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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Sixteen multiple-amplifier sensing charge-coupled devices and characterization techniques targeting the next generation of astronomical instruments

We present a candidate sensor for future spectroscopic applications, such as a Stage-5 Spectroscopic Survey Experiment or the Habitable Worlds Observatory. This type of charge-coupled device (CCD) sensor features multiple in-line amplifiers at its output stage allowing multiple measurements of the same charge packet, either in each amplifier or in the different amplifiers. Recently, the operation of an eight-amplifier sensor has been experimentally demonstrated, and we present the operation of a 16-amplifier sensor. This new sensor enables a noise level of ∼1 erms− with a single sample per amplifier. In addition, it is shown that sub-electron noise can be achieved using multiple samples per amplifier. In addition to demonstrating the performance of the 16-amplifier sensor, we aim to create a framework for future analysis and performance optimization of this type of detectors. New models and techniques are presented to characterize specific parameters, which are absent in conventional CCDs and Skipper CCDs: charge transfer between amplifiers and independent and common noise in the amplifiers and their processing.

16 multiple-amplifer sensing CCD (MAS-CCD)↗
NASA NTRS2005

Hubble Space Telescope Fine Guidance Sensor and Two-Gyro Control Law Design, Implementation, and On-Orbit Performance

For fifteen years, the science mission of the Hubble Space Telescope (HST) required using at least three rate gyros for n Controlling with alternate sensors to replace failing gyros can extend the HST science mission. A two-gyro control law has been designed and implemented using magnetometers, star trackers, and Fine Guidance Sensors (FGSs) to control vehicle rate about the missing gyro axis. The three aforementioned sensors are used in succession to reduce HST boresight jitter to less than 7 milli-arcseconds rms prior to science imaging. The Magnetometer and 2-Gyro (M2G) control law is used for large angle maneuvers and attitude control during earth. occultation of star trackers and FGSs. The Tracker and 2-Gyro (T2G) control law dampens M2G rates and controls attitude in preparation for guide star acquisition with the FGSs. The Fine Guidance Sensor and 2-Gyro (F2G) control law dampens T2G rates and controls HST attitude during science imaging. This paper describes the F2G control law. Details of F2G algorithms are presented, including computation of the FGS-measured star vector using non-linear equations, optimal estimation of HST body rate, design of the F2G control laws and gyro bias observer, SISO and MIMO linear stability analyses, and design of the F2G intramode transition and guide star acquisition logic. Results from an FGS flight spare ground test are presented that define acceptable HST jitter levels for successful guide star acquisition under two-gyro control. HST-specific disturbance and noise models are described that are based upon flight telemetry; these models are used in HSTSIM, a high-fidelity non-linear time domain simulation, to predict HST on-orbit disturbance responses and FGS interferometer Loss of Lock (LOL) characteristics under F2G control. Additional HSTSIM results are presented predicting HST quiescent boresight jitter performance, science maneuver performance, and observer configuration performance during F2G operation. Simulation results are compared to on-orbit data b m F2G flight tests performed in February 2005. Science images and point spread functions from the Advanced Camera for Surveys (ACS) High Resolution Camera (HRC) are presented that compare HST science performance under F2G versus three-gyro control. Images and flight telemetry show that HST boresight jitter with the new F2G control law is usually less than jitter using the three-gyro law, and HST boresight jitter during F2G operation is dependent upon guide star magnitude.

Clapp, Brian R.↗
NASA NTRS1985

Thematic Mapper Data Quality and Performance Assessment in Renewable Resources/agriculture/remote Sensing

Analysis of the early thematic mapper (TM) data indicate the TM sensor and associated ground processing are performing equal to the high expectations and within advertised specifications. The overall TM system with improved resolution, together with additional and more optimumly placed spectral bands shows much promise for benefits in future analysis activities. By selecting man-made features of known dimensions (e.g., highways, airfields, buildings, and isolated water bodies), an assessment was made of the TM performance relative to the specified 30-meter (98-foot) resolution. The increase of spatial resolution of TM (30 m) over MSS (80 M) appears to be significant not only in resolving spectrally distinct classes that were previously undefinable but also in distinguishing within-field variability. An Important result of the early TM evaluation and pre-TM analyses was the development of an integrated system to receive LANDSAT-4 TM (as well as MSS) data and analyze the data via various approaches.

Bizzell, R. M.↗
DOE OSTI2022

DRIPS: Dynamic Rebalancing of Pipelined Streaming Applications on CGRAs

Coarse-grained reconfigurable arrays (CGRAs) provide higher flexibility than application-specific integrated circuits (ASICs) and higher efficiency than fine-grained reconfigurable devices such as Field Programmable Gate Arrays (FPGAs). However, CGRAs are generally designed to support offloading of a single kernel. While their design, based on communicating functional units, appears to naturally suit data streaming applications composed of multiple cooperating kernels, current approaches only statically partition the resources across application kernels. However, emerging streaming applications at the edge (scientific instruments, sensor networks, network processing) perform much more than digital signal processing and often are data and input dependent. This leads to extremely variable kernel execution times, severely impacting the throughput of the entire pipeline if resources are only statically allocated. Therefore, in this paper, we propose DRIPS — a coarse-grained, dynamically, and partially reconfigurable array for data-dependent streaming applications. We present a unified compiler framework to facilitate the mapping of a given streaming application onto the DRIPS CGRA architecture. The experimental results show that DRIPS achieves an average throughput improvement of 1.46$\times$ across a set of representative applications over a statically partitioned solution. The additional area overhead to enable dynamic rebalancing consumes 16.34% of the entire area for a 5x5 CGRA prototype.

Tan, Cheng↗
DOE OSTI2023

Measurement and applications: Exploring the challenges and opportunities of hierarchical federated learning in sensor applications

Sensor applications have become ubiquitous in modern society as the digital age continues to advance. AI-based techniques (e.g., machine learning) are effective at extracting actionable information from large amounts of data. An example would be an automated water irrigation system that uses AI-based techniques on soil quality data to decide how to best distribute water. However, these AI-based techniques are costly in terms of hardware resources, and Internet-of-Things (IoT) sensors are resource-constrained with respect to processing power, energy, and storage capacity. These limitations can compromise the security, performance, and reliability of sensor-driven applications. To address these concerns, cloud computing services can be used by sensor applications for data storage and processing. Unfortunately, cloud-based sensor applications that require real-time processing, such as medical applications (e.g., fall detection and stroke prediction), are vulnerable to issues such as network latency due to the sparse and unreliable networks between the sensor nodes and the cloud server [1]. As users approach the edge of the communications network, latency issues become more severe and frequent. A promising alternative is edge computing, which provides cloud-like capabilities at the edge of the network by pushing storage and processing capabilities from centralized nodes to edge devices that are closer to where the data are gathered, resulting in reduced network delays [2], [3].

Po-Leen Ooi, Melanie↗
DOE OSTI2022

Feature Extraction for Pipeline Defects Inspection Based Upon Distributed Acoustic Fiber Optic Sensing Data

Fiber-optic distributed acoustic sensing (DAS) is becoming an increasingly important tool for real-time monitoring of energy and civil infrastructure structural health such as pipelines. We present a systematic theoretical study of the potential for DAS to be directly coupled with guided ultrasonic waves typically used in conventional acoustic non-destructive evaluation (NDE) methods for real-time pipeline health monitoring. We are referring to this innovative new NDE technique as ultrasonic guided wave and optical fiber sensor fusion. In the practical application of DAS coupled with guided ultrasonic waves, the structural design of (1) the specific guided waves excited, (2) the physical installation of the acoustic transducers and the fiber optic sensors, and (3) the functional performance specifications (gauge length, sensitivity, Etc.) of fiber optic DAS have an important influence on overall capabilities of the monitoring system. Meanwhile, physics-based analysis of acoustic waves is still a challenge due to the complex nature of the Lamb wave when it propagates, scatters, and disperses in the presence of structural defects. In this work, we simulate carbon steel pipes relevant for oil and gas pipeline applications with diameters of approximately 6-12” and wall thickness of 0.5” as the objects to be monitored. By establishing and implementing these capabilities, we seek to pursue an in-depth study on structural parameter optimization of DAS network, measurement range, and signal processing with an ultimate goal of increasing the sensitivity and efficacy of DAS to defect identification for various modes of corrosion expected in practice. To study the characteristics of scattered acoustic waves and performance of DAS for defect identification, we simulated the response of DAS for multiple pipe structures, defect types, and DAS sensor network configuration using finite element software Ansys, then the properties of signal response are extracted to construct defect-sensitive features. The raw data simulated, and the associated features extracted can ultimately be utilized as annotated training data to benchmark various designs for DAS applications, guided acoustic excitation sources, and learning model parameters to enhance early detection of potentially problematic defects.

Pipeline Defects Inspection, Fiber-optic sensors, ↗
DOE OSTI2023

Increased range and contrast in fog with circularly polarized imaging

Fogs, low lying clouds, and other highly scattering environments pose a challenge for many commercial and national security sensing systems. Current autonomous systems rely on optical sensors for navigation whose performance is degraded by highly scattering environments. In our previous simulation work, we have shown that polarized light can penetrate through a scattering environment such as fog. We have demonstrated that circularly polarized light maintains its initial polarization state better than linearly polarized light, even through large numbers of scattering events and thus ranges. This has recently been experimentally verified by other researchers. In this work, we present the design, construction, and testing of active polarization imagers at short-wave infrared and visible wavelengths. We explore multiple polarimetric configurations for the imagers, focusing on linear and circular polarization states. The polarized imagers were tested at the Sandia National Laboratories Fog Chamber under realistic fog conditions. Here, we show that active circular polarization imagers can increase range and contrast in fog better than linear polarization imagers. We show that when imaging typical road sign and safety retro-reflective films, circularly polarized imaging has enhanced contrast throughout most fog densities/ranges compared to linearly polarized imaging and can penetrate over 15 to 25 m into the fog beyond the range limit of linearly polarized imaging, with a strong dependence on the interaction of the polarization state with the target materials.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Oscura: Dark Matter search with 10 kg of skipper-CCDs

The electron-counting capability of the skipper-CCDs has enabled world leading searches for low mass DM-electron scattering in experiments with less than 100 g active mass (SENSEI and DAMIC-M). Oscura is the ongoing effort to develop a 10 kg skipper-CCD experiment for dark matter search. In this talk, I will discuss the current status and plans for the Oscura experiment, including the performance of the prototype sensors and the plans for early science.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Assessment of Flow-Enhanced Electrochemical Sensor Testing and Deployments for MSRs

This report serves as the deliverable for Milestone M3RS-23AN0401061 that is part of Work Package RS-23AN040106 (Flow Enhanced Sensors for MSRs – ANL). The goal of this milestone was to determine performance of the flow enhanced electrochemical sensor (FEES) and modular flow instrumentation testbed (MFIT) in safeguards relevant scenarios. Flow enhanced electrochemical sensors are a type of electroanalytical sensor that has been developed at Argonne National Laboratory to be installed directly into MSR flow conduits to make measurements of the salt composition. These sensors represent a significant improvement in capabilities compared to earlier electroanalytical sensors that instead can only be operated in quiescent conditions. Previous work has focused on testing of the FEES in flowing conditions provided by the MFIT to assess the accuracy and precision of the sensor measurements. To further improve this capability, in FY23 we undertook a campaign of safeguards relevant scenarios in molten salt containing a range of uranium chloride concentrations (0 to 3 wt%). All the testing carried out in FY23 was aided by a control system designed to automatically actuate flow conditions and collect data. This new automation system is estimated to have increased experimental throughput by a factor of four and enabled testing in a variety of complex conditions. The advancements in throughput and repeatability led to improved quantification of actinide concentrations using the in-flow sensors, with a reduction of the mean absolute relative error from 5.6% in FY22 to 3.1% in FY23. In addition to safeguards scenarios run in the MFIT, FY23 work included deployment of a FEES at a partner institution where it will be tested in a flowing salt loop. The FEES was successfully integrated into that loop and is being tested prior to loop startup. In FY23, work also continued on the smaller flow system that we have named the mini-MFIT. This smaller system is capable of rapid prototyping of new sensor designs prior to installation in the larger MFIT radiological flow system. Work was carried out to test this new system in non-radiological molten salts in a separate glovebox. This work is helping us to enhance the accuracy of our salt monitoring capabilities through the integration of multiple types of sensors. The high degree of accuracy required by 10 CFR 74 represents a significant challenge, and further design evolution and integration of the sensors into multimodal sensing frameworks will be needed to further push the measurement accuracy to the needed level.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Transformers and Long Short-Term Memory Transfer Learning for GenIV Reactor Temperature Time Series Forecasting

Automated monitoring of the coolant temperature can enable autonomous operation of generation IV reactors (GenIV), thus reducing their operating and maintenance costs. Automation can be accomplished with machine learning (ML) models trained on historical sensor data. However, the performance of ML usually depends on the availability of large amount of training data, which is difficult to obtain for GenIV, as this technology is still under development. We propose the use of transfer learning (TL), which involves utilizing knowledge across different domains, to compensate for this lack of training data. TL can be used to create pre-trained ML models with data from small-scale research facilities, which can then be fine-tuned to monitor GenIV reactors. In this work, we develop pre-trained Transformer and long short-term memory (LSTM) networks by training them on temperature measurements from thermal hydraulic flow loops operating with water and Galinstan fluids at room temperature at Argonne National Laboratory. The pre-trained models are then fine-tuned and re-trained with minimal additional data to perform predictions of the time series of high temperature measurements obtained from the Engineering Test Unit (ETU) at Kairos Power. The performance of the LSTM and Transformer networks is investigated by varying the size of the lookback window and forecast horizon. The results of this study show that LSTM networks have lower prediction errors than Transformers, but LSTM errors increase more rapidly with increasing lookback window size and forecast horizon compared to the Transformer errors.

LSTM↗
NASA NTRS1972

Fabrication and test of eight sets of STRAP 3 flight hardware

The development and characteristics of the Stellar Tracking Rocket Attitude Positioning (STRAP) system are discussed. The objective of the program was to produce eight sets of reliable flight hardware at a cost that was compatible with the typical sounding rocket mission. The flight system consists of: (1) the attitude control system, (2) the pneumatics systems components, and a fine guidance error sensors star tracker. The performance of the system and the areas where improvements are needed are examined. Line drawings and circuit diagrams are presented to amplify the accomplishments of the project.

Greeb, M. E.↗
NASA NTRS1972

Polar orbiting operational weather satellites.

The progress in the development of operational weather satellites is reviewed, covering their chronology from Explorer 7 of 1959 through Meteor 12 of June, 1972. Special attention is given to the development of the TIROS series satellites with the evolution of their operational sensors, data systems and performance requirements. The topics also include the data collection system designs, to Advanced Very High Resolution Radiometer (AVHRR), the sounder radiometer, the Solar Environment Monitor (SEM), the data processor, and TIROS-N operation and orbital characteristics. It is expected that TIROS-N and its forthcoming advanced versions will provide an effective technology for sensing environmental data on a global scale in the latter half of the decade.

Stampfl, R. A.↗
NASA NTRS1973

Separate surfaces for automatic flight controls.

The purpose of this paper is to describe an investigation of separate surface stability augmentation systems for general aviation aircraft. The program objective were twofold: first, a wind tunnel program to determine control effectiveness of separate surfaces in the presence of main surfaces, and hinge moment feedback from separate surfaces via the main surfaces to the pilot; second, a theoretical study to determine the minimum performance of actuators and sensors that can be tolerated, the best slaving gains to be used with separate surfaces, and control authority needed for proper operation under direct pilot control, under autopilot control, and in failure situations. On the basis of the results obtained, it has been concluded that separate surface systems are feasible and advantageous for use in general aviation aircraft.

Roskam, J.↗
NASA NTRS1974

Scene correction (precision techniques) of ERTS sensor data using digital image processing techniques

Techniques have been developed, implemented, and evaluated to process ERTS Return Beam Vidicon (RBV) and Multispectral Scanner (MSS) sensor data using digital image processing techniques. The RBV radiometry has been corrected to remove shading effects, and the MSS geometry and radiometry have been corrected to remove internal and external radiometric and geometric errors. The results achieved show that geometric mapping accuracy of about one picture element RMS and two picture elements (maximum) can be achieved by the use of nine ground control points. Radiometric correction of MSS and RBV sensor data has been performed to eliminate striping and shading effects to about one count accuracy. Image processing times on general purpose computers of the IBM 370/145 to 168 class are in the range of 29 to 3.2 minutes per MSS scene (4 bands). Photographic images of the fully corrected and annotated scenes have been generated from the processed data and have demonstrated excellent quality and information extraction potential.

Bernstein, R.↗
NASA NTRS1975

Earth observations

The development of the Earth Resources Technology Satellites for making earth observations from space is discussed. The programmatic elements of the ERTS program are defined. The specific results of the program which are considered are as follows: (1) analysis of snowmelt runoff, (2) analysis of Mississippi River floods of 1973, (3) global ice surveys with an electrically scanning microwave radiometer, (4) the microwave signature of snow fields, (5) analysis of strip mining activities, (6) sea surface studies using microwave techniques during the Bering Sea experiment, (7) cloud types and measurement of rainfall, and (8) performance of various remote sensors.

Source record↗