COURSE DESCRIPTION DIGITAL SIGNAL PROCESSING - 3RD COURSE 1ST SEMESTER BIOMEDICAL ENGINEERING ON CAMPUS ACADEMIC YEAR 2020/2021
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COURSE DESCRIPTION DIGITAL SIGNAL PROCESSING 3RD COURSE 1ST SEMESTER BIOMEDICAL ENGINEERING ON CAMPUS ACADEMIC YEAR 2020/2021 POLYTECHNIC SCHOOL
Course Description / Academic year 2020-2021
1. COURSE/SUBJECT IDENTIFICATION
1.- COURSE/SUBJECT:
Name: Digital Signal Processing
Code: 17562
Year (s) course is taught: 3 Semester(s) when the course is taught: 1
Type: Mandatory ECTS of the course: 6 Hours ECTS: 30
Language: English Type of course: Presential
Degree (s) in which the course is taught: Biomedical Engineering
School in which the course is taught: EPS
2.- ORGANIZATION OF THE COURSE:
Department: Information technology
Area of knowledge: Biomedical engineering
2. LECTURERS OF THE COURSE/SUBJECT
1.-LECTURERES:
Responsible of the Course CONTACT
Name: Javier Tejedor Noguerales
Phone (ext): 14874
Email: javier.tejedornoguerales@ceu.es
Office: D.2.2.1
Teaching and Research profile Theory of Signal and Communications
Research Lines Spoken Term Detection, Speech Recognition, Digital Signal
Processing, Machine Learning, Human-Computer Interaction.
2.- TUTORIALS:
For any queries students can contact lecturers by e-mail, phone or visiting their office during the
teacher’s tutorial times published on the students’ Virtual Campus.
2Course Description / Academic year 2020-2021
3. COURSE DESCRIPTION
This is intended to provide an understanding and working familiarity with the fundamentals of digital
signal processing. Its goals are to enable the student to apply digital signal processing concepts to
the field of biomedical engineering, to make it possible to read the technical literature on digital signal
processing, and to provide the background for the study of more advanced topics and applications.
The student will also learn the principles of discrete-time signals and systems and the use of the Z-
transform in the transform analysis of linear and time-invariant (LTI) systems.
4. COMPETENCIES
1.- COMPETENCIES
Code Basic and General Competencies
BAS-2 Que los estudiantes sepan aplicar sus conocimientos a su trabajo o vocación de una forma
profesional y posean las competencias que suelen demostrarse por medio de la
elaboración y defensa de argumentos y la resolución de problemas dentro de su área de
estudio.
BAS-3 Que los estudiantes tengan la capacidad de reunir e interpretar datos relevantes
(normalmente dentro de su área de estudio) para emitir juicios que incluyan una reflexión
sobre temas relevantes de índole social, científica o ética.
BAS-5 Que los estudiantes hayan desarrollado aquellas habilidades de aprendizaje necesarias
para emprender estudios posteriores con un alto grado de autonomía.
CG-7 Ejercer los valores de convivencia, ciudadanía, libertad, equidad y solidaridad.
CG-8 Actuar con honradez, veracidad, rigor, justicia, eficiencia y respeto.
Code Transversal Competencies
CT-5 Capacidad para dominar un idioma extranjero (inglés).
Code Specific Competencies
CE-24 Analizar las propiedades espectrales de una señal determinista o aleatoria y diseñar y
aplicar filtros digitales sobre señales en una o varias dimensiones.
CE-25 Comprender y aplicar la implementación discreta de los métodos transformados de análisis
en una o varias dimensiones.
CE-26 Conocer las técnicas de muestreo y sus propiedades a nivel de señal.
2.- LEARNING OUTCOMES:
Code Learning outcomes
RA1 Determine properties of discrete signals and systems in time and/or Fourier transform
domains.
RA2 Determine the output of LTI discrete systems in time and/or Fourier transform domains.
RA3 Apply sampling schemes (A/D and D/A conversion, downsampling, upsampling,
oversampling, etc.) to the signal processing of discrete or continuous signals.
RA4 Design, characterize and implement LTI systems.
RA5 Use the discrete Fourier Transform (DFT) and its fast implementation (FFT) to visualize the
spectral properties of the signals and filtering.
3Course Description / Academic year 2020-2021
RA6 Apply Z-transform in the transform analysis of LTI systems.
5. LEARNING ACTIVITIES
1.- DISTRIBUTION OF STUDENTS` ASSIGNMENT:
Total hours of the course 180
DESCRIPTION OF LEARNING ACTIVITIES:
Code Name On-campus hours
AF1 Theoretical and practical sessions 39
AF2 Laboratory 28
AF3 Tutoring 3
TOTAL Presence Hours 70
Code Name Not on-campus
hours
AF6 Self student work 110
2.- DESCRIPTION OF LEARNING ACTIVITIES:
Activity Definition
AF1 Theoretical and practical sessions Learning activity performed in the classroom under the
guidance of the professor which covers lecturing of
contents and the analysis and resolution of exercises.
AF2 Laboratory Learning activity performed in specialized laboratories
under the guidance of the professor which aims at
acquiring practical skills.
AF3 Tutoring Training activity outside the classroom that fosters
independent learning, supported the action and guide of a
tutor.
AF6 Self-student work Training activity outside or inside the classroom that fosters
independent, individual or cooperative learning.
6. ASSESMENT OF LEARNING
1.- CLASS ATTENDANCE:
Class attendance is recorded on the student portal but is not evaluated. Justifications of absence
will not be accepted.
2.- ASSESMENT SYSTEM AND CRITERIA:
ORDINARY EXAMINATION (continuous assessment)
Code Name Percentage
4Course Description / Academic year 2020-2021
SE-1 Written partial exam 20%
SE-3 Practice session reports + Continuous assessment exercises 15%
SE-2 Practice exam 15%
SE-3 Project 15%
SE-1 Written final exam 35%
It is necessary to obtain, at least, 4.5 over 10 in the written final exam to pass the subject. It is
necessary to obtain, at least, 4.5 over 10 in the practice exam to pass the subject. It is necessary to
obtain, at least, 5.0 over 10 in the project to pass the subject. Otherwise, the final grade will be the
minimum of those grades (written final exam, practice exam, project). It is necessary to obtain, at
least, 5.0 over 10 in the final grade to pass the subject.
RE-TAKE EXAM/EXTRAORDINARY EXAMINATION
Name Percentage
Final exam 100%
It is necessary to obtain, at least, 5.0 over 10 in the final exam to pass the subject.
3.- DESCRIPTION OF ASSESSMENT CRITERIA:
Assessment criteria Definition
SE-1 Written Exam Written exam theoretical-practical, with short, long, exercises or test
questions.
SE-2 Practice Exam Practice exam related to the practices carried out by the students in the
course.
SE-3 Portfolio Physical or digital handing-in of laboratory/theoretical sessions or parts of a
project results.
5Course Description / Academic year 2020-2021
7. COURSE PROGRAMME
1.- COURSE PROGRAMME:
THEORETICAL:
1. Discrete signals and systems:
- Basic signals.
- Signal operations.
- System properties.
- Linear and time-invariant (LTI) systems.
- Linear constant-coefficient difference equations.
- Frequency response.
- Discrete-time Fourier transform: definition and properties.
- Discrete Fourier series: definition and properties.
- Discrete Fourier transform: definition and properties.
- Linear convolution from Discrete Fourier transform.
- Fast Fourier transform.
2. Sampling:
- Sampling theorem.
- Frequency-domain sampling representation.
- Band limited continuous-time signal reconstruction.
- Discrete-time processing of continuous-time signals.
- Continuous-time processing of discrete-time signals.
- Downsampling and decimation.
- Upsampling (interpolation).
- Multirate signal processing: interchange of filtering and downsampling/upsampling, polyphase
decomposition and polyphase implementation of decimation/interpolation filters.
- Digital processing of analogic signals: antialiasing filter, A/D conversion, D/A conversion,
oversampling.
3. Transform analysis of linear and time-invariant (LTI) systems:
- Frequency response in LTI systems.
- Group delay.
- Inverse system.
- Impulse response of rational system functions.
- Module and phase relationship.
- All-pass systems.
- Minimum-phase systems.
- Linear systems with generalized linear phase.
- Linear phase systems.
PRACTICAL WORK PROGRAMME:
Block 1: Signal processing in OCTAVE:
- Audio processing.
- Fast Fourier transform.
- Audio coding.
Block 2: Frequency selective filters and implementable systems:
- Digital filter design from continuous-time infinite impulse response systems.
- Digital filter design of finite impulse response filters by windowing.
- Digital filter implementation from systems characterized by linear constant-coefficient difference
equations.
8. RECOMMENDED READING
1.- ESSENTIAL BIBLIOGRAPHY:
A. V. Oppenheim, R. W. Schaffer, J. R. Buck. Discrete-time signal processing. Prentice-Hall
(1999).
J. G. Proakis, D. G. Manolakis. Digital signal processing: principles, algorithms and
applications. Prentice-Hall (1995).
6Course Description / Academic year 2020-2021
9. ATTITUDE IN THE CLASSROOM
1.- REGULATIONS
Any irregular act of academic integrity (no reference to cited sources, plagiarism of work or
inappropriate use of prohibited information during examinations) or signing the attendance sheet
for fellow students not present in class will result in the student not being eligible for continuous
assessment and possibly being penalized according to the university regulations.
In case the student gives a practical session report/exercise report to the lecturer after the
deadline, there is a penalty of 1 point per day delayed. It is not allowed to give any practical
session report/exercise report five or more days after the deadline. The project must be given to
the lecturer before or on the deadline fixed by the lecturer.
Once the exam calendar has been announced in the due time and to guarantee the same
conditions for all the students, no exam will be re-taken unless there are some special
circumstances.
10. EXCEPTIONAL MEASURES
Should an exceptional situation occur which prevents continuing with face-to-face teaching
under the conditions previously established to this end, the University will take appropriate
decisions and adopt the necessary measures to guarantee the acquisition of skills and
attainment of learning outcomes as established in this Course Unit Guide. This will be done in
accordance with the teaching coordination mechanisms included in the Internal Quality
Assurance System of each degree.
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