Vertical handover based adaptions for multimedia applications in pervasive systems

This paper proposes an architecture for pervasive computing which utilizes context information to provide adaptations based on vertical handovers (handovers between heterogeneous networks) while supporting application Quality of Service (QoS). The future of mobile computing will see an increase in ubiquitous network connectivity which allows users to roam freely between heterogeneous networks. One of the requirements for pervasive computing is to adapt computing applications or their environment if current applications can no longer be provided with the requested QoS. One of possible adaptations is a vertical handover to a different network. Vertical handover operations include changing network interfaces on a single device or changes between different devices. Such handovers should be performed with minimal user distraction and minimal violation of communication QoS for user applications. The solution utilises context information regarding user devices, user location, application requirements, and network environment. The paper shows how vertical handover adaptations are incorporated into the whole infrastructure of a pervasive system

A novel grid computing approach for probabilistic small signal analysis

Grid computing is an advanced technique for collaboratively solving complicated scientific problems using geographically and organisational dispersed computational, data storage and other recourses. Application of grid computing could provide significant benefits to all aspects of power system that involves using computers. Based on our previous research, this paper presents a novel grid computing approach for probabilistic small signal stability (PSSS) analysis in electric power systems with uncertainties. A prototype computing grid is successfully implemented in our research lab to carry out PSSS analysis on two benchmark systems. Comparing to traditional computing techniques, the gird computing has given better performances for PSSS analysis in terms of computing capacity, speed, accuracy and stability. In addition, a computing grid framework for power system analysis has been proposed based on the recent study.

Magic-angle effects in the interlayer magnetoresistance of quasi-one-dimensional metals due to interchain incoherence

The dependence of the magnetoresistance of quasi-one-dimensional metals on the direction of the magnetic field show dips when the field is tilted at the so-called magic angles determined by the structural dimensions of the materials. There is currently no accepted explanation for these magic-angle effects. We present a possible explanation. Our model is based on the assumption that, the intralayer transport in the second most conducting direction has a small contribution from incoherent electrons. This incoherence is modeled by a small uncertainty in momentum perpendicular to the most conducting (chain) direction. Our model predicts the magic angles seen in interlayer transport measurements for different orientations of the field. We compare our results to predictions by other models and to experiment.

To learn from one's mistakes: The role of impulsivity in positive workplace behaviours

Recentiy a neuropsychological model of learning has been proposed Qackson, 2002) which argues that Responsibility provides a cognitive re-expression of Impulsivit)' in the prediction of functional and dysfunctional behaviour. Jackson argues that primitive, instinctive impulses lead to antisocial behaviours and socio-cognitive regulators such as Responsibility leads to the re-expression of Impulsivity in terms of pro-social behaviours. Study 1 tests and supports the measurement properties of the assessment methodology associated with the model. Study 2 provides evidence in favour of the instinctive basis of Impulsivity and the conscious basis of Responsibility, which reinforces the underlying neuropsychological basis of the model. Study 3 uses structural equation modelling to determine if Responsibility mediates Impulsivity in the prediction of a latent variable representing work performance, work commitment and team performance, but does not mediate Impulsivit}' in the prediction of a latent variable representing sexual proclivity, workplace deviancy, gambling and beer consumption. Results provide strong support for Jackson's model and suggest that Impulsivity and Responsibility are fundamental to both functional and dysfunctional learning

Face recognition robust to head pose from one sample image

Most face recognition systems only work well under quite constrained environments. In particular, the illumination conditions, facial expressions and head pose must be tightly controlled for good recognition performance. In 2004, we proposed a new face recognition algorithm, Adaptive Principal Component Analysis (APCA) [4], which performs well against both lighting variation and expression change. But like other eigenface-derived face recognition algorithms, APCA only performs well with frontal face images. The work presented in this paper is an extension of our previous work to also accommodate variations in head pose. Following the approach of Cootes et al, we develop a face model and a rotation model which can be used to interpret facial features and synthesize realistic frontal face images when given a single novel face image. We use a Viola-Jones based face detector to detect the face in real-time and thus solve the initialization problem for our Active Appearance Model search. Experiments show that our approach can achieve good recognition rates on face images across a wide range of head poses. Indeed recognition rates are improved by up to a factor of 5 compared to standard PCA.