The Experts below are selected from a list of 14346 Experts worldwide ranked by ideXlab platform
Rui Li - One of the best experts on this subject based on the ideXlab platform.
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performance re evaluation on codewords distribution based optimal combination of equal average equal variance equal norm nearest neighbor fast search algorithm for vector quantization Encoding
IEEE Transactions on Image Processing, 2018Co-Authors: Yang Wang, Rui LiAbstract:In the re-evaluated paper, Xie et al. proposed a new fast search algorithm for vector quantization Encoding, which optimized the priority checking order of variance and norm inequality in order to speed up the Encoding Procedure. CPU time of different Encoding algorithms is given to support their algorithm. However, first, some of the experimental data in the re-evaluated paper are unreasonable and unrepeatable. And second, as an improved algorithm of equal-average equal-variance equal-norm nearest neighbor fast search algorithm, the re-evaluated algorithm in fact cannot achieve a better performance than the existing improved equal-average equal-variance nearest neighbor fast search algorithm. In this paper, these two problems are analyzed, re-evaluated, and discussed in detail.
Yang Wang - One of the best experts on this subject based on the ideXlab platform.
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performance re evaluation on codewords distribution based optimal combination of equal average equal variance equal norm nearest neighbor fast search algorithm for vector quantization Encoding
IEEE Transactions on Image Processing, 2018Co-Authors: Yang Wang, Rui LiAbstract:In the re-evaluated paper, Xie et al. proposed a new fast search algorithm for vector quantization Encoding, which optimized the priority checking order of variance and norm inequality in order to speed up the Encoding Procedure. CPU time of different Encoding algorithms is given to support their algorithm. However, first, some of the experimental data in the re-evaluated paper are unreasonable and unrepeatable. And second, as an improved algorithm of equal-average equal-variance equal-norm nearest neighbor fast search algorithm, the re-evaluated algorithm in fact cannot achieve a better performance than the existing improved equal-average equal-variance nearest neighbor fast search algorithm. In this paper, these two problems are analyzed, re-evaluated, and discussed in detail.
Jiliang Yi - One of the best experts on this subject based on the ideXlab platform.
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codewords distribution based optimal combination of equal average equal variance equal norm nearest neighbor fast search algorithm for vector quantization Encoding
IEEE Transactions on Image Processing, 2016Co-Authors: Changfan Zhang, Lingshuang Kong, Jiliang YiAbstract:Vector quantization Encoding requires expensive time to find the closest codeword through the codebook for each input vector. A fast search algorithm for Encoding is proposed in this paper. The multilevel elimination criterion is still derived from the three features (mean, variance, and norm) inequalities constraints, but the order of the three inequalities constraints, instead of the predefined order like other conventional multilevel elimination criterion, is optimized to speed up the Encoding Procedure. In the proposed algorithm, the elimination criterion at the first level is set to mean inequality constraint because of its narrower search width, and the priority order at the second and/or third level of variance and norm inequalities constraints is optimized based on the codewords distribution and the location of input vector in terms of the considered features. The experimental results demonstrate the effectiveness of the proposed algorithm.
Changfan Zhang - One of the best experts on this subject based on the ideXlab platform.
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codewords distribution based optimal combination of equal average equal variance equal norm nearest neighbor fast search algorithm for vector quantization Encoding
IEEE Transactions on Image Processing, 2016Co-Authors: Changfan Zhang, Lingshuang Kong, Jiliang YiAbstract:Vector quantization Encoding requires expensive time to find the closest codeword through the codebook for each input vector. A fast search algorithm for Encoding is proposed in this paper. The multilevel elimination criterion is still derived from the three features (mean, variance, and norm) inequalities constraints, but the order of the three inequalities constraints, instead of the predefined order like other conventional multilevel elimination criterion, is optimized to speed up the Encoding Procedure. In the proposed algorithm, the elimination criterion at the first level is set to mean inequality constraint because of its narrower search width, and the priority order at the second and/or third level of variance and norm inequalities constraints is optimized based on the codewords distribution and the location of input vector in terms of the considered features. The experimental results demonstrate the effectiveness of the proposed algorithm.
Damian Markham - One of the best experts on this subject based on the ideXlab platform.
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Random coding for sharing bosonic quantum secrets
Physical Review A, 2019Co-Authors: Francesco Arzani, Giulia Ferrini, Frédéric Grosshans, Damian MarkhamAbstract:We consider a protocol for sharing quantum states using continuous variable systems. Specifically we introduce an Encoding Procedure where bosonic modes in arbitrary secret states are mixed with several ancillary squeezed modes through a passive interferometer. We derive simple conditions on the interferometer for this Encoding to define a secret sharing protocol and we prove that they are satisfied by almost any interferometer. This implies that, if the interferometer is chosen uniformly at random, the probability that it may not be used to implement a quantum secret sharing protocol is zero. Furthermore, we show that the decoding operation can be obtained and implemented efficiently with a Gaussian unitary using a number of single-mode squeezers that is at most twice the number of modes of the secret, regardless of the number of players. We benchmark the quality of the reconstructed state by computing the fidelity with the secret state as a function of the input squeezing.
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Implementing stabilizer codes by linear optics
2011Co-Authors: Peter Van Loock, Damian MarkhamAbstract:In this contribution, we propose linear optical realizations of arbitrary stabilizer quantum error correction (QEC) codes in any dimension, including discrete and continuous quantum variables. Our protocols use graph states to encode arbitrary logical states in a systematic way. In this fashion, the hardest part of the Encoding Procedure (i.e., the generation of multi‐party correlations robust against local errors) is shifted offline into the preparation of the graph‐state ancilla.