The Experts below are selected from a list of 20160 Experts worldwide ranked by ideXlab platform
Serdar Boztas - One of the best experts on this subject based on the ideXlab platform.
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a new Weight Vector for a tighter levenshtein bound on aperiodic correlation
IEEE Transactions on Information Theory, 2014Co-Authors: Udaya Parampalli, Yong Liang Guan, Serdar BoztasAbstract:The Levenshtein bound on aperiodic correlation, which is a function of the Weight Vector, is tighter than the Welch bound for sequence sets over the complex roots of unity when M ≥ 4 and n ≥ 2, where M denotes the set size and n the sequence length. Although it is known that the tightest Levenshtein bound is equal to the Welch bound for M ∈ {1,2}, it is unknown whether the Levenshtein bound can be tightened for M=3, and Levenshtein, in his paper published in 1999, postulated that the answer may be negative. A new Weight Vector is proposed in this paper, which leads to a tighter Levenshtein bound for M=3, n ≥ 3 and M ≥ 4, n ≥ 2. In addition, the explicit form of the Weight Vector (which is derived by relating the quadratic minimization to the Chebyshev polynomials of the second kind) in Levenshtein's paper is given. Interestingly, this Weight Vector also yields a tighter Levenshtein bound for M=3, n ≥ 3 and M ≥ 4, n ≥ √M, a fact not noticed by Levenshtein.
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quadratic Weight Vector for tighter aperiodic levenshtein bound
International Symposium on Information Theory, 2013Co-Authors: Yong Liang Guan, Udaya Parampalli, Serdar BoztasAbstract:The Levenshtein bound, as a function of the Weight Vector, is only known to be tighter than the Welch bound on aperiodic correlation for K ≥ 4, N ≥ 2, where K and N denoting the set size and the sequence length, respectively. A quadratic Weight Vector is proposed in this paper which leads to a tighter Levenshtein bound for K ≥ 4, N ≥ 2 and K = 3, N ≥ 4. The latter case was left open by Levensthein.
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ISIT - Quadratic Weight Vector for tighter aperiodic Levenshtein bound
2013 IEEE International Symposium on Information Theory, 2013Co-Authors: Yong Liang Guan, Udaya Parampalli, Serdar BoztasAbstract:The Levenshtein bound, as a function of the Weight Vector, is only known to be tighter than the Welch bound on aperiodic correlation for K ≥ 4, N ≥ 2, where K and N denoting the set size and the sequence length, respectively. A quadratic Weight Vector is proposed in this paper which leads to a tighter Levenshtein bound for K ≥ 4, N ≥ 2 and K = 3, N ≥ 4. The latter case was left open by Levensthein.
Yong Liang Guan - One of the best experts on this subject based on the ideXlab platform.
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asymptotically locally optimal Weight Vector design for a tighter correlation lower bound of quasi complementary sequence sets
IEEE Transactions on Signal Processing, 2017Co-Authors: Yong Liang GuanAbstract:A quasi-complementary sequence set (QCSS) refers to a set of two-dimensional matrices with low nontrivial aperiodic auto- and cross-correlation sums. For multicarrier code-division multiple-access applications, the availability of large QCSSs with low correlation sums is desirable. The generalized Levenshtein bound (GLB) is a lower bound on the maximum aperiodic correlation sum of QCSSs. The bounding expression of GLB is a fractional quadratic function of a Weight Vector $\mathbf {w}$ and is expressed in terms of three additional parameters associated with QCSS: the set size $K$ , the number of channels $M$ , and the sequence length $N$ . It is known that a tighter GLB (compared to the Welch bound) is possible only if the condition $M\geq 2$ and $K\geq \overline{K}+1$ , where $\overline{K}$ is a certain function of $M$ and $N$ , is satisfied. A challenging research problem is to determine if there exists a Weight Vector that gives rise to a tighter GLB for all (not just some ) $K\geq \overline{K}+1$ and $M\geq 2$ , especially for large $N$ , i.e., the condition is asymptotically both necessary and sufficient. To achieve this, we analytically optimize the GLB which is (in general) nonconvex as the numerator term is an indefinite quadratic function of the Weight Vector. Our key idea is to apply the frequency domain decomposition of the circulant matrix (in the numerator term) to convert the nonconvex problem into a convex one. Following this optimization approach, we derive a new Weight Vector meeting the aforementioned objective and prove that it is a local minimizer of the GLB under certain conditions.
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a new Weight Vector for a tighter levenshtein bound on aperiodic correlation
IEEE Transactions on Information Theory, 2014Co-Authors: Udaya Parampalli, Yong Liang Guan, Serdar BoztasAbstract:The Levenshtein bound on aperiodic correlation, which is a function of the Weight Vector, is tighter than the Welch bound for sequence sets over the complex roots of unity when M ≥ 4 and n ≥ 2, where M denotes the set size and n the sequence length. Although it is known that the tightest Levenshtein bound is equal to the Welch bound for M ∈ {1,2}, it is unknown whether the Levenshtein bound can be tightened for M=3, and Levenshtein, in his paper published in 1999, postulated that the answer may be negative. A new Weight Vector is proposed in this paper, which leads to a tighter Levenshtein bound for M=3, n ≥ 3 and M ≥ 4, n ≥ 2. In addition, the explicit form of the Weight Vector (which is derived by relating the quadratic minimization to the Chebyshev polynomials of the second kind) in Levenshtein's paper is given. Interestingly, this Weight Vector also yields a tighter Levenshtein bound for M=3, n ≥ 3 and M ≥ 4, n ≥ √M, a fact not noticed by Levenshtein.
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quadratic Weight Vector for tighter aperiodic levenshtein bound
International Symposium on Information Theory, 2013Co-Authors: Yong Liang Guan, Udaya Parampalli, Serdar BoztasAbstract:The Levenshtein bound, as a function of the Weight Vector, is only known to be tighter than the Welch bound on aperiodic correlation for K ≥ 4, N ≥ 2, where K and N denoting the set size and the sequence length, respectively. A quadratic Weight Vector is proposed in this paper which leads to a tighter Levenshtein bound for K ≥ 4, N ≥ 2 and K = 3, N ≥ 4. The latter case was left open by Levensthein.
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ISIT - Quadratic Weight Vector for tighter aperiodic Levenshtein bound
2013 IEEE International Symposium on Information Theory, 2013Co-Authors: Yong Liang Guan, Udaya Parampalli, Serdar BoztasAbstract:The Levenshtein bound, as a function of the Weight Vector, is only known to be tighter than the Welch bound on aperiodic correlation for K ≥ 4, N ≥ 2, where K and N denoting the set size and the sequence length, respectively. A quadratic Weight Vector is proposed in this paper which leads to a tighter Levenshtein bound for K ≥ 4, N ≥ 2 and K = 3, N ≥ 4. The latter case was left open by Levensthein.
Udaya Parampalli - One of the best experts on this subject based on the ideXlab platform.
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a new Weight Vector for a tighter levenshtein bound on aperiodic correlation
IEEE Transactions on Information Theory, 2014Co-Authors: Udaya Parampalli, Yong Liang Guan, Serdar BoztasAbstract:The Levenshtein bound on aperiodic correlation, which is a function of the Weight Vector, is tighter than the Welch bound for sequence sets over the complex roots of unity when M ≥ 4 and n ≥ 2, where M denotes the set size and n the sequence length. Although it is known that the tightest Levenshtein bound is equal to the Welch bound for M ∈ {1,2}, it is unknown whether the Levenshtein bound can be tightened for M=3, and Levenshtein, in his paper published in 1999, postulated that the answer may be negative. A new Weight Vector is proposed in this paper, which leads to a tighter Levenshtein bound for M=3, n ≥ 3 and M ≥ 4, n ≥ 2. In addition, the explicit form of the Weight Vector (which is derived by relating the quadratic minimization to the Chebyshev polynomials of the second kind) in Levenshtein's paper is given. Interestingly, this Weight Vector also yields a tighter Levenshtein bound for M=3, n ≥ 3 and M ≥ 4, n ≥ √M, a fact not noticed by Levenshtein.
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quadratic Weight Vector for tighter aperiodic levenshtein bound
International Symposium on Information Theory, 2013Co-Authors: Yong Liang Guan, Udaya Parampalli, Serdar BoztasAbstract:The Levenshtein bound, as a function of the Weight Vector, is only known to be tighter than the Welch bound on aperiodic correlation for K ≥ 4, N ≥ 2, where K and N denoting the set size and the sequence length, respectively. A quadratic Weight Vector is proposed in this paper which leads to a tighter Levenshtein bound for K ≥ 4, N ≥ 2 and K = 3, N ≥ 4. The latter case was left open by Levensthein.
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ISIT - Quadratic Weight Vector for tighter aperiodic Levenshtein bound
2013 IEEE International Symposium on Information Theory, 2013Co-Authors: Yong Liang Guan, Udaya Parampalli, Serdar BoztasAbstract:The Levenshtein bound, as a function of the Weight Vector, is only known to be tighter than the Welch bound on aperiodic correlation for K ≥ 4, N ≥ 2, where K and N denoting the set size and the sequence length, respectively. A quadratic Weight Vector is proposed in this paper which leads to a tighter Levenshtein bound for K ≥ 4, N ≥ 2 and K = 3, N ≥ 4. The latter case was left open by Levensthein.
Keun Chul Hwang - One of the best experts on this subject based on the ideXlab platform.
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efficient Weight Vector representation for closed loop transmit diversity
IEEE Transactions on Communications, 2004Co-Authors: Keun Chul HwangAbstract:For a closed-loop transmit (Tx) diversity, the Tx Weights are calculated at a receiver, and fed back to a transmitter. As the number of Tx antennas increases, the potential gain of closed-loop Tx diversity may be significant. However, the amount of feedback information, which is the number of Tx Weights that should be fed back, linearly increases, and the performance improvement of a closed-loop Tx diversity system may not be as significant as expected due to delay in the feedback process. Thus, an efficient Tx Weight representation, which can reduce the amount of feedback information, is needed. In this letter, a Tx Weight Vector representation is presented, and its performance is analyzed. Analysis shows that this Weight Vector representation, referred to as basis selection, significantly reduces the amount of feedback information with little performance degradation.
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efficient Weight Vector representation for closed loop transmit diversity
International Conference on Communications, 2002Co-Authors: Keun Chul HwangAbstract:For a closed-loop transmit (Tx) diversity, the Tx Weights are calculated at a receiver, and fed back to a transmitter. As the number of Tx antennas increases, the potential gain of closed-loop Tx diversity may be significant. However, the amount of feedback information linearly increases, and the performance improvement of a closed-loop Tx diversity system may not be as significant as expected due to delay in feedback process. Thus, an efficient Tx Weight representation, which can reduce the amount of feedback information, is needed. A Tx Weight Vector representation is presented, and its performance is analyzed.
Weijie Chen - One of the best experts on this subject based on the ideXlab platform.
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robust l1 norm multi Weight Vector projection support Vector machine with efficient algorithm
Neurocomputing, 2018Co-Authors: Weijie Chen, Chunna Li, Yuanhai Shao, Ju Zhang, Naiyang DengAbstract:Abstract The recently proposed multi-Weight Vector projection support Vector machine (EMVSVM) is an excellent multi-projections classifier. However, the formulation of MVSVM is based on the L2-norm criterion, which makes it prone to be affected by outliers. To alleviate this issue, in this paper, we propose a robust L1-norm MVSVM method, termed as MVSVM L 1 . Specifically, our MVSVM L 1 aims to seek a pair of multiple projections such that, for each class, it maximizes the ratio of the L1-norm between-class dispersion and the L1-norm within-class dispersion. To optimize such L1-norm ratio problem, a simple but efficient iterative algorithm is further presented. The convergence of the algorithm is also analyzed theoretically. Extensive experimental results on both synthetic and real-world datasets confirm the feasibility and effectiveness of the proposed MVSVM L 1 .
