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arXiv 2024年

作者： Jewani, Yordanos Petry, Michael Sanchez-Arias, Rei Amezcua-Correa, Rodrigo Habib, Md Selim The Department of Data Science and Business Analytics Florida Polytechnic University 4700 Research Way LakelandFL33805 United States The Faculty of Electrical Engineering and Information Technology University of Applied Sciences KarlsruheBW-76133 Germany The CREOL University of Central Florida OrlandoFL32816 United States The Department of Electrical Engineering and Computer Science Florida Institute of Technology MelbourneFL32901 United States

Hollow-core anti-resonant fibers (HC-ARFs) have proven to be an indispensable platform for various emerging applications due to their unique and extraordinary optical properties. However, accurately estimating the propagation loss of nested HC-ARFs remains a challenging task due to their complex structure and the lack of precise analytical and theoretical models. To address this challenge, we propose a supervised machine-learning framework that presents an effective solution to accurately predict the propagation loss of a 5-tube nested HC-ARF. Multiple supervised learning models, including random forest, logistic regression, quadratic discriminant analysis, tree-based methods, extreme gradient boosting, and K-nearest neighbors are implemented and compared using a simulated dataset. Among these methods, the random forest algorithm is identified as the most effective, delivering accurate predictions. Notably, this study considers the impact of random structural perturbations on fiber geometry, encompassing random variations in tube wall thicknesses and tube gap separations. In particular, these perturbations involve randomly varying outer and nested tube wall thicknesses, tube angle offsets, and randomly distributed non-circular, anisotropic shapes within the cladding structure. It is worth noting that these specific perturbations have not been previously investigated. Each tube exhibits its unique set of random values, leading to longer simulation times for combinations of these values compared to regular random variables in HC-ARFs with similar tube characteristics. The comprehensive consideration of these factors allows for precise predictions, significantly contributing to the advancement of HC-ARFs for many emerging applications. © 2024, CC BY.

关键词： Fibers

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An Efficient Ensemble Model for Forecasting Time Series Analysis of Crypto Currency data Using Machine Learning

An Efficient Ensemble Model for Forecasting Time Series Anal...

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2021 IEEE International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2021

作者： Saravanakumar, C. Lakshmi, S. Kanaga Prakash, M. Vijayakumar, M. Arun, C. St. Joseph's Institute of Technology Department of Information Technology Chennai India Sri Muthukumaran Institute of Technology Department of Management Studies Chennai India Data Science and Analytics Center Karpagam College of Engineering Coimbatore India St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India R.M.K College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665435215

Time series data are analyzed for predicting the future based on the consistent time period using statistical methods. These analyses are based on the data which are extracted from different sources. Existing method for performing the forecasting process does not fit into the crypto currency data. GARCH and ARIMA models with intelligent mechanisms provide the solution but it is not at the proper level. RNN model takes the data from preexisting intervals and decides the next time series data which is represented as a directed graph. Two types of behavior are considered namely spatial and temporal. The linear model uses the homogeneous data and heterogeneous data analysis is based on the nonlinear model. The nodes and layers are connected based on the input and corresponding activation unit. The memory model is attached to the RNN model for making persistent storage for keeping the processed data for a longer time. The proposed model uses the ensemble method for integrating various RNN models for achieving a high level of accuracy. © 2021 IEEE.

关键词： Time series analysis

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Qpplab: A Generally Applicable Software Package for Detecting, Analyzing, and Visualizing Large-Scale Quasiperiodic Spatiotemporal Patterns (Qpps) of Brain Activity

SSRN

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SSRN 2024年

作者： Xu, Nan Yousefi, Behnaz Anumba, Nmachi LaGrow, Theodore J. Zhang, Xiaodi Keilholz, Shella Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA United States Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University AtlantaGA United States Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Georgia Institute of Technology United States

One prominent brain dynamic process detected in functional neuroimaging data is large-scale quasi-periodic patterns (QPPs) which display spatiotemporal propagations along brain cortical gradients. QPP associates with the infraslow neural activity related to attention and arousal fluctuations and has been identified in both resting and task-evoked brains across various species. Several QPP detection and analysis tools were developed for distinct applications with study-specific parameters methods. This MATLAB package provides a simplified and user-friendly generally applicable toolbox for detecting, analyzing, and visualizing QPPs from fMRI timeseries of the brain. This paper describes the software functions and presents its ease of use on any brain datasets. © 2024, The Authors. All rights reserved.

关键词： Brain

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Cutting-Edge Robotic Arm Design: Harnessing Flex Sensors and Novel Controller Framework to Meet Real-World Innovations

Cutting-Edge Robotic Arm Design: Harnessing Flex Sensors and...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： V. Syambabu K. Kalaiselvi S. Derisha Mahil Hemalatha S Badi Alekhya N. Kanagavalli Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of CSE(AIML) S.A.Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of AIML R.M.D Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

This research paper introduces a sophisticated arm movement tracking system, leveraging a multi-sensor fusion approach for enhanced precision and versatility. The comprehensive system comprises various components, including a dedicated cabinet, six Leap Motion sensors, six microcomputers, a Kinect depth camera, a KVM switcher, a router, and flex sensors. In addition to the aforementioned components, the integration of flex sensors enhances the system's capability to capture nuanced movements and gestures of the human arm. Flex sensors contribute valuable data to the multi-sensor fusion, further refining the arm movement tracking and providing a comprehensive understanding of the intricacies involved. By quantifying the Root Mean Square Error (RMSE) for each filtering technique, including the data from flex sensors, we gained valuable insights into the accuracy and precision achieved by Particle Filtering in refining data from the specified angles. This approach ensures a thorough validation of Particle Filtering's superior performance when compared to Existing filtering Methods. The systematic evaluation of RMSE serves as a robust method for assessing the effectiveness of Particle Filtering, affirming its potential as a high-performing solution for precise angle estimation and tracking within the context of the presented arm movement tracking system that incorporates flex sensors.

关键词： Flexible printed circuits Systematics Filtering Tracking Refining Switches Sensor fusion

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Mobile Augmented Reality Framework with Fusional Localization and Pose Estimation

arXiv

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arXiv 2025年

作者： Hou, Songlin Lin, Fangzhou Huang, Yunmei Peng, Zhe Xiao, Bin Department of Computer Science Worcester Polytechnic Institute Worcester01609 United States Department of Robotics Engineering Worcester Polytechnic Institute Worcester01609 United States Unprecedented-scale Data Analytics Center Tohoku University Sendai980-8577 Japan Department of Forestry and Natural Resources Purdue University West Lafayette47907 United States Department of Computing The Hong Kong Polytechnic University 999-077 Hong Kong

As a novel way of presenting information, augmented reality (AR) enables people to interact with the physical world in a direct and intuitive way. While there are some mobile AR products implemented with specific hardware at a high cost, the software approaches of AR implementation on mobile platforms(such as smartphones, tablet PC, etc.) are still far from practical use. GPS-based mobile AR systems usually perform poorly due to the inaccurate positioning in the indoor environment. Previous vision-based pose estimation methods need to continuously track predefined markers within a short distance, which greatly degrade user experience. This paper first conducts a comprehensive study of the state-of-the-art AR and localization systems on mobile platforms. Then, we propose an effective indoor mobile AR framework. In the framework, a fusional localization method and a new pose estimation implementation are developed to increase the overall matching rate and thus improving AR display accuracy. Experiments show that our framework has higher performance than approaches purely based on images or Wi-Fi signals. We achieve low average error distances (0.61-0.81m) and accurate matching rates (77%-82%) when the average sampling grid length is set to 0.5m. © 2025, CC0.

关键词： Wi-Fi

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A Novel Framework for the Development of Quality-Aware Autonomic IoT Applications

A Novel Framework for the Development of Quality-Aware Auton...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Ankur Singh Bisht Manjunatha R M. P. Karnik A. Amudha Nimesh Raj Shalini M Computer Science and Engineering Graphic Era Hill University Bhimtal Graphic Era Deemed to be University Dehradun Uttarakhand India School of Sciences Data Analytics and Mathematical Sciences JAIN (Deemed to be University) Bangalore Karnataka Computer Engineering Vishwakarma Institute of Technology Pune India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

A tremendous quantity of data is being generated via the Internet of Things (IoT). While our knowledge of how to assess the quality of this data at the network and device levels is expanding, there are still significant obstacles to overcome when it comes to interpreting and assessing the quality at the informational and application levels. If you want to know what aspects impact the overall system quality, this post will go over some of the problems with IoT systems and how to fix them at various OSI levels. Using two digital health apps made possible by the internet of things (IoT), the authors look at how semantics may be used to measure the system’s data quality and how to integrate multimodal information to clinical decision support. Important data quality issues arise from the fact that the Internet of Things (IoT) ecosystem generates massive amounts of data collected from a variety of heterogeneous sources. The reason for this is that the whole operation of the internet of things system is badly affected by low data quality standards. data quality has been the subject of much research outside of the Internet of Things (IoT) domain, and the idea is backed by massive standardization efforts. However, data quality management inside the IoT has received little attention. An essential idea with many existing applications for issue solving, data quality (DQ) is used in the creation and execution of information, handling data, decision making, as well as engineering systems. But the ways DQ is now integrated into data management pipelines aren’t flexible enough to accommodate all kinds of uses, which limits its use in the IoT space. A generic framework is introduced in this research that allows for the implementation of DQ assessment in a wide range of Internet of Things applications. A knowledge graph serves as the foundation of the framework, making it easy to incorporate into new uses. The study’s findings highlight many gaps in the current state of IoT quality a

关键词： Training Support vector machines Accuracy data integrity Semantics Vectors Classification algorithms Internet of Things Cultural differences Standards

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Towards a multimodal neuroimaging-based risk score for Alzheimer’s disease by combining clinical and large N>37000 population data

Towards a multimodal neuroimaging-based risk score for Alzhe...

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

作者： Elaheh Zendehrouh Mohammad S. E. Sendi Vince D. Calhoun Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA Tri-Institutional Center for Translational Research in Neuroimaging Data Science (TReNDS): Georgia State University Georgia Institute of Technology Emory University Atlanta Georgia Mclean Hospital and Harvard Medical School Boston MA

Alzheimer’s disease (AD) is the most prevalent age-related dementia and causes memory, reasoning, and social skills to deteriorate. In recent years many studies have explored the genetic risk of AD, but less work has been done to identify a brain imaging-based AD risk measure. The current study proposed a new neuroimaging-based measure of AD risk, called brain-wide risk score or BRS, based on multimodal brain features. Using the proposed AD BRS, we identified four AD biotypes from a large sample of subjects (N>37,000) from the UK Biobank dataset: one with high AD BRS, one with low AD BRS, and two with moderate AD BRS. Next, we further showed that the cognitive scores of the biotype with lower AD BRS are significantly better than those of other biotypes.

关键词：

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Cooperative Multi-source data Trading

Cooperative Multi-source Data Trading

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jin Cheng Ningning Ding John C. S. Lui Jianwei Huang School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Data Science and Analytics Thrust Information Hub Hong Kong University of Science and Technology Guangzhou Department of Computer Science and Engineering The Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network and CSIJRI Joint Research Centre on Smart Energy Storage The Chinese University of Hong Kong Shenzhen

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In the era of big data, data trading significantly enhances data-driven technologies by facilitating data sharing. Despite the clear advantages often experienced by data users when incorporating multiple sources, the topic of multi-source data trading remains largely unexplored. This paper designs a novel data trading framework, which enables multi-source data trading through multi-source cooperation. The proposed framework aims to improve data usage efficiency and increase seller revenue. In particular, we model data sellers’ cooperative decisions through the Nash bargaining framework and systematically outline the interactions between sellers and buyers as a two-stage Stackelberg game. A key contribution of this work is the consideration of coupling among diverse data products, which is essential but often overlooked in prior studies. We properly classify data’s utility into endogenous and relational categories to disentangle the coupling. Despite the inherent non-convex nature of the optimization problem, we methodically derive the closed-form optimal solutions by decomposing the problem into several subproblems. Interestingly, we reveal that, under our proposed framework, sellers’ revenue initially remains steady with the increase of product coupling level, but begins to rise once the level exceeds a certain threshold due to the substitute effect. Finally, experimental results show that our proposed framework can improve the seller’s profit by up to 46.32% compared to traditional data trading methods in the current data market.

关键词： Couplings Games Big data data models Global communication Optimization

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Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in Artificial Intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

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Computing (ICOCO), IEEE International Conference on

作者： Mahayaudin M. Mansor Nurain Ibrahim Noor Fadhilah Ahmad Radi Nadirah Abdul Rahim Syarul Heiry Yahaya Mohd Aftar Abu Bakar Roslinazairimah Zakaria School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA (Penang Campus) Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability.

关键词： Visualization Technological innovation Ethics Emotion recognition Bibliometrics Decision making Focusing Electronic healthcare Object recognition Artificial intelligence

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Detection of Breast Cancer using Quasi-Cross Focused Transformer-Guided Quantum Network with Lotus Optimization

Detection of Breast Cancer using Quasi-Cross Focused Transfo...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Rajesh Munirathnam Vishal Shukla Deepashree Devaraj Chintan Thacker Ujwal Nandekar Natrayan L Data & Analytics Division TechnoGen Inc. Hillsborough New Jersey USA Department of Mechanical Engineering Ramdeobaba College of Engineering and Management Nagpur Maharashtra Electronics and Instrumentation Engineering RV College of Engineering Bengaluru Karnataka Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Post Limda Gujarat Symbiosis Centre for Advanced Legal Studies and Research (SCALSAR) Symbiosis Law School Pune Symbiosis International (Deemed University) Pune Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai TamilNadu

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Patient outcomes improve if breast cancer is discovered early. Although many deep learning methods have been studied for breast cancer identification, the efficacy of many has been limited. It presents a new method encompassing the Quasi Cross Focused Transformer Guided Quantum Network with Lotus Optimization (QCFT-GQNet-LO) which is novel to resolve these issues. This technique uses Quasi-Cross Filtering (QCF) for image pre-processing, which improves image quality by reducing noise and artefacts. It was evaluated on histopathological images from the MIAS (Mammographic Image Analysis Society) dataset. A Focused Transformer with U-Net (FTUNet) is utilized to precisely segment malignant regions, guaranteeing precise identification of crucial places. The Self-Guided Quantum Network with Triple Attention (SGQNet-TA) controls classification and feature extraction, significantly improving the model's diagnostic abilities. The whole system is optimized using the Lotus Effect Optimization Algorithm (LEOA) to improve the efficiency and robustness. The QCFT-GQNetLO approach is shown to outperform other approaches, with experimentally demonstrated accuracy of 99.9%, and recall rate of 99.8% on the MIAS dataset. This significantly improves diagnostic precision of lesion type categorization and early breast malignancy diagnosis in histopathological images.

关键词： Image segmentation Accuracy Filtering Noise Transformers Breast cancer Robustness Quantum networks Lesions Optimization

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