1,099 research outputs found
Robust equalization of multichannel acoustic systems
In most real-world acoustical scenarios, speech signals captured by distant microphones from a source are reverberated due to multipath propagation, and the reverberation may impair speech intelligibility. Speech dereverberation can be achieved
by equalizing the channels from the source to microphones. Equalization systems can
be computed using estimates of multichannel acoustic impulse responses. However,
the estimates obtained from system identification always include errors; the fact that
an equalization system is able to equalize the estimated multichannel acoustic system does not mean that it is able to equalize the true system. The objective of this
thesis is to propose and investigate robust equalization methods for multichannel
acoustic systems in the presence of system identification errors.
Equalization systems can be computed using the multiple-input/output inverse theorem or multichannel least-squares method. However, equalization systems
obtained from these methods are very sensitive to system identification errors. A
study of the multichannel least-squares method with respect to two classes of characteristic channel zeros is conducted. Accordingly, a relaxed multichannel least-
squares method is proposed. Channel shortening in connection with the multiple-
input/output inverse theorem and the relaxed multichannel least-squares method is
discussed.
Two algorithms taking into account the system identification errors are developed. Firstly, an optimally-stopped weighted conjugate gradient algorithm is
proposed. A conjugate gradient iterative method is employed to compute the equalization system. The iteration process is stopped optimally with respect to system identification errors. Secondly, a system-identification-error-robust equalization
method exploring the use of error models is presented, which incorporates system
identification error models in the weighted multichannel least-squares formulation
Digital Filters
The new technology advances provide that a great number of system signals can be easily measured with a low cost. The main problem is that usually only a fraction of the signal is useful for different purposes, for example maintenance, DVD-recorders, computers, electric/electronic circuits, econometric, optimization, etc. Digital filters are the most versatile, practical and effective methods for extracting the information necessary from the signal. They can be dynamic, so they can be automatically or manually adjusted to the external and internal conditions. Presented in this book are the most advanced digital filters including different case studies and the most relevant literature
X-ray CT Image Reconstruction on Highly-Parallel Architectures.
Model-based image reconstruction (MBIR) methods for X-ray CT use accurate
models of the CT acquisition process, the statistics of the noisy measurements,
and noise-reducing regularization to produce potentially higher quality images
than conventional methods even at reduced X-ray doses. They do this by
minimizing a statistically motivated high-dimensional cost function; the high
computational cost of numerically minimizing this function has prevented MBIR
methods from reaching ubiquity in the clinic. Modern highly-parallel hardware
like graphics processing units (GPUs) may offer the computational resources to
solve these reconstruction problems quickly, but simply "translating" existing
algorithms designed for conventional processors to the GPU may not fully
exploit the hardware's capabilities.
This thesis proposes GPU-specialized image denoising and image reconstruction
algorithms. The proposed image denoising algorithm uses group coordinate
descent with carefully structured groups. The algorithm converges very
rapidly: in one experiment, it denoises a 65 megapixel image in about 1.5
seconds, while the popular Chambolle-Pock primal-dual algorithm running on the
same hardware takes over a minute to reach the same level of accuracy.
For X-ray CT reconstruction, this thesis uses duality and group coordinate
ascent to propose an alternative to the popular ordered subsets (OS) method.
Similar to OS, the proposed method can use a subset of the data to update the
image. Unlike OS, the proposed method is convergent. In one helical CT
reconstruction experiment, an implementation of the proposed algorithm using
one GPU converges more quickly than a state-of-the-art algorithm converges
using four GPUs. Using four GPUs, the proposed algorithm reaches near
convergence of a wide-cone axial reconstruction problem with over 220 million
voxels in only 11 minutes.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113551/1/mcgaffin_1.pd
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