34,434 research outputs found
Structured Random Linear Codes (SRLC): Bridging the Gap between Block and Convolutional Codes
Several types of AL-FEC (Application-Level FEC) codes for the Packet Erasure
Channel exist. Random Linear Codes (RLC), where redundancy packets consist of
random linear combinations of source packets over a certain finite field, are a
simple yet efficient coding technique, for instance massively used for Network
Coding applications. However the price to pay is a high encoding and decoding
complexity, especially when working on , which seriously limits the
number of packets in the encoding window. On the opposite, structured block
codes have been designed for situations where the set of source packets is
known in advance, for instance with file transfer applications. Here the
encoding and decoding complexity is controlled, even for huge block sizes,
thanks to the sparse nature of the code and advanced decoding techniques that
exploit this sparseness (e.g., Structured Gaussian Elimination). But their
design also prevents their use in convolutional use-cases featuring an encoding
window that slides over a continuous set of incoming packets.
In this work we try to bridge the gap between these two code classes,
bringing some structure to RLC codes in order to enlarge the use-cases where
they can be efficiently used: in convolutional mode (as any RLC code), but also
in block mode with either tiny, medium or large block sizes. We also
demonstrate how to design compact signaling for these codes (for
encoder/decoder synchronization), which is an essential practical aspect.Comment: 7 pages, 12 figure
Class-Weighted Convolutional Features for Visual Instance Search
Image retrieval in realistic scenarios targets large dynamic datasets of
unlabeled images. In these cases, training or fine-tuning a model every time
new images are added to the database is neither efficient nor scalable.
Convolutional neural networks trained for image classification over large
datasets have been proven effective feature extractors for image retrieval. The
most successful approaches are based on encoding the activations of
convolutional layers, as they convey the image spatial information. In this
paper, we go beyond this spatial information and propose a local-aware encoding
of convolutional features based on semantic information predicted in the target
image. To this end, we obtain the most discriminative regions of an image using
Class Activation Maps (CAMs). CAMs are based on the knowledge contained in the
network and therefore, our approach, has the additional advantage of not
requiring external information. In addition, we use CAMs to generate object
proposals during an unsupervised re-ranking stage after a first fast search.
Our experiments on two public available datasets for instance retrieval,
Oxford5k and Paris6k, demonstrate the competitiveness of our approach
outperforming the current state-of-the-art when using off-the-shelf models
trained on ImageNet. The source code and model used in this paper are publicly
available at http://imatge-upc.github.io/retrieval-2017-cam/.Comment: To appear in the British Machine Vision Conference (BMVC), September
201
Localized Dimension Growth in Random Network Coding: A Convolutional Approach
We propose an efficient Adaptive Random Convolutional Network Coding (ARCNC)
algorithm to address the issue of field size in random network coding. ARCNC
operates as a convolutional code, with the coefficients of local encoding
kernels chosen randomly over a small finite field. The lengths of local
encoding kernels increase with time until the global encoding kernel matrices
at related sink nodes all have full rank. Instead of estimating the necessary
field size a priori, ARCNC operates in a small finite field. It adapts to
unknown network topologies without prior knowledge, by locally incrementing the
dimensionality of the convolutional code. Because convolutional codes of
different constraint lengths can coexist in different portions of the network,
reductions in decoding delay and memory overheads can be achieved with ARCNC.
We show through analysis that this method performs no worse than random linear
network codes in general networks, and can provide significant gains in terms
of average decoding delay in combination networks.Comment: 7 pages, 1 figure, submitted to IEEE ISIT 201
VLSI single-chip (255,223) Reed-Solomon encoder with interleaver
The invention relates to a concatenated Reed-Solomon/convolutional encoding system consisting of a Reed-Solomon outer code and a convolutional inner code for downlink telemetry in space missions, and more particularly to a Reed-Solomon encoder with programmable interleaving of the information symbols and code correction symbols to combat error bursts in the Viterbi decoder
On the Power Spectral Density of the GSM Signaling Scheme
In this paper, the Power Spectral Density of encoded Gaussian Minimum Shift Keying
(GMSK) which is the Signaling Scheme of the Global System for Mobile Communication
(GSM) is derived by a combined approach of the autocorrelation method and Markov
Process. In the analysis, the Amplitude Modulated Pulse decomposition proposed by P.
Laurent is employed to ease computation. Encoding of the message data utilizes
Convolutional Code of rate1/2. Results are for both the uncoded and coded waveform
comparing variation in power spread over a range of frequency
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