Caricamento in corso...
Applied signal processing laboratory
02TUMLP
A.A. 2025/26
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
Course structure
| Teaching | Hours |
|---|
Lecturers
| Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
|---|
Co-lectures
Context
| SSD | CFU | Activities | Area context | ING-INF/03 | 6 | D - A scelta dello studente | A scelta dello studente |
|---|
2024/25
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 (e.g., pulse-oximeters and EKG processing), tags for findind objects, aerospace communication signals, 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 is made by lectures which introduce a topic, followed by algorithm implementation by Matlab (or Python). The last part of the course focuses on very important applications based on signal processing (including detection of alarm signals, tags for finding objects, GPS, audio processing, image processing, bio-devices like pulse-oximeter and OCT, aerospace signals like PSK/QAM, satellite link budget, etc.). 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 basic signal processing techniques like sampling, digital filtering, correlation and spectral estimation.
- will understand the relationship between signal processing theory and algorithms for different engineering areas.
- will be able to design and perform a Matlab or Python-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.
Students must use their own laptops with Matlab or Python.
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:
- Introduction to Signal Processing (6 hours)
- Signal correlation (6 hours)
- Spectral analysis (6 hours)
- Sampling (6 hours)
- Digital Filtering (6 hours)
- Digital Signal Processing applications: sampling, spectral analysis, correlation (6 hours)
- Digital Signal Processing applications: alarm signal recognition, tags for findind objects (6 hours)
- Digital Signal Processing Applications: bio signals and pulse oximeters (6 hours)
- Digital Signal Processing Applications: introduction to audio processing (6 hours)
- Digital Signal Processing Applications: communication signals for aerospace and satellites (6 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 two parts. The first part introduce the basis of signal processing: sampling, Fourier transform, correlation and digital filtering.
The second part presents some important DSP applications: localization techniques like UWB tags and GPS, alarm signal recognition, bio-signals and pulse-oximetry, basic audio processing and image processing.
During the course, 4 assignments are proposed on DSP applications, consisting in the implementation of a Matlab or Python program and the preparation of a presentation.
Each assignment topic has at least 6 hours of tutoring where the teacher is available to help students with Matlab/Python 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.
Modalità di esame: Prova orale facoltativa; Elaborato progettuale in gruppo;
Exam: Optional oral exam; Group project;
...
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: Optional oral exam; Group project;
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.
There are two examination modes: exam A and exam B.
The exam A is organized as follows. During the course four assignments are proposed.
They must be solved individually or in groups of up to three students.
The students must deliver
• a presentation containing all the results, the figures and the required answers
• all the Matlab or Python programs.
Each assignment is evaluated and receives a grade (max = 30). The students who deliver their assignment within 2 weeks from when it is assigned will receive 2 bonus points (max = 32), to encourage the students to stay aligned to the course.
All the assignments must be submitted by the end of the September session; submissions will not be accepted after this deadline.
At the end of the course the mean value of the assignment grades is computed and proposed as the final grade. Students who reach at least 30.5 get a grade of 30 cum laude.
The students who want to further improve their average assignment mark (at most +4/30) can deliver one or two of the extra assignments which are proposed at the end of the course. For each of them the student can get up to +2/30 points for the final mark.
As an alternative, the exam B is organized as follows.
Students who opt not to submit the assignments must take an oral exam consisting of 4 questions taken from a list of about 40 questions. (maximum mark = 24) The students who want to further improve this mark can deliver some of the extra assignments which are proposed during the course. For each of them the student can get up to +2/30 points for the final mark (with the optional delivery, the maximum mark becomes 30).
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.