The Experts below are selected from a list of 45957 Experts worldwide ranked by ideXlab platform
Michel Fattouche - One of the best experts on this subject based on the ideXlab platform.
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machine learning techniques with Probability Vector for cooperative spectrum sensing in cognitive radio networks
Wireless Communications and Networking Conference, 2016Co-Authors: Pai Zhu, Donglin Wang, Michel FattoucheAbstract:We study cooperative spectrum sensing in cognitive radio networks (CRN) using machine learning techniques in this paper. A low-dimensional Probability Vector is proposed as the feature Vector for machine learning based classification, instead of the N-dimensional energy Vector in a CRN with a single primary user (PU) and N secondary users (SUs). This proposed method down-converts a high-dimensional feature Vector to a constant two-dimensional feature Vector for machine learning techniques while keeping the same spectrum sensing performance if not better. Due to its lower dimension, the Probability Vector based classification is capable of having a smaller training duration and a shorter classification time for testing Vectors.
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WCNC - Machine learning techniques with Probability Vector for cooperative spectrum sensing in cognitive radio networks
2016 IEEE Wireless Communications and Networking Conference, 2016Co-Authors: Pai Zhu, Donglin Wang, Michel FattoucheAbstract:We study cooperative spectrum sensing in cognitive radio networks (CRN) using machine learning techniques in this paper. A low-dimensional Probability Vector is proposed as the feature Vector for machine learning based classification, instead of the N-dimensional energy Vector in a CRN with a single primary user (PU) and N secondary users (SUs). This proposed method down-converts a high-dimensional feature Vector to a constant two-dimensional feature Vector for machine learning techniques while keeping the same spectrum sensing performance if not better. Due to its lower dimension, the Probability Vector based classification is capable of having a smaller training duration and a shorter classification time for testing Vectors.
Pai Zhu - One of the best experts on this subject based on the ideXlab platform.
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machine learning techniques with Probability Vector for cooperative spectrum sensing in cognitive radio networks
Wireless Communications and Networking Conference, 2016Co-Authors: Pai Zhu, Donglin Wang, Michel FattoucheAbstract:We study cooperative spectrum sensing in cognitive radio networks (CRN) using machine learning techniques in this paper. A low-dimensional Probability Vector is proposed as the feature Vector for machine learning based classification, instead of the N-dimensional energy Vector in a CRN with a single primary user (PU) and N secondary users (SUs). This proposed method down-converts a high-dimensional feature Vector to a constant two-dimensional feature Vector for machine learning techniques while keeping the same spectrum sensing performance if not better. Due to its lower dimension, the Probability Vector based classification is capable of having a smaller training duration and a shorter classification time for testing Vectors.
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WCNC - Machine learning techniques with Probability Vector for cooperative spectrum sensing in cognitive radio networks
2016 IEEE Wireless Communications and Networking Conference, 2016Co-Authors: Pai Zhu, Donglin Wang, Michel FattoucheAbstract:We study cooperative spectrum sensing in cognitive radio networks (CRN) using machine learning techniques in this paper. A low-dimensional Probability Vector is proposed as the feature Vector for machine learning based classification, instead of the N-dimensional energy Vector in a CRN with a single primary user (PU) and N secondary users (SUs). This proposed method down-converts a high-dimensional feature Vector to a constant two-dimensional feature Vector for machine learning techniques while keeping the same spectrum sensing performance if not better. Due to its lower dimension, the Probability Vector based classification is capable of having a smaller training duration and a shorter classification time for testing Vectors.
Mohamed Ould-khaoua - One of the best experts on this subject based on the ideXlab platform.
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New Fault-Tolerant Routing Algorithms for K-Ary N-Cube Networks
2011Co-Authors: Jehad Al-sadi, Khaled Day, Mohamed Ould-khaouaAbstract:This paper describes a new fault-tolerant routing algorithm for k-ary n-cubes using the concept of "Probability Vectors". To compute these Vectors, a node determines first its faulty set, which represents the set of all its neighbouring nodes that are faulty or unreachable due to faulty links. Each node then calculates a Probability Vector, where the lth element represents the Probability that a destination node at distance l cannot be reached through a minimal path due to a faulty node or link. The Probability Vectors are used by all the nodes to achieve an efficient fault-tolerant routing in the network. An extensive performance analysis conducted in this study reveals that the proposed algorithm exhibits good fault-tolerance properties in terms of the achieved percentage of reachability and routing distances.
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A Fault-Tolerant Routing Algorithm for 3-D Torus Interconnection Networks
The International Arab Journal of Information Technology, 2003Co-Authors: Jehad Al-sadi, Khaled Day, Mohamed Ould-khaouaAbstract:This paper describes a new fault-tolerant routing algorithm fo r 3 -D tori using the concept of "Probability Vectors". To compute these Vectors, a node determines first its faulty set, which represents the set of all its neighbouring nodes that are faulty or unreachable due to faulty links. Each node then calculates a Probability Vector, where the l th element represents the Probability that a destination node at distance l cannot be reached through a minimal path due to a faulty node or link. The Probability Vectors are used by all the nodes to achieve an efficient fault-tolerant routing in the network. An extensive performance evaluation conducted in this study reveals that the proposed algorithm exhibits good fault-tolerance properties in terms of the achieved percentage of reachability and routing distances.
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ICPADS - A new probabilistic approach for fault-tolerant routing in k-ary n-cubes
Ninth International Conference on Parallel and Distributed Systems 2002. Proceedings., 2002Co-Authors: Jehad Al-sadi, Khaled Day, Mohamed Ould-khaouaAbstract:This paper describes the new fault-tolerant routing algorithm for k-ary n-cubes using the concept of "Probability Vectors" and conducts an extensive performance analysis for the new algorithm. To compute these Vectors, a node determines first its faulty set, which represents the set of all its neighbouring nodes that are faulty or unreachable due to faulty links. Each node then calculates a Probability Vector, where the l/sup th/ element represents the Probability that a destination node at distance l cannot be reached through a minimal path due to a fault node or link. The Probability Vectors are used by all the nodes to achieve an efficient fault-tolerant routing in the network. The extensive performance analysis conducted in this study reveals that the proposed algorithm exhibits good fault-tolerance properties in terms of the achieved average routing distances.
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SAC - Probability Vectors: a new fault-tolerant routing algorithm for k -ary n -cubes
Proceedings of the 2002 ACM symposium on Applied computing - SAC '02, 2002Co-Authors: Jehad Al-sadi, Khaled Day, Mohamed Ould-khaouaAbstract:This paper describes a new fault-tolerant routing algorithm for the k-ary n-cube using the concept of "Probability Vectors". To compute these Vectors, a node determines first its faulty set, which contains all its neighbouring nodes that are faulty or unreachable due to faulty nodes or links. Each node then calculates a Probability Vector, where the i-th element represents the Probability that a destination node at distance i cannot be reached using a minimal path due to a faulty node or link. The Probability Vectors are used by all the nodes to achieve an efficient fault-tolerant routing in the network. Results from a performance analysis presented below show that the new algorithm exhibits good fault-tolerance properties in terms of the achieved percentage of reachablity and routing distances.
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Probability-based Fault-tolerant Routing in Hypercubes
The Computer Journal, 2001Co-Authors: Jehad Al-sadi, Khaled Day, Mohamed Ould-khaouaAbstract:This paper describes a new fault-tolerant routing algorithm for the hypercube, using the concept of Probability Vectors. To compute these Vectors, a node first determines its faulty set, which represents the set of all its neighbouring nodes that are faulty or unreachable due to faulty nodes or links. Each node then calculates a Probability Vector, where the kth element represents the Probability that a destination node at distance k cannot be reached through a minimal path due to a faulty node or link. The Probability Vectors are used by all the nodes to achieve an efficient fault-tolerant routing in the network. A performance comparison with the recently-proposed safety-Vectors algorithm, through extensive simulation, shows that the new algorithm exhibits superior performance in terms of routing distances and percentage of reachability. Received ; revised
Zheng-hai Huang - One of the best experts on this subject based on the ideXlab platform.
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Stationary Probability Vectors of Higher-Order Two-Dimensional Symmetric Transition Probability Tensors
Asia-Pacific Journal of Operational Research, 2020Co-Authors: Zheng-hai HuangAbstract:In this paper, we investigate stationary Probability Vectors of higher-order two-dimensional symmetric transition Probability tensors. We show that there are two special symmetric transition Probability tensors of order m dimension 2, which have and only have two stationary Probability Vectors; and any other symmetric transition Probability tensor of order m dimension 2 has a unique stationary Probability Vector. As a byproduct, we obtain that any symmetric transition Probability tensor of order m dimension 2 has a unique positive stationary Probability Vector, and that any symmetric irreducible transition Probability tensor of order m dimension 2 has a unique stationary Probability Vector.
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Stationary Probability Vectors of Higher-Order Two-Dimensional Symmetric Transition Probability Tensors
Asia-Pacific Journal of Operational Research, 2020Co-Authors: Zheng-hai HuangAbstract:In this paper, we investigate stationary Probability Vectors of higher-order two-dimensional symmetric transition Probability tensors. We show that there are two special symmetric transition Probability tensors of order [Formula: see text] dimension 2, which have and only have two stationary Probability Vectors; and any other symmetric transition Probability tensor of order [Formula: see text] dimension 2 has a unique stationary Probability Vector. As a byproduct, we obtain that any symmetric transition Probability tensor of order [Formula: see text] dimension 2 has a unique positive stationary Probability Vector, and that any symmetric irreducible transition Probability tensor of order [Formula: see text] dimension 2 has a unique stationary Probability Vector.
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Stationary Probability Vectors of higher-order two-dimensional transition Probability tensors
arXiv: Spectral Theory, 2018Co-Authors: Zheng-hai HuangAbstract:In this paper we investigate stationary Probability Vectors of higher-order two-dimensional symmetric transition Probability tensors. We show that there are two special symmetric transition Probability tensors of order $m$ dimension 2, which have and only have two stationary Probability Vectors; and any other symmetric transition Probability tensor of order $m$ dimension 2 has a unique stationary Probability Vector. As a byproduct, we obtain that any symmetric transition Probability tensor of order $m$ dimension 2 has a unique positive stationary Probability Vector; and that any symmetric irreducible transition Probability tensor of order $m$ dimension 2 has a unique stationary Probability Vector.
John M Findlay - One of the best experts on this subject based on the ideXlab platform.
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a Probability Vector and transition matrix analysis of eye movements during visual search
Acta Psychologica, 1995Co-Authors: V Ponsoda, D Scott, John M FindlayAbstract:This report describes a new method for analysing eye movement records during free viewing. The proposed new measures originate from a categorisation of saccade directions. The proportion of each categorised direction and the transition matrix of each two successive categorised directions are then calculated. These new measures are proposed as objective indicators of the strategies employed by an observer engaged in a visual search task. It is concluded that the Probability Vector and transition matrix analysis are particularly suitable as measures of observer strategies in relation to performance on VDU-based tasks. The relation between these measures and other measures related to search efficiency are also discussed.
