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Camera Networks
- Sparse camera networks: Pan-Tilt-Zoom cameras and fixed cameras cooperate in videosurveillance distributed networks to perform tasks of area patrolling, event detection and event tracking. The systems are autonomic: cameras are smart agents able to coordinate to maximize surveillance performance, manage complex tasks, accommodate for camera losses (self-healing).
- Dense camera networks: 3D reconstruction in motion capture systems shows critical issues when scaling with the number of cameras or the complexity of the scene. In this context a distributed approach is proposed to solve the multicamera reconstruction problemin large scale motion capture systems.
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Networked Control Systems
Networked control systems are multiagent, multitask networks with limited resources. They employ cooperation and the distributed coordination of sensors/actuators to fulfill complex tasks that are not possible to a single agent.
Applications of interest include:
- localization and tracking problem for mobile nodes in multiagent networks
- coverage control and information search in robotic networks
- swarm control in large scale multiagent systems
- sensor networks for environmental monitoring: outdoor monitoring of debris-flow phenomena, smart infrastructure deployment and exploitation for intervention in critical scenarios
- smart environments: smart autonomous greenhouse, domotics
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Fusion Plasmas Modeling and Control [videos]
- Sensor selection through convex optimization for global parameter estimation
- Diagnostics: magnetic measurement estimation and magnetic flux map reconstruction from sparse heterogeneous sensors
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Plasma modeling and regulator design, for the real time control of plasma current, position and shape
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Study of linear and nonlinear models for simulation of the whole plasma-plant system
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Imaging and modeling of biological structures [videos]
The ever-increasing availability of biological data from in vivo and in
vitro experiments demands the development of new techniques and
associated tools that are capable to extract quantitative, specific,
and relevant information of use to the researcher.
- Shape representation, shape analysis and metrics defi nition for both isolated structures and reticular complex structures
- Global shape control and shape parameter control for objects undergoing continuous deformation
Applications in this field include:
- tissue engineering: analysis of the shape of cellular structures for stem cells therapy - collaboration with Dr. P. de Coppi (Great Ormond Street Hospital/UCL Institute of Child Health)
- developmental biology: complex reticular shapes - collaboration with Dr. A. Abate (Delft University)
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Methodologies for the analysis and the compensation of atmospheric turbulence in Adaptive Optics systems
- Techniques for the modal representation of atmospheric turbulence Stochastic realization modeling for turbulence simulation through a phase screen approach
- Model parameter estimation and dynamic model identi cation, as Markov Random Fields
- Turbulence compensation, via prediction of phase values
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Time series analysis for event detection and classification
- Machine learning approach to gesture recognition
- Data mining and model learning from sensor network measurements
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