150,970 research outputs found
MÔ HÌNH HÓA KẾT NỐI TRUY NHẬP THUÊ BAO SỐ (DSL) TRONG MẠCH VÒNG NỘI HẠT
ABSTRACTThis paper is posed on the single point subscriber line modeling problem, consisting of two main paragraphs out of the one for introduction and of that for remarks and suggestions for next works. In the second paragraph, a brief report is made on network topology and characteristics of local loop model obtaining from transmission line theory and electrical characteristics of twisted pair segments. Being of distributed nature, parameters of local loop models are not found to be successfully estimated by common techniques with the use of direction time response (TDR) in system identification theory. However, two algorithms well known on the basis of method of direction estimate (MODE) namely MODE-WRELAX and MODE-type are briefly resumed in the second paragraph. In the third one, a method is proposed on the basis of two dimensional Poisson Momentum Function which transforms signals on the both sides of the system, i.e. input and output sides, to spatial-time domain for direction -of-arrival estimation. The fact behind the spatial-time domain is that time-frequency space leading to the usage of MODE-type algorithm for separation of different reflections in frequency domain. In the last paragraph, three different comments on the proposed method of supplying measurements data by Poisson Momentum Function for estimation purpose and suggestions for further study to be carried out.ON MODELLING LOCAL LOOPS FOR DIGITAL SUBSCRIBER LINE (DSL)This paper is posed on the single point subscriber line modeling problem, consisting of two main paragraphs out of the one for introduction and of that for remarks and suggestions for next works. In the second paragraph, a brief report is made on network topology and characteristics of local loop model obtaining from transmission line theory and electrical characteristics of twisted pair segments. Being of distributed nature, parameters of local loop models are not found to be successfully estimated by common techniques with the use of direction time response (TDR) in system identification theory. However, two algorithms well known on the basis of method of direction estimate (MODE) namely MODE-WRELAX and MODE-type are briefly resumed in the second paragraph. In the third one, a method is proposed on the basis of two dimensional Poisson Momentum Function which transforms signals on the both sides of the system, i.e. input and output sides, to spatial-time domain for direction -of-arrival estimation. The fact behind the spatial-time domain is that time-frequency space leading to the usage of MODE-type algorithm for separation of different reflections in frequency domain. In the last paragraph, three different comments on the proposed method of supplying measurements data by Poisson Momentum Function for estimation purpose and suggestions for further study to be carried out
Theoretical and Experimental Analysis of a Randomized Algorithm for Sparse Fourier Transform Analysis
We analyze a sublinear RAlSFA (Randomized Algorithm for Sparse Fourier
Analysis) that finds a near-optimal B-term Sparse Representation R for a given
discrete signal S of length N, in time and space poly(B,log(N)), following the
approach given in \cite{GGIMS}. Its time cost poly(log(N)) should be compared
with the superlinear O(N log N) time requirement of the Fast Fourier Transform
(FFT). A straightforward implementation of the RAlSFA, as presented in the
theoretical paper \cite{GGIMS}, turns out to be very slow in practice. Our main
result is a greatly improved and practical RAlSFA. We introduce several new
ideas and techniques that speed up the algorithm. Both rigorous and heuristic
arguments for parameter choices are presented. Our RAlSFA constructs, with
probability at least 1-delta, a near-optimal B-term representation R in time
poly(B)log(N)log(1/delta)/ epsilon^{2} log(M) such that
||S-R||^{2}<=(1+epsilon)||S-R_{opt}||^{2}. Furthermore, this RAlSFA
implementation already beats the FFTW for not unreasonably large N. We extend
the algorithm to higher dimensional cases both theoretically and numerically.
The crossover point lies at N=70000 in one dimension, and at N=900 for data on
a N*N grid in two dimensions for small B signals where there is noise.Comment: 21 pages, 8 figures, submitted to Journal of Computational Physic
2-D Prony-Huang Transform: A New Tool for 2-D Spectral Analysis
This work proposes an extension of the 1-D Hilbert Huang transform for the
analysis of images. The proposed method consists in (i) adaptively decomposing
an image into oscillating parts called intrinsic mode functions (IMFs) using a
mode decomposition procedure, and (ii) providing a local spectral analysis of
the obtained IMFs in order to get the local amplitudes, frequencies, and
orientations. For the decomposition step, we propose two robust 2-D mode
decompositions based on non-smooth convex optimization: a "Genuine 2-D"
approach, that constrains the local extrema of the IMFs, and a "Pseudo 2-D"
approach, which constrains separately the extrema of lines, columns, and
diagonals. The spectral analysis step is based on Prony annihilation property
that is applied on small square patches of the IMFs. The resulting 2-D
Prony-Huang transform is validated on simulated and real data.Comment: 24 pages, 7 figure
Guide to Spectral Proper Orthogonal Decomposition
This paper discusses the spectral proper orthogonal decomposition and its use in identifying modes, or structures, in flow data. A specific algorithm based on estimating the cross-spectral density tensor with Welch’s method is presented, and guidance is provided on selecting data sampling parameters and understanding tradeoffs among them in terms of bias, variability, aliasing, and leakage. Practical implementation issues, including dealing with large datasets, are discussed and illustrated with examples involving experimental and computational turbulent flow data
Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables
Understanding nonlinear dynamical systems (NLDSs) is challenging in a variety
of engineering and scientific fields. Dynamic mode decomposition (DMD), which
is a numerical algorithm for the spectral analysis of Koopman operators, has
been attracting attention as a way of obtaining global modal descriptions of
NLDSs without requiring explicit prior knowledge. However, since existing DMD
algorithms are in principle formulated based on the concatenation of scalar
observables, it is not directly applicable to data with dependent structures
among observables, which take, for example, the form of a sequence of graphs.
In this paper, we formulate Koopman spectral analysis for NLDSs with structures
among observables and propose an estimation algorithm for this problem. This
method can extract and visualize the underlying low-dimensional global dynamics
of NLDSs with structures among observables from data, which can be useful in
understanding the underlying dynamics of such NLDSs. To this end, we first
formulate the problem of estimating spectra of the Koopman operator defined in
vector-valued reproducing kernel Hilbert spaces, and then develop an estimation
procedure for this problem by reformulating tensor-based DMD. As a special case
of our method, we propose the method named as Graph DMD, which is a numerical
algorithm for Koopman spectral analysis of graph dynamical systems, using a
sequence of adjacency matrices. We investigate the empirical performance of our
method by using synthetic and real-world data.Comment: 34 pages with 4 figures, Published in Neural Networks, 201
Trans-dimensional inversion of modal dispersion data on the New England Mud Patch
© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Bonnel, J., Dosso, S. E., Eleftherakis, D., & Chapman, N. R. Trans-dimensional inversion of modal dispersion data on the New England Mud Patch. IEEE Journal of Oceanic Engineering, 45(1), (2020): 116-130, doi:10.1109/JOE.2019.2896389.This paper presents single receiver geoacoustic inversion of two independent data sets recorded during the 2017 seabed characterization experiment on the New England Mud Patch. In the experimental area, the water depth is around 70 m, and the seabed is characterized by an upper layer of fine grained sediments with clay (i.e., mud). The first data set considered in this paper is a combustive sound source signal, and the second is a chirp emitted by a J15 source. These two data sets provide differing information on the geoacoustic properties of the seabed, as a result of their differing frequency content, and the dispersion properties of the environment. For both data sets, source/receiver range is about 7 km, and modal time-frequency dispersion curves are estimated using warping. Estimated dispersion curves are then used as input data for a Bayesian trans-dimensional inversion algorithm. Subbottom layering and geoacoustic parameters (sound speed and density) are thus inferred from the data. This paper highlights important properties of the mud, consistent with independent in situ measurements. It also demonstrates how information content differs for two data sets collected on reciprocal tracks, but with different acoustic sources and modal content.10.13039/100000006-Office of Naval Research
10.13039/100007297-Office of Naval Research Globa
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