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Science and Technology Innovation (IAECST), International Academic Exchange Conference on

作者： Changhao Li Jun Liu Hongliang Wang Pengfei Xiu Shenyang University of Chemical Technology Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

Aiming to address the inefficiencies in existing internal voxelization algorithms, a solution has been proposed to use a two-dimensional depth buffer table to record the interval range of surface voxels, reducing the number of internal voxels to be checked. The complexity of this algorithm is independent of the model’s complexity. the voxel model is stored using a non-uniformly subdivided octree structure, where if a particular layer’s node and all of its child nodes are internal voxels, there is no need for further subdivision, reducing storage space. Experimental results indicate that the improved algorithm can run correctly while achieving better time and space efficiency.

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Probability Distribution of the Write-Error Rate of Voltage-Controlled Magnetoresistive Random-Access Memories

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Physical Review Applied 2021年第6期16卷 064068-064068页

作者： Hiroko Arai Takahiro Hirofuchi Hiroshi Imamura National Institute of Advanced Industrial Science and Technology (AIST) Research Center for Emerging Computing Technologies Tsukuba Ibaraki 305-8568 Japan National Institute of Advanced Industrial Science and Technology (AIST) Digital Architecture Research Center Aomi Koto-ku Tokyo 135-0064 Japan

In this study, we theoretically explore the probability distribution of the write-error rate (WER) of a voltage-controlled magnetoresistive random-access memory (VC MRAM). A probability density function (PDF) of the WER is analytically obtained using the change-of-variable technique with the assumption that the anisotropy constant of the VC MRAM follows the normal distribution. Furthermore, we assume that the WER of a single memory cell forms a parabola as a function of the anisotropy constant, resulting from numerical simulations by solving the Landau-Lifshitz-Gilbert equation based on the macrospin model. Theoretical analysis facilitates in identifying types of the PDF: a monotonic decreasing function with a peak at the smallest WER, and a function with local maximum.

关键词： Magnetic anisotropy Magnetization dynamics Spintronics Magnetic tunnel junctions Landau-Lifschitz-Gilbert equation Statistical methods

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Data Mining and Text Mining Approach: Query based Information Retrieval using Genetic Algorithm

Data Mining and Text Mining Approach: Query based Informatio...

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Image Processing and Capsule Networks (ICIPCN), International Conference on

作者： S. Ananthi R. Venkateswaran G. Vijaya J. Arun R. Sathya Advanced Research Centre for Future Networks and Digital Twin Sri Eshwar College of Engineering Coimbatore India Faculty-Cyber and Information Security College of Computing & Information SciencesUniversity of Technology and Applied Sciences Salalah Oman Dept. of CSE Sri Krishna College of Engineering and Tech Coimbatore India Dept. of ECE Sri Eshwar College of Engineering Coimbatore India Department of IT Kongunadu College of Engg and Tech Namakkal India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Information Retrieval system facilitates the retrieval efficiency of the user’s requirement from a collection of information resources. In this research work, Genetic Algorithm (GA) with various fitness functions are implemented for retrieving the relevant document for the given textual query. To increase the efficiency of the retrieval system extended keywords are added to the retrieval system using query reformulation method. Furthermore, the selection procedure namely fitness proportionate selection is used to identify the best fit individual among the chosen population. This selection algorithm assumes that, for the chosen population, the fittest individuals have a greater chance of endurance than weaker ones. The performance of the proposed system is evaluated with three different datasets solely consists of 20,322 documents are analyzed using various matching functions and their results are examined.

关键词： Text mining Accuracy Information resources Text recognition Image processing Data preprocessing Information retrieval

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A Web 3.0 Integrated Blockchain Enabled Access System Augmented by Meta-Heuristic Cognitive Learning Framework for Mitigating Threats in IoT Enabled Consumer Electronic Devices

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IEEE Transactions on Consumer Electronics 2025年

作者： Aruna, Etikala Sahayadhas, Arun Kalpana, Ponugoti Khan, Surbhi B. Quasim, Tabrez Almusharrf, Ahlam VELS Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Pallavaram Chennai India AVN Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad501510 India University of Salford School of Science Engineering and Environment SalfordM5 4WT United Kingdom Chandigarh University University Centre for Research and Development Mohali India Chitkara University Centre for Research Impact and Outcome India University of Bisha College of Computing and Information Technology Department of Computer Science and Artificial Intelligence Bisha Saudi Arabia King Khalid Univeersity Saudi Arabia

Consumer Electronic Devices have become an open network model because of the infusion of the Internet of Things (IoT) and other communication technologies such as 5G/6G. Though these devices have provided the high-end sophistication even to common person, but it has proved its darker side by triggering more security breaches and privacy problems. Hence, securing and authenticating these Internet enabled consumer devices has become a probable issue to be solved for safer and secured communication. Therefore, this paper presents a novel fusion of Web 3.0-based Blockchain (WBC) and Deep learning (DL) technique for securing consumer electronic devices in an IoT ecosystem. The proposed framework k(MTD-BCAM) is devised into two components: Multiple-Threat Detection(MTD) and Access Management Mechanism(AMM). In the first component, a DL model is applied for threat detection, whereas WBC is meant for an efficient authentication process. Furthermore, a novel residual fast-gated recurrent neural network is proposed. To reduce the complexity, the komodo Mlipir optimization (KMO) approach is used to tune the hyper-parameters of the network. The comprehensive experimental outcome study of the proposed approach employs NSL-KDD datasets in which the distinct metrics of both DL and Blockchain (BC) are measured and analyzed. Results demonstrated the superior accuracy of the model by achieving 99.78% with less computational time and higher transaction speed. Additionally, the statistical validation and security strength of the model are also analyzed and examined with the varied state-of-art models. © 2025 IEEE. All rights reserved.

关键词： Recurrent neural networks

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Graph Neural Network Based Surrogate Model of Physics Simulations for Geometry Design

arXiv

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arXiv 2023年

作者： Wong, Jian Cheng Ooi, Chin Chun Chattoraj, Joyjit Lestandi, Lucas Dong, Guoying Kizhakkinan, Umesh Rosen, David William Jhon, Mark Hyunpong Dao, My Ha Department of Fluid Dynamics Institute of High Performance Computing A*STAR Singapore Center for Frontier AI Research A*STAR Singapore Department of Computing & Intelligence Institute of High Performance Computing A*STAR Singapore Institut de Recherche en Génie Civil et Mécanique Nantes Université École Centrale Nantes CNRS Nantes France Department of Mechanical Engineering University of Colorado Denver Denver United States Digital Manufacturing and Design Center Singapore University of Technology and Design Singapore Department of Engineering Mechanics Institute of High Performance Computing A*STAR Singapore

Computational Intelligence (CI) techniques have shown great potential as a surrogate model of expensive physics simulation, with demonstrated ability to make fast predictions, albeit at the expense of accuracy in some cases. For many scientific and engineering problems involving geometrical design, it is desirable for the surrogate models to precisely describe the change in geometry and predict the consequences. In that context, we develop graph neural networks (GNNs) as fast surrogate models for physics simulation, which allow us to directly train the models on 2/3D geometry designs that are represented by an unstructured mesh or point cloud, without the need for any explicit or hand-crafted parameterization. We utilize an encoder-processor-decoder-type architecture which can flexibly make prediction at both node level and graph level. The performance of our proposed GNN-based surrogate model is demonstrated on 2 example applications: feature designs in the domain of additive engineering and airfoil design in the domain of aerodynamics. The models show good accuracy in their predictions on a separate set of test geometries after training, with almost instant prediction speeds, as compared to O(hour) for the high-fidelity simulations required otherwise. © 2023, CC BY.

关键词： Forecasting

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Taming Stable Diffusion for MRI Cross-Modality Translation

Taming Stable Diffusion for MRI Cross-Modality Translation

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yingtai Li Shuo Yang Xiaoyan Wu Shan He S. Kevin Zhou School of Biomedical Engineering Division of Life Sciences and Medicine USTC Hefei P.R. China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advanced Research USTC P.R. China iFLYTEK Research iFLYTEK Co. Ltd Hefei P.R. China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing P.R. China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

In this study, we explore using Stable Diffusion (SD) for unsupervised medical image-to-image translation. SD has shown remarkable performances in generating high-quality images and can be easily applied to generate custom contents by injecting standard plug-ins like LoRA, offering a promising solution to tackle the complexity caused by variations in imaging modalities, acquisition parameters, and body parts in medical imaging. However, We empirically find that existing pipelines designed for natural images fail to translate directly to medical images due to weak structural control and inappropriate color preservation. To address these issues, we propose a novel two-branch image translation pipeline. This pipeline decouples the generation of target image along the time axis and employs ControlNet to ensure precise structural preservation. Additionally, we customize SD to generate images of extreme brightness, a common feature in medical imaging. Our results on the BraTS dataset demonstrate that SD with task-specific plug-ins can generate high-quality medical images comparable to those generated by task-specific models. Since the development of these standard plug-ins can be easily done by clinicians without much knowledge of the underlying algorithm, such a mode holds the potential to significantly extend the use of medical image computing algorithms in the clinical environment.

关键词： Translation Image color analysis Computational modeling Biological system modeling Magnetic resonance imaging Pipelines Brightness Complexity theory Standards Biomedical imaging

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Parking Problem by Oblivious Mobile Robots in Infinite Grids

arXiv

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arXiv 2023年

作者： Chakraborty, Abhinav Mukhopadhyaya, Krishnendu Department of Computer Science and Engineering Institute of Technical Education and Research Siksha O Anusandhan University Odisha Bhubaneswar India Advanced Computing and Microelectronics Unit Indian Statistical Institute 203 B. T. Road West Bengal Kolkata700108 India

In this paper, the parking problem of a swarm of mobile robots has been studied. The robots are deployed at the nodes of an infinite grid, which has a subset of prefixed nodes marked as parking nodes. Each parking node pi has a capacity of ki which is given as input and equals the maximum number of robots a parking node can accommodate. As a solution to the parking problem, robots need to partition themselves into groups so that each parking node contains a number of robots that are equal to the capacity of the node in the final configuration. It is assumed that the number of robots in the initial configuration represents the sum of the capacities of the parking nodes. The robots are assumed to be autonomous, anonymous, homogeneous, identical and oblivious. They operate under an asynchronous scheduler. They neither have any agreement on the coordinate axes nor do they agree on a common chirality. All the initial configurations for which the problem is unsolvable have been identified. A deterministic distributed algorithm has been proposed for the remaining configurations, ensuring the solvability of the problem. Copyright © 2023, The Authors. All rights reserved.

关键词： Mobile robots

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Multi-Layer Perceptron Regression Model for Material Characterization in the Microwave Frequency Range with Cross-Dataset Validation

Multi-Layer Perceptron Regression Model for Material Charact...

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International Youth Conference on Radio Electronics, Electrical and Power engineering (REEPE)

作者： Hanima Kannan C H Deepa Gupta Parul Mathur Dhanesh G Kurup Tilesh Sanjay Chaudhari Priyanka Mathur Department of Electronics and Communication Engineering RF and Wireless Systems Laboratory Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Chemical Engineering Creative and Advanced Research Based on Nanomaterials Laboratory Indian Institute of Technology Hyderabad School of Engineering and Applied Science Bennett University Greater Noida U.P. India

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

This paper presents a Multi-Layer Perceptron (MLP) based regression model for characterizing materials at micrwave frequency range. A mathematical model and experimental setup of the Open-Ended Coaxial Probe (OECP) sensor interacting with the material under test (MUT) generate a cross-dataset to validate the MLP regression model, utilizing the frequency-dependent complex dielectric constant for material characterization. This paper utilizes the frequency-dependent response of the OECP sensor (scattering parameters) as input features for the MLP model, while the complex dielectric constant of the MUT serves as the output feature. The optimized MLP model has an $R^{2}$ of 99.991% and MSE of $6.65 \times 10^{-5}$ , with predictions closely aligning with literature values for standard loads such as distilled water, acetone, and ethanol, obtained using a commercially available dielectric assessment software kit. This cost-effective model enables precise dielectric property estimation, with future work extending to unknown materials, mixtures, and electrolyte solutions.

关键词： Power engineering Dielectric constant Predictive models Mathematical models Software Scattering parameters Probes Permittivity Standards Load modeling

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Fundus Image Quality Assessment and Enhancement: a Systematic Review

arXiv

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arXiv 2025年

作者： Li, Heng Li, Haojin Ou, Mingyang Yu, Xiangyang Zhang, Xiaoqing Niu, Ke Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Autonomous Systems SUSTech Shenzhen China Department of Computer Science and Engineering SUSTech Shenzhen China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Computer School Beijing Information Science and Technology University Beijing China Singapore

As an affordable and convenient eye scan, fundus photography holds the potential for preventing vision impairment, especially in resource-limited regions. However, fundus image degradation is common under intricate imaging environments, impacting following diagnosis and treatment. Consequently, image quality assessment (IQA) and enhancement (IQE) are essential for ensuring the clinical value and reliability of fundus images. While existing reviews offer some overview of this field, a comprehensive analysis of the interplay between IQA and IQE, along with their clinical deployment challenges, is lacking. This paper addresses this gap by providing a thorough review of fundus IQA and IQE algorithms, research advancements, and practical applications. We outline the fundamentals of the fundus photography imaging system and the associated interferences, and then systematically summarize the paradigms in fundus IQA and IQE. Furthermore, we discuss the practical challenges and solutions in deploying IQA and IQE, as well as offer insights into potential future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Photography

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Sorting the digital Stream: Big Data-driven Insights into Email Classification for Spam and Ham Detection

Sorting the Digital Stream: Big Data-driven Insights into Em...

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IEEE International Conference on Big Data

作者： Syed Attique Shah Emil Anthony Arputham Awais Ahmed Mohamed Ben Farah Attal Shah Abdul Aziz School of Computing and Digital Technology Birmingham City University STEAMhouse Birmingham United Kingdom School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan China Department of Computer Engineering Faculty of ICT BUITEMS Quetta Pakistan Aragon Institute of Engineering Research Universidad de Zaragoza Zaragoza Spain

In contemporary email communication, the ever- expanding volume of digital correspondence has ushered in an era where big data plays a pivotal role in addressing the challenge of distinguishing between legitimate (ham) and unsolicited (spam) emails. The primary objective of this paper is the meticulous identification and establishment of criteria for the discrimination between ham and spam emails. To achieve this, the study harnesses data from three distinct datasets, aiming to identify common attributes shared across all emails, irrespective of their classification, while concurrently devising methodologies for precise spam detection. Central to this endeavor is the evaluation of the effectiveness of feature selection techniques, specifically Chi-Square and Pearson Correlation, in elevating the accuracy of email classification. The investigation extends to assessing how the combination of these feature selection techniques with the broader machine learning framework can be optimized. This optimization entails the application of diverse preprocessing techniques to the datasets, all designed to amplify the precision of email classification. Furthermore, this research scrutinizes the performance evaluation metrics employed in the assessment of email classifiers. By conducting comprehensive experiments, the study identifies optimal classifiers based on rigorous evaluation metrics. This contributes valuable insights to the toolkit of techniques for proficient email classification within the realm of big data analysis.

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