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Intelligent Instrumentation Systems

Code: 102724
Credits: 6
2026/2027
Degree programme Type Course
Electronic Engineering for Telecommunication OP 4

Contact lecturer

Name :
Javier Martin Martinez
Email :
javier.martin.martinez@uab.cat

Teaching staff

Javier Martin Martinez

Group languages

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

Prerequisites


It is recommended to have taken the subjects of Instrumentation I and II.

Objectives


The main objective of the subject is to understand how the use of artificial intelligence can improve the instrumentation systems that the student already knows about the instrumentation subjects I and II

Learning outcomes

  1. Identify the standards and regulations for telecommunications in the national, European and international areas in the field of electromagnetic compatibility
  2. Autonomously apply new knowledge and proper techniques for the design, development or operation of electronic systems.
  3. Analyse and specify the fundamental parameters of a communications system, in terms of instrumentation.
  4. Evaluate the advantages and disadvantages of different technological alternatives for the deployment or implementation of electronic systems, in terms of disturbance and noise.
  5. Perform the specification, implementation, documentation and fine-tuning of electronic instrumentation and control equipment and systems , considering technical aspects and the relevant regulatory requirements.
  6. Document the instrumentation systems designed, based on current standards.
  7. Analyse and troubleshoot electromagnetic interference and compatibility.
  8. Develop critical thinking and reasoning.
  9. Develop the capacity for analysis and synthesis.
  10. Work autonomously.
  11. Develop independent learning strategies.
  12. Prevent and solve problems.
  13. Work cooperatively.
  14. Communicate efficiently, orally and in writing, knowledge, results and skills, both professionally and to non-expert audiences.
  15. Respect diversity in ideas, people and situations.
  16. Develop curiosity and creativity.

Contents


1) Modeling non-linear sensors.


2) Introduction to aritficial neural networks.


2.1) The perceptron.


2.2) Multilayer networks


2.3) Training of neural networks.


2.4) General applications.


3) Optimization of instrumentation systems through the use of neural networks.

Learning activities and methodology

Title Hours ECTS Learning outcomes
Classes 30 1.2 1, 2, 3, 4, 7, 8, 9
Discussion of the proposed problems 15 0.6 1, 2, 5, 6, 7, 10, 11, 12, 13
Work oriented to learning based in problems 35 1.4 1, 2, 3, 4, 5, 7, 8, 9, 16
Study 20 0.8 1, 2, 4, 7
Problems and cases seminaris 10 0.4 1, 2, 3, 4, 8, 9, 10, 11, 12, 13, 14, 16
report writing 20 0.8 14
Guidance 7 0.28 1, 2, 3, 4, 5, 6, 7

The teaching methodology will combine, in addition to independent work, guided and supervised activities. The guided activities will combine master classes, problem and case seminars and laboratory sessions.

Through the Virtual Campus, students will have access to teaching materials that complement the concepts covered in the classroom. The Virtual Campus will also be used to submit assessable activities.

It is recommended that students attend class with a laptop.
During the course, lectures will alternate with practical cases that students must solve in class using MATLAB. The use of AI is restricted to solving practical cases. Students must explain the purpose of using AI and obtain the instructor's approval.

This subject does not provide for the single evaluation system.

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
Resolution of problems 40% 10 0.4 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
Final report 30% 2 0.08 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14
Short oral exams 30% 1 0.04 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16

Throughout the course, problems will be proposed for students to solve during and outside of class.

The resolution of these problems will account for 40% of the total grade.

Throughout the course, several oral assessments will be given on the exercises being completed. These assessments account for 30% of the grade.

Finally, the student must submit a report on a free-theme project related to the course content, which will account for 30% of the grade.

If the student does not pass the course, the student will be entitled to a make-up exam according to the schedule established by the School.

A grade of Not Assessable will be obtained if the report on the free-theme project is not submitted and less than 15% of the proposed projects are submitted.

The MH classification will be obtained in accordance with the criteria established in the current UAB regulations.

Without prejudice to other disciplinary measures deemed appropriate, and in accordance with current academic regulations, any irregularities committed by the student that may lead to a change in the grade for an assessment will be graded with a zero.

Bibliography

J.C. Alvarez et al., “Instrumentación electrónica”, Thomson-Paraninfo, 2006

P.H. Sydenham, N.H. Hancok and R. Thorn, “Introduction to Measurement Science and Engineering”, John Wiley & Sons, 1989.

Ripley, Brian D. (1996) Pattern Recognition and Neural Networks, Cambridge

Bishop, C.M. (1995) Neural Networks for Pattern Recognition, Oxford: Oxford University Press.

Software

Matlab

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

Type of teaching Group Language Semester Shift
(TE) Theory 320 Spanish second semester afternoon
(PAUL) Classroom practices 321 Spanish second semester afternoon
(PLAB) Practical laboratories 321 Spanish second semester morning-mixed
(PLAB) Practical laboratories 322 Catalan/Spanish second semester morning-mixed