João Pedro Rijo

LO1 Understand the concept of vector space, apply properties and determine a basis.
LO2 Classify sets of vectors with respect to linearity.
LO3 Represent points and vectors and calculate distances.
LO4 Operate with vectors and identify the relative position of planes and lines.
LO5 Determine equations of the line and plane.
LO6 Calculate and interpret inner and outer products.
LO7 Parametrize curves and calculate the normal and tangent vectors.
LO8 Operate with matrices, solve systems of linear equations by matrix calculation and interpret geometrically.
LO9 Calculate inverse matrix and determinant.
LO10 Understand the linear transformation between finite dimensional vector spaces.
LO11 Understand the necessity of complex numbers and the algebraic and polar forms.
LO12 Operate with complex numbers and apply Moivre's formulas and Euler's identity.
LO13 Acquire skills and reasoning adequate to solve problems in topics of Robotics and Intelligent Systems.

PC1 Concept of vector space (VS) and subspace. Linear dependence of vectors and basis of a VS.
PC2 Points and vectors in the plane and in space. Distance between two points and from a point to a line. Plane sections and spherical surface.
PC3 Vectors and operations. Internal product. Parallelism and perpendicularity of vectors. Relative position of lines and planes.
PC4 Vector director and equation of a line.
PC5 Cross product. Vector normal to a plane and equations of the plane.
PC6 Parametrization of curves in plane and in space. Normal and tangent vectors to a curve. Intersection of curves. Polar coordinates.
PC7 Matrices and operations. Inverse of a non singular matrix. Determinant of a square matrix.
PC8 Systems of linear equations. Matrix form and resolution. Linear transformations.
PC9 Quadratic equations. Complex numbers in algebraic and polar forms. Euler's formula.
PC10 The set of complex numbers as real VS . Moivre's formula. Roots of a complex number.

Approval with classification not less than 10 points (scale 1-20) in one of the following modalities:
- Assessment Throughout the Semester: 8 exercises completed during classes (35%) (only the 6 highest-scoring exercises are considered) + exercises solved on Moodle (5%) + final written test (60%). A minimum score of 7 out of 20 is required for each assessment component.
- Exam Assessment: In any exam period, with an individual written exam (100%).

A minimum attendance of 2/3 of the classes is required.

Mariana Catela Jacob Rodrigues

After successfully attending the curricular unit, students should be able to:
LO1: Components and laws
LO2: Analysis of electrical circuits
LO3: Circuits with DC Bridges and AmpOps

CP1: Basic notions of electrical components and circuits
CP1.1: Units and scales
CP1.2: Voltage and current sources
CP1.3: Ohm's Law
CP1.4: Kirchhoff's Laws
CP2: Analysis of electrical circuits
CP2.1: Linearity and overlap
CP2.2: Thevenin and Norton theorems
CP2.3. Maximum Power Transfer Theorem (MPTT)
CP3: Capacitors and coils
CP3.1: Capacitors
CP3.2: Coils
CP3.3: RLC Circuits
CP4: DC Bridges and Operational Amplifiers
CP4.1: AmpOps Basics
CP4.2: Linear operation
CP4.3: DC Circuits and Bridges
CP4.4: Circuits, DC Bridges and Operational Amplifiers

Assessment throughout the semester:

A1: Laboratory and Reports (30%)
A2: 1st Written Test (30%)
A3: 2nd Written Test (40%)

Minimum mark in Laboratory and Report: 8.5
Minimum mark in Written Tests: 8.5
(final average greater than or equal to 9.5)


The possibility of assessment throughout the semester is conditional on:
- Attendance at laboratory classes (100%) *,
- Attendance at lectures (50%),
- Attendance at theoretical-practical classes at least (50%).
*If laboratory absences are recorded for objective reasons, a way of making up the missed laboratory will be agreed with the teacher.


Assessment by exam:

Exam (100%) in the 1st, 2nd and special exam periods.
The exam will include, in addition to the content of the theoretical and practical classes, some content related to the laboratory work carried out.

Filipa Rodrigues Prudêncio

LO1. Understand and Use Models and Units
Students should identify and apply physical models to solve problems related to measurement units and calculations in physics.
LO2. Analyze and Describe One-Dimensional and Two-Dimensional Motion
Students should understand and describe the motion of objects in one and two dimensions, using motion equations to solve kinematic problems.
LO3. Apply Newton’s Laws to Solve Real Problems
Students should use Newton’s Laws to analyze and solve dynamics problems, identifying the forces involved and applying these laws to determine the motion of bodies.
LO4. Explore and Apply Conservation of Energy
Students should understand the principles of energy conservation and apply them to practical problems.
LO5. Understand Electromagnetic Wave Propagation
Students should describe plane and transverse waves and understand the propagation of electromagnetic waves.

CP 1. Models, unities and calculus
CP 2. Unidimensional movement
CP 3. Bidimensional movement
CP 4. Newton's laws
CP 5. Conservation of Energy
CP 6. Electric Field and Magnetic Field
CP 7. Plane Waves and Transverse Waves
CP 8. Propagation of Electromagnetic Waves

The assessment throughout the semester includes two written tests with a weight of 60% in the final grade (30% T1 + 30% T2). Each written test has a minimum passing score of 7 points.
The individual work by the student has a weight of 10% in the final grade, and the submission of group reports carries a weight of 30% in the final grade.
A minimum score of 9.5 points in the sum of all assessment components (60% + 30% + 10%) is required, along with a minimum attendance of no less than two-thirds of the classes.

In the examination assessment mode:
The written exam accounts for 100% of the final grade, and a minimum score of 9.5 points.

António Jorge Filipe Fonseca

After successfully completing the curricular unit, students should be able to:
OA1. Know and become familiar with different data formats.
OA2. Understand a complete data analysis cycle.
OA3. Know how to perform exploratory data analysis using R.
OA4. Know how to model a set of data.
OA5. Implement a data analysis solution to study a specific problem.

CP1. Introduction to Data Analysis
CP2. Introduction to R and RStudio
CP3. Knowledge of problems in data analysis, application examples
CP4. The complete cycle of data analysis
CP5. Data and data formats
CP6. Data preparation
CP7. Odds; descriptive statistics of data and exploratory data analysis
CP8. Data visualization
CP9. Modeling and machine learning of data models
CP10. Model evaluation methods
CP11. Reporting and publishing results

The assessment in the 'over the semester' format is based on two individual tests: a mid-term test and another at the end of the semester (20% each), and a group project (maximum of 3 students) with the preparation of two reports (20% each) and an oral presentation (20%) to be carried out by the group and this is graded individually.

A minimum attendance of at least 2/3 of the classes is required (students may miss 4 classes out of 12).

The Final Exam is a written, individual, closed-book exam covering all the material. Those who have not successfully completed the assessment throughout the semester, with an average grade higher than or equal to 10 (out of 20), take the final exam in period 1, 2 or special.

Joao Paulo Henriques

LO1: Understand the main theories, concepts, and issues related to Work, Organizations, and Technology;
LO2: Understand the main processes of the digital transition directly related to the world of work and its organizations;
LO3: Analyze the multiple social, economic, and political implications brought by the digital transition;
LO4: Explore cases, strategies, and application methods to understand the real impacts of the digital transition on professions, companies, and organizations.

PC1. Is work different today than it was in the past?
PC2. What rights and duties in the world of work?
PC3. How has theory looked at technology?
PC4. What digital technologies are changing work?
PC5. What future for work?
PC6. Is artificial intelligence really that intelligent?
PC7. Where does precariousness begin and end?
PC8. Who is to blame when the machine makes a mistake?
PC9. Do digital technologies change the relationship between unions and companies?
PC10. What digital transformation in Portugal?

Continuous assessment throughout the semester:
Each student will conduct a Flipped Classroom session, which represents 20% of the final grade.

Individual work accounting for 35% of the final grade.

Group work accounting for a total of 35% of the final grade (10% for the group presentation and 25% for the written work).

Attendance and participation in classes represent 10% of the final grade. A minimum attendance of no less than 2/3 of the classes is required.

Each assessment element must have a minimum grade of 8. The final average of the various elements must be equal to or greater than 9.5.

Examination evaluation (1st Period if chosen by the student, 2nd Period, and Special Period): in-person exam representing 100% of the final grade with a minimum grade of 9.5.

José Barros

By the end of this course unit, the student should be able to:
LO1: Understand and analyze real-variable functions, including polynomial, rational, trigonometric, and exponential functions.
LO2: Grasp and apply concepts of limits and continuity to solve problems involving indeterminate forms and asymptotic behavior.
LO3: Calculate and interpret derivatives and differentials, applying differentiation rules to solve problems involving rates of change.
LO4: Understand and apply definite and indefinite integrals, using integration techniques and exploring their applications.
LO5: Analyze sequences and series, understanding convergence criteria and applying them in mathematical contexts.

P1. Real Variable Functions:
1.1. Polynomial, rational, trigonometric, and exponential functions
1.2. Logarithmic and inverse trigonometric functions
1.3. Inverse and composite functions
1.4. Limits and indeterminate forms
1.5. Continuity

P2. Differential Calculus:
2.1. Rates of change
2.2. Derivative at a point and tangent line
2.3. Differentiation rules
2.4. Chain rule
2.5. Intervals of monotonicity and concavity of the graph
2.6. Taylor approximations

P3. Integral Calculus:
3.1. Definite integral, fundamental theorem of calculus, and primitives
3.2. Integration techniques
3.3. Applications of integration
3.4. Improper integrals and convergence

P4. Sequences and Series:
4.1. Sequences: monotonicity and convergence
4.2. Numerical series: sums and convergence criteria

Passing requires a grade of no less than 10 out of 20 in one of the following modalities:
- Assessment throughout the semester: 1 midterm mini-test (10%) + 1 group project of 3-4 members (20%) + Autonomous work (10%) + 1 final test on the first exam date (60%). A minimum grade of 8 out of 20 is required in both the final test and the average of other assessment moments throughout the semester.
- Assessment by exam (100%): Individual written test in any exam period.
- An oral assessment may be conducted after any assessment moment to validate the final grade.

After attending the course, students should be able to:
OA1 - Know the basic electronic components.
OA2 - Design and test basic electronic circuits.
OA3 - Know how to analyse the correct operation of an electronic circuit.
OA4 - Know the main differences between natural and forced commutators.
OA5 - Analyse the operation of converters.
OA5.1- Analyse the operation of AC-DC converters.
OA5.2- Analyze the operation of DC-AC converters.
OA5.3- Analyse the operation of DC-DC converters.
OA5.4- Analyse the operation of AC-AC converters.

CP1. Introduction to Electronics: Semiconductors, Diodes and transistors. Analog and digital electronics.
CP2. Introduction to Power Electronics: Power electronics applications; Power electronics converters classification;
CP3. Single and three phase uncontrolled rectifiers, natural and forced commutation: Applications and semiconductor type selection; Half and full bridge converter
CP4. DC-DC Converters
CP5. Introduction to resonant converters and switched-mode sources: Advantages of this type of converters; Isolated and multiple output switched-mode sources.
CP6. Single-Phase and Three-Phase Inverters
CP7. AC-AC converters without DC Bus Intermediate: Single-phase configurations

Laboratory (40%) + Written exam (60%)
Minimum grade in the laboratory: 8
Minimum exam grade: 8
The possibility of taking the written exam in the normal or special season is subject to:
- Presence in laboratory classes (100%) *,
- Presence in theoretical classes at least (50%),
- Presence in theoretical-practical classes at least (50%).
*If for objective reasons absences are registered in the laboratory, a way to recover the missing laboratory will be agreed with the professor.

Paulo Henrique Contente Rocha

LO1. Understand and use a tool (Python or R) for statistical analysis
LO2. Understand the information gathered using statistics
LO3: Use the most important theoretical distributions to calculate probabilities in real-life problems
LO4: Identify and apply estimation and decision methods to real-life problems

S1: Univariate descriptive statistics
S2: Bivariate descriptive statistics
S3: Main theoretical distributions of discrete random variables
S4. Main theoretical distributions of continuous random variables
S5: Parameter estimation
S6. Decision-making

The approval in this course has to be with a mark of not less than 10, in one of the following methods:
- Periodic Assessment: 2 mini-tests (MT) taken in class (15% each) + Final test taken on the date of the first exam (40%) + autonomous work (10%) + group project (20%),
The average of the mini-tests ( (MT1+MT2)/2 ) has a minimum mark of 7.0.
The final test has a minimum mark of 7.0.
or
- Assessment by Exam (100%).

Sílvia Cavalinhos

The objective of the UC is to develop a technological project in line with the scope of the Course. Contact will be established with project planning considering the main phases: Requirements analysis, development, partial tests and final tests and changes. Contact with laboratory equipment and tools is one of the goals for designing a software, hardware or both project.

I. Introduction to technological innovation along the lines of Europe
II. Planning a technological project and its phases
III. Essential aspects for the development of a project
IV. Definition of material resources
V. Budget of a project
VI. Partial and joint Test Plan
VII. Presentation of a technological project
VIII. Technological project demonstration
IX. Preparation of Technical Report

Periodic grading system:
- Group project: first presentation: 30%; second presentation and exhibition: 40%; final report: 30%. The presentations, demonstrations and defence are in group.

LO1. Develop specific oral communication skills for public presentations.
LO2. Know and identify strategies for effective use of the vocal apparatus.
LO3. Identify and improve body expression. LO4. Learn performance techniques.
The learning objectives will be achieved through practical and reflective activities, supported by an active and participatory teaching method that emphasizes experiential learning. The knowledge acquired involves both theatrical theory and specific oral communication techniques. Students will learn about the fundamentals of vocal expression, character interpretation and improvisation, adapting this knowledge to the context of public performances.

PC1. Preparing for a presentation.
PC2. Non-verbal communication techniques.
PC3. Voice and body communication, audience involvement. PC4. Presentation practice and feedback. The learning objectives will be achieved through practical and reflective activities, supported by the active and participatory teaching method which emphasizes experiential learning. Classes will consist of activities such as: Theatrical experiences and group discussions; Practical activities; Presentations and exhibitions of autonomous work; Individual reflection.

The assessment of the Public Presentations with Theatrical Techniques course aims to gauge the development of students' skills in essential aspects of public presentations. The assessment structure includes activities covering different aspects of the experiential learning process involving both theatrical techniques and specific communication techniques.
Assessment throughout the semester includes activities covering different aspects of the process of preparing a public presentation, including group and individual work activities:
Group activities (50%) [students are challenged to perform in groups of up to 5 elements, made up randomly according to each activity proposal].
1-Practical Presentations: Students will be assessed on the basis of their public presentations throughout the semester:
Description: each group receives a presentation proposal and must identify the elements of the activity and act in accordance with the objective.
The results of their work are presented in class to their colleagues (Time/group: presentation - 5 to 10 min.; reflection - 5 min.). Assessment (oral): based on active participation, organization of ideas and objectivity in communication, vocal and body expression, the use of theatrical techniques and performance. Presentations may be individual or group, depending on the proposed activities.
Individual activities (50%)
1-Exercises and Written Assignments (Autonomous Work):
Description: In addition to the practical presentations, students will be asked to carry out exercises and written tasks related to the content covered in each class. These activities include reflecting on techniques learned, creating a vision board, analyzing academic objectives, student self-assessment throughout the semester, answering theoretical questions and writing presentation scripts.
Assessment: (Oral component and written content), organization, content, correct use of the structure and procedures of the autonomous work proposed in each class, ability to answer questions posed by colleagues and the teacher. Communication skills and the quality of written work will be assessed, with a focus on clarity of presentation. These activities will help to gauge conceptual understanding of the content taught.
There will be no assessment by final exam, and approval will be determined by the weighted average of the assessments throughout the semester.
General considerations: in the assessment, students will be given feedback on their performance in each activity.
To complete the course in this mode, the student must attend 80% of the classes. The student must have more than 7 (seven) points in each of the assessments to be able to remain in evaluation in the course of the semester.

LO1. Acquiring knowledge about the fundamentals and stages of the Design Thinking process
LO2. Develop skills such as critical thinking, collaboration, empathy and creativity.
LO3. To apply Design Thinking in problem solving in several areas, promoting innovation and continuous improvement.

S1. Introduction to Design Thinking and Stage 1: Empathy (3h)
S2. Steps 2 and 3: Problem Definition and Ideation (3h)
S3. Step 4: Prototyping (3h)
S4. Step 5: Testing and application of Design Thinking in different areas (3h)

Semester-long Assessment Mode:

• Class participation (20%): Evaluates students' presence, involvement, and contribution in class discussions and activities.

• Individual work (40%): Students will develop an individual project applying Design Thinking to solve a specific problem. They will be evaluated on the application of the stages of Design Thinking, the quality of the proposed solutions, and creativity.

• Group work (40%): Students will form groups to develop a joint project, applying Design Thinking to solve a real challenge. Evaluation will be based on the application of the steps of Design Thinking, the quality of the solutions, and collaboration among group members.

To complete the course in the Semester-long Assessment mode, the student must attend at least 75% of the classes and must not score less than 7 marks in any of the assessment components. The strong focus on learning through practical and project activities means that this course does not include a final assessment mode.

LO1. Know the structure, language and ethical and normative (APA) procedures for writing academic texts.
LO2. Learn how to use generative models to write academic texts.
LO3. Discuss procedures for the analysis, relevance and reliability of data generated by AI.
LO4. Recognize the ethical implications of using generative AI in an academic context. The learning objectives will be achieved through practical and reflective activities such as:
- Group discussions;
- Analysis of texts;
- Oral defense;
- Practical exercises.

CP1. Introduction: academic writing and generative models:
- Understanding how Generative Artificial Intelligence works: the path towards using generative AI in the academic environment.
CP2. Procedures for planning and constructing argumentative texts with the help of AI:
- Identifying the possibilities and hallucinations in the answers produced by Generative AI.
CP3. Critical analysis of texts produced: identifying and referencing data sources and analyzing their relevance to the objectives of academic work:
- Exploring the possibilities of data validation and the potential use of Generative AI tools in the production of academic papers.
CP4. Opportunities and risks of using AI: good practice guide for accessing, sharing and using Generative AI in an academic context:
- Understand the dynamics in responsible and ethically committed use when carrying out academic work with Generative AI tools.

The assessment of the course aims to gauge the development of students' skills in the informed use of generative models as an aid to the production of academic work. Assessment throughout the semester includes the following activities:
1.Individual activities (50%)
1.1 Participation in activities throughout the semester (10%).
Description: this component aims to assess each student's specific contribution to the activities carried out.
Assessment: Interventions in the classroom; relevance of the student's specific contributions to the debates.
1.2 Simulations of prompts with AI tools in an academic context (20%).
Description: the student must create a clear/justified, well-structured prompt, according to the script proposed by the teacher in class.
Assessment: (submit on moodle), communication skills and teamwork based on the quality of the prompt simulations carried out.
1.3 Oral Defense - group presentation - 5 minutes; debate - 5 minutes (20%).
Description: Each student must present their contributions to the work carried out to the class.
Evaluation: after the student's presentation, there will be a question and answer session.2. group activities (50%)
[students are organized in groups of up to 5 elements, constituted randomly]
2.1 Group presentations, revisions, editing and validation of content produced by AI (20%):
Description: Formation of working groups to review and edit the texts, using the generative models.
Evaluation: (submit to moodle), collection of relevant information, clarity and the innovative nature of the use of properly structured promts.
2.2 Development of strategies for reviewing, editing and validating content produced by AI (10%).
Description: At the end of each stage of the activity, students will have to promote critical evaluations by reflecting on the ethical challenges of integrating AI into an academic environment.
Evaluation: (submit on moodle), work will be corrected and evaluated based on accuracy and compliance with the quality of revisions, edits and the participation of students in the feedback provided to colleagues.
2.3 Final Project Presentation Simulations (20%):
Description: the groups choose a topic and create a fictitious project following the structure of a technical report or scientific text, making a presentation of their project in class (5 minutes) and debating the topic (5 minutes).
Evaluation: (submit on moodle): organization, content, correct use of the structure and procedures of academic work.
General considerations: feedback will be given during the semester. The student must have more than 7 (seven) points in each of the assessments to be able to remain in evaluation in the course of the semester.

Pedro Sousa Romano

At the end of the learning unit, the student must be able to: LG.1. Understand entrepreneurship; LG.2. Create new innovative ideas, using ideation techniques and design thinking; LG.3. Create value propositions, business models, and business plans; LG.5. Develop, test and demonstrate technology-based products, processes and services; LG.6. Analyse business scalability; LG.7. Prepare internationalization and commercialization plans; LG.8. Search and analyse funding sources

I. Introduction to Entrepreneurship;
II. Generation and discussion of business ideas;
III. Value Proposition Design;
IV. Business Ideas Communication;
V. Business Models Creation;
VI. Business Plans Generation;
VII. Minimum viable product (products, processes and services) test and evaluation;
VIII. Scalability analysis;
IX. Internationalization and commercialization;
X. Funding sources

Periodic grading system: - Group project: first presentation: 30%; second presentation: 30%; final report: 40%.

LO1 - automaton architect and programming methods
LO2 - Automation system structure using programmable logic controllers
LO3 - solve sequential control tasks in an automation system by writing the corresponding programmable logic controller programs
LO4 - control automatic drive systems

CP1: Introduction
CP2: Combinational Logic
CP3: Sequential Logic
CP4: Programming Languages
CP5: Algorithms
CP6: Finite Automation
CP7: Programmable Industrial Automation

Compulsory attendance of the student in 90% of Curricular Unit activities. Completion and presentation in laboratory of group project. Assessment weights:
- 5% - Attendance and participation in class.
- 70% - 4 group project work assignments.
- 25% - Mini-test with multiple answers.
The student waives the exam with 10 marks. In case of failure in the regular season the student has access to the exam of the resource season.

Bruno Duarte Damas

By the end of this course unit, the student should be able to:
LO1: Apply fundamental programming concepts.
LO2: Create procedures and functions with parameters.
LO3: Understanding the syntax of the Python programming language.
LO4: Develop programming solutions for problems of intermediate complexity.
LO5: Explain, execute and debug code fragments developed in Python.
LO6: Interpret the results obtained from executing code developed in Python.
LO7: Develop programming projects.

PC1. Integrated development environments. Introduction to programming: Logical sequence and instructions, Data input and output.
PC2. Constants, variables and data types. Logical, arithmetic and relational operations.
PC3. Control structures.
PC4. Lists and Lists of Lists
PC5. Procedures and functions. References and parameters.
PC6. Objects and object classes.
PC7. File Manipulation.
PC8: Graphical Interface.

The course follows a project-based continuous assessment model throughout the semester due to its highly practical nature, and does not include a final exam.

The student is evaluated according to the following parameters:
A1 (30% of the final grade): Learning Tasks validated by teachers, with a minimum grade of 8 points on the average of the tasks. There are 10 learning tasks and the 8 best grades count.

A2 (70% of the final grade): Mandatory Group Project (maximum 3 members) with theoretical-practical discussion (Delivery: 30%, Practical-oral: 40% with a minimum grade of 8). Component A2 has a minimum score of 9.5 points.

Students who do not achieve the minimum grade will have the opportunity to complete a 100% Practical Project with an oral discussion.

Minimum attendance of no less than 2/3 of classes is required.

Mariana Catela Jacob Rodrigues

After successful attendance of the course, students should be able to:
OA1. Identify the main features and components of sensors and actuators and select for applications
OA2. Carry out the design and implementation of specific conditioning circuits for sensors and actuators.
OA3. Design systems based on sensors and actuators for specific applications: industrial, processional agriculture, environmental monitoring.
OA4. Understand, design and implement digital signal processing algorithms.
OA5 Design and implement real-time digital systems characterized by sensors, actuators and specific digital signal processing algorithms.

CP1 Classification sensors; analog and digital sensors, operation, applications.
CP2: Actuators: classification, operation and applications
CP3: Signal conditioning: amplification, analog signal processing -analog filtering, specific control circuits for actuators:
CP4 Elements about analogue and analogue digital conversion..
CP5 Digital signal processing: time-domain and frequency-domain signal analysis; digital filtering algorithms.
CP6 Implementation of digital processing algorithms on real-time computing platforms
CP7 Systems design with sensors and actuators and signal processing modules and applications::industrial, cities and smart homes, transport, precision agriculture.

Laboratory (40%) + Written exam (60%)
Minimum grade in the laboratory: 8
Minimum exam grade: 8
The possibility of taking the written exam in the normal or special season is subject to:
- Presence in laboratory classes (100%) *,
- Presence in theoretical classes at least (50%),
- Presence in theoretical-practical classes at least (50%).
*If for objective reasons absences are registered in the laboratory, a way to recover the missing laboratory will be agreed with the professor.

André Filipe Xavier da Glória

At the end of this UC, the student should be able to:
LG.1. Present the image of the product/service in a website
OA.2. Present the image of the product/service in social networks
OA.3. Describe functionalities of the product/service
OA.4. Describe phases of the development plan
OA.5. Develop a prototype
OA.6. Test the prototype in laboratory
OA.7. Correct the product/service according to tests
OA.8. Optimize the product/service considering economic, social, and environmental aspects
OA.9. Adjust the business plan after development and tests, including commercialization and image
OA.10. Define product/service management and maintenance plan

I. Development of the product/service image
II. Functionalities of the product/service
III. Development plan
IV. Development of the product/service (web/mobile or other)
V. Revision of the business plan
VI. Management and maintenance of the product/service
VII. Certification plan
VIII. Intellectual property, patents, and support documentation
IX. Main aspects for the creation of a startup - juridical, account, registry, contracts, social capital, obligations, taxes

Periodic grading system:
- Group project: first presentation: 30%; second presentation: 30%; final report: 40%. The presentations, demonstrations and Defence are in group.

At the end of this course, students should be able to:
OA1. Understand digital technologies and their impact on the strategy of industrial manufacturing companies, when producing tangible goods and providing services;
OA2.: Characterize organizations, model business processes and understand the role of technological systems in companies;
OA3: Understand the characteristics of ERP systems and their use in the management of organizational processes in companies. Know how to use an ERP, WMS or TMS system.
OA4.: Use a strategic management planning approach to understand the link between the organization's strategy and the supporting technological systems;
OA5.: Consolidate the concepts studied by creating a plan for the development of an innovative business (start-up), articulating organizational aspects and technological systems suited to the company's strategic objectives.

CP1. Characteristics of the industrial manufacturing company
a. The Digital Transformation and Industry 4.0
b. Operation and organizational structure of the company
CP2. Operations and business process management
a. Operations and logistics management.
b. Organizational architecture and industrial processes
c. Business Process Management (BPM)
d. Visual process modeling (with BPMN)
CP3. Information systems in manufacturing
a. Management and information systems
b. IS requirements
c. Automation of production and logistics processes (ERP, WMS, TMS)
CP4. applied project

Evaluation throughout the semester
Group assignment (report + presentation + final discussion) 40%
Individual report (thematic or field visit) 20%
Individual final test 40%

In order to get approval, students must score, at least, 8 points (over 20) in every evaluation component with a weight over 30%.

Final exam
Final exam 100%

Mariana Catela Jacob Rodrigues

OA1 study of microcontroller architecture;
OA2 development and programming of systems with microcontroller with application in automation;
OA3 develop automations based on microcontroller.

CP1:Typical architecture and internal units of a microcontroller,
CP2: Memory types and organization.
CP3: Analog and Digital Signal Interface.
CP4: Timing/counting, Interrupts
CP5:Serial communications (UART,SPI, I2C).
CP6Program development using C and dedicated libraries
CP7Planning and realization of a microcontroller based project.

Written test (40%)
Laboratory Work (30%)
Final mini-project (30%)
Minimum grade 8

Bruno Duarte Damas

After the completion of this course, students should
(OA1) Know the different industrial robotic platforms as well as the main control architectures of robotic systems.
(OA2) Identify the requirements of the systems and/or models to be implemented;
(OA3) Choose the technological approaches best suited to the requirements of the problems.
(OA4) Understand and know how to use the approaches presented in the UC for the development of robotic systems.

CP1: Robotics Fundamentals
CP2: Robot Technology
CP3: Robot Applications in Industry
CP4: Kinematics
CP5: Robot Languages and Programming
CP6: Robot Control Systems
CP7: Block Programming
CP8: Redundancy

The evaluation is based on a project (50%) and a final exam (50%).
The project has two evaluation phases, mid-term delivery and a final oral examination.
Final grade is the average from the project and the exam grades

After attending the course, students should be able to:
OA1. Identify the main elements of a control loop, physical characteristics and metrology elements associated with industrial instruments and controllers.
OA2. Know the main instruments associated with the control of industrial processes.
OA3 Characterize, test and calibrate industrial instrumentation modules
OA4. Understand and implement process controllers considering the main control modes
OA5. Design and implement industrial instrumentation systems based on the use of PLCs and real-time processing platforms.
OA6 Understand and use industrial communication protocols.

CP1: General Concepts on measurement and measuring instruments and on the control of industrial processes,
CP2: Industrial instrument for measuring process quantities: pressure, force, level, flow, temperature, heat, humidity, density, viscosity and pH.
CP3: Signal conditioning for industrial instruments: amplifiers, measuring bridges.
CP4: Valves and Actuators and Motor Control - Applications
CP5:Control modes and digital controllers.
CP6: Programmable Logic Circuits and Applications
CP7: Communication protocols with application in industrial instrumentation and control systems.

Laboratory (40%) + Written exam (60%), Continuous Evaluation
Minimum grade in the lab: 8
Minimum exam grade: 8
The possibility of taking the written exam in the normal or special season is subject to:
- Presence in laboratory classes (100%) *,
- Presence in theoretical classes at least (50%),
- Presence in theoretical-practical classes at least (50%).
*If for objective reasons absences are registered in the laboratory, a way to recover the missing laboratory will be agreed upon with the professor.

João Pedro Duarte Monge

After completing this course, the student should:
OA1: Know the fundamentals of quality control;
OA2: Know the fundamentals of applying artificial vision to quality control;
OA3: Know the fundamentals of applying artificial intelligence to quality control;
OA4: Understand the fundamentals of designing human-machine interfaces for quality control.

P1. Quality Control (QC), quality metrics and non-destructive inspection.
P2. Integration of QC in automation systems and interconnection to the process.
P3. Fundamentals of artificial vision and artificial intelligence essential to QC.
P4. Selection and calibration of sensors, lenses, filters, and lighting for QC based on artificial vision.
P5. Passive and active collection of sensory data, its filtering, processing, and analysis for QC.
P6. Automatic learning of models and their use in detecting and predicting failures/deviations.
P7. Human-machine interfaces in the context of QC.

Periodic Assessment: Group project (60%) + Individual written test (40%).
Assessment by Exam: Individual written exam (100%).

LO1. Understand the main methods of non-supervised learning.
LO2: Evaluate, validate, and interpret the results of non-supervised models.
LO3: Develop knowledge discovery projects from data using non-supervised learning models.
LO4: Acknowledge, through the approach of various problem contexts (e.g., customer segmentation) in which unsupervised learning can effectively provide solutions relevant to these problems.
LO5. Understand the theoretical and practical foundations of reinforced learning.
LO6. Implement and test reinforced learning algorithms in simulated environments to understand the dynamics between actions and consequent rewards.
LO7. Evaluate and optimize the performance of reinforced learning models using appropriate metrics.
LO8. Learn and apply unsupervised and reinforced algorithms in practical case studies.

SY1: Introduction to unsupervised learning: fundamental concepts, types of algorithms and practical applications.
SY2: Dimensionality reduction and data visualization: Principal Component Analysis (PCA), t-SNE and UMAP for dimensionality reduction and visual interpretation.
SY3: Clustering and segmentation techniques: exploration of algorithms such as K-Means, DBSCAN, Expectation-Maximization (EM), hierarchical clustering.
SY4: Outlier analysis and detection using unsupervised techniques: KNN, LOF, iForest.
SY5: Association rules and the Apriori algorithm.
SY6: Self-Organizing Maps (SOMs): application of self-organizing maps for visualization and analysis of complex patterns in large volumes of data.
SY7: Reinforcement learning techniques: Q-Learning, SARSA. Introduction to concepts and practical implementation.
SY8: Exploration vs. exploitation in reinforcement learning: strategies for balancing decision-making.

As this course is of a very practical and applied nature, it follows the 100% project-based assessment model throughout the semester, which is why this course does not include a final exam. The assessment consists of 3 assessment blocks (AB), and each AB consists of one or more assessment moments. It is organized as follows:

- AB1: 1st tutorial + 1st mini-test [20% for the 1st tutorial + 10% for the 1st mini-test = 30%]
- AB2: 2nd tutorial + 2nd mini-test [20% for the 2nd tutorial + 10% for the 2nd mini-test = 30%]
- AB3: 1 final project [40%]

All periodic assessment blocks (AB1, AB2 and AB3) have a minimum mark of 8.5. In any AB, it may be necessary to hold an individual oral discussion to assess knowledge.

The tutorials consist of individual oral discussions to assess the students' performance in the projects proposed for the tutorial.

Mini-tests are used to assess the theoretical knowledge applied to each of the projects also assessed during tutorials.

The final project consists of the development of a practical piece of work that brings together the knowledge and skills acquired throughout the semester, in which external organizations / companies may participate in the proposed challenge.

The 1st Season and 2nd Season can be used for assessment.
Attendance at classes is not compulsory.

After completing this course, the student should:
? OA1: Know the fundamentals of user-centered design of interactive systems;
? OA2: Know the fundamentals of designing and implementing human-robot interfaces;
? OA3: Know the fundamentals of robotic systems simulation;
? OA4: Understand the fundamentals of testing and validating human-robot systems.

? P1. Human factors, user models, user experience, and usability.
? P2. User-centred design, prototyping, and evaluation of interactive systems.
? P3. Basics of visualization and interaction.
? Q4. Fundamentals of human-robot interaction and social robotics.
? Q5. Graphical, natural, and multimodal interfaces in robotics.
? P6. Interfaces based on virtual reality and mixed reality in robotics.
? P7. Simulation of human-robot systems.
? P8. Testing and validation of human-robot systems.

- Periodic Assessment: Group project (60%) + Individual written test (40%).
- Assessment by Exam: Individual written exam (100%).

At the end of the course, the student should be able to:
LO1: Apply co-creation methodologies in the development of innovative triple sustainable projects (with economic, social and environmental value) in organizations.
LO2: Create empathy with the user and his organization (define needs, obstacles, goals, opportunities, current and desired tasks), define the problem and raise the issues addressed by the project.
LO3: Conduct a systematic literature review and competitive landscape analysis (if applicable), related to the identified problem and the issues raised.
LO4: Identify the digital (including data collection), computational and other resources needed to address the problem.
LO5: Apply already consolidated knowledge of project planning, agile management and project development, within the framework of group work.
LO6: Participate in collaborative and co-creation dynamics and make written and oral presentations, in the context of group work.

S1 Co-creation methodologies based on Design Thinking and Design Sprint
C2 Sustainable Development Goals (SDGs) of the United Nations. Creation of value propositions
S3 Presentation of case studies and digital technologies project topics (product, service or process)
S4 Selecting the project topic and framing it in the organization
S5 Problem space: creating empathy with the user and his organization, defining the problem and its related issues, considering business requirements, customer and user needs, and technology challenges
S6 Application of a systematic literature review methodology and its critical analysis. Competition analysis (if applicable)
S7 Identification of digital resources (including data collection), computational, and other resources required for project development
S8 Application of agile project management methodologies, appropriate to the group work to be developed by the students. Communication of results

Course in periodic assessment, not contemplating final exam, given the adoption of the project-based teaching-learning method applied to real situations. Presentations, demonstrations and discussion will be carried out in groups.
Assessment weights:
R1 Report: Project Topic Definition: 5%
R2 Report: Empathy with the User and the Organization and Definition of the Problem. Its presentation and group discussion: 40%
R3 Report: Systematic Literature Review and Project Development Planning. Its presentation and group discussion: 55%

LO1: Correct the user and/or organization problem identified in the Applied Project I course of the 1st semester, developing, in an iterative way, an integrated project with all its components, including requirements gathering, solution prototyping (lo-fi, hi-fi, MVP), and evaluation and field deployment of the innovative solution, regarding product, process or service (PPS).
LO2: Produce design documentation of the PPS innovation solution, including, where applicable, architecture, hardware and software configuration, installation, operation and usage manuals.
LO3: Produce solutions with the potential to be triple sustainable in the field, taking into account the applicable legal framework.
LO4: Produce audiovisual content on the achieved results, to be exploited in several communication channels: social networks, landing page web, presentation to relevant stakeholders, demonstration workshop.

S1 Solution space: ideation of the best technological solution relative to the project, development of user requirements, storyboarding, user/costumer journey, iterative prototyping cycles (low fidelity - lo-fi, high fidelity - hi-fi, minimum viable product - MVP), heuristic evaluation of the solution with experts and evaluation with end users.
S2 Production of solution design documentation, including, where applicable, architecture, technical specifications, hardware and software configuration, installation, operation and use manuals.
S3. Experimental deployment of the solution with the potential to be triple sustainable (with economic, social and environmental value creation), safeguarding the applicable legal framework.
S4. Audiovisual communication on the Web and social networks. Communication in public and its structure. Presentation to relevant actors.
S5. Demonstration in workshop with relevant actors

UC in periodic assessment, not contemplating final exam, given the adoption of the project-based teaching method applied to real situations. Presentations, demonstrations and discussion are carried out in groups.
Evaluation weights:
R1 Solution Ideation Report, with Storyboard, User Journey, User Requirements, Technical Specifications and its audiovisual presentation: 20%.
R2 Solution Prototyping: Lo-fi and Hi-fi Prototypes and Minimum Viable Prototype - MVP (on GitHub), its Demonstration and Evaluation Report: 40%
R3 Solution Design Report with the following elements (if applicable): Architecture (UML Package Diagram, UML Component Diagram), Hardware and Software Configuration, Installation Manual (UML Deployment Diagram, Configuration Tutorial), Operation Manual, User Manual: 20%
R4 Audio-visual presentation of the solution and its demonstration in a Workshop: 20%.

After attending the course, students should be able to:
OA1. Understand the flow of information in office networks with an internet connection;
OA2. Understand the information flow in an industrial automation process, from the sensor to the internet;
OA3. Understand the operation, set up, configure and maintain office computer networks;
OA4. Understand the operation, set up, configure and maintain communication networks for industrial automation;
OA5. Use the OPC (Open Platform Communications) platform to share data at supervisory level and above;
OA6. Build supervision software to support HMI, including synoptics, alarm management and historical data archive.

CP1. Introduction to communication networks based on the OSI (Open Systems Interconnection) model. Architecture of office and industrial automation networks.
CP2. Computer networks (Ethernet, IP, UDP, TCP, sockets).
CP3. Industrial communication networks (Modbus, Profibus).
CP4. Industrial Ethernet (Modbus TCP, Profinet).
CP5. Data sharing and logging using the OPC (Open Platform Communications) platform.
CP6. Supervisory systems (SCADA).

Continuous evaluation consists of interim tests, laboratory projects and/or bibliographic research works.
In case of reproval, the continuous assessment will be replaced by a final written exam.

After completing this UC, the student will be able to:

LO1. Identify the main themes and debates relating to the impact of digital technologies on contemporary societies;
LO2. Describe, explain and analyze these themes and debates in a reasoned manner;
LO3. Identify the implications of digital technological change in economic, social, cultural, environmental and scientific terms;
LO4. Predict some of the consequences and impacts on the social fabric resulting from the implementation of a digital technological solution;
LO5. Explore the boundaries between technological knowledge and knowledge of the social sciences;
LO6. Develop forms of interdisciplinary learning and critical thinking, debating with interlocutors from different scientific and social areas.

S1. The digital transformation as a new civilizational paradigm.
S2. The impact of digital technologies on the economy.
S3. The impacts of digital technologies on work.
S4. The impact of digital technologies on inequalities.
S5. The impacts of digital technologies on democracy.
S6. The impacts of digital technologies on art.
S7. The impacts of digital technologies on individual rights.
S8. The impacts of digital technologies on human relations.
S9. The impacts of digital technologies on the future of humanity.
S10. Responsible Artificial Intelligence.
S11. The impact of quantum computing on future technologies.
S12. The impact of digital technologies on geopolitics.

The assessment process includes the following elements:

A) Ongoing assessment throughout the semester
A1. Group debates on issues and problems related to each of the program contents. Each group will participate in three debates throughout the semester. The performance evaluation of each group per debate will account for 15% of each student's final grade within the group, resulting in a total of 3 x 15% = 45% of each student's final grade.
A2. Participation assessment accounting for 5% of each student's final grade.
A3. Final test covering part of the content from the group debates and part from the lectures given by the instructor, representing 50% of each student's final grade.
A minimum score of 9.5 out of 20 is required in each assessment and attendance at a minimum of 3/4 of the classes is mandatory.

B) Final exam assessment Individual written exam, representing 100% of the final grade.