Particle Filter Based Matching Pursuit Decomposition for Damage Quantification in Composite Structures

2006 ◽  
Author(s):  
Santanu Das ◽  
Ioannis Kyriakides ◽  
Aditi Chattopadhyay ◽  
Antonia Papandreou-Suppappola
Keyword(s):  
Particle Filter ◽  
Composite Structures ◽  
Matching Pursuit ◽  
Damage Quantification ◽  
Matching Pursuit Decomposition

Damage Localization in a Stiffened Composite Panel Using a Lamb Wave Based Tomography Approach

2011 ◽  
Author(s):  
Anthony J. Vizzini ◽  
Yingtao Liu ◽  
Aditi Chattopadhyay
Keyword(s):  
Composite Structures ◽  
Impact Damage ◽  
Matching Pursuit ◽  
Low Velocity Impact ◽  
Composite Panel ◽  
Low Velocity ◽  
Stiffened Panels ◽  
Time Frequency ◽  
Matching Pursuit Decomposition ◽  
High Level

In structural health monitoring (SHM) of aerospace components, such as stiffened panels, detection and localization of damage is an important issue. This paper presents a methodology for determining the existence and location of low velocity impact damage in a stiffened composite panel. Using a matching pursuit decomposition algorithm, converted modes due to damage were extracted in the time-frequency domain. The energy of the converted mode was then used in conjunction with a probabilistic tomography approach that was able to localize the damage with a high level of accuracy. The results obtained confirm the ability of this approach to detect and localize damage in complex composite structures.

Parametric Description of EEG Profiles for Assessment of Sleep Architecture in Disorders of Consciousness

2019 ◽  
Vol 29 (03) ◽  
pp. 1850049 ◽  
Author(s):  
Magdalena Zieleniewska ◽  
Anna Duszyk ◽  
Piotr Różański ◽  
Marcin Pietrzak ◽  
Marta Bogotko ◽  
...  
Keyword(s):  
Matching Pursuit ◽  
Disorders Of Consciousness ◽  
Sleep Architecture ◽  
Quality Of Sleep ◽  
Software Environment ◽  
Continuous Description ◽  
Matching Pursuit Decomposition ◽  
Parametric Description ◽  
Eeg Recordings

We propose a fully parametric approach to the assessment of sleep architecture, based upon the classical electroencephalographic criteria, applicable also to the recordings of patients with disorders of consciousness (DOC). Sleep spindles and slow waves are automatically detected from the matching pursuit decomposition of overnight EEG recordings. Their evolution can be presented in the form of EEG profiles, yielding a continuous description of sleep architecture, compatible with the classical criteria used in sleep staging. We propose assessment of these EEG profiles by five parameters, which can be combined by a linear classifier, assessing the quality of sleep architecture. Proposed methodology is evaluated on 59 overnight EEG recordings from 19 patients from a hospital for children with severe brain damage, in relation to their behavioral diagnosis according to the Coma Recovery Scale-Revised. Presented results indicate robustness of the proposed approach, which may serve as a valuable aid in diagnosis of DOC patients. Complete software environment for computing and presentation of EEG profiles is freely available from http://svarog.pl .

Network anomaly detection based on signal processing techniques

2013 ◽  
Vol 18 (1) ◽  
pp. 15-21
Author(s):  
Tomasz Andrysiak ◽  
Łukasz Saganowski ◽  
Mirosław Maszewski
Keyword(s):  
Signal Processing ◽  
Anomaly Detection ◽  
Network Traffic ◽  
Detection Method ◽  
Matching Pursuit ◽  
Matching Pursuit Decomposition ◽  
Network Anomaly Detection ◽  
Processing Techniques ◽  
Signal Processing Techniques

Abstract The article depicts possibility of using Matching Pursuit decomposition in order to recognize unspecified hazards in network traffic. Furthermore, the work aims to present feasible enhancements to the anomaly detection method, as well as their efficiency on the basis of a wide collection of pattern test traces.

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