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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 253 records · Page 14

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NASA NTRS2002

MIMIC II: a massive temporal ICU patient database to support research in intelligent patient monitoring

Development and evaluation of Intensive Care Unit (ICU) decision-support systems would be greatly facilitated by the availability of a large-scale ICU patient database. Following our previous efforts with the MIMIC (Multi-parameter Intelligent Monitoring for Intensive Care) Database, we have leveraged advances in networking and storage technologies to develop a far more massive temporal database, MIMIC II. MIMIC II is an ongoing effort: data is continuously and prospectively archived from all ICU patients in our hospital. MIMIC II now consists of over 800 ICU patient records including over 120 gigabytes of data and is growing. A customized archiving system was used to store continuously up to four waveforms and 30 different parameters from ICU patient monitors. An integrated user-friendly relational database was developed for browsing of patients' clinical information (lab results, fluid balance, medications, nurses' progress notes). Based upon its unprecedented size and scope, MIMIC II will prove to be an important resource for intelligent patient monitoring research, and will support efforts in medical data mining and knowledge-discovery.

NASA Discipline Cardiopulmonary↗

NASA NTRS2012

Intelligent Systems Approach for Automated Identification of Individual Control Behavior of a Human Operator

Results have been obtained using conventional techniques to model the generic human operator?s control behavior, however little research has been done to identify an individual based on control behavior. The hypothesis investigated is that different operators exhibit different control behavior when performing a given control task. Two enhancements to existing human operator models, which allow personalization of the modeled control behavior, are presented. One enhancement accounts for the testing control signals, which are introduced by an operator for more accurate control of the system and/or to adjust the control strategy. This uses the Artificial Neural Network which can be fine-tuned to model the testing control. Another enhancement takes the form of an equiripple filter which conditions the control system power spectrum. A novel automated parameter identification technique was developed to facilitate the identification process of the parameters of the selected models. This utilizes a Genetic Algorithm based optimization engine called the Bit-Climbing Algorithm. Enhancements were validated using experimental data obtained from three different sources: the Manual Control Laboratory software experiments, Unmanned Aerial Vehicle simulation, and NASA Langley Research Center Visual Motion Simulator studies. This manuscript also addresses applying human operator models to evaluate the effectiveness of motion feedback when simulating actual pilot control behavior in a flight simulator.

Zaychik, Kirill B.↗

NASA NTRS2005

Using EMG to anticipate head motion for virtual-environment applications

In virtual environment (VE) applications, where virtual objects are presented in a see-through head-mounted display, virtual images must be continuously stabilized in space in response to user's head motion. Time delays in head-motion compensation cause virtual objects to "swim" around instead of being stable in space which results in misalignment errors when overlaying virtual and real objects. Visual update delays are a critical technical obstacle for implementing head-mounted displays in applications such as battlefield simulation/training, telerobotics, and telemedicine. Head motion is currently measurable by a head-mounted 6-degrees-of-freedom inertial measurement unit. However, even given this information, overall VE-system latencies cannot be reduced under about 25 ms. We present a novel approach to eliminating latencies, which is premised on the fact that myoelectric signals from a muscle precede its exertion of force, thereby limb or head acceleration. We thus suggest utilizing neck-muscles' myoelectric signals to anticipate head motion. We trained a neural network to map such signals onto equivalent time-advanced inertial outputs. The resulting network can achieve time advances of up to 70 ms.

Clinical Trial↗

NASA NTRS2011

Quantum-Classical Hybrid for Information Processing

Based upon quantum-inspired entanglement in quantum-classical hybrids, a simple algorithm for instantaneous transmissions of non-intentional messages (chosen at random) to remote distances is proposed. The idea is to implement instantaneous transmission of conditional information on remote distances via a quantum-classical hybrid that preserves superposition of random solutions, while allowing one to measure its state variables using classical methods. Such a hybrid system reinforces the advantages, and minimizes the limitations, of both quantum and classical characteristics. Consider n observers, and assume that each of them gets a copy of the system and runs it separately. Although they run identical systems, the outcomes of even synchronized runs may be different because the solutions of these systems are random. However, the global constrain must be satisfied. Therefore, if the observer #1 (the sender) made a measurement of the acceleration v(sub 1) at t =T, then the receiver, by measuring the corresponding acceleration v(sub 1) at t =T, may get a wrong value because the accelerations are random, and only their ratios are deterministic. Obviously, the transmission of this knowledge is instantaneous as soon as the measurements have been performed. In addition to that, the distance between the observers is irrelevant because the x-coordinate does not enter the governing equations. However, the Shannon information transmitted is zero. None of the senders can control the outcomes of their measurements because they are random. The senders cannot transmit intentional messages. Nevertheless, based on the transmitted knowledge, they can coordinate their actions based on conditional information. If the observer #1 knows his own measurements, the measurements of the others can be fully determined. It is important to emphasize that the origin of entanglement of all the observers is the joint probability density that couples their actions. There is no centralized source, or a sender of the signal, because each receiver can become a sender as well. An observer receives a signal by performing certain measurements synchronized with the measurements of the others. This means that the signal is uniformly and simultaneously distributed over the observers in a decentralized way. The signals transmit no intentional information that would favor one agent over another. All the sequence of signals received by different observers are not only statistically equivalent, but are also point-by-point identical. It is important to assume that each agent knows that the other agent simultaneously receives the identical signals. The sequences of the signals are true random, so that no agent could predict the next step with the probability different from those described by the density. Under these quite general assumptions, the entangled observers-agents can perform non-trivial tasks that include transmission of conditional information from one agent to another, simple paradigm of cooperation, etc. The problem of behavior of intelligent agents correlated by identical random messages in a decentralized way has its own significance: it simulates evolutionary behavior of biological and social systems correlated only via simultaneous sensoring sequences of unexpected events.

Zak, Michail↗

NASA NTRS1990

Remote sensing science for the Nineties; Proceedings of IGARSS '90 - 10th Annual International Geoscience and Remote Sensing Symposium, University of Maryland, College Park, May 20-24, 1990. Vols. 1, 2, & 3

Various papers on remote sensing (RS) for the nineties are presented. The general topics addressed include: subsurface methods, radar scattering, oceanography, microwave models, atmospheric correction, passive microwave systems, RS in tropical forests, moderate resolution land analysis, SAR geometry and SNR improvement, image analysis, inversion and signal processing for geoscience, surface scattering, rain measurements, sensor calibration, wind measurements, terrestrial ecology, agriculture, geometric registration, subsurface sediment geology, radar modulation mechanisms, radar ocean scattering, SAR calibration, airborne radar systems, water vapor retrieval, forest ecosystem dynamics, land analysis, multisensor data fusion. Also considered are: geologic RS, RS sensor optical measurements, RS of snow, temperature retrieval, vegetation structure, global change, artificial intelligence, SAR processing techniques, geologic RS field experiment, stochastic modeling, topography and Digital Elevation model, SAR ocean waves, spaceborne lidar and optical, sea ice field measurements, millimeter waves, advanced spectroscopy, spatial analysis and data compression, SAR polarimetry techniques. Also discussed are: plant canopy modeling, optical RS techniques, optical and IR oceanography, soil moisture, sea ice back scattering, lightning cloud measurements, spatial textural analysis, SAR systems and techniques, active microwave sensing, lidar and optical, radar scatterometry, RS of estuaries, vegetation modeling, RS systems, EOS/SAR Alaska, applications for developing countries, SAR speckle and texture.

Source record↗

NASA NTRS1986

Intelligent information extraction from reflectance spectra Absorption band positions

A multiple high-order derivative analysis algorithm has been developed which can automatically extract absorption band positions from low-quality reflectance spectra with little degredation of accuracy. Overlapping bands with comparable widths and intensities can be resolved whose centers are as close as 0.3-0.5 W, with safer resolution limits of 0.6-1.0 W band center separations suggested for overlapping bands that are dissimilar. The segment length for smoothing is continually adjusted to about 0.5 W to minimize signal distortion, and a spectral pattern recognition algorithm predicts the signal spectrum and calculates approximate W across the spectrum using its second derivative. A single-pass cubic spline is applied to the smoothed data, and a sliding segment sixth-order polynomial is fit to the spectrum, with the length of the segment being continuously locally adjusted to 1.0 W across the spectrum. Good reliability and consistency of the algorithm is demonstrated with application to laboratory and earth-based telescope spectra.

Huguenin, R. L.↗

NASA NTRS2000

Intelligent subsystem interface for modular hardware system

A single chip application specific integrated circuit (ASIC) which provides a flexible, modular interface between a subsystem and a standard system bus. The ASIC includes a microcontroller/microprocessor, a serial interface for connection to the bus, and a variety of communications interface devices available for coupling to the subsystem. A three-bus architecture, utilizing arbitration, provides connectivity within the ASIC and between the ASIC and the subsystem. The communication interface devices include UART (serial), parallel, analog, and external device interface utilizing bus connections paired with device select signals. A low power (sleep) mode is provided as is a processor disable option.

Krening, Douglas N.↗

DOE OSTI2026

Material‐Driven Neuronal Oscillators and Filters via Active Reactance in CC‐NDR and VC‐NDR Electro‐Thermal Memristors

The continued scaling of artificial intelligence and telecommunications hardware is increasingly constrained by the power, bandwidth, and area limitations of transistor-based circuits. Neuromorphic processor units, analog oscillators, and active inductors and capacitors rely on complex multi-transistor architectures restricting material choices and incurring energy and footprint overhead. Here, we show that active reactance in electro-thermal memristors provides an intrinsic, material driven route to neuronal oscillator dynamics and signal processing. Using a physics-based compact modeling framework, we bridge negative differential resistance (NDR) and bias-tunable reactance, which underlies spiking dynamics in electro-thermal memristors. Memristors with negative temperature coefficients of resistance (TCR) manifest current-controlled (CC-) NDR and act as active inductors, thus generating spiking above a critical circuit capacitance; whereas memristors with positive TCR manifest voltage-controlled (VC-) NDR and active capacitance, leading to spiking above a critical inductance. By creating a compact model for La 0.7 Ca 0.3 MnO 3 as a representative VC-NDR material and comparing it with LaCoO 3 manifesting CC-NDR, we explain the physical origins of their distinct current-voltage characteristics, reactive phase shifts and consequent spiking behaviors. Finally, we demonstrate tunable filtering enabled by the active reactance of electro-thermal memristors, establishing them as a compact hardware platform for neuronal oscillator functionality and integrated filtering beyond conventional CMOS.

active reactance↗

DOE OSTI2024

Automatic Loss Factor Modeling and Attribution on Unlabeled PV Energy Data

We present a novel approach for modeling the loss factors of photovoltaic power generation systems (PV systems). This method is a white-box machine learning model built on convex optimization that is fast, interpretable, and auditable. It takes as an input the measured daily energy produced by the system, over a multi-year period, and returns a multiplicative decomposition model of the daily energy signal and full attribution of the total energy loss to each feature. The methods section of this paper has two major components: (1) the description of the signal decomposition (SD) model, expressed in the SD framework, and (2) the attribution of total energy losses via Shapley values. We validate the method on synthetic and open-source data sets and compare to similar methods from the literature.

artificial intelligence↗

NASA NTRS1994

Empirical modeling for intelligent, real-time manufacture control

Artificial neural systems (ANS), also known as neural networks, are an attempt to develop computer systems that emulate the neural reasoning behavior of biological neural systems (e.g. the human brain). As such, they are loosely based on biological neural networks. The ANS consists of a series of nodes (neurons) and weighted connections (axons) that, when presented with a specific input pattern, can associate specific output patterns. It is essentially a highly complex, nonlinear, mathematical relationship or transform. These constructs have two significant properties that have proven useful to the authors in signal processing and process modeling: noise tolerance and complex pattern recognition. Specifically, the authors have developed a new network learning algorithm that has resulted in the successful application of ANS's to high speed signal processing and to developing models of highly complex processes. Two of the applications, the Weld Bead Geometry Control System and the Welding Penetration Monitoring System, are discussed in the body of this paper.

Xu, Xiaoshu↗

NASA NTRS1989

Image wavelet decomposition and applications

The general problem of computer vision has been investigated for more that 20 years and is still one of the most challenging fields in artificial intelligence. Indeed, taking a look at the human visual system can give us an idea of the complexity of any solution to the problem of visual recognition. This general task can be decomposed into a whole hierarchy of problems ranging from pixel processing to high level segmentation and complex objects recognition. Contrasting an image at different representations provides useful information such as edges. An example of low level signal and image processing using the theory of wavelets is introduced which provides the basis for multiresolution representation. Like the human brain, we use a multiorientation process which detects features independently in different orientation sectors. So, images of the same orientation but of different resolutions are contrasted to gather information about an image. An interesting image representation using energy zero crossings is developed. This representation is shown to be experimentally complete and leads to some higher level applications such as edge and corner finding, which in turn provides two basic steps to image segmentation. The possibilities of feedback between different levels of processing are also discussed.

Treil, N.↗

DOE OSTI2024

Embedded Fluidic Sensing and Control with Soft Open‐Cell Foams

Abstract The synthesis of soft matter intelligence with circuit‐driven logic has enabled a new class of robots that perform complex tasks or conform to specialized form factors in unique ways that cannot be realized through conventional designs. Translating this hybrid approach to fluidic systems, the present work addresses the need for sheet‐based circuit materials by leveraging the innate porosity of foam—a soft material—to develop pneumatic components that support digital logic, mixed‐signal control, and analog force sensing in wearables and soft robots. Analytical tools and experimental techniques developed in this work serve to elucidate compressible gas flow through porous sheets, and to inform the design of centimeter‐sized foam resistors with fluidic resistances on the order of 10 9 Pa s m −3 . When embedded inside soft robots and wearables, these resistors facilitate diverse functionalities spanning both sensing and control domains, including digital logic using textile logic gates, digital‐to‐analog signal conversion using ladder networks, and analog sensing of forces up to 40 N via compression‐induced changes in resistance. By combining features of both circuit‐based and materials‐based approaches, foam‐enabled fluidic circuits serve as a useful paradigm for future hybrid robotic architectures that fully embody the sensing and computing capabilities of soft fluidic materials.

Rajappan, Anoop↗

NASA NTRS1988

A wide-band high-resolution spectrum analyzer

A two-million-channel, 40 MHz bandwidth, digital spectrum analyzer under development at the Jet Propulsion Laboratory is described. The analyzer system will serve as a prototype processor for the sky survey portion of NASA's Search for Extraterrestrial Intelligence program and for other applications in the Deep Space Network. The analyzer digitizes an analog input, performs a 2 (sup 21) point Discrete Fourier Transform, accumulates the output power, normalizes the output to remove frequency-dependent gain, and automates simple detection algorithms. Due to its built-in frequency-domain processing functions and configuration flexibility, the analyzer is a very powerful tool for real-time signal analysis.

Quirk, Maureen P.↗

NASA NTRSDate not supplied

The Wizard Staff: A Mobile Tool Supporting Human Exploration on The Lunar Surface

The Wizard Staff is a rechargeable, battery powered mobile tool with multiple uses for lunar surface exploration. It supports human activity as a surface survey tool, provides external illumination to enhance safety, delivers networking, communications, and navigation features to remote sites, and assists crew rescue with an emergency beacon and rigid splint structure. As a survey tool, it assists crewed geologic field site exploration with external lights, cameras, and LiDAR to illuminate, map, and record the sample collection process. As a relay station, it provides a surface intranet communications and navigation node for extended exploration far from the lander or habitation module. A separately developed Search-and-Rescue (SAR) handheld device can broadcast a signal to the Gandalf Staff containing crew position and biometric data. The staff then formats the message and sends it orbiting satellite(s) for emergency response via the LunaNET on a dedicated frequency similar to terrestrial applications using an Emergency Position-Indicating Radiobeacon (EPIRB). The staff is designed with an upper and lower segment. Components in the upper segment are the LiDAR, external lighting arrays, cameras, communications equipment, intelligence devices, and user interface for crew operations. The lower segment is a set of FeLiPO4 battery cells packed into the 2” diameter carbon fiber tube. When the battery pack is discharged, a spring connector (designed for the dusty lunar environment) easily disconnects the two segments for replacement with another fully charged battery pack. The lower segment can be used for Incapacitated Crew Rescue (ICR) as a rigid splint. As a stand-alone science platform, the Wizard Staff provides infrastructure for instruments and sensors to record the lunar environment over long periods of time. After a crew installs the components on the lunar surface, the platform delivers data to the lunar lander or base station for analysis. The stand-alone mode requires an auxiliary power source (e.g. solar array) and energy storage system (e.g. battery), so it could also be used as an electrical recharging station. A working prototype of Wizard Staff has been developed with collaboration between NASA (JSC, MSFC, and GSFC), Texas Space Technology Applications and Research (T STAR), and Texas A&M University (TAMU) using $300k of innovation funding in FY’21 and FY’22. The functional staff weights 36# on Earth (6# on the Moon), stands 2.1m tall and delivers external lighting with an array of 40 LED bulbs generating 1460 Lux at 2m range (near field), or 2 Lux at 50m (far field).

Gandalf Staff↗

DOE OSTI2025

Isolating Unisolated Upsilons with Anomaly Detection in CMS Open Data

We present the first study of anti-isolated Upsilon decays to two muons (ϒ→𝜇⁺⁢𝜇⁻) in proton-proton collisions at the Large Hadron Collider. Using a machine learning (ML)-based anomaly detection strategy, we “rediscover” the ϒ in 13 TeV CMS Open Data from 2016, despite overwhelming anti-isolated backgrounds. We elevate the signal significance to 6.4⁢𝜎 using these methods, starting from 1.6⁢𝜎 using the dimuon mass spectrum alone. Moreover, we demonstrate improved sensitivity from using an ML-based estimate of the multifeature likelihood compared to traditional “cut-and-count” methods. This is the first ever detection of anti-isolated Upsilons, which can be useful in the study of heavy-flavor fragmentation in quantum chromodynamics. Our Letter demonstrates that it is possible and practical to find real signals in experimental collider data using ML-based anomaly detection, and we distill a readily accessible benchmark dataset from the CMS Open Data to facilitate future anomaly detection developments.

machine learning↗

DOE OSTI2025

Versatile & Intelligent Biodetection via Environmental Sensing (VIBES)

Reactive health monitoring strategies during events like the COVID-19 pandemic highlighted the need for predictive, threat-agnostic diagnostics that can detect both known diseases and novel chemical or biological threats. To address this, we investigated an optical biosensor as a breath volatile organic compound (VOC) analyzer, aiming to emulate biological olfaction. We assembled and validated the device with thin film metal coated substrate-based sensors. We immobilized small biological recognition elements on the substrates and delivered controlled concentrations of target VOCs. The sensor was irradiated with a visible laser and the sensor signal was recorded. We characterized the laser performance and tested 3 recognition elements for 2 VOCs with varying concentrations (1-100 ppm). We also evaluated enhancement of the signal using nanostructures on the metal film in comparison with planar film substrate. We demonstrated detecting ethanol reliably at concentrations as low as ~2 ppm along with preliminary detection of acetone (<100 ppm). We also found several unexpected factors that influence the sensor behavior that should be addressed to further refine the device’s performance. The nanostructures were, as expected, found to amplify the sensor signals. These findings demonstrate the feasibility of the optical bio-sensing modality for breath VOC monitoring at physiologically relevant levels. This positions LLNL to develop a low-cost, scalable, broad-spectrum health monitoring capability aligned with the Early Detection thrust of the Bioresilience Mission Focus Area and attract external funding.

47 OTHER INSTRUMENTATION↗

NASA NTRS1991

An overview of Space Communication Artificial Intelligence for Link Evaluation Terminal (SCAILET) Project

A software application to assist end-users of the link evaluation terminal (LET) for satellite communications is being developed. This software application incorporates artificial intelligence (AI) techniques and will be deployed as an interface to LET. The high burst rate (HBR) LET provides 30 GHz transmitting/20 GHz receiving (220/110 Mbps) capability for wideband communications technology experiments with the Advanced Communications Technology Satellite (ACTS). The HBR LET can monitor and evaluate the integrity of the HBR communications uplink and downlink to the ACTS satellite. The uplink HBR transmission is performed by bursting the bit-pattern as a modulated signal to the satellite. The HBR LET can determine the bit error rate (BER) under various atmospheric conditions by comparing the transmitted bit pattern with the received bit pattern. An algorithm for power augmentation will be applied to enhance the system's BER performance at reduced signal strength caused by adverse conditions.

Shahidi, Anoosh K.↗

NASA NTRS1992

Bilevel shared control for teleoperators

A shared system is disclosed for robot control including integration of the human and autonomous input modalities for an improved control. Autonomously planned motion trajectories are modified by a teleoperator to track unmodelled target motions, while nominal teleoperator motions are modified through compliance to accommodate geometric errors autonomously in the latter. A hierarchical shared system intelligently shares control over a remote robot between the autonomous and teleoperative portions of an overall control system. Architecture is hierarchical, and consists of two levels. The top level represents the task level, while the bottom, the execution level. In space applications, the performance of pure teleoperation systems depend significantly on the communication time delays between the local and the remote sites. Selection/mixing matrices are provided with entries which reflect how each input's signals modality is weighted. The shared control minimizes the detrimental effects caused by these time delays between earth and space.

Hayati, Samad A.↗