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  • The course provides a modern introduction to design, analysis and implementation of sequential and parallel algorithms. In particular, the course is based on a pragmatic approach to parallel programming of message-passing algorithms through the C language and the MPI library.

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  • In this course we will provide advanced control methodologies, beyond the basic tools for linear systems provided in the undergraduate courses, to analyze structural properties and design control strategies for nonlinear dynamical systems and multi-agent network systems.  In the first part of the course, we will focus on selected design tools, based on optimization methods, to control nonlinear systems. After setting up the bases of nonlinear optimization, optimal control methods for dynamical systems will be introduced. Then, nonlinear control techniques, based on these tools, will be presented. The second part of the course will focus on a novel class of dynamical systems, namely cyber-physical network systems. They are multi-agent systems consisting of many control subsystems that aim at performing global tasks via local communication in a cooperative way. Selected distributed control and optimization techniques will be provided to solve complex tasks in a cooperative way. The proposed techniques will be applied to a number of example domains, including mobile robotics, to bridge the gap between theory and application.

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  • In this course we will provide advanced control methodologies, beyond the basic tools for linear systems provided in the undergraduate courses, to analyze structural properties and design control strategies for nonlinear dynamical systems and multi-agent network systems. In the first part of the course we will investigate analysis tools to characterize structural properties of nonlinear systems as stability of equilibria and trajectories. In the second part, we will focus on selected design tools, based on optimization methods, to control nonlinear systems. The third part of the course will focus on a novel class of dynamical systems, namely cyber-physical network systems. They are multi-agent systems consisting of many control subsystems that aim at performing global tasks via local communication in a cooperative way. Selected distributed control and optimization techniques will be provided to solve complex tasks in a cooperative way. To bridge the gap between theory and application, the proposed techniques will be applied to a number of example domains including mobile robotics.

    Self enrolmentGuest access
  • In this course we will provide advanced control methodologies, beyond the basic tools for linear systems provided in the undergraduate courses, to analyze structural properties and design control strategies for nonlinear dynamical systems and multi-agent network systems. In the first part of the course we will investigate analysis tools to characterize structural properties of nonlinear systems as stability of equilibria and trajectories. In the second part, we will focus on selected design tools, based on optimization methods, to control nonlinear systems. The third part of the course will focus on a novel class of dynamical systems, namely cyber-physical network systems. They are multi-agent systems consisting of many control subsystems that aim at performing global tasks via local communication in a cooperative way. Selected distributed control and optimization techniques will be provided to solve complex tasks in a cooperative way. The proposed techniques will be applied to a number of example domains, including mobile robotics, to bridge the gap between theory and application.

    Self enrolment
  • In this course we will mainly focus on dynamical systems that can be represented as nonlinear, possibly time-varying ordinary differential (or finite-difference) equations. In the first part of the course we will investigate analysis tools to characterize structural properties of nonlinear systems as stability of equilibria and trajectories. In the second part, we will focus on selected design tools to perform feedback control of nonlinear systems. The proposed techniques will be applied to a number of example domains, including mobile robotics. The third part of the course will focus on a novel class of dynamical systems, namely cyber-physical network systems. They are multi-agent systems consisting of many control subsystems that aim at performing global tasks via local communication in a cooperative way. The main challenges of cooperative control will be identified. Selected distributed control, estimation and optimization techniques will be presented.

    Self enrolment
  • In this course we will mainly focus on dynamical systems that can be represented as nonlinear (possibly time-varying) ordinary differential equations. In the first part of the course we will investigate analysis tools to characterize structural properties of nonlinear systems as stability, controllability and maneuverability. In the second part, we will focus on selected design tools to perform aggressive maneuvering of nonlinear systems. The proposed techniques will be applied to a number of example domains, including robotics, avionics (thrust-vectored aircrafts and UAVs), and automotive (car models). The third part of the course will focus on a novel class of dynamical systems, namely multi-agent systems. Multi-agent systems consists of many control systems that aim at performing global tasks via local communication in a cooperative way. The main challenges of cooperative control will be identified. Selected distributed control, estimation and optimization techniques will be presented.

    Self enrolment
  • In this course we will mainly focus on dynamical systems that can be represented as nonlinear (possibly time-varying) ordinary differential equations. In the first part of the course we will investigate analysis tools to characterize structural properties of nonlinear systems as stability, controllability and maneuverability. In the second part, we will focus on selected design tools to perform aggressive maneuvering of nonlinear systems. The proposed techniques will be applied to a number of example domains, including robotics, avionics (thrust-vectored aircrafts and UAVs), and automotive (car models). The third part of the course will focus on a novel class of dynamical systems, namely multi-agent systems. Multi-agent systems consists of many control systems that aim at performing global tasks via local communication in a cooperative way. The main challenges of cooperative control will be identified and preliminary communication and control techniques will be presented.

    Self enrolment
  • Il corso descrive i metodi e le tecnologie di base per la modellistica, l’analisi e la sintesi di sistemi di controllo del moto per robot industriali e di servizio.

    Self enrolment
  • In this course we will mainly focus on dynamical systems that can be represented as nonlinear (possibly time-varying) ordinary differential equations. In the first part of the course we will investigate analysis tools to characterize structural properties of nonlinear systems as stability, controllability and maneuverability. In the second part, we will focus on selected design tools to perform aggressive maneuvering of nonlinear systems. The proposed techniques will be applied to a number of example domains, including robotics, avionics (thrust-vectored aircrafts and UAVs), and automotive (car models). The third part of the course will focus on a novel class of dynamical systems, namely multi-agent systems. Multi-agent systems consists of many control systems that aim at performing global tasks via local communication in a cooperative way. The main challenges of cooperative control will be identified and preliminary communication and control techniques will be presented.

    Self enrolment
  • Il corso fornisce gli strumenti fondamentali per l’analisi di proprietà strutturali e la sintesi di un controllore per sistemi dinamici lineari tempo invarianti in forma di stato. Il corso fornisce, inoltre, strumenti preliminari per l’analisi della stabilità di sistemi non lineari tempo invarianti. In particolare, si forniscono allo studente le metodologie per analizzare la stabilità, la raggiungibilità e l’osservabilità di sistemi lineari tempo invarianti. Sulla base degli strumenti di analisi forniti allo studente, si propongono tecniche di sintesi per il controllo in retroazione e la stima dello stato di sistemi dinamici lineari tempo invarianti. I due strumenti vengono quindi combinati per la progettazione di un regolatore che permette simultaneamente di stimare lo stato e controllarlo. Vengono forniti, infine, strumenti di analisi preliminari per lo studio della stabilità di sistemi non lineari tempo invarianti.

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  • Il corso si propone di fornire i principali strumenti di modellazione e di analisi delle proprietà dei sistemi dinamici lineari tempo-invarianti a tempo continuo e a tempo discreto nello spazio degli stati.

    Self enrolment
  • Il corso mira a fornire i concetti e gli strumenti metodologici di base per l'analisi e la sintesi di sistemi di controllo a tempo continuo, lineari, tempo invarianti a singolo ingresso e singola uscita.

    Self enrolment
  • The course covers all of the fundamental aspects related to the development of a mobile application using Apple iOS. Meant for students without previous programming experience, the course starts covering the new programming language Swift, using the integrated development environment Xcode. After introducing the Swift programming language and the use of Xcode to develop a simple application, the students will create a basic prototype application, and, gradually, they will implement the application by adding new features until they implement a real, usable application.

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  • The course provides a modern introduction to design, analysis and implementation of mobile applications for Apple iOS devices. In particular, the course is based on a pragmatic approach to iOS programming using the Swift programming language and the Xcode IDE.

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  • This course focuses on data mining of very large amounts of data, that is, data so large it does not fit in main memory. Because of the emphasis on size, many of the examples are about the Web or data derived from the Web. Further, the course takes an algorithmic point of view: data mining is about applying algorithms to data, rather than using data to “train” a machine-learning engine of some sort.

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  • Il corso fornisce da una parte una moderna introduzione alla programmazione orientata agli oggetti, in particolare al linguaggio di programmazione Java, dall’altra introduce i concetti fondamentali delle basi di dati.

    Self enrolmentGuest access

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