Engineering topics

Villasenor, J.

Publications and source records attributed to Villasenor, J..

3 records

NASA NTRS2004

Observations of Gamma-Ray Bursts by HETE-2

The High Energy Transient Explorer 2 (HETE-2), launched in October 2000, is currently localizing gamma-ray bursts (GRBs) at a rate of approximately 20/yr, many in real time. As of August 2003, HETE-2 had localized 43 GRBs; 16 localizations had led to the detection of an X-ray, optical, or radio afterglows. The prompt position notification of HETE-2 enabled probing the nature of so-called "dark bursts" for which no optical afterglows were found despite of accurate localizations. In some cases, the optical afterglow was found to be intrinsically faint , and its flux declined rapidly. In another case, the optical emission was likely to be extinguished by the dust in the vicinity of the GRB source. The bright afterglows of GRB021004 and GRB030329 were observed in unprecedented details by telescopes around the world. Strong evidence for the association of long GRBs with the core-collapse supernovae was found. HETE-2 has localized almost as many X-ray rich GRBs as classical GRBs. The nature of the X-ray rich GRBs and X-ray flashes have been studied systematically with HETE-2, and they are found to have many properties in common with the classical GRBs, suggesting that they are a single phenomenon.

Kawai, N.↗

NASA NTRS1997

Visibility of wavelet quantization noise

The discrete wavelet transform (DWT) decomposes an image into bands that vary in spatial frequency and orientation. It is widely used for image compression. Measures of the visibility of DWT quantization errors are required to achieve optimal compression. Uniform quantization of a single band of coefficients results in an artifact that we call DWT uniform quantization noise; it is the sum of a lattice of random amplitude basis functions of the corresponding DWT synthesis filter. We measured visual detection thresholds for samples of DWT uniform quantization noise in Y, Cb, and Cr color channels. The spatial frequency of a wavelet is r 2-lambda, where r is display visual resolution in pixels/degree, and lambda is the wavelet level. Thresholds increase rapidly with wavelet spatial frequency. Thresholds also increase from Y to Cr to Cb, and with orientation from lowpass to horizontal/vertical to diagonal. We construct a mathematical model for DWT noise detection thresholds that is a function of level, orientation, and display visual resolution. This allows calculation of a "perceptually lossless" quantization matrix for which all errors are in theory below the visual threshold. The model may also be used as the basis for adaptive quantization schemes.

NASA Center ARC↗

NASA NTRS1991

Multi-frequency and polarimetric radar backscatter signatures for discrimination between agricultural crops at the Flevoland experimental test site

We describe the calibration and analysis of multi-frequency, multi-polarization radar backscatter signatures over an agriculture test site in the Netherlands. The calibration procedure involved two stages: in the first stage, polarimetric and radiometric calibrations (ignoring noise) were carried out using square-base trihedral corner reflector signatures and some properties of the clutter background. In the second stage, a novel algorithm was used to estimate the noise level in the polarimetric data channels by using the measured signature of an idealized rough surface with Bragg scattering (the ocean in this case). This estimated noise level was then used to correct the measured backscatter signatures from the agriculture fields. We examine the significance of several key parameters extracted from the calibrated and noise-corrected backscatter signatures. The significance is assessed in terms of the ability to uniquely separate among classes from 13 different backscatter types selected from the test site data, including eleven different crops, one forest and one ocean area. Using the parameters with the highest separation for a given class, we use a hierarchical algorithm to classify the entire image. We find that many classes, including ocean, forest, potato, and beet, can be identified with high reliability, while the classes for which no single parameter exhibits sufficient separation have higher rates of misclassification. We expect that modified decision criteria involving simultaneous consideration of several parameters increase performance for these classes.

Freeman, A.↗