DNA; Digital Data Storage Device

This article is dedicated to definition of the term virtual traces in forensics. The author determined the peculiarities of virtual traces, which allowed giving definition to the traces on digital data storage devices. It is noted, the forensic science does not have a universal terminology for definition of traces left as a result of cybercrimes. Solution of this problem remains extremely important, as it would assist the advancement of the theory and practice of forensics in the indicated field. The author analyzed the case law for the purpose of determining peculiarities of utilization of terminology to describe traces detected in the course of studying digital data storage devices. The conclusion is made that most common and suitable term is “virtual traces”. The definition of the concept in questions was proposed leaning on the analysis of peculiarities of virtual traces and mechanism of their emergence on digital data storage devices. Thus, virtual trace is determined as varied in structure, special type of material traces that exists within the limits of digital data storage device and directly tied with it, as well as can be decoded only using special software and technical means.

Download Full-text

GaN Materials Nanostructures Growth Control in the Epitaxial Units

Solid State Phenomena ◽

10.4028/www.scientific.net/ssp.265.627 ◽

2017 ◽

Vol 265 ◽

pp. 627-630 ◽

Cited By ~ 29

Author(s):

A. Zhilenkov

Keyword(s):

Data Storage ◽

High Capacity ◽

Growth Control ◽

Digital Data ◽

Storage Device ◽

Light Sources ◽

New Methods ◽

Full Color ◽

Metal Organic ◽

Digital Data Storage

The article overviews the results of the research into the peculiarities of GaN and AlGaN epitaxy from metal organic compounds, the influence of its peculiarities on the formation of GaN epitaxial layers and gives recommendations on the development of new methods for GaN epitaxial growth as well as on the research how they impact the characteristics of grown layers. The use of such semi-conductors allows obtaining full-color RGB light sources, increasing record density of a digital data storage device, getting high-capacity and efficient sources of white light. The electronic properties of such semi-conductors allow using them as a basis for high-power and high-frequency transistors and other electronic devices the specifications of which are competitive with those of SiC-based devices.

Download Full-text

New Trends in Digital Data Storage for the Internet of Things

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i3.359363 ◽

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

Download Full-text

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

10.1117/12.304980 ◽

1998 ◽

Cited By ~ 3

Author(s):

Kai-Oliver Mueller ◽

Cornelia Denz ◽

Torsten Rauch ◽

Thorsten Heimann ◽

J. Trumpfheller ◽

...

Keyword(s):

Data Storage ◽

Digital Data ◽

Holographic Memory ◽

Digital Data Storage

Download Full-text

Instance Selection

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch161 ◽

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.

Download Full-text