FIDEX: An expert system for satellite diagnostics

A Fault Isolation and Diagnostic Expert system (FIDEX) was developed for communication satellite diagnostics. It was designed specifically for the 30/20 GHz satellite transponder. The expert system was designed with a generic structure and features that make it applicable to other types of space systems. FIDEX is a frame based system that enjoys many of the inherent frame base features, such as hierarchy that describes the transponder's components, with other hierarchies that provide structural and fault information about the transponder. This architecture provides a flexible diagnostic structure and enhances maintenance of the system. FIDEX also includes an inexact reasoning technique and a primitive learning ability. Inexact reasoning was an important feature for this system due to the sparse number of sensors available to provide information on the transponder's performance. FIDEX can determine the most likely faulted component under the constraint of limited information. FIDEX learns about the most likely faults in the transponder by keeping a record of past established faults. FIDEX also has the ability to detect anomalies in the sensors that provide information on the transponders performance

DSN advanced receiver: Breadboard description and test results

A breadboard Advanced Receiver for use in the Deep Space Network was designed, built, and tested in the laboratory. Field testing was also performed during Voyager Uranus encounter at DSS-13. The development of the breadboard is intended to lead towards implementation of the new receiver throughout the network. The receiver is described on a functional level and then in terms of more specific hardware and software architecture. The results of performance tests in the laboratory and in the field are given. Finally, there is a discussion of suggested improvements for the next phase of development

A two-level structure for advanced space power system automation

The tasks to be carried out during the three-year project period are: (1) performing extensive simulation using existing mathematical models to build a specific knowledge base of the operating characteristics of space power systems; (2) carrying out the necessary basic research on hierarchical control structures, real-time quantitative algorithms, and decision-theoretic procedures; (3) developing a two-level automation scheme for fault detection and diagnosis, maintenance and restoration scheduling, and load management; and (4) testing and demonstration. The outlines of the proposed system structure that served as a master plan for this project, work accomplished, concluding remarks, and ideas for future work are also addressed

Measures of Analysis of Time Series (MATS): A MATLAB Toolkit for Computation of Multiple Measures on Time Series Data Bases

In many applications, such as physiology and finance, large time series data bases are to be analyzed requiring the computation of linear, nonlinear and other measures. Such measures have been developed and implemented in commercial and freeware softwares rather selectively and independently. The Measures of Analysis of Time Series ({\tt MATS}) {\tt MATLAB} toolkit is designed to handle an arbitrary large set of scalar time series and compute a large variety of measures on them, allowing for the specification of varying measure parameters as well. The variety of options with added facilities for visualization of the results support different settings of time series analysis, such as the detection of dynamics changes in long data records, resampling (surrogate or bootstrap) tests for independence and linearity with various test statistics, and discrimination power of different measures and for different combinations of their parameters. The basic features of {\tt MATS} are presented and the implemented measures are briefly described. The usefulness of {\tt MATS} is illustrated on some empirical examples along with screenshots.Comment: 25 pages, 9 figures, two tables, the software can be downloaded at http://eeganalysis.web.auth.gr/indexen.ht

EEG-based cognitive control behaviour assessment: an ecological study with professional air traffic controllers

Several models defining different types of cognitive human behaviour are available. For this work, we have selected the Skill, Rule and Knowledge (SRK) model proposed by Rasmussen in 1983. This model is currently broadly used in safety critical domains, such as the aviation. Nowadays, there are no tools able to assess at which level of cognitive control the operator is dealing with the considered task, that is if he/she is performing the task as an automated routine (skill level), as procedures-based activity (rule level), or as a problem-solving process (knowledge level). Several studies tried to model the SRK behaviours from a Human Factor perspective. Despite such studies, there are no evidences in which such behaviours have been evaluated from a neurophysiological point of view, for example, by considering brain activity variations across the different SRK levels. Therefore, the proposed study aimed to investigate the use of neurophysiological signals to assess the cognitive control behaviours accordingly to the SRK taxonomy. The results of the study, performed on 37 professional Air Traffic Controllers, demonstrated that specific brain features could characterize and discriminate the different SRK levels, therefore enabling an objective assessment of the degree of cognitive control behaviours in realistic setting

ATAMM analysis tool

Diagnostics software for analyzing Algorithm to Architecture Mapping Model (ATAMM) based concurrent processing systems is presented. ATAMM is capable of modeling the execution of large grain algorithms on distributed data flow architectures. The tool graphically displays algorithm activities and processor activities for evaluation of the behavior and performance of an ATAMM based system. The tool's measurement capabilities indicate computing speed, throughput, concurrency, resource utilization, and overhead. Evaluations are performed on a simulated system using the software tool. The tool is used to estimate theoretical lower bound performance. Analysis results are shown to be comparable to the predictions