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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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Rigidity Control & UAV Systems 

Rigidity theory has emerged as an efficient tool in the control field of coordinated multi–agent systems, such as multi–robot formations and UAVs swarms that are characterized by sensing, communication and movement capabilities.  

This research is applied to the study of methodologies and the development of algorithms for estimation and control of autonomous aerial vehicles (UAVs).

Applications of interest include:

  • SE(2)-SE(3) rigidity theory
  • UAV attitude estimation and control
  • formation control in multiagent systems
  • navigation and docking control

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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 
  • synchronization and 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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Brain and Biological Networks 

Recently, considerable attention has been devoted to the study of the human brain as a network of different cortical regions that show coherent activity during resting-state: we study how synchronization patterns do emerge in such a complex network and how to model it for a possible control action design.

A parallel activity concerns the analysis and the modeling of the dynamics of mitochondria, multi-functional organelles that have a central role for cell life: these, again, show population emergent behaviors that are designed to fulfill complex tasks for the whole organism.  




Time series analysis for event detection and classification 

 Machine learning approach to gesture recognition: 

With the aim of monitoring the human activity, wearable devices provide an enhanced usability and a seamless human experience with respect to other portable devices (e.g. smartphones) in critical tasks as well as in leisure and sport activities: algorithmic solution especially tackled for these devices need to be developed so as to provide detection/identification accuracy and efficiency. 

 Data mining and model learning from sensor network measurements:

To fully unleash the potential of the IoT and CPS paradigms, however, advanced data processing techniques are required to extract useful information from heaps of raw data: we focus on the problem of processing time series of data, with the twofold aim of: i) extracting recurrent patterns, which represent the normal (i.e., expected) behavior of the monitored parameters, and ii) detecting anomalies, i.e., significant deviations from the normal behavior. 





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
  • Plasma modeling and regulator design, for the real time control of plasma current, position and shape
  • Study of linear and nonlinear models for simulation of the whole plasma-plant system

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