A speech analysis system for use in educational research

This thesis describes work undertaken in order to fulfil a need experienced in the Department of Educational Enquiry at the University of Aston in Birmingham for speech analysis facilities suitable for use in teaching and research work within the Department. The hardware and software developed during the research project provides displays of speech fundamental frequency and intensity in real time. The system is suitable for the provision of visual feedback of these parameters of a subject's speech in a learning situation, and overcomes the inadequacies of equipment currently used for this task in that it provides a clear indication of fundamental frequency contours as the subject is speaking. The thesis considers the use of such equipment in several related fields, and the approaches that have been reported to one of the major problems of speech analysis, namely pitch-period estimation. A number of different systems are described, and their suitability for the present purposes is discussed. Finally, a novel method of pitch-period estimation is developed, and a speech analysis system incorporating this method is described. Comparison is made between the results produced by this system and those produced by a conventional speech spectrograph.

Gas analysis of sediment cores from the Batumi seep area

Detailed knowledge of the extent of post-genetic modifications affecting shallow submarine hydrocarbons fueled from the deep subsurface is fundamental for evaluating source and reservoir properties. We investigated gases from a submarine high-flux seepage site in the anoxic Eastern Black Sea in order to elucidate molecular and isotopic alterations of low-molecular-weight hydrocarbons (LMWHC) associated with upward migration through the sediment and precipitation of shallow gas hydrates. For this, near-surface sediment pressure cores and free gas venting from the seafloor were collected using autoclave technology at the Batumi seep area at 845 m water depth within the gas hydrate stability zone. Vent gas, gas from pressure core degassing, and from hydrate dissociation were strongly dominated by methane (>99.85 mol.% of Sum[C1-C4, CO2]). Molecular ratios of LMWHC (C1/[C2 + C3] > 1000) and stable isotopic compositions of methane (d13C = -53.5 per mill V-PDB; D/H around -175 per mill SMOW) indicated predominant microbial methane formation. C1/C2+ ratios and stable isotopic compositions of LMWHC distinguished three gas types prevailing in the seepage area. Vent gas discharged into bottom waters was depleted in methane by >0.03 mol.% (Sum[C1-C4, CO2]) relative to the other gas types and the virtual lack of 14C-CH4 indicated a negligible input of methane from degradation of fresh organic matter. Of all gas types analyzed, vent gas was least affected by molecular fractionation, thus, its origin from the deep subsurface rather than from decomposing hydrates in near-surface sediments is likely. As a result of the anaerobic oxidation of methane, LMWHC in pressure cores in top sediments included smaller methane fractions [0.03 mol.% Sum(C1-C4, CO2)] than gas released from pressure cores of more deeply buried sediments, where the fraction of methane was maximal due to its preferential incorporation in hydrate lattices. No indications for stable carbon isotopic fractionations of methane during hydrate crystallization from vent gas were found. Enrichments of 14C-CH4 (1.4 pMC) in short cores relative to lower abundances (max. 0.6 pMC) in gas from long cores and gas hydrates substantiates recent methanogenesis utilizing modern organic matter deposited in top sediments of this high-flux hydrocarbon seep area.

Low resistivity Pt interconnects developed by electron beam assisted deposition using novel gas injector system

Electron beam-induced deposition (EBID) is a direct write process where an electron beam locally decomposes a precursor gas leaving behind non-volatile deposits. It is a fast and relatively in-expensive method designed to develop conductive (metal) or isolating (oxide) nanostructures. Unfortunately the EBID process results in deposition of metal nanostructures with relatively high resistivity because the gas precursors employed are hydrocarbon based. We have developed deposition protocols using novel gas-injector system (GIS) with a carbon free Pt precursor. Interconnect type structures were deposited on preformed metal architectures. The obtained structures were analysed by cross-sectional TEM and their electrical properties were analysed ex-situ using four point probe electrical tests. The results suggest that both the structural and electrical characteristics differ significantly from those of Pt interconnects deposited by conventional hydrocarbon based precursors, and show great promise for the development of low resistivity electrical contacts.

A Generalized Fuzzy Linguistic Model for Predicting Component Concentrations in an Optical Gas Sensing System

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NO_x, SO₂ etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

Development of a real time Image analysis system for traffic monitoring applications.

Image processing offers unparalleled potential for traffic monitoring and control. For many years engineers have attempted to perfect the art of automatic data abstraction from sequences of video images. This paper outlines a research project undertaken at Napier University by the authors in the field of image processing for automatic traffic analysis. A software based system implementing TRIP algorithms to count cars and measure vehicle speed has been developed by members of the Transport Engineering Research Unit (TERU) at the University. The TRIP algorithm has been ported and evaluated on an IBM PC platform with a view to hardware implementation of the pre-processing routines required for vehicle detection. Results show that a software based traffic counting system is realisable for single window processing. Due to the high volume of data required to be processed for full frames or multiple lanes, system operations in real time are limited. Therefore specific hardware is required to be designed. The paper outlines a hardware design for implementation of inter-frame and background differencing, background updating and shadow removal techniques. Preliminary results showing the processing time and counting accuracy for the routines implemented in software are presented and a real time hardware pre-processing architecture is described.

A Strategy to Characterize the LISA-Pathfinder Cold Gas Thruster System

The cold gas micro-propulsion system that will be used during the LISA-Pathfinder mission will be one of the most important component used to ensure the "free-fall" of the enclosed test masses. In this paper we present a possible strategy to characterize the effective direction and amplitude gain of each of the 6 thrusters of this system.

Effect of shoes' heel height on the energy cost during jogging

The purpose of this study is to examine the changes of energy cost during a high-heeled continuous jogging.Thirteen healthy female volunteers jointed in this study with heel height of the shoes varied from 1, 4.5 and 7 cm, respectively. Each subjects jogged on the treadmill with K4b2 portable gas analysis system. The results of this study showed that ventilnation, relative oxygen consumption and energy expenditure increased with the increase of heel height and these values shows significantly larger when the heel height reached to 7 cm. Present study suggest that wearing high heel shoes jogging could directly increase energy consumption, causing neuromuscular fatigue.