Robust speaker verification via fusion of speech and lip modalities

This paper investigates the use of lip information, in conjunction with speech information, for robust speaker verification in the presence of background noise. It has been previously shown in our own work, and in the work of others, that features extracted from a speaker's moving lips hold speaker dependencies which are complementary with speech features. We demonstrate that the fusion of lip and speech information allows for a highly robust speaker verification system which outperforms the performance of either sub-system. We present a new technique for determining the weighting to be applied to each modality so as to optimize the performance of the fused system. Given a correct weighting, lip information is shown to be highly effective for reducing the false acceptance and false rejection error rates in the presence of background noise

The use of speech and lip modalities for robust speaker verification under adverse conditions

Investigates the use of lip information, in conjunction with speech information, for robust speaker verification in the presence of background noise. We have previously shown (Int. Conf. on Acoustics, Speech and Signal Proc., vol. 6, pp. 3693-3696, May 1998) that features extracted from a speaker's moving lips hold speaker dependencies which are complementary with speech features. We demonstrate that the fusion of lip and speech information allows for a highly robust speaker verification system which outperforms either subsystem individually. We present a new technique for determining the weighting to be applied to each modality so as to optimize the performance of the fused system. Given a correct weighting, lip information is shown to be highly effective for reducing the false acceptance and false rejection error rates in the presence of background noise

Optimistic linear programming gives logarithmic regret for irreducible MDPs

We present an algorithm called Optimistic Linear Programming (OLP) for learning to optimize average reward in an irreducible but otherwise unknown Markov decision process (MDP). OLP uses its experience so far to estimate the MDP. It chooses actions by optimistically maximizing estimated future rewards over a set of next-state transition probabilities that are close to the estimates, a computation that corresponds to solving linear programs. We show that the total expected reward obtained by OLP up to time T is within C(P) log T of the reward obtained by the optimal policy, where C(P) is an explicit, MDP-dependent constant. OLP is closely related to an algorithm proposed by Burnetas and Katehakis with four key differences: OLP is simpler, it does not require knowledge of the supports of transition probabilities, the proof of the regret bound is simpler, but our regret bound is a constant factor larger than the regret of their algorithm. OLP is also similar in flavor to an algorithm recently proposed by Auer and Ortner. But OLP is simpler and its regret bound has a better dependence on the size of the MDP.

Principles for use of ball projection machines in elite and developmental sport programmes

Use of ball projection machines in the acquisition of interceptive skill has recently been questioned. The use of projection machines in developmental and elite fast ball sports programmes is not a trivial issue, since they play a crucial role in reducing injury incidence in players and coaches. A compelling challenge for sports science is to provide theoretical principles to guide how and when projection machines might be used for acquisition of ball skills and preparation for competition in developmental and elite sport performance programmes. Here, we propose how principles from an ecological dynamics theoretical framework could be adopted by sports scientists, pedagogues and coaches to underpin the design of interventions, practice and training tasks, including the use of hybrid video-projection technologies. The assessment of representative learning design during practice may provide ways to optimize developmental programmes in fast ball sports and inform the principled use of ball projection machines.

Using the Precede-Proceed Model of Health Program Planning in breast cancer nursing research

Aim: In this paper we discuss the use of the Precede-Proceed model when investigating health promotion options for breast cancer survivors. Background: Adherence to recommended health behaviors can optimize well-being after cancer treatment. Guided by the Precede-Proceed approach, we studied the behaviors of breast cancer survivors in our health service area. Data sources: The interview data from the cohort of breast cancer survivors are used in this paper to illustrate the use of Precede-Proceed in this nursing research context. Interview data were collected from June to December 2009. We also searched Medline, CINAHL, PsychInfo and PsychExtra up to 2010 for relevant literature in English to interrogate the data from other theoretical perspectives. Discussion: The Precede-Proceed model is theoretically-complex. The deductive analytic process guided by the model usefully explained some of the health behaviors of cancer survivors, although it could not explicate many other findings. A complementary inductive approach to the analysis and subsequent interpretation by way of Uncertainty in Illness Theory and other psychosocial perspectives provided a comprehensive account of the qualitative data that resulted in contextually-relevant recommendations for nursing practice. Implications for nursing: Nursing researchers using Precede-Proceed should maintain theoretical flexibility when interpreting qualitative data. Perspectives not embedded in the model might need to be considered to ensure that the data are analyzed in a contextually-relevant way. Conclusion: Precede-Proceed provides a robust framework for nursing researchers investigating health promotion in cancer survivors; however additional theoretical lenses to those embedded in the model can enhance data interpretation.

Implementable efficient time and energy consumption trajectories design for an autonomous underwater vehicle

In this paper we consider the implementation of time and energy efficient trajectories onto a test-bed autonomous underwater vehicle. The trajectories are losely connected to the results of the application of the maximum principle to the controlled mechanical system. We use a numerical algorithm to compute efficient trajectories designed using geometric control theory to optimize a given cost function. Experimental results are shown for the time minimization problem.

Study of tissue damage during mechanical peeling of tough skinned vegetables

Peeling is an essential phase of post harvesting and processing industry; however the undesirable losses and waste rate that occur during peeling stage are always the main concern of food processing sector. There are three methods of peeling fruits and vegetables including mechanical, chemical and thermal, depending on the class and type of fruit. By comparison, the mechanical method is the most preferred; this method keeps edible portions of produce fresh and creates less damage. Obviously reducing material losses and increasing the quality of the process has a direct effect on the whole efficiency of food processing industry which needs more study on technological aspects of this industrial segment. In order to enhance the effectiveness of food industrial practices it is essential to have a clear understanding of material properties and behaviour of tissues under industrial processes. This paper presents the scheme of research that seeks to examine tissue damage of tough skinned vegetables under mechanical peeling process by developing a novel FE model of the process using explicit dynamic finite element analysis approach. In the proposed study a nonlinear model which will be capable of simulating the peeling process specifically, will be developed. It is expected that unavailable information such as cutting force, maximum shearing force, shear strength, tensile strength and rupture stress will be quantified using the new FEA model. The outcomes will be used to optimize and improve the current mechanical peeling methods of this class of vegetables and thereby enhance the overall effectiveness of processing operations. Presented paper aims to review available literature and previous works have been done in this area of research and identify current gap in modelling and simulation of food processes.

Exercise for breast cancer survivors: bridging the gap between evidence and practice

Evidence supporting the benefits of exercise following the diagnosis of breast cancer is overwhelming and compelling. Exercise reduces the severity and number of treatment-related side effects, optimizes quality of life during and following treatment, and may optimize survival. Yet, exercise does not uniformly form part of the standards of care provided to women following a breast cancer diagnosis. This commentary summarizes the evidence in support of exercise as a form of adjuvant treatment and identifies and discusses potential issues preventing the formal integration of exercise into breast cancer care. Proposed within the commentary is a model of breast cancer care that incorporates exercise prescription as a key component but also integrates the need for surveillance and management for common breast cancer treatment-related morbidities, as well as education. While future research evaluating the potential cost savings through implementation of such amodel is required, a committed, collaborative approach by clinicians, allied health professionals, and researchers will be instrumental in bridging the gap between research and practice.

A prospective surveillance model for rehabilitation for women with breast cancer

Background: The current model of care for breast cancer is focused on disease treatment followed by ongoing recurrence surveillance. This approach lacks attention to the patients’ physical and functional well-being. Breast cancer treatment sequelae can lead to physical impairments and functional limitations. Common impairments include pain, fatigue, upper extremity dysfunction, lymphedema, weakness, joint arthralgia, neuropathy, weight gain, cardiovascular effects, and osteoporosis. Evidence supports prospective surveillance for early identification and treatment as a means to prevent or mitigate many of these concerns. Purpose: This paper proposes a prospective surveillance model for physical rehabilitation and exercise that can be integrated with disease treatment to create a more comprehensive approach to survivorship health care. The goals of the model are to promote surveillance for common physical impairments and functional limitations associated with breast cancer treatment, to provide education to facilitate early identification of impairments, to introduce rehabilitation and exercise intervention when physical impairments are identified and to promote and support physical activity and exercise behaviors through the trajectory of disease treatment and survivorship. Methods: The model is the result of a multi-disciplinary meeting of research and clinical experts in breast cancer survivorship and representatives of relevant professional and advocacy organizations. Outcomes: The proposed model identifies time points during breast cancer care for assessment of and education about physical impairments. Ultimately, implementation of the model may influence incidence and severity of breast cancer treatment related physical impairments. As such, the model seeks to optimize function during and following treatment and positively influence a growing survivorship community.

Controlled growth of carbon nanotubes for electronic and photovoltaic applications

Carbon nanotubes (CNTs), experimentally observed for the first time twenty years ago, have triggered an unprecedented research effort, on the account of their astonishing structural, mechanical and electronic properties. Unfortunately, the current inability in predicting the CNTs’ properties and the difficulty in controlling their position on a substrate are often limiting factors for the application of this material in actual devices. This research aims at the creation of specific methodologies for controlled synthesis of CNTs, leading to effectively employ them in various fields of electronics, e.g. photovoltaics. Focused Ion Beam (FIB) patterning of Si surfaces is here proposed as a means for ordering the assembly of vertical-aligned CNTs. With this technique, substrates with specific nano-structured morphologies have been prepared, enabling a high degree of control over CNTs’ position and size. On these nano-structured substrates, the growth of CNTs has been realized by chemical vapor deposition (CVD), i.e. thermal decomposition of hydrocarbon gases over a heated catalyst. The most common materials used as catalysts in CVD are transition metals like Fe and Ni; however, their presence in the CNT products often results in shortcomings for electronic applications, especially for those based on silicon, being the metallic impurities incompatible with very-large-scale integration (VLSI) technology. In the present work the role of Ge dots as an alternative catalysts for CNTs synthesis on Si substrates has been thoroughly assessed, finding a close connection between the catalytic activity of such material and the CVD conditions, which can affect both size and morphology of the dots. Successful CNT growths from Ge dots have been obtained by CVD at temperatures ranging from 750 to 1000°C, with mixtures of acetylene and hydrogen in an argon carrier gas. The morphology of the Si surface is observed to play a crucial role for the outcome of the CNT synthesis: natural (i.e. chemical etching) and artificial (i.e. FIB patterning, nanoindentation) means of altering this morphology in a controlled way have been then explored to optimize the CNTs yield. All the knowledge acquired in this study has been finally applied to synthesize CNTs on transparent conductive electrodes (indium-tin oxide, ITO, coated glasses), for the creation of a new class of anodes for organic photovoltaics. An accurate procedure has been established which guarantees a controlled inclusion of CNTs on ITO films, preserving their optical and electrical properties. By using this set of conditions, a CNTenhanced electrode has been built, contributing to improve the power conversion efficiency of polymeric solar cells.

Breaking organizational barriers for greening Australian

Sustainability is an issue for everyone. For instance, the higher education sector is being asked to take an active part in creating a sustainable future, due to their moral responsibility, social obligation, and their own need to adapt to the changing higher education environment. By either signing declarations or making public statements, many universities are expressing their desire to become role models for enhancing sustainability. However, too often they have not delivered as much as they had intended. This is particularly evident in the lack of physical implementation of sustainable practices in the campus environment. Real projects such as green technologies on campus have the potential to rectify the problem in addition to improving building performance. Despite being relatively recent innovations, Green Roof and Living Wall have been widely recognized because of their substantial benefits, such as runoff water reduction, noise insulation, and the promotion of biodiversity. While they can be found in commercial and residential buildings, they only appear infrequently on campuses as universities have been very slow to implement sustainability innovations. There has been very little research examining the fundamental problems from the organizational perspective. To address this deficiency, the researchers designed and carried out 24 semi-structured interviews to investigate the general organizational environment of Australian universities with the intention to identify organizational obstacles to the delivery of Green Roof and Living Wall projects. This research revealed that the organizational environment of Australian universities still has a lot of room to be improved in order to accommodate sustainability practices. Some of the main organizational barriers to the adoption of sustainable innovations were identified including lack of awareness and knowledge, the absence of strong supportive leadership, a weak sustainability-rooted culture and several management challenges. This led to the development of a set of strategies to help optimize the organizational environment for the purpose of better decision making for Green Roof and Living Wall implementation.

Lost in translation (and rotation) : rapid extrinsic calibration for 2D and 3D LIDARs

This paper describes a novel method for determining the extrinsic calibration parameters between 2D and 3D LIDAR sensors with respect to a vehicle base frame. To recover the calibration parameters we attempt to optimize the quality of a 3D point cloud produced by the vehicle as it traverses an unknown, unmodified environment. The point cloud quality metric is derived from Rényi Quadratic Entropy and quantifies the compactness of the point distribution using only a single tuning parameter. We also present a fast approximate method to reduce the computational requirements of the entropy evaluation, allowing unsupervised calibration in vast environments with millions of points. The algorithm is analyzed using real world data gathered in many locations, showing robust calibration performance and substantial speed improvements from the approximations.

Embedding human expert cognition and real-time trajectory planning in autonomous UAS

This thesis presents a new approach to compute and optimize feasible three dimensional (3D) flight trajectories using aspects of Human Decision Making (HDM) strategies, for fixed wing Unmanned Aircraft (UA) operating in low altitude environments in the presence of real time planning deadlines. The underlying trajectory generation strategy involves the application of Manoeuvre Automaton (MA) theory to create sets of candidate flight manoeuvres which implicitly incorporate platform dynamic constraints. Feasible trajectories are formed through the concatenation of predefined flight manoeuvres in an optimized manner. During typical UAS operations, multiple objectives may exist, therefore the use of multi-objective optimization can potentially allow for convergence to a solution which better reflects overall mission requirements and HDM preferences. A GUI interface was developed to allow for knowledge capture from a human expert during simulated mission scenarios. The expert decision data captured is converted into value functions and corresponding criteria weightings using UTilite Additive (UTA) theory. The inclusion of preferences elicited from HDM decision data within an Automated Decision System (ADS) allows for the generation of trajectories which more closely represent the candidate HDM’s decision strategies. A novel Computationally Adaptive Trajectory Decision optimization System (CATDS) has been developed and implemented in simulation to dynamically manage, calculate and schedule system execution parameters to ensure that the trajectory solution search can generate a feasible solution, if one exists, within a given length of time. The inclusion of the CATDS potentially increases overall mission efficiency and may allow for the implementation of the system on different UAS platforms with varying onboard computational capabilities. These approaches have been demonstrated in simulation using a fixed wing UAS operating in low altitude environments with obstacles present.

A simulated annealing algorithm for energy efficient virtual machine placement

Improving energy efficiency has become increasingly important in data centers in recent years to reduce the rapidly growing tremendous amounts of electricity consumption. The power dissipation of the physical servers is the root cause of power usage of other systems, such as cooling systems. Many efforts have been made to make data centers more energy efficient. One of them is to minimize the total power consumption of these servers in a data center through virtual machine consolidation, which is implemented by virtual machine placement. The placement problem is often modeled as a bin packing problem. Due to the NP-hard nature of the problem, heuristic solutions such as First Fit and Best Fit algorithms have been often used and have generally good results. However, their performance leaves room for further improvement. In this paper we propose a Simulated Annealing based algorithm, which aims at further improvement from any feasible placement. This is the first published attempt of using SA to solve the VM placement problem to optimize the power consumption. Experimental results show that this SA algorithm can generate better results, saving up to 25 percentage more energy than First Fit Decreasing in an acceptable time frame.

Modeling resource management in the building design process by information constraint Petri nets

The effect of resource management on the building design process directly influences the development cycle time and success of construction projects. This paper presents the information constraint net (ICN) to represent the complex information constraint relations among design activities involved in the building design process. An algorithm is developed to transform the information constraints throughout the ICN into a Petri net model. A resource management model is developed using the ICN to simulate and optimize resource allocation in the design process. An example is provided to justify the proposed model through a simulation analysis of the CPN Tools platform in the detailed structural design. The result demonstrates that the proposed approach can obtain the resource management and optimization needed for shortening the development cycle and optimal allocation of resources.