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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Helicopter main-rotor speed effects: A comparison of predicted ranges of detection from the aural detection program ICHIN and the electronic detection program ARCAS

NASA LaRC personnel have conducted a strudy of the predicted acoustic detection ranges associated with reduced helicopter main rotor speeds. This was accomplished by providing identical input information to both the aural detection program ICHIN 6, (I Can Hear It Now, version 6) and the electronic acoustic detection program ARCAS (Assessment of Rotorcraft Detection by Acoustics Sensing). In this study, it was concluded that reducing the main rotor speed of the helicopter by 27 percent reduced both the predicted aural and electronic detection ranges by approximately 50 percent. Additionally, ARCAS was observed to function better with narrowband spectral input than with one-third octave band spectral inputs and the predicted electronic range of acoustic detection is greater than the predicted aural detection range.

Mueller, Arnold W.↗
NASA NTRS1985

The application of the detection filter to aircraft control surface and actuator failure detection and isolation

The performance of the detection filter in detecting and isolating aircraft control surface and actuator failures is evaluated. The basic detection filter theory assumption of no direct input-output coupling is violated in this application due to the use of acceleration measurements for detecting and isolating failures. With this coupling, residuals produced by control surface failures may only be constrained to a known plane rather than to a single direction. A detection filter design with such planar failure signatures is presented, with the design issues briefly addressed. In addition, a modification to constrain the residual to a single known direction even with direct input-output coupling is also presented. Both the detection filter and the modification are tested using a nonlinear aircraft simulation. While no thresholds were selected, both filters demonstrated an ability to detect control surface and actuator failures. Failure isolation may be a problem if there are several control surfaces which produce similar effects on the aircraft. In addition, the detection filter was sensitive to wind turbulence and modeling errors.

Bonnice, W. F.↗
NASA NTRS1994

In situ detection of tropospheric OH and HO2 by laser-induced fluorescence in a detection chamber at low pressure

Just as in the method of Hard and O'Brien, ambient air is pulled through an approximately 1 mm diameter inlet into a detection chamber that is maintained at a pressure of 2.4 torr. The ambient air stream travels through the detection chamber with a velocity of greater than 100 m sec(exp -1) in a narrow stream, constrained by the addition of an inert gas flow (0.4 torr). The OH molecule is both excited and detected in the A(exp 2)Sigma (v' = 0) yields X(exp 2)II (v'' = 0) transition at 308 nm. Light from a copper vapor-pumped dye laser (rep. rate = 10kHz; pulse length = 20 ns; linewidth = .1 cm(exp -1), and average power = 15 mW), resonant with the Q(sub 1)(3) transition, is multipassed through a White cell and intercepts the air stream as 24 non-overlapping 2mm by 5mm beams. A fast microchannel plate detector is turned off during the laser pulse to prevent saturation of the detector due to Rayleigh and chamber scattering. It is turned on 30-100 nsec after the end of the laser pulse for 300 ns to collect resonance fluorescence from OH. HO2 is detected by chemical conversion to OH by reaction with reagent NO, followed by OH detection. Both the detection sensitivity and the inlet characteristics must be understood for any in situ instrument. For the calibration of the detection sensitivity, OH is produced quantitatively by the fast H + NO2 yields OH + NO reaction in a low pressure, flowing discharge tube connected to the detection chamber. The inlet transmission of OH inlet is calibrated separately.

Brune, William H.↗
NASA NTRS2005

Aircraft Fault Detection and Classification Using Multi-Level Immune Learning Detection

This work is an extension of a recently developed software tool called MILD (Multi-level Immune Learning Detection), which implements a negative selection algorithm for anomaly and fault detection that is inspired by the human immune system. The immunity-based approach can detect a broad spectrum of known and unforeseen faults. We extend MILD by applying a neural network classifier to identify the pattern of fault detectors that are activated during fault detection. Consequently, MILD now performs fault detection and identification of the system under investigation. This paper describes the application of MILD to detect and classify faults of a generic transport aircraft augmented with an intelligent flight controller. The intelligent control architecture is designed to accommodate faults without the need to explicitly identify them. Adding knowledge about the existence and type of a fault will improve the handling qualities of a degraded aircraft and impact tactical and strategic maneuvering decisions. In addition, providing fault information to the pilot is important for maintaining situational awareness so that he can avoid performing an action that might lead to unexpected behavior - e.g., an action that exceeds the remaining control authority of the damaged aircraft. We discuss the detection and classification results of simulated failures of the aircraft's control system and show that MILD is effective at determining the problem with low false alarm and misclassification rates.

Wong, Derek↗
NASA NTRS2017

Kepler Planet Detection Metrics: Per-Target Detection Contours for Data Release 25

A necessary input to planet occurrence calculations is an accurate model for the pipeline completeness (Burke et al., 2015). This document describes the use of the Kepler planet occurrence rate products in order to calculate a per-target detection contour for the measured Data Release 25 (DR25) pipeline performance. A per-target detection contour measures for a given combination of orbital period, Porb, and planet radius, Rp, what fraction of transit signals are recoverable by the Kepler pipeline (Twicken et al., 2016; Jenkins et al., 2017). The steps for calculating a detection contour follow the procedure outlined in Burke et al. (2015), but have been updated to provide improved accuracy enabled by the substantially larger database of transit injection and recovery tests that were performed on the final version (i.e., SOC 9.3) of the Kepler pipeline (Christiansen, 2017; Burke Catanzarite, 2017a). In the following sections, we describe the main inputs to the per-target detection contour and provide a worked example of the python software released with this document (Kepler Planet Occurrence Rate Tools KeplerPORTs)1 that illustrates the generation of a detection contour in practice. As background material for this document and its nomenclature, we recommend the reader be familiar with the previous method of calculating a detection contour (Section 2 of Burke et al.,2015), input parameters relevant for describing the data quantity and quality of Kepler targets (Burke Catanzarite, 2017b), and the extensive new transit injection and recovery tests of the Kepler pipeline (Christiansen et al., 2016; Burke Catanzarite, 2017a; Christiansen, 2017).

Planet Detection Metrics↗
DOE OSTI2025

Nanopore Activity Assays for Detection of Biomarker Protease Activity: Design and Testing of Substrates for Both Nanopore Sequencing and PCR-Based Detection Methods

The work performed in this project has demonstrated the ability to construct proteolytic enzyme substrates that are PCR and sequencing-readable reporter molecules. Specifically, the goal was to detect those reporter molecules via PCR and Oxford Nanopore Technologies MinION sequencing methods following exposure to the biomarker protease thrombin. The assay development focused on binding the constructed peptide-oligonucleotide chimera to immobilized streptavidin. The action of thrombin on the peptide portion of the molecule released the oligonucleotide for detection. Detection of protease activity was demonstrated in a concentration-dependent manner using MALDI-MS, RT-PCR and DNA sequencing. Additional steps to remove background release of reporter molecules during the assay was used to improve the difference in detected oligonucleotide reporter following protease activity. Additional steps in assay development will be to (1) test the assay in an appropriate matrix, (2) investigate detection using additional DNA sequencing platforms and (3) demonstrate multiplexed detection of multiple protease markers in a single reaction.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2020

Evaluation, Analysis and Results of the DANTi Flight Test Data, the DAIDALUS Detect and Avoid Algorithm, and the DANTi Concept for Detect and Avoid in the Cockpit

The DAIDALUS Detect and Avoid Algorithm [1] was developed to address the operational needs of Unmanned Aerial Systems (UAS) and meet the Minimum Operational Performance Standards for Detect and Avoid [2]. The DANTi (Detect and Avoid iN the cockpit) concept [3], developed at the National Aeronautics and Space Administration (NASA) Langley Research Center, leverages advancements achieved in surveillance and Detect and Avoid technologies for unmanned aircraft systems as a safety enhancing capability for pilots of manned aircraft. Pilots operating under Visual Flight Rules and not receiving Air Traffic Control radar services rely on see and avoid to remain well clear of other aircraft and avoid collisions. The DANTi concept has been conceived as a safety enhancement capability to remain well clear and avoid potential collisions. The DANTi concept uses a traffic display to provide situational awareness, conflict detection, alerting, and guidance to remain well clear.

Detect and Avoid↗
NASA NTRS1992

In situ detection of tropospheric OH, HO2, NO2, and NO by laser-induced fluorescence in detection chambers at reduced pressures

For detection of OH and HO2, we have met or exceeded most of the goals of the proposal for this grant. We have succeeded in building a prototype instrument for the detection of OH and HO2 that has a detection sensitivity of about 2 x 10 exp 4 (OH molecules/cc)/(ct/s), about five times greater than proposed. The current minimum detectable OH of 1.4 x 10 exp 5 OH molecules/cc (S/N=2; 30 second integration) is less than half that proposed, and will be much lower once scattered light levels are reduced. The instrument displays other important properties. First, interfering signals from other gases and OH losses on the inlet appear to be small under laboratory and field conditions. Second, three different calibration methods, two external to the inlet, give similar results, to within 30 percent. Third, the chemical conversion efficiency of HO2 to OH by addition of reagent NO is better than 90 percent. All of these factors give us great confidence that this technique works. The instrument has gone through several variations that have not affected its potential performance but have affected its use. We were able to undergo the first field trials away from State College in June 1992, when we were able to participate informally in the ROSE experiment in rural Alabama, a year ahead of the proposed schedule for field studies. Because this field experiment was our first and the data analysis was complicated by instrument instabilities, we have not yet released the observations. None-the-less, we have gained insight into instrument design. We have not yet completed all of the proposed work. The calibration systems need to be improved to reduce the 50 percent to 100 percent uncertainties to less than 30 percent. Interference signals from ambient gases and possible losses on the inlet under field conditions need to be quantified. Finally, the detection of NO2 and NO with laser induced fluorescence is only now being seriously pursued.

Brune, William H.↗
DOE OSTI2025

Using Temporal Information from Human Mobility Data to Detect Anchor Points

Spatiotemporal mobility data are available in massive quantities, but large quantities of data typically include fewer variables or data fields. Often, the only available fields are User ID, Longitude, Latitude, Timestamp (ULLT). This raises an important question: how much can we infer about human mobility patterns using only these four fields? With ULLT data, we do not know individuals' socioeconomic status information or when they are visiting their anchor points (AP) or locations (such as homes, places of employment, or schools), and it is a modern challenge to use this data to infer these characteristics. When detecting anchor locations with limited input information, verification and validation (VV) are significant challenges. This paper addresses the problem of identifying individuals' anchor locations using only temporal information from spatiotemporal datasets with limited attributes. Our approach does not explicitly use latitude and longitude during analysis. Locationbased information is only employed in the preprocessing stage to identify periods of movement (trips) and stops (dwelling). Beyond this step, all analysis is based on temporal patterns. In theory, if stops and dwell times could be detected through alternative means, our method could function entirely without location-based input. We demonstrate this methodology on the 2017 National Household Travel Survey (NHTS) data, because it includes a carefully designed and collected time use survey with representative sampling and labeled ground truth. The high-quality survey data allows us to test the accuracy of our methods because NHTS contains intended place labels and agent/user characteristics. We have also applied our validated AP identification algorithm on very large-scale GPS based trajectory data for Patterns-of-Life (PoL) assessment and other applications, but due to space limit that could not be presented here.

McBride, Liz [ORNL] (ORCID:0000000286925869)↗
NASA NTRS1980

The physics of heterodyne detection in the far-infrared: Transition from electric-field to photon-absorption detection in a simple system

The history of heterodyne detection is reviewed from the radiowave to the optical regions of the electromagnetic spectrum with emphasion the submillimeter/far infrared. The transition from electric field to photon absorption detection in a simple system is investigated. The response of an isolated two level detector to a coherent source of incident radiation is calculated for both heterodyne and video detection. When the processes of photon absorption and photon emission cannot be distinguished, the relative detected power at double- and sum-frequencies is found to be multiplied by a coefficient, which is less than or equal to unity, and which depends on the incident photon energy and on the effective temperature of the system.

Teich, M. C.↗
NASA NTRS1989

Error detecting capabilities of the shortened Hamming codes adopted for error detection in IEEE Standard 802.3

The error-detecting capabilities of the shortened Hamming codes adopted for error detection in IEEE Standard 802.3 are investigated. These codes are also used for error detection in the data link layer of the Ethernet, a local area network. The weight distributions for various code lengths are calculated to obtain the probability of undetectable error and that of detectable error for a binary symmetric channel with bit-error rate between 0.00001 and 1/2.

Fujiwara, Toru↗
NASA NTRS2014

MODIS NDVI Change Detection Techniques and Products Used in the Near Real Time ForWarn System for Detecting, Monitoring, and Analyzing Regional Forest Disturbances

This presentation discusses MODIS NDVI change detection methods and products used in the ForWarn Early Warning System (EWS) for near real time (NRT) recognition and tracking of regionally evident forest disturbances throughout the conterminous US (CONUS). The latter has provided NRT forest change products to the forest health protection community since 2010, using temporally processed MODIS Aqua and Terra NDVI time series data to currently compute and post 6 different forest change products for CONUS every 8 days. Multiple change products are required to improve detectability and to more fully assess the nature of apparent disturbances. Each type of forest change product reports per pixel percent change in NDVI for a given 24 day interval, comparing current versus a given historical baseline NDVI. EMODIS 7 day expedited MODIS MOD13 data are used to obtain current and historical NDVIs, respectively. Historical NDVI data is processed with Time Series Product Tool (TSPT); and 2) the Phenological Parameters Estimation Tool (PPET) software. While each change products employ maximum value compositing (MVC) of NDVI, the design of specific products primarily differs in terms of the historical baseline. The three main change products use either 1, 3, or all previous years of MVC NDVI as a baseline. Another product uses an Adaptive Length Compositing (ALC) version of MVC to derive an alternative current NDVI that is the freshest quality NDVI as opposed to merely the MVC NDVI across a 24 day time frame. The ALC approach can improve detection speed by 8 to 16 days. ForWarn also includes 2 change products that improve detectability of forest disturbances in lieu of climatic fluctuations, especially in the spring and fall. One compares current MVC NDVI to the zonal maximum under the curve NDVI per pheno-region cluster class, considering all previous years in the MODIS record. The other compares current maximum NDVI to the mean of maximum NDVI for all previous MODIS years.

Spruce, Joseph P.↗
DOE OSTI2025

Interactive Rotated Object Detection for Novel Class Detection in Remotely Sensed Imagery

In this paper we propose IRTR-DETR an Interactive and Real-Time Rotated DEtection TRansformer that extends IRTDETR to predict rotated bounding boxes. IRTR-DETR maintains the Human-In-The-Loop (HIL) workflow of IRTDETR but introduces rotation-aware heads for improved detection of objects with arbitrary orientations. Similarly to IRTDETR IRTR-DETR can be trained with a small labeled sample set in an interactive setting but we show that it can also be pretrained on related but not identical data--such as a building damage dataset--before being applied to tasks like identifying buildings under construction. We demonstrate the efficacy of our approach on the publicly available Tiny-DOTA and xBD dataset as well as two study-cases on proprietary datasets of greenhouses and houses under construction ("waffle homes"). Detecting greenhouses is highly relevant in the context of damage assessment while "waffle homes" aid understanding typical floorplans and building codes in different areas both thereby supporting population modeling emergency response and policy planning. Our method outperforms the state of the art in interactive rotated object detection on the Tiny-DOTA dataset by 5.7 percent and improves upon the non interactive RTDETR by 7.85 to 19.39 percent (depending on the number of provided samples) while maintaining its real-time efficiency.

Burges, Marvin [ORNL] (ORCID:0000000312690769)↗
DOE OSTI2026

SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images

The rapid advancement of generative models has made the detection of AI-generated images a critical challenge for both research and society. Recent works have shown that most state-of-the-art fake image detection methods overfit to their training data and catastrophically fail when evaluated on curated hard test sets with strong distribution shifts. In this work, we argue that it is more principled to learn a tight decision boundary around the real image distribution and treat the fake category as a sink class. To this end, we propose SimLBR, a simple and efficient framework for fake image detection with Latent Blending Regularization (LBR). Our method significantly improves cross-generator generalization, achieving up to +24.85% accuracy and +69.62% recall on the challenging Chameleon benchmark. SimLBR is also highly efficient, training orders of magnitude faster than existing approaches. Furthermore, we emphasize the need for reliability-oriented evaluation in fake image detection, introducing risk-adjusted metrics and worst-case estimates to better assess model robustness. All the code and models are availabe at: https://github.com/mvrl/SimLBR

Dhakal, Aayush [Washington University, St. Louis]↗
DOE OSTI2024

Detection and Quantitation of Hydrogen Emissions Role and Status of Detection Technology

Hydrogen is a critical strategy to decarbonize energy and manufacturing industries. Hydrogen is nontoxic and can be handled safely, but potential for secondary greenhouse impacts. Hydrogen releases arise from a variety of mechanisms (process, design features, "leaks") that contribute to total hydrogen releases. Detection methodologies will be critical to detect and quantify hydrogen emissions. Detection is to be integrated with advanced analytics (AI) and behavior modelling to effectively identify, quantify, and source locate hydrogen releases. Modelling of emissions will contribute to facility safety and reliability. DOE is committed to develop the tools to model and mitigate the impact of hydrogen releases which include: Support modeling to elucidate released hydrogen degradation; Support the development of tools for emissions quantitation; and Support engineering advancements to minimize hydrogen losses along the value chain (including process, design features, and leaks).

detection↗
DOE OSTI2026

Detect the Unobservable: Abnormality Detection in mixed Autonomy for Lane Change Maneuver with Following Vehicles’ Trajectories Only

Highly Automated Vehicles (HAVs) and Advanced Driver-Assistance Systems (ADAS) are transforming modern transportation with enhanced mobility, safety, and efficiency. Despite their advantages, cybersecurity vulnerabilities in these systems can lead to abnormal behavior, posing significant risks to surrounding human-driven vehicles (HDVs) in mixed traffic environments. Here, this article addresses the challenge of detecting abnormal lateral movements of HAVs/ADAS vehicles using only trajectory profiles of following HDVs. Specifically, we propose a novel modeling approach that captures both normal and abnormal lateral behaviors through vehicle kinematics, integrated decision-making processes, vehicle control using symbolic regression for lane change vehicles. Additionally, we introduce an abnormality detection framework that relies on observable HDV data, even in occlusion scenarios. The framework evaluates the sensitivity of various car-following models to detect abnormal behaviors, providing insights into the interaction between HAVs/ADAS and HDVs in mixed autonomy systems.

Connected and Automated vehicles↗
NASA NTRS1980

On the detection of other planetary systems - Detection of intrinsic thermal radiation

There is currently no unambiguous observational evidence for the existence of other planetary systems. One possible way to detect and study such systems is infrared observations of continuum blackbody radiation from planets revolving around other stars. It is shown that the effective temperature of large planets revolving around mid- to late-spectral-type main-sequences stars is set by energy sources internal to the planet rather than by equilibrium with the radiation field of the central star, making them easier to detect than had been previously thought. Consideration is given to the two major observational constraints on detecting planetary companions to nearby stars, namely, angular resolution and sensitivity. A comparison is made between the performance of an ambient (T 200 K), single-aperture telescope and a cooled interferometer. In each case the required aperture (baseline) is large (in the 10-m class), but consistent with Shuttle launch capability.

Black, D. C.↗
NASA NTRS1985

A preliminary evaluation of a failure detection filter for detecting and identifying control element failures in a transport aircraft

The application of the failure detection filter to the detection and identification of aircraft control element failures was evaluated in a linear digital simulation of the longitudinal dynamics of a B-737 Aircraft. Simulation results show that with a simple correlator and threshold detector used to process the filter residuals, the failure detection performance is seriously degraded by the effects of turbulence.

Bundick, W. T.↗