SEARCH · Engineering Papers

Results for “edge network”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 541 records · Page 30

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Peak Prediction Using Multi Layer Perceptron (MLP) for Edge Computing ASICs Targeting Scientific Applications

High data rate detectors play an integral part in scientific research and their development is actively pursued at High Energy Physics (HEP) facilities around the world. Edge Machine Learning (ML) offers the ability to reduce data rates by integrating ML algorithms into Application Specific Integrated Circuits (ASICs) on the front end electronics. In this work, we explore a set of neural network architectures for predicting the peak amplitudes in the detector's sensor response. We have designed and synthesized several MLP based neural networks comparing their inference accuracy, power consumption, and area targeting for minimal latency. The neural networks are synthesized in a commercial 65nm process. The effect of quantizing the network's weights and biases on hardware performance and area is reported. We also conduct design space exploration to compare between design alternatives

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

A Multi-Objective Approach for Optimizing Edge-Based Resource Allocation Using TOPSIS

Existing approaches for allocating resources on edge environments are inefficient and lack the support of heterogeneous edge devices, which in turn fail to optimize the dependency on cloud infrastructures or datacenters. To this extent, we propose in this paper OpERA, a multi-layered edge-based resource allocation optimization framework that supports heterogeneous and seamless execution of offloadable tasks across edge, fog, and cloud computing layers and architectures. By capturing offloadable task requirements, OpERA is capable of identifying suitable resources within nearby edge or fog layers, thus optimizing the execution process. Throughout the paper, we present results which show the effectiveness of our proposed optimization strategy in terms of reducing costs, minimizing energy consumption, and promoting other residual gains in terms of processing computations, network bandwidth, and task execution time. We also demonstrate that by optimizing resource allocation in computation offloading, it is then possible to increase the likelihood of successful task offloading, particularly for computationally intensive tasks that are becoming integral as part of many IoT applications such robotic surgery, autonomous driving, smart city monitoring device grids, and deep learning tasks. The evaluation of our OpERA optimization algorithm reveals that the TOPSIS MCDM technique effectively identifies optimal compute resources for processing offloadable tasks, with a 96% success rate. Moreover, the results from our experiments with a diverse range of use cases show that our OpERA optimization strategy can effectively reduce energy consumption by up to 88%, and operational costs by 76%, by identifying relevant compute resources.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Machine learning without a processor: Emergent learning in a nonlinear analog network

Standard deep learning algorithms require differentiating large nonlinear networks, a process that is slow and power-hungry. Electronic contrastive local learning networks (CLLNs) offer potentially fast, efficient, and fault-tolerant hardware for analog machine learning, but existing implementations are linear, severely limiting their capabilities. These systems differ significantly from artificial neural networks as well as the brain, so the feasibility and utility of incorporating nonlinear elements have not been explored. Here, we introduce a nonlinear CLLN—an analog electronic network made of self-adjusting nonlinear resistive elements based on transistors. We demonstrate that the system learns tasks unachievable in linear systems, including XOR (exclusive or) and nonlinear regression, without a computer. We find our decentralized system reduces modes of training error in order (mean, slope, curvature), similar to spectral bias in artificial neural networks. The circuitry is robust to damage, retrainable in seconds, and performs learned tasks in microseconds while dissipating only picojoules of energy across each transistor. This suggests enormous potential for fast, low-power computing in edge systems like sensors, robotic controllers, and medical devices, as well as manufacturability at scale for performing and studying emergent learning.

Science & Technology - Other Topics↗

NASA NTRS1989

Local parallel models for integration of stereo matching constraints and intrinsic image combination

Parallel relaxation computations such as those of connectionist networks offer a useful model for constraint integration and intrinsic image combination in developing a general-purpose stereo matching algorithm. This paper describes such a stereo algorithm that incorporates hierarchical, surface-structure, and edge-appearance constraints that are redefined and integrated at the level of individual candidate matches. The algorithm produces a high percentage of correct decisions on a wide variety of stereo pairs. Its few errors arise when the correlation measures defined by the constraints are either weakened or ambiguous, as in the case of periodic patterns in the images. Two additional mechanisms are discussed for overcoming the remaining errors.

Stewart, Charles V.↗

NASA NTRS1991

Fuzzy set methods for object recognition in space applications

Progress on the following tasks is reported: (1) fuzzy set-based decision making methodologies; (2) feature calculation; (3) clustering for curve and surface fitting; and (4) acquisition of images. The general structure for networks based on fuzzy set connectives which are being used for information fusion and decision making in space applications is described. The structure and training techniques for such networks consisting of generalized means and gamma-operators are described. The use of other hybrid operators in multicriteria decision making is currently being examined. Numerous classical features on image regions such as gray level statistics, edge and curve primitives, texture measures from cooccurrance matrix, and size and shape parameters were implemented. Several fractal geometric features which may have a considerable impact on characterizing cluttered background, such as clouds, dense star patterns, or some planetary surfaces, were used. A new approach to a fuzzy C-shell algorithm is addressed. NASA personnel are in the process of acquiring suitable simulation data and hopefully videotaped actual shuttle imagery. Photographs have been digitized to use in the algorithms. Also, a model of the shuttle was assembled and a mechanism to orient this model in 3-D to digitize for experiments on pose estimation is being constructed.

Keller, James M.↗

NASA NTRS1998

A 90 GHz Amplifier Assembled Using a Bump-Bonded InP-Based HEMT

We report on the performance of a novel W-band amplifier fabricated utilizing very compact bump bonds. We bump-bonded a high-speed, low-noise InP high electron mobility transistor (HEMT) onto a separately fabricated passive circuit having a GaAs substrate. The compact bumps and small chip size were used for efficient coupling and maximum circuit design flexibility. This new quasi-monolithic millimeter-wave integrated circuit (Q-MMIC) amplifier exhibits a peak gain of 5.8 dB at approx. 90 GHz and a 3 dB bandwidth of greater than 25%. To our knowledge, this is the highest frequency amplifier assembled using bump-bonded technology. Our bump-bonding technique is a useful alternative to the high cost of monolithic millimeter-wave integrated circuits (MMIC's). Effects of the bumps on the circuit appear to be minimal. We used the simple matching circuit for demonstrating the technology - future circuits would have all of the elements (resistors, via holes, bias lines, etc.) included 'in conventional MMIC's. Our design in different from other investigators' efforts in that the bumps are only 8 microns thick by 15 microns wide. The bump sizes were sufficiently small that the devices, originally designed for W-band hybrid circuits, could be bonded without alteration. Figure 3 shows the measured and simulated magnitude of S-parameters from 85-120 GHz, of the InP HEMT bump-bonded to the low noise amplifier (LNA) passive. The maximum gain is 5.8 dB at approx. 90 GHz, and gain extends to 117 GHz. Measurement of a single device (without matching networks) shows approx. 1 dB of gain at 90 GHz. The measured gain of the amplifier agrees well with the design in the center of the measurement band, and the agreement falls off at the band edges. Since no accommodation for the bump-bonding parasitics was made in the design, the result implies that the parasitic elements associated with the bonding itself do not dominate the performance of the LNA circuit. It should be noted that this amplifier was designed for good noise performance, which is why the input and output return losses are poorer than one would expect for an amplifier simply matched for gain. However, noise performance has not been measured at this time. While the agreement between modeled vs. experimental data is not exact, the data prove that bump-bonded technology can be used for amplifiers at frequencies at least as high as 100 GHz. JPL is pursuing this technology as a way to economically and quickly incorporate the best available HEMTs into a circuit with all of the reliability and circuit design flexibility offered by MMIC technology. We are currently using the technology to fabricate 4-stage, wide-band, W-band LNA's. We have also performed pull and shear tests which show that the bump bonds are sufficiently robust for any anticipated application.

Pinsukanjana, Paul R.↗

NASA NTRS2017

Effects of One Year of Spaceflight on Neurocognitive Function

It is known that spaceflight adversely affects human sensorimotor function. With interests in longer duration deep space missions it is important to understand microgravity dose-response relationships. NASA's One Year Mission project allows for comparison of the effects of one year in space with those seen in more typical six month missions to the International Space Station. In the Neuromapping project we are performing structural and functional magnetic resonance brain imaging to identify the relationships between changes in neurocognitive function and neural structural alterations following a six month International Space Station mission. Our central hypothesis is that measures of brain structure, function, and network integrity will change from pre- to post-spaceflight. Moreover, we predict that these changes will correlate with indices of cognitive, sensory, and motor function in a neuroanatomically selective fashion. Our interdisciplinary approach utilizes cutting edge neuroimaging techniques and a broad-ranging battery of sensory, motor, and cognitive assessments that are conducted pre-flight, during flight, and post-flight to investigate potential neuroplastic and maladaptive brain changes in crewmembers following long-duration spaceflight. With the one year mission we had one crewmember participate in all of the same measures pre-, per- and post-flight as in our ongoing study. During this presentation we will provide an overview of the magnitude of changes observed with our brain and behavioral assessments for the one year crewmember in comparison to participants that have completed our six month study to date.

Seidler, R. D.↗

DOE OSTI2023

Moisture Absorbing and Water Self‐Releasing from Hybrid Hydrogel Desiccants

Abstract Atmospheric moisture is a valuable resource for fresh water and potentially sustainable energy. However, direct harvesting water from moisture (the vapor form) remains the most challenging. Hybrid desiccants made from hydrogels embedded with salt offer promise in absorbing moisture. However, the desorption process often requires additional energy to heat the samples. Here, poly(acrylic acid) (PAA) hydrogels embedded with lithium chloride are prepared, demonstratng simultaneous moisture absorption and self‐release of liquid water at room temperature with 50–90% relative humidity. The water self‐releasing process can be separated into two distinct stages: 1) surface release, where water droplets grow on the hydrogel surface due to differences in nucleation and diffusion rates, and 2) bulk release, triggered by the collapse of polymer chains, subsequently releasing water from the hydrogel network. Factors such as salt concentration, hydrogel crosslinking density, and film thickness are investigated to better understand the moisture absorption and water‐releasing processes. Moreover, hydrophobic domains are introduced onto the salt‐embedded PAA films, creating an edge effect that enhances the droplet growth rates. When the hydrophobic domains are patterned, the movement of released water can be guided, resulting in a threefold increase in water removal rate attributed to gravitational force.

Chemistry↗

DOE OSTI2023

Graph Sparsification by Approximate matrix Multiplication

Graphs arising in statistical problems, signal processing, large networks, combinatorial optimization, and data analysis are often dense, which causes both computational and storage bottlenecks. One way of sparsifying a weighted graph, while sharing the same vertices as the original graph but reducing the number of edges, is through spectral sparsification. We study this problem through the perspective of RandNLA. Specifically, we utilize randomized matrix multiplication to give a clean and simple analysis of how sampling according to edge weights gives a spectral approximation to graph Laplacians, without requiring spectral information. Through the CR–MM algorithm, we attain a simple and computationally efficient sparsifier whose resulting Laplacian estimate is unbiased and of minimum variance. Here, we define a new notion of additive spectral sparsifiers, which has not been considered in the literature.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Cross-facility science with the Superfacility Project at LBNL

As data sets from DOE user science facilities grow in both size and complexity there is an urgent need for new capabilities to transfer, analyze and manage the data underlying scientific discoveries. LBNL's Superfacility project brings together experimental and observational research instruments with computational and network facilities at the National Energy Research Scientific Computing Center (NERSC) and the Energy Sciences Network (ESnet) with the goal of enabling user science. Here, we report on recent innovations in the Superfacility project, including advanced data management, API-based automation, real-Time interactive user interfaces, and supported infrastructure for 'edge' services.

Enders, B↗

NASA NTRS2014

The Effects of Long Duration Head Down Tilt Bed Rest on Neurocognitive Performance: The Effects of Exercise Interventions

We are conducting ongoing experiments in which we are performing structural and functional magnetic resonance brain imaging to identify the relationships between changes in neurocognitive function and neural structural alterations following a six month International Space Station mission and following 70 days exposure to a spaceflight analog, head down tilt bedrest. Our central hypothesis is that measures of brain structure, function, and network integrity will change from pre to post intervention (spaceflight, bedrest). Moreover, we predict that these changes will correlate with indices of cognitive, sensory, and motor function in a neuroanatomically selective fashion. Our interdisciplinary approach utilizes cutting edge neuroimaging techniques and a broad ranging battery of sensory, motor, and cognitive assessments that will be conducted pre flight, during flight, and post flight to investigate potential neuroplastic and maladaptive brain changes in crewmembers following long-duration spaceflight. Success in this endeavor would 1) result in identification of the underlying neural mechanisms and operational risks of spaceflight-induced changes in behavior, and 2) identify whether a return to normative behavioral function following re-adaptation to Earth's gravitational environment is associated with a restitution of brain structure and function or instead is supported by substitution with compensatory brain processes. Our ongoing bed rest participants are also engaging in exercise studies directed by Dr. Lori Ploutz Snyder. In this presentation, I will briefly highlight the existing literature linking exercise and fitness to brain and behavioral functions. I will also overview the metrics from my study that could be investigated in relation to the exercise and control subgroups.

Seidler, R. D.↗

NASA NTRS2014

The Effects of Spaceflight and a Spaceflight Analog on Neurocognitive Perfonnance: Extent, Longevity, and Neural Bases

We are conducting ongoing experiments in which we are performing structural and functional magnetic resonance brain imaging to identify the relationships between changes in neurocognitive function and neural structural alterations following a six month International Space Station mission and following 70 days exposure to a spaceflight analog, head down tilt bedrest. Our central hypothesis is that measures of brain structure, function, and network integrity will change from pre to post intervention (spaceflight, bedrest). Moreover, we predict that these changes will correlate with indices of cognitive, sensory, and motor function in a neuroanatomically selective fashion. Our interdisciplinary approach utilizes cutting edge neuroimaging techniques and a broad ranging battery of sensory, motor, and cognitive assessments that will be conducted pre flight, during flight, and post flight to investigate potential neuroplastic and maladaptive brain changes in crewmembers following long-duration spaceflight. Success in this endeavor would 1) result in identification of the underlying neural mechanisms and operational risks of spaceflight-induced changes in behavior, and 2) identify whether a return to normative behavioral function following re-adaptation to Earth's gravitational environment is associated with a restitution of brain structure and function or instead is supported by substitution with compensatory brain processes. With the bedrest study, we will be able to determine the neural and neurocognitive effects of extended duration unloading, reduced sensory inputs, and increased cephalic fluid distribution. This will enable us to parse out the multiple mechanisms contributing to any spaceflight-induced neural structural and behavioral changes that we observe in the flight study. In this presentation I will discuss preliminary results from six participants who have undergone the bed rest protocol. These individuals show decrements in balance and functional mobility, and alterations in brain structure and function, in association with extended bed rest.

Seidler, R. D.↗

DOE OSTI2025

MSU IETC ML for Modbus (AN EDGE)

This study explores machine learning for decoding Modbus RTU data using K-Nearest Neighbors (KNN) models. An initial KNN model trained on 8,000 packets achieved 95.15% accuracy. Although ML improves generalization, accuracy still falls short of deterministic methods. These findings have implications for Modbus traffic analysis, intrusion detection in industrial networks, and adaptive error correction in real-time monitoring systems. By refining ML-based decoding, future work could enable more efficient anomaly detection and predictive maintenance in industrial automation and cybersecurity applications.

Communication Protocol↗

DOE OSTI2025

American-Made Solar Prize: Edgeli Enables DER Integration (CRADA 615) (Final Report)

The purpose of this project was to demonstrate how granular time series data and automated data transformation, and impact assessment tools could speed interconnection approvals for distributed energy resource projects of various types and sizes. Types included community solar, rooftop solar, and EV charging projects. Using software routines to automate the transformation of data (e.g. GIS) to a network database and power flow model then applying scenarios to create hourly (8760) hosting capacity values and voltage and thermal impacts for specific projects, we were able to demonstrate the feasibility of quickly assembling and analyzing key utility data sets for interconnection purposes. The outcomes of this effort will become the foundation for future work that will enhance and encapsulate the software components developed as part of this project, into web services (e.g. APIs) that can be integrated into queue management systems and automate interconnection screening processes.

14 SOLAR ENERGY↗

DOE OSTI2023

Organic Matrix Derived from Host–Microbe Interplay Contributes to Pathological Renal Biomineralization

Matrix stones are a rare form of kidney stones. They feature a high percentage of hydrogel-like organic matter, and their formation is closely associated with urinary tract infections. Herein, comprehensive materials and biochemical approaches were taken to map the organic-inorganic interface and gather insights into the host-microbe interplay in pathological renal biomineralization. Surgically extracted soft and slimy matrix stones were examined using micro-X-ray computed tomography and various microspectroscopy techniques. Higher-mineral-density laminae were positive for calcium-bound Alizarin red. Lower-mineral-density laminae revealed periodic acid-Schiff-positive organic filamentous networks of varied thickness. These organic filamentous networks, which featured a high polysaccharide content, were enriched with zinc, carbon, and sulfur elements. Neutrophil extracellular traps (NETs) along with immune response-related proteins, including calprotectin, myeloperoxidase, CD63, and CD86, also were identified in the filamentous networks. Expressions of NETs and upregulation of polysaccharide-rich mucin secretion are proposed as a part of the host immune defense to "trap" pathogens. These host-microbe derived organic matrices can facilitate heterogeneous nucleation and precipitation of inorganic particulates, resulting in macroscale aggregates known as "matrix stones". These insights into the plausible aggregation of constituents through host-microbe interplay underscore the unique "double-edged sword" effect of the host immune response to pathogens and the resulting renal biominerals.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1993

Meteorological analysis of the November 25 FIRE cirrus-2 case: A well-defined ridge-crest cirrus system over Oklahoma

On the morning of 25 Nov. 1991, a cirrus cloud system formed in a region extending eastward from the continental divide into central Oklahoma and southwestern Kansas. The system moved slowly eastward during the day and maintained a relatively constant scale and shape. From the satellite perspective, this cloud system had a number of morphological similarities to the 27-28 Oct. FIRE (First International Satellite Cloud Climatology Project Regional Experiment) Cirrus-1 case analyzed by Starr and Wylie. By comparison, the present case had much better rawinsonde coverage. The rawinsonde network was much more extensive and the cloud system was relatively slow moving and well-located with respect to the enhanced sounding network and the National Weather Service (NWS) Wind Profiler Network. In addition to the rawinsonde soundings (1200 and 1800 UTC on 25 Nov. and 0000 and 0600 UTC on 26 Nov.) and satellite observations, four aircraft sorties were flown in the northern portion of this cirrus cloud system from mid-morning to mid afternoon. These included microphysical-radiation profiling missions by the NCAR Sabreliner near Tulsa (about midway between COF and OUN) and the NCAR King Air near Ponca City (about 80 km upwind of Tulsa), a microphysical profiling mission by the UND Citation near Tulsa, and a NASA ER-2 overflight. Thus, very good data are available for this case although, unfortunately, the cloud system remained south of the FIRE Hub site at Coffeyville (COF) where extensive ground-based remote sensor was located. The northern edge of the cloud system was visible from COF during most of the day. Given the wealth of available in situ and remote sensing observations, the similarity to the FIRE Cirrus-1 focal case study, and the particularly good match in scale and location of the cloud system with the rawinsonde and profiler networks, analysis of this case may yield very useful results on the life-cycle of this cirrus cloud system. The quality of the meteorological description for this case was assessed based on our preliminary analysis of the rawinsonde observations.

Starr, David↗

DOE OSTI2021

Living on the edge: A continental-scale assessment of forest vulnerability to drought

Globally, forests are facing an increasing risk of mass tree mortality events associated with extreme droughts and higher temperatures. Hydraulic dysfunction is considered a key mechanism of drought-triggered dieback. By leveraging the climate breadth of the Australian landscape and a national network of research sites (Terrestrial Ecosystem Research Network), we conducted a continental-scale study of physiological and hydraulic traits of 33 native tree species from contrasting environments to disentangle the complexities of plant response to drought across communities. Here, we found strong relationships between key plant hydraulic traits and site aridity. Leaf turgor loss point and xylem embolism resistance were correlated with minimum water potential experienced by each species. Across the data set, there was a strong coordination between hydraulic traits, including those linked to hydraulic safety, stomatal regulation and the cost of carbon investment into woody tissue. These results illustrate that aridity has acted as a strong selective pressure, shaping hydraulic traits of tree species across the Australian landscape. Hydraulic safety margins were constrained across sites, with species from wetter sites tending to have smaller safety margin compared with species at drier sites, suggesting trees are operating close to their hydraulic thresholds and forest biomes across the spectrum may be susceptible to shifts in climate that result in the intensification of drought.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Robust Real-Time Modeling of Distribution Systems with Data-Driven Grid-Wise Observability (Final Technical Report)

The overall objective of this project is to leverage existing and emerging sensor measurements to develop data-driven observability enhancement algorithms as well as robust state estimation and parameter identification techniques to enable real-time grid-wise monitoring and modeling of loads and distributed energy resources (DERs). The project resulted in a holistic framework with the following key components: 1) data-driven and machine learning-based grid-edge visibility enhancement, 2) robust branch-current-based state estimation (BCSE), and 3) robust real-time steady-state and dynamic-state modeling of loads and DERs. The research outcomes have provided utility companies better network visibility, higher-fidelity load/DER models, and more accurate assessment of DERs’ impacts, thus, facilitating the integration of renewable energy sources. The synergistic collaborative project among Iowa State University (ISU), Argonne National Laboratory (ANL), Electric Power Research Center (EPRC), SIEMENS Industry, Alliant Energy, Cedar Falls Utilities (CFU), and Maquoketa Valley Electric Cooperative (MVEC) to leverage the team’s extensive expertise and experience in power distribution systems, state estimation, online modeling and identification, and DER integrations. The proposed frameworks have been verified with industry adopted software and attempted integrated with existing tool wherever possible

24 POWER TRANSMISSION AND DISTRIBUTION↗