A New Raw Holoscopic Image Simulator and Data Generation

This article has been accepted for publication in a future proceedings of this conference, but has not been fully edited. Content may change prior to final publication. Citation information: DOI10.1109/ICIVC58118.2023.10270247, 2023 8th International Conference on Image, Vision and Computing (ICIVC

Recognition Of Silently Spoken Word From Eeg Signals Using Dense Attention Network (DAN)

In this paper, we propose a method for recognizing silently spoken words from electroencephalogram (EEG) signals using a Dense Attention Network (DAN). The proposed network learns features from the EEG data by applying the self-attention mechanism on temporal, spectral, and spatial (electrodes) dimensions. We examined the effectiveness of the proposed network in extracting spatio-spectro-temporal in-formation from EEG signals and provide a network for recognition of silently spoken words. The DAN achieved a recognition rate of 80.7% in leave-trials-out (LTO) and 75.1% in leave-subject-out (LSO) cross validation methods. In a direct comparison with other methods, the DAN outperformed other existing techniques in recognition of silently spoken words

Low-delay Single Holoscopic 3D Computer Generated Image To Multiview Images

Qatar National Research Fun

Investigating 3D holoscopic visual content upsampling using super resolution for cultural heritage digitization

This publication was made possible by NPRP grant 9-181-1-036 from the Qatar National Research Fund (a member of Qatar Foundation).NPRP grant 9-181-1-036 from the Qatar National Research Fund (a member of Qatar Foundation)

Machine Learning and Digital Heritage: The CEPROQHA Project Perspective

Through this paper, we aim at investigating the impact of artificial intelligence technologies on cultural heritage promotion and long-term preservation in terms of digitization effectiveness, attractiveness of the assets, and value empowering. Digital tools have been validated to yield sustainable and yet effective preservation for multiple types of content. For cultural data, however, there are multiple challenges in order to achieve sustainable preservation using these digital tools due to the specificities and the high-quality requirements imposed by cultural institutions. With the rise of machine learning and data science technologies, many researchers and heritage organizations are nowadays searching for techniques and methods to value and increase the reliability of cultural heritage digitization through machine learning. The present study investigates some of these initiatives highlighting their added value and potential future improvements. We mostly cover the aspects related to our context which is the long-term cost-effective digital preservation of the Qatari cultural heritage through the CEPROQHA project.Qatar National Research Fund

Characterization and optimization study of Ficus exasperata extract as corrosion inhibitor for mild steel in seawater

This study investigated the characterization of Ficus exasperata extract and the optimization of the process variables on inhibition of mild steel in seawater environment. Box Behnken Design was employed to examine the influence of three process variables: Temperature: 25-29 °C; Time: 3 – 6 days; Inhibitor concentration: 1-5 v/v. Phytochemical screening of the extract was done. The physicochemical constituents of the seawater were also determined. The experimental data was statistically determined and Scanning Electron Microscope (SEM) was used to characterize the mild steel. The result of the phytochemical screening of the Ficus exasperata plant extract (FEPE) showed the presence of contain inhibitive constituents: alkaloids, tannins, saponins, flavonoids and glycosides. The highest inhibition efficiency of 86.31% at a temperature of 29 ℃ for 6days at an inhibition concentration of 3v/v was observed from the experimental run. The optimal process levels of time: 5.74 days, temperature: 27.95 ℃ and inhibitor concentration: 2.90v/v, gave 87.52% as its inhibition efficiency. The result of the SEM from the optimal process level validated showed that more passive film was formed which can be attributed to the adsorption of the Ficus exasperata extract. It can be concluded that the Ficus exasperata was a good eco-friendly inhibitor