Výsledky vyhledávání

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Towards Linking Histological Changes to Liver Viscoelasticity: A Hybrid Analytical-Computational Micromechanics Approach

Autor: Shah, Haritya, Guddati, Murthy N.

Motivated by elastography that utilizes tissue mechanical properties as biomarkers for liver disease, with the eventual objective of quantitatively linking histopathology and bulk mechanical properties, we develop a micromechanical modeling approach

Externí odkaz: http://arxiv.org/abs/2411.13530

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Twin Peak Method for Estimating Tissue Viscoelasticity using Shear Wave Elastography

Autor: Adikary, Shuvrodeb, Urban, Matthew W., Guddati, Murthy N.

Tissue viscoelasticity is becoming an increasingly useful biomarker beyond elasticity and can theoretically be estimated using shear wave elastography (SWE), by inverting the propagation and attenuation characteristics of shear waves. Estimating visc

Externí odkaz: http://arxiv.org/abs/2411.11572

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Solving inverse scattering problems via reduced-order model embedding procedures

Autor: Zimmerling, Jörn, Druskin, Vladimir, Guddati, Murthy, Cherkaev, Elena, Remis, Rob

We present a reduced-order model (ROM) methodology for inverse scattering problems in which the reduced-order models are data-driven, i.e. they are constructed directly from data gathered by sensors. Moreover, the entries of the ROM contain localised

Externí odkaz: http://arxiv.org/abs/2306.08465

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Akademický článek

A Lightweight Deep Convolutional Neural Network Implemented on FPGA and Android Devices for Detection of Breast Cancer Using Ultrasound Images

Autor: Aditya Vinod, Prabhav Guddati, Amit Kumar Panda, Rajesh Kumar Tripathy

Publikováno v: IEEE Access, Vol 12, Pp 179190-179203 (2024)

Breast cancer (BC) continues to be the primary cause of high mortality rates among women globally. Early and automated detection of this disease plays a significant role in clinical standards for better diagnosis and improved survival rates. Ultrason

Externí odkaz: https://doaj.org/article/e441340b1b8d40d2bdabe4548ffae110

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Akademický článek

MOOSE Optimization Module: Physics-constrained optimization

Autor: Zachary M. Prince, Lynn Munday, Dewen Yushu, Max Nezdyur, Murthy Guddati

Publikováno v: SoftwareX, Vol 26, Iss , Pp 101754- (2024)

The MOOSE Optimization Module integrates optimization capabilities within the MOOSE framework, enabling efficient and accurate physics-constrained optimization. This module leverages automatic differentiation to compute Jacobians and employs an autom

Externí odkaz: https://doaj.org/article/d17f35d74fa144e5b9350641784e9d23

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