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Proposed rectangular array of photodetectors in imaging spectrometer includes integral filter deposited directly on imaging array which would not become misaligned or misregistered as result of mechanical shock, vibration, or differential thermal expansion. Need for precise alignment and registration of filter cannot be over-emphasized.
Coplanar-waveguide microwave low-pass filters based on thin films of high-critical-temperature superconductor YBa2Cu3O7 built and tested. Designed to provide passband of 0 to 9.5 GHz. Dimensions suitable for microwave integrated circuits and chosen to provide fairly stringent test of usefulness and practicality of design and of method of fabrication. Insertion loss less than filter made with copper.
Tracking comb filter includes charge-coupled-device delay line and phase-locked loop. Filter removes harmonics of frequency of repetition of welding-current pulses from acoustic-emission signal. Spurious signals reduced almost to fundamental limit.
Experimental real-time optical correlator includes complex-valued holographic filter computed from one of two input scenes. Made more tolerant of errors in patterns to be recognized. Filter function essentially coded hologram computed by Burchhardt's method, in which complex value of each pixel of fast Fourier transform of image represented by three real, nonnegative numbers. In generating optical correlator according to method, phasor of each pixel in Fourier transform represented by intensities in three adjacent pixels.
A novel method is presented of identifying a state space model and a state estimator for linear stochastic systems from input and output data. The method is primarily based on the relations between the state space model and the finite difference model for linear stochastic systems derived through projection filters. It is proven that least squares identification of a finite difference model converges to the model derived from the projection filters. System pulse response samples are computed from the coefficients of the finite difference model. In estimating the corresponding state estimator gain, a z-domain method is used. First the deterministic component of the output is subtracted out, and then the state estimator gain is obtained by whitening the remaining signal. Experimental example is used to illustrate the feasibility of the method.
This paper presents a procedure for estimating the Kalman filter gain from output residuals. The system state space model is assumed to be known, but the process and noise covariance are unknown. The proposed procedure consists of three basic steps. First, the output residuals are computed from the given model and a given set of input-output data. Second, a linear regression model for this part of the response is computed by a least squares solution. Third, the Kalman filter gain is then estimated from the coefficients of this model. Numerical results using experimental data are presented to illustrate the validity of the developed procedure.
This paper describes a program for analysis of output curves from Differential Thermal Analyzer (DTA). The program first extracts probabilistic qualitative features from a DTA curve of a soil sample, and then uses Bayesian probabilistic reasoning to infer the mineral in the soil. The qualifier module employs a simple and efficient extension of scale-space filtering suitable for handling DTA data. We have observed that points can vanish from contours in the scale-space image when filtering operations are not highly accurate. To handle the problem of vanishing points, perceptual organizations heuristics are used to group the points into lines. Next, these lines are grouped into contours by using additional heuristics. Probabilities are associated with these contours using domain-specific correlations. A Bayes tree classifier processes probabilistic features to infer the presence of different minerals in the soil. Experiments show that the algorithm that uses domain-specific correlation to infer qualitative features outperforms a domain-independent algorithm that does not.
Infrared optical multilayer filters and materials were exposed to the space environment of low Earth orbit on LDEF. The effects are summarized of that environment on the physical and optical properties of the filters and materials flown.
A set of ten interference filters were supplied to be mounted on experiment no. A0-147 of LDEF. The results of spectral transmission measurements before delivery and after recovery are presented. Included are descriptions of the construction and materials included in the filters. Additional results from photographic microscopic and surface analytical observations are being gathered and will be presented later.
Binary synthetic discriminant function (BSDF) optical filters which are invariant to scale changes in the target object of more than 50 percent are demonstrated in simulation and experiment. Efficient databases of scale invariant BSDF filters can be designed which discriminate between two very similar objects at any view scaled over a factor of 2 or more. The BSDF technique has considerable advantages over other methods for achieving scale invariant object recognition, as it also allows determination of the object's scale. In addition to scale, the technique can be used to design recognition systems invariant to other geometric distortions.
The progress during the contract period is described. The project involved fabrication of image quality, space-qualified bandpass filters in the 200-350 nm spectral region. Ion-assisted deposition (IAD) was applied to produce stable, reasonably durable filter coatings on space compatible UV substrates. Thin film materials and UV transmitting substrates were tested for resistance to simulated space effects.
A model for the Faraday anomalous dispersion optical filter is presented. The model predicts a bandwidth of 0.6 GHz and a transmission peak of 0.98 for a filter operating on the Cs (D2) line. The model includes hyperfine effects and is valid for arbitrary magnetic fields.
The spectral resolution and statistical significance of a harmonic analysis obtained by low-order MEM can be improved by subjecting the data to an adaptive filter. This adaptive filter consists of projecting the data onto the leading temporal empirical orthogonal functions obtained from singular spectrum analysis (SSA). The combined SSA-MEM method is applied both to a synthetic time series and a time series of AAM data. The procedure is very effective when the background noise is white and less so when the background noise is red. The latter case obtains in the AAM data. Nevertheless, reliable evidence for intraseasonal and interannual oscillations in AAM is detected. The interannual periods include a quasi-biennial one and an LF one, of 5 years, both related to the El Nino/Southern Oscillation. In the intraseasonal band, separate oscillations of about 48.5 and 51 days are ascertained.
The K passband (central wavelength 2.2 microns, FWHM 0.4 micron) is the longest wavelength standard near-IR passband through which deep ground-based imaging is possible. Thermal emission from telescope, instrument, and sky limits the depth to which such imaging can reach by producing strongly temperature-dependent backgrounds in the range 11-13.5 mag/sq arcsec. This paper describes how a passband, which is denoted as K-prime, located slightly shortward of the standard K passband (central wavelength 2.1 microns), yet still within the same atmospheric window, leads to a significantly lower thermal component of the background, reducing the background surface brightness by up to 0.9 mag sq arcsec, and thereby allowing deeper imaging to be obtained in the same integration time. The photometric differences between the K-prime filter and the standard K filter are discussed.
Limitations associated with the binary phase-only filter often used in optical correlators are presently circumvented in the writing of complex-valued data on a gray-scale spatial light modulator through the use of a computer-generated hologram (CGH) algorithm. The CGH encodes complex-valued data into nonnegative real CGH data in such a way that it may be encoded in any of the available gray-scale spatial light modulators. A CdS liquid-crystal light valve is used for the complex-valued CGH encoding; computer simulations and experimental results are compared, and the use of such a CGH filter as the synapse hologram in a holographic optical neural net is discussed.
A comparison is made of the performances of two detection strategies that are based on different data fusion techniques. The strategies detect changes in a linear system. One detection strategy involves combining the estimates and error covariance matrices of distributed Kalman filters, generating a residual from the used estimates, comparing this residual to a threshold, and making a decision. The other detection strategy involves a distributed decision process in which estimates from distributed Kalman filters are used to generate distributed residuals which are compared locally to a threshold. Local decisions are made and these decisions are then fused into a global decision. The performances of each of these detection schemes are compared, and it is concluded that better performance is achieved when local decisions are made and then fused into a global decision.
Distributed-effect sensors, which respond to spatially distributed inputs over a significant gauge length, encompass piezoelectric laminate films, modal-domain optical fiber sensors, and holographic sensors; they can be fabricated with spatially varying sensitivity to a distributed measurand for spatial filtering. Such spatial filters are configurable to extract various structural parameters from distributed measurements that cannot be directly measured by sensors. A modeling is presently conducted for distributed-effect sensors' integration into state-space structural models, noting the effects of fabrication errors on sensor operation.
A novel method is presented for identifying a state-space model and a state estimator for linear stochastic systems from input and output data. The method is primarily based on the relationship between the state-space model and the finite-difference model of linear stochastic systems derived through projection filters. It is proved that least-squares identification of a finite difference model converges to the model derived from the projection filters. System pulse response samples are computed from the coefficients of the finite difference model.