- Details
- Category: Discipline
- Não
- Semestral
- Português
- 4
- 0
- 5
- 1
- IPLUSO6865-25639
- Digital Systems and Microcontrollers
- 25639
- 6865
- Automation and Computer Systems
- Details
- Category: Discipline
- Não
- Semestral
- Português
- 4
- 0
- 4
- 1
- IPLUSO6865-624
- Computer-Aided Design
- 624
- 6865
- Automation and Computer Systems
- Details
- Category: Discipline
- Não
- Provides a wide range of basic knowledge, skills and tools essential for data analysis.
- Semestral
Descrição dos instrumentos de avaliação (individuais e de grupo) ¿ testes, trabalhos práticos, relatórios, projetos... respetivas datas de entrega/apresentação... e ponderação na nota final.
Exemplo:
Descrição
Data limite
Ponderação
1ºteste
40%
2ºteste
40%
2 Trabalhos (10%+10%)
20%
- Descriptive statistics: Basic concepts. Measures of location, measures of dispersion. Graphic presentation. Random experiment. Events. Sample space. Algebra of events. Probability concepts. Axiomatic of Kolmogorov. Conditional probability. Independence. Bayes theorem Discrete and Continuous random variables. Probability and distribuition functions. Mean value, variance and standard deviation Discrete distributions: Uniform, Binomial, Negative Binomial and Poisson distributions Continuous distributions: Uniform, Normal, Exponential, Chi-square and t-Student distributions Statistical inference. Sample distributions. Central limit theorem Estimation by points or by intervals. Confidence interval for mean (know and unknow variance, large and small sample). Confidence interval for variance and standard deviation. Confidence interval for proportion Hypothesis Testing The Linear Regression Model
- Be able to calculate and interpret the most important desciptive statistical measures and identify their properties; to calculate probabilities using different concepts; to calculate conditional probability and apply the principles of multiplication, total probability and Bayes' theorem. Be able to use the concept of random variable and to operate with probability functions and distributions. Know the most important discrete and continuos distributions and some of their properties. Calculate confidence intervals and apply hypothesis tests and interpret the obtained results. Know the Linear Regression Model and evaluate the adjustment of the model to data.
- Mandatory
- Application of acquired knowledge to the study of real problemas and use of statistical software.
- Português
- Murteira, B, Ribeiro, C.S., Silva,J.A. e Pimenta,C. (2015) Introdução à Estatística. Escolar editora. Pestana,d. e Velosa,H. (2006) Introdução à Probabilidade e Estatística. Fundação Calouste Gulbenkian. Reis, E., Melo, P. , Andrade,R. e Calapez,T. (2007) Estatística Aplicada, vol.1 e 2. Edições Sílabo. Ross,S. (2009) Introduction to Probability and Statistics for Engineers and Scientists, 4th edition. Elsevier Academic Press.
- 4
- 0
- 4
- 1
- IPLUSO6865-15
- Probabilities and Statistics
- 15
- 6865
- Automation and Computer Systems
- Details
- Category: Discipline
- Não
- In this study cycle, curricular units in the scientific area of Mathematics play an important role. They are essential for students to acquire solid basic knowledge needed in other curricular units of the study cycle that require mathematical knowledge or new skills and competences acquired through gradual and persistent work in curricular units in this area. The curricular unit of Mathematics I assumes a fundamental and relevant role in the beginning of students' mathematical training. Mathematical contents are essential in the training of qualified staff, either in the understanding and consolidation of the different concepts, or in the specific knowledge of their applicability and in the development of new skills and competences acquired with the work in the curricular unit. Much of the content explored in some topics of the mentioned syllabus has a wide application in other areas of the study cycle.
- Semestral
A avaliação com vista à aprovação da disciplina é composta por duas tipologias, nomeadamente: a avaliação continua e a avaliação final. A avaliação contínua é constituída por duas frequências a realizar durante o semestre, a primeira com uma ponderação de 40% e a segunda com uma ponderação de 50% e a componente de trabalho na sala de aula com uma ponderação de 10 %.São considerados aprovados os alunos que obtenham uma média igual ou superior a 10 valores.Por sua vez também está disponível a avaliação final nas duas épocas de exame previstas. São considerados aprovados os alunos que obtenham uma classificação igual ou superior a 10 valores numa das épocas. Os alunos que desejem fazer melhoria de nota podem fazê-lo na segunda época.Descr
ição
Data limite
Ponderação
1ª Frequência
13-11-2025
40%
2ª Frequência
18-12-2025
50%
Trabalho
08-01-2026
10%
Adicionalmente poderão ser incluídas informações gerais, como por exemplo, referência ao tipo de acompanhamento a prestar ao estudante na realização dos trabalhos; referências bibliográficas e websites úteis; indicações para a redação de trabalho escrito...
- CP1. Algebraic structures. Fields R and C CP2. Vector spaces. Linear combination and independence. Generating set. Basis and dimension CP3. Vector subspace. Intersection and direct sum CP4. Linear systems. Matrix algebra. Inverse CP5. Gaussian characteristic and condensation. Rouché's theorem and dependence of variables CP6. Elementary matrices. Permutations. Determinant and properties CP7. Complementary minors and adjoint. Laplace's formula. Cramer's rule CP8. Operators and linear transformations (LTs). Image and kernel. Similarity. Change of basis CP9. LTs in computer graphics: composite and geometric transformations CP10. Eigenvectors and eigenvalues (EVVs). Invariants. Characteristic polynomial CP11. Diagonalization of matrices. Jordan block and canonical form. Minimal polynomial CP12. EVVs in system stability linear dynamics: difference and power equations of a matrix, differential and exponential matrix equations, Markov processes, input-output and Von Neumann models
- LO1. Understand the concepts of real vector space and vector subspace; LO2. Master the language of vectors and matrices and perform operations; LO3. Classify sets of vectors according to linear independence; LO4. Obtain systems of generators, bases, and the dimension of vector spaces; LO5. Obtain the coordinates of a vector in different bases; LO6. Calculate determinants, interpret their value, and apply properties; LO7. Solve linear systems using matrices and identify dependent variables; LO8. Calculate eigenvalues and eigenvectors; LO9. Understand the definition of the product of complex numbers as the operation between vectors that allows the structure of a field and a vector space over R in C; LO10. Obtain the matrix of a linear transformation in different bases and determine the kernel and image subspaces; LO11. Use Python (or Octave) as an exploratory work tool; LO12. Apply theory to contextual problems and acquire the skills and reasoning for their formulation.
- Mandatory
- Teaching methodologies are based on two strands: (1) Theoretical sessions - where fundamental concepts are conveyed; (2) Theoretical-practical sessions, in which teaching is practically oriented and students are invited to analyze and solve problems involving the concepts presented in the theoretical classes. Students are also encouraged to experiment with various problem-solving strategies.
- Português
- Strang, G. (2009). Introduction to Linear Algebra, Wellesley-Cambridge Press. Almada, T. (2007). Álgebra Linear, Edições Universitárias Lusófonas. Magalhães, L. T. (2001). Álgebra Linear como introdução à Matemática Aplicada, Texto Editora. Blyth, T.S.; Robertson (1998). Basic Linear Algebra, Springer. Monteiro, A.; Pinto, G. (1997). Álgebra Linear e Geometria Analítica. Problemas e exercícios, McGraw-Hill.
- 4
- 0
- 6
- 1
- IPLUSO6865-1
- Mathematics I
- 1
- 6865
- Automation and Computer Systems
- Details
- Category: Discipline
- Não
- In this course, students are expected to analyse engineering problems and apply general principles of abstraction and modularity in order to be able to interconnect the different disciplinary areas of the course: Automation, Electronics and Computer Systems. Develop basic Automation and Computer Systems solutions that integrate components from various disciplinary areas.
- Semestral
Descrição dos instrumentos de avaliação (individuais e de grupo) ¿ testes, trabalhos práticos, relatórios, projetos... respetivas datas de entrega/apresentação... e ponderação na nota final.
Exemplo:
Descrição
Data limite
Ponderação
Teste de avaliação
1ºteste
2ºteste
12-11-2025
19-1-2026
50%, nota minima 9,5 valores
1º Trabalho Prático
2º Trabalho Prático
8-12-2025
21-1-2026
15%
35%
(...)
Adicionalmente poderão ser incluídas informações gerais, como por exemplo, referência ao tipo de acompanhamento a prestar ao estudante na realização dos trabalhos; referências bibliográficas e websites úteis; indicações para a redação de trabalho escrito...
- CP1. Introduction CP2. Teamwork and leadership CP3. Systems engineering CP4. Engineering project management CP5. Preparation of technical documents and presentations CP6. Ethics in Engineering CP7. Introduction to robotics CP8. Introduction to analogue electronics CP9. Introduction to digital electronics CP10 Introduction to communication networks CP11. Introduction to automation CP12. Introduction to dynamic systems and control
- LO1. Analyse complex engineering problems and apply general principles of abstraction and modularity; LO2. Know the different disciplinary areas of Automation, Electronics and Computer Systems; LO3. Understand the fundamental concepts and application possibilities of each subject area; LO4. Develop basic Automation and Computer Systems solutions that integrate components from various disciplinary areas.
- Mandatory
- Students attending the course will understand and analyse engineering problems and apply general principles of abstraction and modularity in order to be able to interconnect the different disciplinary areas of the course: Automation, Electronics and Computer Systems, always in accordance with engineering ethics and teamwork. Develop basic automation and computer systems solutions that integrate components from various disciplinary areas. Know the dynamics of leadership, teamwork and develop the ability to develop technical documents and technical presentations.
- Português
- - Slides fornecidos pelo docente - NASA Systems Engineering Handbook, Rev2, 2020. (https://www.nasa.gov/connect/ebooks/nasa-systems-engineering-handbook) - Systems Engineering Principles and Practice, Kossiakoff, A., Biemer, S.M., Seymour, S.J., Flanigan, D.A., 2020, Wiley and Sons, 3rd Ed. - Feedback Systems: An Introduction for Scientists and Engineers, K. Åström and R. Murray, 2018, Princeton, 2nd Ed. - Folhas de apoio fornecidas pelo docente
- 4
- 0
- 5
- 1
- IPLUSO6865-25637
- Introduction to Systems Engineering
- 25637
- 6865
- Automation and Computer Systems
