577 research outputs found
Enhanced B-Wavelets via Mixed, Composite Packets
A modified B-wavelet construction with enhanced
filter characteristics is considered. The design comprises a superposition
of tessellated, integer dilated, ‘sister’ wavelet functions.
We here propose a cascaded filter-bank realisation of this wavelet
family together with some notable extensions. We prove that
modifications of low-order members exist in the multiresolution
subspace spanned by the half-translates of the original wavelets
and hence that the resulting modified wavelet coefficients can be
computed as convolutions of the undecimated original wavelet
coefficients. Finite impulse response filters are thus designed and
incorporated into a B-wavelet packet architecture such that the
mainlobe-to-sidelobe ratio of the resulting wavelet filter characteristic
is improved. This is achieved by designing the filters so
that zeros are introduced near to the maxima of the harmonics.
It is shown that the numbers of zeros can be balanced with the
length of the corresponding filters by controlling the ‘modification
order’. Several constructions are presented. We prove that two
such constructions satisfy the perfect reconstruction property for
all orders. The resulting modified wavelets preserve many of the
properties of the original B-wavelets such as differentiability,
number of vanishing moments, symmetry, anti-symmetry, finite
support, and the existence of a closed form expression
Combined Industry, Space and Earth Science Data Compression Workshop
The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems
Anisotropic Harmonic Analysis and Integration of Remotely Sensed Data
This thesis develops the theory of discrete directional Gabor frames and several algorithms for the analysis of remotely sensed image data, based on constructions of harmonic analysis. The problems of image registration, image superresolution, and image fusion are separate but interconnected; a general approach using transform methods is the focus of this thesis. The methods of geometric multiresolution analysis are explored, particularly those related to the shearlet transform. Using shearlets, a novel method of image registration is developed that aligns images based on their shearlet features. Additionally, the anisotropic nature of the shearlet transform is deployed to smoothly superrsolve remotely-sensed image with edge features. Wavelet packets, a generalization of wavelets, are utilized for a flexible image fusion algorithm. The interplay between theoretical guarantees for these mathematical constructions, and their effectiveness for image processing is explored throughout
Signal processing for guided wave structural health monitoring
The importance of Structural Health Monitoring (SHM) in several industrial fields
has been continuously growing in the last few years with the increasing need for
the development of systems able to monitor continuously the integrity of complex
structures. In order to be competitive with conventional non destructive evaluation
techniques, SHM must be able to effectively detect the occurrence of damage in
the structure, giving information regarding the damage location. Ultrasonic guided
waves offer the possibility of inspecting large areas of structures from a small number
of sensor positions. However, inspection of complex structures is difficult as the
reflections from different features overlap. Therefore damage detection becomes an
extremely challenging problem and robust signal processing is required in order to
resolve strongly overlapping echoes.
In our work we have considered at first the possibility of employing a deconvolution
approach for enhancing the resolution of ultrasonic time traces and the potential
and the limitations of this approach for reliable SHM applications have been shown.
The effects of noise on the bandwidth of the typical signals in SHM and the effects
of frequency dependent phase shifts are the main detrimental issues that strongly
reduce the performance of deconvolution in SHM applications.
The second part of this thesis is concerned with the evaluation of a subtraction approach
for SHM when changes of environmental conditions are taken into account.
Temperature changes result in imperfect subtraction even for an undamaged structure,
since temperature changes modify the mechanical properties of the material
and therefore the velocity of propagation of ultrasonic guided waves. Compensation
techniques have previously been used effectively to overcome temperature effects, in
order to reduce the residual in the subtraction. In this work the performance of temperature
compensation techniques has been evaluated also in the presence of other
detrimental effects, such as liquid loading and different temperature responses of
materials in adhesive joints. Numerical simulations and experiments have been conducted
and it has been shown that temperature compensation techniques can cope in principle with non temperature effects. It is concluded that subtraction approach
represents a promising method for reliable Structural Health Monitoring. Nonetheless
the feasibility of a subtraction approach for SHM depends on environmental
conditions
The 1995 Science Information Management and Data Compression Workshop
This document is the proceedings from the 'Science Information Management and Data Compression Workshop,' which was held on October 26-27, 1995, at the NASA Goddard Space Flight Center, Greenbelt, Maryland. The Workshop explored promising computational approaches for handling the collection, ingestion, archival, and retrieval of large quantities of data in future Earth and space science missions. It consisted of fourteen presentations covering a range of information management and data compression approaches that are being or have been integrated into actual or prototypical Earth or space science data information systems, or that hold promise for such an application. The Workshop was organized by James C. Tilton and Robert F. Cromp of the NASA Goddard Space Flight Center
Wavelet Theory
The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior
The locally stationary dual-tree complex wavelet model
We here harmonise two significant contributions to the field of wavelet analysis in the past two decades, namely the locally stationary wavelet process and the family of dual-tree complex wavelets. By combining these two components, we furnish a statistical model that can simultaneously access benefits from these two constructions. On the one hand, our model borrows the debiased spectrum and auto-covariance estimator from the locally stationary wavelet model. On the other hand, the enhanced directional selectivity is obtained from the dual-tree complex wavelets over the regular lattice. The resulting model allows for the description and identification of wavelet fields with significantly more directional fidelity than was previously possible. The corresponding estimation theory is established for the new model, and some stationarity detection experiments illustrate its practicality
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