King Abdullah University of Science and Technology

About: King Abdullah University of Science and Technology is a education organization based out in Jeddah, Saudi Arabia. It is known for research contribution in the topics: Catalysis & Membrane. The organization has 6221 authors who have published 22019 publications receiving 625706 citations. The organization is also known as: KAUST.

Unified Statistical Channel Model for Turbulence-Induced Fading in Underwater Wireless Optical Communication Systems

Abstract: A unified statistical model is proposed to characterize turbulence-induced fading in underwater wireless optical communication (UWOC) channels in the presence of air bubbles and temperature gradient for fresh and salty waters, based on experimental data. In this model, the channel irradiance fluctuations are characterized by the mixture exponential–generalized gamma (EGG) distribution. We use the expectation–maximization algorithm to obtain the maximum likelihood parameter estimation of the new model. Interestingly, the proposed model is shown to provide a perfect fit with the measured data under all channel conditions for both types of water. The major advantage of the new model is that it has a simple mathematical form making it attractive from a performance analysis point of view. Indeed, we show that the application of the EGG model leads to closed-form and analytically tractable expressions for key UWOC system performance metrics such as the outage probability, the average bit-error rate, and the ergodic capacity. To the best of our knowledge, this is the first-ever comprehensive channel model addressing the statistics of optical beam irradiance fluctuations in underwater wireless optical channels due to both air bubbles and temperature gradient.

Blade-Coated Hybrid Perovskite Solar Cells with Efficiency > 17%: An In Situ Investigation

Abstract: Blade-coating has recently emerged as a scalable fabrication method for hybrid perovskite solar cells, but it currently underperforms spin-coating, yielding a power conversion efficiency (PCE) of ∼15% for CH3NH3PbI3 (MAPbI3). We investigate the solidification of MAPbI3 films in situ during spin/blade-coating using optical and X-ray scattering methods. We find that the coating method and conditions profoundly influence the crystallization process, which proceeds through intermediate crystalline solvates. The polymorphism and composition of the solvates are mediated by the solvent removal rate dictated by the process temperature in blade-coating. Low to intermediate temperatures (25–80 °C) yield solvates with differing compositions and yield poor PCEs (∼5–8%) and a large spread (±2.5%). The intermediate solvates are not observed at elevated temperatures (>100 °C), pointing to direct crystallization of the perovskite from the sol–gel ink. These conditions yield large and compact spherulitic domains of perovs...

A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis

Abstract: COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis, prognosis and treatment strategies to combat the disease. Although nucleic acid detection has been mainly used as the gold standard to confirm this RNA virus-based disease, it has been shown that such a strategy has a high false negative rate, especially for patients in the early stage, and thus CT imaging has been applied as a major diagnostic modality in confirming positive COVID-19. Despite the various, urgent advances in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis, most of the existing methods can only perform classification, whereas the state-of-the-art segmentation method requires a high level of human intervention. In this paper, we propose a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources. Our method is founded upon two innovations: 1) the first CT scan simulator for COVID-19, by fitting the dynamic change of real patients’ data measured at different time points, which greatly alleviates the data scarcity issue; and 2) a novel deep learning algorithm to solve the large-scene-small-object problem, which decomposes the 3D segmentation problem into three 2D ones, and thus reduces the model complexity by an order of magnitude and, at the same time, significantly improves the segmentation accuracy. Comprehensive experimental results over multi-country, multi-hospital, and multi-machine datasets demonstrate the superior performance of our method over the existing ones and suggest its important application value in combating the disease.