809 research outputs found
Translating the EAH Data Compression Algorithm into Automata Theory
Adaptive codes have been introduced in [Dragos Trinca, cs.DS/0505007] as a
new class of non-standard variable-length codes. These codes associate
variable-length codewords to symbols being encoded depending on the previous
symbols in the input data string. A new data compression algorithm, called EAH,
has been introduced in [Dragos Trinca, cs.DS/0505061], where we have
behaviorally shown that for a large class of input data strings, this algorithm
substantially outperforms the well-known Lempel-Ziv universal data compression
algorithm. In this paper, we translate the EAH encoder into automata theory.Comment: 9 page
Special Cases of Encodings by Generalized Adaptive Codes
Adaptive (variable-length) codes associate variable-length codewords to
symbols being encoded depending on the previous symbols in the input data
string. This class of codes has been presented in [Dragos Trinca,
cs.DS/0505007] as a new class of non-standard variable-length codes.
Generalized adaptive codes (GA codes, for short) have been also presented in
[Dragos Trinca, cs.DS/0505007] not only as a new class of non-standard
variable-length codes, but also as a natural generalization of adaptive codes
of any order. This paper is intended to continue developing the theory of
variable-length codes by establishing several interesting connections between
adaptive codes and other classes of codes. The connections are discussed not
only from a theoretical point of view (by proving new results), but also from
an applicative one (by proposing several applications). First, we prove that
adaptive Huffman encodings and Lempel-Ziv encodings are particular cases of
encodings by GA codes. Second, we show that any (n,1,m) convolutional code
satisfying certain conditions can be modelled as an adaptive code of order m.
Third, we describe a cryptographic scheme based on the connection between
adaptive codes and convolutional codes, and present an insightful analysis of
this scheme. Finally, we conclude by generalizing adaptive codes to
(p,q)-adaptive codes, and discussing connections between adaptive codes and
time-varying codes.Comment: 17 page
Modelling the Eulerian Path Problem using a String Matching Framework
The well-known Eulerian path problem can be solved in polynomial time (more
exactly, there exists a linear time algorithm for this problem). In this paper,
we model the problem using a string matching framework, and then initiate an
algorithmic study on a variant of this problem, called the (2,1)-STRING-MATCH
problem (which is actually a generalization of the Eulerian path problem).
Then, we present a polynomial-time algorithm for the (2,1)-STRING-MATCH
problem, which is the most important result of this paper. Specifically, we get
a lower bound of Omega(n), and an upper bound of O(n^{2}).Comment: 10 page
Adaptive Codes: A New Class of Non-standard Variable-length Codes
We introduce a new class of non-standard variable-length codes, called
adaptive codes. This class of codes associates a variable-length codeword to
the symbol being encoded depending on the previous symbols in the input data
string. An efficient algorithm for constructing adaptive codes of order one is
presented. Then, we introduce a natural generalization of adaptive codes,
called GA codes.Comment: 10 page
High-performance BWT-based Encoders
In 1994, Burrows and Wheeler developed a data compression algorithm which
performs significantly better than Lempel-Ziv based algorithms. Since then, a
lot of work has been done in order to improve their algorithm, which is based
on a reversible transformation of the input string, called BWT (the
Burrows-Wheeler transformation). In this paper, we propose a compression scheme
based on BWT, MTF (move-to-front coding), and a version of the algorithms
presented in [Dragos Trinca, ITCC-2004].Comment: 12 page
EAH: A New Encoder based on Adaptive Variable-length Codes
Adaptive variable-length codes associate a variable-length codeword to the
symbol being encoded depending on the previous symbols in the input string.
This class of codes has been recently presented in [Dragos Trinca,
arXiv:cs.DS/0505007] as a new class of non-standard variable-length codes. New
algorithms for data compression, based on adaptive variable-length codes of
order one and Huffman's algorithm, have been recently presented in [Dragos
Trinca, ITCC 2004]. In this paper, we extend the work done so far by the
following contributions: first, we propose an improved generalization of these
algorithms, called EAHn. Second, we compute the entropy bounds for EAHn, using
the well-known bounds for Huffman's algorithm. Third, we discuss implementation
details and give reports of experimental results obtained on some well-known
corpora. Finally, we describe a parallel version of EAHn using the PRAM model
of computation.Comment: 16 page
Modelling the EAH Data Compression Algorithm using Graph Theory
Adaptive codes associate variable-length codewords to symbols being encoded
depending on the previous symbols in the input data string. This class of codes
has been introduced in [Dragos Trinca, cs.DS/0505007] as a new class of
non-standard variable-length codes. New algorithms for data compression, based
on adaptive codes of order one, have been presented in [Dragos Trinca,
ITCC-2004], where we have behaviorally shown that for a large class of input
data strings, these algorithms substantially outperform the Lempel-Ziv
universal data compression algorithm. EAH has been introduced in [Dragos
Trinca, cs.DS/0505061], as an improved generalization of these algorithms. In
this paper, we present a translation of the EAH algorithm into the graph
theory.Comment: 10 page
Randomized Iterative Reconstruction for Sparse View X-ray Computed Tomography
With the availability of more powerful computers, iterative reconstruction
algorithms are the subject of an ongoing work in the design of more efficient
reconstruction algorithms for X-ray computed tomography. In this work, we show
how two analytical reconstruction algorithms can be improved by correcting the
corresponding reconstructions using a randomized iterative reconstruction
algorithm. The combined analytical reconstruction followed by randomized
iterative reconstruction can also be viewed as a reconstruction algorithm
which, in the experiments we have conducted, uses up to less projection
angles as compared to the analytical reconstruction algorithms and produces the
same results in terms of quality of reconstruction, without increasing the
execution time significantly.Comment: 23 page
IRXCT: Iterative Reconstruction and visualization application for X-ray Computed Tomography
This report describes the IRXCT Windows application for reconstruction and
visualization of tomography tasks.Comment: 12 page
Comparison of Sinogram-based Iterative Reconstruction with Compressed Sensing Techniques in X-ray CT
Performing X-ray computed tomography (CT) examinations with less radiation
has recently received increasing interest: in medical imaging this means less
(potentially harmful) radiation for the patient; in non-destructive testing of
materials/objects such as testing jet engines, the redution of the number of
projection angles (which for large objects is in general high) leads to a
substantial decreasing of the experiment time. In the experiment, less
radiation is usually achieved by either (1) reducing the radiation dose used at
each projection angle or (2) using sparse view X-ray CT, which means
significantly less projection angles are used during the examination. In this
work, we study the performance of the recently proposed sinogram-based
iterative reconstruction algorithm in sparse view X-ray CT and show that it
provides, in some cases, reconstruction accuracy better than that obtained by
some of the Total Variation regularization techniques. The provided accuracy is
obtained with computation times comparable to other techniques. An important
feature of the sinogram-based iterative reconstruction algorithm is that it has
no parameters to be set.Comment: 18 page
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