Publications related to Simultaneous estimation of phase derivative and phase using parallel Kalman filter implementation

Related publications (27)

On distributional autoregression and iterated transportation

We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of Double-struck capital R. An order-1 autoregressive model in this context is to be understood as a Markov chain, where ...

Wiley2024

Functional-Basis Analysis of Non-Stationary Signals in Modern Power Grids: Theory and Implementation in Embedded Systems

Alexandra Cameron Karpilow

Situational awareness strategies are essential for the reliable and secure operation of the electric power grid which represents critical infrastructure in modern society. With the rise of converter-interfaced renewable generation and the consequent shift ...

EPFL2024

Sparse autoregressive neural networks for classical spin systems

Giuseppe Carleo, Dian Wu, Indaco Biazzo

Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

Iop Publishing Ltd2024

3D diffractive optics for linear interconnects and nonlinear processing

Niyazi Ulas Dinç

The optical domain presents potential avenues for enhancing both computing and communication due to its inherent properties of bandwidth, parallelism, and energy efficiency. This research focuses on harnessing 3-Dimensional (3D) diffractive optics for nove ...

EPFL2024

Comparison of Three Imputation Methods for Groundwater Level Timeseries

Andrea Rinaldo

This study compares three imputation methods applied to the field observations of hydraulic head in subsurface hydrology. Hydrogeological studies that analyze the timeseries of groundwater elevations often face issues with missing data that may mislead bot ...

MDPI2023

Distributional Regression and Autoregression via Optimal Transport

Laya Ghodrati

We present a framework for performing regression when both covariate and response are probability distributions on a compact and convex subset of

\R^d

. Our regression model is based on the theory of optimal transport and links the conditional Fr'echet m ...

EPFL2023

Directional sensitivity of cortical neurons towards TMS induced electric fields

Werner Alfons Hilda Van Geit, Aurélien Tristan Jaquier

We derived computationally efficient average response models of different types of cortical neurons, which are subject to external electric fields from Transcranial Magnetic Stimulation. We used 24 reconstructions of pyramidal cells (PC) from layer 2/3, 24 ...

2023

Modeling Structured Data in Attention-based Models

Alireza Mohammadshahi

Natural language processing has experienced significant improvements with the development of Transformer-based models, which employ self-attention mechanism and pre-training strategies. However, these models still present several obstacles. A notable issue ...

EPFL2023

Comparing end-tidal CO2, respiration volume per time (RVT), and average gray matter signal for mapping cerebrovascular reactivity amplitude and delay with breath-hold task BOLD fMRI

Stefano Moia

Cerebrovascular reactivity (CVR), defined as the cerebral blood flow response to a vasoactive stimulus, is an imaging biomarker with demonstrated utility in a range of diseases and in typical development and aging processes. A robust and widely implemented ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2023

On the rate of convergence for the autocorrelation operator in functional autoregression

Victor Panaretos, Alessia Caponera

We consider the problem of estimating the autocorrelation operator of an autoregressive Hilbertian process. By means of a Tikhonov approach, we establish a general result that yields the convergence rate of the estimated autocorrelation operator as a funct ...

ELSEVIER2022

Removing systematics-induced 21-cm foreground residuals by cross-correlating filtered data

Tianyue Chen

Observations of the redshifted 21-cm signal emitted by neutral hydrogen represent a promising probe of large-scale structure in the universe. However, the cosmological 21-cm signal is challenging to observe due to astrophysical foregrounds which are severa ...

American Physical Society2022

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

Andrea Zanoni

We study the problem of learning unknown parameters of stochastic dynamical models from data. Often, these models are high dimensional and contain several scales and complex structures. One is then interested in obtaining a reduced, coarse-grained descript ...

EPFL2022

Simultaneous unwrapping and low pass filtering of continuous phase maps based on autoregressive phase model and wrapped Kalman filtering

Pramod Rastogi, Rishikesh Dilip Kulkarni

We propose a simultaneous noise filtering and phase unwrapping algorithm. Spatial evolution of phase is modeled as an autoregressive Gaussian Markov random field. Accordingly, phase value at a pixel is related to phase values at surrounding pixels in a pro ...

2020

Text as signal. A tutorial with case studies focusing on social media (Twitter)

Erick Jorge Canales Rodriguez

Sentiment analysis is the automated coding of emotions expressed in text. Sentiment analysis and other types of analyses focusing on the automatic coding of textual documents are increasingly popular in psychology and computer science. However, the potenti ...

SPRINGER2022

Point, Plane, Set, Match! Winning the Echo Grand SLAM

Miranda Krekovic

Some of the most important and challenging problems in science are inverse problems. They allow us to understand phenomena that cannot be measured directly. Inverse problems might not always have a unique or stable solution, or might not have any solution ...

EPFL2020

Frequency domain theory for functional time series: Variance decomposition and an invariance principle

Neda Mohammadi Jouzdani

This paper is concerned with frequency domain theory for functional time series, which are temporally dependent sequences of functions in a Hilbert space. We consider a variance decomposition, which is more suitable for such a data structure than the varia ...

INT STATISTICAL INST2020

Dimensionality Reduction and Network Inference for Climate Data Using delta-MAPS: Application to the CESM Large Ensemble Sea Surface Temperature

Athanasios Nenes

A framework for analyzing and benchmarking climate model outputs is built upon delta-MAPS, a recently developed complex network analysis method. The framework allows for the possibility of highlighting quantifiable topological differences across data sets, ...

2019

Sparsely Observed Functional Time Series: Theory and Applications

Tomas Rubin

Functional time series is a temporally ordered sequence of not necessarily independent random curves. While the statistical analysis of such data has been traditionally carried out under the assumption of completely observed functional data, it may well ha ...

EPFL2021

Application of Time Series Methods on Long-Term Structural Monitoring Data for Fatigue Analysis

Eugen Brühwiler, Bartlomiej Wojciech Sawicki

Structural health monitoring (SHM) can be employed to reduce uncertainties in different aspects of structural analysis such as: load modeling, crack development, corrosion rates, etc. Fatigue is one of the main degradation processes of structures that caus ...

2019

Interpreting null models of resting-state functional MRI dynamics: not throwing the model out with the hypothesis

Dimitri Nestor Alice Van De Ville, Raphaël Pierre Liégeois

Null models are useful for assessing whether a dataset exhibits a non-trivial property of interest. These models have recently gained interest in the neuroimaging community as means to explore dynamic properties of functional Magnetic Resonance Imaging (fM ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2021