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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

NASA Tech Briefs, November 1995

The contents include: 1) Mission Accomplished; 2) Resource Report: Marshall Space Flight Center; 3) NASA 1995 Software of the Year Award; 4) Microbolometers Based on Epitaxial YBa2Cu3O(sub 7-x) Thin Films; 5) Garnet Random-Access Memory; 6) Fabrication of SNS Weak Links on SOS Substrates; 7) High-Voltage MOSFET Switching Circuit; 8) Asymmetric Switching for a PWM H-Bridge Power Circuit; 9) Better Ohmic Contacts for InP Semiconductor Devices; 10) Low-Bandgap Thermovoltaic Materials and Devices; 11) Digital Frequency-Differencing Circuit; 12) Imaging Magnetometer; 13) Computer-Assisted Monitoring of a Complex System; 14) Buffered Telemetry Demodulator; 15) Compact Multifunction Inspection Head; 16) Optical Detection of Fractures in Ceramic Diaphragms; 17) Eddy-Current Detection of Cracks in Reinforced Carbon/Carbon; 18) Apparent Thermal Conductivity of Multilayer Insulation; 19) Optimizing Misch-Metal Compositions in Metal Hydride Anodes; 20) Device for Sampling Surface Contamination; 21) Probabilistic Failure Assessment for Fatigue; 22) Probabilistic Fatigue and Flaw-Propagation Analysis; 23) Windows Program for Driving the TDU-850 Printer; 24) Subband/Transform MATLAB Functions for Processing Images; 25) Computing Equilibrium Chemical Compositions; 26) Program Processes Thermocouple Readings; 27) ICAN-Second-Generation Integrated Composite Analyzer; 28) Integrated Composite Analyzer with Damping Capabilities; 29) Computing Efficiency of Transfer of Microwave Power; 30) Program Calculates Power Demands of Electronic Designs; 31) Cost-Estimation Program; 32) Program Estimates Areas Required by Electronic Designs; 33) Program to Balance Mapped Turbopump Assemblies; 34) BiblioTech; 35) Controlling Mirror Tilt With a Bimorph Actuator; 36) Burst-Disk Device Simulates Effect of Pyrotechnic Device; 37) Bearing-Mounting Concept Accommodates Thermal Expansion; 38) Parallel-Plate Acoustic Absorbers for Hot Environments; 39) Adjustable-Length Strut Withstands Large Cyclic Loads; 40) Tool Indicates Contact Angles in Bearing Raceways; 41) Gravity Slides With Magnetic Braking; 42) High-Torque, Lightweight, Pneumatically Driven Wrench for Small Spaces; 43) Device for Testing Compatibility of an O-Ring; 44) Magnetic Heat Pump Containing Flow Diverters; 45) Variable-Tilt Helicopter Rotor Mast; 46) "Beach-Ball" Robotic Rovers; 47) Apparatus Would Measure Temperatures of Ball Bearings; 48) Flexible Borescope for Inspecting Ducts; 49) Texturing Copper To Reduce Secondary Emission of Electrons; 50) Automated Laser Cutting in Three Dimensions; 51) Algorithm Helps Monitor Engine Operation; 52) Flexible Revision of Data-Processing Communications; 53) Software for Managing the Use of Land; 54) Thermal Strap Increases Cryocooling Efficiency; 55) Reversible Nut With Engagement Indication; 56) Control Algorithms for Kinematically Redundant Manipulators; 57) Computed Hydrogen-Flow Splits in a Rocket Engine; 58) Pressure and Thermal Modeling of Rocket Launches; 59) Field of View of a Spacecraft Antenna: Analysis and Software; 60) Digital Controller for Laser-Beam-Steering Subsystem; 61) More About Beam-Steering Subsystem for Laser Communication; 62) Digital Controller for Laser-Beam-Steering Subsystem: Part 2; 63) Interface Circuit Board for Space-Shuttle Communications; 64) Automated Planning of Spacecraft Telecommunications; 65) Artifacts of Spectral Analysis of Instrument Readings; 66) Neural-Network Controller for Vibration Suppression; 67) Adaptive Finite-Element Computation in Fracture Mechanics; 68) Attitude Control for the Cassini Spacecraft; 69) Analytical Model for Fluid Dynamics in a Microgravity Environment; 70) Study of Rocket-Engine Joints Bonded by NVCU/NARloy-Z; 71) Improved Silicon Nitride for Advanced Heat Engines; 72) Parameters for Welding Aluminum/Lithium Alloys; 73) Lightweight Composite Intertank Structure; 74) Foil Patches Seal Small Vacuum Leaks; 75) Data Base on Cables and Connectors; 76) Effect of Clock Mode on Radiation Hardnessf an ADC; and 77) Fault-Tolerant Control for a Robotic Inspection System.

Source record↗
NASA NTRS1992

Learning and optimization with cascaded VLSI neural network building-block chips

To demonstrate the versatility of the building-block approach, two neural network applications were implemented on cascaded analog VLSI chips. Weights were implemented using 7-b multiplying digital-to-analog converter (MDAC) synapse circuits, with 31 x 32 and 32 x 32 synapses per chip. A novel learning algorithm compatible with analog VLSI was applied to the two-input parity problem. The algorithm combines dynamically evolving architecture with limited gradient-descent backpropagation for efficient and versatile supervised learning. To implement the learning algorithm in hardware, synapse circuits were paralleled for additional quantization levels. The hardware-in-the-loop learning system allocated 2-5 hidden neurons for parity problems. Also, a 7 x 7 assignment problem was mapped onto a cascaded 64-neuron fully connected feedback network. In 100 randomly selected problems, the network found optimal or good solutions in most cases, with settling times in the range of 7-100 microseconds.

Duong, T.↗
NASA NTRS1992

Analog hardware for delta-backpropagation neural networks

This is a fully parallel analog backpropagation learning processor which comprises a plurality of programmable resistive memory elements serving as synapse connections whose values can be weighted during learning with buffer amplifiers, summing circuits, and sample-and-hold circuits arranged in a plurality of neuron layers in accordance with delta-backpropagation algorithms modified so as to control weight changes due to circuit drift.

Eberhardt, Silvio P.↗
NASA NTRS2003

Organization of projections from the raphe nuclei to the vestibular nuclei in rats

Previous anatomic and electrophysiological evidence suggests that serotonin modulates processing in the vestibular nuclei. This study examined the organization of projections from serotonergic raphe nuclei to the vestibular nuclei in rats. The distribution of serotonergic axons in the vestibular nuclei was visualized immunohistochemically in rat brain slices using antisera directed against the serotonin transporter. The density of serotonin transporter-immunopositive fibers is greatest in the superior vestibular nucleus and the medial vestibular nucleus, especially along the border of the fourth ventricle; it declines in more lateral and caudal regions of the vestibular nuclear complex. After unilateral iontophoretic injections of Fluoro-Gold into the vestibular nuclei, retrogradely labeled neurons were found in the dorsal raphe nucleus (including the dorsomedial, ventromedial and lateral subdivisions) and nucleus raphe obscurus, and to a minor extent in nucleus raphe pallidus and nucleus raphe magnus. The combination of retrograde tracing with serotonin immunohistofluorescence in additional experiments revealed that the vestibular nuclei receive both serotonergic and non-serotonergic projections from raphe nuclei. Tracer injections in densely innervated regions (especially the medial and superior vestibular nuclei) were associated with the largest numbers of Fluoro-Gold-labeled cells. Differences were observed in the termination patterns of projections from the individual raphe nuclei. Thus, the dorsal raphe nucleus sends projections that terminate predominantly in the rostral and medial aspects of the vestibular nuclear complex, while nucleus raphe obscurus projects relatively uniformly throughout the vestibular nuclei. Based on the topographical organization of raphe input to the vestibular nuclei, it appears that dense projections from raphe nuclei are colocalized with terminal fields of flocculo-nodular lobe and uvula Purkinje cells. It is hypothesized that raphe-vestibular connections are organized to selectively modulate processing in regions of the vestibular nuclear complex that receive input from specific cerebellar zones. This represents a potential mechanism whereby motor activity and behavioral arousal could influence the activity of cerebellovestibular circuits.

NASA Discipline Neuroscience↗
NASA NTRS2003

NASA Tech Briefs, May 2003

Topics covered include: Using Diffusion Bonding in Making Piezoelectric Actuators; Wireless Temperature-Monitoring System; Analog Binaural Circuits for Detecting and Locating Leaks; Mirrors Containing Biomimetic Shape-Control Actuators; Surface-Micromachined Planar Arrays of Thermopiles; Cascade Back-Propagation Learning in Neural Networks; Perovskite Superlattices as Tunable Microwave Devices; Rollable Thin-Shell Nanolaminate Mirrors; Flight Tests of a Ministick Controller in an F/A-18 Airplane; Piezoelectrically Actuated Shutter for High Vacuum; Bio-Inspired Engineering of Exploration Systems; Microscope Cells Containing Multiple Micromachined Wells; Electrophoretic Deposition for Fabricating Microbatteries; Integrated Arrays of Ion-Sensitive Electrodes; Model of Fluidized Bed Containing Reacting Solids and Gases; Membrane Mirrors With Bimorph Shape Actuators; Using Fractional Clock-Period Delays in Telemetry Arraying; Developing Generic Software for Spacecraft Avionics; Numerical Study of Pyrolysis of Biomass in Fluidized Beds; and Assessment of Models of Chemically Reacting Granular Flows.

Source record↗
NASA NTRS1996

Solar Dynamic Power System Fault Diagnosis

The objective of this research is to conduct various fault simulation studies for diagnosing the type and location of faults in the power distribution system. Different types of faults are simulated at different locations within the distribution system and the faulted waveforms are monitored at measurable nodes such as at the output of the DDCU's. These fault signatures are processed using feature extractors such as FFT and wavelet transforms. The extracted features are fed to a clustering based neural network for training and subsequent testing using previously unseen data. Different load models consisting of constant impedance and constant power are used for the loads. Open circuit faults and short circuit faults are studied. It is concluded from present studies that using features extracted from wavelet transforms give better success rates during ANN testing. The trained ANN's are capable of diagnosing fault types and approximate locations in the solar dynamic power distribution system.

Momoh, James A.↗
NASA NTRS1999

Electric organ discharges and electric images during electrolocation

Weakly electric fish use active electrolocation - the generation and detection of electric currents - to explore their surroundings. Although electrosensory systems include some of the most extensively understood circuits in the vertebrate central nervous system, relatively little is known quantitatively about how fish electrolocate objects. We believe a prerequisite to understanding electrolocation and its underlying neural substrates is to quantify and visualize the peripheral electrosensory information measured by the electroreceptors. We have therefore focused on reconstructing both the electric organ discharges (EODs) and the electric images resulting from nearby objects and the fish's exploratory behaviors. Here, we review results from a combination of techniques, including field measurements, numerical and semi-analytical simulations, and video imaging of behaviors. EOD maps are presented and interpreted for six gymnotiform species. They reveal diverse electric field patterns that have significant implications for both the electrosensory and electromotor systems. Our simulations generated predictions of the electric images from nearby objects as well as sequences of electric images during exploratory behaviors. These methods are leading to the identification of image features and computational algorithms that could reliably encode electrosensory information and may help guide electrophysiological experiments exploring the neural basis of electrolocation.

Review, Tutorial↗
NASA NTRS1981

The enchanted loom

The evolution of intelligence began with the movement of Crossopterygian fish onto land. The eventual appearance of large dinosaurs eliminated all but the smallest of mammalian creatures, with the survivors forced to move only nocturnally, when enhanced olfactory and aural faculties were favored and involved a larger grey matter/body mass ratio than possessed by the dinosaurs. Additionally, the mammals made comparisons between the inputs of various senses, implying the presence of significant memory capacity and an ability to abstract survival information. More complex behavior occurred with the advent of tree dwellers (forward-looking eyes), hands, color vision, and the ability to grip and manipulate objects. An extra pound of brain evolved in the human skull in less than a million years. The neural processes that can lead to an action by a creature with a brain are mimicked by the basic AND and OR gates in computers, which are rapidly approaching the circuit density of the human brain. It is suggested that humans will eventually produce computers of higher intelligence than people possess, and computer spacecraft, alive in an electronic sense, will travel outward to explore the universe.

Jastrow, R.↗
NASA NTRS1991

Continuous system modeling

A comprehensive and systematic introduction is presented for the concepts associated with 'modeling', involving the transition from a physical system down to an abstract description of that system in the form of a set of differential and/or difference equations, and basing its treatment of modeling on the mathematics of dynamical systems. Attention is given to the principles of passive electrical circuit modeling, planar mechanical systems modeling, hierarchical modular modeling of continuous systems, and bond-graph modeling. Also discussed are modeling in equilibrium thermodynamics, population dynamics, and system dynamics, inductive reasoning, artificial neural networks, and automated model synthesis.

Cellier, Francois E.↗
NASA NTRS2000

Trainable Gene Regulation Networks with Applications to Drosophila Pattern Formation

This chapter will very briefly introduce and review some computational experiments in using trainable gene regulation network models to simulate and understand selected episodes in the development of the fruit fly, Drosophila melanogaster. For details the reader is referred to the papers introduced below. It will then introduce a new gene regulation network model which can describe promoter-level substructure in gene regulation. As described in chapter 2, gene regulation may be thought of as a combination of cis-acting regulation by the extended promoter of a gene (including all regulatory sequences) by way of the transcription complex, and of trans-acting regulation by the transcription factor products of other genes. If we simplify the cis-action by using a phenomenological model which can be tuned to data, such as a unit or other small portion of an artificial neural network, then the full transacting interaction between multiple genes during development can be modelled as a larger network which can again be tuned or trained to data. The larger network will in general need to have recurrent (feedback) connections since at least some real gene regulation networks do. This is the basic modeling approach taken, which describes how a set of recurrent neural networks can be used as a modeling language for multiple developmental processes including gene regulation within a single cell, cell-cell communication, and cell division. Such network models have been called "gene circuits", "gene regulation networks", or "genetic regulatory networks", sometimes without distinguishing the models from the actual modeled systems.

Mjolsness, Eric↗
NASA NTRS2000

From Coexpression to Coregulation: An Approach to Inferring Transcriptional Regulation Among Gene Classes from Large-Scale Expression Data

We provide preliminary evidence that existing algorithms for inferring small-scale gene regulation networks from gene expression data can be adapted to large-scale gene expression data coming from hybridization microarrays. The essential steps are (I) clustering many genes by their expression time-course data into a minimal set of clusters of co-expressed genes, (2) theoretically modeling the various conditions under which the time-courses are measured using a continuous-time analog recurrent neural network for the cluster mean time-courses, (3) fitting such a regulatory model to the cluster mean time courses by simulated annealing with weight decay, and (4) analysing several such fits for commonalities in the circuit parameter sets including the connection matrices. This procedure can be used to assess the adequacy of existing and future gene expression time-course data sets for determining transcriptional regulatory relationships such as coregulation.

Mjolsness, Eric↗
NASA NTRSDate not supplied

Quantum-Assisted Variational Segmentation for Image-to-Image Wildfire Detection Using Satellite Data

The quantum computing community has been searching for suitable applications to demonstrate the potential of near-term quantum devices. Quantum machine learning is a potential candidate, particularly using models that cannot be efficiently simulated with classical computers [1, 2]. This work focuses on a transition phase of quantum computers where the quantum machine learning model is still simulable classically but projected not to be simulable as the size of the model grows. Ultimately quantum computers may have advantages for high-dimensional real-world problems. Due to the limited number of qubits in current noisy intermediate-scale quantum (NISQ) devices, the direct application of quantum computers in high dimensional data is not feasible. To remedy this problem, an encoder-decoder architecture can be utilized. The encoder model would transform the high-dimensional data into a compact representation, to a level that small quantum computers can be used today (or in the near future), and the decoder would take the quantum processed outputs back to the high-dimensional space. Addressing the two challenges of quantum machine learning, this work investigates a hybrid supervised generative model with a quantum Ising Born machine embedded as the latent distribution. The model contains four main parts (Figure 1.a.): (1) a U-NET architecture responsible for learning segmentation flow, (2) a Prior network responsible for learning an encoded latent distribution of the input data, (3) a Born machine which represents the latent distribution, and (4) a Posterior network in charge of learning the joint encoded latent distribution of inputs and target data. The initial model, proposed by [3], is optimized by (1) maximizing the overlap of the prior and posterior latent distributions, and (2) minimizing the segmentation loss. The proposed model is designed to be investigated in a simulation environment applied to the real-world application of wildfire segmentation. Specifically, the model is designed to solve the patchy wildfire segmentations of Moderate Resolution Imaging Spectroradiometer (MODIS) by taking the MODIS observations and using Visible Infrared Imaging Radiometer Suite’s (VIIRS) consistent wildfire product as the target. The model solves patchy wildfire segmentations and provides insight into the epistemic errors sourced from model variation. The model utilizes the Born machine as a QUBO solver to represent the latent space as a Bernoulli distribution. The proposed configuration allows the variational segmentation model to leverage the true quantum probabilistic nature and derive a more expressive latent configuration, increasing the model performance in describing wildfire segmentations. The quantum probabilistic information of the Born machine is directly incorporated in the Kullback-Leibler divergence loss in the prior and posterior distributions, forcing the Bernoulli latent distribution to maximize the overlap of input and joint input-target distributions. The proposed model is then trained and compared with a baseline only consisting of direct Bernoulli latent distribution with no Born machine representing the latent space. The models are evaluated based on the segmentation metrics, such as precision, recall, intersect of union, with uncertainty boundaries accounting for the stochastic nature of the model. Our findings show that even in low latent-dimensional space (due to the limit in computational power of the classical quantum simulator), we are able to effectively capture the latent representation and hence the model performs better than the baseline. The findings are a projection for scaling the model into higher dimensional latent space with the Born machine surpassing the baseline performance. Figure 1. Sub-figure (a) demonstrates the architecture for the training phase. The model consists of a Prior and Posterior network that encode inputs and joint input-target data into compact representations, respectively. The Born machine represents the latent distribution, and the U-NET branch learns the segmentation patterns of the data. The stochasticity is introduced to the U-NET through its last layer to create meaningful but stochastic segmentations. Sub-figure (b) represents the inference phase where the model takes the stochastic behavior from the prior network and injects that into the U-NET. Each attempt of inference will generate different but similar segmentations from the same distribution of the wildfire event. REFERENCES [1] Coyle, B., Mills, D., Danos, V., & Kashefi, E. (2020). The Born supremacy: quantum advantage and training of an Ising Born machine. npj Quantum Information, 6(1), 1-11. [2] Liu, J. G., & Wang, L. (2018). Differentiable learning of quantum circuit born machines. Physical Review A, 98(6), 062324. [3] Kohl, S., Romera-Paredes, B., Meyer, C., De Fauw, J., Ledsam, J. R., Maier-Hein, K., ... & Ronneberger, O. (2018). A probabilistic u-net for segmentation of ambiguous images. Advances in neural information processing systems, 31.

quantum machine learning↗
NASA NTRS1995

Morphology and kainate-receptor immunoreactivity of identified neurons within the entorhinal cortex projecting to superior temporal sulcus in the cynomolgus monkey

Projections of the entorhinal cortex to the hippocampus are well known from the classical studies of Cajal (Ramon y Cajal, 1904) and Lorente de No (1933). Projections from the entorhinal cortex to neocortical areas are less well understood. Such connectivity is likely to underlie the consolidation of long-term declarative memory in neocortical sites. In the present study, a projection arising in layer V of the entorhinal cortex and terminating in a polymodal association area of the superior temporal gyrus has been identified with the use of retrograde tracing. The dendritic arbors of neurons giving rise to this projection were further investigated by cell filling and confocal microscopy with computer reconstruction. This analysis demonstrated that the dendritic arbor of identified projection neurons was largely confined to layer V, with the exception of a solitary, simple apical dendrite occasionally ascending to superficial laminae but often confined to the lamina dissecans (layer IV). Finally, immunoreactivity for glutamate-receptor subunit proteins GluR 5/6/7 of the dendritic arbor of identified entorhinal projection neurons was examined. The solitary apical dendrite of identified entorhinal projection neurons was prominently immunolabeled for GluR 5/6/7, as was the dendritic arbor of basilar dendrites of these neurons. The restriction of the large bulk of the dendritic arbor of identified entorhinal projection neurons to layer V implies that these neurons are likely to be heavily influenced by hippocampal output arriving in the deep layers of the entorhinal cortex. Immunoreactivity for GluR 5/6/7 throughout the dendritic arbor of such neurons indicates that this class of glutamate receptor is in a position to play a prominent role in mediating excitatory neurotransmission within hippocampal-entorhinal circuits.

NASA Discipline Neuroscience↗
NASA NTRS1992

A mean field neural network for hierarchical module placement

This paper proposes a mean field neural network for the two-dimensional module placement problem. An efficient coding scheme with only O(N log N) neurons is employed where N is the number of modules. The neurons are evolved in groups of N in log N iteration steps such that the circuit is recursively partitioned in alternating vertical and horizontal directions. In our simulations, the network was able to find optimal solutions to all test problems with up to 128 modules.

Unaltuna, M. Kemal↗
NASA NTRS2019

Design of Experiment for the Measurement of Aerosol Droplet Size Distribution of Temperature-Controlled Thermally Atomized Printed Electronic Inks

In-Space Manufacturing (ISM) centers around NASA’s growing need and ability to produce space technologies on demand in space. As the future of long term presence in space and deep space exploration approach, fundamental questions of our dependence on earth resupply to Low Earth Orbit (LEO) remain unanswered. ISM is leading various effort to evaluate the feasibility of producing essential spares and redundant parts on demand to enable a sustainable space-based supply chain model for part resupply. Among the parts and systems being considered, Avionics form the neural network of modern day aircraft and space vehicles providing a wealth of information ranging from Guidance, Navigation, and Control (GN&C) systems to on board Environmental Control and Life Support (ECLS) systems. Recent advances in the use of Aerosol Jet Technology to print Avionics components ranging from electrical traces on a circuit board to complex transistors and sensors raise the possibility of using such technology to reproduce or recreate electronic parts on demand with the help of custom electronics 3D printers. The challenge herein lies within the ability of such printers to generate and deposit an aerosol of electronic material utilizing processes independent of or enhanced by gravity to ensure controllably identical or improved behavior of the aerosol in an International Space Station (ISS) laboratory and on the ground. The behavior as well as the hazards and properties associated with such aerosols in a microgravity environment must be understood well in order to merit a feasible approach to utilizing them for manufacturing in space. In this report, we outline the experimental setup of a modified conventional vaping device to be used as the ideal gravity independent thermal atomization mechanism to generate aerosol. Our objective is to identify the ideal mass, density, and volume of our aerosolized droplets of ink to conclude that there exist a threshold of aerosolized ink droplet sizes that are indeed independent of the effects of gravity and remain stable after atomization. We use a Malvern Spraytec® Spray Particle Size Analyzer to perform real-time laser diffraction measurement of our ink droplets during atomization. The droplet size between conductive ink, dielectric ink and vegetable glycerin have been measured and contrasted. Furthermore, the mechanism of thermal atomization versus traditional pneumatic and ultrasonic atomization for operation in microgravity have been explored.

Wu, Chih-Hao↗
NASA NTRS1988

Implementing neural nets with programmable logic

Networks of Boolean programmable logic modules are presented as one purely digital class of artificial neural nets. The approach contrasts with the continuous analog framework usually suggested. Programmable logic networks are capable of handling many neural-net applications. They avoid some of the limitations of threshold logic networks and present distinct opportunities. The network nodes are called dynamically programmable logic modules. They can be implemented with digitally controlled demultiplexers. Each node performs a Boolean function of its inputs which can be dynamically assigned. The overall network is therefore a combinational circuit and its outputs are Boolean global functions of the network's input variables. The approach offers definite advantages for VLSI implementation, namely, a regular architecture with limited connectivity, simplicity of the control machinery, natural modularity, and the support of a mature technology.

Vidal, Jacques J.↗
NASA NTRS1990

Neural Network Solves "Traveling-Salesman" Problem

Experimental electronic neural network solves "traveling-salesman" problem. Plans round trip of minimum distance among N cities, visiting every city once and only once (without backtracking). This problem is paradigm of many problems of global optimization (e.g., routing or allocation of resources) occuring in industry, business, and government. Applied to large number of cities (or resources), circuits of this kind expected to solve problem faster and more cheaply.

Thakoor, Anilkumar P.↗
NASA NTRS1988

Feedforward, high density, programmable read only neural network based memory system

Neural network-inspired, nonvolatile, programmable associative memory using thin-film technology is demonstrated. The details of the architecture, which uses programmable resistive connection matrices in synaptic arrays and current summing and thresholding amplifiers as neurons, are described. Several synapse configurations for a high-density array of a binary connection matrix are also described. Test circuits are evaluated for operational feasibility and to demonstrate the speed of the read operation. The results are discussed to highlight the potential for a read data rate exceeding 10 megabits/sec.

Daud, Taher↗