Multi-resolution Wavelet Transform Research Articles

Dither Filtering of Real RLG Signal Using Wavelet Transforms

Ring Laser Gyroscopes (RLGs) are widely used in many airborne and navigation systems for accurate measurement of the true rotation of the body movement. But the RLG’s suffer a serious problem at low frequencies known as Lock–in frequency. To avoid lock-in problem, the RLG is vibrated mechanically to a high frequency which is known as Dithering. In order to get the true rotation of the body the dither signal has to be removed. Single stage, multistage and multirate filters are suggested to remove the dither signal. These filters have the disadvantage that either the FIR filter length is too large or the phase characteristics are nonlinear. In this work, multiresolution Wavelet Transform (WT) techniques are used to remove the dither signal. Five level multiresolution analysis is carried out with various types of wavelets like Discrete Meyer and Daubechies 45 (db45) etc. With none of the standard wavelets, the original and reconstructed signals are matched. A new wavelet is designed to remove the dither signal. The required signal can be reconstructed back using the approximation coefficients at level 5. The dither signal is attenuated by 107.0 dB, and the phase characteristics are found to be linear in the pass band. The computational complexity is also less compared to the three stage combined filter reported earlier.

Hybrid Wavelet-ARX approach for modeling association between rainfall and meteorological forcings at river basin scale

Interaction between meteorological and hydrologic processes is challenging to model owing to their high spatio-temporal variability. The understanding of their associations can help to ensure future fresh water security with changing climate. In this study, due to continuously evolving nature of these interactions, the hydrological and meteorological variables are studied on wavelet component level. Multi-Resolution Stationary Wavelet Transformation (MRSWT) is used to transform the independent (climatic variable) and dependent (hydrological variable) time series into their components. The components of the dependent time series are modeled using a kernel-based auto-regressive (AR) model for separating their memory part. The residuals are hypothesized to be the effect of interaction of predictor variables and thus, are modeled using the MRSWT components of meteorological variables in an auto-regressive model with exogenous inputs (ARX). Finally, the predicted residuals (effect of climatic variables) are added to the component estimated by kernel-based AR estimator (memory of dependent series components) to obtain the predicted components of the dependent hydrologic variable, which are then inverse-transformed to obtain the predicted dependent hydrologic variable. The developed hybrid Wavelet-ARX is found to capture the information about relationship between synthetically generated data better than a simple ARX model. The model is then applied to predict total monthly rainfall over Upper Mahanadi Basin and is found to effectively extract the information from the poorly associated hydro meteorological variables. While the potential of Wavelet-ARX is found to be impressive for hydro meteorological applications, additionally, discarding some climatic inputs on the basis of their relative importance may lead to better prediction by the developed model. The developed model is suitable for extracting climatic forcings and is desirable in a changing climate.

Design and Implementation of an Ultra-Low Resource Electrodermal Activity Sensor for Wearable Applications ‡

While modern low-power microcontrollers are a cornerstone of wearable physiological sensors, their limited on-chip storage typically makes peripheral storage devices a requirement for long-term physiological sensing—significantly increasing both size and power consumption. Here, a wearable biosensor system capable of long-term recording of physiological signals using a single, 64 kB microcontroller to minimize sensor size and improve energy performance is described. Electrodermal (EDA) signals were sampled and compressed using a multiresolution wavelet transformation to achieve long-term storage within the limited memory of a 16-bit microcontroller. The distortion of the compressed signal and errors in extracting common EDA features is evaluated across 253 independent EDA signals acquired from human volunteers. At a compression ratio (CR) of 23.3×, the root mean square error (RMSErr) is below 0.016 S and the percent root-mean-square difference (PRD) is below 1%. Tonic EDA features are preserved at a CR = 23.3× while phasic EDA features are more prone to reconstruction errors at CRs > 8.8×. This compression method is shown to be competitive with other compressive sensing-based approaches for EDA measurement while enabling on-board access to raw EDA data and efficient signal reconstructions. The system and compression method provided improves the functionality of low-resource microcontrollers by limiting the need for external memory devices and wireless connectivity to advance the miniaturization of wearable biosensors for mobile applications.

ECG BEAT DETECTION AND CLASSIFICATION SYSTEM USING WAVELET TRANSFORM AND ONLINE SEQUENTIAL ELM

Electrocardiogram (ECG) signals consist of data containing measurements of electrical activity in the heartbeats. These signals include relevant information used to detect abnormalities such as arrhythmia. In this study, a recognition system is proposed for detection and classification of heartbeats in ECG signals. Heartbeats in the ECG data were detected by using the wavelet transform (WT) method and these beats are segmented with determined periods. For obtaining distinctive features from the beats, multi-resolution WT is applied to these segmented signals, and wavelet coefficients are obtained from different frequency levels. Feature vectors are generated on these coefficients by using various statistical methods. The proposed recognition system is trained on feature vectors by using the Online Sequential Extreme Learning Machine (OSELM) classifier during the learning phase to automatically recognize the signals. Five different beat types were obtained from the MIT-BIH arrhythmia dataset. The multi-class dataset that includes five classes and the binary-class dataset that includes two classes were created among these beat types. Performance tests of the proposed wavelet-based-OSELM (W-OSELM) method were realized with these two datasets. The proposed recognition system provided 97.29% correct beat detection rate from raw ECG signals. The classification accuracy is 99.44% for the binary-class dataset and 98.51% for the multi-class dataset. Furthermore, the proposed classifier has shown very fast recognition performance on ECG signals.

Myographic Interference Filtering from ECG Signals Using Multiresolution Wavelet Transform

Myographic Interference Filtering from ECG Signals Using Multiresolution Wavelet Transform

Delineation of Built-Up Areas from Very High-Resolution Satellite Imagery Using Multi-Scale Textures and Spatial Dependence

Very high spatial resolution (VHR) satellite images possess several advantages in terms of describing the details of ground targets. Extracting built-up areas from VHR images has received increasing attention in practical applications, such as land use planning, urbanization monitoring, geographic information database update. In this study, a novel method is proposed for built-up area detection and delineation on VHR satellite images, using multi-resolution space-frequency analysis, spatial dependence modelling and cross-scale feature fusion. First, the image is decomposed by multi-resolution wavelet transformation, and then the high-frequency information at different levels is employed to represent the multi-scale texture and structural characteristics of built-up areas. Subsequently, the local Getis-Ord statistic is introduced to model the spatial patterns of built-up area textures and structures by measuring the spatial dependence among frequency responses at different spatial positions. Finally, the saliency map of built-up areas is produced using a cross-scale feature fusion algorithm, followed by adaptive threshold segmentation to obtain the detection results. The experiments on ZY-3 and Quickbird datasets demonstrate the effectiveness and superiority of the proposed method through comparisons with existing algorithms.

Performance comparison of segmentation algorithms for hand gesture recognition

The gestures presented in diverse backgrounds have to be accurately processed and segmented, for it to be classified precisely by the hand gesture recognition system. This study compares performance of the proposed Image Segmentation Algorithm with a standard Canny Edge Detection Algorithm by comparing the statistical values of the features obtained from the feature extraction stage, thus validating the importance of having a robust preprocessing stage for the hand gestures. The proposed algorithm uses Non-local Mean filter for noise removal and then an improved Global Swarm Optimization based Canny edge detection for extracting the edges. Features are extracted using two dimensional Multi-resolution Discrete Wavelet Transform (2D-DWT) combined with Gray-level Co-occurrence Matrix. The efficiency of the proposed Image Segmentation Algorithm is evaluated using Radial Basis Function Neural Network as the classifier.

Recaptured Image Forensics Algorithm Based on Multi-Resolution Wavelet Transformation and Noise Analysis

With the rapid development of digital cameras and smart phones, the image identification system in current times will be of a great impact. This will cause the form of image information to increase serious security issues. Especially, the emergence of the recaptured image makes conventional digital image forensics algorithm invalid. Therefore, a new image forensics algorithm is urgently needed to identify the recaptured image. In this paper, a new recaptured image identifying algorithm is put forward based on wavelet transformation and noise analysis by analyzing the differences between the real and recaptured images generated in the imaging process. First, the proposed algorithm extracts mean value, variance and skewness as wavelet characteristic from the high-frequency images and low-frequency images by wavelet transformation. Meanwhile, the proposed algorithm analyzes the noise image by means of local binary pattern to extract noise characteristic. Finally, the support vector machine is applied to classify the recaptured image with wavelet characteristics and noise characteristics. The results show the presented method can not only identify the recaptured image obtained from different media but also have better identification rate, and the dimension of the characteristic vector is also lower than those obtained by other algorithms.

Multispectral and Panchromatic Image Fusion using Empirical Wavelet Transform

Pan sharpening is the process of fusion of panchromatic and multispectral image to obtain an output image of high spatial and spectral resolution. It is very important for various remote sensing applications such as image segmentation studies, image classification, temporal change detection etc. The present work demonstrates the application of Empirical Wavelet Transform for the fusion of panchromatic image and multispectral image by simple average fusion rule. The Proposed method is experimented on panchromatic and multispectral images captured by high resolution earth observation satellites such as GeoEye-1, QuickBird, WorldView-2 and World View-3. The effectiveness of our proposed method is evaluated by visual perception and quantitative assessment measures. The experimental analysis shows that the proposed method performs comparable to the existing fusion algorithms such as Multi-resolution Singular Value Decomposition and Discrete Wavelet Transform.

Wavelet based feature extraction of voltage profile for online voltage stability assessment using RBF neural network

Online voltage stability assessment is one of the vital requirements for intricate electric power systems. Due to the restructuring and liberalization, modern power systems tend to operate close to their stability limits with small security margin. In such environment, online voltage stability evaluation plays a significant role in secure operation of power systems. This paper presents a new approach for estimating voltage stability margin VSM, based on application of wavelet feature extraction method to network voltage profile. Voltage profile is adopted as the original input data for VSM estimation, because it contains sufficient information concerning network topology, load level, load-generation patterns and all system controllers. In this approach, in order to provide high discrimination between network voltage profiles, Multi-Resolution Wavelet Transform (MRWT) is utilized to extract the features of voltage profiles. Also, in order to eliminate the redundant features, principle component analysis (PCA) is used to select the most relevant features extracted by MRWT. Radial Basis Function (RBF) neural network is adopted to estimate system VSM using the dominant extracted features of the voltage profile by MRWT and PCA. Using voltage profile as the original data makes the proposed approach capable of estimating system VSM in both static and quasi dynamic conditions. The proposed approach has been implemented in New England 39-bus test system with promising results demonstrating its effectiveness and applicability.

Wavelet-based regularization of the extracted topographic index from high-resolution topography for hydro-geomorphic applications

High-resolution topography, e.g. 1-m digital elevation model (DEM) from light detection and ranging (LiDAR), offers opportunity for accurate identification of topographic features of relevance for hydrologic and geomorphologic modelling. Yet, the computation of some derived topographic properties, such as the topographic index (TI), is characterized by daunting challenges that hamper the full exploration of topography-based models. Particular problems, for example, arise when a distributed (or semi-distributed) rainfall–runoff model is applied to high-resolution DEMs. Indeed, the characteristic dependency between landscape resolution and the computed TI distribution results in the formation of un-physical, unconnected saturated zones, which in turn cause unrealistic representations of rainfall–runoff dynamics. In this study, we present a methodology based on a multi-resolution wavelet transformation that, by means of a soft-thresholding scheme on the wavelet coefficients, filters the noise of high-resolution topography to construct regularized sets of locally smoother topography on which the TI is computed. While the methodology needs a somewhat arbitrary definition of the wavelet coefficients threshold, our study shows that when the information content (entropy) of the TI distribution is used as a filtering efficiency metric, a critical threshold automatically emerges in the landscape reconstruction. The methodology is demonstrated using 1-m LiDAR data for the Elder Creek River basin in California. While the proposed case study uses a TOPMODEL approach, the methodology can be extended to different topography-based models and is not limited to hydrological applications. Copyright © 2012 John Wiley & Sons, Ltd.

Identification of differentially expressed genes using multi-resolution wavelet transformation analysis combined with SAM

Although many statistical methods have been proposed for identifying differentially expressed genes, the optimal approach has still not been resolved. Therefore, it is necessary to develop more efficient methods of finding differentially expressed genes while accounting for noise and false discovery rate (FDR). We propose a method based on multi-resolution wavelet transformation analysis combined with SAM for identifying differentially expressed genes by adjusting the Δ and computing the FDR. This method was applied to a microarray expression dataset from adenoma patients and normal subjects. The number of differentially expressed genes gradually reduced with an increasing Δ value, and the FDR was reduced after wavelet transformation. At a given Δ value, the FDR was also reduced before and after wavelet transformation. In conclusion, a greater number and quality of differentially expressed genes were detected using the method when compared to non-transformed data, and the FDRs were notably more controlled and reduced.

MC CDMA Papr Reduction Techniques Using Discrete Transforms And Companding

High Peak to Average Power Ratio (PAPR) of the transmitted signal is a serious problem in multicarrier modulation systems. In this paper a new technique for reduction in PAPR of the Multicarrier Code Division Multiple Access (MC CDMA) signals based on combining the Discrete Transform either Discrete Cosine Transform (DCT) or multi-resolution Discrete Wavelet Transform (DWT) with companding is proposed. It is analyzed and implemented using MATLAB. Simulation results of reduction in PAPR and power Spectral Density (PSD) of the MC CDMA with companding and without companding are compared with the MC CDMA with DCT and companding, DWT and companding systems. The new technique proposed is to make use of multi-resolution DWT in combination with companding in order to achieve a very substantial reduction in PAPR of the MC CDMA signal.

Color texture image segmentation based on neutrosophic set and wavelet transformation

Efficient and effective image segmentation is an important task in computer vision and pattern recognition. Since fully automatic image segmentation is usually very hard for natural images, interactive schemes with a few simple user inputs are good solutions. In this paper, we propose a fully automatic new approach for color texture image segmentation based on neutrosophic set (NS) and multiresolution wavelet transformation. It aims to segment the natural scene images, in which the color and texture of each region does not have uniform statistical characteristics. The proposed approach combines color information with the texture information on NS and wavelet domain for segmentation. At first, it transforms each color channel and the texture information of the input image into the NS domain independently. The entropy is defined and employed to evaluate the indeterminacy of the image in NS domain. Two operations, α-mean and β-enhancement operations are proposed to reduce the indeterminacy. Finally, the proposed method is employed to perform image segmentation using a γ- K-means clustering. The determination of the cluster number K is carried out with cluster validity analysis. Two different segmentation evaluation criterions were used to determine the segmentations quality. Experiments are conducted on a variety of images, and the results are compared with those new existing segmentation algorithm. The experimental results demonstrate that the proposed approach can segment the color images automatically and effectively.

Color Image Hiding In Cover Speech Signal By Using Multi-resolution Discrete Wavelet Transform

Color Image Hiding In Cover Speech Signal By Using Multi-resolution Discrete Wavelet Transform

Classification of Sleep Apnea through Sub-band Energy of Abdominal Effort Signal Using Wavelets + Neural Networks

Detection and classification of sleep apnea syndrome (SAS) is a critical problem. In this study an efficient method for classification sleep apnea through sub-band energy of abdominal effort using a particularly designed hybrid classifier as Wavelets + Neural Network is proposed. The Abdominal respiration signals were separated into spectral sub-band energy components with multi-resolution Discrete Wavelet Transform (DWT). The energy content of these spectral components was applied to the input of the artificial neural network (ANN). The ANN was configured to give three outputs dedicated to SAS cases; obstructive sleep apnea (OSA), central sleep apnea (CSA) and mixed sleep apnea (MSA). Through the network, satisfactory results that rewarding 85.62% mean accuracy in classifying SAS were obtained.

Improving biometric recognition accuracy and robustness using DWT and SVM watermarking

This paper presents a novel biometric watermarking algorithm for improving the recognition accuracy and protecting the face and fingerprint images from tampering. Multi-resolution Discrete Wavelet Transform is used for embedding the face image in a fingerprint image. An intelligent learning algorithm based on υ-Support Vector Machine (SVM) is introduced to enhance the quality of the extracted face image. The performance of the watermarking algorithm is experimentally validated using existing fingerprint and face recognition algorithms. The results show that the extracted fingerprint and face images are of high quality. The use of SVM enhances the performance of face recognition by at least 10% even when the watermarked image is subjected to certain geometric and frequency attacks such as scaling, cropping, compression and filtering.

A Robust Watermarking Method for Copyright Protection of Digital Images using Wavelet Transformation.

A multiresolution wavelet transformation based robust watermarking method is introduced. The coefficients of watermark are embedded into those of host image at different transformation level using a secret key. Watermark is extracted using the same key and by inverse transformation at each level. Finally, the watermark is estimated by taking mean value of the obtained watermarks. The introduced method is tested on gray and color images with added Gaussian, salt and pepper, Speckle, and JPEG noises. Correlation coefficients are used to compare the embedded and extracted watermarks. Comparison result with an earlier work is presented.

Enhanced Chemical Classification of Raman Images Using Multiresolution Wavelet Transformation

Multiresolution wavelet transformation (MWT) and block thresholding is used to effectively suppress both background and noise interference while minimally distorting Raman spectral features. The performance of MWT as a spectral pre-processing algorithm is demonstrated using both synthetic spectra and experimental hyper-spectral Raman images with large background and noise components. The results are quantified by comparing correlation coefficients of synthetic spectra with either the same or different backgrounds. The improved chemical imaging performance obtained using MWT is demonstrated by comparing principal component analysis (PCA) channel images and spectral angle mapping (SAM) classified images before and after MWT pre-processing.

DISCRIMINATION BETWEEN HEALTHY AND SICK CARDIAC AUTONOMIC NERVOUS SYSTEM BY DETRENDED HEART RATE VARIABILITY ANALYSIS

Multiresolution Wavelet Transform and Detrended Fluctuation Analysis have recently been proven to be excellent methods in the analysis of Heart Rate Variability and in distinguishing between healthy subjects and patients with various dysfunctions of the cardiac nervous system. We argue that it is possible to obtain a distinction between healthy subjects/patients of at least similar quality by, first, detrending the time-series of RR-intervals by subtracting a running average based on a local window with a length of around 32 data points, then calculating the standard deviation of the detrended time-series. The results presented here indicate that the analysis can be based on very short time-series of RR-data (7–8 minutes), which is a considerable improvement relative to 24-hour Holter recordings.