Fundamentals of Signal Processing

Code:

L.EEC025

Acronym:

FPS

Keywords
Classification	Keyword
OFICIAL	Basic Sciences for Electrotechnology

Instance: 2021/2022 - 1S

Active?	Yes
Web Page:	https://moodle.up.pt/course/view.php?id=3853
Responsible unit:	Department of Electrical and Computer Engineering
Course/CS Responsible:	Bachelor in Electrical and Computer Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
L.EEC	300	Syllabus	3	-	6	52	162
M.EEC	70	Syllabus	1	-	6	52	162

Teaching language

Portuguese and english

Objectives

This course aims to motivate students to the fundamental concepts, techniques and tools of analysis and design in the field of Signal Processing (SP). A particular emphasis is given to specific topics, notably sampling and reconstruction of signals; the Z transform; the design and realization of FIR and IIR filters; discrete equivalents of continuous systems; the Discrete Fourier Transform (DFT) and its fast computation through the FFT; practical applications of the DFT mainly in correlation studies and spectral analysis. A central objective is to empower students to solve signal processing-related problems and to motivate them to laboratory experimentation through the design, testing and practical validation of solutions for selected challenges by following a "hands-on" and "learning-by-doing" approach.

Learning outcomes and competences

Attendance and successful completion of this course will enable students

-to understand the process of sampling and signal reconstruction and to anticipate the implications when applied to real signals;

-to design, implement and test digital FIR and IIR filters according to specific operation and signal conditioning requirements, including in adaptive filtering;

-to fully understand the DFT, its circular properties and fast implementation alternatives (FFT);

-to identify and realize potential applications of the DFT, particularly in fast FIR filtering, correlation studies and in spectral analysis.

Working method

B-learning

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Signals and Systems (L.EEC015), or equivalent

Program

1. Characterization and representation of discrete-time signals and systems. Deterministic and discrete-time random signals.
2. Discrete-time Fourier Transform. Properties and transform pairs.
3. Sampling and reconstruction of signals. The sampling theorem and aliasing. Discrete-time processing of continuous-time signals.
4. The Z Transform. Causality and stability conditions. Characterization in the Z domain of FIR and IIR discrete systems.
5. Inverse systems, all-pass systems, minimum-phase, linear-phase, and maximum-phase systems. FIR linear-phase systems.
6. Design of discrete IIR and FIR filters and their realization structures. Adaptive filters.
7. Discrete equivalents of continuous-time systems.
8. The Discrete Fourier Transform (DFT) and its periodic properties.
9. The computation of the DFT using the Fast Fourier Transform (FFT).
10. Application of the FFT in FIR fast-convolution, in correlation studies, and in spectral estimation.

Mandatory literature

Alan V. Oppenheim; Discrete-time signal processing. ISBN: 0-13-083443-2

Complementary Bibliography

Sanjit K. Mitra; Digital signal processing. ISBN: 0-07-122607-9
John G. Proakis; Digital signal processing. ISBN: 0-13-187374-1

Teaching methods and learning activities

The teaching methodology is based on lectures (1.5h/week), practical classes (1.5h/week) and laboratory classes (1h/week). Lectures involve the discussion and illustration of the course topics and include, in specific cases, additional video support that the students may watch. The objective of practical classes is to discuss conventional or Matlab-based exercises, including in a “peer-to-peer learning/teaching” perspective, and to guide students in the preparation of laboratory work in groups of 4 students. The laboratory classes are dedicated to the development of experimental work using Matlab and/or a real-time digital signal processing platform. Lectures (T), practical (TP) and laboratory (PL) classes incorporate a form of evaluation that is weighted at 25% (T) and 75% (TP+PL) in the distributed evaluation at the end of the semester. In turn, this distributed evaluation grade is combined (60% weighting) with a final exam classification (40% weighting) to deliver the final classification.

Software

Matlab

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Participação presencial	12,00
Exame	40,00
Trabalho laboratorial	36,00
Teste	12,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	60,00
Frequência das aulas	52,00
Trabalho laboratorial	26,00
Trabalho de campo	24,00
Total:	162,00

Eligibility for exams

Attending T, TP and PL classes and obtaining an attendance grade is essential for admission to the final exam.

The participation grade (F) is given to students who do not exceed the absence limit (according to the FEUP General Assessment Regulation) and who have taken the online tests and prepared performed the practical and laboratory work requested for distributed evaluation (AD). Online tests are carried out individually and some practical work, as well as laboratory work, are carried out in groups of 4 students. Online tests are performed in T classes (Verification Questions) and represent 25% of AD. TP works are carried out individually, but evaluated in groups, in a “peer-to-peer” perspective, and are weighted at 20% in AD. Laboratory work is assessed in PL classes and is weighted at 55% in AD.

Calculation formula of final grade

The final exam consists of a written exam lasting 2 hours. This exam is closed book but a formulae sheet will be provided.

The final grade (C) is obtained by combining the participation score (F) and the score of the written exam (E> = 7.0) using the formula

 

C = 0.6×F + 0.4×E.

 

The final grade is conditional to a minimum score of 7.0/20 in the written exam.

All grades presume the range [0, 20].

Special assessment (TE, DA, ...)

No student enrolled in the course is exempt from participating in the various distributed evaluation components. If, due to justified and force majeure reasons, a student benefiting from a special status is not able to participate in those components, he/she is subject to the development of a mandatory project based on the signal processing platform adopted in laboratory classes and whose theme and realization objectives must be agreed with the principal professor of the course. This project must be documented through a report and its operation must be demonstrated through a practical laboratory exam.

Classification improvement

Due to the fact that the participation score (F) is based on several components evaluated in different types of classes and throughout the semester, the participation score is not subject to improvement through any modality replacing it at the end of the semester. As a result, only the final exam score (E) can be improved according to the current rules.

Observations

The Zoom link that is required to attend each weekly online lecture will be posted on the Moodle platform a few minutes before the lecture starts.