Wavelet methods in speech recognition

In this thesis, novel wavelet techniques are developed to improve parametrization of speech signals prior to classification. It is shown that non-linear operations carried out in the wavelet domain improve the performance of a speech classifier and consistently outperform classical Fourier methods. This is because of the localised nature of the wavelet, which captures correspondingly well-localised time-frequency features within the speech signal. Furthermore, by taking advantage of the approximation ability of wavelets, efficient representation of the non-stationarity inherent in speech can be achieved in a relatively small number of expansion coefficients. This is an attractive option when faced with the so-called 'Curse of Dimensionality' problem of multivariate classifiers such as Linear Discriminant Analysis (LDA) or Artificial Neural Networks (ANNs). Conventional time-frequency analysis methods such as the Discrete Fourier Transform either miss irregular signal structures and transients due to spectral smearing or require a large number of coefficients to represent such characteristics efficiently. Wavelet theory offers an alternative insight in the representation of these types of signals. As an extension to the standard wavelet transform, adaptive libraries of wavelet and cosine packets are introduced which increase the flexibility of the transform. This approach is observed to be yet more suitable for the highly variable nature of speech signals in that it results in a time-frequency sampled grid that is well adapted to irregularities and transients. They result in a corresponding reduction in the misclassification rate of the recognition system. However, this is necessarily at the expense of added computing time. Finally, a framework based on adaptive time-frequency libraries is developed which invokes the final classifier to choose the nature of the resolution for a given classification problem. The classifier then performs dimensionaIity reduction on the transformed signal by choosing the top few features based on their discriminant power. This approach is compared and contrasted to an existing discriminant wavelet feature extractor. The overall conclusions of the thesis are that wavelets and their relatives are capable of extracting useful features for speech classification problems. The use of adaptive wavelet transforms provides the flexibility within which powerful feature extractors can be designed for these types of application

Tuning the relaxation rates of dual-mode T?/T? nanoparticle contrast agents: a study into the ideal system

Magnetic resonance imaging (MRI) is an excellent imaging modality. However the low sensitivity of the technique poses a challenge to achieving an accurate image of function at the molecular level. To overcome this, contrast agents are used; typically gadolinium based agents for T? weighted imaging, or iron oxide based agents for T? imaging. Traditionally, only one imaging mode is used per diagnosis although several physiological situations are known to interfere with the signal induced by the contrast agents in each individual imaging mode acquisition. Recently, the combination of both T? and T? imaging capabilities into a single platform has emerged as a tool to reduce uncertainties in MR image analysis. To date, contradicting reports on the effect on the contrast of the coupling of a T? and T? agent have hampered the application of these specialised probes. Herein, we present a systematic experimental study on a range of gadolinium-labelled magnetite nanoparticles envisioned to bring some light into the mechanism of interaction between T? and T? components, and advance towards the design of efficient (dual) T? and T? MRI probes. Unexpected behaviours observed in some of the constructs will be discussed. In this study, we demonstrate that the relaxivity of such multimodal probes can be rationally tuned to obtain unmatched potentials in MR imaging, exemplified by preparation of the magnetite-based nanoparticle with the highest T? relaxivity described to date

State-space solutions to the dynamic magnetoencephalography inverse problem using high performance computing

Determining the magnitude and location of neural sources within the brain that are responsible for generating magnetoencephalography (MEG) signals measured on the surface of the head is a challenging problem in functional neuroimaging. The number of potential sources within the brain exceeds by an order of magnitude the number of recording sites. As a consequence, the estimates for the magnitude and location of the neural sources will be ill-conditioned because of the underdetermined nature of the problem. One well-known technique designed to address this imbalance is the minimum norm estimator (MNE). This approach imposes an $L^2$ regularization constraint that serves to stabilize and condition the source parameter estimates. However, these classes of regularizer are static in time and do not consider the temporal constraints inherent to the biophysics of the MEG experiment. In this paper we propose a dynamic state-space model that accounts for both spatial and temporal correlations within and across candidate intracortical sources. In our model, the observation model is derived from the steady-state solution to Maxwell's equations while the latent model representing neural dynamics is given by a random walk process.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS483 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Utilization of Microscale Silicon Cantilevers to Assess Cellular Contractile Function In Vitro

The development of more predictive and biologically relevant in vitro assays is predicated on the advancement of versatile cell culture systems which facilitate the functional assessment of the seeded cells. To that end, microscale cantilever technology offers a platform with which to measure the contractile functionality of a range of cell types, including skeletal, cardiac, and smooth muscle cells, through assessment of contraction induced substrate bending. Application of multiplexed cantilever arrays provides the means to develop moderate to high-throughput protocols for assessing drug efficacy and toxicity, disease phenotype and progression, as well as neuromuscular and other cell-cell interactions. This manuscript provides the details for fabricating reliable cantilever arrays for this purpose, and the methods required to successfully culture cells on these surfaces. Further description is provided on the steps necessary to perform functional analysis of contractile cell types maintained on such arrays using a novel laser and photo-detector system. The representative data provided highlights the precision and reproducible nature of the analysis of contractile function possible using this system, as well as the wide range of studies to which such technology can be applied. Successful widespread adoption of this system could provide investigators with the means to perform rapid, low cost functional studies in vitro, leading to more accurate predictions of tissue performance, disease development and response to novel therapeutic treatment

Laser Ablation System for Solid Sample Analysis by Inductively Coupled Plasma Atomic Emission Spectrometry

A laser ablation sample introduction system, based on a Nd : YAG laser with an X-Y-Zdirectional sampling head, has been designed and constructed for use with inductively coupled plasma optical emission spectrometry. A study has been made of a number of parameters which affect the performance of the system to establish the optimum operating conditions. South African Reference Material (SARM) rock samples have been analysed using the system, and the results obtained have been compared with the certificate values. The importance of using closely matrix-matched samples and standards is demonstrated. Precision studies on SARM 5 (pyroxenite) show that both intra- and inter-sample precisions are typically 10% (relative standard deviation )

Computational Assay of H7N9 Influenza Neuraminidase Reveals R292K Mutation Reduces Drug Binding Affinity

The emergence of a novel H7N9 avian influenza that infects humans is a serious cause for concern. Of the genome sequences of H7N9 neuraminidase available, one contains a substitution of arginine to lysine at position 292, suggesting a potential for reduced drug binding efficacy. We have performed molecular dynamics simulations of oseltamivir, zanamivir and peramivir bound to H7N9, H7N9-R292K, and a structurally related H11N9 neuraminidase. They show that H7N9 neuraminidase is structurally homologous to H11N9, binding the drugs in identical modes. The simulations reveal that the R292K mutation disrupts drug binding in H7N9 in a comparable manner to that observed experimentally for H11N9-R292K. Absolute binding free energy calculations with the WaterSwap method confirm a reduction in binding affinity. This indicates that the efficacy of antiviral drugs against H7N9-R292K will be reduced. Simulations can assist in predicting disruption of binding caused by mutations in neuraminidase, thereby providing a computational ‘assay.