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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Inertial orientation tracker having automatic drift compensation for tracking human head and other similarly sized body

A self contained sensor apparatus generates a signal that corresponds to at least two of the three orientational aspects of yaw, pitch and roll of a human-scale body, relative to an external reference frame. A sensor generates first sensor signals that correspond to rotational accelerations or rates of the body about certain body axes. The sensor may be mounted to the body. Coupled to the sensor is a signal processor for generating orientation signals relative to the external reference frame that correspond to the angular rate or acceleration signals. The first sensor signals are impervious to interference from electromagnetic, acoustic, optical and mechanical sources. The sensors may be rate sensors. An integrator may integrate the rate signal over time. A drift compensator is coupled to the rate sensors and the integrator. The drift compensator may include a gravitational tilt sensor or a magnetic field sensor or both. A verifier periodically measures the orientation of the body by a means different from the drift sensitive rate sensors. The verifier may take into account characteristic features of human motion, such as stillness periods. The drift compensator may be, in part, a Kalman filter, which may utilize statistical data about human head motion.

Foxlin, Eric M.↗
NASA NTRS1981

Solar cell anomaly detection method and apparatus

A method is provided for detecting cracks and other imperfections in a solar cell, which includes scanning a narrow light beam back and forth across the cell in a raster pattern, while monitoring the electrical output of the cell to find locations where the electrical output varies significantly. The electrical output can be monitored on a television type screen containing a raster pattern with each point on the screen corresponding to a point on the solar cell surface, and with the brightness of each point on the screen corresponding to the electrical output from the cell which was produced when the light beam was at the corresponding point on the cell. The technique can be utilized to scan a large array of interconnected solar cells, to determine which ones are defective.

Miller, Emmett L.↗
NASA NTRS1998

Inertial orientation tracker apparatus method having automatic drift compensation for tracking human head and other similarly sized body

A self contained sensor apparatus generates a signal that corresponds to at least two of the three orientational aspects of yaw, pitch and roll of a human-scale body, relative to an external reference frame. A sensor generates first sensor signals that correspond to rotational accelerations or rates of the body about certain body axes. The sensor may be mounted to the body. Coupled to the sensor is a signal processor for generating orientation signals relative to the external reference frame that correspond to the angular rate or acceleration signals. The first sensor signals are impervious to interference from electromagnetic, acoustic, optical and mechanical sources. The sensors may be rate sensors. An integrator may integrate the rate signal over time. A drift compensator is coupled to the rate sensors and the integrator. The drift compensator may include a gravitational tilt sensor or a magnetic field sensor or both. A verifier periodically measures the orientation of the body by a means different from the drift sensitive rate sensors. The verifier may take into account characteristic features of human motion, such as stillness periods. The drift compensator may be, in part, a Kalman filter, which may utilize statistical data about human head motion.

Foxlin, Eric M.↗
NASA NTRS1997

Inertial orientation tracker apparatus having automatic drift compensation for tracking human head and other similarly sized body

A self contained sensor apparatus generates a signal that corresponds to at least two of the three orientational aspects of yaw, pitch and roll of a human-scale body, relative to an external reference frame. A sensor generates first sensor signals that correspond to rotational accelerations or rates of the body about certain body axes. The sensor may be mounted to the body. Coupled to the sensor is a signal processor for generating orientation signals relative to the external reference frame that correspond to the angular rate or acceleration signals. The first sensor signals are impervious to interference from electromagnetic, acoustic, optical and mechanical sources. The sensors may be rate sensors. An integrator may integrate the rate signal over time. A drift compensator is coupled to the rate sensors and the integrator. The drift compensator may include a gravitational tilt sensor or a magnetic field sensor or both. A verifier periodically measures the orientation of the body by a means different from the drift sensitive rate sensors. The verifier may take into account characteristic features of human motion, such as stillness periods. The drift compensator may be, in part, a Kalman filter, which may utilize statistical data about human head motion.

Foxlin, Eric M.↗
NASA NTRS2002

Inertial orientation tracker having gradual automatic drift compensation for tracking human head and other similarly sized body

A self contained sensor apparatus generates a signal that corresponds to at least two of the three orientational aspects of yaw, pitch and roll of a human-scale body, relative to an external reference frame. A sensor generates first sensor signals that correspond to rotational accelerations or rates of the body about certain body axes. The sensor may be mounted to the body. Coupled to the sensor is a signal processor for generating orientation signals relative to the external reference frame that correspond to the angular rate or acceleration signals. The first sensor signals are impervious to interference from electromagnetic, acoustic, optical and mechanical sources. The sensors may be rate sensors. An integrator may integrate the rate signal over time. A drift compensator is coupled to the rate sensors and the integrator. The drift compensator may include a gravitational tilt sensor or a magnetic field sensor or both. A verifier periodically measures the orientation of the body by a means different from the drift sensitive rate sensors. The verifier may take into account characteristic features of human motion, such as stillness periods. The drift compensator may be, in part, a Kalman filter, which may utilize statistical data about human head motion.

Foxlin, Eric M.↗
NASA NTRS2004

Using a micromachined magnetostatic relay in commutating a DC motor

A DC motor is commutated by rotating a magnetic rotor to induce a magnetic field in at least one magnetostatic relay in the motor. Each relay is activated in response to the magnetic field to deliver power to at least one corresponding winding connected to the relay. In some cases, each relay delivers power first through a corresponding primary winding and then through a corresponding secondary winding to a common node. Specific examples include a four-pole, three-phase motor in which each relay is activated four times during one rotation of the magnetic rotor.

Tai, Yu-Chong↗
NASA NTRS2004

inertial orientation tracker having automatic drift compensation using an at rest sensor for tracking parts of a human body

A self contained sensor apparatus generates a signal that corresponds to at least two of the three orientational aspects of yaw, pitch and roll of a human-scale body, relative to an external reference frame. A sensor generates first sensor signals that correspond to rotational accelerations or rates of the body about certain body axes. The sensor may be mounted to the body. Coupled to the sensor is a signal processor for generating orientation signals relative to the external reference frame that correspond to the angular rate or acceleration signals. The first sensor signals are impervious to interference from electromagnetic, acoustic, optical and mechanical sources. The sensors may be rate sensors. An integrator may integrate the rate signal over time. A drift compensator is coupled to the rate sensors and the integrator. The drift compensator may include a gravitational tilt sensor or a magnetic field sensor or both. A verifier periodically measures the orientation of the body by a means different from the drift sensitive sate sensors. The verifier may take into account characteristic features of human motion, such as stillness periods. The drift compensator may be, in part, a Kalman filter, which may utilize statistical data about human head motion.

Foxlin, Eric M.↗
NASA NTRS1989

Method and apparatus for multiplexing switch signals

Apparatus for multiplexing switch state signals comprises a plurality of switches and parallel weighted resistors connected in series between circuit ground and a node at a utilization device. The resistors are weighted as a function of a power of the same base, such as the power of the base 2, for coding the multiplexed switch state signals. A constant current source connected between the node and circuit ground drives current over a single cable conductor through the resistor. Each switch may be independently closed to change the switch state voltage signals multiplexed to the node. An analog-to-digital converter connected between the node and circuit ground demultiplexes the switch state signals received at the node and provides a switch state signal at each analog-to-digital output corresponding to the state of the switches at the moment. A potentiometer may replace a resistor and bypass switch combination in a position where the potentiometer has a maximum value of the lowest power of the base in order to multiplex a true analog voltage signal while switch state signals are unambiguously coded and multiplexed. The potentiometer in the least significant position permits the analog value to be in the range from 0 to a maximum corresponding to the least significant position of the switch state encoding. The invention may be used in redundancy systems by duplicating the invention with corresponding switches in each duplication ganged to open and close simultaneously upon operation of a pushbutton switch.

Hannaford, Blake↗
NASA NTRS2008

Creating User-Friendly Tools for Data Analysis and Visualization in K-12 Classrooms: A Fortran Dinosaur Meets Generation Y

During the summer of 2007, as part of the second year of a NASA-funded project in partnership with Christopher Newport University called SPHERE (Students as Professionals Helping Educators Research the Earth), a group of undergraduate students spent 8 weeks in a research internship at or near NASA Langley Research Center. Three students from this group formed the Clouds group along with a NASA mentor (Chambers), and the brief addition of a local high school student fulfilling a mentorship requirement. The Clouds group was given the task of exploring and analyzing ground-based cloud observations obtained by K-12 students as part of the Students' Cloud Observations On-Line (S'COOL) Project, and the corresponding satellite data. This project began in 1997. The primary analysis tools developed for it were in FORTRAN, a computer language none of the students were familiar with. While they persevered through computer challenges and picky syntax, it eventually became obvious that this was not the most fruitful approach for a project aimed at motivating K-12 students to do their own data analysis. Thus, about halfway through the summer the group shifted its focus to more modern data analysis and visualization tools, namely spreadsheets and Google(tm) Earth. The result of their efforts, so far, is two different Excel spreadsheets and a Google(tm) Earth file. The spreadsheets are set up to allow participating classrooms to paste in a particular dataset of interest, using the standard S'COOL format, and easily perform a variety of analyses and comparisons of the ground cloud observation reports and their correspondence with the satellite data. This includes summarizing cloud occurrence and cloud cover statistics, and comparing cloud cover measurements from the two points of view. A visual classification tool is also provided to compare the cloud levels reported from the two viewpoints. This provides a statistical counterpart to the existing S'COOL data visualization tool, which is used for individual ground-to-satellite correspondences. The Google(tm) Earth file contains a set of placemarks and ground overlays to show participating students the area around their school that the satellite is measuring. This approach will be automated and made interactive by the S'COOL database expert and will also be used to help refine the latitude/longitude location of the participating schools. Once complete, these new data analysis tools will be posted on the S'COOL website for use by the project participants in schools around the US and the world.

Chambers, L. H.↗
NASA NTRS2004

Spitzer 24 Micron Observations of Optical/Near-Infrared-Selected Extremely Red Galaxies: Evidence for Assembly of Massive Galaxies at Z approximately equal to 1-2?

We carried out direct measurement of the fraction of dusty sources in a sample of extremely red galaxies with (R - Ks) >= 5.3 mag and Ks < 20:2 mag, using 24 micron data from the Spitzer Space Telescope. Combining deep 24 micron Ks- and R-band data over an area of ~64 arcmin(sup 2) in ELAIS N1 of the Spitzer First Look Survey (FLS), we find that 50% +/- 6% of our extremely red object (ERO) sample have measurable 24 micron flux above the 3 (sigma) flux limit of 40 (micro)Jy. This flux limit corresponds to a star formation rate (SFR) of 12 solar masses per year ~1, much more sensitive than any previous long-wavelength measurement. The 24 micron-detected EROs have 24 micron/2.2 micron and 24 micron/0.7 micron flux ratios consistent with infrared luminous, dusty sources at z >= 1, and are an order of magnitude too red to be explained by an infrared quiescent spiral or a pure old stellar population at any redshift. Some of these 24 micron-detected EROs could be active galactic nuclei; however, the fraction among the whole ERO sample is probably small, 10%-20%, as suggested by deep X-ray observations as well as optical spectroscopy. Keck optical spectroscopy of a sample of similarly selected EROs in the FLS field suggests that most of the EROs in ELAIS N1 are probably at z ~1. The mean 24 micron flux (167 (micro)Jy) of the 24 micron-detected ERO sample roughly corresponds to the rest-frame 12 micron luminosity, (nu)L(nu)(12 micron, of 3x10(exp 10)(deg) solar luminosities at z ~1. Using the c IRAS (nu)L(nu)(12 (micron) and infrared luminosity LIR(8-1000 (micron), we infer that the (LIR) of the 24 micron- detected EROs is 3 x 10(exp 11) and 1 x 10(exp 12) solar luminosities at z = 1.0 and similar to that of local luminous infrared galaxies (LIRGs) and ultraluminous infrared galaxies (ULIRGs). The corresponding SFR would be roughly 50-170 solar masses per year. If the timescale of this starbursting phase is on the order of 108 yr as inferred for the local LIRGs and ULIRGs, the lower limit on the masses of these 24 micron-detected EROs is 5 x 10(exp 9) to 2 x 10(exp 10) solar masses. It is plausible that some of the starburst EROs are in the midst of a violent transformation to become massive early type galaxies at the epoch of z ~1-2.

high redshift↗
NASA NTRS2008

Using LDPC Code Constraints to Aid Recovery of Symbol Timing

A method of utilizing information available in the constraints imposed by a low-density parity-check (LDPC) code has been proposed as a means of aiding the recovery of symbol timing in the reception of a binary-phase-shift-keying (BPSK) signal representing such a code in the presence of noise, timing error, and/or Doppler shift between the transmitter and the receiver. This method and the receiver architecture in which it would be implemented belong to a class of timing-recovery methods and corresponding receiver architectures characterized as pilotless in that they do not require transmission and reception of pilot signals. Acquisition and tracking of a signal of the type described above have traditionally been performed upstream of, and independently of, decoding and have typically involved utilization of a phase-locked loop (PLL). However, the LDPC decoding process, which is iterative, provides information that can be fed back to the timing-recovery receiver circuits to improve performance significantly over that attainable in the absence of such feedback. Prior methods of coupling LDPC decoding with timing recovery had focused on the use of output code words produced as the iterations progress. In contrast, in the present method, one exploits the information available from the metrics computed for the constraint nodes of an LDPC code during the decoding process. In addition, the method involves the use of a waveform model that captures, better than do the waveform models of the prior methods, distortions introduced by receiver timing errors and transmitter/ receiver motions. An LDPC code is commonly represented by use of a bipartite graph containing two sets of nodes. In the graph corresponding to an (n,k) code, the n variable nodes correspond to the code word symbols and the n-k constraint nodes represent the constraints that the code places on the variable nodes in order for them to form a valid code word. The decoding procedure involves iterative computation of values associated with these nodes. A constraint node represents a parity-check equation using a set of variable nodes as inputs. A valid decoded code word is obtained if all parity-check equations are satisfied. After each iteration, the metrics associated with each constraint node can be evaluated to determine the status of the associated parity check. Heretofore, normally, these metrics would be utilized only within the LDPC decoding process to assess whether or not variable nodes had converged to a codeword. In the present method, it is recognized that these metrics can be used to determine accuracy of the timing estimates used in acquiring the sampled data that constitute the input to the LDPC decoder. In fact, the number of constraints that are satisfied exhibits a peak near the optimal timing estimate. Coarse timing estimation (or first-stage estimation as described below) is found via a parametric search for this peak. The present method calls for a two-stage receiver architecture illustrated in the figure. The first stage would correct large time delays and frequency offsets; the second stage would track random walks and correct residual time and frequency offsets. In the first stage, constraint-node feedback from the LDPC decoder would be employed in a search algorithm in which the searches would be performed in successively narrower windows to find the correct time delay and/or frequency offset. The second stage would include a conventional first-order PLL with a decision-aided timing-error detector that would utilize, as its decision aid, decoded symbols from the LDPC decoder. The method has been tested by means of computational simulations in cases involving various timing and frequency errors. The results of the simulations ined in the ideal case of perfect timing in the receiver.

Jones, Christopher↗
NASA NTRS2008

Symmetric Phase-Only Filtering in Particle-Image Velocimetry

Symmetrical phase-only filtering (SPOF) can be exploited to obtain substantial improvements in the results of data processing in particle-image velocimetry (PIV). In comparison with traditional PIV data processing, SPOF PIV data processing yields narrower and larger amplitude correlation peaks, thereby providing more-accurate velocity estimates. The higher signal-to-noise ratios associated with the higher amplitude correlation peaks afford greater robustness and reliability of processing. SPOF also affords superior performance in the presence of surface flare light and/or background light. SPOF algorithms can readily be incorporated into pre-existing algorithms used to process digitized image data in PIV, without significantly increasing processing times. A summary of PIV and traditional PIV data processing is prerequisite to a meaningful description of SPOF PIV processing. In PIV, a pulsed laser is used to illuminate a substantially planar region of a flowing fluid in which particles are entrained. An electronic camera records digital images of the particles at two instants of time. The components of velocity of the fluid in the illuminated plane can be obtained by determining the displacements of particles between the two illumination pulses. The objective in PIV data processing is to compute the particle displacements from the digital image data. In traditional PIV data processing, to which the present innovation applies, the two images are divided into a grid of subregions and the displacements determined from cross-correlations between the corresponding sub-regions in the first and second images. The cross-correlation process begins with the calculation of the Fourier transforms (or fast Fourier transforms) of the subregion portions of the images. The Fourier transforms from the corresponding subregions are multiplied, and this product is inverse Fourier transformed, yielding the cross-correlation intensity distribution. The average displacement of the particles across a subregion results in a displacement of the correlation peak from the center of the correlation plane. The velocity is then computed from the displacement of the correlation peak and the time between the recording of the two images. The process as described thus far is performed for all the subregions. The resulting set of velocities in grid cells amounts to a velocity vector map of the flow field recorded on the image plane. In traditional PIV processing, surface flare light and bright background light give rise to a large, broad correlation peak, at the center of the correlation plane, that can overwhelm the true particle- displacement correlation peak. This has made it necessary to resort to tedious image-masking and background-subtraction procedures to recover the relatively small amplitude particle-displacement correlation peak. SPOF is a variant of phase-only filtering (POF), which, in turn, is a variant of matched spatial filtering (MSF). In MSF, one projects a first image (denoted the input image) onto a second image (denoted the filter) as part of a computation to determine how much and what part of the filter is present in the input image. MSF is equivalent to cross-correlation. In POF, the frequency-domain content of the MSF filter is modified to produce a unitamplitude (phase-only) object. POF is implemented by normalizing the Fourier transform of the filter by its magnitude. The advantage of POFs is that they yield correlation peaks that are sharper and have higher signal-to-noise ratios than those obtained through traditional MSF. In the SPOF, these benefits of POF can be extended to PIV data processing. The SPOF yields even better performance than the POF approach, which is uniquely applicable to PIV type image data. In SPOF as now applied to PIV data processing, a subregion of the first image is treated as the input image and the corresponding subregion of the second image is treated as the filter. The Fourier transforms from both the firs and second- image subregions are normalized by the square roots of their respective magnitudes. This scheme yields optimal performance because the amounts of normalization applied to the spatial-frequency contents of the input and filter scenes are just enough to enhance their high-spatial-frequency contents while reducing their spurious low-spatial-frequency content. As a result, in SPOF PIV processing, particle-displacement correlation peaks can readily be detected above spurious background peaks, without need for masking or background subtraction.

Wemet, Mark P.↗
NASA NTRS2008

Fuel Savings and Aerodynamic Drag Reduction from Rail Car Covers

The potential for energy savings by reducing the aerodynamic drag of rail cars is significant. A previous study of aerodynamic drag of coal cars suggests that a 25% reduction in drag of empty cars would correspond to a 5% fuel savings for a round trip [1]. Rail statistics for the United States [2] report that approximately 5.7 billion liters of diesel fuel were consumed for coal transportation in 2002, so a 5% fuel savings would total 284 million liters. This corresponds to 2% of Class I railroad fuel consumption nationwide. As part of a DOE-sponsored study, the aerodynamic drag of scale rail cars was measured in a wind tunnel. The goal of the study was to measure the drag reduction of various rail-car cover designs. The cover designs tested yielded an average drag reduction of 43% relative to empty cars corresponding to an estimated round-trip fuel savings of 9%.

Storms, Bruce↗
NASA NTRS2007

Wavefront Sensing With Switched Lenses for Defocus Diversity

In an alternative hardware design for an apparatus used in image-based wavefront sensing, defocus diversity is introduced by means of fixed lenses that are mounted in a filter wheel (see figure) so that they can be alternately switched into a position in front of the focal plane of an electronic camera recording the image formed by the optical system under test. [The terms image-based, wavefront sensing, and defocus diversity are defined in the first of the three immediately preceding articles, Broadband Phase Retrieval for Image-Based Wavefront Sensing (GSC-14899-1).] Each lens in the filter wheel is designed so that the optical effect of placing it at the assigned position is equivalent to the optical effect of translating the camera a specified defocus distance along the optical axis. Heretofore, defocus diversity has been obtained by translating the imaging camera along the optical axis to various defocus positions. Because data must be taken at multiple, accurately measured defocus positions, it is necessary to mount the camera on a precise translation stage that must be calibrated for each defocus position and/or to use an optical encoder for measurement and feedback control of the defocus positions. Additional latency is introduced into the wavefront sensing process as the camera is translated to the various defocus positions. Moreover, if the optical system under test has a large focal length, the required defocus values are large, making it necessary to use a correspondingly bulky translation stage. By eliminating the need for translation of the camera, the alternative design simplifies and accelerates the wavefront-sensing process. This design is cost-effective in that the filterwheel/lens mechanism can be built from commercial catalog components. After initial calibration of the defocus value of each lens, a selected defocus value is introduced by simply rotating the filter wheel to place the corresponding lens in front of the camera. The rotation of the wheel can be automated by use of a motor drive, and further calibration is not necessary. Because a camera-translation stage is no longer needed, the size of the overall apparatus can be correspondingly reduced.

Dean, Bruce H.↗
NASA NTRS2008

An Integrated Approach to Damage Accommodation in Flight Control

In this paper we present an integrated approach to in-flight damage accommodation in flight control. The approach is based on Multiple Models, Switching and Tuning (MMST), and consists of three steps: In the first step the main objective is to acquire a realistic aircraft damage model. Modeling of in-flight damage is a highly complex problem since there is a large number of issues that need to be addressed. One of the most important one is that there is strong coupling between structural dynamics, aerodynamics, and flight control. These effects cannot be studied separately due to this coupling. Once a realistic damage model is available, in the second step a large number of models corresponding to different damage cases are generated. One possibility is to generate many linear models and interpolate between them to cover a large portion of the flight envelope. Once these models have been generated, we will implement a recently developed-Model Set Reduction (MSR) technique. The technique is based on parameterizing damage in terms of uncertain parameters, and uses concepts from robust control theory to arrive at a small number of "centered" models such that the controllers corresponding to these models assure desired stability and robustness properties over a subset in the parametric space. By devising a suitable model placement strategy, the entire parametric set is covered with a relatively small number of models and controllers. The third step consists of designing a Multiple Models, Switching and Tuning (MMST) strategy for estimating the current operating regime (damage case) of the aircraft, and switching to the corresponding controller to achieve effective damage accommodation and the desired performance. In the paper present a comprehensive approach to damage accommodation using Model Set Design,MMST, and Variable Structure compensation for coupling nonlinearities. The approach was evaluated on a model of F/A-18 aircraft dynamics under control effector damage, augmented by nonlinear cross-coupling terms and a structural dynamics model. The proposed approach achieved excellent performance under severe damage effects.

Boskovic, Jovan D.↗
NASA NTRS2010

Measuring a Fiber-Optic Delay Line Using a Mode-Locked Laser

The figure schematically depicts a laboratory setup for determining the optical length of a fiber-optic delay line at a precision greater than that obtainable by use of optical time-domain reflectometry or of mechanical measurement of length during the delay-line-winding process. In this setup, the delay line becomes part of the resonant optical cavity that governs the frequency of oscillation of a mode-locked laser. The length can then be determined from frequency-domain measurements, as described below. The laboratory setup is basically an all-fiber ring laser in which the delay line constitutes part of the ring. Another part of the ring - the laser gain medium - is an erbium-doped fiber amplifier pumped by a diode laser at a wavelength of 980 nm. The loop also includes an optical isolator, two polarization controllers, and a polarizing beam splitter. The optical isolator enforces unidirectional lasing. The polarization beam splitter allows light in only one polarization mode to pass through the ring; light in the orthogonal polarization mode is rejected from the ring and utilized as a diagnostic output, which is fed to an optical spectrum analyzer and a photodetector. The photodetector output is fed to a radio-frequency spectrum analyzer and an oscilloscope. The fiber ring laser can generate continuous-wave radiation in non-mode-locked operation or ultrashort optical pulses in mode-locked operation. The mode-locked operation exhibited by this ring is said to be passive in the sense that no electro-optical modulator or other active optical component is used to achieve it. Passive mode locking is achieved by exploiting optical nonlinearity of passive components in such a manner as to obtain ultra-short optical pulses. In this setup, the particular nonlinear optical property exploited to achieve passive mode locking is nonlinear polarization rotation. This or any ring laser can support oscillation in multiple modes as long as sufficient gain is present to overcome losses in the ring. When mode locking is achieved, oscillation occurs in all the modes having the same phase and same polarization. The frequency interval between modes, often denoted the free spectral range (FSR), is given by c/nL, where c is the speed of light in vacuum, n is the effective index of refraction of the fiber, and L is the total length of optical path around the ring. Therefore, the length of the fiber-optic delay line, as part of the length around the ring, can be calculated from the FSRs measured with and without the delay line incorporated into the ring. For this purpose, the FSR measurements are made by use of the optical and radio-frequency spectrum analyzers. In experimentation on a 10-km-long fiber-optic delay line, it was found that this setup made it possible to measure the length to within a fractional error of about 3 10(exp -6), corresponding to a length error of 3 cm. In contrast, measurements by optical time-domain reflectometry and mechanical measurement were found to be much less precise: For optical time-domain reflectometry, the fractional error was found no less than 10(exp -4) (corresponding to a length error of 1 m) and for mechanical measurement, the fractional error was found to be about 10(exp -2) (corresponding to a length error of 100 m).

Tu, Meirong↗
NASA NTRS2010

Near-field nano-imager

An imaging device. In one embodiment, the imaging device includes a plurality of first electrode strips in parallel to each other along a first direction x, wherein each first electrode strip has an elongated body with a first surface and an opposite, second surface and a thickness n.sub.1. The imaging device also includes a plurality of second electrode strips in parallel to each other along a second direction y that is substantially perpendicular to the first direction x, wherein each second electrode strip has an elongated body with a first surface and an opposite, second surface and a thickness n.sub.2. The plurality of second electrode strips are positioned apart from the plurality of first electrode strips along a third direction z that is substantially perpendicular to the first direction x and the second direction y such that the plurality of first electrode strips and the plurality of second electrode strips are crossing each other accordingly to form a corresponding number of crossing points. And at each crossing point, a semiconductor component is filled between the second surface of a corresponding first electrode strip and the first surface of a corresponding second electrode strip to form an addressable pixel.

Liu, Boyang↗
NASA NTRS2010

Physics of Life: A Model for Non-Newtonian Properties of Living Systems

This innovation proposes the reconciliation of the evolution of life with the second law of thermodynamics via the introduction of the First Principle for modeling behavior of living systems. The structure of the model is quantum-inspired: it acquires the topology of the Madelung equation in which the quantum potential is replaced with the information potential. As a result, the model captures the most fundamental property of life: the progressive evolution; i.e. the ability to evolve from disorder to order without any external interference. The mathematical structure of the model can be obtained from the Newtonian equations of motion (representing the motor dynamics) coupled with the corresponding Liouville equation (representing the mental dynamics) via information forces. All these specific non-Newtonian properties equip the model with the levels of complexity that matches the complexity of life, and that makes the model applicable for description of behaviors of ecological, social, and economical systems. Rather than addressing the six aspects of life (organization, metabolism, growth, adaptation, response to stimuli, and reproduction), this work focuses only on biosignature ; i.e. the mechanical invariants of life, and in particular, the geometry and kinematics of behavior of living things. Living things obey the First Principles of Newtonian mechanics. One main objective of this model is to extend the First Principles of classical physics to include phenomenological behavior on living systems; to develop a new mathematical formalism within the framework of classical dynamics that would allow one to capture the specific properties of natural or artificial living systems such as formation of the collective mind based upon abstract images of the selves and non-selves; exploitation of this collective mind for communications and predictions of future expected characteristics of evolution; and for making decisions and implementing the corresponding corrections if the expected scenario is different from the originally planned one. This approach postulates that even a primitive living species possesses additional, non-Newtonian properties that are not included in the laws of Newtonian or statistical mechanics. These properties follow from a privileged ability of living systems to possess a self-image (a concept introduced in psychology) and to interact with it. The proposed mathematical system is based on the coupling of the classical dynamical system representing the motor dynamics with the corresponding Liouville equation describing the evolution of initial uncertainties in terms of the probability density and representing the mental dynamics. The coupling is implemented by the information-based supervising forces that can be associated with self-awareness. These forces fundamentally change the pattern of the probability evolution, and therefore, lead to a major departure of the behavior of living systems from the patterns of both Newtonian and statistical mechanics. This innovation is meant to capture the signature of life based only on observable behavior, not on any biochemistry. This will not prevent the use of this model for developing artificial living systems, as well as for studying some general properties of behavior of natural, living systems.

Zak, Michail↗