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.

The soundscape of the Anthropocene ocean.

Abstract: Oceans have become substantially noisier since the Industrial Revolution. Shipping, resource exploration, and infrastructure development have increased the anthrophony (sounds generated by human activities), whereas the biophony (sounds of biological origin) has been reduced by hunting, fishing, and habitat degradation. Climate change is affecting geophony (abiotic, natural sounds). Existing evidence shows that anthrophony affects marine animals at multiple levels, including their behavior, physiology, and, in extreme cases, survival. This should prompt management actions to deploy existing solutions to reduce noise levels in the ocean, thereby allowing marine animals to reestablish their use of ocean sound as a central ecological trait in a healthy ocean.

Versatile N-Doped MXene Ink for Printed Electrochemical Energy Storage Application

Abstract: L.H.Y. and Z.D.F. contributed equally to this work. This work was supported by the National Natural Science Foundation of China (51702225), National Key Research and Development Program (2016YFA0200103), and Jiangsu Youth Science Foundation (BK20170336). The authors acknowledge the support from Suzhou Key Laboratory for Advanced Carbon Materials and Wearable Energy Technologies, Suzhou, China.

Impact of Pointing Errors on the Performance of Mixed RF/FSO Dual-Hop Transmission Systems

Abstract: In this work, the performance analysis of a dual-hop relay transmission system composed of asymmetric radio-frequency (RF)/free-space optical (FSO) links with pointing errors is presented. More specifically, we build on the system model presented in to derive new exact closed-form expressions for the cumulative distribution function, probability density function, moment generating function, and moments of the end-to-end signal-to-noise ratio in terms of the Meijer's G function. We then capitalize on these results to offer new exact closed-form expressions for the higher-order amount of fading, average error rate for binary and M-ary modulation schemes, and the ergodic capacity, all in terms of Meijer's G functions. Our new analytical results were also verified via computer-based Monte-Carlo simulation results.

Performance Analysis of Mixed Nakagami- $m$ and Gamma–Gamma Dual-Hop FSO Transmission Systems

Abstract: In this paper, we carry out a unified performance analysis of a dual-hop relay system over the asymmetric links composed of both radio-frequency (RF) and unified free-space optical (FSO) links under the effect of pointing errors. Both fixed and variable gain relay systems are studied. The RF link is modeled by the Nakagami-m fading channel and the FSO link by the Gamma-Gamma fading channel subject to both types of detection techniques (i.e., heterodyne detection and intensity modulation with direct detection). In particular, we derive new unified closed-form expressions for the cumulative distribution function, the probability density function, the moment generating function (MGF), and the moments of the end-to-end signal-to-noise ratio (SNR) of these systems in terms of the Meijer's G function. Based on these formulas, we offer exact closed-form expressions for the outage probability (OP), the higher order amount of fading, and the average bit error rate (BER) of a variety of binary modulations in terms of the Meijer's G function. Furthermore, an exact closed-form expression of the end-to-end ergodic capacity is derived in terms of the bivariate G function. Additionally, by using the asymptotic expansion of the Meijer's G function at the high-SNR regime, we derive new asymptotic results for the OP, the MGF, and the average BER in terms of simple elementary functions.

Fast and flexible convolutional sparse coding

Abstract: Convolutional sparse coding (CSC) has become an increasingly important tool in machine learning and computer vision. Image features can be learned and subsequently used for classification and reconstruction tasks. As opposed to patch-based methods, convolutional sparse coding operates on whole images, thereby seamlessly capturing the correlation between local neighborhoods. In this paper, we propose a new approach to solving CSC problems and show that our method converges significantly faster and also finds better solutions than the state of the art. In addition, the proposed method is the first efficient approach to allow for proper boundary conditions to be imposed and it also supports feature learning from incomplete data as well as general reconstruction problems.