
Digital Signal Processing
Code: 102687Credits: 12
| Degree programme | Type | Course |
|---|---|---|
| Telecommunication Systems Engineering | OB | 3 |
Contact lecturer
- Name :
- José A. Lopez Salcedo
- Email :
- jose.salcedo@uab.cat
Teaching staff
- Sergi Locubiche Serra
- Fran Fabra Cervellera
Group languages
You can consult this information at the end of the document.
Prerequisites
It is recommended to have passed the following courses: Calculus, Algebra, Statistics, Discrete Systems and Signals, Fundamentals of Communications.
Objectives
Once completed the subject, the student will be able to:
- Use vector and matrix algebra normally.
- Operate with numerical series and stochastic processes.
- Rigorously use different probabilistic tools.
- Estimate the parameters of a model from the signal samples at its output.
- Estimate the power spectral density of a random process.
- Design optimal filters in the MMSE sense and implement them in an efficient manner using iterative/adaptive algorithms.
- Apply signal processing techniques to situations in real life.
Learning outcomes
- Autonomously learn new knowledge related with digital signal processing in order to conceive and develop communication systems.
- Propose innovative solutions for problems related with the transmission, reception and the digital treatment of signals.
- Adapt the knowledge and techniques of the digital signal treatment in accordance with the characteristics of communication systems and services as well as fixed or mobile work scenario.
- Analyse and specify the fundamental parameters of communication subsystems from the point of view of the transmission, reception and digital treatment of signals.
- Develop mathematical models to simulate the behaviour of communication subsystems and to evaluate and predict features.
- Analyse the advantages and disadvantages of different technological alternatives or the implementation of communication systems from the point of view of digital signal treatment.
- Be able to analyse, encode, process and transmit multimedia information employing analogue and digital signal processing techniques.
- Apply detection and estimation theory to the design of communication receivers.
- Apply adaptive statistical filtering and control theory to the design of dynamic algorithms for the coding, processing and transmission of multimedia information. Apply multichannel signal processing to the design of fixed and mobile antenna grouping based communication systems.
- Describe the operational principles of radio-navigation, its architecture and the techniques for dealing with its sources of error.
- Apply statistical signal processing to estimate synchronisation parameters in digital communication and radio-navigation receivers.
- Develop critical thinking and reasoning.
- Develop the capacity for analysis and synthesis.
- Develop scientific thinking.
- Develop independent learning strategies.
- Manage available time and resources.
- Adapt to unforeseen situations.
- Work in complex or uncertain surroundings and with limited resources.
- Develop curiosity and creativity.
- Generate innovative and competitive proposals in professional activity.
- Manage information by critically incorporating the innovations of one's professional field, and analysing future trends.
Contents
1. Introduction
- Discrete random processes, frequency representation.
- Fundamentals of matrix algebra.
- The autocorrelation matrix.
2. Estimation theory
- Fundamentals of model-based methodology.
- Classical vs. bayesian estimation.
- MVU criterion and properties of good estimators.
- Maximum likelihood estimation.
- Cramér-Rao lower bound.
- Suboptimal estimation methods.
- Applications in communication and positioning systems.
3. Spectral estimation
- Non-parametric methods.
- Capon or minimum variance method.
- Parametric methods.
- Super-resolution methods.
- Applications in voice coding and multi-antenna signal processing.
4. Wiener filtering and adaptive filtering
- Minimum mean square error (MMSE) estimation.
- Linear prediction.
- Steepest descent method.
- Convergence criteria.
- Least Mean Square (LMS) method.
- Applications in noise cancellation.
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| Problem solving | 40 | 1.6 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 |
| Tutorials | 15 | 0.6 | 7, 8, 9, 10, 11, 12 |
| Laboratory classes | 25 | 1 | 4, 5, 6, 7, 8, 9, 10, 11, 15 |
| Study | 100 | 4 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 21 |
| Exercise classes | 15 | 0.6 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 |
| Prepare laboratory classes | 30 | 1.2 | 1, 2, 3, 4, 5, 6, 12, 13, 15, 20, 21 |
| Lectures | 60 | 2.4 | 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 19 |
Directed activities:
- Lectures, which convey the theoretical contents of the course.
- Exercise classes, where exercises related to the theoretical contents of the course are solved by the lecturer with the participation of the students.
- Laboratory classes for the application of the contents conveyed during the lectures using Matlab.
- Written assessment tests.
Autonomous activities
- Study of the theoretical and practical contents of the course. Preparation of the problem solving. Exam preparation.
- Practical assignments: complete the laboratory reports and consolidate the knowledge acquired during the laboratory classes.
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| Exam 1 | 35% | 2.5 | 0.1 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 21 |
| Exam 2 | 35% | 2.5 | 0.1 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 17, 21 |
| Laboratory: in-situ evaluation | 30% | 10 | 0.4 | 1, 2, 3, 4, 6, 7, 8, 9, 12, 13, 14, 15, 16, 18, 19, 20, 21 |
Continuous evaluation
It consists of two different topologies of assessments. The first one is composed of two exams that assess the achievement of knowledge of the subject, and that form the "MarkTheory". The latter is calculated as the average of the two exams. The second type of assessment is the evaluation that the lab examiner carries out in-situ during the laboratory sessions scheduled in the subject, and it forms the "MarkLab".
Based on the theory and lab marks, the overall mark of the subject is calculated as:
If (MarkTheory >= 3.5) --> MarkContinuousAssessment = (0.7 x MarkTheory) + (0.3 x MarkLab)
If (MarkTheory < 3.5) --> MarkContinuousAssessment = MarkTheory
Therefore, the student should get a theory mark equal or greater than 3.5 to be eligible to have the theory and lab marks averaged.
Single evaluation
This subject does not consider a single assessment system.
Re-assessment
Students who have failed the continuous evaluation will be eligible to attend the re-assesment exam, provided that the mark obtained in theory part (average of the two exams) is equal or greater than 2.5. The re-assessment exam will be carried out within the period of exams published by the School. In this exam, the student can re-assess the part corresponding to the Exam1, the part corresponding to the Exam2, or both. The mark obtained in each part of the re-assessment exam (mark ExamRA1, mark ExamRA2)supersedes the previous mark that the student had in the corresponding continuous evaluation exam. The re-assessment mark is computed as follows:
MarkTheoryRA = [(0.35 x Mark {Exam1 or ExamRA1}) + (0.35 x Mark {Exam2 or ExamRA2})]/ 0.7
The final mark of the course is then computed as follows:
If (MarkTheoryRA >= 3.5) --> FinalMark = (0.7 x MarkTheoryRA) + (0.3 x MarkLab)
If (MarkTheoryRA < 3.5) --> FinalMark = MarkTheoryRA
Laboratory rules
- The assistance to all laboratory classes is compulsory.
- The report of each laboratory session must be delivered at the end of the session. Any delay in the delivery of the report will be penalised in the corresponding mark. In addition, the laboratory professor will be \"in-situ\" evaluating the actual progress and usage of the session made by the student and this item will have an impact on the mark of the report itself.
- Artificial intelligence cannot be used to solve the laboratory exercises or write the reports.
Repeating students
If in previous years they passed the laboratory part of the course, they will maintain the same mark and will not have to repeat the laboratory. If any repeating student wants to do the laboratory sessions again, they must explicitly inform the laboratory professor. Otherwise, there is no need to inform the professor because the option of maintaining the mark is assumed by default.
Honors grades
Awarding an honors grade is the decision of the professor responsible for the course. Honors grades will only be awarded to students who have shown a high level of excellence in the subject, and not by default to the highest grades. UAB regulations indicate that honors grades can only be awarded to students who have obtained a final grade equal to or higher than 9.00, and can be awarded to up to 5% of the total number of students.
Consideration of "Not Assessable"
A student who does not take any of the exams (neither the two continuous assessment exams nor the retake exam) will be considered "Not Assessable".
Use of AI
All content submitted by students (exams, reports, follow-up activities, etc.) must be original and generated solely by them, without the direct intervention of artificial intelligence tools, unless otherwise indicated. This does not prevent students from using AI as a learning tool or support to review content, assist in the creation of code or compare results.
Communication
The Virtual Campus will be the communication platform with students.
Additional considerations
Without prejudice to other disciplinary measures that may deem necessary in accordancetothe academic regulation, any irregularity committed by the student that may alter the mark of an assessment activity will lead this activity to be marked with zero points. For instance, copying or letting copy a laboratory report or any another assessment activity will involve to fail it with a mark equal to zero. Furthermore, such activity will not be able to be re-assessed during the same academic course. If this activity has a minimum mark associated to it, then the subject will be graded as failed.
If the student commits irregularities in various assessment activities of the course, the final mark of the course will be 0 in virtue of point 10, article 116, of the Academic Regulation.
Bibliography
Basic:
- S. M. Kay, Fundamentals of statistical signal processing. Estimation theory, vol. I, Prentice-Hall, 1993.
- M. H. Hayes, Statistical digital signal processing and modeling, John Wiley and Sons, 1996.
- P. Stoica and R. Moses, Spectral analysis of signals, Prentice-Hall, 2005.
- S. Haykin, Adaptive filter theory, Pearson, 2013.
Supplementary:
- A. H. Sayed, Inference and learning from data. Foundations, vol. I, Cambridge University Press, 2022. (tema 1)
- A. H. Sayed, Inference and learning from data. Inference vol. II, Cambridge University Press, 2022. (tema 2)
- D. G. Manolakis, V. K. Ingle, S. M. Kogen, Statistical and adaptive signal processing: spectral estimation, signal modeling, adaptive filtering and array processing, Artech-House, 2005.
- S. M. Kay, Fundamentals of statistical signal processing. Practical algorithm development, vol. III, Pearson, 2013.
- S. Lawrence Marple, Digital spectral analysis, Dover Publications, 2019.
- B. Widrow and S. D. Stearns, Adaptive signal processing, Prentice-Hall, 2985.
- A. Hjorungnes, Complex-Valued Matrix Derivatives: With Applications in Signal Processing and Communications, Cambridge University Press, 2011.
- Fundamentals:
- S. M. Kay, Intuitive probability and random processes using Matlab, Springer, 2006.
- V. K. Ingle and J. G. ProakisManolakis, Digital signal processing using Matlab, Cengage Learning, 2012.
Software
- The laboratory sessions will be carried out using the software MATLAB.
Course groups and languages
The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE
| Type of teaching | Group | Language | Semester | Shift |
|---|---|---|---|---|
| (TE) Theory | 330 | Catalan/Spanish | first semester | morning-mixed |
| (PAUL) Classroom practices | 332 | Catalan/Spanish | first semester | morning-mixed |
| (PLAB) Practical laboratories | 332 | Catalan/Spanish | first semester | morning-mixed |
| (PLAB) Practical laboratories | 333 | Catalan/Spanish | first semester | morning-mixed |
| (PLAB) Practical laboratories | 334 | Catalan/Spanish | first semester | morning-mixed |