Search NASA⌕ Search

SEARCH · Search NASA

Results for “sensor node”

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.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Embedded FPGA developments in 130 nm and 28 nm CMOS for machine learning in particle detector readout

Embedded field programmable gate array (eFPGA) technology allows the implementation of reconfigurable logic within the design of an application-specific integrated circuit (ASIC). This approach offers the low power and efficiency of an ASIC along with the ease of FPGA configuration, particularly beneficial for the use case of machine learning in the data pipeline of next-generation collider experiments. An open-source framework called "FABulous" was used to design eFPGAs using 130 nm and 28 nm CMOS technology nodes, which were subsequently fabricated and verified through testing. The capability of an eFPGA to act as a front-end readout chip was assessed using simulation of high energy particles passing through a silicon pixel sensor. A machine learning-based classifier, designed for reduction of sensor data at the source, was synthesized and configured onto the eFPGA. A successful proof-of-concept was demonstrated through reproduction of the expected algorithm result on the eFPGA with perfect accuracy. Finally, further development of the eFPGA technology and its application to collider detector readout is discussed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Simulations of Sparse Static Detector Networks for City-Scale Radiological/Nuclear Detection

Sparse static detector networks in urban environments can be used in efforts to detect illicit radioactive sources, such as stolen nuclear material or radioactive "dirty bombs." We use detailed simulations to evaluate multiple configurations of detector networks and their ability to detect sources moving through a $6\times 6$ km 2 area of downtown Chicago. A detector network's probability of detecting a source increases with detector density but can also be increased with strategic node placement. Here, we show that the ability to fuse correlated data from a source-carrying vehicle passing by multiple detectors can significantly contribute to the overall detection probability. In this article, we distinguish static sensor deployments operated as networks able to correlate signals between sensors, from deployments operated as arrays where each sensor is operated individually. In particular, we show that additional visual attributes of source-carrying vehicles, such as vehicle color and make, can greatly improve the ability of a detector network to detect illicit sources.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2018

Deployable sensor system using mesh networking and satellite communication

A sensor system may be configured for continuous operation in a low resource environment and/or in extreme environmental conditions. The sensor system may have sufficient processing capabilities to provide scientific computing for pre-processing, quality control, statistical analysis, event classification, data compression and corrections (e.g., spikes in the data), autonomous decisions and actions, triggering other nodes, and information assurance functions that provide data confidentiality, data integrity, authentication, and non-repudiation. The hardware may have both mesh networking and satellite and cellular communication capability, and may be available for relatively low cost. Such a network provides the flexibility to have potentially any number of nodes be completely independent from one another. Thus, the network may scale across a diverse terrain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Deployable sensor system using mesh networking and satellite communication

A sensor system may be configured for continuous operation in a low resource environment and/or in extreme environmental conditions. The sensor system may have sufficient processing capabilities to provide scientific computing for pre-processing, quality control, statistical analysis, event classification, data compression and corrections (e.g., spikes in the data), autonomous decisions and actions, triggering other nodes, and information assurance functions that provide data confidentiality, data integrity, authentication, and non-repudiation. The hardware may have both mesh networking and satellite and cellular communication capability, and may be available for relatively low cost. Such a network provides the flexibility to have potentially any number of nodes be completely independent from one another. Thus, the network may scale across a diverse terrain.

Frigo, Janette↗
DOE OSTI2017

Evolving Deep Networks Using HPC

While a large number of deep learning networks have been studiedand published that produce outstanding results on natural imagedatasets, these datasets only make up a fraction of those to whichdeep learning can be applied. These datasets include text data, audiodata, and arrays of sensors that have very different characteristicsthan natural images. As these “best” networks for natural imageshave been largely discovered through experimentation and cannotbe proven optimal on some theoretical basis, there is no reasonto believe that they are the optimal network for these drasticallydifferent datasets. Hyperparameter search is thus often a very im-portant process when applying deep learning to a new problem. Inthis work we present an evolutionary approach to searching thepossible space of network hyperparameters and construction thatcan scale to 18, 000 nodes. This approach is applied to datasets ofvarying types and characteristics where we demonstrate the abilityto rapidly find best hyperparameters in order to enable practitionersto quickly iterate between idea and result.

Young, Steven↗
DOE OSTI2018

Theoretical Investigations of Nano and Bio Structures

The PI was supported by the DOE grant entitled “Theoretical Investigations of Nano and Bio Structures,” 3/1/13-2/28/17, $440,000, and a one-year no cost extension, 3/1/17-2/28/18. Its main emphasis was on nonequilibrium electron transport and novel nanoscale materials and devices, such as graphene nanoribbons, nanotube-based sensors, nanocircuits able to detect DNA replication, and DNA nanowires. We have also made major enhancements to our RMG suite of electronic structure codes, leading to much increased scalability, excellent parallelization of across both CPU and GPU nodes, and cleaner, refactored code base. For example, RMG performs excellently on Summit, DOE’s flagship supercomputer and the fastest supercomputer in the world. It scales to over 100,000 CPU cores and 20,000 GPUs, and can use multiple GPUs per node. It performs 80x faster per node than on DOE’s previous flagship supercomputer, Titan. This is the final report for grant DE-FG02-98ER45685.

36 MATERIALS SCIENCE↗
DOE OSTI2024

GraMeR: Gra ph Me ta R einforcement learning for multi-objective influence maximization

Influence maximization (IM) is a combinatorial problem of identifying a subset of seed nodes in a network (graph), which when activated, provide a maximal spread of influence in the network for a given diffusion model and a budget for seed set size. IM has numerous applications such as viral marketing, epidemic control, sensor placement and other network-related tasks. However, its practical uses are limited due to the computational complexity of current algorithms. Recently, deep reinforcement learning has been leveraged to solve IM in order to ease the computational burden. However, there are serious limitations in current approaches, including narrow IM formulation that only consider influence via spread and ignore self-activation, low scalability to large graphs, and lack of generalizability across graph families leading to a large running time for every test network. In this work, we address these limitations through a unique approach that involves: (1) Formulating a generic IM problem as a Markov decision process that handles both intrinsic and influence activations; (2)incorporating generalizability via meta-learning across graph families. There are previous works that combine deep reinforcement learning with graph neural network, but this work solves a more realistic IM problem and incorporates generalizability across graphs via meta reinforcement learning. Extensive experiments are carried out in various standard networks to validate performance of the proposed Graph Meta Reinforcement learning (GraMeR) framework. Finally, the results indicate that GraMeR is multiple orders faster and generic than conventional approaches when applied on small to medium scale graphs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

A pillar-layered metal-organic framework as luminescent sensor for selective and reversible response of chloroform

A new 3D metal-organic framework, namely, (Zn{sub 4}(H{sub 2}BPTC){sub 2}(HCOO){sub 4}){sub n} (SNNU-1, H{sub 4}BPTC=biphenyl-3,3',5,5'-tetracarboxylic acid, SNNU=Shaanxi Normal University) has been solvothermal synthesized. Four independent tetrahedral Zn atoms are connected by organic ligands to form a 2D Zn-H{sub 2}BPTC layer, which is further bridged by in-situ generated HCOO{sup -} to give the 3D pillar-layered framework of SNNU-1. Unique Zn and H{sub 2}BPTC all act as 4-connected nodes leading to a new 4,4,4-connected topological net with point symbol of (4·5·6{sup 2}·8{sup 2})(4·5{sup 2}·6{sup 2}·8)(5{sup 2}·6{sup 3}·7). Notably, intense blue emission band is observed for SNNU-1, which exhibits solvent-dependent effect. Compared to other common organic solvents, chloroform can specially improve the photoluminescent intensity of SNNU-1. Further repeated response and release experiments clearly showed that SNNU-1 can act as luminescent sensor for selective and reversible detection of chloroform. - Graphical abstract: Zn{sup 2+} ions are bridged by aromatic tetracarboxylate ligands and inorganic formate anions to give a microporous pillar layered open-framework, which exhibits not only strong photoluminescence but also selective and reversible luminescent sensing for chloroform. - Highlights: • Novel Zn-tetracarboxylate-formate microporous pillar layered open-framework. • New 4,4,4-connected topology and rod-packing net. • Solvent-dependent photoluminescent intensity. • Selective and reversible response for chloroform.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Continuous Test and Transition Infrastructure for Quantum Networking for Science Complex

We propose a network architecture with a separate quantum dataplane and a conventional control plane: (a) Quantum Data Plane: The Quantum Data plane consists of links of dark fibers connecting quantum switches and repeaters, which in turn, connect to quantum computers, memory and sensors. Since the current reach is limited to local areas, it will begin as a collection of site networks at laboratories, (b) Conventional Control Plane: The Control plane provides management access to quantum devices for configuration and provisioning via control nodes with firewall and encryption capabilities. Continuous Test and Transition Infrastructure: We propose an infrastructure with a control plane connecting multiple sites via encrypted tunnels over conventional networks consisting of the following: (a) Site Quantum Networks: Individual site networks supported by their fiber plants connect laboratories that house and connect to their quantum devices for testing and interoperability.(b) Site Control Planes: Sites are connected over individual control planes with control hosts with Software Defined Networking capabilities, which can be peered with other networks via ESnet.(c) Progressive Expansion: Initially, sites will develop individual data planes with their specific quantum devices and fiber connections, and mechanisms to interface with conventional networks. They progressively expand and interconnect, under a wide-area ecosystem of peered control planes for device testing, interoperability development and roll off into production environments.

Rao, Nageswara S.↗
DOE OSTI2021

Risk-informed autonomous adaptive cyber controllers

Technology related to risk-informed autonomous adaptive cyber controllers is disclosed. In one example of the disclosed technology, a method includes generating probabilities of a cyber-attack occurring along an attack surface of a network. The probabilities can be generated using sensor and operational data of a network as inputs to an attack graph. The risk scores can be determined using a plurality of fault trees and the generated probabilities from the attack graph. The respective risk scores can correspond to respective nodes of an event tree. The event tree and the determined risk scores can be used to determine risk estimates for a plurality of configurations of the network. The risk estimates for the plurality of configurations of the network can be used to reconfigure the network to reduce a risk from the cyber-attack.

Veeramany, Arun↗
DOE OSTI2024

Isolating Signatures of Cyberattacks under Stressed Grid Conditions

In a controlled cyber-physical network, such as a power grid, any malicious data injection in the sensor measurements can lead to widespread impact due to the actions of the closed-loop controllers. While fast identification of the attack signatures is imperative for reliable operations, it is challenging to do so in a large dynamical network with tightly coupled nodes. A particularly challenging scenario arises when the cyberattacks are strategically launched during a grid stress condition, caused by non-malicious physical disturbances.

Ghosh, Sanchita [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2018

Inorganic “Conductive Glass” Approach to Rendering Mesoporous Metal–Organic Frameworks Electronically Conductive and Chemically Responsive

A representative mesoporous metal–organic-framework (MOF) material, NU-1000, has been rendered electronically conductive via a robust inorganic approach that permits retention of MOF crystallinity and porosity. The approach is based on condensed-phase grafting of molecular tin species onto the MOF nodes via irreversible reaction with hydroxyl and aqua ligands presented at the node surface, a self-limiting process termed solvothermal installation (of metal ions) in MOFs (SIM, a solution-phase analog of atomic layer deposition in MOFs). Treatment of the modified MOF with aerated steam at 120 °C converts the grafted tin molecules to tetratin(IV)oxy clusters, with the clusters being sited between insulating pairs of zirconia-like nodes (the zirconium component being key to endowing the parent material with requisite chemical and thermal stability). By introducing new O–H presenting ligands on the modified-MOF node, the high-temperature steam step additionally serves to reset the material to reactive form, thus enabling a second self-limiting tin-grafting step to be run (and after further steam treatment, enabling a third). Difference-envelop-density (DED) analyses of synchrotron-derived X-ray scattering data, with and without installed tin species, show that the clusters formed after one cycle are spatially isolated, but that repetitive SIM cycling adds metal and oxygen ions in a way that enshrouds nodes, links clusters, and yields continuous one-dimensional strands of oxy-tin(IV), oriented exclusively along the c axis of the MOF. Two-probe conductivity measurements show that the parent MOF and the version containing isolated oxy-tin(IV) clusters are electrically insulating, but that the versions featuring continuous strands show an electrical conductivity of 1.8 × 10–7 S/cm after three Sn-SIM cycles. When combined with interdigitated microelectrodes, the solvent-free and conductive-glass-modified material (three Sn-SIM cycles) displays a substantial and persistent increase in electrical conductivity during exposure to 5% H2, indicating a role for dissociated H2 as an electronic dopant. The increase can be repetitively reversed by alternating H2 with air, illustrating the ability of the conductive MOF to function as a resistive sensor for H2 and suggesting further potential applications that may capitalize on the combination of high volumetric surface area, high mesoporosity, high chemical and thermal stability, and significant electrical conductivity.

bio-inspired↗
DOE OSTI2024

Cyber-Secure and Safe Operation of Solar Photovoltaic Power Distribution Systems

Solar photovoltaic (PV)-rich power distribution systems are networked Cyber-Physical Systems (CPS). These are control systems where multiple computing nodes and diverse intelligent agents interact with the physical world in real-time. However, the presence of networked components renders them vulnerable to potential cyber-attacks, cyber-intrusions, and other malicious events. This is because these systems depend on the measurements reported from their heterogeneous sensors. This makes them vulnerable to potential cyber-attacks where malicious agents can compromise the sensors or the communication networks carrying the sensor measurements. This paper proposes a novel methodology for enhancing the cyber-security and cyber-resilient post-attack safe operation of solar PV-rich power distribution systems against potential cyber-attacks through the Dynamic Watermarking (DW), using online system identification. The resiliency of the proposed technique is tested and validated with several attack scenarios on both a lab-scale 3kW grid-connected PV inverter and a Hardware-in-the-Loop (HiL) system. The proposed approach can be applied to other types of power distribution systems to enhance their cyber-secure and cyber-resilient safe operation. This paper thereby contributes to the field of cyber-security of Cyber-Physical Energy Systems (CPES).

Kim, Jaewon↗
DOE OSTI2022

Power management for wireless nodes

A system includes a rectifier having an input configured to receive a variable AC input voltage and convert the AC input voltage to a rectified output voltage. A power converter has an input coupled to receive the rectified output voltage and to provide a DC supply voltage at an output thereof. A low drop-out regulator has an input coupled to receive the supply voltage and is configured provide a regulated voltage. A sensor is coupled to receive the regulated voltage as a power source and is configured to sense a condition and provide sensor data indicative of the sensed condition.

Feng, Philip X. L.↗
DOE OSTI2021

Regressing Nuclear Reactor Power Level Using Low-Cost Sensor Network Data

Multisensor networks deployed at nuclear facilities can be leveraged to collect data used as inputs to machine learning models predicting nuclear safeguard relevant information. This work demonstrates an application of this idea by regressing nuclear reactor power levels, a key indicator for nuclear safeguard verification, at the McClellan Nuclear Research Center using data collected by five Merlyn multisensor platforms with LASSO and LSTM models. This work also demonstrates the use of Leave One Node Out to measure the importance of each multisensor for this regression problem providing insight into model explainability and allowing inferential hypotheses about the nuclear facility to be made. This work can be used as a starting point for future development of methods for regression on reactor power levels at nuclear facilities using multisensor network data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Stochastic Gradient-Based Distributed Bayesian Estimation in Cooperative Sensor Networks

Distributed Bayesian inference provides a full quantification of uncertainty offering numerous advantages over point estimates that autonomous sensor networks are able to exploit. However, fully-decentralized Bayesian inference often requires large communication overheads and low network latency, resources that are not typically available in practical applications. In this paper, we propose a decentralized Bayesian inference approach based on stochastic gradient Langevin dynamics, which produces full posterior distributions at each of the nodes with significantly lower communication overhead. We provide analytical results on convergence of the proposed distributed algorithm to the centralized posterior, under typical network constraints. Finally, we also provide extensive simulation results to demonstrate the validity of the proposed approach.

42 ENGINEERING↗
DOE OSTI2021

Fluorescent Detection of Carbon Disulfide by a Highly Emissive and Robust Isoreticular Series of Zr-Based Luminescent Metal Organic Frameworks (LMOFs)

Carbon disulfide (CS 2 ) is a highly volatile neurotoxic species. It is known to cause atherosclerosis and coronary artery disease and contributes significantly to sulfur-based pollutants. Therefore, effective detection and capture of carbon disulfide represents an important aspect of research efforts for the protection of human and environmental health. In this study, we report the synthesis and characterization of two strongly luminescent and robust isoreticular metal organic frameworks (MOFs) Zr 6 (µ 3- O) 4 (OH) 8 (tcbpe) 2 (H 2 O) 4 (here termed 1) and Zr 6 (µ 3- O) 4 (OH) 8 (tcbpe-f) 2 (H 2 O) 4 (here termed 2) and their use as fluorescent sensors for the detection of carbon disulfide. Both MOFs demonstrate a calorimetric bathochromic shift in the optical bandgap and strong luminescence quenching upon exposure to carbon disulfide. The interactions between carbon disulfide and the frameworks are analyzed by in-situ infrared spectroscopy and computational modelling by density functional theory. These results reveal that both the Zr metal node and organic ligand act as the preferential binding sites and interact strongly with carbon disulfide.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗