28 research outputs found
Deconvolution of Quantized-Input Linear Systems: An Information-Theoretic Approach
The deconvolution problem has been drawing the attention of mathematicians, physicists and engineers since the early sixties.
Ubiquitous in the applications, it consists in recovering the unknown input of a convolution system from noisy measurements of the output. It is a typical instance of inverse, ill-posed problem: the existence and uniqueness of the solution are not assured and even small perturbations in the data may cause large deviations in the solution.
In the last fifty years, a large amount of estimation techniques have been proposed by different research communities to tackle deconvolution, each technique being related to a peculiar engineering application or mathematical set. In many occurrences, the unknown input presents some known features, which can be exploited to develop ad hoc algorithms. For example, prior information about regularity and smoothness of the input function are often considered, as well as the knowledge of a probabilistic distribution on the input source: the estimation techniques arising in different scenarios
are strongly diverse.
Less effort has been dedicated to the case where the input is known to be affected by discontinuities and switches, which is becoming an important issue in modern technologies. In fact, quantized signals, that is, piecewise constant functions that can assume only a finite number of values, are nowadays widespread in the applications, given the
ongoing process of digitization concerning most of information and communication systems. Moreover, hybrid systems are often encountered, which are characterized by the introduction of quantized signals into physical, analog communication channels.
Motivated by such consideration, this dissertation is devoted to the study of the deconvolution of continuous systems with quantized input; in particular, our attention will be focused on linear systems. Given the discrete nature of the input, we will
show that the whole problem can be interpreted as a paradigmatic digital transmission problem and we will undertake an Information-theoretic approach to tackle it.
The aim of this dissertation is to develop suitable deconvolution algorithms for quantized-input linear systems, which will be derived from known decoding procedures, and to test them in different scenarios. Much consideration will be given to the
theoretical analysis of these algorithms, whose performance will be rigorously described in mathematical terms
Deconvolution of Quantized-Input Linear Systems : an Information-Theoretic Approach
The deconvolution problem has been drawing the attention of mathematicians, physicists
and engineers since the early sixties.
Ubiquitous in the applications, it consists in recovering the unknown input of a
convolution system from noisy measurements of the output. It is a typical instance of
inverse, ill-posed problem: the existence and uniqueness of the solution are not assured
and even small perturbations in the data may cause large deviations in the solution.
In the last fifty years, a large amount of estimation techniques have been proposed by
di fferent research communities to tackle deconvolution, each technique being related
to a peculiar engineering application or mathematical set. In many occurrences, the
unknown input presents some known features, which can be exploited to develop ad
hoc algorithms. For example, prior information about regularity and smoothness of
the input function are often considered, as well as the knowledge of a probabilistic
distribution on the input source: the estimation techniques arising in diff erent scenarios
are strongly diverse.
Less eff ort has been dedicated to the case where the input is known to be aff ected by
discontinuities and switches, which is becoming an important issue in modern technologies.
In fact, quantized signals, that is, piecewise constant functions that can assume
only a fi nite number of values, are nowadays widespread in the applications, given the
ongoing process of digitization concerning most of information and communication systems.
Moreover, hybrid systems are often encountered, which are characterized by the
introduction of quantized signals into physical, analog communication channels.
Motivated by such consideration, this dissertation is devoted to the study of the
deconvolution of continuous systems with quantized input; in particular, our attention
will be focused on linear systems. Given the discrete nature of the input, we will
show that the whole problem can be interpreted as a paradigmatic digital transmission
problem and we will undertake an Information-theoretic approach to tackle it.
The aim of this dissertation is to develop suitable deconvolution algorithms for
quantized-input linear systems, which will be derived from known decoding procedures,
and to test them in diff erent scenarios. Much consideration will be given to the
theoretical analysis of these algorithms, whose performance will be rigorously described
in mathematical terms
Undergraduate Catalogue 2013-2014
https://scholarship.shu.edu/undergraduate_catalogues/1033/thumbnail.jp
Undergraduate Catalogue 2012-2013
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Undergraduate Catalogue 2014-2015
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Undergraduate Catalogue 2013-2014
https://scholarship.shu.edu/undergraduate_catalogues/1033/thumbnail.jp
Undergraduate Catalogue 2015-2016
https://scholarship.shu.edu/undergraduate_catalogues/1032/thumbnail.jp
Undergraduate Catalogue 2015-2016
https://scholarship.shu.edu/undergraduate_catalogues/1032/thumbnail.jp