Feature Extraction and Signal Processing Approach to Realtime Pattern Recognition.

1978 ◽  
Author(s):  
C. H. Chen
Keyword(s):  
Pattern Recognition ◽  
Signal Processing ◽  
Feature Extraction ◽  
Processing Approach

An Overview of Signal-Based Damage Detection Methods

2011 ◽  
Vol 94-96 ◽  
pp. 834-851 ◽  
Author(s):  
Long Qiao ◽  
Asad Esmaeily
Keyword(s):  
Pattern Recognition ◽  
Signal Processing ◽  
Feature Extraction ◽  
Damage Detection ◽  
Structural Response ◽  
Nonlinear Behavior ◽  
Detection Methods ◽  
Time Frequency ◽  
Frequency Domain Methods ◽  
Feature Extraction And Selection

Deterioration of structures due to aging, cumulative crack growth or excessive response significantly affects the performance and safety of structures during their service life. Recently, signal-based methods have received many attentions for structural health monitoring and damage detection. These methods examine changes in the features derived directly from the measured time histories or their corresponding spectra through proper signal processing methods and algorithms to detect damage. Based on different signal processing techniques for feature extraction, these methods are classified into time-domain methods, frequency-domain methods, and time-frequency (or time-scale)-domain methods. As an enhancement for feature extraction, selection and classification, pattern recognition techniques are deeply integrated into signal-based damage detection. This paper provided an overview of these methods based on two aspects: (1) feature extraction and selection, and (2) pattern recognition. Signal-based methods are particularly more effective for structures with complicated nonlinear behavior and the incomplete, incoherent, and noise-contaminated measurements of structural response.

scholarly journals FEM enhanced signal processing approach for pattern recognition in the SQUID based NDE system

2010 ◽  
Vol 234 (4) ◽  
pp. 042030 ◽  
Author(s):  
F Sarreshtedari ◽  
N M S Jahed ◽  
N Hosseni ◽  
A Pourhashemi ◽  
Marko banzet ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Signal Processing ◽  
Processing Approach

scholarly journals Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 114
Author(s):  
Tiziano Zarra ◽  
Mark Gino K. Galang ◽  
Florencio C. Ballesteros ◽  
Vincenzo Belgiorno ◽  
Vincenzo Naddeo
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Performance Optimization ◽  
Industrial Plant ◽  
Mathematical Treatment ◽  
Extraction Techniques ◽  
Peak Period ◽  
Linear Discriminant ◽  
Artificial Neural

Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor signal, the mathematical treatment of the acquired data, and the validation of the correlation of the odour metric are key topics to control in order to ensure a robust and reliable measurement. The research presents and discusses the use of different pattern recognition and feature extraction techniques in the elaboration and effectiveness of the odour classification monitoring model (OCMM). The effect of the rise, intermediate, and peak period from the original response curve, in collaboration with Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) as a pattern recognition algorithm, were investigated. Laboratory analyses were performed with real odour samples collected in a complex industrial plant, using an advanced smart IOMS. The results demonstrate the influence of the choice of method on the quality of the OCMM produced. The peak period in combination with the Artificial Neural Network (ANN) highlighted the best combination on the basis of high classification rates. The paper provides information to develop a solution to optimize the performance of IOMS.

A Signal Processing Approach to Pharmacokinetic Data Analysis

2021 ◽  
Vol 38 (4) ◽  
pp. 625-635
Author(s):  
C. O. S. Sorzano ◽  
M. A. Pérez-de-la-Cruz Moreno ◽  
F. R. Martín ◽  
C. Montejo ◽  
A. Aguilar-Ros
Keyword(s):  
Signal Processing ◽  
Data Analysis ◽  
Pharmacokinetic Data ◽  
Processing Approach
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