Publication
WSPM: Wavelet-Based Statistical Parametric Mapping
Related publications (62)
The Sparsity of Cycle Spinning for Wavelet-Based Solutions of Linear Inverse Problems
The usual explanation of the efficacy of wavelet-based methods hinges on the sparsity of many real-world objects in the wavelet domain. Yet, standard wavelet-shrinkage techniques for sparse reconstruction are not competitive in practice, one reason being t ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery
Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. In many real applications of fault detection and diagnosis, data tend to be imbalanced, meaning that the number of samples for some fault ...
MDPI2022Running Speed Estimation Using Shoe-Worn Inertial Sensors: Direct Integration, Linear, and Personalized Model
Kamiar Aminian, Mathieu Pascal Falbriard, Abolfazl Soltani
The overground speed is a key component of running analysis. Today, most speed estimation wearable systems are based on GNSS technology. However, these devices can suffer from sparse communication with the satellites and have a high-power consumption. In t ...
2021Deriving Brain Myelin Water Fraction Maps from Relaxometry: a Data-Driven Approach
Jean-Philippe Thiran, Tobias Kober, Tom Hilbert, Erick Jorge Canales Rodriguez, Marco Pizzolato, Gian Franco Piredda, Reto Meuli, Jonas Richiardi
Currently, one of the gold-standard methods to obtain brain myelin water fraction (MWF) maps is the multi-echo spin-echo sequence. To overcome some of its limitations (e.g. long acquisition times), a data-driven approach for deriving MWF maps is proposed h ...
2019Multidimensional Lévy White Noise in Weighted Besov Spaces
Michaël Unser, Julien René Pierre Fageot, Alireza Fallah
In this paper, we study the Besov regularity of a general d-dimensional Lévy white noise. More precisely, we describe new sample paths properties of a given noise in terms of weighted Besov spaces. In particular, we characterize the smoothness and integrab ...
Elsevier2017