Statistical Shape Modelling of the Levator Ani with Thickness Variation
The levator ani is vulnerable to injury during childbirth and effective surgical intervention requires full knowledge of the morphology and mechanical properties of the muscle structure. The shape of the levator ani and regional thickening during different levels of physiological loading can provide an indication of pelvic floor dysfunction. This paper describes a coupled approach for shape and thickness statistical modelling based on harmonic embedding for volumetric analysis of the levator ani. With harmonic embedding, the dynamic information extracted by the statistical modelling captures shape and thickness variation of the levator ani across different subjects and during varying levels of stress. With this study, we demonstrate that the derived model is compact and physiologically meaningful, demonstrating the practical value of the technique.

title: An Embedding Framework for Myocardial Velocity Processing with MRI
abstract: 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: Drug risk assessment with determining the number of sub-populations under finite mixture normal models
abstract: In some biological experiments, it is quite common that laboratory subjects may be different in their patterns of susceptibility to a treatment. In these situations, finite mixture model analysis becomes a useful tool. Under a finite mixture normal model, drug risk assessment problems are studied under the assumption that the population at risk consists of multiple sub-types of different susceptibilities. The EM algorithm and its extension are utilized to obtain the maximum likelihood estimates of the model. Particular discussion is given to the model selection problem based on Bayes factor, which is approximated by the Schwarz criterion. Meantime, the practical significance of the proposed method is illustrated with an actual dose–response data set.

title: Neighbor supporting ad hoc multicast routing protocol
abstract: An ad hoc network is a multi-hop wireless network formed by a collection of mobile nodes without the intervention of fixed infrastructure. Limited bandwidth and a high degree of mobility require that routing protocols for ad hoc networks be robust, simple, and energy-conserving. This paper proposes a new ad hoc multicast routing protocol called Neighbor-Supporting Multicast Protocol (NSMP). NSMP adopts a mesh structure to enhance resilience against mobility. And NSMP utilizes node locality to reduce the overhead of route failure recovery and mesh maintenance. NSMP also attempts to improve route efficiency and reduce data transmissions. Our simulation results show that NSMP delivers packets efficiently while substantially reducing control overhead in various environments.

title: Improving aggregated channel performance through decentralized channel monitoring
abstract: Aggregating low-speed WAN links into a higher-speed logical link promises to improve data-transfer rates to collaborating communities of wireless mobile multihomed devices. Such bandwidth aggregation systems must adapt to link dynamics as the number of links and the channel conditions vary with time due to mobility, power dissipation, and channel interference. A monitoring architecture that accurately measures the link dynamics and promptly feeds this information to the system is vital to realize significant bandwidth aggregation performance gains. In this paper we present various architectural design alternatives for such a monitoring system, and evaluate them using both analysis and simulation. We show that a properly-designed monitoring system can accurately measure and quickly respond to changes in communication link performance while minimizing the control overhead.

title: An Accurate Instruction-Level Energy Consumption Model for Embedded RISC Processors
abstract: Energy consumption of software is becoming an increasingly important issue in designing mobile embedded systems where batteries are used as the main power source. As a consequence, recently, a number of promising techniques have been proposed to optimize software for reduced energy consumption. Such low-power software techniques require an energy consumption model that can be used to estimate or predict the energy consumed by software. We propose a technique to derive an accurate energy consumption model at the instruction level, combining an empirical method and a statistical analysis technique. The result of the proposed approach is given by a model equation that characterizes energy behavior of software based on the properties of the instructions. Experimental results show that the model equation can accurately estimate the energy consumption of random instruction sequences, with an average error of 2.5%

title: Frontiers in Handwriting Recognition