30,912 research outputs found
Optimally Sparse Frames
Frames have established themselves as a means to derive redundant, yet stable
decompositions of a signal for analysis or transmission, while also promoting
sparse expansions. However, when the signal dimension is large, the computation
of the frame measurements of a signal typically requires a large number of
additions and multiplications, and this makes a frame decomposition intractable
in applications with limited computing budget. To address this problem, in this
paper, we focus on frames in finite-dimensional Hilbert spaces and introduce
sparsity for such frames as a new paradigm. In our terminology, a sparse frame
is a frame whose elements have a sparse representation in an orthonormal basis,
thereby enabling low-complexity frame decompositions. To introduce a precise
meaning of optimality, we take the sum of the numbers of vectors needed of this
orthonormal basis when expanding each frame vector as sparsity measure. We then
analyze the recently introduced algorithm Spectral Tetris for construction of
unit norm tight frames and prove that the tight frames generated by this
algorithm are in fact optimally sparse with respect to the standard unit vector
basis. Finally, we show that even the generalization of Spectral Tetris for the
construction of unit norm frames associated with a given frame operator
produces optimally sparse frames
Semi-blind speech-music separation using sparsity and continuity priors
In this paper we propose an approach for the problem of single channel source separation of speech and music signals. Our approach is based on representing each source's power spectral density using dictionaries and nonlinearly projecting the mixture signal spectrum onto the combined span of the dictionary entries. We encourage sparsity and continuity of the dictionary coefficients using penalty terms (or log-priors) in an optimization framework. We propose to use a novel coordinate descent technique for optimization, which nicely handles nonnegativity constraints and nonquadratic penalty terms. We use an adaptive Wiener filter, and spectral subtraction to reconstruct both of the sources from the mixture data after corresponding power spectral densities (PSDs) are estimated for each source. Using conventional metrics, we measure the performance of the system on simulated mixtures of single person speech and piano music sources. The results indicate that the proposed method is a promising technique for low speech-to-music ratio conditions and that sparsity and continuity priors help improve the performance of the proposed system
High-resolution modal analysis
Usual modal analysis techniques are based on the Fourier transform. Due to
the Delta T . Delta f limitation, they perform poorly when the modal overlap mu
exceeds 30%. A technique based on a high-resolution analysis algorithm and an
order-detection method is presented here, with the aim of filling the gap
between the low- and the high-frequency domains (30%<mu<100%). A pseudo-impulse
force is applied at points of interests of a structure and the response is
measured at a given point. For each pair of measurements, the impulse response
of the structure is retrieved by deconvolving the pseudo-impulse force and
filtering the response with the result. Following conditioning treatments, the
reconstructed impulse response is analysed in different frequency-bands. In
each frequency-band, the number of modes is evaluated, the frequencies and
damping factors are estimated, and the complex amplitudes are finally
extracted. As examples of application, the separation of the twin modes of a
square plate and the partial modal analyses of aluminium plates up to a modal
overlap of 70% are presented. Results measured with this new method and those
calculated with an improved Rayleigh method match closely
A frequency-based RF partial discharge detector for low-power wireless sensing
Partial discharge (PD) monitoring has been the subject of significant research in recent years, which has given rise to a range of well-established PD detection and measurement techniques, such as acoustic and RF, on which condition monitoring systems for highvoltage equipment have been based. This paper presents a novel approach to partial discharge monitoring by using a low-cost, low-power RF detector. The detector employs a frequency-based technique that can distinguish between multiple partial discharge events and other impulsive noise sources within a substation, tracking defect severity over time and providing information pertaining to plant health. The detector is designed to operate as part of a wireless condition monitoring network, removing the need for additional wiring to be installed into substations whilst still gaining the benefits of the RF technique. This novel approach to PD detection not only provides a low-cost solution to on-line partial discharge monitoring, but also presents a means to deploy wide-scale RF monitoring without the associated costs of wide-band monitoring systems
Nanostructure of cellulose microfibrils in spruce wood
The structure of cellulose microfibrils in wood is not known in detail, despite the abundance of cellulose in woody biomass and its importance for biology, energy, and engineering. The structure of the microfibrils of spruce wood cellulose was investigated using a range of spectroscopic methods coupled to small-angle neutron and wide-angle X-ray scattering. The scattering data were consistent with 24-chain microfibrils and favored a “rectangular” model with both hydrophobic and hydrophilic surfaces exposed. Disorder in chain packing and hydrogen bonding was shown to increase outwards from the microfibril center. The extent of disorder blurred the distinction between the I alpha and I beta allomorphs. Chains at the surface were distinct in conformation, with high levels of conformational disorder at C-6, less intramolecular hydrogen bonding and more outward-directed hydrogen bonding. Axial disorder could be explained in terms of twisting of the microfibrils, with implications for their biosynthesis
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