SUMMARYThe seamless internetworking among heterogeneous networks is in great demand to provide 'always-on' connectivity services with quality of service (QoS) provision, anywhere at anytime. The integration of wireless-fidelity (Wi-Fi) and wireless metropolitan area networks (WiMAX) networks can combine their best features to provide ubiquitous access, while mediating the weakness of both networks. While it is challenging to obtain optimized handover decision-based dynamic QoS information, users can improve their perceived QoS by using the terminal-controlled handover decision in a single device equipped with multiple radio interfaces. The IEEE 802.21 aims at providing a framework that defines media-independent handover (MIH) mechanism that supports seamless handover across heterogeneous networks. In this paper, an multiple attributes decision making-based terminal-controlled vertical handover decision scheme using MIH services is proposed in the integrated Wi-Fi and WiMAX networks to provide 'always-on' connectivity QoS services. The simulation results show that the proposed scheme provides smaller handover times and lower dropping rate than the RSS-based and cost function-based vertical handover schemes.

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Wireless access in vehicular environment (WAVE) architecture of intelligent transportation system has been standardized in the IEEE 802.11p specification. The WAVE network supports the features of multi-rate and multi-channel and it is going to be widely deployed in realistic roadway environments in order to provide traffic information and convenient services. However, the adopted contention-based medium access control protocol, which confronts the performance anomaly problem, would severely downgrade transmission efficiency between roadside-unit (RSU) and on-board-unit (OBU) because they may use diverse data transmission rates to access channel. As a solution, this paper proposes the vehicular channel access scheme (VCAS) to group a number of OBUs with similar transmission rates into one channel to optimize the channel throughput, while the group sizes of channels are controlled in order to fulfill the fairness requirement. To flexibly compromise the tradeoff between throughput and fairness, a marginal utility model is proposed in this paper. Simulation results demonstrate that the proposed VCAS with marginal utility provides a flexible way to handle versatile vehicular scenarios.

In this paper, we explore the effect of using different convolutional layers, batch normalization and the global average pooling layer upon a convolutional neural network (CNN) based gaze tracking system. A novel method is proposed to label the participant’s face images as gaze points retrieved from eye tracker while watching videos for building a training dataset that is closer to human visual behavior. The participants can swing their head freely; therefore, the most real and natural images can be obtained without too many restrictions. The labeled data are classified according to the coordinate of gaze and area of interest on the screen. Therefore, varied network architectures are applied to estimate and compare the effects including the number of convolutional layers, batch normalization (BN) and the global average pooling (GAP) layer instead of the fully connected layer. Three schemes, including the single eye image, double eyes image and facial image, with data augmentation are used to feed into neural network to train and evaluate the efficiency. The input image of the eye or face for an eye tracking system is mostly a small-sized image with relatively few features. The results show that BN and GAP are helpful in overcoming the problem to train models and in reducing the amount of network parameters. It is shown that the accuracy is significantly improved when using GAP and BN at the mean time. Overall, the face scheme has a highest accuracy of 0.883 when BN and GAP are used at the mean time. Additionally, comparing to the fully connected layer set to 512 cases, the number of parameters is reduced by less than 50% and the accuracy is improved by about 2%. A detection accuracy comparison of our model with the existing George and Routray methods shows that our proposed method achieves better prediction accuracy of more than 6%.

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