Search NASA⌕ Search

SEARCH · Search NASA

Results for “Real-time Inference”

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.

166 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Cryptic Methane-Cycling by Methanogens During Multi-Year Incubation of Estuarine Sediment

As marine sediments are buried, microbial communities transition from sulfate-reduction to methane-production after sulfate is depleted. When this biogenic methane diffuses into the overlying sulfate-rich sediments, it forms a sulfate-methane transition zone (SMTZ) because sulfate reducers deplete hydrogen concentrations and make hydrogenotrophic methanogenesis exergonic in the reverse direction, a process called the anaerobic oxidation of methane (AOM). Microbial participation in these processes is often inferred from geochemistry, genes, and gene expression changes with sediment depth, using sedimentation rates to convert depth to time. Less is known about how natural sediments transition through these geochemical states transition in real-time. We examined 16S rRNA gene amplicon libraries and metatranscriptomes in microcosms of anoxic sediment from the White Oak River estuary, NC, with three destructively sampled replicates with methane added (586-day incubations) and three re-sampled un-amended replicates (895-day incubations). Sulfate dropped to a low value (∼0.3 mM) on similar days for both experiments (312 and 320 days, respectively), followed by a peak in hydrogen, intermittent increases in methane-cycling archaea starting on days 375 and 362 (mostly Methanolinea spp. and Methanosaeta spp., and Methanococcoides sp. ANME-3), and a methane peak 1 month later. However, methane δ 13 C values only show net methanogenesis 6 months after methane-cycling archaea increase and 4 months after the methane peak, when sulfate is consistently below 0.1 mM and hydrogen increases to a stable 0.61 ± 0.13 nM (days 553–586, n = 9). Sulfate-reducing bacteria (mostly Desulfatiglans spp. and Desulfosarcina sp. SEEP-SRB1) increase in relative abundance only during this period of net methane production, suggesting syntrophy with methanogens in the absence of sulfate. The transition from sulfate reduction to methane production in marine sediments occurs through a prolonged period of methane-cycling by methanogens at low sulfate concentrations, and steady growth of sulfate reducers along with methanogens after sulfate is depleted.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Two-Stage Wildlife Event Classification for Edge Deployment

Camera-based wildlife monitoring is often overwhelmed by non-target triggers and slowed by manual review or cloud-dependent inference, which can prevent timely intervention for high stakes human–wildlife conflicts. Our key contribution is a deployable, fully offline edge vision sensor that achieves near-real-time, highly accurate wildlife event classification by combining detector-based empty-image suppression with a lightweight classifier trained with a staged transfer-learning curriculum. Specifically, Stage 1 uses a pretrained You Only Look Once (YOLO)-family detector for permissive animal localization and empty-trigger suppression, and Stage 2 uses a lightweight EfficientNet-based binary classifier to confirm puma on detector crops and gate downstream actions. Our design is robust to low-quality nighttime monochrome imagery (motion blur, low contrast, illumination artifacts, and partial-body captures) and operates using commercially available components in connectivity-limited settings. In field deployments running since May 2025, end-to-end latency from camera trigger to action command is approximately 4 s. Ablation studies using a dataset of labeled wildlife images (pumas, not pumas) show that the two-stage approach substantially reduces false alarms in identifying pumas relative to a full-image classifier while maintaining high recall. On the held-out test set (N = 1434 events), the proposed two-stage cascade achieves precision 0.983, recall 0.975, F1 0.979, accuracy 0.986, and balanced accuracy 0.983, with only 8 false positives and 12 false negatives. The system can be easily adapted for other species, as demonstrated by rapid retraining of the second stage to classify ringtails. Downstream responses (e.g., notifications and optional audio/light outputs) provide flexible actuation capabilities that can be configured to support intervention.

58 GEOSCIENCES↗
DOE OSTI2025

Real-time Anomaly Detection for Liquid Argon Time Projection Chambers

We present a real-time anomaly detection framework for liquid argon time projection chambers (LArTPCs), targeting applications in particle physics experiments such as the Short Baseline Near Detector (SBND) or the future Deep Underground Neutrino Experiment (DUNE). These experiments employ detectors that generate and stream high-resolution but sparse images of neutrino and other particle interactions. Our approach utilizes anomaly detection with autoencoders, compressed through knowledge distillation (KD), to enable the detection of anomalous signals in the data through efficient inference on resource-constrained hardware. The framework is targeted for deployment on computing platforms equipped with field-programmable gate arrays (FPGAs), GPUs, or CPUs, allowing low-latency selection of relevant activity directly from the raw detector data stream. We demonstrate that our approach is suitable for the detection and localization of anomalously "high-multiplicity" activity, and outline promising applications for LArTPC online data filtering and triggering.

FOS: Physical sciences↗
DOE OSTI2024

Emerging Jets Search, Triton Server Deployment, and Track Quality Development: Machine Learning Applications in High Energy Physics

Machine learning is becoming prevalent in high energy physics, with numerous applications in physics analyses and event reconstruction showing great improvements compared to traditional computing methods. This thesis studies three projects which each propose new avenues for machine learning applications within the high energy physics CMS experiment located at CERN. In the first project, a search for a dark matter signal called “emerging jets” is performed, using graph neural networks to greatly increase sensitivity to the signal’s signature within the data. The result of this dark matter search sets the most stringent exclusion limits to date on theoretical emerging jet models. Motivated by inefficiencies encountered when processing the emerging jet graph neural network at Fermi National Accelerator Laboratory’s computing centers, the second project re-optimizes the computing centers for machine learning inference. This re-optimization uses NVIDIA Triton Inference Servers to process users’ analysis code heterogeneously, therefore achieving high processing throughput and decreasing user time-to-insight. The last project focuses on an upgrade to the CMS experiment’s real-time event selection system which improves physics object reconstruction under harsh processing conditions. A boosted decision tree is used to quickly and efficiently quantify a reconstructed particle’s “track quality” in order to remove particle tracks reconstructed erroneously. In summary, this thesis will not only present examples of how high energy physics can greatly benefit by leveraging machine learning techniques for physics analysis and reconstruction, but will also provide guidance on how the field can prepare for the inevitable increase in machine learning applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗