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Applied signal processing laboratory

02TUMMB

A.A. 2022/23

Course Language

Inglese

Course degree

1st degree and Bachelor-level of the Bologna process in Ingegneria Chimica E Alimentare - Torino

Course structure
Teaching Hours
Lezioni 50
Esercitazioni in laboratorio 30
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Garello Roberto Professore Associato ING-INF/03 20 0 10 0 4
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
ING-INF/03 6 D - A scelta dello studente A scelta dello studente
Valutazione CPD /
2022/23
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 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 UWB tags, GPS, audio processing, image processing, bio-devices like pulse-oximeter and OCT, 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 six topics: - Signals and Sampling (10 hours) - Spectral analysis (10 hours) - Signal correlation (10 hours) - Digital Filtering (10 hours) - Digital Signal Processing Applications 1: UWB Airtag, GPS, audio processing, pulse oximeter (10 hours) - Digital Signal Processing Application 2: OCT image processing, photography, bio signals (10 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 5 topics. The first 4 introduce the basis of signal processing: sampling, Fourier transform, correlation and digital filtering. The last two present some important DSP applications: localization techniques like UWB Airtag and GPS, bio-signals and pulse-oximetry, basic audio processing and image processing. For each topic, a homework is assigned, consisting in the implementation of a Matlab or Python program and the preparation of a short report. Each topic corresponds to about 10 hours, further divided in 5 hours of lectures and 5 hours of tutoring where the teacher is available to help students with Matlab/Python implementation. 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.
ModalitÓ di esame: Prova orale facoltativa; Elaborato progettuale individuale;
Exam: Optional oral exam; Individual 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 disabilitÓ 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'UnitÓ Special Needs, al fine di permettere al/la docente la declinazione pi¨ idonea in riferimento alla specifica tipologia di esame.
Exam: Optional oral exam; Individual 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 is organized as follows: For each topic (6) an assignment (to be solved individually) is proposed. The student must deliver a pdf report containing alle the results, the figures and the required answers and all the written Matlab or Python programs. Each assignment is evaluated and receives a mark (max mark = 30). The students who deliver their assignment within 2 weeks from when it is assigned received 2 more bonus points (max mark = 32), 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 students who want to further improve the mark (max +6/30) can: - deliver some of the 3 extra assignments which are proposed for the last two topic on applications. For each of them the student can get up to +2/30 points for the final mark - seat for an oral examination where 3 questions are proposed, extracted from a list of about 50 questions prepared at the end of the course, each question can give up to +2/30 points for the final mark. Students who get at least 33 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.
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