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 ...

Abstract: Multikernel sparse representation (SR) algorithms have made much progress in synthetic aperture radar automatic target recognition (SAR ATR) tasks. These algorithms usually combine all the ...

BM25 is a probabilistic ranking algorithm that calculates relevance scores between queries and documents based on term frequency and inverse document frequency. This library's implementation produces ...

MATLAB is a high-performance language and interactive environment used by millions of engineers and scientists worldwide for technical computing, data analysis, algorithm development, and ...

Abstract: We investigated the cognitive/neural “recycling” underlying the acquisition of programming, a culturally-invented skill. Using fMRI, we found neural representations of algorithms (written in ...

People store large quantities of data in their electronic devices and transfer some of this data to others, whether for professional or personal reasons. Data compression methods are thus of the ...

ABSTRACT: Node renumbering is an important step in the solution of sparse systems of equations. It aims to reduce the bandwidth and profile of the matrix. This allows for the speeding up of the ...

Neurons in cortical networks are very sparsely connected; even neurons whose axons and dendrites overlap are highly unlikely to form a synaptic connection. What is the relevance of such sparse ...

Reinforcement learning (RL) trains agents to make sequential decisions by maximizing cumulative rewards. It has diverse applications, including robotics, gaming, and automation, where agents interact ...