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Embedded Systems

Code: 104378
Credits: 6
2026/2027
Degree programme Type Course
Data Engineering OP 4

Contact lecturer

Name :
Lluís Ribas Xirgo
Email :
lluis.ribas@uab.cat

Teaching staff

Joaquín Saiz Alcaine

Group languages

You can consult this information at the end of the document.

Prerequisites

For a full understanding of the contents of the course, it is necessary to have basic skills in programming and a good knowledge of how programs run on computers. To do this, you must have passed Advanced Programming, as well as Physical Basics for Data Acquisition to understand the principles of digitization of information. It is advisable to have completed Data Structures and Parallel Programming.

Objectives

Embedded systems are responsible for very specific sets of functions that usually act as a high-level interface between applications and the physical world. Therefore, they deal with the treatment of data captured by sensors, some of their processing (edge computing), the transmission of processed data to the applications, and with the control of motors and actuators of all kinds, too. Currently, there are systems embedded in almost any "thing" and, if things are complex, they can carry many such as those that may be in a car.

Because embedded systems are closely related to the physical environment around them, the algorithms they implement must meet many requirements, often very stringent and contradictory to each other. For example, having a high performance and consuming little energy.

Thus, for the development of embedded systems it is necessary to design robust algorithms that can be verified to operate in critical environments and have a development and execution cost within the margins delimited by the requirements of the application.

The aim of this course is for students to achieve the following objectives:

  • To have a basic understanding of the application domains of embedded systems and their most common requirements.
  • To know the development methodology of embedded systems.
  • To have acquired the fundamentals of model-based design.
  • To understand the various models of computation of systems.
  • To have practical skills in designing and implementing state-oriented models of computation.
  • To be able to estimate implementation costs based on system models of computation.

Learning outcomes

  1. Students must be capable of collecting and interpreting relevant data (usually within their area of study) in order to make statements that reflect social, scientific or ethical relevant issues.
  2. Prevent and solve problems, adapt to unforeseen situations and take decisions.
  3. Demonstrate sensitivity towards ethical, social and environmental topics.
  4. Students must develop the necessary learning skills to undertake further training with a high degree of autonomy.
  5. Design the most efficient data acquisition system for a system to support autonomous driving.

Contents

1. Introduction

1.1. Embedded systems development process

1.2. Controllers based on state machines

1.3. State machine programming

2. Models of computation

2.1. Extended Finite State Machines (EFSMs)

2.2. Concurrent and Hierarchical Extended Finite State Machines (HCEFSMs)

2.3. Algorithmic State Machines (ASMs)

2.4. Data Flow Graphs (DFGs)

2.5. Control Data Flow Graphs (CDFGs)

3. Development of embedded systems

3.1. Architecture of complex systems

3.2. Formal verification of state-oriented systems

3.3. Software synthesis

3.4. Simulation

3.5. Real time systems


Learning activities and methodology

Title Hours ECTS Learning outcomes
Theory: Attendance and participation in theory classes 24 0.96 1
Problem-solving: Reporting solutions to proposed problems 24 0.96 2
Tutoring: Additional problem-solving activities 6 0.24 5
Assignment: Project development and report writing 12 0.48 5
Project: Course project development 12 0.48 2, 5
Problem-solving: Problem solution proposals and discussion 12 0.48 3, 5
Theory: Study 26 1.04 4
Project: Course project follow-up reporting 6 0.24 5

Teaching is structured in the following face-to-face activities:

  • Theory classes: Presentations of course contents, with a first part that is devoted to the dissemination of the necessary knowledge for the analysis and the design of cyber-physical systems, and to explain cases that situate in context the knowledge and the abilities that are acquired during the course. The second part will be devoted to the discussion of problems that will be dealt with in the corresponding seminars.
  • Problem-solving seminars: Discussion of small case studies (for example, control of a microwave oven) that serve to consolidate theoretical knowledge regarding the analysis and design of cyber-physical systems.
  • Laboratory practices: Team work at the laboratory, following a walk-through guide under the supervision of a teacher. Each session will deal with a specific aspect regarding the implementation of cyber-physical systems.

As in all areas of Engineering, the development of embedded systems involves making decisions based on often contradictory criteria. In the case studies, care will be taken to include ethical, social and environmental criteria. Similarly, the ability to adjust them to adapt to incidents in the development process and changes in specifications will be encouraged.

Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Assessment

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Continuous assessment assignments 25% 10 0.4 1, 5
Laboratory 25% 12 0.48 2, 4, 5
Midterm exam 25% 2 0.08 1, 3, 5
Make-up exam 50% 2 0.08 1, 3, 5
Final exam 25% 2 0.08 1, 3, 5

a) Procedure and assessment activities’ plan

The assessment is continuous with specific activities (exams and assignments) throughout the course. These assessment activities generate a series of grades that determine the final grade.

The calculation of the final grade n follows the following expression:

n = x·50% + p·25% + c·25%

where x is the grade for the exams; p, that for the laboratory project, and c, that for the continuous assessment.

If x < 5 or p < 5, the final grade n is, at most, a 4.5. In other words, the average of the exams and the project must be passed separately.

The exam grade (x) is the average of the midterm and final exam grades, or the final exam grade alone if it is higher. If the final exam grade is below 3.5, a resit exam must be taken.

Project grade p is obtained from the weighted average of the grades corresponding to each lab session. Six are planned. In case of non-attendance, the absent person will receive a 0 as the grade for the corresponding session.

Continuous assessment grade c is obtained from a weighted average of the problem-solving assignments completed throughout the course. There is no minimum and, therefore, the course can be passed with c = 0 as long as x·50% + p·25% ≥ 5.

b) Assessment activities schedule

The dates of all face-to-face activities, including assessment activities, and submission deadlines will be published on the virtual campus (CV) and may be subject to possible changes in programming for reasons of adaptation to possible incidents: they will always be previously informed through the CV since it is the usual mechanism for exchanging information between teachers and students outside the classroom.

In exceptional cases where the affected person receives approval for the rescheduling request of assessment activities (see “Exam Rescheduling” on the School’s website), an alternative will be offered that fits the course schedule.

c) Re-assessment procedure

In accordance with the coordination of the Degree and the deanship of the School of Engineering, the following activities are not recoverable:

- Project, 25% of the final grade

- Continuous assessment, 25% of the final grade

The average grade of the exams can be recovered with a specific make-up exam.

d) Assessment review procedure

Assessment activities can be reviewed any time after corresponding grades are published and before the deadline for the revision of the final exam.

Should the change of a grade be agreed upon, that grade may not be modified in a later review.

No reviews will be done after the closure of the reviews of the final exam, but for the make-up exam.

e) Grading

A “non-assessable” grade is assigned to students that have not participated in any assessment activity. In any other case, not participating in an assessment activity scores 0 in the weighted average computation.

Honors will be awarded to those who obtain grades greater than or equal to 9.0 in each part, up to 5% of those enrolled in descending order of final grades. They may also be granted in other cases if they do not exceed 5% and the final grade is equal to or greater than 9.0.

f) Irregularities, copies and plagiarism

Copies are evidence that the work or the examination has been done in part or in full without the author's intellectual contribution. This definition also includes attempts to copy in exams and reports, and violations of the norms that ensure intellectual authorship. Plagiarisms refer to the works and texts of other authors that are passed on as their own. They are a crime against intellectual property. To avoid plagiarism, quote the sources you use when writing the corresponding work reports or examinations.

In accordance with the UAB regulations, copies or plagiarisms or any attempt to alter the assessment result, for oneself or for others, like e.g. letting other copy, imply a final grade for the corresponding part (exam, continuous assessment or project) of 0 in the computation of the final score and failing the course. This does not limit the right to act against perpetrators, both in the academic field and in the criminal.

The use of Artificial Intelligence (AI) technologies as an integral part of the development of the work is permitted, provided that the result reflects a significant contribution by the student in the analysis and personal reflection. The student must clearly identify which parts have been generated with this technology, specify the tools used and include a critical reflection on how they have influenced the process and the result of the activity. The lack of transparency in the use of AI is considered a lack of academic honesty and entails a penalty in the grade of the activity, or greater sanctions in serious cases.

g) Assessment of repeaters

There is no differentiated treatment for repeaters, but they can take advantage of their own material from the previous year provided it is informed in the corresponding reports.

h) Single assessment

This course does not have a single assessment procedure.

Bibliography

Ribas-Xirgo, Ll. (2026). Simulator-Based Digital Twin of a Robotics Laboratory. Machines, 14(3), 273. https://doi.org/10.3390/machines140302273 (Details the model of the upper layers of the robot controller stack used in the laboratory sessions and as an example in the course.)

Ribas-Xirgo, Ll. (2025). State Machine Model of the Operation Control of a Differential-Drive Mobile Robot. Preprints. https://doi.org/10.20944/preprints202511.0943.v1 (Details the model of the lower layers of the robot controller stack used in the laboratory sessions and as an example in the course.)

Edward A. Lee and Sanjit A. Seshia. (2017). Introduction to Embedded Systems, A Cyber-Physical Systems Approach, Second Edition, MIT Press. https://ptolemy.berkeley.edu/ (A course with similar contents, from a more formal perspective.)

Ll. Ribas Xirgo. (2014). How to code finite state machines (FSMs) in C. A systematic approach. TR01.102791 Embedded Systems. Universitat Autònoma de Barcelona. https://www.researchgate.net/publication/273636602_How_to_code_finite_state_machines_FSMs_in_C_A_systematic_approach (It explains a method for programming finite state machines in C similar to the one used in the course.)

Ll. Ribas Xirgo. (2011). “Basic structure of a computer”, Chapter 5 in Montse Peiron Guàrdia, Lluís Ribas i Xirgo, Fermín Sánchez Carracedo and A. Josep Velasco González: Fundamentals of Computers. Teaching material of the UOC. UOC OpenCourseWare. https://hdl.handle.net/10609/12901 (It covers the model of state machines, algorithmic machines, and the basic architectures of digital systems, aligning with the corresponding topics of the course.)

Tim Wilmshurst. (2010). Designing Embedded Systems with PIC Microcontrollers. Principles and Applications (Second Edition). Elsevier. (Complementary information to the course, presenting a possible embedded system for robot control.)

Brian Bailey, Grant Martin and Andrew Piziali. (2007). ESL Design and Verification. A Prescription for Electronic System-Level Methodology. Elsevier. (It reviews the entire synthesis process of embedded systems and puts the course material into context. Therefore, it is a good complementary resource.)

M. J. Pont. (2005). Embedded C. Pearson Education Ltd.: Essex, England. (It deals with programming embedded systems, a topic that coincides with the problems and practical part of the course. Therefore, it is a very interesting complementary resource.)

Oliver H. Bailey. (2005). Embedded Systems Desktop Integration. Wordware Publishing. (Complementary information to the course, focusing mainly on communication between hardware and software.)

Software

CoppeliaSim, EDU Version, Coppelia Robotics [https://www.coppeliarobotics.com/]

ZeroBrane Studio, ZeroBrane [https://studio.zerobrane.com/]

Draw.io, diagrams.net [https://app.diagrams.net/]

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE