Concept

Linear canonical transformation

Publications associées (37)

Frequency domain data-driven robust and optimal control

The goal of this thesis is to propose pragmatic solutions to real challenges faced in the industry. The scope of this thesis encompasses two subjects: frequency-based structured controller synthesis for linear time-invariant (LTI) systems on one side, and ...

EPFL2024

Towards super resolution in the compressed domain of learning-based image codecs

Touradj Ebrahimi, Evgeniy Upenik, Michela Testolina

Learning-based image coding has shown promising results during recent years. Unlike the traditional approaches to image compression, learning-based codecs exploit deep neural networks for reducing dimensionality of the input at the stage where a linear tra ...

SPIE2021

Gelfand pairs admit an Iwasawa decomposition

Nicolas Monod

Every Gelfand pair (G, K) admits a decomposition G = K P, where P < G is an amenable subgroup. In particular, the Furstenberg boundary of G is homogeneous. Applications include the complete classification of non-positively curved Gelfand pairs, relying on ...

SPRINGER HEIDELBERG2020

Generalized approximate survey propagation for high-dimensional estimation*

Yue Lu

In generalized linear estimation (GLE) problems, we seek to estimate a signal that is observed through a linear transform followed by a component-wise, possibly nonlinear and noisy, channel. In the Bayesian optimal setting, generalized approximate message ...

IOP PUBLISHING LTD2020

The sliding Frank-Wolfe algorithm and its application to super-resolution microscopy

Emmanuel Emilien Louis Soubies, Quentin Alain Denoyelle, Gabriel Peyré

This paper showcases the theoretical and numerical performance of the Sliding Frank-Wolfe, which is a novel optimization algorithm to solve the BLASSO sparse spikes super-resolution problem. The BLASSO is a continuous (i.e. off-the-grid or grid-less) count ...

IOP PUBLISHING LTD2020

Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees

Nisheeth Vishnoi, Laura Elisa Celis, Vijay Keswani, Lingxiao Huang

Developing classification algorithms that are fair with respect to sensitive attributes of the data is an important problem due to the increased deployment of classification algorithms in societal contexts. Several recent works have focused on studying cla ...

ASSOC COMPUTING MACHINERY2019

Neural VTLN for Speaker Adaptation in TTS

Philip Neil Garner, Bastian Schnell

Vocal tract length normalisation (VTLN) is well established as a speaker adaptation technique that can work with very little adaptation data. It is also well known that VTLN can be cast as a linear transform in the cepstral domain. Building on this latter ...

2019

Theory for the density of interacting quasilocalized modes in amorphous solids

Matthieu Wyart, Thomas Willem Jan de Geus, Wencheng Ji, Marko Popovic

Quasilocalized modes appear in the vibrational spectrum of amorphous solids at low frequency. Though never formalized, these modes are believed to have a close relationship with other important local excitations, including shear transformations and two-lev ...

AMER PHYSICAL SOC2019

FRIDA: FRI-Based DOA Estimation For Arbitrary Array Layouts

Martin Vetterli, Eric Bezzam, Ivan Dokmanic, Robin Scheibler, Hanjie Pan

In this paper we present FRIDA—an algorithm for estimating directions of arrival of multiple wideband sound sources. FRIDA combines multi-band information coherently and achieves state- of-the-art resolution at extremely low signal-to-noise ratios. It work ...

IEEE2017

Signal Analysis Based on Complex Wavelet Signs

Martin Kurt Storath

We propose a signal analysis tool based on the sign (or the phase) of complex wavelet coefficients, which we call a signature. The signature is defined as the fine-scale limit of the signs of a signal's complex wavelet coefficients. We show that the signat ...

Elsevier2017

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