Caricamento in corso...
Applied signal processing laboratory
01TUMLP
A.A. 2023/24
Course Language
Inglese
Degree programme(s)
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 02TUMLP 02TUMLS 02TUMLZ 02TUMMA 02TUMMB 02TUMMC 02TUMMK 02TUMMN 02TUMMO 02TUMMQ 02TUMNX 02TUMOA 02TUMOD 02TUMPC 02TUMPI 02TUMPL
Course structure
| Teaching | Hours |
|---|---|
| Lezioni | 40 |
| Esercitazioni in aula | 10 |
| Esercitazioni in laboratorio | 30 |
Lecturers
| Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
|---|---|---|---|---|---|---|---|
| Garello Roberto | Professore Associato | IINF-03/A | 40 | 0 | 0 | 0 | 6 |
Co-lectures
Context
| SSD | CFU | Activities | Area context | ING-INF/03 ING-INF/03 |
5 3 |
D - A scelta dello studente F - Altre attività (art. 10) |
A scelta dello studente Altre conoscenze utili per l'inserimento nel mondo del lavoro |
|---|
2023/24
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.
Digital Signal Processing is the key technique of many modern applications, including audio coding, processing and synthesis, wireless and body sensor networks, image processing, processing of bio signals, pulse-oximeters and EKG processing, UWB tags for findind objects, GPS, radars, and many others.
Area of interests of signal processing applications include: Aerospace, Automotive, Biomedical, Building structure monitoring, Communications, Computer, Electronics, Energy, Informatics, Mechanical, Remote sensing engineering and the like.
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, microphones, camera, mechanical process, biomedical instruments, and so on.
This course gives to the student an introduction to practical aspects of digital signal processing. The course has a very practical slant with a special focus on MATLAB implementation of digital signal processing algorithms. Most part of the course focuses on very important applications of signal processing, such as digital filters, digital audio analysis and synthesis, processing of images bio-signals and antenna arrays. The course is 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 fundamentals of digital signal processing for what concerns spectral analysis and digital filtering; will also know the main concepts behind digital audio and image processing, and antenna array processing.
• will understand the relationship between signal processing theory and algorithms for different engineering areas.
• will be able to design and perform a Matlab-based development project on signal processing and its report.
• will know in detail how some state-of-the-art applications based on digital signal processing work.
Signal theory, Fourier transform.
Students must use their own laptops with Matlab.
Basics of Fourier analysis. Matlab. Students must use their own laptops with Matlab installed.
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 topics:
• Fundamentals of spectral analysis (25 hours):
o Introduction to signals and sampling.
o Spectral analysis of discrete time signals: DTFT and DFT.
o FFT and spectral analysis of analog signals through DFT.
o Discrete-time correlation functions and random processes.
o Fundamentals of spectral estimation.
• LTI Systems and Digital Filtering (20 hours):
o Introduction to LTI systems and Z-transform.
o Design of digital Finite Impulse Response (FIR) filters.
o Design of digital Infinite Impulse Response (IIR) filters.
• Introduction to audio and image processing (20 hours)
o Introduction to digital audio processing.
o Digital audio synthesis.
o Introduction to image processing.
• Array signal processing and beamforming (15 hours).
o Introduction to linear antenna array processing.
o Planar array processing.
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 into 4 main topics. Each week a new subtopic is introduced, followed by a Matlab application on the same subtopic.
During the course, 4 assignments are proposed on DSP applications, consisting in the implementation of a Matlab program and the preparation of a report. An assignment is made by a certain number of exercises.
Each assignment topic has at least 6 hours of tutoring where the teacher is available to help students with Matlab implementation.
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.
Slides; Video lezioni tratte da anni precedenti;
Lecture slides; Video lectures (previous years);
Modalità di esame: Elaborato progettuale in gruppo; Prova scritta in aula tramite PC con l'utilizzo della piattaforma di ateneo;
Exam: Group project; Computer-based written test in class using POLITO platform;
...
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 disabilità 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'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam: Group project; Computer-based written test in class using POLITO platform;
The scope of the exam is to verify that the student has acquired the fundamentals of digital signal processing included some of its practical applications, and is able to develop Matlab-based projects on engineering problems requiring signal processing.
During the course four assignments are proposed. They must be solved in groups of two students. The group must deliver a report containing all the results, the figures and the required answers and all the Matlab scripts for each assignment. Each assignment is evaluated and receives a mark (max mark = 12). The students who deliver their assignment within one and a half week from when it is assigned received 2 more bonus points (max mark = 14), to encourage the students to stay aligned to the course. At the end of the course the mean value of the assignment marks is computed.
The final exam consists of two parts:
1. A final written lab-based test on Matlab to be solved (similar structure of an assignment) and 2-3 theoretical questions taken from a list of questions presented during the course. Max mark = 18.
2. The mean value of the assignments marks. Max mark = 14.
Students who obtain at least 31 get 30L.
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.