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Found 5 papers, 0 papers with code
Iterative Broadband Source Localization
We show that by using these models, our adapted algorithms can successfully localize broadband sources under a variety of adverse operating scenarios.
Neural Network Approximation of Continuous Functions in High Dimensions with Applications to Inverse Problems
The approach is based on the observation that the existence of a Johnson-Lindenstrauss embedding $A\in\mathbb{R}^{d\times D}$ of a given high-dimensional set $S\subset\mathbb{R}^D$ into a low dimensional cube $[-M, M]^d$ implies that for any H\"older (or uniformly) continuous function $f:S\to\mathbb{R}^p$, there exists a H\"older (or uniformly) continuous function $g:[-M, M]^d\to\mathbb{R}^p$ such that $g(Ax)=f(x)$ for all $x\in S$.
Broadband Beamforming via Linear Embedding
In modern applications multi-sensor arrays are subject to an ever-present demand to accommodate signals with higher bandwidths.
Harmless interpolation in regression and classification with structured features
Our results recover prior independent-features results (with a much simpler analysis), but they furthermore show that harmless interpolation can occur in more general settings such as features that are a bounded orthonormal system.
Thomson's Multitaper Method Revisited
This is useful in problems where many samples are taken, and thus, using many tapers is desirable.
