… examination of autoencoder-based communication systems in various scenarios and channel
conditions. It begins with an introduction to autoencoder communication systems, followed …

… multi-channel input Autoencoder architecture … trend across many signal processing techniques,
including for motor condition monitoring, where Gangsar [7] noted in their state-of-the art …

… trends of machine learning and data mining approaches for human activity recognition
highlighting the fundamental problems and challenges. … learning of each separate channel. A 1-D …

… Although such an approach can be similarly adopted in the present work, thereby reducing
the number of channels at the neural network input from Nf + 1 to a much smaller number, it …

… challenges, this study developed a feature extraction approach based on LSTM-autoencoder …
As a powerful self-supervised representation learning approach, autoencoder is capable of …

… Neural networks that apply unsupervised learning are, for example, autoencoders and … ,
to model the relationship between the different channels of the feature maps. Figure 16 shows …

… structure as a channel autoencoder, where channel refers to the … challenges, we propose
to train a practical but suboptimum autoencoder architecture and stack it with a state-of-the-art …

… in real-world channel effects on signal … tasks but their performance in general settings
may suffer since the crafted features may not fully reflect all the effects that real-world channels …

… Here, ncin is the number of channels in the input image. This way, the autoencoders had the
… Additionally, the trends we derive from our results suggest that this finding likely extends to …

… We first illustarte the setup of a single layer in each channel of our SMCAE. For a single
channel of our SMCAE is basically an autoencoder [5]. Assume an input dataset with n instances …