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  • In Jour Smart Sensing And Intelligent Systems

 

Article | 05-September-2013

Ranking of Sensitive Positions using Statistical and Correlational Analysis

machine based on statistical parameters analysis. While the final list of required number of sensitive positions is generated, the cross-correlation amongst the positions is also taken into consideration to avoid redundancy. Furthermore, a standalone application for implementing the same has been developed on Android platform. The proposed scheme and application can be used for variety of other applications that work on similar principle of acquiring data from multiple sensors.

Nishchal K. Verma, Rahul K. Sevakula, Sonal Dixit, Al Salour

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 4, 1745–1762

Research Article | 20-February-2013

CFM: A FITNESS-MODEL-BASED TOPOLOGY CONTROL ALGORITHM FOR WIRELESS SENSOR NETWORKS

The main objective of wireless sensor network design is to maximize network lifetime. The network topology, which is the important foundation of upper layer protocols, serves as the supportive groundwork for this goal. We constructed the model of sensor networks, and investigated the property of topology with complex network theory. Three statistical parameters were used to describe the network structure, and then some ideal characteristics were concluded for topology. The characteristics of

Linfeng Liu, Jiagao Wu, Fu Xiao, Ruchuan Wang

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 1, 58–76

Article | 01-September-2014

EMPIRICAL MODE DECOMPOSITION AND ROUGH SET ATTRIBUTE REDUCTION FOR ULTRASONIC FLAW SIGNAL CLASSIFICATION

Feature extraction and selection are the most important techniques for ultrasonic flaw signal classification. In this study, empirical mode decomposition (EMD) is first used to obtain the intrinsic mode functions (IMFs) of original ultrasonic signals. Such IMFs and traditional time as well as frequency domain based statistical parameters are extracted as the initial features of flaw signal. After that, spectral clustering method is used for feature value discretization so that rough set

Yu Wang

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 3, 1401–1420

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