A multi-agent architect for CSCW systems: from organisational semiotics perspective

This paper describes a multi-agent architecture to support CSCW systems modelling. Since CSCW involves different organizations, it can be seen as a social model. From this point of view, we investigate the possibility of modelling CSCW by agent technology, and then based on organizational semiotics method a multi-agent architecture is proposed via using EDA agent model. We explain the components of this multi-agent architecture and design process. It is argued that this approach provides a new perspective for modelling CSCW systems.

e-learning services provision and management

With the rapid development of information technology, learners demand effective personalised learning support, which imposes a new learning paradigm in learning content management. Standards as well as best practice in industry and research community have taken place to address the paradigm shift. With respect to this trend, it is recognised that finding learning content which meet personal learning requirements remains challenging. This paper describes a model of e-learning services provision which integrates the best practice in e-learning and Web services technology so that learning content management is capable of supporting applications of learning services.

Practical interface experiments with implant technology

In this paper results are shown to indicate the efficacy of a direct connection between the human nervous system and a computer network. Experimental results obtained thus far from a study lasting for over 3 months are presented, with particular emphasis placed on the direct interaction between the human nervous system and a piece of wearable technology. An overview of the present state of neural implants is given, as well as a range of application areas considered thus far. A view is also taken as to what may be possible with implant technology as a general purpose human-computer interface for the future.

Comparison of the computational cost of a Monte Carlo and deterministic algorithm for computing bilinear forms of matrix powers

In this paper we consider bilinear forms of matrix polynomials and show that these polynomials can be used to construct solutions for the problems of solving systems of linear algebraic equations, matrix inversion and finding extremal eigenvalues. An almost Optimal Monte Carlo (MAO) algorithm for computing bilinear forms of matrix polynomials is presented. Results for the computational costs of a balanced algorithm for computing the bilinear form of a matrix power is presented, i.e., an algorithm for which probability and systematic errors are of the same order, and this is compared with the computational cost for a corresponding deterministic method.

Linear predicted hexagonal search algorithm with moments

A novel Linear Hashtable Method Predicted Hexagonal Search (LHMPHS) method for block based motion compensation is proposed. Fast block matching algorithms use the origin as the initial search center, which often does not track motion very well. To improve the accuracy of the fast BMA's, we employ a predicted starting search point, which reflects the motion trend of the current block. The predicted search centre is found closer to the global minimum. Thus the center-biased BMA's can be used to find the motion vector more efficiently. The performance of the algorithm is evaluated by using standard video sequences, considers the three important metrics: The results show that the proposed algorithm enhances the accuracy of current hexagonal algorithms and is better than Full Search, Logarithmic Search etc.

Linear algorithm and hexagonal search based two-pass algorithm for motion estimation

This paper presents a novel two-pass algorithm constituted by Linear Hashtable Motion Estimation Algorithm (LHMEA) and Hexagonal Search (HEXBS) for block base motion compensation. On the basis of research from previous algorithms, especially an on-the-edge motion estimation algorithm called hexagonal search (HEXBS), we propose the LHMEA and the Two-Pass Algorithm (TPA). We introduced hashtable into video compression. In this paper we employ LHMEA for the first-pass search in all the Macroblocks (MB) in the picture. Motion Vectors (MV) are then generated from the first-pass and are used as predictors for second-pass HEXBS motion estimation, which only searches a small number of MBs. The evaluation of the algorithm considers the three important metrics being time, compression rate and PSNR. The performance of the algorithm is evaluated by using standard video sequences and the results are compared to current algorithms, Experimental results show that the proposed algorithm can offer the same compression rate as the Full Search. LHMEA with TPA has significant improvement on HEXBS and shows a direction for improving other fast motion estimation algorithms, for example Diamond Search.

Collaterally cued labelling framework underpinning semantic-level visual content descriptor

In this paper, we introduce a novel high-level visual content descriptor devised for performing semantic-based image classification and retrieval. The work can be treated as an attempt for bridging the so called "semantic gap". The proposed image feature vector model is fundamentally underpinned by an automatic image labelling framework, called Collaterally Cued Labelling (CCL), which incorporates the collateral knowledge extracted from the collateral texts accompanying the images with the state-of-the-art low-level visual feature extraction techniques for automatically assigning textual keywords to image regions. A subset of the Corel image collection was used for evaluating the proposed method. The experimental results indicate that our semantic-level visual content descriptors outperform both conventional visual and textual image feature models.

A forward constrained selection algorithm for probabilistic neural network

A new probabilistic neural network (PNN) learning algorithm based on forward constrained selection (PNN-FCS) is proposed. An incremental learning scheme is adopted such that at each step, new neurons, one for each class, are selected from the training samples arid the weights of the neurons are estimated so as to minimize the overall misclassification error rate. In this manner, only the most significant training samples are used as the neurons. It is shown by simulation that the resultant networks of PNN-FCS have good classification performance compared to other types of classifiers, but much smaller model sizes than conventional PNN.

A multi-level probabilistic neural network

Based on the idea of an important cluster, a new multi-level probabilistic neural network (MLPNN) is introduced. The MLPNN uses an incremental constructive approach, i.e. it grows level by level. The construction algorithm of the MLPNN is proposed such that the classification accuracy monotonically increases to ensure that the classification accuracy of the MLPNN is higher than or equal to that of the traditional PNN. Numerical examples are included to demonstrate the effectiveness of proposed new approach.

Design and development of a haptic dental training system - hapTEL

This paper presents a novel design of a virtual dental training system (hapTEL) using haptic technology. The system allows dental students to learn and practice procedures such as dental drilling, caries removal and cavity preparation for tooth restoration. This paper focuses on the hardware design, development and evaluation aspects in relation to the dental training and educational requirements. Detailed discussions on how the system offers dental students a natural operational position are documented. An innovative design of measuring and connecting the dental tools to the haptic device is also shown. Evaluation of the impact on teaching and learning is discussed.

A n-Bit Reconfigurable Scalar Quantiser

A reconfigurable scalar quantiser capable of accepting n-bit input data is presented. The data length n can be varied in the range 1... N-1 under partial-run time reconfiguration, p-RTR. Issues as improvement in throughput using this reconfigurable quantiser of p-RTR against RTR for data of variable length are considered. The quantiser design referred to as the priority quantiser PQ is then compared against a direct design of the quantiser DIQ. It is then evaluated that for practical quantiser sizes, PQ shows better area usage when both are targeted onto the same FPGA. Other benefits are also identified.