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Detecting and Characterizing the Fabella with High Frame-Rate Ultrasound Imaging

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Detecting and Characterizing the Fabella with High Frame-Rat...

IEEE International Ultrasonics Symposium

作者： Michael A. Berthaume Matthieu Toulemonde Laura Peralta Kirsten Christensen-Jeffries Enrico Grisan Sevan Harput Division of Mechanical Engineering and Design London South Bank University London UK ULIS Group London UK School of Biomedical Engineering & Imaging Sciences King's College London UK Division of Computer Science and Informatics London South Bank University London UK Division of Electrical and Electronic Engineering London South Bank University London UK

ISBN: (数字)9781728154480

ISBN: (纸本)9781728154497

The fabella is a sesamoid bone usually located in the tendon of the lateral head of the gastrocnemius muscle, behind the knee joint. Prevalence rates in human populations vary widely with an average of 42.5% people having a fabella. Clinically, it is associated with a number of knee ailments, most notably the osteoarthritis of the knee and generalized knee pain (i.e., fabella syndrome). As the function of the fabella remains unknown, the biomechanical consequences of fabella presence/absence can only be speculated. Successfully detecting the fabella, measuring its size and determining its shape, are off importance for clinical and evolutionary researchers. In this work, we compare plane wave imaging with conventional focused imaging and evaluate their performance for detecting and characterizing the fabella.

关键词： Ultrasonic imaging Ultrasonic variables measurement Imaging Coherence Bones Statistics Tendons

Digital Interaction and Machine Intelligence 1

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丛书名： Lecture Notes in Networks and Systems

1000年

作者： Cezary Biele Janusz Kacprzyk Jan W. Owsiński Wiesław Kopeć Jakub Możaryn Andrzej Romanowski Marcin Sikorski

Analysis of the Contribution of Cardiovascular Compartments to the Ballistocardiogram Signal Using Mathematical Modeling 50

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Analysis of the Contribution of Cardiovascular Compartments ...

50th Computing in Cardiology, CinC 2023

作者： Zaid, Mohamed Invernizzi, Raul Sala, Lorenzo Despins, Laurel Popescu, Mihail Keller, James Skubic, Marjorie Sacco, Riccardo Szopos, Marcela Huxley, Virginia H. Guidoboni, Giovanna Maine College of Engineering and Computing University of Maine OronoME United States Politecnico di Milano Department of Mathematics Milan Italy Inrae MaIAGE Universite Paris-Saclay Paris France Sinclair School of Nursing University of Missouri ColumbiaMO United States Health Management and Informatics University of Missouri ColumbiaMO United States Electrical and Computer Engineering University of Missouri ColumbiaMO United States Université Paris Cité Cnrs ParisF-75006 France University of Missouri Department of Medical Pharmacology and Physiology ColumbiaMO United States

ISBN: (纸本)9798350382525

The ballistocardiogram (BCG) captures the mechanics and fluid-dynamic properties of the cardiovascular system and provides useful insights into cardiovascular function. However, the cardiovascular mechanisms generating the BCG are still poorly understood and this limits its clinical applications. The present work examines computer-aided methods for understanding the cardiovascular foundations of the BCG signal. The main goal is to investigate how different cardiovascular compartments contribute to the BCG waveform. This is achieved by comparing BCG signals generated by cardiovascular models of increased complexity, while assessing the impact on pressure-volume loops in the left ventricle. All models describe the circulation as a closed-loop system, which is crucial to capture the dynamic interplay among all cardiovascular compartments, with increasing anatomical and physiological detail. The results highlight the importance of including the ventricles, the main arteries, and the cerebral vasculature in a constructive model for the BCG. The findings emphasize the potential of mathematical modeling to significantly impact cardiovascular diagnostics and healthcare. © 2023 CinC.

关键词： Cardiovascular system

Clinical connectivity map for drug repurposing: Using laboratory tests to bridge drugs and diseases

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arXiv 2020年

作者： Wen, Qianlong Liu, Ruoqi Zhang, Ping Department of Electrical and Computer Engineering Ohio State University 2015 Neil Ave 43210 ColumbusOH United States Department of Computer Science and Engineering Ohio State University 2015 Neil Ave 43210 ColumbusOH United States Department of Biomedical Informatics Ohio State University 1800 Cannon Drive 43210 ColumbusOH United States

Background: Drug repurposing, the process of identifying additional therapeutic uses for existing drugs, has attracted increasing attention from both the pharmaceutical industry and the research community. Many existing computational drug repurposing methods rely on preclinical data (e.g., chemical structures, drug targets), resulting in translational problems for clinical trials. Methods: In this study, we propose a clinical connectivity map framework for drug repurposing by leveraging laboratory tests to analyze complementarity between drugs and diseases. We establish clinical drug effect vectors (i.e., drug-laboratory test associations) by applying a continuous self-controlled case series model on a longitudinal electronic health record data. We establish clinical disease sign vectors (i.e., disease-laboratory tes associations) by applying a Wilcoxon rank sum test on a large-scale national survey data. Finally, we compute a repurposing possibility score for each drug-disease pair by applying a dot product-based scoring function on clinical disease sign vectors and clinical drug effect vectors. Results: We comprehensively evaluate 392 drugs for 6 important chronic diseases (include asthma, coronary heart disease, congestive heart failure, heart attack, type 2 diabetes, and stroke). We discover not only known associations between diseases and drugs, but also many hidden drug-disease associations. For example, clopidogrel and alendronate may be repurposed as candidate drugs for diabetes and cardiovascular diseases respectively. Moreover, we are able to explain the predicted drug-disease associations via the corresponding complementarity between laboratory tests of drug effect vectors and disease sign vectors. Conclusion: The proposed clinical connectivity map framework uses laboratory tests from electronic clinical information to bridge drugs and diseases, which is explainable and has better translational power than existing computational methods. Experimental resul

关键词： Diseases

Proactive Network Traffic Prediction using Generative Adversarial Network

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Proactive Network Traffic Prediction using Generative Advers...

International Conference on Information Networking

作者： Gyurin Byun Van-Vi Vo Syed M. Raza Duc-Tai Le Huigyu Yang Hyunseung Choo Dept. of AI Systems Engineering Sungkyunkwan University Suwon South Korea Convergence Research Institute Sungkyunkwan University Suwon South Korea Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea College of Computing and Informatics Sungkyunkwan University Suwon South Korea Dept. of Superintelligence Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

Proactive traffic management in 5G networks is crucial for optimizing network efficiency, ensuring quality of service, adapting to dynamic conditions, and enhancing the user experience. In this paper, we propose a Generative Adversarial Network (GAN) architecture that leverages spatiotemporal features in network traffic data to predict future traffic. Our approach incorporates a Convolutional Long Short-Term Memory (ConvLSTM) model within the generator of the GAN, which collaborates with the discriminator, utilizing a Convolutional Neural Network (CNN) model, to provide essential feedback for training the generator. This integration ensures that our model not only predicts future traffic with improved accuracy but also adapts to dynamic network conditions. Based on experimental results using network traces, our model significantly outperforms the baseline, reducing prediction error by 12% while forecasting network traffic for the next 1 minute. These findings represent a significant advancement in proactive network management, particularly in addressing the challenges posed by real-time streaming and other latency-sensitive applications in 5G networks.

关键词： Training Adaptation models 5G mobile communication Telecommunication traffic Predictive models Generative adversarial networks Generators

Near-Field Channel Modeling for Holographic MIMO Communications

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arXiv 2024年

作者： Gong, Tierui Wei, Li Huang, Chongwen Alexandropoulos, George C. Debbah, Mérouane Yuen, Chau School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens15784 Greece Department of Electrical Engineering and Computer Science Khalifa University of Science and Technology Abu Dhabi127788 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France

Empowered by the latest progress on innovative metamaterials/metasurfaces and advanced antenna technologies, holographic multiple-input multiple-output (H-MIMO) emerges as a promising technology to fulfill the extreme goals of the sixth-generation (6G) wireless networks. The antenna arrays utilized in H-MIMO comprise massive (possibly to extreme extent) numbers of antenna elements, densely spaced less than half-a-wavelength and integrated into a compact space, realizing an almost continuous aperture. Thanks to the expected low cost, size, weight, and power consumption, such apertures are expected to be largely fabricated for near-field communications. In addition, the physical features of H-MIMO enable manipulations directly on the electromagnetic (EM) wave domain and spatial multiplexing. To fully leverage this potential, near-field H-MIMO channel modeling, especially from the EM perspective, is of paramount significance. In this article, we overview near-field H-MIMO channel models elaborating on the various modeling categories and respective features, as well as their challenges and evaluation criteria. We also present EM-domain channel models that address the inherit computational and measurement complexities. Finally, the article is concluded with a set of future research directions on the topic. Copyright © 2024, The Authors. All rights reserved.

关键词： Antenna arrays

The Effects of Extended Locational Marginal Pricing in Wholesale Electricity Markets

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The Effects of Extended Locational Marginal Pricing in Whole...

North American Power Symposium

作者： Gregory Thompson Chao Li Muhong Zhang Kory W. Hedman School of Computing Informatics and Decision Systems Engineering Arizona State University School of Electrical Computer and Energy Engineering Arizona State University

ISBN: (纸本)9781479912537

In this paper, two pricing mechanisms in electricity markets are studied, the locational marginal price (LMP) and the extended locational marginal price (ELMP). The effects of these two pricing mechanisms are compared with regards to the allocation of the market surplus between generators and loads. The two pricing mechanisms are also analyzed with regards to the required uplift payments. The results confirm that uplift payments are reduced under the ELMP pricing mechanism. However, ELMP does not appropriately represent the marginal market clearing price for the market dispatch solution.

关键词： Extended locational marginal price Locational marginal price Non-convex markets Pricing mechanisms Uplifts

Modeling blockage in high directional wireless systems

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arXiv 2024年

作者： Koutsonas, Evangelos Boulogeorgos, Alexandros-Apostolos A. Trevlakis, Stylianos E. Ali, Tanweer Tsiftis, Theodoros A. Department of Electrical and Computer Engineering University of Western Macedonia Kozani50100 Greece Research & Development Department InnoCube P.C. Thessaloniki55535 Greece Department of Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Department of Informatics & Telecommunications University of Thessaly Lamia35100 Greece Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo315100 China

While the wireless word moves towards higher frequency bands, new challenges arises, due to the inherent characteristics of the transmission links, such as high path and penetration losses. Penetration losses causes blockages that in turn can significantly reduce the signal strength at the receiver. Most published contributions consider a binary blockage stage, i.e. either fully blocked or blockage-free links. However, in realistic scenarios, a link can be partially blocked. Motivated by this, in this paper, we present two low-complexity models that are based on tight approximations and accommodates the impact of partial blockage in high-frequency links. To demonstrate the applicability of the derived framework, we present closed-form expressions for the outage probability for the case in which the distance between the center of the receiver plane and the blocker’s shadow center follow uniform distribution. Numerical results verify the derived framework and reveal how the transmission parameters affect blockage. Copyright © 2024, The Authors. All rights reserved.

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Biologically-Informed Excitatory and Inhibitory Balance for Robust Spiking Neural Network Training

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arXiv 2024年

作者： Kilgore, Joseph A. Kopsick, Jeffrey D. Ascoli, Giorgio A. Adam, Gina C. Department of Electrical and Computer Engineering George Washington University WashingtonDC20052 United States Center for Neural Informatics Structures & Plasticity Krasnow Institute for Advanced Study George Mason University FairfaxVA22030 United States

Spiking neural networks drawing inspiration from biological constraints of the brain promise an energy-efficient paradigm for artificial intelligence. However, challenges exist in identifying guiding principles to train these networks in a robust fashion. In addition, training becomes an even more difficult problem when incorporating biological constraints of excitatory and inhibitory connections. In this work, we identify several key factors, such as low initial firing rates and diverse inhibitory spiking patterns, that determine the overall ability to train spiking networks with various ratios of excitatory to inhibitory neurons on AI-relevant datasets. The results indicate networks with the biologically realistic 80:20 excitatory:inhibitory balance can reliably train at low activity levels and in noisy environments. Additionally, the Van Rossum distance, a measure of spike train synchrony, provides insight into the importance of inhibitory neurons to increase network robustness to noise. This work supports further biologically-informed large-scale networks and energy efficient hardware implementations. Copyright © 2024, The Authors. All rights reserved.

关键词： Neural networks