This course offers a comprehensive introduction to signal processing concepts. Students will
explore the fundamentals of continuous and discrete signals, revisit Fourier Transforms, study
sampling and quantization, and examine the Discrete Fourier Transform, and the Fast Fourier Trans-
form.
- Teacher: Sarra DEHILI
This course provides a comprehensive introduction to the modeling and analysis of stochastic processes, which are fundamental for understanding random phenomena encountered in engineering systems.
Students explore the distinction between deterministic and random signals, and study key properties such as stationarity, ergodicity, autocorrelation, and power spectral density (PSD). The course also covers probabilistic descriptions of random processes, including distributions, moments, and joint behaviors.
A major component of the course focuses on spectral analysis and estimation techniques, where students learn both classical and parametric methods for analyzing signals in the frequency domain. Emphasis is placed on evaluating estimation performance through criteria such as bias and variance.
The module further introduces modern estimation and filtering techniques, including the Wiener filter and the Kalman filter, widely used in real-time systems. Practical applications such as noise reduction, signal detection, and sensor fusion are highlighted throughout the course.
By the end of the course, students are able to model, analyze, and design estimation algorithms for complex systems, particularly in areas such as signal processing, communications, and embedded or autonomous systems.
- Teacher: Mohamed LAIDI
This course aims to introduce students to the principles and working mechanisms of actuators, including electric, hydraulic, and mechanical systems. It also focuses on the integration of sensors and actuators in embedded systems, providing students with the skills to design, interface, and calibrate sensor-actuator networks. Emphasis is placed on noise filtering, signal conditioning, and the implementation of control loops for real-time applications in automation and robotics.
- Teacher: Mohamed Kaouane
his course aims to provide students with the theoretical and practical foundations of relational databases, covering the entire process from data design to data manipulation and querying. It enables students to understand how to model, organize, query, and maintain data in a consistent, reliable, and efficient manner.
The course begins with the study of conceptual and logical data modeling, focusing on the Entity–Relationship (ER) model. Students learn how to analyze real-world systems, identify entities, relationships, attributes, and constraints, and transform a conceptual schema into a logical relational schema ready for implementation.
The course then addresses functional dependencies and normalization, which are essential concepts for ensuring data consistency and minimizing redundancy. Different normal forms (1NF, 2NF, 3NF, and BCNF) are studied and applied to relational database schemas.
Next, the course introduces relational algebra, a formal query language that allows the manipulation of relations using fundamental operations such as selection, projection, join, union, and difference. This part provides a theoretical understanding of how queries are processed in relational database management systems.
Finally, the course covers SQL (Structured Query Language), the standard language used for defining, manipulating, and querying relational databases. Students learn how to create tables, define integrity constraints, insert, update, and delete data, and write both simple and advanced SQL queries.
By the end of this course, students will be able to design a correct relational database, implement it using a database management system, and efficiently retrieve and manipulate data using SQL.
- Teacher: Mohamed MAHSEUR