25,171 research outputs found
Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting
Solar radiation prediction is an important challenge for the electrical
engineer because it is used to estimate the power developed by commercial
photovoltaic modules. This paper deals with the problem of solar radiation
prediction based on observed meteorological data. A 2-day forecast is obtained
by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS
are used to exploit the correlation between solar radiation and
timescale-related variations of wind speed, humidity, and temperature. The
input to the selected WRNN is provided by timescale-related bands of wavelet
coefficients obtained from meteorological time series. The experimental setup
available at the University of Catania, Italy, provided this information. The
novelty of this approach is that the proposed WRNN performs the prediction in
the wavelet domain and, in addition, also performs the inverse wavelet
transform, giving the predicted signal as output. The obtained simulation
results show a very low root-mean-square error compared to the results of the
solar radiation prediction approaches obtained by hybrid neural networks
reported in the recent literature
3D weak lensing with spin wavelets on the ball
We construct the spin flaglet transform, a wavelet transform to analyze spin
signals in three dimensions. Spin flaglets can probe signal content localized
simultaneously in space and frequency and, moreover, are separable so that
their angular and radial properties can be controlled independently. They are
particularly suited to analyzing of cosmological observations such as the weak
gravitational lensing of galaxies. Such observations have a unique 3D
geometrical setting since they are natively made on the sky, have spin angular
symmetries, and are extended in the radial direction by additional distance or
redshift information. Flaglets are constructed in the harmonic space defined by
the Fourier-Laguerre transform, previously defined for scalar functions and
extended here to signals with spin symmetries. Thanks to various sampling
theorems, both the Fourier-Laguerre and flaglet transforms are theoretically
exact when applied to bandlimited signals. In other words, in numerical
computations the only loss of information is due to the finite representation
of floating point numbers. We develop a 3D framework relating the weak lensing
power spectrum to covariances of flaglet coefficients. We suggest that the
resulting novel flaglet weak lensing estimator offers a powerful alternative to
common 2D and 3D approaches to accurately capture cosmological information.
While standard weak lensing analyses focus on either real or harmonic space
representations (i.e., correlation functions or Fourier-Bessel power spectra,
respectively), a wavelet approach inherits the advantages of both techniques,
where both complicated sky coverage and uncertainties associated with the
physical modeling of small scales can be handled effectively. Our codes to
compute the Fourier-Laguerre and flaglet transforms are made publicly
available.Comment: 24 pages, 4 figures, version accepted for publication in PR
A statistical multiresolution approach for face recognition using structural hidden Markov models
This paper introduces a novel methodology that combines the multiresolution feature of the discrete wavelet transform (DWT) with the local interactions of the facial structures expressed through the structural hidden Markov model (SHMM). A range of wavelet filters such as Haar, biorthogonal 9/7, and Coiflet, as well as Gabor, have been implemented in order to search for the best performance. SHMMs perform a thorough probabilistic analysis of any sequential pattern by revealing both its inner and outer structures simultaneously. Unlike traditional HMMs, the SHMMs do not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the SHMMs. Therefore, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. SHMMs have not previously been applied to the problem of face identification. The results reported in this application have shown that SHMM outperforms the traditional hidden Markov model with a 73% increase in accuracy
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