Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Michael Lunglmayr"'
Publikováno v:
Signals, Vol 2, Iss 2, Pp 189-200 (2021)
The frequency estimation of multiple complex sinusoids in the presence of noise is important for many signal processing applications. As already discussed in the literature, this problem can be reformulated as a sparse representation problem. In this
Externí odkaz:
https://doaj.org/article/9323731a127e485ea6212910df4fd2dc
Publikováno v:
EURASIP Journal on Wireless Communications and Networking, Vol 2020, Iss 1, Pp 1-26 (2020)
Abstract Detection of level shifts in a noisy signal, or trend break detection, is a problem that appears in several research fields, from biophysics to optics and economics. Although many algorithms have been developed to deal with such a problem, a
Externí odkaz:
https://doaj.org/article/52094c5047634eb1af1bf065ab2ce01d
Root tracking using time-varying autoregressive moving average models and sigma-point Kalman filters
Autor:
Kyriaki Kostoglou, Michael Lunglmayr
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2020, Iss 1, Pp 1-16 (2020)
Abstract Root tracking is a powerful technique that provides insight into the mechanisms of various time-varying processes. The poles and the zeros of a signal-generating system determine the spectral characteristics of the signal under consideration
Externí odkaz:
https://doaj.org/article/622791ccb19b436bba785ee47f77efb5
Autor:
Michael Lunglmayr, Michael Kalcher, Harald Enzinger, Daniel Gruber, Mario Huemer, Stefan Trampitsch
Publikováno v:
IEEE Transactions on Microwave Theory and Techniques. 69:271-283
This article presents a novel digital predistortion (DPD) approach to compensate for nonlinear dynamic distortions caused by the supply network of capacitive radio frequency digital-to-analog converters (RF-DACs). The developed DPD concept recreates
Publikováno v:
IEEE Transactions on Circuits and Systems II: Express Briefs. 67:3507-3511
The least mean squares (LMS) filter is one of the most important adaptive filters used in digital signal processing applications. We present a performance improvement method for LMS filters based on null space projection. The approach uses buffering
Publikováno v:
EURASIP Journal on Wireless Communications and Networking, Vol 2020, Iss 1, Pp 1-26 (2020)
Eurasip journal on wireless communications and networking, 2020(1)
Eurasip journal on wireless communications and networking, 2020(1)
Detection of level shifts in a noisy signal, or trend break detection, is a problem that appears in several research fields, from biophysics to optics and economics. Although many algorithms have been developed to deal with such a problem, accurate a
Autor:
Andreas Gebhard, Matthias Wagner, Mario Huemer, Harald Pretl, Thomas Paireder, Christina Auer, Christian Motz, Oliver Lang, Ram Sunil Kanumalli, Michael Lunglmayr
Publikováno v:
IEEE Transactions on Microwave Theory and Techniques. 67:1946-1961
Transceivers operating in frequency division duplex experience a transmitter leakage (TxL) signal into the receiver due to the limited duplexer stopband isolation. This TxL signal in combination with the second-order nonlinearity of the receive mixer
Autor:
Harald Gietler, Christoph Unterrieder, Marc Kanzian, Mario Huemer, Michael Lunglmayr, Matteo Agostinelli
Publikováno v:
IEEE Transactions on Industry Applications. 55:2076-2087
The importance of online system identification (SI) in power electronics is ever increasing. It enables the tracking of system parameters, which in turn can be used for online controller tuning. Hence, SI is a key element for improving a converter's
Publikováno v:
Communications in Computer and Information Science ISBN: 9783030871000
DEXA Workshops
DEXA Workshops
Data acquisition is crucial for efficient AI systems. We present a bio-inspired prototype implementation of discrepancy-based adaptive threshold-based sampling on a low-cost microcontroller. We show measurement results demonstrating that an adaptive
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::25ec7fa0fdf161137ccb38a6b470e018
https://doi.org/10.1007/978-3-030-87101-7_12
https://doi.org/10.1007/978-3-030-87101-7_12
In recent years, machine learning methods became increasingly important for a manifold number of applications. However, they often suffer from high computational requirements impairing their efficient use in real-time systems, even when employing ded
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa0cbb18de796b06daa8c5840a5bb2ab