Applying SVMs and weight-based factor analysis to unsupervised adaptation for speaker verification

This paper presents an extended study on the implementation of support vector machine(SVM) based speaker verification in systems that employ continuous progressive model adaptation using the weight-based factor analysis model. The weight-based factor analysis model compensates for session variations in unsupervised scenarios by incorporating trial confidence measures in the general statistics used in the inter-session variability modelling process. Employing weight-based factor analysis in Gaussian mixture models (GMM) was recently found to provide significant performance gains to unsupervised classification. Further improvements in performance were found through the integration of SVM-based classification in the system by means of GMM supervectors. This study focuses particularly on the way in which a client is represented in the SVM kernel space using single and multiple target supervectors. Experimental results indicate that training client SVMs using a single target supervector maximises performance while exhibiting a certain robustness to the inclusion of impostor training data in the model. Furthermore, the inclusion of low-scoring target trials in the adaptation process is investigated where they were found to significantly aid performance.

Analysis of cardiac and epileptic signals using higher order spectra

The theory of nonlinear dyamic systems provides some new methods to handle complex systems. Chaos theory offers new concepts, algorithms and methods for processing, enhancing and analyzing the measured signals. In recent years, researchers are applying the concepts from this theory to bio-signal analysis. In this work, the complex dynamics of the bio-signals such as electrocardiogram (ECG) and electroencephalogram (EEG) are analyzed using the tools of nonlinear systems theory. In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death. The Electrocardiogram (ECG) is an important biosignal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computerbased intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and four classes of arrhythmia. This thesis presents some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. Several features were extracted from the HOS and subjected an Analysis of Variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, seven features were extracted from the heart rate signals using HOS and fed to a support vector machine (SVM) for classification. The performance evaluation protocol in this thesis uses 330 subjects consisting of five different kinds of cardiac disease conditions. The classifier achieved a sensitivity of 90% and a specificity of 89%. This system is ready to run on larger data sets. In EEG analysis, the search for hidden information for identification of seizures has a long history. Epilepsy is a pathological condition characterized by spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model (GMM) classifier and a Support Vector Machine (SVM) classifier. Results show that the classifiers were able to achieve 93.11% and 92.67% classification accuracy, respectively, with selected HOS based features. About 2 hours of EEG recordings from 10 patients were used in this study. This thesis introduces unique bispectrum and bicoherence plots for various cardiac conditions and for normal, background and epileptic EEG signals. These plots reveal distinct patterns. The patterns are useful for visual interpretation by those without a deep understanding of spectral analysis such as medical practitioners. It includes original contributions in extracting features from HRV and EEG signals using HOS and entropy, in analyzing the statistical properties of such features on real data and in automated classification using these features with GMM and SVM classifiers.

Inheritance of Resistance to Rice Tungro Spherical Virus in a Near-Isogenic Line Derived from Utri Merah and in Rice Cultivar TKM6

Resistance to rice virus diseases is an important requirement in many Southeast Asian rice breeding programs. Inheritance of resistance to rice tungro spherical virus (RTSV) in TW5, a near-isogenic line derived from Indonesian rice cultivar Utri Merah, was compared to that in TKM6, an Indian rice cultivar. Both TKM6 and Utri Merah are cultivars resistant to RTSV infections. Crosses were made between TKM6 and TN1, a susceptible cultivar, and between TW5 and TN1, and F3 lines were evaluated for their resistance to RTSV using two RTSV inoculum sources and a serological assay (ELISA). In TKM6, the resistance to the mixture of RTSV-V + RTBV inoculum source was controlled by a single recessive gene, whereas in TW5, the resistance was controlled by two recessive genes. A single recessive gene, however, controlled the resistance in TW5 when another RTSV variant, RTSV-VI, was used, suggesting that the resistance in TW5 depends on the nature of the RTSV inoculum used. RT-PCR, sequence, and phylogenetic analyses confirmed that RTSV-VI inoculum differs from RTSV-V inoculum and accurate phenotyping of the resistance to RTSV requires the use of a genetic marker.

Patient education to prevent falls among older hospital inpatients : a randomized controlled trial

Background Falls are a common adverse event during hospitalization of older adults, and few interventions have been shown to prevent then. Methods This study was a 3-group randomized trial to evaluate the efficacy of 2 forms of multimedia patient education compared with usual care for the prevention of in-hospital falls. Older hospital patients (n = 1206) admitted to a mixture of acute (orthopedic, respiratory, and medical) and subacute (geriatric and neurorehabilitation) hospital wards at 2 Australian hospitals were recruited between January 2008 and April 2009. The interventions were a multimedia patient education program based on the health-belief model combined with trained health professional follow-up (complete program), multi-media patient education materials alone (materials only), and usual care (control). Falls data were collected by blinded research assistants by reviewing hospital incident reports, hand searching medical records, and conducting weekly patient interviews. Results Rates of falls per 1000 patient-days did not differ significantly between groups (control, 9.27; materials only, 8.61; and complete program, 7.63). However, there was a significant interaction between the intervention and presence of cognitive impairment. Falls were less frequent among cognitively intact patients in the complete program group (4.01 per 1000 patient-days) than among cognitively intact patients in the materials-only group (8.18 per 1000 patient-days) (adjusted hazard ratio, 0.51; 95% confidence interval, 0.28-0.93]) and control group (8.72 per 1000 patient-days) (adjusted hazard ratio, 0.43; 95% confidence interval, 0.24-0.78). Conclusion Multimedia patient education with trained health professional follow-up reduced falls among patients with intact cognitive function admitted to a range of hospital wards.

Performance analysis of hybrid liquid desiccant solar cooling system

This thesis investigates the coefficient of performance (COP) of a hybrid liquid desiccant solar cooling system. This hybrid cooling system includes three sections: 1) conventional air-conditioning section; 2) liquid desiccant dehumidification section and 3) air mixture section. The air handling unit (AHU) with mixture variable air volume design is included in the hybrid cooling system to control humidity. In the combined system, the air is first dehumidified in the dehumidifier and then mixed with ambient air by AHU before entering the evaporator. Experiments using lithium chloride as the liquid desiccant have been carried out for the performance evaluation of the dehumidifier and regenerator. Based on the air mixture (AHU) design, the electrical coefficient of performance (ECOP), thermal coefficient of performance (TCOP) and whole system coefficient of performance (COPsys) models used in the hybrid liquid desiccant solar cooing system were developed to evaluate this system performance. These mathematical models can be used to describe the coefficient of performance trend under different ambient conditions, while also providing a convenient comparison with conventional air conditioning systems. These models provide good explanations about the relationship between the performance predictions of models and ambient air parameters. The simulation results have revealed the coefficient of performance in hybrid liquid desiccant solar cooling systems substantially depends on ambient air and dehumidifier parameters. Also, the liquid desiccant experiments prove that the latent component of the total cooling load requirements can be easily fulfilled by using the liquid desiccant dehumidifier. While cooling requirements can be met, the liquid desiccant system is however still subject to the hysteresis problems.

A statistical framework for natural feature representation

This paper presents a robust stochastic framework for the incorporation of visual observations into conventional estimation, data fusion, navigation and control algorithms. The representation combines Isomap, a non-linear dimensionality reduction algorithm, with expectation maximization, a statistical learning scheme. The joint probability distribution of this representation is computed offline based on existing training data. The training phase of the algorithm results in a nonlinear and non-Gaussian likelihood model of natural features conditioned on the underlying visual states. This generative model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The instantiated likelihoods are expressed as a Gaussian mixture model and are conveniently integrated within existing non-linear filtering algorithms. Example applications based on real visual data from heterogenous, unstructured environments demonstrate the versatility of the generative models.

A stochastic model for natural feature representation

This paper presents a robust stochastic model for the incorporation of natural features within data fusion algorithms. The representation combines Isomap, a non-linear manifold learning algorithm, with Expectation Maximization, a statistical learning scheme. The representation is computed offline and results in a non-linear, non-Gaussian likelihood model relating visual observations such as color and texture to the underlying visual states. The likelihood model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The likelihoods are expressed as a Gaussian Mixture Model so as to permit convenient integration within existing nonlinear filtering algorithms. The resulting compactness of the representation is especially suitable to decentralized sensor networks. Real visual data consisting of natural imagery acquired from an Unmanned Aerial Vehicle is used to demonstrate the versatility of the feature representation.

Rich probabilistic representations for bearing only decentralised data fusion

The aim of this paper is to demonstrate the validity of using Gaussian mixture models (GMM) for representing probabilistic distributions in a decentralised data fusion (DDF) framework. GMMs are a powerful and compact stochastic representation allowing efficient communication of feature properties in large scale decentralised sensor networks. It will be shown that GMMs provide a basis for analytical solutions to the update and prediction operations for general Bayesian filtering. Furthermore, a variant on the Covariance Intersect algorithm for Gaussian mixtures will be presented ensuring a conservative update for the fusion of correlated information between two nodes in the network. In addition, purely visual sensory data will be used to show that decentralised data fusion and tracking of non-Gaussian states observed by multiple autonomous vehicles is feasible.

Applying ISOMAP to the learning of hyperspectral image

In this paper, we present the application of a non-linear dimensionality reduction technique for the learning and probabilistic classification of hyperspectral image. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. It gives much greater information content per pixel on the image than a normal colour image. This should greatly help with the autonomous identification of natural and manmade objects in unfamiliar terrains for robotic vehicles. However, the large information content of such data makes interpretation of hyperspectral images time-consuming and userintensive. We propose the use of Isomap, a non-linear manifold learning technique combined with Expectation Maximisation in graphical probabilistic models for learning and classification. Isomap is used to find the underlying manifold of the training data. This low dimensional representation of the hyperspectral data facilitates the learning of a Gaussian Mixture Model representation, whose joint probability distributions can be calculated offline. The learnt model is then applied to the hyperspectral image at runtime and data classification can be performed.

Empirical Bayes method in the study of traffic safety via heterogeneous negative multinomial model

In the study of traffic safety, expected crash frequencies across sites are generally estimated via the negative binomial model, assuming time invariant safety. Since the time invariant safety assumption may be invalid, Hauer (1997) proposed a modified empirical Bayes (EB) method. Despite the modification, no attempts have been made to examine the generalisable form of the marginal distribution resulting from the modified EB framework. Because the hyper-parameters needed to apply the modified EB method are not readily available, an assessment is lacking on how accurately the modified EB method estimates safety in the presence of the time variant safety and regression-to-the-mean (RTM) effects. This study derives the closed form marginal distribution, and reveals that the marginal distribution in the modified EB method is equivalent to the negative multinomial (NM) distribution, which is essentially the same as the likelihood function used in the random effects Poisson model. As a result, this study shows that the gamma posterior distribution from the multivariate Poisson-gamma mixture can be estimated using the NM model or the random effects Poisson model. This study also shows that the estimation errors from the modified EB method are systematically smaller than those from the comparison group method by simultaneously accounting for the RTM and time variant safety effects. Hence, the modified EB method via the NM model is a generalisable method for estimating safety in the presence of the time variant safety and the RTM effects.

Essays on irrigation development, farm production and unaccounted costs : theory and empirical evidence

A review of the literature related to issues involved in irrigation induced agricultural development (IIAD) reveals that: (1) the magnitude, sensitivity and distribution of social welfare of IIAD is not fully analysed; (2) the impacts of excessive pesticide use on farmers’ health are not adequately explained; (3) no analysis estimates the relationship between farm level efficiency and overuse of agro-chemical inputs under imperfect markets; and (4) the method of incorporating groundwater extraction costs is misleading. This PhD thesis investigates these issues by using primary data, along with secondary data from Sri Lanka. The overall findings of the thesis can be summarised as follows. First, the thesis demonstrates that Sri Lanka has gained a positive welfare change as a result of introducing new irrigation technology. The change in the consumer surplus is Rs.48,236 million, while the change in the producer surplus is Rs. 14,274 millions between 1970 and 2006. The results also show that the long run benefits and costs of IIAD depend critically on the magnitude of the expansion of the irrigated area, as well as the competition faced by traditional farmers (agricultural crowding out effects). The traditional sector’s ability to compete with the modern sector depends on productivity improvements, reducing production costs and future structural changes (spillover effects). Second, the thesis findings on pesticides used for agriculture show that, on average, a farmer incurs a cost of approximately Rs. 590 to 800 per month during a typical cultivation period due to exposure to pesticides. It is shown that the value of average loss in earnings per farmer for the ‘hospitalised’ sample is Rs. 475 per month, while it is approximately Rs. 345 per month for the ‘general’ farmers group during a typical cultivation season. However, the average willingness to pay (WTP) to avoid exposure to pesticides is approximately Rs. 950 and Rs. 620 for ‘hospitalised’ and ‘general’ farmers’ samples respectively. The estimated percentage contribution for WTP due to health costs, lost earnings, mitigating expenditure, and disutility are 29, 50, 5 and 16 per cent respectively for hospitalised farmers, while they are 32, 55, 8 and 5 per cent respectively for ‘general’ farmers. It is also shown that given market imperfections for most agricultural inputs, farmers are overusing pesticides with the expectation of higher future returns. This has led to an increase in inefficiency in farming practices which is not understood by the farmers. Third, it is found that various groundwater depletion studies in the economics literature have provided misleading optimal water extraction quantity levels. This is due to a failure to incorporate all production costs in the relevant models. It is only by incorporating quality changes to quantity deterioration, that it is possible to derive socially optimal levels. Empirical results clearly show that the benefits per hectare per month considering both the avoidance costs of deepening agro-wells by five feet from the existing average, as well as the avoidance costs of maintaining the water salinity level at 1.8 (mmhos/Cm), is approximately Rs. 4,350 for farmers in the Anuradhapura district and Rs. 5,600 for farmers in the Matale district.

Influence of parameters on the heterogeneous photocatalytic degradation of pesticides and phenolic contaminants in wastewater : a short review

In recent years, the application of heterogeneous photocatalytic water purification process has gained wide attention due to its effectiveness in degrading and mineralizing the recalcitrant organic compounds as well as the possibility of utilizing the solar UV and visible light spectrum. This paper aims to review and summarize the recently published works on the titanium dioxide (TiO2) photocatalytic oxidation of pesticides and phenolic compounds, predominant in storm and waste water effluents. The effect of various operating parameters on the photocatalytic degradation of pesticides and phenols are discussed. Results reported here suggested that the photocatalytic degradation of organic compounds depends on the type of photocatalyst and composition, light intensity, initial substrate concentration, amount of catalyst, pH of the reaction medium, ionic components in water, solvent types, oxidizing agents/electron acceptors, catalyst application mode, and calcinations temperature in water environment. A substantial amount of research has focused on the enhancement of TiO2 photocatalysis by modification with metal, non-metal and ion doping. Recent developments in TiO2 photocatalysis for the degradation of various pesticides and phenols are also highlighted in this review. It is evident from the literature survey that photocatalysis has shown good potential for the removal of various organic pollutants. However, still there is a need to find out the practical utility of this technique on commercial scale.

Researching the 'reception' of indigenous affairs in Australia

The Reporting and Reception of Indigenous Issues in the Australian Media was a three year project financed by the Australian government through its Australian Research Council Large Grants Scheme and run by Professor John Hartley (of Murdoch and then Edith Cowan University, Western Australia). The purpose of the research was to map the ways in which indigeneity was constructed and circulated in Australia's mediasphere. The analysis of the 'reporting' element of the project was almost straightforward: a mixture of content analysis of a large number of items in the media, and detailed textual analysis of a smaller number of key texts. The discoveries were interesting - that when analysis approaches the media as a whole, rather than focussing exclusively on news or serious drama genres, then representation of indigeneity is not nearly as homogenous as has previously been assumed. And if researchers do not explicitly set out to uncover racism in every text, it is by no means guaranteed they will find it1. The question of how to approach the 'reception' of these issues - and particularly reception by indigenous Australians - proved to be a far more challenging one. In attempting to research this area, Hartley and I (working as a research assistant on the project) often found ourselves hampered by the axioms that underlie much media research. Traditionally, the 'reception' of media by indigenous people in Australia has been researched in ethnographic ways. This research repeatedly discovers that indigenous people in Australia are powerless in the face of new forms of media. Indigenous populations are represented as victims of aggressive and powerful intrusions: ‘What happens when a remote community is suddenly inundated by broadcast TV?’; ‘Overnight they will go from having no radio and television to being bombarded by three TV channels’; ‘The influence of film in an isolated, traditionally oriented Aboriginal community’2. This language of ‘influence’, ‘bombarded’, and ‘inundated’, presents metaphors not just of war but of a war being lost. It tells of an unequal struggle, of a more powerful force impinging upon a weaker one. What else could be the relationship of an Aboriginal audience to something which is ‘bombarding’ them? Or by which they are ‘inundated’? This attitude might best be summed up by the title of an article by Elihu Katz: ‘Can authentic cultures survive new media?’3. In such writing, there is little sense that what is being addressed might be seen as a series of discursive encounters, negotiations and acts of meaning-making in which indigenous people — communities and audiences —might be productive. Certainly, the points of concern in this type of writing are important. The question of what happens when a new communication medium is summarily introduced to a culture is certainly an important one. But the language used to describe this interaction is a misleading one. And it is noticeable that such writing is fascinated with the relationship of only traditionally-oriented Aboriginal communities to the media of mass communication.

Thermal stability of the ‘cave’ mineral brushite CaHPO4•2H2O -mechanism of formation and decomposition

Thermogravimetry combined with evolved gas mass spectrometry has been used to ascertain the stability of the ‘cave’ mineral brushite. X-ray diffraction shows that brushite from the Jenolan Caves is very pure. Thermogravimetric analysis coupled with ion current mass spectrometry shows a mass loss at 111°C due to loss of water of hydration. A further decomposition step occurs at 190°C with the conversion of hydrogen phosphate to a mixture of calcium ortho-phosphate and calcium pyrophosphate. TG-DTG shows the mineral is not stable above 111°C. A mechanism for the formation of brushite on calcite surfaces is proposed, and this mechanism has relevance to the formation of brushite in urinary tracts.

Recent developments in photocatalytic degradation of pesticides and phenols in storm and wastewater effluent

The heterogeneous photocatalytic oxidation process offers a versatile promise in the detoxification and disinfection of wastewater containing hazardous organic compounds such as pesticides and phenolic compounds in storm and wastewater effluent. This process has gained wide attention due to its effectiveness in degrading and mineralizing the organic compounds into harmless and often useful components. To develop an efficient photocatalytic process, titanium dioxide has been actively studied in recent years due to its excellent performance as a photocatalyst under UV light irradiation. This paper aims at critically evaluating and highlighting the recent developments of the heterogeneous photocatalytic systems with a special focus on storm and wastewater treatment applications.