29 research outputs found
Random neural network based epileptic seizure episode detection exploiting electroencephalogram signals
Epileptic seizures are caused by abnormal electrical activity in the brain that manifests itself in a variety of ways, including confusion and loss of awareness. Correct identification of epileptic seizures is critical in the treatment and management of patients with epileptic disorders. One in four patients present resistance against seizures episodes and are in dire need of detecting these critical events through continuous treatment in order to manage the specific disease. Epileptic seizures can be identified by reliably and accurately monitoring the patients’ neuro and muscle activities, cardiac activity, and oxygen saturation level using state-of-the-art sensing techniques including electroencephalograms (EEGs), electromyography (EMG), electrocardiograms (ECGs), and motion or audio/video recording that focuses on the human head and body. EEG analysis provides a prominent solution to distinguish between the signals associated with epileptic episodes and normal signals; therefore, this work aims to leverage on the latest EEG dataset using cutting-edge deep learning algorithms such as random neural network (RNN), convolutional neural network (CNN), extremely random tree (ERT), and residual neural network (ResNet) to classify multiple variants of epileptic seizures from non-seizures. The results obtained highlighted that RNN outperformed all other algorithms used and provided an overall accuracy of 97%, which was slightly improved after cross validation
impact of dehazing on underwater marker detection for augmented reality
Underwater augmented reality is a very challenging task and amongst several issues, one of the most crucial aspects involves real-time tracking. Particles present in water combined with the uneven absorption of light decrease the visibility in the underwater environment. Dehazing methods are used in many areas to improve the quality of digital image data that is degraded by the influence of the environment. This paper describes the visibility conditions affecting underwater scenes and shows existing dehazing techniques that successfully improve the quality of underwater images. Four underwater dehazing methods are selected for evaluation of their capability of improving the success of square marker detection in underwater videos. Two reviewed methods represent approaches of image restoration: Multi-Scale Fusion, and Bright Channel Prior. Another two methods evaluated, the Automatic Color Enhancement and the Screened Poisson Equation, are methods of image enhancement. The evaluation uses diverse test data set to evaluate different environmental conditions. Results of the evaluation show an increased number of successful marker detections in videos pre-processed by dehazing algorithms and evaluate the performance of each compared method. The Screened Poisson method performs slightly better to other methods across various tested environments, while Bright Channel Prior and Automatic Color Enhancement shows similarly positive results
Development and integration of digital technologies addressed to raise awareness and access to European underwater cultural heritage. An overview of the H2020 i-MARECULTURE project
The Underwater Cultural Heritage (UCH)
represents a vast historical and scientific resource that, often, is
not accessible to the general public due the environment and depth
where it is located. Digital technologies (Virtual Museums, Virtual
Guides and Virtual Reconstruction of Cultural Heritage) provide
a unique opportunity for digital accessibility to both scholars and
general public, interested in having a better grasp of underwater
sites and maritime archaeology. This paper presents the
architecture and the first results of the Horizon 2020 iMARECULTURE
(Advanced VR, iMmersive Serious Games and
Augmented REality as Tools to Raise Awareness and Access to
European Underwater CULTURal heritage) project that aims to
develop and integrate digital technologies for supporting the wide
public in acquiring knowledge about UCH. A Virtual Reality (VR)
system will be developed to allow users to visit the underwater sites
through the use of Head Mounted Displays (HMDs) or digital
holographic screens. Two serious games will be implemented for
supporting the understanding of the ancient Mediterranean
seafaring and the underwater archaeological excavations. An
Augmented Reality (AR) system based on an underwater tablet
will be developed to serve as virtual guide for divers that visit the
underwater archaeological sites