
Discrete-Time Signals and Systems
Code: 102712Credits: 6
| Degree programme | Type | Course |
|---|---|---|
| Electronic Engineering for Telecommunications | OB | 2 |
| Telecommunication Systems Engineering | OB | 2 |
Contact lecturer
- Name :
- Fran Fabra Cervellera
- Email :
- franciscojose.fabra@uab.cat
Teaching staff
- Rafael Terris Gallego
- Alda Xhafa
- Miquel Garcia Fernandez
Group languages
You can consult this information at the end of the document.
Prerequisites
This subject can be considered as the continuation of the subject 'Fundamentals of Signals and Systems'; therefore, it is recommended to have completed and passed 'Fundamentals of Signals and Systems'.
Objectives
The processing of sequences of numbers, also known as discrete signals, is a task present in virtually all information transmission, processing and storage systems, even when the source signals can be analog. The aim of the course is to provide the student with the fundamental knowledge to describe the discrete signals and the systems that deal with them, both in the temporal domain and in the frequency or transformed domains.
The specific goals are:
- To understand the representation of discrete signals over time, as well as their properties.
- To analyze the systems for the discrete signals processing over time and propose alternative ways of describing them.
- To represent signals and systems in transformed domains: in the frequency domain and in the Z domain.
- To design basic digital filters.
- To relate discrete signals with the periodic sampling of analog signals and with their reconstruction.
- To apply the Matlab programming environment to solve digital signal processing problems.
- To characterize discrete signals.
Learning outcomes
Electronic Engineering for Telecommunications
- KU111 (Estimate the effects of analogue and digital signal filtering.) Estimate the effects of analogue and digital signal filtering.
- SU116 (Apply the basic concepts of linear systems and related functions and transforms to analyze and solve problems in the field of engineering.) Apply the basic concepts of linear systems and related functions and transforms to analyze and solve problems in the field of engineering.
- SU117 (Analyse analogue and digital signal processing schemes, paying specific attention to those related to communications systems.) Analyse analogue and digital signal processing schemes, paying specific attention to those related to communications systems.
Telecommunication Systems Engineering
- KU111 (Estimate the effects of analogue and digital signal filtering.) Estimate the effects of analogue and digital signal filtering.
- SU116 (Apply the basic concepts of linear systems and related functions and transforms to analyze and solve problems in the field of engineering.) Apply the basic concepts of linear systems and related functions and transforms to analyze and solve problems in the field of engineering.
- SU117 (Analyse analogue and digital signal processing schemes, paying specific attention to those related to communications systems.) Analyse analogue and digital signal processing schemes, paying specific attention to those related to communications systems.
Contents
1. Signals and discrete systems
- Signals: properties, transformations and basic signals
- Systems: properties, basic systems
- Convolution
- Description of systems using finite difference equations
2. Frequency representation
- Fourier transform (FT): definition, properties, convolution theorem
- Discrete Fourier Transform (DFT): definition, properties, circular convolution
- Correlation and spectrum
- Decimation and interpolation
3. Sampling and reconstruction
- Periodic sampling
- Sampling representation in the frequency domain
- Reconstruction of limited band signals: Nyquist Theorem
- Modification of the sampling frequency
4. Representation of signals and systems in the Z domain
- The Z-transform: definition and properties
- The inverse Z-transform
- Frequency response and transfer function
5. System analysis
- Inverse, minimum-phase and all-pass systems
- Linear phase systems
- Introduction to the design of IR and IIR filters
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| Problem-solving lectures | 12 | 0.48 | |
| Individual work of the student: practices preparation | 12 | 0.48 | |
| Theory lectures | 36 | 1.44 | |
| Individual work of the student: study and exercices resolution | 64 | 2.56 | |
| Laboratory sessions | 12 | 0.48 | |
| Tutorship | 2 | 0.08 |
Class activities:
- Theory lectures: presentation of the theoretical contents
- Problem-solving lectures: solving problems related to the theory, with the participation of the students themselves.
- Laboratory sessions: application of the techniques shown in the theory lectures to different representative cases of real systems and implementation with standard software for digital signal processing (Matlab).
- Partial and final exams (recovery).
Autonomous activities:
- Study of the theoretical and practical contents of the subject. Problem solving and preparation of deliveries with solutions of some sets of problems or challenges. Preparation of the exams.
- Practical works: realization and deepening of laboratory sessions. Preparation of the laboratory sessions report.
In addition, students can attend sessions of individual or group tutoring to resolve questions that may arise during the course.
By default, all class activities are individual with the exception of laboratory sessions, which can be made in groups up to two members.
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| Practices | 20% | 5 | 0.2 | KU111, SU116, SU117 |
| Recovery Exam | 80% | 2 | 0.08 | KU111, SU116, SU117 |
| Follow-up Activities | Up to 20%, if it increases the final grade. | 1 | 0.04 | KU111, SU116, SU117 |
| Partial Exam 2 | 48% | 2 | 0.08 | KU111, SU116, SU117 |
| Partial Exam 1 | 32% | 2 | 0.08 | KU111, SU116, SU117 |
Principles of evaluation
The assessment is structured so that students can choose a format of continuous evaluation or a format where most of the weight of the assessment is concentrated at the end of the course (which can also be used as a recovery mechanism for the continuous evaluation). This allows the student to adapt the pace of completion of the assessment elements to their needs and preferences.
Evaluation elements
There will be the following evaluation elements:
- Follow-up activiites
- Practices
- Partial Exam 1
- Partial Exam 2
- Recovery Exam
The follow-up activities (NS) will consist of participation in class, carrying out problems or tests in class and/or outside of class. The follow-up note will only be used to raise the final grade (maximum of 20%).
The grade of practices (NP) will be evaluated based on the reports that must be submitted at the start and / or end of the practice sessions, the work and possible exercices done during or after the sessions, and the possible additional exercises deliveries. It is not necessary to approve each practice individually. Laboratory sessions are an activity that is not recoverable.
The Partial Exam 1 (ExP1) will be held approximately halfway through the course. It does not release matter why the contigunts of this course are cumulative; that is to say, they must master the first issues to be able to follow the last ones.
The Partial Exam 2 (ExP2) will be carried out approximately the last week of face-to-face activities (theoretical classes).
The Recovery Exam (ExR) will be carried out once the face-to-face activities have been finalized, during the time period specifically devoted to examinations.
Academic Integrity and Authorship Verification
The teaching staff reserves the right to require an individual oral interview or verification assessment whenever there are indications of plagiarism, cheating, or lack of authorship in an assessed activity. Failure to satisfactorily demonstrate authorship may result in the activity being graded in accordance with the applicable academic regulations.
Calculation of the final grade
- Continuous assessment note: AC = max {0.4*ExP1 + 0.6*ExP2, ExP2}
- Overall note of the exams (NE):
- NE = AC if the recovery exam is not peformed.
- NE = ExR if the recovery exam is performed.
- The final grade (NF) of the subject is: NF = max {0.8 x NE + 0.2 x NP, 0.8 x (0.8 x NE + 0.2 x NP) + 0.2 x NS}
- To approve it is necessary that NF >= 5.
Repeating students
You can keep the Lab Grade from previous years. It is the option that is considered by default if they do not go back to doing the practices.
Honor grades
Granting an Honor qualification is a decision of the faculty responsible for the subject. Honors will be awarded only to students who have shown a great level of excellence in the subject, and not bydefault to those who have removed the highest marks. The regulations of the UAB indicate that Honors can only be awarded to students who have obtained a final grade of 9.00 or more. It can be granted MH up to 5% of the total number of students enrolled.
Consideration of \"Not Evaluable\"
The final grade will be \"Not Evaluable\" only when the student is not present at any exam, neither of the continuous evaluation nor recovery exam.
Consideration in case of copy or plagiarism
Notwithstanding other disciplinary measures that are deemed appropriate, and in accordance with the current academic regulations, the evidenceorreports where the student has committed irregularities (eg plagiarism, deception, copying, the fact to leave copy, etc.) that could lead to a variation of the qualification.
Communication
The Virtual Campus will be the communication platform with the students.
Single evaluation
This subject does not consider a single assessment system.
Review procedure
A regular, in-person review period will be established after each exam.
Use of AI
In this course, the use of Artificial Intelligence (AI) technologies is allowed as an integral part of the development of coursework, provided that the final result reflects a significant contribution from the student in terms of analysis and personal reflection. The student must clearly identify which parts have been generated using this technology, specify the tools employed, and include a critical reflection on how these have influenced both the process and the final result of the activity. Lack of transparency in the use of AI will be considered an act of academic dishonesty and may result in a grade penalty for the activity, or more severe sanctions in serious cases.
Bibliography
Recommended bibliography
- Hsu P. Hwei, Señales y sistemas, 2a. edición, The McGraw-Hill Companies Inc, 2012.
- John G. Proakis, Dimitris G. Manolakis, Tratamiento digital de señales, 4ª edición, Pearson Prentice Hall, 2007.
- Alan V. Oppenheim, Ronald W. Schafer, Tratamiento de señales en tiempo discreto, 3ª edición, Pearson, 2012.
Other resources
- John G. Proakis, Dimitris G. Manolakis, Digital Signal Processing, Pearson, 2016.
- Alan V. Oppenheim, Ronald W. Schafer, Discrete-Time Signal Processing, 3nd edition, Prentice-Hall, 2009.
- S. Haykin, B. Van Veen, Signals and Systems, Wiley, 2002.
- S. Haykin, B. Van Veen, Señales y Sistemas, Limusa, 2001.
- M. J. Roberts, Señales y Sistemas, Mc Graw Hill, 2005.
- J.B. Mariño Acebal, F. Vallverdú Bayés, J.A. Rodríguez Fonollosa, A. Moreno Bilbao, Tratamiento Digital de la Señal: Una introducción experimental, Edicions UPC, 1999.
Software
During the practical sessions, MATLAB software will be used.
Likewise, MALTAB will also be used as a support for the theoretical and problem classes.
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 | 31 | Catalan/Spanish | first semester | morning-mixed |
| (TE) Theory | 32 | Catalan/Spanish | first semester | morning-mixed |
| (TE) Theory | 51 | Catalan/Spanish | first semester | afternoon |
| (PAUL) Classroom practices | 311 | Catalan/Spanish | first semester | morning-mixed |
| (PLAB) Practical laboratories | 311 | Catalan/Spanish | first semester | morning-mixed |
| (PAUL) Classroom practices | 312 | Catalan/Spanish | first semester | morning-mixed |
| (PLAB) Practical laboratories | 312 | Catalan/Spanish | first semester | morning-mixed |
| (PLAB) Practical laboratories | 313 | Catalan/Spanish | first semester | morning-mixed |
| (PLAB) Practical laboratories | 316 | Catalan/Spanish | first semester | morning-mixed |
| (PLAB) Practical laboratories | 317 | Catalan/Spanish | first semester | morning-mixed |
| (PLAB) Practical laboratories | 318 | Catalan/Spanish | first semester | morning-mixed |
| (PLAB) Practical laboratories | 319 | Catalan/Spanish | first semester | morning-mixed |
| (PLAB) Practical laboratories | 320 | Catalan/Spanish | first semester | morning-mixed |
| (PAUL) Classroom practices | 321 | Catalan/Spanish | first semester | morning-mixed |
| (PLAB) Practical laboratories | 321 | Catalan/Spanish | first semester | morning-mixed |
| (PAUL) Classroom practices | 322 | Catalan/Spanish | first semester | morning-mixed |
| (PLAB) Practical laboratories | 322 | Catalan/Spanish | first semester | morning-mixed |
| (PLAB) Practical laboratories | 323 | Catalan/Spanish | first semester | morning-mixed |
| (PAUL) Classroom practices | 511 | Catalan/Spanish | first semester | afternoon |