Machine learning for faster and smarter fluorescence lifetime imaging microscopy
Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique in biomedical
research that uses the fluorophore decay rate to provide additional contrast in fluorescence …
research that uses the fluorophore decay rate to provide additional contrast in fluorescence …
Small training dataset convolutional neural networks for application-specific super-resolution microscopy
Significance Machine learning (ML) models based on deep convolutional neural networks
have been used to significantly increase microscopy resolution, speed [signal-to-noise ratio …
have been used to significantly increase microscopy resolution, speed [signal-to-noise ratio …
Improving fluorescence lifetime imaging microscopy phasor accuracy using convolutional neural networks
Introduction: Although a powerful biological imaging technique, fluorescence lifetime
imaging microscopy (FLIM) faces challenges such as a slow acquisition rate, a low signal-to …
imaging microscopy (FLIM) faces challenges such as a slow acquisition rate, a low signal-to …
Convolutional neural network denoising in fluorescence lifetime imaging microscopy (FLIM)
Fluorescence lifetime imaging microscopy (FLIM) systems are limited by their slow
processing speed, low signal-to-noise ratio (SNR), and expensive and challenging …
processing speed, low signal-to-noise ratio (SNR), and expensive and challenging …
Machine Learning‐Driven Discovery of Thermoset Shape Memory Polymers With High Glass Transition Temperature Using Variational Autoencoders
A Teimouri, G Li - Journal of Polymer Science, 2024 - Wiley Online Library
The discovery of high‐performance shape memory polymers (SMPs) with enhanced glass
transition temperatures (Tg) is of paramount importance in fields such as geothermal energy …
transition temperatures (Tg) is of paramount importance in fields such as geothermal energy …
Deep learning-based super-resolution fluorescence microscopy on small datasets
Fluorescence microscopy has enabled a dramatic development in modern biology by
visualizing biological organ-isms with micrometer scale resolution. However, due to the …
visualizing biological organ-isms with micrometer scale resolution. However, due to the …
Autoencoder Based Nonlinear Feature Extraction from EDA Signals for Emotion Recognition
YR Veeranki, LR Mercado-Diaz… - 2024 IEEE …, 2024 - ieeexplore.ieee.org
The field of emotion recognition plays a pivotal role in enhancing the quality and
effectiveness of human-computer interaction. Electrodermal Activity (EDA) signals, which …
effectiveness of human-computer interaction. Electrodermal Activity (EDA) signals, which …
Low dosage 3D volume fluorescence microscopy imaging using compressive sensing
Fluorescence microscopy has been a significant tool to observe long-term imaging of
embryos (in vivo) growth over time. However, cumulative exposure is phototoxic to such …
embryos (in vivo) growth over time. However, cumulative exposure is phototoxic to such …
[图书][B] Overcoming fundamental limits of three-dimensional in vivo fluorescence imaging using machine learning
VV Mannam - 2022 - search.proquest.com
In vivo fluorescence imaging is a powerful tool for understanding and characterizing
biological systems. For example, with the help of in vivo fluorescence imaging, one can …
biological systems. For example, with the help of in vivo fluorescence imaging, one can …
Low-energy convolutional neural networks (CNNs) using Hadamard method
V Mannam - 2022 IEEE International Conference on Big Data …, 2022 - ieeexplore.ieee.org
The growing demand for the internet of things (IoT) makes it necessary to implement
computer vision tasks such as object recognition in low-power devices. Convolutional neural …
computer vision tasks such as object recognition in low-power devices. Convolutional neural …