A Computer System for Controlling Interviewer Costs

1968 ◽  
Vol 5 (3) ◽  
pp. 312-318 ◽  
Author(s):  
Charles S. Mayer
Keyword(s):  
Computer System ◽  
Computer Analysis

A computer analysis of inputs supplied by interviewers provides a set of management statistics that explains reasons for high-cost interviewer performance.

scholarly journals COMPUTERIZED MEIOTIC MAPPING IN ASPERGILLUS NIDULANS

Genetics ◽  
1972 ◽  
Vol 72 (4) ◽  
pp. 595-605
Author(s):  
Gordon L Dorn
Keyword(s):  
Genetic Analysis ◽  
Aspergillus Nidulans ◽  
Computer System ◽  
Computer Analysis ◽  
Gene Sequences ◽  
Gene Group ◽  
Advantages And Disadvantages ◽  
Meiotic Mapping ◽  
Coefficient Of Coincidence

ABSTRACT A computer system for the storage and processing of microbial meiotic data has been developed. Based on the language Fortran 4, the program retrieves relevant data and determines the order, map distances, and coefficient of coincidence for any three-gene group. Meiotic data from Aspergillus nidulans were used to test the program. A total of 61 three-gene sequences were processed, and the results were found to be compatible with the published values. The advantages and disadvantages of computer analysis for genetic analysis are discussed.

Some Recent Results in Quiescent Prominence Spectrometry

1979 ◽  
Vol 44 ◽  
pp. 41-47
Author(s):  
Donald A. Landman
Keyword(s):  
Real Time ◽  
Computer System ◽  
Spectral Response ◽  
Absolute Intensity ◽  
Solar Observatory ◽  
Target Size ◽  
Small Target ◽  
Signal Averaging ◽  
Quiescent Prominence

This paper describes some recent results of our quiescent prominence spectrometry program at the Mees Solar Observatory on Haleakala. The observations were made with the 25 cm coronagraph/coudé spectrograph system using a silicon vidicon detector. This detector consists of 500 contiguous channels covering approximately 6 or 80 Å, depending on the grating used. The instrument is interfaced to the Observatory’s PDP 11/45 computer system, and has the important advantages of wide spectral response, linearity and signal-averaging with real-time display. Its principal drawback is the relatively small target size. For the present work, the aperture was about 3″ × 5″. Absolute intensity calibrations were made by measuring quiet regions near sun center.

Computer analysis of side-band holography in STEM

1991 ◽  
Vol 49 ◽  
pp. 684-685
Author(s):  
M.A. Gribelyuk ◽  
J.M. Cowley
Keyword(s):  
Interference Pattern ◽  
Computer Analysis ◽  
Side Band ◽  
Diffraction Plane ◽  
Illumination System ◽  
Probe Size

Recently the use of a biprism in a STEM instrument has been suggested for recording of a hologram. A biprism is inserted in the illumination system and creates two coherent focussed beams at the specimen level with a probe size d= 5-10Å. If one beam passes through an object and another one passes in vacuum, an interference pattern, i.e. a hologram can be observed in diffraction plane (Fig.1).

Remote real-time computer system for medical research and diagnosis

JAMA ◽  
1966 ◽  
Vol 196 (11) ◽  
pp. 967-972
Author(s):  
J. F. Dickson
Keyword(s):  
Medical Research ◽  
Real Time ◽  
Computer System

Pattern-Recognition: A Comparison of the Performance of Clinicians and Non-Clinicians - with a Note on the Performance of a Computer-Based System

1972 ◽  
Vol 11 (01) ◽  
pp. 32-37 ◽  
Author(s):  
F. T. DE DOMBAL ◽  
J. C. HORROCKS ◽  
J. R. STANILAND ◽  
P. J. GUILLOU
Keyword(s):  
Pattern Recognition ◽  
Computer System ◽  
Computer Aided Diagnosis ◽  
Clinical Interview ◽  
Computer Performance ◽  
Pattern Size ◽  
Computer Aided ◽  
Computer Based ◽  
Aided Diagnosis ◽  
Better Than

This paper describes a series of 10,500 attempts at »pattern-recognition« by two groups of humans and a computer based system. There was little difference between the performances of 11 clinicians and 11 other persons of comparable intellectual capability. Both groups’ performances were related to the pattern-size, the accuracy diminishing rapidly as the patterns grew larger. By contrast the computer system increased its accuracy as the patterns increased in size.It is suggested (a) that clinicians are very little better than others at pattem-recognition, (b) that the clinician is incapable of analysing on a probabilistic basis the data he collects during a traditional clinical interview and examination and (c) that the study emphasises once again a major difference between human and computer performance. The implications as - regards human- and computer-aided diagnosis are discussed.

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