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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Fiber-Optic Terahertz Data-Communication Networks

Network protocols implemented in optical domain. Fiber-optic data-communication networks utilize fully available bandwidth of single-mode optical fibers. Two key features of method: use of subpicosecond laser pulses as carrier signals and spectral phase modulation of pulses for optical implementation of code-division multiple access as multiplexing network protocol. Local-area network designed according to concept offers full crossbar functionality, security of data in transit through network, and capacity about 100 times that of typical fiber-optic local-area network in current use.

Chua, Peter L.↗
DOE OSTI2026

Network of Modified Portable Optical Particle Spectrometers Instrument Handbook

The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s portable optical particle spectrometer (POPS) network (Figure 1) provides an aerosol size distribution spectrum from 150 nm to 5 μm in 16 logarithmically spaced bins. The data emanating from this network of up to four matching POPS in weatherproof enclosures comprises one-minute averages of the aerosol size and number spectrum and the co-located temperature and relative humidity. The total particle concentration and sample flow rate are also provided. The sample stream is not dried before its measurement. Two size spectra are provided: the default size distribution corresponds to the calibration using polystyrene latex spheres (PSL), while the second size distribution corresponds to the calibration using size-selected dry ammonium sulfate aerosol. The principal scientific application of the data provided by the ARM POPS is to determine the aerosol size distribution across a region in three disparate locations – the size distribution is broadly relevant to studies of pollution and of conditions in the boundary layer. A high particle concentration can be an indicator of unhealthy levels of pollution; the dispersion of particles in the atmosphere is a predictor of long-range visibility; and particles serve as collectors of condensing gases including water vapor, and thus are a factor in the formation and properties of fogs and clouds, and in the initiation of precipitation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

High-Speed Optical Wide-Area Data-Communication Network

Proposed fiber-optic wide-area network (WAN) for digital communication balances input and output flows of data with its internal capacity by routing traffic via dynamically interconnected routing planes. Data transmitted optically through network by wavelength-division multiplexing in synchronous or asynchronous packets. WAN implemented with currently available technology. Network is multiple-ring cyclic shuffle exchange network ensuring traffic reaches its destination with minimum number of hops.

Monacos, Steve P.↗
NASA NTRS1986

Research in high speed fiber optics local area networks

The design of high speed local area networks (HSLAN) for communication among distributed devices requires solving problems in three areas: the network medium and its topology, the medium access control, and the network interface. Considerable progress was already made in the first two areas. Accomplishments are divided into two groups according to their theoretical or experimental nature. A brief summary is given.

Tobagi, F. A.↗
NASA NTRS1988

High speed fiber optics local area networks: Design and implementation

The design of high speed local area networks (HSLAN) for communication among distributed devices requires solving problems in three areas: (1) the network medium and its topology; (2) the medium access control; and (3) the network interface. Considerable progress has been made in all areas. Accomplishments are divided into two groups according to their theoretical or experimental nature. A brief summary is given in Section 2, including references to papers which appeared in the literature, as well as to Ph.D. dissertations and technical reports published at Stanford University.

Tobagi, Fouad A.↗
NASA NTRS1985

A synchronous fiber optic ring local area network for multigigabit/s mixed-traffic communication

A synchronous-ring fiber optic local area network is reported that facilitates the simultaneous transmission of packet and real-time traffic at gigabit/s rates, minimizes the amount of high-speed logic, and simplifies the user interface to the network. The novelty of the technique is based on (1) suspending in transit around the ring's circumference an integral number of data frames and (2) achieving this condition by skewing the frame clock rate a small amount. Rather than use the whole data frame as one packet destined to a specific user, many individual channels are instead time-multiplexed into the data frame. This technique only becomes feasible for local networks as data rates approach the Gbit/s range. This departure from other synchronous rings results in several advantages both in terms of system performance and hardware simplicity.

Bergman, L. A.↗
NASA NTRS2000

Optical Circuit Switched Protocol

The present invention is a system and method embodied in an optical circuit switched protocol for the transmission of data through a network. The optical circuit switched protocol is an all-optical circuit switched network and includes novel optical switching nodes for transmitting optical data packets within a network. Each optical switching node comprises a detector for receiving the header, header detection logic for translating the header into routing information and eliminating the header, and a controller for receiving the routing information and configuring an all optical path within the node. The all optical path located within the node is solely an optical path without having electronic storage of the data and without having optical delay of the data. Since electronic storage of the header is not necessary and the initial header is eliminated by the first detector of the first switching node. multiple identical headers are sent throughout the network so that subsequent switching nodes can receive and read the header for setting up an optical data path.

Monacos, Steve P.↗
NASA NTRS1988

Optical production systems using neural networks and symbolic substitution

Two optical implementations of production systems are advanced. The production systems operate on a knowledge base where facts and rules are encoded as formulas in propositional calculus. The first implementation is a binary neural network. An analog neural network is used to include reasoning with uncertainties. The second implementation uses a new optical symbolic substitution correlator. This implementation is useful when a set of similar situations has to be handled in parallel on one processor.

Botha, Elizabeth↗
NASA NTRS1985

Fiber optic configurations for local area networks

A number of fiber optic configurations for a new class of demand assignment multiple-access local area networks requiring a physical ordering among stations are proposed. In such networks, the data transmission and linear-ordering functions may be distinguished and be provided by separate data and control subnetworks. The configurations proposed for the data subnetwork are based on the linear, star, and tree topologies. To provide the linear-ordering function, the control subnetwork must always have a linear unidirectional bus structure. Due to the reciprocity and excess loss of optical couplers, the number of stations that can be accommodated on a linear fiber optic bus is severely limited. Two techniques are proposed to overcome this limitation. For each of the data and control subnetwork configurations, the maximum number of stations as a function of the power margin, for both reciprocal and nonreciprocal couplers, is computed.

Nassehi, M. M.↗
DOE OSTI2024

ReLU, Sparseness, and the Encoding of Optic Flow in Neural Networks

Accurate self-motion estimation is critical for various navigational tasks in mobile robotics. Optic flow provides a means to estimate self-motion using a camera sensor and is particularly valuable in GPS- and radio-denied environments. The present study investigates the influence of different activation functions—ReLU, leaky ReLU, GELU, and Mish—on the accuracy, robustness, and encoding properties of convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs) trained to estimate self-motion from optic flow. Our results demonstrate that networks with ReLU and leaky ReLU activation functions not only achieved superior accuracy in self-motion estimation from novel optic flow patterns but also exhibited greater robustness under challenging conditions. The advantages offered by ReLU and leaky ReLU may stem from their ability to induce sparser representations than GELU and Mish do. Our work characterizes the encoding of optic flow in neural networks and highlights how the sparseness induced by ReLU may enhance robust and accurate self-motion estimation from optic flow.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

A Study of an Optical Lunar Surface Communications Network with High Bandwidth Direct to Earth Link

A lunar surface systems study explores the application of optical communications to support a high bandwidth data link from a lunar relay satellite and from fixed lunar assets. The results show that existing 1-m ground stations could provide more than 99% coverage of the lunar terminal at 100Mb/s data rates from a lunar relay satellite and in excess of 200Mb/s from a fixed terminal on the lunar surface. We have looked at the effects of the lunar regolith and its removal on optical samples. Our results indicate that under repeated dust removal episodes sapphire rather than fused silica would be a more durable material for optical surfaces. Disruption tolerant network protocols can minimize the data loss due to link dropouts. We report on the preliminary results of the DTN protocol implemented over the optical carrier.

lunar optical communications↗
DOE OSTI2024

Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks

As an optical processor, a diffractive deep neural network (D2NN) utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing, completing its tasks at the speed of light propagation through thin optical layers. With sufficient degrees of freedom, D2NNs can perform arbitrary complex-valued linear transformations using spatially coherent light. Similarly, D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are nonnegative, acting on diffraction-limited optical intensity patterns at the input field of view. Here, we expand the use of spatially incoherent D2NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent light. Through simulations, we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products, a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination. The findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors.

36 MATERIALS SCIENCE↗
NASA NTRS1991

Automated target recognition and tracking using an optical pattern recognition neural network

The on-going development of an automatic target recognition and tracking system at the Jet Propulsion Laboratory is presented. This system is an optical pattern recognition neural network (OPRNN) that is an integration of an innovative optical parallel processor and a feature extraction based neural net training algorithm. The parallel optical processor provides high speed and vast parallelism as well as full shift invariance. The neural network algorithm enables simultaneous discrimination of multiple noisy targets in spite of their scales, rotations, perspectives, and various deformations. This fully developed OPRNN system can be effectively utilized for the automated spacecraft recognition and tracking that will lead to success in the Automated Rendezvous and Capture (AR&C) of the unmanned Cargo Transfer Vehicle (CTV). One of the most powerful optical parallel processors for automatic target recognition is the multichannel correlator. With the inherent advantages of parallel processing capability and shift invariance, multiple objects can be simultaneously recognized and tracked using this multichannel correlator. This target tracking capability can be greatly enhanced by utilizing a powerful feature extraction based neural network training algorithm such as the neocognitron. The OPRNN, currently under investigation at JPL, is constructed with an optical multichannel correlator where holographic filters have been prepared using the neocognitron training algorithm. The computation speed of the neocognitron-type OPRNN is up to 10(exp 14) analog connections/sec that enabling the OPRNN to outperform its state-of-the-art electronics counterpart by at least two orders of magnitude.

Chao, Tien-Hsin↗
NASA NTRS1993

Neural-network-directed alignment of optical systems using the laser-beam spatial filter as an example

This report describes an effort at NASA Lewis Research Center to use artificial neural networks to automate the alignment and control of optical measurement systems. Specifically, it addresses the use of commercially available neural network software and hardware to direct alignments of the common laser-beam-smoothing spatial filter. The report presents a general approach for designing alignment records and combining these into training sets to teach optical alignment functions to neural networks and discusses the use of these training sets to train several types of neural networks. Neural network configurations used include the adaptive resonance network, the back-propagation-trained network, and the counter-propagation network. This work shows that neural networks can be used to produce robust sequencers. These sequencers can learn by example to execute the step-by-step procedures of optical alignment and also can learn adaptively to correct for environmentally induced misalignment. The long-range objective is to use neural networks to automate the alignment and operation of optical measurement systems in remote, harsh, or dangerous aerospace environments. This work also shows that when neural networks are trained by a human operator, training sets should be recorded, training should be executed, and testing should be done in a manner that does not depend on intellectual judgments of the human operator.

Decker, Arthur J.↗