Electronics

Base Knowledge

It does not require experience in electronics.

Teaching Methodologies

1 – Theoretical classes: Presentation of fundamental concepts and techniques of analysis and design, essential for practical classes. Analog electronic circuits and simple embedded systems will be studied, with support from the PSpice, LTspice, and Tinkercad platforms. Topics such as energy literacy, decarbonization, IoT, and IIoT will also be introduced.

2 – Practical classes: Solving exercises and developing practical projects focused on the analysis, design, and simulation of low-complexity analog circuits using PSpice and Python. This also includes implementing two final projects with an Arduino Nano: a traffic light control system and an electronic lock.

Learning Results

The course aims to help students understand electronics at various levels of abstraction, from physical fundamentals to analog and digital electronics, and to explain the role of the analog computer in connecting the digital computer to the environment. It also aims to raise awareness of energy literacy and decarbonization issues, promoting greater awareness of the need to change behaviors and lifestyles.

In the theoretical component, students study electrical quantities, elementary circuit analysis, and semiconductor physics to understand how devices such as diodes, transistors, and operational amplifiers operate. They should also understand forms of information processing in analog electronics, including discrete components and integrated circuits, as well as the architecture and requirements of embedded systems and basic notions of IoT and IIoT.

In the laboratory component, the aim is to develop practical skills in handling electronic components, analyzing analog electronic systems, and using circuit simulation tools such as PSpice, LTspice, and Tinkercad. Students should also gain knowledge of simulating dynamic systems in Python and of using embedded systems design tools.

Program

· Introduction:

• Definitions of basic electrical quantities;

• Fundamental elements of a circuit;

• Electrical resistance, Ohm’s law, Joule effect and superconductors;

• Renewable energy sources, energy efficiency, sustainability and the circular economy, highlighting the contribution of information technology to the sustainable management of resources.

· Circuit analysis techniques:

• Laws of Kirchhoff;

• Analysis of elementary circuits;

• Analysis Methods: Mesh analysis and Nodal analysis;

• Thevenin’s theorem;

• Simulation of electrical and electronic circuits using simulation tools: Pspice, LTspice and Tinkercad.

· Semiconductors:

• The atom;

• Materials used in Electronics;

• Electron and hole;

• Extrinsic and intrinsic semiconductors;

• N-Type semiconductor;

• P-Type semiconductor;

• PN junction – Diode.

· Diode:

• Diode operation;

• V-I characteristic;

• Diode model;

• Circuit analysis;

• Analysis and simulation of circuits with diodes using the Python programming language;

• Most relevant applications in the field of computer engineering.

· Electricity and the human body:

• Voltage characteristics of grid electricity;

• Root mean square value;

• Severity of electrical current in the human body;

• Main physiological effects of electric current on the human body.

· Bipolar junction transistor:

• Structure;

• Basic operation;

• Characteristics and operation;

• BJT as an amplifier;

• BJT AC models;

• Common-emitter amplifier.

• Analysis of BJT circuits (DC and AC).

· Operational amplifier:

• Differential amplifier;

• Integrated circuits;

• Characteristics of amp-ops and schematic symbol;

• Circuit model;

• Characteristics of an Ideal amp-op;

• Ideal amp-op analysis;

• Different types of feedback;

• Mathematical operations;

• Amp-op versus analog computer.

· Digital to Analog Converters (CAD):

• Sample and hold circuit;

• Nyquist’s theorem;

• Aliasing;

• Quantization block;

• Characteristics;

• CAD architectures.

· Embedded Systems (ES):

• Fundamental concepts;

• Structure;

• Microcontrollers;

• Processor types: GPP, ASIP e ASIC;

• Instruction Set Architecture: CISC versus RISC;

• Von Neumann versus Harvard architectures

• Memories in ES: RAM, ROM e FLASH;

• Arduino nano development board;

• Integrated development environment;

• Design of embedded systems using the Tinkercad simulation tool;

• Sensors;

• Actuators;

• User interface.

· Internet of Things versus Industrial Internet of Things

• Applications, challenges, definitions and advantages;

• History (M2M and IoT) and evolution;

• Architectures and main components;

• Sensor technologies used in IoT systems and their applications;

• Communication protocols: Wi-Fi, Bluetooth, Ethernet and MQQT;

• Control systems (Feedback control and Feedforward control) and real-time control;

• Components of an industrial automation system: PLC, SCADA, HMI and MES;

• Synergy between Industrial Automation and IoT (IIoT).

Curricular Unit Teachers

Acácio Manuel Raposo Amaral

Grading Methods

The final grade depends on two components:

The written component (midterm test or exam) accounts for 75% of the final grade, and a minimum score of 35% is required.

  • Students who take the first midterm test are not allowed to take the first final exam but may take the second.
  • If a student submits the first midterm test, they opt for continuous assessment, regardless of whether they take the second midterm test.
  • If a student misses the second midterm test (SMT), the final grade will be calculated with a score of 0 for that test (SMT).

The practical component includes six assignments and makes up 25% of the final grade.


    Internship(s)

    NAO

    Bibliography

    Main Bibliography:

    1. Amaral, A. M. R. (2021). Electrónica Aplicada. Lisboa: Edições Silabo (Cota: 1-1-414 (ISEC)).

    Complementar:

    1. Amaral, A. M. R. (2017). Electrónica Analógica: Princípios, Análise e Projectos. Lisboa: Edições Silabo (Cota: 1-1-398 (ISEC)).

    2. Amaral, A. M. R. (2015). Análise de Circuitos e Dispositivos Electrónicos (2ª ed.). Porto: Publindústria (1-1-388 (ISEC)).

    3. Nilsson, J. W., Riedel S. A. (2011). Electric Circuits (9ª ed.). New Jersey: Pearson Education, Inc (Cota: 1-3-289 (ISEC)).

    4. Malvino, A., Bates, D. (2016). Eletronic Principles (8ª ed). New York: McGraw-Hill (Cota: 1-1-144(ISEC))

    5. Millman, J., Halkias C. (1972). Integrated Eletronics: Analog and Digital Circuits and Systems. Tokyo: McGraw-Hill. (Cota: 1-1-217 (ISEC))

    6. Psarommatis, F., May, G. (Abril 2026). A Comprehensive Review of Digital Product Passports in Sustainable Manufacturing: Current State, Challenges, and Future Directions. Circular Economy and Sustainability, 6, pp. 1-52.

    7. Amaral, A.M.R. (Setembro 2025). Intelligent Estimation of ESR and C in AECs for Buck Converters Using Signal Processing and ML Regression. Computers, Materials & Continua, 85, pp. 3825-3859.

    8. Amaral, A.M.R. (Junho 2025). Enhancing Power Converter Reliability Through a Logistic Regression-Based Non-Invasive Fault Diagnosis Technique. Applied Sciences, 15, pp. 1-33.

    9. Laadjal, K., Amaral, A. M. R., Serra J., Cardoso, A. J. M (Outubro 2024). An Intelligent Framework for Accurate Identification of Open-Circuit Faults in Asymmetrical Six-Phase Induction Motors. In 2024 IEEE Energy Conversion Congress and Exposition (ECCE). Phoenix, AZ, USA (pp. 3723-3728).

    10. Amaral, A. M. R., Laadjal, K., Cardoso, A. J. M (Outubro 2024). Assessment of the Integrity of Aluminum Electrolytic Capacitors Using a Logistic Regression Model. In 2024 IEEE International Conference on Artificial Intelligence & Green Energy. Yasmine Hammamet, Tunisia (pp. 1-6).

    11. Amaral, A. M. R., Laadjal, K., Cardoso, A. J. M (Outubro 2024). Advancements in Fault Diagnosis Techniques for Aluminum Capacitors Using STLSP and Autoencoder. In 2024 IEEE International Conference on Artificial Intelligence & Green Energy. Yasmine Hammamet, Tunisia (pp. 1-6).

    12. Amaral, A. M. R., Laadjal, K., Cardoso, A. J. M (Agosto 2024). Optimized Preventive Diagnostic Algorithm for Assessing Aluminum Electrolytic Capacitor Condition Using Discrete Wavelet Transform and Kalman Filter. Electronics, 13, pp. 1-26.

    13. Laadjal, K., Amaral, A.M.R., Sahraoui, M., Cardoso, A. J. M (Setembro 2023). Machine Learning Based Method for Impedance Estimation and Unbalance Supply Voltage Detection in Induction Motors. Sensors, 23, pp. 1-25.

    14. Amaral, A. M. R., Laadjal, K., Cardoso, A. J. M (2023, July). Decision Tree Regressor-Based Approach for DC-Link Electrolytic Capacitors Health Monitoring. In 2023 AEIT International Conference of Electrical and Electronic Technologies for Automotive, Modena, Italy (pp. 1-6).

    15. Amaral, A. M. R., Laadjal, K., Cardoso, A. J. M (Junho 2023). Advanced Fault-Detection Technique for DC-Link Aluminum Electrolytic Capacitors Based on a Random Forest Classifier. Electronics, 12, pp. 1-18.

    16. Piscicelli, L. (2023). The sustainability impact of a digital circular economy. Current Opinion in Environmental Sustainability, 61, 101251.

    17. Amaral, A. M. R., Cardoso, A. J.M. (Setembro 2022). Simulation Tool to Evaluate Fault Diagnosis Techniques for DC-DC Converters. Symmetry, 14, pp. 1-16.

    18. Amaral, A. M. R., Cardoso, A. J. M. (Maio 2022). Using Python for the Simulation of a Closed Loop PI Controller for a Buck Converter. Signals, 3, pp. 313-325.

    19. Ellen MacArthur Foundation. (2021). Completing the picture: How the circular economy tackles climate change. Ellen MacArthur Foundation.

    20. Amaral, A. M. R., Cardoso, A. J. M. (Maio 2010). Simple Experimental Techniques to Characterize Capacitors in a Wide Range of Frequencies and Temperatures. IEEE Transactions on Instrumentation and Measurement, 59(5), pp. 1258-1267.