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M. Goyal Laxmi Publications PDF

9

Statistical Techniques

9.1. THE STATISTICAL METHODS

Statistical methods are devices by which complex and numerical data are so systematically treated as to present a comprehensible and intelligible view of them. In other words, the statistical method is a technique used to obtain, analyse and present numerical data.

9.2. LIMITATION OF STATISTICAL METHODS

There are certain limitations to the statistical methods.

1. Statistical laws are not exact laws like mathematical or chemical laws. They are derived by taking a majority of cases and are not true for every individual. Thus, the statistical inferences are uncertain.

2. Statistical technique applies only to data reducible to quantitative forms.

3. Statistical technique is the same for the social as for physical sciences.

4. Statistical results might lead to fallacious conclusions if they are quoted short of their context.

9.3. FREQUENCY CHARTS

To represent frequency distribution by means of a diagram makes the unwieldly data intelligible and conveys to the eye the general run of the observations. The graphs and diagrams have a more lasting effect on the brain. When data of two items is compared with one another it is always easier to compare through graphs and diagrams. Forecasting also becomes easier by the help of graphs. Graphs help us in interpolation of values of the variables.

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Medium 9781601323262

Energy Efficient Data-Driven Networking Processor with Autonomous Load Distribution Capability

Hamid R. Arabnia, Lou D'Alotto, Hiroshi Ishii, Minoru Ito, Kazuki Joe, Hiroaki Nishikawa, Georgios Sirakoulis, William Spataro, Giuseppe A. Trunfio, George A. Gravvanis, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti CSREA Press PDF

514

Int'l Conf. Par. and Dist. Proc. Tech. and Appl. | PDPTA'14 |

Energy Efficient Data-Driven Networking Processor with Autonomous Load Distribution Capability

Shuji SANNOMIYA and Hiroaki NISHIKAWA

Graduate School of Systems and Information Engineering, University of Tsukuba,

Tsukuba Science City, Ibaraki, Japan

Abstract— Energy efficiency is one of the crucial issues for the processors realizing battery-operated devices to lengthen the lifetime of wireless network systems. CUE, a data-driven processor, is one of promising energy-efficient processors because of its real-time multiprocessing with essential power consumption. The CUE is realized by an elastic circular pipeline whose elastic capability enables to preserve processing time naturally even when the number of valid data exceeds a given design target temporarily due to the fluctuation of input traffic.

In this paper, a data-driven networking processor with autonomous load distribution capability is proposed to provide higher-level elastic capability against the larger input traffic fluctuation without any additional controls resulting in the degradation of the energy efficiency. In the processor proposed, the CUE’s in chip multiprocessor or many-core architectures are deconstructed and every processor module is placed on a parallelized circular pipeline in order to integrate the distributed elastic capabilities.

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Prof. Rachna Sharma and Prof. Sudipto Das Laxmi Publications PDF
Medium 9781944131425

LXCCC-10n.pdf

Ramesh Bangia Laxmi Publications PDF

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An Energy based Routing Algorithm using the Centers of Local Clustering in Wireless Sensor Networks

Hamid R. Arabnia, Leonidas Deligiannidis, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti CSREA Press PDF

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