Novel Pitch Detection Algorithm With Application to Speech Coding

This thesis introduces a novel method for accurate pitch detection and speech segmentation, named Multi-feature, Autocorrelation (ACR) and Wavelet Technique (MAWT). MAWT uses feature extraction, and ACR applied on Linear Predictive Coding (LPC) residuals, with a wavelet-based refinement step. MAWT opens the way for a unique approach to modeling: although speech is divided into segments, the success of voicing decisions is not crucial. Experiments demonstrate the superiority of MAWT in pitch period detection accuracy over existing methods, and illustrate its advantages for speech segmentation. These advantages are more pronounced for gain-varying and transitional speech, and under noisy conditions

Improved micro-contact resistance model that considers material deformation, electron transport and thin film characteristics

This paper reports on an improved analytic model forpredicting micro-contact resistance needed for designing microelectro-mechanical systems (MEMS) switches. The originalmodel had two primary considerations: 1) contact materialdeformation (i.e. elastic, plastic, or elastic-plastic) and 2) effectivecontact area radius. The model also assumed that individual aspotswere close together and that their interactions weredependent on each other which led to using the single effective aspotcontact area model. This single effective area model wasused to determine specific electron transport regions (i.e. ballistic,quasi-ballistic, or diffusive) by comparing the effective radius andthe mean free path of an electron. Using this model required thatmicro-switch contact materials be deposited, during devicefabrication, with processes ensuring low surface roughness values(i.e. sputtered films). Sputtered thin film electric contacts,however, do not behave like bulk materials and the effects of thinfilm contacts and spreading resistance must be considered. Theimproved micro-contact resistance model accounts for the twoprimary considerations above, as well as, using thin film,sputtered, electric contact

An evaluation of partial differential equations based digital inpainting algorithms

Partial Differential equations (PDEs) have been used to model various phenomena/tasks in different scientific and engineering endeavours. This thesis is devoted to modelling image inpainting by numerical implementations of certain PDEs. The main objectives of image inpainting include reconstructing damaged parts and filling-in regions in which data/colour information are missing. Different automatic and semi-automatic approaches to image inpainting have been developed including PDE-based, texture synthesis-based, exemplar-based, and hybrid approaches. Various challenges remain unresolved in reconstructing large size missing regions and/or missing areas with highly textured surroundings. Our main aim is to address such challenges by developing new advanced schemes with particular focus on using PDEs of different orders to preserve continuity of textural and geometric information in the surrounding of missing regions. We first investigated the problem of partial colour restoration in an image region whose greyscale channel is intact. A PDE-based solution is known that is modelled as minimising total variation of gradients in the different colour channels. We extend the applicability of this model to partial inpainting in other 3-channels colour spaces (such as RGB where information is missing in any of the two colours), simply by exploiting the known linear/affine relationships between different colouring models in the derivation of a modified PDE solution obtained by using the Euler-Lagrange minimisation of the corresponding gradient Total Variation (TV). We also developed two TV models on the relations between greyscale and colour channels using the Laplacian operator and the directional derivatives of gradients. The corresponding Euler-Lagrange minimisation yields two new PDEs of different orders for partial colourisation. We implemented these solutions in both spatial and frequency domains. We measure the success of these models by evaluating known image quality measures in inpainted regions for sufficiently large datasets and scenarios. The results reveal that our schemes compare well with existing algorithms, but inpainting large regions remains a challenge. Secondly, we investigate the Total Inpainting (TI) problem where all colour channels are missing in an image region. Reviewing and implementing existing PDE-based total inpainting methods reveal that high order PDEs, applied to each colour channel separately, perform well but are influenced by the size of the region and the quantity of texture surrounding it. Here we developed a TI scheme that benefits from our partial inpainting approach and apply two PDE methods to recover the missing regions in the image. First, we extract the (Y, Cb, Cr) of the image outside the missing region, apply the above PDE methods for reconstructing the missing regions in the luminance channel (Y), and then use the colourisation method to recover the missing (Cb, Cr) colours in the region. We shall demonstrate that compared to existing TI algorithms, our proposed method (using 2 PDE methods) performs well when tested on large datasets of natural and face images. Furthermore, this helps understanding of the impact of the texture in the surrounding areas on inpainting and opens new research directions. Thirdly, we investigate existing Exemplar-Based Inpainting (EBI) methods that do not use PDEs but simultaneously propagate the texture and structure into the missing region by finding similar patches within the rest of image and copying them into the boundary of the missing region. The order of patch propagation is determined by a priority function, and the similarity is determined by matching criteria. We shall exploit recently emerging Topological Data Analysis (TDA) tools to create innovative EBI schemes, referred to as TEBI. TDA studies shapes of data/objects to quantify image texture in terms of connectivity and closeness properties of certain data landmarks. Such quantifications help determine the appropriate size of patch propagation and will be used to modify the patch propagation priority function using the geometrical properties of curvature of isophotes, and to improve the matching criteria of patches by calculating the correlation coefficients from the spatial, gradient and Laplacian domains. The performance of this TEBI method will be tested by applying it to natural dataset images, resulting in improved inpainting when compared with other EBI methods. Fourthly, the recent hybrid-based inpainting techniques are reviewed and a number of highly performing innovative hybrid techniques that combine the use of high order PDE methods with the TEBI method for the simultaneous rebuilding of the missing texture and structure regions in an image are proposed. Such a hybrid scheme first decomposes the image into texture and structure components, and then the missing regions in these components are recovered by TEBI and PDE based methods respectively. The performance of our hybrid schemes will be compared with two existing hybrid algorithms. Fifthly, we turn our attention to inpainting large missing regions, and develop an innovative inpainting scheme that uses the concept of seam carving to reduce this problem to that of inpainting a smaller size missing region that can be dealt with efficiently using the inpainting schemes developed above. Seam carving resizes images based on content-awareness of the image for both reduction and expansion without affecting those image regions that have rich information. The missing region of the seam-carved version will be recovered by the TEBI method, original image size is restored by adding the removed seams and the missing parts of the added seams are then repaired using a high order PDE inpainting scheme. The benefits of this approach in dealing with large missing regions are demonstrated. The extensive performance testing of the developed inpainting methods shows that these methods significantly outperform existing inpainting methods for such a challenging task. However, the performance is still not acceptable in recovering large missing regions in high texture and structure images, and hence we shall identify remaining challenges to be investigated in the future. We shall also extend our work by investigating recently developed deep learning based image/video colourisation, with the aim of overcoming its limitations and shortcoming. Finally, we should also describe our on-going research into using TDA to detect recently growing serious “malicious” use of inpainting to create Fake images/videos

Machine Learning and Data Mining Applications in Power Systems

This Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods to facilitate the development of modern electric power systems, grids and devices, and smart grids and protection devices, as well as to develop tools for more accurate and efficient power system analysis. Conventional signal processing is no longer adequate to extract all the relevant information from distorted signals through filtering, estimation, and detection to facilitate decision-making and control actions. Machine learning algorithms, optimization techniques and efficient numerical algorithms, distributed signal processing, machine learning, data-mining statistical signal detection, and estimation may help to solve contemporary challenges in modern power systems. The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency; the dynamic optimization of grid operations; demand response; the incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and the integration of smart appliances and consumer devices. Signal processing offers the tools needed to convert measurement data to information, and to transform information into actionable intelligence. This Special Issue includes fifteen articles, authored by international research teams from several countries

Strain state detection in composite structures: Review and new challenges

Developing an advanced monitoring system for strain measurements on structural components represents a significant task, both in relation to testing of in-service parameters and early identification of structural problems. This paper aims to provide a state-of-the-art review on strain detection techniques in composite structures. The review represented a good opportunity for direct comparison of different novel strain measurement techniques. Fibers Bragg grating (FBG) was discussed as well as non-contact techniques together with semiconductor strain gauges (SGs), specifically infrared (IR) thermography and the digital image correlation (DIC) applied in order to detect strain and failure growth during the tests. The challenges of the research community are finally discussed by opening the current scenario to new objectives and industrial applications

Asteroseismology in Binary Stars with Applications of Bayesian Inference Tools

Space missions like Kepler have revolutionized asteroseismology, the science that infers the stellar interiors by studying oscillation frequency spectra of pulsating stars. Great advancements have been made in understanding solar-like oscillators. However, this is not the case for variable stars of intermediate masses, such asScutiand Doradus variables. By studying these stars in eclipsing binaries (EBs), model independent funda- mental parameters such as mass and radius can be inferred. On one hand, this synergy constrains the parameter space and facilitates the asteroseismic modeling, and this is shown for the Scuti type pulsating EB KIC 9851944. On the other hand, studies of binary stars must address the complexities such as mass transfer. KIC 8262223 is such an example, which consists of a mass-gaining Scuti primary and a pre-He white dwarf secondary. Some of the eccentric binary systems, the ‘heartbeat’ stars, show tidally excited oscillations. After briefly reviewing the linear theory of tidally forced stellar oscillations, we study the tidal pulsating binary KIC 3230227 and demonstrate that both amplitude and phase can be used to identify the tidally excited pulsation modes. We also discuss the variability of a Slowly Pulsating B-star KOI-81 and a Cataclysmic variable KIC 9406652. In the second part of this dissertation, we apply Bayesian statistics to some problems in binaries and asteroseismology with the help of packages BUGS and JAGS. Special attention is paid to the inverse problems (tomography) encountered in studying the double-line spectroscopic binaries

Dragline gear monitoring under fluctuating conditions

The aim of this study is to apply computed order tracking with subsequent rotation domain averaging and statistical analysis to typical mining environments. Computed order tracking is a fault detection method that is unaffected by varying speed conditions often found in industry and has been proven effective in laboratory conditions. However in the controlled environment of a laboratory it is difficult to test the robustness of the order-tracking procedure. The need thus exists to adjust the order tracking procedure so that it will be effective in the mining environment. The procedure needs to be adjusted to function with a two pulse per revolution speed input. The drag gear aboard a dragline rotates in two directions. This gives the unique opportunity to observe the performance of the order tracking method in a bi-directional rotating environment allowing relationships between the results of each operating direction to be investigated. A monitoring station was set up aboard a dragline and data was captured twice daily for a period spanning one year. The data captured consisted of accelerometer and proximity sensor data. The key on the shaft triggers the proximity sensors allowing speed and direction to be measured. The rudimentary measured speed is interpolated using various documented speed interpolation techniques and by a newly developed speed interpolation technique. The interpolated speed is then used to complete the order tracking procedure that re-samples the vibration data with reference to the speed. The results indicate that computed order tracking can be successfully implemented in typical mining environments. Furthermore there is a distinct relationship between vibration data taken in both rotational directions: one direction provides a better indication of incipient failure. It is thus important not to choose a direction randomly when monitoring rotating machinery of this kind.Dissertation (MEng)--University of Pretoria, 2008.Mechanical and Aeronautical Engineeringunrestricte

Rolling contact fatigue failures in silicon nitride and their detection

The project investigates the feasibility of using sensor-based detection and processing systems to provide a reliable means of monitoring rolling contact fatigue (RCF) wear failures of silicon nitride in hybrid bearings. To fulfil this investigation, a decision was made early in the project to perform a series of hybrid rolling wear tests using a twin disc machine modified for use on hybrid bearing elements.The initial part of the thesis reviews the current understanding of the general wear mechanisms and RCF with a specific focus to determine the appropriate methods for their detection in hybrid bearings. The study focusses on vibration, electrostatic and acoustic emission (AE) techniques and reviews their associated sensing technologies currently deployed with a view of adapting them for use in hybrids. To provide a basis for the adaptation, an understanding of the current sensor data enhancement and feature extraction methods is presented based on a literature review.The second part describes the test equipment, its modifications and instrumentation required to capture and process the vibration, electrostatic and AE signals generated in hybrid elements. These were identified in an initial feasibility test performed on a standard twin disc machine. After a detailed description of the resulting equipment, the thesis describes the calibration tests aimed to provide base data for the development of the signal processing methods.The development of the signal processing techniques is described in detail for each of the sensor types. Time synchronous averaging (TSA) technique is used to identify the location of the signal sources along the surfaces of the specimens and the signals are enhanced by additional filtering techniques.The next part of the thesis describes the main hybrid rolling wear tests; it details the selection of the run parameters and the samples seeded with surface cracks to cover a variety of situations, the method of execution of each test run, and the techniques to analyse the results.The research establishes that two RCF fault types are produced in the silicon nitride rolling element reflecting essentially different mechanisms in their distinct and separate development; i) cracks, progressing into depth and denoted in this study as C-/Ring crack Complex (CRC) and ii) Flaking, progressing primarily on the surface by spalls. Additionally and not reported in the literature, an advanced stage of the CRC fault type composed of multiple and extensive c-cracks is interpreted as the result of induced sliding in these runs. In general, having reached an advanced stage, both CRC and Flaking faults produce significant wear in the steel counterface through abrasion, plastic deformation or 3-body abrasion in at least three possible ways, all of which are described in details