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Kumar et al. [35] proposed an approach in detecting the macula by using bit plane decomposition and mathematical morphology methods.
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For example, we can use the bit-plane decomposition [50] to compute the local binary codes on each 1-b plane, and then integrate them together.
Moreover, by using gray code representation rather than classic bit planes decomposition, we reduce the impact of small changes in intensity of patches that could produce significant variations in the corresponding binary code [17].
Finally, modes are interpolated using a plane wave decomposition.
The second is led by the plane wave decomposition of the δ-function.
To perform directional and spatial characterization of recorded sounds, a plane wave decomposition with an adaptive beamforming technique, such as minimum variance distortionless response (MVDR) spatial spectrum estimation, was employed.
Conventionally plane wave decomposition (PWD) of a low frequency sound field using a spherical microphone array (SMA) would suffer from low spatial resolution.
First, upper bounds expressing the required number of samples in the plane wave decomposition as a function of a preset accuracy are rigorously derived.
A general plane wave decomposition procedure that determines both the wave amplitudes (or the reflection coefficient) and the wavenumbers is proposed for in-duct measurements.
Therefore, particular attention is paid to the plane wave decomposition for 2-D homogeneous space Green functions in very lossy media, as needed in the PML-MLFMA.
In this article, a beamforming algorithm is presented in order to estimate the plane wave decomposition of the initial sound field.
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