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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Manar AL-Mahdawi Aythem Khairi Kareem Ahmed Adil Nafea Ali Muwafaq Shaban Salah A. S. Aliesawi Mohammed M AL-Ani dept. Solid State Physics College of Science AL-Nahrain University Baghdad Iraq dept. of Heet Education General Directorate of Education in Anbar Ministry of Education Heet Anbar Iraq dept. Artificail inteligence College of Computer Science and IT University of Anbar Ramadi Iraq dept. Information System College of Computer Science and IT University of Anbar Ramadi Iraq dept. Computer Science College of Computer Science and Information Technology University of Anbar Ramadi Iraq dept. Center for Artificial Intelligence Technology (CAIT) Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Selangor Malaysia

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

The prediction of proton energy shows a key part in various scientific and technological studies including particle physics, medical imaging, and radiation therapy. In the last years the regression techniques study a lot trying to discover the difficult relationships among proton properties energy, needing to be studying of additional advanced techniques. This study proposed Artificial Neural Networks (ANN) as a tool for predicting types of proton energy (Max, Total and A vg). This paper proposed a comprehensive methodology for developing an ANN model for proton energy predection, covering data preparation, model architecture design, evaluation, and prediction. This proposed can ability to learn difficult models and non-linear relationships from large datasets, making them well-suited for this task. The ANN proposed achieved the highest R2 on the Total of proton energy predation of 0.96 % while the best mean_squared_error on 0.03668 for testing dataset. The results displayed the efficiency and accuracy of the proposed ANN model in predicted proton energy. The findings highlight the ability of ANNs as a tool for proton energy prediction.

关键词： Protons Performance evaluation High energy physics Estimation Artificial neural networks Predictive models Data models

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Camera Proxy based Contrastive Learning with Hard Sampling for Unsupervised Person Re-identification

Camera Proxy based Contrastive Learning with Hard Sampling f...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yimin Liu Meibin Qi Qiang Wu Yanfang Yang Xiaohong Li Jian Zhang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia School of Physics Hefei University of Technology Hefei China

Because of the advantages of dealing with large-scale unlabelled data, unsupervised learning has recently attracted more attention for person re-identification. Particularly, the combination of the unsupervised learning paradigm with contrastive learning shows promising efficiency in network optimization. This work adopts the successful camera-aware contrastive learning approach and further explores its capability on the camera proxy level to improve the data pair consistency. Thus, it is more robust to the camera change, which still challenges the unsupervised person re-identification. This work proposed a Camera Proxy-based Contrastive Learning framework, which explicitly considers inter-camera scenario and intra-camera scenario. Moreover, this work is motivated by the strategy of selecting a hard negative sample in triplet loss learning and further extends it to contrastive learning for both negative and positive pair creation on the camera proxy level. Extensive experiments demonstrate the superiority of the proposed framework over state-of-the-art approaches on purely unsupervised re-identification.

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Magnetotransport Signatures of the Radial Rashba Spin-Orbit Coupling in Proximitized Graphene

arXiv

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arXiv 2024年

作者： Kang, Wun-Hao Barth, Michael Garcia-Ruiz, Aitor Mreńca-Kolasińska, Alina Liu, Ming-Hao Kochan, Denis Department of Physics National Cheng Kung University Tainan70101 Taiwan National Cheng Kung University Tainan70101 Taiwan Institute for Theoretical Physics University of Regensburg Regensburg93040 Germany National Graphene Institute University of Manchester ManchesterM13 9PL United Kingdom AGH University Faculty of Physics and Applied Computer Science Kraków30-059 Poland Institute of Physics Slovak Academy of Sciences Bratislava84511 Slovakia

Graphene-based van der Waals heterostructures take advantage of tailoring spin-orbit coupling (SOC) in the graphene layer by proximity effect. At long-wavelength—saddled by the electronic states near the Dirac points—the proximitized features can be effectively modeled by the Hamiltonian involving novel SOC terms and allow for an admixture of the tangential and radial spin textures—by the so-called Rashba angle θR. Taking such effective models we perform realistic large-scale magneto-transport calculations—transverse magnetic focusing and Dyakonov-Perel spin relaxation—and show that there are unique qualitative and quantitative features allowing for an unbiased experimental disentanglement of the conventional Rashba SOC from its novel radial counterpart, called here the radial Rashba SOC. Along with that, we propose a scheme for a direct estimation of the Rashba angle by exploring the magneto-response symmetries when swapping an in-plane magnetic field. To complete the story, we analyze the magneto-transport signatures in the presence of an emergent Dresselhaus SOC and also provide some generic ramifications about possible scenarios of the radial superconducting diode effect. © 2024, CC0.

关键词： Magnetos

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Symbrain: A large-scale dataset of MRI images for neonatal brain symmetry analysis

arXiv

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arXiv 2024年

作者： Gucciardi, Arnaud El Ghazouali, Safouane Venturini, Francesca Groznik, Vida Michelucci, Umberto University of Ljubljana Faculty of Computer and Information Science Ljubljana Slovenia TOELT llc Machine Learning Research and Development LAB Dübendorf Switzerland Institute of Applied Mathematics and Physics Zurich University of Applied Sciences Winterthur Switzerland Faculty of Mathematics Natural Sciences and Information Technologies University of Primorska Koper Slovenia

This paper presents an annotated dataset of brain MRI images designed to advance the field of brain symmetry study. Magnetic resonance imaging (MRI) has gained interest in analyzing brain symmetry in neonatal infants, and challenges remain due to the vast size differences between fetal and adult brains. Classification methods for brain structural MRI use scales and visual cues to assess hemisphere symmetry, which can help diagnose neonatal patients by comparing hemispheres and anatomical regions of interest in the brain. Using the Developing Human Connectome Project dataset, this work presents a dataset comprising cerebral images extracted as slices across selected portions of interest for clinical evaluation . All the extracted images are annotated with the brain’s midline. All the extracted images are annotated with the brain’s midline. From the assumption that a decrease in symmetry is directly related to possible clinical pathologies, the dataset can contribute to a more precise diagnosis because it can be used to train deep learning model application in neonatal cerebral MRI anomaly detection from postnatal infant scans thanks to computer vision. Such models learn to identify and classify anomalies by identifying potential asymmetrical patterns in medical MRI images. Furthermore, this dataset can contribute to the research and development of methods using the relative symmetry of the two brain hemispheres for crucial diagnosis and treatment planning. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Aicaregaitrehabilitationbyfusion: Multi-Model and Multi-Modalities Sensor Data Fusion for Ai-Iot Enabled Realtime Electrical Stimulation Device for Pre-Fog and Post-Fog to Person with Parkinson's

SSRN

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SSRN 2024年

作者： Ghayvat, Hemant Geddam, Rebakah Awais, Muhammad Tiwari, Prayag Milrad, Marcelo Kumar, Neeraj Computer Science and Media Technology Department Faculty of Technology Linnaeus University P G Vejdes väg 351 95 Växjö35234 Sweden Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat Ahmedabad382481 India Department of Imaging Physics The University of Texas MD Anderson Cancer Center Houston77030 United States School of Information Technology Halmstad University Halmstad11042 Sweden Department of Computer Science and Engineering Thapar University Punjab Patiala India School of Computer Science University of Petroleum and Energy Studies Dehradun Uttarakhand Patiala147001 India

People with Parkinson's disease (PD) frequently experience the Freezing of Gait (FoG), a locomotive issue that reduces their quality of life and increases their risk of falling. These people are given timely bio-feedback cues to assist them in controlling their gait utilizing wearable FoG sensing technology. However, the currently used FoG detection methods are cumbersome and intrusive. As a result, the current study intends to incorporate the FoG detection method into a single sensor node using characteristics with low computational load and specialized hardware, which restricts the area and memory use. For the classification, a real-time learning neural network is used, allowing the method to adjust to the needs of a patient as needed. Machine learning techniques based on Support Vector Machines (SVM) and a tri-axial accelerometer are used in the research to design a new gadget for FoG detection that will be worn by the patient around their waist. The proposed device's categorization performance is highly competitive, comfortable to wear, and real-time adaptable. The findings indicate that an electrical stimulation-based cueing strategy has a beneficial, universal effect on gait and FoG in PD. Federated Learning (FL), a machine learning strategy, provides encouraging options for customization while protecting delicate health data. This study examines how federated learning can be used to wearable electrical stimulation devices for more effective, individualized Parkinson's treatment. © 2024, The Authors. All rights reserved.

关键词： Deep learning

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The Effect of Data Augmentation on the Accuracy of Fish Species Classification Using Deep Learning

The Effect of Data Augmentation on the Accuracy of Fish Spec...

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International Conference on Informatics and Computing (ICIC)

作者： Ellya Helmud Catur Edi Widodo Oky Dwi Nurhayati Doctor Information System Postgraduate Diponegoro University Semarang Indonesia Faculty of Information Technology Information Systems Study Program Atma Luhur Institute of Science and Business Pangkalpinang Indonesia Physics Department Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a convolutional neural network. The dataset used consists of $\mathbf{3 0 1 0}$ fish images, divided into training, validation, and testing sets. The convolutional neural network model was trained both with and without data augmentation. Evaluation results show that the model trained with data augmentation achieved an accuracy of $95 \%$ with a loss value of 0.0983, slightly better than the model without augmentation which achieved an accuracy of $94.56 \%$ with a loss value of $\mathbf{0. 1 7 9 4}$. This indicates that data augmentation techniques are effective in improving model performance, likely because augmentation helps the model generalize better to variations in fish image data. The results of this research demonstrate the significant potential of convolutional neural network for fish image classification tasks. The developed model can serve as a foundation for the development of computer vision-based applications such as automatic fish species identification in fisheries or educational applications. Further research can be conducted by exploring different convolutional neural network architectures, more advanced data augmentation techniques, and larger datasets to improve model performance.

关键词： Deep learning Training Accuracy Computational modeling computer architecture Fish Data augmentation Data models Convolutional neural networks Image classification

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Probing collectivity in heavy-ion collisions with fluctuations of the pT spectrum

arXiv

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arXiv 2024年

作者： Parida, Tribhuban Samanta, Rupam Ollitrault, Jean-Yves Department of Physical Sciences Indian Institute of Science Education and Research Berhampur Govt ITI Transit Campus Odisha Berhampur760010 India AGH University of Krakow Faculty of Physics and Applied Computer Science aleja Mickiewicza 30 Cracow30-059 Poland Université Paris Saclay CNRS CEA Institut de physique théorique Gif-sur-Yvette91191 France

Event-by-event fluctuations in the initial stages of ultrarelativistic nucleus-nucleus collisions depend little on rapidity. The hydrodynamic expansion which occurs in later stages then gives rise to correlations among outgoing particles which depend weakly on their relative rapidity. Azimuthal correlations, through which anisotropic flow (vn(pT)) is defined, have been the most studied. Here we study a new observable introduced in 2020 by Schenke, Shen and Teaney and dubbed v0(pT), which quantifies the relative change in the pT spectrum induced by a fluctuation. We describe how it can be measured. Using hydrodynamic simulations, we make quantitative predictions for v0(pT) of charged and identified hadrons. We then discuss how v0(pT) relates to two phenomena which have been measured: The increase of the mean transverse momentum in ultracentral collisions, and the event-by-event fluctuations of the transverse momentum per particle [pT]. We show that v0(pT) determines the dependence of these quantities on the pT cuts implemented in the analysis. We quantitatively explain the rise of σpT observed by ATLAS as the upper pT-cut is increased from 2 to 5 GeV/c. Copyright © 2024, The Authors. All rights reserved.

关键词： Heavy ions

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Digital Twins Approaches and Methods Review

Digital Twins Approaches and Methods Review

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Telecommunications Conference (ITC-Egypt), International

作者： Mohammed E. Helal Hamed Shawky Zied Ahmed Khairy Mahmoud Manal Helal Ali E. Takieldeen Samia M. Abd-Alhalem Faculty of Engineering AASTMT Cairo Egypt Air Defense College Alexandria University Alexandria Egypt School of Physics Engineering & Computer Science Hertfordshire University HATFIELD UK Faculty of Artificial Intelligence Delta University for Science and Technology Gamasa Egypt

This paper investigates the recent advances in Digital Twin technologies. The aim is to compare the approaches, available open source and proprietary technologies and methods, their features, and their integration capabilities. The motivation is to enable better design decisions based on the available literature and case studies. Various tools for 3D reconstruction and visualisation, IoT and sensor integration, Physical simulations and other complete platforms provide complete solutions. A conclusion of current challenges and future work identified that the lack of standardisation and interoperability makes the life-time of a digital twin short, with a high cost and time to build and rebuild if required.

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Non-homogeneous Dirichlet problems with concave-convex reaction

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Atti della Accademia Nazionale dei Lincei, Classe di Scienze Fisiche, Matematiche e Naturali, Rendiconti Lincei Matematica e Applicazioni 2021年第4期32卷 799-818页

作者： Bonanno, Gabriele Livrea, Roberto Rădulescu, Vicenţiu D. Messina98166 Italy Department of Mathematics and Computer Science University of Palermo Via Archirafi 34 Palermo90123 Italy Faculty of Applied Mathematics AGH University of Science and Technology Kraków30-059 Poland Department of Mathematics University of Craiova Street A.I. Cuza No. 13 Craiova200585 Romania ''Simion Stoilow'' Institute of Mathematics The Romanian Academy P.O. Box 1-764 Bucharest014700 Romania

The variational methods are adopted for establishing the existence of at least two nontrivial solutions for a Dirichlet problem driven by a non-homogeneous differential operator of p-Laplacian type. A large class of nonlinear terms is considered, covering the concave-convex case. In particular, two positive solutions to the problem are obtained under a (p - 1)-superlinear growth at infinity, provided that a behaviour less than (p - 1)-linear of the nonlinear term in a suitable set is requested. © 2021 European Mathematical Society Publishing House. All rights reserved.

关键词： Mathematical operators

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A novel watermark method for image protection based on periodic haar piecewise-linear transform 1

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10th International Conference on Multimedia Communications, Services and Security, MCSS 2020

作者： Dziech, Andrzej Bogacki, Piotr Derkacz, Jan The Faculty of Computer Science Electronics and Telecommunications The Department of Telecommunications AGH University of Science and Technology Krakow Poland

ISBN: (数字)9783030590000

ISBN: (纸本)9783030589998

In this paper, a novel watermark method based on Periodic Haar Piecewise-Linear (PHL) transform is proposed. This solution provides high embedding capacity with negligible degradation of image quality. The PHL transform is based on integration and differentiation of periodic Haar functions. Its major advantage is the fact that it is notably fast and easy to implement and beyond that it provides good reconstruction quality. The procedure for constructing two-dimensional PHL functions and transform is introduced. Furthermore, the analysis of embedding capacity and imperceptibility of the proposed algorithm is performed. PSNR (Peak Signal to Noise Ratio) is taken as a measure of perceptual quality. The method is compared with DCT approach in certain aspects of watermarking algorithms. © Springer Nature Switzerland AG 2020.

关键词： Signal to noise ratio

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