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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Non-destructive, in-situ evaluation of water presence using thermal contrast and cooled detector

Exemplary systems for detecting water include: a light source positioned to transmit thermal radiation through a sample; a lens assembly positioned to: receive the thermal radiation transmitted through the sample; and focus the transmitted thermal radiation onto a filter positioned between the lens assembly and a detector; and a cooling subsystem for cooling the filter and the detector to a temperature below that of the sample. The filter (1) selectively transmits first portions of the thermal radiation received from the lens assembly and characterized by a wavelength at least partially overlapping a predefined water absorption band and/or a predefined water absorption line; and (2) selectively blocks second portions of the thermal radiation received from the lens assembly and characterized by a wavelength outside the predefined water absorption band and/or the predefined water absorption line. Methods for detecting presence of water in a sample are also disclosed.

Bora, Mihail↗
DOE OSTI2022

Non-destructive, in-situ evaluation of water presence using thermal contrast and cooled detector

Exemplary systems for detecting water include: a light source positioned to transmit thermal radiation through a sample; a lens assembly positioned to: receive the thermal radiation transmitted through the sample; and focus the transmitted thermal radiation onto a filter positioned between the lens assembly and a detector; and a cooling subsystem for cooling the filter and the detector to a temperature below that of the sample. Methods for detecting presence of water in a sample are also disclosed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Non-destructive, in-situ evaluation of water presence using thermal contrast and cooled detector

Exemplary methods for detecting presence of water in a sample include: heating a light source to a predetermined temperature at which the light source emits thermal radiation; placing a sample between the light source and a detector; transmitting the thermal radiation from the light source through the sample and onto the detector; and determining a presence or an absence of water within the sample based on the thermal radiation transmitted onto the detector. Exemplary systems for detecting presence of water in a sample are also disclosed.

Bora, Mihail↗
DOE OSTI2023

Serial Moire scanning phase contrast x-ray imaging

Method include emitting x-rays from an x-ray source, directing a first portion of the x-rays through an object grating situated adjacent to an object while the object is scanned relative to the object grating along a scan direction, directing a second portion of the x-rays through the object and subsequently through a detector grating without transmitting through the object grating, wherein the object grating and detector grating are adjacently arranged in a field of view of the x-rays sequentially with respect to each other in the scan direction, and receiving the first portion transmitted through the object and object grating with a first portion of a detector and receiving the second portion transmitted through the object and the detector grating with a second portion of the detector adjacent to the first portion of the detector. Systems are also disclosed, along with related techniques for beam hardening correction.

Miller, Erin A.↗
DOE OSTI2025

Contrasting Time-Frequency Representations for Unknown Waveform Detection

In real-world applications like spectrum management and interference detection, dealing with unseen electromagnetic waveforms is critical. Although some methods attempt to simulate open set data using generator models, they face challenges in generating synthetic samples for open set while simultaneously selecting an optimal discriminator for accurate classification. This results in difficulties capturing distinctive features across classes, especially in dynamic scenarios where new classes emerge. To detect unseen waveforms, we propose combining time and frequency domain features with cosine similarity loss to enhance feature distinctiveness and enabling more accurate predictions. This approach efficiently captures more comprehensive information than single-domain representations or approaches without cosine loss. Additionally, our model avoids generic feature vectors by extracting class-specific features during training, resulting in improved class representation. The experiment results show that this combined feature approach with cosine loss outperforms single-domain models and improves accuracy by 10\% over models without cosine loss.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Contrasting Time-Frequency Representations for Unknown Waveform Detection

Identifying unseen electromagnetic waveforms is critical for many applications, like interference management, electronic warfare and spectrum management. Traditionally this is done using statistical methods for anomaly detection, which has evolved to deep learning models for identifying the unseen data, formally termed as open set recognition. Some prior methods use a generative model to emulate open set data, which face challenges in generating synthetic samples for open set while simultaneously selecting an optimal discriminator for accurate classification. To alleviate this issue, we propose a discriminative model that effectively combines time and frequency domain features of communication signals for accurate predictions. We further introduce a cosine similarity loss that makes the domain specific features unique to enhance the prediction rate. Additionally, our model avoids generic feature vectors by extracting class-specific features during training, resulting in improved class representation. The experiment results show that this combined feature approach with cosine loss outperforms single-domain models and improves accuracy by 10% over models without cosine loss.

99 - GENERAL AND MISCELLANEOUS↗