The Digital signal processing II represents the natural continuation of its predecessor Digital signal processing I, and introduces some advanced techniques of digital signal processing. First, some techniques and basic tools for the design of frequency selective digital filters are studied, specifically for FIR filters (the windowing based method) and IIR filters (design from continuous-time filters using different domain conversion methodologies). Secondly, an introduction of the optimum filtering and adaptive filters is presented to solve some linear processing problems such as dynamic system´s tracking, noise reduction techniques or prediction from the statistical characteristics of the incoming signals. Finally, an introduction to artificial neural networks is presented. Neural networks are nonlinear processing units inspired in human neurons which are able to solve some non linear problems such as classification and pattern recognition.
Type Subject
Previous Knowledge

Sampling theorem for bandlimited signals and time and frequency characterization for analog signals and systems. Time and frequency characterization (convolution, impulse response, Fourier Transform and Discrete Fourier Transform) of discrete signals and systems. Z transform.


Digital signal and image processing students learn the following knowledge and develop the following skills:

1. Acquire the basic knowledge from the study and characterization of discrete signals and systems, specially the study of frequency selective digital filter design methods, optimum and adaptive filter theory and basic neural networks concepts, to allow the implementation, analysis and design of digital signal and pattern recognition systems.
2. Identify, formulate and solve digital signal and image processing problems in a multidisciplinary environment, individually or as a member of a team.
3. Analyze, design and make use of systems, procedures and algorithms in order to achieve the proposed goals in a specific digital signal processing problem, making use of open source simulation, analysis and application development tools in this area (SciLab), and to analyze and understand the given results.
4. To use new e-learning techniques and tools (virtual campus, study guide, sharing documents, forums)


1 Digital filter design techniques
1.1 Digital filter design introduction
1.2 The window method for design of FIR filters
1.3 Design of FIR filters by the Kaiser window
1.4 IIR filter design from analog filters
1.5 Used analog filters for the design of digital filters
1.6 Frequency transformations

2 Optimum filtering and adaptive filtering
2.1 Introduction: The estimation problem
2.2 Linear optimum estimation. The Wienner filter
2.3 Introduction to adaptive filters

3 Neural networks
3.1 Introduction to neural networks
3.2 Perceptrons
3.3 Radial basis function networks (RBF)
3.4 Self organizing maps
3.5 LVQ networks
3.6 Some neural networks applications


Two methodologies are applied depending on the profile selected by the student at the time of registering. In the attended methodology theory is given through the teacher´s lectures, providing the basic knowledge to enable the student to develop the complete program of activities. In the semi-attended methodology, theory is developed by the student with the aid of an electronic study guide. In this case the student has a more active paper. In the study guide basic contents and bibliographic references are pointed out to allow the student to progress in his own learning.

Aside from the theoretical background, both learning methodologies share the following aspects: practice demonstrations, problems classes, practices classes, and the all the individual work the student has to do as homework (advanced problems and practice demonstrations)

Teacher´s lectures and work with the study guide are complemented with problems classes and practical demonstrations. Therefore, theoretical comprehension is improved through visual examples with the simulation software SciLab and also with the discussion of key concepts, allowing students to develop some practical skills, the ability to solve problems, the ability to be more creative in order to face new situations or to work as a member of a team.

During the course some theoretical and practical problems are given as homework. Students have a specific software to solve some of these problems, where they can evaluate and share their results with other students. Auxiliary practices teachers are available for asking questions about these practices. Students receive continuous support through meetings with the teacher, where they get some advice concerning their whole learning process and achievement. Some self-evaluation tests are also available in the electronic study guide in order to evaluate one´s degree of comprehension after every chapter.

Finally, both students and teachers plan virtual meetings in order to promote the discussion of certain key concepts during the course. In these meetings the teacher can interact with a reduced number of interested students, sharing specific application examples in SciLab or to solve some questions by sharing electronic documents.


The student evaluation will be completed with:
A. Exams
D. Homework
G. Computer assignments
K. Laboratory reports
M. Participation in the virtual campus

The subject mark will have a possible contribution of a continuous assessment mark where the student´s effort during the whole course will be reflected. The final mark will have a contribution of the 40% of this continuous assessment mark, if the normal mark is at least 3.5 and this contribution is positive with respect to the normal mark solely.

The normal mark will have a theoretical part (80%) and a practical part (20%). The mark for the theory part will be given from the exam problems (80%). The mark for the practices part will be given through the exam practice problems (10%), and the evaluation of a written report on the results of the practices (10%).

The continuous assessment mark will be computed with the results of different activities: homework assignments and participation in discussion forums.

Evaluation Criteria

Objective 1:
-Students should prove to have a general and basic knowledge of some advanced digital signal processing studied techniques (digital filter design studied techniques, optimum and adaptive filter theory and artificial neural network basic theory), and show an ability to connect the different conceptual blocks [A, D, G].

Objective 2:
-Student should show abilities for analysis and synthesis in solving exercises: to posing different ways of reaching the desired objectives and choosing the simpler, faster and better way of achieving the goals planned with the given restrictions [A, D].

Objective 3:
-Students should prove to have elementary computing skills in practical development software (SciLab) and the given modules and functions during the posed practices problems [G].
-Student should show ability to work as a member of a interdisciplinary team and ability to put the acquired theoretical knowledge into practice.[G,K].

Objective 4:
-Student should be capable of working in a e-learning environment with several documents and knowledge sources (problems, study guide, specific bibliography, transparencies, discussion forums) and should show an ability for self-learning and autonomous work, ability to adapt to new situations, ability to communicate with non expert persons and ability for information management [M].

Basic Bibliography

Oppenheim, Alan V; Schafer, Ronald W., Discrete-Time Signal Processing, Prentice-Hall, New Jersey, 1999

Morán, José Antonio; Socoró, Joan Claudi; Cobo, Germán; Sevillano, Xavier; Guia d´estudi de Processament digital del senyal II, Enginyeria La Salle, 2011

Socoró, Joan Claudi; Cobo, Germán; Morán, José Antonio; Calzada, Àngel; Monzó, Carlos; Sevillano, Xavier; Problemes de Processament digital del senyal II, Enginyeria La Salle, 2011

Trilla, Alexandre; Sevillano, Xavier; Pràctiques de Processament digital del senyal II, Enginyeria La Salle, 2011

Additional Material

Haykin, Simon, Neural Networks, Prentice-Hall, New Jersey, 1999

Proakis, John G.; Manolakis, Dimitris G., Digital Signal Processing, Macmillan Publishing Company, New York, 1992

Haykin, Simon, Adaptive filter theory, Prentice-Hall, New Jersey, 1991