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arXiv 2021年

作者： Trichili, Abderrahmen Ragheb, Amr Briantcev, Dmitrii Esmail, Maged A. Altamimi, Majid Ashry, Islam Ooi, Boon S. Alshebeili, Saleh Alouini, Mohamed-Slim The Computer Electrical and Mathematical Sciences & Engineering King Abdullah University of Science and Technology King Saud University Prince Sultan University

Progress in optical wireless communication (OWC) has unleashed the potential to transmit data in an ultra-fast manner without incurring large investments and bulk infrastructure. OWC includes wireless data transmissions in three optical sub-bands;ultraviolet, visible, and infrared. This paper discusses installing infrared OWC, known as free space optics (FSO), systems on top of installed radio frequency (RF) networks for outdoor applications to benefit from the reliability of RF links and the unlicensed broad optical spectrum, and the large data rates carried by laser beams propagating in free space. We equally review commercially available solutions and the hardware requirements for RF and FSO technology co-existence. The potential of hybrid RF/FSO for space communication is further discussed. Finally, open problems and future research directions are presented. © 2021, CC BY.

关键词： Optical communication

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Voltage controlled domain wall motion based neuron and stochastic magnetic tunnel junction synapse for neuromorphic computing applications

TechRxiv

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TechRxiv 2021年

作者： Lone, Aijaz H. Amara, Selma Fariborzi, Hossein Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

The present work discusses the proposal of a spintronic neuromorphic system with spin orbit torque driven domain wall motion-based neuron and synapse. We propose a voltage-controlled magnetic anisotropy domain wall motion based magnetic tunnel junction neuron. We investigate how the electric field at the gate (pinning site), generated by the voltage signals from pre-neurons, modulates the domain wall motion, which reflects in the non-linear switching behaviour of neuron magnetization. For the implementation of synaptic weights, we propose 3-terminal MTJ with stochastic domain wall motion in the free layer. We incorporate intrinsic pinning effects by creating triangular notches on the sides of the free layer. The pinning of domain wall and intrinsic thermal noise of device lead to the stochastic behaviour of domain wall motion. The control of this stochasticity by the spin orbit torque is shown to realize the potentiation and depression of the synaptic weight. The micromagnetics and spin transport studies in synapse and neuron are carried out by developing a coupled micromagnetic Non-Equilibrium Green’s Function (MuMag-NEGF) model. The minimization of the writing current pulse width by leveraging the thermal noise and demagnetization energy is also presented. Finally, we discuss the implementation of digit recognition by the proposed system using a spike time dependent algorithm. © 2021, CC BY.

关键词： Electric fields

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Multi-frame Correlated Representation Network for Video Super-Resolution

Multi-frame Correlated Representation Network for Video Supe...

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International Conference on computer, Information and Telecommunication Systems (CITS)

作者： Shuo Jin Meiqin Liu Yu Guo Chao Yao Mohammad S. Obaidat Institute of Information Science Beijing Jiaotong University Beijing China School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China King Abdullah II School of Information Technology The University of Jordan Amman Jordan The Amity University Noida UP India

Video super-resolution aims to reconstruct the high-resolution video from its degraded observation. Existing methods primarily utilize explicit alignment strategies, including optical flow and motion compensation, and implicit alignment strategies, including deformable convolution and non-local attention, to model the temporal information. However, these alignment strategies with motion estimation and motion compensation (MEMC) introduce inaccurate inter-frame information to some extent, which largely affects the reconstruction performance. To avoid the error compensation information, we propose to model the temporal information from the perspective of the self-similarity between frames and design a multi-frame correlated representation network (MCRNet) for video super-resolution. To address the issue of temporal information distortion caused by large-scale pixel displacement and object occlusion, MCRNet extracts temporal information through similar regions on multiple frames, which are aggregated with allocated weights for information compensation. Moreover, we design a multi-scale non-local information fusion module for non-local correlation matching of spatio-temporal features in the multi-scale space, which maintains the scale consistency of spatio-temporal features. Experimental results indicate that MCRNet achieves promising gains over other competing methods which employ explicit and implicit alignment strategies on different datasets.

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A Portable Device for Real-time Continuous Drug Monitoring in Freely Moving Small Animals

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Biosensors and Bioelectronics 2025年

作者： Yurii Tsyban Saptami Suresh Shetty Yanyan Li José Ilton de Oliveira Filho Jasmeen S. Merzaban Khaled Nabil Salama Bioengineering Program Biological and Environmental Sciences and Engineering (BESE) Division King Abdullah University of Science and Technology Thuwal 23955-6900 Kingdom of Saudi Arabia Bioscience Program Biological and Environmental Sciences and Engineering (BESE) Division King Abdullah University of Science and Technology Thuwal 23955-6900 Kingdom of Saudi Arabia Electrical Engineering and Computer Program Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology Thuwal 23955-6900 Kingdom of Saudi Arabia Center of Excellence on Smart Health King Abdullah University of Science and Technology Thuwal 23955-6900 Kingdom of Saudi Arabia

The large size and operational constraints of benchtop potentiostats, which typically require the use of anesthetized animals, have posed significant challenges for conducting real-time, long-term pharmacokinetic studies in freely moving subjects. This research addresses these challenges by developing a compact, portable electrochemical sensing platform capable of monitoring drug concentrations in real time within natural, dynamic environments. The system integrates lightweight, miniaturized electronics, and multiple electrochemical techniques with onboard data processing to eliminate the need for external computational support, while its energy-efficient design reduces reliance on large batteries. Experimental evaluations demonstrate that the device reliably measures drug concentrations using aptamer-based sensors and processes signals with baseline correction and drift compensation in situ. Unlike implantable systems, our externally mounted device offers the flexibility of quick attachment and replacement without surgical intervention, preserving animal welfare. These advancements open new avenues for pharmacokinetic research and closed-loop drug delivery systems by enabling continuous, high-frequency measurements in awake, freely moving small animals.

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BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression

arXiv

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arXiv 2022年

作者： Zhao, Haoyu Li, Boyue Li, Zhize Richtárik, Peter Chi, Yuejie Department of Computer Science Princeton University PrincetonNJ08540 United States Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

Communication efficiency has been widely recognized as the bottleneck for large-scale decentralized machine learning applications in multi-agent or federated environments. To tackle the communication bottleneck, there have been many efforts to design communication-compressed algorithms for decentralized nonconvex optimization, where the clients are only allowed to communicate a small amount of quantized information (aka bits) with their neighbors over a predefined graph topology. Despite significant efforts, the state-of-the-art algorithm in the nonconvex setting still suffers from a slower rate of convergence O((G/T)2/3) compared with their uncompressed counterpart, where G measures the data heterogeneity across different clients, and T is the number of communication rounds. This paper proposes BEER, which adopts communication compression with gradient tracking, and shows it converges at a faster rate of O(1/T). This significantly improves over the state-of-the-art rate, by matching the rate without compression even under arbitrary data heterogeneity. Numerical experiments are also provided to corroborate our theory and confirm the practical superiority of BEER in the data heterogeneous regime. Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

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URL Spam Detection Using Machine Learning Classifiers

URL Spam Detection Using Machine Learning Classifiers

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Omar Almomani Adeeb Alsaaidah Mosleh M. Abualhaj Mohammed Amin Almaiah Ammar Almomani Shahzad Memon Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Computer Science King Abdullah the II IT School The University of Jordan Amman Jordan Department of Information Technology Al-Huson University College Al-Balqa Applied University Irbid Jordan Department of Computer Information Science Higher Colleges of Technology Sharjah United Arab Emirates Department of Computer Science and Digital Technologies Faculty of Architecture Computing and Engineering University of East London UK

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Cybersecurity has emerged as one of the most prevalent and significant challenges in recent years due to the advancement of technology. Among the most frequent and hazardous cybersecurity threats are spam URLs (Uniform Resource Locators), which are also one of the most popular methods for user fraud. Users are the victims of this attack, which also steals their data and infects their devices with harmful software. The detection of spam URLs has become very important in protecting the user. Therefore, this study aims to investigate the efficiency of machine learning classifiers in detecting spam URLs. The following machine learning classifiers were chosen: Random Forest, Decision Tree, and SVM. The evaluation was based on the ISCXURL2016 dataset, which is divided into three groups: All Features, BestFirst Features, and Infogain Features and evaluation matrices were the Accuracy, Precision, Sensitivity, and F-measure. The results obtained showed that Random Forest with All Features is superior to others with an accuracy of 99.75%, Precision of 99.74%, and Sensitivity of 99. 79%, and F-measure 99.76 %.

关键词： Uniform resource locators Support vector machines Sensitivity Accuracy Phishing Metaheuristics Feature extraction Malware Decision trees Random forests

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Hybrid Channel Attention Regression U-Net (ARU-Net): An Enhanced Architecture for Brain Tumour Segmentation in Magnetic Resonance Imaging

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IEEE Access 2025年 13卷 102278-102298页

作者： Siddiqui, Noman Ahmed Qadri, Muhammad Tahir Ali, Zain Anwar Akhter, Muhammad Ovais Alsanad, Ahmed Alhogail, Areej abdullah Gumaei, Abdul H. Sir Syed University of Engineering and Technology Electronic Engineering Department Karachi75290 Pakistan Co. Kildare MaynoothW23 A3HY Ireland Bahria University Electrical Engineering Department Karachi73500 Pakistan King Saud University College of Computer and Information Science Riyadh11543 Saudi Arabia Prince Sattam bin Abdulaziz University College of Computer Engineering and Sciences Al-Kharj11942 Saudi Arabia

This research presents an accurate segmentation of brain tumours which is important in diagnosis and treatment planning of magnetic resource imaging (MRI) scans. Despite existing methodologies based on deep learning models, it remains difficult to distinguish between a tumour region and a normal brain tissue. To tackle this problem, a recent work proposed a new brain tumour segmentation technique on MRI, known as the Hybrid Attention Regression U-Net (ARU-Net) architecture. To enhance the effectiveness of segmentation, the proposed architecture utilises modern approaches including attention mechanisms, feature fusion processes, and regression-based output layer. It specifically uses Spatial and channel-wise attention mechanisms to capture important regions and inter channel dependencies. Spatial attention module in this architecture is based on improved Convolutional Block Attention Module which constrain attention on informative areas and suppress irrelevant or noisy ones. On the other hand, the channel-wise attention part, the Squeeze-and-Excitation (SE) blocks, recalibrates channel-wise feature responses for higher emphasis on salient features. The performance of proposed method has been evaluated comprehensively on the BraTS-2021 dataset where it shows enhanced accuracy over existing methods in detecting the tumour area. From the point of epoch curve, F1 score, the area under the curve, specificity, sensitivity, and accuracy in augmented and non-augmented datasets validated the stability of the proposed ARU-Net. This underlines its roles in segmentation of brain tumour, which if optimally developed holds the promise to significantly contribute for diagnosis and treatment planning. © 2013 IEEE.

关键词： Image segmentation

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NeuS-PIR: Learning Relightable Neural Surface using Pre-Integrated Rendering

arXiv

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arXiv 2023年

作者： Mao, Shi Wu, Chenming Shen, Zhelun Wang, Yifan Wu, Dayan Zhang, Liangjun Baidu Research China King Abdullah University of Science and Technology Saudi Arabia Paul G. Allen School of Computer Science University of Washington United States Institute of Information Engineering Chinese Academy of Science Beijing100084 China

This paper presents a method, namely NeuS-PIR, for recovering relightable neural surfaces using pre-integrated rendering from multi-view images or video. Unlike methods based on NeRF and discrete meshes, our method utilizes implicit neural surface representation to reconstruct high-quality geometry, which facilitates the factorization of the radiance field into two components: a spatially varying material field and an all-frequency lighting representation. This factorization, jointly optimized using an adapted differentiable pre-integrated rendering framework with material encoding regularization, in turn addresses the ambiguity of geometry reconstruction and leads to better disentanglement and refinement of each scene property. Additionally, we introduced a method to distil indirect illumination fields from the learned representations, further recovering the complex illumination effect like inter-reflection. Consequently, our method enables advanced applications such as relighting, which can be seamlessly integrated with modern graphics engines. Qualitative and quantitative experiments have shown that NeuS-PIR outperforms existing methods across various tasks on both synthetic and real datasets. Source code is available at https://***/Sheldonmao/NeuSPIR. Copyright © 2023, The Authors. All rights reserved.

关键词： Factorization

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On Time Uniform Wong-Zakai Approximation Theorems

arXiv

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arXiv 2023年

作者： Del Moral, Pierre Hu, Shulan Jasra, Ajay Ruzayqat, Hamza Wang, Xinyu Institut de Mathematiques de Bordeaux Bordeaux33405 France School of Statistics and Mathematics Zhongnan University of Economics and Law China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Wenlan School of Business Zhongnan University of Economics and Law China

We consider the long time behavior of Wong-Zakai approximations of stochastic differential equations. These piecewise smooth diffusion approximations are of great importance in many areas, such as those with ordinary differential equations associated to random smooth fluctuations;e.g. robust filtering problems. In many examples, the mean error estimate bounds that have been derived in the literature can grow exponentially with respect to the time horizon. We show in a simple example that indeed mean error estimates do explode exponentially in the time parameter, i.e. in that case a Wong-Zakai approximation is only useful for extremely short time intervals. Under spectral conditions, we present some quantitative time-uniform convergence theorems, i.e. time-uniform mean error bounds, yielding what seems to be the first results of this type for Wong-Zakai diffusion *** Codes 60H35(Primary), 65C30(Secondary) Copyright © 2023, The Authors. All rights reserved.

关键词： Brownian movement

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A Trusted Lesion-assessment Network for Interpretable Diagnosis of Coronary Artery Disease in Coronary CT Angiography 39

A Trusted Lesion-assessment Network for Interpretable Diagno...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ma, Xinghua Fang, Xinyan Zou, Mingye Luo, Gongning Wang, Wei Wang, Kuanquan Qiu, Zhaowen Gao, Xin Li, Shuo Faculty of Computing Harbin Institute of Technology Harbin China Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia Faculty of Computing Harbin Institute of Technology Shenzhen China College of Computer and Control Engineering Northeast Forestry University Harbin China Department of Computer and Data Science Case Western Reserve University Cleveland United States Department of Biomedical Engineering Case Western Reserve University Cleveland United States

ISBN: (纸本)157735897X

Coronary Artery Disease (CAD) poses a significant threat to cardiovascular patients worldwide, underscoring the critical importance of automated CAD diagnostic technologies in clinical practice. Previous technologies for lesion assessment in Coronary CT Angiography (CCTA) images have been insufficient in terms of interpretability, resulting in solutions that lack clinical reliability in both network architecture and prediction outcomes, even when diagnoses are accurate. To address the limitation of interpretability, we introduce the Trusted Lesion-Assessment Network (TLA-Net), which provides a clinically reliable solution for multi-view CAD diagnosis: (1) The causality-informed evidence collection constructs a causal graph for the diagnostic process and implements causal interventions, preventing confounders’ interference and enhancing the transparency of the network architecture. (2) The clinically-aligned uncertainty integration hierarchically combines Dirichlet distributions from various views based on clinical priors, offering confidence coefficients for prediction outcomes that align with physicians’ image analysis procedures. Experimental results on a dataset of 2,618 lesions demonstrate that TLA-Net, supported by its interpretable methodological design, exhibits superior performance with outstanding generalization, domain adaptability, and robustness. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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