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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Rifle-like camera for long distance face recognition

An improved long-range facial recognition system is provided. The facial recognition system includes an integrated, rifle-like mounting platform with modular and interchangeable components, including a machine-vision camera, a facial recognition module, and an aiming scope. The mounting platform includes an elongated frame, a hand grip, a shoulder stock, and an underbody support attachment for an optional bipod. The mounting platform provides an intuitive, shoulder-operated support structure for stabilizing the machine-vision camera and reducing vibrations that otherwise inhibit long distance imaging.

Bolme, David S.↗
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↗
NASA NTRS2003

Design of a Borescope for Extravehicular Non-Destructive Applications

Anomalies such as corrosion, structural damage, misalignment, cracking, stress fiactures, pitting, or wear can be detected and monitored by the aid of a borescope. A borescope requires a source of light for proper operation. Today s current lighting technology market consists of incandescent lamps, fluorescent lamps and other types of electric arc and electric discharge vapor lamp. Recent advances in LED technology have made LEDs viable for a number of applications, including vehicle stoplights, traffic lights, machine-vision-inspection, illumination, and street signs. LEDs promise significant reduction in power consumption compared to other sources of light. This project focused on comparing images taken by the Olympus IPLEX, using two different light sources. One of the sources is the 50-W internal metal halide lamp and the other is a 1 W LED placed at the tip of the insertion tube. Images acquired using these two light sources were quantitatively compared using their histogram, intensity profile along a line segment, and edge detection. Also, images were qualitatively compared using image registration and transformation [l]. The gray-level histogram, edge detection, image profile and image registration do not offer conclusive results. The LED light source, however, produces good images for visual inspection by an operator. Analysis using pattern recognition using Eigenfaces and Gaussian Pyramid in face recognition may be more useful.

Bachnak, Rafic↗
DOE OSTI2022

Grassmannian Diffusion Maps--Based Dimension Reduction and Classification for High-Dimensional Data

This work introduces the Grassmannian diffusion maps (GDMaps), a novel nonlinear dimensionality reduction technique that defines the affinity between points through their representation as low-dimensional subspaces corresponding to points on the Grassmann manifold. Here, the method is designed for applications, such as image recognition and data-based classification of constrained high-dimensional data where each data point itself is a high-dimensional object (i.e., a large matrix) that can be compactly represented in a lower-dimensional subspace. The GDMaps is composed of two stages. The first is a pointwise linear dimensionality reduction wherein each high-dimensional object is mapped onto the Grassmann manifold representing the low-dimensional subspace on which it resides. The second stage is a multipoint nonlinear kernel-based dimension reduction using diffusion maps to identify the subspace structure of the points on the Grassmann manifold. To this end, an appropriate Grassmannian kernel is used to construct the transition matrix of a random walk on a graph connecting points on the Grassmann manifold. Spectral analysis of the transition matrix yields low-dimensional Grassmannian diffusion coordinates embedding the data into a low-dimensional reproducing kernel Hilbert space. Further, a novel data classification/recognition technique is developed based on the construction of an overcomplete dictionary of reduced dimension whose atoms are given by the Grassmannian diffusion coordinates. Three examples are considered. First, a "toy" example shows that the GDMaps can identify an appropriate parametrization of structured points on the unit sphere. The second example demonstrates the ability of the GDMaps to revealing the intrinsic subspace structure of high-dimensional random field data. In the last ex- ample, a face recognition problem is solved considering face images subject to varying illumination conditions, changes in face expressions, and occurrence of occlusions. The technique presented high recognition rates (i.e., 95% in the best case) using a fraction of the data required by conventional methods.

42 ENGINEERING↗
NASA NTRS1994

Three-D Artificial Neural Network (3DANN) technology. Blueprint for the future

Irvine Sensors Corporation (ISC), working closely with JPL under BMDO/ONR sponsorship, is developing a radically new neural computing technology. Primarily aimed at discrimination and target recognition for BMDO missile interceptor applications, it appears to have near term commercial applicability to such problems as handwriting and face recognition, just to name two. In its earliest form it will be able to perform inner product computation using 262 thousand 64x64 templates (weighted synapse arrays) where the 64(exp 5) weights can all be changed every millisecond. Internal switching provides an inherent capability to zoom, translate, or rotate the templates. The 3D silicon architecture is manufactured on a commercial, high volume DRAM production line at very low cost, enabling its commercialization. Two technology thrusts are beginning: in the first, the 64 layer capability of 3DANN-I will be extended to 1024 layers and beyond. In the second layer size will be shrunk to 2-3 millimeters to reduce layer costs. Our workshop goal is to expose this technology to the neural network community as an emerging tool for their use and to obtain their desire for its future development.

Carson, John↗
DOE OSTI2024

Long-Range Biometric Identification in Real World Scenarios: A Comprehensive Evaluation Framework Based on Missions

The considerable body of data available for evaluating biometric recognition systems in Research and Development (R&D) environments has contributed to the increasingly common problem of target performance mismatch. Biometric algorithms are frequently tested against data that may not reflect the real world applications they target. From a Testing and Evaluation (T&E) standpoint, this domain mismatch causes difficulty assessing when improvements in State-of-the-Art (SOTA) research actually translate to improved applied outcomes. This problem can be addressed with thoughtful preparation of data and experimental methods to reflect specific use-cases and scenarios.To that end, this paper evaluates research solutions for identifying individuals at ranges and altitudes, which could support various application areas such as counterterrorism, protection of critical infrastructure facilities, military force protection, and border security. We address challenges including image quality issues and reliance on face recognition as the sole biometric modality. By fusing face and body features, we propose developing robust biometric systems for effective long-range identification from both the ground and steep pitch angles. Preliminary results show promising progress in whole-body recognition. This paper presents these early findings and discusses potential future directions for advancing long-range biometric identification systems based on mission-driven metrics.

Aykac, Deniz↗
NASA NTRS1976

An investigation of potential applications of OP-SAPS: Operational sampled analog processors

The impact of charge-coupled device (CCD) processors on future instrumentation was investigated. The CCD devices studied process sampled analog data and are referred to as OP-SAPS - operational sampled analog processors. Preliminary studies into various architectural configurations for systems composed of OP-SAPS show that they have potential in such diverse applications as pattern recognition and automatic control. It appears probable that OP-SAPS may be used to construct computing structures which can serve as special peripherals to large-scale computer complexes used in real time flight simulation. The research was limited to the following benchmark programs: (1) face recognition, (2) voice command and control, (3) terrain classification, and (4) terrain identification. A small amount of effort was spent on examining a method by which OP-SAPS may be used to decrease the limiting ground sampling distance encountered in remote sensing from satellites.

Parrish, E. A.↗
NASA NTRS2019

Understanding Machine Learning in Earth Science: A Natural Language Processing Approach

Machine learning (ML) is being increasingly utilized in Earth science research. Benefits of ML include efficiency, reduction of human error, and ability to extract hidden patterns within data. However, the mutual lack of each other’s domain knowledge by ML and Earth science stands as a barrier to timely and effective implementation. Earth science, in particular, faces challenges in generating sample data, compared to those of traditional ML problems such as face recognition or stock predictions, where data is abundant and not lacking in ground truth, which is necessary for labeling. Earth science data are more varying in formats, such as HDF5 and image resolutions, and are not standardized across instruments, even within a given Earth science discipline. Previous studies have been done to outline the specific challenges that Earth science faces with ML, while others have focused on using existing publications to mine information efficiently. Other resources such as Scikit-Learn have developed decision trees for choosing appropriate machine learning algorithms, but application within Earth science subjects becomes much more complex. For the current study, we propose a methodology and tool that aids in implementation of ML in Earth science using natural language processing (NLP). Our work comprises three main parts: (1) analyzing existing publications related to ML and Earth science, using natural language processing: (2) extracting from the publications information on ML models subjects in Earth Science: and (3) visualizing the extracted relationships as a network graph. The resulting network graph should aid the Earth science communities in applying optimal ML algorithms and guiding data preparation through visualization of similar studies. The network graph and analysis of document similarity will be the basis of our next step, which is to develop a decision tree for selecting optimal machine learning methodologies for specified Earth science applications.

Zheng, Laura↗
NASA NTRS1994

How Captain Amerika uses neural networks to fight crime

Artificial neural network models can make amazing computations. These models are explained along with their application in problems associated with fighting crime. Specific problems addressed are identification of people using face recognition, speaker identification, and fingerprint and handwriting analysis (biometric authentication).

Rogers, Steven K.↗
NASA NTRS1997

Cognitive Sciences

Session MP4 includes short reports on: (1) Face Recognition in Microgravity: Is Gravity Direction Involved in the Inversion Effect?; (2) Motor Timing under Microgravity; (3) Perceived Self-Motion Assessed by Computer-Generated Animations: Complexity and Reliability; (4) Prolonged Weightlessness Reference Frames and Visual Symmetry Detection; (5) Mental Representation of Gravity During a Locomotor Task; and (6) Haptic Perception in Weightlessness: A Sense of Force or a Sense of Effort?

Source record↗
NASA NTRS1997

The heuristic and motivational value of video reinforcement

Four rhesus monkeys (Macaca mulatta) were tested on joystick-based computer tasks in which they could choose to be reinforced either with pellets-only or with pellets + video. A variety of videotapes were used to reinforce task performance. The monkeys significantly preferred to be rewarded with a pellet and 10 s of a blank screen than a pellet plus 10 s of videotape. When they did choose to see videotaped images, however, they were significantly more likely to view video of themselves than video of their roommate or of unfamiliar conspecifics. These data support earlier findings of individual differences in preference for video reinforcement, and have clear implications for the study of face-recognition and self-recognition by nonhuman primates.

NASA Discipline Space Human Factors↗
NASA NTRS1989

Sparse distributed memory

Sparse distributed memory was proposed be Pentti Kanerva as a realizable architecture that could store large patterns and retrieve them based on partial matches with patterns representing current sensory inputs. This memory exhibits behaviors, both in theory and in experiment, that resemble those previously unapproached by machines - e.g., rapid recognition of faces or odors, discovery of new connections between seemingly unrelated ideas, continuation of a sequence of events when given a cue from the middle, knowing that one doesn't know, or getting stuck with an answer on the tip of one's tongue. These behaviors are now within reach of machines that can be incorporated into the computing systems of robots capable of seeing, talking, and manipulating. Kanerva's theory is a break with the Western rationalistic tradition, allowing a new interpretation of learning and cognition that respects biology and the mysteries of individual human beings.

Denning, Peter J.↗
DOE OSTI2026

The BRIAR Dataset: A Comprehensive Whole-Body Biometric Recognition Benchmark at Extreme Distances and Altitudes (Collections 1-6)

The Biometric Recognition and Identification at Altitude and Range (BRIAR) program aims to extend biometric capabilities into severe operational environments characterized by long ranges, atmospheric turbulence, and elevated viewpoints. This paper introduces the program’s two final government collections expanding activities, environments, viewpoints, distances, and modalities, and adding appearance and environmental stressors. We refine curation/evaluation and include sequestered subsets to support controlled assessments. These updates strengthen BRIAR as a comprehensive resource for whole-body, face, and gait recognition at altitude and range, while maintaining privacy-first practices.

Yoon, Rocky [ORNL] (ORCID:0009000491499165)↗
NASA NTRS2000

Endpoint Security Using Biometric Authentication for Secure Remote Mission Operations

We propose a flexible security authentication solution for the spacecraft end-user, which will allow the user to interact over Internet with the spacecraft, its instruments, or with the ground segment from anywhere, anytime based on the user's pre-defined set of privileges. This package includes biometrics authentication products, such as face, voice or fingerprint recognition, authentication services and procedures, such as: user registration and verification over the Internet and user database maintenance, with a configurable schema of spacecraft users' privileges. This fast and reliable user authentication mechanism will become an integral part of end-to-end ground-to-space secure Internet communications and migration from current practice to the future. All modules and services of the proposed package are commercially available and built to the NIST BioAPI standard, which facilitates "pluggability" and interoperability.

Donohue, John T.↗
NASA NTRS2012

Using Arm and Hand Gestures to Command Robots during Stealth Operations

Command of support robots by the warfighter requires intuitive interfaces to quickly communicate high degree-of-freedom (DOF) information while leaving the hands unencumbered. Stealth operations rule out voice commands and vision-based gesture interpretation techniques, as they often entail silent operations at night or in other low visibility conditions. Targeted at using bio-signal inputs to set navigation and manipulation goals for the robot (say, simply by pointing), we developed a system based on an electromyography (EMG) "BioSleeve", a high density sensor array for robust, practical signal collection from forearm muscles. The EMG sensor array data is fused with inertial measurement unit (IMU) data. This paper describes the BioSleeve system and presents initial results of decoding robot commands from the EMG and IMU data using a BioSleeve prototype with up to sixteen bipolar surface EMG sensors. The BioSleeve is demonstrated on the recognition of static hand positions (e.g. palm facing front, fingers upwards) and on dynamic gestures (e.g. hand wave). In preliminary experiments, over 90% correct recognition was achieved on five static and nine dynamic gestures. We use the BioSleeve to control a team of five LANdroid robots in individual and group/squad behaviors. We define a gesture composition mechanism that allows the specification of complex robot behaviors with only a small vocabulary of gestures/commands, and we illustrate it with a set of complex orders.

stealth operations↗
DOE OSTI2021

Quantum pattern recognition algorithms for charged particle tracking

High-energy physics is facing a daunting computing challenge with the large datasets expected from the upcoming High-Luminosity Large Hadron Collider in the next decade and even more so at future colliders. A key challenge in the reconstruction of events of simulated data and collision data is the pattern recognition algorithms used to determine the trajectories of charged particles. The field of quantum computing shows promise for transformative capabilities and is going through a cycle of rapid development and hence might provide a solution to this challenge. This article reviews current studies of quantum computers for charged particle pattern recognition in high-energy physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Language Barriers in Organismal Biology: What Can Journals Do Better?

Synopsis In the field of organismal biology, as in much of academia, there is a strong incentive to publish in internationally recognized, highly regarded, English-language journals to promote career advancement. This expectation has created a linguistic hegemony in scientific publishing, whereby scholars for whom English is an additional language face additional barriers to achieving the same scientific recognition as scholars who speak English as a first language. Here, we surveyed the author guidelines of 230 journals in organismal biology with impact factors of 1.5 or greater for linguistically inclusive and equitable practices and policies. We looked for efforts that reflect first steps toward reducing barriers to publication for authors globally, including the presence of statements that encouraged submissions from authors of diverse nationalities and backgrounds, policies regarding manuscript rejection based on perceived inadequacies of the English language, the existence of bias-conscious reviewer practices, whether translation and editing resources or services are available, allowance for non-English abstracts, summaries, or translations, and whether journals offer license options that would permit authors (or other scholars) to translate their work and publish it elsewhere. We also directly contacted a subset of journals to verify whether the information on their author guidelines page accurately reflects their policies and the accommodations they would make. We reveal that journals and publishers have made little progress toward beginning to recognize or reduce language barriers. Counter to our predictions, journals associated with scientific societies did not appear to have more inclusive policies compared to non-society journals. Many policies lacked transparency and clarity, which can generate uncertainty, result in avoidable manuscript rejections, and necessitate additional time and effort from both prospective authors and journal editors. We highlight examples of equitable policies and summarize actions that journals can take to begin to alleviate barriers to scientific publishing.

Nolde-Lopez, B.↗