Wavelets Research Papers - Academia.edu (original) (raw)

In this paper, we describe an intelligent signal analysis system employing the wavelet transformation towards solving vehicle engine diagnosis problems. Vehicle engine diagnosis often involves multiple signal analysis. The developed system first partitions a leading signal into small segments representing physical events or stateds based on wavelet mutli-resolution analysis. Second, by applying the segmentation result of the leading signal to the other signals, the detailed properties of each segment, including inter-signal relationships, are extracted to form a feature vector. Finally a fuzzy intelligent system is used to learn diagnostic features from a training set containing feature vectors extracted from signal segments at various vehicle states. The fuzzy system applies its diagnostic knowledge to classify signals as abnormal or normal. The implementation of the system is described and experiment results are presented.

- by
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- Engineering, Artificial Intelligence, Technology, Machine Learning

The increasing demand of World Wide Web raises the need of predicting the user's web page request. The most widely used approach to predict the web pages is the pattern discovery process of Web usage mining. This process involves inevitability of many techniques like Markov model, association rules and clustering. Fuzzy theory with different techniques has been introduced for the better results. Our focus is on Markov models. This paper is introducing the vague Rules with Markov models for more accuracy using the vague set theory.

- by Kamran Iqbal
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- Time Series, Resource Allocation, Distributed System, Neural Network

Abstrak Kemampuan manusia untuk membedakan tekstur-tekstur yang berbeda secara perceptual merupakan permasalahan utama dalam visi mesin (machine vision) yang mengacu pada banyak pola tekstural dan kondisi iluminasi. Sehingga dalam beberapa dekade ini banyak dikembangkan penelitian teknik multiskala (multiscale) yang dapat mengisolasi daerah (region) yang mempunyai tekstur homogen secara perceptual dalam sebuah citra. Dari beberapa penelitian kebanyakan menggunakan citra gray-level yang mengabaikan unsur warna dari citra.Penelitian ini melakukan segmentasi citra berwarna berdasarkan karakteristik color-texture dengan menggunakan teknik multiskala, yaitu dengan menerapkan transformasi wavelet diskrit (Discrete Wavelet Transform) untuk mendapatkan region-region yang mempunyai tekstur yang homogen. Dalam metode ini, suatu citra disegmentasi dalam beberapa region dengan mengelompokkan (clustering) fitur color-texture yang diperhitungkan dari koefisien waveletnya. Percobaan dilakukan dengan menggunakan citra tekstur gray-level dan berwarna, serta citra riil. Dari hasil percobaan yang dilakukan, dapat dilihat bahwa hasil segmentasi region pada citra yang berwarna lebih dapat membedakan dua region yang mempunyai tekstur sama tetapi mempunyai warna yang agak berbeda. Pemilihan transformasi ruang warna juga mempengaruhi hasil segmentasi, dimana dari beberapa percobaan yang dilakukan transformasi ruang warna linier K-L (Karhunen-Loeve) memberikan hasil yang lebih baik dibandingkan transformasi ruang warna nonlinier HSV (Hue Saturation Value)

- by Fajar Astuti
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- Wavelets, Segmentation, Color texture

An electrophysiological phenomenon running up and down the spine, elicited by light pressure contact at very precise points and thereafter taking the external appearance of an undulatory motion of the spine, is analyzed from its standing wave, coherence, and synchronization-at-a-distance properties. This standing spinal wave can be elicited in both normal and quadriplegic subjects, which demonstrates that the neuronal circuitry is embedded in the spine. The latter, along with the inherent rhythmicity of the motion, its wave properties, and the absence of external sensory input once the phenomenon is elicited reveal a Central Pattern Generator (CPG). The major investigative tool is surface electromyographic (sEMG) wavelet signal analysis at various points along the paraspinal muscles. Statistical correlation among the various points is used to establish the standing wave phenomenon on a specific subband of the Daubechies wavelet decomposition of the sEMG signals. More precisely, ∼10 Hz coherent bursts reveal synchronization between sensory-motor loops at a distance larger, and a frequency slower, than those already reported. As a potential therapeutic application, it is shown that partial recovery from spinal cord injury can be assessed by the correlation between the sEMG signals on both sides of the injury. ;15(5):461-64. PMID: 19450165 Reorganizational Healing, (ROH), is an emerging wellness, growth and behavioral change paradigm. Through its three central elements (the Four Seasons of Wellbeing, the Triad of Change, and the Five Energetic Intelligences) Reorganizational Healing takes an approach to help create a map for individuals to self-assess and draw on strengths to create sustainable change. Reorganizational Healing gives individuals concrete tools to explore and use the meanings of their symptoms, problems, and life-stressors as catalysts to taking new and sustained action to create a more fulfilling and resilient life. 1-15

- by Edmond Jonckheere
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- Biomedical Engineering, Spinal Cord Injury, Signal Analysis, Wavelets

This paper relies on wavelet multiresolution analysis to capture the dependence structure of currency markets and reveal the complex dynamics across different timescales. It investigates the nature and direction of causal relationships among the most widely traded currencies denoted relative to the United States Dollar (USD), namely Euro (EUR), Great Britain Pound (GBP) and Japanese Yen (JPY). The timescale analysis involves the estimation of linear vis-à-vis nonlinear and spectral causality of wavelet components and aggregate series as well as the detection of short-vs. long-run linkages and cross-scale correlations. Moreover, this study attempts to probe into the micro-foundations of across-scale heterogeneity in the causality pattern on the basis of trader behavior with different time horizons. New stylized properties emerge in the volatility structure and the implications for the flow of information across scales are inferred. The examined period starts from the introduction of the Euro and covers the dot-com bubble, the financial crisis of 2007-2010 and the Eurozone debt crisis. Technically, this paper presents an invariant discrete wavelet transform that deals efficiently with phase shifts, dyadic-length and boundary effects. It also proposes a new entropy-based methodology for the determination of the optimal decomposition level. Overall, there is no indication of a global causal behavior that dominates at all timescales. When the nonlinear effects are accounted for, the evidence of dynamical bidirectional causality implies that the pattern of leads and lags changes over time. These results may prove useful to quantify the process of integration as well as influence the greater predictability of currency markets.

- by Stelios Bekiros
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- Foreign Exchange Market, Forecasting, Neural Networks, Causality

In order to measure the D structure of a number of objects a comparably new technique in computer vision exists, namely time of flight (TOF) cameras. The overall principle is rather easy and has been applied using sound or light for a long time in all kind of sonar and lidar systems. However in this approach one uses modulated light waves and receives the signals by a parallel pixel array structure. Out of the travelling time at each pixel one can estimate the depth structure of a distant object. The technique requires measuring the intensity differences and ratios of several pictures with extremely high accuracy; therefore one faces in practice rather high noise levels. Object features as reflectance and roughness influence the measurement results. This leads to partly high noise levels with variances dependent on the illumination and material parameters. It can be shown that a reciprocal relation between the variance of the phase and the squared amplitude of the signals exists. On...

- by Bernhard Moser
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- Engineering, Geography, Computer Science, Computer Vision

Power Quality is the Major Concern in modern Electrical Distribution System. Supplying Un Interrupted Power to the customers is the first priority of distribution companies. In recent days, raise of un balanced loads the voltage levels are deviated, further causing many power quality issues. There is a constant need of monitoring the voltage, current levels of the distribution system. To achieve this, these parameters like voltage, current and power are continuously measured. Decomposed Signals are more effective than the Original Signals to analysis the Signal for detecting Power Quality disturbances. So, Signal Processing techniques are affective techniques to measure the voltage, current and Power. In this Paper, dual tree complex wavelets transform (DTCWT) is used to decompose the signal. A formula is proposed measure the power in distribution system using dual tree complex wavelet transform. The Proposed formula Validated by taking different cases and it is carried out in MATLAB Software.

Outliers in financial data can lead to model parameter estimation biases, invalid inferences and poor volatility forecasts. Therefore, their detection and correction should be taken seriously when modeling financial data. This paper focuses on these issues and proposes a general detection and correction method based on wavelets that can be applied to a large class of volatility models. The effectiveness of our proposal is tested by an intensive Monte Carlo study for six well known volatility models and compared to alternative proposals in the literature, before applying it to three daily stock market indexes. The Monte Carlo experiments show that our method is both very effective in detecting isolated outliers and outlier patches and much more reliable than other wavelet-based procedures since it detects a significant smaller number of false outliers.

- by Helena Veiga
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- Stock Market, Parameter estimation, Wavelets, Volatility Forecasting

Given the central role which demand plays in the cost of health care Money Matters has recently been exploring the issues around forecasting demand. Having been involved in health care planning for over 18 years I have serious reservations about the central assumption in NHS planning that demographic change or the ageing population is driving demand.. In this respect increases in accident & emergency (A&E) attendances which are far above that suggested by demography are a common international feature. Fig. 1 elegantly demonstrates the complete lack of demographic involvement in that the A&E trends for England over the past 30 years behave in a way which has more to do with wavelet analysis, i.e. recurring peaks and troughs, rather than any other form of recognisable trend.

- by Rodney Jones
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- Health Sciences, Emergency Medicine, Epidemiology, Forecasting

The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a new image which is more suitable for human and machine perception or further image-processing tasks such as segmentation, feature extraction and object recognition. Di erent fusion methods have been proposed in literature, including multiresolution analysis. This paper is an image fusion tutorial based on wavelet decomposition, i.e. a multiresolution image fusion approach. We can fuse images with the same or di erent resolution level, i.e. range sensing, visual CCD, infrared, thermal or medical. The tutorial performs a synthesis between the multiscale-decomposition-based image approach (Proc. IEEE 87 (1999) 1315), the ARSIS concept (Photogramm. Eng. Remote Sensing 66 (1) (2000) 49) and a multisensor scheme (Graphical Models Image Process. 57 (3) (1995) 235). Some image fusion examples illustrate the proposed fusion approach. A comparative analysis is carried out against classical existing strategies, including those of multiresolution.

- by GONZALO PAJARES MARTINSANZ
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- Image Processing, Remote Sensing, Pattern Recognition, Wavelets

In this paper it is shown the performing of an optical transform to state the scalar diffraction in the formulation of the wavelet transform and the 'wave equations'. From there, a bridge is build between equations of spherical waves presented in 1678 by Huygens and the continuous wavelet transform. For such a purpose, wavelets are introduced that meet the principles of waves and the properties of wavelets. The following equations are applied in solution to show a correspondence between the Huygens-Fresnel diffraction and the wavelet transform.

- by Victor Vermehren and +1
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- Fourier and Wavelet Processing, Wavelets, Wave Equations, Christiaan Huygens

Head injury is a major reason for morbidity and mortality worldwide and traumatic head injuries represent the major cause of neurological disability to a clot or hematoma caused by Haemorrhage (ICH) and is the The most common cause of ICH normally reported in our country are road traffic accidents (RTA) followed by falls and assaults. India is a populous country with over a billion every 100,000 population with deprived of these doctors. The unavailability of these specialists is a grave concern to the w care to the nation. The mainstay in the diagnosis of an ICH is the CT (Computed Tomography) scan of the head which is the definitive tool for accurate diagnosis of an ICH following trauma and provides an objective assessment of structural damage to brain. Accurate segmentation of the haemorrhage. This study is on segment Keywords: Intracranial decomposition; Brain haemorrhage segmentation is the first step before detecting the been done on the brain haemorrhage detection using methods like Convolutional neural network other efficient and advanced deep learning techniques. But that is resource intensive. It is also nec efficient when there is a large dataset Hssayeni and colleagues multiple slices and made it public. Second, used deep learning methods to perform segmentation and got a dice coefficient of 31% which is good compared to and colleagues [12] propose entropy based automatic unsupervised brain intracranial haemorrhage segmentation which comprises of FCM clustering, thresholding and edge based active contour methods and they get a better result with the combination than FCM clustering and active use deep learning to diagnose brain haemorrhage. They have used LeNet, GoogleNet and Inception dataset consisting of 100 cases collected from 115 hospitals and discovered LeNet is the among the three. Arjun Majumdar and colleagues haemorrhage instead of Head injury is a major reason for morbidity and mortality worldwide and traumatic head injuries represent the major cause of neurological disability. A traumatic brain injury to a clot or hematoma caused by an accident or any other trauma. (ICH) and is the most common and serious consequence of head injury which can be life The most common cause of ICH normally reported in our country are road traffic accidents (RTA) followed by falls and assaults. India is a populous country with over a billion every 100,000 population with most of them in the urban setup, Indian rural population of more than 70% is deprived of these doctors. The unavailability of these specialists is a grave concern to the w care to the nation. The mainstay in the diagnosis of an ICH is the CT (Computed Tomography) scan of the head which is the definitive tool for accurate diagnosis of an ICH following trauma and provides an objective assessment of ctural damage to brain. Accurate segmentation of the. This study is on segmentation of the brain haemorrhage Intracranial haemorrhage; Discrete wavelet transforms I. RELATED WORK Brain haemorrhage segmentation is the first step before detecting the been done on the brain haemorrhage detection using methods like Convolutional neural network other efficient and advanced deep learning techniques. But that is resource intensive. It is also nec efficient when there is a large dataset, which is not easily available in case of brain haemorrhage. Hssayeni and colleagues [1][2] have contributed in two ways, they collected a new dataset of 82 CT scans with ade it public. Second, used deep learning methods to perform segmentation and got a dice coefficient of 31% which is good compared to other deep learning techniques on small datasets. Indrajeet Kumar propose entropy based automatic unsupervised brain intracranial haemorrhage segmentation which comprises of FCM clustering, thresholding and edge based active contour methods and they get a better result with the combination than FCM clustering and active contour methods alone. use deep learning to diagnose brain haemorrhage. They have used LeNet, GoogleNet and Inception dataset consisting of 100 cases collected from 115 hospitals and discovered LeNet is the among the three. Arjun Majumdar and colleagues [8] haemorrhage instead of traditional methods and achieve a Head injury is a major reason for morbidity and mortality worldwide and traumatic head injuries traumatic brain injury (TBI) is damage to the brain, secondary an accident or any other trauma. This hematoma is known as an Intracranial most common and serious consequence of head injury which can be life The most common cause of ICH normally reported in our country are road traffic accidents (RTA) followed by falls and assaults. India is a populous country with over a billion people and there is approximately one radiologist for of them in the urban setup, Indian rural population of more than 70% is deprived of these doctors. The unavailability of these specialists is a grave concern to the w care to the nation. The mainstay in the diagnosis of an ICH is the CT (Computed Tomography) scan of the head which is the definitive tool for accurate diagnosis of an ICH following trauma and provides an objective assessment of ctural damage to brain. Accurate segmentation of the haemorrhage is the first step before detecting the brain haemorrhage images using discrete wavelet transforms. iscrete wavelet transforms; Segmentation; RELATED WORK Brain haemorrhage segmentation is the first step before detecting the haemorrhage in the brain. A lot of work has been done on the brain haemorrhage detection using methods like Convolutional neural network other efficient and advanced deep learning techniques. But that is resource intensive. It is also nec which is not easily available in case of brain haemorrhage. have contributed in two ways, they collected a new dataset of 82 CT scans with ade it public. Second, used deep learning methods to perform segmentation and got a dice deep learning techniques on small datasets. Indrajeet Kumar propose entropy based automatic unsupervised brain intracranial haemorrhage segmentation which comprises of FCM clustering, thresholding and edge based active contour methods and they get a better result contour methods alone. Tong Duc Phong and colleagues use deep learning to diagnose brain haemorrhage. They have used LeNet, GoogleNet and Inception dataset consisting of 100 cases collected from 115 hospitals and discovered LeNet is the most time [8] use a modified version of U-Net to detect the brain traditional methods and achieve an overall specificity of 98.6% on the small dataset. Brain Haemorrhage Segmentation using Dircrete Wavelet Transform. the terms of the Creative Commons Attribution License; Which Permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source Head injury is a major reason for morbidity and mortality worldwide and traumatic head injuries (TBI) is damage to the brain, secondary This hematoma is known as an Intracranial most common and serious consequence of head injury which can be life-threatening. The most common cause of ICH normally reported in our country are road traffic accidents (RTA) followed by falls people and there is approximately one radiologist for of them in the urban setup, Indian rural population of more than 70% is deprived of these doctors. The unavailability of these specialists is a grave concern to the well-being of the health care to the nation. The mainstay in the diagnosis of an ICH is the CT (Computed Tomography) scan of the head which is the definitive tool for accurate diagnosis of an ICH following trauma and provides an objective assessment of is the first step before detecting the brain images using discrete wavelet transforms. Thresholding; Wavelet haemorrhage in the brain. A lot of work has been done on the brain haemorrhage detection using methods like Convolutional neural network [2][3][5][11] and other efficient and advanced deep learning techniques. But that is resource intensive. It is also necessary and which is not easily available in case of brain haemorrhage. Murtada D. have contributed in two ways, they collected a new dataset of 82 CT scans with ade it public. Second, used deep learning methods to perform segmentation and got a dice deep learning techniques on small datasets. Indrajeet Kumar propose entropy based automatic unsupervised brain intracranial haemorrhage segmentation which comprises of FCM clustering, thresholding and edge based active contour methods and they get a better result Tong Duc Phong and colleagues [13] use deep learning to diagnose brain haemorrhage. They have used LeNet, GoogleNet and Inception-ResNet and a most time-consuming model Net to detect the brain overall specificity of 98.6% on the small dataset.

An important obstacle in the life expectancy of high voltage equipment is the progressive and systematic damage to insulation systems caused by Partial Discharge (PD). The evolution of PD activity is symptomatic of the insulation state. The characterization of these PD patterns may provide additional information into the nature and extent of various defects. By looking at a new generation of recently developed piezoelectric sensors with notable features (economic, large broadband frequency response, flexible, miniature, etc.), this study will investigate the applicability of this technology as an efficient partial discharge detector. We will provide experimental validation through a comparative study using conventional system measurements which are widely recognized within the industry.

- by IAEME Publication
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- Wavelets, Dielectrics, Partial Discharge, Piezoelectric Sensors

In the last few years, several quantitative inversion methods have been proposed to analyze pulsed phase thermographic data: statistical methods [1], Neural Networks [2] and wavelets , with a wide range of reported accuracies. In the present paper a new approach is proposed based on absolute phase contrast computations defined in a similar way as for absolute temperature contrast . Phase contrast data is then used to estimate the blind frequency, i.e. the frequency at which the defect becomes visible for the 'first' time . It was found an excellent agreement between defect depth z, and the corresponding blind frequencies f b . Experimental tests on Plexiglas ® and aluminum specimens demonstrate the potential of the technique on retrieving the depth of flat-bottomed holes. We also discuss temporal aliasing and its relationship with the phase delay images. As will be stressed, the unavoidable differences between the Continuous and the Discrete Fourier Transform of a time-dependent temperature decay signal can be effectively minimized not only by selecting a sampling frequency rate according to Shannon's Sampling Theorem (as is well-known [6]), but also by choosing an appropriate truncation window size .

- by Clemente Ibarra Castanedo
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- Neural Networks, Neural Network, Wavelets, Inverse Method

The Haar wavelets Second-order boundary-value problems Cantilever beam Obstacle problems Multi-point boundary-value problems Radiation fin a b s t r a c t An efficient numerical method based on uniform Haar wavelets is proposed for the numerical solution of second-order boundary-value problems (BVPs) arising in the mathematical modeling of deformation of beams and plate deflection theory, deflection of a cantilever beam under a concentrated load, obstacle problems and many other engineering applications. The Haar wavelet basis permits to enlarge the class of functions used so far in the collocation framework. The performance of the Haar wavelets is compared with the Walsh wavelets, semi-orthogonal B-spline wavelets, spline functions, Adomian decomposition method (ADM), finite difference method, and Runge-Kutta method coupled with nonlinear shooting method. A more accurate solution can be obtained by wavelet decomposition in the form of a multi-resolution analysis of the function which represents the solution of a given problem. Through this analysis the solution is found on the coarse grid points, and then refined towards higher accuracy by increasing the level of the Haar wavelets. Neumann's boundary conditions which are problematic for most of the numerical methods are automatically coped with. The main advantage of the Haar wavelet based method is its efficiency and simple applicability for a variety of boundary conditions. The convergence analysis of the proposed method alongside numerical procedure for multipoint boundary-value problems are given to test wider applicability and accuracy of the method.

- by Imran Aziz and +1
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- Applied Mathematics, Numerical Analysis, Numerical Method, Wavelets

Wavelet transform has emerged over recent years as a favoured tool for the investigation of biomedical signals, which are highly non-stationary by their nature. A relevant wavelet-based approach in the analysis of biomedical signals exploits the capability of wavelet transform to separate the signal energy among different frequency bands (i.e., different scales), realizing a good compromise between temporal and frequency resolution. The rationale of this paper is twofold: (i) to present a mathematical formalization of energy calculation from wavelet coefficients, in order to obtain uniformly time distributed atoms of energy across all the scales; (ii) to show two different applications of the wavelet-based energetic approach to biomedical signals. One application concerns the study of epileptic brain electrical activity, with the aim of identifying typical patterns of energy redistribution during the seizure. Results obtained from this method provide interesting indications on the complex spatio-temporal dynamics of the seizure. The other application concerns the electro-oculographic tracings, with the purpose of realizing an automatic detector of a particular type of eye movements (slow eye movements), important to identify sleep phases. The algorithm is able to identify this eye movement pattern efficiently, characterizing it in rigorous energetic terms. The energetic approach built within the framework of the multiresolution decomposition appears as a powerful and versatile tool for the investigation and characterization of transient events in biomedical signals. support and frequencies are not localized in time. Consequently, Fourier analysis provides only globally timeaveraged information, whereas it obscures any local behaviour within the signal. Hence, it is suitable for extracting frequency information from stationary signals.

- by elena gardella
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- Applied Mathematics, Signal Processing, Numerical Analysis, Algorithm

La clasificación automática de las imágenes del Diagnóstico de Esparcimiento Thomson constituye uno de los ejes de la automatización del funcionamiento del TJ – II. En consecuencia el sistema de adquisición de datos del Diagnóstico fue sincronizado con el funcionamiento del TJ – II y un clasificador automático de imágenes desarrollado, funcionando este hace algunos años. Este trabajo se centra en el diseño y entrenamiento de nuevos clasificadores para las
imágenes del Diagnóstico de Esparcimiento Thomson. En el mismo se presenta un conjunto de clasificadores desarrollados para la clasificación de las imágenes del Diagnóstico. Los referidos clasificadores fueron diseñados para el funcionamiento en dos etapas, una de preprocesamiento de las imágenes, en la cual se utilizan las
transformadas wavelet, y otra de clasificación, en la cual se recurre a distintas técnicas de aprendizaje, las redes neuronales Learning Vector Quantization, las Máquinas de Vectores Soporte y template matching. Se presenta también una serie de experimentos realizados con los referidos clasificadores y con base en los resultados obtenidos se efectúa un análisis comparativo en cuanto a las potencialidades de los
mismos. Por otro lado, se presenta en el trabajo un servidor de clasificación desarrollado con base en uno de los clasificadores aquí presentados. El referido servidor se integra al sistema de adquisición de datos del Diagnóstico y permite efectuar la clasificación de las imágenes, a petición de un programa cliente, de forma sincronizada con el funcionamiento del TJ – II.
Palabras Claves: redes neuronales, Learning Vector Quantization, Máquina de Vectores Soporte, template matching, clasificador
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Automatic classification of Thomson Scattering Diagnostic’s images is among the central ideas about the TJ – II automation. Consequently, the Diagnostic’s data acquisition program has been synchronized with the TJ – II operation and an automatic image classifier implemented, and it has been operating for years. This work aims to design and train a set of new classifiers for the Thomson Scattering Diagnostic’s images. In this work a set of classifiers developed for the Diagnostic’s images are shown. They were designed to operate in two stages, firstly the preprocessing stage, in which the wavelet transform is used, and then the classification
stage, in which several learning techniques are used, as Learning Vector Quantization neural networks, Support Vector Machines and template matching. Also, a set of experiments carried out with the designed classifiers are shown and based on the tests performances a comparative analysis is made. On the other hand, in the work a classification server, which were developed using one of the showed classifiers, is presented. The server is integrated in the Diagnostic’s data
acquisition system and allows image classification, at request of a client program, in a synchronized way with TJ – II operation.
Keywords: neural networks, Learning Vector Quantization, Support vector Machines, template matching, classifier

- by Lazaro Makili
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- Pattern Recognition, Support Vector Machines, Wavelets, Image Classification

Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.

This paper deals with a new scheme for the diagnosis of localised defects in ball bearings based on the wavelet transform and neuro-fuzzy classification. Vibration signals for normal bearings, bearings with inner race faults and ball faults were acquired from a motor-driven experimental system. The wavelet transform was used to process the accelerometer signals and to generate feature vectors. An adaptive neural-fuzzy inference system (ANFIS) was trained and used as a diagnostic classifier. For comparison purposes, the Euclidean vector distance method as well as the vector correlation coefficient method were also investigated. The results demonstrate that the developed diagnostic method can reliably separate different fault conditions under the presence of load variations. r

- by Kenneth Loparo
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- Mechanical Engineering, Civil Engineering, Wavelets, Wavelet Transform

Multiresolution analysis and wavelets provide useful and efficient tools for representing functions at multiple levels of detail. Wavelet representations have been used in a broad range of applications, including image compression, physical simulation, and numerical analysis. In this article, we present a new class of wavelets, based on subdivision surfaces, that radically extends the class of representable functions. Whereas previous two-dimensional methods were restricted to functions defined on ‫ޒ‬ 2 , the subdivision wavelets developed here may be applied to functions defined on compact surfaces of arbitrary topological type. We envision many applications of this work, including continuous level-of-detail control for graphics rendering, compression of geometric models, and acceleration of global illumination algorithms. Level-ofdetail control for spherical domains is illustrated using two examples: shape approximation of a polyhedral model, and color approximation of global terrain data.

- by Toni So
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- Information Systems, Numerical Analysis, Level Of Detail (LOD), Wavelets

Wavelet analysis, although used extensively in disciplines such as signal processing, engineering, medical sciences, physics and astronomy, has not fully entered the economics discipline yet. In this survey article, wavelet analysis is introduced in an intuitive manner, and the existing economics and finance literature that utilizes wavelets is surveyed and explored. Extensive examples of exploratory wavelet analysis are given, most using Canadian, US and Finnish industrial production data. Finally, potential and possible future applications for wavelet analysis in economics are discussed.

- by Patrick Crowley
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- Image Processing, Economic Growth, Wavelets, Business Cycles

Image processing and analysis based on the continuous or discrete image transforms are classic techniques. The image transforms are widely used in image filtering, data description, etc. Nowadays the wavelet theorems make up very popular methods of image processing, denoising and compression. Considering that the Haar functions are the simplest wavelets, these forms are used in many methods of discrete image transforms and processing. The image transform theory is a well known area characterized by a precise mathematical background, but in many cases some transforms have particular properties which are not still investigated. This paper for the first time presents graphic dependences between parts of Haar and wavelets spectra. It also presents a method of image analysis by means of the wavelets-Haar spectrum. Some properties of the Haar and wavelets spectrum were investigated. The extraction of image features immediately from spectral coefficients distribution were shown. In this pa...

A compressão de dados é um dos factores que mais contribuiu para o grande crescimento das tecnologias da informação e da comunicação. Sem compressão, a maioria dos produtos tecnológicos de consumo e entretenimento, que são hoje banais, nunca teria chegado a existir, destacando-se, entre muitos outros, o DVD, as câmaras fotográficas digitais, os leitores de MP3, o YouTube e o streaming de vídeo, as redes sem fios e a televisão digital. De facto, as tecnologias de compressão multimédia permitem representar a informação de uma forma mais eficiente, reduzindo os grandes volumes de espaço de armazenamento que ocupa e, portanto, a largura de banda que consome para se transmitir nas redes e na Internet. Este livro tem como objectivo principal apresentar uma introdução fundamentada, clara, acessível e abrangente aos conceitos e tecnologias relacionadas com métodos, algoritmos e normas internacionais de compressão de informação multimédia. Ao longo do livro são abordados, entre outros, os seguintes temas: Conceitos e normas sobre compressão de informação multimédia, incluindo texto, imagens, áudio e vídeo; Entropia e algoritmos de compressão sem perdas: Shannon-Fano, Huffman, Aritmética e LZW; Psicoacústica, métodos e normas de compressão de áudio, incluindo voz (ADPCM, vocoders) e áudio de alta-fidelidade (transformadas, MP3, AAC, MP4 e Dolby AC-3); Teoria rate-distortion, métodos de compressão de imagem (DPCM, transformadas DCT, waveletet Haar, quantificadores lineares e não-lineares); Normas internacionais JPEG e JPEG2000; Compressão de vídeo, redundância espacial e temporal, quantificação de vídeo para aplicações multimédia e de videoconferência; Normas MPEG.

- by Derek Abbott
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- Microelectronics, Wavelets, Denoising, Hilbert transform

Decision making both on individual and organizational level is always accompanied by the search of other's opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining and sentiment analysis are the formalization for studying and construing opinions and sentiments. The digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.

With the computational power available today, machine learning is becoming a
very active field finding its applications in our everyday life. One of its biggest
challenge is the classification task involving data representation (the preprocess-
ing part in a machine learning algorithm). In fact, classify linearly separable
data is easily done. The aim of the preprocesing part is to obtain well repre-
sented data by mapping raw data into a feature space where simple classifiers
can be used efficiently. For example, everything around audio processing uses
MFCC until now. This toolbox gives the basic tools for audio representation
using the C++ programming language by providing an implementation of the
Scattering Network [4] which brings a new and powerful solution for these tasks.
The toolkit of reference in scattering analysis is the SCATNET from Mallat et al.
1
. This tool is an attempt to have some of the scatnet features more tractable
in large dataset. Furthermore, the use of this toolbox is not limited to ma-
chine learning preprocessing. It can also be used for more advanced biological
analysis such as animal communication behaviours analysis or any biological
study related to signal analysis. One motivation for this work is the collabora-
tion between DI ENS and the university of Toulon through the SABIOB Scaled
Acoustic project.[15] [14]. This toolbox gives out of the box executables that can
be used by simple bash commands. Examples are given in the README file.
Finally, for each presented algorithm, a graph is provided in order to summarize
how the computation is done in this toolbox.

- by Randall BALESTRIERO
- •
- Computer Engineering, Bioacoustics, Machine Learning, Signal Processing

We propose a form of semi-nonparametric regression based on wavelet analysis. Traditional time series methods usually involve either the time or the frequency domain, but wavelets can combine the information from both of these. While wavelet transforms are typically restricted to equally spaced observations an integer power of 2 in number, we show how to go beyond these constraints. We use our methods to construct \patios" for 21 important international commodity price series. These graph the magnitude of the variations in the series at di erent time scales for various subperiods of the full sample.

- by Russell Davidson
- •
- Computational Economics, Econometrics, Time Series, Wavelets
- by taibi hebib
- •
- Wavelets
- by Bijan Najafi
- •
- Algorithms, Geriatrics, Biomedical Engineering, Decomposition

The trend for coloring gray scale mages becomes very interesting research
point nowadays since it is utilized to increase the visual appeal of images such as old
black and white photos, movies or scientific illustrations, and different techniques for
doing that exist in the literature. There are many traditional ways for coloring like
hand-coloring and pseudocoloring, but the trend for automatic computer based
coloring is an important goal for many researchers in this field. From our side of view
all these trials are still depend on human interaction in the coloring process.
In this thesis, we propose a new system for automatic gray image coloring. The
proposed system is constructed of texture based recognition system that recognizes the
objects in textural images like natural scenes, and then retrieve their actual colors from
a previous knowledge about their classes. This procedure is a trial of simulating the
human vision in this area.
The proposed Texture Recognition based Image Coloring System (TRICKS) is
composite of three stages; segmentation stage to segment the image into different
regions according to their textural characteristics, classification stage to classify or
recognize each region to be one of a predefined set of texture classes. For each class, a
hue color is attached which is transferred to the regions of this class in the final stage,
the coloring stage.
This thesis is a research work for studying different gray image features, segmentation
and classification techniques, and different color modes to select the suitable
techniques for achieving the best coloring results.

- by DrNoura Semary
- •
- Wavelets, Texture Classification, Colorization

a b s t r a c t a r t i c l e i n f o Available online xxxx

- by Elif Tülay
- •
- Psychophysiology, Brain and Cognitive Development, Wavelets, Literature Review

In the present technical report the Discrete Wavelet Transform is introduced. The z- transform and the discrete Fourier transform along with their properties are first reviewed. Then the up-sampling and down-sampling processes are described. Subband transforms, two-channel analysis/synthesis filter banks and quadrature mirror filters follow. Finally, the discrete wavelet transform is introduced and its subband implementation is described.

- by Athanassios Skodras
- •
- Wavelets, Fourier Analysis, Subband Analysis

The present contribution presents a review of the research on the use of wavelets as a medium of fault detection and fault tolerant control of induction machines. Modeling of induction motor in the stator short winding and stator open winding faults has been illustrated. The authors provide comprehensive information about the wavelet application to fault diagnosis, including a summary of wavelet types (continuous and discrete), faults, methods and their validation in the diagnosis and frequency characteristics components of healthy and faulty induction motors. Inverter faults and artificial intelligence methods used for fault diagnosis are reviewed in this paper. Case studies using stator current sensing, DC voltage sensor and the gate drive signal for fault detection of an induction motor are also presented. Finally, a case study of inverter fault detection is illustrated experimentally using an induction motor drive controlled by the Texas Instrument TMS 320F28335 DSP.

En este trabajo, se desarrolló una interfaz en MatLab preparada para cuantificar de manera sistemática el desempeño de distintos algoritmos para reconstruir imágenes médicas obtenidas por ultrasonido. Los filtros seleccionados utilizan umbrales duros y blandos aplicados a los coeficientes wavelet de la imagen exportada del ecógrafo. Las transformadas elegidas son transformadas discreta wavelet (DWT) [26], [59], transformadas Daubechies wavelets simétricas (SDW) [54], [55], onditas complejas de doble árbol (DT-CWT) [47], [66], phaselet [28], [29] y curvelet [15][81]. Según el estado del arte actual, estas transformadas corresponden al conjunto de las que obtienen mejores resultados de reducción de ruido tipo speckle.
La plataforma integra distintas métricas objetivas que utilizan referencia externa (pico de relación señal ruido - PSNR), que utilizan referencia reducida (ganancia de contraste – CG) y que no necesitan referencia alguna (resolución lateral - RL, resolución axial - RA). Así mismo, la misma fue desarrollada para cuantificar estás métricas, no solo sobre la imagen total, sino también en entornos definidos por el usuario, variando posición y cantidad de pixeles.
En este trabajo, se propone un método consistente que implica obtener la imagen mediante la captura de un fantoma conocido (en este caso, un fantoma multipropósito comercial), con zonas de distintas impedancias y tamaños. De esta manera, generar la imagen utilizada como referencia a partir de las especificaciones técnicas brindadas por el fabricante.
Se realizaron pruebas sobre imágenes obtenidas a partir de la captura un fantoma Gammex 403 GS LE por un ecógrafo Esaote MyLabTM 25, aplicando umbrales duros y blandos a los coeficientes wavelet. Se compararon los distintos descriptores en entornos de objetivos infinitesimales y quistes de distinta impedancia. A partir de estas pruebas, se puede concluir que los valores de mejora sobre la PSNR no siempre reflejan una mejora en otros índices importantes.

- by Martin Baggio
- •
- Medical Imaging, Signal Processing, Ultrasound Imaging, Digital Signal Processing

Wavelets have been favorably applied in almost all aspects of digital wireless communication systems including data compression, source and channel coding, signal denoising, channel modeling and design of transceivers. The main property of wavelets in these applications is in their flexibility and ability to characterize signals accurately. In this paper recent trends and developments in the use of wavelets in wireless communications are reviewed. Major applications of wavelets in wireless channel modeling, interference mitigation, denoising, OFDM modulation, multiple access, Ultra Wideband communications, cognitive radio and wireless networks are surveyed. The confluence of information and communication technologies and the possibility of ubiquitous connectivity have posed a challenge to developing technologies and architectures capable of handling large volumes of data under severe resource constraints such as power and bandwidth. Wavelets are uniquely qualified to address this challenge. The flexibility and adaptation provided by wavelets have made wavelet technology a strong candidate for future wireless communication.

- by Mohamed Belahreche
- •
- Digital Watermarking, Wavelets, Image Watermarking
- by Ilya (w-495) Nikitin
- •
- Image Processing, Video Art, Soft Computing, Data Compression

The paper proposes a new image encryption scheme based on chaotic encryption. It provides a fast encryption algorithm based on coupled chaotic map. The image is encrypted using a pseudorandom key stream generator. The image is partially encrypted by selecting most important components of image. To obtain most important components of an image, discrete wavelet transform is applied.

Uma sinopse sobre séries ortogonais generalizadas (e.g. Legendre-Fourier), com ênfase na representação trigonométrica de Fourier, é introduzida. A apresentação envolve o fenômeno de Gibbs, e critérios para a convergência de séries (condições de Dirichlet, teorema de Fourier, teorema de Fejér). Implicações do “Reino de Fourier” na Engenharia Acústica: consonâncias & dissonâncias, instrumentos musicais. Apresenta-se a teoria dos tapers de Tukey, a janela de Lanczos e o uso da série de Fourier para modelar fractais determinísticos. A passagem para o contínuo conduz à transformada de Fourier, cujas propriedades são revisadas. O princípio da incerteza de Gabor-Heisenberg é conectado à teoria de Fourier. Cálculos computacionais do espectro conduzem à transformada discreta de Fourier (DFT) e mecanismos para o cálculo eficiente do espectro (algoritmos rápidos). Os resultados de Heidman sobre complexidade multiplicativa para a DFT são apresentados. A análise espectral clássica evolui: a análise moderna com base em wavelets funciona ligada à abordagem Fourier. O teorema da amostragem é discutido, com uma demonstração do tipo “viva Fourier”, assim como o teorema 2BT sobre dimensionalidade de sinais. Aplicações modernas na descontaminação de sinais são argumentadas sob enfoque pragmático. A adaptação da análise de Fourier para sinais estocásticos conduz às séries estocásticas de Fourier, e expansões de Kahunen-Loève. Até a modelagem não-linear para sistemas com base em séries de Volterra é apresentada. Por fim, o reino de Fourier conquista definitivamente o mundo finito e digital, migrando para a transformada de Fourier de corpo finito (transformada de Galois-Fourier).

- by Meinard Müller and +1
- •
- Signal Processing, Digital Signal Processing, Fourier and Wavelet Processing, Wavelets

The aim of this study was investigate noises and interferences which disturb the surface electromyography signal (sEMG). It was shown that the noises and interferences are caused by various sources. Sources of interference and noise can be divided into internal and external. The internal noise are caused by the electrodes, EMG signals of other muscles; noise associated with the functioning of other organs such as the heart or stomach. The external noses are due to electrical environment the most prominent of which is the direct interference of the power hum, produced by the incorrect grounding of other devices and electro motors. The block diagram of the noise sources was developed and with accordance with the diagram EMG signal was simulated. Denosing of simulated EMG signal was fulfilled by different wavelets and compare with digital filtering. The smallest error was observed in the case when using wavelet db4 of level 6.

- by Vladimir Bonilla and +2
- •
- Digital Signal Processing, Fourier and Wavelet Processing, Wavelets, EMG

Identifying spatio-temporal synchrony in a complex, interacting and oscillatory coupled-system is a challenge. In particular, the characterization of statistical relationships between environmental or biophysical variables with the multivariate data of pandemic is a difficult process because of the intrinsic variability and non-stationary nature of the time-series in space and time. This paper presents a methodology to address these issues by examining the bivariate relationship between Covid-19 and temperature time-series in the time-localized frequency domain by using Singular Value Decomposition (SVD) and continuous cross-wavelet analysis. First, the dominant spatio-temporal trends are derived by using the eigen decomposition of SVD. The Covid-19 incidence data and the temperature data of the corresponding period are transformed into significant eigen-state vectors for each spatial unit. The Morlet Wavelet transformation is performed to analyse and compare the frequency structure of the dominant trends derived by the SVD. The result provides cross-wavelet transform and wavelet coherence measures in the ranges of time period for the corresponding spatial units. Additionally, wavelet power spectrum and paired wavelet coherence statistics and phase difference are estimated. The result suggests statistically significant coherency at various frequencies providing insight into spatio-temporal dynamics. Moreover, it provides information about the complex conjugate dynamic relationships in terms phases and phase differences.

- by Sergio Bravo-Solorio
- •
- Image Processing, Soft Computing, Data Compression, Machine Vision

A methodological analyze of a time series issued from several space geodesy techniques (Doris stations position, mean sea level at global and local scales) is performed trough this thesis.
This study concerns, in the first step, an comparative analyze of the time series of position of six DORIS stations issued from three international analysis centers (Institute of Astronomy, Russian Academy of Sciences, Russia /INA, Institut Géographique National, IGN France; and CNES/ CLS Centre d’Analyse, France /LCA), using the wavelet spectral technique for the determination of the correlation between the different analysis centers. The obtained results confirm the strong correlation between the solutions of the two analysis centers IGN and INA.
To highlight the efficiency of the technique used, an application using data from satellite altimetry is steadily successfully performed for showing the strong correlation between the variations of the global mean sea level and the fluctuations of the ENSO (El Niño Southern Oscillation) phenomena as well as their relative phases in the time – frequency space.
As a similar technique to the wavelet analysis, the SSA (Singular Spectrum Analysis) is applied for the time series of the mean sea and ocean levels. The trends extracted well confirm the rise of the mean sea and oceans levels, and therefore these trends highlight the strong regional signature.
Key words: Time series, Position of stations, sea level, wavelet, correlation, SSA.

- by taibi hebib
- •
- Wavelets