Engineering topics

Adorf, Hans-Martin

Publications and source records attributed to Adorf, Hans-Martin.

4 records

NASA NTRS1992

Restoration of HST images with missing data

Missing data are a fairly common problem when restoring Hubble Space Telescope observations of extended sources. On Wide Field and Planetary Camera images cosmic ray hits and CCD hot spots are the prevalent causes of data losses, whereas on Faint Object Camera images data are lossed due to reseaux marks, blemishes, areas of saturation and the omnipresent frame edges. This contribution discusses a technique for 'filling in' missing data by statistical inference using information from the surrounding pixels. The major gain consists in minimizing adverse spill-over effects to the restoration in areas neighboring those where data are missing. When the mask delineating the support of 'missing data' is made dynamic, cosmic ray hits, etc. can be detected on the fly during restoration.

Adorf, Hans-Martin↗

NASA NTRS1992

Scientific computing in the 1990ies: An astronomical perspective

The compute performance, storage capability, degree of networking, and usability of modern computer hardware have enormously progressed in the past decade. These hardware advances are not paralleled by an equivalent increase in software productivity. Among astronomers the need is gradually perceived to discuss questions such as whether we are prepared to meet the pending challenge of vector and massively parallel computers. Therefore, a moderated, time-limited and access-restricted, wide-area network discussion forum is proposed for having a first, broad-minded go at the question of whether our current software efforts are heading in the right direction. The main topics, goals, means, and form of the proposed discussion process are presented.

Adorf, Hans-Martin↗

NASA NTRS1992

A low-cost vector processor boosting compute-intensive image processing operations

Low-cost vector processing (VP) is within reach of everyone seriously engaged in scientific computing. The advent of affordable add-on VP-boards for standard workstations complemented by mathematical/statistical libraries is beginning to impact compute-intensive tasks such as image processing. A case in point in the restoration of distorted images from the Hubble Space Telescope. A low-cost implementation is presented of the standard Tarasko-Richardson-Lucy restoration algorithm on an Intel i860-based VP-board which is seamlessly interfaced to a commercial, interactive image processing system. First experience is reported (including some benchmarks for standalone FFT's) and some conclusions are drawn.

Adorf, Hans-Martin↗

NASA NTRS1989

Learning in stochastic neural networks for constraint satisfaction problems

Researchers describe a newly-developed artificial neural network algorithm for solving constraint satisfaction problems (CSPs) which includes a learning component that can significantly improve the performance of the network from run to run. The network, referred to as the Guarded Discrete Stochastic (GDS) network, is based on the discrete Hopfield network but differs from it primarily in that auxiliary networks (guards) are asymmetrically coupled to the main network to enforce certain types of constraints. Although the presence of asymmetric connections implies that the network may not converge, it was found that, for certain classes of problems, the network often quickly converges to find satisfactory solutions when they exist. The network can run efficiently on serial machines and can find solutions to very large problems (e.g., N-queens for N as large as 1024). One advantage of the network architecture is that network connection strengths need not be instantiated when the network is established: they are needed only when a participating neural element transitions from off to on. They have exploited this feature to devise a learning algorithm, based on consistency techniques for discrete CSPs, that updates the network biases and connection strengths and thus improves the network performance.

Johnston, Mark D.↗