检索结果-内蒙古大学图书馆

EEG Epileptic Data Classification Using the Schrodinger Operator’s Spectrum

学校读者我要写书评

暂无评论

EEG Epileptic Data Classification Using the Schrodinger Oper...

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Maria Sara Nour Sadoun Muhammad Mahboob Ur Rahman Tareq Al-Naffouri Taous-Meriem Laleg-Kirati Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE) King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia National Institute for Research in Digital Science and Technology Paris-Saclay France

Epilepsy is a common brain disorder characterized by recurrent, unprovoked seizures which affects over 65 million people. Visual inspection of Electroencephalograms (EEG) is common for diagnosis; however, it requires time and expertise. Therefore, an accurate computer-aided epileptic seizure diagnosis system would be valuable. A new research tendency when tackling epileptic seizure detection tends towards minimizing human manual intervention by designing frameworks with autonomous feature engineering. In this optic, this paper proposes a new approach for EEG epileptic data classification. Features derived from the Semi-Classical Signal Analysis (SCSA) method, a quantum-inspired signal processing method well-suited for the characterization of pulse-shaped physiological signals, are proposed. In addition nonlinear dynamical features that proved efficient in characterizing nonlinear dynamics of neural activity have been extracted. Moreover, hyperparameters’ optimization, correlation analysis and feature selection have been performed. The selected features are fed into five different machine learning classifiers. The performance of the proposed approach has been analyzed using Bonn university database. The results show that all classifiers yield a performance accuracy of 93% and *** relevance— The paper contributes to the design of methods and algorithms to build reliable software solutions to assist medical experts and reduce epilepsy disease’s diagnosis time and errors.

关键词：

Controlling the Skyrmion Density and Size for Quantized Convolutional Neural Networks

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Lone, Aijaz H. Ganguly, Arnab Li, Hanrui El-Atab, Nazek Das, Gobind Fariborzi, H. Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Physics Khalifa University Abu Dhabi12788 United Arab Emirates

Skyrmion devices show energy-efficient and high-integration data storage and computing capabilities. Herein, we present the results of experimental and micromagnetic investigations of the creation and stability of magnetic skyrmions in the Ta/IrMn/CoFeB/MgO thin-film system. We investigate the magnetic-field dependence of the skyrmion density and size using polar magneto-optic Kerr effect (MOKE) microscopy supported by a micromagnetic study. The evolution of the topological charge with time under a magnetic field is investigated, and the transformation dynamics are explained. Furthermore, considering the voltage control of these skyrmion devices, we evaluate the dependence of the skyrmion size and density on the Dzyaloshinskii–Moriya interaction and the magnetic anisotropy. We furthermore propose a skyrmion-based synaptic device based on the results of the MOKE and micromagnetic investigations. We demonstrate the spin-orbit torque–controlled discrete topological resistance states with high linearity and uniformity in the device. The discrete nature of the topological resistance (weights) makes it a candidate to realize hardware implementation of weight quantization in a quantized neural network (QNN). The neural network is trained and tested on the CIFAR-10 dataset, where the devices act as synapses to achieve a recognition accuracy of ~87%, which is comparable to the result of ideal software-based methods. © 2023, CC BY.

关键词： Topology

Monolayer C5N: A Promising Building Block for the Anode of K-Ion Batteries

学校读者我要写书评

暂无评论

Physical Review Applied 2022年第3期17卷 034055-034055页

作者： Junjie Jin Geetanjali Deokar Pedro M. F. J. Costa Udo Schwingenschlögl Applied Physics Program Physical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia

Intercalation-type batteries based on the alkali metal K, an earth-abundant element, are attracting increasing attention as alternatives to Li-ion batteries. However, the lack of a high-performance anode limits the success of K-ion batteries. Here, we study the performance of monolayer C5N as a potential building block for the anode. Our theoretical data show that the metallic monolayer can accommodate 2.25 K atoms per formula unit, which corresponds to a specific capacity of 814 mA h g−1, and provides a low diffusion barrier of only 0.11 eV. The volume change of bulk C5N (47%) is found to be smaller than that of graphite (61%; commercial anode material). These predictions will serve the design of future anode materials for K-ion batteries.

关键词： Lithium-alternative batteries 2-dimensional systems Batteries Carbon-based materials Energy materials Nitrides Ab initio molecular dynamics Density functional calculations

Enhancement of Antenna Gain With M&W Slot Model on EBG Reflector Structure Utilizing Reflection Phase Diagram Analysis

学校读者我要写书评

暂无评论

Enhancement of Antenna Gain With M&W Slot Model on EBG Refle...

International electrical engineering Congress (iEECON)

作者： Payu Chakunna Nopphadon Phusam Pisit Mahatthanajatuphat Wilaiporn Lee Thanakarn Suangun School of Engineering University of Phayao Phayao Thailand Regular Program in Mathematics-Science Yothinburana School Bangkok Thailand Department of Electrical and Computer Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This research demonstrates the improvement in antenna gain by utilizing an EBG reflector. The EBG reflector includes a unit cell with grooves shaped like the letters M and W to mitigate surface waves at a frequency of 2.45 GHz. The suggested EBG reflector is constructed on an FR-4 substrate exhibiting a uniform dielectric constant of 4.3, a loss tangent of 0.025, and a thickness of 1.6 mm. Furthermore, the impact of boosting the antenna gain can be analyzed by the reflection phase diagram, which exhibits values ranging from +90° to -90° at the resonant frequency of the reflector. The proposed EBG reflector comprises 5x5 unit cells. The testing results of the reflector's performance, when integrated with a general dipole antenna, indicate that it can effectively respond to the resonant frequency of 2.45 GHz, with a |S 11 | level below -10 dB within the frequency range of 2.2 GHz to 2.6 GHz, thereby adequately covering the IEEE 802.11 b/g/n band. The suggested EBG reflector exhibits a gain of around 6.5 dBi at 2.45 GHz, indicating its efficacy in enhancing gain relative to the standard PEC reflector. Moreover, the radiation pattern is also directional.

关键词： Slot antennas Dipole antennas Resonant frequency Reflector antennas Metamaterials Reflection Gain Standards Periodic structures Antenna radiation patterns

InfRS: Incremental Few-shot Object Detection in Remote Sensing Images

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Li, Wuzhou Zhou, Jiawei Li, Xiang Cao, Yi Jin, Guang Zhang, Xuemin The School of Remote Sensing and Information Engineering Wuhan University Wuhan430072 China The Electronic Information School Wuhan University Wuhan430072 China The Electrical and Computer Engineering King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

Few-shot detection in remote sensing images has witnessed significant advancements recently. Despite these progresses, the capacity for continuous conceptual learning still poses a significant challenge to existing methodologies. In this paper, we explore the intricate task of incremental few-shot object detection (iFSOD) in remote sensing images. We present a pioneering transfer-learning-based technique, termed InfRS, designed to enable the incremental learning of novel classes using a restricted set of examples, while simultaneously preserving the knowledge learned from previously seen classes without the need to revisit old data. Specifically, we pretrain the detector using sufficient data from base datasets and then generate a set of class-wise prototypes that represent the intrinsic characteristics of the data. In the incremental learning session, we design a Hybrid Prototypical Contrastive (HPC) encoding module for learning discriminative representations. Furthermore, we develop a prototypical calibration strategy based on the Wasserstein distance to overcome the catastrophic forgetting problem. Comprehensive evaluations conducted with two aerial imagery datasets show that our InfRS effectively addresses the iFSOD issue in remote sensing imagery. Code will be released. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

Predicting Electric Vehicle Charging Stations Occupancy: A Federated Deep Learning Framework

学校读者我要写书评

暂无评论

Predicting Electric Vehicle Charging Stations Occupancy: A F...

IEEE Conference on Vehicular technology (VTC)

作者： Lydia Douaidi Sidi-Mohammed Senouci Ines El Korbi Fouzi Harrou DRIVE Lab University of Burgundy Nevers France Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Electric vehicles (EVs) have long been recognized as a solution to the shortage of fossil fuels and the environmental problems associated with increasing CO2 emissions. However, charging an electric vehicle can take significant time at certain charging stations. Additionally, the limited deployment of charging stations is a significant barrier to the widespread adoption of electric mobility (e-mobility). In fact, many drivers struggle to locate a convenient charging station before their vehicle’s battery runs out. This study introduces a novel approach to addressing the issue of congestion at public charging stations and reducing the amount of time drivers spend waiting in line by predicting their occupancy. Previous research has relied on traditional Deep Learning (DL) techniques for prediction, which require centralized data collection. Nevertheless, each Charging Station Operator (CSO) holds sensitive data about its charging stations and users that cannot be shared with external parties. To address these privacy concerns, we propose a Federated Deep Learning approach where each CSO trains a DL model locally and then sends the model updates (or parameters) to a server for aggregation. Experiments on a real-world dataset demonstrate that predicting occupancy using the Federated Deep Learning approach achieves promising results (86,21% of accuracy and 91,49% of f1-score ), guarantees privacy, minimizes data transfer costs over the network, and allows individual CSOs to benefit from the rich datasets of others without sharing their sensitive data.

关键词：

Unbiased Approximations for Stationary Distributions of McKean-Vlasov SDEs

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Awadelkarim, Elsiddig Chada, Neil K. Jasra, Ajay 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 Department of Mathematics City University of Hong Kong Hong Kong School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China

We consider the development of unbiased estimators, to approximate the stationary distribution of Mckean-Vlasov stochastic differential equations (MVSDEs). These are an important class of processes, which frequently appear in applications such as mathematical finance, biology and opinion dynamics. Typically the stationary distribution is unknown and indeed one cannot simulate such processes exactly. As a result one commonly requires a time-discretization scheme which results in a discretization bias and a bias from not being able to simulate the associated stationary distribution. To overcome this bias, we present a new unbiased estimator taking motivation from the literature on unbiased Monte Carlo. We prove the unbiasedness of our estimator, under assumptions. In order to prove this we require developing ergodicity results of various discrete time processes, through an appropriate discretization scheme, towards the invariant measure. Numerous numerical experiments are provided, on a range of MVSDEs, to demonstrate the effectiveness of our unbiased estimator. Such examples include the Currie-Weiss model, a 3D neuroscience model and a parameter estimation problem. © 2024, CC BY-NC-SA.

关键词： Stochastic systems

From axioms over graphs to vectors, and back again: evaluating the properties of graph-based ontology embeddings

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Zhapa-Camacho, Fernando Hoehndorf, Robert Computational Bioscience Research Center Computer Electrical & Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology 4700 KAUST Thuwal23955 Saudi Arabia

Several approaches have been developed that generate embeddings for Description Logic ontologies and use these embeddings in machine learning. One approach of generating ontologies embeddings is by first embedding the ontologies into a graph structure, i.e., introducing a set of nodes and edges for named entities and logical axioms, and then applying a graph embedding to embed the graph in Rn. Methods that embed ontologies in graphs (graph projections) have different formal properties related to the type of axioms they can utilize, whether the projections are invertible or not, and whether they can be applied to asserted axioms or their deductive closure. We analyze, qualitatively and quantitatively, several graph projection methods that have been used to embed ontologies, and we demonstrate the effect of the properties of graph projections on the performance of predicting axioms from ontology embeddings. We find that there are substantial differences between different projection methods, and both the projection of axioms into nodes and edges as well ontological choices in representing knowledge will impact the success of using ontology embeddings to predict axioms. © 2023, CC BY.

关键词： Ontology

Precision-Adaptive Task Offloading and Resource Allocation for Efficient Positioning and Sensing in Near-Field IoV Systems

学校读者我要写书评

暂无评论

IEEE Internet of Things Journal 2025年第13期12卷 22635-22646页

作者： Dai, Cheng Bao, Song Chen, Songlin Garg, Sahil Kaddoum, Georges Sichuan University College of Computer Science Sichuan Chengdu China École de technologie supérieure Electrical Engineering Department MontréalQCH3C 1K3 Canada Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India École de technologie supérieure MontréalQCH3C1K3 Canada King Saud University College of Computer and Information Sciences Department of Information Systems Riyadh11543 Saudi Arabia

With the rapid advancement of sixth-generation (6G) network communication technology, improvements in data transmission rates, latency, and reliability have driven substantial growth in Internet of Vehicles (IoV) applications. Among these, the integration of 6G-enabled extremely large-scale antenna arrays (ELAAs) has extended the range of near-field (NF) communication, enabling their application in IoV to facilitate efficient and accurate environmental sensing. Through NF communication, vehicles can achieve high-accuracy localization and perception by analyzing the signal phase, channel state information, and beamforming calculations. However, positioning and sensing tasks place substantial computational and energy demands on edge devices, often exceeding traditional capacity limits. To address this challenge, task offloading has emerged as a solution, with mobile edge computing (MEC) offering a lower-latency alternative to centralized cloud computing by processing tasks at the network edge. Despite these advantages, MEC’s limited resources present challenges as the number of connected vehicles increases. Existing approaches to resource allocation often overlook the varied accuracy requirements of IoV tasks, where high-accuracy tasks like indoor navigation require stringent performance standards, while lower-accuracy tasks may tolerate reduced precision to save resources. Motivated by this, we propose an accuracy-based classification scheme for IoV positioning and sensing tasks, which dynamically adjusts accuracy requirements to reduce delay and energy consumption. Our approach maps total energy, accuracy loss, and delay to an overall quality of service (QoS) metric, and employs an optimization algorithm that leverages gradient descent and greedy strategies to balance resource allocation and accuracy selection. Extensive simulations demonstrate the effectiveness of the proposed scheme in reducing delay and energy consumption while maintaining high accuracy, signific

关键词： Resource allocation