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Doğan, D. (author), Leus, G.J.T. (author)
We consider the problem of recovering complex-valued block sparse signals with unknown borders. Such signals arise naturally in numerous applications. Several algorithms have been developed to solve the problem of unknown block partitions. In pattern-coupled sparse Bayesian learning (PCSBL), each coefficient involves its own hyperparameter...
journal article 2024
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Joseph, G. (author)
The convergence of expectation-maximization (EM)-based algorithms typically requires continuity of the likelihood function with respect to all the unknown parameters (optimization variables). The requirement is not met when parameters comprise both discrete and continuous variables, making the convergence analysis nontrivial. This paper...
journal article 2024
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Wervers, Jurgen (author)
Ambiguities are an often encountered nuisance in signal processing and are the source of some of the fundamental trade-offs encountered in radar systems. The goal of this thesis is to extract unambiguous information about targets by combining a limited amount of measurements on a video integration level. A novel framework is proposed to reach...
master thesis 2023
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Doğan, D. (author), Leus, G.J.T. (author)
We consider the problem of recovering block-sparse signals with unknown boundaries. Such signals arise naturally in various applications. Recent literature introduced a pattern-coupled or clustered Gaussian prior, in which each coefficient involves its own hyperparameter as well as its immediate neighbors' hyperparameters. Some methods use a...
conference paper 2023
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Trestioreanu, Ilinca (author)
Is there a way to incorporate fairness in the opponent modeling component of an automated agent? Since opponent modeling plays an important role in a negotiation strategy, it is reasonable to research how fairness can be integrated into this component, as it influences the outcome of the negotiation. A first step towards finding an answer to...
bachelor thesis 2022
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Zhou, H. (author)
Applying deep neural networks (DNNs) for system identification (SYSID) has attracted more andmore attention in recent years. The DNNs, which have universal approximation capabilities for any measurable function, have been successfully implemented in SYSID tasks with typical network structures, e.g., feed-forward neural networks and recurrent...
doctoral thesis 2022
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Knuyt, Jerry (author)
The loss of wildlife due to illegal poaching activity poses threats on both the survival of iconic animal species and the livelihood of local communities. This research proposes a distributed surveillance model in which a UAV swarm autonomously coordinates continuous surveillance in a dynamic environment. The adaptive behaviour of poachers has...
master thesis 2022
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Joshi, A. (author), Thakolkaran, P. (author), Zheng, Y. (author), Escande, Maxime (author), Flaschel, Moritz (author), De Lorenzis, Laura (author), Kumar, Siddhant (author)
Within the scope of our recent approach for Efficient Unsupervised Constitutive Law Identification and Discovery (EUCLID), we propose an unsupervised Bayesian learning framework for discovery of parsimonious and interpretable constitutive laws with quantifiable uncertainties. As in deterministic EUCLID, we do not resort to stress data, but...
journal article 2022
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Joseph, G. (author), Varshney, Pramod K. (author)
In this paper, we consider the problem of estimating the states of a linear dynamical system whose inputs are jointly sparse and outputs at a few unknown time instants are missing. We model the missing data mechanism using a Markov chain with two states representing the missing and non-missing data. This mechanism with memory governed by the...
conference paper 2022
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Ayala-Romero, Jose A. (author), Garcia-Saavedra, Andres (author), Costa-Perez, Xavier (author), Iosifidis, G. (author)
Virtualized base stations (vBS) can be implemented in diverse commodity platforms and are expected to bring unprecedented operational flexibility and cost efficiency to the next generation of cellular networks. However, their widespread adoption is hampered by their complex configuration options that affect in a non-traditional fashion both...
journal article 2022
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Zhou, H. (author), Chahine, I. (author), Zheng, Wei Xing (author), Pan, W. (author)
This paper proposes a sparse Bayesian treatment of deep neural networks (DNNs) for system identification. Although DNNs show impressive approximation ability in various fields, several challenges still exist for system identification problems. First, DNNs are known to be too complex that they can easily overfit the training data. Second, the...
journal article 2022
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Wan, Zixuan (author)
An end-to-end framework is developed to discover physical laws directly from videos, which can help facilitate the study on robust prediction, system stability analysis and gain the physical insight of a dynamic process. In this work, a video information extraction module is proposed to detect and collect the pixel position of moving objects,...
master thesis 2021
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WANG, CHENXU (author)
Least Squares Support Vector Machines (LS-SVMs) are state-of-the-art learning algorithms that have been widely used for pattern recognition. The solution for an LS-SVM is found by solving a system of linear equations, which involves the computational complexity of O(N^3). When datasets get larger, solving LS-SVM problems with standard methods...
master thesis 2021
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Chahine, Ibrahim (author)
System identification is a mature field in physical sciences and an emerging field in social sciences, with a vast range of applications. Nevertheless, it remains of great focus in academia. The main challenge is the efficient use of data to generate good model fits. System identification involves multi-disciplinary techniques from statistical,...
master thesis 2021
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Sakthivel, Ramkumar (author)
Human pose estimation, a challenging computer vision task of estimating various human body joints' locations, has a wide range of applications such as pedestrian tracking for autonomous cars, baby monitoring, video surveillance, human action recognition, virtual reality, gaming, gait analysis, etc. A majority of the research on the development...
master thesis 2020
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Lucassen, Max (author)
Least-squares support-vector-machines are a frequently used supervised learning method for nonlinear regression and classification. The method can be implemented by solving either its primal problem or dual problem. In the dual problem a linear system needs to be solved, yet for large-scale problems this can be impractical as current methods...
master thesis 2020
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Manss, C. (author), Shutin, Dmitriy (author), Leus, G.J.T. (author)
For swarm systems, distributed processing is of paramount importance, and Bayesian methods are preferred for their robustness. Existing distributed sparse Bayesian learn- ing (SBL) methods rely on the automatic relevance deter- mination (ARD), which involves a computationally complex reweighted l1-norm optimization, or they use loopy belief...
journal article 2020
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Tang, Yajie (author)
Radio astronomy image formation can be treated as a linear inverse problem. However, due to physical limitations, this inverse problem is ill-posed. To overcome the ill-posedness, side information should be involved. Based on the sparsity assumption of the sky image, we involve l1-regularization. We formulate the image formation problem into a...
master thesis 2019
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SHARMA, Sparsh (author)
The increasing complexity of mechanical systems has resulted in an increased usage and dependence on data driven modelling techniques in order to obtain simple yet accurate models of these systems. Neural networks have emerged as a popular modelling choice due to their proven ability to learn complex nonlinear relationships between inputs and...
master thesis 2019
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Gerstoft, Peter (author), Nannuru, Santosh (author), Mecklenbrauker, Christoph F. (author), Leus, G.J.T. (author)
The paper considers direction of arrival (DOA) estimation from long-term observations in a noisy environment. In such an environment the noise source might evolve, causing the stationary models to fail. Therefore a heteroscedastic Gaussian noise model is introduced where the variance can vary across observations and sensors. The source...
conference paper 2018
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