Compiling Fortran 8x array features for the connection machine computer system

1988 ◽  
Author(s):  
Eugene Albert ◽  
Kathleen Knobe ◽  
Joan D. Lukas ◽  
Guy L. Steele
Keyword(s):  
Computer System ◽  
Connection Machine

Compiling Fortran 8x array features for the connection machine computer system

1988 ◽  
Vol 23 (9) ◽  
pp. 42-56 ◽  
Author(s):  
Eugene Albert ◽  
Kathleen Knobe ◽  
Joan D. Lukas ◽  
Guy L. Steele
Keyword(s):  
Computer System ◽  
Connection Machine

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.

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.

HUBUNGAN GAYA BELAJAR SISWA TERHADAP ASPEK PENGETAHUAN SISWA PADA MATA PELAJARAN SISTEM KOMPUTER DI SMK IT RAHMATAN KARIMAH BENGKULU TENGAH

2020 ◽  
Vol 1 (01) ◽  
pp. 13-20
Author(s):  
Dian Saputra
Keyword(s):  
Learning Styles ◽  
Computer System ◽  
Learning Style ◽  
Descriptive Analysis ◽  
Visual Learning ◽  
Auditory Learning ◽  
Analysis Techniques ◽  
Post Test ◽  
The Subject ◽  
The Relationship

This study aims to find out the relationship between learning style and students’ knowledge aspect on Computer System Subject at SMK IT Rahmatan Karimah of  Central Bengkulu, the type of research is quantitative and the subject of research is grade X in SMK IT Rahmatan Karimah of  Central Bengkulu. Data collection techniques using observation, Questionnaire and documentation. Data analysis techniques used were Descriptive Analysis, and inferential Statistical Analysis. The results of visual learning style post-test were 11 people with a mean of 76.36, an auditory learning style of 8 people at a mean of 62.14, a kinesthetic learning style of 3 people at a mean of 50.33, apart from that (r x y = 2.35) and the magnitude of r is reflected in the table (r table = 0.4132). Then rxy > r table ie = 2.35> 0.4132. In other words, Ho is rejected and Ha is accepted. It has a significant relationship between the learning styles of students and students’ knowledge aspect on Computer System Subject of grade X TKJ in SMK IT Rahmatan Karimah of  Central Bengkulu

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