Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Video”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

INS Nuclear Material Accounting and Control Instructional Video Materials- FY 25 Guides

This training video provides an essential guide to understanding and utilizing Tamper Indicating Devices (TIDs), with a specific focus on a common Mylar (Adhesive) variety during the application process. TIDs are a critical part of nuclear security, used to detect unauthorized access to sensitive materials or facilities. The Mylar (adhesive) TID employes a tamper-evident film integrated into the TID that visually indicates any tampering attempts. This video will explain how an example Mylar (adhesive) TID works, demonstrate proper installation techniques, and highlight how to interpret its indicators to ensure the security of nuclear materials.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Iterative Learning Control for Video-rate Atomic Force Microscopy

We present a control scheme for video-rate atomic force microscopy with rosette pattern. The controller structure involves a feedback internal-model-based controller and a feedforward iterative learning controller. The iterative learning controller is designed to improve tracking performance of the feedback-controlled scanner by rejecting the repetitive disturbances arising from the system nonlinearities. We investigate the performance of two inversion techniques for constructing the learning filter. We conduct tracking experiments using a two-degree-of-freedom microelectromechanical system (MEMS) nanopositioner at frame rates ranging from 5 to 20 frames per second. Furthermore, the results reveal that the algorithm converges rapidly and the iterative learning controller significantly reduces both the transient and steady-state tracking errors. We acquire and report a series of high-resolution time-lapsed video-rate AFM images with the rosette pattern.

42 ENGINEERING↗
DOE OSTI2021

Experimental Images and Videos of Foam Stability (Half-life)

The experimental data obtained in this project is the thermal stability data of various foams measured using the setup established at Temple University during this study. The setup is installed with a portable digital camera which can take images and videos of foam evolution at a given pressure and temperature condition. Consequently, the half-life data was recorded from the images/videos, which are used as a measure of the thermal stability for foams. Over the 3 years of this project, four different surfactants and five different stabilizing agents were studied. The surfactants are, Alfa Olefin Sulfonate (AOS), Sodium Dodecyl Sulphate (SDS), Tergitol (NP-40), and Cetyltrimethylammonium chloride (CTAC). The stabilizing agents are, guar gum, bentonite clay, crosslinking agents, silicon dioxide nanoparticles (60 to 70nm), and graphene oxide dispersions. Foam stability was evaluated at different temperatures between 100C and 200cC, while the foam generation pressure varied between atmospheric pressure (14.7 psi) and 1000 psi. The images are saved as .jpg file and videos are saved as .avi files.

15 GEOTHERMAL ENERGY↗
NASA NTRS1996

Video-task acquisition in rhesus monkeys (Macaca mulatta) and chimpanzees (Pan troglodytes): a comparative analysis

This study describes video-task acquisition in two nonhuman primate species. The subjects were seven rhesus monkeys (Macaca mulatta) and seven chimpanzees (Pan troglodytes). All subjects were trained to manipulate a joystick which controlled a cursor displayed on a computer monitor. Two criterion levels were used: one based on conceptual knowledge of the task and one based on motor performance. Chimpanzees and rhesus monkeys attained criterion in a comparable number of trials using a conceptually based criterion. However, using a criterion based on motor performance, chimpanzees reached criterion significantly faster than rhesus monkeys. Analysis of error patterns and latency indicated that the rhesus monkeys had a larger asymmetry in response bias and were significantly slower in responding than the chimpanzees. The results are discussed in terms of the relation between object manipulation skills and video-task acquisition.

NASA Discipline Space Human Factors↗
NASA NTRS1996

Measuring eye movements during locomotion: filtering techniques for obtaining velocity signals from a video-based eye monitor

Video-based eye-tracking systems are especially suited to studying eye movements during naturally occurring activities such as locomotion, but eye velocity records suffer from broad band noise that is not amenable to conventional filtering methods. We evaluated the effectiveness of combined median and moving-average filters by comparing prefiltered and postfiltered records made synchronously with a video eye-tracker and the magnetic search coil technique, which is relatively noise free. Root-mean-square noise was reduced by half, without distorting the eye velocity signal. To illustrate the practical use of this technique, we studied normal subjects and patients with deficient labyrinthine function and compared their ability to hold gaze on a visual target that moved with their heads (cancellation of the vestibulo-ocular reflex). Patients and normal subjects performed similarly during active head rotation but, during locomotion, patients held their eyes more steadily on the visual target than did subjects.

NASA Discipline Neuroscience↗
NASA NTRS1994

NASA's K/Ka-Band Broadband Aeronautical Terminal for Duplex Satellite Video Communications

JPL has recently begun the development of a Broadband Aeronautical Terminal (BAT) for duplex video satellite communications on commercial or business class aircraft. The BAT is designed for use with NASA's K/Ka-band Advanced Communications Technology Satellite (ACTS). The BAT system will provide the systems and technology groundwork for an eventual commercial K/Ka-band aeronautical satellite communication system. With industry/government partnerships, three main goals will be addressed by the BAT task: 1) develop, characterize and demonstrate the performance of an ACTS based high data rate aeronautical communications system; 2) assess the performance of current video compression algorithms in an aeronautical satellite communication link; and 3) characterize the propagation effects of the K/Ka-band channel for aeronautical communications.

communications↗
DOE OSTI2022

DefectTrack: a deep learning-based multi-object tracking algorithm for quantitative defect analysis of in-situ TEM videos in real-time

Abstract In-situ irradiation transmission electron microscopy (TEM) offers unique insights into the millisecond-timescale post-cascade process, such as the lifetime and thermal stability of defect clusters, vital to the mechanistic understanding of irradiation damage in nuclear materials. Converting in-situ irradiation TEM video data into meaningful information on defect cluster dynamic properties (e.g., lifetime) has become the major technical bottleneck. Here, we present a solution called the DefectTrack , the first dedicated deep learning-based one-shot multi-object tracking (MOT) model capable of tracking cascade-induced defect clusters in in-situ TEM videos in real-time. DefectTrack has achieved a Multi-Object Tracking Accuracy (MOTA) of 66.43% and a Mostly Tracked (MT) of 67.81% on the test set, which are comparable to state-of-the-art MOT algorithms. We discuss the MOT framework, model selection, training, and evaluation strategies for in-situ TEM applications. Further, we compare the DefectTrack with four human experts in quantifying defect cluster lifetime distributions using statistical tests and discuss the relationship between the material science domain metrics and MOT metrics. Our statistical evaluations on the defect lifetime distribution suggest that the DefectTrack outperforms human experts in accuracy and speed.

42 ENGINEERING↗
DOE OSTI2024

Event-to-Video Conversion for Overhead Object Detection

Collecting overhead imagery using an event camera is desirable due to the energy efficiency of the image sensor compared to standard cameras. However, event cameras complicate downstream image processing, especially for complex tasks such as object detection. In this paper, we investigate the viability of event streams for overhead object detection. We demonstrate that across a number of standard modeling approaches, there is a significant gap in performance between dense event representations and corresponding RGB frames. We establish that this gap is, in part, due to a lack of overlap between the event representations and the pre-training data that the object detectors were initially trained on through a number of experiments. Then, apply an off-the-shelf event-to-video conversion tool that converts event streams into gray-scale video to close this gap. We demonstrate that this approach results in a large performance increase, outperforming even event-specific object detection techniques on our overhead target task. These results suggest that better aligning event representations with existing large pre-trained models may result in greater short-term performance gains compared to end-to-end event-specific architectural improvements.

machine learning (ML), computer vision, Neuromorph↗
DOE OSTI2024

Avian Activity Classification Using Recurrent Networks to Fuse Videos with Metadata on Imbalanced Datasets

Activity classification plays a crucial role in various real-life scenarios involving both humans and animals. There is an increasing need for precise activity classification focused on avian-solar interactions, as the usage of solar energy facilities, such as photovoltaic array power stations, has been observed to impact bird species richness, behavior, and activity. However, there has been no work to develop an automated system to monitor and classify these avian-solar interactions. All current methods rely on human observers, which is time and human resources costly and subject to errors related to searcher efficiency. With the recent success of Deep Learning models in activity classification problems, this paper develops a recurrent neural network-based model to automatically classify six avian activities around solar energy facilities. Our proposed model integrates critical feature engineering metadata with video frame data, enabling improved learning and more accurate activity classification. Furthermore, we address the challenge of data imbalance during training and demonstrate the efficacy of our model in detecting and classifying different activities within video tracks. Additionally, we analyze the saliency/backpropagation map of the trained proposed model and validate its decision-making rationale.

Avian activity classification; bidirectional LSTM;↗
DOE OSTI2024

Temperature, Humidity, and Time-Lapse Video Data from the East River Watershed, Water Year 2024

A new version of this dataset is available at doi:10.15485/3001338 and is the first citation in the 'Related References' section. It is expands on this dataset by appending another water year of data collection and additional logger sites.This dataset contains time-lapse imagery and distributed measurements of air temperature, relative humidity, dew point, and soil temperature across the East River basin from 3 October 2023 to 12 August 2024. Instruments were deployed at 14 sites as part of the DOE Grant: Seasonal Cycles Unravel Mysteries of Missing Mountain Water organized by Jessica Lundquist (University of Washington), Rosemary Carroll (Desert Research Institute), and Ethan Gutmann (National Center for Atmospheric Research). The data are published to support studies of surface climate or hydrologic processes in complex terrain. Measurements were collected with low-cost data loggers installed 2 m high on evergreen trees or buried just below the soil surface. Time-lapse cameras were deployed at three sites. Imagery from sites AP BONUS and AP5 provides insight into large-scale seasonal snow cover variability. Imagery from site EL2 shows smaller-scale snow patterns across a nearby meadow.Dataset files are organized by site and variable (air measurements, ground measurements, or time-lapse video). Air and ground measurements are packaged in LoggerData.zip, and time-lapse imagery is compiled into short videos stored in TimelapseVideos.zip. File-level metadata contains details for each file included in the dataset. A data dictionary provides units and descriptions for column or row names in all files. The locations metadata file describes site characteristics, locations, and associated GPS methods.Dataset update 2025-03-03: Resolved header and datetime formatting inconsistencies within LoggerData.zip files KP1_Air, KP3_Air, KP3_Ground, KP6_Ground, AP3_Ground, AP4_Air, and AP6_Air.Dataset update 2025-11-18: Modified abstract and related references sections to include new version of dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Temperature, Humidity, and Time-Lapse Video Data from Yosemite National Park, Water Year 2024

This dataset contains time-lapse imagery and distributed measurements of air temperature, relative humidity, dew point, and soil temperature across Yosemite National Park from October 2023 to September 2024. Instruments were deployed at 10 sites in two cross-valley transects as part of the DOE Grant: Seasonal Cycles Unravel Mysteries of Missing Mountain Water organized by Jessica Lundquist (University of Washington), Rosemary Carroll (Desert Research Institute), and Ethan Gutmann (National Center for Atmospheric Research). The data are intended to support hydrologic modeling efforts to better resolve the fate of mountain water, and are published to support studies of surface climate or hydrologic processes in complex terrain. Measurements were collected with low-cost data loggers installed 2 m high on evergreen trees or buried just below the soil surface. A time-lapse camera at one site captures valley-scale seasonal snow cover variability.Dataset files are organized by site and variable (air measurements, ground measurements, or time-lapse video). Air and ground measurements are packaged in LoggerData.zip, and time-lapse imagery is compiled into a short video stored in TimelapseVideos.zip. File-level metadata contains details for each file included in the dataset. A data dictionary provides units and descriptions for column or row names in all files. The locations metadata file describes site characteristics, locations, and associated GPS methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Temperature, Humidity, and Time-Lapse Video Data from the East River Watershed, Water Years 2024 and 2025

This dataset contains time-lapse imagery and distributed measurements of air temperature, relative humidity, dew point, and soil temperature across the East River basin from 3 October 2023 to 8 August 2025. Instruments were deployed at 19 sites as part of the DOE Grant: Seasonal Cycles Unravel Mysteries of Missing Mountain Water organized by Jessica Lundquist (University of Washington), Rosemary Carroll (Desert Research Institute), and Ethan Gutmann (National Center for Atmospheric Research). The data are published to support studies of surface climate or hydrologic processes in complex terrain. Measurements were collected with low-cost data loggers installed 2 m high on evergreen trees or buried just below the soil surface. Time-lapse cameras were deployed at three sites. Imagery from sites AP BONUS and AP5 (Avery Picnic) provides insight into large-scale seasonal snow cover variability. Imagery from site EL2 (Emerald Lake) shows smaller-scale snow patterns across a nearby meadow. Dataset files are organized by site and variable (air measurements, ground measurements, or time-lapse video). Air and ground measurements are packaged in LoggerData.zip, and time-lapse imagery is compiled into short videos stored in TimelapseVideos.zip. File-level metadata contains details for each file included in the dataset. A data dictionary provides units and descriptions for column or row names in all files. The locations metadata file describes site characteristics, locations, and associated GPS methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Videos and front speeds of frontal ring-opening metathesis polymerization (FROMP) of DCPD/ENB with norbornene-functionalized PDMS comonomers

This dataset contains videos and front speed measurements for 16 frontal ring-opening metastasis polymerization experiments of dicyclopentadiene (DCPD)/5-ethylidene-2-norbornene (ENB) resins and norbornene-functionalized polydimethylsiloxane (nor-PDMS) comonomers. Each run was carried out in a 10 mm diameter glass test tube and recorded to quantify front propagation behavior. Reported front speeds were extracted by video tracking and reported maximum front temperatures were measured with a thermocouple.

Clarke, Brandon R.↗
DOE OSTI2021

Deliberate Motion Analytics Fused Radar and Video Test Results Deployed Beyond the Perimeter Fence in a High Noise Environment

Security systems that protect the nation’s critical facilities must be capable of detecting physical intrusions in all weather conditions. Intrusion detection sensors in a perimeter with a high nuisance alarm rate (NAR) significantly undermine detection performance and degrade security system effectiveness. This research demonstrated a fused sensor system that can differentiate foliage and weather-induced nuisance alarms from those caused by intruders, providing reliable detection within a two-fence perimeter or beyond the fence. A key element of this work is the creation and application of a “deliberate motion algorithm” that fuses alarm data from radar and video analytics to create video motion detection fused radar system. The two-layer architecture of the algorithm uses machine learning, multi-hypothesis tracking, and Dynamic Bayes Nets to differentiate intruder alarms from weather induced alarms.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

C.9 Scribe NMAC Layer Video Development

This task incorporates NMAC insider threat principles and good practices into video format for aiding in training activities. It was labeled as Task C.9 under the FY24 Annex 1 for NA-211 SSM A&I Pillar. For activity development, the setting of a research and test reactor, RTR, was selected as the focus of this fiscal year. More specifically, a TRIGA reactor was selected for facility and reactor inspiration. FY 23/24 developed materials Research Reactor Facility Measures against the Insider Threat, LLNL-MI-839144, was used as a reference for selecting relevant, suggested mitigation measures to incorporate into the script and subsequent videos. Throughout the initial phases of development, discipline SMEs met with Scribe SMEs from Sandia National Laboratory to create the initial story arc and assess the limits of Scribe/Odin technologies available for the project. As a result, the decision for a two-fold deliverable was developed and utilized as the basis for script development efforts over the first half of the fiscal year.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Two-Dimensional Video Disdrometer (VDIS) Instrument Handbook

In order to improve the quantitative description of precipitation processes in climate models, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility has been collecting observations of the drop size spectra of rain events since early in 2006 with impact disdrometers. While ARM is primarily interested in their contribution to the understanding of precipitation processes, disdrometers can also be used for traffic control, airport observation systems, and hydrology. The latest disdrometers employ microwave or laser technologies. Two-dimensional (2D) video disdrometers (VDIS) make the most detailed and complete observations of hydrometeor shape and fall velocity. Four of ARM’s five video disdrometers are deployed; the last is a spare. This handbook provides a detailed description of the instruments and their datastreams.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1967

Video synchronization processor overcomes poor signal-to-noise ratio

Video synchronization processor overcomes poor signal-to-noise ratio which occurs during adverse signal conditions caused by flame attenuation, antenna pattern nulls, and near-horizon tracking. The system maintains sync lock far below the point where excessive noise would normally render the video useless.

Webb, D. L.↗