Signals and Systems

Credits
6
Types
Compulsory
Requirements
This subject has not requirements, but it has got previous capacities
Department
TSC
The set of techniques and algorithms that allow studying, detecting, transforming, processing, transmitting and classifying signals is called signal processing. This course provides an introduction to the fundamental theory of this discipline for both one-dimensional and two-dimensional signals. During the course the signals and the systems in the time domain and the frequency domain are characterized by the Fourier transform in its analog and discrete versions. The sampling theorem allows the processing of analog signals by means of discrete techniques and to be able to apply computationally efficient algorithms. The course introduces the concept of filter and filtering of signals, and filters are designed using specs in the frequency domain.

Teachers

Person in charge

  • M. Asuncion Moreno Bilbao ( )

Others

  • Olga Muñoz Medina ( )

Weekly hours

Theory
2
Problems
1.5
Laboratory
0.5
Guided learning
0.4
Autonomous learning
5.6

Competences

Technical Competences

Technical competencies

  • CE5 - Design and apply techniques of signal processing, choosing between different technological tools, including those of Artificial vision, speech recognition and multimedia data processing.

Transversal Competences

Transversals

  • CT5 - Solvent use of information resources. Manage the acquisition, structuring, analysis and visualization of data and information in the field of specialty and critically evaluate the results of such management.

Basic

  • CB5 - That the students have developed those learning skills necessary to undertake later studies with a high degree of autonomy

Generic Technical Competences

Generic

  • CG1 - To design computer systems that integrate data of provenances and very diverse forms, create with them mathematical models, reason on these models and act accordingly, learning from experience.
  • CG2 - Choose and apply the most appropriate methods and techniques to a problem defined by data that represents a challenge for its volume, speed, variety or heterogeneity, including computer, mathematical, statistical and signal processing methods.
  • CG5 - To be able to draw on fundamental knowledge and sound work methodologies acquired during the studies to adapt to the new technological scenarios of the future.

Objectives

  1. 1. The student must be able to understand and be proficient on the basic concepts of signals, linear systems and related functions and transformations.
    Related competences: CG2, CG5, CB5,
  2. 2. The student must know how to do the mathematical analysis of signals and systems in the time and frequency,domains both in analogue and digital environments.
    Related competences: CG5, CB5,
  3. 3. The student must know how to interpret and use discrete signals and systems in 1D and 2D in the temporal / spatial and frequency domains.
    Related competences: CE5, CG1, CG2,
  4. 4. The student must be able to apply the frequency representation of signals and systems to solve various applications.
    Related competences: CE5, CT5, CG2,
  5. 5. The student must be able to evaluate discrete filters and apply them to real systems
    Related competences: CE5, CG2, CG5, CB5,
  6. 6. The student must know how to correctly formulate a problem from the proposed statement and identify the options for its resolution, apply the appropriate resolution method, and validate the solution.
    Related competences: CT5, CG2, CB5,
  7. 7. The student must know how to identify, model and solve problems from open situations. Also to explore and apply the alternatives for resolution. He will work with approximations.
    Related competences: CE5, CG1, CG2, CG5,
  8. 8. The student must know how to use autonomously the tools, instruments and software applications available in the laboratories of the basic and advanced subjects. He should know their performances and limitations.
    Related competences: CE5, CT5, CG1, CG2,
  9. 9. The student should know additional tools useful for processing discrete generic signals in the time and transformed domains.
    Related competences: CE5, CG1, CG2,
  10. 10. The student must be able to evaluate advantages and disadvantages of different technological alternatives to implement analysis systems for analog and discrete signal.
    Related competences: CE5, CT5, CG2,

Contents

  1. Signals and Systems
    Signals and systems.
    Characterization of signals and sequences, energy and power.
    Analog and discrete systems, properties.
    Impulse response and convolution equation.
    Discrete systems represented by equations in differences.
    Impulse response (FIR and IIR systems)
  2. Fourier transform of analog signals.
    Definition and properties.
    Frequency response.
    Examples: Filtering, packaging, modulation.
  3. Sampling.
    Sampling Theorem.
    Interpolation formula.
    Conversion A/D, D/A.
  4. Fourier transform of discrete signals.
    Definition and Properties
    Discrete Fourier Transform. DFT.
  5. Filtering.
    Z transform.
    Design of filters. Specifications template. Design tools.
    Linear phase filters, filters runs all over.
  6. Interpolation and decimation.
    Interpolation and decimation.
    Changing the sampling frequency.

Activities

Activity Evaluation act


Topic 1

Theory classes and problems corresponding to topic 1
Objectives: 1 2 6
Contents:
Theory
6h
Problems
4h
Laboratory
2h
Guided learning
2h
Autonomous learning
18h

Topic 2

Theory classes and problems corresponding to topic 2
Objectives: 1 2 4 7 6
Contents:
Theory
6h
Problems
4h
Laboratory
0h
Guided learning
0h
Autonomous learning
15h

Topic 3

Theory classes and problems corresponding to topic 3
Objectives: 1 2 4 3 7 6
Contents:
Theory
4h
Problems
2h
Laboratory
0h
Guided learning
0h
Autonomous learning
6h

Topic 4

Theory classes, problems and lab corresponding to topic 4
Objectives: 2 4 3 7 9 8 10 6
Contents:
Theory
8h
Problems
6h
Laboratory
4h
Guided learning
2h
Autonomous learning
27h

Topic 5

Theory classes and problems corresponding to topic 5
Objectives: 4 3 7 5 9 8 10 6
Contents:
Theory
4h
Problems
4h
Laboratory
2h
Guided learning
2h
Autonomous learning
12h

Topic 6

Theory classes and problems corresponding to topic 6
Objectives: 2 4 3 7 6
Contents:
Theory
2h
Problems
2h
Laboratory
0h
Guided learning
0h
Autonomous learning
6h

Teaching methodology

The course is based on face-to-face theory and laboratory classes. The theory classes follow the program defined in this teaching guide.

Within the theory classes, the dialogue between the teacher and the students is promoted, providing problems and joint activities based on particular aspects of the topic being discussed.

The laboratory classes focus on the topics of Fourier Transform, filtering and processing of signals. They are based on computer programs and are guided by a text.

Evaluation methodology

The final grade of the course is obtained from:

- Quizzes: Q (0-5%)
- The mid-term exam: P (30%-25%)
- The final exam: F (60%)
- Practices: L (10%)

The quizzes are optional, and only those quizzes with a grade higher than the mid-term exam will count for the final grade. For each student, the mid-term exam will count between 30% and 25% depending on the grades obtained in the theory quizzes. In turn, the weighting of the quizzes can vary between 0% and 5%, depending on the number of quizzes that exceed their mid-term exam grade.

Final grade= max( 0,3-0,25 C + 0-0,05 Q + 0,1 L +0,6 F; 0,1 L + 0,9 F)

In the case of taking a Re-evaluation exam, the final grade is:

Final grade = 0,9 R+0,1 L

Bibliography

Basic:

Complementary:

Previous capacities

The knowledge acquired in the subjects of the Degree in the previous semester.

Addendum

Contents

No hi ha canvis respecte la informació publicada a la guia docent

Teaching methodology

No es modifica per la teoria i els problemes. Estan previstes 4 classes pràctiques que es realitzaran de forma no presencial. Les pràctiques podran realitzar-se en parelles o individualment. El termini per a realitzar-les serà de 2 dies. El professorat estarà disponible a la classe virtual durant l'horari establert per a les classes presencials i la resta del temps atendrà els dubtes per videoconferència amb cita prèvia mitjançant correu electrònic.

Evaluation methodology

No hi ha canvis respecte la informació publicada a la guia docent

Contingency plan

Per a cada tema es proposaran problemes a realitzar en grups de fins a 4 persones i es lliuraran una setmana després de finalitzar el tema. L'avaluació d'aquests lliurables es valorarà amb un increment en la nota final de fins a 0,25 punts per lliurable sempre que aquest obtingui una qualificació ≥ 7. La nota máxima de la assignatura estarà fixada a 10 punts Les pràctiques podran realitzar-se en parelles o individualment. El termini per a realitzar-les serà de dos dies. El professorat estarà disponible a la classe virtual durant l'horari establert per a les classes presencials i la resta del temps atendrà els dubtes per videoconferència amb cita prèvia mitjançant correu electrònic. Els exàmens a realitzar durant l'alarma sanitària es realitzaran en el mateix horari previst per al cas presencial. Es supervisaran via videoconferència i els resultats de l'examen es pujaran a la plataforma Atenea.