Wavelet Theory And Its Application To Pattern Recognition

Tang, Yu-Wen; Liu, J.; Yang, Longfei; Ma, Hong

doi:10.1142/9789812385529

Cited by 27 publications

(28 citation statements)

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“…In applications of wavelets for change detection, wavelet decomposition coefficients have been used to identify edges in images and change points in time series data [2,3]. In the context of solution monitoring (SM) for nuclear safeguards, wavelet analysis is an appealing option for change detection because wavelets are localized in time and frequency and can detect both abrupt and gradual changes.…”

Section: Open Accessmentioning

confidence: 99%

Change Detection Using Wavelets in Solution Monitoring Data for Nuclear Safeguards

Longo

Burr

Myers

2013

Axioms

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Wavelet analysis is known to be a good option for change detection in many contexts. Detecting changes in solution volumes that are measured with both additive and relative error is an important aspect of safeguards for facilities that process special nuclear material. This paper qualitatively compares wavelet-based change detection to a lag-one differencing option using realistic simulated solution volume data for which the true change points are known. We then show quantitatively that Haar wavelet-based change detection is effective for finding the approximate location of each change point, and that a simple piecewise linear optimization step is effective to refine the initial wavelet-based change point estimate

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Section: Open Accessmentioning

confidence: 99%

Change Detection Using Wavelets in Solution Monitoring Data for Nuclear Safeguards

Longo

Burr

Myers

2013

Axioms

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“…including data and image compression [30,31], pattern recognition [32], transient detection [33], texture analysis [34], and noise reduction [35], and more recently, in bioinformatics [36] e.g., genome sequence analysis [37], analysis of microarray data [38], and retrieval of protein structures from databases [39].…”

Section: Wavelet Transformmentioning

confidence: 99%

Predicting protective bacterial antigens using random forest classifiers

EL-Manzalawy

Dobbs

Honavar

2012

Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine

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Identifying protective antigens from bacterial pathogens is important for developing vaccines. Most computational methods for predicting protein antigenicity rely on sequence similarity between a query protein sequence and at least one known antigen. Such methods limit our ability to predict novel antigens (i.e., antigens that are not homologous to any known antigen). Therefore, there is an urgent need for alignment-free computational methods for reliable prediction of protective antigens.We evaluated the discriminative power of four different amino acid composition derived feature representations using three classification methods (Logistic Regression, Support Vector Machine, and Random Forest) on a cross validation data set of 193 protective bacterial antigens and 193 non-antigenic bacterial proteins. Our results show that, with all four data representations, Random Forest classifiers consistently outperform other classifiers. We compared HRF50, one of the best performing Random Forest classifiers with VaxiJen and SignalP on independent test sets derived from the Chlamydia trachomatis and Bartonella proteomes. Our results show that our HRF50 predictor outperforms VaxiJen and is competitive with SignalP and ANTIGENpro in predicting protective antigens. We further showed that when we combine SignalP with HRF50, the resulting method, which we call BacGen, yields performance that is comparable to or better than that of ANTIGENpro in predicting antigens in bacterial sequences. We conclude that amino acid sequence composition derived features can be effectively used to design alignment-free methods for predicting protein antigenicity using Random Forest classifiers. BacGen is available as an online server at

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“…Wavelet analysis [5][6][7] is a method for time-frequency analysis. It has an advantage over the traditional methods in the signal analysis.…”

Section: Principle Of Wavelet Transformmentioning

confidence: 99%

Improving feature extraction in fingerprint of medicinal herbs via wavelet transform and fractal technique

Tang

Wang

et al. 2006

Journal of Chemometrics

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In this paper, hybrid features combing both high-frequency and low-frequency components of wavelet transform are applied to fingerprint of medicinal herbs. Through the fingerprints of medicinal herbs by wavelet transform and the fractal dimensions, 13 features are obtained, which are called fractal-wavelet features. In this new approach, the information of each sample can be acquired to the maximum degree. These novel hybrid features have been applied to recognition of the different types of ginseng. Experiments have been conducted, and the result of recognition can match the real situation. Experiments indicate this method is better than the traditional ones.

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Wavelet Theory And Its Application To Pattern Recognition

Abstract: All rights resewed. This book, or parts thereof; may not be reproduced in any form or by any meam, electronic or mchanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written petmissionfrom the Publisher.

Cited by 27 publications

References 0 publications

Change Detection Using Wavelets in Solution Monitoring Data for Nuclear Safeguards

Change Detection Using Wavelets in Solution Monitoring Data for Nuclear Safeguards

Predicting protective bacterial antigens using random forest classifiers

Improving feature extraction in fingerprint of medicinal herbs via wavelet transform and fractal technique

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