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Concept

Chirplet transform

Related publications (6)

Reservoir Computing meets Recurrent Kernels and Structured Transforms

Florent Gérard Krzakala, Jonathan Yuelin Dong

Reservoir Computing is a class of simple yet efficient Recurrent Neural Networks where internal weights are fixed at random and only a linear output layer is trained. In the large size limit, such random neural networks have a deep connection with kernel m ...

Curran Associates, Inc.2020

Intrinsic structural transitions of the pyramidal I 〈c+a〉 dislocation in magnesium

William Curtin

The stability of a mixed < c + a > dislocation on the pyramidal I plane in magnesium is studied using molecular dynamics simulations. The dislocation is metastable and undergoes a thermally-activated transition to either a sessile, basal-dissociated < c + ...

Elsevier2016

Local Geometric Transformations in Image Analysis

Zsuzsanna Püspöki

The characterization of images by geometric features facilitates the precise analysis of the structures found in biological micrographs such as cells, proteins, or tissues. In this thesis, we study image representations that are adapted to local geometric ...

EPFL2016

Optimized Kaiser-Bessel Window Functions for Computed Tomography

Michaël Unser, Cédric René Jean Vonesch, John Paul Ward, Masih Nilchian

Kaiser-Bessel window functions are frequently used to discretize tomographic problems because they have two desirable properties: 1) their short support leads to a low computational cost and 2) their rotational symmetry makes their imaging transform indepe ...

Ieee-Inst Electrical Electronics Engineers Inc2015

A Dual-Tree Rational-Dilation Complex Wavelet Transform

In this correspondence, we introduce a dual-tree rational-dilation complex wavelet transform for oscillatory signal processing. Like the short-time Fourier transform and the dyadic dual-tree complex wavelet transform, the introduced transform employs quadr ...

2011

Exact and Efficient Signal Reconstruction in Frequency-Domain Optical-Coherence Tomography

Michaël Unser, Chandra Sekhar Seelamantula

We address the problem of tomogram reconstruction in frequency-domain optical-coherence tomography. We propose a new technique for suppressing the autocorrelation artifacts that are commonly encountered with the conventional Fourier-transform-based approac ...

OSA2008