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๐Ÿ” jonah gabry ๐Ÿ“‚ Engineering
Showing 10 results for "jonah gabry" in Engineering
Engineering Preprint PDF DOI

Multiple-beam Interference Spectroscopy: Instrument Analysis and Spectrum Reconstruction

Mohamad Jouni, Daniele Picone, Mauro Dalla Mura ยท 2024

Hyperspectral imaging systems based on multiple-beam interference (MBI), such as Fabry-Perot interferometry, are attracting interest due to their compact design, high throughput, and fine resolution. โ€ฆ

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Engineering Preprint PDF DOI

Ultraspherical/Gegenbauer polynomials to unify 2D/3D Ambisonic directivity designs

Franz Zotter ยท 2024

This report on axisymmetric ultraspherical/Gegenbauer polynomials and their use in Ambisonic directivity design in 2D and 3D presents an alternative mathematical formalism to what can be read in, e.g.โ€ฆ

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Engineering Preprint PDF DOI

Interferometer response characterization algorithm for multi-aperture Fabry-Perot imaging spectrometers

Daniele Picone, Silvere Gousset, Mauro Dalla-Mura, Yann Ferrec, Etienne le-Coarer ยท 2023

In recent years, the demand for hyperspectral imaging devices has grown significantly, driven by their ability of capturing high-resolution spectral information. Among the several possible optical desโ€ฆ

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Engineering Preprint PDF DOI

Circularly Polarized Fabry-Perot Cavity Sensing Antenna Design using Generative Model

Kainat Yasmeen, Kumar Vijay Mishra, A. V. Subramanyam, Shobha Sundar Ram ยท 2022

We consider the problem of designing a circularly polarized Fabry-P'erot cavity (FPC) antenna for S-band sensing applications such as satellite navigation and communication. The spatial distribution oโ€ฆ

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Engineering Preprint PDF DOI

A machine learning accelerated inverse design of underwater acoustic polyurethane coatings with cylindrical voids

Hansani Weeratunge, Zakiya Shireen, Sagar Iyer, Richard Sandberg, Saman Halgamuge, Adrian Menzel, Andrew Phillips, Elnaz Hajizadeh ยท 2022

Here, we report the development of a detailed "Materials Informatics" framework for the design of acoustic coatings for underwater sound attenuation through integrating Machine Learning (ML) and statiโ€ฆ

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Engineering Preprint PDF DOI

Signal Processing Techniques to Reduce the Limit of Detection for Thin Film Biosensors

Simon J. Ward, Rabeb Layouni, Sofia Arshavsky-Graham, Ester Segal, Sharon M. Weiss ยท 2021

The ultimate detection limit of optical biosensors is often limited by various noise sources, including those introduced by the optical measurement setup. While sophisticated modifications to instrumeโ€ฆ

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Engineering Preprint PDF DOI

Digital synthesis of multistage etalons for enhancing the FSR

Faiza Iftikhar, Usman Khan, M. Imran Cheema ยท 2019

Fabry-Perot fiber etalons (FPE) built from three or more reflectors are attractive for a variety of applications including communications and sensing. For accelerating a research and development work,โ€ฆ

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Engineering Preprint PDF DOI

More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing

Henry Kvinge, Elin Farnell, Julia R. Dupuis, Michael Kirby, Chris Peterson, Elizabeth C. Schundler ยท 2019

Compressive sensing (CS) is a method of sampling which permits some classes of signals to be reconstructed with high accuracy even when they were under-sampled. In this paper we explore a phenomenon iโ€ฆ

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Engineering Preprint PDF DOI

Total variation vs L1 regularization: a comparison of compressive sensing optimization methods for chemical detection

Elin Farnell, Henry Kvinge, Julia R. Dupuis, Michael Kirby, Chris Peterson, Elizabeth C. Schundler ยท 2019

One of the fundamental assumptions of compressive sensing (CS) is that a signal can be reconstructed from a small number of samples by solving an optimization problem with the appropriate regularizatiโ€ฆ

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Engineering Preprint PDF DOI

A data-driven approach to sampling matrix selection for compressive sensing

Elin Farnell, Henry Kvinge, John P. Dixon, Julia R. Dupuis, Michael Kirby, Chris Peterson, Elizabeth C. Schundler, Christian W. Smith ยท 2019

Sampling is a fundamental aspect of any implementation of compressive sensing. Typically, the choice of sampling method is guided by the reconstruction basis. However, this approach can be problematicโ€ฆ

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