By Topic

A universal scheme of hidden information detection from original signals via wavelet neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Li Jinping ; Sch. of Inf. Sci. & Eng., Jinan Univ., Shandong, China ; Han Yanbin ; Zhong Hongbo ; Yin Jianqin

A universal mathematical scheme for hidden information detection from various original n-dimensional signals is presented. The key points include the establishment of functional expression and the employment of cascade neural networks. The former indicates the detection of hidden information is dependent upon the shape of whole signals, not the specific values and the sampling number of the signals, the latter refers to the architecture of neural networks consisting of two neural networks, the first extracts main features of signals, and the second detects hidden information from the extracted features. Since wavelet functions play important role in signal analysis and feature extraction, thus wavelet neural networks constitute the first neural networks. The general scheme of feature extraction from original signals in L2(RM) by wavelet neural networks is presented. The application in signal processing of chemical chromatographic spectra of solution shows satisfactory results.

Published in:

Control, Automation, Robotics and Vision Conference, 2004. ICARCV 2004 8th  (Volume:3 )

Date of Conference:

6-9 Dec. 2004