Computer Storage and Manipulation of Numbers

2018 ◽  
pp. 23-38
Author(s):  
Victor N. Kaliakin
Keyword(s):  
Computer Storage

Microdensitometer—computer correlation analysis of ultrastructural periodicity

1978 ◽  
Vol 36 (2) ◽  
pp. 32-33
Author(s):  
D.R. Ensor ◽  
C.G. Jensen ◽  
J.A. Fillery ◽  
R.J.K. Baker
Keyword(s):  
Correlation Analysis ◽  
Background Noise ◽  
Computer Control ◽  
Optical Diffraction ◽  
Screw Thread ◽  
Straight Line ◽  
Computer Storage ◽  
Correlation Process ◽  
Electron Microscope Image ◽  
Fixed Beam

Because periodicity is a major indicator of structural organisation numerous methods have been devised to demonstrate periodicity masked by background “noise” in the electron microscope image (e.g. photographic image reinforcement, Markham et al, 1964; optical diffraction techniques, Horne, 1977; McIntosh,1974). Computer correlation analysis of a densitometer tracing provides another means of minimising "noise". The correlation process uncovers periodic information by cancelling random elements. The technique is easily executed, the results are readily interpreted and the computer removes tedium, lends accuracy and assists in impartiality.A scanning densitometer was adapted to allow computer control of the scan and to give direct computer storage of the data. A photographic transparency of the image to be scanned is mounted on a stage coupled directly to an accurate screw thread driven by a stepping motor. The stage is moved so that the fixed beam of the densitometer (which is directed normal to the transparency) traces a straight line along the structure of interest in the image.

Experiences with the Use of Automation for Collecting and Recording Medical-History Data

1969 ◽  
Vol 8 (02) ◽  
pp. 53-59 ◽  
Author(s):  
John Mayne
Keyword(s):  
Medical History ◽  
English Language ◽  
Medical Information ◽  
Experimental Studies ◽  
Video Screen ◽  
History Data ◽  
Computer Storage ◽  
Computer Controlled ◽  
Questionnaire Studies ◽  
Health Questionnaires

For the past several years, experimental studies have been undertaken at the Mayo Clinic to evaluate the feasibility of utilizing electronic data processing to handle medical information, especially the medical information which makes up a medical record. We have experimented with automated techniques for collecting and storing medical-history data, specifically with techniques for computer generation and processing of health questionnaires, for computer-controlled administration of health questionnaires, and for computer-controlled entry and retrieval of medical-history data directly by physicians in ordinary English language by use of a video-screen and light-pen computer terminal.The questionnaire studies are concerned with ways of entering into computer storage medical-history data obtained from patients without physician involvement; the video-screen studies are concerned with entry into computer storage of medical-history data obtained by physicians in their interview with the patient. The paper describes our experiences in these studies.

scholarly journals Iterative Methods for the Computation of the Perron Vector of Adjacency Matrices

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1522
Author(s):  
Anna Concas ◽  
Lothar Reichel ◽  
Giuseppe Rodriguez ◽  
Yunzi Zhang
Keyword(s):  
Iterative Methods ◽  
Adjacency Matrix ◽  
Lanczos Method ◽  
Power Method ◽  
Arnoldi Method ◽  
Multiple Eigenvalues ◽  
Computer Storage ◽  
Adjacency Matrices ◽  
Lanczos Methods ◽  
Perron Vector

The power method is commonly applied to compute the Perron vector of large adjacency matrices. Blondel et al. [SIAM Rev. 46, 2004] investigated its performance when the adjacency matrix has multiple eigenvalues of the same magnitude. It is well known that the Lanczos method typically requires fewer iterations than the power method to determine eigenvectors with the desired accuracy. However, the Lanczos method demands more computer storage, which may make it impractical to apply to very large problems. The present paper adapts the analysis by Blondel et al. to the Lanczos and restarted Lanczos methods. The restarted methods are found to yield fast convergence and to require less computer storage than the Lanczos method. Computed examples illustrate the theory presented. Applications of the Arnoldi method are also discussed.

Computer storage elements

1965 ◽  
Vol 36 (142) ◽  
pp. 59
Author(s):  
A.J. Cole
Keyword(s):  
Computer Storage

Putting the horse before the cart: rapid access to data banks by the ‘SIGNPOSTS’* method

1992 ◽  
Vol 18 (1) ◽  
pp. 3-9
Author(s):  
Audrey M. Adams
Keyword(s):  
Image Processing ◽  
Document Image ◽  
Access To Information ◽  
Document Image Processing ◽  
Rapid Access ◽  
Efficient Access ◽  
Computer Storage ◽  
Efficient Retrieval ◽  
Access To Data ◽  
Data Banks

Document Image Processing (DIP) provides an efficient computer storage of information. Efficient retrieval of information is equally important and requires good indexing. This is a semantics prob lem only human indexers can solve. Data banks even more than books require good indexers to provide efficient access to information. ‘SIGNPOSTS’ provides a matrix for establishing a fully articulated index for databases.

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