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Article
Publication date: 1 January 1983

S. SITHARAMA IYENGAR, PAUL O'NEILL and AVIS O'NEILL

The last decade has witnessed a growing concern among computer scientists to understand the complex interactions between humans and computer hardware. The work described in this…

60

Abstract

The last decade has witnessed a growing concern among computer scientists to understand the complex interactions between humans and computer hardware. The work described in this paper is an experimental study of a user‐computer interaction on a time‐sharing computer terminal network over a period of 1 year. The user‐system interaction described in this paper refers to a university environment. The user‐system performance variables considered are arrival patterns of jobs, inter‐arrival time, connect time, cpu time and think time. The users of the systems are grouped into on‐ and off‐campus users; a two‐way analysis of variance without replications established that arrival volume depended upon the weekday but not upon the user group. The pattern of arrivals throughout one day required an empirical distribution. Coefficient of variation indicated hyper‐exponential distributions for inter‐arrival time, connect time and cpu time, but an exponential distribution for think time. Furthermore, the experimental research described in this paper supports the fact that a hypothesis to characterize the interaction between the user and the computing system can be developed for an efficient use of the system.

Details

Kybernetes, vol. 12 no. 1
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 1 February 1986

S. SITHARAMA IYENGAR, JOHN FULLER, SIDARTH AMBARDAR and N. PARAMESWARAN

A comparison of the Halstead and McCabe methods of measuring program complexity with a recently proposed metric, which is based on the analysis of dependency computations using a…

43

Abstract

A comparison of the Halstead and McCabe methods of measuring program complexity with a recently proposed metric, which is based on the analysis of dependency computations using a data flowgraph model, is presented. The sensitivity of the metric to changes in the data structure is discussed. Comments and criticisms of the measures are included.

Details

Kybernetes, vol. 15 no. 2
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 21 August 2009

Hua Cao, Nathan E. Brener and S. Sitharama Iyengar

The purpose of this paper is to develop a 3D route planner, called 3DPLAN, which employs the Fast‐Pass A* algorithm to find optimum paths in the large grid.

218

Abstract

Purpose

The purpose of this paper is to develop a 3D route planner, called 3DPLAN, which employs the Fast‐Pass A* algorithm to find optimum paths in the large grid.

Design/methodology/approach

The Fast‐Pass A* algorithm, an improved best‐first search A* algorithm, has a major advantage compared to other search methods because it is guaranteed to give the optimum path.

Findings

In spite of this significant advantage, no one has previously used A* in 3D searches. Most researchers think that the computational cost of using A* for 3D route planning would be prohibitive. This paper shows that it is quite feasible to use A* for 3D searches if one employs the new mobility and threat heuristics that have been developed.

Practical implications

This paper reviews the modification of the previous 3DPLAN in the ocean dynamical environment. The test mobility map is replaced with more realistic mobility map that consists of travel times of each grid point to each of its 26 neighbors using the actual current velocity data from the Navy Coastal Ocean Model – East Asian Seas version. Numerical comparison between the A* and genetic algorithms (GA) shows that the A* algorithm has significantly faster running time than GA.

Originality/value

These new heuristics substantially speed up the A* algorithm so that the run times are quite reasonable for the large grids that are typical of 3D searches.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 2 no. 3
Type: Research Article
ISSN: 1756-378X

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