O presente trabalho apresenta uma comparação de duas técnicas para a análise tempo - frequência em análise de qualidade de energia elétrica para sinais de tensão que contenham distúrbios individuais ou simultâneos. Dessa forma, o objetivo, desta dissertação, é encontrar uma ferramenta que forneça as características e parâmetros para a localização, identificação e classificação de tais distúrbios. O estudo consiste na análise do desempenho da Transformada Wavelet Discreta e da Transformada-S, principalmente, quando os sinais são analisados na presença de múltiplos distúrbios. Ambas as transformadas fornecem informação importante nos domínios do tempo e da frequência. No entanto, essas ferramentas não tem sido amplamente exploradas para análise de múltiplos distúrbios. Neste contexto, ambas as transformadas são testadas para conhecer seus desempenhos e suas capacidades de identificação e localização de eventos de qualidade de energia elétrica. Para finalizar, é projetado um sistema classificador baseado em arvore de decisão capaz de reconhecer quinze tipos de distúrbios diferentes. / This work presents a comparison of two methods for time-frequency analysis applied in Power Quality signals containing single or multiple disturbances. In this way, the aim of this work is to apply tools that supply the parameters and characteristics to identify, locate and classify Power Quality disturbances. For that, the proposed method analyzes the performance of the Wavelet and S transforms, mainly when the signals are with more than one disturbance type. Both mathematical tools supply important information on the time and frequency domain. However, these tools have not been thoroughly used to analyze multiple events locate Power Quality events. In this contest, both transforms are tested in order to assess their performance to identify and locate electrical power quality events. According to a decision tree classifier, fifteen types of single and combined power disturbances are well recognized.

Neste trabalho, buscou-se compreender a morfologia e os componentes do sinal da deglutição. Na busca desse entendimento diversas técnicas foram empregadas. No intuito de fazer marcações fidedignas em trechos específicos do sinal, o qual foi analisado simultaneamente com a imagem da videofluoroscopia da deglutição, considerado o melhor método atual na avaliação da dinâmica da deglutição. Os parâmetros numéricos utilizados para análise também foram abrangentes e com base em técnicas atuais de processamento de sinais, como emprego de transformada Wavelet. Quanto à classificação dos sinais, foram utilizados classificadores modernos como floresta de caminhos ótimos, máquinas de vetores de suporte, redes neurais artificiais e classificador Bayesiano, dando maior ênfase ao primeiro, por possuir um custo computacional bem menor quando comparado aos outros 3, e consequentemente convergindo mais rapidamente ao resultado. Foram avaliados 84 sinais, divididos em 2 grupos separados pela consistência do bolo alimentar oferecido (líquido e pastoso). Na distinção e/ou caracterização desses tipos foi definido um subconjunto com 4 variáveis que proporcionou uma boa acurácia na separação das classes representantes de cada tipo de bolo alimentar. / This work proposes to understand the morphology and the components of the swallowing signal. In pursuit of this understanding, a variety of techniques were employed. In order to make reliable markings on specific portions of the signal, the signal was examined simultaneously with videofluoroscopic swallowing, which is considered the best method in the evaluation of swallowing dynamics. The parameters used for numerical analysis were based on current signal processing techniques, such as: Wavelet transform, Optimum path forest, Support vector machines, Artificial neural networks and Bayesian classifier, emphasizing the first technique, due to a much lower computational cost when compared to the previous, and, consequently, the results converged much faster. Eighty four signals, divided into 2 groups separated by the consistency of food bolus offered (liquid and thickened), were evaluated. For distinction and/or characterization of such types, a subset with 4 variables was defined, providing a good accuracy in the separation of these classes representing each type of consistency of the food bolus.

Field programmable grid arrays (FPGA) are increasingly being adopted as the primary on-board computing system for autonomous deep space vehicles. There is a need to support several complex applications for navigation and image processing in a rapidly responsive on-board FPGA-based computer. This requires exploring and combining several design concepts such as systolic arrays, hardware-software partitioning, and partial dynamic reconfiguration. A microprocessor/co-processor design that can accelerate two single precision oating-point algorithms, extended Kalman lter and a discrete wavelet transform, is presented. This research makes three key contributions. (i) A polymorphic systolic array framework comprising of recofigurable partial region-based sockets to accelerate algorithms amenable to being mapped onto linear systolic arrays. When implemented on a low end Xilinx Virtex4 SX35 FPGA the design provides a speedup of at least 4.18x and 6.61x over a state of the art microprocessor used in spacecraft systems for the extended Kalman lter and discrete wavelet transform algorithms, respectively. (ii) Switchboxes to enable communication between static and partial reconfigurable regions and a simple protocol to enable schedule changes when a socket's contents are dynamically reconfigured to alter the concurrency of the participating systolic arrays. (iii) A hybrid partial dynamic reconfiguration method that combines Xilinx early access partial reconfiguration, on-chip bitstream decompression, and bitstream relocation to enable fast scaling of systolic arrays on the PolySAF. This technique provided a 2.7x improvement in reconfiguration time compared to an o-chip partial reconfiguration technique that used a Flash card on the FPGA board, and a 44% improvement in BRAM usage compared to not using compression.

This research set out to develop a novel technique to decompose Electroencephalograph (EEG) signal into sets of constituent peaks in order to better describe the underlying nature of these signals. It began with the question; can a localised, single stimulation of sensory nervous tissue in the body be detected in the brain? Flash Visual Evoked Potential (VEP) tests were carried out on 3 participants by presenting a flash and recording the response in the occipital region of the cortex. By focussing on analysis techniques that retain a perspective across different domains � temporal (time), spectral (frequency/scale) and epoch (multiple events) � useful information was detected across multiple domains, which is not possible in single domain transform techniques. A comprehensive set of algorithms to decompose evoked potential data into sets of peaks was developed and tested using wavelet transform singularity detection methods. The set of extracted peaks then forms the basis for a subsequent clustering analysis which identifies sets of localised peaks that contribute the most towards the standard evoked response. The technique is quite novel as no closely similar work in research has been identified. New and valuable insights into the nature of an evoked potential signal have been identified. Although the number of stimuli required to calculate an Evoked Potential response has not been reduced, the amount of data contributing to this response has been effectively reduced by 75%. Therefore better examination of a small subset of the evoked potential data is possible. Furthermore, the response has been meaningfully decomposed into a small number (circa 20) of constituent peaksets that are defined in terms of the peak shape (time location, peak width and peak height) and number of peaks within the peak set. The question of why some evoked potential components appear more strongly than others is probed by this technique. Delineation between individual peak sizes and how often they occur is for the first time possible and this representation helps to provide an understanding of how particular evoked potentials components are made up. A major advantage of this techniques is the there are no pre-conditions, constraints or limitations. These techniques are highly relevant to all evoked potential modalities and other brain signal response applications � such as in brain-computer interface applications. Overall, a novel evoked potential technique has been described and tested. The results provide new insights into the nature of evoked potential peaks with potential application across various evoked potential modalities.

Since twenty years wavelet transform was recognized as a privileged tool for analysis of the fractals objects. We exploited the self-similarity of the wavelet transform to detect singularity which is a fractal signal characterization .In a first part, we use the wavelet transform modulus maxima lines (WTMM) as a tool for analysis of synthetics fractals signals. In second part we applied this technique to the data of wells located in the Algerian Sahara, we proposed then an automatic algorithm of segmentation which is applied thereafter to a simple resolution well-logs data another with a high resolution. We demonstrate the potentialities of the method in the segmentation of different geological formations. We finalize this work by planning a multilayer perceptron neuronal machine. We used the precedents results as information able to detect the lithology and the nature of the pores fluid . Keywords: Wavelet transform, WTMM, Segmentation, simple resolution, High resolution.

<p>A method for constructing a highly scalable bit stream for video coding is presented in detail and implemented in a demo application with a GUI in the Windows Vista operating system.</p><p>The video codec uses the Discrete Wavelet Transform in both spatial and temporal directions together with a zerotree quantizer to achieve a highly scalable bit stream in the senses of quality, spatial resolution and frame rate.</p> / <p>I detta arbete presenteras en metod för att skapa en mycket skalbar videoström. Metoden implementeras sedan i sin helhet i programspråken C och C++ med ett grafiskt användargränssnitt på operativsystemet Windows Vista.</p><p>I metoden används den diskreta wavelettransformen i såväl de spatiella dimensionerna som tidsdimensionen tillsammans med en nollträdskvantiserare för att åstakomma en skalbar videoström i avseendena bildkvalitet, skärmupplösning och antal bildrutor per sekund.</p>

<p>For demanding applications, for example image or video processing, there may be computations that aren’t very suitable for digital signal processors. While a DSP processor is appropriate for some tasks, the instruction set could be extended in order to achieve higher performance for the tasks that such a processor normally isn’t actually design for. The platform used in this project is flexible in the sense that new hardware can be designed to speed up certain computations.</p><p>This thesis analyzes the computational complex parts of JPEG2000. In order to achieve sufficient performance for JPEG2000, there may be a need for hardware acceleration.</p><p>First, a JPEG2000 decoder was implemented for a DSP processor in assembler. When the firmware had been written, the cycle consumption of the parts was measured and estimated. From this analysis, the bottlenecks of the system were identified. Furthermore, new processor instructions are proposed that could be implemented for this system. Finally the performance improvements are estimated.</p>

The Applied Miniaturisation Laboratory (AML) has recently built a laser-induced fluorescent capillary electrophoresis (LIF-CE) genetic analysis instrument, called the Tricorder Tool Kit (TTK). By using a photodiode instead of photomultiplier tubes in the optical detection, the AML has lowered the cost and size compared to commercial LIF-CE products. However, maintaining an adequate signal-to-noise (SNR) and limit of detection (LOD) is a challenge. By implementing a multistage amplifier, we increased the bandwidth and voltage swing while maintaining the transimpedance gain compared to the previous design. We also developed signal processing algorithms for post-experiment processing of CE. Using wavelet transform, iterative polynomial baseline fitting, and Jansson's deconvolution, we improved the SNR, reduced baseline variations, and separated overlapping peaks in CE signals. By improving the electronics and signal processing, we lowered the LOD of the TTK, which is a step towards the realisation of inexpensive point-of-care molecular medical diagnosis instruments. / Computer, Microelectronic Devices, Circuits and Systems

The main goal of this research is to estimate the model parameters and to detect multiple change points in the long memory parameter of Gaussian ARFIMA(p, d, q) processes. Our approach is Bayesian and inference is done on wavelet domain. Long memory processes have been widely used in many scientific fields such as economics, finance and computer science. Wavelets have a strong connection with these processes. The ability of wavelets to simultaneously localize a process in time and scale domain results in representing many dense variance-covariance matrices of the process in a sparse form. A wavelet-based Bayesian estimation procedure for the parameters of Gaussian ARFIMA(p, d, q) process is proposed. This entails calculating the exact variance-covariance matrix of given ARFIMA(p, d, q) process and transforming them into wavelet domains using two dimensional discrete wavelet transform (DWT2). Metropolis algorithm is used for sampling the model parameters from the posterior distributions. Simulations with different values of the parameters and of the sample size are performed. A real data application to the U.S. GNP data is also reported. Detection and estimation of multiple change points in the long memory parameter is also investigated. The reversible jump MCMC is used for posterior inference. Performances are evaluated on simulated data and on the Nile River dataset.

Safeguarding creative content in a digital form has become increasingly difficult. It is progressively easier to copy, modify and redistribute digital media, which causes great declines in business profits. For example, the International Federation of the Phonographic Industry estimates that in 2001 the worldwide sales of pirated music CDs were 475 million US dollars. While a large amount of time and money is committed to creating intellectual property, legal means have not proven to be sufficient for the protection of this property. Digital watermarking is a steganographic technique that has been proposed as a possible solution to this problem. A digital watermark hides embedded information about the origin, status, owner and/or destination of the data, often without the knowledge of the viewer or user. This dissertation examines a technique for digital watermarking which utilizes properties of the Discrete Wavelet Transform (DWT). Research has been done in this field, but which wavelet family is superior is not adequately addressed. This dissertation studies the influence of the wavelet family when using a blind, nonvisible watermark in digital media. The digital watermarking algorithm uses a database of multiple images with diverse properties. Various watermarks are embedded. Eight different families of wavelets with dissimilar properties are compared. How effective is each wavelet? To objectively measure the success of the algorithm, the influence of the mother wavelet, the imperceptibility of the embedded watermark and the readability of the extracted watermark, the Peak Signal-to-Noise Ratio and the Image Quality Index for each wavelet family and image are obtained. Two common categories of digital watermarking attacks are removing the watermark and rendering the watermark undetectable. To simulate and examine the effect of attacks on the images, noise is added to the image data. Also, to test the effect of reducing an image in size, each image containing the embedded watermark is compressed. The dissertation asks the questions: “Is the wavelet family chosen to implement the algorithm for a blind, nonvisible watermark in digital images of consequence? If so, which family is superior?” This dissertation conclusively shows that the Haar transform is the best for blind, non-visible digital watermarking.