Related papers: Identification of molecular line emission using Co…

This paper reports on the use of a convolutional neural network (CNN) methodology to analyse fluorescence images of calcium-ion Coulomb crystals in the gas phase. A transfer-learning approach is adopted using the publicly available RESNET50…

In the present work, 3D convolutional neural networks (CNNs) are trained to link random heterogeneous, two-phase materials of arbitrary phase fractions to their elastic macroscale stiffness thus replacing explicit homogenization…

An emerging application of Raman spectroscopy is monitoring the state of chemical reactors during biologic drug production. Raman shift intensities scale linearly with the concentrations of chemical species and thus can be used to…

Line intensity maps (LIMs) are in principle sensitive to a large amount of information about faint, distant galaxies which are invisible to conventional surveys. However, actually extracting that information from a confused,…

Efficient and precise classification of histological cell nuclei is of utmost importance due to its potential applications in the field of medical image analysis. It would facilitate the medical practitioners to better understand and…

This paper describes the architecture and performance of ORACLE, an approach for detecting a unique radio from a large pool of bit-similar devices (same hardware, protocol, physical address, MAC ID) using only IQ samples at the physical…

In this paper, we examine the strength of deep learning technique for diagnosing lung cancer on medical image analysis problem. Convolutional neural networks (CNNs) models become popular among the pattern recognition and computer vision…

We present our results from training and evaluating a convolutional neural network (CNN) to predict galaxy shapes from wide-field survey images of the first data release of the Dark Energy Survey (DES DR1). We use conventional shape…

Computer Aided Diagnosis has emerged as an indispensible technique for validating the opinion of radiologists in CT interpretation. This paper presents a deep 3D Convolutional Neural Network (CNN) architecture for automated CT scan-based…

Nanopore sensing is a versatile technique for the analysis of molecules on the single-molecule level. However, extracting information from data with established algorithms usually requires time-consuming checks by an experienced researcher…

Tensor decomposition methods are widely used for model compression and fast inference in convolutional neural networks (CNNs). Although many decompositions are conceivable, only CP decomposition and a few others have been applied in…

Convolutional neural networks (CNNs) are widely used in computer vision. They can be used not only for conventional digital image material to recognize patterns, but also for feature extraction from digital imagery representing spectral and…

Polycrystalline materials have numerous applications due to their unique properties, which are often determined by the grain boundaries. Hence, quantitative characterization of grain as well as interface orientation is essential to optimize…

Remote observations of comets, especially using high spectral resolution millimeter spectroscopy, have enabled the detection of over 25 molecules in comets for the last twenty years. Among the molecules identified at radio wavelengths,…

This paper presents a data processing algorithm with machine learning for polarization extraction and event selection applied to photoelectron track images taken with X-ray polarimeters. The method uses a convolutional neural network (CNN)…

Light plays a vital role in vision either human or machine vision, the perceived color is always based on the lighting conditions of the surroundings. Researchers are working to enhance the color detection techniques for the application of…

This work proposes a spectral convolutional neural network (CNN) operating on laser induced breakdown spectroscopy (LIBS) signals to learn to (1) disentangle spectral signals from the sources of sensor uncertainty (i.e., pre-process) and…

Orion KL has served as a benchmark for spectral line searches throughout the (sub)millimeter regime. The main goal is to systematically study spectral characteristics of Orion KL in the 1.3 cm band. We carried out a spectral line survey…

Galaxies can be described by features of their optical spectra such as oxygen emission lines, or morphological features such as spiral arms. Although spectroscopy provides a rich description of the physical processes that govern galaxy…

The new generation of observatories and instruments (VLT/ERIS, JWST, ELT) motivate the development of robust methods to detect and characterise faint and close-in exoplanets. Molecular mapping and cross-correlation for spectroscopy use…