[pt] O sistema hidroelétrico é responsável por 83,7% da energia elétrica gerada no país. Assim sendo, a geração de energia elétrica no Brasil depende basicamente das vazões naturais que afluem aos aproveitamentos hidroelétricos distribuídos por doze bacias hidrográficas no país. Sendo o Operador Nacional do Sistema Elétrico (ONS) o órgão responsável por elaborar a previsão e a geração de cenários de vazões naturais médias diárias, semanais e mensais para todos os locais de aproveitamentos hidroelétricos do Sistema Interligado Nacional (SIN), a qualidade da previsão da vazão natural é de suma importância para este órgão. A qualidade dessa previsão impacta diretamente no planejamento e em programas de operação do SIN, tal como o Programa Mensal de Operação - PMO. Mesmo com a melhoria na qualidade da previsão de vazões por meio da criação e adoção dos mais diversos modelos determinísticos e estocásticos nos últimos anos, os erros de previsão são, ainda, significativos. Deste modo, o objetivo principal desta dissertação foi propor um novo modelo capaz de proporcionar um significativo ganho de qualidade na previsão de vazões nas regiões dos aproveitamentos hidrelétricos das bacias hidrográficas do país. O modelo proposto, baseado em redes neurais, tem como ferramenta primordial a utilização de transformadas wavelets, que filtram os dados históricos de vazões, ou seja, as entradas das redes neurais de previsão, dividindo esses dados de entrada (sinais) em diversas escalas, no intuito de que as redes neurais possam melhor analisá-los. Para verificar a eficácia do modelo proposto, aqui denominado MIP (Modelo Inteligente de Previsão), procedeu-se um estudo de caso que realiza a previsão de vazões naturais incrementais médias diárias e semanais no trecho incremental entre as Usinas Hidroelétricas (UHE) Porto Primavera, Rosana e Itaipu da Bacia do Rio Paraná, chegando-se a um erro de aproximadamente 3,5% para previsão de vazões um dia à frente, 16% para 12 dias à frente, e 9% para previsão média semanal. Esta dissertação objetiva, também, investigar a eficácia do uso de informações das precipitações observadas e previstas na previsão de vazão, em conjunção com o uso do histórico de vazões. / [en] The hydroelectricity system is responsible for 83.7% of the electric energy generated at Brazil. Therefore, the generation of electric power in Brazil depends basically on the natural flow rates distributed by twelve basins in the country. The quality of prediction of natural flow is of crucial importance for the Brazilian governmental agency, ONS (from the portuguese language Electrical National Operator System), responsible for preparing the forecast and the generation of scenarios of daily, weekly and monthly average natural streamflows of all places of hydroelectric exploitations of SIN (from the portuguese language National Linked System). The quality of that forecast impacts directly in the planning and operation programs of SIN, for example, the PMO (from the portuguese language Monthly Operation Program). Even with the improvement in the quality of river flow forecasts through the creation and adoption of the various deterministic and stochastic models in recent years, the errors of forecasting are still significant. Thus, the main goal of this dissertation was proposing a new model capable of providing a significant improvement in Streamflow forecasts in regions of exploitations of hydroelectric basins of the country. The proposed model, based on neural networks, has the primary tool the use of wavelet transforms, to filter streamflows historical data, or the entries of predict neural networks, dividing the input data (signals) in several scales, in order that the neural networks can better analyse them. In order to check the effectiveness of the proposed model, here called MIP (from the portuguese language Forecast Intelligent Model), it was developed a case study to forecast daily and weekly average of natural incremental streamflows between the Hydroelectric Plants: Porto Primavera, Rosana e Itaipu belonging to the the Parana River Basin. The model reaches up an error of about 3,5% to estimates of streamflows one day ahead, 16% to 12 days ahead, and 9% for average weekly forecast. This thesis aims to also investigate the effectiveness of the use of information of observed and predicted rainfall in the forecast flow, in conjunction with the use of the historical streamflows.

This thesis primarily addresses the problem of automatic measurement of ore textures by image analysis in a way that is relevant to mineral processing. Specifically, it addresses the following major hypotheses: • Automatic logging of drill core by image analysis provides a feasible alternative to manual logging by geologists. • Image analysis can quantify process mineralogy by physically meaningful parameters. • Multi-scale image analysis, over a wide range of size scales, provides potential benefits to process mineralogy that are additional to those available from small-scale analysis alone, and also better retains the information content of manual logging. • Image analysis can provide physically meaningful, ore-texture-related, additive regionalised variables that can be input to geostatistical models and the definition of domains. The central focus of the thesis is the development of an automatic, multi-scale method to identify and measure objects in an image, using a specially-developed skeleton termed the morphological CWT skeleton. This skeleton is a multi-scale extension of the morphological skeleton commonly used in image analysis, and is derived from the continuous wavelet transform (CWT). Objects take the form of hierarchical segments from image segmentation based on the CWT. Only the Mexican hat, also known as the Laplacian-of-Gaussian, wavelet is used, although other wavelet shapes are possible. The natural scale of each object is defined to be the size scale at which its CWT signal (the contrast between the interior and exterior of the object) is strongest. In addition to the natural scale, the analysis automatically records the mineral composition of both the interior and exterior of each object, and shape descriptors of the object. The measurements of natural scale, mineral composition and shape are designed to relate to: • The size to which ore must be broken in order to liberate objects. • Minerals that need to be separated by physical or chemical means once objects have been liberated. • Capability to distinguish qualitatively different ore-texture types that may have different geological origins and for which different processing regimes may provide an economic benefit. Measurements are taken over size scales from three pixels to hundreds of pixels. For the major case study the pixel size is about 50 µm, but the methodology is equally applicable to photomicrographs in which the pixel size is about 4 µm. The methodology for identifying objects in images contributes to the field of scale-space image segmentation, and has advantages in performing the following actions automatically: • Finding optimal size scales in hierarchical image segmentation (natural scale). • Merging segments that are similar and spatially close together (although not necessarily touching), using the structure of the morphological CWT skeleton, thus aiding recognition of complex structures in an image. • Defining the contrast between each segment and its surrounding segments appropriately for the size scale of the segment, in a way that extends well beyond the segment boundary. For process mineralogy this contrast quantifies mineral associations at different size scales. The notion of natural scale defined in this thesis may have applications to other fields of image processing, such as mammography and cell measurements in biological microscopy. The objects identified in images are input to cluster analysis, using a finite mixture model to group the objects into object populations according to their size, composition and shape descriptors. Each image is then characterised by the abundances of different object populations that occur in it. These abundances form additive, regionalised variables that can be input into geostatistical block models. The images are themselves input to higher-level cluster analysis based on a hidden Markov model. A collection of images is divided into different ore texture types, based on differences in the abundances of the textural object populations. The ore texture types help to define geostatistical domains in an ore body. Input images for the methodology take the form of mineral maps, in which a particular mineral has been assigned to each pixel in the image prior to analysis. A method of analysing unmapped, raw colour images of ore is also outlined, as is a new model for fracture of ore. The major case study in the thesis is an analysis of approximately 1000 metres of continuously-imaged drill core from four drill holes in the Ernest Henry iron-oxide-copper-gold ore deposit (Queensland, Australia). Thirty-one texture-related variables are used to summarise the individual half-metres of drill core, and ten major ore texture types are identified. Good agreement is obtained between locations of major changes in ore type found by automatic image analysis, and those identified from manual core logging carried out by geologists. The texture-related variables are found to explain a significant amount of the variation in comminution hardness of ore within the deposit, over and above that explained by changes in abundances of the component minerals. The thesis also contributes new algorithms with wide applicability in image processing: • A fast algorithm for computing the continuous wavelet transform of a signal or image: The new algorithm is simpler in form and several times faster than the best previously-published algorithms. It consists of a single finite impulse response (FIR) filter. • A fast algorithm for computing Euclidean geodesic distance. This algorithm runs in O(1) arithmetic operations per pixel processed, which has not been achieved by any previously published algorithm. Geodesic distance is widely used in image processing, for segmentation and shape characterisation.

Concrete structures can often be modeled as plates, for example, bridges, tunnel walls and pipes. Near-surface damage in concrete structures mostly takes the form of cracking. Surface-breaking cracks affect concrete properties and structural integrity; therefore, the nondestructive evaluation of crack depth is important for structural monitoring, strengthening and rehabilitation. On the other hand, material damping is a fundamental parameter for the dynamic analysis of material specimens and structures. Monitoring damping changes is useful for the assessment of material conditions and structural deterioration. The main objective of this research is to develop new methodologies for depth evaluation of surface-breaking cracks and the evaluation of damping in concrete plates. Nondestructive techniques based on wave propagation are useful because they are non-intrusive, efficient and cost effective. Previous studies for the depth evaluation of surface-breaking cracks in concrete have used diffracted compressional waves (P-waves). However, surface waves exhibit better properties for the characterization of near surface defects, because (a) surface waves dominate the surface response, they carry 67% of the wave propagation energy, and present lower geometrical attenuation because the propagating wave front is cylindrical; and (b) the penetration depth of Rayleigh waves (R-waves) depends on their frequency. Most of the R-wave energy concentrates at a depth of one-third of their wavelengths. The transmission of R-waves through a surface-breaking crack depends on the crack depth; this depth sensitivity is the basis for the so-called Fourier transmission coefficient (FTC) method. R-waves only exist in a half-space (one traction-free surface); whereas in the case of a plate (two traction-free surfaces), Lamb modes are generated. Fundamental Lamb modes behave like R-waves at high frequencies, because their wavelengths are small relative to the plate thickness. Lamb modes are not considered in the standard FTC method, and the FTC method is also affected by the selected spacing between receivers. The FTC calculation requires the use of an explicit time window for the identification of the arrival of surface waves, and the selection of a reliable frequency range. This research presents theoretical, numerical and experimental results. Theoretical aspects of Lamb modes are discussed, and a theoretical transfer function is derived, which can be used to study changes of Lamb modes in the time and frequency domains as a function of distance. The maximum amplitude of the wavelet transform varies with distance because of the dispersion of Lamb modes and the participation of higher Lamb modes in the response. Numerical simulations are conducted to study the wave propagation of Lamb modes through a surface-breaking crack with different depths. The surface response is found to be dominated by the fundamental Lamb mode. Using the 2D Fourier transform, the incident, transmitted and reflected fundamental Lamb modes are extracted. A transmission ratio between the transmitted and incident modes is calculated, which is sensitive to crack depths (d) normalized to the wavelength (λ) in a range (d / λ) = 0.1 to 1/3. A new wavelet transmission coefficient (WTC) method for the depth evaluation of surface-breaking cracks in concrete is proposed to overcome the main limitations of the FTC method. The WTC method gives a global coefficient that is correlated with the crack depth, which does not require time windowing and the pre-selection of a frequency bandwidth. To reduce the effects of wave reflections, which are present in the FTC method because of the non-equal spacing configuration, a new equal spacing configuration is used in the WTC method. The effects of Lamb mode dispersion are also reduced. In laboratory tests, an ultrasonic transmitter with central frequency at 50kHz is used as a source; the 50kHz frequency is appropriate for the concrete plate tested (thickness 80mm), because the fundamental Lamb modes have converged to the Rayleigh wave mode. The new method has also been used in-situ at Hanson Pipe and Precast Inc., Cambridge, Ontario, Canada, and it shows potential for practical applications. In general, the evaluation of material damping is more difficult than the measurement of wave velocity; the dynamic response and attenuation of structural vibrations are predominantly controlled by damping, and the damping is typically evaluated using the modal analysis technique, which requires considerable efforts. The existing methods based on surface waves, use the Fourier transform to measure material damping; however, an explicit time window is required for the spectral ratio method to extract the arrival of surface wave; in addition, a slope of the spectral ratio varies for different frequency ranges, and thus a reliable frequency range needs to be determined. This research uses the wavelet transform to measure material damping in plates, where neither an explicit time window nor the pre-selection of a frequency bandwidth are required. The measured material damping represents an average damping for a frequency range determined by source. Both numerical and experimental results show good agreement and the potential for practical applications.

Concrete structures can often be modeled as plates, for example, bridges, tunnel walls and pipes. Near-surface damage in concrete structures mostly takes the form of cracking. Surface-breaking cracks affect concrete properties and structural integrity; therefore, the nondestructive evaluation of crack depth is important for structural monitoring, strengthening and rehabilitation. On the other hand, material damping is a fundamental parameter for the dynamic analysis of material specimens and structures. Monitoring damping changes is useful for the assessment of material conditions and structural deterioration. The main objective of this research is to develop new methodologies for depth evaluation of surface-breaking cracks and the evaluation of damping in concrete plates. Nondestructive techniques based on wave propagation are useful because they are non-intrusive, efficient and cost effective. Previous studies for the depth evaluation of surface-breaking cracks in concrete have used diffracted compressional waves (P-waves). However, surface waves exhibit better properties for the characterization of near surface defects, because (a) surface waves dominate the surface response, they carry 67% of the wave propagation energy, and present lower geometrical attenuation because the propagating wave front is cylindrical; and (b) the penetration depth of Rayleigh waves (R-waves) depends on their frequency. Most of the R-wave energy concentrates at a depth of one-third of their wavelengths. The transmission of R-waves through a surface-breaking crack depends on the crack depth; this depth sensitivity is the basis for the so-called Fourier transmission coefficient (FTC) method. R-waves only exist in a half-space (one traction-free surface); whereas in the case of a plate (two traction-free surfaces), Lamb modes are generated. Fundamental Lamb modes behave like R-waves at high frequencies, because their wavelengths are small relative to the plate thickness. Lamb modes are not considered in the standard FTC method, and the FTC method is also affected by the selected spacing between receivers. The FTC calculation requires the use of an explicit time window for the identification of the arrival of surface waves, and the selection of a reliable frequency range. This research presents theoretical, numerical and experimental results. Theoretical aspects of Lamb modes are discussed, and a theoretical transfer function is derived, which can be used to study changes of Lamb modes in the time and frequency domains as a function of distance. The maximum amplitude of the wavelet transform varies with distance because of the dispersion of Lamb modes and the participation of higher Lamb modes in the response. Numerical simulations are conducted to study the wave propagation of Lamb modes through a surface-breaking crack with different depths. The surface response is found to be dominated by the fundamental Lamb mode. Using the 2D Fourier transform, the incident, transmitted and reflected fundamental Lamb modes are extracted. A transmission ratio between the transmitted and incident modes is calculated, which is sensitive to crack depths (d) normalized to the wavelength (λ) in a range (d / λ) = 0.1 to 1/3. A new wavelet transmission coefficient (WTC) method for the depth evaluation of surface-breaking cracks in concrete is proposed to overcome the main limitations of the FTC method. The WTC method gives a global coefficient that is correlated with the crack depth, which does not require time windowing and the pre-selection of a frequency bandwidth. To reduce the effects of wave reflections, which are present in the FTC method because of the non-equal spacing configuration, a new equal spacing configuration is used in the WTC method. The effects of Lamb mode dispersion are also reduced. In laboratory tests, an ultrasonic transmitter with central frequency at 50kHz is used as a source; the 50kHz frequency is appropriate for the concrete plate tested (thickness 80mm), because the fundamental Lamb modes have converged to the Rayleigh wave mode. The new method has also been used in-situ at Hanson Pipe and Precast Inc., Cambridge, Ontario, Canada, and it shows potential for practical applications. In general, the evaluation of material damping is more difficult than the measurement of wave velocity; the dynamic response and attenuation of structural vibrations are predominantly controlled by damping, and the damping is typically evaluated using the modal analysis technique, which requires considerable efforts. The existing methods based on surface waves, use the Fourier transform to measure material damping; however, an explicit time window is required for the spectral ratio method to extract the arrival of surface wave; in addition, a slope of the spectral ratio varies for different frequency ranges, and thus a reliable frequency range needs to be determined. This research uses the wavelet transform to measure material damping in plates, where neither an explicit time window nor the pre-selection of a frequency bandwidth are required. The measured material damping represents an average damping for a frequency range determined by source. Both numerical and experimental results show good agreement and the potential for practical applications.

Abstract In a petrochemical plant, to exert economic efficiency and spacing convenience for transporting fluid or gas, the pipelines used in the plant are often buried along the road. The buried pipelines are usually wrapped in the soil that only the guided wave method is a convenient technique to perform the nondestructive testing for the pipelines. However, the viscosity of soil causes the attenuation of the guided wave during the test, the accuracy and the detection distance will then be affected. Thus, the objectives of this thesis are to study the characteristics, such as the detection distance and the refraction signal, of the T(0,1) guided wave when propagating along pipelines wrapped in the soil at different depths. The thesis would be divided into two parts: experiment and numerical simulation. Four different depths, 0.5, 1.0, 1.5 and 2.0 m, are used in the experiment to evaluate the characteristics of reflected signals and its attenuation. Wavelet transform, which would enhance the capability of distinguishing guided wave defect, is used to improve the attenuation of defected refraction signal caused by soil. In the numerical simulation, this research applies the transient simulation by finite element method to analyze the wave propagation behavior of T(0,1) mode guided wave of buried pipeline, which is incorporated with Two-dimensional Fourier transform for modal identification. The result of experiment shows that the attenuation of the guided wave is caused by the leakage and the viscosity of the soil. The decay rate is proportional to the depth and due to the viscosity of the soil is proportional to the excitation frequency. This phenomenon is more obvious when the pipeline is buried deeper. The reflected signal amplitude of each characteristic would decrease along with the increasing soil depth, but the overall trends did not changed. The result of wavelet transform shows that the capability of distinguishing of the guided wave detection defect of buried pipeline, which attenuation of refraction signal caused by soil would be improved. The result of the numerical simulation indicates that the T(0,1) mode would not cause mode conversion and dispersion due to its propagation through the buried pipeline with different depths of soil. The soil caused leakage of the T(0,1) mode in the form of shear waves. The attenuation rate of guided wave and its detection distance in the study could be the reference of site selection for detection and defect refraction signal determination, which could effectively raise the efficiency of on-site detection.

In this study, emitter identification techniques have been investigated and a schema has been proposed to solve the emitter identification problem in Electronic Warfare systems. Clustering technique, histogram based deinterleaving techniques and a continuous wavelet transform based deinterleaving technique have been reviewed. A receiver simulator software has been developed to test the performance of these techniques and to compare them against each other. To compensate the disadvantages of these techniques, a schema utilizing the beneficial points of them has been developed. With the modifications proposed a resultant schema has been obtained. Proposed schema uses clustering and deinterleaving together with other proposed modifications. Tests made through out this study have shown that this usage improves performance of emitter identification system. Hence, proposed schema can be used to identify the emitters in real EW systems.

Transient regimes experienced by dynamic systems may have severe impacts on the operation of the aircraft. They are often regulated by dynamic constraints, requiring the dynamic signals to remain within bounds whose values vary with time. The verification of these peculiar types of constraints, which generally requires high-fidelity time-domain simulation, intervenes late in the system development process, thus potentially causing costly design iterations. The research objective of this thesis is to develop a methodology that integrates the verification of dynamic constraints in the early specification of dynamic systems. In order to circumvent the inefficiencies of time-domain simulation, multivariate dynamic surrogate models of the original time-domain simulation models are generated using wavelet neural networks (or wavenets). Concurrently, an alternate approach is formulated, in which the envelope of the dynamic response, extracted via a wavelet-based multiresolution analysis scheme, is subject to transient constraints. Dynamic surrogate models using sigmoid-based neural networks are generated to emulate the transient behavior of the envelope of the time-domain response. The run-time efficiency of the resulting dynamic surrogate models enables the implementation of a data farming approach, in which the full design space is sampled through a Monte-Carlo Simulation. An interactive visualization environment, enabling what-if analyses, is developed; the user can thereby instantaneously comprehend the transient response of the system (or its envelope) and its sensitivities to design and operation variables, as well as filter the design space to have it exhibit only the design scenarios verifying the dynamic constraints. The proposed methodology, along with its foundational hypotheses, is tested on the design and optimization of a 350VDC network, where a generator and its control system are concurrently designed in order to minimize the electrical losses, while ensuring that the transient undervoltage induced by peak demands in the consumption of a motor does not violate transient power quality constraints.

Many scientific data sets are contaminated by noise, either because of data acquisition process, or because of naturally occurring phenomena. A first step in analyzing such data sets is denoising, i.e., removing additive noise from a noisy image. For images, noise suppression is a delicate and a difficult task. A trade of between noise reduction and the preservation of actual image features has to be made in a way that enhances the relevant image content. The beginning chapter in this thesis is introductory in nature and discusses the Popular denoising techniques in spatial and frequency domains. Wavelet transform has wide applications in image processing especially in denoising of images. Wavelet systems are a set of building blocks that represent a signal in an expansion set involving indices for time and scale. These systems allow the multi-resolution representation of signals. Several well known denoising algorithms exist in wavelet domain which penalize the noisy coefficients by threshold them. We discuss the wavelet transform based denoising of images using bit planes. This approach preserves the edges in an image. The proposed approach relies on the fact that wavelet transform allows the denoising strategy to adapt itself according to directional features of coefficients in respective sub-bands. Further, issues related to low complexity implementation of this algorithm are discussed. The proposed approach has been tested on different sets images under different noise intensities. Studies have shown that this approach provides a significant reduction in normalized mean square error (NMSE). The denoised images are visually pleasing. Many of the image compression techniques still use the redundancy reduction property of the discrete cosine transform (DCT). So, the development of a denoising algorithm in DCT domain has a practical significance. In chapter 3, a DCT based denoising algorithm is presented. In general, the design of filters largely depends on the a-priori knowledge about the type of noise corrupting the image and image features. This makes the standard filters to be application and image specific. The most popular filters such as average, Gaussian and Wiener reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high frequency details making the image non-smooth. An integrated approach to design filters based on DCT is proposed in chapter 3. This algorithm reorganizes DCT coefficients in a wavelet transform manner to get the better energy clustering at desired spatial locations. An adaptive threshold is chosen because such adaptively can improve the wavelet threshold performance as it allows additional local information of the image to be incorporated in the algorithm. Evaluation results show that the proposed filter is robust under various noise distributions and does not require any a-priori Knowledge about the image. Inpainting is another application that comes under the category of image processing. In painting provides a way for reconstruction of small damaged portions of an image. Filling-in missing data in digital images has a number of applications such as, image coding and wireless image transmission for recovering lost blocks, special effects (e.g., removal of objects) and image restoration (e.g., removal of solid lines, scratches and noise removal). In chapter 4, a wavelet based in painting algorithm is presented for reconstruction of small missing and damaged portion of an image while preserving the overall image quality. This approach exploits the directional features that exist in wavelet coefficients in respective sub-bands. The concluding chapter presents a brief review of the three new approaches: wavelet and DCT based denoising schemes and wavelet based inpainting method.

Ο κυματιδικός μετασχηματισμός αποτελεί το πλέον σύγχρονο μαθηματικό εργαλείο για την ανάλυση σήματος σε βάση συναρτήσεων. Σε σχέση με άλλες παρόμοιες τεχνικές (π.χ. Fourier) παρουσιάζει εμφανή πλεονεκτήματα με κυρίοτερο την τοπικότητα στο χρόνο των συναρτήσεων βάσης. Η δύναμη του κυματιδιακού μετασχηματισμού βρίσκεται στη διακριτή του έκδοση (Discrete Wavelet Transform), που υπολογίζεται με τη βοήθεια διατάξεων FIR φίλτρων ακολουθούμενων από υποδειγματοληψία. Η ταχύτερη και πιο σύγχρονη τεχνική υπολογισμού του DWT ονομάζεται σχήμα lifting και βασίζεται στην παραγοντοποίηση των πινάκων μετασχηματισμού σε γινόμενο αραιών πινάκων. Στο πλαίσιο της εργασίας σχεδιάστηκε και υλοποιήθηκε σε γλώσσα VHDL μία VLSI αρχιτεκτονική ικανή να εκτελεί οποiοδήποτε φίλτρο (ευθύ και αντίστροφο) του DWT τροποποιημένο με τη μέθοδο lifting. Τα φίλτρα είναι αποθηκευμένα σαν μικροπρογράμματα σε μνήμη ελέγχου για ευκολία στη σχεδίαση και δυνατότητα επαναπροσδιορισμού του συστήματος. Το σύστημα εξομοιώθηκε για ορθή λειτουργία κατά την εκτέλεση των φίλτρων του προτύπου JPEG2000, ενώ έγινε και σύνθεση σε FPGA. / The wavelet transform is the most powerful mathematical tool for analysing signals into function bases. Comparing with other such technics (e.g. Fourier transform), wavelets show obvious advantages, with the most important being the spatial locality of the basis functions. The real power of wavelet transform is the Discrete Wavelet Tranfsorm (DWT), which is a filtering operation followed by downsampling. Recently, a new, fast approach for calculating these filter banks has been developed, named the lifting scheme. This method is based on the factorization of the transform matrices into a product of some sparse matrices. Α VLSI architecture that executes wavelet filters (forward and inverse) modified by the lifting scheme is designed and implemented in VHDL code. The filters are considered as microprogramms placed in the system

This thesis aims at detecting and classifying the power system transmission line faults. To deal with the problem of an extremely large data set with different fault situations, a three step optimized Neural Network approach has been proposed. The approach utilizes Discrete Wavelet Transform for detection and two different types of self-organized, unsupervised Adaptive Resonance Theory Neural Networks for classification. The fault scenarios are simulated using Alternate Transients Program and the performance of this highly improved scheme is compared with the existing techniques. The simulation results prove that the proposed technique handles large data more efficiently and time of operation is considerably less when compared to the existing methods.