نتایج جستجو برای: hidden node effect

تعداد نتایج: 1859524  

Journal: :SIAM Journal of Applied Mathematics 2011
Fabrice Guillemin Charles Knessl Johan van Leeuwaarden

Wireless networks equipped with CSMA are scheduled in a fully distributed manner. A disadvantage of such distributed control in multihop networks is the hidden node problem, which causes the effect of stealing, in which a downstream node steals the channel from an upstream node with probability p. Aziz, Starobinski, and Thiran [IEEE/ACM Trans. Networking, to appear] have recently shown that the...

2013
Francesco Segreto Daniele Tosi Giovanni Francesco Marangi Alfonso Luca Pendolino Stefano Santoro Pierluigi Gigliofiorito Paolo Persichetti

functional and aesthetic results [3]. In case of more extensive defects involving both scrotal and perineal skin loss, we favor split thickness skin grafts for defect coverage. As noted by Huettinger et al. [1], graft fixation remains a challenge. We have developed a VAC fixation technique, the so-called “sandwich technique” [4], that we have used successfully for many years now. We feel that i...

2016
N. Arun Vignesh P. Poongodi

In Wireless Local Area Network, wireless node will suffer due to the hidden terminal node. This hidden terminal node leads to high collision, high drop rate of packets and high delay. To avoid this hidden terminal node, wireless nodes use the control messages to know the status of the destination nodes. Another issue in the WLAN is less coverage range with lesser number of nodes. To increase th...

Journal: :CoRR 2018
Feng Li Sibo Yang Huanhuan Huang Wei Wu

This paper is concerned with the sparsification of the input-hidden weights of ELM (Extreme Learning Machine). For ordinary feedforward neural networks, the sparsification is usually done by introducing certain regularization technique into the learning process of the network. But this strategy can not be applied for ELM, since the input-hidden weights of ELM are supposed to be randomly chosen ...

Journal: :Neurocomputing 2007
Guang-Bin Huang Lei Chen

Unlike the conventional neural network theories and implementations, Huang et al. [Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Transactions on Neural Networks 17(4) (2006) 879–892] have recently proposed a new theory to show that single-hidden-layer feedforward networks (SLFNs) with randomly generated additive or radial basis functi...

2010
Katsuhiro Naito Yasunori Fukuda Kazuo Mori Hideo Kobayashi

In this paper, we propose an autonomous transmission timing control scheme for collision avoidance in ad hoc multicasting. In ad hoc multicasting, packet collisions due to hidden node problems at upstream nodes cause packet losses at all downstream nodes. Therefore, collision avoidance mechanisms are important to improve packet delivery ratio at destination nodes. In this paper, we extend an On...

1999
Masahiro Kuroda Takashi Sakakura Takashi Watanabe Tadanori Mizuno Yoshiki Shimotsuma

The optimistic consistency scheme has been established with respect to data consistency and availability in distributed systems. Wireless networks are becoming popular in data communication and suitable for data sharing among users using multicast capability, but have hidden node problems. This paper proposes a simple data coherency protocol for mobile devices that is less data traffic in data ...

1999
Karl-Heinz Temme Ralph Heider Claudio Moraga

Neuro-fuzzy modeling has been intensively studied since the early nineties. Recently a method has been disclosed, that uses a classical feedforward neural network with just one hidden layer. Nodes of the hidden layer use the logistic function as activation function meanwhile the output node has a linear activation function. This paper introduces a generalization of the logistic function and eva...

Journal: :IEEE Trans. Industrial Electronics 2003
Sai-Ho Ling F. H. Frank Leung Hak-Keung Lam Yim-Shu Lee Peter Kwong-Shun Tam

This paper presents a neural network with a novel neuron model. In this model, the neuron has two activation functions and exhibits a node-to-node relationship in the hidden layer. This neural network provides better performance than a traditional feedforward neural network, and fewer hidden nodes are needed. The parameters of the proposed neural network are tuned by a genetic algorithm with ar...

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