Live Breaking News & Updates on Stokes Raman

Stay updated with breaking news from Stokes raman. Get real-time updates on events, politics, business, and more. Visit us for reliable news and exclusive interviews.

What is the Role of Vibrational Spectroscopy in Surgery and Diagnostics? | Webinars

What is the Role of Vibrational Spectroscopy in Surgery and Diagnostics? | Webinars
photonics.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from photonics.com Daily Mail and Mail on Sunday newspapers.

Frederic Leblond , About This , Stokes Raman ,

New Phase-Modulation Boosts 3D Chemical Imaging

New Phase-Modulation Boosts 3D Chemical Imaging
miragenews.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from miragenews.com Daily Mail and Mail on Sunday newspapers.

Weiqi Wang , Zhiwei Huang , Department Of Biomedical Engineering , Optical Bioimaging Laboratory , College Of Design , National University Of Singapore , Advanced Photonics , Professor Zhiwei Huang , Biomedical Engineering , National University , Stokes Raman , Gold Open Access ,

Enhanced 3D chemical imaging with phase-modulation

Enhanced 3D chemical imaging with phase-modulation
phys.org - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from phys.org Daily Mail and Mail on Sunday newspapers.

Zhiwei Huang , Weiqi Wang , College Of Design , Optical Bioimaging Laboratory , Department Of Biomedical Engineering , National University Of Singapore , Advanced Photonics , Professor Zhiwei Huang , Biomedical Engineering , National University , Stokes Raman , Stimulated Raman ,

"Log-Gaussian gamma processes for training Bayesian neural networks in " by Teemu Härkönen, Erik M. Vartiainen et al.

We propose an approach utilizing gamma-distributed random variables, coupled with log-Gaussian modeling, to generate synthetic datasets suitable for training neural networks. This addresses the challenge of limited real observations in various applications. We apply this methodology to both Raman and coherent anti-Stokes Raman scattering (CARS) spectra, using experimental spectra to estimate gamma process parameters. Parameter estimation is performed using Markov chain Monte Carlo methods, yielding a full Bayesian posterior distribution for the model which can be sampled for synthetic data generation. Additionally, we model the additive and multiplicative background functions for Raman and CARS with Gaussian processes. We train two Bayesian neural networks to estimate parameters of the gamma process which can then be used to estimate the underlying Raman spectrum and simultaneously provide uncertainty through the estimation of parameters of a probability distribution. We apply the trai ....

Monte Carlo , Stokes Raman ,