Acoustical Imaging: Techniques and Applications for by Woon Siong Gan

By Woon Siong Gan

The expertise of acoustical imaging has complex speedily during the last sixty years, and now represents a cosmopolitan procedure utilized to a variety of fields together with non-destructive trying out, scientific imaging, underwater imaging and SONAR, and geophysical exploration. Acoustical Imaging: suggestions and purposes for Engineers introduces the fundamental physics of acoustics and acoustical imaging, earlier than progressing to extra complicated themes reminiscent of 3D and 4D imaging, elasticity conception, gauge invariance estate of acoustic equation of movement and acoustic metamaterials. the writer attracts jointly the several applied sciences in sonar, seismic and ultrasound imaging, highlighting the similarities among subject parts and their universal underlying theory.

Key features:

  • Comprehensively covers the entire vital purposes of acoustical imaging.
  • Introduces the gauge invariance estate of acoustic equation of movement, with purposes within the elastic constants of isotropic solids, time reversal acoustics, destructive refraction, double adverse acoustical metamaterial and acoustical cloaking.
  • Contains modern remedies on newest theories of sound propagation in random media, together with statistical therapy and chaos theory.
  • Includes a bankruptcy dedicated to new acoustics in keeping with metamaterials, a box based via the writer, together with a brand new idea of elasticity and new concept of sound propagation in solids and fluids and super power in numerous novel applications.

Covers the recent issues on acoustical imaging together with time reversal acoustics, adverse refraction and acoustical cloaking.

Acoustical Imaging: recommendations and purposes for Engineers is a accomplished reference on acoustical imaging and varieties a worthy source for engineers, researchers, senior undergraduate and graduate students.

Content:
Chapter 1 creation (pages 1–4):
Chapter 2 Physics of Acoustics and Acoustical Imaging (pages 5–14):
Chapter three sign Processing (pages 15–36):
Chapter four universal Methodologies of Acoustical Imaging (pages 37–61):
Chapter five Time?Reversal Acoustics and Superresolution (pages 63–87):
Chapter 6 Nonlinear Acoustical Imaging (pages 89–123):
Chapter 7 High?Frequencies Acoustical Imaging (pages 125–139):
Chapter eight Statistical remedy of Acoustical Imaging (pages 141–168):
Chapter nine Nondestructive trying out (pages 169–194):
Chapter 10 scientific Ultrasound Imaging (pages 195–262):
Chapter eleven Underwater Acoustical Imaging (pages 263–287):
Chapter 12 Geophysical Exploration (pages 289–324):
Chapter thirteen Quantum Acoustical Imaging (pages 325–341):
Chapter 14 unfavorable Refraction, Acoustical Metamaterials and Acoustical Cloaking (pages 343–368):
Chapter 15 New Acoustics according to Metamaterials (pages 369–406):
Chapter sixteen destiny instructions and destiny applied sciences (pages 407–408):

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Additional info for Acoustical Imaging: Techniques and Applications for Engineers

Example text

Image enhancement means the sharpening of image features such as edges, boundaries, or contrast to improve the resolution of the acoustical images. It is a form of digital image processing. From Rayleigh’s criterion, the resolution of the acoustical image is limited to λ/2, where λ is the sound wavelength. Hence, to increase the clarity of the images, we have to resort to digital image processing, an engineering method. The enhancement process does not increase the inherent information content in the data, but it does increase the dynamic range of the chosen features to enable them to be easily detected.

N − 1},{bl (n) , l = 0, . . . , N − 1} are one-dimensional complete orthonormal sets of basis vectors. 37) The Z-Transform The Z-transform is a generalization of the Fourier transform to the two-dimensional complex regime. 38) m,n=∞ where z1 and z2 are complex variables. The Z-transform of the impulse response of a linear shift invariant discrete system is called its transfer function. 39) This shows the transfer function of the systems, the ratio of the Z-transforms of the output, and the input sequences.

The terms used in statistics, such as means and covariance functions, will also be used here. 5 A quantizer (Jain [2]) We start with a one-dimensional (1D) linear system to represent the input signals. Examples of stochastic models used here are covariance models, 1D autoregressive (AR) models, 1D moving average (MA) models, and 1D autoregressive moving average (ARMA) models. 1 Autoregressive Models Let u(n) be a real, stationary random sequence with zero mean and covariance r(n). 50) where Sε (z) = SDF o f ε (n) .

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