Objective
By the end of the course, students should be able to:
Syllabus
Learning Outcome
Objective
Syllabus
This course will provide graduate students with a panorama of functional analysis and approximation theory in multiple dimensions, adopting a systematic dual point of view (functions defined through a collection of measurements, weak formulations). The emphasis will be laid on the simplest, albeit modern mathematical concepts and mechanisms, with a view to avoid extraneous formalism and more abstract (e.g., topological) considerations. This knowledge will be used to devise methods for solving exactly or approximately various inverse problems; e.g., resulting from partial differential equations.
This knowledge will be used to model engineering problems (e.g., data acquisition, sampling), to devise methods for solving exactly or approximately the inverse problems that are related (e.g., resulting from partial differential equations), and to analyse the error resulting from the approximations.
Course contents: elements of
Learning Outcome
The goal of this course is, for the students, to learn the most important mathematical ideas that are needed to be able to solve mathematically and numerically a number of multivariate problems that arise in engineering fields (including physics, signal processing). It is not meant to provide very detailed, complete mathematical coverage of each theory encountered, though.
At the end of this course, the students are expected to be able to
Objective
A series of lectures on current research in signal processing.
Syllabus
Learning Outcome
Objective
A series of lectures on current research in solid state technology.
Syllabus
Learning Outcome
Objective
A series of lectures on current research in wireless communication technology.
Syllabus
Learning Outcome