PLEASE NOTE THAT THIS SUBJECT IS TAUGHT ONLY IN SPANISH/BASQUE, ALTHOUGHT IT IS ENGLISH FRIENDLY.



This course is a compulsory course in the 2nd year of the Engineering Degree in Artificial Intelligence (taught during the first term).



The subject is designed to introduce students to both the theoretical and practical aspects of Digital Signal Processing. For this reason, the subject uses concepts previously learned in first-year subjects of the degree in the area of mathematics (Mathematical Analysis, Algebra), statistics (Statistical Methods in Engineering) and programming (Basic Programming, Programming Methodology, Modular Programming and Object Oriented).



Within the context of the degree in Artificial Intelligence, the course provides students with the necessary tools to process signals of various kinds and transform them so that they can be used as input to intelligent information processing systems that are presented in multiple subjects of the degree (Data Mining, Artificial Intelligence, Machine Learning and Neural Networks, Biomedical and Physiological Data Analysis, Speech Processing, among others).



In the professional field, the subject enables students to digitally process any type of signal (sound, image, information from sensors, time series, etc.) in multiple fields (audiovisual, industry, medicine, meteorology, etc.). In this way, it serves as a link to other areas such as Data Science, Big Data, Industry 4.0, Robotics, Physiological Computing, etc.

PLEASE NOTE THAT THIS SUBJECT IS TAUGHT ONLY IN SPANISH/BASQUE, ALTHOUGHT IT IS ENGLISH FRIENDLY.



According to the verified report of the degree in Artificial Intelligence, the competences (specific, transversal) and learning outcomes are presented.



The specific competences acquired by taking the subject are:

M07CE1 - Knowledge of the representation of signals and systems in the time and frequency domains, both in continuous and discrete time.

M07CE2 - Ability to understand, analyse, evaluate and apply the most appropriate digital signal processing strategies to deal with a given physical process.



The transversal competences acquired by taking this subject are:

CT1 - Autonomy and Self-regulation

Ability to use self-management and self-regulation techniques.

CT3 - Communication and Multilingualism

Ability to communicate ideas and arguments in a comprehensible way and according to established formal criteria.

CT8 - Teamwork

Value teamwork, accepting the potential of diversity as a learning opportunity. Carry out with responsibility the tasks that correspond to them in order to achieve the objectives and the collective result.



The specific learning outcomes provided by the subject are the following:

RA1 - Know how to use digital signal processing software and critically interpret the results obtained.

RA2 - Master and be able to apply the mechanisms of transformation of continuous signals to digital: sampling and quantification.

RA3 - Know the main methods of calculating the Fourier transform and know how to apply them to digital signals.

RA4 - Know the main parameters of FIR and IIR digital filters, and know how to design and apply them to digital signals.



Based on these competences and learning outcomes, the following objectives are contemplated in the subject:

O1 - To introduce students to the basic concepts related to Digital Processing: signals, systems, time and frequency analysis, filters.

O2 - To deepen in these concepts for signals of different nature, and to show the methods used in digital systems to capture, process and produce this type of signals.

O3 - To show different practical applications of these techniques and alternatives for their implementation.

O4 - To put into practice the concepts studied, applying them in the laboratory to real cases of signal processing using the MATLAB platform (other alternatives such as SCILAB, Octave, Python, etc. may also be used).

PLEASE NOTE THAT THIS SUBJECT IS TAUGHT ONLY IN SPANISH/BASQUE, ALTHOUGHT IT IS ENGLISH FRIENDLY.



Theme 1

1.1 Introduction to digital signal processing

1.2 Signals and systems Why digital processing?

Introduction to the subject in which the basic definitions of the PDS are shown.



Theme 2

2.1 Digital signals

2.2 Definitions and properties. Digitization. Basic signals and operations

2.3 Analysis of different types of signals (sound, image, physiological, etc.)

Practical part: Specific Project 1 (PE1) – Introduction to Matlab

After introducing the main types of signals, a specific project will be carried out in order to learn the functionalities of the Matlab platform.



Theme 3

3.1 Time domain analysis

3.2 Usual operations. Windowing and short-term operations. Correlation

Practical part: Specific Project 2 (SP2) - Time Domain Analysis

This chapter presents the treatment of signals in the time domain. It introduces short-time analysis of signals and the possible features to be extracted, e.g. correlation. It ends with a project in which a sound signal is analysed to automatically extract the frequencies present by means of correlation.



Theme 4

4.1 Frequency domain analysis

4.2 Starting idea. Fourier series and transform. Application to two-dimensional systems

4.3 Projects: Analysis of digital signals in the frequency domain

Practical part: Specific Project 3 (SP3) - Frequency domain analysis

This chapter presents the treatment of signals in the frequency domain after applying the Fourier transform. Window analysis is again used in order to extract features in the frequency domain. It ends with a project in which several sound signals (DTFM, melodies) are analysed in order to extract the frequencies present in them by analysing the spectra.



Theme 5

5.1 Filters

5.2 LTI systems. FIR filters. Z transform. IIR filters. Non-linear filters

Practical Part: Specific Project 4 (SP4) - LTI Filters: FIR

Practical Part: Specific Project 5 (SP5) - LTI Filters: IIR

This topic introduces the two types of LTI filters, FIR and IIR, together with the Z-transform. In this topic, two specific projects with FIR and IIR filters respectively are developed in practical applications, e.g. removal of unwanted noise in signals.



Theme 6

6.1 Applications of digital signal processing.

6.2 Areas of application and examples.

The last topic of the course explores the concept of sampling, quantification and aliasing. In addition, applications of PDS are considered that point towards possible final projects.

Practical part: Final Project (FP) - Application of PDS in a given context.

This is a project of medium/high complexity in which what has been learnt in the course is applied.

PLEASE NOTE THAT THIS SUBJECT IS TAUGHT ONLY IN SPANISH/BASQUE, ALTHOUGHT IT IS ENGLISH FRIENDLY.



There are four types of activities:



- Autonomous study by the students of the material available in the virtual classroom for each subject in which the theoretical/practical concepts to be used are presented, as well as a proposal of exercises associated with them. In addition to directly accessible information, students can use bibliographic references as support material.



- Presentation and exercise classes in which, in a participative way, the theoretical/practical concepts of each topic are shared and the doubts associated with them are clarified, always emphasizing their usefulness and practical aspects. In these sessions, the initially proposed exercises ("on paper") will be shared in order to deepen the theoretical foundations. Exercises will also be proposed on each topic that the students will have to solve and that will be evaluated with the corresponding feedback.



- Development of specific projects in which students in groups ( preferably in groups of 2 or 3) apply the theoretical/practical concepts learnt to real problems, using specific signal processing software. For each of these sessions, a technical report of the results must be submitted that will be evaluated with the corresponding feedback.



- Development of a final project (medium/high complexity level) in which the students (preferably in groups of 2 or 3) will apply the theoretical/practical knowledge previously learned in the course.



In order to facilitate student learning, specific projects will be monitored by providing feedback based on previously established and shared evaluation criteria. In this way, students are aware of their level of learning and take steps to improve it if necessary.

• Continuous Assessment System
• Final Assessment System
• Tools and qualification percentages:
  • The percentages and types of assessment are specified in the following sections (%): 100

PLEASE NOTE THAT THIS SUBJECT IS TAUGHT ONLY IN SPANISH/BASQUE, ALTHOUGHT IT IS ENGLISH FRIENDLY.



The assessment systems considered are the continuous assessment system and the final assessment system. In the ordinary call, the continuous assessment system is the one that will be used in preference, as indicated in the current regulations of the UPV/EHU. The mark is calculated as follows:



- Theory: classroom exercises and written tests 50% (5 points: 2 points for topics 1-2-3, 2.5 points for topics 4-5, and 0.5 points for topic 5).

- Practical: specific projects 35% and final project 15%. This part of the course uses a PBL type methodology and involves the autonomous completion of the proposed projects, with the delivery of the corresponding technical reports (transversal communication skills) by each group of two or three people (collaborative learning). In addition, there will be individual written evaluations that will weigh the marks of the practical part in the following way:

Minimun mark Weigthing to be applied

0 0.3

3 0.5

5 0.7

8 1.0





For the final assessment mode, the students will have to submit the reports corresponding to the specific projects and the final project at least two weeks before the date of the ordinary call (date of the final theory test). In this case, the examination will weigh 60% and the practical part 40% (based on previously submitted projects). There will be an individual written evaluation that will weigh the overall mark of the practical part (see the weighting in the continuous assessment).



In order to pass the subject, in any modality, it is necessary to pass both the practical and theoretical parts of the subject separately.



Students who, fulfilling the conditions to continue in the continuous assessment system, decide to opt for the final or global assessment, must inform the teacher responsible for the subject by email before the beginning of the second week of the grouped timetable of the four-month period established in the centre's calendar.

PLEASE NOTE THAT THIS SUBJECT IS TAUGHT ONLY IN SPANISH/BASQUE, ALTHOUGHT IT IS ENGLISH FRIENDLY.



In the case of the extraordinary call, the final mark is calculated based on two parts:



- Theory (60%): It is assessed by means of a knowledge test on the date of the extraordinary call.



- Practical (40%): This is assessed on the basis of the technical reports corresponding to the specific and final projects, which must be submitted before the date of the theory test. There will be an individual written evaluation that will weigh the overall mark of the practical part.



In order to pass the course it is necessary to pass both parts (theoretical and practical).

PLEASE NOTE THAT THIS SUBJECT IS TAUGHT ONLY IN SPANISH/BASQUE, ALTHOUGHT IT IS ENGLISH FRIENDLY.

For the correct development of the subject it is required:
- a PC type personal computer.
- and specific software for signal processing (MATLAB, etc.), for the laboratory practices.

The centre provides both resources. In addition, students have the possibility of carrying out the practical projects on their own computers using the UPV/EHU's MATLAB corporate licence and free software (SCILAB, Octave, Python, etc.).

Basic bibliography

PLEASE NOTE THAT THIS SUBJECT IS TAUGHT ONLY IN SPANISH/BASQUE, ALTHOUGHT IT IS ENGLISH FRIENDLY.



J. G. Proakis, D.G. Manolakis: "Tratamiento digital de señales". Prentice-Hall, 1997.

R.G. Lyons: “Understanding Digital Signal Processing”. Prentice Hall, 2010.

A. Lárez: “Procesamiento Digital de Señales: parte 1”. Eleunion, 2022.

J. G. Proakis, D.G. Manolakis: Digital Signal Processing: Principles, Algorithms, and Applications. 4th Edition, Pearson Education, Inc., New Delhi, 2007.

A. V. Oppenheim, R. W. Schafer: "Digital Signal Processing". Prentice-Hall, 1988.

A.V. Oppenheim, R. W. Schafer: “Discrete-Time Signal Processing”. Prentice Hall, 2009.

S. S. Soliman, M.D. Srinath: "Señales y Sistemas continuos y discretos", Prentice Hall, 1999.

In-depth bibliography

PLEASE NOTE THAT THIS SUBJECT IS TAUGHT ONLY IN SPANISH/BASQUE, ALTHOUGHT IT IS ENGLISH FRIENDLY.

E. Soria: "Tratamiento Digital de Señales: Problemas y ejercicios resueltos", Pearson Prentice Hall, 2003.
S.I. Abood: “Digital Signal Processing: A Primer with MATLAB”. CRC Press, 2020.
C. S. Burrus: "Ejercicios de tratamiento de señal utilizando MATLAB v4". Prentice-Hall, 1997.
B. Gold, N. Morgan: "Speech and audio Signal Processing: Processing and perception of speech and music", Wiley 2000.
J. R. Deller, J. G. Proakis: "Discrete-Time Processing of Speech Signals". MacMillan, 1993.

Journals

PLEASE NOTE THAT THIS SUBJECT IS TAUGHT ONLY IN SPANISH/BASQUE, ALTHOUGHT IT IS ENGLISH FRIENDLY.

Digital Signal Processing (Elsevier)
Signal Processing (Elsevier)
IEEE Signal Processing Letters

Web addresses

PLEASE NOTE THAT THIS SUBJECT IS TAUGHT ONLY IN SPANISH/BASQUE, ALTHOUGHT IT IS ENGLISH FRIENDLY.

www.mathworks.com
www.scilab.org
www.dsprelated.com
www.gnu.org/software/octave
www.scipy.org