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Applied signal processing laboratory

01TUMLP

A.A. 2019/20

Course Language

English

Course degree

1st degree and Bachelor-level of the Bologna process in Electronic And Communications Engineering - Torino

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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 1
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
ING-INF/03
ING-INF/03
3
5
F - Altre (art. 10, comma 1, lettera f)
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. 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.
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 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 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 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.
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 scritta; progetto individuale;
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.
Exam: written test; 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.


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