IEEE

Polynomial Phase Signal Denoising via Sparse Representations Over a SMAF-Based Dictionary Learning Algorithm

Abstract: In this paper, we address the problem of denoising polynomial phase signals (PPS) by removing additive white Gaussian noise. Our approach is based on sparse representation using a trained ...
IEEE

A Real-Time Neural Representation via Algorithm-Hardware Synergy for Sparse-View CT Reconstruction

Abstract: Sparse-view computed tomography (SVCT) is an advancement in computed tomography (CT) technology that aims to reduce the radiation dose during imaging. Reconstructing high-quality images from ...
Frontiers

Graph Learning and Deep Unfolding for Electromagnetic Field Modelling and Antenna Systems

Traditional computational electromagnetics (CEM) methods—such as MoM, FEM, or FDTD—offer high fidelity, but struggle to scale ...
GitHub

MoEEG: A Sparse Mixture-of-Experts Transformer for Universal EEG Representation Learning

MoEEG has two variants, Base and Large, which share an identical model architecture but differ in hyperparameter configurations: the Base model features an embedding dimension of 128 with 4 attention ...
GitHub

On the Role of Discrete Representation in Sparse Mixture of Experts

Sparse Mixture of Experts (SMoE) is an effective solution for scaling up model capacity without increasing the computational costs. A crucial component of SMoE is the router, responsible for directing ...