Search NASA⌕ Search

SEARCH · Search NASA

Results for “network architectures”

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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Self-growing neural network architecture using crisp and fuzzy entropy

The paper briefly describes the self-growing neural network algorithm, CID3, which makes decision trees equivalent to hidden layers of a neural network. The algorithm generates a feedforward architecture using crisp and fuzzy entropy measures. The results for a real-life recognition problem of distinguishing defects in a glass ribbon, and for a benchmark problen of telling two spirals apart are shown and discussed.

Cios, Krzysztof J.↗
NASA NTRS2012

Establishment of a Spaceport Network Architecture

Since the beginning of the space age, the main actors in space exploration have been governmental agencies, enabling a privileged access to space, but with very restricted and rare missions. The last decade has seen the rise of space tourism, and the founding of ambitious private space mining companies, showing the beginnings of a new exploration era, that is based on a more generalized and regular access to space and which is not limited to the Earth's vicinity. However, the cost of launching sufficient mass into orbit to sustain these inspiring challenges is prohibitive, and the necessary infrastructures to support these missions is still lacking. To provide easy and affordable access into orbital and deep space destinations, there is the need to create a network of spaceports via specific waypoint locations coupled with the use of natural resources, or In Situ Resource Utilization (ISRU), to provide a more economical solution. As part of the International Space University Space Studies Program 2012, the international and intercultural team of Operations and Service Infrastructure for Space (OASIS) proposes an interdisciplinary answer to the problem of economical space access and transportation. This paper presents a summary of a detailed report [1] of the different phases of a project for developing a network of spaceports throughout the Solar System in a timeframe of 50 years. The requirements, functions, critical technologies and mission architecture of this network of spaceports are outlined in a roadmap of the important steps and phases. The economic and financial aspects are emphasized in order to allow a sustainable development of the network in a public-private partnership via the formation of an International Spaceport Authority (ISPA). The approach includes engineering, scientific, financial, legal, policy, and societal aspects. Team OASIS intends to provide guidelines to make the development of space transportation via a spaceports logistics network feasible, and believes that this pioneering effort will revolutionize space exploration, science and commerce, ultimately contributing to permanently expand humanity into space.

Larson, Wiley J.↗
NASA NTRS1991

Evolving earth-based and in-situ satellite network architectures for Mars communications and navigation support

Results of on-going studies to develop navigation/telecommunications network concepts to support future robotic and human missions to Mars are presented. The performance and connectivity improvements provided by the relay network will permit use of simpler, lower performance, and less costly telecom subsystems for the in-situ mission exploration elements. Orbiting relay satellites can serve as effective navigation aids by supporting earth-based tracking as well as providing Mars-centered radiometric data for mission elements approaching, in orbit, or on the surface of Mars. The relay satellite orbits may be selected to optimize navigation aid support and communication coverage for specific mission sets.

Hastrup, Rolf↗
DOE OSTI2024

Neuro-Spark: A Submicrosecond Spiking Neural Networks Architecture for In-Sensor Filtering

Neuro-Spark, which is a new neuromorphic architecture with a field-programmable gate array (FPGA) implementation for ultrafast spiking neural network (SNN) inference at the edge, facilitates smart-pixel in-sensor filtering for high-energy physics experiments at the Large Hadron Collider (LHC). Utilizing the evolutionary optimization for neuromorphic systems (EONS) training method, we generate compact SNN models with 91% signal efficiency, akin to convolutional neural networks but with half the parameters. However, deploying near the detector poses a challenge because the SNN must handle a sustained input data rate exceeding 1013 GB/s. To overcome this, we propose a novel hardware architecture that uses high-level synthesis to construct a tuned architecture for the EONS-trained SNN. In addition to the analysis and validation with an AMD Xilinx Artix-A7 FPGA, our solution consumes only ç24% of FPGA LUT and flipflops. We also introduce an innovative quantization method that reduces FPGA resource utilization by ç15% without compromising accuracy. Our FPGA implementation achieves computing latency of ç10 ns for smart-pixel application inference on an edge FPGA.

Miniskar, Narasinga Rao↗
DOE OSTI2022

Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends

A sustainable computing scenario demands more energy-efficient processors. Neuromorphic systems mimic biological functions by employing spiking neural networks for achieving brain-like efficiency, speed, adaptability, and intelligence. Current trends in neuromorphic technologies address the challenges of investigating novel materials, systems, and architectures for enabling high-integration and extreme low-power brain-inspired computing. This review collects the most recent trends in exploiting the physical properties of nonvolatile memory technologies for implementing efficient in-memory and in-device computing with spike-based neuromorphic architectures.

42 ENGINEERING↗
NASA NTRS2018

Improvements to Integrated Tradespace Analysis of Communications Architectures (ITACA) Network Loading Analysis Tool

NASA's SCENIC project aims to simplify and reduce the cost of space mission planning by replicating the analysis capabilities of commercially licensed software which are integrated with relevant analysis parameters specific to SCaN assets and SCaN supported user missions. SCENIC differs from current tools that perform similar analyses in that it 1) does not require any licensing fees, 2) will provide an all-in-one package for various analysis capabilities that normally requires add-ons or multiple tools to complete. As part of SCENIC's capabilities, the ITACA network loading analysis tool will be responsible for assessing the loading on a given network architecture and generating a network service schedule. ITACA will allow users to evaluate the quality of service of a given network architecture and determine whether or not the architecture will satisfy the mission's requirements. ITACA is currently under development, and the following improvements were made during the fall of 2017: optimization of runtime, augmentation of network asset pre-service configuration time, augmentation of Brent's method of root finding, augmentation of network asset FOV restrictions, augmentation of mission lifetimes, and the integration of a SCaN link budget calculation tool. The improvements resulted in (a) 25% reduction in runtime, (b) more accurate contact window predictions when compared to STK(Registered Trademark) contact window predictions, and (c) increased fidelity through the use of specific SCaN asset parameters.

analysis↗
NASA NTRS1990

MSAT network architecture

The Mobile Satellite (MSAT) communications system will support mobile voice and data services using circuit switched and packet switched facilities with interconnection to the public switched telephone network and private networks. Control of the satellite network will reside in a Network Control System (NCS) which is being designed to be extremely flexible to provide for the operation of the system initially with one multi-beam satellite, but with capability to add additional satellites which may have other beam configurations. The architecture of the NCS is described. The signalling system must be capable of supporting the protocols for the assignment of circuits for mobile public telephone and private network calls as well as identifying packet data networks. The structure of a straw-man signalling system is discussed.

Davies, N. G.↗
NASA NTRS2000

On-Board Fiber-Optic Network Architectures for Radar and Avionics Signal Distribution

Continued progress in both civil and military avionics applications is overstressing the capabilities of existing radio-frequency (RF) communication networks based on coaxial cables on board modem aircrafts. Future avionics systems will require high-bandwidth on- board communication links that are lightweight, immune to electromagnetic interference, and highly reliable. Fiber optic communication technology can meet all these challenges in a cost-effective manner. Recently, digital fiber-optic communication systems, where a fiber-optic network acts like a local area network (LAN) for digital data communications, have become a topic of extensive research and development. Although a fiber-optic system can be designed to transport radio-frequency (RF) signals, the digital fiber-optic systems under development today are not capable of transporting microwave and millimeter-wave RF signals used in radar and avionics systems on board an aircraft. Recent advances in fiber optic technology, especially wavelength division multiplexing (WDM), has opened a number of possibilities for designing on-board fiber optic networks, including all-optical networks for radar and avionics RF signal distribution. In this paper, we investigate a number of different novel approaches for fiber-optic transmission of on-board VHF and UHF RF signals using commercial off-the-shelf (COTS) components. The relative merits and demerits of each architecture are discussed, and the suitability of each architecture for particular applications is pointed out. All-optical approaches show better performance than other traditional approaches in terms of signal-to-noise ratio, power consumption, and weight requirements.

Alam, Mohammad F.↗
DOE OSTI2026

Competing Effects of Network Architecture and Composition on Polydomain Liquid Crystal Elastomers

Main-chain liquid crystal elastomers (LCEs) are synthesized to investigate the interplay of the composition and network structure on LCE nematic-to-isotropic (N–I) transitions. We focus on networks synthesized from liquid crystalline oligomers reacted with tri- or tetrafunctional nonmesogenic cross-linker molecules. We find that coupling between mesogens and the polymer backbone increases with the degree of cross-linking. However, this enhanced coupling competes with mesogenic dilution arising from the cross-linker molecules to determine the N–I transition temperature (T NI ). When cross-linker molecules are dilute, the degree of cross-linking directly correlates to the change in T NI from the oligomer to LCE (ΔT NI ) through mesogen–backbone coupling. In this regime, ΔT NI ranges from 2.9 to 12.2 °C and 2.9–13.9 °C for tri- and tetrafunctional cross-linkers, respectively. At high cross-linker concentrations, deviations from this linear relationship appear. Further, the fractional mesogen content within an oligomer chain induces molecular weight-dependent mesogenic dilution effects arising from the flexible spacer molecules. Analysis of the N–I transition peak reveals a maximum latent heat per gram of mesogen (ΔH NI,mes ) for this system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Surrogate modeling of Cellular-Potts agent-based models as a segmentation task using the U-Net neural network architecture

The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigate in vitro vasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations by a factor of 562 times compared to single-core CPM code execution on CPU. Over short timescales of up to 3 recursive evaluations, or 300 MCS, our model captures the emergent behaviors demonstrated by the original Cellular-Potts model such as vessel sprouting, extension and anastomosis, and contraction of vascular lacunae. This approach demonstrates the potential for deep learning to serve as a step toward efficient surrogate models for CPM simulations, enabling faster evaluation of computationally expensive CPM simulations of biological processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Network Architecture Verification & Validation Tool

The NAVV Tool is an automation of Linux commands run Zeek IDS software on a packet capture to create a Microsoft Excel spreadsheet table breaking down network traffic observed. The tool automates the Zeek software analysis, the collation of logs, and then the dissection of the Conn.log and DNS.logs to create a summary table within a Excel. This spreadsheet can then be updated with network segments using CIDR formatting and labels along with inventory information including name and IP address. Using the tool again will integrate these label and color coding into the existing analysis table to aid in conducting an evaluation of the network traffic.

Nichols, DonovanW↗
DOE OSTI2022

A comparison of neural network architectures for data-driven reduced-order modeling

The popularity of deep convolutional autoencoders (CAEs) has engendered new and effective reduced-order models (ROMs) for the simulation of large-scale dynamical systems. Despite this, it is still unknown whether deep CAEs provide superior performance over established linear techniques or other network-based methods in all modeling scenarios. To elucidate this, the effect of autoencoder architecture on its associated ROM is studied through the comparison of deep CAEs against two alternatives: a simple fully connected autoencoder, and a novel graph convolutional autoencoder. Through benchmark experiments, it is shown that the superior autoencoder architecture for a given ROM application is highly dependent on the size of the latent space and the structure of the snapshot data, with the proposed architecture demonstrating benefits on data with irregular connectivity when the latent space is sufficiently large.

42 ENGINEERING↗
DOE OSTI2023

Improving Knowledge Base with Network Architecture Diagrams

In order to ensure Cyber Security Team (CST) has access to updated and in-depth information regarding Fermilab systems, capabilities, procedures, tools, and training, the CST created the Knowledge Base. This consists of a wealth of information for current and future CST employees to improve knowledge retention and transfer. The Knowledge Base, however, uses outdated network diagrams that lack many of the previous and upcoming changes to the architecture. For this reason, updated diagrams have been created to reflect the current position of the CST capabilities. By employing the knowledge learned at Fermi National Accelerator Laboratory, three diagrams have been created to highlight the current state of the CST’s operations and capabilities.

Blum, Ethan↗
NASA NTRS1991

Experiments on neural network architectures for fuzzy logic

The use of fuzzy logic to model and manage uncertainty in a rule-based system places high computational demands on an inference engine. In an earlier paper, the authors introduced a trainable neural network structure for fuzzy logic. These networks can learn and extrapolate complex relationships between possibility distributions for the antecedents and consequents in the rules. Here, the power of these networks is further explored. The insensitivity of the output to noisy input distributions (which are likely if the clauses are generated from real data) is demonstrated as well as the ability of the networks to internalize multiple conjunctive clause and disjunctive clause rules. Since different rules with the same variables can be encoded in a single network, this approach to fuzzy logic inference provides a natural mechanism for rule conflict resolution.

Keller, James M.↗
DOE OSTI2025

Software-Defined Data Center Network Architecture using VXLAN-based BGP EVPN for Dynamic Workflows in a Supercomputing Environment (VXLAN-based BGP EVPN Fabric for HPC) v1

This software repository automates the deployment of a multi-vendor VXLAN-based BGP EVPN architecture, leveraging Containerlab to instantiate a stretched CLOS topology. It integrates Linux, Nokia SR Linux, and Arista cEOS, using BGP for underlay, overlay, and topology extension. The software enables rapid prototyping and testing of advanced network configurations. Its key advantage lies in providing a dynamic, programmable environment for research and development of critical technologies supporting dynamic workflows within supercomputing environments, surpassing the limitations of static, vendor-locked alternatives by fostering interoperability and agility.

Kumar, Ronal [Lawrence Berkeley National Laborator↗