Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Labeling”

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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Colorimetric and Label-Free Optical Detection of Pb 2+ Ions via Colloidal Gold Nanoparticles

The detection of Lead (Pb) heavy metal in water is crucial in many chemical processes as it is associated with serious health hazard. Here, we report the selective and precise colorimetric detection of Pb 2+ ions in water exploiting the mechanisms of aggregation and self-assembly of Glutathione (GSH) functionalized gold nanoparticles (GNPs). The carboxyl functional groups are available to create coordination complexes with Pb 2+ inducing aggregation amongst the GSH-GNPs in the presence of Pb 2+ due to the chelation of the GSH ligands. The resulting aggregation of amongst the GSHGNPs in the presence of Pb2+ increases the aggregate size depending on available Pb 2+ ions affecting the plasmonic coupling. This causes in a substantial shift in the plasmon wavelength to a longer wavelength side with increasing Pb 2+ concentration, resulting in a red-to-blue colorimetric or visual change, enabling instant determination of lead content in water.

calorimetric detection↗
NASA NTRS2005

Identifying Objects via Encased X-Ray-Fluorescent Materials - the Bar Code Inside

Systems for identifying objects by means of x-ray fluorescence (XRF) of encased labeling elements have been developed. The XRF spectra of objects so labeled would be analogous to the external bar code labels now used to track objects in everyday commerce. In conjunction with computer-based tracking systems, databases, and labeling conventions, the XRF labels could be used in essentially the same manner as that of bar codes to track inventories and to record and process commercial transactions. In addition, as summarized briefly below, embedded XRF labels could be used to verify the authenticity of products, thereby helping to deter counterfeiting and fraud. A system, as described above, is called an encased core product identification and authentication system (ECPIAS). The ECPIAS concept is a modified version of that of a related recently initiated commercial development of handheld XRF spectral scanners that would identify alloys or detect labeling elements deposited on the surfaces of objects. In contrast, an ECPIAS would utilize labeling elements encased within the objects of interest. The basic ECPIAS concept is best illustrated by means of an example of one of several potential applications: labeling of cultured pearls by labeling the seed particles implanted in oysters to grow the pearls. Each pearl farmer would be assigned a unique mixture of labeling elements that could be distinguished from the corresponding mixtures of other farmers. The mixture would be either incorporated into or applied to the surfaces of the seed prior to implantation in the oyster. If necessary, the labeled seed would be further coated to make it nontoxic to the oyster. After implantation, the growth of layers of mother of pearl on the seed would encase the XRF labels, making these labels integral, permanent parts of the pearls that could not be removed without destroying the pearls themselves. The XRF labels would be read by use of XRF scanners, the spectral data outputs of which would be converted to alphanumeric data in a digital equivalent data system (DEDS), which is the subject of the previous article. These alphanumeric data would be used to track the pearls through all stages of commerce, from the farmer to the retail customer.

Schramm, Harry F.↗
NASA NTRS2019

Ask-The-Expert: Minimizing Human Review for Big Data Analytics Through Active Learning

In this CIF project, we worked toward semi-automating knowledge discovery from anomaly detection algorithms through the use of active learning. Active learning is an area of research within machine learning that uses an "expert in the loop" to learn from large data sets that have very few annotations or labels available, and where providing such labels is expensive. In our case, the task can be defined as the identification of safety events from flight operational data. Since traditional anomaly detection algorithms cannot differentiate between operationally relevant and irrelevant statistical anomalies, Subject Matter Experts (SMEs) have a lengthy and expensive burden of investigating every example identified by the detection algorithm, classifying and labeling them as relevant or irrelevant. Active learningidentifies the unlabeled example for which a label would most improve the classifier, asks the domain expert for a label, and repeats this process until there are no more resources (time, budget) available for labeling or a minimum required performance is reached. A positive label indicates an operationally significant safety event whereas a negative label indicates otherwise. Based on these few labels we propose to build an active learning system that utilizes the SME's time in the most effective manner by iteratively asking for labels for as few informative instances as possible. Our work was proposed to be a stepping stone toward implementation and deployment of the system with user interface to be pursued by the Aviation Operations and Safety Program (AOSP) given its interest in safety monitoring and discovery of safety incidents.

aviation safety↗
NASA NTRS1993

Structure identification in fuzzy inference using reinforcement learning

In our previous work on the GARIC architecture, we have shown that the system can start with surface structure of the knowledge base (i.e., the linguistic expression of the rules) and learn the deep structure (i.e., the fuzzy membership functions of the labels used in the rules) by using reinforcement learning. Assuming the surface structure, GARIC refines the fuzzy membership functions used in the consequents of the rules using a gradient descent procedure. This hybrid fuzzy logic and reinforcement learning approach can learn to balance a cart-pole system and to backup a truck to its docking location after a few trials. In this paper, we discuss how to do structure identification using reinforcement learning in fuzzy inference systems. This involves identifying both surface as well as deep structure of the knowledge base. The term set of fuzzy linguistic labels used in describing the values of each control variable must be derived. In this process, splitting a label refers to creating new labels which are more granular than the original label and merging two labels creates a more general label. Splitting and merging of labels directly transform the structure of the action selection network used in GARIC by increasing or decreasing the number of hidden layer nodes.

Berenji, Hamid R.↗
NASA NTRS1998

Cell Kinetic and Histomorphometric Analysis of Microgravitational Osteopenia: PARE.03B

Previous methods of identifying cells undergoing DNA synthesis (S-phase) utilized H-3 thymidine (3HT) autoradiography. 5-Bromo-2'-deoxyuridine (BrdU) immunohistochemistry is a nonradioactive alternative method. This experiment compared the two methods using the nuclear volume model for osteoblast histogenesis in two different embedding media. Twenty Sprague-Dawley rats were used, with half receiving 3HT (1 micro Ci/g) and the other half BrdU (50 microgram/g). Condyies were embedded (one side in paraffin, the other in plastic) and S-phase nuclei were identified using either autoradiography or immunohistochemistry. The fractional distribution of preosteoblast cell types and the percentage of labeled cells (within each cell fraction and label index) were calculated and expressed as mean q standard error. Chi-Square analysis showed only a minor difference in the fractional distribution of cell types. However, there were significant differences (p less than 0.05) by ANOVA, in the nuclear labeling of specific cell types. With the exception of the less-differentiated A+A'cells, more BrdU label was consistently detected in paraffin than in plastic-embedded sections. In general, more nuclei were labeled with 3H-thymidine than with BrdU in both types of embedding media. Labeling index data (labeled cells/total cells sampled x 100) indicated that BrdU in paraffin, but not plastic gave the same results as 3HT in either embedding method. Thus, we conclude that the two labeling methods do not yield the same results for the nuclear volume model and that embedding media is an important factor whenusing BrdU. As a result of this work, 3HT was chosen for used in the PARE.03 flight experiments.

Roberts, W. Eugene↗
NASA NTRS1998

Cell Kinetic and Histomorphometric Analysis of Microgravitational Osteopenia: PARE.03B

Previous methods of identifying cells undergoing DNA synthesis (S-phase) utilized 3H-thymidine (3HT) autoradiography. 5-Bromo-2'-deoxyuridine (BrdU) immunohistochemistry is a nonradioactive alternative method. This experiment compared the two methods using the nuclear volume model for osteoblast histogenesis in two different embedding media. Twenty Sprague-Dawley rats were used, with half receiving 3HT (1 micro-Ci/g) and the other half BrdU (50 micro-g/g). Condyles were embedded (one side in paraffin, the other in plastic) and S-phase nuclei were identified using either autoradiography or immunohistochemistry. The fractional distribution of preosteoblast cell types and the percentage of labeled cells (within each cell fraction and label index) were calculated and expressed as mean +/- standard error. Chi-Square analysis showed only a minor difference in the fractional distribution of cell types. However, there were,significant differences (p less than 0.05) by ANOVA, in the nuclear labeling of specific cell types. With the exception of the less-differentiated A+A' cells, more BrdU label was consistently detected in paraffin than in plastic-embedded sections. In general, more nuclei were labeled with 3H-thymidine than with BrdU in both types of embedding media (Fig 2.). Labeling index data (labeled cells/total cells sampled x 100) indicated that BrdU in paraffin, but not plastic gave the same results as 3HT in either embedding method. Thus, we conclude that the two labeling methods do not yield the same results.

Roberts, W. Eugene↗
NASA NTRS1985

Polar transport of auxin across gravistimulated roots of maize and its enhancement by calcium

The effect of Ca on the polar movement of [3H]indoleacetic acid ([3H]IAA) in gravistimulated roots was examined using 3-day-old seedlings of maize (Zea mays L.). Transport of label was measured by placing an agar donor block containing [3H]IAA on one side of the elongation zone and measuring movement of label across the root into an agar receiver block on the opposite side. In vertically oriented roots, movement of label across the elongation zone into the receiver was slight and was not enhanced by incorporating 10 millimolar CaCl2 into the receiver block. In horizontally oriented roots, movement of label across the root was readily detectable and movement to a receiver on the bottom was about 3-fold greater than movement in the opposite direction. This polarity was abolished in roots from which the caps were removed prior to gravistimulation. When CaCl2 was incorporated into the receivers, movement of label across horizontally oriented intact roots was increased about 3-fold in both the downward and upward direction. The ability of Ca to enhance the movement of label from [3H]IAA increased with increasing Ca concentration in the receiver up to 5 to 10 millimolar CaCl2. With the inclusion of CaCl2 in the receiver blocks, gravity-induced polar movement of label into receiver blocks from applied [3H]IAA was detectable within 30 minutes, and asymmetric distribution of label within the tissue was detectable within 20 minutes. The results indicate that gravistimulation induces a physiological asymmetry in the auxin transport system of maize roots and that Ca increases the total transport of auxin across the root.

NASA Program Space Biology↗
NASA NTRS2000

Exploiting the dynamics of S-phase tracers in developing brain: interkinetic nuclear migration for cells entering versus leaving the S-phase

Two S-phase markers for in vivo studies of cell proliferation in the developing central nervous system, tritiated thymidine ((3)H-TdR) and bromodeoxyuridine (BUdR), were compared using double-labeling techniques in the developing mouse cortex at embryonic day 14 (E14). The labeling efficiencies and detectability of the two tracers were approximately equivalent, and there was no evidence of significant tracer interactions that depend on order of administration. For both tracers, the loading time needed to label an S-phase cell to detectability is estimated at <0.2 h shortly after the injection of the label, but, as the concentration of the label falls, it increases to approximately 0.65 h after about 30 min. Thereafter, cells that enter the S-phase continue to become detectably labeled for approximately 5-6 h. The approximate equivalence of these two tracers was exploited to observe directly the numbers and positions of nuclei entering (labeled with the second tracer only) and leaving (labeled with the first tracer only) the S-phase. As expected, the numbers of nuclei entering and leaving the S-phase both increased as the interval between the two injections lengthened. Also, nuclei leaving the S-phase rapidly move towards the ventricular surface during G2, but, unexpectedly, the distribution of the entering nuclei does not differ significantly from the distribution of the nuclei in the S-phase. This indicates that: (1) the extent and rate of abventricular nuclear movement during G1 is variable, such that not all nuclei traverse the entire width of the ventricular zone, and (2) interkinetic nuclear movements are minimal during S-phase. Copyright 2000 S. Karger AG, Basel.

Non-NASA Center↗
NASA NTRSDate not supplied

FloodPlanet: High-Resolution Commercial Imagery for Training and Validation of Deep Learning-Based Models of Inundation Extent

Flooding events are becoming increasingly frequent worldwide and are known to cause extensive damage. Public optical and radar satellite imagery can be used to detect large areas of inundation in rural areas, however, long revisit times and coarse spatial resolution limit applications for short-lived events and urban areas. Commercial constellations such as those operated by Planet offer increased spatial and temporal resolution and can supplement mapping efforts to provide more information to disaster response, relief, and mitigation efforts. Deep learning requires high quality labeled data for training across coincident sensors. The FloodPlanet dataset presented here contains labeled surface water for 18 events across the world based on Planetscope imagery with coincident Harmonized Landsat Sentinel-2 ( HLS) or Sentinel-1 and builds upon the previously existing Sen1Floods11, xBD, and NASA Sentinel-1 datasets. Sen1Floods11 includes 4,831 512x512 pixel overlapping tiles of coincident Sentinel-1 and Sentinel-2 data observing 11 flood events across the world from 2017-2019. The dataset contains a combination of automated and hand-labeled surface water for use in training and validation of inundation modeling efforts. The xBD dataset identifies flood-damaged buildings and indicates the scale of damage to each (none, minor, moderate, and major) from four flood events which occurred in the United States, India, Nepal, and Bangladesh from the same time period. The NASA dataset contains hand-labeled water bodies observed in Sentinel-1 imagery during five flood events within the 2017-2019 period. The effort presented here utilizes observations from these previously investigated flood events to generate labels of surface water at the 3-5m spatial resolution provided by Planetscope and facilitate the comparison between public and commercial data. A data pipeline was built which uses clustering algorithms to pick the most suitable overlapping chips between the public data and PlanetScope data for manual labeling. Labels were created manually using NASA’s ImageLabeler tool and include areas of high- and low-confidence water. The high confidence designation is reserved for areas of open, unobstructed water while low confidence is used for areas of suspected water beneath vegetation, clouds, or cloud shadows. Expected to be released in late 2022, the FloodPlanet dataset will include tiled imagery with a unique ID for each 1024x1024 pixel tile, 7 bands of HLS data, and high- and low-confidence flood labels in both shapefile and tiff formats. The authors will follow Spatial Temporal Access Catalog (STAC) guidelines to release FloodPlanet on the Radiant Earth ML hub, which hosts public datasets for machine learning.

Alexander Melancon↗
NASA NTRS1995

Chemical Topography of Efferent Projections from the Median Preoptic Nucleus to Pontine Monoaminergic Cell Groups in the Rat

This study examined efferent output from the median preoptic nucleus (MNPO) to pontine noradrenergic and serotonergic cell groups using an anterograde tracing technique (Phaseolus vulgaris leucoagglutinin, PHA-L) combined with glucose oxidase immunocytochemistry to serotonin (5-HT) or to dopamine-beta-hydroxylase (DBH). Injections of PHA-L into the ventral MNPO resulted in moderate axonal labeling within the region of the B7 and B8 serotonergic groups in the dorsal raphe. PHA-L labeled fibers and punctate processes were observed in close apposition to many of the 5-HT immunoreactive neurons in these regions. In contrast, sparse terminal labeling was found within the B5 group in the raphe pontis nucleus, and only trace fiber labeling observed in the B3 and B6 groups. Efferents from the MNPO also provided moderate innervation to the A6 and A7 noradrenergic groups. PHA-L labeled punctate processes were found most frequently in close apposition to DBH-immunoreactive neurons at mid- to caudal levels of the locus coeruleus. Some labeled axons were also present within the A7 and A5 groups. Additionally, a close apposition between labeled MNPO efferents and 5-HT fibers within the lateral parabrachial nucleus was observed. The results indicate the MNPO provides a topographic innervation of monoaminergic groups in the upper brainstem.

Zardetto-Smith, Andrea M.↗
NASA NTRS1995

Chemical Topography of Efferent Projections from the Median Preoptic Nucleus to Pontine Monoaminergic Cell Groups in the Rat

This study examined efferent output from the median preoptic nucleus (MnPO) to pontine noradrenergic and serotonergic cell groups using an anterograde tracing technique (Phaseolus vulgaris leucoagglutinin, PHA-L) combined with glucose oxidase immunocytochemistry to scrotonin (5-HT) or to dopamine-(beta)-hydroxylase (DBH). Injections of PHA-L into the ventral MNPO resulted in moderate axonal labeling within the region of the B7 and B8 serotonergic groups in the dorsal raphe. PHA-L labeled fibers and punctate processes were observed in close apposition to many of the 5-HT immunoreactive neurons in these regions, In contrast, sparse terminal labeling was found within the B5 group in the raphe pontis nucleus, and only trace fiber labeling observed in the B3 and B6 groups. Efferents from the MNPO also provided moderate innervation to the A6 and A7 noradrenergic groups. PHA-L labeled punctate processes were found most frequently in close apposition to DBH-immunorcactive neurons at mid- to caudal levels of the locus coeruleus. Some labeled axons were also present within the A7 and A5 groups. Additionally, a close apposition between labeled MNPO efferents and 5-HT fibers within the lateral parabrachial nucleus was observed, The results indicate the MNPO provides a topographic innerva- tion of monoaminergic groups in the upper brainstem.

Zardetto-Smith, Andrea M.↗
NASA NTRS2002

The origin of mesoderm in phoronids

Descriptive studies of phoronid development have concluded that the mesoderm of these animals originates from the endoderm during gastrulation. This interpretation has been tested by labeling one blastomere of 4- through 16-cell embryos and examining the position and germ layers occupied by the labeled clones of cells in the larva. No 2 injections gave rise to identical clones of cells, suggesting that the cleavage program does not generate cells of unique identity and that cell fates are established at later developmental time points. In many cases, a relatively large sector composed of ectodermal cells was labeled. When these labeled cells were adjacent to the mouth or anus of the larva, muscle and mesenchyme cells originated from the labeled clones. Under these circumstances, nerve cells also originated from these labeled sectors. These labeling studies also showed that endodermal cells can give rise to mesodermal and neural cells. These results suggest that nerve and muscle cells are induced to form at ectodermal-endodermal boundaries from both germ layers. These marking experiments also confirmed the observation that nerve cells originate both from the apical organ and the trunk region and show for the first time that the intestine originates by ingression of posterior ectoderm.

NASA Discipline Evolutionary Biology↗
NASA NTRS2009

Active Learning with Irrelevant Examples

An improved active learning method has been devised for training data classifiers. One example of a data classifier is the algorithm used by the United States Postal Service since the 1960s to recognize scans of handwritten digits for processing zip codes. Active learning algorithms enable rapid training with minimal investment of time on the part of human experts to provide training examples consisting of correctly classified (labeled) input data. They function by identifying which examples would be most profitable for a human expert to label. The goal is to maximize classifier accuracy while minimizing the number of examples the expert must label. Although there are several well-established methods for active learning, they may not operate well when irrelevant examples are present in the data set. That is, they may select an item for labeling that the expert simply cannot assign to any of the valid classes. In the context of classifying handwritten digits, the irrelevant items may include stray marks, smudges, and mis-scans. Querying the expert about these items results in wasted time or erroneous labels, if the expert is forced to assign the item to one of the valid classes. In contrast, the new algorithm provides a specific mechanism for avoiding querying the irrelevant items. This algorithm has two components: an active learner (which could be a conventional active learning algorithm) and a relevance classifier. The combination of these components yields a method, denoted Relevance Bias, that enables the active learner to avoid querying irrelevant data so as to increase its learning rate and efficiency when irrelevant items are present. The algorithm collects irrelevant data in a set of rejected examples, then trains the relevance classifier to distinguish between labeled (relevant) training examples and the rejected ones. The active learner combines its ranking of the items with the probability that they are relevant to yield a final decision about which item to present to the expert for labeling. Experiments on several data sets have demonstrated that the Relevance Bias approach significantly decreases the number of irrelevant items queried and also accelerates learning speed.

Wagstaff, Kiri↗
NASA NTRS2011

Java Image I/O for VICAR, PDS, and ISIS

This library, written in Java, supports input and output of images and metadata (labels) in the VICAR, PDS image, and ISIS-2 and ISIS-3 file formats. Three levels of access exist. The first level comprises the low-level, direct access to the file. This allows an application to read and write specific image tiles, lines, or pixels and to manipulate the label data directly. This layer is analogous to the C-language "VICAR Run-Time Library" (RTL), which is the image I/O library for the (C/C++/Fortran) VICAR image processing system from JPL MIPL (Multimission Image Processing Lab). This low-level library can also be used to read and write labeled, uncompressed images stored in formats similar to VICAR, such as ISIS-2 and -3, and a subset of PDS (image format). The second level of access involves two codecs based on Java Advanced Imaging (JAI) to provide access to VICAR and PDS images in a file-format-independent manner. JAI is supplied by Sun Microsystems as an extension to desktop Java, and has a number of codecs for formats such as GIF, TIFF, JPEG, etc. Although Sun has deprecated the codec mechanism (replaced by IIO), it is still used in many places. The VICAR and PDS codecs allow any program written using the JAI codec spec to use VICAR or PDS images automatically, with no specific knowledge of the VICAR or PDS formats. Support for metadata (labels) is included, but is format-dependent. The PDS codec, when processing PDS images with an embedded VIAR label ("dual-labeled images," such as used for MER), presents the VICAR label in a new way that is compatible with the VICAR codec. The third level of access involves VICAR, PDS, and ISIS Image I/O plugins. The Java core includes an "Image I/O" (IIO) package that is similar in concept to the JAI codec, but is newer and more capable. Applications written to the IIO specification can use any image format for which a plug-in exists, with no specific knowledge of the format itself.

Deen, Robert G.↗