01TUMLP

A.A. 2019/20

Course Language

Inglese

Course degree

1st degree and Bachelor-level of the Bologna process in Electronic And Communications Engineering (Ingegneria Elettronica E Delle Comunicazioni) - Torino

Borrow

02TUMJM 02TUMLI 02TUMLM 02TUMLN 02TUMLZ 02TUMMA 02TUMMC 02TUMMK 02TUMMN 02TUMMO 02TUMNX 02TUMOA 02TUMOD 02TUMPC 02TUMPI 02TUMPL

Course structure

Teaching | Hours |
---|---|

Lezioni | 20 |

Esercitazioni in aula | 60 |

Teachers

Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
---|---|---|---|---|---|---|---|

Garello Roberto | Professore Associato | ING-INF/03 | 20 | 0 | 0 | 0 | 4 |

Teaching assistant

Context

SSD | CFU | Activities | Area context |
---|---|---|---|

ING-INF/03 ING-INF/03 |
3 5 |
F - Altre attivitą (art. 10) D - A scelta dello studente |
Altre conoscenze utili per l'inserimento nel mondo del lavoro A scelta dello studente |

2019/20

Signal processing is the treatment of signals to enable their recognition, classification, analysis, transmission, storing, processing, or enhancement. Such signals may come from many different sources, like sensors, speech, camera, mechanical process, biomedical instruments, and so on. This course aims to give the student an introduction to practical aspects of digital signal processing. The course is made by lectures which introduce a topic, followed by direct experimentation and algorithm implementation by Matlab. The course can be useful for students interested to apply signal processing to digital communication, electronics implementation, signal recognition and processing in different engineering areas.

Signal processing is the treatment of signals to enable their recognition, classification, analysis, transmission, storing, processing, or enhancement. Such signals may come from many different sources, like sensors, speech, camera, mechanical process, biomedical instruments, and so on. This course aims to give the student an introduction to practical aspects of digital signal processing. The course is made by lectures which introduce a topic, followed by direct experimentation and algorithm implementation by Matlab. The course can be useful for students interested to apply signal processing to digital communication, electronics implementation, signal recognition and processing in different engineering areas.

At the end of the course, the student will know the basic signal processing techniques like sampling, digital filtering and spectral estimation. The student will understand the relationship between theory and algorithms for signal processing applied to different engineering area including transmission, sensor data and basic audio processing. The student will be able to design and perform a Matlab-based development project on signal processing.

At the end of the course, the student:
- will know the basic signal processing techniques like sampling, digital filtering and spectral estimation.
- will understand the relationship between signal processing theory and algorithms for different engineering areas. Examples will cover bio and sensor signals, basic audio processing, array signal processing.
- will be able to design and perform a Matlab-based development project on signal processing.

Signal theory, Fourier transform.
Students must use their own laptops with Matlab.

Signal theory, Fourier transform.
Students must use their own laptops with Matlab.

The course will cover the following topics (10 hours each):
- Sampling
- Digital filtering
- SIgnal correlation
- Spectral estimation
- Processing of signals from mechanical and bio sensors
- Introduction to audio processing
- Spread Spectrum and Code Division Multiple Access techniques (20 hours)

The course will cover the following seven topics:
- Signals and Sampling (10 hours)
- Signal correlation (10 hours)
- Spectral estimation (10 hours)
- Digital filtering (10 hours)
- Processing of signals from bio and mechanical sensors (10 hours)
- Introduction to audio processing (10 hours)
- Array Signal Processing and introduction to 5G beamforming (20 hours)

The course is organized in 7 topics.
The first 2 introduce the basis of signal processing: samping and correlation.
The next 2 present applications to spectral application and digital filtering, that can be useful for many different areas.
The other 2 present case studies on mechanical and bio sensor processing and audio processing.
The last one is more complex (and requires 20 hours instead of 10 as the other ones) and introduces Code Division Multiple Access which is a fundamental technique for digital transmission.
For each topic, a homework will be assigned, consisting in the implementation of a Matlab program and a short report.
Each topic corresponds to about 10 hours (20 hours for the last one), further divided in 5 hours of lectures and 5 hours of Matlab implementation (10 and 10 for the last one). To realize the homeworks, the groups may be composed by 1 or 2 students.

The course is organized in 7 topics. The first 2 introduce the basis of signal processing: samping and correlation.
The next 2 present applications to spectral application and digital filtering, that can be useful for many different areas.
The other 2 present case studies on mechanical and bio sensor processing and audio processing.
The last one is more complex (and requires 20 hours instead of 10 as the other ones) and introduces array signal processing which is a fundamental technique for 5G beamforming.
For each topic, a homework will be assigned, consisting in the implementation of a Matlab program and a short report.
Each topic corresponds to about 10 hours (20 hours for the last one), further divided in 5 hours of lectures and 5 hours of Matlab implementation (10 and 10 for the last one). To solve the homework, students must work alone with their notebook.

Dutoit, Thierry, Marques, Ferran, "Applied Signal Processing: A MATLAB-Based Proof of Concept", Springer.
Samuel D. Stearns, Donald R. Hush, "Digital Signal Processing with Examples in MATLAB, CRC Press.
Course notes provided by the teacher.

Dutoit, Thierry, Marques, Ferran, "Applied Signal Processing: A MATLAB-Based Proof of Concept", Springer.
Samuel D. Stearns, Donald R. Hush, "Digital Signal Processing with Examples in MATLAB, CRC Press.
Course notes provided by the teacher.

...
The scope of the exam is to verify that the student has acquired the basis of signal processing and is able to develop Matlab-based projects on engineering problems requiring signal processing.
The exam will consist of:
- A two hours written examinations (no books, no notes, no calculator), consisting in 5 questions (1 page answer each) randomly extracted from a list of about 25 questions prepared at the end of the course. Max mark = 15/30;
- The evaluation of the Matlab-based projects. Max mark = 15/30.
The final mark will be the average of the two marks.

Gli studenti e le studentesse con disabilitą o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unitą Special Needs, al fine di permettere al/la docente la declinazione pił idonea in riferimento alla specifica tipologia di esame.

The scope of the exam is to verify that the student has acquired the basis of signal processing and is able to develop Matlab-based projects on engineering problems requiring signal processing.
The exam will consist of:
- A two hours written examinations (no books, no notes, no calculator), consisting in 5 questions (1 page answer each) randomly extracted from a list of about 20 questions prepared at the end of the course. Three points for each correct answer, max mark = 15/30;
- The evaluation of the Matlab-based projects computed as the arithmetic average of the marks received by the proposed assignments. Max mark = 15/30.
The final mark will be the average of the two marks. Students who get 15 for both the two parts receive 30 cum laude.

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.

© Politecnico di Torino

Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY

Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY