Management of services differentiation and guarantee in IEEE 802.11e wireless LANs

In this paper, we study the management and control of service differentiation and guarantee based on enhanced distributed function coordination (EDCF) in IEEE 802.11e wireless LANs. Backoff-based priority schemes are the major mechanism for Quality of Service (QoS) provisioning in EDCF. However, control and management of the backoff-based priority scheme are still challenging problems. We have analysed the impacts of backoff and Inter-frame Space (IFS) parameters of EDCF on saturation throughput and service differentiation. A centralised QoS management and control scheme is proposed. The configuration of backoff parameters and admission control are studied in the management scheme. The special role of access point (AP) and the impact of traffic load are also considered in the scheme. The backoff parameters are adaptively re-configured to increase the levels of bandwidth guarantee and fairness on sharing bandwidth. The proposed management scheme is evaluated by OPNET. Simulation results show the effectiveness of the analytical model based admission control scheme. ©2005 IEEE.

Comparison between conventional and stochastic pinning control

Theoretical developments on pinning control of complex dynamical networks have mainly focused on the deterministic versions of the model dynamics. However, the dynamical behavior of most real networks is often affected by stochastic noise components. In this paper the pinning control of a stochastic version of the coupled map lattice network with spatiotemporal characteristics is studied. The control of these complex dynamical networks have functional uncertainty which should be considered when calculating stabilizing control signals. Two feedback control methods are considered: the conventional feedback control and modified stochastic feedback control. It is shown that the typically-used conventional control method suffers from the ignorance of model uncertainty leading to a reduction and potentially a collapse in the control efficiency. Numerical verification of the main result is provided for a chaotic coupled map lattice network. © 2011 IEEE.

A distributed and key-position fiber sensing system based on LPFG and WFBG assisted OTDR

An enhanced fiber sensing system used for distributed bending and key-position sensing is reported by integrating WFBGs, LPFG and OTDR, which also achieves strain and temperature sensitivities up to 0.047mv/με and 0.675mv/°C respectively. © 2014 OSA.

Ultrahigh step-up dc-dc converter for distributed generation by three Degrees of Freedom (3DoF) approach

This paper proposes a novel dc-dc converter topology to achieve an ultrahigh step-up ratio while maintaining a high conversion efficiency. It adopts a three degree of freedom approach in the circuit design. It also demonstrates the flexibility of the proposed converter to combine with the features of modularity, electrical isolation, soft-switching, low voltage stress on switching devices, and is thus considered to be an improved topology over traditional dc-dc converters. New control strategies including the two-section output voltage control and cell idle control are also developed to improve the converter performance. With the cell idle control, the secondary winding inductance of the idle module is bypassed to decrease its power loss. A 400-W dc-dc converter is prototyped and tested to verify the proposed techniques, in addition to a simulation study. The step-up conversion ratio can reach 1:14 with a peak efficiency of 94% and the proposed techniques can be applied to a wide range of high voltage and high power distributed generation and dc power transmission.

Learning user and product distributed representations using a sequence model for sentiment analysis

In product reviews, it is observed that the distribution of polarity ratings over reviews written by different users or evaluated based on different products are often skewed in the real world. As such, incorporating user and product information would be helpful for the task of sentiment classification of reviews. However, existing approaches ignored the temporal nature of reviews posted by the same user or evaluated on the same product. We argue that the temporal relations of reviews might be potentially useful for learning user and product embedding and thus propose employing a sequence model to embed these temporal relations into user and product representations so as to improve the performance of document-level sentiment analysis. Specifically, we first learn a distributed representation of each review by a one-dimensional convolutional neural network. Then, taking these representations as pretrained vectors, we use a recurrent neural network with gated recurrent units to learn distributed representations of users and products. Finally, we feed the user, product and review representations into a machine learning classifier for sentiment classification. Our approach has been evaluated on three large-scale review datasets from the IMDB and Yelp. Experimental results show that: (1) sequence modeling for the purposes of distributed user and product representation learning can improve the performance of document-level sentiment classification; (2) the proposed approach achieves state-of-The-Art results on these benchmark datasets.