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Mathematics & Statistics

Image & signal processing

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Displaying 26 - 50 of 311

Investigating the Influence of Sampling Frequency on X-Space MPI Image Reconstructions

March 19, 2023
Author(s)
Mark-Alexander Henn, Klaus Natorf Quelhas, Solomon I. Woods
In this presentation we employ a direct X-space deconvolution to estimate particle distributions from MPI data. We report on how the accuracy of those estimations changes as a function of sampling frequency and compare the findings to the MPI core operator

Parallel MPI image reconstructions in GPU using CUDA

March 19, 2023
Author(s)
Klaus Natorf Quelhas, Mark-Alexander Henn, Ricardo Farias, Weston L. Tew, Solomon I. Woods
This work shows that it is possible to obtain faster MPI image reconstructions by implementing the algorithms in parallel in Graphics Processing Units (GPUs) using NVIDIA's CUDA (Compute Unified Device Architecture). While the parallel Kaczmarz's algorithm

Colloquium: Advances in automation of quantum dot devices control

February 17, 2023
Author(s)
Justyna Zwolak, Jacob Taylor
Arrays of quantum dots (QDs) are a promising candidate system to realize scalable, coupled qubit systems and serve as a fundamental building block for quantum computers. In such semiconductor quantum systems, devices now have tens of individual

Separable Shape Tensors for Aerodynamic Design

January 31, 2023
Author(s)
Zachary J. Grey, Andrew Glaws, Olga Doronina
Airfoil shape design is a classical problem in engineering and manufacturing. In this work, we combine principled physics-based considerations for the shape design problem with modern computational techniques using a data-driven approach. Modern and

Restricted Domain Compressive Sensing for Antenna Metrology

November 21, 2022
Author(s)
Marc Valdez, Alex Yuffa, Michael Wakin
In this paper, we prove a compressive sensing guarantee for restricted measurement domains in spherical near-field to far-field transformations for antenna metrology. We achieve this by first defining Slepian functions on a measurement sub-domain $R$ of

Application of machine learning to reflection high-energy electron diffraction images for automated structural phase mapping

June 29, 2022
Author(s)
Haotong Liang, Valentin Stanev, A. Gilad Kusne, Yuuto Tsukahara, Ama Itou, Ryota Takahashi, Mikk Lippmaa, Ichiro Takeuchi
We have developed a phase mapping method based on machine learning analysis of reflection high-energy electron diffraction (RHEED) images. RHEED produces diffraction patterns containing a wealth of static and dynamic information and is commonly used to

Phase retrieval based on deep learning in grating interferometer

April 25, 2022
Author(s)
Ohsung Oh, Youngju Kim, Daeseung Kim, Daniel Hussey, Seung Wook Lee
Grating interferometry is a promising technique to obtain differential phase contrast images with illumination source of low intrinsic transverse coherence. However, retrieving the phase contrast image from the differential phase contrast image is

Gauging Difficulty of Image Segmentation

March 25, 2022
Author(s)
Marek Franaszek
Image segmentation is the first step in a complex process of object recognition. This report presents a method to gauge the difficulty of segmentation by calculating a scalar parameter Q for an image. This parameter depends on a distribution of the

Open Media Forensics Challenge 2022 Evaluation Plan

March 3, 2022
Author(s)
Haiying Guan, Yooyoung Lee, Lukas Diduch
This document describes the system evaluation tasks supported by the Open Media Forensics Challenge (OpenMFC) 2022. The evaluation plan covers resources, task definitions, task conditions, file formats for system inputs and outputs, evaluation metrics

Toward Robust Autotuning of Noisy Quantum Dot Devices

February 25, 2022
Author(s)
Joshua Ziegler, Thomas McJunkin, Emily Joseph, Sandesh Kalantre, Benjamin Harpt, Donald Savage, Max Lagally, Mark Eriksson, Jacob Taylor, Justyna Zwolak
The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy or otherwise low-quality data is processed by an autonomous system. In this

A Convolutional Neural Networks-Based Approach for Texture Directionality Detection

January 12, 2022
Author(s)
Marcin Kociolek, Michal Kozlowski, Antonio Cardone
The perceived texture directionality is an important, not fully explored image characteristic. In many applications texture directionality detection is of fundamental importance. Several approaches have been proposed, such as the fast Fourier-based method

AbsorbanceQ: An App for Generating Absorbance Images from Brightfield Images

January 4, 2022
Author(s)
Stephen Zimmerman, Carl Simon Jr., Greta Babakhanova
The AbsorbanceQ app converts brightfield microscope images into absorbance images that can be analyzed and compared across different operators, microscopes and time. Absorbance-based measurements are comparable, which is useful when the aim is to

NUScon: A community-driven platform for quantitative evaluation of nonuniform sampling in NMR

November 25, 2021
Author(s)
Yulia Pustovalova, Frank Delaglio, Darien Craft, Hari Arthanari, Ad Bax, Martin Billeter, Mark Bostock, Hesam Dashti, Flemming Hansen, Sven Hyberts, Bruce Johnson, Krzysztof Kazimierczuk, Hengfa Lu, Mark W. Maciejewski, Tomas Miljenovic, Mehdi Mobli, Daniel Nietlispach, Vladislav Orekhov, Robert Powers, Xiaobo Qu, Scott Robson, David Rovnyak, Gerhard Wagner, Jinfa Ying, Matthew Zambrello, Jeffrey C. Hoch, David Donoho, Adam D. Schuyler
Although the concepts of non-uniform sampling (NUS) and non-Fourier spectral reconstruction in multidimensional NMR began to emerge four decades ago (Bodenhausen and Ernst, 1981; Barna and Laue, 1987), it is only relatively recently that NUS has become

IN-PROCESS DATA FUSION FOR PROCESS MONITORING AND CONTROL OF METAL ADDITIVE MANUFACTURING

November 17, 2021
Author(s)
Zhuo Yang, Yan Lu, Simin Li, Jennifer Li, Yande Ndiaye, Hui Yang, Sundar Krishnamurty
To accelerate the adoption of Metal Additive Manufacturing (MAM) for production, an understanding of MAM process-structure-property (PSP) relationships is indispensable for quality control. A multitude of physical phenomena involved in MAM necessitates the
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