s integral formula and the limitations imposed by unstable open loop poles. Youla parametrization to design stable compensation. Kalman filters, Riccati equations and robust control design.

CONTROL DESIGN FOR MULTIVARIABLE NONLINEAR SYSTEMS
Relative degree for a single input single output nonlinear system. State feedback control design for input-output linearization. State feedback linearization when the relative degree is equal to the state space dimension. The definition of nonlinear inverse systems. Relative degrees or decoupling indices for multivariable (multi-input, multi-output) nonlinear systems. The definition of the decoupling matrix. State feedback control design for input-output linearization when the decoupling matrix is full rank using the Penrose pseudoinverse. State feedback linearization when the sum of relative degrees is equal to the state space dimension and the decoupling matrix is full rank.

CASE STUDIES OF NONLINEAR MECHANICAL CONTROL SYSTEMS
Control of bycicles, robots, vehicles and aircrafts

POWERTRAIN TECHNOLOGIES FOR FUTURE MOBILITY (ex Internal Combustion Engines)

Simona Ranieri 1st year, 2nd year, 9 CFU, a.a. 2023-2024, a.a. 2024-25, a.a.2022-2023, ALL blocks, Block A, Block B, Block C, Block C1, Block C2, Block D, Block E, II semester, Subjects , , , ,
POWERTRAIN TECHNOLOGIES FOR FUTURE MOBILITY (ex Internal Combustion Engines)
1 YEAR (Block C)

2 YEAR (Blocks A|B|D|E)

 II semester  9 CFU
Stefano CORDINER (6/9 cfu)
Lorenzo BARTOLUCCI (3/9 cfu)
 A.Y. 2021-22

Internal Combustion Engines
Since A.Y. 2022-23

POWERTRAIN TECHNOLOGIES FOR FUTURE MOBILITY
Code: 80300079
SSD: ING/IND/08
(by Mechanical Engineering)

PREREQUISITES: Technical Physics, Fluid Machinery

FORMATIVE OBJECTIVES

LEARNING OUTCOMES:

The course aims to provide students with in-depth scientific training to correctly address the problems of designing, choosing and managing new propulsion systems for sustainable mobility starting from current solutions with internal combustion engines as well as creating the conditions for the development of innovative and low environmental impact solutions. To this end, students will develop in-depth knowledge of the operating principles of propulsion systems for transport and will learn simulation procedures for their verification and sizing. Finally, particular attention is dedicated to the most recent technological development of internal combustion engine technology aimed at overcoming current limits in terms of emissions and efficiency and defining innovative scenarios for sustainable mobility.

KNOWLEDGE AND UNDERSTANDING:
Course aim is to provide the students with tools for the analysis of the performances and the evaluation of proper design solution 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:

Legislation evolution on Internal Combustin Engines. Definition of the performance of the propulsion systems and their operating characteristics in relation to the mission, driving cycles. Generalities on reciprocating internal combustion engines: Characteristics and classification, thermodynamic and performance analysis of reciprocating internal combustion engines.
Air supply for 4-stroke engines: volumetric efficiency and its evaluation; Design elements of intake systems: quasi-stationary effects; valve sizing; influence of other engine parameters; Variable Valve Actuation systems. 2-stroke engines: construction schemes; Non-stationary phenomena in intake and exhaust ducts: inertia and wave propagation; variable geometry systems; calculation models; Supercharging.
In cylinder charge Motion: Turbulence; swirl, squish, tumble; stratified charge engines.
Traditional and alternative fuels; Properties of motor fuels. Generalities: combustibles; stoichiometric air; calorific value Gaseous fuels: natural gas, hydrogen and mixtures. bio-ethanol, bio-diesel and DME. Characteristics and their use in engines: technical solutions, performance and emissions.
Fuel supply Premixed combustion engines; Non-pre-mixed combustion engines.
Combustion : Analytical elements of combustion; thermodynamics of combustion processes; calculation of the chemical composition and of the adiabatic equilibrium temperature ; transport phenomena ; chemical kinetics.
Pollutant emissions and abatement systems; Emissions: formation mechanisms, effects on health and the environment, measurement of emissions; influence of engine parameters; Innovative combustion solutions, Advanced Thermodynamic Cycles. Sustainable mobility. Operating principles of hybrid vehicles: series and parallel solution; motors a.c. and electrical employees; regenerative braking; lithium batteries, performance and prospects. Plug-in hybrid vehicles, i.c. engines “range extender”. Innovative control logics for optimal powersplitting between the different energy sources. Electric vehicles, characteristics and prospects. Numerical simulation tools will be presented for all course topics

ATTENDANCE

Course attendance is strongly recommended. During the course, students are invited to interact with the Professor during the class or office hours for any clarification or insight in specific topics related to the program.

Digital Signal Processing (block C1-C2-opt)

Simona Ranieri 1st year, 6 CFU, a.a.2022-2023, Block C, Block C1, Block C2, c-choice2, c1-choice31, c2-choice2, II semester, Subjects , , , , ,
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
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

(Prof. M.RUGUERI)

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.

 

VERIFICATION CRITERIA

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.

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.