10 papers · ranked by Valyu relevance
Shang Zhang, Yuhan Dong, Hongyan Fu, Shao-Lun Huang + 1 more
The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically…
Chunsheng Yan
Title: Summary Spectroscopic techniques are indispensable for material characterization, yet their weak signals remain highly prone to interference from environmental noise, instrumental artifacts, sample impurities, scattering effects, and radiation-based distortions (e.g., fluorescence and cosmic rays). These…
Nan Xia, Yunbao Huang, Haiyan Li, Pu Li + 2 more
In the experiment of inertial confinement fusion, soft X-ray spectrum unfolding can provide important information to optimize the design of the laser and target. As the laser beams increase, there are limited locations for installing detection channels to obtain measurements, and the soft X-ray spectrum can be…
Jose Velasco, Daniel Pizarro, Javier Macias-Guarasa
This paper presents a novel approach for indoor acoustic source localization using sensor arrays. The proposed solution starts by defining a generative model, designed to explain the acoustic power maps obtained by Steered Response Power (SRP) strategies. An optimization approach is then proposed to fit the model to…
Xiaonan Pan, Miao Yuan, Jianrui Zhang, Xiaojun Yu + 1 more
Optical coherence tomography (OCT) is an optical imaging modality that provides high-resolution cross-sectional imaging of biological tissues noninvasively. In Fourier-domain OCT, axial resolution is governed by both the center wavelength and the spectral bandwidth of the light source; therefore, limited or…
Shiang Hu, Pedro A. Valdes-Sosa
Neural oscillations excitability shape sensory, motor, and cognitive processes of the brain operation. To quantify the spectrum of brain magnetic and electrical recordings has become the main methodology to study neural oscillations. However, there is still lacking a valid approach, although the literatures on the…
Gabriel Barello, Adam S. Charles, Jonathan W. Pillow
The sparse coding model posits that the visual system has evolved to efficiently code natural stimuli using a sparse set of features from an overcomplete dictionary. The classic sparse coding model suffers from two key limitations, however: (1) computing the neural response to an image patch requires minimizing a…
Martin Wilson
Accurate analysis of metabolite levels from ^1^H MRS data is a significant challenge, typically requiring the estimation of approximately 100 parameters from a single spectrum. Signal overlap, spectral noise and common artefacts further complicate analysis, leading to instability and reports of poor agreement between…
Amir M Shamaei, Jana Starcukova, Zenon Starcuk
While the recommended analysis method for magnetic resonance spectroscopy data is linear combination model (LCM) fitting, the supervised deep learning (DL) approach for quantification of MR spectroscopy (MRS) and MR spectroscopic imaging (MRSI) data recently showed encouraging results; however, supervised learning…
Sebastian Reinhard, Dominic A. Helmerich, Dominik Boras, Markus Sauer + 1 more
Localization-based super-resolution microscopy resolves macromolecular structures down to a few nanometers by computationally reconstructing fluorescent emitter coordinates from diffraction-limited spots. The most commonly used algorithms are based on fitting parametric models of the point spread function (PSF) to a…