Zobrazeno 1 - 10
of 4 594
pro vyhledávání: '"Frangi, A"'
Autor:
Stabile, André de F., Vizzaccaro, Alessandra, Salles, Loïc, Colombo, Alessio, Frangi, Attilio, Touzé, Cyril
The direct parametrisation method for invariant manifolds is adjusted to consider a varying parameter. More specifically, the case of systems experiencing a Hopf bifurcation in the parameter range of interest are investigated, and the ability to pred
Externí odkaz:
http://arxiv.org/abs/2411.09769
We propose the use of the Extended Kalman Filter (EKF) for online data assimilation and update of a dynamic model, preliminary identified through the Sparse Identification of Nonlinear Dynamics (SINDy). This data-driven technique may avoid biases due
Externí odkaz:
http://arxiv.org/abs/2411.04842
Autor:
Deng, Jinqiu, Chen, Ke, Li, Mingke, Zhang, Daoping, Chen, Chong, Frangi, Alejandro F., Zhang, Jianping
Diffeomorphic image registration is crucial for various medical imaging applications because it can preserve the topology of the transformation. This study introduces DCCNN-LSTM-Reg, a learning framework that evolves dynamically and learns a symmetri
Externí odkaz:
http://arxiv.org/abs/2411.02888
Publikováno v:
Maldonado-Garcia, C., Zakeri, A., Frangi, A.F., Ravikumar, N. (2025). Predictive Intelligence in Medicine. PRIME 2024. LNCS, vol 15155, Springer, Cham
Early identification of patients at risk of cardiovascular diseases (CVD) is crucial for effective preventive care, reducing healthcare burden, and improving patients' quality of life. This study demonstrates the potential of retinal optical coherenc
Externí odkaz:
http://arxiv.org/abs/2410.14423
Autor:
Nabavi, Shahabedin, Hamedani, Kian Anvari, Moghaddam, Mohsen Ebrahimi, Abin, Ahmad Ali, Frangi, Alejandro F.
This study proposes an attention-based statistical distance-guided unsupervised domain adaptation model for multi-class cardiovascular magnetic resonance (CMR) image quality assessment. The proposed model consists of a feature extractor, a label pred
Externí odkaz:
http://arxiv.org/abs/2409.00375
Autor:
Frangi, Giorgio
It has been pointed out in [1] that the familiar reciprocity relation between the conductivity $\sigma$ and resistivity $\rho$ should not be expected to hold in all possible settings, but, as argued in [2], is rather a property that may (or may not)
Externí odkaz:
http://arxiv.org/abs/2406.16124
Autor:
Frangi, Giorgio, Grozdanov, Sašo
Conventional wisdom teaches us that the electrical conductivity in a material is the inverse of its resistivity. In this work, we show that when both of these transport coefficients are defined in linear response through the Kubo formulae as two-poin
Externí odkaz:
http://arxiv.org/abs/2406.16123
Autor:
Conti, Paolo, Kneifl, Jonas, Manzoni, Andrea, Frangi, Attilio, Fehr, Jörg, Brunton, Steven L., Kutz, J. Nathan
The simulation of many complex phenomena in engineering and science requires solving expensive, high-dimensional systems of partial differential equations (PDEs). To circumvent this, reduced-order models (ROMs) have been developed to speed up computa
Externí odkaz:
http://arxiv.org/abs/2405.20905
Autor:
Abadi, Ehsan, Badano, Aldo, Bakic, Predrag, Bliznakova, Kristina, Bosmans, Hilde, Carton, Ann-Katherine, Frangi, Alejandro, Glick, Stephen, Kinahan, Paul, Lo, Joseph, Maidment, Andrew, Ria, Francesco, Samei, Ehsan, Sechopoulos, Ioannis, Segars, Paul, Tanaka, Rie, Vancoillie, Liesbeth
This submission comprises the proceedings of the 1st Virtual Imaging Trials in Medicine conference, organized by Duke University on April 22-24, 2024. The listed authors serve as the program directors for this conference. The VITM conference is a pio
Externí odkaz:
http://arxiv.org/abs/2405.05359
Publikováno v:
Comput. Methods Appl. Mech. Eng., 431, 117264, 2024
Measured data from a dynamical system can be assimilated into a predictive model by means of Kalman filters. Nonlinear extensions of the Kalman filter, such as the Extended Kalman Filter (EKF), are required to enable the joint estimation of (possibly
Externí odkaz:
http://arxiv.org/abs/2404.07536