An Embedding Framework for Myocardial Velocity Processing with MRI
This paper presents an embedding framework for myocardial velocity processing with MRI based on a hollow, semi-spherical template. The relationship between vector bundle and manifold mapping is analysed and three different mapping methods that include tangent mapping, normal mapping, and normal-tangent mapping are assessed for their practical value of myocardial contractility analysis. The proposed method provides a basis for consistent volume matching and vector correspondence, in addition to the ease of calculating biomechanical indices such as radial, circumferential and longitudinal strain rates without the concern of boundary effects. Detailed analysis results with both synthetic and in vivo MR velocity data sets are provided.

title: Subject Specific Finite Element Modelling of the Levator Ani
abstract: Understanding of the dynamic behaviour of the levator ani is important to the assessment of pelvic floor dysfunction. Whilst shape modelling allows the depiction of 3D morphological variation of the levator ani between different patient groups, it is insufficient to determine the underlying behaviour of how the muscle deforms during contraction and strain. The purpose of this study is to perform a subject specific finite element analysis of the levator ani with open access magnetic resonance imaging. The method is based on a Mooney-Rivlin hyperelastic model and permits dynamic study of subjects under natural physiological loadings. The value of the proposed modelling framework is demonstrated with dynamic 3D data from nulliparous, female subjects.

title: Embedding of Complete Binary Trees into Meshes with Row-Column Routing
abstract: This paper considers the problem of embedding complete binary trees into meshes using the row-column routing and obtained the following results: a complete binary tree with 2/sup p/-1 nodes can be embedded (1) with link congestion one into a /sup 9///sub 8//spl radic/(2/sup p/)/spl times//sup 9///sub 8//spl radic/(2/sup p/) mesh when p is even and a /spl radic/(/sup 9///sub 8/2/sup p/)/spl times//spl radic/(/sup 9///sub 8/2/sup p/) mesh when p is odd, and (2) with link congestion two into a /spl radic/(2/sup p/)/spl times//spl radic/(2/sup p/) mesh when p is even, and a /spl radic/(2/sup p-1/)/spl times//spl radic/(2/sup p-1/) mesh when p is odd.

title: An interactive video delivery and caching system using video summarization
abstract: With the advance of high-speed network technologies, the availability and popularity of streaming media content over the Internet has grown rapidly in recent years. The delivery and caching of streaming media must be handled in a different fashion than that of traditional non-streaming objects such as HTML or image files, because of its distinct characteristics and user viewing patterns. We propose a novel scheme that provides users with the video summary (a number of key-frame images) before they download the file, and options for them to select the starting playback position. We introduce the content analysis service to achieve these functionalities. The video content analysis performs shot boundary detection, key-frame selection, and face detection and tracking. The results of the processing are a segmented video sequence and an XML-based meta-data describing the video content. We also design a caching system that utilizes our video abstraction and summarization technique. Our integrated video delivery and caching system combines content-aware segmentation, prefix caching, prefetching, and cooperative caching. We describe how our scheme can be applied in three proposed caching architectures.

title: Endomorphic modelling for two-dimensional time-varying autoregressive model signals
abstract: Physical signals are often non-stationary. We assume that images are non-stationary and each pixel is the output of a different set of two-dimensional autoregressive (AR) model coefficients; therefore, texture cannot be characterised by applying AR modelling techniques to the entire image. A multistage modelling technique, which appears in the literature as "endomorphic modelling", is applied to this kind of signal. The signal is divided into sub-sets of samples and each sub-set is modelled using an AR model. A new image is formed using the same AR model coefficient estimated from different blocks, then the new image is re-modelled using AR models again. This process ends when the variance of the estimated parameters is sufficiently small. The simulation results show that the variances obtained from the estimated coefficients at the second stage are much smaller than the ones from the first stage

title: Scalability study of the ad hoc on-demand distance vector routing protocol
abstract: As mobile networking continues to experience increasing popularity, the need to connect large numbers of wireless devices will become more prevalent. Many recent proposals for ad hoc routing have certain characteristics that may limit their scalability to large networks. This paper examines five different combinations of modifications that may be incorporated into virtually any on-demand protocol in order to improve its scalability. The scalability of current on-demand routing protocols is evaluated through the selection of a representative from this class of protocols. The performance of the un-modified on-demand protocol is compared against that of it combined with each of the scalability modifications. Each scheme's behavior is analyzed in networks as large as 10,000 nodes through detailed simulation. Based on the observations, conclusions are drawn as to the expected scalability improvement that can be achieved by each modification.

title: Noise-robust automatic speech recognition using mainlobe-resilient time-frequency quantile-based noise estimation
abstract: In standard speech recognition systems in which training data are clean speech, the presence of background noise in received signal can severely deteriorate the recognition performance. This paper presents a simple noise-robust speech recognition system based on a modified noise spectral estimation method called mainlobe-resilient time-frequency quantile-based noise estimation (M-R T-F QBNE), which focuses on the mainlobes at harmonic frequencies. We estimate the global signal-to-noise ratio (SNR) and select a recognition model, which is best matched to the SNR operating range. Experimental results show that the recognition accuracy of the proposed recognition system is higher than that of the AURORA2 clean training baseline by 23%. Compared to multicondition training, the proposed method achieves comparable recognition accuracy.