Estimating wildfire growth from noisy and incomplete incident data using a state space model

Hyperspectral Unmixing from Incomplete and Noisy Data

In hyperspectral images, once the pure spectra of the materials are known, hyperspectral unmixing seeks to find their relative abundances throughout the scene. We present a novel variational model for hyperspectral unmixing from incomplete noisy data, which combines a spatial regularity prior with the knowledge of the pure spectra. The material abundances are found by minimizing the resulting c...

Image Reconstruction from Incomplete Data

Estimating a State-Space Model from Point Process Observations

A widely used signal processing paradigm is the state-space model. The state-space model is defined by two equations: an observation equation that describes how the hidden state or latent process is observed and a state equation that defines the evolution of the process through time. Inspired by neurophysiology experiments in which neural spiking activity is induced by an implicit (latent) stim...

Image Reconstruction from Incomplete Data

Estimating Oil Products Demand by State-space Model and Relevant Guidelines for Price Liberalization

The purpose of this paper is to estimate oil products demand by the state-space model, taking into account the implications for price liberalization using the Kalman filter technique in the framework of a time-varying pattern. For this purpose, we use the data of the Energy Balance Sheet and the National Iranian Oil Refining and Distribution Company during the period of 1994-2017. Our model res...