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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

ORNL Biometric Eye Model for Iris Recognition

Iris recognition has been proven to be an accurate and reliable biometric. However, the recognition of non-ideal iris images such as off angle images is still an unsolved problem. We propose a new biometric targeted eye model and a method to reconstruct the off-axis eye to its frontal view allowing for recognition using existing methods and algorithms. This allows for existing enterprise level algorithms and approaches to be largely unmodified by using our work as a pre-processor to improve performance. In addition, we describe the `Limbus effect' and its importance for an accurate segmentation of off-axis irides. Our method uses an anatomically accurate human eye model and ray-tracing techniques to compute a transformation function, which reconstructs the iris to its frontal, non-refracted state. Then, the same eye model is used to render a frontal view of the reconstructed iris. The proposed method is fully described and results from synthetic data are shown to establish an upper limit on performance improvement and establish the importance of the proposed approach over traditional linear elliptical unwrapping methods. Our results with synthetic data demonstrate the ability to perform an accurate iris recognition with an image taken as much as 70 degrees off-axis.

Santos-Villalobos, Hector J↗
DOE OSTI2014

Face recognition system and method using face pattern words and face pattern bytes

The present invention provides a novel system and method for identifying individuals and for face recognition utilizing facial features for face identification. The system and method of the invention comprise creating facial features or face patterns called face pattern words and face pattern bytes for face identification. The invention also provides for pattern recognitions for identification other than face recognition. The invention further provides a means for identifying individuals based on visible and/or thermal images of those individuals by utilizing computer software implemented by instructions on a computer or computer system and a computer readable medium containing instructions on a computer system for face recognition and identification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Face Recognition Algorithm Bias: Performance Differences on Images of Children and Adults

In this work, we examine if current state-of-the-art deep- learning enhanced face recognition systems exhibit a negative bias for children as compared to adults. The systems selected for this work are five top performing1 commercial off-the-shelf (COTS) face recognition systems, two government off-the-shelf (GOTS) face recognition systems and one open-source face recognition solution. The datasets used to evaluate the performance of the systems are both unconstrained in age, pose, illumination, and expression and are publicly available. These datasets are indicative of photo journalistic face datasets published and evaluated on over the last few years. Our findings show a negative bias (i.e., a considerable degradation in performance) for each algorithm on children when compared to the performance obtained on adults. Genuine and imposter distributions high- light the performance bias between the datasets further sup- porting the need for a deeper investigation into algorithm bias as a function of age cohorts. To combat the performance decline on the child demographic, several score- level fusion strategies were evaluated. This work identifies the best score-level fusion technique for child demographic.

Srinivas, Nisha↗
DOE OSTI2023

Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Dataset

Face recognition technology has advanced significantly in recent years due largely to the availability of large and increasingly complex training datasets for use in deep learning models. These datasets, however, typically comprise images scraped from news sites or social media plat-forms and, therefore, have limited utility in more advanced security, forensics, and military applications. These applications require lower resolution, longer ranges, and ele-vated viewpoints. To meet these critical needs, we collected and curated the first and second subsets of a large multi-modal biometric dataset designed for use in the research and development (R&D) of biometric recognition technolo-gies under extremely challenging conditions. Thus far, the dataset includes more than 350,000 still images and over 1,300 hours of video footage of approximately 1,000 sub-jects. To collect this data, we used Nikon DSLR cameras, a variety of commercial surveillance cameras, specialized long-rage R&D cameras, and Group 1 and Group 2 UAV platforms. The goal is to support the development of algorithms capable of accurately recognizing people at ranges up to 1,000 m and from high angles of elevation. These ad-vances will include improvements to the state of the art in face recognition and will support new research in the area of whole-body recognition using methods based on gait and anthropometry. This paper describes methods used to col-lect and curate the dataset, and the dataset's characteristics at the current stage.

Brogan, Joel↗
DOE OSTI2023

CholecTriplet2021: A benchmark challenge for surgical action triplet recognition

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of ‹ instrument, verb, target › combination delivers more comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. Here, the challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and the assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. Here, a total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms from the competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Assessing the Impact of Corneal Refraction and Iris Tissue Non-planarity on Iris Recognition

Assumptions regarding eye morphology are implicit in iris recognition algorithms. The cornea is assumed to have little to no effect on the view of the iris texture when the eye gaze is non-frontal, the iris is assumed flat, and the eye is assumed to be imaged via an orthographic projection. If these assumptions hold, affine transformations may be used to rectify non-frontally posed images to a frontal view and the rubber sheet model may be accurately used to normalize the iris annulus to a rectangular image. This work examines how iris recognition performance degrades when the first two assumptions are violated. Using a computer renderable eye model, a data set is created varying the presence of the cornea and a parameterized non-planarity of the iris shape across a large range of eye gaze angles. Matching scores are created using a commercial matcher. Thus, when comparing the relative impact of each assumption violation, it is observed that iris non-planarity presents a more significant problem than corneal refractive distortion with regard to iris recognition accuracy in non-frontal images.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

High-fidelity Recognition of Organotrifluoroborate Anions (R-BF 3 - ) as Designer Guest Molecules

The recognition of boron compounds is well developed as boronic acids but untapped as organotrifluoroborate anions (R-BF 3 - ). Here, we are exploring the development of these and other designer anions as anion-recognition motifs by considering them as substituted versions of the parent inorganic ion. To this end, we demonstrate strong and reliable binding of organic trifluoroborates, R-BF 3 - , by cyanostar macrocycles that are size-complementary to the inorganic BF 4 - progenitors. We find that recognition is modulated by the substituent's sterics and that the affinities are retained using the common K + salts of R-BF 3 - anions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Glycosylated Peptoid Nanosheets as a Multivalent Scaffold for Protein Recognition

Glycoproteins adhered on the cellular membrane play an important role in a wide range of cellular functions. Their significance is particularly relevant in the recognition process between infectious pathogens (such as viruses, bacteria, toxins) and their host cells. Multivalent interactions at the pathogen-cell interfaces govern binding events and can result in a strong and specific interaction. Here in this paper, we report an approach to mimic the cell surface presentation of carbohydrate ligands by the multivalent display of sugars on the surface of peptoid nanosheets. The constructs provide a highly organized 2D platform for recognition of carbohydrate-binding proteins. The sugars were displayed using different linker lengths or within loops containing 2-6 hydrophilic peptoid monomers. Both the linkers and the loops contained one alkyne-bearing monomer, to which different saccharides were attached by copper-catalyzed azide-alkyne cycloaddition reactions. Peptoid nanosheets functionalized with different saccharide groups were able to selectively bind multivalent lectins, Concanavalin A and Wheat Germ Agglutinin, as observed by fluorescence microscopy and a homogeneous Förster resonance energy transfer (FRET)-based binding assay. To evaluate the potential of this system as sensor for threat agents, the ability of functionalized peptoid nanosheets to bind Shiga toxin was also studied. Peptoid nanosheets were functionalized with globotriose, the natural ligand of Shiga toxin, and the effective binding of the nanomaterial was verified by the FRET-based binding assay. In all cases, evidence for multivalent binding was observed by systematic variation of the ligand display density on the nanosheet surface. These cell surface mimetic nanomaterials may find utility in the inactivation of pathogens or as selective molecular recognition elements.

bioinspired polymers↗
DOE OSTI2014

Quantification of the transferability of a designed protein specificity switch reveals extensive epistasis in molecular recognition

Re-engineering protein–protein recognition is an important route to dissecting and controlling complex interaction networks. Experimental approaches have used the strategy of “second-site suppressors,” where a functional interaction is inferred between two proteins if a mutation in one protein can be compensated by a mutation in the second. Mimicking this strategy, computational design has been applied successfully to change protein recognition specificity by predicting such sets of compensatory mutations in protein–protein interfaces. To extend this approach, it would be advantageous to be able to “transplant” existing engineered and experimentally validated specificity changes to other homologous protein–protein complexes. Here, we test this strategy by designing a pair of mutations that modulates peptide recognition specificity in the Syntrophin PDZ domain, confirming the designed interaction biochemically and structurally, and then transplanting the mutations into the context of five related PDZ domain–peptide complexes. We find a wide range of energetic effects of identical mutations in structurally similar positions, revealing a dramatic context dependence (epistasis) of designed mutations in homologous protein–protein interactions. To better understand the structural basis of this context dependence, we apply a structure-based computational model that recapitulates these energetic effects and we use this model to make and validate forward predictions. The context dependence of these mutations is captured by computational predictions, our results both highlight the considerable difficulties in designing protein–protein interactions and provide challenging benchmark cases for the development of improved protein modeling and design methods that accurately account for the context.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Protocadherin cis -dimer architecture and recognition unit diversity

Clustered protocadherins (Pcdhs) mediate numerous neural patterning functions, including neuronal self-recognition and non–self-discrimination to direct self-avoidance among vertebrate neurons. Individual neurons stochastically express a subset of Pcdh isoforms, which assemble to form a stochastic repertoire of cis -dimers. We describe herein the structure of a PcdhγB7 cis -homodimer, which includes the membrane-proximal extracellular cadherin domains EC5 and EC6. The structure is asymmetric with one molecule contributing interface surface from both EC5 and EC6, and the other only from EC6. Structural and sequence analyses suggest that all Pcdh isoforms will dimerize through this interface. Site-directed mutants at this interface interfere with both Pcdh cis -dimerization and cell surface transport. The structure explains the known restrictions of cis -interactions of some Pcdh isoforms, including α-Pcdhs, which cannot form homodimers. The asymmetry of the interface approximately doubles the size of the recognition repertoire, and restrictions on cis -interactions among Pcdh isoforms define the limits of the Pcdh recognition unit repertoire.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Human N -acetylglucosaminyltransferase II substrate recognition uses a modular architecture that includes a convergent exosite

Asn-linked oligosaccharides are extensively modified during transit through the secretory pathway, first by trimming of the nascent glycan chains and subsequently by initiating and extending multiple oligosaccharide branches from the trimannosyl glycan core. Trimming and branching pathway steps are highly ordered and hierarchal based on the precise substrate specificities of the individual biosynthetic enzymes. A key committed step in the synthesis of complex-type glycans is catalyzed by N-acetylglucosaminyltransferase II (MGAT2), an enzyme that generates the second GlcNAcβ1,2- branch from the trimannosyl glycan core using UDP-GlcNAc as the sugar donor. We determined the structure of human MGAT2 as a Mn 2+ -UDP donor analog complex and as a GlcNAcMan 3 GlcNAc 2 -Asn acceptor complex to reveal the structural basis for substrate recognition and catalysis. The enzyme exhibits a GT-A Rossmann-like fold that employs conserved divalent cation-dependent substrate interactions with the UDP-GlcNAc donor. MGAT2 interactions with the extended glycan acceptor are distinct from other related glycosyltransferases. These interactions are composed of a catalytic subsite that binds the Man-α1,6- monosaccharide acceptor and a distal exosite pocket that binds the GlcNAc-β1,2Man-α1,3Manβ- substrate “recognition arm.” Recognition arm interactions are similar to the enzyme–substrate interactions for Golgi α-mannosidase II, a glycoside hydrolase that acts just before MGAT2 in the Asn-linked glycan biosynthetic pathway. Here, these data suggest that substrate binding by MGAT2 employs both conserved and convergent catalytic subsite modules to provide substrate selectivity and catalysis. More broadly, the MGAT2 active-site architecture demonstrates how glycosyltransferases create complementary modular templates for regiospecific extension of glycan structures in mammalian cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Wire-cell 3D pattern recognition techniques for neutrino event reconstruction in large LArTPCs: algorithm description and quantitative evaluation with MicroBooNE simulation

Wire-Cell is a 3D event reconstruction package for liquid argon time projection chambers. Through geometry, time, and drifted charge from multiple readout wire planes, 3D space points with associated charge are reconstructed prior to the pattern recognition stage. Pattern recognition techniques, including track trajectory and dQ/dx (ionization charge per unit length) fitting, 3D neutrino vertex fitting, track and shower separation, particle-level clustering, and particle identification are then applied on these 3D space points as well as the original 2D projection measurements. A deep neural network is developed to enhance the reconstruction of the neutrino interaction vertex. Compared to traditional algorithms, the deep neural network boosts the vertex efficiency by a relative 30% for charged-current ν e interactions. Therefore, this pattern recognition achieves 80–90% reconstruction efficiencies for primary leptons, after a 65.8% (72.9%) vertex efficiency for charged-current ν e (ν μ ) interactions. Based on the resulting reconstructed particles and their kinematics, we also achieve 15-20% energy reconstruction resolutions for charged-current neutrino interactions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Stereoselective recognition of morphine enantiomers by μ -opioid receptor

Stereospecific recognition of chiral molecules plays a crucial role in biological systems. The μ-opioid receptor (MOR) exhibits binding affinity towards (-)-morphine, a well-established gold standard in pain management, while it shows minimal binding affinity for the (+)-morphine enantiomer, resulting in a lack of analgesic activity. Understanding how MOR stereoselectively recognizes morphine enantiomers has remained a puzzle in neuroscience and pharmacology for over half-a-century due to the lack of direct observation techniques. To unravel this mystery, we constructed the binding and unbinding processes of morphine enantiomers with MOR via molecular dynamics simulations to investigate the thermodynamics and kinetics governing MOR's stereoselective recognition of morphine enantiomers. Our findings reveal that the binding of (-)-morphine stabilizes MOR in its activated state, exhibiting a deep energy well and a prolonged residence time. In contrast, (+)-morphine fails to sustain the activation state of MOR. Furthermore, the results suggest that specific residues, namely D114 2.50 and D147 3.32 , are deprotonated in the active state of MOR bound to (-)-morphine. This work highlights that the selectivity in molecular recognition goes beyond binding affinities, extending into the realm of residence time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Cross-Domain Recognition by Identifying Joint Subspaces of Source Domain and Target Domain

This paper introduces here a new method to solve the cross-domain recognition problem. Different from the traditional domain adaption methods which rely on a global domain shift for all classes between the source and target domains, the proposed method is more flexible to capture individual class variations across domains. By adopting a natural and widely used assumption that the data samples from the same class should lay on an intrinsic low-dimensional subspace, even if they come from different domains, the proposed method circumvents the limitation of the global domain shift, and solves the cross-domain recognition by finding the joint subspaces of the source and target domains. Specifically, given labeled samples in the source domain, we construct a subspace for each of the classes. Then we construct subspaces in the target domain, called anchor subspaces, by collecting unlabeled samples that are close to each other and are highly likely to belong to the same class. The corresponding class label is then assigned by minimizing a cost function which reflects the overlap and topological structure consistency between subspaces across the source and target domains, and within the anchor subspaces, respectively. We further combine the anchor subspaces to the corresponding source subspaces to construct the joint subspaces. Subsequently, one-versus-rest support vector machine classifiers are trained using the data samples belonging to the same joint subspaces and applied to unlabeled data in the target domain. We evaluate the proposed method on two widely used datasets: 1) object recognition dataset for computer vision tasks and 2) sentiment classification dataset for natural language processing tasks. Comparison results demonstrate that the proposed method outperforms the comparison methods on both datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

TU-C-17A-03: An Integrated Contour Evaluation Software Tool Using Supervised Pattern Recognition for Radiotherapy

Purpose: Radiotherapy (RT) contours delineated either manually or semiautomatically require verification before clinical usage. Manual evaluation is very time consuming. A new integrated software tool using supervised pattern contour recognition was thus developed to facilitate this process. Methods: The contouring tool was developed using an object-oriented programming language C# and application programming interfaces, e.g. visualization toolkit (VTK). The C# language served as the tool design basis. The Accord.Net scientific computing libraries were utilized for the required statistical data processing and pattern recognition, while the VTK was used to build and render 3-D mesh models from critical RT structures in real-time and 360° visualization. Principal component analysis (PCA) was used for system self-updating geometry variations of normal structures based on physician-approved RT contours as a training dataset. The inhouse design of supervised PCA-based contour recognition method was used for automatically evaluating contour normality/abnormality. The function for reporting the contour evaluation results was implemented by using C# and Windows Form Designer. Results: The software input was RT simulation images and RT structures from commercial clinical treatment planning systems. Several abilities were demonstrated: automatic assessment of RT contours, file loading/saving of various modality medical images and RT contours, and generation/visualization of 3-D images and anatomical models. Moreover, it supported the 360° rendering of the RT structures in a multi-slice view, which allows physicians to visually check and edit abnormally contoured structures. Conclusion: This new software integrates the supervised learning framework with image processing and graphical visualization modules for RT contour verification. This tool has great potential for facilitating treatment planning with the assistance of an automatic contour evaluation module in avoiding unnecessary manual verification for physicians/dosimetrists. In addition, its nature as a compact and stand-alone tool allows for future extensibility to include additional functions for physicians’ clinical needs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Transcranial Direct Current Stimulation (tDCS) Improves Empathy and Recognition of Facial Emotions Conveying Threat in Adults with Autism Spectrum Disorder (ASD): A Randomized Controlled Pilot Study

Introduction: Empathy is critical for human interactions to become shared and meaningful, and it is facilitated by the expression and processing of facial emotions. Deficits in empathy and facial emotion recognition are associated with individuals with autism spectrum disorder (ASD), with specific concerns over inaccurate recognition of facial emotion expressions conveying a threat. Yet, the number of evidenced interventions for facial emotion recognition and processing (FERP), emotion, and empathy remains limited, particularly for adults with ASD. Transcranial direct current stimulation (tDCS), a noninvasive brain stimulation, may be a promising treatment modality to safely accelerate and/or enhance treatment interventions to increase their efficacy. Methods: This study investigates the effectiveness of FERP, emotion, and empathy treatment interventions paired with tDCS for adults with ASD. Differences in scores on the Empathy Quotient (EQ) and on a FERP test were predicted for verum tDCS when compared to sham. Verum/sham tDCS was randomly assigned in a within-subjects, double-blinded design in adults with ASD without intellectual disability. Results: Verum tDCS significantly improved EQ scores and FERP scores for emotions that conveyed threat. Conclusions: These results suggest the potential for increasing the efficacy of treatment interventions by pairing them with tDCS for individuals with ASD.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2010

Proposal for the development of 3D Vertically Integrated Pattern Recognition Associative Memory (VIPRAM)

Future particle physics experiments looking for rare processes will have no choice but to address the demanding challenges of fast pattern recognition in triggering as detector hit density becomes significantly higher due to the high luminosity required to produce the rare process. The authors propose to develop a 3D Vertically Integrated Pattern Recognition Associative Memory (VIPRAM) chip for HEP applications, to advance the state-of-the-art for pattern recognition and track reconstruction for fast triggering.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2013

Non-Cooperative Facial Recognition Video Dataset Collection Plan

The Pacific Northwest National Laboratory (PNNL) will produce a non-cooperative (i.e. not posing for the camera) facial recognition video data set for research purposes to evaluate and enhance facial recognition systems technology. The aggregate data set consists of 1) videos capturing PNNL role players and public volunteers in three key operational settings, 2) photographs of the role players for enrolling in an evaluation database, and 3) ground truth data that documents when the role player is within various camera fields of view. PNNL will deliver the aggregate data set to DHS who may then choose to make it available to other government agencies interested in evaluating and enhancing facial recognition systems. The three operational settings that will be the focus of the video collection effort include: 1) unidirectional crowd flow 2) bi-directional crowd flow, and 3) linear and/or serpentine queues.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗