US researchers say a self-supervised machine-learning tool can identify long-term physical defects in solar assets weeks or years before conventional inspections, potentially reducing operations and ...

The software tool developed by Stony Brook University uses self-supervised learning to detect long-term solar equipment damage weeks or years before manual inspections find it.

In a study published in Frontiers in Science, scientists from Purdue University and the Georgia Institute of Technology ...

Abstract: The spectrum advantage of millimeter wave (mmWave) can support large-bandwidth and high-rate transmission for B5G/6G. However, the penetration losses and significant atmospheric absorption ...

Abstract: This paper proposes an adaptive beamforming algorithm based on the subarray technology of coprime array to avoid the occurrence of grating lobes and reduce complexity, in response to the ...