Image and Mathematical Modeling

Scientific Activity

To cover all these research areas, the INIT is divided into 10 sections

Image and Mathematical Modeling

  • Stochastic and Markov processes, data analysis, free and non-parametric distribution methods, multivariate analysis, statistical inference techniques, statistical prediction techniques, time series, spatial and temporal-spatial statistics, analysis of large volumes of data, crystallography, mineralogy, earth sciences, air pollution, soils, natural resources, environmental management.

 

  • Medical image segmentation and analysis, randomized Markov fields, MCMC methods, closed randomized sets, stereology, radiology, image fusion.

 

  • Differential geometry, stereology, medical images, image volume processing, sectional, media, scalar and Ricci curvatures, differential invariants, variational calculus.

 

  • Bohr duality and compaction, unitary duality, Lie groups, automatic continuity, Banach algebras, dynamic systems, valuations, singularities, desingularització methods, evaluation codes.
Projects (Jorge Mateu)
Projects (Amelia Simó)

RESEARCHERS

Vicente Cervera Mateu – Section Leader
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Vicente Cervera Mateu – Section Leader

Ximo Gual
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Ximo Gual

Jorge Mateu
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Jorge Mateu

Vicente Palmer
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Vicente Palmer

Salvador Hernández
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Salvador Hernández

Statistical analysis of events in space-time on networks and trajectories. (SpTNet) December 2016 – December 2020 Statistical analysis of events in space-time on networks and trajectories. (SpTNet) December 2016 – December 2020
MODELOS ESTOCÁSTICOS E INFERENCIA PARA PROCESOS MARCADOS ESPACIO-TEMPORALES SOBRE REDES
NEURO-ML: MACHINE LEARNING THROUGH BIO-INSPIRED MODELS (AND VICE VERSA)

Statistical analysis of events in space-time on networks and trajectories. (SpTNet) December 2016 – December 2020

New technologies have opened the door to the era of big data and with it the emergence of data science. In a massive world of information, the need for statistical treatment of events that evolve in space-time on a network system is increasingly common. When these events are monitored over time they give information on the displacement and provide trajectories over networks. The theory of stochastic space- time processes on networks provide the theoretical and methodological support for this type of data.

This project aims to address the statistical modeling of this type of event in these new scenarios. We will begin with the parametric analysis of first-order
characteristics, which are necessary to arrive at those of second order and higher orders. We intend to characterize all the orders (as far as possible) of a specific process on networks to determine the underlying structure of space-time interaction existing on the network.

MODELOS ESTOCÁSTICOS E INFERENCIA PARA PROCESOS MARCADOS ESPACIO-TEMPORALES SOBRE REDES

MODELOS ESTOCÁSTICOS E INFERENCIA PARA PROCESOS MARCADOS ESPACIO-TEMPORALES SOBRE REDES

UJI-B2021-37

NEURO-ML: MACHINE LEARNING THROUGH BIO-INSPIRED MODELS (AND VICE VERSA)

NEURO-ML: MACHINE LEARNING THROUGH BIO-INSPIRED MODELS (AND VICE VERSA)

UJI-A2022-12

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