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Applied signal processing laboratory

02TUMLN

A.A. 2019/20

Course Language

Inglese

Course degree

1st degree and Bachelor-level of the Bologna process in Automotive Engineering - Torino

Course structure
Teaching Hours
Lezioni 20
Esercitazioni in aula 60
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
ING-INF/03 6 D - A scelta dello studente 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 - Signal correlation - Spectral estimation - Digital filtering - Processing of signals from mechanical and bio sensors
The course will cover the following five 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)
The course is organized in 5 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 last one presents case studies on mechanical and bio sensor processing. 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, further divided in 5 hours of lectures and 5 hours of Matlab implementation. To realize the homeworks, the groups may be composed by 1 or 2 students.
The course is organized in 5 topics. The first 2 introduce the basis of signal processing: sampling and correlation. o The next 2 present applications to spectral application and digital filtering, that can be useful for many different areas. The last one presents case studies on mechanical and bio sensor processing. For each topic, a homework will be assigned, consisting in the implementation of a Matlab program and a short report. o Each topic corresponds to about 10 hours, further divided in 5 hours of lectures and 5 hours of Matlab 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 scritta (in aula); 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 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.


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