15 citations · 51 across the 10 of their papers we have counts for
11 papers
Reduced-Rank Estimation for Ill-Conditioned Stochastic Linear Model with High Signal-to-Noise Ratio
Tomasz Piotrowski, Isao Yamada
Reduced-rank approach has been used for decades in robust linear estimation of both deterministic and random vector of parameters in linear model y=Hx+\sqrt{epsilon}n. In practical…
Inverse Feasibility in Over-the-Air Federated Learning
Tomasz Piotrowski, Rafail Ismayilov, Matthias Frey +1
We introduce the concept of inverse feasibility for linear forward models as a tool to enhance OTA FL algorithms. Inverse feasibility is defined as an upper bound on the condition…
ToFFi -- Toolbox for Frequency-based Fingerprinting of Brain Signals
Michał K. Komorowski, Krzysztof Rykaczewski, Tomasz Piotrowski +5
Spectral fingerprints (SFs) are unique power spectra signatures of human brain regions of interest (ROIs, Keitel & Gross, 2016). SFs allow for accurate ROI identification and can s…
The fixed point iteration of positive concave mappings converges geometrically if a fixed point exists: implications to wireless systems
Tomasz Piotrowski, Renato L. G. Cavalcante
We prove that the fixed point iteration of arbitrary positive concave mappings with nonempty fixed point set converges geometrically for any starting point. We also show that posit…
Relaxed Zero-Forcing Beamformer under Temporally-Correlated Interference
Takehiro Kono, Masahiro Yukawa, Tomasz Piotrowski
The relaxed zero-forcing (RZF) beamformer is a quadratically-and-linearly constrained minimum variance beamformer. The central question addressed in this paper is whether RZF perfo…
Fixed points of nonnegative neural networks
Tomasz J. Piotrowski, Renato L. G. Cavalcante, Mateusz Gabor
We use fixed point theory to analyze nonnegative neural networks, which we define as neural networks that map nonnegative vectors to nonnegative vectors. We first show that nonnega…
Iterative Neural Networks with Bounded Weights
Tomasz Piotrowski, Krzysztof Rykaczewski
A recent analysis of a model of iterative neural network in Hilbert spaces established fundamental properties of such networks, such as existence of the fixed points sets, converge…
supFunSim: : spatial filtering toolbox for EEG
Krzysztof Rykaczewski, Jan Nikadon, Włodzisław Duch +1
Recognition and interpretation of brain activity patterns from EEG or MEG signals is one of the most important tasks in cognitive neuroscience, requiring sophisticated methods of s…
Localization of Brain Activity from EEG/MEG Using MV-PURE Framework
Tomasz Piotrowski, Jan Nikadon, Alexander Moiseev
We consider the problem of localization of sources of brain electrical activity from electroencephalographic (EEG) and magnetoencephalographic (MEG) measurements using spatial filt…
MV-PURE Spatial Filters with Application to EEG/MEG Source Reconstruction
Tomasz Piotrowski, Jan Nikadon, David Gutierrez
In this paper we propose spatial filters for a linear regression model which are based on the minimum-variance pseudo-unbiased reduced-rank estimation (MV-PURE) framework. As a sam…
Performance of the stochastic MV-PURE estimator in highly noisy settings
Tomasz Piotrowski, Isao Yamada
The stochastic minimum-variance pseudo-unbiased reduced-rank estimator (stochastic MV-PURE estimator) has been developed to provide linear estimation with robustness against high n…