… autoencoder (VAE) for data-driven channel estimation. The underlying true and unknown
channel … a VAE-based channel estimator can approximate the MMSE channel estimator. We …

… [12] to channel charting. Accordingly, we first employ a convolutional autoencoder (CAE) to
… Guillaud, “Trends and challenges in wireless channel modeling for evolving radio access,” …

… features, common sense of the state of art and trends emerged in ratings. In comparison to
our … We use the sum of RGB channels to reduce the impact of brightness. The dark channel …

… To stay current with academic trends, in this article, we choose to stay focused on the pure
… captured from each channel as a separate piece of data. This is so that all channels of the …

… Compared to a three-channel RGB image or to few spectral bands in multispectral,
hyperspectral images detain more detailed … This trend was viewed for both blue and black inks. …

… contextualize conditional variational autoencoder into side-channel analysis in … autoencoders
for DLSCA and we present our main contribution: the Conditional Variational AutoEncoder …

… convolutional autoencoder to extract features from the input, where the autoencoder is trained
… 224.75μW/mm 2 when processing 16 input channels at 7.68 MHz and 0.55 V. Our design …

… In this study, for the first time, a deep wavelet autoencoder is proposed, evolving by a two-…
-varying shallow underwater channel effects. In this approach, the autoencoder is an automatic …

… However, late fusion may require more computational resources, as multiple autoencoders
… of multiple autoencoders can be challenging. 6) JOINT LEARNING Autoencoders are trained …

In this work, we propose a Wavelet-based Deep Auto Encoder-Decoder Network (WDAED)
based image compression which takes care of the various frequency components present in …