Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Nour Zalmai"'
Publikováno v:
IEEE Transactions on Image Processing. 31:4663-4678
Variations of L1 -regularization including, in particular, total variation regularization, have hugely improved computational imaging. However, sharper edges and fewer staircase artifacts can be achieved with convex-concave regularizers. We present a
Publikováno v:
MEDI 2021-10th International Conference on Model and Data Engineering
MEDI 2021-10th International Conference on Model and Data Engineering, Jun 2021, Tallinn, Estonia. ⟨10.1007/978-3-030-87657-9_8⟩
MEDI 2021-10th International Conference on Model and Data Engineering, Jun 2021, Tallinn, Estonia
Advances in Model and Data Engineering in the Digitalization Era ISBN: 9783030876562
MEDI Workshops
MEDI 2021-10th International Conference on Model and Data Engineering, Jun 2021, Tallinn, Estonia. ⟨10.1007/978-3-030-87657-9_8⟩
MEDI 2021-10th International Conference on Model and Data Engineering, Jun 2021, Tallinn, Estonia
Advances in Model and Data Engineering in the Digitalization Era ISBN: 9783030876562
MEDI Workshops
Symposium on Intelligent and Autonomous Systems (SIAS 2021)Tallinn, Estonia. June 21-23, 2021; International audience; In this paper, a novel model related to the safety of autonomous vehicles (AVs) is presented. A simulation platform is designed to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff93e14a003672864d9e9fe83a3c9bce
https://hal.science/hal-03331190/document
https://hal.science/hal-03331190/document
Publikováno v:
IEEE Transactions on Signal Processing. 66:3768-3783
This paper introduces a toolbox for model-based detection, separation, and reconstruction of signals that is especially suited for biomedical signals, such as electrocardiograms (ECGs) or electromyograms (EMGs). The modeling is based on autonomous li
Autor:
Nour Zalmai, Hans-Andrea Loeliger, Reto A. Wildhaber, Dominik Bruegger, Marcel Jacomet, Hildegard Tanner, Josef Goette, Hampus Malmberg, Andreas Haeberlin
Publikováno v:
IEEE transactions on biomedical circuits and systems. 12(4)
The rapid progress of invasive therapeutic options for cardiac arrhythmias increases the need for accurate diagnostics. The surface electrocardiogram (ECG) is still the standard of noninvasive diagnostics but lacks atrial signal resolution. By contra
Publikováno v:
ICASSP
Most of the algorithms for tomographic reconstruction face the same problem: high computational complexity. In order to tackle this problem, this paper proposes a general multi-resolution approach that enables a flexible choice of reconstruction focu
Publikováno v:
EUSIPCO
The paper addresses the problem of fitting, at any given time, a parameterized signal generated by an autonomous linear state space model (LSSM) to discrete-time observations. When the cost function is the squared error, the fitting can be accomplish
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d9d2d94b41693fe82d4d26e9c4dac52d
Unsupervised Feature Extraction, Signal Labeling, and Blind Signal Separation in a State Space World
Publikováno v:
EUSIPCO
The paper addresses the problem of joint signal separation and estimation in a single-channel discrete-time signal composed of a wandering baseline and overlapping repetitions of unknown (or known) signal shapes. All signals are represented by a line
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e718e33ddd46b67ae69740680a68b6c7
Publikováno v:
GlobalSIP
A new approach to image segmentation (grayscale or color) is proposed. It uses a (improper) Markov random field prior with sparsifying NUV terms (normal with unknown variance), which favors piecewise smooth images with sharp edges. The proposed algor
We introduce a model-based approach for computationally efficient signal detection and discrimination, which is relevant for biological signals. Due to its low computational complexity and low memory need, this approach is well-suited for low power d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c83d54c56014c61c7037a2d525194d83
Publikováno v:
EUSIPCO
The paper proposes a new prior model for gray-scale images in 2D and 3D, and a pertinent algorithm for tomographic image reconstruction. Using ideas from sparse Bayesian learning, the proposed prior is a Markov random field with individual unknown va