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Weissbart, L.J.A. (author), Chmielewski, Łukasz (author), Picek, S. (author), Batina, Lejla (author)Profiling attacks, especially those based on machine learning, proved to be very successful techniques in recent years when considering the side-channel analysis of symmetric-key crypto implementations. At the same time, the results for implementations of asymmetric-key cryptosystems are very sparse. This paper considers several machine learning...journal article 2020
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Guédon, Annetje C.P. (author), Meij, S.E.P. (author), Osman, Karim N.M.M.H. (author), Kloosterman, Helena A. (author), van Stralen, Karlijn J. (author), Grimbergen, Matthijs C.M. (author), Eijsbouts, Quirijn A.J. (author), van den Dobbelsteen, J.J. (author), Twinanda, Andru P. (author)perating room planning is a complex task as pre-operative estimations of procedure duration have a limited accuracy. This is due to large variations in the course of procedures. Therefore, information about the progress of procedures is essential to adapt the daily operating room schedule accordingly. This information should ideally be...journal article 2020
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Castillo, J.M. (author), Arif, M. (author), Starmans, M.P.A. (author), Niessen, W.J. (author), Bangma, C.H. (author), Schoots, Ivo G. (author), Veenland, J.F. (author)The computer-aided analysis of prostate multiparametric MRI (mpMRI) could improve significant-prostate-cancer (PCa) detection. Various deep-learning-and radiomics-based methods for significant-PCa segmentation or classification have been reported in the literature. To be able to assess the generalizability of the performance of these methods,...journal article 2022
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de Bruin, T.D. (author), Kober, J. (author), Tuyls, Karl (author), Babuska, R. (author)Deep reinforcement learning makes it possible to train control policies that map high-dimensional observations to actions. These methods typically use gradient-based optimization techniques to enable relatively efficient learning, but are notoriously sensitive to hyperparameter choices and do not have good convergence properties. Gradient...journal article 2020
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Cuperman, Rafael (author), Jansen, K.M.B. (author), Ciszewski, M.G. (author)Action statistics in sports, such as the number of sprints and jumps, along with the details of the corresponding locomotor actions, are of high interest to coaches and players, as well as medical staff. Current video-based systems have the disadvantage that they are costly and not easily transportable to new locations. In this study, we...journal article 2022
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Pezzotti, Nicola (author), Yousefi, Sahar (author), Elmahdy, Mohamed S. (author), van Gemert, Jeroen Hendrikus Fransiscus (author), Schuelke, Christophe (author), Doneva, Mariya (author), Nielsen, Tim (author), Lelieveldt, B.P.F. (author), Staring, M. (author)Adaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence to accelerate MR acquisition. Starting from undersampled k-space data, an iterative learning-based reconstruction scheme inspired by compressed sensing theory is...journal article 2020
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Ewald, Vincentius (author), Sridaran Venkat, Ramanan (author), Asokkumar, Aadhik (author), Benedictus, R. (author), Boller, Christian (author), Groves, R.M. (author)Predictive maintenance, as one of the core components of Industry 4.0, takes a proactive approach to maintain machines and systems in good order to keep downtime to a minimum and the airline maintenance industry is not an exception to this. To achieve this goal, practices in Structural Health Monitoring (SHM) complement the existing Non...journal article 2021
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Classification of airborne laser scanning point cloud using point-based convolutional neural networkZhu, Jianfeng (author), Sui, Lichun (author), Zang, Y. (author), Zheng, He (author), Jiang, Wei (author), Zhong, Mianqing (author), Ma, Fei (author)In various applications of airborne laser scanning (ALS), the classification of the point cloud is a basic and key step. It requires assigning category labels to each point, such as ground, building or vegetation. Convolutional neural networks have achieved great success in image classification and semantic segmentation, but they cannot be...journal article 2021
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Li, Bo (author), Niessen, W.J. (author), Klein, Stefan (author), de Groot, Marius (author), Ikram, M. Arfan (author), Vernooij, Meike W. (author), Bron, Esther E. (author)This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net. To optimally exploit information available in longitudinal data, this method concurrently learns a multi-class segmentation and nonlinear registration. Segmentation and registration are modeled using a convolutional neural network and...journal article 2021
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Šabanovič, Eldar (author), Kojis, Paulius (author), Šukevičius, Šarūnas (author), Shyrokau, B. (author), Ivanov, Valentin (author), Dhaens, Miguel (author), Skrickij, Viktor (author)With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this...journal article 2021
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Jeon, Jaemin (author), Kim, Jaeyong (author), Lee, Jong Jun (author), Shin, D. (author), Kim, Yoon Young (author)This study presents a new modeling technique to estimate the stiffness matrix of a thin-walled beam-joint structure using deep learning. When thin-walled beams meet at joints, significant sectional deformations occur, such as warping and distortion. These deformations should be considered in the one-dimensional beam analysis, but it is...journal article 2022
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Lumban-Gaol, Y. A. (author), Chen, Z. (author), Smit, M. (author), Li, X. (author), Erbaşu, M. A. (author), Verbree, E. (author), Balado Frías, J. (author), Meijers, B.M. (author), van der Vaart, C.G. (author)Point cloud data have rich semantic representations and can benefit various applications towards a digital twin. However, they are unordered and anisotropically distributed, thus being unsuitable for a typical Convolutional Neural Networks (CNN) to handle. With the advance of deep learning, several neural networks claim to have solved the...journal article 2021
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Jamali-Rad, H. (author), Szabó, Attila (author)Semantic segmentation is one of the most fundamental problems in computer vision with significant impact on a wide variety of applications. Adversarial learning is shown to be an effective approach for improving semantic segmentation quality by enforcing higher-level pixel correlations and structural information. However, state-of-the-art...journal article 2021
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Sabidussi, E. R. (author), Klein, S. (author), Caan, M. W.A. (author), Bazrafkan, S. (author), den Dekker, A. J. (author), Sijbers, J. (author), Niessen, W.J. (author), Poot, D.H.J. (author)In this paper, we propose the use of Recurrent Inference Machines (RIMs) to perform T<sub>1</sub> and T<sub>2</sub> mapping. The RIM is a neural network framework that learns an iterative inference process based on the signal model, similar to conventional statistical methods for quantitative MRI (QMRI), such as the Maximum Likelihood...journal article 2021
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Mohammadpourfard, Mostafa (author), Weng, Yang (author), Khalili, Abdullah (author), Genc, Istemihan (author), Shefaei, A. (author), Mohammadi-Ivatloo, Behnam (author)The expansion of power systems over large geographical areas renders centralized processing inefficient. Therefore, the distributed operation is increasingly adopted. This work introduces a new type of attack against distributed state estimation of power systems, which operates on inter-area boundary buses. We show that the developed attack...journal article 2022
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Wang, Dandan (author), Xu, Jinlan (author), Gao, Fei (author), Wang, C.C. (author), Gu, Renshu (author), Lin, Fei (author), Rabczuk, Timon (author), Xu, Gang (author)In this paper, a deep learning framework combined with isogeometric analysis (IGA for short) called IGA-Reuse-Net is proposed for efficient reuse of numerical simulation on a set of topology-consistent models. Compared with previous data-driven numerical simulation methods only for simple computational domains, our method can predict high...journal article 2022
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Pastor Serrano, O. (author), Lathouwers, D. (author), Perko, Z. (author)Background and objective: One of the main problems with biomedical signals is the limited amount of patient-specific data and the significant amount of time needed to record the sufficient number of samples needed for diagnostic and treatment purposes. In this study, we present a framework to simultaneously generate and classify biomedical...journal article 2021
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Merkx, D.G.M. (author), Scholten, Sebastiaan (author), Frank, Stefan L. (author), Ernestus, Mirjam (author), Scharenborg, O.E. (author)Many computational models of speech recognition assume that the set of target words is already given. This implies that these models learn to recognise speech in a biologically unrealistic manner, i.e. with prior lexical knowledge and explicit supervision. In contrast, visually grounded speech models learn to recognise speech without prior...journal article 2022
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Ossenkoppele, B.W. (author), Luijten, Ben (author), Bera, Deep (author), de Jong, N. (author), Verweij, M.A. (author), van Sloun, Ruud J.G. (author)There is an increased desire for miniature ultrasound probes with small apertures to provide volumetric images at high frame rates for in-body applications. Satisfying these increased requirements makes simultaneous achievement of a good lateral resolution a challenge. As micro-beamforming is often employed to reduce data rate and cable count...journal article 2023
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Chen, Zhijun (author), Lu, Zhe (author), Chen, Qiushi (author), Zhong, Hongliang (author), Zhang, Yishi (author), Xue, J. (author), Wu, Chaozhong (author)Short-term traffic flow prediction is a core branch of intelligent traffic systems (ITS) and plays an important role in traffic management. The graph convolution network (GCN) is widely used in traffic prediction models to efficiently handle the graphical structural data of road networks. However, the influence weights among different road...journal article 2022