THE KAUST Repository is an initiative of the University Library to expand the impact of conference papers, technical reports, peer-reviewed articles, preprints, theses, images, data sets, and other research-related works of King Abdullah University of Science and Technology (KAUST). 

Theses and DissertationsResearch Publications

Files in the repository are accessible through popular web search engines and are given persistent web addresses so links will not become broken over time.

KAUST researchers: To add your research to the repository, click on Deposit your Research, log in with your KAUST user name and password, and deposit the item in the appropriate collection.

Deposit your Research

If you have any questions, please contact repository@kaust.edu.sa.

  • Optimal 3D trajectory planning for AUVs using ocean general circulation models

    Albarakati, Sultan Saud; Lima, Ricardo; Giraldi, Loic; Hoteit, Ibrahim; Knio, Omar (Ocean Engineering, Elsevier Ltd, 2019-09-15) [Article]
    In this paper, we consider the autonomous underwater vehicle (AUV) trajectory planning problem under the influence of a realistic 3D current as simulated by an ocean general circulation model (OGCM). Attention is focused on the case of a deterministic steady OGCM field, which is used to specify data for both the ocean current and for ocean bathymetry. A general framework for optimal trajectory planning is developed for this setting, accounting for the 3D ocean current and for static obstacle avoidance constraints. A nonlinear programming approach is used for this purpose, which leads to a low complexity discrete-time model that can be efficiently solved. To demonstrate the efficiency of the model, we consider the optimal time trajectory planning of an AUV operating in the Red Sea and Gulf of Aden, with velocity, and bathymetric data provided by an eddy-resolving MITgcm. Different optimal-time trajectory planning scenarios are implemented to demonstrate the capabilities of the model to identify trajectories that adapt to favorable and adverse currents and to avoid obstacles corresponding to a complex bathymetry environment. The simulations are also used to evaluate the performance of the proposed approach, and to illustrate the application of advanced visualization tools to interpret the model predictions.
  • A Multi-Domain Connectome Convolutional Neural Network for Identifying Schizophrenia from EEG Connectivity Patterns

    Phang, Chun-Ren; Noman, Fuad Mohammed; Hussain, Hadri; Ting, Chee-Ming; Ombao, Hernando (IEEE Journal of Biomedical and Health Informatics, Institute of Electrical and Electronics Engineers (IEEE), 2019-09-13) [Article]
    Objective: We exploit altered patterns in brain functional connectivity as features for automatic discriminative analysis of neuropsychiatric patients. Deep learning methods have been introduced to functional network classification only very recently for fMRI, and the proposed architectures essentially focused on a single type of connectivity measure. Methods: We propose a deep convolutional neural network (CNN) framework for classification of electroencephalogram (EEG)-derived brain connectome in schizophrenia (SZ). To capture complementary aspects of disrupted connectivity in SZ, we explore combination of various connectivity features consisting of time and frequency-domain metrics of effective connectivity based on vector autoregressive model and partial directed coherence, and complex network measures of network topology. We design a novel multi-domain connectome CNN (MDC-CNN) based on a parallel ensemble of 1D and 2D CNNs to integrate the features from various domains and dimensions using different fusion strategies. We also consider an extension to dynamic brain connectivity using the recurrent neural networks. Results: Hierarchical latent representations learned by the multiple convolutional layers from EEG connectivity reveal apparent group differences between SZ and healthy controls (HC). Results on a large resting-state EEG dataset show that the proposed CNNs significantly outperform traditional support vector machine classifiers. The MDC-CNN with combined connectivity features further improves performance over single-domain CNNs using individual features, achieving remarkable accuracy of 91.69% with a decision-level fusion. Conclusion: The proposed MDC-CNN by integrating information from diverse brain connectivity descriptors is able to accurately discriminate SZ from HC. Significance: The new framework is potentially useful for developing diagnostic tools for SZ and other disorders.
  • Label-Free Detection of Ovarian Cancer Antigen CA125 by Surface Enhanced Raman Scattering.

    TunÇ, İlknur; Susapto, Hepi Hari (Journal of nanoscience and nanotechnology, American Scientific Publishers, 2019-09-08) [Article]
    Surface-enhanced Raman spectroscopy (SERS) has drawn attention in recent years for imaging biologicalmolecules as an analytical tool due to its label-free approach. The SERS approach can be used in tracking organic molecules and monitoring unique Raman spectra of the organic molecules bound to metal nanoparticles (NPs). In this paper, the molecular specifity of Raman Spectroscopy was used together with self-assembled monolayer of metallic AuNPs as a sensor platform in order to detect CA125 antibody-antigen probe molecules. Highly enhanced electromagnetic fields localized around neighboring AuNPs provide hot-spot construction due to the spatial distribution of SERS enhancement on the CA125 proteins at nM concentration level. Time resolved SERS mapping of CA125 antibody and antigen couples was recorded. Even though blinking behavior was observed for some cases, vast variety SERS signals from CA125 proteins were highly reproducible. Blinking behavior is attributed to single molecular detection. Distinguished feature of SERS mapping images of CA125 antibody and antigen with such a low concentration level is very promising for this technique to be used for diagnostic purposes.
  • The future of Blue Carbon science.

    Macreadie, Peter I; Anton Gamazo, Andrea; Raven, John A; Beaumont, Nicola; Connolly, Rod M; Friess, Daniel A; Kelleway, Jeffrey J; Kennedy, Hilary; Kuwae, Tomohiro; Lavery, P. S.; Lovelock, Catherine E; Smale, Dan A; Apostolaki, Eugenia T; Atwood, Trisha B; Baldock, Jeff; Bianchi, Thomas S; Chmura, Gail L; Eyre, Bradley D; Fourqurean, J. W.; Hall-Spencer, Jason M; Huxham, Mark; Hendriks, Iris E; Krause-Jensen, Dorte; Laffoley, Dan; Luisetti, Tiziana; Marbà, Núria; Masqué, Pere; McGlathery, Karen J; Megonigal, J Patrick; Murdiyarso, Daniel; Russell, Bayden D; Santos, Rui; Serrano, Oscar; Silliman, Brian R; Watanabe, Kenta; Duarte, Carlos M. (Nature communications, Springer Science and Business Media LLC, 2019-09-07) [Article]
    The term Blue Carbon (BC) was first coined a decade ago to describe the disproportionately large contribution of coastal vegetated ecosystems to global carbon sequestration. The role of BC in climate change mitigation and adaptation has now reached international prominence. To help prioritise future research, we assembled leading experts in the field to agree upon the top-ten pending questions in BC science. Understanding how climate change affects carbon accumulation in mature BC ecosystems and during their restoration was a high priority. Controversial questions included the role of carbonate and macroalgae in BC cycling, and the degree to which greenhouse gases are released following disturbance of BC ecosystems. Scientists seek improved precision of the extent of BC ecosystems; techniques to determine BC provenance; understanding of the factors that influence sequestration in BC ecosystems, with the corresponding value of BC; and the management actions that are effective in enhancing this value. Overall this overview provides a comprehensive road map for the coming decades on future research in BC science.
  • Flexible tag design for semi-continuous wireless data acquisition from marine animals

    Karimi, Muhammad Akram; Zhang, Qingle; Kuo, Yen Hung; Shaikh, Sohail F.; Kaidarova, Altynay; Geraldi, Nathan; Hussain, Muhammad Mustafa; Kosel, Jürgen; Duarte, Carlos M.; Shamim, Atif (Flexible and Printed Electronics, IOP Publishing, 2019-09-06) [Article]
    Acquisition of sensor data from tagged marine animals has always been a challenge. Presently, we come across two extreme mechanisms to acquire marine data. For continuous data acquisition, hundreds of kilometers of optical fiber links are used which in addition to being expensive, are impractical in certain circumstances. On the other extreme, data is retrieved in an offline and invasive manner after removing the sensor tag from the animal's skin. This paper presents a semi-continuous method of acquiring marine data without requiring tags to be removed from the sea animal. Marine data is temporarily stored in the tag's memory, which is then automatically synced to floating receivers as soon as the animal rises to the water surface. To ensure effective wireless communication in an unpredictable environment, a quasi-isotropic antenna has been designed which works equally well irrespective of the orientation of the tagged animal. In contrast to existing rigid wireless devices, the tag presented in this work is flexible and thus convenient for mounting on marine animals. The tag has been initially tested in air as a standalone unit with a communication range of 120m. During tests in water, with the tag mounted on the skin of a crab, a range of 12m has been observed. In a system-level test, the muscle activity of a small giant clam (Tridacna maxima) has been recorded in real time via the non-invasive wireless tag.

View more