The Experts below are selected from a list of 63 Experts worldwide ranked by ideXlab platform

Tapani Ristaniemi - One of the best experts on this subject based on the ideXlab platform.

  • The Max-Product Algorithm Viewed as Linear Data-Fusion: A Distributed Detection Scenario
    IEEE Transactions on Wireless Communications, 2020
    Co-Authors: Younes Abdi, Tapani Ristaniemi
    Abstract:

    In this paper, we disclose the statistical behavior of the max-Product algorithm configured to solve a maximum a posteriori estimation problem in a network of distributed agents. Specifically, we first build a distributed hypothesis test conducted by a max-Product Iteration over a binary-valued pairwise Markov random field and show that the decision variables obtained are linear combinations of the local log-likelihood ratios observed in the network. Then, we use these linear combinations to formulate the system performance in terms of the false-alarm and detection probabilities. Our findings indicate that, in the hypothesis test concerned, the optimal performance of the max-Product algorithm is obtained by an optimal linear data-fusion scheme and the behavior of the max-Product algorithm is very similar to the behavior of the sum-Product algorithm. Consequently, we demonstrate that the optimal performance of the max-Product Iteration is closely achieved via a linear version of the sum-Product algorithm, which is optimized based on statistics received at each node from its one-hop neighbors. Finally, we verify our observations via computer simulations.

Younes Abdi - One of the best experts on this subject based on the ideXlab platform.

  • The Max-Product Algorithm Viewed as Linear Data-Fusion: A Distributed Detection Scenario
    IEEE Transactions on Wireless Communications, 2020
    Co-Authors: Younes Abdi, Tapani Ristaniemi
    Abstract:

    In this paper, we disclose the statistical behavior of the max-Product algorithm configured to solve a maximum a posteriori estimation problem in a network of distributed agents. Specifically, we first build a distributed hypothesis test conducted by a max-Product Iteration over a binary-valued pairwise Markov random field and show that the decision variables obtained are linear combinations of the local log-likelihood ratios observed in the network. Then, we use these linear combinations to formulate the system performance in terms of the false-alarm and detection probabilities. Our findings indicate that, in the hypothesis test concerned, the optimal performance of the max-Product algorithm is obtained by an optimal linear data-fusion scheme and the behavior of the max-Product algorithm is very similar to the behavior of the sum-Product algorithm. Consequently, we demonstrate that the optimal performance of the max-Product Iteration is closely achieved via a linear version of the sum-Product algorithm, which is optimized based on statistics received at each node from its one-hop neighbors. Finally, we verify our observations via computer simulations.

Bundit Pibaljommee - One of the best experts on this subject based on the ideXlab platform.

  • Semigroups of linear tree languages
    Asian-European Journal of Mathematics, 2018
    Co-Authors: Pongsakorn Kitpratyakul, Bundit Pibaljommee
    Abstract:

    A linear tree language of type [Formula: see text] is a set of linear terms, terms in which each variable occurs at most once, of that type. We investigate a semigroup consisting of the collection of all linear tree languages such that Products of any element in the collection are nonempty and the operation of the corresponding linear Product especially idempotent elements, Green’s relations [Formula: see text], [Formula: see text], and [Formula: see text], and some of its subsemigroups. We discover that this semigroup is neither factorizable nor locally factorizable. We also study the linear Product Iteration and show that any Iteration is idempotent in this semigroup. Moreover, we study a semigroup with the complement of the universe set of the above semigroup together with the same linear Product operation.

  • Semigroups of linear tree languages
    Asian-european Journal of Mathematics, 2017
    Co-Authors: Pongsakorn Kitpratyakul, Bundit Pibaljommee
    Abstract:

    A linear tree language of type τ is a set of linear terms, terms in which each variable occurs at most once, of that type. We investigate a semigroup consisting of the collection of all linear tree languages such that Products of any element in the collection are nonempty and the operation of the corresponding linear Product especially idempotent elements, Green’s relations ℋ, 𝒟, and 𝒥, and some of its subsemigroups. We discover that this semigroup is neither factorizable nor locally factorizable. We also study the linear Product Iteration and show that any Iteration is idempotent in this semigroup. Moreover, we study a semigroup with the complement of the universe set of the above semigroup together with the same linear Product operation.

Pongsakorn Kitpratyakul - One of the best experts on this subject based on the ideXlab platform.

  • Semigroups of linear tree languages
    Asian-European Journal of Mathematics, 2018
    Co-Authors: Pongsakorn Kitpratyakul, Bundit Pibaljommee
    Abstract:

    A linear tree language of type [Formula: see text] is a set of linear terms, terms in which each variable occurs at most once, of that type. We investigate a semigroup consisting of the collection of all linear tree languages such that Products of any element in the collection are nonempty and the operation of the corresponding linear Product especially idempotent elements, Green’s relations [Formula: see text], [Formula: see text], and [Formula: see text], and some of its subsemigroups. We discover that this semigroup is neither factorizable nor locally factorizable. We also study the linear Product Iteration and show that any Iteration is idempotent in this semigroup. Moreover, we study a semigroup with the complement of the universe set of the above semigroup together with the same linear Product operation.

  • Semigroups of linear tree languages
    Asian-european Journal of Mathematics, 2017
    Co-Authors: Pongsakorn Kitpratyakul, Bundit Pibaljommee
    Abstract:

    A linear tree language of type τ is a set of linear terms, terms in which each variable occurs at most once, of that type. We investigate a semigroup consisting of the collection of all linear tree languages such that Products of any element in the collection are nonempty and the operation of the corresponding linear Product especially idempotent elements, Green’s relations ℋ, 𝒟, and 𝒥, and some of its subsemigroups. We discover that this semigroup is neither factorizable nor locally factorizable. We also study the linear Product Iteration and show that any Iteration is idempotent in this semigroup. Moreover, we study a semigroup with the complement of the universe set of the above semigroup together with the same linear Product operation.

Ristaniemi Tapani - One of the best experts on this subject based on the ideXlab platform.

  • The Max-Product Algorithm Viewed as Linear Data-Fusion: A Distributed Detection Scenario
    2020
    Co-Authors: Abdi Younes, Ristaniemi Tapani
    Abstract:

    In this paper, we disclose the statistical behavior of the max-Product algorithm configured to solve a maximum a posteriori (MAP) estimation problem in a network of distributed agents. Specifically, we first build a distributed hypothesis test conducted by a max-Product Iteration over a binary-valued pairwise Markov random field and show that the decision variables obtained are linear combinations of the local log-likelihood ratios observed in the network. Then, we use these linear combinations to formulate the system performance in terms of the false-alarm and detection probabilities. Our findings indicate that, in the hypothesis test concerned, the optimal performance of the max-Product algorithm is obtained by an optimal linear data-fusion scheme and the behavior of the max-Product algorithm is very similar to the behavior of the sum-Product algorithm. Consequently, we demonstrate that the optimal performance of the max-Product Iteration is closely achieved via a linear version of the sum-Product algorithm which is optimized based on statistics received at each node from its one-hop neighbors. Finally, we verify our observations via computer simulations.Comment: Revised and restructured with the main contents unchange