We propose an efficient iterative data interpolation method using continuity along reflectors in seismic images via curvelet and discrete cosine transforms. The curvelet transform is a new multiscale transform that provides sparse representations for images that comprise smooth objects separated by piece-wise smooth discontinuities (e.g. seismic images). The advantage of using curvelets is that these frames are sparse for high-frequency caustic-free solutions of the wave-equation. Since we are dealing with less than ideal data (e.g. bandwidth-limited), we compliment the curvelet frames with the discrete cosine transform. The latter is motivated by the successful data continuation with the discrete Fourier transform. By choosing generic basis functions we circumvent the necessity to make parametric assumptions (e.g. through linear/parabolic Radon or demigration) regarding the shape of events in seismic data. Synthetic and real data examples demonstrate that our algorithm provides interpolated traces that accurately reproduce the wavelet shape as well as the AVO behavior along events in shot gathers.

Seismic transitions of the subsurface are typically considered as zero-order singularities (step functions). According to this model, the conventional deconvolution problem aims at recovering the seismic reflectivity as a sparse spike train. However, recent multiscale analysis on sedimentary records revealed the existence of accumulations of varying order singularities in the subsurface, which give rise to fractional-order discontinuities. This observation not only calls for a richer class of seismic reflection waveforms, but it also requires a different methodology to detect and characterize these reflection events. For instance, the assumptions underlying conventional deconvolution no longer hold. Because of the bandwidth limitation of seismic data, multiscale analysis methods based on the decay rate of wavelet coefficients may yield ambiguous results. We avoid this problem by formulating the estimation of the singularity orders by a parametric nonlinear inversion method.

The surface of the weld pool contains information that can be exploited to emulate a skilled human welder to better understand and control the welding process. Of the existing techniques, the method that uses the pool's specular nature to an advantage and which is relatively more cost effective, and suitable for welding environment is the one that utilizes reflected images to reconstruct 3D weld pool surface by using structured light and image processing techniques. In this thesis, an improvement has been made to the existing method by changing welding direction to obtain a denser reflected dot-matrix pattern allowing more accurate surface measurement. Then, the reflected images, obtained by capturing the reflection of a structured laser dot-matrix pattern from the pool surface through a high-speed camera with a narrow band-pass filter, are processed by a newly proposed algorithm to find the position of each reflected dot relative to its actual projection dot. This is a complicated process owing to the increased density of dots and noise induced due to the harsh environment. The obtained correspondence map may later be used by a surface reconstruction algorithm to derive the three-dimensional pool surface based on the reflection law.

The current technology used for location of permanent faults in high voltage direct current (HVDC) transmission lines and cables is based on the travelling-wave principle. This technology has served well for the conventional point-to-point HVDC systems, but is inadequate to handle emerging HVDC transmission configurations such as schemes with very long overhead lines or cables, schemes with a combination of cable and overhead line segments, and multi-terminal HVDC (MTHVDC) schemes. This research investigated accurate and economical ways to locate the faults on dc transmission lines in the aforementioned emerging HVDC transmission configurations. The accuracy of travelling-wave based fault location methods is highly dependent on the accuracy of measuring the time of arrival of the fault generated travelling waves. Investigations showed that post-processing of detection signals such as the line terminal voltages or surge capacitor currents with continuous wavelet transform yields consistent and accurate fault location results. This method was applied for fault location in HVDC systems with extra-long overhead lines and cables using only the terminal measurements. Simulation results verified the effectiveness of this method in locating the faults in a 2400 km long overhead line and a 300 km long underground cable. A new algorithm was proposed to locate the faults in a two-terminal HVDC system consisting of multiple segments of overhead lines and cables, using only the terminal measurements. Application of the proposed algorithm was analysed through detailed simulations. Correct performance was verified under various scenarios. A new algorithm was developed for locating the faults in a star-connected MTHVDC network. This algorithm is also required only the terminal measurements. Its effectiveness was verified through detailed simulations. Finally, a novel measurement scheme for detection of travelling-wave arrival times was proposed. A prototype of this measurement scheme which uses a Rogowski coil to measure the transient currents through the surge capacitors at the line terminals was implemented. Its effectiveness was validated through field tests in a real HVDC transmission system. The proposed measurement scheme could capture significantly clean signals in an actual substation environment, confirming the practicability of implementing the proposed new algorithms.

Temporal and wavelet characteristics of initial breakdown pulses are meticulously studied especially during the earliest moment of lightning events. Any possible features during the earliest moment that may exist which lead to either negative cloud-to-ground (CG), positive cloud-to-ground, cloud or isolated breakdown flashes in Sweden are investigated. Moreover, the occurrence of narrow bipolar pulses (NBPs) as part of a CG event that has been recorded from tropical thunderstorms are also included in the investigation. Electric field signatures selected from a collection of waveforms recorded using fast electric field broadband antenna system installed in Uppsala, Sweden and Skudai, South Malaysia are then carefully analyzed in order to observe any similarities or/and differences of their features. Temporal analysis reveals that there are significant distinctions within the first 1 ms among different types of lightning flashes. It is found that a negative CG flash tends to radiate pulses more frequently than other flashes and a cloud flash tends to radiate shorter pulses than other flashes but less frequently when compared to negative CG and isolated breakdown flashes. Perhaps, the ionization process during the earliest moment of negative CG flashes is more rapid than other discharges. Using a wavelet transformation, it can be suggested that the first electric field pulse of both negative CG and cloud flashes experiences a more rapid and extensive ionization process compared to positive CG and isolated breakdown flashes. Further temporal analysis on NBPs found to occur as part of CG flashes show the disparity of the normalized electric field amplitude between the NBPs prior to and after the first return stroke. This indicates that the NBPs intensities were influenced by the return stroke events and they occurred in the same thundercloud. The similarity between the temporal characteristics of NBPs as part of CG flashes and isolated NBPs suggests that their breakdown mechanisms might be similar.

This thesis proposes a bearing condition monitoring system using acceleration and acoustic emission (AE) signals. Bearings are perhaps the most omnipresent machine elements and their condition is often critical to the success of an operation or process. Consequently, there is a great need for a timely knowledge of the health status of bearings. Generally, bearing monitoring is the prediction of the component's health or status based on signal detection, processing and classification in order to identify the causes of the problem. As the monitoring system uses both acceleration and acoustic emission signals, it is considered a multi-sensor system. This has the advantage that not only do the two sensors provide increased reliability they also permit a larger range of rotating speeds to be monitored successfully. When more than one sensor is used, if one fails to work properly the other is still able to provide adequate monitoring. Vibration techniques are suitable for higher rotating speeds whilst acoustic emission techniques for low rotating speeds. Vibration techniques investigated in this research concern the use of the continuous wavelet transform (CWT), a joint time- and frequency domain method, This gives a more accurate representation of the vibration phenomenon than either time-domain analysis or frequency- domain analysis. The image processing technique, called binarising, is performed to produce binary image from the CWT transformed image in order to reduce computational time for classification. The back-propagation neural network (BPNN) is used for classification. The AE monitoring techniques investigated can be categorised, based on the features used, into: 1) the traditional AE parameters of energy, event duration and peak amplitude and 2) the statistical parameters estimated from the Weibull distribution of the inter-arrival times of AE events in what is called the STL method. Traditional AE parameters of peak amplitude, energy and event duration are extracted from individual AE events. These events are then ordered, selected and normalised before the selected events are displayed in a three-dimensional Cartesian feature space in terms of the three AE parameters as axes. The fuzzy C-mean clustering technique is used to establish the cluster centres as signatures for different machine conditions. A minimum distance classifier is then used to classify incoming AE events into the different machine conditions. The novel STL method is based on the detection of inter-arrival times of successive AE events. These inter-arrival times follow a Weibull distribution. The method provides two parameters: STL and L63 that are derived from the estimated Weibull parameters of the distribution's shape (y), characteristic life (0) and guaranteed life (to). It is found that STL and 43 are related hyperbolically. In addition, the STL value is found to be sensitive to bearing wear, the load applied to the bearing and the bearing rotating speed. Of the three influencing factors, bearing wear has the strongest influence on STL and L63. For the proposed bearing condition monitoring system to work, the effects of load and speed on STL need to be compensated. These issues are resolved satisfactorily in the project.

Forest disturbances and the spatial patterns they create affect ecosystem processes through their influence on forest vegetation from individual trees to landscapes. In the boreal and mixed-wood forests of eastern Canada the main agents of disturbance are logging, fire, and defoliation by the spruce budworm (SBW, Choristoneura fumiferana). These disturbances are similar in that they remove forest biomass and influence forest succession but also distinct in that logging creates patterns that are different than those created by natural disturbances. All disturbances are indirectly linked to each other through their mutual effects on forest spatial structure and succession. Through such feedbacks, spatial disturbance legacies can facilitate or constrain further disturbances, including forest management. Surprisingly, the long term spatial consequences of interactions among multiple natural and anthropogenic disturbances remain largely unexplored. This thesis investigates how, and at what spatial scale, legacies in forest composition and age structure influence natural disturbance dynamics, and how natural disturbances constrain forest management. I address four specific questions: (i) For how long do spatial legacies of different forest management strategies persist on the landscape? (ii) How do interactions among logging, fire, SBW, and succession affect timber availability and long term forest patterns in age and composition? (iii) How do these patterns differ from those created by each disturbance individually? And, (iv) How can management be used to reduce the extent and severity of fires and SBW defoliation through the manipulation of forest structure? The key scientific innovations of this thesis are: (i) Characterization of the duration and influence of spatial legacies on forest disturbances and sustainability; (ii) Development of a dynamic spatial forest simulation model that includes distinct successional rules that respond to different types of disturbance and shifts in disturbance regimes; and, (iii) Development and application of a wavelet-based significance testing framework to identify key scales of expression in forest spatial patterns. These innovations provide a scientific basis for landscape level forest management strategies designed to reduce the long term impacts of defoliating insects and to meet multiple objectives.

Complex wavelet structural similarity (CW-SSIM) index has been recognized as a novel image similarity measure of broad potential applications due to its robustness to small geometric distortions such as translation, scaling and rotation of images. Nevertheless, how to make the best use of it in image classification problems has not been deeply investi- gated. In this study, we introduce a series of novel image classification algorithms based on CW-SSIM and use handwritten digit and face image recognition as examples for demonstration, including CW-SSIM based nearest neighbor method, CW-SSIM based k means method, CW-SSIM based support vector machine method (SVM) and CW-SSIM based SVM using affinity propagation. Among the proposed approaches, the best compromise between accuracy and complexity is obtained by the CW-SSIM support vector machine algorithm, which combines an unsupervised clustering method to divide the training images into clusters with representative images and a supervised learning method based on support vector machines to maximize the classification accuracy. Our experiments show that such a conceptually simple image classification method, which does not involve any registration, intensity normalization or sophisticated feature extraction processes, and does not rely on any modeling of the image patterns or distortion processes, achieves competitive performance with reduced computational cost.

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Zugl.: Marburg, Univ., Diss., 2007