Wavelet Transforms: Introduction to Theory and Applications

Rao, Raghuveer M.

doi:10.1117/1.482718

Cited by 296 publications

(273 citation statements)

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“…It is associated to the points {12,13,14,15}. The probability of correct decision is (14) and the probability of error for Type 4 is expressed as Combining and rearranging Equations (9), (11), (13) and (15), the average probability of error for the circular 16-QAM scheme is given by The analysis can also determine the exact BER for other circular M-ary QAM. Note that the process of obtaining BER analysis for the square scheme is excluded since it is available in much literature.…”

Section:  mentioning

confidence: 99%

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Performance Analysis of an Optimal Circular 16-QAM for Wavelet Based OFDM Systems

Abdullah

Mahmoud²,

Hussain³

2009

IJCNS

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The BER performance for an optimal circular 16-QAM constellation is theoretically derived and applied in wavelet based OFDM system in additive white Gaussian noise channel. Signal point constellations have been discussed in much literature. An optimal circular 16-QAM is developed. The calculation of the BER is based on the four types of the decision boundaries. Each decision boundary is determined based on the space distance d following the pdf Gaussian distribution with respect to the in-phase and quadrature components nI and nQ with the assumption that they are statistically independent to each other. The BER analysis for other circular M-ary QAM is also analyzed. The system is then applied to wavelet based OFDM. The wavelet transform is considered because it offers a better spectral containment feature compared to conventional OFDM using Fourier transform. The circular schemes are slightly better than the square schemes in most SNR values. All simulation results have met the theoretical calculations. When applying to wavelet based OFDM, the circular modulation scheme has also performed slightly less errors as compared to the square modulation scheme.

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Section:  mentioning

confidence: 99%

“…Let  and be two scaling functions and let ψ and ˆb e two wavelet functions, then we can express the biorthogonal scaling and wavelet functions as follows [5,14]:…”

Section: Biorthogonal Waveletsmentioning

confidence: 99%

Performance Analysis of an Optimal Circular 16-QAM for Wavelet Based OFDM Systems

Abdullah

Mahmoud²,

Hussain³

2009

IJCNS

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“…Wavelet is a waveform with an effectively limited duration and zero average value. Mathematically, consider a function ψ with the following properties [18][19][20]: 1. The function integrates with zero (Equation (1)):…”

Section: Wavelet Transformmentioning

confidence: 99%

Wavelet-based acoustic emission characterization of damage mechanism in composite materials under mode I delamination at different interfaces

Oskouei¹,

Ahmadi

Hajikhani³

2009

Express Polym. Lett.

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Abstract. In this paper, acoustic emission (AE) monitoring with a wavelet-based signal processing technique is developed to detect the damage types during mode I delamination on glass/polyester composite materials. Two types of specimen at different midplane layups, woven/woven (T3) and unidirectional/unidirectional (T5), leading to different levels of damage evolution, were studied. Double cantilever beam (DCB) is applied to simulate delamination process for all specimens. Firstly, the obtained AE signals are decomposed into various wavelet levels. Each level includes detail and approximation that are called components and related to a specific frequency range. Secondly, the energy distribution criterion is applied to find the more significant components each one of which is in relation to a distinct type of damage. The results show that the energy of AE signals has been concentrated in three significant components for both of the specimens. There is a difference in energy distribution of similar components of two specimens. It indicates that there is a dissimilar dominant damage mechanism for two different interfaces during the delamination process. Additionally, the microscopic observation (SEM) is used to determine how the different fracture mechanisms are related to the dominant corresponding wavelet components.

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“…Wavelet transform of a signal, on the other hand, decomposes signal in both time and frequency domain [6][7][8], which turns out to be very useful in fault detection. In wavelet transform we take a real/complex valued continuous time function with two main properties -a) it will integrate to zero; b) it is square integrable.…”

Section: Overview Of Wavelet Transformmentioning

confidence: 99%

Frequency Specification Testing of Analog Filters Using Wavelet Transform of Dynamic Supply Current

2005

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Wavelet Transforms: Introduction to Theory and Applications

Cited by 296 publications

References 0 publications

Performance Analysis of an Optimal Circular 16-QAM for Wavelet Based OFDM Systems

Performance Analysis of an Optimal Circular 16-QAM for Wavelet Based OFDM Systems

Wavelet-based acoustic emission characterization of damage mechanism in composite materials under mode I delamination at different interfaces

Frequency Specification Testing of Analog Filters Using Wavelet Transform of Dynamic Supply Current

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