Advanced Transport Operating System (ATOPS) color displays software description microprocessor system

This document describes the software created for the Sperry Microprocessor Color Display System used for the Advanced Transport Operating Systems (ATOPS) project on the Transport Systems Research Vehicle (TSRV). The software delivery known as the 'baseline display system', is the one described in this document. Throughout this publication, module descriptions are presented in a standardized format which contains module purpose, calling sequence, detailed description, and global references. The global reference section includes procedures and common variables referenced by a particular module. The system described supports the Research Flight Deck (RFD) of the TSRV. The RFD contains eight cathode ray tubes (CRTs) which depict a Primary Flight Display, Navigation Display, System Warning Display, Takeoff Performance Monitoring System Display, and Engine Display

Advanced Transport Operating System (ATOPS) color displays software description: MicroVAX system

This document describes the software created for the Display MicroVAX computer used for the Advanced Transport Operating Systems (ATOPS) project on the Transport Systems Research Vehicle (TSRV). The software delivery of February 27, 1991, known as the 'baseline display system', is the one described in this document. Throughout this publication, module descriptions are presented in a standardized format which contains module purpose, calling sequence, detailed description, and global references. The global references section includes subroutines, functions, and common variables referenced by a particular module. The system described supports the Research Flight Deck (RFD) of the TSRV. The RFD contains eight Cathode Ray Tubes (CRTs) which depict a Primary Flight Display, Navigation Display, System Warning Display, Takeoff Performance Monitoring System Display, and Engine Display

A fast version of the k-means classification algorithm for astronomical applications

Context. K-means is a clustering algorithm that has been used to classify large datasets in astronomical databases. It is an unsupervised method, able to cope very different types of problems. Aims. We check whether a variant of the algorithm called single-pass k-means can be used as a fast alternative to the traditional k-means. Methods. The execution time of the two algorithms are compared when classifying subsets drawn from the SDSS-DR7 catalog of galaxy spectra. Results. Single-pass k-means turn out to be between 20 % and 40 % faster than k-means and provide statistically equivalent classifications. This conclusion can be scaled up to other larger databases because the execution time of both algorithms increases linearly with the number of objects. Conclusions. Single-pass k-means can be safely used as a fast alternative to k-means

Contraction of Unconnected Diagrams using Least Cost Parsing

A free-hand diagram editor allows the user to place diagram components on the pane without any restrictions. This increase in flexibility often comes at the cost of editing performance, though. In particular it is tedious to manually establish the spatial relations between diagram components that are required by the visual language. Even worse are certain graph-like languages where it is a quite annoying task to explicitly link the node components. In this paper diagram contraction is proposed for solving these issues. The editor user can just roughly arrange a set of diagram components. On request the editor automatically creates a correct diagram from these components while preserving their layout as far as possible. Moreover, for several languages diagram contraction corresponds to linking node components appropriately. Such auto-linking is considered useful. It even has been integrated into first commercial modeling tools. The proposed approach can be applied to visual languages that are specified by means of hypergraph grammars. For syntax analysis an error-tolerant hypergraph parser is used, which computes a cost function by attribute evaluation. That way, unfavorable derivation (sub-)trees can be excluded at an early stage, and combinatorial explosion is mostly prevented