483 research outputs found
Woven Graph Codes: Asymptotic Performances and Examples
Constructions of woven graph codes based on constituent block and
convolutional codes are studied. It is shown that within the random ensemble of
such codes based on -partite, -uniform hypergraphs, where depends
only on the code rate, there exist codes satisfying the Varshamov-Gilbert (VG)
and the Costello lower bound on the minimum distance and the free distance,
respectively. A connection between regular bipartite graphs and tailbiting
codes is shown. Some examples of woven graph codes are presented. Among them an
example of a rate woven graph code with
based on Heawood's bipartite graph and containing constituent rate
convolutional codes with overall constraint lengths is
given. An encoding procedure for woven graph codes with complexity proportional
to the number of constituent codes and their overall constraint length
is presented.Comment: Submitted to IEEE Trans. Inform. Theor
Scalable video/image transmission using rate compatible PUM turbo codes
The robust delivery of video over emerging wireless networks poses many challenges due to the heterogeneity of access networks, the variations in streaming devices, and the expected variations in network conditions caused by interference and coexistence. The proposed approach exploits the joint optimization of a wavelet-based scalable video/image coding framework and a forward error correction method based on PUM turbo codes. The scheme minimizes the reconstructed image/video distortion at the decoder subject to a constraint on the overall transmission bitrate budget. The minimization is achieved by exploiting the rate optimization technique and the statistics of the transmission channel
A rate R=5/20 hypergraph-based woven convolutional code with free distance 120
A rate R=5/20 hypergraph-based woven convolu- tional code with overall constraint length 67 and constituent con- volutional codes is presented. It is based on a 3-partite, 3-uniform, 4-regular hypergraph and contains rate R=3/4 constituent convolutional codes with overall constraint length 5. Although the code construction is based on low-complexity codes, the free distance of this construction, computed with the BEAST algorithm, is dfree=120, which is remarkably large
Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders
Convolutional autoencoders have emerged as popular methods for unsupervised
defect segmentation on image data. Most commonly, this task is performed by
thresholding a pixel-wise reconstruction error based on an distance.
This procedure, however, leads to large residuals whenever the reconstruction
encompasses slight localization inaccuracies around edges. It also fails to
reveal defective regions that have been visually altered when intensity values
stay roughly consistent. We show that these problems prevent these approaches
from being applied to complex real-world scenarios and that it cannot be easily
avoided by employing more elaborate architectures such as variational or
feature matching autoencoders. We propose to use a perceptual loss function
based on structural similarity which examines inter-dependencies between local
image regions, taking into account luminance, contrast and structural
information, instead of simply comparing single pixel values. It achieves
significant performance gains on a challenging real-world dataset of
nanofibrous materials and a novel dataset of two woven fabrics over the state
of the art approaches for unsupervised defect segmentation that use pixel-wise
reconstruction error metrics
Evolutionary Algorithm Aided Interleaver Design for Serially Concatenated Codes
In this paper, we propose an algorithm for designing the interleavers of Serially Concatenated Codes (SCCs), in order to increase the Minimum Hamming Distance (MHD) between the legitimate permutations of the encoded bit sequence and hence to improve the corresponding error floor. Unlike previous so-called Code Matched Interleaver (CMI) designs, our approach is capable of creating interleavers for serial concatenations of both irregular and non-linear codes, as well as achieving MHDs that are arbitrarily close to the maximum possible, provided that a sufficiently high off-line complexity is affordable. However, owing to the efficiency of the proposed approach, only a relatively low number of algorithm generations are required to achieve significant improvements to the error floor of low-delay wireless sensor network, speech and audio schemes, for example. Indeed, we demonstrate that our interleavers are capable of completely eradicating the error floors that would otherwise be apparent, if classic random or S-random interleavers were employed
Decoding of woven convolutional codes and simulation results
An iterative decoding scheme for woven convolutional codes is presented. It is called pipeline decoding and operates in a window sliding over the received sequence. This exploits the nature of convolutional codes as sequences and suits the concept of convolutional encoding and decoding as a continuous process. The pipeline decoder is analyzed in terms of decoding delay and decoding complexity. Additional interleaving for woven convolutional constructions is introduced by employing a convolutional scrambler. It is shown that some types of interleaving preserve the lower bound on the free distance of the original woven construction. Simulation results for woven convolutional codes are presente
Deep filter banks for texture recognition, description, and segmentation
Visual textures have played a key role in image understanding because they
convey important semantics of images, and because texture representations that
pool local image descriptors in an orderless manner have had a tremendous
impact in diverse applications. In this paper we make several contributions to
texture understanding. First, instead of focusing on texture instance and
material category recognition, we propose a human-interpretable vocabulary of
texture attributes to describe common texture patterns, complemented by a new
describable texture dataset for benchmarking. Second, we look at the problem of
recognizing materials and texture attributes in realistic imaging conditions,
including when textures appear in clutter, developing corresponding benchmarks
on top of the recently proposed OpenSurfaces dataset. Third, we revisit classic
texture representations, including bag-of-visual-words and the Fisher vectors,
in the context of deep learning and show that these have excellent efficiency
and generalization properties if the convolutional layers of a deep model are
used as filter banks. We obtain in this manner state-of-the-art performance in
numerous datasets well beyond textures, an efficient method to apply deep
features to image regions, as well as benefit in transferring features from one
domain to another.Comment: 29 pages; 13 figures; 8 table
Codes on Graphs and More
Modern communication systems strive to achieve reliable and efficient information transmission and storage with affordable complexity. Hence, efficient low-complexity channel codes providing low probabilities for erroneous receptions are needed. Interpreting codes as graphs and graphs as codes opens new perspectives for constructing such channel codes. Low-density parity-check (LDPC) codes are one of the most recent examples of codes defined on graphs, providing a better bit error probability than other block codes, given the same decoding complexity. After an introduction to coding theory, different graphical representations for channel codes are reviewed. Based on ideas from graph theory, new algorithms are introduced to iteratively search for LDPC block codes with large girth and to determine their minimum distance. In particular, new LDPC block codes of different rates and with girth up to 24 are presented. Woven convolutional codes are introduced as a generalization of graph-based codes and an asymptotic bound on their free distance, namely, the Costello lower bound, is proven. Moreover, promising examples of woven convolutional codes are given, including a rate 5/20 code with overall constraint length 67 and free distance 120. The remaining part of this dissertation focuses on basic properties of convolutional codes. First, a recurrent equation to determine a closed form expression of the exact decoding bit error probability for convolutional codes is presented. The obtained closed form expression is evaluated for various realizations of encoders, including rate 1/2 and 2/3 encoders, of as many as 16 states. Moreover, MacWilliams-type identities are revisited and a recursion for sequences of spectra of truncated as well as tailbitten convolutional codes and their duals is derived. Finally, the dissertation is concluded with exhaustive searches for convolutional codes of various rates with either optimum free distance or optimum distance profile, extending previously published results
Convolutional Codes in Rank Metric with Application to Random Network Coding
Random network coding recently attracts attention as a technique to
disseminate information in a network. This paper considers a non-coherent
multi-shot network, where the unknown and time-variant network is used several
times. In order to create dependencies between the different shots, particular
convolutional codes in rank metric are used. These codes are so-called
(partial) unit memory ((P)UM) codes, i.e., convolutional codes with memory one.
First, distance measures for convolutional codes in rank metric are shown and
two constructions of (P)UM codes in rank metric based on the generator matrices
of maximum rank distance codes are presented. Second, an efficient
error-erasure decoding algorithm for these codes is presented. Its guaranteed
decoding radius is derived and its complexity is bounded. Finally, it is shown
how to apply these codes for error correction in random linear and affine
network coding.Comment: presented in part at Netcod 2012, submitted to IEEE Transactions on
Information Theor
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