IME’s Graduate Program in Electrical Engineering was restructured in 2023 following the introduction of its doctoral program. It aims to train highly qualified professionals and researchers and produce advanced knowledge in Electrical Engineering, particularly in Electronic Defense and Communications Systems. This field encompasses three research areas: Automation, Control and System Operation; Communications Systems; and Signal Processing and Robotic and Autonomous Systems.
This research area investigates and validates control synthesis in the time and frequency domains through simulations and practical implementations. Topics include robust, optimal, adaptive, nonlinear and intelligent control, as well as control of linear time-varying systems. Research also covers identification and optimization of physical systems, modeling and applications of classical and modern control, and control and verification of discrete-event and hybrid systems, with applications in electronic defense systems. Work on electric power systems includes modeling, analysis, operational planning, protection and power quality. The main objective is to train specialists and generate knowledge in the following topics:
a) Supervisory Control of Discrete-Event Systems (DES): Hierarchical control systems; timed DES control; fault-tolerant control; model checking; Industry 4.0 applications: smart factories, cyber-physical systems, the Internet of Things and cybersecurity.
b) Modeling, Identification and Optimization of Dynamic Systems: System modeling for modern control techniques; frequency-domain and time-domain system identification; model order reduction methods; dynamic system simulation.
c) Optimal and Modern Control: Optimal control; robust control under structured and unstructured uncertainties; parametric robust control based on the quality of Bayesian identification; linear parameter-varying control; integral quadratic constraints (IQC)—theory and applications.
d) Modeling, Analysis and Control of Electric Power Systems: System modeling and identification; small-signal analysis and control; voltage stability; detection of disturbances caused by cyberattacks; real-time operation and operational planning; dynamics, protection and control; power quality; equipment performance.
a) Acoustic Signal Processing: Signal enhancement methods robust to urban acoustic noise; acoustic masks to improve auditory intelligibility; acoustic interference processing (reverberation and noise); acoustically sensitive environments; robot hearing; mitigation of self-generated acoustic interference in robots and drones; acoustic intelligibility; acoustic sensor selection for assisted hearing; simultaneous localization and mapping (SLAM) using acoustic sensors; urban acoustic environment classification using AI and neural networks; voice-based identification and authentication (granted patent); noise-robust sound source localization in urban environments; adaptive time-frequency filters for auditory representation; acoustic identification of emotional states; stochastic and unsupervised learning for classification of acoustic signals and images (audiovisual).
b) Digital Image Processing: Image processing; video streaming.
c) Digital Signal Processing—Theory and Applications: Adaptive filtering; sensor array signal processing; subband signal processing; graph signal processing.
d) 3D Point Cloud Processing: LiDAR point cloud registration; indoor mapping using RGB-D data; unsupervised learning on 3D point clouds.
e) GNSS Signal Processing: Real-time precise point positioning; Kalman filtering for GNSS navigation; stochastic models for signal error mitigation; modeling atmospheric effects (ionosphere and troposphere) on signals; time-series analysis of GNSS station coordinates.
f) Pattern Recognition in Images and Acoustic Signals: Deep learning for object detection, semantic segmentation and instance segmentation in images; convolutional neural networks for acoustic pattern recognition; unsupervised learning for images and acoustic signals; hyperspectral image processing and analysis.
g) Autonomous and Intelligent Robotic Systems: Multi-agent frameworks for heterogeneous drone swarms; deep learning for mobile robot systems in competitive tasks (robot soccer); critical embedded systems for real-time problem solving; SLAM techniques for autonomous navigation of mobile ground platforms; unmanned aerial vehicle prototyping and development; machine learning for humanoid robot walking; metaheuristic algorithms for drone swarm applications in smart cities; flying ad hoc networks (FANETs) for communication within drone swarms; deep learning for 3D reconstruction using images from multiple drones.
a) Optical Devices and Systems: Analysis of devices for optical communications and sensing; integrated optical devices; modeling, simulation and development of free-space optical (FSO) systems; modeling, simulation and development of optical sensors; optical sensor design and characterization.
b) Radar Systems: Modeling and simulation of radar transmitters and receivers; performance assessment of algorithms for detection and estimation of reflecting-object characteristics; radar cross-section characterization of distributed targets; signature characterization; micro-Doppler of distributed targets; analysis of radar waveform interception and deception techniques; phased-array radar design; passive radar; radar waveform design and analysis; joint communication and sensing.
c) Digital Transmission Systems and Techniques: Communications system performance assessment; physical-layer and link-layer channel modeling and simulation; modulation and equalization for fading channels; multicarrier transmission—OFDM and variants; cognitive radio systems.
d) Electromagnetic Theory, Microwaves, Wave Propagation, Antennas and Telecommunications Systems: Antennas and radio propagation channels; RF and microwave circuits; electromagnetic compatibility; radio system dimensioning; measurement of dielectric properties of materials.