102 citations · 222 across the 29 of their papers we have counts for
27 papers
EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals
Andac Demir, Toshiaki Koike-Akino, Ye Wang +2
Convolutional neural networks (CNN) have been frequently used to extract subject-invariant features from electroencephalogram (EEG) for classification tasks. This approach holds th…
Protograph-Based Design for QC Polar Codes
Toshiaki Koike-Akino, Ye Wang
We propose a new family of polar coding which realizes high coding gain, low complexity, and high throughput by introducing a protograph-based design. The proposed technique called…
Distributed Coding of Quantized Random Projections
Maxim Goukhshtein, Petros T. Boufounos, Toshiaki Koike-Akino +1
In this paper we propose a new framework for distributed source coding of structured sources, such as sparse signals. Our framework capitalizes on recent advances in the theory of…
Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders
Mo Han, Ozan Ozdenizci, Toshiaki Koike-Akino +2
Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological st…
Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction
Mo Han, Ozan Ozdenizci, Ye Wang +2
Recent developments in biosignal processing have enabled users to exploit their physiological status for manipulating devices in a reliable and safe manner. One major challenge of…
Huffman-Coded Sphere Shaping for Extended-Reach Single-Span Links
Pavel Skvortcov, Ian Phillips, Wladek Forysiak +4
Huffman-coded sphere shaping (HCSS) is an algorithm for finite-length probabilistic constellation shaping, which provides nearly optimal energy efficiency at low implementation com…