Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Jan Østergaard"'
Publikováno v:
Frontiers in Neuroscience, Vol 16 (2022)
ObjectivesComprehension of speech in adverse listening conditions is challenging for hearing-impaired (HI) individuals. Noise reduction (NR) schemes in hearing aids (HAs) have demonstrated the capability to help HI to overcome these challenges. The o
Externí odkaz:
https://doaj.org/article/a1bb0ab8872d4469a7576e54c82b08f7
Publikováno v:
Entropy, Vol 23, Iss 8, p 947 (2021)
We study the increase in per-sample differential entropy rate of random sequences and processes after being passed through a non minimum-phase (NMP) discrete-time, linear time-invariant (LTI) filter G. For LTI discrete-time filters and random process
Externí odkaz:
https://doaj.org/article/40d9d9b192d64d96a9e35c7265336370
Autor:
Milan S. Derpich, Jan Østergaard
Publikováno v:
Entropy, Vol 23, Iss 5, p 533 (2021)
We present novel data-processing inequalities relating the mutual information and the directed information in systems with feedback. The internal deterministic blocks within such systems are restricted only to be causal mappings, but are allowed to b
Externí odkaz:
https://doaj.org/article/4b165b94721d4edcba6790e78c50d071
Publikováno v:
Entropy, Vol 22, Iss 10, p 1124 (2020)
We propose a new estimator to measure directed dependencies in time series. The dimensionality of data is first reduced using a new non-uniform embedding technique, where the variables are ranked according to a weighted sum of the amount of new infor
Externí odkaz:
https://doaj.org/article/a8ee3acd3f2242d896ae749a5e9f7111
Publikováno v:
Entropy, Vol 22, Iss 8, p 842 (2020)
In this paper, we derive lower and upper bounds on the OPTA of a two-user multi-input multi-output (MIMO) causal encoding and causal decoding problem. Each user’s source model is described by a multidimensional Markov source driven by additive i.i.
Externí odkaz:
https://doaj.org/article/c6a441dd4b2e4c1a993a879810023a54
Autor:
Christian Schou Oxvig, Patrick Steffen Pedersen, Thomas Arildsen, Jan Østergaard, Torben Larsen
Publikováno v:
Journal of Open Research Software, Vol 2, Iss 1, Pp e29-e29 (2014)
Magni is an open source Python package that embraces compressed sensing and Atomic Force Microscopy (AFM) imaging techniques. It provides AFM-specific functionality for undersampling and reconstructing images from AFM equipment and thereby accelerati
Externí odkaz:
https://doaj.org/article/345805e1ed2d46cdbf680f37b18f4337
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2011 (2011)
Externí odkaz:
https://doaj.org/article/3729e4051f0041c9a39c4de694b7b3ca
Publikováno v:
Morante, M, Østergaard, J & Theodoridis, S 2023, INTERPRETABLE NONNEGATIVE INCOHERENT DEEP DICTIONARY LEARNING FOR FMRI DATA ANALYSIS . in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) .
Extracting information from fMRI data constitutes a broad active area of research. Current techniques still present several limitations; some ignore relevant aspects regarding the brain functioning or lack of interpretability. In an effort to overcom
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5d9ec91c13b71a9a89b5bff31d2b93ee
https://vbn.aau.dk/da/publications/9a6e8f6c-28d1-48c0-86fe-82817e4bc8e7
https://vbn.aau.dk/da/publications/9a6e8f6c-28d1-48c0-86fe-82817e4bc8e7
Listening effort can be defined as a measure of cognitive resources used by listeners to perform a listening task. Several methods have been proposed to assess listening effort, but the reliability of these methods has not yet been thoroughly establi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1542363779533968bb9b3dc5ac399a28
https://doi.org/10.22541/au.167850559.95803105/v1
https://doi.org/10.22541/au.167850559.95803105/v1
Publikováno v:
IFAC-PapersOnLine. 55:247-252
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::87e918a40872618e3b4a6b82740fa478
https://doi.org/10.1016/j.ifacol.2022.09.354
https://doi.org/10.1016/j.ifacol.2022.09.354