A trail detection method using statistical analysis of trail features in dense forest

Author(s):  
Jeonghyeok Kim ◽  
Sanggil Kang ◽  
Heezin Lee
2014 ◽  
Vol 513-517 ◽  
pp. 1005-1008
Author(s):  
Xiao Ming Yao ◽  
Hong Yu Chen ◽  
Hong Lei Li ◽  
Xiao Yi Zhou

Data tampering as one of the primary security issues in RFID-enabled applications has been presented in recent years and proposals based on watermarking have been put forward to address different aspects of tampering in RFID tags. However, most of current researches are focused on the way of generating the watermark from the data to be protected and embedding it into the tag field (usually the field of serial number or SN) used as the cover medium, thus the innate structural coding relationship as a new clue to guess out the hidden watermark might be ignored. In this paper, this flaw has been fully considered, and a novel tamper detection method using CFB based encryption to hide the location clues is presented. Although it cant resist the attack from statistical analysis either, theoretical analysis has demonstrated that our scheme outperforms its previous counterparts in data security.


2009 ◽  
Vol 14 (6) ◽  
pp. 661-675
Author(s):  
Hye-Jeong Cho ◽  
Ji-Eun Kim ◽  
Chae-Bong Sohn ◽  
Kwang-Sue Chung ◽  
Seoung-Jun Oh

1966 ◽  
Vol 24 ◽  
pp. 188-189
Author(s):  
T. J. Deeming

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.


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