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

Tatsuya Akutsu - One of the best experts on this subject based on the ideXlab platform.

  • Enumerating Chemical Graphs with Mono-block 2-Augmented Tree Structure from Given Upper and Lower Bounds on Path Frequencies.
    arXiv: Data Structures and Algorithms, 2020
    Co-Authors: Yuui Tamura, Hiroshi Nagamochi, Yuhei Nishiyama, Chenxi Wang, Yanming Sun, Aleksandar Shurbevski, Tatsuya Akutsu
    Abstract:

    We consider a problem of enumerating chemical graphs from given constraints concerning their structures, which has an important application to a novel method for the inverse QSAR/QSPR recently proposed. In this paper, the structure of a chemical graph is specified by a feature vector each of whose entries represents the Frequency of a prescribed Path. We call a graph a 2-augmented tree if it is obtained from a tree (an acyclic graph) by adding edges between two pairs of nonadjacent vertices. Given a set of feature vectors as the interval between upper and lower bounds of feature vectors, we design an efficient algorithm for enumerating chemical 2-augmented trees that satisfy the Path Frequency specified by some feature vector in the set. We implemented the proposed algorithm and conducted some computational experiments.

  • inferring a graph from Path Frequency
    Discrete Applied Mathematics, 2012
    Co-Authors: Tatsuya Akutsu, Daiji Fukagawa, Jesper Jansson, Kunihiko Sadakane
    Abstract:

    This paper considers the problem of inferring a graph from the number of occurrences of vertex-labeled Paths, which is closely related to the pre-image problem for graphs: to reconstruct a graph from its feature space representation. It is shown that both exact and approximate versions of the problem can be solved in polynomial time in the size of an output graph by using dynamic programming algorithms if the graphs are trees whose maximum degree is bounded by a constant and the lengths of given Paths and alphabet size are bounded by constants. On the other hand, it is shown that this problem is strongly NP-hard even for trees of bounded degree if the maximum length of Paths is not bounded. The problem of inferring a string from the number of occurrences of fixed size substrings is also studied.

  • enumerating treelike chemical graphs with given Path Frequency
    Journal of Chemical Information and Modeling, 2008
    Co-Authors: Hiroki Fujiwara, Jiexun Wang, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu
    Abstract:

    The enumeration of chemical graphs satisfying given constraints is one of the fundamental problems in chemoinformatics. In this paper, we consider the problem of enumerating (i.e., listing) all treelike chemical graphs from a given Path Frequency. We propose an exact algorithm for enumerating all solutions to this problem on the basis of the branch-and-bound method. To further improve the efficiency of the enumeration, we introduce a new variant of the compound enumeration problem by adding a specification on the number of multiple bonds to the input and design another exact enumeration algorithm. The experimental results show that our algorithms can efficiently solve instances with larger sizes that are impossible to solve by the previous methods. In particular, we apply the latter algorithm to the enumeration problem of the special treelike chemical structures—alkane isomers. The theoretical and experimental results show that our algorithm works at least as fast as the state-of-the-art algorithms specia...

  • inferring a graph from Path Frequency
    Combinatorial Pattern Matching, 2005
    Co-Authors: Tatsuya Akutsu, Daiji Fukagawa
    Abstract:

    We consider the problem of inferring a graph (and a sequence) from the numbers of occurrences of vertex-labeled Paths, which is closely related to the pre-image problem for graphs in machine learning: to reconstruct a graph from its feature space representation. We show that this problem can be solved in polynomial time in the size of an output graph if graphs are trees of bounded degree and the lengths of given Paths are bounded by a constant. On the other hand, we show that this problem is strongly NP-hard even for planar graphs of bounded degree.

Ismail Guvenc - One of the best experts on this subject based on the ideXlab platform.

  • joint frame synchronization and channel estimation sparse recovery approach and usrp implementation
    IEEE Access, 2019
    Co-Authors: Ozgur Ozdemir, Ridha Hamila, Naofal Aldhahir, Chethan Kumar Anjinappa, Ismail Guvenc
    Abstract:

    Correlation-based techniques used for frame synchronization can suffer significant performance degradation over multi-Path Frequency-selective channels. In this paper, we propose a joint frame synchronization and channel estimation (JFSCE) framework as a remedy to this problem. This framework, however, increases the size of the resulting combined channel vector which should capture both the channel impulse response vector and the frame boundary offset and, therefore, its estimation becomes more challenging. On the other hand, because the combined channel vector is sparse, sparse channel estimation methods can be applied. We propose several JFSCE methods using popular sparse signal recovery algorithms which exploit the sparsity of the combined channel vector. Subsequently, the sparse channel vector estimate is used to design a sparse equalizer. Our simulation results and experimental measurements using software defined radios show that in some scenarios our proposed method improves the overall system performance significantly, in terms of the mean square error between the transmitted and the equalized symbols compared to the conventional method.

  • joint frame synchronization and channel estimation sparse recovery approach and usrp implementation
    arXiv: Signal Processing, 2019
    Co-Authors: Ozgur Ozdemir, Ridha Hamila, Naofal Aldhahir, Chethan Kumar Anjinappa, Ismail Guvenc
    Abstract:

    Correlation-based techniques used for frame synchronization can suffer significant performance degradation over multi-Path Frequency-selective channels. In this paper, we propose a joint frame synchronization and channel estimation (JFSCE) framework as a remedy to this problem. This framework, however, increases the size of the resulting combined channel vector which should capture both the channel impulse response (CIR) vector and the frame boundary offset and, therefore, its estimation becomes more challenging. On the other hand, because the combined channel vector is sparse, sparse channel estimation methods can be applied. We propose several JFSCE methods using popular sparse signal recovery (SSR) algorithms which exploit the sparsity of the combined channel vector. Subsequently, the sparse channel vector estimate is used to design a sparse equalizer. Our simulation results and experimental measurements using software defined radios (SDRs) show that in some scenarios our proposed method improves the overall system performance significantly, in terms of the mean square error (MSE) between the transmitted and the equalized symbols compared to the conventional method.

  • sparsity aware joint frame synchronization and channel estimation algorithm and usrp implementation
    Military Communications Conference, 2017
    Co-Authors: Ozgur Ozdemir, Ridha Hamila, Naofal Aldhahir, Ismail Guvenc
    Abstract:

    Conventional correlation-based frame synchronization techniques can suffer significant performance degradation over multi-Path Frequency-selective channels. As a remedy, in this paper we consider joint frame synchronization and channel estimation. This, however, increases the length of the resulting combined channel and its estimation becomes more challenging. On the other hand, since the combined channel is a sparse vector, sparse channel estimation methods can be applied. We propose a joint frame synchronization and channel estimation method using the orthogonal matching pursuit (OMP) algorithm which exploits the sparsity of the combined channel vector. Subsequently, the channel estimate is used to design the equalizer. Our simulation results and experimental outcomes using software defined radios show that the proposed approach improves the overall system performance in terms of the mean square error (MSE) between the transmitted and the equalized symbols compared to the conventional method.

Ozgur Ozdemir - One of the best experts on this subject based on the ideXlab platform.

  • joint frame synchronization and channel estimation sparse recovery approach and usrp implementation
    IEEE Access, 2019
    Co-Authors: Ozgur Ozdemir, Ridha Hamila, Naofal Aldhahir, Chethan Kumar Anjinappa, Ismail Guvenc
    Abstract:

    Correlation-based techniques used for frame synchronization can suffer significant performance degradation over multi-Path Frequency-selective channels. In this paper, we propose a joint frame synchronization and channel estimation (JFSCE) framework as a remedy to this problem. This framework, however, increases the size of the resulting combined channel vector which should capture both the channel impulse response vector and the frame boundary offset and, therefore, its estimation becomes more challenging. On the other hand, because the combined channel vector is sparse, sparse channel estimation methods can be applied. We propose several JFSCE methods using popular sparse signal recovery algorithms which exploit the sparsity of the combined channel vector. Subsequently, the sparse channel vector estimate is used to design a sparse equalizer. Our simulation results and experimental measurements using software defined radios show that in some scenarios our proposed method improves the overall system performance significantly, in terms of the mean square error between the transmitted and the equalized symbols compared to the conventional method.

  • joint frame synchronization and channel estimation sparse recovery approach and usrp implementation
    arXiv: Signal Processing, 2019
    Co-Authors: Ozgur Ozdemir, Ridha Hamila, Naofal Aldhahir, Chethan Kumar Anjinappa, Ismail Guvenc
    Abstract:

    Correlation-based techniques used for frame synchronization can suffer significant performance degradation over multi-Path Frequency-selective channels. In this paper, we propose a joint frame synchronization and channel estimation (JFSCE) framework as a remedy to this problem. This framework, however, increases the size of the resulting combined channel vector which should capture both the channel impulse response (CIR) vector and the frame boundary offset and, therefore, its estimation becomes more challenging. On the other hand, because the combined channel vector is sparse, sparse channel estimation methods can be applied. We propose several JFSCE methods using popular sparse signal recovery (SSR) algorithms which exploit the sparsity of the combined channel vector. Subsequently, the sparse channel vector estimate is used to design a sparse equalizer. Our simulation results and experimental measurements using software defined radios (SDRs) show that in some scenarios our proposed method improves the overall system performance significantly, in terms of the mean square error (MSE) between the transmitted and the equalized symbols compared to the conventional method.

  • sparsity aware joint frame synchronization and channel estimation algorithm and usrp implementation
    Military Communications Conference, 2017
    Co-Authors: Ozgur Ozdemir, Ridha Hamila, Naofal Aldhahir, Ismail Guvenc
    Abstract:

    Conventional correlation-based frame synchronization techniques can suffer significant performance degradation over multi-Path Frequency-selective channels. As a remedy, in this paper we consider joint frame synchronization and channel estimation. This, however, increases the length of the resulting combined channel and its estimation becomes more challenging. On the other hand, since the combined channel is a sparse vector, sparse channel estimation methods can be applied. We propose a joint frame synchronization and channel estimation method using the orthogonal matching pursuit (OMP) algorithm which exploits the sparsity of the combined channel vector. Subsequently, the channel estimate is used to design the equalizer. Our simulation results and experimental outcomes using software defined radios show that the proposed approach improves the overall system performance in terms of the mean square error (MSE) between the transmitted and the equalized symbols compared to the conventional method.

Ridha Hamila - One of the best experts on this subject based on the ideXlab platform.

  • joint frame synchronization and channel estimation sparse recovery approach and usrp implementation
    IEEE Access, 2019
    Co-Authors: Ozgur Ozdemir, Ridha Hamila, Naofal Aldhahir, Chethan Kumar Anjinappa, Ismail Guvenc
    Abstract:

    Correlation-based techniques used for frame synchronization can suffer significant performance degradation over multi-Path Frequency-selective channels. In this paper, we propose a joint frame synchronization and channel estimation (JFSCE) framework as a remedy to this problem. This framework, however, increases the size of the resulting combined channel vector which should capture both the channel impulse response vector and the frame boundary offset and, therefore, its estimation becomes more challenging. On the other hand, because the combined channel vector is sparse, sparse channel estimation methods can be applied. We propose several JFSCE methods using popular sparse signal recovery algorithms which exploit the sparsity of the combined channel vector. Subsequently, the sparse channel vector estimate is used to design a sparse equalizer. Our simulation results and experimental measurements using software defined radios show that in some scenarios our proposed method improves the overall system performance significantly, in terms of the mean square error between the transmitted and the equalized symbols compared to the conventional method.

  • joint frame synchronization and channel estimation sparse recovery approach and usrp implementation
    arXiv: Signal Processing, 2019
    Co-Authors: Ozgur Ozdemir, Ridha Hamila, Naofal Aldhahir, Chethan Kumar Anjinappa, Ismail Guvenc
    Abstract:

    Correlation-based techniques used for frame synchronization can suffer significant performance degradation over multi-Path Frequency-selective channels. In this paper, we propose a joint frame synchronization and channel estimation (JFSCE) framework as a remedy to this problem. This framework, however, increases the size of the resulting combined channel vector which should capture both the channel impulse response (CIR) vector and the frame boundary offset and, therefore, its estimation becomes more challenging. On the other hand, because the combined channel vector is sparse, sparse channel estimation methods can be applied. We propose several JFSCE methods using popular sparse signal recovery (SSR) algorithms which exploit the sparsity of the combined channel vector. Subsequently, the sparse channel vector estimate is used to design a sparse equalizer. Our simulation results and experimental measurements using software defined radios (SDRs) show that in some scenarios our proposed method improves the overall system performance significantly, in terms of the mean square error (MSE) between the transmitted and the equalized symbols compared to the conventional method.

  • sparsity aware joint frame synchronization and channel estimation algorithm and usrp implementation
    Military Communications Conference, 2017
    Co-Authors: Ozgur Ozdemir, Ridha Hamila, Naofal Aldhahir, Ismail Guvenc
    Abstract:

    Conventional correlation-based frame synchronization techniques can suffer significant performance degradation over multi-Path Frequency-selective channels. As a remedy, in this paper we consider joint frame synchronization and channel estimation. This, however, increases the length of the resulting combined channel and its estimation becomes more challenging. On the other hand, since the combined channel is a sparse vector, sparse channel estimation methods can be applied. We propose a joint frame synchronization and channel estimation method using the orthogonal matching pursuit (OMP) algorithm which exploits the sparsity of the combined channel vector. Subsequently, the channel estimate is used to design the equalizer. Our simulation results and experimental outcomes using software defined radios show that the proposed approach improves the overall system performance in terms of the mean square error (MSE) between the transmitted and the equalized symbols compared to the conventional method.

Naofal Aldhahir - One of the best experts on this subject based on the ideXlab platform.

  • joint frame synchronization and channel estimation sparse recovery approach and usrp implementation
    IEEE Access, 2019
    Co-Authors: Ozgur Ozdemir, Ridha Hamila, Naofal Aldhahir, Chethan Kumar Anjinappa, Ismail Guvenc
    Abstract:

    Correlation-based techniques used for frame synchronization can suffer significant performance degradation over multi-Path Frequency-selective channels. In this paper, we propose a joint frame synchronization and channel estimation (JFSCE) framework as a remedy to this problem. This framework, however, increases the size of the resulting combined channel vector which should capture both the channel impulse response vector and the frame boundary offset and, therefore, its estimation becomes more challenging. On the other hand, because the combined channel vector is sparse, sparse channel estimation methods can be applied. We propose several JFSCE methods using popular sparse signal recovery algorithms which exploit the sparsity of the combined channel vector. Subsequently, the sparse channel vector estimate is used to design a sparse equalizer. Our simulation results and experimental measurements using software defined radios show that in some scenarios our proposed method improves the overall system performance significantly, in terms of the mean square error between the transmitted and the equalized symbols compared to the conventional method.

  • joint frame synchronization and channel estimation sparse recovery approach and usrp implementation
    arXiv: Signal Processing, 2019
    Co-Authors: Ozgur Ozdemir, Ridha Hamila, Naofal Aldhahir, Chethan Kumar Anjinappa, Ismail Guvenc
    Abstract:

    Correlation-based techniques used for frame synchronization can suffer significant performance degradation over multi-Path Frequency-selective channels. In this paper, we propose a joint frame synchronization and channel estimation (JFSCE) framework as a remedy to this problem. This framework, however, increases the size of the resulting combined channel vector which should capture both the channel impulse response (CIR) vector and the frame boundary offset and, therefore, its estimation becomes more challenging. On the other hand, because the combined channel vector is sparse, sparse channel estimation methods can be applied. We propose several JFSCE methods using popular sparse signal recovery (SSR) algorithms which exploit the sparsity of the combined channel vector. Subsequently, the sparse channel vector estimate is used to design a sparse equalizer. Our simulation results and experimental measurements using software defined radios (SDRs) show that in some scenarios our proposed method improves the overall system performance significantly, in terms of the mean square error (MSE) between the transmitted and the equalized symbols compared to the conventional method.

  • sparsity aware joint frame synchronization and channel estimation algorithm and usrp implementation
    Military Communications Conference, 2017
    Co-Authors: Ozgur Ozdemir, Ridha Hamila, Naofal Aldhahir, Ismail Guvenc
    Abstract:

    Conventional correlation-based frame synchronization techniques can suffer significant performance degradation over multi-Path Frequency-selective channels. As a remedy, in this paper we consider joint frame synchronization and channel estimation. This, however, increases the length of the resulting combined channel and its estimation becomes more challenging. On the other hand, since the combined channel is a sparse vector, sparse channel estimation methods can be applied. We propose a joint frame synchronization and channel estimation method using the orthogonal matching pursuit (OMP) algorithm which exploits the sparsity of the combined channel vector. Subsequently, the channel estimate is used to design the equalizer. Our simulation results and experimental outcomes using software defined radios show that the proposed approach improves the overall system performance in terms of the mean square error (MSE) between the transmitted and the equalized symbols compared to the conventional method.