39,691 research outputs found
Adaptive multichannel control of time-varying broadband noise and vibrations
This paper presents results obtained from a number of applications in which a recent adaptive algorithm for broadband multichannel active noise control is used. The core of the algorithm uses the inverse of the minimum-phase part of the secondary path for improvement of the speed of convergence. A further improvement of the speed of convergence is obtained by using double control filters for elimination of adaptation loop delay. Regularization was found to be necessary for robust operation. The regularization technique which is used preserves the structure to eliminate the adaptation loop delay. Depending on the application at hand, a number of extensions are used for this algorithm. For an application with rapidly changing disturbance spectra, the core algorithm was extended with an iterative affine projection scheme, leading to improved convergence rates as compared to the standard nomalized lms update rules. In another application, in which the influence of the parametric uncertainties was critical, the core algorithm was extended with low authority control loops operating at high sample rates. In addition, results of other applications are given, such as control of acoustic energy density and control of time-varying periodic and non-periodic vibrations
Rapidly converging multichannel controllers for broadband noise and vibrations
Applications are given of a preconditioned adaptive algorithm for broadband multichannel active noise control. Based on state-space descriptions of the relevant transfer functions, the algorithm uses the inverse of the minimum-phase part of the secondary path in order to improve the speed of convergence. A further improvement of the convergence rate is obtained by using double control filters for elimination of adaptation loop delay. Regularization was found to be essential for robust operation. The particular regularization technique preserves the structure to eliminate the adaptation loop delay. Depending on the application at hand, a number of extensions are used for this algorithm, such as for applications with rapidly changing disturbance spectra, applications with large parametric uncertainty, applications with control of time-varying acoustic energy density
Multi-modal Blind Source Separation with Microphones and Blinkies
We propose a blind source separation algorithm that jointly exploits
measurements by a conventional microphone array and an ad hoc array of low-rate
sound power sensors called blinkies. While providing less information than
microphones, blinkies circumvent some difficulties of microphone arrays in
terms of manufacturing, synchronization, and deployment. The algorithm is
derived from a joint probabilistic model of the microphone and sound power
measurements. We assume the separated sources to follow a time-varying
spherical Gaussian distribution, and the non-negative power measurement
space-time matrix to have a low-rank structure. We show that alternating
updates similar to those of independent vector analysis and Itakura-Saito
non-negative matrix factorization decrease the negative log-likelihood of the
joint distribution. The proposed algorithm is validated via numerical
experiments. Its median separation performance is found to be up to 8 dB more
than that of independent vector analysis, with significantly reduced
variability.Comment: Accepted at IEEE ICASSP 2019, Brighton, UK. 5 pages. 3 figure
Mitigation of artifacts due to isolated acoustic heterogeneities in photoacoustic computed tomography using a variable data truncation-based reconstruction method
Photoacoustic computed tomography (PACT) is an emerging computed imaging
modality that exploits optical contrast and ultrasonic detection principles to
form images of the absorbed optical energy density within tissue. If the object
possesses spatially variant acoustic properties that are unaccounted for by the
reconstruction method, the estimated image can contain distortions. While
reconstruction methods have recently been developed to compensate for this
effect, they generally require the object's acoustic properties to be known a
priori. To circumvent the need for detailed information regarding an object's
acoustic properties, we previously proposed a half-time reconstruction method
for PACT. A half-time reconstruction method estimates the PACT image from a
data set that has been temporally truncated to exclude the data components that
have been strongly aberrated. However, this method can be improved upon when
the approximate sizes and locations of isolated heterogeneous structures, such
as bones or gas pockets, are known. To address this, we investigate PACT
reconstruction methods that are based on a variable data truncation (VDT)
approach. The VDT approach represents a generalization of the half-time
approach, in which the degree of temporal truncation for each measurement is
determined by the distance between the corresponding ultrasonic transducer
location and the nearest known bone or gas void location. Computer-simulated
and experimental data are employed to demonstrate the effectiveness of the
approach in mitigating artifacts due to acoustic heterogeneities
Inertial Coupling Method for particles in an incompressible fluctuating fluid
We develop an inertial coupling method for modeling the dynamics of
point-like 'blob' particles immersed in an incompressible fluid, generalizing
previous work for compressible fluids. The coupling consistently includes
excess (positive or negative) inertia of the particles relative to the
displaced fluid, and accounts for thermal fluctuations in the fluid momentum
equation. The coupling between the fluid and the blob is based on a no-slip
constraint equating the particle velocity with the local average of the fluid
velocity, and conserves momentum and energy. We demonstrate that the
formulation obeys a fluctuation-dissipation balance, owing to the
non-dissipative nature of the no-slip coupling. We develop a spatio-temporal
discretization that preserves, as best as possible, these properties of the
continuum formulation. In the spatial discretization, the local averaging and
spreading operations are accomplished using compact kernels commonly used in
immersed boundary methods. We find that the special properties of these kernels
make the discrete blob a particle with surprisingly physically-consistent
volume, mass, and hydrodynamic properties. We develop a second-order
semi-implicit temporal integrator that maintains discrete
fluctuation-dissipation balance, and is not limited in stability by viscosity.
Furthermore, the temporal scheme requires only constant-coefficient Poisson and
Helmholtz linear solvers, enabling a very efficient and simple FFT-based
implementation on GPUs. We numerically investigate the performance of the
method on several standard test problems...Comment: Contains a number of corrections and an additional Figure 7 (and
associated discussion) relative to published versio
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
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