Objectives of Programme's Learning Outcomes

• Imagine, implement and operate systems/solutions/software to address a complex problem in the field of electrical engineering as an essential measurement and control vector in modern society by integrating needs, contexts and issues (technical, economic, societal, ethical and environmental).
• Identify complex problems to be solved and develop the specifications with the client by integrating needs, contexts and issues (technical, economic, societal, ethical and environmental).
• Based on modelling and experimentation, design one or more systems/solutions/software addressing the problem raised; evaluate them in light of various parameters of the specifications.
• Implement a chosen system/solution in the form of a schema, a flowchart, an algorithm, a prototype, software and/or digital model.
• Evaluate the approach and results for their adaptation (optimisation and quality).
• Mobilise a structured set of scientific knowledge and skills and specialised techniques in order to carry out electrical engineering missions, with a focus on signals and systems, using their expertise and adaptability.
• Master and appropriately mobilise knowledge, models, methods and techniques related to the basics of electricity, electronics, automatic control, signal processing and analysis, telecommunications, hardware and software instrumentation, mathematical modelling and analysis of dynamic systems, control methods, more specific applications related to signal processing, and control of biomedical and biological systems.
• Analyse and model a problem by critically selecting theories and methodological approaches (modelling, calculations), and taking into account multidisciplinary aspects.

Learning Outcomes of UE

This course focuses on dynamic systems with discrete states and transitions for the modeling of technological systems such as automated production systems and process control systems and transport systems.

The practical part of the course is devoted to a concrete application of control and control of a complex system.

Content of UE

REVIEW OF SYSTEM THEORY FUNDAMENTALS: Basic concepts<em>, Time-driven</em> vs. <em>event-driven</em> systems, Examples of Discrete Event Systems (DES): automated manufacturing;  traffic systems, The queueing system model. UNTIMED MODELS OF DISCRETE-EVENT SYSTEMS, State Automata, Analysis: stability, reachability, deadlocks. The Poisson counting process and Markov chain models INTRODUCTION TO DISCRETE EVENT (MONTE-CARLO) SIMULATION, Basic concepts in discrete event simulation, Model construction and applications, Introduction to estimation theory MARKOV DECISION PROCESSES. Solving resource contention problems: admission control, routing,  scheduling

Prior Experience

Not applicable

Type of Assessment for UE in Q1

• Oral examination

Q1 UE Assessment Comments

Not applicable

Type of Assessment for UE in Q3

• Oral examination

Q3 UE Assessment Comments

Not applicable

Type of Resit Assessment for UE in Q1 (BAB1)

• N/A

Q1 UE Resit Assessment Comments (BAB1)

Not applicable