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Mario Huemer

5 accepted papers

2024

Efficient Functional Link Adaptive Filters Based On Nearest Kronecker Product Decomposition

ICASSP 2024accepted

Functional link adaptive filters (FLAFs) utilize expansion blocks to nonlinearly augment the input signal to a higher dimensional space, after which an adaptive weight algorithm is applied. These filters are useful for nonlinear system identification tasks, as they can update a large number of coeff…

Cited by 0SourceScholar
2020

A Hybrid Approach for Thermographic Imaging With Deep Learning

ICASSP 2020accepted

We propose a hybrid method for reconstructing thermographic images by combining the recently developed virtual wave concept with deep neural networks. The method can be used to detect defects inside materials in a non-destructive way. We propose two architectures along with a thorough evaluation tha…

Cited by 0SourceScholar
2019

Waveform Modeling by Adaptive Weighted Hermite Functions

ICASSP 2019accepted

Modern medical science demands sophisticated signal representation methods in order to cope with the increasing amount of data. Important criteria for these methods are mainly low computational and storage costs, whereas the underlying mathematical model should still be interpretable and meaningful…

Cited by 0SourceScholar
2017

Design of space-time block coded unique word OFDM systems

ICASSP 2017accepted

In this paper we develop space-time block codes for unique word - orthogonal frequency division multiplexing (UW-OFDM) systems to fully exploit the diversity gain when the channel state information is not available at the transmitter. To this end, we propose two novel space-time block codes (STBCs)…

Cited by 0SourceScholar