Wavelet packet based transient signal classification

Learned, Rachel E.; Karl, W.C.; Willsky, Alan S.

doi:10.1109/tftsa.1992.274223

Cited by 31 publications

(10 citation statements)

References 10 publications

(5 reference statements)

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“…The wavelet packet transform (WPT) offers a great deal of freedom in dealing with different types of transient signals. Indeed the development of the wavelet transform (WT) [7][8][9] and wavelet packets [10][11][12] has sparked considerable activity in signal representation and in transient and non stationary signal analysis. [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Time Domain Signal Analysis Using Wavelet Packet Decomposition Approach

Gokhale¹,

Khanduja²

2010

IJCNS

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This paper explains a study conducted based on wavelet packet transform techniques. In this paper the key idea underlying the construction of wavelet packet analysis (WPA) with various wavelet basis sets is elaborated. Since wavelet packet decomposition can provide more precise frequency resolution than wavelet decomposition the implementation of one dimensional wavelet packet transform and their usefulness in time signal analysis and synthesis is illustrated. A mother or basis wavelet is first chosen for five wavelet filter families such as Haar, Daubechies (Db4), Coiflet, Symlet and dmey. The signal is then decomposed to a set of scaled and translated versions of the mother wavelet also known as time and frequency parameters. Analysis and synthesis of the time signal is performed around 8 seconds to 25 seconds. This was conducted to determine the effect of the choice of mother wavelet on the time signals. Results are also prepared for the comparison of the signal at each decomposition level. The physical changes that are occurred during each decomposition level can be observed from the results. The results show that wavelet filter with WPA are useful for analysis and synthesis purpose. In terms of signal quality and the time required for the analysis and synthesis, the Haar wavelet has been seen to be the best mother wavelet. This is taken from the analysis of the signal to noise ratio (SNR) value which is around 300 dB to 315 dB for the four decomposition levels.

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Section: Introductionmentioning

confidence: 99%

Time Domain Signal Analysis Using Wavelet Packet Decomposition Approach

Gokhale¹,

Khanduja²

2010

IJCNS

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“…(Desai & Shazeer, 1991;Etemad & Chellappa, 1998;Leraned, 1992)). To construct effective classification systems, feature extraction of signals is an essential process.…”

Section: Image Feature Extractionmentioning

confidence: 99%

Image Classification Technique using Modified Particle Swarm Optimization

Shukran¹,

Chung²,

Yeh³

et al. 2011

MAS

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Image classification is becoming ever more important as the amount of available multimedia data increases. With the rapid growth in the number of images, there is an increasing demand for effective and efficient image indexing mechanisms. For large image databases, successful image indexing will greatly improve the efficiency of content based image classification. One attempt to solve the image indexing problem is using image classification to get high-level concepts. In such systems, an image is usually represented by various low-level features, and high-level concepts are learned from these features. PSO has recently attracted growing research interest due to its ability to learn with small samples and to optimize high-dimensional data. Therefore, this paper will introduce the related work on image feature extraction. Then, several techniques of image feature extraction will be introduced which include two main methods. These methods are RGB and Discrete Cosine Transformation (DCT). Finally, several experimental designs and results concerning the application of the proposed image classification using modified PSO classifier will be described in detail.

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“…If the window is long in comparison to the signal duration, there will be time averaging of the spectral information in that window. On the other hand, the window must be long enough so that there is no excessive frequency distortion of the signal spectrum 16 . Crash-event signals are not stationary but transient and strongly localised in time.…”

Section: Waveletsmentioning

confidence: 99%

Signal Processing, Wavelets and high-speed Image Interpretation of Bird Impact

Karthikeyan¹,

Ramachandra²,

Vizhian³

et al. 2011

DSJ

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Bird impact on aircraft has been well documented and has been of interest for researchers in aircraft design. The process, though of very short duration, is complex in nature. A bird, which can be treated as a soft body behaves more like a fluid at high-speeds. When aircraft components become targets of bird strikes, the impact can have consequences for safety, and hence the study of the phenomena has engineering implications. In this paper, strain signals from a specially instrumented stiff fixture, high-speed imaging and wavelets are used to describe the nature of the phenomenon. Gelatin-based artificial birds were impacted on the fixture fired through an air gun at two different velocities. high-speed imaging showed different behaviours with a rebound at low-velocity (~50 m/s) and a flow behaviour at high-velocity (~100 m/s). High sampling data acquisition was used to measure the dynamic strain exerted on the fixture during bird impact. Time histories of strain signals obtained in the raw form were processed to get a Fourier spectrum and continuous wavelet transform to gain more information about different frequency patterns and the temporal distribution of the frequencies, when such impacts occurred. The frequency content for low-velocity and high-velocity impacts is characterised. It can be noted that the behaviour as described by earlier researchers was seen here as well at higher velocities, though at lower velocities, the bird behaved more like a solid. Many aircraft have approach speeds that are about 60 m/s rather than 100-200 m/s, making it important to study behaviour at lower velocities as well. The short time interval events identified in the signals provide insight into the nature of the loads on targets. This information can aid in tuning simulation models of the birds which use Lagrangian, Eulerian and smooth particle hydrodynamic models.

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Wavelet packet based transient signal classification

Cited by 31 publications

References 10 publications

Time Domain Signal Analysis Using Wavelet Packet Decomposition Approach

Time Domain Signal Analysis Using Wavelet Packet Decomposition Approach

Image Classification Technique using Modified Particle Swarm Optimization

Signal Processing, Wavelets and high-speed Image Interpretation of Bird Impact

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