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2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond, DroneCom 2020

作者： Jacob, Sunil Menon, Varun G. Parvathi, R. Shynu, P.G. Shemim, Fathima K. S. Mahapatra, Bandana Mukherjee, Mithun Centre for Robotics SCMS School of Engineering and Technology India Department of Computer Science and Engineering SCMS School of Engineering and Technology India Department of Electronics and Communication Engineering SCMS School of Engineering and Technology India School of Information Technology and Engineering VIT University Vellore India Department of Computing and IT University of Bolton Ras Al Khaimah United Arab Emirates Department of Data Science Symbiosis Skills and Open University India Guangdong University of Petrochemical Technology Maoming China

ISBN: (纸本)9781450381055

The number of vehicular collisions is on a toll worldwide. Despite enforcing stringent laws and incorporating various safety features, the causalities are still on the rise. Existing techniques such as vision zero strategy and safe system approach provides only postcrash aid. Although numerous works have been carried out on Intelligent Transportation Systems (ITS), a well-coordinated vehicular collision avoidance system is still missing. In this paper, we utilize the tremendous opportunity provided by ITS, Light Detection and Ranging (LIDAR), Wireless Sensor Networks (WSN), 5G, and propose an effective system using drones with swarm intelligence that can automatically control the vehicles to prevent the collision. The proposed method, Bidirectional Multi- Tier IoT drone with Swarm optimization (BMTD-IoT-S) uses intelligent coordination of the drone swarms with the vehicular networks and always ensures a safe distance between the vehicles using the principle of magnetic levitation. The system is further investigated for optimizing the power, altitude, and angular frequency allocation for static and dynamic BMTD-IoT-S'. The results from simulation confirm the excellent performance of the system in ensuring collision avoidance. © 2020 Association for computing Machinery.

关键词： Collision avoidance

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SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI

arXiv

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arXiv 2022年

作者： Xu, Junshen Moyer, Daniel Grant, P. Ellen Golland, Polina Iglesias, Juan Eugenio Adalsteinsson, Elfar Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA United States Fetal-Neonatal Neuroimaging and Developmental Science Center Boston Children’s Hospital BostonMA United States Harvard Medical School BostonMA United States Centre for Medical Image Computing Department of Medical Physics and Biomedical Engineering University College London United Kingdom Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School Boston United States Institute for Medical Engineering and Science MIT CambridgeMA United States

Volumetric reconstruction of fetal brains from multiple stacks of MR slices, acquired in the presence of almost unpredictable and often severe subject motion, is a challenging task that is highly sensitive to the initialization of slice-to-volume transformations. We propose a novel slice-to-volume registration method using Transformers trained on synthetically transformed data, which model multiple stacks of MR slices as a sequence. With the attention mechanism, our model automatically detects the relevance between slices and predicts the transformation of one slice using information from other slices. We also estimate the underlying 3D volume to assist slice-to-volume registration and update the volume and transformations alternately to improve accuracy. Results on synthetic data show that our method achieves lower registration error and better reconstruction quality compared with existing state-of-the-art methods. Experiments with real-world MRI data are also performed to demonstrate the ability of the proposed model to improve the quality of 3D reconstruction under severe fetal motion. Copyright © 2022, The Authors. All rights reserved.

关键词： Brain

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DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

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Intelligence-Based Medicine 2025年 11卷 100192-100192页

作者： Amin, Mohammad Nahar, Khalid M.O. Gharaibeh, Hasan Nasayreh, Ahmad Alsalmanc, Neda'a Alomar, Alaa Malkawi, Majd Alqasem, Noor Smerat, Aseel Zitar, Raed Abu Nusier, Shawd Ezugwu, Absalom E. Abualigah, Laith Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Faculty of Computer Studies Arab Open University P.O.Box 84901 Riyadh 11681 Saudi Arabia Department of Computer Science Faculty of Information Technology Jordan University of Science and Technology Irbid 22110 Jordan Department of Medicine Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid 21163 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates Department of Health and Clinical Outcomes Research Saint Louis University St. Louis MO United States Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency. © 2024 The Authors

关键词： Brain tumor Medical image analysis MRI Vision Transformer

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Machine Learning and Physics-Driven Modelling and Simulation of Multiphase Systems

SSRN

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SSRN 2024年

作者： Basha, Nausheen Arcucci, Rossella Angeli, Panagiota Anastasiou, Charitos Abadie, Thomas Casas, César Quilodrán Chen, Jianhua Cheng, Sibo Chagot, Loïc Galvanin, Federico Heaney, Claire E. Hossein, Fria Hu, Jinwei Kovalchuk, Nina Kalli, Maria Kahouadji, Lyes Kerhouant, Morgan Lavino, Alessio Liang, Fuyue Nathanael, Konstantia Magri, Luca Lettieri, Paola Materazzi, Massimiliano Erigo, Matteo Pico, Paula Pain, Christopher C. Shams, Mosayeb Simmons, Mark Traverso, Tullio Valdes, Juan Pablo Wolffs, Zef Zhu, Kewei Zhuang, Yilin Matar, Omar K. Department of Chemical Engineering Imperial College London United Kingdom Department of Earth Science & Engineering Imperial College London United Kingdom Department of Chemical Engineering University College London United Kingdom School of Chemical Engineering University of Birmingham United Kingdom Data Science Institute Department of Computing Imperial College London United Kingdom State Key Laboratory of Multiphase Complex Systems Institute of Process Engineering Chinese Academy of Sciences Beijing China Centre for AI-Physics Modelling Imperial-X White City Campus Imperial College London United Kingdom The Alan Turing Institute British Library 96 Euston Road LondonNW1 2DB United Kingdom Department of Aeronautics Imperial College London United Kingdom Institute of Physics University of Amsterdam Science Park Amsterdam904 Netherlands Nikhef Science Park Amsterdam105 Netherlands Department of Computer Science University of York United Kingdom

We highlight the work of a multi-university collaborative programme, PREMIERE (PREdictive Modelling with QuantIfication of UncERtainty for MultiphasE Systems), which is at the intersection of multi-physics and machine learning, aiming to enhance predictive capabilities in complex multiphase flow systems across diverse length and time scales. Our contributions encompass a variety of approaches, including the Design of Experiments for nanoparticle synthesis optimisation, Generalised Latent Assimilation models for drop coalescence prediction, Bayesian regularised artificial neural networks, eXtreme Gradient Boosting for microdroplet formation prediction, and a sub-sampling based adversarial neural network for predicting slug flow behaviour in two-phase pipe flows. Additionally, we introduce a generalised latent assimilation technique, Long Short-Term Memory networks for sequence forecasting mixing performance in stirred and static mixers, active learning via Bayesian optimisation to recover coalescence model parameters for high current density electrolysers, Gaussian process regression for drop size distribution predictions for sprays, and acoustic emission signal inversion using gradient boosting machines to characterise particle size distribution in fluidised beds. We also offer perspectives on the development of a shape optimisation framework that leverages the use of a multi-fidelity multiphase emulator. The results presented have applications in chemical synthesis, microfluidics, product manufacturing, and green hydrogen generation. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Generalization of multivariable Laplace transform based on Tsallis q-exponential and its inverse using Post-Widder’s method

arXiv

引用

arXiv 2022年

作者： Samu Shahabuddin, Naina Mohammed Karthikeyan, Jeevanandham Ahmed Abduljaffer, Basherrudin Mahmud Ali, Manirul Radhakrishnan, Chandrashekar Department of Physics Government Arts College Tiruppur District Tamilnadu Udumalpet642126 India School of Physics Madurai Kamaraj University Madurai625021 India Centre for Quantum Science and Technology Chennai Institute of Technology Chennai600069 India Department of Computer Science Data Science and Engineering NYU Shanghai 567 Yangsi West Road Pudong New District Shanghai200124 China Centre for Quantum Information Communication and Computing Indian Institute of Technology Madras Chennai600036 India

A generalization of the multivariate Laplace transform based on the Tsallis q-exponential is given in the present work for a new type of kernel. We also define the inverse transform for this generalized multivariate transform based on the complex integration method. We prove identities corresponding to the Laplace transform and inverse transform like the q-convolution theorem, the action of generalized derivative and generalized integration on the Laplace transform. We then derive a q-generalization of the inverse Laplace transform based on the Post-Widder’s method which bypasses the necessity for a complex contour integration. This extension of the Post-Widder’s method has been carried out for the multivariate case as well. We demonstrate the usefulness of this in computing the Laplace and inverse Laplace transform of some elementary functions. The inverse Laplace transform has been calculated using both the complex integration method as well as the Post-Widder’s methods and the results obtained through these two methods agree with each other. © 2022, CC BY.

关键词： Integration

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Short-time simulation of quantum dynamics by Pauli measurements

arXiv

引用

arXiv 2024年

作者： Faehrmann, Paul K. Eisert, Jens Kieferová, Mária Kueng, Richard Dahlem Center for Complex Quantum Systems Freie Universität Berlin Berlin14195 Germany Institute for Integrated Circuits and Quantum Computing Johannes Kepler University Linz Austria Helmholtz-Zentrum Berlin für Materialien und Energie Hahn-Meitner-Platz 1 Berlin14109 Germany Fraunhofer Heinrich Hertz Institute Berlin10587 Germany Centre for Quantum Software and Information School of Computer Science Faculty of Engineering & Information Technology University of Technology SydneyNSW2007 Australia

Simulating the dynamics of complex quantum systems is a central application of quantum devices. Here, we propose leveraging the power of measurements to simulate short-time quantum dynamics of physically prepared quantum states in classical post-processing using a truncated Taylor series approach. While limited to short simulation times, our hybrid quantum-classical method is equipped with rigorous error bounds. It is extendable to estimate low-order Taylor approximations of smooth, time-dependent functions of tractable linear combinations of measurable operators. These insights can be made use of in the context of Hamiltonian learning and device verification, short-time imaginary time evolution, or the application of intractable operations to sub-universal quantum simulators in classical post-processing. © 2024, CC BY.

关键词： Hamiltonians

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Innovations in Simulation: Experiences With Cloud-Based Simulation Experimentation

Innovations in Simulation: Experiences With Cloud-Based Simu...

引用

Simulation Winter Conference

作者： Simon J. E. Taylor Anastasia Anagnostou Nura Tijjani Abubakar Tamas Kiss James DesLauriers Gabor Terstyanszky Peter Kacsuk Jozsef Kovacs Shane Kite Gary Pattison James Petry Modelling & Simulation Group Brunel University London Uxbridge UK Centre for Parallel Computing School of Computer Science and Engineering University of Westminster London UK Institute for Computer Science and Control (SZTAKI) Budapest HUNGARY Saker Solutions Ltd Warwickshire UK

ISBN: (数字)9781728194998

ISBN: (纸本)9781728195001

The amount of simulation experimentation that can be performed in a project can be restricted by time, especially if a model takes a long time to simulate and many replications are required. Cloud computing presents an attractive proposition to speeding up, or extending, simulation experimentation as computing resources can be hired on demand rather than having to invest in costly infrastructure. However, it is not common practice for simulation users to take advantage of this and, arguably, rather than speeding up simulation experimentation users tend to make compromises by using unnecessary model simplification techniques. This may be due to a lack of awareness of what Cloud computing can offer. Based on several years' experience of innovation in this area, this article presents our experiences in developing Cloud computing applications for simulation experimentation and discusses what future innovations might be created for the widespread benefit of our simulation community.

关键词： Technological innovation Cloud computing Computational modeling Optimization

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Securing the road ahead: Machine learning-driven DDoS attack detection in VANET cloud environments

Cyber Security and Applications

引用

Cyber Security and Applications 2024年 2卷

作者： Setia, Himanshu Chhabra, Amit Singh, Sunil K. Kumar, Sudhakar Sharma, Sarita Arya, Varsha Gupta, Brij B. Wu, Jinsong Department of CSE Chandigarh College of Engineering and Technology Sector 26 Chandigarh India Department of CSE Chandigarh College of Engineering and Technology Chandigarh India Department of ECE Chandigarh College of Engineering and Technology Chandigarh India Department of Business Administration Asia University Taiwan Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan School of Artificial Intelligence Guilin University of Electronic Technology 510004 China Department of Electrical Engineering University of Chile 8370451 Chile Department of Electrical and Computer Engineering Lebanese American University Beirut 1102 Lebanon Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India School of Computing Skyline University College P.O. Box 1797 Sharjah United Arab Emirates Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India

Vehicular ad-hoc network (VANET) technology has gained prominence, especially in the context of the emerging field of VANET Cloud as an integral part of connected and autonomous vehicles. The automotive industry's move towards automation and the integration of vehicles into the digital ecosystem has revolutionized wireless network communications. Nevertheless, security remains a paramount concern in these advanced technological landscapes. Safeguarding system integrity and data privacy is of utmost importance before the widespread adoption of VANET Cloud solutions. This study addresses the critical challenge of security within the context of VANET Cloud. Specifically, the focus is on anticipating and mitigating Distributed Denial of Service (DDoS) attacks, which can potentially disrupt the functioning of connected vehicles and associated cloud-based services. To tackle this issue, an innovative architectural framework is proposed to capture and analyze network flows within the VANET Cloud environment. Additionally, it leverages machine learning techniques for classification and predictive analytics with an accuracy of 99.59%. The architecture presented in this research offers the potential to significantly enhance security measures in VANET Cloud deployments. Its adaptability ensures practical applicability to real-world systems, enabling timely responses to security threats and breaches. © 2024

关键词： Cloud DDoS Fuzzification Machine learning NS2 Simulation VANET

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Performance Evaluation of the Weakly Hard Real-Time Tasks for Global Multiprocessor Scheduling Approach

Performance Evaluation of the Weakly Hard Real-Time Tasks fo...

引用

computer science and Data engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Habibah Ismail Dayang N. A. Jawawi Ismail Ahmedy Mohd Adham Isa Centre of Foundation Studies Universiti Teknologi MARA Dengkil Selangor Malaysia School of Computing Faculty of Engineering Universiti Teknologi Malaysia Skudai Johor Malaysia Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia

ISBN: (纸本)9781665495530

Real-time systems can be classified into three categories, based on the “seriousness” of deadline misses either by hard, soft, or weakly hard real-time tasks. The consequences of deadlines miss for a hard real-time task cannot be tolerated because some failure can affect the system performance whereas some deadline misses can be tolerated for soft real-time tasks. Meanwhile, in a weakly hard real-time task, the distribution of its met and missed deadlines is stated and specified precisely. Due to the complexity and significantly increased functionality in system computation, attention has been given to multiprocessor scheduling. Studies have shown that current multiprocessor scheduling of weakly hard real-time tasks used imprecise computation model based on iterative algorithms. This algorithm decomposed into two parts; mandatory and optional parts, unfortunately, the result analysis is precise only if its mandatory and optional parts are both executed. Even, the use of hierarchical scheduling algorithm, such as two-level scheduling under PFair algorithm may cause high overhead due to frequent preemptions and migrations. Furthermore, this algorithm incurs significant run-time overhead due to their quantum-based scheduling. In order to cater for the limitations and stated problems, an alternative multiprocessor scheduling approach, called global scheduling is proposed. The proposed scheduling approach aims to improve the probability of deadline satisfactions as much as possible and at the same time achieve a higher utilization of the task sets, with less task migrations. Thus, in this paper, performance measurement parameters are used as performance evaluation of the proposed scheduling approach.

关键词： Performance evaluation Analytical models Upper bound Processor scheduling Computational modeling Predictive models Real-time systems

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The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring (vol 30, pg 61, 2024)

引用

NATURE MEDICINE 2024年第4期30卷 1213-1213页

作者： Marcus, Hani J. Ramirez, Pedro T. Khan, Danyal Z. Horsfall, Hugo Layard Hanrahan, John G. Williams, Simon C. Beard, David J. Bhat, Rani Catchpole, Ken Cook, Andrew Hutchison, Katrina Martin, Janet Melvin, Tom Stoyanov, Danail Rovers, Maroeska Raison, Nicholas Dasgupta, Prokar Noonan, David Stocken, Deborah Sturt, Georgia Vanhoestenberghe, Anne Vasey, Baptiste McCulloch, Peter Department of Neurosurgery National Hospital of Neurology and Neurosurgery London UK Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS) London UK Department of Obstetrics and Gynaecology Houston Methodist Hospital Neal Cancer Center Houston TX USA RCS Surgical Interventional Trials Unit (SITU) & Robotic and Digital Surgery Initiative (RADAR) Nuffield Dept Orthopaedics Rheumatology and Musculo-skeletal Sciences University of Oxford Oxford UK Department of Gynaecological Oncology Apollo Hospital Bengaluru India Department of Anaesthesia and Perioperative Medicine Medical University of South Carolina Charleston SC USA NIHR Coordinating Centre and Clinical Trials Unit University of Southampton Southampton UK Department of Philosophy Macquarie University Sydney Australia Department of Anesthesia & Perioperative Medicine University of Western Ontario Ontario Canada Department of Medical Gerontology School of Medicine Trinity College Dublin Dublin Republic of Ireland Department of Medical Imaging Radboudumc Nijmegen the Netherlands Department of Biomedical Engineering and Imaging Sciences King’s College London London UK King’s Health Partners Academic Surgery King’s College London London UK Moon Surgical Paris France RCSEng Surgical Trials Centre Leeds Institute of Clinical Trials Research University of Leeds Leeds UK Bowel Research UK London UK School of Biomedical Engineering & Imaging Sciences King’s College London London UK Department of Surgery Geneva University Hospital Geneva Switzerland Nuffield Department of Surgical Sciences University of Oxford John Radcliffe Hospital Oxford UK PRISMA Lab University of Naples Federico II Naples Italy Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich Switzerland School of Computer Science University of Birmingham Birmingham UK Division of Cardiac Surgery Brigham and Women’s Hospital Harvard Medical Schoo

The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot’s development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes. Long-term monitoring needs to transition toward collaborative, transparent and inclusive consortiums for real-world data collection. Here, the Idea, Development, Exploration, Assessment and Long-term monitoring (IDEAL) Robotics Colloquium proposes recommendations for evaluation during development, comparative study and clinical monitoring of surgical robots—providing practical recommendations for developers, clinicians, patients and healthcare systems. Multiple perspectives are considered, including economics, surgical training, human factors, ethics, patient perspectives and sustainability. Further work is needed on standardized metrics, health economic assessment models and global applicability of recommendations.

关键词： Medical research Surgery

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