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IEEE International Symposium on Biomedical Imaging

作者： Julian Lukas Laue Christian Adolph Eren Bora Yilmaz Carsten Meyer Department of Computer Science Ostfalia University of Applied Sciences Wolfenbüttel Germany L3S Research Center Leibniz University Hannover Hannover Germany Deptartment of Computer Science Faculty of Engineering Kiel University Germany Dept. of Radiology and Neuroradiology Section Biomedical Imaging UKSH Kiel Germany

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Vertebral body (VB) fractures can have severe implications for the patient’s well-being, but often remain undetected. Automatic vertebral fracture assessment is challenging due to severe class imbalance in fracture grades, scarcity of annotated data, and often subtle visual differences between the grades. In this paper, we show that leveraging unlabeled data, collected from eight diverse, publicly available datasets in a self-supervised way via the BYOL framework, can noticeably improve performance. Our pre-trained models outperformed the non-pre-trained baselines in 17 out of 18 comparisons, 11 of which were statistically significant, when evaluated on the widely-used VerSe dataset, our in-house dataset, and combinations thereof. Furthermore, our models reached an average AUROC value of 99.6% on the VerSe test corpus, the highest recorded in the literature so far.

关键词： Training Visualization Sensitivity Biological system modeling Context modeling Biomedical imaging

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Microwave Adaptive Method for Non-destructive Testing of Thermophysical Characteristics of Building Materials and Products 3

Microwave Adaptive Method for Non-destructive Testing of The...

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3rd International Conference on Control Systems, mathematical Modeling, Automation and Energy Efficiency, SUMMA 2021

作者： Chernyshova, Tatyana Udalova, Anastasiya Chernyshov, Vladimir Institute of Power Engineering Tool Engineering and Radio Electronics Tambov State Technical University Tambov Russia Tambov State Technical University Department of Radio Engineering Tambov Russia Tambov State Technical University Department of Applied Computer Science in Law Tambov Russia

ISBN: (纸本)9781665439817

The article proposes a method for determining the thermophysical characteristics, which consists in measuring the temperature of two points of the sample surface, subjected to pulsed thermal action from microwave radiation of a given power focused into a line. The number of pulses and the repetition rate are adaptively set by the system when the established excess temperature at the control point is equal to the set value. To implement the proposed method, a microprocessor information-measuring system is presented. The accuracy of the proposed method is increased due to the control of high-level values of temperatures, the removal of information in the frequency-pulse form, and the heating of a large volume of the material under study. Metrological analysis and experimental studies of the proposed method have shown sufficient accuracy for thermophysical measurements, which will allow it to be applied in various fields of industry and construction heat engineering. © 2021 IEEE.

关键词： Nondestructive examination

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Leveraging Consumer Technology for Healthcare Systems Using Blockchain Based Bio-Sensor Devices

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1-1页

作者： Shabaz, Mohammad Rahman, Mohammad Zia Ur Alsaadi, Mahmood Raparthi, Mohan Maaliw, Renato R. Keshta, Ismail Soni, Mukesh Patni, Jagdish Chandra Byeon, Haewon Model Institute of Engineering and Technology Jammu India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Dist A.P. India Department of computer science Al-Maarif University College Al Anbar Iraq Dallas Texas College of Engineering Southern Luzon State University Lucban Quezon Philippines Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Dr. D. Y. Patil Vidyapeeth Pune India School of advance Computing Alliance University Bengaluru India Department of Digital Anti-Aging Healthcare Inje University Gimhae Republic of Korea

Wireless biosensor devices have significantly enhanced level of convenience associated with patient treatment. Presently, most uncertified aggregate signature methods struggle to withstand key attacks that employ only specific keys. This paper presents an uncertified parallel key-isolated aggregate signature framework that utilizes blockchain technology in wireless medical sensor networks and biosensor devices for consumer electronics to tackle the challenges. The proximity of the signature verification and aggregation processes to end users is increased by integrating a bio-sensor device for consumer electronics into the cutting-edge framework. This action simultaneously enhances the safeguarding of patient confidentiality and mitigates computational burden on primary cloud server. By integrating the advantages of key-isolated and uncertified technologies, the proposed solution circumvents exposure concerns, difficult certificate administration, and key storage. This study demonstrates that the proposed method is resistant to Type I, Type II, and wholly selected key attacks when implemented in a random oracle model. This scheme shows a reduction in calculation overhead of 82.97%, 74.03%, 84.58%, and 86.79% correspondingly when compared to other schemes. Analysis of performance indicates that in comparison to pertinent uncertified signature systems, this solution can reduce communication overhead by a minimum of 25% and computational cost by a minimum of 74.03%. IEEE

关键词： Blockchain

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THE WEIGHTED AND SHIFTED SEVEN-STEP BDF METHOD FOR PARABOLIC EQUATIONS

arXiv

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arXiv 2024年

作者： Akrivis, Georgios Chen, Minghua Yu, Fan Department of Computer Science and Engineering University of Ioannina Ioannina451 10 Greece Institute of Applied and Computational Mathematics FORTH Heraklion Crete700 13 Greece School of Mathematics and Statistics Gansu Key Laboratory of Applied Mathematics and Complex Systems Lanzhou University Lanzhou730000 China

Stability of the BDF methods of order up to five for parabolic equations can be established by the energy technique via Nevanlinna–Odeh multipliers. The nonexistence of Nevanlinna–Odeh multipliers makes the six-step BDF method special;however, the energy technique was recently extended by the authors in [Akrivis et al., SIAM J. Numer. Anal. 59 (2021) 2449–2472] and covers all six stable BDF methods. The seven-step BDF method is unstable for parabolic equations, since it is not even zero-stable. In this work, we construct and analyze a stable linear combination of two non zero-stable schemes, the seven-step BDF method and its shifted counterpart, referred to as WSBDF7 method. The stability regions of the WSBDFq, q 6 7, with a weight ϑ > 1, increase as ϑ increases, are larger than the stability regions of the classical BDFq, corresponding to ϑ = 1. We determine novel and suitable multipliers for the WSBDF7 method and establish stability for parabolic equations by the energy technique. The proposed approach is applicable for mean curvature flow, gradient flows, fractional equations and nonlinear equations. Copyright © 2024, The Authors. All rights reserved.

关键词： Nonlinear equations

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Convolutional Bi-LSTM Based Human Gait Recognition Using Video Sequences

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computers, Materials & Continua 2021年第8期68卷 2693-2709页

作者： Javaria Amin Muhammad Almas Anjum Muhammad Sharif Seifedine Kadry Yunyoung Nam ShuiHua Wang University of Wah Wah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan COMSATS University Islamabad Wah CampusWah CanttPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics University of LeicesterLeicesterUK

Recognition of human gait is a difficult assignment,particularly for unobtrusive surveillance in a video and human identification from a large ***,a method is proposed for the classification and recognition of different types of human *** proposed approach is consisting of two *** phase I,the new model is proposed named convolutional bidirectional long short-term memory(Conv-BiLSTM)to classify the video frames of human *** this model,features are derived through convolutional neural network(CNN)named ResNet-18 and supplied as an input to the LSTM model that provided more distinguishable temporal *** phase II,the YOLOv2-squeezeNet model is designed,where deep features are extricated using the fireconcat-02 layer and fed/passed to the tinyYOLOv2 model for recognized/localized the human gaits with predicted *** proposed method achieved up to 90%correct prediction scores on CASIA-A,CASIA-B,and the CASIA-C benchmark *** proposed method achieved better/improved prediction scores as compared to the recent existing works.

关键词： Bi-LSTM YOLOv2 open neural network resNet-18 gait squeezeNet

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Estimating Atmospheric Variables from Digital Typhoon Satellite Images via Conditional Denoising Diffusion Models

arXiv

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arXiv 2024年

作者： Ling, Zhangyue Nath, Pritthijit Quilodrán-Casas, César Department of Computing Imperial College London United Kingdom Department of Applied Math and Theoretical Physics University of Cambridge United Kingdom Department of Earth Science and Engineering Imperial College London United Kingdom Grantham Institute for Climate Change and the Environment Imperial College London United Kingdom National Center for Artificial Intelligence CENIA Chile

This study explores the application of diffusion models in the field of typhoons, predicting multiple ERA5 meteorological variables simultaneously from Digital Typhoon satellite images. The focus of this study is taken to be Taiwan, an area very vulnerable to typhoons. By comparing the performance of Conditional Denoising Diffusion Probability Model (CDDPM) with Convolutional Neural Networks (CNN) and Squeeze-and-Excitation Networks (SENet), results suggest that the CDDPM performs best in generating accurate and realistic meteorological data. Specifically, CDDPM achieved a PSNR of 32.807, which is approximately 7.9% higher than CNN and 5.5% higher than SENet. Furthermore, CDDPM recorded an RMSE of 0.032, showing a 11.1% improvement over CNN and 8.6% improvement over SENet. A key application of this research can be for imputation purposes in missing meteorological datasets and generate additional high-quality meteorological data using satellite images. It is hoped that the results of this analysis will enable more robust and detailed forecasting, reducing the impact of severe weather events on vulnerable regions. Code accessible at https://***/TammyLing/Typhoon-forecasting. © 2024, CC BY-NC-SA.

关键词： Convolutional neural networks

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ON ADDITIVE COMPLEMENTS IN THE COMPLEMENT OF A SET OF NATURAL NUMBERS

arXiv

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arXiv 2024年

作者： Patil, Bhuwanesh Rao Mohan Department of Applied Science and Humanities Government Engineering College Bihar Nawada805111 India Stat-Math Unit Indian Statistical Institute S. J. S. Sansanwal Marg New Delhi110016 India

Let A be a set of natural numbers. A set B, a set of natural numbers, is an additive complement of the set A if all sufficiently large natural numbers can be represented in the form x + y, where x ∈ A and y ∈ B. Erdős proposed a conjecture that every infinite set of natural numbers has a sparse additive complement, and in 1954, Lorentz proved this conjecture. This article describes the existence or non-existence of those additive complements of the set A that is a subset of the complement of A. We provide a ratio test to verify the existence of such additive complements. In precise, we prove that if A = {ai : i ∈ N} is a set of natural numbers such that ai i+1 for i ∈ N and lim infn→∞(an+1/an) > 1, then there exists a set B ⊂ N \ A such that B is a sparse additive complement of the set A. Copyright © 2024, The Authors. All rights reserved.

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A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling

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Transportation Research Procedia 2022年 62卷 374-382页

作者： José Carlos García-García Ricardo García-Ródenas Julio Alberto López-Gómez José Ángel Martín-Baos Department of Mathematics School of Computer Science University of Castilla-La Mancha Ciudad Real Spain Institute of Applied Mathematics in Science and Engineering (IMACI) University of Castilla-La Mancha Ciudad Real Spain Department of Information Systems and Technologies School of Computer Science University of Castilla-La Mancha Ciudad Real Spain

Traditionally, Random Utility Maximization (RUM) models have been widely applied to travel mode choice modelling. Currently, Machine Learning (ML) models are being applied as an alternative to RUM models, since they provide better results in terms of prediction capability and they can manage large volumes of data. In this paper, a comprehensive comparison between classic RUM models and ML models, including single and ensemble classifiers as well as Deep Neural Networks (DNNs), is provided in order to assess systematically the performance of different models over two different datasets which have different sizes and nature of data. Numerical experiments show Random Forest (RF) is the best classifier in terms of accuracy index and the computational cost to train the model.

关键词： Random Utility Maximization (RUM) models Deep Neural Networks (DNN) Machine Learning (ML) models travel behaviour

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A New Fixed Point Noise Cancellation Method for Suppressing Power Line Interference in Electrocardiogram Signals

A New Fixed Point Noise Cancellation Method for Suppressing ...

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E-Health and Bioengineering Conference (EHB)

作者： Mohammed Mujahid Ulla Faiz Saumya Kareem Reni Izzet Kale ECE Department Presidency University Bengaluru India Department of Computer Science and Engineering Applied DSP and VLSI Research Group University of Westminster London United Kingdom

ISBN: (数字)9781665485579

ISBN: (纸本)9781665485586

In this article, a new fixed point Leaky Sign Regressor Least Mean Mixed Norm (LSRLMMN) powered adaptive noise cancellation technique is being used for eliminating the Power Line Interference (PLI) noise embedded in the ElectroCardioGram (ECG) signal. The fixed point LSRLMMN powered noise cancellation technique used in this article has been completely quantized. The intention for the extensive quantization study and modeling approach was with a view to the physical integrated circuit implementation. All the modeling and simulation studies were carried out at the bit-level with various loss of precision schemes to ensure compliance with the set specification. The filter coefficients and all the data paths are quantized in order to establish at a high-level behavioral level of the parameters for a decreased complexity in integrated circuit implementation.

关键词： Quantization (signal) Upper bound Adaptive filters Interference Filtering algorithms Electrocardiography Noise cancellation

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Interpreting Industrial IoT Data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka

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IEEE Transactions on Fuzzy Systems 2024年第8期32卷 4671-4684页

作者： Bożena Małysiak-Mrozek Bartłomiej Ryba Marek Molęda Che-Lun Hung Witold Pedrycz Weiping Ding Dariusz Mrozek Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei City Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Department of Measurements and Control Systems Silesian University of Technology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Türkiye School of Artificial Intelligence and Computer Science Nantong University Nantong China Faculty of Data Science City University of Macau Macau China

In industrial settings, querying data streams from Internet of Things (IoT) devices benefits from utilizing elastic criteria to enhance the interpretability of the current state of the monitored environment. Fuzzy sets provide this elasticity, enabling the aggregation and representation of similar values in a human-comprehensible manner. However, many sensor signals exhibit temporal oscillations, leading to varying interpretations of the signal based on its current trend (rising or falling). This hysteresis in signal (and subsequently of the production device) interpretation inspired us to introduce this phenomenon into data stream processing, resulting in the novel concept of hysteretic fuzzy sets. This article demonstrates how fuzzy searching and grouping can be applied to IoT sensor signals in flexible Big Data stream processing on Apache Kafka. We illustrate the impact of data stream querying with KSQL queries involving fuzzy sets (encompassing fuzzy filtering of data stream events, fuzzy transformation of data stream attributes, fuzzy grouping, and joining) on the flexibility of executed operations and computational resources utilized by the Kafka processing engine. Finally, our experiments with hysteretic fuzzy sets while analyzing sensor signals in power plants demonstrate that this novel approach effectively reduces the number of alarms while monitoring the state of the production machine.

关键词： Streams Fuzzy sets Internet of Things Big Data Monitoring Production Real-time systems

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