Sensorial Systems


To endow students with knowledge on characteristics and applications of sensors of diverse types and to deepen their knowledge about image processing.

 To know:

  • Knowledge of the C# programming language
  • Implementation of image processing techniques with emphasis on the efficiency
  • Real time programming
  • Selection, project and implementation of sensor based circuits
  • Sensors calibration

To do:

  • Development of structured programming
  • Usage of software libraries
  • Project and implementation of sensors based circuits

General characterization





Responsible teacher

José Manuel Matos Ribeiro da Fonseca


Weekly - 4

Total - 56

Teaching language



Basic knowledge in Electric Circuits Theory, Eletronics and Programming languages, namely, C or C# (C sharp) are recommended.


Interfacing sensors to the IBM PC, Willis J. Tompkins, John G. Webster, Prentice Hall

Multi-sensor fusion - fundamentals and applications with software, Richard Brooks, S. Iyengar. Prentice Hall 

Digital Image Processing. Rafael Gonzalez, Paul Wintz. Addison-Wesley

Image Analysis: principles and practice, pp. 36 a 36 e 106 a 117. Joyce-Loebl

Digital Image Processing and Computer Vision, pp. 130 a 173. Robert Schalkoff

Fuzzy Algorithms, pp. 85 a 93, Zheru Chi, Hong Yan, Tuan Pham. Worls Scientific, Fuzzy Clustering

Computer Graphics - Principles and Practice, pp. 550 a 555. Foley, van DAM, Feiner, Hughes. Addison-Wesley

Teaching method

The Problem-solving sessions (TP) introduce the concepts and solve problems with the active participation of the students. In the laboratory sessions dedicated to image processing the students consolidate the concepts by developing a small image processing software project.

The fundamental concepts of the generic sensors component are presented on the TP sessions. On the laboratory sessions the students develop their knowledge of real world sensors by projecting and implementing small sensor based circuits that they calibrate and report the results.

In the last week of the course both practical assignments are presented and discussed with the teachers. The practical projects and their defense represent 40% of the final evaluation and the mid-term tests (or the final examination) the remaining 60%.

Evaluation method

Evaluation method:

  • 60 % Theoretical component
      • 0,75 * GTT + 0,25 GTM
      •  GTT = Average grade of 2 tests or Exam

      •  GTM = Moodle Theoretical grade

        • average grade of 9 moodle tests of a total of 10
        • Moodle tests are to be done outsitde classes
      • Conditions: GTT >= 9.5 e GTM >= 9.5

  • 40 % Pratical component
    • 80% - 1st Project
    • 20% - 2nd Project


     Final Grade = TG * 0,6 + ( PPG1 *0,8 + PPG2 * 0,2 ) * 0,4  


  • A penalty of 1 value per day of delay in the delivery of practical 
    work is considered
  • It is necessary to have a grade of not less than 9.5 in both practical 
    and theoretical components
  • Knowledge assessment tests and exams will be in person and without 
    consultation. Students who take remote assessment tests (tests or examss
    (because they have an officially recognized status that allows them to
    do so) may be called upon to defend the grade obtained in an oral
    discussion with teachers that will be carried out in person or remote
    according to the circumstances
  • The tests carried out remotely will be considered with consultation
    for what they will have stated appropriate to the circumstances.

Subject matter

- Introduction – Typical steps of image processing

 Image formation: Pinhole, Lens, Aperture vs Depth of field and Aperture vs Shutter speed, Image sensors

- Basics of image processing: Geometric image transformations, Translation, Rotation and Scaling, Spatial methods, Linear and non-linear averaging, image averaging, median, k-nearest neighbor, Sigma, Roberts, Sobel and Quadtree, Binarization and binary image processing,Histogram, c-means and Otsu

 - Image segmentation: Connected components and projections

- Feature extraction: Basic features calculation: chain code, Fresnell, Skeletoning (medial axis and Zhang and Suen)

- Sensors: Definitions, Sensors characterization, Sensors technology and applications. Examples of real world sensors – positioning, level, displacement, presence and movement, speed and acceleration, strength, flux, acoustic, humidity, powder, light and temperature.