Spectral representation-based dimension reduction for simulating multivariate non-stationary ground motions
Sign Up to like & getrecommendations! Published in 2018 at "Soil Dynamics and Earthquake Engineering"
DOI: 10.1016/j.soildyn.2018.04.058
Abstract: Abstract A framework of spectral representation-based dimension reduction for simulating multivariate non-stationary stochastic ground motion processes is addressed in this paper. By means of introducing random functions serving as constraints correlating with the orthogonal random… read more here.
Keywords: ground; spectral representation; dimension reduction; based dimension ... See more keywords
Deep Latent Spectral Representation Learning-Based Hyperspectral Band Selection for Target Detection
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2019.2952091
Abstract: Hyperspectral images (HSIs) can provide discriminative spectral signatures regarding the physical nature of different materials. It is this unique nature that makes HSIs to be of great interest in many fields. However, HSI application faces… read more here.
Keywords: representation; spectral representation; band selection; deep latent ... See more keywords
Spatio–Spectral Representation Learning for Electroencephalographic Gait-Pattern Classification
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Neural Systems and Rehabilitation Engineering"
DOI: 10.1109/tnsre.2018.2864119
Abstract: The brain plays a pivotal role in locomotion by coordinating muscles through interconnections that get established by the peripheral nervous system. To date, many attempts have been made to reveal the underlying mechanisms of humans’… read more here.
Keywords: representation learning; classification; pattern classification; gait pattern ... See more keywords
Asymptotic Spectral Representation of Linear Convolutional Layers
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2022.3140718
Abstract: By stacking a number of convolutional layers, convolutional neural networks (CNNs) have made remarkable performance boosts in many artificial intelligence applications. While the convolution operation is well-understood, it is still a mystery why repeated convolutions… read more here.
Keywords: upper bounds; spectral representation; representation linear; asymptotic spectral ... See more keywords