Results 181 to 190 of about 329,555 (323)

Numerical evaluation of image homogeneity, signal‐to‐noise ratio, and specific absorption rate for human brain imaging at 1.5, 3, 7, 10.5, and 14T in an 8‐channel transmit/receive array

Journal of Magnetic Resonance Imaging, 2015
Z. Cao, Joshua H. Park, Z. Cho, C. Collins   +3 more
semanticscholar   +1 more source

Disentangling Coincident Cell Events Using Deep Transfer Learning and Compressive Sensing

Advanced Intelligent Systems, EarlyView.
Overlapping cells during detection distort single‐cell measurements and reduce diagnostic accuracy. A hybrid framework combining a fully convolutional neural network with compressive sensing to disentangle overlapping signals directly from raw time‐series data is presented.
Moritz Leuthner, Rafael Vorländer, Oliver Hayden   +2 more
wiley   +1 more source

Efficient Super-Resolution Bayesian Eletromagnetic Brain Imaging. [PDF]

IEEE Trans Biomed Eng
Cai C, Qi X, Yan J, Shang Y, Sekihara K, Wu W, Nagarajan SS.   +6 more
europepmc   +1 more source

Signal to noise ratio (SNR) comparison for lockin thermographic data processing methods in CFRP specimen

Proceedings of the 2010 International Conference on Quantitative InfraRed Thermography, 2010
F.J. Madruga, P. Albendea, C. Ibarra-Castanedo, J.M. López-Higuera   +3 more
openaire   +1 more source

Biomarker‐Agnostic Detection of Ovarian Cancer from Blood Plasma Using a Machine Learning‐Driven Electronic Nose

Advanced Intelligent Systems, EarlyView.
This study introduces a biomarker‐agnostic diagnostic strategy for ovarian cancer, utilizing a machine learning‐enhanced electronic nose to analyze volatile organic compound signatures from blood plasma. By overcoming the dependence on specific biomarkers, this approach enables accurate detection, staging, and cancer type differentiation, offering a ...
Ivan Shtepliuk, Lingyin Meng, Christer Borgfeldt, Jens Eriksson, Donatella Puglisi   +4 more
wiley   +1 more source

A framework for spontaneous Brillouin noise: unveiling fundamental limits in Brillouin metrology. [PDF]

Light Sci Appl
Jin S, Yao S, Yang Z, Du Z, Hong X, Soto MA, Xie J, Zhang L, Yang F, Wu J.   +9 more
europepmc   +1 more source

Improved Signal-to-Noise Ratio Estimation for Speech Enhancement

IEEE Transactions on Audio, Speech, and Language Processing, 2006
C. Plapous, Claude Marro, P. Scalart
semanticscholar   +1 more source

Predicting Materials Thermodynamics Enabled by Large Language Model‐Driven Dataset Building and Machine Learning

Advanced Intelligent Systems, EarlyView.
Illustration of text data mining of rare earth mineral thermodynamic parameters with the large language model‐powered LMExt. A dataset is built with mined thermodynamic properties. Subsequently, a machine learning model is trained to predict formation enthalpy from the dataset.
Juejing Liu, Haydn Anderson, Noah I. Waxman, Vsevolod Kovalev, Byron Fisher, Elizabeth Li, Xiaofeng Guo   +6 more
wiley   +1 more source

Hybrid Unsupervised-Supervised Learning Framework for Rainfall Prediction Using Satellite Signal Strength Attenuation. [PDF]

Sensors (Basel)
Laon P, Sahavisit T, Pourbunthidkul S, Puangragsa S, Wichittrakarn P, Phasukkit P, Houngkamhang N.   +6 more
europepmc   +1 more source

Effects of signal‐to‐noise ratio on the accuracy and reproducibility of diffusion tensor imaging–derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T

Journal of Magnetic Resonance Imaging, 2007
J. Farrell, B. Landman, Craig K. Jones, Seth A. Smith, Jerry L Prince, Peter C. M. van Zijl, S. Mori   +6 more
semanticscholar   +1 more source