Deep learning methods combined with large datasets have recently shown significant progress in solving several medical tasks.
We investigate deep learning for video compressive sensing within the scope of snapshot compressive imaging (SCI).
In this article, we propose multiple machine learning (ML) based physical-layer receiver solutions for demodulating orthogonal frequency-division multiplexing (OFDM) signals that are subject to hig
Accurate path gain models are critical for coverage prediction and RF planning in wireless communications.
Genetic Algorithms (GAs) have been shown to be a very effective optimisation tool on a wide variety of problems. However, they are not without their drawbacks.
End-to-end learning of communications systems is a fascinating novel concept that has so far only been validated by simulations for block-based transmissions.
In a tight binding superlattice, due to the weak coupling between wells, the minibands are very narrow ( 1 meV for many representative III-V multiquantum well structures such as GaAs/AlGaAs).
Deep centers in AlGaN/GaN high electron mobility transistors (HEMTs) on SiC substrate have been characterized by capacitance deep level transient spectroscopy (DLTS) and conductance deep level tran
The effects of lattice relaxation on the deep levels due to substitutional impurities in semiconductors are investigated using an extension of a previously developed formalism.
In this work we investigate AlGaN/GaN HEMTs structures grown by metalorganic chemical-vapor deposition on SiC substrates.
