Invariant pattern-recognition filter based on the wavelet transform

1996 ◽  
Author(s):  
Zeev Zalevsky ◽  
David Mendlovic ◽  
Carlos Ferreira
Keyword(s):  
Pattern Recognition ◽  
Wavelet Transform

Techniques for Decomposition of EMG Signals

2011 ◽  
pp. 467-477
Author(s):  
Arun Kumar Wadhwani ◽  
Sulochana Wadhwani
Keyword(s):  
Pattern Recognition ◽  
Wavelet Transform ◽  
Muscle Fatigue ◽  
Cross Correlation ◽  
Neuromuscular Disorders ◽  
Clinical Importance ◽  
Neuromuscular Control ◽  
Diagnostic Information ◽  
Self Organizing Map ◽  
Statistical Pattern

The information extracted from the EMG recordings is of great clinical importance and is used for the diagnosis and treatment of neuromuscular disorders and to study muscle fatigue and neuromuscular control mechanism. Thus there is a necessity of efficient and effective techniques, which can clearly separate individual MUAPs from the complex EMG without loss of diagnostic information. This chapter deals with the techniques of decomposition based on statistical pattern recognition, cross-correlation, Kohonen self-organizing map and wavelet transform.

scholarly journals Terrorist Group Behavior Prediction by Wavelet Transform-Based Pattern Recognition

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Ze Li ◽  
Duoyong Sun ◽  
Bo Li ◽  
Zhanfeng Li ◽  
Aobo Li
Keyword(s):  
Pattern Recognition ◽  
Social Network ◽  
Wavelet Transform ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Group Behavior ◽  
Terrorist Group ◽  
Attack Behavior ◽  
Terrorist Groups ◽  
Terrorist Acts

Predicting terrorist attacks by group networks is an important but difficult issue in intelligence and security informatics. Effective prediction of the behavior not only facilitates the understanding of the dynamics of organizational behaviors but also supports homeland security’s missions in prevention, preparedness, and response to terrorist acts. There are certain dynamic characteristics of terrorist groups, such as periodic features and correlations between the behavior and the network. In this paper, we propose a comprehensive framework that combines social network analysis, wavelet transform, and the pattern recognition approach to investigate the dynamics and eventually predict the attack behavior of terrorist group. Our ideas rely on social network analysis to model the terrorist group and extract relevant features for group behaviors. Next, based on wavelet transform, the group networks (features) are predicted and mutually checked from two aspects. Finally, based on the predicted network, the behavior of the group is recognized based on the correlation between the network and behavior. The Al-Qaeda data are investigated with the proposed framework to show the strength of our approaches. The results show that the proposed framework is highly accurate and is of practical value in predicting the behavior of terrorist groups.

WAVELET ORTHONORMAL DECOMPOSITIONS FOR EXTRACTING FEATURES IN PATTERN RECOGNITION

1999 ◽  
Vol 13 (06) ◽  
pp. 803-831 ◽  
Author(s):  
YUAN Y. TANG ◽  
JIMING LIU ◽  
HONG MA ◽  
BING F. LI
Keyword(s):  
Pattern Recognition ◽  
Wavelet Transform ◽  
Two Dimensional ◽  
Classification Rate ◽  
One Dimensional ◽  
Feature Vectors ◽  
Novel Approach

In this paper, a novel approach based on the wavelet orthonormal decomposition is presented to extract features in pattern recognition. The proposed approach first reduces the dimensionality of a two-dimensional pattern, and thereafter performs wavelet transform on the derived one-dimensional pattern to generate a set of wavelet transform subpatterns, namely, several uncorrelated functions. Based on these functions, new features are readily computed to represent the original two-dimensional pattern. As an application, experiments were conducted using a set of printed characters with varying orientations and fonts. The results obtained from these experiments have consistently shown that the proposed feature vectors can yield an excellent classification rate in pattern recognition.

Sign in / Sign up

Export Citation Format

Share Document