Multi-action recognition via stochastic modelling of optical flow and gradients

In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%.

Coil enhancements for high efficiency wireless power transfer applications

Achieving high efficiency with improved power transfer range and misalignment tolerance is the major design challenge in realizing Wireless Power Transfer (WPT) systems for industrial applications. Resonant coils must be carefully designed to achieve highest possible system performance by fully utilizing the available space. High quality factor and enhanced electromagnetic coupling are key indices which determine the system performance. In this paper, design parameter extraction and quality factor optimization of multi layered helical coils are presented using finite element analysis (FEA) simulations. In addition, a novel Toroidal Shaped Spiral (TSS) coil is proposed to increase power transfer range and misalignment tolerance. The proposed shapes and recommendations can be used to design high efficiency WPT resonator in a limited space.

Quantifying uncertainty in parameter estimates for stochastic models of collective cell spreading using approximate Bayesian computation

Wound healing and tumour growth involve collective cell spreading, which is driven by individual motility and proliferation events within a population of cells. Mathematical models are often used to interpret experimental data and to estimate the parameters so that predictions can be made. Existing methods for parameter estimation typically assume that these parameters are constants and often ignore any uncertainty in the estimated values. We use approximate Bayesian computation (ABC) to estimate the cell diffusivity, D, and the cell proliferation rate, λ, from a discrete model of collective cell spreading, and we quantify the uncertainty associated with these estimates using Bayesian inference. We use a detailed experimental data set describing the collective cell spreading of 3T3 fibroblast cells. The ABC analysis is conducted for different combinations of initial cell densities and experimental times in two separate scenarios: (i) where collective cell spreading is driven by cell motility alone, and (ii) where collective cell spreading is driven by combined cell motility and cell proliferation. We find that D can be estimated precisely, with a small coefficient of variation (CV) of 2–6%. Our results indicate that D appears to depend on the experimental time, which is a feature that has been previously overlooked. Assuming that the values of D are the same in both experimental scenarios, we use the information about D from the first experimental scenario to obtain reasonably precise estimates of λ, with a CV between 4 and 12%. Our estimates of D and λ are consistent with previously reported values; however, our method is based on a straightforward measurement of the position of the leading edge whereas previous approaches have involved expensive cell counting techniques. Additional insights gained using a fully Bayesian approach justify the computational cost, especially since it allows us to accommodate information from different experiments in a principled way.

Real-time assessment of GNSS observation noise with single receivers

Stochastic modelling is critical in GNSS data processing. Currently, GNSS data processing commonly relies on the empirical stochastic model which may not reflect the actual data quality or noise characteristics. This paper examines the real-time GNSS observation noise estimation methods enabling to determine the observation variance from single receiver data stream. The methods involve three steps: forming linear combination, handling the ionosphere and ambiguity bias and variance estimation. Two distinguished ways are applied to overcome the ionosphere and ambiguity biases, known as the time differenced method and polynomial prediction method respectively. The real time variance estimation methods are compared with the zero-baseline and short-baseline methods. The proposed method only requires single receiver observation, thus applicable to both differenced and un-differenced data processing modes. However, the methods may be subject to the normal ionosphere conditions and low autocorrelation GNSS receivers. Experimental results also indicate the proposed method can result on more realistic parameter precision.

Maximum principle and numerical method for the multi-term time–space Riesz–Caputo fractional differential equations

The maximum principle for the space and time–space fractional partial differential equations is still an open problem. In this paper, we consider a multi-term time–space Riesz–Caputo fractional differential equations over an open bounded domain. A maximum principle for the equation is proved. The uniqueness and continuous dependence of the solution are derived. Using a fractional predictor–corrector method combining the L1 and L2 discrete schemes, we present a numerical method for the specified equation. Two examples are given to illustrate the obtained results.

Multi-layered model of individual HIV infection progression and mechanisms of phenotypical expression

Cite as: Perrin, Dimitri (2008) Multi-layered model of individual HIV infection progression and mechanisms of phenotypical expression. PhD thesis, Dublin City University.

Prediction of GMA welding characteristic parameter by artificial neural network system

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

A framework for identification of similarities between multiple algorithms

This thesis in software engineering presents a novel automated framework to identify similar operations utilized by multiple algorithms for solving related computing problems. It provides a new effective solution to perform multi-application based algorithm analysis, employing fundamentally light-weight static analysis techniques compared to the state-of-art approaches. Significant performance improvements are achieved across the objective algorithms through enhancing the efficiency of the identified similar operations, targeting discrete application domains.

Structural damage alarming and localization of cable-supported bridges using multi-novelty indices: A feasibility study

This paper presents a feasibility study on structural damage alarming and localization of long-span cable-supported bridges using multi-novelty indices formulated by monitoring-derived modal parameters. The proposed method which requires neither structural model nor damage model is applicable to structures of arbitrary complexity. With the intention to enhance the tolerance to measurement noise/uncertainty and the sensitivity to structural damage, an improved novelty index is formulated in terms of auto-associative neural networks (ANNs) where the output vector is designated to differ from the input vector while the training of the ANNs needs only the measured modal properties of the intact structure under in-service conditions. After validating the enhanced capability of the improved novelty index for structural damage alarming over the commonly configured novelty index, the performance of the improved novelty index for damage occurrence detection of large-scale bridges is examined through numerical simulation studies of the suspension Tsing Ma Bridge (TMB) and the cable-stayed Ting Kau Bridge (TKB) incurred with different types of structural damage. Then the improved novelty index is extended to formulate multi-novelty indices in terms of the measured modal frequencies and incomplete modeshape components for damage region identification. The capability of the formulated multi-novelty indices for damage region identification is also examined through numerical simulations of the TMB and TKB.

Multi-parametric hydrogels support 3D in vitro bioengineered microenvironment models of tumour angiogenesis

Tumour microenvironment greatly influences the development and metastasis of cancer progression. The development of three dimensional (3D) culture models which mimic that displayed in vivo can improve cancer biology studies and accelerate novel anticancer drug screening. Inspired by a systems biology approach, we have formed 3D in vitro bioengineered tumour angiogenesis microenvironments within a glycosaminoglycan-based hydrogel culture system. This microenvironment model can routinely recreate breast and prostate tumour vascularisation. The multiple cell types cultured within this model were less sensitive to chemotherapy when compared with two dimensional (2D) cultures, and displayed comparative tumour regression to that displayed in vivo. These features highlight the use of our in vitro culture model as a complementary testing platform in conjunction with animal models, addressing key reduction and replacement goals of the future. We anticipate that this biomimetic model will provide a platform for the in-depth analysis of cancer development and the discovery of novel therapeutic targets.

Green: Exploring the aestheticized use of chroma-key techniques and technologies in two intermedial productions

Peggy Shaw’s RUFF, (USA 2013) and Queensland Theatre Company’s collaboration with Queensland University of Technology, Total Dik!, (Australia 2013) overtly and evocatively draw on an aestheticized use of the cinematic techniques and technologies of Chroma Key to reveal the tensions in their production and add layers to their performances. In doing so they offer invaluable insight where the filmic and theatrical approaches overlap. This paper draws on Eckersall, Grehan and Scheer’s New Media Dramaturgy (2014) to reposition the frame as a contribution to intermedial theatre and performance practices in light of increasing convergence between seemingly disparate discourses. In RUFF, the scenic environment replicates a chroma-key ‘studio’ which facilitates the reconstruction of memory displaced after a stroke. RUFF uses the screen and projections to recall crooners, lounge singers, movie stars, rock and roll bands, and an eclectic line of eccentric family members living inside Shaw. While the show pays tribute to those who have kept her company across decades of theatrical performance, use of non-composited chroma-key technique as a theatrical device and the work’s taciturn revelation of the production process during performance, play a central role in its exploration of the juxtaposition between its reconstructed form and content. In contrast Total Dik! uses real-time green screen compositing during performance as a scenic device. Actors manipulate scale models, refocus cameras and generate scenes within scenes in the construction of the work’s examination of an isolated Dictator. The ‘studio’ is again replicated as a site for (re)construction, only in this case Total Dik! actively seeks to reveal the process of production as the performance plays out. Building on RUFF, and other works such as By the Way, Meet Vera Stark, (2012) and Hotel Modern’s God’s Beard (2012), this work blends a convergence of mobile technologies, models, and green screen capture to explore aspects of transmedia storytelling in a theatrical environment (Jenkins, 2009, 2013). When a green screen is placed on stage, it reads at once as metaphor and challenge to the language of theatre. It becomes, or rather acts, as a ‘sign’ that alludes to the nature of the reconstructed, recomposited, manipulated and controlled. In RUFF and in Total Dik!, it is also a place where as a mode of production and subsequent reveal, it adds weight to performance. These works are informed by Auslander (1999) and Giesenkam (2007) and speak to and echo Lehmann’s Postdramatic Theatre (2006). This paper’s consideration of the integration of studio technique and live performance as a dynamic approach to multi-layered theatrical production develops our understanding of their combinatory use in a live performance environment.

Decreasing menopausal symptoms in women undertaking a web-based multi-modal lifestyle intervention: The Women's Wellness Program

Menopausal transition can be challenging for many women. This study tested the effectiveness of an intervention delivered in different modes in decreasing menopausal symptoms in midlife women. The Women's Wellness Program (WWP) intervention was delivered to 225 Australian women aged between 40 and 65 years through three modes (i.e., on-line independent, face-to-face with nurse consultations, and on-line with virtual nurse consultations). All women in the study were provided with a 12-week Program Book outlining healthy lifestyle behaviors while women in the consultation groups were supported by a registered nurse who provide tailored health education and assisted with individual goal setting for exercise, healthy eating, smoking and alcohol consumption. Pre- and post-intervention data were collected on menopausal symptoms (Greene Climacteric Scale), health related quality of life (SF12), and modifiable lifestyle factors. Linear mixed-effect models showed an average 0.87 and 1.23 point reduction in anxiety (p < 0.01) and depression scores (p < 0.01) over time in all groups. Results also demonstrated reduced vasomotor symptoms (β = −0.19, SE = 0.10, p = 0.04) and sexual dysfunction (β = −0.17, SE = 0.06, p < 0.01) in all participants though women in the face-to-face group generally reported greater reductions than women in the other groups. This lifestyle intervention embedded within a wellness framework has the potential to reduce menopausal symptoms and improve quality of life in midlife women thus potentially enhancing health and well-being in women as they age. Of course, study replication is needed to confirm the intervention effects.

Process visualization techniques for multi-perspective process comparisons

Organizations executing similar business processes need to understand the differences and similarities in activities performed across work environments. Presently, research interest is directed towards the potential of visualization for the display of process models, to support users in their analysis tasks. Although recent literature in process mining and comparison provide several methods and algorithms to perform process and log comparison, few contributions explore novel visualization approaches. This paper analyses process comparison from a design perspective, providing some practical visualization techniques as anal- ysis solutions (/to support process analysis). The design of the visual comparison has been tackled through three different points of view: the general model, the projected model and the side-by-side comparison in order to support the needs of business analysts. A case study is presented showing the application of process mining and visualization techniques to patient treatment across two Australian hospitals.

Towards strongly consistent online HMM parameter estimation using one-step Kerridge inaccuracy

In this paper, we propose a novel online hidden Markov model (HMM) parameter estimator based on the new information-theoretic concept of one-step Kerridge inaccuracy (OKI). Under several regulatory conditions, we establish a convergence result (and some limited strong consistency results) for our proposed online OKI-based parameter estimator. In simulation studies, we illustrate the global convergence behaviour of our proposed estimator and provide a counter-example illustrating the local convergence of other popular HMM parameter estimators.

Investigation of the effects of the fatty acid profile on fuel properties using a Multi-Criteria Decision Analysis

The structural features of fatty acids in biodiesel, including degree of unsaturation, percentage of saturated fatty acids and average chain length, influence important fuel properties such as cetane number, iodine value, density, kinematic viscosity, higher heating value and oxidation stability. The composition of fatty acid esters within the fuel should therefore be in the correct ratio to ensure fuel properties are within international biodiesel standards such as ASTM 6751 or EN 14214. This study scrutinises the influence of fatty acid composition and individual fatty acids on fuel properties. Fuel properties were estimated based on published equations, and measured according to standard procedure ASTM D6751 and EN 14214 to confirm the influences of the fatty acid profile. Based on fatty acid profile-derived calculations, the cetane number of the microalgal biodiesel was estimated to be 11.6, but measured 46.5, which emphasises the uncertainty of the method used for cetane number calculation. Multi-criteria decision analysis (MCDA), PROMETHEE-GAIA, was used to determine the influence of individual fatty acids on fuel properties in the GAIA plane. Polyunsaturated fatty acids increased the iodine value and had a negative influence on cetane number. Kinematic viscosity was negatively influenced by some long chain polyunsaturated fatty acids such as C20:5 and C22:6 and some of the more common saturated fatty acids C14:0 and C18:0. The positive impact of average chain length on higher heating value was also confirmed in the GAIA plane