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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Automated identification of OB associations in M31

A new identification of OB associations in M31 has been performed using the Path Linkage Criterion (PLC) technique of Battinelli (1991). We found 174 associations with a very small contamination (less than 5%) by random clumps of stars. The expected total number and average size of OB associations in the region of M 31 covered by our data set (Magnier et al. 1992) are approximately 280 and approximately 90 pc, respectively. M31 associations therefore have sizes similar to those of OB associations observed in nearby galaxies, so that we can consider them to be classical OB associations. This list of OB associations will be used for the study of the spatial distribution of OB associations and their correlation with other objects. Taking into account the fact that we do not cover the entire disk of M31, we extrapolate a total number of association in M31 of approximately 420.

Magnier, Eugene A.↗
NASA NTRS2005

An Astronomical Pattern-Matching Algorithm for Automated Identification of Whale Sharks

The largest shark species alive today, whale sharks (Rhincodon typus) are rare and poorly studied. Directed fisheries, high value in international trade, a highly migratory nature, and generally low abundance make this species vulnerable to exploitation. Mark- and-recapture studies have provided our current understanding of whale shark demographics and life history, but conventional tagging has met with limited success. To aid in conservation and management efforts, and to further our knowledge of whale shark biology, an identification technology that maximizes the scientific value of individual sighting is needed.

Arzoumanian, Z.↗
NASA NTRS2007

Automated Identification of Nucleotide Sequences

STITCH is a computer program that processes raw nucleotide-sequence data to automatically remove unwanted vector information, perform reverse-complement comparison, stitch shorter sequences together to make longer ones to which the shorter ones presumably belong, and search against the user s choice of private and Internet-accessible public 16S rRNA databases. ["16S rRNA" denotes a ribosomal ribonucleic acid (rRNA) sequence that is common to all organisms.] In STITCH, a template 16S rRNA sequence is used to position forward and reverse reads. STITCH then automatically searches known 16S rRNA sequences in the user s chosen database(s) to find the sequence most similar to (the sequence that lies at the smallest edit distance from) each spliced sequence. The result of processing by STITCH is the identification of the most similar well-described bacterium. Whereas previously commercially available software for analyzing genetic sequences operates on one sequence at a time, STITCH can manipulate multiple sequences simultaneously to perform the aforementioned operations. A typical analysis of several dozen sequences (length of the order of 103 base pairs) by use of STITCH is completed in a few minutes, whereas such an analysis performed by use of prior software takes hours or days.

Osman, Shariff↗
NASA NTRS1974

Automated Firearms Identification System (AFIDS), phase 1

Items critical to the future development of an automated firearms identification system (AFIDS) have been examined, with the following specific results: (1) Types of objective data, that can be utilized to help establish a more factual basis for determining identity and nonidentity between pairs of fired bullets, have been identified. (2) A simulation study has indicated that randomly produced lines, similar in nature to the individual striations on a fired bullet, can be modeled and that random sequences, when compared to each other, have predictable relationships. (3) A schematic diagram of the general concept for AFIDS has been developed and individual elements of this system have been briefly tested for feasibility. Future implementation of such a proposed system will depend on such factors as speed, utility, projected total cost and user requirements for growth. The success of the proposed system, when operational, would depend heavily on existing firearms examiners.

Blackwell, R. J.↗
DOE OSTI2025

Leveraging large language models to automate the identification of healthcare access barriers for veterans

Objective: To develop and evaluate an automated system for identifying healthcare barriers focusing on transportation issues in veterans’ clinical notes using large language models (LLMs) and to assess the impact of different prompting strategies on classification performance and explanation consistency. Methods: We developed a hybrid system combining pattern matching for templated notes with LLM analysis for free-text notes. Using 2000 manually annotated clinical notes, we compared four prompting strategies (dual-role short, dual-role long, analysis-first, analysis-only) across Mistral-7B and Llama-3.1 models. We evaluated classification performance using standard metrics and assessed explanation consistency through embedding similarity analysis. Results: The analysis-first strategy achieved superior performance, with Mistral-7B reaching an F1 score of 0.914, outperforming traditional machine learning approaches (GBM: 0.786, BERT: 0.811). LLMs demonstrated higher explanation consistency within models (mean cosine similarity 0.887–0.908) compared to cross-model similarities (0.767–0.872). Pattern matching successfully handled 6.7% of templated notes deterministically. Mistral-7B showed greater internal consistency but higher abstention rates compared to Llama-3.1. Conclusion: Requiring LLMs to analyze evidence before classification improves both accuracy and explanation consistency for identifying transportation barriers in clinical notes. This approach enables automated barrier detection at scale while providing clinically relevant explanations, supporting both population-level healthcare planning and individual patient care decisions.

Healthcare access barriers↗
NASA NTRS2011

Automated Purgatoid Identification: Final Report

Driving on Mars is hazardous: technical problems and unforeseen natural hazards can end a mission quickly at the worst, or result in long delays at best. This project is focused on helping to mitigate hazards posed to rovers by purgatoids: small (less than 1 m high, less than 10 m wide), ripple-like eolian bedforms commonly found scattered across the Meridiani Planum region of Mars. Due to the poorly consolidated nature of purgatoids and multiple past episodes of rovers getting stuck in them, identification and avoidance of these eolian bedforms is an important feature of rover path planning (NASA, 2011).

MatLab↗
DOE OSTI2026

Automated Bacterial Identification and Morphological Feature Analysis in Low‐Dose Cryo‐EM Using YOLOv11

Bacteria rapidly adapt to environmental cues through morphological and ultrastructural changes that correlate with physiology and behavior. Cryogenic transmission electron microscopy (cryo‐TEM) can capture these phenotypic changes in near‐native, vitrified states, but manual analysis of low‐dose micrographs is labor intensive and limits throughput. Here, we present an end‐to‐end workflow that combines low‐dose cryo‐TEM imaging with a YOLOv11‐based instance‐segmentation model to automatically identify bacteria and quantify key structural features directly from the micrographs. This workflow enables (i) robust bacterial localization and counting from low‐magnification atlas/montage images, (ii) automated measurements of cell‐envelope (outer–inner membrane) thickness and anisotropy from higher‐magnification views, and (iii) detection and quantification of bacteria–flagella interactions, including overlap length and curvature metrics for interacting versus noninteracting flagella. Using Pantoea sp. YR343 grown under distinct media conditions, we show that the automated measurements agree with manual annotations while substantially reducing analysis time. Together, these tools provide a practical framework for scalable bacterial identification and quantitative phenotyping in low‐dose cryo‐TEM datasets and establish a foundation for extending cryo‐TEM image analysis toward higher‐throughput studies of microbial heterogeneity and biointerfaces.

YOLOv11↗
NASA NTRS1971

Automated Drug Identification for Urban Hospitals

Many urban hospitals are becoming overloaded with drug abuse cases requiring chemical analysis for identification of drugs. In this paper, the requirements for chemical analysis of body fluids for drugs are determined and a system model for automated drug analysis is selected. The system as modeled, would perform chemical preparation of samples, gas-liquid chromatographic separation of drugs in the chemically prepared samples, infrared spectrophotometric analysis of the drugs, and would utilize automatic data processing and control for drug identification. Requirements of cost, maintainability, reliability, flexibility, and operability are considered.

Shirley, Donna L.↗
NASA NTRS1993

Real-time automated failure identification in the Control Center Complex (CCC)

A system which will provide real-time failure management support to the Space Station Freedom program is described. The system's use of a simplified form of model based reasoning qualifies it as an advanced automation system. However, it differs from most such systems in that it was designed from the outset to meet two sets of requirements. First, it must provide a useful increment to the fault management capabilities of the Johnson Space Center (JSC) Control Center Complex (CCC) Fault Detection Management system. Second, it must satisfy CCC operational environment constraints such as cost, computer resource requirements, verification, and validation, etc. The need to meet both requirement sets presents a much greater design challenge than would have been the case had functionality been the sole design consideration. The choice of technology, discussing aspects of that choice and the process for migrating it into the control center is overviewed.

Kirby, Sarah↗
NASA NTRS1974

Automated drug identification system

System speeds up analysis of blood and urine and is capable of identifying 100 commonly abused drugs. System includes computer that controls entire analytical process by ordering various steps in specific sequences. Computer processes data output and has readout of identified drugs.

Campen, C. F., Jr.↗
NASA NTRS1996

Evaluation of Automated Yeast Identification System

One hundred and nine teleomorphic and anamorphic yeast isolates representing approximately 30 taxa were used to evaluate the accuracy of the Biolog yeast identification system. Isolates derived from nomenclatural types, environmental, and clinica isolates of known identity were tested in the Biolog system. Of the isolates tested, 81 were in the Biolog database. The system correctly identified 40, incorrectly identified 29, and was unable to identify 12. Of the 28 isolates not in the database, 18 were given names, whereas 10 were not. The Biolog yeast identification system is inadequate for the identification of yeasts originating from the environment during space program activities.

McGinnis, M. R.↗
NASA NTRS2003

Automated Rock Identification for Future Mars Exploration Missions

A key task for human or robotic explorers on the surface of Mars is choosing which particular rock or mineral samples should be selected for more intensive study. The usual challenges of such a task are compounded by the lack of sensory input available to a suited astronaut or the limited downlink bandwidth available to a rover. Additional challenges facing a human mission include limited surface time and the similarities in appearance of important minerals (e.g. carbonates, silicates, salts). Yet the choice of which sample to collect is critical. To address this challenge we are developing science analysis algorithms to interface with a Geologist's Field Assistant (GFA) device that will allow robotic or human remote explorers to better sense and explore their surroundings during limited surface excursions. We aim for our algorithms to interpret spectral and imaging data obtained by various sensors. The algorithms, for example, will identify key minerals, rocks, and sediments from mid-IR, Raman, and visible/near-IR spectra as well as from high resolution and microscopic images to help interpret data and to provide high-level advice to the remote explorer. A top-level system will consider multiple inputs from raw sensor data output by imagers and spectrometers (visible/near-IR, mid-IR, and Raman) as well as human opinion to identify rock and mineral samples.

Gulick, V. C.↗
NASA NTRS1992

Evaluation of the Biolog automated microbial identification system

Biolog's identification system was used to identify 39 American Type Culture Collection reference taxa and 45 gram-negative isolates from water samples. Of the reference strains, 98% were identified to genus level and 76% to species level within 4 to 24 h. Identification of some authentic strains of Enterobacter, Klebsiella, and Serratia was unreliable. A total of 93% of the water isolates were identified.

NASA Program Environmental Health↗
NASA NTRS2001

Automated landmark identification for spacecraft navigation

An integrated approach to surface relative optical landmark tracking for spacecraft is being developed. Landmarks are defined as full digital topography/albedo maps and are determined from previous imaging and navigation data. Initially, this technique will speed up ground based optical navigation. Ultimately, it will enable on-board trajectory determination during orbital and landing maneuvers.

landmark↗
NASA NTRS2010

Aligning Astronomical Telescopes via Identification of Stars

A proposed method of automated, precise alignment of a ground-based astronomical telescope would eliminate the need for initial manual alignment. The method, based on automated identification of known stars and other celestial objects in the telescope field of view, would also eliminate the need for an initial estimate of the aiming direction. The method does not require any equipment other than a digital imaging device such as a charge-coupled-device digital imaging camera and control computers of the telescope and camera, all of which are standard components in professional astronomical telescope systems and in high-end amateur astronomical telescope systems. The method could be implemented in software running in the telescope or camera control computer or in an external computer communicating with the telescope pointing mount and camera control computers.

Whorton, Mark↗
NASA NTRS2020

Back to the Future: Surveying the Northern Hemisphere and Reprocessing the Southern TESS Data Set

TESS launched 18 April 2018 to conduct a two-year, near all-sky survey for at least 50 small, nearby exoplanets for which masses can be ascertained and whose atmospheres can be characterized by ground- and space-based follow-on observations. TESS has completed its survey of the southern hemisphere and begun its survey of the northern hemisphere, identifying >1000 candidate exoplanets and unveiling a plethora of exciting non-exoplanet astrophysics results, such as asteroseismology, asteroids, and supernova. The TESS Science Processing Operations Center (SPOC) processes the data downlinked every two weeks to generate a range of data products hosted at the Mikulski Archive for Space Telescopes (MAST). For each sector (~1 month) of observations, the SPOC calibrates the image data for both 30-min Full Frame Images (FFIs) and up to 20,000 pre-selected 2-min target star postage stamps. Data products for the 2-min targets include simple aperture photometry and systematic error-corrected flux time series. The SPOC also conducts searches for transiting exoplanets in the 2-min data for each sector and generates Data Validation time series and associated reports for each transit-like feature identified in the search. Multi-sector searches for exoplanets are conducted periodically to discover longer period planets, including those in the James Webb Continuous Viewing Zone (CVZ), which are observed for up to one year. Starting with Sector 8, scattered light from the Earth and Moon contaminated significant portions of the data in each orbit. We have developed algorithms for automated identification of the scattered light features at the individual target level. Previously, data for all stars on a CCD affected by scattered light were manually excluded. The automated flagging will allow us to retain significantly more data for stars that are not affected by the scattered light even though it is occurring elsewhere on the CCD. We also discuss enhancements to the SPOC pipeline and the newly available FFI light curves. The TESS Mission is funded by NASA's Science Mission Directorate as an Astrophysics Explorer Mission.

Jenkins, Jon M.↗
NASA NTRS2008

Cell-Detection Technique for Automated Patch Clamping

A unique and customizable machinevision and image-data-processing technique has been developed for use in automated identification of cells that are optimal for patch clamping. [Patch clamping (in which patch electrodes are pressed against cell membranes) is an electrophysiological technique widely applied for the study of ion channels, and of membrane proteins that regulate the flow of ions across the membranes. Patch clamping is used in many biological research fields such as neurobiology, pharmacology, and molecular biology.] While there exist several hardware techniques for automated patch clamping of cells, very few of those techniques incorporate machine vision for locating cells that are ideal subjects for patch clamping. In contrast, the present technique is embodied in a machine-vision algorithm that, in practical application, enables the user to identify good and bad cells for patch clamping in an image captured by a charge-coupled-device (CCD) camera attached to a microscope, within a processing time of one second. Hence, the present technique can save time, thereby increasing efficiency and reducing cost. The present technique involves the utilization of cell-feature metrics to accurately make decisions on the degree to which individual cells are "good" or "bad" candidates for patch clamping. These metrics include position coordinates (x,y) in the image plane, major-axis length, minor-axis length, area, elongation, roundness, smoothness, angle of orientation, and degree of inclusion in the field of view. The present technique does not require any special hardware beyond commercially available, off-the-shelf patch-clamping hardware: A standard patchclamping microscope system with an attached CCD camera, a personal computer with an imagedata- processing board, and some experience in utilizing imagedata- processing software are all that are needed. A cell image is first captured by the microscope CCD camera and image-data-processing board, then the image data are analyzed by software that implements the present machine-vision technique. This analysis results in the identification of cells that are "good" candidates for patch clamping (see figure). Once a "good" cell is identified, a patch clamp can be effected by an automated patchclamping apparatus or by a human operator. This technique has been shown to enable reliable identification of "good" and "bad" candidate cells for patch clamping. The ultimate goal in further development of this technique is to combine artificial-intelligence processing with instrumentation and controls in order to produce a complete "turnkey" automated patch-clamping system capable of accurately and reliably patch clamping cells with a minimum intervention by a human operator. Moreover, this technique can be adapted to virtually any cellular-analysis procedure that includes repetitive operation of microscope hardware by a human.

McDowell, Mark↗