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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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EEG baselines covering wide range of states of wakefulness and sleep in astronaut candidates estimated by computation and pattern recognition techniques
Input/output model of system with finite settling time, using error correcting technique employed in pattern recognition
VIGILANTE is an ultrafast smart sensor testbed for generic Automatic Target Recognition (ATR) applications with a series of capability demonstration focussed on cruise missile defense (CMD). VIGILANTE's sensor/processor architecture is based on next-generation UV/visible/IR sensors and a tera-operations per second sugar-cube processor, as well as supporting airborne vehicle. Excellent results of efficient ATR methodologies that use an eigenvectors/neural network combination and feature-based precision tracking have been demonstrated in the laboratory environment.
Fingerprint recognition is typically done by matching graphs or templates of extracted minutia from whole fingerprint images.
A bottom-up visual attention model (the saliency model) is tested to enhance the performance of Automated Target Recognition (ATR). JPL has developed an ATR system that identifies regions of interest (ROI) using a trained OT-MACH filter, and then classifies potential targets as true- or false-positives using machine-learning techniques. In this project, saliency is used as a pre-processing step to reduce the space for performing OT-MACH filtering. Saliency parameters, such as output level and orientation weight, are tuned to detect known target features. Preliminary results are promising and future work entails a rigrous and parameter-based search to gain maximum insight about this method.
Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.
The task for this project was to design, develop, test, and deploy a facial recognition system for the Kennedy Space Center Augmented/Virtual Reality Lab. This system will serve as a means of user authentication as part of the NUI of the lab. The overarching goal is to create a seamless user interface that will allow the user to initiate and interact with AR and VR experiences without ever needing to use a mouse or keyboard at any step in the process.
Increasingly, unmanned aircraft systems (UAS) are being applied to wildfire incidents for tasks such as mapping, aerial ignition, and delivery. As a result, incident reporting systems for wildfires are beginning to accumulate data related to UAS mishaps in wildfire response. In this research, we apply state-of-the-art natural language processing (NLP) techniques to develop a custom Named Entity Recognition (NER) model which extracts a Failure Modes and Effects Analysis (FMEA)-style survey of wildfire UAS mishaps reported in SAFECOM. The custom NER model is built by fine-tuning an existing (BERT) model, resulting in a generalizable NER model that can extract engineering relevant entities including failure modes, causes, effects, control processes, and recommendations from any failure-relevant text. Similar mishaps are clustered and reported as single rows within the FMEA. For each cluster, frequency, severity, and overall risk are computed. The methodology can be applied as part of a broader safety management system to track trends in mishaps and discover knowledge that can be utilized to improve safety outcomes and system performance.
Increasingly, unmanned aircraft systems (UAS) are being applied to wildfire incidents for tasks such as mapping, aerial ignition, and delivery. As a result, aviation incident reporting systems for wildfires are beginning to accumulate data related to UAS mishaps in wildfire response. In this research, we apply state-of-the-art natural language processing (NLP) techniques to develop a custom Named Entity Recognition (NER) model which extracts entities relevant to safety analysts. The custom NER model is built by fine-tuning an existing Bidirectional Encoder Representations from Transformers (BERT) model, resulting in a generalizable NER model that can extract engineering relevant entities including failure modes, causes, effects, control processes, and recommendations from failure-relevant text. This model performs passably, with a weighted average f1 score of 0.33 across entity types, indicating more labeled training data is needed. Extracted entities are used to form a Failure Modes and Effects Analysis (FMEA)-style survey of wildfire UAS mishaps reported using the SAFECOM system. Similar mishaps are manually clustered and reported as single rows within an FMEA. Foreach cluster, we compute frequency, severity, and overall riskin accordance with FAA standards. This methodology can beapplied as part of a broader safety management system totrack trends in mishaps (e.g., likelihood, severity) and discoverknowledge (e.g., causes, effects) that can be utilized to improvesafety outcomes and system performance.
Increasingly, unmanned aircraft systems (UAS) are being applied to wildfire incidents for tasks such as mapping, aerial ignition, and delivery. As a result, incident reporting systems for wildfires are beginning to accumulate data related to UAS mishaps in wildfire response. In this research, we apply state-of-the-art natural language processing (NLP) techniques to develop a custom Named Entity Recognition (NER) model which extracts a Failure Modes and Effects Analysis (FMEA)-style survey of wildfire UAS mishaps reported in SAFECOM. The custom NER model is built by fine-tuning an existing (BERT) model, resulting in a generalizable NER model that can extract engineering relevant entities including failure modes, causes, effects, control processes, and recommendations from any failure-relevant text. Similar mishaps are clustered and reported as single rows within the FMEA. For each cluster, frequency, severity, and overall risk are computed. The methodology can be applied as part of a broader safety management system to track trends in mishaps and discover knowledge that can be utilized to improve safety outcomes and system performance.
We developed multiple machine learning methods for the detection and classification of new wireless communication waveforms, which is critical for targeted attacks in wireless networks and electronic warfare. Our machine learning models are capable of dynamically detecting security threats in near real time through our advanced open set recognition (OSR) approach. This model has demonstrated significant improvements in the detection of unknown waveforms, thereby enhancing the security and reliability of mission critical communications. Our approach to detecting uncertain security threats is novel; we advanced OSR techniques by incorporating domain knowledge of wireless signals. Specifically, we combined time and frequency domain model features to enhance the model’s performance. Utilizing an OSR approach eliminates the need for training data to be distributed similarly to the deployment environment and removes the requirement for the training set to contains all possible threat classes. This is crucial because it is often infeasible to determine and characterize all potential security threats in advance. Our model were trained on simulated data, generated in partnership with the University at Albany, State of New York. The data set contained a diverse array of wireless signals, including those with additive white Gaussian noise and multipath signals, with and without line of sight. This comprehensive training set allowed us to optimize our models to detect unknown waveforms under various challenging scenarios, such as low signal-to-noise ratios. By training on various waveforms, varying signal-to-noise ratio, and different sample sizes under normal conditions, our models were fine tuned to perform effectively in challenging environments.
This presentation applies open set recognition to classify unknown waveforms, enabling systems to not only identify known types but also reliably detect when waveforms fall outside the training distribution. This approach enhances robustness by avoiding forced misclassification of novel or anomalous signals.
Abstract The heterogeneous nuclear ribonucleoprotein (hnRNP) R‐like family is a class of RNA binding proteins in the hnRNP superfamily with diverse functions in RNA processing. Here, we present the 1.90 Å X‐ray crystal structure and solution NMR studies of the first RNA recognition motif (RRM) of human hnRNPR. We find that this domain adopts an extended RRM (eRRM1) featuring a canonical RRM with a structured N‐terminal extension (N ext ) motif that docks against the RRM and extends the β‐sheet surface. The adjoining loop is structured and forms a tryptophan cage motif to position the N ext motif for docking to the RRM. Combining mutagenesis, solution NMR spectroscopy, and thermal denaturation studies, we evaluate the importance of residues in the N ext –RRM interface and adjoining loop on eRRM folding and conformational dynamics. We find that these sites are essential for protein solubility, conformational ordering, and thermal stability. Consistent with their importance, mutations in the N ext –RRM interface and loop are associated with several cancers in a survey of somatic mutations in cancer studies. Sequence and structure comparison of the human hnRNPR eRRM1 to experimentally verified and predicted hnRNPR‐like proteins reveals conserved features in the eRRM.
Two-dimensional transition metal dichalcogenides (2D-TMDs)-based memtransistors have emerged as promising candidates for neuromorphic hardware due to their exceptional ability to emulate synaptic behavior. However, many existing 2D-TMDs memtransistors rely on polycrystalline channels with grain boundaries or defects introduced through postgrowth treatments, raising concerns about material integrity and the preservation of intrinsic properties. Here, in this work, we demonstrate a monocrystalline monolayer MoS 2 memtransistor fabricated on a silicon nitride (SiN X ) substrate, achieving a large resistive switching ratio of 10 4 , a dynamic range exceeding 90, along with highly linear and symmetric weight updates, minimal cycle-to-cycle variability, and low device-to-device variability. These attributes are critical for enabling high-performance neuromorphic hardware. Based on experimental data, we further show that these artificial synapses enable a recognition accuracy of more than 97% on the MNIST handwritten digits data set. Our findings present a straightforward approach to realizing 2D-TMDs memtransistors through dielectric engineering, offering a promising platform for next-generation neuromorphic computing systems.
The widespread oligonucleotide/oligosaccharide-binding (OB)-fold recognizes diverse substrates from sugars to nucleic acids and proteins, and plays key roles in genome maintenance, transcription, translation, and tRNA metabolism. OB-containing bacterial Trbp and yeast Arc1p proteins are thought to recognize the tRNA elbow or anticodon regions. Here we report a 2.6 Å co-crystal structure of Aquifex aeolicus Trbp111 bound to tRNA Ile , which reveals that Trbp recognizes tRNAs solely by capturing their 3' ends. Structural, mutational, and biophysical analyses show that the Trbp/EMAPII-like OB fold precisely recognizes the single-stranded structure, 3' terminal location, and specific sequence of the 3' CA dinucleotide — a universal feature of mature tRNAs. Arc1p supplements its OB – tRNA 3' end interaction with additional contacts that involve an adjacent basic region and the tRNA body. This study uncovers a previously unrecognized mode of tRNA recognition by an ancient protein fold, and provides insights into protein-mediated tRNA aminoacylation, folding, localization, trafficking, and piracy.
Abstract Significant progress has been made in probing the state of an earthquake fault by applying machine learning to continuous seismic waveforms. The breakthroughs were originally obtained from laboratory shear experiments and numerical simulations of fault shear, then successfully extended to slow-slipping faults. Here we apply the Wav2Vec-2.0 self-supervised framework for automatic speech recognition to continuous seismic signals emanating from a sequence of moderate magnitude earthquakes during the 2018 caldera collapse at the Kīlauea volcano on the island of Hawai’i. We pre-train the Wav2Vec-2.0 model using caldera seismic waveforms and augment the model architecture to predict contemporaneous surface displacement during the caldera collapse sequence, a proxy for fault displacement. We find the model displacement predictions to be excellent. The model is adapted for near-future prediction information and found hints of prediction capability, but the results are not robust. The results demonstrate that earthquake faults emit seismic signatures in a similar manner to laboratory and numerical simulation faults, and artificial intelligence models developed for encoding audio of speech may have important applications in studying active fault zones.
The complex distribution of functional groups in carbohydrates, coupled with their strong solvation in water, makes them challenging targets for synthetic receptors. Despite extensive research into various molecular frameworks, most synthetic carbohydrate receptors have exhibited low affinities, and their interactions with sugars in aqueous environments remain poorly understood. In this work, we present a simple pyridinium-based hydrogen-bonding receptor derived from a subtle structural modification of a well-known tetralactam macrocycle. This small structural change resulted in a dramatic enhancement of glucose binding affinity, increasing from 56 M −1 to 3001 M −1 . Remarkably, the performance of our synthetic lectin surpasses that of the natural lectin, concanavalin A, by over fivefold. X-ray crystallography of the macrocycle–glucose complex reveals a distinctive hydrogen bonding pattern, which allows for a larger surface overlap between the receptor and glucose, contributing to the enhanced affinity. Furthermore, this receptor possesses allosteric binding sites, which involve chloride binding and trigger receptor aggregation. This unique allosteric process reveals the critical role of structural flexibility in this hydrogen-bonding receptor for the effective recognition of sugars. We also demonstrate the potential of this synthetic lectin as a highly sensitive glucose sensor in aqueous solutions.
In this work, experimental results leading to flowsheet design are presented showing how a calixarene-crown ether based solvent-extraction process can meet the challenge of cesium removal from nuclear tank wastes stored at the US Department of Energy Hanford site. Cleanup of legacy Cold War nuclear waste stored in underground tanks represents one of the greatest environmental challenges facing the US Department of Energy in terms of risk, cost, and effectiveness of applicable science and technology. Planning for the cleanup at the Hanford Site calls for the removal of the radioactive fission product 137Cs from its alkaline salt waste, including the use of modular processes that can be deployed near the tank farms. To meet the resulting need for extremely high selectivity, the Next-Generation Caustic-Side Solvent Extraction (NG-CSSX) process employing a calix[4]arene-crown ether in modified kerosene has been adapted to remove sub-millimolar cesium in competition with molar sodium and potassium in a high-nitrate alkaline matrix. Potassium loading in the solvent was determined in extraction, scrubbing, and stripping, leading to an empirical model closely approximating cesium distribution ratios for a variety of Hanford waste types. Process chemistry has been developed based on this molecular-recognition approach, focusing on the competitive effect of potassium loading and the mitigating process modifications needed, including extending the scrub section. The result is a modular flowsheet design that can achieve cesium decontamination factors well in excess of 15,000 even for the worst-case Hanford waste.