SIAM Team (Signal, Image, AutoMatic) is an interdisciplinary team whose research revolves around the four essential steps in the general study of a system: perception, observation, modeling, and control. The two systems targeted for applying these methods are mainly vehicles and biological systems. The non-standard behavior of the systems studied, as well as the consideration of their autonomy, led the team’s researchers to implement original methods on very varied aspects: behavior at the safety limit for vehicles, air vehicles lighter than air, cooperation of autonomous vehicles, mobility of cells in the ameboid phase and consideration of the micro-environment.

This expertise contributes to the definition of the team and its influence. Based on heterogeneous applications, the team has imagined methods that cut across the usual tools, thus offering the possibility of linking disciplines and, therefore, communities that are not naturally linked. Let us cite a few examples: the decomposition of statistical tensors for the analysis of 3D images, the theory of statistical decision applied to the recognition of cell types, the consideration of deformable models with aero-elastic forces to control airships, the classification of the constrained stability of non-Hamiltonian systems.

The team, therefore, relies on these multidisciplinary interactions to establish its research project in a favorable local context (installation of the CHSF in 2010, of the IRBA in 2009, extension of Genopole, and rapprochement with IFFSTAR-LIVIC).

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Optimal control Observers Vehicle dynamics LMIs Linear matrix inequalities Nonlinear system Semantic segmentation Networked control systems Renewable source integration Contour extraction Time-frequency Machine learning Lyapunov methods PID control Home Electrical Appliances HEAs Optical flow Nonlinear control Disturbance observer Synchrosqueezing DC MicroGrids Ridge extraction UAVs LBP Sliding mode control Stability criteria Distributed control LMI constraints Convolutional neural networks Autonomous Vehicles Interval observer Protocols Cyberattack Pooling function Fault-Tolerant Control Linear Matrix Inequality LMI Heuristic algorithms Games Structure from Motion Fuzzy control Linear systems Multi-task learning Fault detection Time delay Model predictive control Linear matrix inequalities LMIs Adaptive systems Optimization Photovoltaic system Autonomous driving Apprentissage profond Adaptation models Roads UAV Neural Networks Markov processes Calibration LPV systems Adaptive fuzzy control Object detection Deep learning Observer-based control Gait rehabilitation Segmentation Fuzzy systems Robust control Reinforcement learning Localization Stability Deep Learning State estimation LMI Fuzzy logic systems Model Predictive Control Cameras Actuator saturation Grid stability Clinical gait analysis Consensus control Multi-agent systems Takagi-Sugeno Nonlinear systems Lyapunov Theory Identification Actuators Costs Fault tolerant control Event-triggered mechanism Estimation Sensor faults Stability analysis Topology Linear matrix inequality Fault estimation Adaptive control Six-rotor unmanned aerial vehicles Neural networks Fault-tolerant control Rank aggregation Autonomous aerial vehicles Unknown input