A Holographic Memory for Digital Data Storage

1997 ◽  
Vol 08 (04) ◽  
pp. 749-765
Author(s):  
C. P. Yang ◽  
S. H. Lin ◽  
M. L. Hsieh ◽  
K. Y. Hsu ◽  
T. C. Hsieh
Keyword(s):  
Data Storage ◽  
Personal Computer ◽  
Bit Error Rate ◽  
Error Rate ◽  
Optical Memory ◽  
Digital Data ◽  
Holographic Memory ◽  
Optical Signals ◽  
Digital Data Storage

A read-only holographic memory for digital data storage is experimentally demonstrated. Techniques for coding and decoding of optical signals, and the interface techniques between the optical memory and a personal computer are described. The performance of the optical memory and the techniques for improving the bit error rate (BER) are presented.

Analog and digital data storage in a phase-coded holographic memory

1998 ◽  
Author(s):  
Kai-Oliver Mueller ◽  
Cornelia Denz ◽  
Torsten Rauch ◽  
Thorsten Heimann ◽  
J. Trumpfheller ◽  
...  
Keyword(s):  
Data Storage ◽  
Digital Data ◽  
Holographic Memory ◽  
Digital Data Storage

Digital Data Storage and Computing by a Vector Holographic Memory System

2001 ◽  
Vol 40 (Part 1, No. 3B) ◽  
pp. 1841-1845 ◽  
Author(s):  
Katsunori Kawano ◽  
Jiro Minabe ◽  
Tsutomu Ishii
Keyword(s):  
Data Storage ◽  
Memory System ◽  
Digital Data ◽  
Holographic Memory ◽  
Digital Data Storage

Digital data storage in a phase-encoded holographic memory system: data quality and security

2003 ◽  
Author(s):  
Gernot Berger ◽  
Kai-Oliver Mueller ◽  
Cornelia Denz ◽  
Istvan Foldvari ◽  
Agnes Peter
Keyword(s):  
Data Quality ◽  
Data Storage ◽  
Memory System ◽  
Digital Data ◽  
Holographic Memory ◽  
System Data ◽  
Digital Data Storage

New Trends in Digital Data Storage for the Internet of Things

2018 ◽  
Vol 6 (3) ◽  
pp. 359-363
Author(s):  
A. Saxena ◽  
◽  
S. Sharma ◽  
S. Dangi ◽  
A. Sharma ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Data Storage ◽  
Digital Data ◽  
The Internet ◽  
Digital Data Storage ◽  
The Internet Of Things

Instance Selection

2011 ◽  
pp. 1041-1045
Author(s):  
Huan Liu
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Data Reduction ◽  
Effective Means ◽  
Digital Data ◽  
Data Selection ◽  
Instance Selection ◽  
Redundant Data ◽  
Performance Deterioration ◽  
Digital Data Storage

The amounts of data become increasingly large in recent years as the capacity of digital data storage worldwide has significantly increased. As the size of data grows, the demand for data reduction increases for effective data mining. Instance selection is one of the effective means to data reduction. This article introduces basic concepts of instance selection, its context, necessity and functionality. It briefly reviews the state-of-the-art methods for instance selection. Selection is a necessity in the world surrounding us. It stems from the sheer fact of limited resources. No exception for data mining. Many factors give rise to data selection: data is not purely collected for data mining or for one particular application; there are missing data, redundant data, and errors during collection and storage; and data can be too overwhelming to handle. Instance selection is one effective approach to data selection. It is a process of choosing a subset of data to achieve the original purpose of a data mining application. The ideal outcome of instance selection is a model independent, minimum sample of data that can accomplish tasks with little or no performance deterioration.

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