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This paper investigates an adaptive wavelet collocation time domain method for the numerical solution of Maxwell's equations. In this method a computational grid is dynamically adapted at each time step by using the wavelet decomposition of…

Numerical Analysis · Mathematics 2012-04-06 Haojun Li , Kirankumar R. Hiremath , Andreas Rieder , Wolfgang Freude

An inverse modeling technique is introduced that combines elements of coupled logistic map models and wavelet analysis for the purpose of analyzing partial synchronization states in high-dimensional systems. Using Embedded Complex Logistic…

Adaptation and Self-Organizing Systems · Physics 2007-05-23 Sandy Shaw

We describe an algorithm for the extraction of the angular power spectrum of an intensity field, such as the cosmic microwave background (CMB), from interferometer data. This new method, based on the gridding of interferometer visibilities…

Inspired by edge detection based on the decay behavior of wavelet coefficients, we introduce a (near) linear-time algorithm for detecting the local regularity in non-uniformly sampled multivariate signals. Our approach quantifies regularity…

Numerical Analysis · Mathematics 2025-07-21 Sara Avesani , Gianluca Giacchi , Michael Multerer

The problem of detecting chirps is present in many applications of Signal Processing. Proper denoising, which involves filtering the signals after their acquisition, improves the efficacy of their detection. This manuscript describes how a…

Computational Engineering, Finance, and Science · Computer Science 2023-05-24 Alejandro Silva

We introduce a qualitative, shape-based, timescale-independent time-domain transform used to extract local dynamics from sociotechnical time series---termed the Discrete Shocklet Transform (DST)---and an associated similarity search…

Identifying the qualitative changes in time-series data provides insights into the dynamics associated with such data. Such qualitative changes can be detected through topological approaches, which first embed the data into a…

Data Analysis, Statistics and Probability · Physics 2019-03-27 Quoc Hoan Tran , Yoshihiko Hasegawa

An adaptive algorithm for spectral proper orthogonal decomposition (SPOD) of mixed broadband-tonal turbulent flows is developed. Sharp peak resolution at tonal frequencies is achieved by locally minimizing the bias of the spectrum. Smooth…

Fluid Dynamics · Physics 2024-06-25 Brandon C. Y. Yeung , Oliver T. Schmidt

Detection of B-mode polarization of the cosmic microwave background (CMB) radiation is one of the frontiers of observational cosmology. Because they are an order of magnitude fainter than E-modes, it is quite a challenge to detect B-modes.…

Cosmology and Nongalactic Astrophysics · Physics 2013-01-23 Ata Karakci , P. M. Sutter , Le Zhang , Emory F. Bunn , Andrei Korotkov , Peter Timbie , Gregory S. Tucker , Benjamin D. Wandelt

Emission from the interstellar medium can be a significant contaminant of measurements of the intensity and polarization of the cosmic microwave background (CMB). For planning CMB observations, and for optimizing foreground-cleaning…

Cosmology and Nongalactic Astrophysics · Physics 2021-04-21 Ben Thorne , Lloyd Knox , Karthik Prabhu

We present two novel methods for the estimation of the angular power spectrum of cosmic microwave background (CMB) anisotropies. We assume an absolute CMB experiment with arbitrary asymmetric beams and arbitrary sky coverage. The methods…

Cosmology and Nongalactic Astrophysics · Physics 2017-01-25 Elina Keihänen , Kimmo Kiiveri , Hannu Kurki-Suonio , Martin Reinecke

We propose a novel robust decentralized graph clustering algorithm that is provably equivalent to the popular spectral clustering approach. Our proposed method uses the existing wave equation clustering algorithm that is based on…

Machine Learning · Computer Science 2024-02-05 Hongyu Zhu , Stefan Klus , Tuhin Sahai

We develop a timescale synthesis-based probabilistic approach for the modeling of locally stationary signals. Inspired by our previous work, the model involves zero-mean, complex Gaussian wavelet coefficients, whose distribution varies as a…

Statistics Theory · Mathematics 2020-02-10 Adrien Meynard , Bruno Torrésani

The standard approach to analyzing brain electrical activity is to examine the spectral density function (SDF) and identify predefined frequency bands that have the most substantial relative contributions to the overall variance of the…

Spectral graph convolution, an important tool of data filtering on graphs, relies on two essential decisions: selecting spectral bases for signal transformation and parameterizing the kernel for frequency analysis. While recent techniques…

Machine Learning · Computer Science 2025-05-15 Nian Liu , Xiaoxin He , Thomas Laurent , Francesco Di Giovanni , Michael M. Bronstein , Xavier Bresson

We describe a sampling method to estimate the polarized CMB signal from observed maps of the sky. We use a Metropolis-within-Gibbs algorithm to estimate the polarized CMB map, containing Q and U Stokes parameters at each pixel, and its…

Temporal or spatial structures are readily extracted from complex data by modal decompositions like Proper Orthogonal Decomposition (POD) or Dynamic Mode Decomposition (DMD). Subspaces of such decompositions serve as reduced order models…

Fluid Dynamics · Physics 2019-02-25 Jörn Sesterhenn , Amir Shahirpour

Extracting cosmological information from microwave sky observations requires accurate estimation of the underlying Cosmic Microwave Background (CMB) by removing foreground contamination, instrumental noise, and the effects of beam…

Instrumentation and Methods for Astrophysics · Physics 2026-05-12 Obasho M , Shambhavi Jaiswal , Santanu Das , Krishna Mohan Parattu

With the temperature power spectrum of the cosmic microwave background (CMB) at least four orders of magnitude larger than the B-mode polarisation power spectrum, any instrumental imperfections that couple temperature to polarisation must…

Cosmology and Nongalactic Astrophysics · Physics 2017-02-22 Christopher G. R. Wallis , A. Bonaldi , Michael L. Brown , Richard A. Battye

Time series data is ubiquitous in the real-world problems across various domains including healthcare, social media, and crime surveillance. Detecting anomalies, or irregular and rare events, in time series data, can enable us to find…

Machine Learning · Computer Science 2021-10-05 Abilasha S , Sahely Bhadra , Deepak P , Anish Mathew
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