Estudo comparativo entre algoritmos das transformadas discretas de Fourier e Wavelet
Wilson Hissamu Shirado; Márcio de Abreu Moreira; Jandira Guenka Palma; Sylvio Barbon Júnior
2015-01-01
Este trabalho apresenta um estudo comparativo das complexidades dos algoritmos das Transformadas Discretas de Fourier, Wavelet e Transformada Rápida de Fourier. As formalizações matemáticas e algumas características dos algoritmos são apresentadas, assim como alguns conceitos de complexidade assintótica. Por fim, é realizado um ensaio prático para comparação dos algoritmos, abrangendo questões como tempo de execução, vantagens e desvantagens de cada transformada assim como avaliações a respei...
Estudo comparativo entre algoritmos das transformadas discretas de Fourier e Wavelet
Directory of Open Access Journals (Sweden)
Wilson Hissamu Shirado
2015-11-01
Full Text Available Este trabalho apresenta um estudo comparativo das complexidades dos algoritmos das Transformadas Discretas de Fourier, Wavelet e Transformada Rápida de Fourier. As formalizações matemáticas e algumas características dos algoritmos são apresentadas, assim como alguns conceitos de complexidade assintótica. Por fim, é realizado um ensaio prático para comparação dos algoritmos, abrangendo questões como tempo de execução, vantagens e desvantagens de cada transformada assim como avaliações a respeito das diferentes resoluções tempo/frequência de cada algoritmo.
Directory of Open Access Journals (Sweden)
Antonio CedeÃ±o Pozo Ing.,
2013-04-01
Gaussian noise in industrial signals. To that aim, we perform experiments on a set of pattern signals proposed by Donoho and other representative measurements obtained from real processes in Cuban1s Nickel plants. Our results indicate that, for this kind of data, the Neigh Shrink algorithm outperforms. Palabras clave: Transformada wavelet, Ruido, SeÃ±ales industriales, Ruido gaussiano, Keywords: Wavelet transform, Noise, Industrial signals
Generalização de modelos digitais de terreno com base em transformada wavelet
Directory of Open Access Journals (Sweden)
Clovis Gaboardi
Full Text Available Os sistemas de laser scanner permitem a obtenção de modelos digitais de terreno de alta resolução e exatidão. Porém, quando se necessita trabalhar em aplicações com uma resolução menor que a originalmente gerada, a grande quantidade de dados acarreta a necessidade de generalização. Este trabalho tem por objetivo verificar o comportamento da transformada wavelet na generalização de modelos digitais do terreno sob a forma de grades regulares, obtidas a partir de dados do laser scanner. As transformadas wavelets foram implementadas em programas na linguagem Matlab. Foram utilizadas as wavelets de Haar, Daubechies e Symlet. A generalização por krigagem foi utilizada para a comparação dos resultados. Os resultados obtidos nos experimentos realizados permitem afirmar que a transformada wavelet pode ser utilizada como alternativa para a generalização de MDT em razão da facilidade de programação, baixo custo computacional, alta velocidade de processamento e exatidão compatível com a resolução obtida no MDT generalizado, além de ser um método natural de análise multirresolução.
Directory of Open Access Journals (Sweden)
Víctor Gómez
2013-01-01
Full Text Available En este artículo se propone y se evalúa experimentalmente un método de diagnóstico de fallas en rodamientos utilizando la clasificación de patrones provenientes de las señales de las vibraciones mecánicas. El método utiliza pre-procesamientos con las transformadas de Fourier y wavelet packet para luego alimentar una red neuronal clasificadora que determina el tipo de fallo. Para evaluar las variables de entrada se realiza un análisis de varianza ANOVA comparando el efecto que tienen los factores: velocidad, carga, falla en pista externa y falla en elemento rodante sobre cada uno de los parámetros propuestos como entradas para las redes neuronales artificiales (RNA. Una vez seleccionadas las variables de entrada más adecuadas, se realiza la búsqueda del clasificador más apropiado explorando diversas configuraciones de red neuronal. Se han entrenado alrededor de 2000 RNA con el propósito de encontrar el clasificador más adecuado. Los resultados de validación muestran que para el algoritmo de entrenamiento tipo gradiente conjugado escalado (trainscg se alcanza un porcentaje de éxito en la clasificación del 88,5 %, mientras que para el algoritmo de entrenamiento de Levenberg-Marquardt (trainlm se logra un 91,8 %. Adicionalmente, se resalta que en 7 ocasiones se logró el 100 % de aciertos en la clasificación.
Directory of Open Access Journals (Sweden)
J. Rodríguez Matienzo.
2008-01-01
Full Text Available La determinación del estado técnico de los sistemas estructurales representa un factor importante en la correcta explotación de los mismos. Existen diversos métodos, en su mayoría visuales, pero que requieren el acceso directo a zonas del sistema y pueden ser poco prácticos cuando los volúmenes de la estructura son grandes [9]. El análisis modal puede ser también empleado, pero también tiene algunas limitaciones. Se ha propuesto a la transformada wavelet como una herramienta de grandes posibilidades en la detección de grietas en estado incipiente. El problema debe ser estudiado tanto práctica como teóricamente, y en el segundo aspecto la correcta representación de la grieta es de importancia fundamental. En este trabajo se representa una grieta cerrada transversal en una viga de Euler-Bernoulli, según la propuesta de Bovsunovsky [1], con modelos continuos y por elementos finitos. Se aplica la transformada wavelet al desplazamiento en los modos de vibración.The determination of the state o a structure represents an important factor in the use of structural systems. This is known as structural health monitoring (SHM. Several methods, the most of them visuals, are employed. These require a direct access to the possible damaged zone and can be very time consuming and costly, a reason by which the researchers in this field are focused in others method, like modal analysis. The wavelet transform is also a very powerful tool in crack detection in early stages. This problem should be studied in a practical and theoretical way. In this paper a transverse closed crack is represented in an Euler-Bernoulli beam, as Bovsunovsky. Continuous and finite element models are used, and the wavelet transform is applied.
Directory of Open Access Journals (Sweden)
Claudia C. Botero Suárez
2007-07-01
Full Text Available Este artículo describe la detección de actividad precrisis mediante la aplicación de la correlación cruzada junto con la transformada Wavelet. La transformada Wavelet es aplicada a los datos EEG puros para la reducción y pre-procesamiento de las señales. Esta técnica de extracción de características provee las señales simplificadas para ser procesadas por medio de la técnica de correlación cruzada. El análisis ha sido realizado con un grupo de datos tanto precrisis como intercrisis, (incluyendo crisis agudas inducidas y crisis espontáneas recurrentes, con el fin de determinar su sensitividad y especificidad (tasa de falsas predicciones. Son determinados, adicionalmente, el período de ocurrencia de crisis y el horizonte de previsión de crisis.This paper describes the detection of a pre-crisis activity through the application of Cross-Correlation together with the Wavelet Transform. The Wavelet Transform is applied in the data reduction and pre-processing of signals. This feature extract technique provides the simplified signals to process by means of the Cross-Correlation technique. The analysis with a group of pre-crisis and inter-crisis data (including both induced acute crises and recurrent spontaneous crises, to determinate its sensitivity and its specificity (False Prediction Rate has been done. The seizure occurrence period and the seizure prediction horizon are calculated additionally.
Directory of Open Access Journals (Sweden)
J. Rodríguez Matienzo
2008-01-01
Full Text Available La determinación del estado técnico de los sistemas estructurales representa un factor importante en la correcta explotaciónde los mismos. Existen diversos métodos, en su mayoría visuales, pero que requieren el acceso directo a zonas del sistema ypueden ser poco prácticos cuando los volúmenes de la estructura son grandes [9]. El análisis modal puede ser tambiénempleado, pero también tiene algunas limitaciones. Se ha propuesto a la transformada wavelet como una herramienta degrandes posibilidades en la detección de grietas en estado incipiente. El problema debe ser estudiado tanto práctica comoteóricamente, y en el segundo aspecto la correcta representación de la grieta es de importancia fundamental. En este trabajose representa una grieta cerrada transversal en una viga de Euler-Bernoulli, según la propuesta de Bovsunovsky [1], conmodelos continuos y por elementos finitos. Se aplica la transformada wavelet al desplazamiento en los modos de vibración.Palabras claves: detección de grietas, wavelet, análisis modal._____________________________________________________________________________Abstract:The determination of the state o a structure represents an important factor in the use of structural systems. This is known asstructural health monitoring (SHM. Several methods, the most of them visuals, are employed. These require a direct accessto the possible damaged zone an can be very time consuming and costly, a reason by which the researchers in this field arefocused in others method, like modal analysis. The wavelet transform is also a very powerful toll in crack detection in earlystages. This problem should be studied in a practical and theoretical way. In this paper a transverse closed crack isrepresented in an Euler-Bernoulli beam, as Bovsunovsky. Continuous and finite element models are used, and the wavelettransform is applied to the eigenmodes.Key words: crack detection, wavelet, modal analysis.
Estudo comparativo sobre filtragem de sinais instrumentais usando transformadas de Fourier e Wavelet
Directory of Open Access Journals (Sweden)
Galvão Roberto Kawakami Harrop
2001-01-01
Full Text Available A comparative study of the Fourier (FT and the wavelet transforms (WT for instrumental signal denoising is presented. The basic principles of wavelet theory are described in a succinct and simplified manner. For illustration, FT and WT are used to filter UV-VIS and plasma emission spectra using MATLAB software for computation. Results show that FT and WT filters are comparable when the signal does not display sharp peaks (UV-VIS spectra, but the WT yields a better filtering when the filling factor of the signal is small (plasma spectra, since it causes low peak distortion.
Energy Technology Data Exchange (ETDEWEB)
Jauregui Correa, Juan Carlos; Rubio Cerda, Eduardo; Gonzalez Brambila, Oscar [CIATEQ, A.C., Queretaro (Mexico)
2007-11-15
The modern processes of signal analysis that measure mechanical vibrations are based on the fast transform of Fourier (FFT), nevertheless, this method is not able to identify transient phenomena nor of nonlinear nature. Although many efforts have been made to try to identify these phenomena in the frequency spectra, it is not possible to correlate the spectra with the physical characteristics of this type of phenomena. Within these phenomena on the rubbing of a rotor against the housing or trunnion of a bearing, this phenomenon has a nonlinear behavior, as it is demonstrated in this paper. In the first part a method based on the of signal analysis type wavelets is presented and how this technique can be used to predict transient and nonlinear phenomena. Once defined the method, its application in the identification of the friction of rotors is demonstrated. With this, one demonstrates that the method presented in this paper allows to also identifying in real time the rubbing phenomenon and also that it can be used as an of analysis technique in the preventive maintenance systems. [Spanish] Los procesos modernos de analisis de senales que miden vibraciones mecanicas se basan en la transformada rapida de Fourier (FFT por sus siglas en ingles), sin embargo, este metodo no es capaz de identificar fenomenos transitorios ni de naturaleza no lineal. A pesar de que se han hecho muchos esfuerzos para tratar de identificar estos fenomenos en los espectros de frecuencia, no es posible correlacionar el espectro con las caracteristicas fisicas de este tipo de fenomenos. Dentro de estos fenomenos sobre el rozamiento de un rotor contra la carcasa o munon de una chumacera, este fenomeno tiene un comportamiento no lineal, como se demuestra en este trabajo. En la primera parte se presenta un metodo basado en el analisis de senales tipo wavelets y como esta tecnica puede utilizarse para predecir fenomenos transitorios y no lineales. Una vez definido el metodo, se demuestra su
Directory of Open Access Journals (Sweden)
Daniel José Isernia‐Trebols
2011-01-01
Full Text Available Se presenta el Análisis Modal realizado a varios Modelos de Elementos Finitos de una armaduraplana con el objeto de detectar la presencia de una grieta transversal en uno de sus elementos apartir del análisis de los desplazamientos nodales del primer modo a flexión por medio de latransformada de Wavelet. La grieta es representada por una zona de rigidez reducida calculada porun proceso similar al desarrollado por Bovsunovsky y Matveev para una viga en voladizo en base acriterios de la energía de deformación en la punta de la grieta. Se probaron varias funciones motherwavelet para encontrar la mas adecuada para detectar discontinuidades producto de la grieta. Losresultados teóricos muestran que es posible detectar una grieta transversal parcial en una armaduraplana con la transformada wavelet.Palabras claves: pórticos planos, grietas, análisis modal, transformada wavelet.___________________________________________________________________AbstractThe results of a modal analysis performed in several finite elements models of a planar truss with andwithout a partial crack, and crack detection by the wavelet transform of nodal displacements in thefirst mode are shown. The crack is represented by a zone with reduced bending stiffness, followingthe idea developed by A P. Bovsunovsky and V. V. Matveev for a cantilever beam, using the conceptof strain energy in the crack tip. Several mother wavelet were used in order to find the mostappropriated for the detection of discontinuities produced by the crack, and its usefulness in crackdetection is probed.Key words: planar truss, crack detection, modal analysis, wavelet transform.
Directory of Open Access Journals (Sweden)
Miguel Martínez
2009-01-01
Full Text Available En el presente documento, se presenta un algoritmo que permite distinguir de forma clara y precisa, las faltas transitorias de las permanentes. Adicionalmente se determina el instante de extinción del arco secundario, para así evitar o controlar de forma efectiva y segura la operación de reconexión monofásica en sistemas de transporte de energía. El método de identificación se basa en la determinación de las características de alta frecuencia que posee la señal de tensión de la fase en falta antes del despeje y de la corriente en una fase sana y las relaciona de forma independiente mediante una auto-correlación cruzada. Para el análisis de la señal y extracción de los componentes de alta frecuencia, se ha utilizado la transformada Wavelet. El algoritmo propuesto se probó en un sistema base de 380 kV, funcionando de forma correcta en todos los escenarios planteados y logrando una identificación precisa en los primeros 25 ms de la falta.
Aplicación de las transformada Wavelet en la interpretación de cortes sísmicos de reflexión
Directory of Open Access Journals (Sweden)
Margarita Fernández-Limia
2001-03-01
Full Text Available El reconocimiento estadístico de patrones sísmicos de reflexión desempeña un papel muy importante en la interpretación de estos datos. El mismo se basa en la comparación de estos patrones con otros de referencia, correspondientes a zonas bien estudiadas desde el punto de vista geológico y que se caracterizan por una clase de textura dada. En este trabajo se muestran los resultados de la segmentación supervisada de un corte sísmico de reflexión correspondiente a una región de Cuba. Dicha segmentación se llevó a cabo empleando el método de la transformada wavelet packet, utilizando la base ortogonal Daubechies-2 y la distancia de Mahalanobis como función de discriminación. Los descriptores de textura se obtuvieron a base de los coeficientes de esta transformación. Los patrones de referencia fueron tomados en correspondencia con zonas de diversas texturas, ubicadas a diferentes profundidades a lo largo de un pozo perforado en la región. El resultado de la segmentación constituye una valiosa ayuda para realizar la calibración geológica de las reflexiones sísmicas en el área investigada. Una vez identificadas las zonas texturales correspondientes a cuatro patrones de referencia, se realizó una segunda segmentación, tomando en este caso un solo patrón, correspondiente a una de las zonas del pozo con características de interés para la exploración petrolera. El trabajo demuestra que la técnica empleada en la interpretación de cortes sísmicos, la cual proporciona incluso una valoración cuantitativa de los mismos, puede constituir una herramienta adicional para los especialistas, que les permita complementar y comprobar sus métodos de prospección y análisis.
Directory of Open Access Journals (Sweden)
Aballe, Alvaro
1999-12-01
Full Text Available The most important difficulty to use electrochemical noise measurement resides in the interpretation of the experimental data. In the literature, three different main approaches have been proposed for processing the experimental records: the statistical, the spectral and the Chaos-Theory based methods. The purpose of this work is to introduce an alternative tool to analyse electrochemical noise: the wavelet transform. It has been found that this tool could be especially usefully to distinguish localised and uniform corrosion processes that are developing simultaneously. To show that, a current noise record corresponding to working electrodes suffering from both kind of corrosion process is analysed by means of the wavelet transform. These results are compared with the ones coming form the Fourier. In addition, a brief theoretical description of the tool is presented and some applications in the field of the industrial monitoring are suggested.
La dificultad más importante para usar la medida del ruido electroquímico reside en la interpretación de los datos experimentales. En la literatura, se han propuesto básicamente tres aproximaciones diferentes para procesar los registros experimentales: la estadística, la espectral y los métodos basados en la teoría del caos. El propósito de este trabajo es introducir una herramienta alternativa para el análisis del ruido electroquímico: la transformada de wavelets. Se ha encontrado que esta herramienta podría ser especialmente útil para distinguir procesos localizados y uniformes que se desarrollan simultáneamente. Con este objetivo, se han analizado mediante la transformada de wavelets registros de ruido de corriente correspondientes a electrodos de trabajo que sufren ambos tipos de procesos de corrosión. Estos resultados son comparados con los obtenidos al aplicar la transformada de Fourier. Además, se presenta brevemente una descripción teórica de la herramienta y
Martingalas discretas. Aplicaciones
Directory of Open Access Journals (Sweden)
Miguel A. Marmolejo
2009-12-01
Full Text Available Debido a su amplio rango de aplicaciones, la teoría de las martingalas es parte fundamental de la probabilidad. En este artículo se presentan las nociones básicas de las martingalas discretas y se recopilan algunas de sus aplicaciones en probabilidad y análisis, dando idea de los diferentes contextos donde se usan.
Directory of Open Access Journals (Sweden)
Rubén Medina
2014-05-01
Full Text Available En este artículo se implementa un nuevo servicio en la Web que ofrece a los usuarios la posibilidad de realizar la fusión de imágenes de satélite provenientes de diferentes sensores remotos y/o con diferentes resoluciones espaciales. A lo largo del artículo tres temáticas importantes son abordadas. La primera temática corresponde al servicio Web, éste servicio es implementado usando software libre y cuenta con una sencilla interfaz donde el usuario puede interactuar y principalmente puede realizar una solicitud del servicio de fusión. Adicionalmente, en la aplicación Web se desarrolló un módulo que permite obtener datos georreferenciados de diferentes fuentes externas para crear un nuevo servicio (Mashup a través de las API’s, de manera rápida y fácil utilizando OpenStreetMaps. La segunda temática se ocupa del análisis de la transformada rápida de wavelet haar (TRWH, estos conceptos matemáticos se abordan a partir de un ejemplo usando una matriz que se descompone en coeficientes de detalle y de aproximación de segundo nivel. La última temática detalla la metodología propuesta, paso a paso, para realizar la fusión de imágenes usando la TRWH. Igualmente con el fin de determinar la eficiencia de la TRWH cinco wavelets diferentes fueron implementadas en Matlab para fusionar el mismo par de imágenes satelitales. Las imágenes resultantes fueron evaluadas tanto en la calidad espacial como en la espectral a través de cuatro índices. Los mejores resultados de la evaluación fueron obtenidos con la TRWH la cual preserva la riqueza espectral de la imagen multiespectral original y mejora su calidad espacial.
Vázquez Méndez, Miguel Ernesto
2013-01-01
A transformada de Laplace é un método de gran eficiencia á hora de resolver un certo tipo de ecuacións diferenciais e integrais. Ademais do interese que pode ter desde o punto de vista puramente matemático, constitúe unha ferramenta básica na enxeñaría de control moderna e resulta especialmente útil á hora de resolver determinados problemas de valor inicial con termos non homoxéneos de natureza descontinua ou impulsiva. Este tipo de problemas aparece con relativa frecuencia en enxeñarí...
Energy Technology Data Exchange (ETDEWEB)
Masotti, Paulo Henrique Ferraz
2006-07-01
The monitoring and diagnosis area is presenting an impressive development in recent years with the introduction of new diagnosis techniques as well as with the use the computers in the processing of the information and of the diagnosis techniques. The contribution of the artificial intelligence in the automation of the defect diagnosis is developing continually and the growing automation in the industry meets this new techniques. In the nuclear area, the growing concern with the safety in the facilities requires more effective techniques that have been sought to increase the safety level. Some nuclear power stations have already installed in some machines, sensors that allow the verification of their operational conditions. In this way, the present work can also collaborate in this area, helping in the diagnosis of the operational condition of the machines. This work presents a new technique for characteristic extraction based on the Zero Crossing of Wavelet Transform, contributing with the development of this dynamic area. The technique of artificial intelligence was used in this work the Paraconsistent Logic of Annotation with Two values (LPA2v), contributing with the automation of the diagnosis of defects, because this logic can deal with contradictory results that the techniques of feature extraction can present. This work also concentrated on the identification of defects in its initial phase trying to use accelerometers, because they are robust sensors, of low cost and can be easily found the industry in general. The results obtained in this work were accomplished through the use of an experimental database, and it was observed that the results of diagnoses of defects shown good results for defects in their initial phase. (author)
Directory of Open Access Journals (Sweden)
Márcia Werlang
2014-01-01
Full Text Available Partial least squares (PLS calibration models were compared for the determination of biodiesel and vegetable oil in different blends using infrared spectra data (FTIR/ATR. Eighty binary and ternary blends containing biodiesel, vegetable oil and diesel were made, 48 were employed to compose the calibration set and 32 for prediction set. Initially the spectral signals of samples containing biodiesel and vegetable oil between 0 and 8 %(v/v were compressed using Discrete Wavelet Transform. The best models with compressed or uncompressed signals were compared, using the interval partial least squares algorithm (iPLS, also identifying which region of the infrared showed better correlation. The results using the compressed data showed similar errors in the determination of the vegetable oil and of the biodiesel in blends. The combination of the regression algorithm iPLS applied to data obtained by FTIR/ATR has shown considerable promise in the development of a simple, rapid and non-destructive method for the determination of adulteration with vegetable oil in biodiesel or biodiesel/diesel blends.
Antileishmanial and immunomodulatory activity of Xylopia discreta.
López, R; Cuca, L E; Delgado, G
2009-10-01
This study aimed at determining the in vitro antileishmanial activity of the essential oil and eight extracts obtained from Xylopia discreta. J774 and U937 macrophages were exposed to the different substances to establish the median lethal concentration (LC(50)). The median effective concentration (EC(50)) was obtained by determining the reduction of Leishmania panamensis-infected cells. A selectivity index (SI) (LC(50)/EC(50)) >or= 20 defined a specific activity for one Xylopia discreta leaf extracts and for the essential oil, being these the two that showed the highest activity (SI = 64.8 and 110, respectively in J774 cells). To assess the substances' immunomodulatory activity, pro- and anti-inflammatory soluble mediators produced after treating infected macrophages were quantified by flow cytometry. The leaf methanol extract and the essential oil induced a differential production of monocyte chemoattractant protein-1, a chemokine associated with a Leishmania-resistant phenotype (Th1).
Directory of Open Access Journals (Sweden)
Rómulo Pérez
2014-04-01
Full Text Available En este trabajo las mediciones recabadas por una estación experimental instalada en un Transformador de 100 MVA de la Subestación Barquisimeto de Venezuela son procesadas para eliminar factores de ruido que introducen errores en la identificación de parámetros del modelo térmico para el cálculo de la temperatura superior del aceite. Se usa una metodología para el control de calidad y eliminación del ruido en las mediciones recabadas basada en experiencias propias y reforzadas con experiencias de reconocidos autores internacionales, la cual aplica la Transformada Discreta de Wavelet DWT para obtener datos que muestran buenos indicadores de calidad en las principales variables del modelo térmico, como lo son la corriente de carga, la temperatura ambiente y la temperatura del aceite superior. Finalmente se comparan los resultados de la modelación térmica antes y después de ser procesados los datos, donde se evidencia un notable incremento en la exactitud del modelo. In this work measurement get of experimental station connected in a power transformer of 100 MVA in a Barquisimeto Substation in Venezuela are processing to eliminate noise that introduce mistake in the parameters identification for top oil temperature model calculation. A methodology based in your experiences with experiences of international authors for the control of quality and elimination of the noise in the successfully obtained measurements is used. It’s apply the Discreet Wavelet Transform (DWT to collect data that show good indicators of quality in the main values of the thermal model, as the load current, the ambient temperature and the top oil temperature. Finally is compared thermal model results after and beforedata processing, where at increase in the exactitude of the thermal model is demonstrated.
Directory of Open Access Journals (Sweden)
Fabrício Soares
2014-08-01
Full Text Available Este artigo apresenta o desenvolvimento de um modelo de predição de séries temporais financeiras com o uso da Rede Neural Artificial TLFN Distribuída (Time Lagged FeedForward Network - Rede Neural Alimentada para frente Atrasada no Tempo, treinada com o algoritmo backpropagation temporal e com o pré-processamento dos sinais de entrada realizado com as Transformadas Wavelets Discretas. A metodologia demonstra como a análise de multirresolução feita com o algoritmo piramidal de Mallat colaborou para o aumento da capacidade de generalização da rede neural, otimizando as previsões feitas pelo modelo implementado. Com a finalidade de demonstrar a eficácia desta metodologia, foi realizado um estudo de caso envolvendo a séries histórica de cotações das cotas, negociadas no mercado secundário, do Fundo de Investimento Imobiliário Almirante Barroso.
Aboufadel, Edward
1999-01-01
An accessible and practical introduction to wavelets. With applications in image processing, audio restoration, seismology, and elsewhere, wavelets have been the subject of growing excitement and interest over the past several years. Unfortunately, most books on wavelets are accessible primarily to research mathematicians. Discovering Wavelets presents basic and advanced concepts of wavelets in a way that is accessible to anyone with only a fundamental knowledge of linear algebra. The basic concepts of wavelet theory are introduced in the context of an explanation of how the FBI uses wavelets
DESARROLLO DEL PENSAMIENTO COMPUTACIONAL EN LA FORMACIÓN EN MATEMÁTICA DISCRETA
Directory of Open Access Journals (Sweden)
AUGUSTO FLORES P.
2011-12-01
Full Text Available Este documento se refiere al desarrollo del pensamiento computacional en la formación en matemáticas discretas. En primer lugar, se detallan cuatro componentes principales del pensamiento computacional: pensamiento abstracto, pensamiento lógico, pensamiento modelado y pensamiento constructivo. En segundo lugar, se describe parte del contenido de las matemáticas discretas, que tiene estrecha relación con el pensamiento computacional, a través de un ejemplo de aplicación correspondiente. Por último, se hace un mapeo de las unidades de conocimiento de las matemáticas discretas con los detalles subsecuentes del pensamiento computacional.
Cheng, Lizhi; Luo, Yong; Chen, Bo
2014-01-01
This book could be divided into two parts i.e. fundamental wavelet transform theory and method and some important applications of wavelet transform. In the first part, as preliminary knowledge, the Fourier analysis, inner product space, the characteristics of Haar functions, and concepts of multi-resolution analysis, are introduced followed by a description on how to construct wavelet functions both multi-band and multi wavelets, and finally introduces the design of integer wavelets via lifting schemes and its application to integer transform algorithm. In the second part, many applications are discussed in the field of image and signal processing by introducing other wavelet variants such as complex wavelets, ridgelets, and curvelets. Important application examples include image compression, image denoising/restoration, image enhancement, digital watermarking, numerical solution of partial differential equations, and solving ill-conditioned Toeplitz system. The book is intended for senior undergraduate stude...
Giesemann, Jens; Greiner, Martin; Lipa, Peter
1997-02-01
The generators of binary multiplicative cascade models with a non-overlapping branching structure are given by the Haar wavelets. We construct specific generalizations of these models for which any given wavelet represents the generators of the local cascade branchings. Such “wavelet cascades”, for which we calculate spatial correlation functions, have spatially overlapping branches and are therefore useful for modeling recombination effects in hierarchical branching processes.
Chan, Y T
1995-01-01
Since the study of wavelets is a relatively new area, much of the research coming from mathematicians, most of the literature uses terminology, concepts and proofs that may, at times, be difficult and intimidating for the engineer. Wavelet Basics has therefore been written as an introductory book for scientists and engineers. The mathematical presentation has been kept simple, the concepts being presented in elaborate detail in a terminology that engineers will find familiar. Difficult ideas are illustrated with examples which will also aid in the development of an intuitive insight. Chapter 1 reviews the basics of signal transformation and discusses the concepts of duals and frames. Chapter 2 introduces the wavelet transform, contrasts it with the short-time Fourier transform and clarifies the names of the different types of wavelet transforms. Chapter 3 links multiresolution analysis, orthonormal wavelets and the design of digital filters. Chapter 4 gives a tour d'horizon of topics of current interest: wave...
Wavelet applications in engineering electromagnetics
National Research Council Canada - National Science Library
Sarkar, Tapan; Salazar-Palma, Magdalena; Wicks, Michael C
2002-01-01
... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Road Map of the Book . . . . . . Introduction . . . . . . . . Why Use Wavelets? . . . . . . What Are Wavelets? . . . . . . What Is the Wavelet Transform? . . . Use...
Wavelets and Wavelet Packets on Quantum Computers
Klappenecker, Andreas
1999-01-01
We show how periodized wavelet packet transforms and periodized wavelet transforms can be implemented on a quantum computer. Surprisingly, we find that the implementation of wavelet packet transforms is less costly than the implementation of wavelet transforms on a quantum computer.
Certain problems concerning wavelets and wavelets packets
International Nuclear Information System (INIS)
Siddiqi, A.H.
1995-09-01
Wavelets is the outcome of the synthesis of ideas that have emerged in different branches of science and technology, mainly in the last decade. The concept of wavelet packets, which are superpositions of wavelets, has been introduced a couple of years ago. They form bases which retain many properties of wavelets like orthogonality, smoothness and localization. The Walsh orthornomal system is a special case of wavelet packet. The wavelet packets provide at our disposal a library of orthonormal bases, each of which can be used to analyze a given signal of finite energy. The optimal choice is decided by the entropy criterion. In the present paper we discuss results concerning convergence, coefficients, and approximation of wavelet packets series in general and wavelets series in particular. Wavelet packet techniques for solutions of differential equations are also mentioned. (author). 117 refs
A Parallel Watermarking application on a G
Edgar C. García Cano; Rabil Bassem S.; Robert Sabourin
2013-01-01
Debido al gran volumen de información que fluye a través de Internet, las marcas de agua se utilizan ampliamente para proteger la autenticidad e integridad de la información. La inserción y la extracción de marcas de agua se pueden hacer en el dominio espacial o de otros dominios de frecuencia, como la Transformada Discreta del Coseno (DCT) y la Transformada Discreta Wavelet (DWT). La inserción y la extracción en dominios como DCT tienen un gran costo computacional en comparación con los m...
Lecciones aprendidas en la impartición de la asignatura Matemáticas Discretas
Sánchez Ansola, Eduardo; Acosta Sánchez, Rolando; Rosete Suárez, Alejandro; Fernández Oliva, Perla
2016-01-01
En el presente trabajo se exponen las principales experiencias del colectivo de profesores que ha impartido la asignatura Matemáticas Discretas en la F acultad de Ingeniería Informática del “Instituto Superior Politécnico José Antonio Echeverría” (CUJAE) durante el período que comprende los cursos desde 2007-2008 hasta 2014-2015. Los cursos han sido concebidos hacia la búsqueda de un aprendizaje desarrollador por parte del estudiante, su formación como un profesional capaz de aprender por sí ...
National Research Council Canada - National Science Library
Dinh, H
2001-01-01
...%. This is achieved with the use of Cubic Spline wavelet- a biorthogonal third order wavelet. This paper reports that the use of wavelets reduces the error in detection of QRS complexes and that wavelet functions that support symmetry and compactness provide better results.
Energy Technology Data Exchange (ETDEWEB)
Vieira, Fabio P.B.; Bevilacqua, Joyce S., E-mail: fpbvieira@gmail.com, E-mail: joyce.bevilacqua@gmail.com [Universidade de Sao Paulo (IME/USP), Sao Paulo, SP (Brazil). Instituto de Matematica e Estatistica; Rodrigues Junior, Orlando, E-mail: rodrijr@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)
2014-07-01
The use of electron paramagnetic resonance spectrometers - EPR - in radiation dosimetry is known for more than four decades. It is an important tool in the retrospective determination of doses absorbed. To estimate the dose absorbed by the sample, it is necessary to know the amplitude of the peak to peak signature of the substance in its EPR spectrum. This information can be compromised by the presence of spurious information: noise - of random and low intensity nature; and the behavior of the baseline - coming from the coupling between the resonator tube and the sample analyzed. Due to the intrinsic characteristics of the three main components of the signal, i.e. signature, noise, and baseline - the analysis in the frequency domain allows, through post-processing techniques to filter spurious information. In this work, an algorithm that retrieves the signature of a substance has been implemented. The Discrete Fourier Transform is applied to the signal and without user intervention, the noise is filtered. From the filtered signal, recovers the signature by Inverse Discrete Fourier Transform. The peak to peak amplitude, and the absorbed dose is calculated with an error of less than 1% for signals wherein the base line is linearized. Some more general cases are under investigation and with little user intervention, you can get the same error.
Equações discretas no ensino médio: modelos de dinâmicas populacionais
Maligeri, Glaucía Cristina Alecci Meneghim [UNESP
2013-01-01
As equações discretas fornecem as ferramentas matemáticas básicas para a correta modelagem da dinâmica populacional ao se tomar como hipótese tempos discretos. Neste trabalho, apresentamos a teoria das equações discretas e algumas aplicações no capítulo 01, dando ênfase aos modelos de dinâmica populacional conhecidos como modelo de Malthus e de Verhulst junto com exemplos que evidenciam tais modelos na modelagem de populações reais. No capítulo 02 apresentamos duas propostas didáticas; a prim...
Análisis del electroencefalograma con transformada de fourier y modelos paramétricos
Delgado R., J. Alberto
2011-01-01
El análisis tradicional del electroencefalograma (EEG) se realiza en el tiempo. Para ello se selecciona una sección del registro donde la contaminación es baja y posteriormente se cuenta el número de picos. Con el propósito de mejorar el estudio del EEG reduciendo el factor subjetivo, se han aplicado dos técnicas para el análisis espectral. La primera se refiere a la Transformada de Fourier y el periodograma modificado; la segunda ajusta un modelo Autorregresivo (AR) al EEG y con base ...
Correlation Structure of Wavelet Cascades
Greiner, Martin; Giesemann, Jens
The following sections are included: * Introduction * Some Basics about Wavelets * Multiresolution analysis * Dilation equations * Wavelet transformation * Multiplicative Haar-Wavelet Cascade * Binary random multiplicative branching processes * n-point correlation densities * Haar-wavelet transformed correlation densities * Daubechies-wavelet transformed correlation densities * Multiplicative Daubechies-Wavelet Cascade * Random multiplicative branching processes on a D4-wavelet tree * n-point correlation densities * Wavelet transformed correlation densities * Scaling behavior of moments * Conclusion * REFERENCES
Energy Technology Data Exchange (ETDEWEB)
Szu, H.; Hsu, C. [Univ. of Southwestern Louisiana, Lafayette, LA (United States)
1996-12-31
Human sensors systems (HSS) may be approximately described as an adaptive or self-learning version of the Wavelet Transforms (WT) that are capable to learn from several input-output associative pairs of suitable transform mother wavelets. Such an Adaptive WT (AWT) is a redundant combination of mother wavelets to either represent or classify inputs.
Análisis del electroencefalograma con transformada de Fourier y modelos paramétricos
Directory of Open Access Journals (Sweden)
J. Alberto Delgado R.
1991-03-01
Full Text Available El análisis tradicional del electroencefalograma (EEG se realiza en el tiempo. Para ello se selecciona una sección del registro donde la contaminación es baja y posteriormente se cuenta el número de picos. Con el propósito de mejorar el estudio del EEG reduciendo el factor subjetivo, se han aplicado dos técnicas para el análisis espectral. La primera se refiere a la Transformada de Fourier y el periodograma modificado; la segunda ajusta un modelo Autorregresivo (AR al EEG y con base en este se obtiene un estimador de la densidad espectral. Adicionalmente, los parámetros del AR permiten monitorear la evolución del EEG.Instructor Asistente Ingenlerla Eléctrica
Lecciones aprendidas en la impartición de la asignatura Matemáticas Discretas
Directory of Open Access Journals (Sweden)
Sánchez Ansola, Eduardo
2016-06-01
Full Text Available En el presente trabajo se exponen las principales experiencias del colectivo de profesores que ha impartido la asignatura Matemáticas Discretas en la F acultad de Ingeniería Informática del “Instituto Superior Politécnico José Antonio Echeverría” (CUJAE durante el período que comprende los cursos desde 2007-2008 hasta 2014-2015. Los cursos han sido concebidos hacia la búsqueda de un aprendizaje desarrollador por parte del estudiante, su formación como un profesional capaz de aprender por sí mismo, así como hacia su desarrollo integral como futuro profesional de la Ingeniería Informática. Para ello, se han tenido en cuenta las categorías fundamentales de la didáctica desarrolladora. Además, se ha considerado la integración de la asignatura con el resto de las asignaturas del año y su tributo a las asignaturas de años superiores. Al finalizar el trabajo se muestran los resultados docentes alcanzados por los estudiantes en los cursos en cuestión y algunos análisis correspondientes a estos resultados. Estos análisis permiten obtener una retroalimentación de lo acaecido en estos cursos y así, mejorar la asignatura para los siguientes cursos.
Directory of Open Access Journals (Sweden)
Arillo, A.
1998-12-01
Full Text Available In this paper the chaetotaxy of the legs of a member of the subfamily Antilloppiinae (Neoppia discreta Ruiz, Mínguez et Subías, 1988 is studied for the first time.En el presente trabajo se realiza por primera vez el estudio de la quetotaxia de las patas de un miembro de la subfamilia Antilloppiinae, Neoppia discreta Ruiz, Mínguez et Subías, 1988.
Hramov, Alexander E; Makarov, Valeri A; Pavlov, Alexey N; Sitnikova, Evgenia
2015-01-01
This book examines theoretical and applied aspects of wavelet analysis in neurophysics, describing in detail different practical applications of the wavelet theory in the areas of neurodynamics and neurophysiology and providing a review of fundamental work that has been carried out in these fields over the last decade. Chapters 1 and 2 introduce and review the relevant foundations of neurophysics and wavelet theory, respectively, pointing on one hand to the various current challenges in neuroscience and introducing on the other the mathematical techniques of the wavelet transform in its two variants (discrete and continuous) as a powerful and versatile tool for investigating the relevant neuronal dynamics. Chapter 3 then analyzes results from examining individual neuron dynamics and intracellular processes. The principles for recognizing neuronal spikes from extracellular recordings and the advantages of using wavelets to address these issues are described and combined with approaches based on wavelet neural ...
Skopina, Maria; Protasov, Vladimir
2016-01-01
This book presents a systematic study of multivariate wavelet frames with matrix dilation, in particular, orthogonal and bi-orthogonal bases, which are a special case of frames. Further, it provides algorithmic methods for the construction of dual and tight wavelet frames with a desirable approximation order, namely compactly supported wavelet frames, which are commonly required by engineers. It particularly focuses on methods of constructing them. Wavelet bases and frames are actively used in numerous applications such as audio and graphic signal processing, compression and transmission of information. They are especially useful in image recovery from incomplete observed data due to the redundancy of frame systems. The construction of multivariate wavelet frames, especially bases, with desirable properties remains a challenging problem as although a general scheme of construction is well known, its practical implementation in the multidimensional setting is difficult. Another important feature of wavelet is ...
Wavelets, vibrations and scalings
Meyer, Yves
1997-01-01
Physicists and mathematicians are intensely studying fractal sets of fractal curves. Mandelbrot advocated modeling of real-life signals by fractal or multifractal functions. One example is fractional Brownian motion, where large-scale behavior is related to a corresponding infrared divergence. Self-similarities and scaling laws play a key role in this new area. There is a widely accepted belief that wavelet analysis should provide the best available tool to unveil such scaling laws. And orthonormal wavelet bases are the only existing bases which are structurally invariant through dyadic dilations. This book discusses the relevance of wavelet analysis to problems in which self-similarities are important. Among the conclusions drawn are the following: 1) A weak form of self-similarity can be given a simple characterization through size estimates on wavelet coefficients, and 2) Wavelet bases can be tuned in order to provide a sharper characterization of this self-similarity. A pioneer of the wavelet "saga", Meye...
Wavelets in scientific computing
DEFF Research Database (Denmark)
Nielsen, Ole Møller
1998-01-01
the FWT can be used as a front-end for efficient image compression schemes. Part II deals with vector-parallel implementations of several variants of the Fast Wavelet Transform. We develop an efficient and scalable parallel algorithm for the FWT and derive a model for its performance. Part III...... supported wavelets in the context of multiresolution analysis. These wavelets are particularly attractive because they lead to a stable and very efficient algorithm, namely the fast wavelet transform (FWT). We give estimates for the approximation characteristics of wavelets and demonstrate how and why...... is an investigation of the potential for using the special properties of wavelets for solving partial differential equations numerically. Several approaches are identified and two of them are described in detail. The algorithms developed are applied to the nonlinear Schrödinger equation and Burgers' equation...
The Discrete Wavelet Transform
1991-06-01
B-1 ,.iii FIGURES 1.1 A wavelet filter bank structure ..................................... 2 2.1 Diagram illustrating the dialation and...abstract decompositions of discrete time series. Their wide sweeping significance, however, lies in their interpretation as wavelet transforms. In a general...parameter transform wn in the scale- time plane. Following terminology to be intro- duced, wi is the (decimated) discrete wavelet transform. become the
Wavelet Analyses and Applications
Bordeianu, Cristian C.; Landau, Rubin H.; Paez, Manuel J.
2009-01-01
It is shown how a modern extension of Fourier analysis known as wavelet analysis is applied to signals containing multiscale information. First, a continuous wavelet transform is used to analyse the spectrum of a nonstationary signal (one whose form changes in time). The spectral analysis of such a signal gives the strength of the signal in each…
Wavelet Enhanced Appearance Modelling
DEFF Research Database (Denmark)
Stegmann, Mikkel Bille; Forchhammer, Søren; Cootes, Timothy F.
2004-01-01
and computational requirements. This paper extends the previous work of Wolstenholme and Taylor where Haar wavelet coefficient subsets were modelled rather than pixel intensities. In addition to a detailed review of the method and a discussion of the integration into an AAM-framework, we demonstrate that the more...... recent bi-orthogonal CDF 9-7 wavelet offers advantages over the traditional Haar wavelet in terms of synthesis quality and accuracy. Further, we demonstrate that the inherent frequency separation in wavelets allows for simple band-pass filtering, e.g. edge-emphasis. Experiments using Haar and CDF 9......-7 wavelets on face images have shown that segmentation accuracy degrades gracefully with increasing compression ratio. Further, a proposed weighting scheme emphasizing edges was shown to be significantly more accurate at compression ratio 1:1, than a conventional AAM. At higher compression ratios the scheme...
Directory of Open Access Journals (Sweden)
Jorge Andrés Cuellar Gil
2011-05-01
Full Text Available Se trabajó con espectroscopía infrarroja transformada de Fourier (FT-IR para diferenciar diez especies de micobacterias. Mycobacterium intracelullare y M. fortuitum (ATCC, M.flavensces, M. smegmatis, M. chelone, M. gordonae, M. triviale, M. vaccae, M. terrae y M.nonchromogenicum (IP. Como control de diferenciación de género se incluyó Staphylococcusaureus, Streptococcus viridans y Streptococcus pyogenes, Klebsiella pneumoniae y Escherichia coli,cada especie se corrió por triplicado en KBr y ATR. Los espectros se analizaron según el método de diferenciación de componentes principales, y se realizaron derivadas deprimer orden (D1 en modalidad de transmisión usando la pastilla de KBr y la base ATR, además se diseñó una biblioteca espectral con la primera derivada de ATR. Lasensibilidad de detección fue de 100% al trabajar con KBr y el nivel de diferenciación fue de 100% en tres de cuatro muestras problema.
Nucleos reproductores discretos y una aplicación a las funciones elípticas discretas
García Arroyo, Mauricio
2006-01-01
La definición de una función analítica discreta fue propuesta originalmente por Jaqueline Ferrand (Lelong) en su artículo Fonctions préharmoniques et fonctions préholomorphes, publicado en 1944. Ahí se establecen las bases de la teoría en que se sustenta esta tesis, y que fue ampliamente desarrollada por R.J. Duffin en la década de los años 50.
Fractional Calculus and Shannon Wavelet
Directory of Open Access Journals (Sweden)
Carlo Cattani
2012-01-01
Full Text Available An explicit analytical formula for the any order fractional derivative of Shannon wavelet is given as wavelet series based on connection coefficients. So that for any 2(ℝ function, reconstructed by Shannon wavelets, we can easily define its fractional derivative. The approximation error is explicitly computed, and the wavelet series is compared with Grünwald fractional derivative by focusing on the many advantages of the wavelet method, in terms of rate of convergence.
Fang, Li-Zhi
1998-01-01
Recent advances have shown wavelets to be an effective, and even necessary, mathematical tool for theoretical physics. This book is a timely overview of the progress of this new frontier. It includes an introduction to wavelet analysis, and applications in the fields of high energy physics, astrophysics, cosmology and statistical physics. The topics are selected for the interests of physicists and graduate students of theoretical studies. It emphasizes the need for wavelets in describing and revealing structure in physical problems, which is not easily accomplishing by other methods.
Siddiqi, A. H.
2012-07-01
In this chapter, the role of wavelet methods applied to identification and characterization of oil reservoir is elaborated. The market rate of petroleum product is very much related to exploration, drilling and production cost. The main goal of researchers working in oil industry is to develop tools and techniques for minimizing cost of exploration and production. Efforts of researchers working in applications of wavelet methods in different parts of the world to achieve this goal is reviewed. Wavelet based solution of Buckley-Leverett equation modelling reservoir is discussed. Variants of Buckley-Leverett equations including its higher dimension versions are introduced. Wavelet methods for inverse problems associated with Buckley-Leverett equation, which are quite useful for oil recovery, are also explained in this chapter.
Blatter, Christian
1998-01-01
The Wavelet Transform has stimulated research that is unparalleled since the invention of the Fast Fourier Transform and has opened new avenues of applications in signal processing, image compression, radiology, cardiology, and many other areas. This book grew out of a short course for mathematics students at the ETH in Zurich; it provides a solid mathematical foundation for the broad range of applications enjoyed by the wavelet transform. Numerous illustrations and fully worked out examples enhance the book.
Haar wavelets with applications
Lepik, Ülo
2014-01-01
This is the first book to present a systematic review of applications of the Haar wavelet method for solving Calculus and Structural Mechanics problems. Haar wavelet-based solutions for a wide range of problems, such as various differential and integral equations, fractional equations, optimal control theory, buckling, bending and vibrations of elastic beams are considered. Numerical examples demonstrating the efficiency and accuracy of the Haar method are provided for all solutions.
Lecture notes on wavelet transforms
Debnath, Lokenath
2017-01-01
This book provides a systematic exposition of the basic ideas and results of wavelet analysis suitable for mathematicians, scientists, and engineers alike. The primary goal of this text is to show how different types of wavelets can be constructed, illustrate why they are such powerful tools in mathematical analysis, and demonstrate their use in applications. It also develops the required analytical knowledge and skills on the part of the reader, rather than focus on the importance of more abstract formulation with full mathematical rigor. These notes differs from many textbooks with similar titles in that a major emphasis is placed on the thorough development of the underlying theory before introducing applications and modern topics such as fractional Fourier transforms, windowed canonical transforms, fractional wavelet transforms, fast wavelet transforms, spline wavelets, Daubechies wavelets, harmonic wavelets and non-uniform wavelets. The selection, arrangement, and presentation of the material in these ...
Utilización de Simulación Discreta como Estrategia de Aprendizaje de Logística Empresarial
Zuluaga Mazo Abdul; Gómez Montoya, Rodrigo Andres; Cano Arenas, Jose Alejandro
2014-01-01
El artículo tiene como objetivo presentar una nueva metodología para el aprendizaje de gestión logística basado en la utilización de la herramienta computacional de simulación discreta. Como resultado de la investigación, se obtiene una metodología que facilita el aprendizaje de la gestión logística y centra el proceso de formación en los estudiantes. Para medir los resultados de la metodología se llevó a cabo un estudio con estudiantes de noveno semestre de Ingeniería Industrial, los cuales ...
Target recognition by wavelet transform
Li Zheng Dong; He Wu Liang; Pei Chun Lan; Peng Wen; SongChen; Zheng Xiao Dong
2002-01-01
Wavelet transform has an important character of multi-resolution power, which presents pyramid structure, and this character coincides the way by which people distinguish object from coarse to fineness and from large to tiny. In addition to it, wavelet transform benefits to reducing image noise, simplifying calculation, and embodying target image characteristic point. A method of target recognition by wavelet transform is provided
International Nuclear Information System (INIS)
Ludu, A.; Greiner, M.
1995-09-01
A non-linear associative algebra is realized in terms of translation and dilation operators, and a wavelet structure generating algebra is obtained. We show that this algebra is a q-deformation of the Fourier series generating algebra, and reduces to this for certain value of the deformation parameter. This algebra is also homeomorphic with the q-deformed su q (2) algebra and some of its extensions. Through this algebraic approach new methods for obtaining the wavelets are introduced. (author). 20 refs
Electromagnetic spatial coherence wavelets
International Nuclear Information System (INIS)
Castaneda, R.; Garcia-Sucerquia, J.
2005-10-01
The recently introduced concept of spatial coherence wavelets is generalized for describing the propagation of electromagnetic fields in the free space. For this aim, the spatial coherence wavelet tensor is introduced as an elementary amount, in terms of which the formerly known quantities for this domain can be expressed. It allows analyzing the relationship between the spatial coherence properties and the polarization state of the electromagnetic wave. This approach is completely consistent with the recently introduced unified theory of coherence and polarization for random electromagnetic beams, but it provides a further insight about the causal relationship between the polarization states at different planes along the propagation path. (author)
Monochromatic Electromagnetic Wavelets and the Huygens Principle
Sheng, Yunlong; Deschênes, Sylvain; Caulfield, H. John
1998-02-01
For the first time, to our knowledge, optical diffraction is shown to be a wavelet transform with the electromagnetic wavelets. We show that the optical wavelets proposed by Onural Opt. Lett. 18, 846 (1993) are the Huygens wavelets under a Fresnel approximation, and the electromagnetic wavelets proposed by Kaiser A Friendly Guide to Wavelets (Birkhauser, Boston, Mass., 1994) reduce to Hyugens wavelets in the case of a monochromatic field.
Wavelets theory, algorithms, and applications
Montefusco, Laura
2014-01-01
Wavelets: Theory, Algorithms, and Applications is the fifth volume in the highly respected series, WAVELET ANALYSIS AND ITS APPLICATIONS. This volume shows why wavelet analysis has become a tool of choice infields ranging from image compression, to signal detection and analysis in electrical engineering and geophysics, to analysis of turbulent or intermittent processes. The 28 papers comprising this volume are organized into seven subject areas: multiresolution analysis, wavelet transforms, tools for time-frequency analysis, wavelets and fractals, numerical methods and algorithms, and applicat
Wavelets in functional data analysis
Morettin, Pedro A; Vidakovic, Brani
2017-01-01
Wavelet-based procedures are key in many areas of statistics, applied mathematics, engineering, and science. This book presents wavelets in functional data analysis, offering a glimpse of problems in which they can be applied, including tumor analysis, functional magnetic resonance and meteorological data. Starting with the Haar wavelet, the authors explore myriad families of wavelets and how they can be used. High-dimensional data visualization (using Andrews' plots), wavelet shrinkage (a simple, yet powerful, procedure for nonparametric models) and a selection of estimation and testing techniques (including a discussion on Stein’s Paradox) make this a highly valuable resource for graduate students and experienced researchers alike.
WAVELET ANALYSIS OF ABNORMAL ECGS
Directory of Open Access Journals (Sweden)
Vasudha Nannaparaju
2014-02-01
Full Text Available Detection of the warning signals by the heart can be diagnosed from ECG. An accurate and reliable diagnosis of ECG is very important however which is cumbersome and at times ambiguous in time domain due to the presence of noise. Study of ECG in wavelet domain using both continuous Wavelet transform (CWT and discrete Wavelet transform (DWT, with well known wavelet as well as a wavelet proposed by the authors for this investigation is found to be useful and yields fairly reliable results. In this study, Wavelet analysis of ECGs of Normal, Hypertensive, Diabetic and Cardiac are carried out. The salient feature of the study is that detection of P and T phases in wavelet domain is feasible which are otherwise feeble or absent in raw ECGs.
Søgaard, Andreas
For the LHC Run 2 and beyond, experiments are pushing both the energy and the intensity frontier so the need for robust and efficient pile-up mitigation tools becomes ever more pressing. Several methods exist, relying on uniformity of pile-up, local correlations of charged to neutral particles, and parton shower shapes, all in $y − \\phi$ space. Wavelets are presented as tools for pile-up removal, utilising their ability to encode position and frequency information simultaneously. This allows for the separation of individual hadron collision events by angular scale and thus for subtracting of soft, diffuse/wide-angle contributions while retaining the hard, small-angle components from the hard event. Wavelet methods may utilise the same assumptions as existing methods, the difference being the underlying, novel representation. Several wavelet methods are proposed and their effect studied in simple toy simulation under conditions relevant for the LHC Run 2. One full pile-up mitigation tool (‘wavelet analysis...
Indian Academy of Sciences (India)
In the last decade, a new mathematical microscope has allowed scientists and engineers to view the details of time varying and transient phenomena, in a manner hitherto not possible through con- ventional tools. This invention, which goes by the name of wavelet transform, has created revolu- tionary changes in the areas ...
REYES ESTÉVEZ, L.R.; CASTELLANOS LEÓN, F.; LAGUNEZ RINERA, L.; PECH PÉREZ, A.
2011-01-01
Numerosas técnicas de control en el procesado de alimentos lácteos han sido estudiadas [1,2, 3]. Ello tiene relevancia en la aplicación de sistemas de certificación de higiene tales como HACCP. Las mediciones ultrasónicas tienen una clara ventaja al medir sin contacto fisico dichos alimentos y por lo tanto, reducir el riesgo de contaminación. En la producción de yogurt cambios bioquímicos modifican la densidad de la leche, afectando la velocidad de propagación de la onda ultrasónica [4, 5]. S...
Directory of Open Access Journals (Sweden)
D. Galán Martínez
2000-07-01
Full Text Available Una de las herramientas matemáticas más utilizadas en ingeniería en el estudio de los denominados sistemas de control dedatos muestreados es la transformada Z. La transformada Z como método operacional puede ser utilizada en la resoluciónde ecuaciones en diferencias finitas; las cuales formulan la dinámica de los sistemas de control de datos muestreados. Estatransformada juega un papel similar que el de la transformada de Laplace en el análisis de los sistemas de control de tiempocontinuo.El presente trabajo tiene como objetivo la confección de un programa para computadora digital, utilizando el asistentematemático DERIVE, para la determinación de la transformada Z inversa de una función algebraica racional, las cualesmodelan matemáticamente los sistemas de control de datos muestreados lineales que aparecen con mucha frecuencia en elestudio de los procesos de ingeniería.Palabras claves: Algoritmo, transformada Z, DERIVE, función algebraica racional, modelo matemático._______________________________________________________________________AbstractOne of the mathematical tools more used in engineering in the study of the denominated systems of data control samples isthe transformed Z. The transformed Z like as an operational method can be used in the resolution of equations in finitedifferences; which formulate the dynamics of the systems of data control samples. This transformed plays a similar paperthat the Laplace transformed in the analysis of the systems of control in continuous time.The present work has as objective the confection of a program for digital computer, using the mathematical assistantDERIVES, for the determination of the Z inverse transformed of a rational algebraic function, which model mathematicallythe systems of lineal data control samples that appear very frecuently in the study of the engineering processesKey words: algorithm, Z inverse transformed, Derives, Digital computer program, Rational
Wavelet frames and their duals
DEFF Research Database (Denmark)
Lemvig, Jakob
2008-01-01
This thesis is concerned with computational and theoretical aspects of wavelet frame analysis in higher dimensions and, in particular, with the study of so-called dual frames of wavelet frames. A frame is a system of "simple" functions or building blocks which deliver ways of analyzing signals....... The signals are then represented by linear combinations of the building blocks with coefficients found by an associated frame, called a dual frame. A wavelet frame is a frame where the building blocks are stretched (dilated) and translated versions of a single function; such a frame is said to have wavelet...... structure. The dilation of the wavelet building blocks in higher dimension is done via a square matrix which is usually taken to be integer valued. In this thesis we step away from the "usual" integer, expansive dilation and consider more general, expansive dilations. In most applications of wavelet frames...
Estimasi Regresi Wavelet Thresholding Dengan Metode Bootstrap
Suparti, Suparti; Mustofa, Achmad; Rusgiyono, Agus
2007-01-01
Wavelet is a function that has the certainly characteristic for example, it oscillate about zero point ascillating, localized in the time and frequency domain and construct the orthogonal bases in L2(R) space. On of the wavelet application is to estimate non parametric regression function. There are two kinds of wavelet estimator, i.e., linear and non linear wavelet estimator. The non linear wavelet estimator is called a thresholding wavelet rstimator. The application of the bootstrap method...
International Nuclear Information System (INIS)
Zahra, Noor e; Sevindir, Huliya A.; Aslan, Zafar; Siddiqi, A. H.
2012-01-01
The aim of this study is to provide emerging applications of wavelet methods to medical signals and images, such as electrocardiogram, electroencephalogram, functional magnetic resonance imaging, computer tomography, X-ray and mammography. Interpretation of these signals and images are quite important. Nowadays wavelet methods have a significant impact on the science of medical imaging and the diagnosis of disease and screening protocols. Based on our initial investigations, future directions include neurosurgical planning and improved assessment of risk for individual patients, improved assessment and strategies for the treatment of chronic pain, improved seizure localization, and improved understanding of the physiology of neurological disorders. We look ahead to these and other emerging applications as the benefits of this technology become incorporated into current and future patient care. In this chapter by applying Fourier transform and wavelet transform, analysis and denoising of one of the important biomedical signals like EEG is carried out. The presence of rhythm, template matching, and correlation is discussed by various method. Energy of EEG signal is used to detect seizure in an epileptic patient. We have also performed denoising of EEG signals by SWT.
Energy Technology Data Exchange (ETDEWEB)
Zahra, Noor e; Sevindir, Huliya A.; Aslan, Zafar; Siddiqi, A. H. [Sharda University, SET, Department of Electronics and Communication, Knowledge Park 3rd, Gr. Noida (India); University of Kocaeli, Department of Mathematics, 41380 Kocaeli (Turkey); Istanbul Aydin University, Department of Computer Engineering, 34295 Istanbul (Turkey); Sharda University, SET, Department of Mathematics, 32-34 Knowledge Park 3rd, Greater Noida (India)
2012-07-17
The aim of this study is to provide emerging applications of wavelet methods to medical signals and images, such as electrocardiogram, electroencephalogram, functional magnetic resonance imaging, computer tomography, X-ray and mammography. Interpretation of these signals and images are quite important. Nowadays wavelet methods have a significant impact on the science of medical imaging and the diagnosis of disease and screening protocols. Based on our initial investigations, future directions include neurosurgical planning and improved assessment of risk for individual patients, improved assessment and strategies for the treatment of chronic pain, improved seizure localization, and improved understanding of the physiology of neurological disorders. We look ahead to these and other emerging applications as the benefits of this technology become incorporated into current and future patient care. In this chapter by applying Fourier transform and wavelet transform, analysis and denoising of one of the important biomedical signals like EEG is carried out. The presence of rhythm, template matching, and correlation is discussed by various method. Energy of EEG signal is used to detect seizure in an epileptic patient. We have also performed denoising of EEG signals by SWT.
Directory of Open Access Journals (Sweden)
Kristopher Chandía Valenzuela
2006-08-01
Full Text Available ABSTRACT As it is known, quantum inductive circuits with charge discreteness show Bloch-like oscillations in electrical current under a dc external voltage. In this paper, the effect of a superimposed ac voltage in the circuit is considered. The Shapiro effect is found to be related to the existence of resonance. Surprisingly, in the limit of low frequency (no resonance, the electrical averaged current exists and has always the same sign. Eventually this allows for an experimental method to measure discrete charge effect in quantum mesoscopic circuits.RESUMEN Es sabido que circuitos cuánticos inductivos con carga discreta, cuando se someten a un voltaje continuo externo, presentan oscilaciones de Bloch en la corriente. En este trabajo se considera, además, la superposición de un voltaje alterno en el circuito. El efecto Shapiro, relacionado con la existencia de resonancias, es encontrado de modo explícito. Sorprendentemente, en el límite de bajas frecuencias (sin resonancia la corriente eléctrica promediada existe y tiene siempre el mismo signo. Eventualmente, esto entrega un método experimental para medir los efectos de la discretización de la carga en circuitos cuánticos mesosocópicos.
Wavelet Analysis for Molecular Dynamics
2015-06-01
2480. 4. Ismail AE, Rutledge GC, Stephanopoulos G. Topological coarse graining of polymer chains using wavelet-accelerated Monte Carlo. I. Freely...jointed chains. J Chem Phys. 2005;122:234901. 5. Ismail AE, Stephanopoulos G, Rutledge GC. Topological coarse graining of polymer chains using wavelet
Noise reduction by wavelet thresholding
National Research Council Canada - National Science Library
Jansen, Maarten
2001-01-01
.... I rather present new material and own insights in the que stions involved with wavelet based noise reduction . On the other hand , the presented material does cover a whole range of methodologies, and in that sense, the book may serve as an introduction into the domain of wavelet smoothing. Throughout the text, three main properties show up ever again: spar...
A generalized wavelet extrema representation
Energy Technology Data Exchange (ETDEWEB)
Lu, Jian; Lades, M.
1995-10-01
The wavelet extrema representation originated by Stephane Mallat is a unique framework for low-level and intermediate-level (feature) processing. In this paper, we present a new form of wavelet extrema representation generalizing Mallat`s original work. The generalized wavelet extrema representation is a feature-based multiscale representation. For a particular choice of wavelet, our scheme can be interpreted as representing a signal or image by its edges, and peaks and valleys at multiple scales. Such a representation is shown to be stable -- the original signal or image can be reconstructed with very good quality. It is further shown that a signal or image can be modeled as piecewise monotonic, with all turning points between monotonic segments given by the wavelet extrema. A new projection operator is introduced to enforce piecewise inonotonicity of a signal in its reconstruction. This leads to an enhancement to previously developed algorithms in preventing artifacts in reconstructed signal.
Jameson, Leland
1996-01-01
Wavelets can provide a basis set in which the basis functions are constructed by dilating and translating a fixed function known as the mother wavelet. The mother wavelet can be seen as a high pass filter in the frequency domain. The process of dilating and expanding this high-pass filter can be seen as altering the frequency range that is 'passed' or detected. The process of translation moves this high-pass filter throughout the domain, thereby providing a mechanism to detect the frequencies or scales of information at every location. This is exactly the type of information that is needed for effective grid generation. This paper provides motivation to use wavelets for grid generation in addition to providing the final product: source code for wavelet-based grid generation.
An Introduction to Wavelet Theory and Analysis
Energy Technology Data Exchange (ETDEWEB)
Miner, N.E.
1998-10-01
This report reviews the history, theory and mathematics of wavelet analysis. Examination of the Fourier Transform and Short-time Fourier Transform methods provides tiormation about the evolution of the wavelet analysis technique. This overview is intended to provide readers with a basic understanding of wavelet analysis, define common wavelet terminology and describe wavelet amdysis algorithms. The most common algorithms for performing efficient, discrete wavelet transforms for signal analysis and inverse discrete wavelet transforms for signal reconstruction are presented. This report is intended to be approachable by non- mathematicians, although a basic understanding of engineering mathematics is necessary.
Multiscale Clock Ensembling Using Wavelets
2010-11-01
allows an energy decomposition of the signal as well, referred to as the wavelet variance. This variance is defined by ) var ()( 2 llX Wv (11...and it can be shown that for a very wide class of signals and for an appropriately chosen wavelet that ) var ()( 1 2 Xv l lX . One such...42 nd Annual Precise Time and Time Interval (PTTI) Meeting 527 MULTISCALE CLOCK ENSEMBLING USING WAVELETS Ken Senior Naval Center
Wavelet spectra of JACEE events
International Nuclear Information System (INIS)
Suzuki, Naomichi; Biyajima, Minoru; Ohsawa, Akinori.
1995-01-01
Pseudo-rapidity distributions of two high multiplicity events Ca-C and Si-AgBr observed by the JACEE are analyzed by a wavelet transform. Wavelet spectra of those events are calculated and compared with the simulation calculations. The wavelet spectrum of the Ca-C event somewhat resembles that simulated with the uniform random numbers. That of Si-AgBr event, however, is not reproduced by simulation calculations with Poisson random numbers, uniform random numbers, or a p-model. (author)
Iris Recognition Using Wavelet
Directory of Open Access Journals (Sweden)
Khaliq Masood
2013-08-01
Full Text Available Biometric systems are getting more attention in the present era. Iris recognition is one of the most secure and authentic among the other biometrics and this field demands more authentic, reliable and fast algorithms to implement these biometric systems in real time. In this paper, an efficient localization technique is presented to identify pupil and iris boundaries using histogram of the iris image. Two small portions of iris have been used for polar transformation to reduce computational time and to increase the efficiency of the system. Wavelet transform is used for feature vector generation. Rotation of iris is compensated without shifts in the iris code. System is tested on Multimedia University Iris Database and results show that proposed system has encouraging performance.
Wavelet transforms and their applications
Debnath, Lokenath
2015-01-01
This textbook is an introduction to wavelet transforms and accessible to a larger audience with diverse backgrounds and interests in mathematics, science, and engineering. Emphasis is placed on the logical development of fundamental ideas and systematic treatment of wavelet analysis and its applications to a wide variety of problems as encountered in various interdisciplinary areas. Numerous standard and challenging topics, applications, and exercises are included in this edition, which will stimulate research interest among senior undergraduate and graduate students. The book contains a large number of examples, which are either directly associated with applications or formulated in terms of the mathematical, physical, and engineering context in which wavelet theory arises. Topics and Features of the Second Edition: · Expanded and revised the historical introduction by including many new topics such as the fractional Fourier transform, and the construction of wavelet bases in various spaces ...
Wavelet theory and its applications
Energy Technology Data Exchange (ETDEWEB)
Faber, V.; Bradley, JJ.; Brislawn, C.; Dougherty, R.; Hawrylycz, M.
1996-07-01
This is the final report of a three-year, Laboratory-Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). We investigated the theory of wavelet transforms and their relation to Laboratory applications. The investigators have had considerable success in the past applying wavelet techniques to the numerical solution of optimal control problems for distributed- parameter systems, nonlinear signal estimation, and compression of digital imagery and multidimensional data. Wavelet theory involves ideas from the fields of harmonic analysis, numerical linear algebra, digital signal processing, approximation theory, and numerical analysis, and the new computational tools arising from wavelet theory are proving to be ideal for many Laboratory applications. 10 refs.
Wavelets and multiscale signal processing
Cohen, Albert
1995-01-01
Since their appearance in mid-1980s, wavelets and, more generally, multiscale methods have become powerful tools in mathematical analysis and in applications to numerical analysis and signal processing. This book is based on "Ondelettes et Traitement Numerique du Signal" by Albert Cohen. It has been translated from French by Robert D. Ryan and extensively updated by both Cohen and Ryan. It studies the existing relations between filter banks and wavelet decompositions and shows how these relations can be exploited in the context of digital signal processing. Throughout, the book concentrates on the fundamentals. It begins with a chapter on the concept of multiresolution analysis, which contains complete proofs of the basic results. The description of filter banks that are related to wavelet bases is elaborated in both the orthogonal case (Chapter 2), and in the biorthogonal case (Chapter 4). The regularity of wavelets, how this is related to the properties of the filters and the importance of regularity for t...
From Fourier analysis to wavelets
Gomes, Jonas
2015-01-01
This text introduces the basic concepts of function spaces and operators, both from the continuous and discrete viewpoints. Fourier and Window Fourier Transforms are introduced and used as a guide to arrive at the concept of Wavelet transform. The fundamental aspects of multiresolution representation, and its importance to function discretization and to the construction of wavelets is also discussed. Emphasis is given on ideas and intuition, avoiding the heavy computations which are usually involved in the study of wavelets. Readers should have a basic knowledge of linear algebra, calculus, and some familiarity with complex analysis. Basic knowledge of signal and image processing is desirable. This text originated from a set of notes in Portuguese that the authors wrote for a wavelet course on the Brazilian Mathematical Colloquium in 1997 at IMPA, Rio de Janeiro.
Wavelet Analysis of Protein Motion
BENSON, NOAH C.
2014-01-01
As high-throughput molecular dynamics simulations of proteins become more common and the databases housing the results become larger and more prevalent, more sophisticated methods to quickly and accurately mine large numbers of trajectories for relevant information will have to be developed. One such method, which is only recently gaining popularity in molecular biology, is the continuous wavelet transform, which is especially well-suited for time course data such as molecular dynamics simulations. We describe techniques for the calculation and analysis of wavelet transforms of molecular dynamics trajectories in detail and present examples of how these techniques can be useful in data mining. We demonstrate that wavelets are sensitive to structural rearrangements in proteins and that they can be used to quickly detect physically relevant events. Finally, as an example of the use of this approach, we show how wavelet data mining has led to a novel hypothesis related to the mechanism of the protein γδ resolvase. PMID:25484480
A wavelet phase filter for emission tomography
International Nuclear Information System (INIS)
Olsen, E.T.; Lin, B.
1995-01-01
The presence of a high level of noise is a characteristic in some tomographic imaging techniques such as positron emission tomography (PET). Wavelet methods can smooth out noise while preserving significant features of images. Mallat et al. proposed a wavelet based denoising scheme exploiting wavelet modulus maxima, but the scheme is sensitive to noise. In this study, the authors explore the properties of wavelet phase, with a focus on reconstruction of emission tomography images. Specifically, they show that the wavelet phase of regular Poisson noise under a Haar-type wavelet transform converges in distribution to a random variable uniformly distributed on [0, 2π). They then propose three wavelet-phase-based denoising schemes which exploit this property: edge tracking, local phase variance thresholding, and scale phase variation thresholding. Some numerical results are also presented. The numerical experiments indicate that wavelet phase techniques show promise for wavelet based denoising methods
Wavelet analysis for nonstationary signals
International Nuclear Information System (INIS)
Penha, Rosani Maria Libardi da
1999-01-01
Mechanical vibration signals play an important role in anomalies identification resulting of equipment malfunctioning. Traditionally, Fourier spectral analysis is used where the signals are assumed to be stationary. However, occasional transient impulses and start-up process are examples of nonstationary signals that can be found in mechanical vibrations. These signals can provide important information about the equipment condition, as early fault detection. The Fourier analysis can not adequately be applied to nonstationary signals because the results provide data about the frequency composition averaged over the duration of the signal. In this work, two methods for nonstationary signal analysis are used: Short Time Fourier Transform (STFT) and wavelet transform. The STFT is a method of adapting Fourier spectral analysis for nonstationary application to time-frequency domain. To have a unique resolution throughout the entire time-frequency domain is its main limitation. The wavelet transform is a new analysis technique suitable to nonstationary signals, which handles the STFT drawbacks, providing multi-resolution frequency analysis and time localization in a unique time-scale graphic. The multiple frequency resolutions are obtained by scaling (dilatation/compression) the wavelet function. A comparison of the conventional Fourier transform, STFT and wavelet transform is made applying these techniques to: simulated signals, arrangement rotor rig vibration signal and rotate machine vibration signal Hanning window was used to STFT analysis. Daubechies and harmonic wavelets were used to continuos, discrete and multi-resolution wavelet analysis. The results show the Fourier analysis was not able to detect changes in the signal frequencies or discontinuities. The STFT analysis detected the changes in the signal frequencies, but with time-frequency resolution problems. The wavelet continuos and discrete transform demonstrated to be a high efficient tool to detect
Asymptotic expansion of the wavelet transform with error term
Pathak, R S; Pathak, Ashish
2014-01-01
UsingWong's technique asymptotic expansion for the wavelet transform is derived and thereby asymptotic expansions for Morlet wavelet transform, Mexican Hat wavelet transform and Haar wavelet transform are obtained.
Signal Analysis by New Mother Wavelets
International Nuclear Information System (INIS)
Niu Jinbo; Qi Kaiguo; Fan Hongyi
2009-01-01
Based on the general formula for finding qualified mother wavelets [Opt. Lett. 31 (2006) 407] we make wavelet transforms computed with the newly found mother wavelets (characteristic of the power 2n) for some optical Gaussian pulses, which exhibit the ability to measure frequency of the pulse more precisely and clearly. We also work with complex mother wavelets composed of new real mother wavelets, which offer the ability of obtaining phase information of the pulse as well as amplitude information. The analogy between the behavior of Hermite-Gauss beams and that of new wavelet transforms is noticed. (general)
From Calculus to Wavelets: ANew Mathematical Technique
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 2; Issue 4. From Calculus to Wavelets: A New Mathematical Technique Wavelet Analysis Physical Properties. Gerald B Folland. General Article Volume 2 Issue 4 April 1997 pp 25-37 ...
Quasi-Wavelet Models for Atmospheric Turbulence
National Research Council Canada - National Science Library
Goedecke, George
2002-01-01
...). The "quasi-wavelet" (QW) model discussed in this paper is an attempt to develop a mathematical representation for the turbulence that more closely resembles this physical picture than Fourier modes or customary wavelets...
Vílchez Q., Enrique; González S., Elizabeth
2013-01-01
Resumen: Se presentan los resultados de una investigación de carácter descriptivo realizada sobre una muestra de 68 estudiantes inscritos en cursos vinculados con álgebra lineal y matemáticas discretas, específicamente en dos instituciones de enseñanza superior pública en Costa Rica: la Universidad Nacional y la Universidad de Costa Rica. El estudio pretendió analizar a través de la participación de tres grupos pilotos (dos de álgebra y uno de matemática discreta) el impacto en términos de e...
Vilchez, Enrique; Gonzáles, Elizabeth
2014-01-01
Resumen: Se presentan los resultados de una investigación de carácter descriptivo realizada sobre una muestra de 68 estudiantes inscritos en cursos vinculados con álgebra lineal y matemáticas discretas, específicamente en dos instituciones de enseñanza superior pública en Costa Rica: la Universidad Nacional y la Universidad de Costa Rica. El estudio pretendió analizar a través de la participación de tres grupos pilotos (dos de álgebra y uno de matemática discreta) el impacto en términos de e...
Directory of Open Access Journals (Sweden)
Manuel Guillermo Forero Vargas
2001-07-01
Full Text Available Las máscaras de convolución son útiles para extractar información relevante de una imagen. Este artículo presenta una técnica para la evaluación del efecto de estas máscaras a través de la transformada de Fourier, haciendo uso de su propiedad de convolución y su aplicación en la encriptación de imágenes.
Echeverry Correa, Julián David; López, Andrés Felipe; López, Juan Fernando
2007-01-01
Se presenta en este trabajo una metodología de caracterización basada en la representación tiempo frecuencia de las señales fonocardiográficas con el fin de hacer reconocimiento de valvulopatías cardíacas. La naturaleza de estas patologías las hace susceptibles a ser caracterizadas por medio de representaciones en el espacio conjunto tiempo-frecuencia. Se emplea la transformada de Gabor para llevar los registros a este tipo bidimensional de representación. Los porcentajes de clasificación, me...
Texture analysis using Gabor wavelets
Naghdy, Golshah A.; Wang, Jian; Ogunbona, Philip O.
1996-04-01
Receptive field profiles of simple cells in the visual cortex have been shown to resemble even- symmetric or odd-symmetric Gabor filters. Computational models employed in the analysis of textures have been motivated by two-dimensional Gabor functions arranged in a multi-channel architecture. More recently wavelets have emerged as a powerful tool for non-stationary signal analysis capable of encoding scale-space information efficiently. A multi-resolution implementation in the form of a dyadic decomposition of the signal of interest has been popularized by many researchers. In this paper, Gabor wavelet configured in a 'rosette' fashion is used as a multi-channel filter-bank feature extractor for texture classification. The 'rosette' spans 360 degrees of orientation and covers frequencies from dc. In the proposed algorithm, the texture images are decomposed by the Gabor wavelet configuration and the feature vectors corresponding to the mean of the outputs of the multi-channel filters extracted. A minimum distance classifier is used in the classification procedure. As a comparison the Gabor filter has been used to classify the same texture images from the Brodatz album and the results indicate the superior discriminatory characteristics of the Gabor wavelet. With the test images used it can be concluded that the Gabor wavelet model is a better approximation of the cortical cell receptive field profiles.
Energy Technology Data Exchange (ETDEWEB)
Zeng, X.; Yamazaki, K. [Tokyo Gakugei University, Tokyo (Japan); Oguchi, Y. [Hosei University, Tokyo (Japan)
1997-10-22
A study has been performed on wavelet analysis of seismic waves. In the wavelet analysis of seismic waves, there is a possibility that the results according to different wavelet functions may come out with great difference. The study has carried out the following analyses: an analysis of amplitude and phase using wavelet transform which uses wavelet function of Morlet on P- and S-waves generated by natural earthquakes and P-wave generated by an artificial earthquake, and an analysis using continuous wavelet transform, which uses a constitution of complex wavelet function constructed by a completely diagonal scaling function of Daubechies and the wavelet function. As a result, the following matters were made clear: the result of detection of abnormal components or discontinuity depends on the wavelet function; if the Morlet wavelet function is used to properly select angular frequency and scale, equiphase lines in a phase scalogram concentrate on the discontinuity; and the result of applying the complex wavelet function is superior to that of applying the wavelet function of Morlet. 2 refs., 5 figs.
Oversampling of wavelet frames for real dilations
DEFF Research Database (Denmark)
Bownik, Marcin; Lemvig, Jakob
2012-01-01
We generalize the Second Oversampling Theorem for wavelet frames and dual wavelet frames from the setting of integer dilations to real dilations. We also study the relationship between dilation matrix oversampling of semi-orthogonal Parseval wavelet frames and the additional shift invariance gain...
Handling Wavelet Expansions in numerical Methods
Metselaar, Arend Aalberthus Roeland
2002-01-01
Wavelet expansions have drawn a lot of attention in recent decades. Wavelets originate from signal analysis, and one of the purposes is data compression. The ability to compress data can also be used to reduce the amount of computation work in a numerical simulation.A family of wavelets forms a
Fundamental papers in wavelet theory
Walnut, David F
2006-01-01
This book traces the prehistory and initial development of wavelet theory, a discipline that has had a profound impact on mathematics, physics, and engineering. Interchanges between these fields during the last fifteen years have led to a number of advances in applications such as image compression, turbulence, machine vision, radar, and earthquake prediction. This book contains the seminal papers that presented the ideas from which wavelet theory evolved, as well as those major papers that developed the theory into its current form. These papers originated in a variety of journals from differ
Wavelet-based associative memory
Jones, Katharine J.
2004-04-01
Faces provide important characteristics of a person"s identification. In security checks, face recognition still remains the method in continuous use despite other approaches (i.e. fingerprints, voice recognition, pupil contraction, DNA scanners). With an associative memory, the output data is recalled directly using the input data. This can be achieved with a Nonlinear Holographic Associative Memory (NHAM). This approach can also distinguish between strongly correlated images and images that are partially or totally enclosed by others. Adaptive wavelet lifting has been used for Content-Based Image Retrieval. In this paper, adaptive wavelet lifting will be applied to face recognition to achieve an associative memory.
Wavelet series approximation using wavelet function with compactly ...
African Journals Online (AJOL)
The Wavelets generated by Scaling Function with Compactly Support are useful in various applications especially for reconstruction of functions. Generally, the computational process will be faster if Scaling Function support descends, so computational errors are summarized from one level to another level. In this article, the ...
Directory of Open Access Journals (Sweden)
García de los Salmones Sánchez, M.M.
2009-01-01
Full Text Available El presente trabajo trata de cuantificar el valor de marca y el posicionamiento de las entidades financieras en dos mercados diferentes: consumidores finales y microempresas. Así mismo, se busca identificar las dimensiones de imagen de mayor peso a la hora de elegir a una entidad como la de mejor valoración global. A través del modelo de elección discreta y del análisis del efecto halo ,se comprueba la importancia del trato personal al cliente como la variable de mayor peso en la elección de marca. Además, se observa la mayor intensidad del efecto halo para las cajas de ahorros en el caso de los consumidores particulares. Sin embargo, en el segmento de microempresas, el valor de marca es superior para los bancos.
A MATEMÁTICA DISCRETA E SUAS APLICAÇÕES: um modelo para conexão de sistemas computacionais em nuvem
Lozano, Abel; UERJ/UNIGRANRIO; Siqueira, Angelo; UNIGRANRIO; Jurkiewicz, Samuel; COPPE/UFRJ; Pinto, Valessa; UNIGRANRIO
2015-01-01
A Teoria dos Grafos é uma importante área da Matemática Discreta capaz de modelar e solucionar problemas reais, através do desenvolvimento de algoritmos eficientes. Esta teoria permite a construção das ideias básicas que permeiam os processos algorítmicos e é base da ciência da computação moderna. Além disso, é uma área da Matemática onde não são necessários grandes pré-requisitos, podendo abordar praticamente todos os conceitos básicos envolvidos em qualquer nível de ensino. Neste trabalho, ...
Optimal Wavelets for Speech Signal Representations
Directory of Open Access Journals (Sweden)
Shonda L. Walker
2003-08-01
Full Text Available It is well known that in many speech processing applications, speech signals are characterized by their voiced and unvoiced components. Voiced speech components contain dense frequency spectrum with many harmonics. The periodic or semi-periodic nature of voiced signals lends itself to Fourier Processing. Unvoiced speech contains many high frequency components and thus resembles random noise. Several methods for voiced and unvoiced speech representations that utilize wavelet processing have been developed. These methods seek to improve the accuracy of wavelet-based speech signal representations using adaptive wavelet techniques, superwavelets, which uses a linear combination of adaptive wavelets, gaussian methods and a multi-resolution sinusoidal transform approach to mention a few. This paper addresses the relative performance of these wavelet methods and evaluates the usefulness of wavelet processing in speech signal representations. In addition, this paper will also address some of the hardware considerations for the wavelet methods presented.
A Wavelet Perspective on the Allan Variance.
Percival, Donald B
2016-04-01
The origins of the Allan variance trace back 50 years ago to two seminal papers, one by Allan (1966) and the other by Barnes (1966). Since then, the Allan variance has played a leading role in the characterization of high-performance time and frequency standards. Wavelets first arose in the early 1980s in the geophysical literature, and the discrete wavelet transform (DWT) became prominent in the late 1980s in the signal processing literature. Flandrin (1992) briefly documented a connection between the Allan variance and a wavelet transform based upon the Haar wavelet. Percival and Guttorp (1994) noted that one popular estimator of the Allan variance-the maximal overlap estimator-can be interpreted in terms of a version of the DWT now widely referred to as the maximal overlap DWT (MODWT). In particular, when the MODWT is based on the Haar wavelet, the variance of the resulting wavelet coefficients-the wavelet variance-is identical to the Allan variance when the latter is multiplied by one-half. The theory behind the wavelet variance can thus deepen our understanding of the Allan variance. In this paper, we review basic wavelet variance theory with an emphasis on the Haar-based wavelet variance and its connection to the Allan variance. We then note that estimation theory for the wavelet variance offers a means of constructing asymptotically correct confidence intervals (CIs) for the Allan variance without reverting to the common practice of specifying a power-law noise type a priori. We also review recent work on specialized estimators of the wavelet variance that are of interest when some observations are missing (gappy data) or in the presence of contamination (rogue observations or outliers). It is a simple matter to adapt these estimators to become estimators of the Allan variance. Finally we note that wavelet variances based upon wavelets other than the Haar offer interesting generalizations of the Allan variance.
Online Wavelet Complementary velocity Estimator.
Righettini, Paolo; Strada, Roberto; KhademOlama, Ehsan; Valilou, Shirin
2018-02-01
In this paper, we have proposed a new online Wavelet Complementary velocity Estimator (WCE) over position and acceleration data gathered from an electro hydraulic servo shaking table. This is a batch estimator type that is based on the wavelet filter banks which extract the high and low resolution of data. The proposed complementary estimator combines these two resolutions of velocities which acquired from numerical differentiation and integration of the position and acceleration sensors by considering a fixed moving horizon window as input to wavelet filter. Because of using wavelet filters, it can be implemented in a parallel procedure. By this method the numerical velocity is estimated without having high noise of differentiators, integration drifting bias and with less delay which is suitable for active vibration control in high precision Mechatronics systems by Direct Velocity Feedback (DVF) methods. This method allows us to make velocity sensors with less mechanically moving parts which makes it suitable for fast miniature structures. We have compared this method with Kalman and Butterworth filters over stability, delay and benchmarked them by their long time velocity integration for getting back the initial position data. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Wavelet Approximation in Data Assimilation
Tangborn, Andrew; Atlas, Robert (Technical Monitor)
2002-01-01
Estimation of the state of the atmosphere with the Kalman filter remains a distant goal because of high computational cost of evolving the error covariance for both linear and nonlinear systems. Wavelet approximation is presented here as a possible solution that efficiently compresses both global and local covariance information. We demonstrate the compression characteristics on the the error correlation field from a global two-dimensional chemical constituent assimilation, and implement an adaptive wavelet approximation scheme on the assimilation of the one-dimensional Burger's equation. In the former problem, we show that 99%, of the error correlation can be represented by just 3% of the wavelet coefficients, with good representation of localized features. In the Burger's equation assimilation, the discrete linearized equations (tangent linear model) and analysis covariance are projected onto a wavelet basis and truncated to just 6%, of the coefficients. A nearly optimal forecast is achieved and we show that errors due to truncation of the dynamics are no greater than the errors due to covariance truncation.
Wavelet Primal Sketch Representation Using Marr Wavelet Pyramid and Its Reconstruction
D. Van De Ville M. Unser
2009-01-01
Based on the class of complex gradient Laplace operators we show the design of a non separable two dimensional wavelet basis from a single and analytically defined generator wavelet function. The wavelet decomposition is implemented by an efficient FFT based filterbank. By allowing for slight redundancy we obtain the Marr wavelet pyramid decomposition that features improved translation invariance and steerability. The link with Marr's theory of early vision is due to the replication of the es...
Modeling Network Traffic in Wavelet Domain
Directory of Open Access Journals (Sweden)
Sheng Ma
2004-12-01
Full Text Available This work discovers that although network traffic has the complicated short- and long-range temporal dependence, the corresponding wavelet coefficients are no longer long-range dependent. Therefore, a "short-range" dependent process can be used to model network traffic in the wavelet domain. Both independent and Markov models are investigated. Theoretical analysis shows that the independent wavelet model is sufficiently accurate in terms of the buffer overflow probability for Fractional Gaussian Noise traffic. Any model, which captures additional correlations in the wavelet domain, only improves the performance marginally. The independent wavelet model is then used as a unified approach to model network traffic including VBR MPEG video and Ethernet data. The computational complexity is O(N for developing such wavelet models and generating synthesized traffic of length N, which is among the lowest attained.
Cross wavelet analysis: significance testing and pitfalls
Directory of Open Access Journals (Sweden)
D. Maraun
2004-01-01
Full Text Available In this paper, we present a detailed evaluation of cross wavelet analysis of bivariate time series. We develop a statistical test for zero wavelet coherency based on Monte Carlo simulations. If at least one of the two processes considered is Gaussian white noise, an approximative formula for the critical value can be utilized. In a second part, typical pitfalls of wavelet cross spectra and wavelet coherency are discussed. The wavelet cross spectrum appears to be not suitable for significance testing the interrelation between two processes. Instead, one should rather apply wavelet coherency. Furthermore we investigate problems due to multiple testing. Based on these results, we show that coherency between ENSO and NAO is an artefact for most of the time from 1900 to 1995. However, during a distinct period from around 1920 to 1940, significant coherency between the two phenomena occurs.
Wavelet analysis of epileptic spikes
Latka, M; Kozik, A; West, B J; Latka, Miroslaw; Was, Ziemowit; Kozik, Andrzej; West, Bruce J.
2003-01-01
Interictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous, pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after long-term monitoring became common in investigation of epileptic patients. The traditional definition of a spike is based on its amplitude, duration, sharpness, and emergence from its background. However, spike detection systems built solely around this definition are not reliable due to the presence of numerous transients and artifacts. We use wavelet transform to analyze the properties of EEG manifestations of epilepsy. We demonstrate that the behavior of wavelet transform of epileptic spikes across scales can constitute the foundation of a relatively simple yet effective detection algorithm.
Bivariate hard thresholding in wavelet function estimation
Piotr Fryzlewicz
2007-01-01
We propose a generic bivariate hard thresholding estimator of the discrete wavelet coefficients of a function contaminated with i.i.d. Gaussian noise. We demonstrate its good risk properties in a motivating example, and derive upper bounds for its mean-square error. Motivated by the clustering of large wavelet coefficients in real-life signals, we propose two wavelet denoising algorithms, both of which use specific instances of our bivariate estimator. The BABTE algorithm uses basis averaging...
Multispectral Image Enhancement Through Adaptive Wavelet Fusion
2016-09-14
AFRL-AFOSR-UK-TR-2017-0005 Multispectral image enhancement through adaptive wavelet fusion Alexander Toet Nederlandse Organisatie voor Toegepast...image enhancement through adaptive wavelet fusion 5a. CONTRACT NUMBER 5b. GRANT NUMBER FA9550-15-1-0433 5c. PROGRAM ELEMENT NUMBER 61102F 6. AUTHOR(S...efficient. 15. SUBJECT TERMS adaptive wavelet fusion, Multispectral image enhancement, Multispectral image fusion, multiband image interpolation
Discrete frequency slice wavelet transform
Yan, Zhonghong; Tao, Ting; Jiang, Zhongwei; Wang, Haibin
2017-11-01
This paper introduces a new kind of Time-Frequency Representation (TFR) method called Discrete Frequency Slice Wavelet Transform (DFSWT). It is an improved version of Frequency Slice Wavelet Transform (FSWT). The previous researches on FSWT show that it is a new efficient TFR in an easy way without strict limitation as traditional wavelet theory. DFSWT as well as FSWT are defined directly in frequency domain, and still keep its properties in time-frequency domain as FSWT decomposition, reconstruction and filter design, etc. However, the original signal is decomposed and reconstructed on a Chosen Frequency Domains (CFD) as need of application. CFD means that the decomposition and reconstruction are not completed on all frequency components. At first, it is important to discuss the necessary condition of CFD to reconstruct the original signal. And then based on norm l2, an optimization algorithm is introduced to reconstruct the original signal even accurately. Finally, for a test example, the TFR analysis of a real life signal is shown. Some conclusions are drawn that the concept of CFD is very useful to application, and the DFSWT can become a simple and easy tool of TFR method, and also provide a new idea of low speed sampling of high frequency signal in applications.
Wavelets for Sparse Representation of Music
DEFF Research Database (Denmark)
Endelt, Line Ørtoft; Harbo, Anders La-Cour
2004-01-01
We are interested in obtaining a sparse representation of music signals by means of a discrete wavelet transform (DWT). That means we want the energy in the representation to be concentrated in few DWT coefficients. It is well-known that the decay of the DWT coefficients is strongly related...... to the number of vanishing moments of the mother wavelet, and to the smoothness of the signal. In this paper we present the result of applying two classical families of wavelets to a series of musical signals. The purpose is to determine a general relation between the number of vanishing moments of the wavelet...
Directory of Open Access Journals (Sweden)
Helberte João França Almeida
Full Text Available Resumo Elabora-se um modelo computacional de escolha ternária baseado em agentes para avaliar a evolução da distribuição de frequência de preditores de inflação. A cada período de reavaliação das estratégias de previsão, cada agente escolhe um dentre três preditores (estático, adaptativo e VAR para prever a inflação mensal. O processo de seleção de preditores é formalizado como uma dinâmica de escolha discreta baseada em dois atributos, a saber, acurácia menos o custo médio do preditor (atributos privados e habilidades cognitivas heterogêneas (dispersão nas habilidades cognitivas. O modelo computacional baseado em agentes calibrado apresenta persistência da heterogeneidade de expectativas inflacionárias, ou seja, preditores de inflação menos acurados acabam coexistindo com o preditor mais acurado devido à dispersão das habilidades cognitivas dos agentes.
Infinite matrices, wavelet coefficients and frames
Directory of Open Access Journals (Sweden)
N. A. Sheikh
2004-01-01
Full Text Available We study the action of A on f∈L2(ℝ and on its wavelet coefficients, where A=(almjklmjk is a double infinite matrix. We find the frame condition for A-transform of f∈L2(ℝ whose wavelet series expansion is known.
Application of wavelets in speech processing
Farouk, Mohamed Hesham
2014-01-01
This book provides a survey on wide-spread of employing wavelets analysis in different applications of speech processing. The author examines development and research in different application of speech processing. The book also summarizes the state of the art research on wavelet in speech processing.
Wavelet=Galerkin discretization of hyperbolic equations
Energy Technology Data Exchange (ETDEWEB)
Restrepo, J.M.; Leaf, G.K.
1994-12-31
The relative merits of the wavelet-Galerkin solution of hyperbolic partial differential equations, typical of geophysical problems, are quantitatively and qualitatively compared to traditional finite difference and Fourier-pseudo-spectral methods. The wavelet-Galerkin solution presented here is found to be a viable alternative to the two conventional techniques.
Some applications of wavelets to physics
International Nuclear Information System (INIS)
Thompson, C.R.
1992-01-01
A thorough description of a fast wavelet transform algorithm (FWT) and its inverse (IFWT) are given. The effects of noise in the wavelet transform are studied, in particular the effects on signal reconstruction. A model for additive white noise on the coefficients is presented along with two methods that can help to suppress the effects of noise corruption of the signal. Problems of improper sampling are studied, including the propagation of uncertainty through the FWT and IFWT. Interpolation techniques and data compression are also studied. The FWT and IFWT are generalized for analysis of two dimensional images. Methods for edge detection are discussed as well as contrast improvement and data compression. Finally, wavelets are applied to electromagnetic wave propagation problems. Formulas relating the wavelet and Fourier transforms are given, and expansions of time-dependent electromagnetic fields using both fixed and moving wavelet bases are studied
Wavelets and the Lifting Scheme
DEFF Research Database (Denmark)
la Cour-Harbo, Anders; Jensen, Arne
The objective of this article is to give a concise introduction to the discrete wavelet transform (DWT) based on a technique called lifting. The lifting technique allows one to give an elementary, but rigorous, definition of the DWT, with modest requirements on the reader. A basic knowledge...... of linear algebra and signal processing will suffice. The lifting based definition is equivalent to the usual filer bank based definition of the DWT. The article does not discuss applications in any detail. The reader is referred to other articles in this collection....
Complex Wavelet Based Modulation Analysis
DEFF Research Database (Denmark)
Luneau, Jean-Marc; Lebrun, Jérôme; Jensen, Søren Holdt
2008-01-01
Low-frequency modulation of sound carry important information for speech and music. The modulation spectrum i commonly obtained by spectral analysis of the sole temporal envelopes of the sub-bands out of a time-frequency analysis. Processing in this domain usually creates undesirable distortions...... polynomial trends. Moreover an analytic Hilbert-like transform is possible with complex wavelets implemented as an orthogonal filter bank. By working in an alternative transform domain coined as “Modulation Subbands”, this transform shows very promising denoising capabilities and suggests new approaches for joint...
Wavelets and the lifting scheme
DEFF Research Database (Denmark)
la Cour-Harbo, Anders; Jensen, Arne
2012-01-01
The objective of this article is to give a concise introduction to the discrete wavelet transform (DWT) based on a technique called lifting. The lifting technique allows one to give an elementary, but rigorous, definition of the DWT, with modest requirements on the reader. A basic knowledge...... of linear algebra and signal processing will suffice. The lifting based definition is equivalent to the usual filer bank based definition of the DWT. The article does not discuss applications in any detail. The reader is referred to other articles in this collection....
Wavelets and the lifting scheme
DEFF Research Database (Denmark)
la Cour-Harbo, Anders; Jensen, Arne
2009-01-01
The objective of this article is to give a concise introduction to the discrete wavelet transform (DWT) based on a technique called lifting. The lifting technique allows one to give an elementary, but rigorous, definition of the DWT, with modest requirements on the reader. A basic knowledge...... of linear algebra and signal processing will suffice. The lifting based definition is equivalent to the usual filer bank based definition of the DWT. The article does not discuss applications in any detail. The reader is referred to other articles in this collection....
Wavelet analysis of multifractal functions
Jaffard, Stephane
1995-09-01
Multifractal signals are characterized by a local Holder exponent that may change completely from point to point. We show that wavelet methods are an extremely efficient tool for determining the exact Holder exponent of a function, or at least, for getting some information about this Holder exponent, such as the Spectrum of Singularities. We construct functions that have a given Holder exponent in a deterministic setting and also in a probabilistic setting (we then obtain the Multifractional Brownian Motion); we also study the Multifractal Formalism for Functions and give some results about its validity.
Seamless multiresolution isosurfaces using wavelets
Energy Technology Data Exchange (ETDEWEB)
Udeshi, T.; Hudson, R.; Papka, M. E.
2000-04-11
Data sets that are being produced by today's simulations, such as the ones generated by DOE's ASCI program, are too large for real-time exploration and visualization. Therefore, new methods of visualizing these data sets need to be investigated. The authors present a method that combines isosurface representations of different resolutions into a seamless solution, virtually free of cracks and overlaps. The solution combines existing isosurface generation algorithms and wavelet theory to produce a real-time solution to multiple-resolution isosurfaces.
Wavelet Neural Network Using Multiple Wavelet Functions in Target Threat Assessment
Guo, Lihong; Duan, Hong
2013-01-01
Target threat assessment is a key issue in the collaborative attack. To improve the accuracy and usefulness of target threat assessment in the aerial combat, we propose a variant of wavelet neural networks, MWFWNN network, to solve threat assessment. How to select the appropriate wavelet function is difficult when constructing wavelet neural network. This paper proposes a wavelet mother function selection algorithm with minimum mean squared error and then constructs MWFWNN network using the above algorithm. Firstly, it needs to establish wavelet function library; secondly, wavelet neural network is constructed with each wavelet mother function in the library and wavelet function parameters and the network weights are updated according to the relevant modifying formula. The constructed wavelet neural network is detected with training set, and then optimal wavelet function with minimum mean squared error is chosen to build MWFWNN network. Experimental results show that the mean squared error is 1.23 × 10−3, which is better than WNN, BP, and PSO_SVM. Target threat assessment model based on the MWFWNN has a good predictive ability, so it can quickly and accurately complete target threat assessment. PMID:23509436
Mammography image compression using Wavelet
International Nuclear Information System (INIS)
Azuhar Ripin; Md Saion Salikin; Wan Hazlinda Ismail; Asmaliza Hashim; Norriza Md Isa
2004-01-01
Image compression plays an important role in many applications like medical imaging, televideo conferencing, remote sensing, document and facsimile transmission, which depend on the efficient manipulation, storage, and transmission of binary, gray scale, or color images. In Medical imaging application such Picture Archiving and Communication System (PACs), the image size or image stream size is too large and requires a large amount of storage space or high bandwidth for communication. Image compression techniques are divided into two categories namely lossy and lossless data compression. Wavelet method used in this project is a lossless compression method. In this method, the exact original mammography image data can be recovered. In this project, mammography images are digitized by using Vider Sierra Plus digitizer. The digitized images are compressed by using this wavelet image compression technique. Interactive Data Language (IDLs) numerical and visualization software is used to perform all of the calculations, to generate and display all of the compressed images. Results of this project are presented in this paper. (Author)
Maximally Localized Radial Profiles for Tight Steerable Wavelet Frames.
Pad, Pedram; Uhlmann, Virginie; Unser, Michael
2016-05-01
A crucial component of steerable wavelets is the radial profile of the generating function in the frequency domain. In this paper, we present an infinite-dimensional optimization scheme that helps us find the optimal profile for a given criterion over the space of tight frames. We consider two classes of criteria that measure the localization of the wavelet. The first class specifies the spatial localization of the wavelet profile, and the second that of the resulting wavelet coefficients. From these metrics and the proposed algorithm, we construct tight wavelet frames that are optimally localized and provide their analytical expression. In particular, one of the considered criterion helps us finding back the popular Simoncelli wavelet profile. Finally, the investigation of local orientation estimation, image reconstruction from detected contours in the wavelet domain, and denoising indicate that optimizing wavelet localization improves the performance of steerable wavelets, since our new wavelets outperform the traditional ones.
Discrete wavelet transformations an elementary approach with applications
Van Fleet, Patrick
2008-01-01
An "applications first" approach to discrete wavelet transformations. Discrete Wavelet Transformations provides readers with a broad elementary introduction to discrete wavelet transformations and their applications. With extensive graphical displays, this self-contained book integrates concepts from calculus and linear algebra into the construction of wavelet transformations and their various applications, including data compression, edge detection in images, and signal and image denoising. The book begins with a cursory look at wavelet transformation development and illustrates its
Applications of a fast, continuous wavelet transform
Energy Technology Data Exchange (ETDEWEB)
Dress, W.B.
1997-02-01
A fast, continuous, wavelet transform, based on Shannon`s sampling theorem in frequency space, has been developed for use with continuous mother wavelets and sampled data sets. The method differs from the usual discrete-wavelet approach and the continuous-wavelet transform in that, here, the wavelet is sampled in the frequency domain. Since Shannon`s sampling theorem lets us view the Fourier transform of the data set as a continuous function in frequency space, the continuous nature of the functions is kept up to the point of sampling the scale-translation lattice, so the scale-translation grid used to represent the wavelet transform is independent of the time- domain sampling of the signal under analysis. Computational cost and nonorthogonality aside, the inherent flexibility and shift invariance of the frequency-space wavelets has advantages. The method has been applied to forensic audio reconstruction speaker recognition/identification, and the detection of micromotions of heavy vehicles associated with ballistocardiac impulses originating from occupants` heart beats. Audio reconstruction is aided by selection of desired regions in the 2-D representation of the magnitude of the transformed signal. The inverse transform is applied to ridges and selected regions to reconstruct areas of interest, unencumbered by noise interference lying outside these regions. To separate micromotions imparted to a mass-spring system (e.g., a vehicle) by an occupants beating heart from gross mechanical motions due to wind and traffic vibrations, a continuous frequency-space wavelet, modeled on the frequency content of a canonical ballistocardiogram, was used to analyze time series taken from geophone measurements of vehicle micromotions. By using a family of mother wavelets, such as a set of Gaussian derivatives of various orders, features such as the glottal closing rate and word and phrase segmentation may be extracted from voice data.
Wavelet Applications for Flight Flutter Testing
Lind, Rick; Brenner, Marty; Freudinger, Lawrence C.
1999-01-01
Wavelets present a method for signal processing that may be useful for analyzing responses of dynamical systems. This paper describes several wavelet-based tools that have been developed to improve the efficiency of flight flutter testing. One of the tools uses correlation filtering to identify properties of several modes throughout a flight test for envelope expansion. Another tool uses features in time-frequency representations of responses to characterize nonlinearities in the system dynamics. A third tool uses modulus and phase information from a wavelet transform to estimate modal parameters that can be used to update a linear model and reduce conservatism in robust stability margins.
Significance tests for the wavelet cross spectrum and wavelet linear coherence
Directory of Open Access Journals (Sweden)
Z. Ge
2008-12-01
Full Text Available This work attempts to develop significance tests for the wavelet cross spectrum and the wavelet linear coherence as a follow-up study on Ge (2007. Conventional approaches that are used by Torrence and Compo (1998 based on stationary background noise time series were used here in estimating the sampling distributions of the wavelet cross spectrum and the wavelet linear coherence. The sampling distributions are then used for establishing significance levels for these two wavelet-based quantities. In addition to these two wavelet quantities, properties of the phase angle of the wavelet cross spectrum of, or the phase difference between, two Gaussian white noise series are discussed. It is found that the tangent of the principal part of the phase angle approximately has a standard Cauchy distribution and the phase angle is uniformly distributed, which makes it impossible to establish significance levels for the phase angle. The simulated signals clearly show that, when there is no linear relation between the two analysed signals, the phase angle disperses into the entire range of [−π,π] with fairly high probabilities for values close to ±π to occur. Conversely, when linear relations are present, the phase angle of the wavelet cross spectrum settles around an associated value with considerably reduced fluctuations. When two signals are linearly coupled, their wavelet linear coherence will attain values close to one. The significance test of the wavelet linear coherence can therefore be used to complement the inspection of the phase angle of the wavelet cross spectrum. The developed significance tests are also applied to actual data sets, simultaneously recorded wind speed and wave elevation series measured from a NOAA buoy on Lake Michigan. Significance levels of the wavelet cross spectrum and the wavelet linear coherence between the winds and the waves reasonably separated meaningful peaks from those generated by randomness in the data set. As
Wavelet primal sketch representation using Marr wavelet pyramid and its reconstruction
Van De Ville, Dimitri; Unser, Michael
2009-08-01
Based on the class of complex gradient-Laplace operators, we show the design of a non-separable two-dimensional wavelet basis from a single and analytically defined generator wavelet function. The wavelet decomposition is implemented by an efficient FFT-based filterbank. By allowing for slight redundancy, we obtain the Marr wavelet pyramid decomposition that features improved translation-invariance and steerability. The link with Marr's theory of early vision is due to the replication of the essential processing steps (Gaussian smoothing, Laplacian, orientation detection). Finally, we show how to find a compact multiscale primal sketch of the image, and how to reconstruct an image from it.
Resolução espacial de um modelo digital de elevação definida pela função wavelet
Directory of Open Access Journals (Sweden)
Alexandre ten Caten
2012-03-01
Full Text Available O objetivo deste trabalho foi definir a resolução espacial mais apropriada para representar a variabilidade da elevação, declividade, curvatura em perfil e índice de umidade topográfica de um terreno, por meio de avaliações com a transformada wavelet. Os dados utilizados no estudo têm sua origem em três transectos de 27 km, posicionados em áreas do Planalto, Rebordo do Planalto e Depressão Central na região central do Estado do Rio Grande do Sul. As variáveis - elevação, declividade, curvatura em perfil e índice de umidade topográfica - foram derivadas de um modelo digital de elevação Topodata com resolução de 30 m. A avaliação da resolução com a máxima variabilidade foi realizada pela aplicação da wavelet-mãe, denominada Morlet. Os resultados foram analisados a partir do isograma e do escalograma dos coeficientes wavelet e indicaram que sensores remotos com resolução espacial próxima a 32 e 40 m podem ser utilizados em pesquisas que considerem os atributos de terreno, como declividade, curvatura em perfil e índice de umidade topográfica, ou, ainda, fenômenos ambientais correlacionados a eles. No entanto, não foi possível estabelecer um valor conclusivo para a resolução espacial mais adequada para a variável elevação.
Using wavelet features for analyzing gamma lines
International Nuclear Information System (INIS)
Medhat, M.E.; Abdel-hafiez, A.; Hassan, M.F.; Ali, M.A.; Uzhinskii, V.V.
2004-01-01
Data processing methods for analyzing gamma ray spectra with symmetric bell-shaped peaks form are considered. In many cases the peak form is symmetrical bell shaped in particular a Gaussian case is the most often used due to many physical reasons. The problem is how to evaluate parameters of such peaks, i.e. their positions, amplitudes and also their half-widths, that is for a single peak and overlapped peaks. Through wavelet features by using Marr wavelet (Mexican Hat) as a correlation method, it could be to estimate the optimal wavelet parameters and to locate peaks in the spectrum. The performance of the proposed method and others shows a better quality of wavelet transform method
Coherent states, wavelets, and their generalizations
Ali, Syed Twareque; Gazeau, Jean-Pierre
2014-01-01
This second edition is fully updated, covering in particular new types of coherent states (the so-called Gazeau-Klauder coherent states, nonlinear coherent states, squeezed states, as used now routinely in quantum optics) and various generalizations of wavelets (wavelets on manifolds, curvelets, shearlets, etc.). In addition, it contains a new chapter on coherent state quantization and the related probabilistic aspects. As a survey of the theory of coherent states, wavelets, and some of their generalizations, it emphasizes mathematical principles, subsuming the theories of both wavelets and coherent states into a single analytic structure. The approach allows the user to take a classical-like view of quantum states in physics. Starting from the standard theory of coherent states over Lie groups, the authors generalize the formalism by associating coherent states to group representations that are square integrable over a homogeneous space; a further step allows one to dispense with the group context altoget...
Applying wavelet entropy principle in fault classification
Energy Technology Data Exchange (ETDEWEB)
El Safty, S.; El-Zonkoly, A. [Arab Academy for Science and Technology, Miami, Alexandria, P.O.1029 (Egypt)
2009-11-15
The ability to detect and classify the type of fault plays a great role in the protection of power system. This procedure is required to be precise with no time consumption. In this paper detection of fault type has been implemented using wavelet analysis together with wavelet entropy principle. The simulation of power system is carried out using PSCAD/EMTDC. Different types of faults were studied obtaining various current waveforms. These current waveforms were decomposed using wavelet analysis into different approximation and details. The wavelet entropies of such decompositions are analyzed reaching a successful methodology for fault classification. The suggested approach is tested using different fault types and proven successful identification for the type of fault. (author)
Building nonredundant adaptive wavelets by update lifting
H.J.A.M. Heijmans (Henk); B. Pesquet-Popescu; G. Piella (Gema)
2002-01-01
textabstractAdaptive wavelet decompositions appear useful in various applications in image and video processing, such as image analysis, compression, feature extraction, denoising and deconvolution, or optic flow estimation. For such tasks it may be important that the multiresolution representations
Image Registration Using Redundant Wavelet Transforms
National Research Council Canada - National Science Library
Brown, Richard
2001-01-01
.... In our research, we present a fundamentally new wavelet-based registration algorithm utilizing redundant transforms and a masking process to suppress the adverse effects of noise and improve processing efficiency...
Wavelets theory and applications for manufacturing
Gao, Robert X
2011-01-01
With the aim of facilitating signal processing in manufacturing, this book presents a systematic description of the fundamentals on wavelet transform and the ways of applying it to the condition monitoring and health diagnosis of rotating machine components.
Framelets and wavelets algorithms, analysis, and applications
Han, Bin
2017-01-01
Marking a distinct departure from the perspectives of frame theory and discrete transforms, this book provides a comprehensive mathematical and algorithmic introduction to wavelet theory. As such, it can be used as either a textbook or reference guide. As a textbook for graduate mathematics students and beginning researchers, it offers detailed information on the basic theory of framelets and wavelets, complemented by self-contained elementary proofs, illustrative examples/figures, and supplementary exercises. Further, as an advanced reference guide for experienced researchers and practitioners in mathematics, physics, and engineering, the book addresses in detail a wide range of basic and advanced topics (such as multiwavelets/multiframelets in Sobolev spaces and directional framelets) in wavelet theory, together with systematic mathematical analysis, concrete algorithms, and recent developments in and applications of framelets and wavelets. Lastly, the book can also be used to teach on or study selected spe...
Wavelet analysis of cardiac optical mapping data.
Xiong, Feng; Qi, Xiaoyan; Nattel, Stanley; Comtois, Philippe
2015-10-01
Optical mapping technology is an important tool to study cardiac electrophysiology. Transmembrane fluorescence signals from voltage-dependent dyes need to be preprocessed before analysis to improve the signal-to-noise ratio. Fourier analysis, based on spectral properties of stationary signals, cannot directly provide information on the spectrum changes with respect to time. Fourier filtering has the disadvantage of causing degradation of abrupt waveform changes such as those in action potential signals. Wavelet analysis has the ability to offer simultaneous localization in time and frequency domains, suitable for the analysis and reconstruction of irregular, non-stationary signals like the fast action-potential upstroke, and better than conventional filters for denoising. We applied discrete wavelet transformation for temporal processing of optical mapping signals and wavelet packet analysis approaches to process activation maps from simulated and experimental optical mapping data from canine right atrium. We compared the results obtained with the wavelet approach to a variety of other methods (Fast Fourier Transformation (FFT) with finite or infinite response filtering, and Gaussian filters). Temporal wavelet analysis improved signal-to-noise ratio (SNR) better than FFT filtering for 5-10dB SNR, and caused less distortion of the action potential waveform over the full range of simulated noise (5-20dB). Spatial wavelet filtering produced more efficient denoising and/or more accurate conduction velocity estimates than Gaussian filtering. Propagation patterns were also best revealed by wavelet filtering. Wavelet analysis is a promising tool, facilitating accurate action potential characterization, activation map formation, and conduction velocity estimation. Copyright © 2015 Elsevier Ltd. All rights reserved.
Optimal wavelet denoising for smart biomonitor systems
Messer, Sheila R.; Agzarian, John; Abbott, Derek
2001-03-01
Future smart-systems promise many benefits for biomedical diagnostics. The ideal is for simple portable systems that display and interpret information from smart integrated probes or MEMS-based devices. In this paper, we will discuss a step towards this vision with a heart bio-monitor case study. An electronic stethoscope is used to record heart sounds and the problem of extracting noise from the signal is addressed via the use of wavelets and averaging. In our example of heartbeat analysis, phonocardiograms (PCGs) have many advantages in that they may be replayed and analysed for spectral and frequency information. Many sources of noise may pollute a PCG including foetal breath sounds if the subject is pregnant, lung and breath sounds, environmental noise and noise from contact between the recording device and the skin. Wavelets can be employed to denoise the PCG. The signal is decomposed by a discrete wavelet transform. Due to the efficient decomposition of heart signals, their wavelet coefficients tend to be much larger than those due to noise. Thus, coefficients below a certain level are regarded as noise and are thresholded out. The signal can then be reconstructed without significant loss of information in the signal. The questions that this study attempts to answer are which wavelet families, levels of decomposition, and thresholding techniques best remove the noise in a PCG. The use of averaging in combination with wavelet denoising is also addressed. Possible applications of the Hilbert Transform to heart sound analysis are discussed.
A Novel Method of Estimating Statistically Matched Wavelet: Part 1-Compactly Supported Wavelet
Directory of Open Access Journals (Sweden)
Anubha Gupta
2004-10-01
Full Text Available Issue of finding a wavelet matched to signal has been addressed by various researchers in past. This paper presents a new method of estimating wavelet that is matched to a given signal in the statistical sense. The key idea lies in the estimation of analysis wavelet filter from a given signal and is similar to a sharpening filter used in image enhancement. The output of analysis wavelet filter branch after decimation is written in terms of filter weights and input signal samples. It is then viewed to be equivalent to difference of middle sample and its smoother estimate from the neighborhood which then needs to be minimized. To achieve this, minimum mean square error (MMSE criterion is employed using the autocorrelation function of input signal. Since wavelet expansion acts like Karhunen-Loève type expansion for generalized 1/f processes, it is assumed that the given signal is a sample function of an nth order fractional Brownian motion. Its autocorrelation function is used with MMSE criterion to estimate analysis wavelet filter. Next, a method is proposed to design 2-band FIR perfect reconstruction biorthogonal filter bank. This result in compactly supported wavelet matched statistically to given signal. Further, it is shown that compactly supported wavelet with desired support can be designed from a given signal. The theory is supported with number of simulation examples.
Detección automática de NEOs en imágenes CCD utilizando la transformada de Hough
Ruétalo, M.; Tancredi, G.
El interés y la dedicación por los objetos que se acercan a la órbita de la Tierra (NEOs) ha aumentado considerablemente en los últimos años, tanto que se han iniciado varias campañas de búsqueda sistemática para aumentar la población identificada de éstos. El uso de placas fotográficas e identificación visual está siendo sustituído, progresivamente, por el uso de cámaras CCD y paquetes de detección automática de los objetos en las imágenes digitales. Una parte muy importante para la implementación exitosa de un programa automatizado de detección de este tipo es el desarrollo de algoritmos capaces de identificar objetos de baja relación señal-ruido y con requerimientos computacionales no elevados. En el presente trabajo proponemos la utilización de la transformada de Hough (utilizada en algunas áreas de visión artificial) para detectar automáticamente trazas, aproximadamente rectilíneas y de baja relación señal-ruido, en imágenes CCD. Desarrollamos una primera implementación de un algoritmo basado en ésta y lo probamos con una serie de imágenes reales conteniendo trazas con picos de señales de entre ~1 σ y ~3 σ por encima del nivel del ruido de fondo. El algoritmo detecta, sin inconvenientes, la mayoría de los casos y en tiempos razonablemente adecuados.
Image wavelet decomposition and applications
Treil, N.; Mallat, S.; Bajcsy, R.
1989-01-01
The general problem of computer vision has been investigated for more that 20 years and is still one of the most challenging fields in artificial intelligence. Indeed, taking a look at the human visual system can give us an idea of the complexity of any solution to the problem of visual recognition. This general task can be decomposed into a whole hierarchy of problems ranging from pixel processing to high level segmentation and complex objects recognition. Contrasting an image at different representations provides useful information such as edges. An example of low level signal and image processing using the theory of wavelets is introduced which provides the basis for multiresolution representation. Like the human brain, we use a multiorientation process which detects features independently in different orientation sectors. So, images of the same orientation but of different resolutions are contrasted to gather information about an image. An interesting image representation using energy zero crossings is developed. This representation is shown to be experimentally complete and leads to some higher level applications such as edge and corner finding, which in turn provides two basic steps to image segmentation. The possibilities of feedback between different levels of processing are also discussed.
Wavelet transforms as solutions of partial differential equations
Energy Technology Data Exchange (ETDEWEB)
Zweig, G.
1997-10-01
This is the final report of a three-year, Laboratory Directed Research and Development (LDRD) project at Los Alamos National Laboratory (LANL). Wavelet transforms are useful in representing transients whose time and frequency structure reflect the dynamics of an underlying physical system. Speech sound, pressure in turbulent fluid flow, or engine sound in automobiles are excellent candidates for wavelet analysis. This project focused on (1) methods for choosing the parent wavelet for a continuous wavelet transform in pattern recognition applications and (2) the more efficient computation of continuous wavelet transforms by understanding the relationship between discrete wavelet transforms and discretized continuous wavelet transforms. The most interesting result of this research is the finding that the generalized wave equation, on which the continuous wavelet transform is based, can be used to understand phenomena that relate to the process of hearing.
Application of Hermitian wavelet to crack fault detection in gearbox
Li, Hui; Zhang, Yuping; Zheng, Haiqi
2011-05-01
The continuous wavelet transform enables one to look at the evolution in the time scale joint representation plane. This advantage makes it very suitable for the detection of singularity generated by localized defects in the mechanical system. However, most of the applications of the continuous wavelet transform have widely focused on the use of Morlet wavelet transform. The complex Hermitian wavelet is constructed based on the first and the second derivatives of the Gaussian function to detect signal singularities. The Fourier spectrum of Hermitian wavelet is real; therefore, Hermitian wavelet does not affect the phase of a signal in the complex domain. This gives a desirable ability to extract the singularity characteristic of a signal precisely. In this study, Hermitian wavelet is used to diagnose the gear localized crack fault. The simulative and experimental results show that Hermitian wavelet can extract the transients from strong noise signals and can effectively diagnose the localized gear fault.
Lifting wavelet method of target detection
Han, Jun; Zhang, Chi; Jiang, Xu; Wang, Fang; Zhang, Jin
2009-11-01
Image target recognition plays a very important role in the areas of scientific exploration, aeronautics and space-to-ground observation, photography and topographic mapping. Complex environment of the image noise, fuzzy, all kinds of interference has always been to affect the stability of recognition algorithm. In this paper, the existence of target detection in real-time, accuracy problems, as well as anti-interference ability, using lifting wavelet image target detection methods. First of all, the use of histogram equalization, the goal difference method to obtain the region, on the basis of adaptive threshold and mathematical morphology operations to deal with the elimination of the background error. Secondly, the use of multi-channel wavelet filter wavelet transform of the original image de-noising and enhancement, to overcome the general algorithm of the noise caused by the sensitive issue of reducing the rate of miscarriage of justice will be the multi-resolution characteristics of wavelet and promotion of the framework can be designed directly in the benefits of space-time region used in target detection, feature extraction of targets. The experimental results show that the design of lifting wavelet has solved the movement of the target due to the complexity of the context of the difficulties caused by testing, which can effectively suppress noise, and improve the efficiency and speed of detection.
Wavelet processing techniques for digital mammography
Laine, Andrew F.; Song, Shuwu
1992-09-01
This paper introduces a novel approach for accomplishing mammographic feature analysis through multiresolution representations. We show that efficient (nonredundant) representations may be identified from digital mammography and used to enhance specific mammographic features within a continuum of scale space. The multiresolution decomposition of wavelet transforms provides a natural hierarchy in which to embed an interactive paradigm for accomplishing scale space feature analysis. Similar to traditional coarse to fine matching strategies, the radiologist may first choose to look for coarse features (e.g., dominant mass) within low frequency levels of a wavelet transform and later examine finer features (e.g., microcalcifications) at higher frequency levels. In addition, features may be extracted by applying geometric constraints within each level of the transform. Choosing wavelets (or analyzing functions) that are simultaneously localized in both space and frequency, results in a powerful methodology for image analysis. Multiresolution and orientation selectivity, known biological mechanisms in primate vision, are ingrained in wavelet representations and inspire the techniques presented in this paper. Our approach includes local analysis of complete multiscale representations. Mammograms are reconstructed from wavelet representations, enhanced by linear, exponential and constant weight functions through scale space. By improving the visualization of breast pathology we can improve the chances of early detection of breast cancers (improve quality) while requiring less time to evaluate mammograms for most patients (lower costs).
Wavelet-frame-based microcalcification detection
Chang, Charles C.; Wu, Hsien-Hsun S.; Liu, Jyh-Charn S.; Chui, Charles K.
1997-10-01
As the leading cause of death for adult women under 54 years of age in the United States, breast cancer accounts for 29% of all cancers in women. Early diagnosis of breast cancer is the most effective approach to reduce death rate. The rapid climbing of the health care cost further reiterates the importance of cost-effective, accurate screening tools for breast cancer. This paper proposes a wavelet frame based computer algorithm for screening of microcalcifications on digitized mammographical imagery. Despite its simplicity, the discrete wavelet transform (DWT) of compactly supported wavelets has been effectively used for detection of various types of signals. However, the shifting variant property of DWT makes it very unstable for detection of minute microcalcifications. Although increasing the sampling rate will improve the detection probability, this approach will drastically increase the size of mammographical images. The wavelet frame transform can be easily derived from the DWT algorithm by eliminating its down sampling step. The subtle difference between DWT and WF in down sampling is shown to be critical to the accuracy of microcalcifications detection. Without any down sampling, local image information at different scales is preserved. By joint thresholding of wavelet coefficients at different scales, one can accurately pin point suspected microcalcifications. A simple partitioning technique enables the detection algorithm to process image blocks independently. Four different partitioning techniques have been compared, and the method of repeating the end value on each partition boundary has the least significant impact on the detection accuracy.
Signorini Cylindrical Waves and Shannon Wavelets
Directory of Open Access Journals (Sweden)
Carlo Cattani
2012-01-01
Full Text Available Hyperelastic materials based on Signorini’s strain energy density are studied by using Shannon wavelets. Cylindrical waves propagating in a nonlinear elastic material from the circular cylindrical cavity along the radius are analyzed in the following by focusing both on the main nonlinear effects and on the method of solution for the corresponding nonlinear differential equation. Cylindrical waves’ solution of the resulting equations can be easily represented in terms of this family of wavelets. It will be shown that Hankel functions can be linked with Shannon wavelets, so that wavelets can have some physical meaning being a good approximation of cylindrical waves. The nonlinearity is introduced by Signorini elastic energy density and corresponds to the quadratic nonlinearity relative to displacements. The configuration state of elastic medium is defined through cylindrical coordinates but the deformation is considered as functionally depending only on the radial coordinate. The physical and geometrical nonlinearities arising from the wave propagation are discussed from the point of view of wavelet analysis.
Nuclear data compression and reconstruction via discrete wavelet transform
Energy Technology Data Exchange (ETDEWEB)
Park, Young Ryong; Cho, Nam Zin [Korea Advanced Institute of Science and Technology, Taejon (Korea, Republic of)
1997-12-31
Discrete Wavelet Transforms (DWTs) are recent mathematics, and begin to be used in various fields. The wavelet transform can be used to compress the signal and image due to its inherent properties. We applied the wavelet transform compression and reconstruction to the neutron cross section data. Numerical tests illustrate that the signal compression using wavelet is very effective to reduce the data saving spaces. 7 refs., 4 figs., 3 tabs. (Author)
Construction of a class of Daubechies type wavelet bases
International Nuclear Information System (INIS)
Li Dengfeng; Wu Guochang
2009-01-01
Extensive work has been done in the theory and the construction of compactly supported orthonormal wavelet bases of L 2 (R). Some of the most distinguished work was done by Daubechies, who constructed a whole family of such wavelet bases. In this paper, we construct a class of orthonormal wavelet bases by using the principle of Daubechies, and investigate the length of support and the regularity of these wavelet bases.
A Comparative Study on Optimal Structural Dynamics Using Wavelet Functions
Directory of Open Access Journals (Sweden)
Seyed Hossein Mahdavi
2015-01-01
Full Text Available Wavelet solution techniques have become the focus of interest among researchers in different disciplines of science and technology. In this paper, implementation of two different wavelet basis functions has been comparatively considered for dynamic analysis of structures. For this aim, computational technique is developed by using free scale of simple Haar wavelet, initially. Later, complex and continuous Chebyshev wavelet basis functions are presented to improve the time history analysis of structures. Free-scaled Chebyshev coefficient matrix and operation of integration are derived to directly approximate displacements of the corresponding system. In addition, stability of responses has been investigated for the proposed algorithm of discrete Haar wavelet compared against continuous Chebyshev wavelet. To demonstrate the validity of the wavelet-based algorithms, aforesaid schemes have been extended to the linear and nonlinear structural dynamics. The effectiveness of free-scaled Chebyshev wavelet has been compared with simple Haar wavelet and two common integration methods. It is deduced that either indirect method proposed for discrete Haar wavelet or direct approach for continuous Chebyshev wavelet is unconditionally stable. Finally, it is concluded that numerical solution is highly benefited by the least computation time involved and high accuracy of response, particularly using low scale of complex Chebyshev wavelet.
A simple output voltage control scheme for single phase wavelet ...
African Journals Online (AJOL)
DR OKE
Wavelet based techniques have been extensively used in various power engineering applications. Recently, wavelet has also been proposed to generate switching signal for single-phase pulse-width-modulated (PWM) dc-ac inverter. The main advantage of the wavelet modulated (WM) scheme is that a single synthesis ...
X-ray volume rendering by hierarchical wavelet splatting
Westenberg, Michel A.; Roerdink, Jos B.T.M.; Sanfeliu, A; Villanueva, JJ; Vanrell, M; Alquezar, R; Huang, T; Serra, J
2000-01-01
This paper is concerned with X-ray volume visualization by means of wavelet splatting, a wavelet-based extension to splatting. Wavelet splatting allows multiresolution visualization of volume data. While a user is interacting with the data, only low resolution images are computed. When interaction
a pyramid algorithm for the haar discrete wavelet packet transform
African Journals Online (AJOL)
PROF EKWUEME
derivation of the fast Haar discrete wavelet packet transform (FHDWPT) and its inverse. It is found out that the. FHDWPT is computationally as efficient as the fast Fourier transform (FFT). KEYWORDS: Wavelet, Packets, Haar, Pyramid, Algorithm. INTRODUCTION. Wavelet-based digital signal processing techniques are ...
Improvement of electrocardiogram by empirical wavelet transform
Chanchang, Vikanda; Kumchaiseemak, Nakorn; Sutthiopad, Malee; Luengviriya, Chaiya
2017-09-01
Electrocardiogram (ECG) is a crucial tool in the detection of cardiac arrhythmia. It is also often used in a routine physical exam, especially, for elderly people. This graphical representation of electrical activity of heart is obtained by a measurement of voltage at the skin; therefore, the signal is always contaminated by noise from various sources. For a proper interpretation, the quality of the ECG should be improved by a noise reduction. In this article, we present a study of a noise filtration in the ECG by using an empirical wavelet transform (EWT). Unlike the traditional wavelet method, EWT is adaptive since the frequency spectrum of the ECG is taken into account in the construction of the wavelet basis. We show that the signal-to-noise ratio increases after the noise filtration for different noise artefacts.
Partially coherent imaging and spatial coherence wavelets
International Nuclear Information System (INIS)
Castaneda, Roman
2003-03-01
A description of spatially partially coherent imaging based on the propagation of second order spatial coherence wavelets and marginal power spectra (Wigner distribution functions) is presented. In this dynamics, the spatial coherence wavelets will be affected by the system through its elementary transfer function. The consistency of the model with the both extreme cases of full coherent and incoherent imaging was proved. In the last case we obtained the classical concept of optical transfer function as a simple integral of the elementary transfer function. Furthermore, the elementary incoherent response function was introduced as the Fourier transform of the elementary transfer function. It describes the propagation of spatial coherence wavelets form each object point to each image point through a specific point on the pupil planes. The point spread function of the system was obtained by a simple integral of the elementary incoherent response function. (author)
Image encryption using the fractional wavelet transform
International Nuclear Information System (INIS)
Vilardy, Juan M; Useche, J; Torres, C O; Mattos, L
2011-01-01
In this paper a technique for the coding of digital images is developed using Fractional Wavelet Transform (FWT) and random phase masks (RPMs). The digital image to encrypt is transformed with the FWT, after the coefficients resulting from the FWT (Approximation, Details: Horizontal, vertical and diagonal) are multiplied each one by different RPMs (statistically independent) and these latest results is applied an Inverse Wavelet Transform (IWT), obtaining the encrypted digital image. The decryption technique is the same encryption technique in reverse sense. This technique provides immediate advantages security compared to conventional techniques, in this technique the mother wavelet family and fractional orders associated with the FWT are additional keys that make access difficult to information to an unauthorized person (besides the RPMs used), thereby the level of encryption security is extraordinarily increased. In this work the mathematical support for the use of the FWT in the computational algorithm for the encryption is also developed.
Multiresolution signal decomposition transforms, subbands, and wavelets
Akansu, Ali N; Haddad, Paul R
2001-01-01
The uniqueness of this book is that it covers such important aspects of modern signal processing as block transforms from subband filter banks and wavelet transforms from a common unifying standpoint, thus demonstrating the commonality among these decomposition techniques. In addition, it covers such ""hot"" areas as signal compression and coding, including particular decomposition techniques and tables listing coefficients of subband and wavelet filters and other important properties.The field of this book (Electrical Engineering/Computer Science) is currently booming, which is, of course
New Algorithm For Calculating Wavelet Transforms
Directory of Open Access Journals (Sweden)
Piotr Lipinski
2009-04-01
Full Text Available In this article we introduce a new algorithm for computing Discrete Wavelet Transforms (DWT. The algorithm aims at reducing the number of multiplications, required to compute a DWT. The algorithm is general and can be used to compute a variety of wavelet transform (Daubechies and CDF. Here we focus on CDF 9/7 filters, which are used in JPEG2000 compression standard. We show that the algorithm outperforms convolution-based and lifting-based algorithms in terms of number of multiplications.
Clifford wavelets, singular integrals, and Hardy spaces
Mitrea, Marius
1994-01-01
The book discusses the extensions of basic Fourier Analysis techniques to the Clifford algebra framework. Topics covered: construction of Clifford-valued wavelets, Calderon-Zygmund theory for Clifford valued singular integral operators on Lipschitz hyper-surfaces, Hardy spaces of Clifford monogenic functions on Lipschitz domains. Results are applied to potential theory and elliptic boundary value problems on non-smooth domains. The book is self-contained to a large extent and well-suited for graduate students and researchers in the areas of wavelet theory, Harmonic and Clifford Analysis. It will also interest the specialists concerned with the applications of the Clifford algebra machinery to Mathematical Physics.
Wavelet methods in mathematical analysis and engineering
Damlamian, Alain
2010-01-01
This book gives a comprehensive overview of both the fundamentals of wavelet analysis and related tools, and of the most active recent developments towards applications. It offers a stateoftheart in several active areas of research where wavelet ideas, or more generally multiresolution ideas have proved particularly effective. The main applications covered are in the numerical analysis of PDEs, and signal and image processing. Recently introduced techniques such as Empirical Mode Decomposition (EMD) and new trends in the recovery of missing data, such as compressed sensing, are also presented.
Orthonormal Wavelet Bases for Quantum Molecular Dynamics
International Nuclear Information System (INIS)
Tymczak, C.; Wang, X.
1997-01-01
We report on the use of compactly supported, orthonormal wavelet bases for quantum molecular-dynamics (Car-Parrinello) algorithms. A wavelet selection scheme is developed and tested for prototypical problems, such as the three-dimensional harmonic oscillator, the hydrogen atom, and the local density approximation to atomic and molecular systems. Our method shows systematic convergence with increased grid size, along with improvement on compression rates, thereby yielding an optimal grid for self-consistent electronic structure calculations. copyright 1997 The American Physical Society
A study of biorthogonal multiple vector-valued wavelets
International Nuclear Information System (INIS)
Han Jincang; Cheng Zhengxing; Chen Qingjiang
2009-01-01
The notion of vector-valued multiresolution analysis is introduced and the concept of biorthogonal multiple vector-valued wavelets which are wavelets for vector fields, is introduced. It is proved that, like in the scalar and multiwavelet case, the existence of a pair of biorthogonal multiple vector-valued scaling functions guarantees the existence of a pair of biorthogonal multiple vector-valued wavelet functions. An algorithm for constructing a class of compactly supported biorthogonal multiple vector-valued wavelets is presented. Their properties are investigated by means of operator theory and algebra theory and time-frequency analysis method. Several biorthogonality formulas regarding these wavelet packets are obtained.
On extensions of wavelet systems to dual pairs of frames
DEFF Research Database (Denmark)
Christensen, Ole; Kim, Hong Oh; Kim, Rae Young
2015-01-01
It is an open problem whether any pair of Bessel sequences with wavelet structure can be extended to a pair of dual frames by adding a pair of singly generated wavelet systems. We consider the particular case where the given wavelet systems are generated by the multiscale setup with trigonometric...... masks and provide a positive answer under extra assumptions. We also identify a number of conditions that are necessary for the extension to dual (multi-) wavelet frames with any number of generators, and show that they imply that an extension with two pairs of wavelet systems is possible. Along the way...
Polar Wavelet Transform and the Associated Uncertainty Principles
Shah, Firdous A.; Tantary, Azhar Y.
2018-02-01
The polar wavelet transform- a generalized form of the classical wavelet transform has been extensively used in science and engineering for finding directional representations of signals in higher dimensions. The aim of this paper is to establish new uncertainty principles associated with the polar wavelet transforms in L2(R2). Firstly, we study some basic properties of the polar wavelet transform and then derive the associated generalized version of Heisenberg-Pauli-Weyl inequality. Finally, following the idea of Beckner (Proc. Amer. Math. Soc. 123, 1897-1905 1995), we drive the logarithmic version of uncertainty principle for the polar wavelet transforms in L2(R2).
Solution of wave-like equation based on Haar wavelet
Directory of Open Access Journals (Sweden)
Naresh Berwal
2012-11-01
Full Text Available Wavelet transform and wavelet analysis are powerful mathematical tools for many problems. Wavelet also can be applied in numerical analysis. In this paper, we apply Haar wavelet method to solve wave-like equation with initial and boundary conditions known. The fundamental idea of Haar wavelet method is to convert the differential equations into a group of algebraic equations, which involves a finite number or variables. The results and graph show that the proposed way is quite reasonable when compared to exact solution.
Parallel object-oriented, denoising system using wavelet multiresolution analysis
Kamath, Chandrika; Baldwin, Chuck H.; Fodor, Imola K.; Tang, Nu A.
2005-04-12
The present invention provides a data de-noising system utilizing processors and wavelet denoising techniques. Data is read and displayed in different formats. The data is partitioned into regions and the regions are distributed onto the processors. Communication requirements are determined among the processors according to the wavelet denoising technique and the partitioning of the data. The data is transforming onto different multiresolution levels with the wavelet transform according to the wavelet denoising technique, the communication requirements, and the transformed data containing wavelet coefficients. The denoised data is then transformed into its original reading and displaying data format.
Quantum dynamics and electronic spectroscopy within the framework of wavelets
International Nuclear Information System (INIS)
Toutounji, Mohamad
2013-01-01
This paper serves as a first-time report on formulating important aspects of electronic spectroscopy and quantum dynamics in condensed harmonic systems using the framework of wavelets, and a stepping stone to our future work on developing anharmonic wavelets. The Morlet wavelet is taken to be the mother wavelet for the initial state of the system of interest. This work reports daughter wavelets that may be used to study spectroscopy and dynamics of harmonic systems. These wavelets are shown to arise naturally upon optical electronic transition of the system of interest. Natural birth of basis (daughter) wavelets emerging on exciting an electronic two-level system coupled, both linearly and quadratically, to harmonic phonons is discussed. It is shown that this takes place through using the unitary dilation and translation operators, which happen to be part of the time evolution operator of the final electronic state. The corresponding optical autocorrelation function and linear absorption spectra are calculated to test the applicability and correctness of the herein results. The link between basis wavelets and the Liouville space generating function is established. An anharmonic mother wavelet is also proposed in the case of anharmonic electron–phonon coupling. A brief description of deriving anharmonic wavelets and the corresponding anharmonic Liouville space generating function is explored. In conclusion, a mother wavelet (be it harmonic or anharmonic) which accounts for Duschinsky mixing is suggested. (paper)
Optimization of wavelet decomposition for image compression and feature preservation.
Lo, Shih-Chung B; Li, Huai; Freedman, Matthew T
2003-09-01
A neural-network-based framework has been developed to search for an optimal wavelet kernel that can be used for a specific image processing task. In this paper, a linear convolution neural network was employed to seek a wavelet that minimizes errors and maximizes compression efficiency for an image or a defined image pattern such as microcalcifications in mammograms and bone in computed tomography (CT) head images. We have used this method to evaluate the performance of tap-4 wavelets on mammograms, CTs, magnetic resonance images, and Lena images. We found that the Daubechies wavelet or those wavelets with similar filtering characteristics can produce the highest compression efficiency with the smallest mean-square-error for many image patterns including general image textures as well as microcalcifications in digital mammograms. However, the Haar wavelet produces the best results on sharp edges and low-noise smooth areas. We also found that a special wavelet whose low-pass filter coefficients are 0.32252136, 0.85258927, 1.38458542, and -0.14548269) produces the best preservation outcomes in all tested microcalcification features including the peak signal-to-noise ratio, the contrast and the figure of merit in the wavelet lossy compression scheme. Having analyzed the spectrum of the wavelet filters, we can find the compression outcomes and feature preservation characteristics as a function of wavelets. This newly developed optimization approach can be generalized to other image analysis applications where a wavelet decomposition is employed.
On optimisation of wavelet algorithms for non-perfect wavelet compression of digital medical images
Ricke, J
2001-01-01
Aim: Optimisation of medical image compression. Evaluation of wavelet-filters for wavelet-compression. Results: Application of filters with different complexity results in significant variations in the quality of image reconstruction after compression specifically in low frequency information. Filters of high complexity proved to be advantageous despite of heterogenous results during visual analysis. For high frequency details, complexity of filters did not prove to be of significant impact on image after reconstruction.
Energy Technology Data Exchange (ETDEWEB)
Penha, Rosani Maria Libardi da
1999-07-01
Mechanical vibration signals play an important role in anomalies identification resulting of equipment malfunctioning. Traditionally, Fourier spectral analysis is used where the signals are assumed to be stationary. However, occasional transient impulses and start-up process are examples of nonstationary signals that can be found in mechanical vibrations. These signals can provide important information about the equipment condition, as early fault detection. The Fourier analysis can not adequately be applied to nonstationary signals because the results provide data about the frequency composition averaged over the duration of the signal. In this work, two methods for nonstationary signal analysis are used: Short Time Fourier Transform (STFT) and wavelet transform. The STFT is a method of adapting Fourier spectral analysis for nonstationary application to time-frequency domain. To have a unique resolution throughout the entire time-frequency domain is its main limitation. The wavelet transform is a new analysis technique suitable to nonstationary signals, which handles the STFT drawbacks, providing multi-resolution frequency analysis and time localization in a unique time-scale graphic. The multiple frequency resolutions are obtained by scaling (dilatation/compression) the wavelet function. A comparison of the conventional Fourier transform, STFT and wavelet transform is made applying these techniques to: simulated signals, arrangement rotor rig vibration signal and rotate machine vibration signal Hanning window was used to STFT analysis. Daubechies and harmonic wavelets were used to continuos, discrete and multi-resolution wavelet analysis. The results show the Fourier analysis was not able to detect changes in the signal frequencies or discontinuities. The STFT analysis detected the changes in the signal frequencies, but with time-frequency resolution problems. The wavelet continuos and discrete transform demonstrated to be a high efficient tool to detect
Application of wavelet transform to seismic data; Wavelet henkan no jishin tansa eno tekiyo
Energy Technology Data Exchange (ETDEWEB)
Nakagami, K.; Murayama, R.; Matsuoka, T. [Japan National Oil Corp., Tokyo (Japan)
1996-05-01
Introduced herein is the use of the wavelet transform in the field of seismic exploration. Among applications so far made, there are signal filtering, break point detection, data compression, and the solution of finite differential equations in the wavelet domain. In the field of data compression in particular, some examples of practical application have been introduced already. In seismic exploration, it is expected that the wavelet transform will separate signals and noises in data in a way different from the Fourier transform. The continuous wavelet transform displays time change in frequency easy to read, but is not suitable for the analysis and processing large quantities of data. On the other hand, the discrete wavelet transform, being an orthogonal transform, can handle large quantities of data. As compared with the conventional Fourier transform that handles only the frequency domain, the wavelet transform handles the time domain as well as the frequency domain, and therefore is more convenient in handling unsteady signals. 9 ref., 8 figs.
Wavelet Transforms: Application to Data Analysis - II
Indian Academy of Sciences (India)
Sachin P Nanavati (top right) has joined the Scientific and. Engineering Computing. Group of C-DAC, Pune. .... tic variations in the data set. For that purpose, one first plots the power spectrum. As defined in Part 1 of .... cessing, Pearson Education. Inc., Delhi, 2003. [ 4] S Nanavati andP Panigrahi,. Wavelets: Applications to.
Wave Forecasting Using Neuro Wavelet Technique
Directory of Open Access Journals (Sweden)
Pradnya Dixit
2014-12-01
Full Text Available In the present work a hybrid Neuro-Wavelet Technique is used for forecasting waves up to 6 hr, 12 hr, 18 hr and 24 hr in advance using hourly measured significant wave heights at an NDBC station 41004 near the east coast of USA. The NW Technique is employed by combining two methods, Discrete Wavelet Transform and Artificial Neural Networks. The hourly data of previously measured significant wave heights spanning over 2 years from 2010 and 2011 is used to calibrate and test the models. The discrete wavelet transform of NWT analyzes frequency of signal with respect to time at different scales. It decomposes time series into low (approximate and high (detail frequency components. The decomposition of approximate can be carried out up to desired multiple levels in order to provide more detail and approximate components which provides relatively smooth varying amplitude series. The neural network is trained with decorrelated approximate and detail wavelet coefficients. The outputs of networks during testing are reconstructed back using inverse DWT. The results were judged by drawing the wave plots, scatter plots and other error measures. The developed models show reasonable accuracy in prediction of significant wave heights from 6 to 24 hours. To compare the results traditional ANN models were also developed at the same location using the same data and for same time interval.
Information retrieval system utilizing wavelet transform
Brewster, Mary E.; Miller, Nancy E.
2000-01-01
A method for automatically partitioning an unstructured electronically formatted natural language document into its sub-topic structure. Specifically, the document is converted to an electronic signal and a wavelet transform is then performed on the signal. The resultant signal may then be used to graphically display and interact with the sub-topic structure of the document.
Modelling spatial density using continuous wavelet transforms
Indian Academy of Sciences (India)
Space debris; wavelets; Mexican hat; Laplace distribution; random search; parameter estimation. ... Author Affiliations. D Sudheer Reddy1 N Gopal Reddy2 A K Anilkumar3. Digital Mapping and Modelling Division, Advanced Data Processing Research Institute, Secunderabad 500 009, India; Department of Mathematics, ...
Wavelet Transform - A New Mathematical Microscope
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 9; Issue 3. Wavelet Transform - A New Mathematical Microscope. Sachin P Nanavati Prasanta K Panigrahi. General Article Volume 9 Issue 3 March 2004 pp 50-64. Fulltext. Click here to view fulltext PDF. Permanent link:
Multiscale wavelet representations for mammographic feature analysis
Laine, Andrew F.; Song, Shuwu
1992-12-01
This paper introduces a novel approach for accomplishing mammographic feature analysis through multiresolution representations. We show that efficient (nonredundant) representations may be identified from digital mammography and used to enhance specific mammographic features within a continuum of scale space. The multiresolution decomposition of wavelet transforms provides a natural hierarchy in which to embed an interactive paradigm for accomplishing scale space feature analysis. Choosing wavelets (or analyzing functions) that are simultaneously localized in both space and frequency, results in a powerful methodology for image analysis. Multiresolution and orientation selectivity, known biological mechanisms in primate vision, are ingrained in wavelet representations and inspire the techniques presented in this paper. Our approach includes local analysis of complete multiscale representations. Mammograms are reconstructed from wavelet coefficients, enhanced by linear, exponential and constant weight functions localized in scale space. By improving the visualization of breast pathology we can improve the changes of early detection of breast cancers (improve quality) while requiring less time to evaluate mammograms for most patients (lower costs).
Nonlinear wavelet regression function estimator for censored ...
African Journals Online (AJOL)
Let (Y;C;X) be a vector of random variables where Y; C and X are, respectively, the interest variable, a right censoring and a covariable (predictor). In this paper, we introduce a new nonlinear wavelet-based estimator of the regression function in the right censorship model. An asymptotic expression for the mean integrated ...
Wavelet Transform-A New Mathematical Microscope
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 9; Issue 3. Wavelet Transform - A New Mathematical Microscope. Sachin P Nanavati Prasanta K Panigrahi. General Article Volume 9 Issue 3 March 2004 pp 50-64. Fulltext. Click here to view fulltext PDF. Permanent link:
Wavelet based multicarrier code division multiple access ...
African Journals Online (AJOL)
This paper presents the study on Wavelet transform based Multicarrier Code Division Multiple Access (MC-CDMA) system for a downlink wireless channel. The performance of the system is studied for Additive White Gaussian Noise Channel (AWGN) and slowly varying multipath channels. The bit error rate (BER) versus ...
Wavelet Transforms: Application to Data Analysis - II
Indian Academy of Sciences (India)
GENERAL I ARTICLE. Wavelet Transforms: Application to Data Analysis - II. Jatan K Modi, Sachin P Nanavati, Amit S Phadke and Prasanta K Panigrahi. Jatan K Modi (top left) is currently a visiting faculty at DDIT, Gujarat. His areas of interest are artificial intelligence, compilers and image processing. Sachin P Nanavati (top ...
monthly energy consumption forecasting using wavelet analysis
African Journals Online (AJOL)
User
ABSTRACT. Monthly energy forecasts help heavy consumers of electric power to prepare adequate budget to pay their electricity bills and also draw the attention of management and stakeholders to electric- ity consumption levels so that energy efficiency measures are put in place to reduce cost. In this paper, a wavelet ...
Monthly Energy Consumption Forecasting Using Wavelet Analysis ...
African Journals Online (AJOL)
Monthly energy forecasts help heavy consumers of electric power to prepare adequate budget to pay their electricity bills and also draw the attention of management and stakeholders to electricity consumption levels so that energy efficiency measures are put in place to reduce cost. In this paper, a wavelet transform and ...
Adaptive wavelet algorithms for solving operator equations
Gantumur, T.
2006-01-01
This thesis treats various aspects of adaptive wavelet algorithms for solving operator equations. For a separable Hilbert space H, a linear functional f in H', and a boundedly invertible linear operator A:H->H', we consider the problem of finding u from H satisfying Au=f. Typically A is given by a
Conductance calculations with a wavelet basis set
DEFF Research Database (Denmark)
Thygesen, Kristian Sommer; Bollinger, Mikkel; Jacobsen, Karsten Wedel
2003-01-01
. The linear-response conductance is calculated from the Green's function which is represented in terms of a system-independent basis set containing wavelets with compact support. This allows us to rigorously separate the central region from the contacts and to test for convergence in a systematic way...
Wavelet Transform for Processing Power Quality Disturbances
Directory of Open Access Journals (Sweden)
H. Y. Zhu
2007-01-01
Full Text Available The emergence of power quality as a topical issue in power systems in the 1990s largely coincides with the huge advancements achieved in the computing technology and information theory. This unsurprisingly has spurred the development of more sophisticated instruments for measuring power quality disturbances and the use of new methods in processing and analyzing the measurements. Fourier theory was the core of many traditional techniques and it is still widely used today. However, it is increasingly being replaced by newer approaches notably wavelet transform and especially in the post-event processing of the time-varying phenomena. This paper reviews the use of wavelet transform approach in processing power quality data. The strengths, limitations, and challenges in employing the methods are discussed with consideration of the needs and expectations when analyzing power quality disturbances. Several examples are given and discussions are made on the various design issues and considerations, which would be useful to those contemplating adopting wavelet transform in power quality applications. A new approach of combining wavelet transform and rank correlation is introduced as an alternative method for identifying capacitor-switching transients.
Directory of Open Access Journals (Sweden)
Jikai Chen
2016-12-01
Full Text Available In a power system, the analysis of transient signals is the theoretical basis of fault diagnosis and transient protection theory. Shannon wavelet entropy (SWE and Shannon wavelet packet entropy (SWPE are powerful mathematics tools for transient signal analysis. Combined with the recent achievements regarding SWE and SWPE, their applications are summarized in feature extraction of transient signals and transient fault recognition. For wavelet aliasing at adjacent scale of wavelet decomposition, the impact of wavelet aliasing is analyzed for feature extraction accuracy of SWE and SWPE, and their differences are compared. Meanwhile, the analyses mentioned are verified by partial discharge (PD feature extraction of power cable. Finally, some new ideas and further researches are proposed in the wavelet entropy mechanism, operation speed and how to overcome wavelet aliasing.
Classification of mammographic microcalcifications using wavelets
Chitre, Yateen S.; Dhawan, Atam P.; Moskowitz, Myron; Sarwal, Alok; Bonasso, Christine; Narayan, Suresh B.
1995-05-01
Breast cancer is the leading cause of death among women. Breast cancer can be detected earlier by mammography than any other non-invasive examination. About 30% to 50% of breast cancers demonstrate tiny granulelike deposits of calcium called microcalcifications. It is difficult to distinguish between benign and malignant cases based on an examination of calcification regions, especially in hard-to-diagnose cases. We investigate the potential of using energy and entropy features computed from wavelet packets for their correlation with malignancy. Two types of Daubechies discrete filters were used as prototype wavelets. The energy and entropy features were computed for 128 benign and 63 malignant cases and analyzed using a multivariate cluster analysis and a univariate statistical analysis to reduce the feature set to a `five best set of features.' The efficacy of the reduced feature set to discriminate between the malignant and benign categories was evaluated using different multilayer perceptron architectures. The multilayer perceptron was trained using the backpropagation algorithm for various training and test set sizes. For each case 40 partitions of the data set were used to set up the training and test sets. The performance of the features was evaluated by computing the best area under the relative operating characteristic (ROC) curve and the average area under the ROC curve. The performance of the features computed from the wavelet packets was compared to a second set of features consisting of the wavelet packet features, image structure features and cluster features. The classification results are encouraging and indicate the potential of using features derived from wavelet packets in discriminating microcalcification regions into benign and malignant categories.
Optimal selection of mother wavelet for accurate infant cry classification.
Saraswathy, J; Hariharan, M; Nadarajaw, Thiyagar; Khairunizam, Wan; Yaacob, Sazali
2014-06-01
Wavelet theory is emerging as one of the prevalent tool in signal and image processing applications. However, the most suitable mother wavelet for these applications is still a relative question mark amongst researchers. Selection of best mother wavelet through parameterization leads to better findings for the analysis in comparison to random selection. The objective of this article is to compare the performance of the existing members of mother wavelets and to select the most suitable mother wavelet for accurate infant cry classification. Optimal wavelet is found using three different criteria namely the degree of similarity of mother wavelets, regularity of mother wavelets and accuracy of correct recognition during classification processes. Recorded normal and pathological infant cry signals are decomposed into five levels using wavelet packet transform. Energy and entropy features are extracted at different sub bands of cry signals and their effectiveness are tested with four supervised neural network architectures. Findings of this study expound that, the Finite impulse response based approximation of Meyer is the best wavelet candidate for accurate infant cry classification analysis.
Wavelet-Based MPNLMS Adaptive Algorithm for Network Echo Cancellation
Directory of Open Access Journals (Sweden)
Doroslovački Miloš
2007-01-01
Full Text Available The μ-law proportionate normalized least mean square (MPNLMS algorithm has been proposed recently to solve the slow convergence problem of the proportionate normalized least mean square (PNLMS algorithm after its initial fast converging period. But for the color input, it may become slow in the case of the big eigenvalue spread of the input signal's autocorrelation matrix. In this paper, we use the wavelet transform to whiten the input signal. Due to the good time-frequency localization property of the wavelet transform, a sparse impulse response in the time domain is also sparse in the wavelet domain. By applying the MPNLMS technique in the wavelet domain, fast convergence for the color input is observed. Furthermore, we show that some nonsparse impulse responses may become sparse in the wavelet domain. This motivates the usage of the wavelet-based MPNLMS algorithm. Advantages of this approach are documented.
Wavelet-Based MPNLMS Adaptive Algorithm for Network Echo Cancellation
Directory of Open Access Journals (Sweden)
Hongyang Deng
2007-03-01
Full Text Available The ÃŽÂ¼-law proportionate normalized least mean square (MPNLMS algorithm has been proposed recently to solve the slow convergence problem of the proportionate normalized least mean square (PNLMS algorithm after its initial fast converging period. But for the color input, it may become slow in the case of the big eigenvalue spread of the input signal's autocorrelation matrix. In this paper, we use the wavelet transform to whiten the input signal. Due to the good time-frequency localization property of the wavelet transform, a sparse impulse response in the time domain is also sparse in the wavelet domain. By applying the MPNLMS technique in the wavelet domain, fast convergence for the color input is observed. Furthermore, we show that some nonsparse impulse responses may become sparse in the wavelet domain. This motivates the usage of the wavelet-based MPNLMS algorithm. Advantages of this approach are documented.
Comparative study of wavelet denoising in myoelectric control applications.
Sharma, Tanu; Veer, Karan
2016-01-01
Here, the wavelet analysis has been investigated to improve the quality of myoelectric signal before use in prosthetic design. Effective Surface Electromyogram (SEMG) signals were estimated by first decomposing the obtained signal using wavelet transform and then analysing the decomposed coefficients by threshold methods. With the appropriate choice of wavelet, it is possible to reduce interference noise effectively in the SEMG signal. However, the most effective wavelet for SEMG denoising is chosen by calculating the root mean square value and signal power values. The combined results of root mean square value and signal power shows that wavelet db4 performs the best denoising among the wavelets. Furthermore, time domain and frequency domain methods were applied for SEMG signal analysis to investigate the effect of muscle-force contraction on the signal. It was found that, during sustained contractions, the mean frequency (MNF) and median frequency (MDF) increase as muscle force levels increase.
FPGA Implementations of Bireciprocal Lattice Wave Discrete Wavelet Filter Banks
Directory of Open Access Journals (Sweden)
Jassim M. Abdul-Jabbar
2012-06-01
Full Text Available In this paper, a special type of IIR filter banks; that is the bireciprocal lattice wave digital filter (BLWDF bank, is presented to simulate scaling and wavelet functions of six-level wavelet transform. 1st order all-pass sections are utilized for the realization of such filter banks in wave lattice structures. The resulting structures are a bireciprocal lattice wave discrete wavelet filter banks (BLW-DWFBs. Implementation of these BLW-DWFBs are accomplished on Spartan-3E FPGA kit. Implementation complexity and operating frequency characteristics of such discrete wavelet 5th order filter bank is proved to be comparable to the corresponding characteristics of the lifting scheme implementation of Bio. 5/3 wavelet filter bank. On the other hand, such IIR filter banks possess superior band discriminations and perfect roll-off frequency characteristics when compared to their Bio. 5/3 wavelet FIR counterparts.
Joint multifractal analysis based on wavelet leaders
Jiang, Zhi-Qiang; Yang, Yan-Hong; Wang, Gang-Jin; Zhou, Wei-Xing
2017-12-01
Mutually interacting components form complex systems and these components usually have long-range cross-correlated outputs. Using wavelet leaders, we propose a method for characterizing the joint multifractal nature of these long-range cross correlations; we call this method joint multifractal analysis based on wavelet leaders (MF-X-WL). We test the validity of the MF-X-WL method by performing extensive numerical experiments on dual binomial measures with multifractal cross correlations and bivariate fractional Brownian motions (bFBMs) with monofractal cross correlations. Both experiments indicate that MF-X-WL is capable of detecting cross correlations in synthetic data with acceptable estimating errors. We also apply the MF-X-WL method to pairs of series from financial markets (returns and volatilities) and online worlds (online numbers of different genders and different societies) and determine intriguing joint multifractal behavior.
Wavelets and their applications past and future
Coifman, Ronald R.
2009-04-01
As this is a conference on mathematical tools for defense, I would like to dedicate this talk to the memory of Louis Auslander, who through his insights and visionary leadership, brought powerful new mathematics into DARPA, he has provided the main impetus to the development and insertion of wavelet based processing in defense. My goal here is to describe the evolution of a stream of ideas in Harmonic Analysis, ideas which in the past have been mostly applied for the analysis and extraction of information from physical data, and which now are increasingly applied to organize and extract information and knowledge from any set of digital documents, from text to music to questionnaires. This form of signal processing on digital data, is part of the future of wavelet analysis.
Detecting Impulses in Mechanical Signals by Wavelets
Directory of Open Access Journals (Sweden)
Yang W-X
2004-01-01
Full Text Available The presence of periodical or nonperiodical impulses in vibration signals often indicates the occurrence of machine faults. This knowledge is applied to the fault diagnosis of such machines as engines, gearboxes, rolling element bearings, and so on. The development of an effective impulse detection technique is necessary and significant for evaluating the working condition of these machines, diagnosing their malfunctions, and keeping them running normally over prolong periods. With the aid of wavelet transforms, a wavelet-based envelope analysis method is proposed. In order to suppress any undesired information and highlight the features of interest, an improved soft threshold method has been designed so that the inspected signal is analyzed in a more exact way. Furthermore, an impulse detection technique is developed based on the aforementioned methods. The effectiveness of the proposed technique on the extraction of impulsive features of mechanical signals has been proved by both simulated and practical experiments.
Wavelet-Based Monitoring for Biosurveillance
Directory of Open Access Journals (Sweden)
Galit Shmueli
2013-07-01
Full Text Available Biosurveillance, focused on the early detection of disease outbreaks, relies on classical statistical control charts for detecting disease outbreaks. However, such methods are not always suitable in this context. Assumptions of normality, independence and stationarity are typically violated in syndromic data. Furthermore, outbreak signatures are typically of unknown patterns and, therefore, call for general detectors. We propose wavelet-based methods, which make less assumptions and are suitable for detecting abnormalities of unknown form. Wavelets have been widely used for data denoising and compression, but little work has been published on using them for monitoring. We discuss monitoring-based issues and illustrate them using data on military clinic visits in the USA.
An introduction to random vibrations, spectral & wavelet analysis
Newland, D E
2005-01-01
One of the first engineering books to cover wavelet analysis, this classic text describes and illustrates basic theory, with a detailed explanation of the workings of discrete wavelet transforms. Computer algorithms are explained and supported by examples and a set of problems, and an appendix lists ten computer programs for calculating and displaying wavelet transforms.Starting with an introduction to probability distributions and averages, the text examines joint probability distributions, ensemble averages, and correlation; Fourier analysis; spectral density and excitation response relation
On transforms between Gabor frames and wavelet frames
DEFF Research Database (Denmark)
Christensen, Ole; Goh, Say Song
2013-01-01
We describe a procedure that enables us to construct dual pairs of wavelet frames from certain dual pairs of Gabor frames. Applying the construction to Gabor frames generated by appropriate exponential Bsplines gives wavelet frames generated by functions whose Fourier transforms are compactly...... supported splines with geometrically distributed knot sequences. There is also a reverse transform, which yields pairs of dual Gabor frames when applied to certain wavelet frames....
Wavelet denoising for quantum noise removal in chest digital tomosynthesis.
Gomi, Tsutomu; Nakajima, Masahiro; Umeda, Tokuo
2015-01-01
Quantum noise impairs image quality in chest digital tomosynthesis (DT). A wavelet denoising processing algorithm for selectively removing quantum noise was developed and tested. A wavelet denoising technique was implemented on a DT system and experimentally evaluated using chest phantom measurements including spatial resolution. Comparison was made with an existing post-reconstruction wavelet denoising processing algorithm reported by Badea et al. (Comput Med Imaging Graph 22:309-315, 1998). The potential DT quantum noise decrease was evaluated using different exposures with our technique (pre-reconstruction and post-reconstruction wavelet denoising processing via the balance sparsity-norm method) and the existing wavelet denoising processing algorithm. Wavelet denoising processing algorithms such as the contrast-to-noise ratio (CNR), root mean square error (RMSE) were compared with and without wavelet denoising processing. Modulation transfer functions (MTF) were evaluated for the in-focus plane. We performed a statistical analysis (multi-way analysis of variance) using the CNR and RMSE values. Our wavelet denoising processing algorithm significantly decreased the quantum noise and improved the contrast resolution in the reconstructed images (CNR and RMSE: pre-balance sparsity-norm wavelet denoising processing versus existing wavelet denoising processing, Pwavelet denoising processing versus existing wavelet denoising processing, Pwavelet denoising processing, Pwavelet denoising processing algorithm caused MTF deterioration. A balance sparsity-norm wavelet denoising processing algorithm for removing quantum noise in DT was demonstrated to be effective for certain classes of structures with high-frequency component features. This denoising approach may be useful for a variety of clinical applications for chest digital tomosynthesis when quantum noise is present.
Wavelet Decomposition of the Financial Market
Czech Academy of Sciences Publication Activity Database
Vošvrda, Miloslav; Vácha, Lukáš
2007-01-01
Roč. 16, č. 1 (2007), s. 38-54 ISSN 1210-0455 R&D Projects: GA ČR GA402/04/1026; GA ČR(CZ) GA402/06/1417 Grant - others:GA UK(CZ) 454/2004/A-EK FSV Institutional research plan: CEZ:AV0Z10750506 Keywords : agents' trading strategies * heterogeneous agents model with stochastic memory * worst out algorithm * wavelet Subject RIV: AH - Economics
Cardiac Arrhythmia Classification by Wavelet Transform
Hadji Salah; Ellouze Noureddine
2015-01-01
Cardiovascular diseases are the major public health parameter; they are the leading causes of mortality in the world. In fact many studies have been implemented to reduce the risk, including promoting education, prevention, and monitoring of patients at risk. In this paper we propose to develop classification system heartbeats. This system is based mainly on Wavelet Transform to extract features and Kohonen self-organization map the arrhythmias are considered in this study: N,(Normal), V(Prem...
Wavelet Scattering on the Pitch Spiral
Lostanlen, Vincent; Mallat, Stéphane
2016-01-01
We present a new representation of harmonic sounds that linearizes the dynamics of pitch and spectral envelope, while remaining stable to deformations in the time-frequency plane. It is an instance of the scattering transform, a generic operator which cascades wavelet convolutions and modulus nonlinearities. It is derived from the pitch spiral, in that convolutions are successively performed in time, log-frequency, and octave index. We give a closed-form approximation of spiral scattering coe...
Stability of wavelet frames with matrix dilations
DEFF Research Database (Denmark)
Christensen, Ole; Sun, Wenchang
2006-01-01
Under certain assumptions we show that a wavelet frame {Tau(A(j), b(j,k))psi} (j,k is an element of Z) := {vertical bar detA(j)vertical bar(-1/2) psi(A(j)(-1)(x - b(j,k)))} (j,k is an element of Z) in L-2(R-d) remains a frame when the dilation matrices A(j) and the translation parameters b...
Structural Pounding Detection by Using Wavelet Scalogram
Directory of Open Access Journals (Sweden)
Shutao Xing
2012-01-01
Full Text Available Structural pounding can cause considerable damage and even lead to collapse of structures. Most research focuses on modeling, parameter investigation, and mitigation approaches. With the development of structural health monitoring, the on-line detection of pounding becomes possible. The detection of pounding can provide useful information of potential damage of structures. This paper proposed using wavelet scalograms of dynamic response to detect pounding and examined the feasibility of this method. Numerical investigations were performed on a pounding system that consisted of a damped single-degree-of-freedom (SDOF structure and a rigid barrier. Hertz contact model was used to simulate pounding behavior. The responses and pounding forces of the system under harmonic and earthquake excitations were numerically solved. The wavelet scalograms of acceleration responses were used to identify poundings. It was found that the scalograms can indicate the occurrence of pounding and occurrence time very well. The severity of the poundings was also approximately estimated. Experimental studies were carried out, in which shake table tests were conducted on a bridge model that underwent pounding between its different components during ground motion excitation. The wavelet scalograms of the bridge responses indicated pounding occurrence quite well. Hence the conclusions from the numerical studies were verified experimentally.
Wavelets in Recognition of Bird Sounds
Directory of Open Access Journals (Sweden)
Juha T. Tanttu
2007-01-01
Full Text Available This paper presents a novel method to recognize inharmonic and transient bird sounds efficiently. The recognition algorithm consists of feature extraction using wavelet decomposition and recognition using either supervised or unsupervised classifier. The proposed method was tested on sounds of eight bird species of which five species have inharmonic sounds and three reference species have harmonic sounds. Inharmonic sounds are not well matched to the conventional spectral analysis methods, because the spectral domain does not include any visible trajectories that computer can track and identify. Thus, the wavelet analysis was selected due to its ability to preserve both frequency and temporal information, and its ability to analyze signals which contain discontinuities and sharp spikes. The shift invariant feature vectors calculated from the wavelet coefficients were used as inputs of two neural networks: the unsupervised self-organizing map (SOM and the supervised multilayer perceptron (MLP. The results were encouraging: the SOM network recognized 78% and the MLP network 96% of the test sounds correctly.
Denoising solar radiation data using coiflet wavelets
Energy Technology Data Exchange (ETDEWEB)
Karim, Samsul Ariffin Abdul, E-mail: samsul-ariffin@petronas.com.my; Janier, Josefina B., E-mail: josefinajanier@petronas.com.my; Muthuvalu, Mohana Sundaram, E-mail: mohana.muthuvalu@petronas.com.my [Department of Fundamental and Applied Sciences, Faculty of Sciences and Information Technology, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31750 Tronoh, Perak Darul Ridzuan (Malaysia); Hasan, Mohammad Khatim, E-mail: khatim@ftsm.ukm.my [Jabatan Komputeran Industri, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor (Malaysia); Sulaiman, Jumat, E-mail: jumat@ums.edu.my [Program Matematik dengan Ekonomi, Universiti Malaysia Sabah, Beg Berkunci 2073, 88999 Kota Kinabalu, Sabah (Malaysia); Ismail, Mohd Tahir [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM Minden, Penang (Malaysia)
2014-10-24
Signal denoising and smoothing plays an important role in processing the given signal either from experiment or data collection through observations. Data collection usually was mixed between true data and some error or noise. This noise might be coming from the apparatus to measure or collect the data or human error in handling the data. Normally before the data is use for further processing purposes, the unwanted noise need to be filtered out. One of the efficient methods that can be used to filter the data is wavelet transform. Due to the fact that the received solar radiation data fluctuates according to time, there exist few unwanted oscillation namely noise and it must be filtered out before the data is used for developing mathematical model. In order to apply denoising using wavelet transform (WT), the thresholding values need to be calculated. In this paper the new thresholding approach is proposed. The coiflet2 wavelet with variation diminishing 4 is utilized for our purpose. From numerical results it can be seen clearly that, the new thresholding approach give better results as compare with existing approach namely global thresholding value.
Teolis, Anthony; Baras, John S.
1995-04-01
We present a highly powerful, modular, and interactive software tool for the analysis of time- frequency coherent signals via wavelet transformations. A major design goal of the Wavelet Signal Processing Workstation (WSPW) is to maximize ease of use while minimizing programming complexity. As such, the WSPW makes ample use of graphical mouse driven user interfaces and, in turn, allows powerful signal processing, classification, and identification techniques to be rapidly implemented and tested. Because it has been developed using MATLAB, the WSPW is easily extensible and inherently portable between varying system architectures. Although the emphasis of this paper is on the wavelet representation of signals, the WSPW has proven itself a valuable tool in applications including radar source identification and signal classification.
Bayesian Wavelet Shrinkage of the Haar-Fisz Transformed Wavelet Periodogram
2015-01-01
It is increasingly being realised that many real world time series are not stationary and exhibit evolving second-order autocovariance or spectral structure. This article introduces a Bayesian approach for modelling the evolving wavelet spectrum of a locally stationary wavelet time series. Our new method works by combining the advantages of a Haar-Fisz transformed spectrum with a simple, but powerful, Bayesian wavelet shrinkage method. Our new method produces excellent and stable spectral estimates and this is demonstrated via simulated data and on differenced infant electrocardiogram data. A major additional benefit of the Bayesian paradigm is that we obtain rigorous and useful credible intervals of the evolving spectral structure. We show how the Bayesian credible intervals provide extra insight into the infant electrocardiogram data. PMID:26381141
Wavelet-based verification of the quantitative precipitation forecast
Yano, Jun-Ichi; Jakubiak, Bogumil
2016-06-01
This paper explores the use of wavelets for spatial verification of quantitative precipitation forecasts (QPF), and especially the capacity of wavelets to provide both localization and scale information. Two 24-h forecast experiments using the two versions of the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) on 22 August 2010 over Poland are used to illustrate the method. Strong spatial localizations and associated intermittency of the precipitation field make verification of QPF difficult using standard statistical methods. The wavelet becomes an attractive alternative, because it is specifically designed to extract spatially localized features. The wavelet modes are characterized by the two indices for the scale and the localization. Thus, these indices can simply be employed for characterizing the performance of QPF in scale and localization without any further elaboration or tunable parameters. Furthermore, spatially-localized features can be extracted in wavelet space in a relatively straightforward manner with only a weak dependence on a threshold. Such a feature may be considered an advantage of the wavelet-based method over more conventional "object" oriented verification methods, as the latter tend to represent strong threshold sensitivities. The present paper also points out limits of the so-called "scale separation" methods based on wavelets. Our study demonstrates how these wavelet-based QPF verifications can be performed straightforwardly. Possibilities for further developments of the wavelet-based methods, especially towards a goal of identifying a weak physical process contributing to forecast error, are also pointed out.
Wavelet Approach to Data Analysis, Manipulation, Compression, and Communication
National Research Council Canada - National Science Library
Chui, Charles K
2007-01-01
...; secondly, based on minimum-energy criteria, new data processing tools, particularly variational algorithms and optimal wavelet thresholding methods, with applications to image restoration, were introduced...
Discrete Wavelet Transform-Partial Least Squares Versus Derivative ...
African Journals Online (AJOL)
Discrete Wavelet Transform-Partial Least Squares Versus Derivative Ratio Spectrophotometry for Simultaneous Determination of Chlorpheniramine Maleate and Dexamethasone in the Presence of Parabens in Pharmaceutical Dosage Form.
[Wavelet entropy analysis of spontaneous EEG signals in Alzheimer's disease].
Zhang, Meiyun; Zhang, Benshu; Chen, Ying
2014-08-01
Wavelet entropy is a quantitative index to describe the complexity of signals. Continuous wavelet transform method was employed to analyze the spontaneous electroencephalogram (EEG) signals of mild, moderate and severe Alzheimer's disease (AD) patients and normal elderly control people in this study. Wavelet power spectrums of EEG signals were calculated based on wavelet coefficients. Wavelet entropies of mild, moderate and severe AD patients were compared with those of normal controls. The correlation analysis between wavelet entropy and MMSE score was carried out. There existed significant difference on wavelet entropy among mild, moderate, severe AD patients and normal controls (Pwavelet entropy for mild, moderate, severe AD patients was significantly lower than that for normal controls, which was related to the narrow distribution of their wavelet power spectrums. The statistical difference was significant (Pwavelet entropy of EEG and the MMSE score were significantly correlated (r= 0. 601-0. 799, PWavelet entropy is a quantitative indicator describing the complexity of EEG signals. Wavelet entropy is likely to be an electrophysiological index for AD diagnosis and severity assessment.
Multiscale Object Recognition and Feature Extraction Using Wavelet Networks
National Research Council Canada - National Science Library
Jaggi, Seema; Karl, W. C; Krim, Hamid; Willsky, Alan S
1995-01-01
In this work we present a novel method of object recognition and feature generation based on multiscale object descriptions obtained using wavelet networks in combination with morphological filtering...
International Nuclear Information System (INIS)
Khawaja, Z; Mazeran, P-E; Bigerelle, M; Guillemot, G; Mansori, M El
2011-01-01
This article presents a multi-scale theory based on wavelet decomposition to characterize the evolution of roughness in relation with a finishing process or an observed surface property. To verify this approach in production conditions, analyses were developed for the finishing process of the hardened steel by abrasive belts. These conditions are described by seven parameters considered in the Tagushi experimental design. The main objective of this work is to identify the most relevant roughness parameter and characteristic length allowing to assess the influence of finishing process, and to test the relevance of the measurement scale. Results show that wavelet approach allows finding this scale.
Rosandra Santos Mottola Lemos
2004-01-01
Neste trabalho, apresentamos uma solução analítica para as equações difusivas unidimensionais da Teoria Geral de Perturbação em uma placa heterogênea, isto é, apresentamos as soluções analíticas para os problemas de autovalor para o fluxo de nêutrons e para o fluxo adjunto de nêutrons, para o cálculo do fator de multiplicação efetivo (keff), para o problema de fonte fixa e para o problema de função auxiliar. Resolvemos todos os problemas mencionados aplicando a Transformada de Laplace em uma ...
Wavelet based image visibility enhancement of IR images
Jiang, Qin; Owechko, Yuri; Blanton, Brendan
2016-05-01
Enhancing the visibility of infrared images obtained in a degraded visibility environment is very important for many applications such as surveillance, visual navigation in bad weather, and helicopter landing in brownout conditions. In this paper, we present an IR image visibility enhancement system based on adaptively modifying the wavelet coefficients of the images. In our proposed system, input images are first filtered by a histogram-based dynamic range filter in order to remove sensor noise and convert the input images into 8-bit dynamic range for efficient processing and display. By utilizing a wavelet transformation, we modify the image intensity distribution and enhance image edges simultaneously. In the wavelet domain, low frequency wavelet coefficients contain original image intensity distribution while high frequency wavelet coefficients contain edge information for the original images. To modify the image intensity distribution, an adaptive histogram equalization technique is applied to the low frequency wavelet coefficients while to enhance image edges, an adaptive edge enhancement technique is applied to the high frequency wavelet coefficients. An inverse wavelet transformation is applied to the modified wavelet coefficients to obtain intensity images with enhanced visibility. Finally, a Gaussian filter is used to remove blocking artifacts introduced by the adaptive techniques. Since wavelet transformation uses down-sampling to obtain low frequency wavelet coefficients, histogram equalization of low-frequency coefficients is computationally more efficient than histogram equalization of the original images. We tested the proposed system with degraded IR images obtained from a helicopter landing in brownout conditions. Our experimental results show that the proposed system is effective for enhancing the visibility of degraded IR images.
Noisy signal filtration using complex wavelet basis sets
Yaseen, A. S.; Pavlova, O. N.; Pavlov, A. N.
2017-07-01
Methods of noisy signal filtration using a discrete wavelet transform (DWT) with real basis sets of the Daubechies family are compared to methods employing a double-density dual-tree complex wavelet transform (DDCWT) with excess (nonorthonormalized) basis sets. Recommendations concerning the choice of filter parameters for minimization of the error of noisy signal filtration are formulated.
CLOSED FORM OF THE STEERED ELONGATED HERMITE-GAUSS WAVELETS
Papari, Giuseppe; Campisi, Patrizio; Petkov, Nicolai
2010-01-01
We provide a closed form, both in the spatial and in the frequency domain, of a family of wavelets which arise from steering elongated Hermite-Gauss filters. These wavelets have interesting mathematical properties, as they form new dyadic families of eigenfunctions of the 2D Fourier transform, and
Multidimensional filter banks and wavelets research developments and applications
Levy, Bernard
1997-01-01
Multidimensional Filter Banks and Wavelets: Reserach Developments and Applications brings together in one place important contributions and up-to-date research results in this important area. Multidimensional Filter Banks and Wavelets: Research Developments and Applications serves as an excellent reference, providing insight into some of the most important research issues in the field.
Polarized spectral features of human breast tissues through wavelet ...
Indian Academy of Sciences (India)
Abstract. Fluorescence characteristics of human breast tissues are investigated through wavelet transform and principal component analysis (PCA). Wavelet transform of polar- ized fluorescence spectra of human breast tissues is found to localize spectral features that can reliably differentiate different tissue types.
Wavelet based denoising of power quality events for characterization
African Journals Online (AJOL)
distribution of pure sine voltage wave, voltage sag, swell, transients, harmonics, impulse, notching, fluctuation and flicker are obtained using wavelet transform. The presence of noise degrades the detection capability of wavelet based method and therefore effect of noise on different signal is analyzed. The noise corrupted ...
Journal Afrika Statistika ISSN 0852-0305 Nonlinear wavelet ...
African Journals Online (AJOL)
7, 2012, pages 391–411. Nonlinear wavelet regression function estimator for censored dependent data. 392 regularity (discontinuities, cusps, sharp spikes, etc.) of the underlying curves to be estimated. This is a remarkable property of the wavelet method when compared to other common estimation techniques, such as the ...
Space-time adaptive wavelet methods for parabolic evolution problems
Schwab, C.; Stevenson, R.
2009-01-01
With respect to space-time tensor-product wavelet bases, parabolic initial boundary value problems are equivalently formulated as bi-infinite matrix problems. Adaptive wavelet methods are shown to yield sequences of approximate solutions which converge at the optimal rate. In case the spatial domain
The canonical and alternate duals of a wavelet frame
DEFF Research Database (Denmark)
Bownik, Marcin; Lemvig, Jakob
We show that there exists a frame wavelet $\\psi$ with fast decay in the time domain and compact support in the frequency domain generating a wavelet system whose canonical dual frame cannot be generated by an arbitrary number of generators. On the other hand, there exists infinitely many alternate...
Data driven design of an orthogonal wavelet with vanishing moments
Peeters, Ralf; Karel, Joël
2014-01-01
We present a framework to design an orthogonal wavelet with compact support and vanishing moments, tuned to a given application. This is achieved by optimizing a criterion, such that a prototype signal, which is characteristic for the application, becomes sparse in the wavelet domain. This approach
Wavelet based feature extraction and visualization in hyperspectral tissue characterization.
Denstedt, Martin; Bjorgan, Asgeir; Milanič, Matija; Randeberg, Lise Lyngsnes
2014-12-01
Hyperspectral images of tissue contain extensive and complex information relevant for clinical applications. In this work, wavelet decomposition is explored for feature extraction from such data. Wavelet methods are simple and computationally effective, and can be implemented in real-time. The aim of this study was to correlate results from wavelet decomposition in the spectral domain with physical parameters (tissue oxygenation, blood and melanin content). Wavelet decomposition was tested on Monte Carlo simulations, measurements of a tissue phantom and hyperspectral data from a human volunteer during an occlusion experiment. Reflectance spectra were decomposed, and the coefficients were correlated to tissue parameters. This approach was used to identify wavelet components that can be utilized to map levels of blood, melanin and oxygen saturation. The results show a significant correlation (p wavelet components. The tissue parameters could be mapped using a subset of the calculated components due to redundancy in spectral information. Vessel structures are well visualized. Wavelet analysis appears as a promising tool for extraction of spectral features in skin. Future studies will aim at developing quantitative mapping of optical properties based on wavelet decomposition.
Wavelet transform of generalized functions in K′{Mp} spaces
Indian Academy of Sciences (India)
Using convolution theory in K { M p } space we obtain bounded results for the wavelet transform. Calderón-type reproducing formula is derived in distribution sense as an application of the same. An inversion formula for the wavelet transform of generalized functions is established.
The canonical and alternate duals of a wavelet frame
DEFF Research Database (Denmark)
Lemvig, Jakob; Bownik, Marcin
2007-01-01
We show that there exists a frame wavelet ψ with fast decay in the time domain and compact support in the frequency domain generating a wavelet system whose canonical dual frame cannot be generated by an arbitrary number of generators. On the other hand, there exists infinitely many alternate duals...
Polarized spectral features of human breast tissues through wavelet ...
Indian Academy of Sciences (India)
Fluorescence characteristics of human breast tissues are investigated through wavelet transform and principal component analysis (PCA). Wavelet transform of polarized fluorescence spectra of human breast tissues is found to localize spectral features that can reliably differentiate different tissue types. The emission range ...
Wavelet Based Diagnosis and Protection of Electric Motors
Khan, M. Abdesh Shafiel Kafiey; Rahman, M. Azizur
2010-01-01
In this chapter, a short review of conventional Fourier transforms and new wavelet based faults diagnostic and protection techniques for electric motors is presented. The new hybrid wavelet packet transform (WPT) and neural network (NN) based faults diagnostic algorithm is developed and implemented for electric motors. The proposed WPT and NN
SVD-based digital image watermarking using complex wavelet ...
Indian Academy of Sciences (India)
Keywords. Digital image watermarking; complex wavelet transform; singular ... In watermarking trial, SVD is applied to the image matrix; then watermark ..... IEEE. Trans. on Multimedia 4(1): 121–128. Loo P, Kingsbury N G 2000 Digital watermarking using complex wavelets. Int. Conf. on Image. Processing 29–32. Loo P ...
Fault diagnosis in gear using wavelet envelope power spectrum ...
African Journals Online (AJOL)
An experimental data set is used to compare the diagnostic capability of the fast Fourier transform power spectrum to the wavelet envelope power spectrum as respectively computed using Laplace and Morlet wavelet functions. The gear testing apparatus was used for experimental studies to obtain the vibration signal from ...
Electrocardiogram de-noising based on forward wavelet transform ...
Indian Academy of Sciences (India)
Abstract. In this paper, we propose a new technique of Electrocardiogram (ECG) signal de-noising based on thresholding of the coefficients obtained from the appli- cation of the Forward Wavelet Transform Translation Invariant (FWT_TI) to each. Bionic Wavelet coefficient. The De-noise De-noised ECG is obtained from the ...
Directional wavelet based features for colonic polyp classification.
Wimmer, Georg; Tamaki, Toru; Tischendorf, J J W; Häfner, Michael; Yoshida, Shigeto; Tanaka, Shinji; Uhl, Andreas
2016-07-01
In this work, various wavelet based methods like the discrete wavelet transform, the dual-tree complex wavelet transform, the Gabor wavelet transform, curvelets, contourlets and shearlets are applied for the automated classification of colonic polyps. The methods are tested on 8 HD-endoscopic image databases, where each database is acquired using different imaging modalities (Pentax's i-Scan technology combined with or without staining the mucosa), 2 NBI high-magnification databases and one database with chromoscopy high-magnification images. To evaluate the suitability of the wavelet based methods with respect to the classification of colonic polyps, the classification performances of 3 wavelet transforms and the more recent curvelets, contourlets and shearlets are compared using a common framework. Wavelet transforms were already often and successfully applied to the classification of colonic polyps, whereas curvelets, contourlets and shearlets have not been used for this purpose so far. We apply different feature extraction techniques to extract the information of the subbands of the wavelet based methods. Most of the in total 25 approaches were already published in different texture classification contexts. Thus, the aim is also to assess and compare their classification performance using a common framework. Three of the 25 approaches are novel. These three approaches extract Weibull features from the subbands of curvelets, contourlets and shearlets. Additionally, 5 state-of-the-art non wavelet based methods are applied to our databases so that we can compare their results with those of the wavelet based methods. It turned out that extracting Weibull distribution parameters from the subband coefficients generally leads to high classification results, especially for the dual-tree complex wavelet transform, the Gabor wavelet transform and the Shearlet transform. These three wavelet based transforms in combination with Weibull features even outperform the state
Some Results on the Wavelet Packet Decomposition of Nonstationary Processes
Directory of Open Access Journals (Sweden)
Sami Touati
2002-11-01
Full Text Available Wavelet/wavelet packet decomposition has become a very useful tool in describing nonstationary processes. Important examples of nonstationary processes encountered in practice are cyclostationary processes or almost-cyclostationary processes. In this paper, we study the statistical properties of the wavelet packet decomposition of a large class of nonstationary processes, including in particular cyclostationary and almost-cyclostationary processes. We first investigate in a general framework, the existence and some properties of the cumulants of wavelet packet coefficients. We then study more precisely the almost-cyclostationary case, and determine the asymptotic distributions of wavelet packet coefficients. Finally, we particularize some of our results in the cyclostationary case before providing some illustrative simulations.
Denoising in Wavelet Packet Domain via Approximation Coefficients
Directory of Open Access Journals (Sweden)
Zahra Vahabi
2012-01-01
Full Text Available In this paper we propose a new approach in the wavelet domain for image denoising. In recent researches wavelet transform has introduced a time-Frequency transform for computing wavelet coefficient and eliminating noise. Some coefficients have effected smaller than the other's from noise, so they can be use reconstruct images with other subbands. We have developed Approximation image to estimate better denoised image. Naturally noiseless subimage introduced image with lower noise. Beside denoising we obtain a bigger compression rate. Increasing image contrast is another advantage of this method. Experimental results demonstrate that our approach compares favorably to more typical methods of denoising and compression in wavelet domain.100 images of LIVE Dataset were tested, comparing signal to noise ratios (SNR,soft thresholding was %1.12 better than hard thresholding, POAC was %1.94 better than soft thresholding and POAC with wavelet packet was %1.48 better than POAC.
International Conference and Workshop on Fractals and Wavelets
Barnsley, Michael; Devaney, Robert; Falconer, Kenneth; Kannan, V; PB, Vinod
2014-01-01
Fractals and wavelets are emerging areas of mathematics with many common factors which can be used to develop new technologies. This volume contains the selected contributions from the lectures and plenary and invited talks given at the International Workshop and Conference on Fractals and Wavelets held at Rajagiri School of Engineering and Technology, India from November 9-12, 2013. Written by experts, the contributions hope to inspire and motivate researchers working in this area. They provide more insight into the areas of fractals, self similarity, iterated function systems, wavelets and the applications of both fractals and wavelets. This volume will be useful for the beginners as well as experts in the fields of fractals and wavelets.
Coherent states versus De Broglie-Wavelets
International Nuclear Information System (INIS)
Barut, A.O.
1993-08-01
There are two types of nonspreading localized wave forms representing a stable, individual, indivisible, single quantum particle with interference properties endowed with classical (hidden) parameters, i.e. initial positions and velocity: coherent states and wavelets. The first is exactly known for oscillator, the second for free particles. Their relation and their construction is discussed from a new unified point of view. We then extend this contraction to the Coulomb problem, where with the introduction of a new time variable T, nonspreading states are obtained. (author). 10 refs
Lung tissue classification using wavelet frames.
Depeursinge, Adrien; Sage, Daniel; Hidki, Asmâa; Platon, Alexandra; Poletti, Pierre-Alexandre; Unser, Michael; Müller, Henning
2007-01-01
We describe a texture classification system that identifies lung tissue patterns from high-resolution computed tomography (HRCT) images of patients affected with interstitial lung diseases (ILD). This pattern recognition task is part of an image-based diagnostic aid system for ILDs. Five lung tissue patterns (healthy, emphysema, ground glass, fibrosis and microdules) selected from a multimedia database are classified using the overcomplete discrete wavelet frame decompostion combined with grey-level histogram features. The overall multiclass accuracy reaches 92.5% of correct matches while combining the two types of features, which are found to be complementary.
Wavelet analysis of the nuclear phase space
International Nuclear Information System (INIS)
Jouault, B.; Sebille, F.; Mota, V. de la.
1997-01-01
The description of transport phenomena in nuclear matter is addressed in a new approach based on the mathematical theory of wavelets and the projection methods of statistical physics. The advantage of this framework is to offer the opportunity to use information concepts common to both the formulation of physical properties and the mathematical description. This paper focuses on two features, the extraction of relevant informations using the geometrical properties of the underlying phase space and the optimization of the theoretical and numerical treatments based on convenient choices of the representation spaces. (author)
Wavelet Packet Entropy for Heart Murmurs Classification
Directory of Open Access Journals (Sweden)
Fatemeh Safara
2012-01-01
Full Text Available Heart murmurs are the first signs of cardiac valve disorders. Several studies have been conducted in recent years to automatically differentiate normal heart sounds, from heart sounds with murmurs using various types of audio features. Entropy was successfully used as a feature to distinguish different heart sounds. In this paper, new entropy was introduced to analyze heart sounds and the feasibility of using this entropy in classification of five types of heart sounds and murmurs was shown. The entropy was previously introduced to analyze mammograms. Four common murmurs were considered including aortic regurgitation, mitral regurgitation, aortic stenosis, and mitral stenosis. Wavelet packet transform was employed for heart sound analysis, and the entropy was calculated for deriving feature vectors. Five types of classification were performed to evaluate the discriminatory power of the generated features. The best results were achieved by BayesNet with 96.94% accuracy. The promising results substantiate the effectiveness of the proposed wavelet packet entropy for heart sounds classification.
Cryptocurrency price drivers: Wavelet coherence analysis revisited.
Phillips, Ross C; Gorse, Denise
2018-01-01
Cryptocurrencies have experienced recent surges in interest and price. It has been discovered that there are time intervals where cryptocurrency prices and certain online and social media factors appear related. In addition it has been noted that cryptocurrencies are prone to experience intervals of bubble-like price growth. The hypothesis investigated here is that relationships between online factors and price are dependent on market regime. In this paper, wavelet coherence is used to study co-movement between a cryptocurrency price and its related factors, for a number of examples. This is used alongside a well-known test for financial asset bubbles to explore whether relationships change dependent on regime. The primary finding of this work is that medium-term positive correlations between online factors and price strengthen significantly during bubble-like regimes of the price series; this explains why these relationships have previously been seen to appear and disappear over time. A secondary finding is that short-term relationships between the chosen factors and price appear to be caused by particular market events (such as hacks / security breaches), and are not consistent from one time interval to another in the effect of the factor upon the price. In addition, for the first time, wavelet coherence is used to explore the relationships between different cryptocurrencies.
Wavelet transform techniques and signal analysis
International Nuclear Information System (INIS)
Perez, R.B.; Mattingly, J.K.; Tennessee Univ., Knoxville, TN; Perez, J.S.
1993-01-01
Traditionally, the most widely used signal analysis tool is the Fourier transform which, by producing power spectral densities (PSDs), allows time dependent signals to be studied in the frequency domain. However, the Fourier transform is global -- it extends over the entire time domain -- which makes it ill-suited to study nonstationary signals which exhibit local temporal changes in the signal's frequency content. To analyze nonstationary signals, the family of transforms commonly designated as short-time Fourier transforms (STFTs), capable of identifying temporally localized changes in the signal's frequency content, were developed by employing window functions to isolate temporal regions of the signal. For example, the Gabor STFT uses a Gaussian window. However, the applicability of STFTs is limited by various inadequacies. The Wavelet transform (NW), recently developed by Grossman and Morlet and explored in depth by Daubechies (2) and Mallat, remedies the inadequacies of STFTs. Like the Fourier transform, the WT can be implemented as a discrete transform (DWT) or as a continuous (integral) transform (CWT). This paper briefly illustrates some of the potential applications of the wavelet transform algorithms to signal analysis
Wavelet Analysis of Ripples in Spilling Breakers
Liu, Xinan; Seer, Gunther; Duncan, James H.
2003-11-01
The wavelength distributions of ripples along the crests of weak spilling breakers are investigated with continuous and descrete wavelet transforms. The waves were generated mechanically via a side-band instability method in a tank that is 48.0 m long, 1.2 m wide and 1.0 m deep. The crest profile histories were measured with a photographic technique that employs a high-speed camera, a laser light sheet and fluorescent dye. The amplitude and location of each length scale was determined by multi-resolution decomposition and the phase speeds of these length scales were determined with wavelet cross-correlation. It is found that at the beginning of the breaking process the length scales of the ripples fall in a narrow range from 4 mm to 11 mm and the phase speeds of the ripples relative to the wave crest are less than 10 cm/s, independent of length scale. In the later stages of breaking, a wide range of ripple wavelengths appear at each instant in time. The phase speed of the ripples (relative the wave crest) increases from 15 to 35 cm/s as the wavelength decreases. However, the phase speeds of all wavelengths are less than the value obtained from the linear-theory dispersion curve for gravity-capillary waves in still water. This work was supported by the National Science Foundation under grant OCE9818910.
Solar activity explored with new wavelet methods
Directory of Open Access Journals (Sweden)
H. Lundstedt
2005-06-01
Full Text Available In order to improve the forecasts of the impact of solar activity on the terrestrial environment on time scales longer than days, improved understanding and forecasts of the solar activity are needed. The first results of a new approach of modelling and forecasting solar activity are presented. Time series of solar activity indicators, such as sunspot number, group sunspot number, F10.7, E10.7, solar magnetic mean field, Mount Wilson plage and sunspot index, have been studied with new wavelet methods; ampligrams and time-scale spectra. Wavelet power spectra of the sunspot number for the period 1610 up to the present show not only that a dramatic increase in the solar activity took place after 1940 but also that an interesting change occurred in 1990. The main 11-year solar cycle was further studied with ampligrams for the period after 1850. time-scale spectra were used to examine the processes behind the variability of the solar activity. Several interesting deterministic and more stochastic features were detected in the time series of the solar activity indicators. The solar nature of these features will be further studied. Keywords. Solar physics, astrophysics and astronomy (Magnetic fields; Stellar interiors and dynamo theory – Space plasma physics (nonlinear phenomena
Directory of Open Access Journals (Sweden)
Daniel Mendoza Casseres
2013-01-01
Full Text Available La acreditación es el reconocimiento de alta calidad de los programas académicos de las Instituciones de Educación Superior. Uno de los primeros pasos para iniciar la acreditación consiste en ponderar los factores. El método que se utilice debe reflejar los niveles de importancia de los factores para juzgar la calidad total de los programas. La complejidad en la ponderación de los factores se origina en las interacciones de juicios entre directivos, profesores, egresados y estudiantes que podrían conllevar a altas inconsistencias en la ponderación. En esta investigación se propone la aplicación de la Teoría de decisión multicriterio discreta para ponderar factores dentro de unos niveles de inconsistencia aceptables. Se presentan resultados en la ponderación de factores para la acreditación de alta calidad en una facultad de ingeniería, utilizando una herramienta cuantitativa.
Study on Optimal Selection of Wavelet Vanishing Moments for ECG Denoising.
Peng, Ziran; Wang, Guojun
2017-07-04
The frequency characteristics of wavelets and the vanishing moments of wavelet filters are both important parameters of wavelets. Clarifying the relationship between the wavelet frequency characteristics and the vanishing moments of the wavelet filter can provide a theoretical basis for selecting the best wavelet. In this paper, the frequency characteristics of wavelets were analyzed by mathematical modeling, the mathematical relationship between wavelet frequency characteristics and vanishing moments was clarified, the optimal wavelet base function was selected hierarchically according to the amplitude frequency characteristics of ECG signal, and an accurate notch filter was realized according to the frequency characteristics of the noise. The experimental results showed that the optimal orthogonal wavelet analysis for the ECG signals with different frequency characteristics could make the high frequency energy distribution sparser, and the method proposed in this paper could effectively preserve the singularity of the signal and reduce the signal distortion.
Space-based RF signal classification using adaptive wavelet features
Energy Technology Data Exchange (ETDEWEB)
Caffrey, M.; Briles, S.
1995-04-01
RF signals are dispersed in frequency as they propagate through the ionosphere. For wide-band signals, this results in nonlinearly- chirped-frequency, transient signals in the VHF portion of the spectrum. This ionospheric dispersion provide a means of discriminating wide-band transients from other signals (e.g., continuous-wave carriers, burst communications, chirped-radar signals, etc.). The transient nature of these dispersed signals makes them candidates for wavelet feature selection. Rather than choosing a wavelet ad hoc, we adaptively compute an optimal mother wavelet via a neural network. Gaussian weighted, linear frequency modulate (GLFM) wavelets are linearly combined by the network to generate our application specific mother wavelet, which is optimized for its capacity to select features that discriminate between the dispersed signals and clutter (e.g., multiple continuous-wave carriers), not for its ability to represent the dispersed signal. The resulting mother wavelet is then used to extract features for a neutral network classifier. The performance of the adaptive wavelet classifier is the compared to an FFT based neural network classifier.
Low-complexity wavelet filter design for image compression
Majani, E.
1994-01-01
Image compression algorithms based on the wavelet transform are an increasingly attractive and flexible alternative to other algorithms based on block orthogonal transforms. While the design of orthogonal wavelet filters has been studied in significant depth, the design of nonorthogonal wavelet filters, such as linear-phase (LP) filters, has not yet reached that point. Of particular interest are wavelet transforms with low complexity at the encoder. In this article, we present known and new parameterizations of the two families of LP perfect reconstruction (PR) filters. The first family is that of all PR LP filters with finite impulse response (FIR), with equal complexity at the encoder and decoder. The second family is one of LP PR filters, which are FIR at the encoder and infinite impulse response (IIR) at the decoder, i.e., with controllable encoder complexity. These parameterizations are used to optimize the subband/wavelet transform coding gain, as defined for nonorthogonal wavelet transforms. Optimal LP wavelet filters are given for low levels of encoder complexity, as well as their corresponding integer approximations, to allow for applications limited to using integer arithmetic. These optimal LP filters yield larger coding gains than orthogonal filters with an equivalent complexity. The parameterizations described in this article can be used for the optimization of any other appropriate objective function.
Evolutionary Spectra Estimation of Field Measurement Typhoon Processes Using Wavelets
Directory of Open Access Journals (Sweden)
Guang-Dong Zhou
2015-01-01
Full Text Available This paper presents a wavelet-based method for estimating evolutionary power spectral density (EPSD of nonstationary stochastic oscillatory processes and its application to field measured typhoon processes. The EPSD, which is deduced in a closed form based on the definition of the EPSD and the algorithm of the continuous wavelet transform, can be formulated as a sum of squared moduli of the wavelet functions in time domain modulated by frequency-dependent coefficients that relate to the squared values of wavelet coefficients and two wavelet functions with different time shifts. A parametric study is conducted to examine the efficacy of the wavelet-based estimation method and the accuracy of different wavelets. The results indicate that all of the estimated EPSDs have acceptable accuracy in engineering application and the Morlet transform can provide desirable estimations in both time and frequency domains. Finally, the proposed method is adopted to investigate the time-frequency characteristics of the Typhoon Matsa measured in bridge site. The nonstationary energy distribution and stationary frequency component during the whole process are found. The work in this paper may promote an improved understanding of the nonstationary features of typhoon winds.
Signal-dependent independent component analysis by tunable mother wavelets
International Nuclear Information System (INIS)
Seo, Kyung Ho
2006-02-01
The objective of this study is to improve the standard independent component analysis when applied to real-world signals. Independent component analysis starts from the assumption that signals from different physical sources are statistically independent. But real-world signals such as EEG, ECG, MEG, and fMRI signals are not statistically independent perfectly. By definition, standard independent component analysis algorithms are not able to estimate statistically dependent sources, that is, when the assumption of independence does not hold. Therefore before independent component analysis, some preprocessing stage is needed. This paper started from simple intuition that wavelet transformed source signals by 'well-tuned' mother wavelet will be simplified sufficiently, and then the source separation will show better results. By the correlation coefficient method, the tuning process between source signal and tunable mother wavelet was executed. Gamma component of raw EEG signal was set to target signal, and wavelet transform was executed by tuned mother wavelet and standard mother wavelets. Simulation results by these wavelets was shown
Wavelet-Coded OFDM for Next Generation Mobile Communications
DEFF Research Database (Denmark)
Cavalcante, Lucas Costa Pereira; Vegas Olmos, Juan José; Tafur Monroy, Idelfonso
2016-01-01
In this work, we evaluate the performance of Wavelet-Coding into offering robustness for OFDM signals against the combined effects of varying fading and noise bursts. Wavelet-Code enables high diversity gains with a low complex receiver, and, most notably, without compromising the system’s spectral...... efficiency. The results show that the Wavelet-Coded OFDM system achieves a BER of 10−3 with nearly 6 dB less SNR than the convolutional coded OFDM system in frequency selective channels with a normalized channel response variation rate of ζ = 10−4.The proposed system fits as a key enabler for the use of mm...
EEG Artifact Removal Using a Wavelet Neural Network
Nguyen, Hoang-Anh T.; Musson, John; Li, Jiang; McKenzie, Frederick; Zhang, Guangfan; Xu, Roger; Richey, Carl; Schnell, Tom
2011-01-01
!n this paper we developed a wavelet neural network. (WNN) algorithm for Electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural network and the time/frequency property of wavelet. We. compared the WNN algorithm with .the ICA technique ,and a wavelet thresholding method, which was realized by using the Stein's unbiased risk estimate (SURE) with an adaptive gradient-based optimal threshold. Experimental results on a driving test data set show that WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy data.
Identification Method of Mud Shale Fractures Base on Wavelet Transform
Xia, Weixu; Lai, Fuqiang; Luo, Han
2018-01-01
In recent years, inspired by seismic analysis technology, a new method for analysing mud shale fractures oil and gas reservoirs by logging properties has emerged. By extracting the high frequency attribute of the wavelet transform in the logging attribute, the formation information hidden in the logging signal is extracted, identified the fractures that are not recognized by conventional logging and in the identified fracture segment to show the “cycle jump”, “high value”, “spike” and other response effect is more obvious. Finally formed a complete wavelet denoising method and wavelet high frequency identification fracture method.
Analysis of Ultrasonic Transmitted Signal for Apple using Wavelet Transform
International Nuclear Information System (INIS)
Kim, Ki Bok; Lee, Sang Dae; Choi, Man Yong; Kim, Man Soo
2005-01-01
This study was conducted to analyze the ultrasonic transmitted signal for apple using wavelet transform. Fruit consists of nonlinear visco-elastic properties such as flesh, an ovary and rind and lienee most ultrasonic wave is attenuated and its frequency is shifted during passing the fruit. Thus it is not easy to evaluate the internal quality of the fruit using typical ultrasonic parameters such as wave velocity, attenuation, and frequency spectrum. The discrete wavelet transform was applied to the ultrasonic transmitted signal for apple. The magnitude of the first peak frequency of the wavelet basis from the ultrasonic transmitted signal showed a close correlation to the storage time of apple
Wavelets an elementary treatment of theory and applications
Koornwinder, T H
1993-01-01
Nowadays, some knowledge of wavelets is almost mandatory for mathematicians, physicists and electrical engineers. The emphasis in this volume, based on an intensive course on Wavelets given at CWI, Amsterdam, is on the affine case. The first part presents a concise introduction of the underlying theory to the uninitiated reader. The second part gives applications in various areas. Some of the contributions here are a fresh exposition of earlier work by others, while other papers contain new results by the authors. The areas are so diverse as seismic processing, quadrature formulae, and wavelet
Comparative study of wavelets of the first and second generation
International Nuclear Information System (INIS)
Ososkov, G.A.; Shitov, A.B.; Stadnik, A.V.
2001-01-01
In order to compare efficiency a comprehensive set of benchmarking tests is developed, which is used to compare abilities of continuous wavelet transform of the vanishing momenta type as well as the second generation wavelets constructed on the basis of the lifting scheme. It is based on processing of various types of pure and contaminated harmonic signals, delta-function, study of the signal phase dependence and the gain-frequency characteristics. The results of a comparative multiscale analysis allow one to reveal advantages and flaws of the considered types of wavelets
Design of minimum entropy wavelet filters using genetic algorithms
Jasper, Warren J.; Joines, Jeff
2005-11-01
This paper presents a method to design a wavelet-filter that minimizes entropy in the wavelet transform. Filters that minimize entropy in images tend to filter out texture while highlighting features of interest. The design of the wavelet filter is couched as a non-convex optimization problem which is solved using a hybridized Genetic Algorithm. As an example, three distinct filters are tuned to detect horizontal, vertical and blob defects in woven fabrics. The effects of shifting on the optimized set of coefficients is also explored.
Wavelets for computer-aided breast cancer diagnosis
Myers, Lemuel R., Jr.; Kocur, Catherine M.; Rogers, Steven K.; Eisenbies, Chris; Hoffmeister, Jeffrey W.
1995-04-01
More than 50 million women over the age of 40 are currently at risk for breast cancer in the United States. Computer-aided diagnosis, used as a `second opinion' to radiologists, will aid in decreasing the number of false readings of mammograms. A novel feature extraction method is presented that provides increased classification power. Wavelets, previously only exploited for their segmentation benefits, are explored as features for classification. Daubechies4, Daubechies20, and biorthogonal wavelets are each investigated. Applied to 94 difficult-to- diagnose digitized microcalcification cases, performance is 74 percent correct classifications. Feature selection techniques are presented which further improve wavelet classification performance to 88 percent correct classification.
Pseudo-stochastic signal characterization in wavelet-domain
International Nuclear Information System (INIS)
Zaytsev, Kirill I; Zhirnov, Andrei A; Alekhnovich, Valentin I; Yurchenko, Stanislav O
2015-01-01
In this paper we present the method for fast and accurate characterization of pseudo-stochastic signals, which contain a large number of similar but randomly-located fragments. This method allows estimating the statistical characteristics of pseudo-stochastic signal, and it is based on digital signal processing in wavelet-domain. Continuous wavelet transform and the criterion for wavelet scale power density are utilized. We are experimentally implementing this method for the purpose of sand granulometry, and we are estimating the statistical parameters of test sand fractions
Hardware Architectures for the Orthogonal and Biorthogonal Wavelet Transform
Directory of Open Access Journals (Sweden)
G. Knowles
2002-01-01
Full Text Available In this note, optimal hardware architectures for the orthogonal and biorthogonal wavelet transforms are presented. The approach used here is not the standard lifting method, but takes advantage of the symmetries inherent in the coefficients of the transforms and the decimation/interpolation operators. The design is based on a highly optimized datapath, which seamlessly integrates both orthogonal and biorthogonal transforms, data extension at the edges and the forward and inverse transforms. The datapath design could be further optimized for speed or low power. The datapath is controlled by a small fast control unit which is hard programmed according to the wavelet or wavelets required by the application.
Neural network signature verification using Haar wavelet and Fourier transforms
McCormack, Daniel K. R.; Brown, B. M.; Pedersen, John F.
1993-08-01
This paper discusses the use of neural network's for handwritten signature verification using the Fourier and Haar wavelet transforms as methods of encoding signature images. Results will be presented that discuss a neural network's ability to generalize to unseen signatures using wavelet encoded training data. These results will be discussed with reference to both Backpropagation networks and Cascade-Correlation networks. Backpropagation and Cascade- Correlation networks are used to compare and contrast the generalization ability of Haar wavelet and Fourier transform encoded signature data.
Complex Wavelet Bases, Steerability, and the Marr-Like Pyramid
Van De Ville, Dimitri; Unser, Michael
2008-01-01
Our aim in this paper is to tighten the link between wavelets, some classical image-processing operators, and David Marr's theory of early vision. The cornerstone of our approach is a new complex wavelet basis that behaves like a smoothed version of the Gradient-Laplace operator. Starting from first principles, we show that a single-generator wavelet can be defined analytically and that it yields a semi-orthogonal complex basis of L-2 (R-2), irrespective of the dilation matrix used. We also p...
Complex Wavelet Bases, Steerability, and the Marr-Like Pyramid
D. Van De Ville M. Unser
2008-01-01
Our aim in this paper is to tighten the link between wavelets some classical image processing operators and David Marr's theory of early vision. The cornerstone of our approach is a new complex wavelet basis that behaves like a smoothed version of the Gradient Laplace operator. Starting from first principles we show that a single generator wavelet can be defined analytically and that it yields a semi orthogonal complex basis of L2(R2) irrespective of the dilation matrix used. We also provide ...
Directory of Open Access Journals (Sweden)
Li Song
2010-04-01
Full Text Available Abstract Background Quantitative proteomics technologies have been developed to comprehensively identify and quantify proteins in two or more complex samples. Quantitative proteomics based on differential stable isotope labeling is one of the proteomics quantification technologies. Mass spectrometric data generated for peptide quantification are often noisy, and peak detection and definition require various smoothing filters to remove noise in order to achieve accurate peptide quantification. Many traditional smoothing filters, such as the moving average filter, Savitzky-Golay filter and Gaussian filter, have been used to reduce noise in MS peaks. However, limitations of these filtering approaches often result in inaccurate peptide quantification. Here we present the WaveletQuant program, based on wavelet theory, for better or alternative MS-based proteomic quantification. Results We developed a novel discrete wavelet transform (DWT and a 'Spatial Adaptive Algorithm' to remove noise and to identify true peaks. We programmed and compiled WaveletQuant using Visual C++ 2005 Express Edition. We then incorporated the WaveletQuant program in the Trans-Proteomic Pipeline (TPP, a commonly used open source proteomics analysis pipeline. Conclusions We showed that WaveletQuant was able to quantify more proteins and to quantify them more accurately than the ASAPRatio, a program that performs quantification in the TPP pipeline, first using known mixed ratios of yeast extracts and then using a data set from ovarian cancer cell lysates. The program and its documentation can be downloaded from our website at http://systemsbiozju.org/data/WaveletQuant.
RFI Mitigation in Microwave Radiometry Using Wavelets
Directory of Open Access Journals (Sweden)
José Miguel Tarongí
2009-09-01
Full Text Available The performance of microwave radiometers can be seriously degraded by the presence of radio-frequency interference (RFI. Spurious signals and harmonics from lower frequency bands, spread-spectrum signals overlapping the “protected” band of operation, or out-of-band emissions not properly rejected by the pre-detection filters due to the finite rejection modify the detected power and the estimated antenna temperature from which the geophysical parameters will be retrieved. In recent years, techniques to detect the presence of RFI have been developed. They include time- and/or frequency domain analyses, or statistical analysis of the received signal which, in the absence of RFI, must be a zero-mean Gaussian process. Current mitigation techniques are mostly based on blanking in the time and/or frequency domains where RFI has been detected. However, in some geographical areas, RFI is so persistent in time that is not possible to acquire RFI-free radiometric data. In other applications such as sea surface salinity retrieval, where the sensitivity of the brightness temperature to salinity is weak, small amounts of RFI are also very difficult to detect and mitigate. In this work a wavelet-based technique is proposed to mitigate RFI (cancel RFI as much as possible. The interfering signal is estimated by using the powerful denoising capabilities of the wavelet transform. The estimated RFI signal is then subtracted from the received signal and a “cleaned” noise signal is obtained, from which the power is estimated later. The algorithm performance as a function of the threshold type, and the threshold selection method, the decomposition level, the wavelet type and the interferenceto-noise ratio is presented. Computational requirements are evaluated in terms of quantization levels, number of operations, memory requirements (sequence length. Even though they are high for today’s technology, the algorithms presented can be applied to recorded data
Zhang, Yao; Zheng, Lihua; Li, Minzan; Deng, Xiaolei; Sun, Hong
2012-11-01
The visible and NIR spectral reflectance were measured for apple leaves by using a spectrophotometer in fruit-bearing, fruit-falling and fruit-maturing period respectively, and the nitrogen content of each sample was measured in the lab. The analysis of correlation between nitrogen content of apple tree leaves and their hyperspectral data was conducted. Then the low frequency signal and high frequency noise reduction signal were extracted by using wavelet packet decomposition algorithm. At the same time, the original spectral reflectance was denoised taking advantage of the wavelet filtering technology. And then the principal components spectra were collected after PCA (Principal Component Analysis). It was known that the model built based on noise reduction principal components spectra reached higher accuracy than the other three ones in fruit-bearing period and physiological fruit-maturing period. Their calibration R2 reached 0.9529 and 0.9501, and validation R2 reached 0.7285 and 0.7303 respectively. While in the fruit-falling period the model based on low frequency principal components spectra reached the highest accuracy, and its calibration R2 reached 0.9921 and validation R2 reached 0.6234. The results showed that it was an effective way to improve ability of predicting apple tree nitrogen content based on hyperspectral analysis by using wavelet packet algorithm.
Fast wavelet based sparse approximate inverse preconditioner
Energy Technology Data Exchange (ETDEWEB)
Wan, W.L. [Univ. of California, Los Angeles, CA (United States)
1996-12-31
Incomplete LU factorization is a robust preconditioner for both general and PDE problems but unfortunately not easy to parallelize. Recent study of Huckle and Grote and Chow and Saad showed that sparse approximate inverse could be a potential alternative while readily parallelizable. However, for special class of matrix A that comes from elliptic PDE problems, their preconditioners are not optimal in the sense that independent of mesh size. A reason may be that no good sparse approximate inverse exists for the dense inverse matrix. Our observation is that for this kind of matrices, its inverse entries typically have piecewise smooth changes. We can take advantage of this fact and use wavelet compression techniques to construct a better sparse approximate inverse preconditioner. We shall show numerically that our approach is effective for this kind of matrices.
Network Anomaly Detection Based on Wavelet Analysis
Directory of Open Access Journals (Sweden)
Ali A. Ghorbani
2008-11-01
Full Text Available Signal processing techniques have been applied recently for analyzing and detecting network anomalies due to their potential to find novel or unknown intrusions. In this paper, we propose a new network signal modelling technique for detecting network anomalies, combining the wavelet approximation and system identification theory. In order to characterize network traffic behaviors, we present fifteen features and use them as the input signals in our system. We then evaluate our approach with the 1999 DARPA intrusion detection dataset and conduct a comprehensive analysis of the intrusions in the dataset. Evaluation results show that the approach achieves high-detection rates in terms of both attack instances and attack types. Furthermore, we conduct a full day's evaluation in a real large-scale WiFi ISP network where five attack types are successfully detected from over 30 millions flows.
Doctoral Research in Wavelet NDE and Novel Dielectrics
National Research Council Canada - National Science Library
DeFacio, Brian
1999-01-01
.... The wavelet studies were directed toward nondestructive evaluation using ultrasonic waves. The novel dielectrics were mainly the PBGS, photonic band structures-periodic arrays of a dielectric with permittivity e...
Optimization and Assessment of Wavelet Packet Decompositions with Evolutionary Computation
Directory of Open Access Journals (Sweden)
Schell Thomas
2003-01-01
Full Text Available In image compression, the wavelet transformation is a state-of-the-art component. Recently, wavelet packet decomposition has received quite an interest. A popular approach for wavelet packet decomposition is the near-best-basis algorithm using nonadditive cost functions. In contrast to additive cost functions, the wavelet packet decomposition of the near-best-basis algorithm is only suboptimal. We apply methods from the field of evolutionary computation (EC to test the quality of the near-best-basis results. We observe a phenomenon: the results of the near-best-basis algorithm are inferior in terms of cost-function optimization but are superior in terms of rate/distortion performance compared to EC methods.
Novel signal shape descriptors through wavelet transforms and dimensionality reduction
Hughes, Nicholas P.; Tarassenko, Lionel
2003-11-01
The wavelet transform is a powerful tool for capturing the joint time-frequency characteristics of a signal. However, the resulting wavelet coefficients are typically high-dimensional, since at each time sample the wavelet transform is evaluated at a number of distinct scales. Unfortunately, modelling these coefficients can be problematic because of the large number of parameters needed to capture the dependencies between different scales. In this paper we investigate the use of algorithms from the field of dimensionality reduction to extract informative and compact descriptions of shape from wavelet coefficients. These low-dimensional shape descriptors lead to models that are governed by only a small number of parameters and can be learnt successfully from limited amounts of data. The validity of our approach is demonstrated on the task of automatically segmenting an electrocardiogram signal into its constituent waveform features.
An Improved Spectral Background Subtraction Method Based on Wavelet Energy.
Zhao, Fengkui; Wang, Jian; Wang, Aimin
2016-12-01
Most spectral background subtraction methods rely on the difference in frequency response of background compared with characteristic peaks. It is difficult to extract accurately the background components from the spectrum when characteristic peaks and background have overlaps in frequency domain. An improved background estimation algorithm based on iterative wavelet transform (IWT) is presented. The wavelet entropy principle is used to select the best wavelet basis. A criterion based on wavelet energy theory to determine the optimal iteration times is proposed. The case of energy dispersive X-ray spectroscopy is discussed for illustration. A simulated spectrum with a prior known background and an experimental spectrum are tested. The processing results of the simulated spectrum is compared with non-IWT and it demonstrates the superiority of the IWT. It has great significance to improve the accuracy for spectral analysis. © The Author(s) 2016.
Image denoising based on wavelet cone of influence analysis
Pang, Wei; Li, Yufeng
2009-11-01
Donoho et al have proposed a method for denoising by thresholding based on wavelet transform, and indeed, the application of their method to image denoising has been extremely successful. But this method is based on the assumption that the type of noise is only additive Gaussian white noise, which is not efficient to impulse noise. In this paper, a new image denoising algorithm based on wavelet cone of influence (COI) analyzing is proposed, and which can effectively remove the impulse noise and preserve the image edges via undecimated discrete wavelet transform (UDWT). Furthermore, combining with the traditional wavelet thresholding denoising method, it can be also used to restrain more widely type of noise such as Gaussian noise, impulse noise, poisson noise and other mixed noise. Experiment results illustrate the advantages of this method.
Wavelet transform based power quality events classification using ...
African Journals Online (AJOL)
WT) energy features by artificial neural network (ANN) and SVM classifiers. The proposed scheme utilizes wavelet based feature extraction to be used for the artificial neural networks in the classification. Six different PQ events are considered in ...
IMAGE SPLICING DETECTION BASED ON DEMOSAICKING AND WAVELET TRANSFORMATION
Directory of Open Access Journals (Sweden)
Endina Putri Purwandari
2015-03-01
Full Text Available Image splicing is a form of digital image manipulation by combining two or more image into a new image. The application was developed through a passive approach using demosaicking and wavelet transformation method. This research purposed a method to implement the demosaicking and wavelet transform for digital image forgery detection with a passive approach. This research shows that (1 demosaicking can be used as a comparison image in forgery detection; (2 the application of demosaicking and wavelet transformation can improve the quality of the input image (3 demosaicking and wavelet algorithm are able to estimate whether the input image is real or fake image with a passive approach and estimate the manipulation area from the input image.
Selection of the wavelet function for the frequencies estimation
International Nuclear Information System (INIS)
Garcia R, A.
2007-01-01
At the moment the signals are used to diagnose the state of the systems, by means of the extraction of their more important characteristics such as the frequencies, tendencies, changes and temporary evolutions. This characteristics are detected by means of diverse analysis techniques, as Autoregressive methods, Fourier Transformation, Fourier transformation in short time, Wavelet transformation, among others. The present work uses the one Wavelet transformation because it allows to analyze stationary, quasi-stationary and transitory signals in the time-frequency plane. It also describes a methodology to select the scales and the Wavelet function to be applied the one Wavelet transformation with the objective of detecting to the dominant system frequencies. (Author)
SYMMETRY, HAMILTONIAN PROBLEMS AND WAVELETS IN ACCELERATOR PHYSICS
Energy Technology Data Exchange (ETDEWEB)
FEDOROVA,A.; ZEITLIN,M.; PARSA,Z.
2000-03-31
In this paper the authors consider applications of methods from wavelet analysis to nonlinear dynamical problems related to accelerator physics. In this approach they take into account underlying algebraical, geometrical and topological structures of corresponding problems.
Hand posture recognizer based on separator wavelet networks
Bouchrika, Tahani; Jemai, Olfa; Zaied, Mourad; Ben Amar, Chokri
2015-12-01
This paper presents a novel hand posture recognizer based on separator wavelet networks (SWNs). Aiming at creating a robust and rapid hand posture recognizer, we have contributed by proposing a new training algorithm for the wavelet network classifier based on fast wavelet transform (FWN). So, the contribution resides in reducing the number of WNs modeling training data. To make that, inspiring from the adaboost feature selection method, we thought to create SWNs (n-1 WNs for n classes) instead of modeling each training sample by its wavelet network (WN). By proposing the new training algorithm, the recognition phase will be positively influenced. It will be more rapid thanks to the reduction of the number of comparisons between test images WNs and training WNs. Comparisons with other works, employing universal hand posture datasets are presented and discussed. Obtained results have shown that the new hand posture recognizer is comparable to previously established ones.
Dynamic wavelet-based tool for gearbox diagnosis
Omar, Farag K.; Gaouda, A. M.
2012-01-01
This paper proposes a novel wavelet-based technique for detecting and localizing gear tooth defects in a noisy environment. The proposed technique utilizes a dynamic windowing process while analyzing gearbox vibration signals in the wavelet domain. The gear vibration signal is processed through a dynamic Kaiser's window of varying parameters. The window size, shape, and sliding rate are modified towards increasing the similarity between the non-stationary vibration signal and the selected mother wavelet. The window parameters are continuously modified until they provide maximum wavelet coefficients localized at the defected tooth. The technique is applied on laboratory data corrupted with high noise level. The technique has shown accurate results in detecting and localizing gear tooth fracture with different damage severity.
Processing of pulse oximeter data using discrete wavelet analysis.
Lee, Seungjoon; Ibey, Bennett L; Xu, Weijian; Wilson, Mark A; Ericson, M Nance; Coté, Gerard L
2005-07-01
A wavelet-based signal processing technique was employed to improve an implantable blood perfusion monitoring system. Data was acquired from both in vitro and in vivo sources: a perfusion model and the proximal jejunum of an adult pig. Results showed that wavelet analysis could isolate perfusion signals from raw, periodic, in vitro data as well as fast Fourier transform (FFT) methods. However, for the quasi-periodic in vivo data segments, wavelet analysis provided more consistent results than the FFT analysis for data segments of 50, 10, and 5 s in length. Wavelet analysis has thus been shown to require less data points for quasi-periodic data than FFT analysis making it a good choice for an indwelling perfusion monitor where power consumption and reaction time are paramount.
A Secret Image Sharing Method Using Integer Wavelet Transform
Directory of Open Access Journals (Sweden)
Li Ching-Chung
2007-01-01
Full Text Available A new image sharing method, based on the reversible integer-to-integer (ITI wavelet transform and Shamir's threshold scheme is presented, that provides highly compact shadows for real-time progressive transmission. This method, working in the wavelet domain, processes the transform coefficients in each subband, divides each of the resulting combination coefficients into shadows, and allows recovery of the complete secret image by using any or more shadows . We take advantages of properties of the wavelet transform multiresolution representation, such as coefficient magnitude decay and excellent energy compaction, to design combination procedures for the transform coefficients and processing sequences in wavelet subbands such that small shadows for real-time progressive transmission are obtained. Experimental results demonstrate that the proposed method yields small shadow images and has the capabilities of real-time progressive transmission and perfect reconstruction of secret images.
Wavelet subspaces invariant under groups of translation operators
Indian Academy of Sciences (India)
R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22
Tα(Vj ) ⊂ Vj for all j ∈ Z and for all α ∈ R. Madych proved that the only translation invariant MRAs are those for which the Fourier transform of the scaling function is the characteristic function of a set. In other words, the associated wavelet is an MSF wavelet. Walter [10,11] modified the definition of translation invariance to ...
Efficient object recognition using boundary representation and wavelet neural network.
Pan, Hong; Xia, Liang-Zheng
2008-12-01
Wavelet neural networks combine the functions of time-frequency localization from the wavelet transform and of self-studying from the neural network, which make them particularly suitable for the classification of complex patterns. In this paper, an efficient object recognition method using boundary representation and the wavelet neural network is proposed. The method employs a wavelet neural network (WNN) to characterize the singularities of the object curvature representation and to perform the object classification at the same time and in an automatic way. The local time-frequency attributes of the singularities on the object boundary are detected by making a preliminary wavelet analysis of the curvature representation. Then, the discriminative scale-translation features of the singularities are stored as the initial scale-translation parameters of the wavelet nodes in the WNN. These parameters are trained to their optimum status during the learning stage. With our approach, as opposed to matching features by convolving the signal with wavelet functions at a large number of scales, the computational burden is significantly reduced. Only a few convolutions are performed at the optimum scale-translation grids during the classification, which makes it suitable for real-time recognition tasks. Compared with the artificial-neural-network-based approaches preceded by wavelet filter banks with fixed scale-translation parameters, the support vector machine (SVM) using traditional Fourier descriptors and K-nearest-neighbor ( K-NN) classifier based on the state-of-the-art shape descriptors, our scheme demonstrates superior and stable discrimination performance under various noisy and affine conditions.
Smoothing the wavelet periodogram using the Haar-Fisz transform
Piotr Fryzlewicz; Guy P. Nason
2004-01-01
The wavelet periodogram is hard to smooth because of the low signal-to-noise ratio and non-stationary covariance structure. This article introduces a method for smoothing a local wavelet periodogram by applying a Haar-Fisz transform which approximately Gaussianizes and approximately stabilizes the variance of the periodogram. Consequently, smoothing the transformed periodogram can take advantage of the wide variety of existing techniques suitable for homogeneous Gaussian data. This article de...
Parametric modelling of thresholds across scales in wavelet regression
Anestis Antoniadis; Piotr Fryzlewicz
2006-01-01
We propose a parametric wavelet thresholding procedure for estimation in the ‘function plus independent, identically distributed Gaussian noise’ model. To reflect the decreasing sparsity of wavelet coefficients from finer to coarser scales, our thresholds also decrease. They retain the noise-free reconstruction property while being lower than the universal threshold, and are jointly parameterised by a single scalar parameter. We show that our estimator achieves near-optimal risk rates for the...
Haar-Fisz estimation of evolutionary wavelet spectra
Piotr Fryzlewicz; Guy P. Nason
2006-01-01
We propose a new 'Haar–Fisz' technique for estimating the time-varying, piecewise constant local variance of a locally stationary Gaussian time series. We apply our technique to the estimation of the spectral structure in the locally stationary wavelet model. Our method combines Haar wavelets and the variance stabilizing Fisz transform. The resulting estimator is mean square consistent, rapidly computable and easy to implement, and performs well in practice. We also introduce the 'Haar–Fisz t...
An empirical analysis of dynamic multiscale hedging using wavelet decomposition
Conlon, Thomas; Cotter, John
2011-01-01
This paper investigates the hedging effectiveness of a dynamic moving window OLS hedging model, formed using wavelet decomposed time-series. The wavelet transform is applied to calculate the appropriate dynamic minimum-variance hedge ratio for various hedging horizons for a number of assets. The effectiveness of the dynamic multiscale hedging strategy is then tested, both in- and out-of-sample, using standard variance reduction and expanded to include a downside risk metric, the time horizon ...
Haar wavelets-based approach for quantifying credit portfolio losses
Masdemont Soler, Josep
2011-01-01
This paper proposes a new methodology to compute Value at Risk (VaR) for quantifying losses in credit portfolios. We approximate the cumulative distribution of the loss function by a finite combination of Haar wavelet basis functions and calculate the coefficients of the approximation by inverting its Laplace transform. The Wavelet Approximation (WA) method is specially suitable for non-smooth distributions, often arising in small or concentrated portfolios, when the hypothesis of the Basel I...
Haar wavelets based approach for quantifying credit portfolio loses
Masdemont Soler, Josep; Ortiz-Gracia, Luís
2011-01-01
This paper proposes a new methodology to compute Value at Risk (VaR) for quantifying losses in credit portfolios. We approximate the cumulative distribution of the loss function by a finite combination of Haar wavelet basis functions and calculate the coefficients of the approximation by inverting its Laplace transform. The Wavelet Approximation (WA) method is particularly suitable for non-smooth distributions, often arising in small or concentrated portfolios, when the hypothe...
Frame scaling function sets and frame wavelet sets in Rd
International Nuclear Information System (INIS)
Liu Zhanwei; Hu Guoen; Wu Guochang
2009-01-01
In this paper, we classify frame wavelet sets and frame scaling function sets in higher dimensions. Firstly, we obtain a necessary condition for a set to be the frame wavelet sets. Then, we present a necessary and sufficient condition for a set to be a frame scaling function set. We give a property of frame scaling function sets, too. Some corresponding examples are given to prove our theory in each section.
Beginning an African Stock Markets Integration? A Wavelet Analysis
Gourène, Grakolet Arnold Zamereith; Mendy, Pierre; Elegbe, Aguin Franck Yvon
2017-01-01
This paper examines the integration of the six largest African stock markets at different timescales. We want to see whether the numerous measures and reforms put in place to integrate the African stock markets are efficient. First, we used the Wavelet Multiple Correlation and the Wavelet Multiple Cross-Correlation proposed by Fernandez-Macho (2012). Then, we combine the spillovers index based on generalized vector autoregressive proposed by Diebold and Yilmaz (2012) with the Maxi...
Wavelet based methods for improved wind profiler signal processing
Directory of Open Access Journals (Sweden)
V. Lehmann
2001-08-01
Full Text Available In this paper, we apply wavelet thresholding for removing automatically ground and intermittent clutter (airplane echoes from wind profiler radar data. Using the concept of discrete multi-resolution analysis and non-parametric estimation theory, we develop wavelet domain thresholding rules, which allow us to identify the coefficients relevant for clutter and to suppress them in order to obtain filtered reconstructions.Key words. Meteorology and atmospheric dynamics (instruments and techniques – Radio science (remote sensing; signal processing
POWER QUALITY DIAGNOSIS IN DISTRIBUTION NETWORK USING WAVELET TRANSFORM
H. H. Goh; H. L. Ting; Q. S. Chua; S. W. Lee; K. C. Goh; K. T.K. Teo
2014-01-01
Power quality is one of the most concerns to the electric power suppliers, equipment manufacturers and the users of various electrical and electronic equipments. This paper presents the use of a continuous wavelet transform to detect and analyse voltage sags and swells. Characteristics which include duration and magnitude of the investigated signals are measured. Unlike other approaches where the detection is performed directly in the time domain, while the detection by using wavelet transfor...
A short introduction to frames, Gabor systems, and wavelet systems
DEFF Research Database (Denmark)
Christensen, Ole
2014-01-01
In this article we present a short survey of frame theory in Hilbert spaces. We discuss Gabor frames and wavelet frames, and a recent transform that allows to move results from one setting into the other and vice versa.......In this article we present a short survey of frame theory in Hilbert spaces. We discuss Gabor frames and wavelet frames, and a recent transform that allows to move results from one setting into the other and vice versa....
Controlled wavelet domain sparsity for x-ray tomography
Purisha, Zenith; Rimpeläinen, Juho; Bubba, Tatiana; Siltanen, Samuli
2018-01-01
Tomographic reconstruction is an ill-posed inverse problem that calls for regularization. One possibility is to require sparsity of the unknown in an orthonormal wavelet basis. This, in turn, can be achieved by variational regularization, where the penalty term is the sum of the absolute values of the wavelet coefficients. The primal-dual fixed point algorithm showed that the minimizer of the variational regularization functional can be computed iteratively using a soft-thresholding operation. Choosing the soft-thresholding parameter \
Wavelet analysis for ground penetrating radar applications: a case study
Javadi, Mehdi; Ghasemzadeh, Hasan
2017-10-01
Noises may significantly disturb ground penetrating radar (GPR) signals, therefore, filtering undesired information using wavelet analysis would be challenging, despite the fact that several methods have been presented. Noises are gathered by probe, particularly from deep locations, and they may conceal reflections, suffering from small altitudes, because of signal attenuation. Multiple engineering fields need data analysis to distinguish valued material, based on information obtained by underground observations. Using wavelets as one of the useful methods for analyzing data is considered in this paper. However, optimal wavelet analysis would be challenging in the realm of exploring GPR signals. There is no doubt that accounting for wavelet function, decomposition level, threshold estimation method and threshold transformation, in the matter of de-noising and investigating signals, is of great importance; they must be chosen with judgment as they influence the results enormously if they are not carefully designated. Multiple wavelet functions are applied to perform de-noising and reconstruction on synthetic noisy signals generated by the finite-difference time-domain (FDTD) method to account for the most appropriate function for the purpose. In addition, various possible decomposition levels, threshold estimation methods and threshold transformations in the de-noising procedure are tested. The optimal wavelet analysis is also evaluated by examining real data acquired from several antenna frequencies which are common in engineering practice.
Analysis and removing noise from speech using wavelet transform
Tomala, Karel; Voznak, Miroslav; Partila, Pavol; Rezac, Filip; Safarik, Jakub
2013-05-01
The paper discusses the use of Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT) wavelet in removing noise from voice samples and evaluation of its impact on speech quality. One significant part of Quality of Service (QoS) in communication technology is the speech quality assessment. However, this part is seriously overlooked as telecommunication providers often focus on increasing network capacity, expansion of services offered and their enforcement in the market. Among the fundamental factors affecting the transmission properties of the communication chain is noise, either at the transmitter or the receiver side. A wavelet transform (WT) is a modern tool for signal processing. One of the most significant areas in which wavelet transforms are used is applications designed to suppress noise in signals. To remove noise from the voice sample in our experiment, we used the reference segment of the voice which was distorted by Gaussian white noise. An evaluation of the impact on speech quality was carried out by an intrusive objective algorithm Perceptual Evaluation of Speech Quality (PESQ). DWT and SWT transformation was applied to voice samples that were devalued by Gaussian white noise. Afterwards, we determined the effectiveness of DWT and SWT by means of objective algorithm PESQ. The decisive criterion for determining the quality of a voice sample once the noise had been removed was Mean Opinion Score (MOS) which we obtained in PESQ. The contribution of this work lies in the evaluation of efficiency of wavelet transformation to suppress noise in voice samples.
Using wavelet transforms in gearbox vibration monitoring data
International Nuclear Information System (INIS)
Badi, M.N.M.; Engin, S.N.; Esat, I.I.
1996-01-01
Gearbox vibration signals are composed of transient changes and they are non-stationary (i.e. their spectra change with time). In a mechanical system consisting of various rotating parts, different components contribute to the overall vibration signals at different times and with different levels. Thus, if a signal analysis method used as an alternative to the standard methods can localize the information on the time axis, it can be possible to determine the approximate location of the fault or damage. Hence further techniques have to be developed such that the localized information in the time domain can be mapped to the frequency domain. Wavelet transforms which is an adjustable windowed analysis method have been used to compensate for the inadequate information provided by other signal analysis methods used before. In order to get more meaningful interpretation of the data mean-square wavelet contour maps were used. These maps show how the mean-square value of the signal is distributed between wavelet levels. Mesh diagrams are then generated for those maps for two different types of wavelet transforms which are dilation wavelets and harmonic wavelets
Advanced noise filtering of EC signals through wavelet transformations
International Nuclear Information System (INIS)
Gorecan, I.
2004-01-01
In this paper, various filtering methods are considered for the purpose of reducing the level of noise in the EC signals, and comparisons are made. The most widely used method for digital signal analysis is Fourier analysis. Unfortunately, this method is best put to use on stationary signals where the loss of time information isn't critical. EC signal's characteristics vary significantly over time in the stochastic sense. Important events (indications) are represented as transient, highly localized changes in the signal and therefore the information is not easily extractable from the spectral domain. Wavelet analysis offers a distinct approach to signal analysis because low frequency information can be analyzed on larger scales while short intervals are used for high frequency content. In this way, time information is not lost in the transformed domain. While Fourier analysis is used to decompose the signal into sinusoids with varying frequencies, wavelet transformation decomposes the signal into shifted and scaled copies of the base (mother) wavelet function. Wavelet families like Haar and Daubechies wavelets are compared. Methods for determining the optimal decomposition tree as well as several post-decomposition thresholding techniques are discussed, including automatic threshold selection. Application of wavelet de-noising algorithms implemented in the INETEC Eddy One EC Data Analysis software is presented on real-world signals collected from WWER steam generator tubes.(author)
Filter design for molecular factor computing using wavelet functions.
Li, Xiaoyong; Xu, Zhihong; Cai, Wensheng; Shao, Xueguang
2015-06-23
Molecular factor computing (MFC) is a new strategy that employs chemometric methods in an optical instrument to obtain analytical results directly using an appropriate filter without data processing. In the present contribution, a method for designing an MFC filter using wavelet functions was proposed for spectroscopic analysis. In this method, the MFC filter is designed as a linear combination of a set of wavelet functions. A multiple linear regression model relating the concentration to the wavelet coefficients is constructed, so that the wavelet coefficients are obtained by projecting the spectra onto the selected wavelet functions. These wavelet functions are selected by optimizing the model using a genetic algorithm (GA). Once the MFC filter is obtained, the concentration of a sample can be calculated directly by projecting the spectrum onto the filter. With three NIR datasets of corn, wheat and blood, it was shown that the performance of the designed filter is better than that of the optimized partial least squares models, and commonly used signal processing methods, such as background correction and variable selection, were not needed. More importantly, the designed filter can be used as an MFC filter in designing MFC-based instruments. Copyright © 2015 Elsevier B.V. All rights reserved.
Wavelet-LMS algorithm-based echo cancellers
Seetharaman, Lalith K.; Rao, Sathyanarayana S.
2002-12-01
This paper presents Echo Cancellers based on the Wavelet-LMS Algorithm. The performance of the Least Mean Square Algorithm in Wavelet transform domain is observed and its application in Echo cancellation is analyzed. The Widrow-Hoff Least Mean Square Algorithm is most widely used algorithm for Adaptive filters that function as Echo Cancellers. The present day communication signals are widely non-stationary in nature and some errors crop up when Least Mean Square Algorithm is used for the Echo Cancellers handling such signals. The analysis of non-stationary signals often involves a compromise between how well transitions or discontinuities can be located. The multi-scale or multi-resolution of signal analysis, which is the essence of wavelet transform, makes Wavelets popular in non-stationary signal analysis. In this paper, we present a Wavelet-LMS algorithm wherein the wavelet coefficients of a signal are modified adaptively using the Least Mean Square Algorithm and then reconstructed to give an Echo-free signal. The Echo Canceller based on this Algorithm is found to have a better convergence and a comparatively lesser MSE (Mean Square error).
Classification of nodules in mammograms image by using wavelet transform
Santaella, Cesar H. M.; Schiabel, Homero; Patrocinio, Ana C.; Nunes, Fatima d. L. d. S.; Romero, Roseli A. F.
2003-05-01
This work presents a classifier for mammographic masses using the wavelet transform as characteristics generator. It considers the BI-RADS classification, dividing mass according to their shapes: circulate, nodular and speculate. We developed procedures with two steps: the first involves a model applying one wavelet technique performing the contours analysis with simulated mass images. This procedure was used to choose the best wavelet that could generate the desired characteristics. The second procedure had the objective of applying the chosen wavelet to masses from segmented images. Both methods have as answers three classes of shape. A root-mean-square function is applied to obtain the energy measure for each level of wavelet decomposition. Thus the shape feature vectors are formed with the coefficients of the details and coefficients of approximation extracted by the energy of wavelet decomposition levels. Linear Discriminan Analysis (LDA) by using Fischer Discriminant was used to reduce the number of characteristics for the feature vector. The Mahalanobis distance was used by the classifier to verify aimed the pertinence of the images for each one the previously given classes. To test actual images, the leave-one-out method was used to the classifier training. The classifier has registered good results, compared to others reports in the corresponding literature.
Complex wavelet bases, steerability, and the marr-like pyramid.
Van De Ville, Dimitri; Unser, Michael
2008-11-01
Our aim in this paper is to tighten the link between wavelets, some classical image-processing operators, and David Marr's theory of early vision. The cornerstone of our approach is a new complex wavelet basis that behaves like a smoothed version of the Gradient-Laplace operator. Starting from first principles, we show that a single-generator wavelet can be defined analytically and that it yields a semi-orthogonal complex basis of L2 (R2), irrespective of the dilation matrix used. We also provide an efficient FFT-based filterbank implementation. We then propose a slightly redundant version of the transform that is nearly translation-invariant and that is optimized for better steerability (Gaussian-like smoothing kernel).We call it the Marr-like wavelet pyramid because it essentially replicates the processing steps in Marr's theory of early vision.We use it to derive a primal wavelet sketch which is a compact description of the image by a multiscale, subsampled edge map. Finally, we provide an efficient iterative algorithm for the reconstruction of an image from its primal wavelet sketch.
Directory of Open Access Journals (Sweden)
Enrique Vílchez Quesada
2014-05-01
Full Text Available Recibido 29 de octubre de 2013 • Corregido 2 de marzo de 2014 • Aceptado 2 de abril de 2014. Este artículo corresponde a un trabajo científico derivado del proyecto de investigación en docencia adscrito a la Escuela de Informática de la Universidad Nacional de Costa Rica (UNA, titulado: Facebook como herramienta de enseñanza y aprendizaje para el curso EIF-203 Estructuras discretas para informática a través del uso de cuadernos interactivos. El objetivo general del proyecto consistió en analizar técnica y pedagógicamente la red social Facebook como un entorno de enseñanza y aprendizaje. En este documento se presenta una serie de estrategias de enseñanza creadas para complementar la docencia en el contexto del curso EIF-203, utilizando como plataforma educativa las redes sociales Facebook y Twitter. Las estrategias compartidas constituyen una intensa búsqueda de nuevas metodologías para mejorar los procesos de enseñanza e, idealmente, los procesos de aprendizaje en la materia EIF-203, integrada dentro del plan de estudios de la carrera Ingeniería en Sistemas de Información de la UNA. Estas fueron implementadas en un grupo de 31 estudiantes de la Sede Interuniversitaria de Alajuela, durante el I semestre 2013. Lo anterior permitió evaluar las estrategias de enseñanza desarrolladas utilizando una metodología de carácter cuantitativo. El instrumento empleado (un cuestionario se validó mediante una prueba de fiabilidad “Alfa de Cronbach” recurriendo a una muestra de 65 participantes. Los resultados obtenidos forman parte del análisis expuesto en el presente trabajo.
Directory of Open Access Journals (Sweden)
Rosario C. Garza Ríos
2016-12-01
Full Text Available En el presente trabajo se presentan los resultados alcanzados al integrar la metodología Seis Sigma, las técnicas de simulación discreta y las técnicas multicriteriales para la mejora de un servicio de belleza en que se deseaba obtener la mejor solución de compromiso entre los ingresos, los costos, la utilización de los recursos y la satisfacción del cliente. Se utilizó la metodología DMAIC proponiéndose un procedimiento en el que se define para cada fase las herramientas de simulación, de toma de decisiones multiatributo, estadísticas y de control y gestión de la calidad. El uso de la simulación permitió analizar las diferentes acciones de mejoras y determinar los valores de las variables de interés definidas por el grupo administrativo. Se utilizó dentro de las técnicas multicriteriales, el índice PRES el cual permitió ordenar las acciones considerando las preferencias de los expertos. ------------------------------------ In this paper we show the results achieved by integrating Six Sigma, discrete simulation techniques and multi-criteria techniques for improving a beauty service that desires to obtain the best compromise solution between incomes, costs, use of resources and customer satisfaction. DMAIC methodology was used to propose a procedure that defines, for each phase, simulation tools, multi-attribute decision making, statistics and quality control and management. The use of simulation allowed us to analyze the different actions for improvements and determine the values of the variables of interest being defined by the administrative group. We used, between multi-criteria techniques, PRES index for ranking the actions according to experts' preferences.
Energy Technology Data Exchange (ETDEWEB)
Garcia R, A. [ININ, Carretera Mexico-Toluca S/N, 52750 La Marquesa, Ocoyoacac, Estado de Mexico (Mexico)]. e-mail: ramador@nuclear.inin.mx
2007-07-01
At the moment the signals are used to diagnose the state of the systems, by means of the extraction of their more important characteristics such as the frequencies, tendencies, changes and temporary evolutions. This characteristics are detected by means of diverse analysis techniques, as Autoregressive methods, Fourier Transformation, Fourier transformation in short time, Wavelet transformation, among others. The present work uses the one Wavelet transformation because it allows to analyze stationary, quasi-stationary and transitory signals in the time-frequency plane. It also describes a methodology to select the scales and the Wavelet function to be applied the one Wavelet transformation with the objective of detecting to the dominant system frequencies. (Author)
Zhang, Hong; Zhang, Sheng; Wang, Ping; Qin, Yuzhe; Wang, Huifeng
2017-07-01
Particulate matter with aerodynamic diameter below 10 μm (PM 10 ) forecasting is difficult because of the uncertainties in describing the emission and meteorological fields. This paper proposed a wavelet-ARMA/ARIMA model to forecast the short-term series of the PM 10 concentrations. It was evaluated by experiments using a 10-year data set of daily PM 10 concentrations from 4 stations located in Taiyuan, China. The results indicated the following: (1) PM 10 concentrations of Taiyuan had a decreasing trend during 2005 to 2012 but increased in 2013. PM 10 concentrations had an obvious seasonal fluctuation related to coal-fired heating in winter and early spring. (2) Spatial differences among the four stations showed that the PM 10 concentrations in industrial and heavily trafficked areas were higher than those in residential and suburb areas. (3) Wavelet analysis revealed that the trend variation and the changes of the PM 10 concentration of Taiyuan were complicated. (4) The proposed wavelet-ARIMA model could be efficiently and successfully applied to the PM 10 forecasting field. Compared with the traditional ARMA/ARIMA methods, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. Wavelet analysis can filter noisy signals and identify the variation trend and the fluctuation of the PM 10 time-series data. Wavelet decomposition and reconstruction reduce the nonstationarity of the PM 10 time-series data, and thus improve the accuracy of the prediction. This paper proposed a wavelet-ARMA/ARIMA model to forecast the PM 10 time series. Compared with the traditional ARMA/ARIMA method, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. The proposed model could be efficiently and successfully applied to the PM 10 forecasting field.
Kernel wavelet-Reed-Xiaoli: an anomaly detection for forward-looking infrared imagery.
Mehmood, Asif; Nasrabadi, Nasser M
2011-06-10
This paper describes a new kernel wavelet-based anomaly detection technique for long-wave (LW) forward-looking infrared imagery. The proposed approach called kernel wavelet-Reed-Xiaoli (wavelet-RX) algorithm is essentially an extension of the wavelet-RX algorithm (combination of wavelet transform and RX anomaly detector) to a high-dimensional feature space (possibly infinite) via a certain nonlinear mapping function of the input data. The wavelet-RX algorithm in this high-dimensional feature space can easily be implemented in terms of kernels that implicitly compute dot products in the feature space (kernelizing the wavelet-RX algorithm). In the proposed kernel wavelet-RX algorithm, a two-dimensional wavelet transform is first applied to decompose the input image into uniform subbands. A number of significant subbands (high-energy subbands) are concatenated together to form a subband-image cube. The kernel RX algorithm is then applied to this subband-image cube. Experimental results are presented for the proposed kernel wavelet-RX, wavelet-RX, and the classical constant false alarm rate (CFAR) algorithm for detecting anomalies (targets) in a large database of LW imagery. The receiver operating characteristic plots show that the proposed kernel wavelet-RX algorithm outperforms the wavelet-RX as well as the classical CFAR detector.
Enhancing seismic P phase arrival picking based on wavelet denoising and kurtosis picker
Shang, Xueyi; Li, Xibing; Weng, Lei
2018-01-01
P phase arrival picking of weak signals is still challenging in seismology. A wavelet denoising is proposed to enhance seismic P phase arrival picking, and the kurtosis picker is applied on the wavelet-denoised signal to identify P phase arrival. It has been called the WD-K picker. The WD-K picker, which is different from those traditional wavelet-based pickers on the basis of a single wavelet component or certain main wavelet components, takes full advantage of the reconstruction of main detail wavelet components and the approximate wavelet component. The proposed WD-K picker considers more wavelet components and presents a better P phase arrival feature. The WD-K picker has been evaluated on 500 micro-seismic signals recorded in the Chinese Yongshaba mine. The comparison between the WD-K pickings and manual pickings shows the good picking accuracy of the WD-K picker. Furthermore, the WD-K picking performance has been compared with the main detail wavelet component combining-based kurtosis (WDC-K) picker, the single wavelet component-based kurtosis (SW-K) picker, and certain main wavelet component-based maximum kurtosis (MMW-K) picker. The comparison has demonstrated that the WD-K picker has better picking accuracy than the other three-wavelet and kurtosis-based pickers, thus showing the enhanced ability of wavelet denoising.
Spectral Laplace-Beltrami wavelets with applications in medical images.
Tan, Mingzhen; Qiu, Anqi
2015-05-01
The spectral graph wavelet transform (SGWT) has recently been developed to compute wavelet transforms of functions defined on non-Euclidean spaces such as graphs. By capitalizing on the established framework of the SGWT, we adopt a fast and efficient computation of a discretized Laplace-Beltrami (LB) operator that allows its extension from arbitrary graphs to differentiable and closed 2-D manifolds (smooth surfaces embedded in the 3-D Euclidean space). This particular class of manifolds are widely used in bioimaging to characterize the morphology of cells, tissues, and organs. They are often discretized into triangular meshes, providing additional geometric information apart from simple nodes and weighted connections in graphs. In comparison with the SGWT, the wavelet bases constructed with the LB operator are spatially localized with a more uniform "spread" with respect to underlying curvature of the surface. In our experiments, we first use synthetic data to show that traditional applications of wavelets in smoothing and edge detectio can be done using the wavelet bases constructed with the LB operator. Second, we show that multi-resolutional capabilities of the proposed framework are applicable in the classification of Alzheimer's patients with normal subjects using hippocampal shapes. Wavelet transforms of the hippocampal shape deformations at finer resolutions registered higher sensitivity (96%) and specificity (90%) than the classification results obtained from the direct usage of hippocampal shape deformations. In addition, the Laplace-Beltrami method requires consistently a smaller number of principal components (to retain a fixed variance) at higher resolution as compared to the binary and weighted graph Laplacians, demonstrating the potential of the wavelet bases in adapting to the geometry of the underlying manifold.
Smoke detection using GLCM, wavelet, and motion
Srisuwan, Teerasak; Ruchanurucks, Miti
2014-01-01
This paper presents a supervised smoke detection method that uses local and global features. This framework integrates and extends notions of many previous works to generate a new comprehensive method. First chrominance detection is used to screen areas that are suspected to be smoke. For these areas, local features are then extracted. The features are among homogeneity of GLCM and energy of wavelet. Then, global feature of motion of the smoke-color areas are extracted using a space-time analysis scheme. Finally these features are used to train an artificial intelligent. Here we use neural network, experiment compares importance of each feature. Hence, we can really know which features among those used by many previous works are really useful. The proposed method outperforms many of the current methods in the sense of correctness, and it does so in a reasonable computation time. It even has less limitation than conventional smoke sensors when used in open space. Best method for the experimental results is to use all the mentioned features as expected, to insure which is the best experiment result can be achieved. The achieved with high accuracy of result expected output is high value of true positive and low value of false positive. And show that our algorithm has good robustness for smoke detection.
Wavelet coherence analysis of change blindness
International Nuclear Information System (INIS)
Memon, I.; Kalhoro, M.S.
2013-01-01
Change blindness is the incapability of the brain to detect substantial visual changes in the presence of other visual interruption. The objectives of this study are to examine the EEG (Electroencephalographic) based changes in functional connectivity of the brain due to the change blindness. The functional connectivity was estimated using the wavelet-based MSC (Magnitude Square Coherence) function of ERPs (Event Related Potentials). The ERPs of 30 subjects were used and were recorded using the visual attention experiment in which subjects were instructed to detect changes in visual stimulus presented before them through the computer monitor. The two-way ANOVA statistical test revealed significant increase in both gamma and theta band MSCs, and significant decrease in beta band MSC for change detection trials. These findings imply that change blindness might be associated to the lack of functional connectivity in gamma and theta bands and increase of functional connectivity in beta band. Since gamma, theta, and beta frequency bands reflect different functions of cognitive process such as maintenance, encoding, retrieval, and matching and work load of VSTM (Visual Short Term Memory), the change in functional connectivity might be correlated to these cognitive processes during change blindness. (author)
Time-localized wavelet multiple regression and correlation
Fernández-Macho, Javier
2018-02-01
This paper extends wavelet methodology to handle comovement dynamics of multivariate time series via moving weighted regression on wavelet coefficients. The concept of wavelet local multiple correlation is used to produce one single set of multiscale correlations along time, in contrast with the large number of wavelet correlation maps that need to be compared when using standard pairwise wavelet correlations with rolling windows. Also, the spectral properties of weight functions are investigated and it is argued that some common time windows, such as the usual rectangular rolling window, are not satisfactory on these grounds. The method is illustrated with a multiscale analysis of the comovements of Eurozone stock markets during this century. It is shown how the evolution of the correlation structure in these markets has been far from homogeneous both along time and across timescales featuring an acute divide across timescales at about the quarterly scale. At longer scales, evidence from the long-term correlation structure can be interpreted as stable perfect integration among Euro stock markets. On the other hand, at intramonth and intraweek scales, the short-term correlation structure has been clearly evolving along time, experiencing a sharp increase during financial crises which may be interpreted as evidence of financial 'contagion'.
Estimation of GRACE observation error covariance in wavelet domain
Behzadpour, Saniya; Mayer-Gürr, Torsten; Flury, Jakob; Goswami, Sujata
2017-04-01
We present a wavelet-based error covariance estimator in the GRACE gravity parameter estimation procedure and study its impact on the recovered gravity field solutions based on the ITSG-Grace2016 scheme. So far, stationarity was the main assumption in modelling the noise in range rate observations and a stationary covariance function was used in the observation whitening (decorrelation) step performed before the least-squares adjustment. We have shown this assumption is violated as the noise has time-variable behaviour and should be modelled in the framework of non-stationary stochastic processes. The Discrete Wavelet Transform (DWT) is of particular interest for analysis of non-stationary and transient time series. This transform operates unconditional of the input process type and tends to achieve the desirable decorrelating property for a large class of stochastic processes, including stationary random processes and some non-stationary random processes such as fractional Brownian motions and fractionally differenced processes. In order to perform the gravity parameter estimation in wavelet domain, both observation and design matrices are transformed by a discrete wavelet transform. In this case, the dense variance-covariance matrix of the noise is diagonalized by exploiting the decorrelation property of the transform. Implementation of gravity parameter estimation in wavelet domain, estimation of the empirical error covariance matrix using the residual coefficients, and comparison of the results with the ITSG-Grace2016 solution will be discussed.
Physical wavelets and their sources: real physics in complex spacetime
International Nuclear Information System (INIS)
Kaiser, Gerald
2003-01-01
A thorough review of acoustic and electromagnetic wavelets is given, including a first account of recent progress in understanding their sources. These physical wavelets, introduced in 1994, are families of 'small' solutions of the wave and Maxwell equations generated from a single member by group operations including translations, Lorentz transformations, and scaling. They are parametrized by complex spacetime points z = x - iy, where x gives the centre of their region of origin and y gives the extension and orientation of this region in spacetime. They are thus pulsed beams whose origin, direction and focus are all governed by z and which give, by superposition, 'wavelet representations' of acoustic and electromagnetic waves. Recently this idea has been developed substantially by the rigorous understanding of the source distributions required to launch and absorb the wavelets, defined as extended delta functions. The unexpected simplicity and complex structure of the sources in the Fourier domain suggests their potential use in the construction of fast algorithms for the analysis and synthesis of acoustic and electromagnetic waves. The review begins with a brief account of the physical wavelets associated with massive (Klein-Gordon and Dirac) fields, which are relativistic coherent states. (topical review)
Wavelet-Based Speech Enhancement Using Time-Frequency Adaptation
Wang, Kun-Ching
2003-12-01
Wavelet denoising is commonly used for speech enhancement because of the simplicity of its implementation. However, the conventional methods generate the presence of musical residual noise while thresholding the background noise. The unvoiced components of speech are often eliminated from this method. In this paper, a novel algorithm of wavelet coefficient threshold (WCT) based on time-frequency adaptation is proposed. In addition, an unvoiced speech enhancement algorithm is also integrated into the system to improve the intelligibility of speech. The wavelet coefficient threshold (WCT) of each subband is first temporally adjusted according to the value of a posterior signal-to-noise ratio (SNR). To prevent the degradation of unvoiced sounds during noise, the algorithm utilizes a simple speech/noise detector (SND) and further divides speech signal into unvoiced and voiced sounds. Then, we apply appropriate wavelet thresholding according to voiced/unvoiced (V/U) decision. Based on the masking properties of human auditory system, a perceptual gain factor is adopted into wavelet thresholding for suppressing musical residual noise. Simulation results show that the proposed method is capable of reducing noise with little speech degradation and the overall performance is superior to several competitive methods.
Wavelet Domain Radiofrequency Pulse Design Applied to Magnetic Resonance Imaging
Huettner, Andrew M.; Mickevicius, Nikolai J.; Ersoz, Ali; Koch, Kevin M.; Muftuler, L. Tugan; Nencka, Andrew S.
2015-01-01
A new method for designing radiofrequency (RF) pulses with numerical optimization in the wavelet domain is presented. Numerical optimization may yield solutions that might otherwise have not been discovered with analytic techniques alone. Further, processing in the wavelet domain reduces the number of unknowns through compression properties inherent in wavelet transforms, providing a more tractable optimization problem. This algorithm is demonstrated with simultaneous multi-slice (SMS) spin echo refocusing pulses because reduced peak RF power is necessary for SMS diffusion imaging with high acceleration factors. An iterative, nonlinear, constrained numerical minimization algorithm was developed to generate an optimized RF pulse waveform. Wavelet domain coefficients were modulated while iteratively running a Bloch equation simulator to generate the intermediate slice profile of the net magnetization. The algorithm minimizes the L2-norm of the slice profile with additional terms to penalize rejection band ripple and maximize the net transverse magnetization across each slice. Simulations and human brain imaging were used to demonstrate a new RF pulse design that yields an optimized slice profile and reduced peak energy deposition when applied to a multiband single-shot echo planar diffusion acquisition. This method may be used to optimize factors such as magnitude and phase spectral profiles and peak RF pulse power for multiband simultaneous multi-slice (SMS) acquisitions. Wavelet-based RF pulse optimization provides a useful design method to achieve a pulse waveform with beneficial amplitude reduction while preserving appropriate magnetization response for magnetic resonance imaging. PMID:26517262
Wavelet-based analysis of circadian behavioral rhythms.
Leise, Tanya L
2015-01-01
The challenging problems presented by noisy biological oscillators have led to the development of a great variety of methods for accurately estimating rhythmic parameters such as period and amplitude. This chapter focuses on wavelet-based methods, which can be quite effective for assessing how rhythms change over time, particularly if time series are at least a week in length. These methods can offer alternative views to complement more traditional methods of evaluating behavioral records. The analytic wavelet transform can estimate the instantaneous period and amplitude, as well as the phase of the rhythm at each time point, while the discrete wavelet transform can extract the circadian component of activity and measure the relative strength of that circadian component compared to those in other frequency bands. Wavelet transforms do not require the removal of noise or trend, and can, in fact, be effective at removing noise and trend from oscillatory time series. The Fourier periodogram and spectrogram are reviewed, followed by descriptions of the analytic and discrete wavelet transforms. Examples illustrate application of each method and their prior use in chronobiology is surveyed. Issues such as edge effects, frequency leakage, and implications of the uncertainty principle are also addressed. © 2015 Elsevier Inc. All rights reserved.
Extracting reflection with wavelet transform in vibroseis signal processing
Jiang, Zhongjin; Qiu, Xiaojun; Lin, Jun; Chen, Zubin
2006-09-01
In conventional vibroseis signal processing, algorithms including cross correlation and deconvolution are applied to convert the raw trace data into a seismic section. However, their performance deteriorates when the trace data are corrupted by the ambient noise, so the mathematical tool for time-frequency analysis and wavelet transform is applied in this paper to overcome the difficulty. A time-frequency cross correlation (TFCC) algorithm based on wavelet transform is proposed to extract the reflection from the trace data by detecting the reflected sweeps and estimating their time delay. The source signal and the trace data are transformed into time-frequency domain with respect to a same wavelet basis function; then time-frequency cross correlation is performed between the source signal and the trace data. The reflected sweeps are converted into time-frequency correlation wavelets in the result; meanwhile, the trace data are converted into seismic section. In wavelet decomposition, the high-frequency noise can be suppressed automatically. In the time-frequency representation of the trace data, the ambient noise and the reflected sweeps can be separated from each other. So in the TFCC algorithm, the interference of the ambient noise can be decreased considerably, and the weak reflections can be extracted clearly. Real vibroseis trace data were processed with the TFCC algorithm and the conventional cross correlation. The results showed the superiority of the proposed new algorithm in vibroseis signal processing.
La discreta arquitectura de Klas Anshelm
Directory of Open Access Journals (Sweden)
José Ignacio Linazasoro
2011-05-01
Full Text Available
Besides studying the masters, in times like these it is also convenient to study other minor architects which have contributed so much to the history of architecture and cities with their greater discretion. This is the case of Klas Anshelm, known for his relationship to Sigurd Lewerentz, with whom he had permanent contact during the last years of the great master. in the early 1950's he built the Kunsthallen. It is a work of extreme clarity and simplicity, with illumination coming from great skylights that guaranteed light and adaptability from the exhibition space. This small work with an essential design is fully integrated within the historic part of the city, both in terms of its scale and its materiality. In 1961, he designed and constructed the extension for the Lund City Hall, completely within the heart of the city. It forms a whole with the old neoclassical city hall. Two halls in the shape of an almond stand out within its floor plan composition scheme. Between 1971 and 1973 he builds the Malmö Art Gallery, his final important work. The building is designed in terms of light and material, and consisting of a single floor illuminated by square skylights, facing north. It is a space where the quality of the light, the control of scale, and its material unity characterize the exhibition space, giving it much flexibility. This architecture carried out by Klas Anshelm without traces from its "author" is the counterpoint to so many architect in these past years, who seem to have forgotten the aims for which buildings where designed.
Wavelet neural networks with applications in financial engineering, chaos, and classification
Alexandridis, Antonios K
2014-01-01
Through extensive examples and case studies, Wavelet Neural Networks provides a step-by-step introduction to modeling, training, and forecasting using wavelet networks. The acclaimed authors present a statistical model identification framework to successfully apply wavelet networks in various applications, specifically, providing the mathematical and statistical framework needed for model selection, variable selection, wavelet network construction, initialization, training, forecasting and prediction, confidence intervals, prediction intervals, and model adequacy testing. The text is ideal for
A time-scale analysis of systematic risk: wavelet-based approach
Khalfaoui Rabeh, K; Boutahar Mohamed, B
2011-01-01
The paper studies the impact of different time-scales on the market risk of individual stock market returns and of a given portfolio in Paris Stock Market by applying the wavelet analysis. To investigate the scaling properties of stock market returns and the lead/lag relationship between them at different scales, wavelet variance and crosscorrelations analyses are used. According to wavelet variance, stock returns exhibit long memory dynamics. The wavelet cross-correlation analysis s...
Shukla, K K
2013-01-01
Due to its inherent time-scale locality characteristics, the discrete wavelet transform (DWT) has received considerable attention in signal/image processing. Wavelet transforms have excellent energy compaction characteristics and can provide perfect reconstruction. The shifting (translation) and scaling (dilation) are unique to wavelets. Orthogonality of wavelets with respect to dilations leads to multigrid representation. As the computation of DWT involves filtering, an efficient filtering process is essential in DWT hardware implementation. In the multistage DWT, coefficients are calculated
Directory of Open Access Journals (Sweden)
LEONARDO BRANDÃO
2013-03-01
Full Text Available Resumo: Com aproximadamente 4 milhões de praticantes no país, o skate vem se revelando uma das atividades corporais de maior visibilidade entre os jovens. No entanto, sua prática apresenta peculiaridades que merecem ser observadas. Sua relação com os usos da cidade talvez seja a principal delas. Neste artigo, discorremos sobre a constituição histórica do skate de rua a partir do estudo com revistas especializadas nesta atividade que foram publicadas durante as décadas de 1970 e 1980. Concluímos que a cidade, representada pelos skatistas como um paraíso de infindáveis possibilidades de diversão, acabou sendo transformada não somente pelo olhar transfigurativo do skatista, que lhe emprestou novos sentidos e funções, mas ela mesma acabou se modificando para disciplinar os filhos “rebeldes” que seu processo de urbanização ajudou a criar.Palavras-chave: História – Cidade – Skate. Abstract: With approximately 4 million practitioners in the country, skateboarding has been revealing one of the most visible body activities among youth. However, its practice has peculiarities which deserve to be observed. Its relationship with the uses of the city is perhaps the main one of them. In this article, we discuss the historical constitution of the street skateboard from the study of magazines specialized in this activity published during the 1970s and 1980s. We conclude that the city, represented by skateboarders as a paradise of endless possibilities for fun, ended up being transformed not only by the skater’s transfigurative look, which lent it new meanings and functions, but it ended up itself changing to discipline their "rebellious" children whcih its urbanization process helped create.Keywords: History – City – Skateboard.
Directory of Open Access Journals (Sweden)
Elkin Flórez
2009-01-01
Full Text Available Uno de los problemas comunes que se presentan en las ruedas de los trenes, es la presencia de planos. Éstos generan un impacto suficiente como para afectar el funcionamiento normal del tren. La detección temprana de planos en las ruedas permite realizar las correcciones necesarias (tornear la superficie de la rueda a fin de evitar daños a los componentes del tren que degraden la prestación del servicio a los usuarios. Aunque existen muchos sensores de vibración en el mercado para detectar vibraciones generadas por el paso de un tren, no hay aún una herramienta estándar que permitan detectar la presencia de planos en las ruedas del mismo. Este estudio presenta la selección apropiada de una ventana temporal para utilizar la Transformada de Fourier en Tiempos Cortos (STFT, por sus siglas en inglés en el análisis de señales de vibración, tomadas al pie del carril y generadas al paso de un tren, que permita determinar la presencia de dichos planos. Para ello, en primer lugar se generó, utilizando la herramienta Matlab, una señal que simule la presencia de un plano y que permita conocer como la STFT, implementando diferentes ventanas temporales (Rectangular, Gauss, Hanning y Chebyshev, permite descubrir la presencia del mismo, en el dominio conjunto tiempo-frecuencia. Seguidamente, se aplica la STFT a señales tomadas en campo. Los resultados obtenidos demuestran que la STFT es una herramienta efectiva para detectar planos en la rueda de los trenes, si la función ventana y sus parámetros se seleccionan correctamente al realizar un análisis tiempo-frecuencia.
On Parseval Wavelet Frames with Two or Three Generators via the Unitary Extension Principle
DEFF Research Database (Denmark)
Christensen, Ole; Kim, Hong Oh; Kim, Rae Young
2014-01-01
The unitary extension principle (UEP) by A. Ron and Z. Shen yields a sufficient condition for the construction of Parseval wavelet frames with multiple generators. In this paper we characterize the UEP-type wavelet systems that can be extended to a Parseval wavelet frame by adding just one UEP-ty...
Wavelet characterization of Hörmander symbol class Sm Sm Sm ...
Indian Academy of Sciences (India)
R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22
Our new idea is to analyse the symbol operators in phase space with relative wavelets, and to establish the kernel distribution property and the operator's continuity on the basis of the wavelets coefficients in phase space. Keywords. Hörmander's symbol; wavelet; kernel distribution; operator's continuity. 1. Introduction.
Frequency domain volume rendering by the wavelet X-ray transform
Westenberg, Michel A.; Roerdink, Jos B.T.M.
We describe a wavelet-based X-ray rendering method in the frequency domain with a smaller time complexity than wavelet splatting. Standard Fourier volume rendering is summarized and interpolation and accuracy issues are briefly discussed. We review the implementation of the fast wavelet transform in
An Extension of Fourier-Wavelet Volume Rendering by View Interpolation
Westenberg, Michel A.; Roerdink, Jos B.T.M.
2001-01-01
This paper describes an extension to Fourier-wavelet volume rendering (FWVR), which is a Fourier domain implementation of the wavelet X-ray transform. This transform combines integration along the line of sight with a simultaneous 2-D wavelet transform in the view plane perpendicular to this line.
Computing connection coefficients of compactly supported wavelets on bounded intervals
Energy Technology Data Exchange (ETDEWEB)
Romine, C.H.; Peyton, B.W.
1997-04-01
Daubechies wavelet basis functions have many properties that make them desirable as a basis for a Galerkin approach to solving PDEs: they are orthogonal, with compact support, and their connection coefficients can be computed. The method developed by Latto et al. to compute connection coefficients does not provide the correct inner product near the endpoints of a bounded interval, making the implementation of boundary conditions problematic. Moreover, the highly oscillatory nature of the wavelet basis functions makes standard numerical quadrature of integrals near the boundary impractical. The authors extend the method of Latto et al. to construct and solve a linear system of equations whose solution provides the exact computation of the integrals at the boundaries. As a consequence, they provide the correct inner product for wavelet basis functions on a bounded interval.
Prediction of Hydrophobic Cores of Proteins Using Wavelet Analysis.
Hirakawa; Kuhara
1997-01-01
Information concerning the secondary structures, flexibility, epitope and hydrophobic regions of amino acid sequences can be extracted by assigning physicochemical indices to each amino acid residue, and information on structure can be derived using the sliding window averaging technique, which is in wide use for smoothing out raw functions. Wavelet analysis has shown great potential and applicability in many fields, such as astronomy, radar, earthquake prediction, and signal or image processing. This approach is efficient for removing noise from various functions. Here we employed wavelet analysis to smooth out a plot assigned to a hydrophobicity index for amino acid sequences. We then used the resulting function to predict hydrophobic cores in globular proteins. We calculated the prediction accuracy for the hydrophobic cores of 88 representative set of proteins. Use of wavelet analysis made feasible the prediction of hydrophobic cores at 6.13% greater accuracy than the sliding window averaging technique.
Wavelet-based histogram equalization enhancement of gastric sonogram images.
Fu, J C; Lien, H C; Wong, S T
2000-01-01
The gray levels of gastric sonogram images are usually concentrated at the zero end of the spectrum, making the image too low in contrast and too dark for the naked eye. Though histogram equalization can enhance the contrast by redistributing the gray levels, it has the drawback that it reduces the information in the processed image. In this paper, a wavelet-based enhancement algorithm post-processor is used to further enhance the image and compensate for the information loss during histogram equalization. Experimental results show that the wavelet-based enhancement algorithm can enhance the contrast and significantly increase the informational entropy of the image. Because the combination of the histogram equalization and wavelet approach can dramatically increase the contrast and maintain information rate in gastric sonograms, it has the potential to improve clinical diagnosis and research.
Apple Shape Classification Method Based on Wavelet Moment
Directory of Open Access Journals (Sweden)
Jiangsheng Gui
2014-09-01
Full Text Available Shape is not only an important indicator for assessing the grade of the apple, but also the important factors for increasing the value of the apple. In order to improve the apple shape classification accuracy rate, an approach for apple shape sorting based on wavelet moments was proposed, the image was first subjected to a normalization process using its regular moments to obtain scale and translation invariance, the rotation invariant wavelet moment features were then extracted from the scale and translation normalized images and the method of cluster analysis was used for finished the shape classification. This method performs better than traditional approaches such as Fourier descriptors and Zernike moments, because of that Wavelet moments can provide time-domain and frequency domain window, which was verified by experiments. The normal fruit shape, mild deformity and severe deformity classification accuracy is 86.21 %, 85.82 %, 90.81 % by our method.
Voice activity detection algorithm using perceptual wavelet entropy neighbor slope.
Lee, Gihyoun; Na, Sung Dae; Cho, Jin-Ho; Kim, Myoung Nam
2014-01-01
This paper presents a voice activity detection (VAD) approach using a perceptual wavelet entropy neighbor slope (PWENS) in a low signal-to-noise (SNR) environment and with a variety of noise types. The basis for our study is to use acoustic features that have large entropy variance for each wavelet critical band. The speech signal is decomposed by the proposed perceptual wavelet packet decomposition (PWPD), and the VAD function is extracted by PWENS. Finally, VAD is decided by the proposed VAD decision rule using two memory buffers. In order to evaluate the performance of the VAD decision, many speech samples and a variety of SNR conditions were used in the experiment. The performance of the VAD decision is confirmed using objective indexes such as a graph of the VAD decision and the relative error rate.
Image and audio wavelet integration for home security video compression
Cheng, Yu-Shen; Huang, Gen-Dow
2002-03-01
We present a novel wavelet compression algorithm for both audio and image with acceptable test by human perception. It is well known that Discrete Wavelet Transform (DWT) provides global multiple resolution decomposition that is the significant feature for the audio and image compressions. Experimental simulations show that the proposed audio and image model can satisfy the current industrial communication requirements in terms of the processing time and the compression fidelity. Development of wavelet-based compression algorithm considers the trade-off for hardware implementations. As a result, this high-performance video codec can develop compact, low power, high-speed, portable, cost-effective, and low-weight video compression for multimedia and home security applications.
Real-time video codec using reversible wavelets
Huang, Gen Dow; Chiang, David J.; Huang, Yi-En; Cheng, Allen
2003-04-01
This paper describes the hardware implementation of a real-time video codec using reversible Wavelets. The TechSoft (TS) real-time video system employs the Wavelet differencing for the inter-frame compression based on the independent Embedded Block Coding with Optimized Truncation (EBCOT) of the embedded bit stream. This high performance scalable image compression using EBCOT has been selected as part of the ISO new image compression standard, JPEG2000. The TS real-time video system can process up to 30 frames per second (fps) of the DVD format. In addition, audio signals are also processed by the same design for the cost reduction. Reversible Wavelets are used not only for the cost reduction, but also for the lossless applications. Design and implementation issues of the TS real-time video system are discussed.
Fault Identification of Gearbox Degradation with Optimized Wavelet Neural Network
Directory of Open Access Journals (Sweden)
Hanxin Chen
2013-01-01
Full Text Available A novel intelligent method based on wavelet neural network (WNN was proposed to identify the gear crack degradation in gearbox in this paper. The wavelet packet analysis (WPA is applied to extract the fault feature of the vibration signal, which is collected by two acceleration sensors mounted on the gearbox along the vertical and horizontal direction. The back-propagation (BP algorithm is studied and applied to optimize the scale and translation parameters of the Morlet wavelet function, the weight coefficients, threshold values in WNN structure. Four different gear crack damage levels under three different loads and three various motor speeds are presented to obtain the different gear fault modes and gear crack degradation in the experimental system. The results show the feasibility and effectiveness of the proposed method by the identification and classification of the four gear modes and degradation.
Wavelet Methods for Solving Fractional Order Differential Equations
Directory of Open Access Journals (Sweden)
A. K. Gupta
2014-01-01
Full Text Available Fractional calculus is a field of applied mathematics which deals with derivatives and integrals of arbitrary orders. The fractional calculus has gained considerable importance during the past decades mainly due to its application in diverse fields of science and engineering such as viscoelasticity, diffusion of biological population, signal processing, electromagnetism, fluid mechanics, electrochemistry, and many more. In this paper, we review different wavelet methods for solving both linear and nonlinear fractional differential equations. Our goal is to analyze the selected wavelet methods and assess their accuracy and efficiency with regard to solving fractional differential equations. We discuss challenges faced by researchers in this field, and we emphasize the importance of interdisciplinary effort for advancing the study on various wavelets in order to solve differential equations of arbitrary order.
Wavelet Filter Banks for Super-Resolution SAR Imaging
Sheybani, Ehsan O.; Deshpande, Manohar; Memarsadeghi, Nargess
2011-01-01
This paper discusses Innovative wavelet-based filter banks designed to enhance the analysis of super resolution Synthetic Aperture Radar (SAR) images using parametric spectral methods and signal classification algorithms, SAR finds applications In many of NASA's earth science fields such as deformation, ecosystem structure, and dynamics of Ice, snow and cold land processes, and surface water and ocean topography. Traditionally, standard methods such as Fast-Fourier Transform (FFT) and Inverse Fast-Fourier Transform (IFFT) have been used to extract Images from SAR radar data, Due to non-parametric features of these methods and their resolution limitations and observation time dependence, use of spectral estimation and signal pre- and post-processing techniques based on wavelets to process SAR radar data has been proposed. Multi-resolution wavelet transforms and advanced spectral estimation techniques have proven to offer efficient solutions to this problem.
ECG Signal Analysis and Arrhythmia Detection using Wavelet Transform
Kaur, Inderbir; Rajni, Rajni; Marwaha, Anupma
2016-12-01
Electrocardiogram (ECG) is used to record the electrical activity of the heart. The ECG signal being non-stationary in nature, makes the analysis and interpretation of the signal very difficult. Hence accurate analysis of ECG signal with a powerful tool like discrete wavelet transform (DWT) becomes imperative. In this paper, ECG signal is denoised to remove the artifacts and analyzed using Wavelet Transform to detect the QRS complex and arrhythmia. This work is implemented in MATLAB software for MIT/BIH Arrhythmia database and yields the sensitivity of 99.85 %, positive predictivity of 99.92 % and detection error rate of 0.221 % with wavelet transform. It is also inferred that DWT outperforms principle component analysis technique in detection of ECG signal.
Long memory analysis by using maximal overlapping discrete wavelet transform
Shafie, Nur Amalina binti; Ismail, Mohd Tahir; Isa, Zaidi
2015-05-01
Long memory process is the asymptotic decay of the autocorrelation or spectral density around zero. The main objective of this paper is to do a long memory analysis by using the Maximal Overlapping Discrete Wavelet Transform (MODWT) based on wavelet variance. In doing so, stock market of Malaysia, China, Singapore, Japan and United States of America are used. The risk of long term and short term investment are also being looked into. MODWT can be analyzed with time domain and frequency domain simultaneously and decomposing wavelet variance to different scales without loss any information. All countries under studied show that they have long memory. Subprime mortgage crisis in 2007 is occurred in the United States of America are possible affect to the major trading countries. Short term investment is more risky than long term investment.
Anisotropy in wavelet-based phase field models
Korzec, Maciek
2016-04-01
When describing the anisotropic evolution of microstructures in solids using phase-field models, the anisotropy of the crystalline phases is usually introduced into the interfacial energy by directional dependencies of the gradient energy coefficients. We consider an alternative approach based on a wavelet analogue of the Laplace operator that is intrinsically anisotropic and linear. The paper focuses on the classical coupled temperature/Ginzburg--Landau type phase-field model for dendritic growth. For the model based on the wavelet analogue, existence, uniqueness and continuous dependence on initial data are proved for weak solutions. Numerical studies of the wavelet based phase-field model show dendritic growth similar to the results obtained for classical phase-field models.
Evaluation of the wavelet image two-line coder
DEFF Research Database (Denmark)
Rein, Stephan Alexander; Fitzek, Frank Hanns Paul; Gühmann, Clemens
2015-01-01
This paper introduces the wavelet image two-line (Wi2l) coding algorithm for low complexity compression of images. The algorithm recursively encodes an image backwards reading only two lines of a wavelet subband, which are read in blocks of 512 bytes from flash memory. It thus only requires very...... little memory, i.e., a memory array for two wavelet subband lines, an array to store intermediate tree level data, and an array for writing binary data. A picture of 256×256 pixels would require 1152 bytes of memory. Computation time for the coding is derived analytically and measured on a real system...... refers to low memory, minimum write access to flash memory, usage of integer operations only, and low conceptual complexity (ease of implementation). As demonstrated in this paper, a compression performance similar to JPEG 2000 and the more recent Google WebP picture compression is achieved...
Content based image retrieval based on wavelet transform coefficients distribution.
Lamard, Mathieu; Cazuguel, Guy; Quellec, Gwénolé; Bekri, Lynda; Roux, Christian; Cochener, Béatrice
2007-01-01
In this paper we propose a content based image retrieval method for diagnosis aid in medical fields. We characterize images without extracting significant features by using distribution of coefficients obtained by building signatures from the distribution of wavelet transform. The research is carried out by computing signature distances between the query and database images. Several signatures are proposed; they use a model of wavelet coefficient distribution. To enhance results, a weighted distance between signatures is used and an adapted wavelet base is proposed. Retrieval efficiency is given for different databases including a diabetic retinopathy, a mammography and a face database. Results are promising: the retrieval efficiency is higher than 95% for some cases using an optimization process.
Content Based Image Retrieval based on Wavelet Transform coefficients distribution
Lamard, Mathieu; Cazuguel, Guy; Quellec, Gwénolé; Bekri, Lynda; Roux, Christian; Cochener, Béatrice
2007-01-01
In this paper we propose a content based image retrieval method for diagnosis aid in medical fields. We characterize images without extracting significant features by using distribution of coefficients obtained by building signatures from the distribution of wavelet transform. The research is carried out by computing signature distances between the query and database images. Several signatures are proposed; they use a model of wavelet coefficient distribution. To enhance results, a weighted distance between signatures is used and an adapted wavelet base is proposed. Retrieval efficiency is given for different databases including a diabetic retinopathy, a mammography and a face database. Results are promising: the retrieval efficiency is higher than 95% for some cases using an optimization process. PMID:18003013
Wavelet analysis of MR functional data from the cerebellum
International Nuclear Information System (INIS)
Karen, Romero Sánchez; Vásquez Reyes Marcos, A.; González Gómez Dulce, I.; Hernández López, Javier M.; Silvia, Hidalgo Tobón; Pilar, Dies Suarez; Eduardo, Barragán Pérez; Benito, De Celis Alonso
2014-01-01
The main goal of this project was to create a computer algorithm based on wavelet analysis of BOLD signals, which automatically diagnosed ADHD using information from resting state MR experiments. Male right handed volunteers (infants with ages between 7 and 11 years old) were studied and compared with age matched controls. Wavelet analysis, which is a mathematical tool used to decompose time series into elementary constituents and detect hidden information, was applied here to the BOLD signal obtained from the cerebellum 8 region of all our volunteers. Statistical differences between the values of the a parameters of wavelet analysis was found and showed significant differences (p<0.02) between groups. This difference might help in the future to distinguish healthy from ADHD patients and therefore diagnose ADHD
Wavelet analysis of MR functional data from the cerebellum
Energy Technology Data Exchange (ETDEWEB)
Karen, Romero Sánchez, E-mail: alphacentauri-hp@hotmail.com, E-mail: marcos-vaquezr@hotmail.com, E-mail: isabeldgg@hotmail.com; Vásquez Reyes Marcos, A., E-mail: alphacentauri-hp@hotmail.com, E-mail: marcos-vaquezr@hotmail.com, E-mail: isabeldgg@hotmail.com; González Gómez Dulce, I., E-mail: alphacentauri-hp@hotmail.com, E-mail: marcos-vaquezr@hotmail.com, E-mail: isabeldgg@hotmail.com; Hernández López, Javier M., E-mail: javierh@fcfm.buap.mx [Faculty of Physics and Mathematics, BUAP, Puebla, Pue (Mexico); Silvia, Hidalgo Tobón, E-mail: shidbon@gmail.com [Infant Hospital of Mexico, Federico Gómez, Mexico DF. Mexico and Physics Department, Universidad Autónoma Metropolitana. Iztapalapa, Mexico DF. (Mexico); Pilar, Dies Suarez, E-mail: pilydies@yahoo.com, E-mail: neurodoc@prodigy.net.mx; Eduardo, Barragán Pérez, E-mail: pilydies@yahoo.com, E-mail: neurodoc@prodigy.net.mx [Infant Hospital of Mexico, Federico Gómez, Mexico DF. (Mexico); Benito, De Celis Alonso, E-mail: benileon@yahoo.com [Faculty of Physics and Mathematics, BUAP, Puebla, Pue. Mexico and Foundation for Development Carlos Sigüenza. Puebla, Pue. (Mexico)
2014-11-07
The main goal of this project was to create a computer algorithm based on wavelet analysis of BOLD signals, which automatically diagnosed ADHD using information from resting state MR experiments. Male right handed volunteers (infants with ages between 7 and 11 years old) were studied and compared with age matched controls. Wavelet analysis, which is a mathematical tool used to decompose time series into elementary constituents and detect hidden information, was applied here to the BOLD signal obtained from the cerebellum 8 region of all our volunteers. Statistical differences between the values of the a parameters of wavelet analysis was found and showed significant differences (p<0.02) between groups. This difference might help in the future to distinguish healthy from ADHD patients and therefore diagnose ADHD.
Image superresolution of cytology images using wavelet based patch search
Vargas, Carlos; García-Arteaga, Juan D.; Romero, Eduardo
2015-01-01
Telecytology is a new research area that holds the potential of significantly reducing the number of deaths due to cervical cancer in developing countries. This work presents a novel super-resolution technique that couples high and low frequency information in order to reduce the bandwidth consumption of cervical image transmission. The proposed approach starts by decomposing into wavelets the high resolution images and transmitting only the lower frequency coefficients. The transmitted coefficients are used to reconstruct an image of the original size. Additional details are added by iteratively replacing patches of the wavelet reconstructed image with equivalent high resolution patches from a previously acquired image database. Finally, the original transmitted low frequency coefficients are used to correct the final image. Results show a higher signal to noise ratio in the proposed method over simply discarding high frequency wavelet coefficients or replacing directly down-sampled patches from the image-database.
Dual-tree complex wavelet for medical image watermarking
International Nuclear Information System (INIS)
Mavudila, K.R.; Ndaye, B.M.; Masmoudi, L.; Hassanain, N.; Cherkaoui, M.
2010-01-01
In order to transmit medical data between hospitals, we insert the information for each patient in the image and its diagnosis, the watermarking consist to insert a message in the image and try to find it with the maximum possible fidelity. This paper presents a blind watermarking scheme in wavelet transform domain dual tree (DTT), who increasing the robustness and preserves the image quality. This system is transparent to the user and allows image integrity control. In addition, it provides information on the location of potential alterations and an evaluation of image modifications which is of major importance in a medico-legal framework. An example using head magnetic resonance and mammography imaging illustrates the overall method. Wavelet techniques can be successfully applied in various image processing methods, namely in image de noising, segmentation, classification, watermarking and others. In this paper we discussed the application of dual tree complex wavelet transform (D T-CWT), which has significant advantages over classic discrete wavelet transform (DWT), for certain image processing problems. The D T-CWT is a form of discreet wavelet transform which generates complex coefficients by using a dual tree of wavelet filters to obtain their real and imaginary parts. The main part of the paper is devoted to profit the exceptional quality for D T-CWT, compared to classical DWT, for a blind medical image watermarking, our schemes are using for the performance bivariate shrinkage with local variance estimation and are robust of attacks and favourably preserves the visual quality. Experimental results show that embedded watermarks using CWT give good image quality and are robust in comparison with the classical DWT.
Context Modeler for Wavelet Compression of Spectral Hyperspectral Images
Kiely, Aaron; Xie, Hua; Klimesh, matthew; Aranki, Nazeeh
2010-01-01
A context-modeling sub-algorithm has been developed as part of an algorithm that effects three-dimensional (3D) wavelet-based compression of hyperspectral image data. The context-modeling subalgorithm, hereafter denoted the context modeler, provides estimates of probability distributions of wavelet-transformed data being encoded. These estimates are utilized by an entropy coding subalgorithm that is another major component of the compression algorithm. The estimates make it possible to compress the image data more effectively than would otherwise be possible. The following background discussion is prerequisite to a meaningful summary of the context modeler. This discussion is presented relative to ICER-3D, which is the name attached to a particular compression algorithm and the software that implements it. The ICER-3D software is summarized briefly in the preceding article, ICER-3D Hyperspectral Image Compression Software (NPO-43238). Some aspects of this algorithm were previously described, in a slightly more general context than the ICER-3D software, in "Improving 3D Wavelet-Based Compression of Hyperspectral Images" (NPO-41381), NASA Tech Briefs, Vol. 33, No. 3 (March 2009), page 7a. In turn, ICER-3D is a product of generalization of ICER, another previously reported algorithm and computer program that can perform both lossless and lossy wavelet-based compression and decompression of gray-scale-image data. In ICER-3D, hyperspectral image data are decomposed using a 3D discrete wavelet transform (DWT). Following wavelet decomposition, mean values are subtracted from spatial planes of spatially low-pass subbands prior to encoding. The resulting data are converted to sign-magnitude form and compressed. In ICER-3D, compression is progressive, in that compressed information is ordered so that as more of the compressed data stream is received, successive reconstructions of the hyperspectral image data are of successively higher overall fidelity.
Wavelet-based ground vehicle recognition using acoustic signals
Choe, Howard C.; Karlsen, Robert E.; Gerhart, Grant R.; Meitzler, Thomas J.
1996-03-01
We present, in this paper, a wavelet-based acoustic signal analysis to remotely recognize military vehicles using their sound intercepted by acoustic sensors. Since expedited signal recognition is imperative in many military and industrial situations, we developed an algorithm that provides an automated, fast signal recognition once implemented in a real-time hardware system. This algorithm consists of wavelet preprocessing, feature extraction and compact signal representation, and a simple but effective statistical pattern matching. The current status of the algorithm does not require any training. The training is replaced by human selection of reference signals (e.g., squeak or engine exhaust sound) distinctive to each individual vehicle based on human perception. This allows a fast archiving of any new vehicle type in the database once the signal is collected. The wavelet preprocessing provides time-frequency multiresolution analysis using discrete wavelet transform (DWT). Within each resolution level, feature vectors are generated from statistical parameters and energy content of the wavelet coefficients. After applying our algorithm on the intercepted acoustic signals, the resultant feature vectors are compared with the reference vehicle feature vectors in the database using statistical pattern matching to determine the type of vehicle from where the signal originated. Certainly, statistical pattern matching can be replaced by an artificial neural network (ANN); however, the ANN would require training data sets and time to train the net. Unfortunately, this is not always possible for many real world situations, especially collecting data sets from unfriendly ground vehicles to train the ANN. Our methodology using wavelet preprocessing and statistical pattern matching provides robust acoustic signal recognition. We also present an example of vehicle recognition using acoustic signals collected from two different military ground vehicles. In this paper, we will
Denoising for Different Noisy Chaotic Signal Based on Wavelet Transform
Directory of Open Access Journals (Sweden)
Jun Ma
2014-01-01
Full Text Available In a complete Chaotic radar ranging system, its effective range is often limited by the randomness of the chaotic signal itself and other transmission channel noises or interferences. In order to improve the precision and accuracy of radar ranging system, wavelet transform is proposed to remove different kinds of noise embedded in chaotic signals. White Gaussian noise, colored Gaussian noise as well as sine-wave signal are respectively applied for simulation analysis. Applied for simulation analysis, the experimental results show that wavelet transform can not only remove the chaotic signal mixed in some of the different types of noise, and can also improve the noise ratio.
CHARACTERIZATION OF RENAL BLOOD FLOW REGULATION BASED ON WAVELET COEFFICIENTS
DEFF Research Database (Denmark)
Pavlov, A.N.; Pavlova, O.N.; Mosekilde, Erik
2010-01-01
. A reduction in the variability of the wavelet coefficients in hypertension is observed at both the microscopic level of the blood flow in efferent arterioles of individual nephrons and at the macroscopic level of the blood pressure in the main arteries. The reduction is manifest in both of the main frequency...... domains of nephron autoregulation and is likely to reflect a reduced flexibility of the cardiovascular system during hypertension.......The purpose of this study is to demonstrate the possibility of revealing new characteristic features of renal blood flow autoregulation in healthy and pathological states through the application of discrete wavelet transforms to experimental time series for normotensive and hypertensive rats...
Wind Speed Forecasting Using Hybrid Wavelet Transform—ARMA Techniques
Directory of Open Access Journals (Sweden)
Diksha Kaur
2015-01-01
Full Text Available The objective of this paper is to develop a novel wind speed forecasting technique, which produces more accurate prediction. The Wavelet Transform (WT along with the Auto Regressive Moving Average (ARMA is chosen to form a hybrid whose combination is expected to give minimum Mean Absolute Prediction Error (MAPE. A simulation study has been conducted by comparing the forecasting results using the Wavelet-ARMA with the ARMA and Artificial Neural Network (ANN-Ensemble Kalman Filter (EnKF hybrid technique to verify the effectiveness of the proposed hybrid method. Results of the proposed hybrid show significant improvements in the forecasting error.
FUSION OF WAVELET AND CURVELET COEFFICIENTS FOR GRAY TEXTURE CLASSIFICATION
Directory of Open Access Journals (Sweden)
M. Santhanalakshmi
2014-05-01
Full Text Available This study presents a framework for gray texture classification based on the fusion of wavelet and curvelet features. The two main frequency domain transformations Discrete Wavelet Transform (DWT and Discrete Curvelet Transform (DCT are analyzed. The features are extracted from the DWT and DCT decomposed image separately and their performance is evaluated independently. Then feature fusion technique is applied to increase the classification accuracy of the proposed approach. Brodatz texture images are used for this study. The results show that, only two texture images D105 and D106 are misclassified by the fusion approach and 99.74% classification accuracy is obtained.
Sonar target enhancement by shrinkage of incoherent wavelet coefficients.
Hunter, Alan J; van Vossen, Robbert
2014-01-01
Background reverberation can obscure useful features of the target echo response in broadband low-frequency sonar images, adversely affecting detection and classification performance. This paper describes a resolution and phase-preserving means of separating the target response from the background reverberation noise using a coherence-based wavelet shrinkage method proposed recently for de-noising magnetic resonance images. The algorithm weights the image wavelet coefficients in proportion to their coherence between different looks under the assumption that the target response is more coherent than the background. The algorithm is demonstrated successfully on experimental synthetic aperture sonar data from a broadband low-frequency sonar developed for buried object detection.
Wavelets and Sentiment in the Heterogeneous Agents Model
Czech Academy of Sciences Publication Activity Database
Vácha, Lukáš; Vošvrda, Miloslav
2008-01-01
Roč. 15, č. 25 (2008), s. 41-56 ISSN 1212-074X R&D Projects: GA ČR GP402/08/P207; GA ČR GA402/07/1113; GA ČR(CZ) GA402/06/0990 Institutional research plan: CEZ:AV0Z10750506 Keywords : heterogeneous agents model * market sentiment * Hurst exponent * wavelets Subject RIV: AH - Economics http://library.utia.cas.cz/separaty/2008/E/vacha-wavelets and sentiment in the heterogeneous agents model.pdf
Wavelet Transformation for Damage Identication in Wind Turbine Blades
DEFF Research Database (Denmark)
Ulriksen, Martin Dalgaard; Skov, Jonas falk; Kirkegaard, Poul Henning
2014-01-01
The present paper documents a proposed modal and wavelet analysis-based structural health monitoring (SHM) method for damage identification in wind turbine blades. A finite element (FE) model of a full-scale wind turbine blade is developed and introduced to a transverse surface crack. Hereby, post......-damage mode shapes are derived through modal analysis and subsequently analyzed with continuous two-dimensional wavelet transformation for damage identification, namely detection, localization and assessment. It is found that valid damage identification is obtained even when utilizing the mode shape...
Speckle Suppression in Ultrasonic Images Based on Undecimated Wavelets
Argenti, Fabrizio; Torricelli, Gionatan
2003-12-01
An original method to denoise ultrasonic images affected by speckle is presented. Speckle is modeled as a signal-dependent noise corrupting the image. Noise reduction is approached as a Wiener-like filtering performed in a shift-invariant wavelet domain by means of an adaptive rescaling of the coefficients of an undecimated octave decomposition. The scaling factor of each coefficient is calculated from local statistics of the degraded image, the parameters of the noise model, and the wavelet filters. Experimental results demonstrate that excellent background smoothing as well as preservation of edge sharpness and fine details can be obtained.
Haar wavelets, fluctuations and structure functions: convenient choices for geophysics
Directory of Open Access Journals (Sweden)
S. Lovejoy
2012-09-01
Full Text Available Geophysical processes are typically variable over huge ranges of space-time scales. This has lead to the development of many techniques for decomposing series and fields into fluctuations Δv at well-defined scales. Classically, one defines fluctuations as differences: (Δv_{diff} = v(x+Δx-v(x and this is adequate for many applications (Δx is the "lag". However, if over a range one has scaling Δv ∝ Δx^{H}, these difference fluctuations are only adequate when 0 < H < 1. Hence, there is the need for other types of fluctuations. In particular, atmospheric processes in the "macroweather" range ≈10 days to 10–30 yr generally have −1 < H < 0, so that a definition valid over the range −1 < H < 1 would be very useful for atmospheric applications. A general framework for defining fluctuations is wavelets. However, the generality of wavelets often leads to fairly arbitrary choices of "mother wavelet" and the resulting wavelet coefficients may be difficult to interpret. In this paper we argue that a good choice is provided by the (historically first wavelet, the Haar wavelet (Haar, 1910, which is easy to interpret and – if needed – to generalize, yet has rarely been used in geophysics. It is also easy to implement numerically: the Haar fluctuation (Δv_{Haar} at lag Δx is simply equal to the difference of the mean from x to x+ Δx/2 and from x+Δx/2 to x+Δx. Indeed, we shall see that the interest of the Haar wavelet is this relation to the integrated process rather than its wavelet nature per se.
Using numerical multifractal simulations, we show that it is quite accurate, and we compare and contrast it with another similar technique, detrended fluctuation analysis. We find that, for estimating scaling exponents, the two methods are very similar, yet
Elastic wavelets and their application to problems of solitary wave propagation
Directory of Open Access Journals (Sweden)
Cattani, Carlo
2008-03-01
Full Text Available The paper can be referred to that direction in the wavelet theory, which was called by Kaiser "the physical wavelets". He developed the analysis of first two kinds of physical wavelets - electromagnetic (optic and acoustic wavelets. Newland developed the technique of application of harmonic wavelets especially for studying the harmonic vibrations. Recently Cattani and Rushchitsky proposed the 4th kind of physical wavelets - elastic wavelets. This proposal was based on three main elements: 1. Kaiser's idea of constructing the physical wavelets on the base of specially chosen (admissible solutions of wave equations. 2. Developed by one of authors theory of solitary waves (with profiles in the form of Chebyshov-Hermite functions propagated in elastic dispersive media. 3. The theory and practice of using the wavelet "Mexican Hat" system, the mother and farther wavelets (and their Fourier transforms of which are analytically represented as the Chebyshov-Hermite functions of different indexes. An application of elastic wavelets to studying the evolution of solitary waves of different shape during their propagation through composite materials is shown on many examples.
The use of wavelet transforms in the solution of two-phase flow problems
International Nuclear Information System (INIS)
Moridis, G.J.; Nikolaou, M.; You, Yong
1994-10-01
In this paper we present the use of wavelets to solve the nonlinear Partial Differential.Equation (PDE) of two-phase flow in one dimension. The wavelet transforms allow a drastically different approach in the discretization of space. In contrast to the traditional trigonometric basis functions, wavelets approximate a function not by cancellation but by placement of wavelets at appropriate locations. When an abrupt chance, such as a shock wave or a spike, occurs in a function, only local coefficients in a wavelet approximation will be affected. The unique feature of wavelets is their Multi-Resolution Analysis (MRA) property, which allows seamless investigational any spatial resolution. The use of wavelets is tested in the solution of the one-dimensional Buckley-Leverett problem against analytical solutions and solutions obtained from standard numerical models. Two classes of wavelet bases (Daubechies and Chui-Wang) and two methods (Galerkin and collocation) are investigated. We determine that the Chui-Wang, wavelets and a collocation method provide the optimum wavelet solution for this type of problem. Increasing the resolution level improves the accuracy of the solution, but the order of the basis function seems to be far less important. Our results indicate that wavelet transforms are an effective and accurate method which does not suffer from oscillations or numerical smearing in the presence of steep fronts
Ansari, Naushad; Gupta, Anubha
2017-08-01
This paper proposes a joint framework wherein lifting-based, separable, image-matched wavelets are estimated from compressively sensed images and are used for the reconstruction of the same. Matched wavelet can be easily designed if full image is available. Also compared with the standard wavelets as sparsifying bases, matched wavelet may provide better reconstruction results in compressive sensing (CS) application. Since in CS application, we have compressively sensed images instead of full images, existing methods of designing matched wavelets cannot be used. Thus, we propose a joint framework that estimates matched wavelets from compressively sensed images and also reconstructs full images. This paper has three significant contributions. First, a lifting-based, image-matched separable wavelet is designed from compressively sensed images and is also used to reconstruct the same. Second, a simple sensing matrix is employed to sample data at sub-Nyquist rate such that sensing and reconstruction time is reduced considerably. Third, a new multi-level L-Pyramid wavelet decomposition strategy is provided for separable wavelet implementation on images that leads to improved reconstruction performance. Compared with the CS-based reconstruction using standard wavelets with Gaussian sensing matrix and with existing wavelet decomposition strategy, the proposed methodology provides faster and better image reconstruction in CS application.
Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold
2017-08-11
This paper presents a hybrid fuzzy wavelet neural network (HFWNN) realized with the aid of polynomial neural networks (PNNs) and fuzzy inference-based wavelet neurons (FIWNs). Two types of FIWNs including fuzzy set inference-based wavelet neurons (FSIWNs) and fuzzy relation inference-based wavelet neurons (FRIWNs) are proposed. In particular, a FIWN without any fuzzy set component (viz., a premise part of fuzzy rule) becomes a wavelet neuron (WN). To alleviate the limitations of the conventional wavelet neural networks or fuzzy wavelet neural networks whose parameters are determined based on a purely random basis, the parameters of wavelet functions standing in FIWNs or WNs are initialized by using the C-Means clustering method. The overall architecture of the HFWNN is similar to the one of the typical PNNs. The main strategies in the design of HFWNN are developed as follows. First, the first layer of the network consists of FIWNs (e.g., FSIWN or FRIWN) that are used to reflect the uncertainty of data, while the second and higher layers consist of WNs, which exhibit a high level of flexibility and realize a linear combination of wavelet functions. Second, the parameters used in the design of the HFWNN are adjusted through genetic optimization. To evaluate the performance of the proposed HFWNN, several publicly available data are considered. Furthermore a thorough comparative analysis is covered.
Hosseinbor, Ameer Pasha; Kim, Won Hwa; Adluru, Nagesh; Acharya, Amit; Vorperian, Houri K; Chung, Moo K
2014-01-01
Recently, the HyperSPHARM algorithm was proposed to parameterize multiple disjoint objects in a holistic manner using the 4D hyperspherical harmonics. The HyperSPHARM coefficients are global; they cannot be used to directly infer localized variations in signal. In this paper, we present a unified wavelet framework that links Hyper-SPHARM to the diffusion wavelet transform. Specifically, we will show that the HyperSPHARM basis forms a subset of a wavelet-based multiscale representation of surface-based signals. This wavelet, termed the hyperspherical diffusion wavelet, is a consequence of the equivalence of isotropic heat diffusion smoothing and the diffusion wavelet transform on the hypersphere. Our framework allows for the statistical inference of highly localized anatomical changes, which we demonstrate in the first-ever developmental study on the hyoid bone investigating gender and age effects. We also show that the hyperspherical wavelet successfully picks up group-wise differences that are barely detectable using SPHARM.
A Note on Directional Wavelet Transform: Distributional Boundary Values and Analytic Wavefront Sets
Directory of Open Access Journals (Sweden)
Felipe A. Apolonio
2012-01-01
Full Text Available By using a particular class of directional wavelets (namely, the conical wavelets, which are wavelets strictly supported in a proper convex cone in the k-space of frequencies, in this paper, it is shown that a tempered distribution is obtained as a finite sum of boundary values of analytic functions arising from the complexification of the translational parameter of the wavelet transform. Moreover, we show that for a given distribution f∈′(ℝn, the continuous wavelet transform of f with respect to a conical wavelet is defined in such a way that the directional wavelet transform of f yields a function on phase space whose high-frequency singularities are precisely the elements in the analytic wavefront set of f.
Dependence and risk assessment for oil prices and exchange rate portfolios: A wavelet based approach
Aloui, Chaker; Jammazi, Rania
2015-10-01
In this article, we propose a wavelet-based approach to accommodate the stylized facts and complex structure of financial data, caused by frequent and abrupt changes of markets and noises. Specifically, we show how the combination of both continuous and discrete wavelet transforms with traditional financial models helps improve portfolio's market risk assessment. In the empirical stage, three wavelet-based models (wavelet-EGARCH with dynamic conditional correlations, wavelet-copula, and wavelet-extreme value) are considered and applied to crude oil price and US dollar exchange rate data. Our findings show that the wavelet-based approach provides an effective and powerful tool for detecting extreme moments and improving the accuracy of VaR and Expected Shortfall estimates of oil-exchange rate portfolios after noise is removed from the original data.
Momoh, James A.; Wang, Yanchun; Dolce, James L.
1997-01-01
This paper describes the application of neural network adaptive wavelets for fault diagnosis of space station power system. The method combines wavelet transform with neural network by incorporating daughter wavelets into weights. Therefore, the wavelet transform and neural network training procedure become one stage, which avoids the complex computation of wavelet parameters and makes the procedure more straightforward. The simulation results show that the proposed method is very efficient for the identification of fault locations.
Control de brazo electrónico usando señales electromiográficas
Directory of Open Access Journals (Sweden)
Jorge Andrés García-Pinzón
2015-05-01
Full Text Available Los trabajos enfocados en la extracción de patrones en señales electromiográficas (SEMG han venido creciendo debido a sus múltiples aplicaciones. En este artículo se presenta una aplicación en la cual se implementa un sistema electrónico para el registro de las SEMG de la extremidad superior en un sujeto, con el fin de controlar de forma remota un brazo electrónico. Se realizó una etapa de preprocesamiento de las señales registradas, para eliminar información poco relevante, y reconocimiento de zonas de interés; enseguida se extraen los patrones y se clasifican. Las técnicas utilizadas fueron: análisis wavelet (AW, análisis de componentes principales (ACP, transformada de fourier (TF, transformada del coseno discreta (TDC, energía, máquinas de soporte vectorial (MSV o SVM y redes neuronales (RNA. En este artículo se demuestra que la metodología planteada permite realizar un proceso de clasificación con un rendimiento superior al 95%. Se registraron más de 4000 señales.
ADQUISICIÓN Y PROCESAMIENTO DE SEÑALES EMG PARA CONTROLAR MOVIMIENTO DE UN BRAZO HIDRAULICO
Directory of Open Access Journals (Sweden)
Jorge Andrés García Pinzon
2014-06-01
Full Text Available En este artículo se presenta el diseño e implementación de un sistema electrónico para el registro de las señales electromiográficas de la extremidad superior del sujeto (humano. Seguidamente al proceso de la implementación del sistema electrónico, en este trabajo se realiza una etapa de pre-procesamiento y procesamiento de las señales registradas, las técnicas utilizadas para éste fin son: análisis wavelet (AW, análisis de componentes principales (ACP, transformada de fourier (TF, transformada del coseno discreta (DCT, máquinas de soporte vectorial (SVM y redes neuronales artificiales (RNA; estas técnicas se usaron para eliminar información poco relevante, reconocer zonas de interés, extraer patrones en cada grupo de señales y clasificar una nueva señal que controle en forma precisa el movimiento que quiere ejecutar el sujeto con el brazo Hidráulico. Dentro de las técnicas de control de procesos Industriales se busca realizar una aplicación con el fin de poder hacer control a dos grados de libertad más el efector final del brazo hidráulico del laboratorio de automatización y mantenimiento de equipos industriales de la Universidad de Pamplona.
A Wavelet-Based Optimization Method for Biofuel Production
Directory of Open Access Journals (Sweden)
Maurizio Carlini
2018-02-01
Full Text Available On a global scale many countries are still heavily dependent on crude oil to produce energy and fuel for transport, with a resulting increase of atmospheric pollution. A possible solution to obviate this problem is to find eco-sustainable energy sources. A potential choice could be the use of biodiesel as fuel. The work presented aims to characterise the transesterification reaction of waste peanut frying oil using colour analysis and wavelet analysis. The biodiesel production, with the complete absence of mucilages, was evaluated through a suitable set of energy wavelet coefficients and scalograms. The physical characteristics of the biodiesel are influenced by mucilages. In particular the viscosity, that is a fundamental parameter for the correct use of the biodiesel, might be compromised. The presence of contaminants in the samples can often be missed by visual analysis. The low and high frequency wavelet analysis, by investigating the energy change of wavelet coefficient, provided a valid characterisation of the quality of the samples, related to the absence of mucilages, which is consistent with the experimental results. The proposed method of this work represents a preliminary analysis, before the subsequent chemical physical analysis, that can be develop during the production phases of the biodiesel in order to optimise the process, avoiding the presence of impurities in suspension in the final product.
An improved adaptive wavelet shrinkage for ultrasound despeckling
Indian Academy of Sciences (India)
Ultrasound imaging is the most widely used medical diagnostic technique for clinical decision making, due to its ability to make real time imaging for moving structures, low cost and safety. However, its usefulness is degraded by the presence of signal dependent speckle noise. Several wavelet-based denoising schemes ...
receive signal strength prediction in the gsm band using wavelet
African Journals Online (AJOL)
user
wavelet to detect the signal attenuation with reference to multipath and shadow fading, but did not group and predict the average power received in their study area. In. [3], a simple approach to a statistical path loss model for indoor communications was developed using power law. The work concluded that the model can ...
Wavelet packet-based insufficiency murmurs analysis method
Choi, Samjin; Jiang, Zhongwei
2007-12-01
In this paper, the aortic and mitral insufficiency murmurs analysis method using the wavelet packet technique is proposed for classifying the valvular heart defects. Considering the different frequency distributions between the normal sound and insufficiency murmurs in frequency domain, we used two properties such as the relative wavelet energy and the Shannon wavelet entropy which described the energy information and the entropy information at the selected frequency band, respectively. Then, the signal to murmur ratio (SMR) measures which could mean the ratio between the frequency bands for normal heart sounds and for aortic and mitral insufficiency murmurs allocated to 15.62-187.50 Hz and 187.50-703.12 Hz respectively, were employed as a classification manner to identify insufficiency murmurs. The proposed measures were validated by some case studies. The 194 heart sound signals with 48 normal and 146 abnormal sound cases acquired from 6 healthy volunteers and 30 patients were tested. The normal sound signals recorded by applying a self-produced wireless electric stethoscope system to subjects with no history of other heart complications were used. Insufficiency murmurs were grouped into two valvular heart defects such as aortic insufficiency and mitral insufficiency. These murmur subjects included no other coexistent valvular defects. As a result, the proposed insufficiency murmurs detection method showed relatively very high classification efficiency. Therefore, the proposed heart sound classification method based on the wavelet packet was validated for the classification of valvular heart defects, especially insufficiency murmurs.
An improved adaptive wavelet shrinkage for ultrasound despeckling
Indian Academy of Sciences (India)
Preservation Index (EPI). A comparison of the results shows that the proposed fil- ter achieves an improvement in terms of quantitative measures and in terms of visual quality of the images. Keywords. Wavelet; translation invariance; inter and intra scale dependency; speckle; adaptive thresholding; ultrasound images. ∗.
Wavelet packet transform-based robust video watermarking technique
Indian Academy of Sciences (India)
Abstract. In this paper, a wavelet packet transform (WPT)-based robust video watermarking algorithm is proposed. A visible meaningful binary image is used as the watermark. First, sequent frames are extracted from the video clip. Then, WPT is applied on each frame and from each orientation one sub-band is selected ...
An improved adaptive wavelet shrinkage for ultrasound despeckling
Indian Academy of Sciences (India)
Abstract. Ultrasound imaging is the most widely used medical diagnostic tech- nique for clinical decision making, due to its ability to make real time imaging for moving structures, low cost and safety. However, its usefulness is degraded by the presence of signal dependent speckle noise. Several wavelet-based denoising ...
Brain MRI tumor image fusion combined with Shearlet and wavelet
Zhang, Changjiang; Fang, Mingchao
2017-11-01
In order to extract the effective information in different modalities of the tumor region in brain Magnetic resonance imaging (MRI) images, we propose a brain MRI tumor image fusion method combined with Shearlet and wavelet transform. First, the source images are transformed into Shearlet domain and wavelet domain. Second, the low frequency component of Shearlet domain is fused by Laplace pyramid decomposition. Then the low-frequency fusion image is obtained through inverse Shearlet transform. Third, the high frequency subimages in wavelet domain are fused. Then the high-frequency fusion image is obtained through inverse wavelet transform. Finally, the low-frequency fusion image and high-frequency fusion image are summated to get the final fusion image. Through experiments conducted on 10 brain MRI tumor images, the result shown that the proposed fusion algorithm has the best fusion effect in the evaluation indexes of spatial frequency, edge strength and average gradient. The main spatial frequency of 10 images is 29.22, and the mean edge strength and average gradient is 103.77 and 10.42. Compared with different fusion methods, we find that the proposed method effectively fuses the information of multimodal brain MRI tumor images and improves the clarity of the tumor area well.
Image Denoising Using Singular Value Difference in the Wavelet Domain
Directory of Open Access Journals (Sweden)
Min Wang
2018-01-01
Full Text Available Singular value (SV difference is the difference in the singular values between a noisy image and the original image; it varies regularly with noise intensity. This paper proposes an image denoising method using the singular value difference in the wavelet domain. First, the SV difference model is generated for different noise variances in the three directions of the wavelet transform and the noise variance of a new image is used to make the calculation by the diagonal part. Next, the single-level discrete 2-D wavelet transform is used to decompose each noisy image into its low-frequency and high-frequency parts. Then, singular value decomposition (SVD is used to obtain the SVs of the three high-frequency parts. Finally, the three denoised high-frequency parts are reconstructed by SVD from the SV difference, and the final denoised image is obtained using the inverse wavelet transform. Experiments show the effectiveness of this method compared with relevant existing methods.
Adaptive Single-Pole Autoreclosure Scheme Based on Wavelet ...
African Journals Online (AJOL)
Adaptive autoreclosing is a fast emerging technology for improving power system marginal sta-bility during faults. It avoids reclosing unto permanent faults ... the latter, predict opti-mal reclosure times. Keywords: Adaptive autoreclosure, Artificial neural networks, Autoreclosure, Signal processing, Stability, Wavelet transform ...
Artificial neural network coupled with wavelet transform for ...
Indian Academy of Sciences (India)
Snow Water Equivalent (SWE) is an important parameter in hydrologic engineering involving the stream- flow forecasting of high-elevation watersheds. In this paper, the application of classic Artificial Neural. Network model (ANN) and a hybrid model combining the wavelet and ANN (WANN) is investigated in estimating the ...
On some problems caused by wavelet filtering in calculated spectra
International Nuclear Information System (INIS)
Por, G.
1999-08-01
It is shown that de-noising a measured time signal by wavelet technique produces a rather good result in time domain, while it has unwanted consequences in spectrum estimation. Therefore it can be used for reconstruction of the picture of the physical process, but it should be avoided, when the aim is to reveal eigenfrequencies or transient behaviour of the spectra
A Wavelet-Based Approach to Fall Detection
Directory of Open Access Journals (Sweden)
Luca Palmerini
2015-05-01
Full Text Available Falls among older people are a widely documented public health problem. Automatic fall detection has recently gained huge importance because it could allow for the immediate communication of falls to medical assistance. The aim of this work is to present a novel wavelet-based approach to fall detection, focusing on the impact phase and using a dataset of real-world falls. Since recorded falls result in a non-stationary signal, a wavelet transform was chosen to examine fall patterns. The idea is to consider the average fall pattern as the “prototype fall”.In order to detect falls, every acceleration signal can be compared to this prototype through wavelet analysis. The similarity of the recorded signal with the prototype fall is a feature that can be used in order to determine the difference between falls and daily activities. The discriminative ability of this feature is evaluated on real-world data. It outperforms other features that are commonly used in fall detection studies, with an Area Under the Curve of 0.918. This result suggests that the proposed wavelet-based feature is promising and future studies could use this feature (in combination with others considering different fall phases in order to improve the performance of fall detection algorithms.
Optimization of Wavelet-Based De-noising in MRI
Czech Academy of Sciences Publication Activity Database
Bartušek, Karel; Přinosil, J.; Smékal, Z.
2011-01-01
Roč. 20, č. 1 (2011), s. 85-93 ISSN 1210-2512 R&D Projects: GA ČR GA102/09/0314 Institutional research plan: CEZ:AV0Z20650511 Keywords : wavelet transformation * filtering technique * magnetic resonance imaging Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering Impact factor: 0.739, year: 2011
a pyramid algorithm for the haar discrete wavelet packet transform
African Journals Online (AJOL)
PROF EKWUEME
computer-aided signal processing of non-stationary signals, this paper develops a pyramid algorithm for the discrete wavelet packet ... Edith T. Luhanga, School of Computational and Communication Sciences and Engineering, Nelson Mandela African. Institute of ..... Mathematics, Washington University. 134. EDITH T.
Accelerating Wavelet Lifting on Graphics Hardware Using CUDA
Laan, Wladimir J. van der; Jalba, Andrei C.; Roerdink, Jos B.T.M.
The Discrete Wavelet Transform (DWT) has a wide range of applications from signal processing to video and image compression. We show that this transform, by means of the lifting scheme, can be performed in a memory and computation-efficient way on modern, programmable GPUs, which can be regarded as
Study and analysis of wavelet based image compression techniques ...
African Journals Online (AJOL)
This paper presented comprehensive study with performance analysis of very recent Wavelet transform based image compression techniques. Image compression is one of the necessities for such communication. The goals of image compression are to minimize the storage requirement and communication bandwidth.
Analytic discrete cosine harmonic wavelet transform based OFDM ...
Indian Academy of Sciences (India)
harmonic wavelet transform, DCT is employed in place of DFT. Employing DCT has lowered leakage effect while providing smooth transition from one signal period to the other, without dis- continuity. A new DCHWT OFDM for BPSK and QPSK modulated signals have been discussed in our previous work (Suma et al 2012).
An optimal adaptive wavelet method without coarsening of the iterands
Gantumur, T.; Harbrecht, H.; Stevenson, R.
2007-01-01
In this paper, an adaptive wavelet method for solving linear operator equations is constructed that is a modification of the method from [Math. Comp, 70 (2001), pp. 27-75] by Cohen, Dahmen and DeVore, in the sense that there is no recurrent coarsening of the iterands. Despite this, it will be shown
Artificial neural network coupled with wavelet transform for ...
Indian Academy of Sciences (India)
Snow Water Equivalent (SWE) is an important parameter in hydrologic engineering involving the stream-flow forecasting of high-elevation watersheds. In this paper, the application of classic Artificial Neural Network model (ANN) and a hybrid model combining the wavelet and ANN (WANN) is investigated in estimating the ...
receive signal strength prediction in the gsm band using wavelet ...
African Journals Online (AJOL)
In this work, GSM receive signal strength was monitored in an indoor environment. Samples of GSM receive signal strength was measured on a Mobile Equipment (ME). One-dimensional multilevel wavelet decomposition technique was used to predict the fading phenomenon of the GSM receive signal strength measured.
Computation of differential operators in aggregated wavelet frame coordinates
Stevenson, R.; Werner, M.
2008-01-01
Adaptive wavelet algorithms for solving operator equations have been shown to converge with the best possible rates in linear complexity. For the latter statement, all costs are taken into account, i.e. also the cost of approximating entries from the infinite stiffness matrix with respect to the
3D Wavelet-Based Filter and Method
Moss, William C.; Haase, Sebastian; Sedat, John W.
2008-08-12
A 3D wavelet-based filter for visualizing and locating structural features of a user-specified linear size in 2D or 3D image data. The only input parameter is a characteristic linear size of the feature of interest, and the filter output contains only those regions that are correlated with the characteristic size, thus denoising the image.
On the equivalence of brushlet and wavelet bases
DEFF Research Database (Denmark)
Borup, Lasse; Nielsen, Morten
We prove that the Meyer wavelet basis and a class of brushlet systems associated with exponential type partitions of the frequency axis form a family of equivalent (unconditional) bases for the Besov and Triebel-Lizorkin function spaces. This equivalence is then used to obtain new results...
A wavelet-based approach to fall detection.
Palmerini, Luca; Bagalà, Fabio; Zanetti, Andrea; Klenk, Jochen; Becker, Clemens; Cappello, Angelo
2015-05-20
Falls among older people are a widely documented public health problem. Automatic fall detection has recently gained huge importance because it could allow for the immediate communication of falls to medical assistance. The aim of this work is to present a novel wavelet-based approach to fall detection, focusing on the impact phase and using a dataset of real-world falls. Since recorded falls result in a non-stationary signal, a wavelet transform was chosen to examine fall patterns. The idea is to consider the average fall pattern as the "prototype fall".In order to detect falls, every acceleration signal can be compared to this prototype through wavelet analysis. The similarity of the recorded signal with the prototype fall is a feature that can be used in order to determine the difference between falls and daily activities. The discriminative ability of this feature is evaluated on real-world data. It outperforms other features that are commonly used in fall detection studies, with an Area Under the Curve of 0.918. This result suggests that the proposed wavelet-based feature is promising and future studies could use this feature (in combination with others considering different fall phases) in order to improve the performance of fall detection algorithms.
Wavelet based free-form deformations for nonrigid registration
W. Sun (William); W.J. Niessen (Wiro); S. Klein (Stefan)
2014-01-01
textabstractIn nonrigid registration, deformations may take place on the coarse and fine scales. For the conventional B-splines based free-form deformation (FFD) registration, these coarse- and fine-scale deformations are all represented by basis functions of a single scale. Meanwhile, wavelets have
The efficacy of estimation for influence coefficients in wavelet basis
Metselaar, A.A.R.; Traas, C.R.
1999-01-01
We use wavelets for the discretisation of an integral equation. Upper bounds are derived for elements of the resulting matrix. These upper bounds are used to compute only those elements that may exceed a certain threshold. Numerical experiments are presented in which this manner of computing a
Discrete wavelet transforms over finite sets which are translation invariant
L. Kamstra
2001-01-01
textabstractThe discrete wavelet transform was originally a linear operator that works on signals that are modeled as functions from the integers into the real or complex numbers. However, many signals have discrete function values. This paper builds on two recent developments: the extension of
On the equivalence of brushlet and wavelet bases
DEFF Research Database (Denmark)
Nielsen, Morten; Borup, Lasse
2005-01-01
We prove that the Meyer wavelet basis and a class of brushlet systems associated with exponential type partitions of the frequency axis form a family of equivalent (unconditional) bases for the Besov and Triebel-Lizorkin function spaces. This equivalence is then used to obtain new results...
A simple output voltage control scheme for single phase wavelet ...
African Journals Online (AJOL)
International Journal of Engineering, Science and Technology ... Wavelet based techniques have been extensively used in various power engineering applications. ... But, unlike other popular PWM schemes e.g. sinusoidal PWM, which offers independent control to both magnitude and frequency of fundamental inverter ...
Investment horizon heterogeneity and wavelet: Overview and further research directions
Chakrabarty, Anindya; De, Anupam; Gunasekaran, Angappa; Dubey, Rameshwar
2015-07-01
Wavelet based multi-scale analysis of financial time series has attracted much attention, lately, from both the academia and practitioners from all around the world. The unceasing metamorphosis of the discipline of finance from its humble beginning as applied economics to the more sophisticated depiction as applied physics and applied psychology has revolutionized the way we perceive the market and its complexities. One such complexity is the presence of heterogeneous horizon agents in the market. In this context, we have performed a generous review of different aspects of horizon heterogeneity that has been successfully elucidated through the synergy between wavelet theory and finance. The evolution of wavelet has been succinctly delineated to bestow necessary information to the readers who are new to this field. The migration of wavelet into finance and its subsequent branching into different sub-divisions have been sketched. The pertinent literature on the impact of horizon heterogeneity on risk, asset pricing and inter-dependencies of the financial time series are explored. The significant contributions are collated and classified in accordance to their purpose and approach so that potential researcher and practitioners, interested in this subject, can be benefited. Future research possibilities in the direction of "agency cost mitigation" and "synergy between econophysics and behavioral finance in stock market forecasting" are also suggested in the paper.
A Multi-wavelet type limiter for discontinuous Galerkin approximations
Cheruvu, V.; Ryan, J.K.
2010-01-01
In this report, we present a multi-wavelet type limiter for the discontinuous Galerkin method for limiting the solution when spurious oscillations develop near a shock. This limiting leads to a loss of information in the approximation that can be detrimental to a higher order approximation (k > 2).
Multiresolution wavelet-ANN model for significant wave height forecasting.
Digital Repository Service at National Institute of Oceanography (India)
Deka, P.C.; Mandal, S.; Prahlada, R.
(ANN) modeling. The transformed output data are used as inputs to ANN models. Various decomposition levels have been tried for a db3 wavelet to obtain optimal results. It is found that the performance of hybrid WLNN is better than that of ANN when lead...
Wavelet Packet Transform Based Driver Distraction Level Classification Using EEG
Directory of Open Access Journals (Sweden)
Mousa Kadhim Wali
2013-01-01
Full Text Available We classify the driver distraction level (neutral, low, medium, and high based on different wavelets and classifiers using wireless electroencephalogram (EEG signals. 50 subjects were used for data collection using 14 electrodes. We considered for this research 4 distraction stimuli such as Global Position Systems (GPS, music player, short message service (SMS, and mental tasks. Deriving the amplitude spectrum of three different frequency bands theta, alpha, and beta of EEG signals was based on fusion of discrete wavelet packet transform (DWPT and FFT. Comparing the results of three different classifiers (subtractive fuzzy clustering probabilistic neural network, -nearest neighbor was based on spectral centroid, and power spectral features extracted by different wavelets (db4, db8, sym8, and coif5. The results of this study indicate that the best average accuracy achieved by subtractive fuzzy inference system classifier is 79.21% based on power spectral density feature extracted by sym8 wavelet which gave a good class discrimination under ANOVA test.
Wavelet based denoising of power quality events for characterization
African Journals Online (AJOL)
user
and feature extraction. Practically, electromagnetic noise is generated in every device that generates, consumes, or transmits power. Besides degrading the detection capability of wavelet and other higher time resolution based PQ monitoring systems it also hinders the recovery of important information from the captured ...
Wind Speed Forecasting by Wavelet Neural Networks: A Comparative Study
Directory of Open Access Journals (Sweden)
Chuanan Yao
2013-01-01
Full Text Available Due to the environmental degradation and depletion of conventional energy, much attention has been devoted to wind energy in many countries. The intermittent nature of wind power has had a great impact on power grid security. Accurate forecasting of wind speed plays a vital role in power system stability. This paper presents a comparison of three wavelet neural networks for short-term forecasting of wind speed. The first two combined models are two types of basic combinations of wavelet transform and neural network, namely, compact wavelet neural network (CWNN and loose wavelet neural network (LWNN in this study, and the third model is a new hybrid method based on the CWNN and LWNN models. The efficiency of the combined models has been evaluated by using actual wind speed from two test stations in North China. The results show that the forecasting performances of the CWNN and LWNN models are unstable and are affected by the test stations selected; the third model is far more accurate than the other forecasting models in spite of the drawback of lower computational efficiency.
Optical asymmetric image encryption using gyrator wavelet transform
Mehra, Isha; Nishchal, Naveen K.
2015-11-01
In this paper, we propose a new optical information processing tool termed as gyrator wavelet transform to secure a fully phase image, based on amplitude- and phase-truncation approach. The gyrator wavelet transform constitutes four basic parameters; gyrator transform order, type and level of mother wavelet, and position of different frequency bands. These parameters are used as encryption keys in addition to the random phase codes to the optical cryptosystem. This tool has also been applied for simultaneous compression and encryption of an image. The system's performance and its sensitivity to the encryption parameters, such as, gyrator transform order, and robustness has also been analyzed. It is expected that this tool will not only update current optical security systems, but may also shed some light on future developments. The computer simulation results demonstrate the abilities of the gyrator wavelet transform as an effective tool, which can be used in various optical information processing applications, including image encryption, and image compression. Also this tool can be applied for securing the color image, multispectral, and three-dimensional images.
Wavelet Transforms: Application to Data Analysis-I
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 9; Issue 11. Wavelet Transforms: Application to Data Analysis – I. Jatan K Modi Sachin P ... Physical Research laboratory Navrangpura Ahmedabad 380 009, India. National PARAM Superconducting Facility, Centre for Development of Advanced ...
An improved adaptive wavelet shrinkage for ultrasound despeckling
Indian Academy of Sciences (India)
2016-08-26
Aug 26, 2016 ... Ultrasound imaging is the most widely used medical diagnostic technique for clinical decision making, due to its ability to make real time imaging for moving structures, low cost and safety. However, its usefulness is degraded by the presence of signal dependent speckle noise. Several wavelet-based ...
Rolling bearings control by comparing the wavelet of scaling ...
African Journals Online (AJOL)
The method for bearing monitoring is proposed, which makes it possible to automate the process of defect detection and to increase the resolving power during vibrationacoustic control performance. The result of the study showed that the application of analysis algorithms with the use of wavelet transformation allows to ...
Analytic discrete cosine harmonic wavelet transform based OFDM ...
Indian Academy of Sciences (India)
Abstract. An OFDM based on Analytic Discrete Cosine Harmonic Wavelet Trans- form (ADCHWT_OFDM) has been proposed in this paper. Analytic DCHWT has been realized by applying DCHWT to the original signal and to its Hilbert trans- form. ADCHWT has been found to be computationally efficient and very effective.
Enhancement of damage indicators in wavelet and curvature analysis
Indian Academy of Sciences (India)
in MATLAB wavelet tool-box suite are used for the computation of the approximate and detail functions. Two level multi-resolution analyses is found to be sufficient to capture the disturbances in the deformation profile. The ratio of detail D2 magnitudes as compared to the original signal values for displacement mode shapes ...
Wavelet packet transform-based robust video watermarking technique
Indian Academy of Sciences (India)
In this paper, a wavelet packet transform (WPT)-based robust video watermarking algorithm is proposed. A visible meaningful binary image is used as the watermark. First, sequent frames are extracted from the video clip. Then, WPT is applied on each frame and from each orientation one sub-band is selected based on ...
Wavelet-Based Processing for Fiber Optic Sensing Systems
Hamory, Philip J. (Inventor); Parker, Allen R., Jr. (Inventor)
2016-01-01
The present invention is an improved method of processing conglomerate data. The method employs a Triband Wavelet Transform that decomposes and decimates the conglomerate signal to obtain a final result. The invention may be employed to improve performance of Optical Frequency Domain Reflectometry systems.
Divergence-Free Wavelets on the Hypercube : General Boundary Conditions
Stevenson, R.
2016-01-01
On the n-dimensional hypercube, for given k∈N, wavelet Riesz bases are constructed for the subspace of divergence-free vector fields of the Sobolev space Hk((0,1)n)n with general homogeneous Dirichlet boundary conditions, including slip or no-slip boundary conditions. Both primal and suitable dual
Directory of Open Access Journals (Sweden)
João Eduardo Azevedo Ramos da Silva
2011-01-01
Full Text Available O Brasil é o maior produtor de cana-de-açúcar do mundo, sendo responsável pela geração de 3 milhões de empregos em toda a sua cadeia de suprimentos. A alimentação constante das moendas durante o período de safra depende do bom gerenciamento das operações de corte, carregamento e transporte de cana-de-açúcar das fazendas para as usinas. A entrega de cana-de-açúcar deve ser garantida para que o risco de parada da produção industrial de açúcar e álcool seja evitado. Este trabalho foca o desenvolvimento e a utilização de um modelo de simulação discreta para a determinhação do sistema de turnos de trabalho dos operadores de equipamentos agrícolas de uma usina no Estado de São Paulo, considerando os requisitos de moagem, o risco da falta de matéria-prima e o limite permitido das jornadas de trabalho. Quatro cenários foram propostos para avaliação, sendo o primeiro com a troca de turnos unificada às 7 e às 19 horas; e os demais com a entrada em operação de uma das frentes de corte e carregamento com 2, 4 e 6 horas de defasagem em relação às demais frentes (escalonamento. O cenário com escalonamento de quatro horas de defasagem apresentou o melhor desempenho ao conciliar a entrega de cana, as jornadas de trabalho e o risco de desabastecimento. A discussão é focada no caso selecionado, mas estudos similares podem ser aplicados a outras usinas de açúcar e álcool ou a outros processos do setor sucroalcooleiro.Brazil is the world´s greatest producer of sugarcane generating 3 million jobs within its supply chain. The continuous feeding of sugarcane mills during the harvest season strongly depends on the good management of harvest and operations of loading and transporting the sugarcane from farms to mills. A guaranteed delivery of sugarcane must be developed to avoid the risk of stopping the production of sugar and alcohol. This study focuses on the development and use of a computer simulation model to evaluate
Wavelet transform analysis of chromatin texture changes during heat shock.
Herbomel, G; Grichine, A; Fertin, A; Delon, A; Vourc'h, C; Souchier, C; Usson, Y
2016-06-01
Texture analysis can be a useful tool to investigate the organization of chromatin. Approaches based on multiscale analysis and in particular the 'à trou' wavelet analysis has already been used for microscopy (Olivo Marin). In order to analyse texture changes, the statistical properties of the wavelet coefficient images were summarized by the first four statistical orders: mean, standard deviation, skewness and kurtosis of the coefficient image histogram. The 'à trou' transform provided a representation of the wavelet coefficients and texture parameters with the same statistical robustness throughout the scale spaces. It was applied for quantifying chromatin texture and heat-induced chromatin changes in living cells. We investigated the changes by both laser scanning and spinning disk confocal microscopies and compared the texture parameters before and after increasing duration of heat shock exposure (15 min, 30 min and 1 h). Furthermore, as activation of the heat shock response also correlates with a rapid localization of HSF1 within a few nuclear structures termed nuclear stress bodies (nSBs), we compared the dynamics of nSBs formation with that of textural changes during 1 h of continuous heat shock. Next, we studied the recovery phase following a 1-h heat shock. Significant differences were observed, particularly affecting the perinucleolar region, even for the shortest heat shock time affecting mostly the skewness and standard deviation. Furthermore, progressive changes could be observed according to the duration of heat shock, mostly affecting fine details (pixel-wise changes) as revealed by the parameters, obtained from the first- and second-order wavelet coefficients. 'A trou' wavelet texture analysis provided a sensitive and efficient tool to investigate minute changes of chromatin. © 2015 The Authors Journal of Microscopy © 2015 Royal Microscopical Society.
Classification of sleep stages using multi-wavelet time frequency entropy and LDA.
Fraiwan, L; Lweesy, K; Khasawneh, N; Fraiwan, M; Wenz, H; Dickhaus, H
2010-01-01
The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomnographic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal. The use of different mother wavelets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method. Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78. This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.
Multi-Level Wavelet Shannon Entropy-Based Method for Single-Sensor Fault Location
Directory of Open Access Journals (Sweden)
Qiaoning Yang
2015-10-01
Full Text Available In actual application, sensors are prone to failure because of harsh environments, battery drain, and sensor aging. Sensor fault location is an important step for follow-up sensor fault detection. In this paper, two new multi-level wavelet Shannon entropies (multi-level wavelet time Shannon entropy and multi-level wavelet time-energy Shannon entropy are defined. They take full advantage of sensor fault frequency distribution and energy distribution across multi-subband in wavelet domain. Based on the multi-level wavelet Shannon entropy, a method is proposed for single sensor fault location. The method firstly uses a criterion of maximum energy-to-Shannon entropy ratio to select the appropriate wavelet base for signal analysis. Then multi-level wavelet time Shannon entropy and multi-level wavelet time-energy Shannon entropy are used to locate the fault. The method is validated using practical chemical gas concentration data from a gas sensor array. Compared with wavelet time Shannon entropy and wavelet energy Shannon entropy, the experimental results demonstrate that the proposed method can achieve accurate location of a single sensor fault and has good anti-noise ability. The proposed method is feasible and effective for single-sensor fault location.
Daily water level forecasting using wavelet decomposition and artificial intelligence techniques
Seo, Youngmin; Kim, Sungwon; Kisi, Ozgur; Singh, Vijay P.
2015-01-01
Reliable water level forecasting for reservoir inflow is essential for reservoir operation. The objective of this paper is to develop and apply two hybrid models for daily water level forecasting and investigate their accuracy. These two hybrid models are wavelet-based artificial neural network (WANN) and wavelet-based adaptive neuro-fuzzy inference system (WANFIS). Wavelet decomposition is employed to decompose an input time series into approximation and detail components. The decomposed time series are used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for WANN and WANFIS models, respectively. Based on statistical performance indexes, the WANN and WANFIS models are found to produce better efficiency than the ANN and ANFIS models. WANFIS7-sym10 yields the best performance among all other models. It is found that wavelet decomposition improves the accuracy of ANN and ANFIS. This study evaluates the accuracy of the WANN and WANFIS models for different mother wavelets, including Daubechies, Symmlet and Coiflet wavelets. It is found that the model performance is dependent on input sets and mother wavelets, and the wavelet decomposition using mother wavelet, db10, can further improve the efficiency of ANN and ANFIS models. Results obtained from this study indicate that the conjunction of wavelet decomposition and artificial intelligence models can be a useful tool for accurate forecasting daily water level and can yield better efficiency than the conventional forecasting models.
Parkale, Yuvraj V; Nalbalwar, Sanjay L
2016-01-01
Compressed sensing is a novel signal compression technique in which signal is compressed while sensing. The compressed signal is recovered with the only few numbers of observations compared to conventional Shannon-Nyquist sampling, and thus reduces the storage requirements. In this study, we have proposed the 1-D discrete wavelet transform (DWT) based sensing matrices for speech signal compression. The present study investigates the performance analysis of the different DWT based sensing matrices such as: Daubechies, Coiflets, Symlets, Battle, Beylkin and Vaidyanathan wavelet families. First, we have proposed the Daubechies wavelet family based sensing matrices. The experimental result indicates that the db10 wavelet based sensing matrix exhibits the better performance compared to other Daubechies wavelet based sensing matrices. Second, we have proposed the Coiflets wavelet family based sensing matrices. The result shows that the coif5 wavelet based sensing matrix exhibits the best performance. Third, we have proposed the sensing matrices based on Symlets wavelet family. The result indicates that the sym9 wavelet based sensing matrix demonstrates the less reconstruction time and the less relative error, and thus exhibits the good performance compared to other Symlets wavelet based sensing matrices. Next, we have proposed the DWT based sensing matrices using the Battle, Beylkin and the Vaidyanathan wavelet families. The Beylkin wavelet based sensing matrix demonstrates the less reconstruction time and relative error, and thus exhibits the good performance compared to the Battle and the Vaidyanathan wavelet based sensing matrices. Further, an attempt was made to find out the best-proposed DWT based sensing matrix, and the result reveals that sym9 wavelet based sensing matrix shows the better performance among all other proposed matrices. Subsequently, the study demonstrates the performance analysis of the sym9 wavelet based sensing matrix and state-of-the-art random
Denoising method of heart sound signals based on self-construct heart sound wavelet
Directory of Open Access Journals (Sweden)
Xiefeng Cheng
2014-08-01
Full Text Available In the field of heart sound signal denoising, the wavelet transform has become one of the most effective measures. The selective wavelet basis is based on the well-known orthogonal db series or biorthogonal bior series wavelet. In this paper we present a self-construct wavelet basis which is suitable for the heart sound denoising and analyze its constructor method and features in detail according to the characteristics of heart sound and evaluation criterion of signal denoising. The experimental results show that the heart sound wavelet can effectively filter out the noise of the heart sound signals, reserve the main characteristics of the signal. Compared with the traditional wavelets, it has a higher signal-to-noise ratio, lower mean square error and better denoising effect.
Baxes, Gregory A. (Inventor); Linger, Timothy C. (Inventor)
2011-01-01
Systems and methods are provided for progressive mesh storage and reconstruction using wavelet-encoded height fields. A method for progressive mesh storage includes reading raster height field data, and processing the raster height field data with a discrete wavelet transform to generate wavelet-encoded height fields. In another embodiment, a method for progressive mesh storage includes reading texture map data, and processing the texture map data with a discrete wavelet transform to generate wavelet-encoded texture map fields. A method for reconstructing a progressive mesh from wavelet-encoded height field data includes determining terrain blocks, and a level of detail required for each terrain block, based upon a viewpoint. Triangle strip constructs are generated from vertices of the terrain blocks, and an image is rendered utilizing the triangle strip constructs. Software products that implement these methods are provided.
Detection of Unilateral Hearing Loss by Stationary Wavelet Entropy.
Zhang, Yudong; Nayak, Deepak Ranjan; Yang, Ming; Yuan, Ti-Fei; Liu, Bin; Lu, Huimin; Wang, Shuihua
2017-01-01
Sensorineural hearing loss is correlated to massive neurological or psychiatric disease. T1-weighted volumetric images were acquired from fourteen subjects with right-sided hearing loss (RHL), fifteen subjects with left-sided hearing loss (LHL), and twenty healthy controls (HC). We treated a three-class classification problem: HC, LHL, and RHL. Stationary wavelet entropy was employed to extract global features from magnetic resonance images of each subject. Those stationary wavelet entropy features were used as input to a single-hidden layer feedforward neuralnetwork classifier. The 10 repetition results of 10-fold cross validation show that the accuracies of HC, LHL, and RHL are 96.94%, 97.14%, and 97.35%, respectively. Our developed system is promising and effective in detecting hearing loss. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
Improving the quality of brain CT image from Wavelet filters
International Nuclear Information System (INIS)
Pita Machado, Reinaldo; Perez Diaz, Marlen; Bravo Pino, Rolando
2012-01-01
An algorithm to reduce Poisson noise is described using Wavelet filters. Five tomographic images of patients and a head anthropomorphic phantom were used. They were acquired with two different CT machines. Due to the original images contain the acquisition noise; some simulated free noise lesions were added to the images and after that the whole images were contaminated with noise. Contaminated images were filtered with 9 Wavelet filters at different decomposition levels and thresholds. Image quality of filtered and unfiltered images was graded using the Signal to Noise ratio, Normalized Mean Square Error and the Structural Similarity Index, as well as, by the subjective JAFROC methods with 5 observers. Some filters as Bior 3.7 and dB45 improved in a significant way head CT image quality (p<0.05) producing an increment in SNR without visible structural distortions
Wavelet/scalar quantization compression standard for fingerprint images
Energy Technology Data Exchange (ETDEWEB)
Brislawn, C.M.
1996-06-12
US Federal Bureau of Investigation (FBI) has recently formulated a national standard for digitization and compression of gray-scale fingerprint images. Fingerprints are scanned at a spatial resolution of 500 dots per inch, with 8 bits of gray-scale resolution. The compression algorithm for the resulting digital images is based on adaptive uniform scalar quantization of a discrete wavelet transform subband decomposition (wavelet/scalar quantization method). The FBI standard produces archival-quality images at compression ratios of around 15 to 1 and will allow the current database of paper fingerprint cards to be replaced by digital imagery. The compression standard specifies a class of potential encoders and a universal decoder with sufficient generality to reconstruct compressed images produced by any compliant encoder, allowing flexibility for future improvements in encoder technology. A compliance testing program is also being implemented to ensure high standards of image quality and interchangeability of data between different implementations.
Compression of Ultrasonic NDT Image by Wavelet Based Local Quantization
Cheng, W.; Li, L. Q.; Tsukada, K.; Hanasaki, K.
2004-02-01
Compression on ultrasonic image that is always corrupted by noise will cause `over-smoothness' or much distortion. To solve this problem to meet the need of real time inspection and tele-inspection, a compression method based on Discrete Wavelet Transform (DWT) that can also suppress the noise without losing much flaw-relevant information, is presented in this work. Exploiting the multi-resolution and interscale correlation property of DWT, a simple way named DWCs classification, is introduced first to classify detail wavelet coefficients (DWCs) as dominated by noise, signal or bi-effected. A better denoising can be realized by selective thresholding DWCs. While in `Local quantization', different quantization strategies are applied to the DWCs according to their classification and the local image property. It allocates the bit rate more efficiently to the DWCs thus achieve a higher compression rate. Meanwhile, the decompressed image shows the effects of noise suppressed and flaw characters preserved.
An Improved Wavelet Correction for Zero Shifted Accelerometer Data
Directory of Open Access Journals (Sweden)
Timothy S. Edwards
2003-01-01
Full Text Available Accelerometer data from shock measurements often contains a spurious DC drifting phenomenon known as zero shifting. This erroneous signal can be caused by a variety of sources. The most conservative approach when dealing with such data is to discard it and collect a different set with steps taken to prevent the zero shifting. This approach is rarely practical, however. The test article may have been destroyed or it may be impossible or prohibitively costly to recreate the test. A method has been proposed by which wavelets may be used to correct the acceleration data. By comparing the corrected accelerometer data to an independent measurement of the acceleration from a laser vibrometer this paper shows that the corrected data, in the cases presented, accurately represents the shock. A method is presented by which the analyst may accurately choose the wavelet correction parameters. The comparisons are made in the time and frequency domains, as well as with the shock response spectrum.
Wavelet domain image restoration with adaptive edge-preserving regularization.
Belge, M; Kilmer, M E; Miller, E L
2000-01-01
In this paper, we consider a wavelet based edge-preserving regularization scheme for use in linear image restoration problems. Our efforts build on a collection of mathematical results indicating that wavelets are especially useful for representing functions that contain discontinuities (i.e., edges in two dimensions or jumps in one dimension). We interpret the resulting theory in a statistical signal processing framework and obtain a highly flexible framework for adapting the degree of regularization to the local structure of the underlying image. In particular, we are able to adapt quite easily to scale-varying and orientation-varying features in the image while simultaneously retaining the edge preservation properties of the regularizer. We demonstrate a half-quadratic algorithm for obtaining the restorations from observed data.
Speech Subvocal Signal Processing using Packet Wavelet and Neuronal Network
Directory of Open Access Journals (Sweden)
Luis E. Mendoza
2013-11-01
Full Text Available This paper presents the results obtained from the recording, processing and classification of words in the Spanish language by means of the analysis of subvocal speech signals. The processed database has six words (forward, backward, right, left, start and stop. In this work, the signals were sensed with surface electrodes placed on the surface of the throat and acquired with a sampling frequency of 50 kHz. The signal conditioning consisted in: the location of area of interest using energy analysis, and filtering using Discrete Wavelet Transform. Finally, the feature extraction was made in the time-frequency domain using Wavelet Packet and statistical techniques for windowing. The classification was carried out with a backpropagation neural network whose training was performed with 70% of the database obtained. The correct classification rate was 75%±2.
Grating geophone signal processing based on wavelet transform
Li, Shuqing; Zhang, Huan; Tao, Zhifei
2008-12-01
Grating digital geophone is designed based on grating measurement technique benefiting averaging-error effect and wide dynamic range to improve weak signal detected precision. This paper introduced the principle of grating digital geophone and its post signal processing system. The signal acquisition circuit use Atmega 32 chip as core part and display the waveform on the Labwindows through the RS232 data link. Wavelet transform is adopted this paper to filter the grating digital geophone' output signal since the signal is unstable. This data processing method is compared with the FIR filter that widespread use in current domestic. The result indicates that the wavelet algorithm has more advantages and the SNR of seismic signal improve obviously.
Wavelet analysis of polarization maps of polycrystalline biological fluids networks
Ushenko, Y. A.
2011-12-01
The optical model of human joints synovial fluid is proposed. The statistic (statistic moments), correlation (autocorrelation function) and self-similar (Log-Log dependencies of power spectrum) structure of polarization two-dimensional distributions (polarization maps) of synovial fluid has been analyzed. It has been shown that differentiation of polarization maps of joint synovial fluid with different physiological state samples is expected of scale-discriminative analysis. To mark out of small-scale domain structure of synovial fluid polarization maps, the wavelet analysis has been used. The set of parameters, which characterize statistic, correlation and self-similar structure of wavelet coefficients' distributions of different scales of polarization domains for diagnostics and differentiation of polycrystalline network transformation connected with the pathological processes, has been determined.
Improving 3D Wavelet-Based Compression of Hyperspectral Images
Klimesh, Matthew; Kiely, Aaron; Xie, Hua; Aranki, Nazeeh
2009-01-01
Two methods of increasing the effectiveness of three-dimensional (3D) wavelet-based compression of hyperspectral images have been developed. (As used here, images signifies both images and digital data representing images.) The methods are oriented toward reducing or eliminating detrimental effects of a phenomenon, referred to as spectral ringing, that is described below. In 3D wavelet-based compression, an image is represented by a multiresolution wavelet decomposition consisting of several subbands obtained by applying wavelet transforms in the two spatial dimensions corresponding to the two spatial coordinate axes of the image plane, and by applying wavelet transforms in the spectral dimension. Spectral ringing is named after the more familiar spatial ringing (spurious spatial oscillations) that can be seen parallel to and near edges in ordinary images reconstructed from compressed data. These ringing phenomena are attributable to effects of quantization. In hyperspectral data, the individual spectral bands play the role of edges, causing spurious oscillations to occur in the spectral dimension. In the absence of such corrective measures as the present two methods, spectral ringing can manifest itself as systematic biases in some reconstructed spectral bands and can reduce the effectiveness of compression of spatially-low-pass subbands. One of the two methods is denoted mean subtraction. The basic idea of this method is to subtract mean values from spatial planes of spatially low-pass subbands prior to encoding, because (a) such spatial planes often have mean values that are far from zero and (b) zero-mean data are better suited for compression by methods that are effective for subbands of two-dimensional (2D) images. In this method, after the 3D wavelet decomposition is performed, mean values are computed for and subtracted from each spatial plane of each spatially-low-pass subband. The resulting data are converted to sign-magnitude form and compressed in a
Efficient regularization with wavelet sparsity constraints in photoacoustic tomography
Frikel, Jürgen; Haltmeier, Markus
2018-02-01
In this paper, we consider the reconstruction problem of photoacoustic tomography (PAT) with a flat observation surface. We develop a direct reconstruction method that employs regularization with wavelet sparsity constraints. To that end, we derive a wavelet-vaguelette decomposition (WVD) for the PAT forward operator and a corresponding explicit reconstruction formula in the case of exact data. In the case of noisy data, we combine the WVD reconstruction formula with soft-thresholding, which yields a spatially adaptive estimation method. We demonstrate that our method is statistically optimal for white random noise if the unknown function is assumed to lie in any Besov-ball. We present generalizations of this approach and, in particular, we discuss the combination of PAT-vaguelette soft-thresholding with a total variation (TV) prior. We also provide an efficient implementation of the PAT-vaguelette transform that leads to fast image reconstruction algorithms supported by numerical results.
Doubly Fed Induction Generator Analysis Through Wavelet Technique
Directory of Open Access Journals (Sweden)
K.Ram Mohan Rao
2009-01-01
Full Text Available Because of the intermittent nature of wind, its integration to the power system is still promising with respect to power qualityand stability. For the large penetration of wind energy, this paper using an embedded time-frequency localization features inwavelet, provides deep insight to the character of transient signals for a proposed test system comprising one thermal plantand three DFIG-based wind plants. The test system is first simulated and the results are mapped onto the wavelet formatfor accurate detection & better resolution of the characters of transients. This is found that the presence of lower frequencybandwidth signals accompanies relatively more energy and larger magnitude wavelet coefficients are the root cause for thestability and quality
Elastic Wave Propagation in Concrete and Continuous Wavelet Transform
Chiang, Chih-Hung; Gi, Yu-Fung; Pan, Chi-Ling; Cheng, Chia-Chi
2005-04-01
Elastic wave methods, such as the ultrasonic pulse velocity and the impact echo, are often subject to multiple reflections at the boundaries of various constituents of concrete. Current study aims to improve the feature identification of elastic wave propagation due to buried objects in concrete slabs and cylinders. Embedded steel reinforcement, steel and PVC tubes, wooden disks, and rubber spheres are tested. The received signals are analyzed using continuous wavelet transform. As a result, signals are decomposed into distinctive frequency bands with transient information preserved. The interpretation of multiple reflections at different boundary conditions thus becomes more straightforward. Features related to reflections from steel bar, PVC tube, and steel tube can be readily identified in the magnitude plot of wavelet coefficients. Vibration modes of the concrete slab corresponding to different buried objects can also be separated based on corresponding time duration.
Mathematical principles of signal processing Fourier and wavelet analysis
Brémaud, Pierre
2002-01-01
Fourier analysis is one of the most useful tools in many applied sciences. The recent developments of wavelet analysis indicates that in spite of its long history and well-established applications, the field is still one of active research. This text bridges the gap between engineering and mathematics, providing a rigorously mathematical introduction of Fourier analysis, wavelet analysis and related mathematical methods, while emphasizing their uses in signal processing and other applications in communications engineering. The interplay between Fourier series and Fourier transforms is at the heart of signal processing, which is couched most naturally in terms of the Dirac delta function and Lebesgue integrals. The exposition is organized into four parts. The first is a discussion of one-dimensional Fourier theory, including the classical results on convergence and the Poisson sum formula. The second part is devoted to the mathematical foundations of signal processing - sampling, filtering, digital signal proc...
Ground extraction from airborne laser data based on wavelet analysis
Xu, Liang; Yang, Yan; Jiang, Bowen; Li, Jia
2007-11-01
With the advantages of high resolution and accuracy, airborne laser scanning data are widely used in topographic mapping. In order to generate a DTM, measurements from object features such as buildings, vehicles and vegetation have to be classified and removed. However, the automatic extraction of bare earth from point clouds acquired by airborne laser scanning equipment remains a problem in LIDAR data filtering nowadays. In this paper, a filter algorithm based on wavelet analysis is proposed. Relying on the capability of detecting discontinuities of continuous wavelet transform and the feature of multi-resolution analysis, the object points can be removed, while ground data are preserved. In order to evaluate the performance of this approach, we applied it to the data set used in the ISPRS filter test in 2003. 15 samples have been tested by the proposed approach. Results showed that it filtered most of the objects like vegetation and buildings, and extracted a well defined ground model.
Wavelet-cellular neural network architecture and learning algorithm
Bal, Abdullah; Ucan, Osman N.; Pastaci, Halit; Alam, Mohammad S.
2004-04-01
Cellular Neural Networks (CNN) provides fast parallel computational capability for image processing applications. The behavior of the CNN is defined by two template matrices. In this paper, adjustment of these template-matrix coefficients have been realized using supervised learning algorithm based on back-propagation technique and wavelet function. Back-propagation algorithm has been modified for dynamic behavior of CNN. Wavelet function is utilized to provide the activation function derivation in this learning algorithm. The supervised learning algorithm is then executed to obtain a compact CNN architecture, called as Wave-CNN. The proposed new learning algorithm and Wave-CNN architecture performance have been tested for 2D image processing applications.
Wavelet analysis of the seismograms for tsunami warning
Directory of Open Access Journals (Sweden)
A. Chamoli
2010-10-01
Full Text Available The complexity in the tsunami phenomenon makes the available warning systems not much effective in the practical situations. The problem arises due to the time lapsed in the data transfer, processing and modeling. The modeling and simulation needs the input fault geometry and mechanism of the earthquake. The estimation of these parameters and other aprior information increases the utilized time for making any warning. Here, the wavelet analysis is used to identify the tsunamigenesis of an earthquake. The frequency content of the seismogram in time scale domain is examined using wavelet transform. The energy content in high frequencies is calculated and gives a threshold for tsunami warnings. Only first few minutes of the seismograms of the earthquake events are used for quick estimation. The results for the earthquake events of Andaman Sumatra region and other historic events are promising.
Application of the Discrete Wavelet Transform in the Ranging Algorithm of Radio Fuze
International Nuclear Information System (INIS)
Chen, X L; Yang, J W; Yang, J; Wang, Y K
2006-01-01
Echo signal of radio fuze is a special transient signal whose wave parameters and arrival time are unknown. In this paper, an echo detection method of radio fuze based on discrete wavelet transform is introduced. The method adopts special wavelet basis function and scale factor, and obtain signal arriving time to realize distance measurement by the relationship that discrete wavelet coefficient of echo signal arrives peak at the corresponding time. Simulating results show that the method is feasible in radio fuze ranging application
Shoaib, Muhammad; Shamseldin, Asaad Y.; Melville, Bruce W.; Khan, Mudasser Muneer
2016-04-01
In order to predict runoff accurately from a rainfall event, the multilayer perceptron type of neural network models are commonly used in hydrology. Furthermore, the wavelet coupled multilayer perceptron neural network (MLPNN) models has also been found superior relative to the simple neural network models which are not coupled with wavelet. However, the MLPNN models are considered as static and memory less networks and lack the ability to examine the temporal dimension of data. Recurrent neural network models, on the other hand, have the ability to learn from the preceding conditions of the system and hence considered as dynamic models. This study for the first time explores the potential of wavelet coupled time lagged recurrent neural network (TLRNN) models for runoff prediction using rainfall data. The Discrete Wavelet Transformation (DWT) is employed in this study to decompose the input rainfall data using six of the most commonly used wavelet functions. The performance of the simple and the wavelet coupled static MLPNN models is compared with their counterpart dynamic TLRNN models. The study found that the dynamic wavelet coupled TLRNN models can be considered as alternative to the static wavelet MLPNN models. The study also investigated the effect of memory depth on the performance of static and dynamic neural network models. The memory depth refers to how much past information (lagged data) is required as it is not known a priori. The db8 wavelet function is found to yield the best results with the static MLPNN models and with the TLRNN models having small memory depths. The performance of the wavelet coupled TLRNN models with large memory depths is found insensitive to the selection of the wavelet function as all wavelet functions have similar performance.
2D Anisotropic Wavelet Entropy with an Application to Earthquakes in Chile
Directory of Open Access Journals (Sweden)
Orietta Nicolis
2015-06-01
Full Text Available We propose a wavelet-based approach to measure the Shannon entropy in the context of spatial point patterns. The method uses the fully anisotropic Morlet wavelet to estimate the energy distribution at different directions and scales. The spatial heterogeneity and complexity of spatial point patterns is then analyzed using the multiscale anisotropic wavelet entropy. The efficacy of the approach is shown through a simulation study. Finally, an application to the catalog of earthquake events in Chile is considered.
Application of wavelet theory to power distribution systems for fault detection
Energy Technology Data Exchange (ETDEWEB)
Momoh, J. [Howard Univ., Washington, DC (United States). Dept. of Electrical Engineering; Rizy, D.T. [Oak Ridge National Lab., TN (United States)
1996-03-01
In this paper an investigation of the wavelet transform as a means of creating a feature extractor for Artificial Neural Network (ANN) training is presented. The study includes a teresstrial-based 3 phase delta power distribution system. Faults were injected into the system and data was obtained from experimentation. Graphical representations of the feature extractors obtained in the time domain, the frequency domain and the wavelet domain are presented to ascertain the superiority of the wavelet ``reform feature extractor.
Study on characteristic points of boiling curve by using wavelet analysis and genetic algorithm
International Nuclear Information System (INIS)
Wei Huiming; Su Guanghui; Qiu Suizheng; Yang Xingbo
2009-01-01
Based on the wavelet analysis theory of signal singularity detection,the critical heat flux (CHF) and minimum film boiling starting point (q min ) of boiling curves can be detected and analyzed by using the wavelet multi-resolution analysis. To predict the CHF in engineering, empirical relations were obtained based on genetic algorithm. The results of wavelet detection and genetic algorithm prediction are consistent with experimental data very well. (authors)
Accurate Wavelet Neural Network for Efficient Controlling of an Active Magnetic Bearing System
Youssef Harkouss; Souhad Mcheik; Roger Achkar
2010-01-01
Problem statement: The synthesis of a command by the neural network has an excellent advantage over the classical one such as PID. This study presented a fast and accurate Wavelet Neural Network (WNN) approach for efficient controlling of an Active Magnetic Bearing (AMB) system. Approach: The proposed approach combined neural network with the wavelet theory. Wavelet theory may be exploited in deriving a good initialization for the neural network and thus improved conv...
Feature extraction of hyperspectral images using wavelet and matching pursuit
Hsu, Pai-Hui
Since hyperspectral images contain rich and fine spectral information, an improvement of land use/cover classification accuracy is highly expected from the utilization of such images. However, the traditional statistics-based classification methods which have been successfully applied to multispectral data in the past are not as effective as to hyperspectral data. One major reason is that the number of spectral bands is too large relative to the number of training samples. This problem is caused by curse of dimensionality, which refers to the fact that the sample size required for training a specific classifier grows exponentially with the number of spectral bands. A simple but sometimes very effective way to overcome this problem is to reduce the dimensionality of hyperspectral images. This can be done by feature extraction that a small number of salient features are extracted from the hyperspectral data when confronted with a limited size of training samples. In this paper, a new feature extraction method based on the matching pursuit (MP) is proposed to extract useful features for the classification of hyperspectral images. The matching pursuit algorithm uses a greedy strategy to find an adaptive and optimal representation of the hyperspectral data iteratively from a highly redundant wavelet packets dictionary. An AVIRIS data set was tested to illustrate the classification performance after matching pursuit method was applied. In addition, some existing feature extraction methods based on the wavelet transform are also compared with the matching pursuit method in terms of the classification accuracies. The experiment results showed that the wavelet and matching pursuit method exactly provide an effective tool for feature extraction. The classification problem caused by curse of dimensionality can be avoided by matching pursuit and wavelet-based dimensionality reduction.
Reliable epileptic seizure detection using an improved wavelet neural network
Directory of Open Access Journals (Sweden)
Zarita Zainuddin
2013-05-01
Full Text Available BackgroundElectroencephalogram (EEG signal analysis is indispensable in epilepsy diagnosis as it offers valuable insights for locating the abnormal distortions in the brain wave. However, visual interpretation of the massive amounts of EEG signals is time-consuming, and there is often inconsistent judgment between experts. AimsThis study proposes a novel and reliable seizure detection system, where the statistical features extracted from the discrete wavelet transform are used in conjunction with an improved wavelet neural network (WNN to identify the occurrence of seizures. Method Experimental simulations were carried out on a well-known publicly available dataset, which was kindly provided by the Epilepsy Center, University of Bonn, Germany. The normal and epileptic EEG signals were first pre-processed using the discrete wavelet transform. Subsequently, a set of statistical features was extracted to train a WNNs-based classifier. ResultsThe study has two key findings. First, simulation results showed that the proposed improved WNNs-based classifier gave excellent predictive ability, where an overall classification accuracy of 98.87% was obtained. Second, by using the 10th and 90th percentiles of the absolute values of the wavelet coefficients, a better set of EEG features can be identified from the data, as the outliers are removed before any further downstream analysis.ConclusionThe obtained high prediction accuracy demonstrated the feasibility of the proposed seizure detection scheme. It suggested the prospective implementation of the proposed method in developing a real time automated epileptic diagnostic system with fast and accurate response that could assist neurologists in the decision making process.
Hybrid image encoding based on wavelet transform and DPCM
Tian, Jinwen; Liu, Bin; Liu, Jianguo
1998-09-01
In this paper, our purposes are image compression code by wavelet transformation, and develop a novel encoding algorithm structure of DPCM/WT on the basis of the DPCM/DCT. We have proposed a new encoding algorithm structure of DPCM/WT based on object driving and data flow driving, the novel algorithm in property is superior to DPCM/DCT in compression ratio, fidelity and real time processing.
Wavelet analysis to detect regime shifts in animal movement
Directory of Open Access Journals (Sweden)
C. Gaucherel
2011-06-01
Full Text Available Animals most often move in a non-homogeneous way as a long movement path through a heterogeneous landscape that corresponds to a sequence of various behavioural states. Hence, a large majority of movement analyses make the assumption that long movements combine typical behaviours like intensive search or resting which are separated by sharp transitions. This study aimed at providing an alternative method for identifying intensive search areas using sharp as well as more continuous (smooth transitions. I proposed analyzing movement data over temporal and spatial scales by the use of the wavelet analysis and drew inferences about the behaviours that shape movements. I computed a synthetic index built with wavelet time-spectra of turning angle and speed parameters, this method offered a robust and automatic way to characterize movement transitions. The first step was to work on simulated movements to define the confidence levels of detection. The second was to illustrate the use of wavelet analysis on the movements of wandering albatrosses. As a result, this study outlined two fundamental areas of interest in animal movement analysis: i it is relevant to select behavioural modes with continuous transitions between them along the animal's movement, as it is done with usual segmentation methods; ii to suppose that every behaviour and every transition between them is intrinsically multiscale (i.e. with a scaling property appeared to be an interesting approach to identify and characterize them. The mathematical robustness and predictive ability of wavelet analysis make it a promising road towards multiscale movement ecology that fuses insights from the study of animal behaviour and environmental properties.
Wavelet and adaptive filtration of the nuclear magnetic resonance signal
Czech Academy of Sciences Publication Activity Database
Bartušek, Karel
2002-01-01
Roč. 11, - (2002), s. 13 - 18 ISSN 0862-9846. [Datastat'01. Brno, 27.08.2001-30.08.2001] R&D Projects: GA ČR GA102/96/1136; GA AV ČR IAA2065201 Institutional research plan: CEZ:AV0Z2065902 Keywords : Wavelet filtration * adaptive filtration * magnetic resonance signal Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering
Haar Wavelets-Based Methods for Credit Risk Portfolio Modeling
Ortiz Gracia, Luis
2011-01-01
In this dissertation we have investigated the credit risk measurement of a credit portfolio by means of the wavelets theory. Banks became subject to regulatory capital requirements under Basel Accords and also to the supervisory review process of capital adequacy, this is the economic capital. Concentration risks in credit portfolios arise from an unequal distribution of loans to single borrowers (name concentration) or different industry or regional sectors (sector concentration) an...
Wavelet-based de-noising techniques in MRI
Czech Academy of Sciences Publication Activity Database
Bartušek, Karel; Přinosil, J.; Smékal, Z.
2011-01-01
Roč. 104, č. 3 (2011), s. 480-488 ISSN 0169-2607 R&D Projects: GA MŠk ED0017/01/01; GA ČR GAP102/11/0318 Institutional research plan: CEZ:AV0Z20650511 Keywords : wavelet transformation * filtering technique * magnetic resonance imaging Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering Impact factor: 1.516, year: 2011
Classification of Transient Phenomena in Distribution System using wavelet Transform
Sedighi, Alireza
2014-05-01
An efficient procedure for classification of transient phenomena in distribution systems is proposed in this paper. The proposed method has been applied to classify some transient phenomena such as inrush current, load switching, capacitor switching and single phase to ground fault. The new scheme is based on wavelet transform algorithm. All of the events for feature extraction and test are simulated using Electro Magnetic Transient Program (EMTP). Results show high accuracy of proposed method.
A new wavelet-based measure of image focus
Czech Academy of Sciences Publication Activity Database
Kautsky, J.; Flusser, Jan; Zitová, Barbara; Šimberová, Stanislava
2002-01-01
Roč. 23, č. 14 (2002), s. 1785-1794 ISSN 0167-8655 R&D Projects: GA ČR GA102/00/1711; GA ČR GP102/01/P065 Institutional research plan: CEZ:AV0Z1075907 Keywords : image blurring * focus measure * wavelet transform Subject RIV: JD - Computer Applications, Robotics Impact factor: 0.409, year: 2002 http://library.utia.cas.cz/prace/20020126.pdf
Curie temperature determination via thermogravimetric and continuous wavelet transformation analysis
Energy Technology Data Exchange (ETDEWEB)
Hasier, John; Nash, Philip [Thermal Processing Technology Center, IIT, Chicago, IL (United States); Riolo, Maria Annichia [University of Michigan, Center for the Study of Complex Systems, Ann Arbor, MI (United States)
2017-12-15
A cost effective method for conversion of a vertical tube thermogravimetric analysis system into a magnetic balance capable of measuring Curie Temperatures is presented. Reference and preliminary experimental data generated using this system is analyzed via a general-purpose wavelet based Curie point edge detection technique allowing for enhanced speed, ease and repeatability of magnetic balance data analysis. The Curie temperatures for a number of Heusler compounds are reported. (orig.)
Modeling of Geological Objects and Geophysical Fields Using Haar Wavelets
A. S. Dolgal
2014-01-01
This article is a presentation of application of the fast wavelet transform with basic Haar functions for modeling the structural surfaces and geophysical fields, characterized by fractal features. The multiscale representation of experimental data allows reducing significantly a cost of the processing of large volume data and improving the interpretation quality. This paper presents the algorithms for sectionally prismatic approximation of geological objects, for preliminary estimation of th...
Wavelet brain angiography suggests arteriovenous pulse wave phase locking
2017-01-01
When a stroke volume of arterial blood arrives to the brain, the total blood volume in the bony cranium must remain constant as the proportions of arterial and venous blood vary, and by the end of the cardiac cycle an equivalent volume of venous blood must have been ejected. I hypothesize the brain to support this process by an extraluminally mediated exchange of information between its arterial and venous circulations. To test this I introduce wavelet angiography methods to resolve single moving vascular pulse waves (PWs) in the brain while simultaneously measuring brain pulse motion. The wavelet methods require angiographic data acquired at significantly faster rate than cardiac frequency. I obtained these data in humans from brain surface optical angiograms at craniotomy and in piglets from ultrasound angiograms via cranial window. I exploit angiographic time of flight to resolve arterial from venous circulation. Initial wavelet reconstruction proved unsatisfactory because of angiographic motion alias from brain pulse motion. Testing with numerically simulated cerebral angiograms enabled the development of a vascular PW cine imaging method based on cross-correlated wavelets of mixed high frequency and high temporal resolution respectively to attenuate frequency and motion alias. Applied to the human and piglet data, the method resolves individual arterial and venous PWs and finds them to be phase locked each with separate phase relations to brain pulse motion. This is consistent with arterial and venous PW coordination mediated by pulse motion and points to a testable hypothesis of a function of cerebrospinal fluid in the ventricles of the brain. PMID:29140981
Estimation of long memory in volatility using wavelets
Czech Academy of Sciences Publication Activity Database
Kraicová, Lucie; Baruník, Jozef
2017-01-01
Roč. 21, č. 3 (2017), č. článku 20160101. ISSN 1081-1826 R&D Projects: GA ČR GA13-32263S EU Projects: European Commission 612955 - FINMAP Institutional support: RVO:67985556 Keywords : long memory * wavelets * whittle Subject RIV: AH - Economics OBOR OECD: Applied Economics, Econometrics Impact factor: 0.649, year: 2016 http://library.utia.cas.cz/separaty/2017/E/barunik-0478480.pdf
On exploiting wavelet bases in statistical region-based segmentation
DEFF Research Database (Denmark)
Stegmann, Mikkel Bille; Forchhammer, Søren
2002-01-01
Statistical region-based segmentation methods such as the Active Appearance Models establish dense correspondences by modelling variation of shape and pixel intensities in low-resolution 2D images. Unfortunately, for high-resolution 2D and 3D images, this approach is rendered infeasible due to ex...... 9-7 wavelet on cardiac MRIs and human faces show that the segmentation accuracy is minimally degraded at compression ratios of 1:10 and 1:20, respectively....
Application of Wavelets and Quaternions to NIR Spectra Classification
International Nuclear Information System (INIS)
Barcala Riveira, J. M.; Fernandez Marron, J. L.; Alberdi Primicia, J.; Navarrete Marin, J. J.; Oller Gonzalez, J.C.
2003-01-01
This document describes how multi resolution analysis can combine with the use of quaternions to identify near infrared spectra. The method is applied to spectra of plastics usually present in domestic wastes. First, Haar wavelet is applied to spectrum. With the coefficients obtained, a quaternion is built. We named this quaternion a characteristic quaternion. Distances to characteristic quaternions are used to classify new quaternions. (Author) 54 refs
Wavelet brain angiography suggests arteriovenous pulse wave phase locking.
Directory of Open Access Journals (Sweden)
William E Butler
Full Text Available When a stroke volume of arterial blood arrives to the brain, the total blood volume in the bony cranium must remain constant as the proportions of arterial and venous blood vary, and by the end of the cardiac cycle an equivalent volume of venous blood must have been ejected. I hypothesize the brain to support this process by an extraluminally mediated exchange of information between its arterial and venous circulations. To test this I introduce wavelet angiography methods to resolve single moving vascular pulse waves (PWs in the brain while simultaneously measuring brain pulse motion. The wavelet methods require angiographic data acquired at significantly faster rate than cardiac frequency. I obtained these data in humans from brain surface optical angiograms at craniotomy and in piglets from ultrasound angiograms via cranial window. I exploit angiographic time of flight to resolve arterial from venous circulation. Initial wavelet reconstruction proved unsatisfactory because of angiographic motion alias from brain pulse motion. Testing with numerically simulated cerebral angiograms enabled the development of a vascular PW cine imaging method based on cross-correlated wavelets of mixed high frequency and high temporal resolution respectively to attenuate frequency and motion alias. Applied to the human and piglet data, the method resolves individual arterial and venous PWs and finds them to be phase locked each with separate phase relations to brain pulse motion. This is consistent with arterial and venous PW coordination mediated by pulse motion and points to a testable hypothesis of a function of cerebrospinal fluid in the ventricles of the brain.
Quantum Channels, Wavelets, Dilations and Representations of $O_n$
Kribs, David W.
2003-01-01
We show that the representations of the Cuntz C$^\\ast$-algebras $O_n$ which arise in wavelet analysis and dilation theory can be classified through a simple analysis of completely positive maps on finite-dimensional space. Based on this analysis, an application in quantum information theory is obtained; namely, a structure theorem for the fixed point set of a unital quantum channel. We also include some open problems motivated by this work.
The Marr Conjecture and Uniqueness of Wavelet Transforms
Allen, Ben; Kon, Mark
2013-01-01
The inverse question of identifying a function from the nodes (zeroes) of its wavelet transform arises in a number of fields. These include whether the nodes of a heat or hypoelliptic equation solution determine its initial conditions, and in mathematical vision theory the Marr conjecture, on whether an image is mathematically determined by its edge information. We prove a general version of this conjecture by reducing it to the moment problem, using a basis dual to the Taylor monomial basis ...
Adaptive wavelet tight frame construction for accelerating MRI reconstruction
Directory of Open Access Journals (Sweden)
Genjiao Zhou
2017-09-01
Full Text Available The sparsity regularization approach, which assumes that the image of interest is likely to have sparse representation in some transform domain, has been an active research area in image processing and medical image reconstruction. Although various sparsifying transforms have been used in medical image reconstruction such as wavelet, contourlet, and total variation (TV etc., the efficiency of these transforms typically rely on the special structure of the underlying image. A better way to address this issue is to develop an overcomplete dictionary from the input data in order to get a better sparsifying transform for the underlying image. However, the general overcomplete dictionaries do not satisfy the so-called perfect reconstruction property which ensures that the given signal can be perfectly represented by its canonical coefficients in a manner similar to orthonormal bases, resulting in time consuming in the iterative image reconstruction. This work is to develop an adaptive wavelet tight frame method for magnetic resonance image reconstruction. The proposed scheme incorporates the adaptive wavelet tight frame approach into the magnetic resonance image reconstruction by solving a l0-regularized minimization problem. Numerical results show that the proposed approach provides significant time savings as compared to the over-complete dictionary based methods with comparable performance in terms of both peak signal-to-noise ratio and subjective visual quality.
Wavelets in music analysis and synthesis: timbre analysis and perspectives
Alves Faria, Regis R.; Ruschioni, Ruggero A.; Zuffo, Joao A.
1996-10-01
Music is a vital element in the process of comprehending the world where we live and interact with. Frequency it exerts a subtle but expressive influence over a society's evolution line. Analysis and synthesis of music and musical instruments has always been associated with forefront technologies available at each period of human history, and there is no surprise in witnessing now the use of digital technologies and sophisticated mathematical tools supporting its development. Fourier techniques have been employed for years as a tool to analyze timbres' spectral characteristics, and re-synthesize them from these extracted parameters. Recently many modern implementations, based on spectral modeling techniques, have been leading to the development of new generations of music synthesizers, capable of reproducing natural sounds with high fidelity, and producing novel timbres as well. Wavelets are a promising tool on the development of new generations of music synthesizers, counting on its advantages over the Fourier techniques in representing non-periodic and transient signals, with complex fine textures, as found in music. In this paper we propose and introduce the use of wavelets addressing its perspectives towards musical applications. The central idea is to investigate the capacities of wavelets in analyzing, extracting features and altering fine timbre components in a multiresolution time- scale, so as to produce high quality synthesized musical sounds.
Seismic random noise attenuation using modified wavelet thresholding
Directory of Open Access Journals (Sweden)
Qi-sheng Zhang
2017-01-01
Full Text Available In seismic exploration, random noise deteriorates the quality of acquired data. This study analyzed existing denoising methods used in seismic exploration from the perspective of random noise. Wavelet thresholding offers a new approach to reducing random noise in simulation results, synthetic data, and real data. A modified wavelet threshold function was developed by considering the merits and demerits of conventional soft and hard thresholding schemes. A MATLAB (matrix laboratory simulation model was used to compare the signal-to-noise ratios (SNRs and mean square errors (MSEs of the soft, hard, and modified threshold functions. The results demonstrated that the modified threshold function can avoid the pseudo-Gibbs phenomenon and produce a higher SNR than the soft and hard threshold functions. A seismic convolution model was built using seismic wavelets to verify the effectiveness of different denoising methods. The model was used to demonstrate that the modified thresholding scheme can effectively reduce random noise in seismic data and retain the desired signal. The application of the proposed tool to a real raw seismogram recorded during a land seismic exploration experiment located in north China clearly demonstrated its efficiency for random noise attenuation.
RAINFALL ANALYSIS IN KLANG RIVER BASIN USING CONTINUOUS WAVELET TRANSFORM
Directory of Open Access Journals (Sweden)
Celso A. G. Santos
2016-01-01
Full Text Available The rainfall characteristics within Klang River basin is analyzed by the continuous wavelet transform using monthly rainfall data (1997–2009 from a raingauge and also using daily rainfall data (1998–2013 from the Tropical Rainfall Measuring Mission (TRMM. The wavelet power spectrum showed that some frequency components were presented within the rainfall time series, but the observed time series is short to provide accurate information, thus the daily TRMM rainfall data were used. In such analysis, two main frequency components, i.e., 6 and 12 months, showed to be present during the entire period of 16 years. Such semiannual and annual frequencies were confirmed by the global wavelet power spectra. Finally, the modulation in the 8–16-month and 256– 512-day bands were examined by an average of all scales between 8 and 16 months, and 256 and 512 days, respectively, giving a measure of the average monthly/daily variance versus time, where the periods with low or high variance could be identified.
Adaptive Wavelet Coding Applied in a Wireless Control System
Gama, Felipe O. S.; O. Salazar, Andrés
2017-01-01
Wireless control systems can sense, control and act on the information exchanged between the wireless sensor nodes in a control loop. However, the exchanged information becomes susceptible to the degenerative effects produced by the multipath propagation. In order to minimize the destructive effects characteristic of wireless channels, several techniques have been investigated recently. Among them, wavelet coding is a good alternative for wireless communications for its robustness to the effects of multipath and its low computational complexity. This work proposes an adaptive wavelet coding whose parameters of code rate and signal constellation can vary according to the fading level and evaluates the use of this transmission system in a control loop implemented by wireless sensor nodes. The performance of the adaptive system was evaluated in terms of bit error rate (BER) versus Eb/N0 and spectral efficiency, considering a time-varying channel with flat Rayleigh fading, and in terms of processing overhead on a control system with wireless communication. The results obtained through computational simulations and experimental tests show performance gains obtained by insertion of the adaptive wavelet coding in a control loop with nodes interconnected by wireless link. These results enable the use of this technique in a wireless link control loop. PMID:29236048
Adaptive Wavelet Coding Applied in a Wireless Control System
Directory of Open Access Journals (Sweden)
Felipe O. S. Gama
2017-12-01
Full Text Available Wireless control systems can sense, control and act on the information exchanged between the wireless sensor nodes in a control loop. However, the exchanged information becomes susceptible to the degenerative effects produced by the multipath propagation. In order to minimize the destructive effects characteristic of wireless channels, several techniques have been investigated recently. Among them, wavelet coding is a good alternative for wireless communications for its robustness to the effects of multipath and its low computational complexity. This work proposes an adaptive wavelet coding whose parameters of code rate and signal constellation can vary according to the fading level and evaluates the use of this transmission system in a control loop implemented by wireless sensor nodes. The performance of the adaptive system was evaluated in terms of bit error rate (BER versus E b / N 0 and spectral efficiency, considering a time-varying channel with flat Rayleigh fading, and in terms of processing overhead on a control system with wireless communication. The results obtained through computational simulations and experimental tests show performance gains obtained by insertion of the adaptive wavelet coding in a control loop with nodes interconnected by wireless link. These results enable the use of this technique in a wireless link control loop.
FITUR BERBASIS FRAKTAL DARI KOEFISIEN WAVELET UNTUK KLASIFIKASI CITRA DAUN
Directory of Open Access Journals (Sweden)
Ardhon Rakhmadi
2017-07-01
Full Text Available Semakin banyak dan beragamnya jenis tanaman di dunia mengakibatkan semakin sulit untuk mengidentifikasi dan mengklasifikasi tanaman secara manual. Daun merupakan bagian dari tanaman yang sering dipakai untuk identifikasi dan klasifikasi tanaman. Metode klasifikasi daun secara automatis telah banyak dikembangkan oleh para peneliti. Pada penelitian sebelumnya sistem klasifikasi daun otomatis dibangun menggunakan fitur berbasis fraktal yaitu dimensi fraktal dan lacunarity. Sistem klasifikasi daun otomatis berbasis dimensi fraktal dan lacunarity dapat mengklasifikasi daun dengan akurasi tinggi namun memerlukan banyak langkah preprocessing sehingga mengakibatkan komputasi sistem meningkat. Pada penelitian ini diusulkan penggunaan metode praproses dan ekstraksi wavelet pada ekstraksi fitur citra daun. Ekstraksi fitur menggunakan teknik perhitungan statistika sederhana pada koefisien wavelet sehingga komputasi menjadi lebih ringan. Hasil ekstraksi fitur citra daun akan menjadi data masukan untuk sistem klasifikasi Support Vector Machine (SVM. Hasil eksperimen menunjukkan bahwa metode ekstraksi fitur statistik pada dekomposisi wavelet lebih unggul dibandingkan dengan metode ekstraksi fitur berbasis fraktal (dimensi fraktal dan lacunarity dari penelitian sebelumnya dengan akurasi 96.66% dan waktu komputasi 329.33 detik.
Exploratory wavelet analysis of dengue seasonal patterns in Colombia.
Fernández-Niño, Julián Alfredo; Cárdenas-Cárdenas, Luz Mery; Hernández-Ávila, Juan Eugenio; Palacio-Mejía, Lina Sofía; Castañeda-Orjuela, Carlos Andrés
2015-12-04
Dengue has a seasonal behavior associated with climatic changes, vector cycles, circulating serotypes, and population dynamics. The wavelet analysis makes it possible to separate a very long time series into calendar time and periods. This is the first time this technique is used in an exploratory manner to model the behavior of dengue in Colombia. To explore the annual seasonal dengue patterns in Colombia and in its five most endemic municipalities for the period 2007 to 2012, and for roughly annual cycles between 1978 and 2013 at the national level. We made an exploratory wavelet analysis using data from all incident cases of dengue per epidemiological week for the period 2007 to 2012, and per year for 1978 to 2013. We used a first-order autoregressive model as the null hypothesis. The effect of the 2010 epidemic was evident in both the national time series and the series for the five municipalities. Differences in interannual seasonal patterns were observed among municipalities. In addition, we identified roughly annual cycles of 2 to 5 years since 2004 at a national level. Wavelet analysis is useful to study a long time series containing changing seasonal patterns, as is the case of dengue in Colombia, and to identify differences among regions. These patterns need to be explored at smaller aggregate levels, and their relationships with different predictive variables need to be investigated.
Construction and decomposition of biorthogonal vector-valued wavelets with compact support
International Nuclear Information System (INIS)
Chen Qingjiang; Cao Huaixin; Shi Zhi
2009-01-01
In this article, we introduce vector-valued multiresolution analysis and the biorthogonal vector-valued wavelets with four-scale. The existence of a class of biorthogonal vector-valued wavelets with compact support associated with a pair of biorthogonal vector-valued scaling functions with compact support is discussed. A method for designing a class of biorthogonal compactly supported vector-valued wavelets with four-scale is proposed by virtue of multiresolution analysis and matrix theory. The biorthogonality properties concerning vector-valued wavelet packets are characterized with the aid of time-frequency analysis method and operator theory. Three biorthogonality formulas regarding them are presented.
Research on Mechanical Fault Diagnosis Scheme Based on Improved Wavelet Total Variation Denoising
Directory of Open Access Journals (Sweden)
Wentao He
2016-01-01
Full Text Available Wavelet analysis is a powerful tool for signal processing and mechanical equipment fault diagnosis due to the advantages of multiresolution analysis and excellent local characteristics in time-frequency domain. Wavelet total variation (WATV was recently developed based on the traditional wavelet analysis method, which combines the advantages of wavelet-domain sparsity and total variation (TV regularization. In order to guarantee the sparsity and the convexity of the total objective function, nonconvex penalty function is chosen as a new wavelet penalty function in WATV. The actual noise reduction effect of WATV method largely depends on the estimation of the noise signal variance. In this paper, an improved wavelet total variation (IWATV denoising method was introduced. The local variance analysis on wavelet coefficients obtained from the wavelet decomposition of noisy signals is employed to estimate the noise variance so as to provide a scientific evaluation index. Through the analysis of the numerical simulation signal and real-word failure data, the results demonstrated that the IWATV method has obvious advantages over the traditional wavelet threshold denoising and total variation denoising method in the mechanical fault diagnose.
Compression of digital hologram for three-dimensional object using Wavelet-Bandelets transform.
Bang, Le Thanh; Ali, Zulfiqar; Quang, Pham Duc; Park, Jae-Hyeung; Kim, Nam
2011-04-25
In the transformation based compression algorithms of digital hologram for three-dimensional object, the balance between compression ratio and normalized root mean square (NRMS) error is always the core of algorithm development. The Wavelet transform method is efficient to achieve high compression ratio but NRMS error is also high. In order to solve this issue, we propose a hologram compression method using Wavelet-Bandelets transform. Our simulation and experimental results show that the Wavelet-Bandelets method has a higher compression ratio than Wavelet methods and all the other methods investigated in this paper, while it still maintains low NRMS error.
A Wavelet Algorithm for Fourier-Bessel Transform Arising in Optics
Directory of Open Access Journals (Sweden)
Nagma Irfan
2015-01-01
Full Text Available The aim of the paper is to propose an efficient and stable algorithm that is quite accurate and fast for numerical evaluation of the Fourier-Bessel transform of order ν, ν>-1, using wavelets. The philosophy behind the proposed algorithm is to replace the part tf(t of the integral by its wavelet decomposition obtained by using CAS wavelets thus representing Fν(p as a Fourier-Bessel series with coefficients depending strongly on the input function tf(t. The wavelet method indicates that the approach is easy to implement and thus computationally very attractive.
Wavelet neural network and its application in fault diagnosis of rolling bearing
Wang, Guo-Feng; Wang, Tai-Yong
2005-12-01
In order to realize diagnosis of rolling bearing of rotating machines, the wavelet neural network was proposed. This kind of artificial neural network takes wavelet function as neuron of hidden layer so as to realize nonlinear mapping between fault and symptoms. A algorithm based on minimum mean square error was given to obtain the weight value of network, dilation and translation parameter of wavelet function. To testify the correctness of wavelet neural network, it was adopted in diagnosing the fault type and location of rolling bearing. The final result shows that it can recognize the fault of outer race, inner race and roller accurately.
Implementation of Wavelet-Based Robust Differential Control for Electric Vehicle Application
DEFF Research Database (Denmark)
Daya, Febin; Padmanaban, Sanjeevikumar; Blaabjerg, Frede
2015-01-01
This research letter presents the modeling and simulation of electronic differential, employing a novel wavelet controller for two brushless dc motors. The proposed controller uses discrete wavelet transform to decompose the error between actual and reference speed. Error signal that is actually...... given by the electronic differential based on throttle and steering angle is decomposed into frequency components. Numerical simulation results are provided for both wavelet and proportional-integral-derivate controllers. In comparison, the proposed wavelet control technique provides greater stability...... and ensures smooth control of the two back driving wheels....
Optical Flow of Small Objects Using Wavelets, Bootstrap Methods, and Synthetic Discriminant Filters
National Research Council Canada - National Science Library
Hewer, Gary
1997-01-01
...) targets in highly cluttered and noisy environments. In this paper; we present a novel wavelet detection algorithm which incorporates adaptive CFAR detection statistics using the bootstrap method...
Directory of Open Access Journals (Sweden)
Mohamed Ali
2017-10-01
Full Text Available This work, Bernoulli wavelet method is formed to solve nonlinear fuzzy Volterra-Fredholm integral equations. Bernoulli wavelets have been Created by dilation and translation of Bernoulli polynomials. First we introduce properties of Bernoulli wavelets and Bernoulli polynomials, and then we used it to transform the integral equations to the system of algebraic equations. We compared the result of the proposed method with the exact solution to show the convergence and advantages of the new method. The results got by present wavelet method are compared with that of by collocation method based on radial basis functions method. Finally, the numerical examples explain the accuracy of this method.
Clifford Continuous Wavelet Transforms in Ll0,2 and Ll0,3
International Nuclear Information System (INIS)
Bernstein, S.
2008-01-01
We consider Clifford-valued functions defined on R n . From the viewpoint of square integrable group representations a continuous wavelet transform is an irreducible continuous unitary representation of the affin group on the real line but also on R n . We will demonstrate that different Clifford continuous wavelet transforms can be obtained inside the calculus with similar properties than the real valued transform. Nevertheless, the Clifford wavelet transform is neither just a special vector transform nor just a wavelet transform applied to each component of the Clifford-valued function.
Directory of Open Access Journals (Sweden)
MIHAIL PRICOP
2016-06-01
Full Text Available Vulnerable and critical mechanical systems are bearings and drive belts. Signal analysis of vibration highlights the changes in root mean square, the frequency spectrum (frequencies and amplitudes in the time- frequency (Short Time Fourier Transform and Wavelet Transform, are the most used method for faults diagnosis and location of rotating machinery. This article presents the results of an experimental study applied on a di agnostic platform of rotating machinery through three Wavelet methods: (Discrete Wavelet Transform -DWT, Continuous Wavelet Transform -CWT, Wavelet Packet Transform -WPT with different mother wavelet. Wavelet Transform is used to decompose the original sig nal into sub -frequency band signals in order to obtain multiple data series at different resolutions and to identify faults appearing in the complex rotation systems. This paper investigates the use of different mother wavelet functions for drive belts and bearing fault diagnosis. The results demonstrate the possibility of using different mother wavelets in rotary systems diagnosis detecting and locating in this way the faults in bearings and drive belts.
Iterative support detection-based split Bregman method for wavelet frame-based image inpainting.
He, Liangtian; Wang, Yilun
2014-12-01
The wavelet frame systems have been extensively studied due to their capability of sparsely approximating piece-wise smooth functions, such as images, and the corresponding wavelet frame-based image restoration models are mostly based on the penalization of the l1 norm of wavelet frame coefficients for sparsity enforcement. In this paper, we focus on the image inpainting problem based on the wavelet frame, propose a weighted sparse restoration model, and develop a corresponding efficient algorithm. The new algorithm combines the idea of iterative support detection method, first proposed by Wang and Yin for sparse signal reconstruction, and the split Bregman method for wavelet frame l1 model of image inpainting, and more important, naturally makes use of the specific multilevel structure of the wavelet frame coefficients to enhance the recovery quality. This new algorithm can be considered as the incorporation of prior structural information of the wavelet frame coefficients into the traditional l1 model. Our numerical experiments show that the proposed method is superior to the original split Bregman method for wavelet frame-based l1 norm image inpainting model as well as some typical l(p) (0 ≤ p wavelet frame coefficients.
Garg, Nidhi; Ryait, Hardeep S; Kumar, Amod; Bisht, Amandeep
2018-01-01
WPS is a non-invasive method to investigate human health. During signal acquisition, noises are also recorded along with WPS. Clean WPS with high peak signal to noise ratio is a prerequisite before use in disease diagnosis. Wavelet Transform is a commonly used method in the filtration process. Apart from its extensive use, the appropriate factors for wavelet denoising algorithm is not yet clear in WPS application. The presented work gives an effective approach to select various factors for wavelet denoise algorithm. With the appropriate selection of wavelet and factors, it is possible to reduce noise in WPS. In this work, all the factors of wavelet denoising are varied successively. Various evaluation parameters such as MSE, PSNR, PRD and Fit Coefficient are used to find out the performance of the wavelet denoised algorithm at every one step. The results obtained from computerized WPS illustrates that the presented approach can successfully select the mother wavelet and other factors for wavelet denoise algorithm. The selection of db9 as mother wavelet with sure threshold function and single rescaling function using UWT has been a better option for our database. The empirical results proves that the methodology discussed here could be effective in denoising WPS of any morphological pattern.
Medical image denoising using dual tree complex thresholding wavelet transform and Wiener filter
Directory of Open Access Journals (Sweden)
Hilal Naimi
2015-01-01
Full Text Available Image denoising is the process to remove the noise from the image naturally corrupted by the noise. The wavelet method is one among various methods for recovering infinite dimensional objects like curves, densities, images, etc. The wavelet techniques are very effective to remove the noise because of their ability to capture the energy of a signal in few energy transform values. The wavelet methods are based on shrinking the wavelet coefficients in the wavelet domain. We propose in this paper, a denoising approach basing on dual tree complex wavelet and shrinkage with the Wiener filter technique (where either hard or soft thresholding operators of dual tree complex wavelet transform for the denoising of medical images are used. The results proved that the denoised images using DTCWT (Dual Tree Complex Wavelet Transform with Wiener filter have a better balance between smoothness and accuracy than the DWT and are less redundant than SWT (StationaryWavelet Transform. We used the SSIM (Structural Similarity Index Measure along with PSNR (Peak Signal to Noise Ratio and SSIM map to assess the quality of denoised images.
Fast wavelet-based image characterization for highly adaptive image retrieval.
Quellec, Gwénolé; Lamard, Mathieu; Cazuguel, Guy; Cochener, Béatrice; Roux, Christian
2012-04-01
Adaptive wavelet-based image characterizations have been proposed in previous works for content-based image retrieval (CBIR) applications. In these applications, the same wavelet basis was used to characterize each query image: This wavelet basis was tuned to maximize the retrieval performance in a training data set. We take it one step further in this paper: A different wavelet basis is used to characterize each query image. A regression function, which is tuned to maximize the retrieval performance in the training data set, is used to estimate the best wavelet filter, i.e., in terms of expected retrieval performance, for each query image. A simple image characterization, which is based on the standardized moments of the wavelet coefficient distributions, is presented. An algorithm is proposed to compute this image characterization almost instantly for every possible separable or nonseparable wavelet filter. Therefore, using a different wavelet basis for each query image does not considerably increase computation times. On the other hand, significant retrieval performance increases were obtained in a medical image data set, a texture data set, a face recognition data set, and an object picture data set. This additional flexibility in wavelet adaptation paves the way to relevance feedback on image characterization itself and not simply on the way image characterizations are combined.
Kamble, Saurabh Prakash; Thawkar, Shashank; Gaikwad, Vinayak G.; Kothari, D. P.
2017-12-01
Detection of disturbances is the first step of mitigation. Power electronics plays a crucial role in modern power system which makes system operation efficient but it also bring stationary disturbances in the power system and added impurities to the supply. It happens because of the non-linear loads used in modern day power system which inject disturbances like harmonic disturbances, flickers, sag etc. in power grid. These impurities can damage equipments so it is necessary to mitigate these impurities present in the supply very quickly. So, digital signal processing techniques are incorporated for detection purpose. Signal processing techniques like fast Fourier transform, short-time Fourier transform, Wavelet transform etc. are widely used for the detection of disturbances. Among all, wavelet transform is widely used because of its better detection capabilities. But, which mother wavelet has to use for detection is still a mystery. Depending upon the periodicity, the disturbances are classified as stationary and non-stationary disturbances. This paper presents the importance of selection of mother wavelet for analyzing stationary disturbances using discrete wavelet transform. Signals with stationary disturbances of various frequencies are generated using MATLAB. The analysis of these signals is done using various mother wavelets like Daubechies and bi-orthogonal wavelets and the measured root mean square value of stationary disturbance is obtained. The measured value obtained by discrete wavelet transform is compared with the exact RMS value of the frequency component and the percentage differences are presented which helps to select optimum mother wavelet.
Research on fault diagnosis for RCP rotor based on wavelet analysis
International Nuclear Information System (INIS)
Chen Zhihui; Xia Hong; Wang Taotao
2008-01-01
Wavelet analysis is with the characteristics of noise reduction and multiscale resolution, and can be used to effectively extract the fault features of the typical failures of the main pumps. Simulink is used to simulate the typical faults: Misalignment Fault, Crackle Fault of rotor, and Initial Bending Fault, then the Wavelet method is used to analyze the vibration signal. The result shows that the extracted fault feature from wavelet analysis can effectively identify the fault signals. The Wavelet analysis is a practical method for the diagnosis of main coolant pump failure, and is with certain value for application and significance. (authors)
Energy Technology Data Exchange (ETDEWEB)
Matsushima, J.; Rokugawa, S.; Kato, Y. [The University of Tokyo, Tokyo (Japan). Faculty of Engineering; Yokota, T.; Miyazaki, T. [Geological Survey of Japan, Tsukuba (Japan); Ichie, Y. [The University of Tokyo, Tokyo (Japan)
1996-10-01
Data processing techniques have been investigated for clarifying structures and physical properties of geothermal reservoirs in the deep underground by seismic exploration using multiple wells. They include the initial motion time-distance tomography, amplitude tomography, diffracted wave tomography, and structure imaging using reflected wave or scattered wave. When applying these data processing methods to observed records, weak and minor signals essentially required are canceled due to averaging the analytical fields. In this study, influence of inhomogeneous media on the wavefield was evaluated. Data were analyzed considering frequency by using wavelet transform by which time-frequency can be easily analyzed. From the time-frequency analysis using wavelet transform, it was illustrated that high frequency scattered waves, generated by scatterer like cracks or by irregularity on the reflection surface, arrive behind direct P-wave and direct S-wave. 5 refs., 8 figs.
Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction.
Zhang, Zitong; Telesford, Qawi K; Giusti, Chad; Lim, Kelvin O; Bassett, Danielle S
2016-01-01
Wavelet methods are widely used to decompose fMRI, EEG, or MEG signals into time series representing neurophysiological activity in fixed frequency bands. Using these time series, one can estimate frequency-band specific functional connectivity between sensors or regions of interest, and thereby construct functional brain networks that can be examined from a graph theoretic perspective. Despite their common use, however, practical guidelines for the choice of wavelet method, filter, and length have remained largely undelineated. Here, we explicitly explore the effects of wavelet method (MODWT vs. DWT), wavelet filter (Daubechies Extremal Phase, Daubechies Least Asymmetric, and Coiflet families), and wavelet length (2 to 24)-each essential parameters in wavelet-based methods-on the estimated values of graph metrics and in their sensitivity to alterations in psychiatric disease. We observe that the MODWT method produces less variable estimates than the DWT method. We also observe that the length of the wavelet filter chosen has a greater impact on the estimated values of graph metrics than the type of wavelet chosen. Furthermore, wavelet length impacts the sensitivity of the method to detect differences between health and disease and tunes classification accuracy. Collectively, our results suggest that the choice of wavelet method and length significantly alters the reliability and sensitivity of these methods in estimating values of metrics drawn from graph theory. They furthermore demonstrate the importance of reporting the choices utilized in neuroimaging studies and support the utility of exploring wavelet parameters to maximize classification accuracy in the development of biomarkers of psychiatric disease and neurological disorders.