Brunel University Research Archive(BURA) preserves and enables easy and open access to all
types of digital content. It showcases Brunel's research outputs.
Research contained within BURA is open access, although some publications may be subject
to publisher imposed embargoes. All awarded PhD theses are also archived on BURA.
Browsing by Subject GAN
Showing results 1 to 4 of 4
| Issue Date | Title | Author(s) |
|---|---|---|
| 29-Sep-2023 | Deep Learning for Detecting Multi-Level Driver Fatigue Using Physiological Signals: A Comprehensive Approach | Peivandi, M; Ardabili, SZ; Sheykhivand, S; Danishvar, S |
| 23-Aug-2021 | Development and deployment of a generative model-based framework for text to photorealistic image generation | Pande, S; Chouhan, S; Sonavane, R; Walambe, R; Ghinea, G; Kotecha, K |
| 2015 | Nanocathodoluminescence reveals mitigation of the stark shift in InGaN quantum wells by Si doping | Griffiths, JT; Zhang, S; Rouet-Leduc, B; Fu, WY; Bao, A; Zhu, D; Wallis, DJ; Howkins, A; Boyd, I; Stowe, D; Kappers, MJ; Humphreys, CJ; Oliver, RA |
| 7-Jan-2024 | A Novel Approach for Automatic Detection of Driver Fatigue Using EEG Signals Based on Graph Convolutional Networks | Ardabili, SZ; Bahmani, S; Lahijan, LZ; Khaleghi, N; Sheykhivand, S; Danishvar, S |