Capturing high-dimensional (HD) data is a long-term challenge in signal processing and
related fields. Snapshot compressive imaging (SCI) uses a 2D detector to capture HD (≥ …

We consider using untrained neural networks to solve the reconstruction problem of
snapshot compressive imaging (SCI), which uses a two-dimensional (2D) detector to …

Snapshot compressive imaging (SCI) systems aim to capture high-dimensional (≥ 3 D)
images in a single shot using 2D detectors. SCI devices consist of two main parts: a …

Spectral compressive imaging (SCI) is able to encode a high-dimensional hyperspectral
image into a two-dimensional snapshot measurement, and then use algorithms to …

The present work studies a novel efficient compressed based cryptosystem which combines
a dynamical parameter coming from high dimensional system, a dynamics S-box and a 2D …

This paper studies the effect of the electromagnetic flux on the dynamics of an introduced
model of heterogeneous coupled neurons. Analytical investigation of the coupled neurons …

Snapshot compressive imaging (SCI) can record a 3D datacube by a 2D measurement and
algorithmically reconstruct the desired 3D information from that 2D measurement. The …

Over the last few decades, ultrafast laser processing has become a widely used tool for
manufacturing microstructures and nanostructures. The real-time monitoring of laser …

Compressive sensing (CS) is a technique frequently adopted in wireless communications.
By utilizing CS, a receiver could sense the state of channels with sub-Nyquist analog to …

In this contribution, the nonlinear dynamics of a non-autonomous model of two neurons
based on the Hopfield neural network is considered. Using activation gradients as …