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Toomarian, N.

Publications and source records attributed to Toomarian, N..

17 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Advanced Exploration Systems Atmosphere Resource Recovery and Environmental Monitoring

In September 2011, the Atmosphere Resource Recovery and Environmental Monitoring (ARREM) project was commissioned by NASA's Advanced Exploration Systems program to advance Atmosphere Revitalization Subsystem (ARS) and Environmental Monitoring Subsystem (EMS) technologies for enabling future crewed space exploration missions beyond low Earth orbit. The ARREM project's period of performance covered U.S. Government fiscal years 2012-2014. The ARREM project critically assessed the International Space Station (ISS) ARS and EMS architectures and process technologies as the foundation for an architecture suitable for deep space exploration vehicles. The project's technical content included technical tasks focused on improving the reliability and life cycle cost of ARS and EMS technologies as well as reducing future flight project developmental risk and design, development, test, and evaluation costs. Targeted technology development and maturation tasks, including key technical trade assessments, were accomplished and integrated ARS architectures were demonstrated. The ARREM project developed, demonstrated, and tested leading process technology candidates and subsystem architectures that met or exceeded key figures of merit, addressed capability gaps, and significantly improved the efficiency, safety, and reliability over the state-of-the-art ISS figures of merit. Promising EMS instruments were developed and functionally demonstrated in a simulated cabin environment. The project's technical approach and results are described and recommendations for continued development are provided.

Perry, J.↗
NASA NTRS2011

On-Orbit Measurements of the ISS Atmosphere by the Vehicle Cabin Atmosphere Monitor

We report on trace gas and major atmospheric constituents results obtained by the Vehicle Cabin Atmosphere Monitor (VCAM) during operations aboard the International Space Station (ISS). VCAM is an autonomous environmental monitor based on a miniature gas chromatograph/mass spectrometer. It was flown to the ISS on shuttle mission STS-131 and commenced operations on 6/10/10. VCAM provides measurements of ppb-to-ppm levels of volatile trace-gas constituents, and of the atmospheric major constituents (nitrogen, oxygen, argon, and carbon dioxide) in a space vehicle or station. It is designed to operate autonomously and maintenance-free, approximately once per day, with a self-contained gas supply sufficient for a one-year lifetime. VCAM is designed to detect and identify 90% of the target compounds at their 180-day Spacecraft Maximum Allowable Concentration levels.

cabin atmosphere↗
NASA NTRS2001

Analysis of biomaterial using Neural Computing Methodology

This paper describes a novel learning methodology used to analyze bio-materials. The premise of this research is to help pathologists quickly identify anomalous cells in a cost efficient method.

biomaterial neural computing methodology neural ne↗
NASA NTRS2001

Quantum dots based rad-hard computing and sensors

Quantum Dots (QDs) are solid state structures made of semiconductors or metals that confine a small number of electrons into a small space. The confinement of electrons is achieved by the placement of some insulating material(s) around a central, well conducting region. Thus, they can ve viewed as artificial atoms.

quantum↗
NASA NTRS2001

Quantum Dots Based Rad-Hard Computing and Sensors

Quantum Dots (QDs) are solid-state structures made of semiconductors or metals that confine a small number of electrons into a small space. The confinement of electrons is achieved by the placement of some insulating material(s) around a central, well-conducting region. Thus, they can be viewed as artificial atoms. They therefore represent the ultimate limit of the semiconductor device scaling. Additional information is contained in the original extended abstract.

Fijany, A.↗
NASA NTRS1996

Spectral imaging applications: Remote sensing, environmental monitoring, medicine, military operations, factory automation and manufacturing

This paper reviews the activities at OKSI related to imaging spectroscopy presenting current and future applications of the technology. The authors discuss the development of several systems including hardware, signal processing, data classification algorithms and benchmarking techniques to determine algorithm performance. Signal processing for each application is tailored by incorporating the phenomenology appropriate to the process, into the algorithms. Pixel signatures are classified using techniques such as principal component analyses, generalized eigenvalue analysis and novel very fast neural network methods. The major hyperspectral imaging systems developed at OKSI include the Intelligent Missile Seeker (IMS) demonstration project for real-time target/decoy discrimination, and the Thermal InfraRed Imaging Spectrometer (TIRIS) for detection and tracking of toxic plumes and gases. In addition, systems for applications in medical photodiagnosis, manufacturing technology, and for crop monitoring are also under development.

National Defense↗
NASA NTRS1996

Detection of Structural Abnormalities Using Neural Nets

This paper describes a feed-forward neural net approach for detection of abnormal system behavior based upon sensor data analyses. A new dynamical invariant representing structural parameters of the system is introduced in such a way that any structural abnormalities in the system behavior are detected from the corresponding changes to the invariant.

abnormal system behavior system behavior sensor da↗
NASA NTRS1994

Communications and control for electric power systems: Power system stability applications of artificial neural networks

This report investigates the application of artificial neural networks to the problem of power system stability. The field of artificial intelligence, expert systems, and neural networks is reviewed. Power system operation is discussed with emphasis on stability considerations. Real-time system control has only recently been considered as applicable to stability, using conventional control methods. The report considers the use of artificial neural networks to improve the stability of the power system. The networks are considered as adjuncts and as replacements for existing controllers. The optimal kind of network to use as an adjunct to a generator exciter is discussed.

Toomarian, N.↗
NASA NTRS1993

Massively Parallel Neurocomputing for Aerospace Applications

An innovative hybrid, analog-digital charge-domain technology, for the massively parallel VLSI implementation of certain large scale matrix-vector operations, has recently been introduced. It employs arrays of Charge Coupled/Charge Injection Device cells holding an analog matrix of charge, which process digital vectors in parallel by means of binary, non-destructive charge transfer operations. The impact of this technology on massively parallel processing is discussed.

massively↗
NASA NTRS1993

Actuator tuning for nonlinear truss structures - A neural network approach

The optimal actuator placement problem encompasses finding their best locations and best gains. The latter is referred to as tuning the actuators. This paper deals with tuning a given set of actuators in presence of known structural nonlinearities arising from gaps at some joints. For this purpose, an efficient neural network-based algorithm is proposed and illustrated using numerical simulations.

Toomarian, N.↗
NASA NTRS1991

Adjoint-operators and non-adiabatic learning algorithms in neural networks

Adjoint sensitivity equations are presented, which can be solved simultaneously (i.e., forward in time) with the dynamics of a nonlinear neural network. These equations provide the foundations for a new methodology which enables the implementation of temporal learning algorithms in a highly efficient manner.

Toomarian, N.↗
NASA NTRS1990

Application of adjoint operators to neural learning

A technique for the efficient analytical computation of such parameters of the neural architecture as synaptic weights and neural gain is presented as a single solution of a set of adjoint equations. The learning model discussed concentrates on the adiabatic approximation only. A problem of interest is represented by a system of N coupled equations, and then adjoint operators are introduced. A neural network is formalized as an adaptive dynamical system whose temporal evolution is governed by a set of coupled nonlinear differential equations. An approach based on the minimization of a constrained neuromorphic energylike function is applied, and the complete learning dynamics are obtained as a result of the calculations.

Barhen, J.↗
NASA NTRS1987

Optimization of the computational load of a hypercube supercomputer onboard a mobile robot

A combinatorial optimization methodology is developed, which enables the efficient use of hypercube multiprocessors onboard mobile intelligent robots dedicated to time-critical missions. The methodology is implemented in terms of large-scale concurrent algorithms based either on fast simulated annealing, or on nonlinear asynchronous neural networks. In particular, analytic expressions are given for the effect of single-neuron perturbations on the systems' configuration energy. Compact neuromorphic data structures are used to model effects such as precedence constraints, processor idling times, and task-schedule overlaps. Results for a typical robot-dynamics benchmark are presented.

Barhen, Jacob↗