2,518 research outputs found
Multi-level Turbo Decoding Assisted Soft Combining Aided Hybrid ARQ
Hybrid Automatic Repeat reQuest (ARQ) plays an essential role in error control. Combining the incorrectly received packet replicas in hybrid ARQ has been shown to reduce the resultant error probability, while improving the achievable throughput. Hence, in this contribution, multi-level turbo codes have been amalgamated both with hybrid ARQ and efficient soft combining techniques for taking into account the Log- Likelihood Ratios (LLRs) of retransmitted packet replicas. In this paper, we present a soft combining aided hybrid ARQ scheme based on multi-level turbo codes, which avoid the capacity loss of the twin-level turbo codes that are typically employed in hybrid ARQ schemes. More specifically, the proposed receiver dynamically appends an additional parallel concatenated Bahl, Cocke, Jelinek and Raviv (BCJR) algorithm based decoder in order to fully exploit each retransmission, thereby forming a multi-level turbo decoder. Therefore, all the extrinsic information acquired during the previous BCJR operations will be used as a priori information by the additional BCJR decoders, whilst their soft output iteratively enhances the a posteriori information generated by the previous decoding stages. We also present link- level Packet Loss Ratio (PLR) and throughput results, which demonstrate that our scheme outperforms some of the previously proposed benchmarks
TIPs for the Analysis of Poverty in Mexico, 1992-2005
This paper proposes some changes to the official methodology that is currently in use to measure the state of poverty in Mexico. Among other suggestions, it is recommended the use of bootstrapping to estimate confidence intervals for the poverty statistics, as well as the use of dominance analysis when making intertemporal comparisons. In particular, since poverty lines change over time, the paper proposes the use of TIP curves for that end. Using the eight surveys that were made during the period 1992-2005, the paper presents a large number of absolute poverty statistics and TIP curves, as well as comparisons among them.Poverty, confidence intervals, standard error, bootstrap, resampling, FGT measures, TIP curves, dominance, Mexico
Fast Decoder for Overloaded Uniquely Decodable Synchronous Optical CDMA
In this paper, we propose a fast decoder algorithm for uniquely decodable
(errorless) code sets for overloaded synchronous optical code-division
multiple-access (O-CDMA) systems. The proposed decoder is designed in a such a
way that the users can uniquely recover the information bits with a very simple
decoder, which uses only a few comparisons. Compared to maximum-likelihood (ML)
decoder, which has a high computational complexity for even moderate code
lengths, the proposed decoder has much lower computational complexity.
Simulation results in terms of bit error rate (BER) demonstrate that the
performance of the proposed decoder for a given BER requires only 1-2 dB higher
signal-to-noise ratio (SNR) than the ML decoder.Comment: arXiv admin note: substantial text overlap with arXiv:1806.0395
Two-View Geometry Scoring Without Correspondences
Camera pose estimation for two-view geometry traditionally relies on RANSAC.
Normally, a multitude of image correspondences leads to a pool of proposed
hypotheses, which are then scored to find a winning model. The inlier count is
generally regarded as a reliable indicator of "consensus". We examine this
scoring heuristic, and find that it favors disappointing models under certain
circumstances. As a remedy, we propose the Fundamental Scoring Network (FSNet),
which infers a score for a pair of overlapping images and any proposed
fundamental matrix. It does not rely on sparse correspondences, but rather
embodies a two-view geometry model through an epipolar attention mechanism that
predicts the pose error of the two images. FSNet can be incorporated into
traditional RANSAC loops. We evaluate FSNet on fundamental and essential matrix
estimation on indoor and outdoor datasets, and establish that FSNet can
successfully identify good poses for pairs of images with few or unreliable
correspondences. Besides, we show that naively combining FSNet with MAGSAC++
scoring approach achieves state of the art results
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