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Introduction to compressed sensing
- Oggetto:
Introduction to compressed sensing
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Academic year 2017/2018
- Teacher
- Keijo Ruotsalainen
- Type
- Basic
- Course disciplinary sector (SSD)
- MAT/05 - analisi matematica
MAT/08 - analisi numerica - Delivery
- Formal authority
- Language
- English
- Attendance
- Obligatory
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Sommario del corso
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Program
The basic problem in several practical problems of science and technology is the task of inferring quantities of interest from measured information. When the information retrieval is linear, the problem reduces to solving a linear a system of equations Ax = y where A ∈ Cm×D is the linear information retrieval process, x ∈ CD the signal to be reconstructed and y ∈ Cm the measured data. In Big Data application then both m and D are Big Numbers. If we have random signals, then we may include the noise n ∈ Cm: Ax + n = y.
In this lecture series, some basic ideas of compressed sensing will be presented: performing data collection and compression simultaneously. With some simple examples it will be demonstrated that under certain conditions it is possible to reconstruct signals when the number of measurements is less than the signal length, in contrary to Shannon's sampling theorem.
Suggested readings and bibliography
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Note
Period: May-July 2018
Lecture date: Lectures are always at Politecnico at 14.30-17.00
Mo 21.5 Room 1D
Mo 28.5 Room 2D
Th 31.5 Room 8D
Mo 4.6 Room Buzano at DISMA
Th 7.6 Room Buzano at DISMA
Mo 11.6 Room 1D
Th 14.6 Room Buzano at DISMA
Mo 25.6 Room 1D
Th 28.6 Room Buzano at DISMA
Th 2.7 Room Buzano at DISMANote that all lectures take place at Politecnico
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