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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

Method and apparatus for holding two separate metal pieces together for welding

A method of holding two separate metal pieces together for welding is described including the steps of overlapping a portion of one of the metal pieces on a portion of the other metal piece, encasing the overlapping metal piece in a compressible device, drawing the compressible device into an enclosure, and compressing a portion of the compressible device around the overlapping portions of the metal pieces for holding the metal pieces under constant and equal pressure during welding. The preferred apparatus for performing the method utilizes a support mechanism to support the two separate metal pieces in an overlapping configuration; a compressible device surrounding the support mechanism and at least one of the metal pieces, and a compressing device surrounding the compressible device for compressing the compressible device around the overlapping portions of the metal pieces, thus providing constant and equal pressure at all points on the overlapping portions of the metal pieces.

Mcclure, S. R.↗
NASA NTRS2002

Micro-Pulse Lidar Signals: Uncertainty Analysis

Micro-pulse lidar (MPL) systems are small, autonomous, eye-safe lidars used for continuous observations of the vertical distribution of cloud and aerosol layers. Since the construction of the first MPL in 1993, procedures have been developed to correct for various instrument effects present in MPL signals. The primary instrument effects include afterpulse, laser-detector cross-talk, and overlap, poor near-range (less than 6 km) focusing. The accurate correction of both afterpulse and overlap effects are required to study both clouds and aerosols. Furthermore, the outgoing energy of the laser pulses and the statistical uncertainty of the MPL detector must also be correctly determined in order to assess the accuracy of MPL observations. The uncertainties associated with the afterpulse, overlap, pulse energy, detector noise, and all remaining quantities affecting measured MPL signals, are determined in this study. The uncertainties are propagated through the entire MPL correction process to give a net uncertainty on the final corrected MPL signal. The results show that in the near range, the overlap uncertainty dominates. At altitudes above the overlap region, the dominant source of uncertainty is caused by uncertainty in the pulse energy. However, if the laser energy is low, then during mid-day, high solar background levels can significantly reduce the signal-to-noise of the detector. In such a case, the statistical uncertainty of the detector count rate becomes dominant at altitudes above the overlap region.

Welton, Ellsworth J.↗
NASA NTRS2011

Orbit Determination of the SELENE Satellites Using Multi-Satellite Data Types and Evaluation of SELENE Gravity Field Models

The SELENE mission, consisting of three separate satellites that use different terrestrial-based tracking systems, presents a unique opportunity to evaluate the contribution of these tracking systems to orbit determination precision. The tracking data consist of four-way Doppler between the main orbiter and one of the two sub-satellites while the former is over the far side, and of same-beam differential VLBI tracking between the two sub-satellites. Laser altimeter data are also used for orbit determination. The contribution to orbit precision of these different data types is investigated through orbit overlap analysis. It is shown that using four-way and VLBI data improves orbit consistency for all satellites involved by reducing peak values in orbit overlap differences that exist when only standard two-way Doppler and range data are used. Including laser altimeter data improves the orbit precision of the SELENE main satellite further, resulting in very smooth total orbit errors at an average level of 18m. The multi-satellite data have also resulted in improved lunar gravity field models, which are assessed through orbit overlap analysis using Lunar Prospector tracking data. Improvements over a pre-SELENE model are shown to be mostly in the along-track and cross-track directions. Orbit overlap differences are at a level between 13 and 21 m with the SELENE models, depending on whether l-day data overlaps or I-day predictions are used.

Goossens, S.↗
NASA NTRS2015

Detection of CFRP Composite Manufacturing Defects Using a Guided Wave Approach

NASA Langley Research Center is investigating a guided-wave based defect detection technique for as-fabricated carbon fiber reinforced polymer (CFRP) composites. This technique will be extended to perform in-process cure monitoring, defect detection and size determination, and ultimately a closed-loop process control to maximize composite part quality and consistency. The overall objective of this work is to determine the capability and limitations of the proposed defect detection technique, as well as the number and types of sensors needed to identify the size, type, and location of the predominant types of manufacturing defects associated with laminate layup and cure. This includes, porosity, gaps, overlaps, through-the-thickness fiber waviness, and in-plane fiber waviness. The present study focuses on detection of the porosity formed from variations in the matrix curing process, and on local overlaps intentionally introduced during layup of the prepreg. By terminating the cycle prematurely, three 24-ply unidirectional composite panels were manufactured such that each subsequent panel had a higher final degree of cure, and lower level of porosity. It was demonstrated that the group velocity, normal to the fiber direction, of a guided wave mode increased by 5.52 percent from the first panel to the second panel and 1.26 percent from the second panel to the third panel. Therefore, group velocity was utilized as a metric for degree of cure and porosity measurements. A fully non-contact guided wave hybrid system composed of an air-coupled transducer and a laser Doppler vibrometer (LDV) was used for the detection and size determination of an overlap By transforming the plate response from the time-space domain to the frequency-wavenumber domain, the total wavefield was then separated into the incident and backscatter waves. The overlap region was accurately imaged by using a zero-lag cross-correlation (ZLCC) imaging condition, implying the incident and backscattered waves are in phase over the overlap boundaries.

Hudson, Tyler B.↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRS1985

Application of homomorphic signal processing to stress wave factor analysis

The stress wave factor (SWF) signal, which is the output of an ultrasonic testing system where the transmitting and receiving transducers are coupled to the same face of the test structure, is analyzed in the frequency domain. The SWF signal generated in an isotropic elastic plate is modelled as the superposition of successive reflections. The reflection which is generated by the stress waves which travel p times as a longitudinal (P) wave and s times as a shear (S) wave through the plate while reflecting back and forth between the bottom and top faces of the plate is designated as the reflection with p, s. Short-time portions of the SWF signal are considered for obtaining spectral information on individual reflections. If the significant reflections are not overlapped, the short-time Fourier analysis is used. A summary of the elevant points of homomorphic signal processing, which is also called cepstrum analysis, is given. Homomorphic signal processing is applied to short-time SWF signals to obtain estimates of the log spectra of individual reflections for cases in which the reflections are overlapped. Two typical SWF signals generated in aluminum plates (overlapping and non-overlapping reflections) are analyzed.

Karagulle, H.↗
NASA NTRS1985

Gamma-ray burster recurrence time scales

In the decade since gamma-ray bursters (GRBs) were discovered by Klebesadel et al. (1973), many models have been proposed to explain the GRB phenomenon. A difficulty is related to the small number of predictions which would make it possible to evaluate the models. One verifiable prediction is the recurrence time scale, tau-gamma. One method to measure tau-gamma is to look for possible cases of recurrence as indicated by overlapping error boxes. The analysis considered in the present investigation is composed of three procedures. One of these involves a search through known error regions for cases where the error regions overlap. In addition, the number of overlaps expected by chance coincidence alone has been determined, and a calculation has been performed regarding the number of overlaps which are expected due to recurrence for various assumed tau-gamma and luminosity functions.

Schaefer, B. E.↗
NASA NTRS1985

Application of homomorphic signal processing to stress wave factor analysis

The stress wave factor (SWF) signal, which is the output of an ultrasonic testing system where the transmitting and receiving transducers are coupled to the same face of the test structure, is analyzed in the frequency domain. The SWF signal generated in an isotropic elastic plate is modelled as the superposition of successive reflections. The reflection which is generated by the stress waves which travel P times as a longitudinal (P) wave and s times as a shear (S) wave through the plate while reflecting back and forth between the bottom and top faces of the plate is designated as the reflection with P, s. Short-time portions of the SWF signal are considered for obtaining spectral information on individual reflections. If the significant reflections are not overlapped, the short-time Fourier analysis is used. A summary of the elevant points of homomorphic signal processing, which is also called cepstrum analysis, is given. Homomorphic signal processing is applied to short-time SWF signals to obtain estimates of the log spectra of individual reflections for cases in which the reflections are overlapped. Two typical SWF signals generated in aluminum plates (overlapping and non-overlapping reflections) are analyzed.

Williams, J. H., Jr.↗
NASA NTRS1986

Transformation of two and three-dimensional regions by elliptic systems

Efforts in transferring computational work from the LRC computer to the IRIS Graphics Workstation at MSU are reported and the computation of a conservative solution of a simple hyperbolic equation on an overlapping grid is discussed. Several conclusions concerning computations on overlapping grids are apparent. Problems only occur when there is a major difference in grid spacing on the individual component grids. In the case of hyperbolic equations, it is necessary that both interpolation and extrapolation be applied at the grid boundaries. When interpolated values are used at outflow boundary points, excessive oscillations in the numerical solution may be the result. The same conclusions would be valid for more complicated systems of hyperbolic equations such as the Euler equations for inviscid flow. Some of the solution values would be extrapolated at the overlap boundary, the exact number depending on the number of characteristics pointing out of the overlap region. It is also possible that similar boundary conditions may be needed for some parabolic equations such as high Reynolds number viscous flow equations. Efforts were also expended on the development of three-dimensional conservative interpolation procedures. Finally, the investigation of grid smoothing procedures were initiated during this reporting period. It was decided that the first grid smoothing algorithms will be based on the concepts of variational grid generation.

Mastin, C. W.↗
NASA NTRS1988

Microplate and shear zone models for oceanic spreading center reorganizations

The kinematics of rift propagation and the resulting goemetries of various tectonic elements for two plates is reviewed with no overlap zone. The formation and evolution of overlap regions using schematic models is discussed. The models are scaled in space and time to approximate the Easter plate, but are simplified to emphasize key elements. The tectonic evolution of overlap regions which act as rigid microplates and shear zones is discussed, and the use of relative motion and structural data to discriminate between the two types of models is investigated. The effect of propagation rate and rise time on the size, shape, and deformation of the overlap region is demonstrated.

Engeln, Joseph F.↗
NASA NTRS1992

Real-time edge-enhanced optical correlator

The performance of five symbol lock detectors are compared. They are the square-law detector with overlapping (SQOD) and non-overlapping (SQNOD) integrators, the absolute value detectors with overlapping and non-overlapping (AVNOD) integrators and the signal power estimator detector (SPED). The analysis considers various scenarios when the observation interval is much larger or equal to the symbol synchronizer loop bandwidth, which has not been considered in previous analyses. Also, the case of threshold setting in the absence of signal is considered. It is shown that the SQOD outperforms all others when the threshold is set in the presence of signal, independent of the relationship between loop bandwidth and observation period. On the other hand, the SPED outperforms all others when the threshold is set in the presence of noise only.

Shihabi, Mazen M.↗
NASA NTRS1992

Locus of the single-channel bottleneck in dual-task interference

Two experiments used the locus-of-cognitive-slack method to determine whether dual-task interference occurs before or after the response selection stage. The experiments used the overlapping tasks paradigm, in which two signals, each requiring a different speeded choice response, are presented in rapid succession. In Experiment 1, stimulus-response (S-R) compatibility was manipulated by varying whether Task 2 stimuli were mapped onto their responses by a rule or arbitrarily. Compatibility effects were additive with the effects of degree of task overlap, manipulated by varying the stimulus onset asynchrony between the signals. Experiment 2 examined 2 additional forms of S-R compatibility: symbolic compatability (arrows vs. letters) and spatial compatibility (the 'Simon' effect). Effects of symbolic compatibility were additive with effects of degree of task overlap, whereas the effects of spatial compatibility and degree of task overlap were underadditive. It is argued that only a central-bottleneck model provides a consistent account of these results. The nature of the central bottleneck is considered.

Mccann, Robert S.↗
NASA NTRS1993

Intercomparison of haloe and ER-2 aircraft H2O and CH4 observations collected during the second Airborne Arctic Stratospheric Experiment (AASE-II)

HALOE observations of H2O and CH4 are compared with in situ techniques aboard the ER-2 aircraft during the northern winter of 1991/92, in particular for the dates 911208, 920108, 920217, 920222, and 920320 when the spatial coincidences are close, within +/- 1 deg latitude and +/- 12 deg longitude. The results reveal the limitations of comparing high resolution in situ aircraft data with a remote sounding limb scanner. Of the five comparison dates, three had HALOE/ER-2 coincidences which occurred near the edge of the Arctic vortex; the vertical variability in the HALOE results and the horizontal variability in the ER-2 observations on these days show that the vortex edge is not a region where exact agreement can be expected except by chance. On a 4th comparison date, there was substantial overlap away from the vortex edge, although for some species the aircraft data show considerable variability near the coincidence point. On the 5th comparison date, the ER-2 had no overlap in altitude with the lowest HALOE observations; however, a short linear interpolation over about 1 km altitude results in smooth composite profiles. Generally speaking the agreement between HALOE and the ER-2 at overlap altitudes is about 12 percent in the case of water vapor, which shows low horizontal and vertical variability. The agreement for methane, with limited data having altitude overlap, is better than 6 percent.

Tuck, A. F.↗
NASA NTRS1996

User's Manual for FOMOCO Utilities-Force and Moment Computation Tools for Overset Grids

In the numerical computations of flows around complex configurations, accurate calculations of force and moment coefficients for aerodynamic surfaces are required. When overset grid methods are used, the surfaces on which force and moment coefficients are sought typically consist of a collection of overlapping surface grids. Direct integration of flow quantities on the overlapping grids would result in the overlapped regions being counted more than once. The FOMOCO Utilities is a software package for computing flow coefficients (force, moment, and mass flow rate) on a collection of overset surfaces with accurate accounting of the overlapped zones. FOMOCO Utilities can be used in stand-alone mode or in conjunction with the Chimera overset grid compressible Navier-Stokes flow solver OVERFLOW. The software package consists of two modules corresponding to a two-step procedure: (1) hybrid surface grid generation (MIXSUR module), and (2) flow quantities integration (OVERINT module). Instructions on how to use this software package are described in this user's manual. Equations used in the flow coefficients calculation are given in Appendix A.

Chan, William M.↗
NASA NTRS2005

Regression Analysis of Long-Term Profile Ozone Data Set from BUV Instruments

We have produced a profile merged ozone data set (MOD) based on the SBUV/SBUV2 series of nadir-viewing satellite backscatter instruments, covering the period from November 1978 - December 2003. In 2004, data from the Nimbus 7 SBUV and NOAA 9, ll, and 16 SBUV/2 instruments were reprocessed using the Version 8 (V8) algorithm and most recent calibrations. More recently, data from the Nimbus 4 BUT instrument, which was operational from 1970 - 1977, were also reprocessed using the V8 algorithm. As part of the V8 profile calibration, the Nimbus 7 and NOAA 9 (1993-1997 only) instrument calibrations have been adjusted to match the NOAA 11 calibration, which was established based on comparisons with SSBUV shuttle flight data. Differences between NOAA 11, Nimbus 7 and NOAA 9 profile zonal means are within plus or minus 5% at all levels when averaged over the respective periods of data overlap. NOAA 16 SBUV/2 data have insufficient overlap with NOAA 11, so its calibration is based on pre-flight information. Mean differences over 4 months of overlap are within plus or minus 7%. Given the level of agreement between the data sets, we simply average the ozone values during periods of instrument overlap to produce the MOD profile data set. Initial comparisons of coincident matches of N4 BUV and Arosa Umkehr data show mean differences of 0.5 (0.5)% at 30km; 7.5 (0.5)% at 35 km; and 11 (0.7)% at 40 km, where the number in parentheses is the standard error of the mean. In this study, we use the MOD profile data set (1978-2003) to estimate the change in profile ozone due to changing stratospheric chlorine levels. We use a standard linear regression model with proxies for the seasonal cycle, solar cycle, QBO, and ozone trend. To account for the non-linearity of stratospheric chlorine levels since the late 1990s, we use a time series of Effective Chlorine, defined as the global average of Chlorine + 50 * Bromine at 1 hPa, as the trend proxy. The Effective Chlorine data are taken from the 3-D Goddard CTM. We will show the latest trend results using this statistical model. In addition, the Nimbus 4 BUV data offer an opportunity to test the physical properties of our statistical model. From ground-based comparisons we will establish an uncertainty range for the Nimbus 4 data. We then extrapolate our statistical model fit backwards in time and compare to the Nimbus 4 data. We compare the characteristics of the residual, defined as the difference between the data and statistical regression fit, during the Nimbus 4 time period and the 1978-2003 period over which the statistical model coefficients were estimated, and present these results.

Stolarski, Richard S.↗
NASA NTRS2008

WGM Resonators for Terahertz-to-Optical Frequency Conversion

Progress has been made toward solving some practical problems in the implementation of terahertz-to-optical frequency converters utilizing whispering-gallery-mode (WGM) resonators. Such frequency converters are expected to be essential parts of non-cryogenic terahertz- radiation receivers that are, variously, under development or contemplated for a variety of applications in airborne and spaceborne instrumentation for astronomical and military uses. In most respects, the basic principles of terahertz-to-optical frequency conversion in WGM resonators are the same as those of microwave (sub-terahertz)-to-optical frequency conversion in WGM resonators, various aspects of which were discussed in the three preceeding articles. To recapitulate: In a receiver following this approach, a preamplified incoming microwave signal (in the present case, a terahertz signal) is up-converted to an optical signal by a technique that exploits the nonlinearity of the electromagnetic response of a whispering-gallery-mode (WGM) resonator made of LiNbO3 or another suitable electro-optical material. Upconversion takes place by three-wave mixing in the resonator. To ensure the required interaction among the optical and terahertz signals, the WGM resonator must be designed and fabricated to function as an electro-optical modulator while simultaneously exhibiting (1) resonance at the required microwave and optical operating frequencies and (2) phase matching among the microwave and optical signals circulating in the resonator. Downstream of the WGM resonator, the up-converted signal is processed photonically by use of a tunable optical filter or local oscillator and is then detected. The practical problems addressed in the present development effort are the following: Satisfaction of the optical and terahertz resonance-frequency requirement is a straightforward matter, inasmuch as the optical and terahertz spectra can be measured. However, satisfaction of the phase-matching requirement is more difficult. The approach followed in the present development is to perform computer simulations of the microwave and optical signals circulating in the resonator to test for phase matching. To enable excitation of the terahertz WGM resonator mode, it is also necessary to ensure phase matching between that mode and the incoming terahertz radiation. In the present development, the incoming signal is coupled into the WGM resonator via a tapered waveguide in the form of a fused silica rod. The phase-matching requirement is satisfied at one point along the taper; the rod is positioned with this point in proximity to the WGM resonator. To maximize the conversion efficiency, it is necessary to maximize the spatial overlap among the terahertz and optical modes in the WGM resonator. In the absence of a special design effort to address this issue, there would be little such overlap because, as a consequence of a large difference between wavelengths, the optical and terahertz modes would be concentrated at different depths from the rim of a WGM resonator. In the present development, overlap is ensured by constructing the WGM resonator as a ring (see figure) so thin that the optical and terahertz modes are effectively forced to overlap.

Strekalov,Dmitry↗