Processamento embarcado aplicado a um sistema de deteccão de vazamentos

Embedded systems are widely spread nowadays. An example is the Digital Signal Processor (DSP), which is a high processing power device. This work s contribution consist of exposing DSP implementation of the system logic for detecting leaks in real time. Among the various methods of leak detection available today this work uses a technique based on the pipe pressure analysis and usesWavelet Transform and Neural Networks. In this context, the DSP, in addition to do the pressure signal digital processing, also communicates to a Global Positioning System (GPS), which helps in situating the leak, and to a SCADA, sharing information. To ensure robustness and reliability in communication between DSP and SCADA the Modbus protocol is used. As it is a real time application, special attention is given to the response time of each of the tasks performed by the DSP. Tests and leak simulations were performed using the structure of Laboratory of Evaluation of Measurement in Oil (LAMP), at Federal University of Rio Grande do Norte (UFRN)

Modulação quantizada para sistemas com codificação wavelet sujeitos ao desvanecimento rayleigh

Wavelet coding is an efficient technique to overcome the multipath fading effects, which are characterized by fluctuations in the intensity of the transmitted signals over wireless channels. Since the wavelet symbols are non-equiprobable, modulation schemes play a significant role in the overall performance of wavelet systems. Thus the development of an efficient design method is crucial to obtain modulation schemes suitable for wavelet systems, principally when these systems employ wavelet encoding matrixes of great dimensions. In this work, it is proposed a design methodology to obtain sub-optimum modulation schemes for wavelet systems over Rayleigh fading channels. In this context, novels signal constellations and quantization schemes are obtained via genetic algorithm and mathematical tools. Numerical results obtained from simulations show that the wavelet-coded systems derived here have very good performance characteristics over fading channels

Estudo da detecção preventiva da osteoporose pela aplicação da variação da permissividade elétrica a partir da sondagem eletromagnética

This paper proposes a method based on the theory of electromagnetic waves reflected to evaluate the behavior of these waves and the level of attenuation caused in bone tissue. For this, it was proposed the construction of two antennas in microstrip structure with resonance frequency at 2.44 GHz The problem becomes relevant because of the diseases osteometabolic reach a large portion of the population, men and women. With this method, the signal is classified into two groups: tissue mass with bony tissues with normal or low bone mass. For this, techniques of feature extraction (Wavelet Transform) and pattern recognition (KNN and ANN) were used. The tests were performed on bovine bone and tissue with chemicals, the methodology and results are described in the work

Uma ferramenta para Análise Multiresolução de dados não regularmente amostrados

Digital signal processing (DSP) aims to extract specific information from digital signals. Digital signals are, by definition, physical quantities represented by a sequence of discrete values and from these sequences it is possible to extract and analyze the desired information. The unevenly sampled data can not be properly analyzed using standard techniques of digital signal processing. This work aimed to adapt a technique of DSP, the multiresolution analysis, to analyze unevenly smapled data, to aid the studies in the CoRoT laboratory at UFRN. The process is based on re-indexing the wavelet transform to handle unevenly sampled data properly. The was efective presenting satisfactory results

Avaliação de desempenho da codificação wavelet em canais seletivos em frequência

Wavelet coding has emerged as an alternative coding technique to minimize the fading effects of wireless channels. This work evaluates the performance of wavelet coding, in terms of bit error probability, over time-varying, frequency-selective multipath Rayleigh fading channels. The adopted propagation model follows the COST207 norm, main international standards reference for GSM, UMTS, and EDGE applications. The results show the wavelet coding s efficiency against the inter symbolic interference which characterizes these communication scenarios. This robustness of the presented technique enables its usage in different environments, bringing it one step closer to be applied in practical wireless communication systems

Compressão Seletiva de Imagens Coloridas com Detecção Automática de Regiões de Interesse

There has been an increasing tendency on the use of selective image compression, since several applications make use of digital images and the loss of information in certain regions is not allowed in some cases. However, there are applications in which these images are captured and stored automatically making it impossible to the user to select the regions of interest to be compressed in a lossless manner. A possible solution for this matter would be the automatic selection of these regions, a very difficult problem to solve in general cases. Nevertheless, it is possible to use intelligent techniques to detect these regions in specific cases. This work proposes a selective color image compression method in which regions of interest, previously chosen, are compressed in a lossless manner. This method uses the wavelet transform to decorrelate the pixels of the image, competitive neural network to make a vectorial quantization, mathematical morphology, and Huffman adaptive coding. There are two options for automatic detection in addition to the manual one: a method of texture segmentation, in which the highest frequency texture is selected to be the region of interest, and a new face detection method where the region of the face will be lossless compressed. The results show that both can be successfully used with the compression method, giving the map of the region of interest as an input

The wavelet method as an alternative for reducing ionospheric effects from single-frequency GPS receivers

The ionospheric effect is one of the major errors in GPS data processing over long baselines. As a dispersive medium, it is possible to compute its influence on the GPS signal with the ionosphere-free linear combination of L1 and L2 observables, requiring dual-frequency receivers. In the case of single-frequency receivers, ionospheric effects are either neglected or reduced by using a model. In this paper, an alternative for single-frequency users is proposed. It involves multiresolution analysis (MRA) using a wavelet analysis of the double-difference observations to remove the short- and medium-scale ionosphere variations and disturbances, as well as some minor tropospheric effects. Experiments were carried out over three baseline lengths from 50 to 450 km, and the results provided by the proposed method were better than those from dual-frequency receivers. The horizontal root mean square was of about 0.28 m (1 sigma).

Análise das curvas de luz do CoroT usando diferentes processos comparativos: estimando períodos de rotação estelar

One of the main goals of CoRoT Natal Team is the determination of rotation period for thousand of stars, a fundamental parameter for the study of stellar evolutionary histories. In order to estimate the rotation period of stars and to understand the associated uncertainties resulting, for example, from discontinuities in the curves and (or) low signal-to-noise ratio, we have compared three different methods for light curves treatment. These methods were applied to many light curves with different characteristics. First, a Visual Analysis was undertaken for each light curve, giving a general perspective on the different phenomena reflected in the curves. The results obtained by this method regarding the rotation period of the star, the presence of spots, or the star nature (binary system or other) were then compared with those obtained by two accurate methods: the CLEANest method, based on the DCDFT (Date Compensated Discrete Fourier Transform), and the Wavelet method, based on the Wavelet Transform. Our results show that all three methods have similar levels of accuracy and can complement each other. Nevertheless, the Wavelet method gives more information about the star, from the wavelet map, showing the variations of frequencies over time in the signal. Finally, we discuss the limitations of these methods, the efficiency to give us informations about the star and the development of tools to integrate different methods into a single analysis

Análise sísmica usando transformada de Curvelet

Oil prospecting is one of most complex and important features of oil industry Direct prospecting methods like drilling well logs are very expensive, in consequence indirect methods are preferred. Among the indirect prospecting techniques the seismic imaging is a relevant method. Seismic method is based on artificial seismic waves that are generated, go through the geologic medium suffering diffraction and reflexion and return to the surface where they are recorded and analyzed to construct seismograms. However, the seismogram contains not only actual geologic information, but also noise, and one of the main components of the noise is the ground roll. Noise attenuation is essential for a good geologic interpretation of the seismogram. It is common to study seismograms by using time-frequency transformations that map the seismic signal into a frequency space where it is easier to remove or attenuate noise. After that, data is reconstructed in the original space in such a way that geologic structures are shown in more detail. In addition, the curvelet transform is a new and effective spectral transformation that have been used in the analysis of complex data. In this work, we employ the curvelet transform to represent geologic data using basis functions that are directional in space. This particular basis can represent more effectively two dimensional objects with contours and lines. The curvelet analysis maps real space into frequencies scales and angular sectors in such way that we can distinguish in detail the sub-spaces where is the noise and remove the coefficients corresponding to the undesired data. In this work we develop and apply the denoising analysis to remove the ground roll of seismograms. We apply this technique to a artificial seismogram and to a real one. In both cases we obtain a good noise attenuation

Representações espectrais de sistemas complexos: aplicações à síntese de superfícies brownianas fracionárias anisotrópicas, filtragem de sinais e identificação de correlações

In this thesis, we study the application of spectral representations to the solution of problems in seismic exploration, the synthesis of fractal surfaces and the identification of correlations between one-dimensional signals. We apply a new approach, called Wavelet Coherency, to the study of stratigraphic correlation in well log signals, as an attempt to identify layers from the same geological formation, showing that the representation in wavelet space, with introduction of scale domain, can facilitate the process of comparing patterns in geophysical signals. We have introduced a new model for the generation of anisotropic fractional brownian surfaces based on curvelet transform, a new multiscale tool which can be seen as a generalization of the wavelet transform to include the direction component in multidimensional spaces. We have tested our model with a modified version of the Directional Average Method (DAM) to evaluate the anisotropy of fractional brownian surfaces. We also used the directional behavior of the curvelets to attack an important problem in seismic exploration: the atenuation of the ground roll, present in seismograms as a result of surface Rayleigh waves. The techniques employed are effective, leading to sparse representation of the signals, and, consequently, to good resolutions

Mathematical tests about the existence and applications of PPS-wavelets in function approximation problems

Neural networks and wavelet transform have been recently seen as attractive tools for developing eficient solutions for many real world problems in function approximation. Function approximation is a very important task in environments where computation has to be based on extracting information from data samples in real world processes. So, mathematical model is a very important tool to guarantee the development of the neural network area. In this article we will introduce one series of mathematical demonstrations that guarantee the wavelets properties for the PPS functions. As application, we will show the use of PPS-wavelets in pattern recognition problems of handwritten digit through function approximation techniques.

Comparative study between RBF and radial-PPS neural networks

The study of function approximation is motivated by the human limitation and inability to register and manipulate with exact precision the behavior variations of the physical nature of a phenomenon. These variations are referred to as signals or signal functions. Many real world problem can be formulated as function approximation problems and from the viewpoint of artificial neural networks these can be seen as the problem of searching for a mapping that establishes a relationship from an input space to an output space through a process of network learning. Several paradigms of artificial neural networks (ANN) exist. Here we will be investigated a comparative of the ANN study of RBF with radial Polynomial Power of Sigmoids (PPS) in function approximation problems. Radial PPS are functions generated by linear combination of powers of sigmoids functions. The main objective of this paper is to show the advantages of the use of the radial PPS functions in relationship traditional RBF, through adaptive training and ridge regression techniques.

IFTrace: Video segmentation of deformable objects using the Image Foresting Transform

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Multidimensional polynomial powers of sigmoid (PPS) Wavelet neural networks

Wavelet functions have been used as the activation function in feedforward neural networks. An abundance of R&D has been produced on wavelet neural network area. Some successful algorithms and applications in wavelet neural network have been developed and reported in the literature. However, most of the aforementioned reports impose many restrictions in the classical backpropagation algorithm, such as low dimensionality, tensor product of wavelets, parameters initialization, and, in general, the output is one dimensional, etc. In order to remove some of these restrictions, a family of polynomial wavelets generated from powers of sigmoid functions is presented. We described how a multidimensional wavelet neural networks based on these functions can be constructed, trained and applied in pattern recognition tasks. As an example of application for the method proposed, it is studied the exclusive-or (XOR) problem.

Robust fault diagnosis in power distribution systems based on fuzzy ARTMAP neural network-aided evidence theory

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)