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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
DOE OSTI2012

Prognostic Value of Subclassification Using MRI in the T4 Classification Nasopharyngeal Carcinoma Intensity-Modulated Radiotherapy Treatment

Purpose: To subclassify patients with the T4 classification nasopharyngeal carcinoma (NPC), according to the seventh edition of the American Joint Committee on Cancer staging system, using magnetic resonance imaging (MRI), and to evaluate the prognostic value of subclassification after intensity-modulated radiotherapy (IMRT). Methods and Materials: A total of 140 patients who underwent MRI and were subsequently histologically diagnosed with nondisseminated classification T4 NPC received IMRT as their primary treatment and were included in this retrospective study. T4 patients were subclassified into two grades: T4a was defined as a primary nasopharyngeal tumor with involvement of the masticator space only; and T4b was defined as involvement of the intracranial region, cranial nerves, and/or orbit. Results: The 5-year overall survival (OS) rate and distant metastasis-free survival (DMFS) rate for T4a patients (82.5% and 87.0%, respectively), were significantly higher than for T4b patients (62.6% and 66.8%; p = 0.033 and p = 0.036, respectively). The T4a/b subclassification was an independent prognostic factor for OS (hazard ratio = 2.331, p = 0.032) and DMFS (hazard ratio = 2.602, p = 0.034), and had no significant effect on local relapse-free survival. Conclusions: Subclassification of T4 patients, as T4a or T4b, using MRI according to the site of invasion, has prognostic value for the outcomes of IMRT treatment in NPC.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2005

Data Format Classification for Autonomous Software Defined Radios

We present maximum-likelihood (ML) coherent and noncoherent classifiers for discriminating between NRZ and Manchester coded (biphase-L) data formats for binary phase-shift-keying (BPSK) modulation. Such classification of the data format is an essential element of so-called autonomous software defined radio (SDR) receivers (similar to so-called cognitive SDR receivers in the military application) where it is desired that the receiver perform each of its functions by extracting the appropriate knowledge from the received signal and, if possible, with as little information of the other signal parameters as possible. Small and large SNR approximations to the ML classifiers are also proposed that lead to simpler implementation with comparable performance in their respective SNR regions. Numerical performance results obtained by a combination of computer simulation and, wherever possible, theoretical analyses, are presented and comparisons are made among the various configurations based on the probability of misclassification as a performance criterion. Extensions to other modulations such as QPSK are readily accomplished using the same methods described in the paper.

classification↗
DOE OSTI2026

A unified large language model–based framework for heterogeneous PV image diagnosis

With advances in imaging technologies, modern photovoltaic (PV) systems generate large volumes of heterogeneous image data, including visible, electroluminescence (EL), and infrared (IR) images. Existing PV image analysis models, particularly deep learning approaches, are typically task-specific and lack cross-modality generalization. To address this limitation, this paper proposes an open-source large language model (LLM)–based unified framework for heterogeneous PV image diagnostics. Through task-aware diagnostic prompting, the framework enables analysis of visible, EL, and IR images within a single pipeline, supporting both zero-shot and few-shot inference and binary and multiclass classification. It is compatible with state-of-the-art multimodal LLMs, including ChatGPT, Gemini, Claude, Qwen, and CLIP. The framework is evaluated on PV module condition classification (clean, soiling, snow, hail, and bird droppings) using visible images, cell crack detection using EL images, and hotspot detection using IR images. GPT-5.1 in few-shot mode achieves the best performance, with classification accuracy exceeding 97.3%. Open-source models such as Qwen and CLIP also deliver competitive results on visible images (around 90% accuracy), though their performance is more limited on EL and IR modalities. On the full ELPV dataset, the framework achieves 83.5% zero-shot accuracy, within 2.8% of the supervised CNN baseline, confirming scalability to larger benchmarks. Practical aspects such as reproducibility, response latency, and confidence estimation are systematically analyzed. The framework operates across PV image modalities without modality- or task-specific training, making it well suited as a rapid pre-screening tool to support downstream detailed diagnostics. A benchmark dataset of diverse labeled PV images is also released.

Li, Baojie↗
DOE OSTI2018

Site Classification for Standard Modular Hydropower Development: Characterizing Stream Reaches by Module Need

The objective of site classification for Standard Modular Hydropower (SMH) development is to develop a framework for classifying potential SMH sites in terms useful for informing SMH development, such as module need and module design requirements. While the current vision for SMH has focused on applications for new stream-reach development, these site classifications are equally useful for other applications, such as modular development at non-powered dams and other hydropower co-development opportunities. We used K-means clustering to classify sites (i.e., stream reaches) into clusters (i.e., groups of sites with similar attributes) based on simultaneous consideration of physical and biological attributes of those sites. Stream reaches included were confined to those with mean annual flow > 50 cfs and < 25,000 cfs (~350,000 reaches). Site Classification was conducted separately for six modules: hydroelectric generation, water quality, sediment passage, fish passage, foundation support, and recreation. About 340,000 stream reaches across the contiguous US were classified into 10 groups for each of the 6 different module categories. For each module category, clusters are visualized on US maps and described based on the defining variables that differentiate clusters from each other.

13 HYDRO ENERGY↗
NASA NTRS1980

CCD data processor for maximum likelihood feature classification

The paper describes an advanced technology development which utilizes a high speed analog/binary CCD correlator to perform the matrix multiplications necessary to implement onboard feature classification. The matrix manipulation module uses the maximum likelihood classification algorithm assuming a Gaussian probability density function. The module will process 16 element multispectral vectors at rates in excess of 500 thousand multispectral vector elements per second. System design considerations for the optimum use of this module are discussed, test results from initial device fabrication runs are presented, and the performance in typical processing applications is described

Benz, H. F.↗
NASA NTRS1992

An improved classification tree analysis of high cost modules based upon an axiomatic definition of complexity

Identification of high cost modules has been viewed as one mechanism to improve overall system reliability, since such modules tend to produce more than their share of problems. A decision tree model was used to identify such modules. In this current paper, a previously developed axiomatic model of program complexity is merged with the previously developed decision tree process for an improvement in the ability to identify such modules. This improvement was tested using data from the NASA Software Engineering Laboratory.

Tian, Jianhui↗
DOE OSTI2022

Deep Learning-Assisted Real-Time Forward Modeling of Electromagnetic Logging in Complex Formations

Higher dimensional (i.e., 2-D and 3-D) modeling is indispensable to correctly evaluate the responses of electromagnetic logging tools in complex formation environments. However, limited by the high computational cost of rigorous modeling such as finite difference method and the finite element method, the real-time applications in the well logging industry primarily rely on the 1-D forward solver, which would result in erroneous formation evaluation for complex scenarios. As a result, aiming at realizing fast modeling for electromagnetic logging tools in complex formations, this paper proposes a general framework assisted by deep neural networks (DNNs). The framework consists of three modules: earth model classification, parameter extraction, and surrogate construction. Separate DNNs are trained and tested for different modules. The accuracy and efficiency of the DNN assisted fast modeling are validated by several experiments. Here, this study finds that the fast modeling assisted by DNNs is able to calculate the tool responses and reconstruct the subsurface formations in real-time.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2016

DeepSAT: A Deep Learning Approach to Tree-Cover Delineation in 1-m NAIP Imagery for the Continental United States

High resolution tree cover classification maps are needed to increase the accuracy of current land ecosystem and climate model outputs. Limited studies are in place that demonstrates the state-of-the-art in deriving very high resolution (VHR) tree cover products. In addition, most methods heavily rely on commercial softwares that are difficult to scale given the region of study (e.g. continents to globe). Complexities in present approaches relate to (a) scalability of the algorithm, (b) large image data processing (compute and memory intensive), (c) computational cost, (d) massively parallel architecture, and (e) machine learning automation. In addition, VHR satellite datasets are of the order of terabytes and features extracted from these datasets are of the order of petabytes. In our present study, we have acquired the National Agriculture Imagery Program (NAIP) dataset for the Continental United States at a spatial resolution of 1-m. This data comes as image tiles (a total of quarter million image scenes with ~60 million pixels) and has a total size of ~65 terabytes for a single acquisition. Features extracted from the entire dataset would amount to ~8-10 petabytes. In our proposed approach, we have implemented a novel semi-automated machine learning algorithm rooted on the principles of "deep learning" to delineate the percentage of tree cover. Using the NASA Earth Exchange (NEX) initiative, we have developed an end-to-end architecture by integrating a segmentation module based on Statistical Region Merging, a classification algorithm using Deep Belief Network and a structured prediction algorithm using Conditional Random Fields to integrate the results from the segmentation and classification modules to create per-pixel class labels. The training process is scaled up using the power of GPUs and the prediction is scaled to quarter million NAIP tiles spanning the whole of Continental United States using the NEX HPC supercomputing cluster. An initial pilot over the state of California spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles has produced true positive rates of around 88 percent for fragmented forests and 74 percent for urban tree cover areas, with false positive rates lower than 2 percent for both landscapes.

Imagery↗
DOE OSTI2016

Determination of the parameters of a holographic layer from its spectral characteristics

Methods for estimating the main parameters of holographic sensors (refractive index modulation depth and hologram thickness) from transmission spectra in the absence of absorption and light scattering are discussed. The consideration is performed for layers oriented parallel to the holographic layer surface under normal light incidence. Direct numerical solution of the problem of light propagation in a periodic nonabsorbing medium is used to study the reflection and transmission spectra of the holographic layer in a wide range of variation in its thickness and the refractive index modulation depth. A classification of the reflection regimes from the holographic layer is proposed (from weak reflection to the photonic crystal regime). A comparison with the results obtained by the coupled-wave analysis is performed, and the limitations of this method at a significant spectral detuning from resonance and under conditions of strong reflection are revealed. It is shown that the main hologram parameters can be estimated from the experimental transmission spectrum of the phase hologram (in the case of strong reflection) based on the spectral dip parameters. (holographic sensors)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2011

Lowest weight representations of super Schroedinger algebras in one dimensional space

Lowest weight modules, in particular, Verma modules over the N=1,2 super Schroedinger algebras in (1 + 1) dimensional spacetime are investigated. The reducibility of the Verma modules is analyzed via explicitly constructed singular vectors. The classification of the irreducible lowest weight modules is given for both massive and massless representations. A vector field realization of the N=1,2 super Schroedinger algebras is also presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Stable dipole solitons and soliton complexes in the nonlinear Schrödinger equation with periodically modulated nonlinearity

We develop a general classification of the infinite number of families of solitons and soliton complexes in the one-dimensional Gross-Pitaevskii/nonlinear Schrödinger equation with a nonlinear lattice pseudopotential, i.e., periodically modulated coefficient in front of the cubic term, which takes both positive and negative local values. This model finds direct implementations in atomic Bose-Einstein condensates and nonlinear optics. The most essential finding is the existence of two branches of dipole solitons (DSs), which feature an antisymmetric shape, being essentially squeezed into a single cell of the nonlinear lattice. This soliton species was not previously considered in nonlinear lattices. We demonstrate that one branch of the DS family (namely, which obeys the Vakhitov-Kolokolov criterion) is stable, while unstable DSs spontaneously transform into stable fundamental solitons (FSs). The results are obtained in numerical and approximate analytical forms, the latter based on the variational approximation. Some stable bound states of FSs are found too.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1983

Method of and system for classifying emergency locating transmitters and emergency positions indicating radio beacons

During a distress call, a distress location transmitter 10 generates a high frequency carrier signal 40 that is modulated by a predetermined distress waveform characteristic 29. The classification of user associated with the distress call is identified by periodically interrupting modulation 42; user classification is determined by the repetition rate of the interruptions, the interruption periods, or both.

Wren, Paul E.↗
DOE OSTI2016

Network analysis of transcriptomics expands regulatory landscapes in Synechococcus sp. PCC 7002

Cyanobacterial regulation of gene expression must contend with a genome organization that lacks apparent functional context, as the majority of cellular processes and metabolic pathways are encoded by genes found at disparate locations across the genome. In addition, the fact that coordinated regulation of cyanobacterial cellular machinery takes place with significantly fewer transcription factors, compared to other Eubacteria, suggests the involvement of post-transcriptional mechanisms and regulatory adaptations which are not fully understood. Global transcript abundance from model cyanobacterium Synechococcus sp. PCC 7002 grown under 42 different conditions was analyzed using context-likelihood of relatedness. The resulting 903-gene network, which was organized into 11 modules, not only allowed classification of cyanobacterial responses to specific environmental variables but provided insight into the transcriptional network topology and led to the expansion of predicted regulons. When used in conjunction with genome sequence, the global transcript abundance allowed identification of putative post-transcriptional changes in expression as well as novel potential targets of both DNA binding proteins and asRNA regulators. The results offer a new perspective into the multi-level regulation that governs cellular adaptations of fast-growing physiologically robust cyanobacterium Synechococcus sp. PCC 7002 to changing environmental variables. It also extends a methodological knowledge-based framework for studying multi-scale regulatory mechanisms that operate in cyanobacteria. Finally, it provides valuable context for integrating systems-level data to enhance evidence-driven genomic annotation, especially in organisms where traditional context analyses cannot be implemented due to lack of operon-based functional organization.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2010

Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules

Background: Elucidating the activation pattern of molecular pathways across a given tumour type is a key challenge necessary for understanding the heterogeneity in clinical response and for developing novel more effective therapies. Gene expression signatures of molecular pathway activation derived from perturbation experiments in model systems as well as structural models of molecular interactions ("model signatures”) constitute an important resource for estimating corresponding activation levels in tumours. However, relatively few strategies for estimating pathway activity from such model signatures exist and only few studies have used activation patterns of pathways to refine molecular classifications of cancer. Methods: Here we propose a novel network-based method for estimating pathway activation in tumours from model signatures. We find that although the pathway networks inferred from cancer expression data are highly consistent with the prior information contained in the model signatures, that they also exhibit a highly modular structure and that estimation of pathway activity is dependent on this modular structure. We apply our methodology to a panel of 438 estrogen receptor negative (ER-) and 785 estrogen receptor positive (ER+) breast cancers to infer activation patterns of important cancer related molecular pathways. Results: We show that in ER negative basal and HER2+ breast cancer, gene expression modules reflecting T-cell helper-1 (Th1) and T-cell helper-2 (Th2) mediated immune responses play antagonistic roles as major risk factors for distant metastasis. Using Boolean interaction Cox-regression models to identify non-linear pathway combinations associated with clinical outcome, we show that simultaneous high activation of Th1 and low activation of a TGF-beta pathway module defines a subtype of particularly good prognosis and that this classification provides a better prognostic model than those based on the individual pathways. In ER+ breast cancer, we find that simultaneous high MYC and RAS activity confers significantly worse prognosis than either high MYC or high RAS activity alone. We further validate these novel prognostic classifications in independent sets of 173 ER- and 567 ER+ breast cancers. Conclusion: We have proposed a novel method for pathway activity estimation in tumours and have shown that pathway modules antagonize or synergize to delineate novel prognostic subtypes. Specifically, our results suggest that simultaneous modulation of T-helper differentiation and TGF-beta pathways may improve clinical outcome of hormone insensitive breast cancers over treatments that target only one of these pathways.

59 BASIC BIOLOGICAL SCIENCES↗