| 07-06-2022 |
Separación ciega de fuentes mediante Transformada Fraccional de Fourier
El proyecto busca solucionar parcialmente el problema de separación de fuentes (extraer señales individuales a partir de mezclas de señales) en audio mediante correlación fraccional.
Keywords:
source-separation
fractional-fourier
Prerequisitos:
IEE2103
Tiene un método de evaluación Nota 1-7, con 10 créditos y tiene 1/2 vacantes disponibles |
Mentor(es): Ver en la plataforma |
| 26-12-2022 |
Prerequisites:
IMT2113
Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |
| 07-06-2022 |
Blind source separation based on the Fractional Fourier Transform
Keywords:
source-separation
fractional-fourier
Prerequisites:
IEE2103
Evaluation method: Nota 1-7, with 1/2 available vacants |
Mentor(s): Open in the plataform |
| 29-10-2021 |
Multiresolution analysis and superresolution
Multiresolution analysis consists in constructing a filtration of L2 of closed subspaces Vj such that each one represents functions at a scale 2j. The ortogonal projection onto Vj represents the approximation at scale 2j whereas the difference between the projections onto Vj and Vj+1 represents the details at scale 2j. A typical signal distortion process consists in removing structure at small scales. This is modeled through convolutions and resampling. Is it possible to leverage multiresolution analysis to recover the missing details? In this case we do not want to solve the problem for any function, thus constraining the worst-case, but only for those that are of interest and have been distorted by the process under study. The goal of this iPre is to review the existing literature connecting multiresolution analysis with this problem, and to propose a mathematical model that would allow us to answer this question.
Keywords:
análisis de fourier
superresolución
Prerequisites:
IMT2113
Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |
| 07-06-2021 |
Study about the electrophysiological decoding of cognitive processes
This study will analyze the rhythmic activity of the electrophysiological signals produced by the brain, which is one of the most approachable manifestations of the brain activity underlying cognitive functions. One of the most important tasks in this approach is to select the relevant information and filter the environmental and internal noise. Therefore, an algorithm must be implemented that allows the raw data to be extracted and transformed into a signal that corresponds to the cognitive processes that are being evaluated. In this course, the student must implement algorithms for classification, decoding and description of the oscillatory activity of the EEG signal in humans, during the development of tasks that mainly assess memory and attention. For this we will use standard computational signal analysis tools.
Prerequisites:
None.
Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |
| 08-04-2021 |
Mathematical methods for the deconvolution problem
One of the main properties of an optical system is its resolution. This is defined as the minimum separation between two ideal point sources so that they can be distinguished from one another when observed through the system. In practice, the diffraction of light imposes a physical limit to the resolution of the system. For a linear system, this process is typically modeled by a convolution by the Point Spread Function (PSF). For this reason, a technique that improves the resolution of the system can be interpreted as a deconvolution method. The objective of this project is to study mathematical methods proposed in the literature in the past decade, which combine applied Fourier analysis, convex optimization, and probability, for which there exists conditions that ensure they solve the superresolution problem in a computationally efficient manner.
Prerequisites:
IMT2113
Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |