Computational Processing, Representation and Analysis of Data

Code:

PRODEM065

Acronym:

PRACD

Keywords
Classification	Keyword
OFICIAL	Mechanical Engineering

Instance: 2023/2024 - 1S

Active?	Yes
Web Page:	http://www.fe.up.pt/~tavares
Responsible unit:	Industrial Design Section
Course/CS Responsible:	Doctoral Program in Mechanical Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
PRODEM	7	Syllabus since 2009/10	1	-	6	28	162

Teaching language

Suitable for English-speaking students

Objectives

GENERAL GOALS:

Currently, the Computational Processing, Representation and Analysis of Data constitute essential tools for an adequate communication form based on a transformation of the data used in computational representations that traduces efficiently and precisely the information contained in the same data.

The several types of Computational Processing, Representation and Analysis of Data have application in different areas of the human knowledge as Medicine, Engineering and Science; namely, in medical diagnosis, meteorological maps, automobile industry, study of physical processes, etc.

With this course is intended to address theoretical and computational fundaments of the Computational Processing, Representation and Analysis of Data, progressively necessary as the computational simulations and analyzes are become more powerful and realistic, involving therefore huger volumes of Data of higher dimension and complexity.

 

SPECIFIC OBJECTIVES:

To provide knowledge to the students on a set of computational techniques that allow the achievement of representations of the information contained in a data set in order to assurance its efficient analysis, taking into account operations of data acquisition, processing and transformation in adequate structures for algorithms of data processing and representation.

Learning outcomes and competences

EXPECTED OUTCOMES:

At the end of the course of Computational Processing, Representation and Analysis of Data, the students should be capable of:

1. Understand the human visual perception system;

2. Know the main algorithms of computational data processing;

3. Know the main methods of computational data representation;

4. Explain the principles and develop computational systems of Processing, Representation and Analysis of Data.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

It is desirable that students have prior knowledge of computer programming.

Program

Programmatic Content:

The main subjects to consider in the course of Computational Processing, Representation and Analysis of Data are: Pipeline of Data Representation, Data Representation, Algorithms for Data Representation, Data Manipulation and Representation and Processing of Image Data.

During the course, some application examples in several domains of the knowledge of the techniques studied of Computational Processing, Representation and Analysis of Data will be verified and analyzed, as in engineering, biomechanics and medicine.

The programmatic content of the course is organized in nine modules: 1) Presentation and Introduction, 2) Introduction to the Object-Oriented Programming, 3) Pipeline of Data Representation, 4) Data Representation, 5) Algorithms for Data Representation, 6) Algorithms for Data Improved Representation, 7) Data Manipulation, 8) Representation and Processing of Image Data, 9)Application Examples.

Mandatory literature

Gonzalez, Rafael C; Digital image processing using Matlab. ISBN: 0-13-008519-7
Freeman, Ralph D. 340; Developmental neurobiology of vision. ISBN: 0-306-40306-4
Kitware; The VTK user.s guide. ISBN: 1-930934-18-1
Foley, James D. 070; Computer Graphics. ISBN: 0-201-12110-7
Yoo, Terry S. 340; Insight into images. ISBN: 1-56881-217-5
Ware, Colin; Information visualization. ISBN: 1-55860-819-2
Schroeder, Will; Martin, Ken; Lorensen, Bill; The Visualization toolkit. ISBN: 1930934076

Teaching methods and learning activities

The course of Computational Processing, Representation and Analysis of Data is based on theoretician-practical lessons.

The theoretician-practical lessons of the course will be dedicated to present the methods and computational algorithms and for the analysis of its application in several study cases; manly, in real cases involving pertinent problems in engineering.

Whenever possible, researchers, whose scientific works are relevant in the domain of Computational Processing, Representation and Analysis of Data, will be invited to present their works; those presentations will also be open to the related scientific community.

 

When the number of students enrolled is small, the course works in a tutorial basis.

Software

VTK – The Visualization Toolkit
CMIS – Contour Matching Image Software
ITK – Insight Segmentation and Registration Toolkit
VolView – Interactive System for Volume Visualization

keywords

Physical sciences > Mathematics > Algorithms
Humanities > Information science > Information management > Information processing
Technological sciences > Technology > Computer technology > Image processing
Technological sciences > Technology > Graphic techniques > Computer graphics

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Defesa pública de dissertação, de relatório de projeto ou estágio, ou de tese	10,00
Trabalho escrito	25,00
Trabalho laboratorial	65,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Elaboração de projeto	65,00
Elaboração de relatório/dissertação/tese	10,00
Estudo autónomo	65,00
Frequência das aulas	25,00
Total:	165,00

Eligibility for exams

To complete this course the students must attain the required frequency of the practical classes.

The students must also undergo all components of evaluation, which includes the delivery of the practical project, and its presentation and discussion.

Calculation formula of final grade

The evaluation system of the course of Computational Processing, Representation and Analysis of Data is composed by the accomplishment, presentation and discussion of an individual practical project, which can be complemented with the completion of a written exam.

The practical project to be developed must have an integrator view and be concerned to a concrete problem of the engineering area. The subject of each project will have to be agreed between each student and the professor. The work done will have to be described in a written report to be delivered to the professor until the end of the semester and public presented and discussed.

The final grade of the practical project and also of the course will be obtained considering the computational work developed - 65%, written report - 25%, and public presentation - 10%.

Examinations or Special Assignments

Not applicable.

Special assessment (TE, DA, ...)

On the special examination periods, the students that are dismissed from attending classes, accordingly to the terms of items a) and b) of number 3 of Article 4 of the General Evaluation Rules, will be called to do a written exam and a practical project.

Classification improvement

According to Article 10 of General Evaluation Rules of FEUP.