Data Analysis and Transformation

Recommended Prerequisites

Mathematical Analysis I, Mathematical Analysis II, Linear Algebra and Analytic Geometry, Statistics, Introduction to Programming and Problem Solving, Information Theory.

Teaching Methods

 - Theoretical classes (2 hours weekly) for presentation and discussion of the matter and problem solving, establishing links with the theoretical-practical classes, using slides and computer demonstrations.

 - Theoretical-practical classes (1 hour weekly) for introduction to analytical and computational resolution of practical exercises.

 - Practical Laboratorial classes (2 hours weekly) to support the execution of practical works.

Learning Outcomes

This course corresponds to a broad and integrated vision of the tools of data analysis, modelling and transformation for the computational treatment of the dynamic phenomena and processes of the material and immaterial worlds. This is covered in specific contexts of Informatics Engineering and of its interconnection with other domains.

 The course aims to develop the following skills:

- Understanding and interpretation of dynamic phenomena and processes of the real and virtual worlds;

- Ability for data analysis supported by computational tools;

- Deepening of mathematical reasoning to information extraction;

- Ability to solve practical problems supported on the analysis and transformation of one and multi-dimensional data in time, space and frequency domains.

The course also contributes to the development of skills at the level of teamwork, critical thinking, argumentative capacity, research and self-learning.

Work Placement(s)

No

Syllabus

Chapter 1: Introduction

Chapter 2: Linear Systems

Chapter 3: Fourier Transforms

Chapter 4: Time-Frequency Analysis

Chapter 5: Other Transforms

Head Lecturer(s)

Alberto Jorge Lebre Cardoso

Assessment Methods

Assessment
- 4 Practical works in groups of two elements (Report with the resolution of exercises and supporting code) and two oral defenses: 30% (minimum: 47.5%) - 2 Tests (optional): 20% (contributing to the exam grade) - 1 Exam: 70% (minimum: 47.5%): 100.0%

Bibliography

- Steven Smith, “The Scientist and Engineer’s Guide to Digital Signal Processing”, 1997, eBook: http://www.dspguide.com/

- A. V. Oppenheim, R. W. Shafer, Discrete-time signal processing, 2nd ed. Prentice-Hall, 1999

- K. Sayood, Introduction to data compression, 2nd edition, Morgan Kaufman, 2000

- J. Stein, Digital Signal Processing – a computer science perspective, Wiley, 2000

- E. Kamen, B. Heck, Fundamentals of Signals and Systems - Using Matlab, Prentice Hall, 1997