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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Solid-state Image Sensor with Focal-plane Digital Photon-counting Pixel Array

A solid-state focal-plane imaging system comprises an NxN array of high gain. low-noise unit cells. each unit cell being connected to a different one of photovoltaic detector diodes, one for each unit cell, interspersed in the array for ultra low level image detection and a plurality of digital counters coupled to the outputs of the unit cell by a multiplexer(either a separate counter for each unit cell or a row of N of counters time shared with N rows of digital counters). Each unit cell includes two self-biasing cascode amplifiers in cascade for a high charge-to-voltage conversion gain (greater than 1mV/e(-)) and an electronic switch to reset input capacitance to a reference potential in order to be able to discriminate detection of an incident photon by the photoelectron (e(-))generated in the detector diode at the input of the first cascode amplifier in order to count incident photons individually in a digital counter connected to the output of the second cascade amplifier. Reseting the input capacitance and initiating self-biasing of the amplifiers occurs every clock cycle of an integratng period to enable ultralow light level image detection by the may of photovoltaic detector diodes under such ultralow light level conditions that the photon flux will statistically provide only a single photon at a time incident on anyone detector diode during any clock cycle.

Fossum, Eric R.↗
NASA NTRS2017

Radiation and Temperature Hard Multi-Pixel Avalanche Photodiodes

The structure and method of fabricating a radiation and temperature hard avalanche photodiode with integrated radiation and temperature hard readout circuit, comprising a substrate, an avalanche region, an absorption region, and a plurality of Ohmic contacts are presented. The present disclosure provides for tuning of spectral sensitivity and high device efficiency, resulting in photon counting capability with decreased crosstalk and reduced dark current.

Bensaoula, Abdelhak↗
NASA NTRS2019

Pixel-Based Model For High Latitude Dust Detection

Dust has implications on the energy budget, ocean biodiversity, and economy at regional and global scales. Dust detection relies on spectral sensitivity at visible (RGB) and infrared wavelengths. Radiative properties of high latitude dust and the background surface albedo in these regions (>40°N, >40°S) complicate current dust detection methods. Leveraging supervised machine learning (ML) methods, we propose a new method accounting for regional differences of dust occurrence.

High latitude dust↗
NASA NTRS2019

Pixel Based Model For High Latitude Dust Detection

Current methods of dust detection rely on spectral sensitivity at visible (RGB) and infrared wavelengths. However, their application on different regions needs to be tuned to mitigate errors associated with background properties. High latitude dust (HLD) regions are characterized by surface with variable albedos and land cover, thus further complicating the dust detection. Leveraging supervised machine learning (ML) methods, we propose a new method accounting for regional differences of dust occurrence.

High latitude dust↗
NASA NTRS2019

TES Pixel Optimization for the ATHENA X-IFU Instrument

ATHENA is a European led x-ray observatory due for launch in the early 2030s. The X-ray Integral Field Unit (X-IFU) instrument on ATHENA will have an array of 3168 TES microcalorimeters, 2.5 eV resolution (at 7 keV), 5 arc second angular resolution and a 5 arc minute field of view. As one example, will allow unprecedented views of composition and dynamics of galaxy clusters.

Wakeham, Nick↗
NASA NTRS2019

Pixel Based Model for High Latitude Dust Detection

High Latitude Dust (HLD, ≥ 50°𝑁 𝑎𝑛𝑑 ≥ 40°𝑆 ) load has implications on the energy budget, ocean biodiversity and economy on a regional and global scale. Current methods of dust detection rely on spectral sensitivity at visible (RGB) and infrared wavelengths. The characteristics of HLD vary according to the sediments and sedimentary processes operating on the land surface that are the source of the dust particles. Leveraging machine learning (ML) methods, we propose a new detection method based on convolutional neural network (CNN) using true color images.

Priftis, Georgios↗