Multiresolution Wavelet Research Articles

Changing patterns of Asian currencies’ co-movement with the US dollar and the Chinese renminbi: Evidence from a wavelet multiresolution analysis

ABSTRACTWe extend the Frankel–Wei approach by using wavelet analysis to evaluate the relative importance of the dollar and the renminbi as anchor currencies at different time scales. We find that Asian currencies’ co-movement with the dollar weakened after the global financial crisis, while that with the renminbi strengthened particularly after China introduced a new exchange rate management system in 2015. The evidence suggests that emerging Asian economies have recently attached more importance to the renminbi as an anchor in exchange rate management.

Haar wavelets multi-resolution collocation analysis of unsteady inverse heat problems

ABSTRACT In this paper, two different Haar wavelet collocation multi-resolution procedures are proposed for linear partial differential equations (PDEs) with an unknown space-dependent heat source and an unknown solution. An appropriate transformation is used to convert a non-homogeneous PDE into a homogeneous form. Two techniques based on multi-resolution Haar wavelets collocation methods are proposed for numerical evaluation of the unknown space-dependent heat source. In homogeneous form, first-order finite-difference approximation is used to discretize the time derivative and finite Haar wavelets series is used for approximation of the space derivatives. Unlike other numerical methods, the proposed methods have well-conditioned Haar coefficient matrices and need not be supplemented by any regularization technique. Several numerical experiments are carried out to validate accuracy, simple applicability and well-conditioned behaviour of the Haar system coefficient matrices of the proposed algorithms.

Frequency-Division Combination Forecasting of Stock Market Based on Wavelet Multiresolution Analysis

Put forward a novel combination forecasting method (M-ARIMA-BP) that could make a more accurate and concise prediction of stock market based on wavelet multiresolution analysis. This innovative method operated by parsing of the low-frequency trend series and the high-frequency volatility series of stock market and gives an insight into the price series. Using the daily closing price data of SSE (Shanghai Stock Exchange) Composite Index and Shenzhen Component Index as samples, compared with conventional wavelet prediction model, ARIMA model, and BP neural network model, the empirical results show that the new algorithm M-ARIMA-BP can improve the accuracy of volatility forecasting and perform better in predicting prices rising and falling.

Elementary factorisation of Box spline subdivision

When a subdivision scheme is factorised into lifting steps, it admits an in–place and invertible implementation, and it can be the predictor of many multiresolution biorthogonal wavelet transforms. In the regular setting where the underlying lattice hierarchy is defined by ℤs and a dilation matrix M, such a factorisation should deal with every vertex of each subset in ℤs/Mℤs in the same way. We define a subdivision scheme which admits such a factorisation as being uniformly elementary factorable. We prove a necessary and sufficient condition on the directions of the Box spline and the arity of the subdivision for the scheme to admit such a factorisation, and recall some known keys to construct it in practice.

BUILT-UP AREA DETECTION FROM HIGH-RESOLUTION SATELLITE IMAGES USING MULTI-SCALE WAVELET TRANSFORM AND LOCAL SPATIAL STATISTICS

Abstract. Recently, built-up area detection from high-resolution satellite images (HRSI) has attracted increasing attention because HRSI can provide more detailed object information. In this paper, multi-resolution wavelet transform and local spatial autocorrelation statistic are introduced to model the spatial patterns of built-up areas. First, the input image is decomposed into high- and low-frequency subbands by wavelet transform at three levels. Then the high-frequency detail information in three directions (horizontal, vertical and diagonal) are extracted followed by a maximization operation to integrate the information in all directions. Afterward, a cross-scale operation is implemented to fuse different levels of information. Finally, local spatial autocorrelation statistic is introduced to enhance the saliency of built-up features and an adaptive threshold algorithm is used to achieve the detection of built-up areas. Experiments are conducted on ZY-3 and Quickbird panchromatic satellite images, and the results show that the proposed method is very effective for built-up area detection.

Determination of Flicker Contribution Level by Using Proposed Index

Depending on different load characteristics, various power quality disturbances such as sag-swell, harmonics, inter-harmonics, and flicker, appear in electric power systems. Among these power quality disturbances, the flicker is one of the critical power quality phenomenon due to the lack of source detection and disturbance responsibility sharing method. The measurement of the flicker level is defined in the IEC 61000-4-15 standard by a flicker meter. This meter deals only with voltage signals and is not sufficient to understand the contribution of the load and background power systems separately. Thus, this paper proposes new approaches to the evaluation of flicker characteristics using the real on-site measurements taken from two different iron and steel factories. The novelty of this paper is investigating the flicker characteristics in light of statistical methods, spectral and multi-resolution wavelet analysis, and the information theory based wavelet energy entropy analysis together, and proposing a new index to interpret the flicker responsibility of load and power system. This proposed index is called Flicker Contribution Ratio (FCR) and represents the percentage flicker disturbance responsibility of both load and power system.DOI: http://dx.doi.org/10.5755/j01.eie.24.2.20631

Wavelet multiresolution complex network for decoding brain fatigued behavior from P300 signals

Brain–computer interface (BCI) enables users to interact with the environment without relying on neural pathways and muscles. P300 based BCI systems have been extensively used to achieve human–machine interaction. However, the appearance of fatigue symptoms during operation process leads to the decline in classification accuracy of P300. Characterizing brain cognitive process underlying normal and fatigue conditions constitutes a problem of vital importance in the field of brain science. We in this paper propose a novel wavelet decomposition based complex network method to efficiently analyze the P300 signals recorded in the image stimulus test based on classical ‘Oddball’ paradigm. Initially, multichannel EEG signals are decomposed into wavelet coefficient series. Then we construct complex network by treating electrodes as nodes and determining the connections according to the 2-norm distances between wavelet coefficient series. The analysis of topological structure and statistical index indicates that the properties of brain network demonstrate significant distinctions between normal status and fatigue status. More specifically, the brain network reconfiguration in response to the cognitive task in fatigue status is reflected as the enhancement of the small-worldness.

Robust front-end for audio, visual and audio–visual speech classification

Fil: Terissi, Lucas Daniel. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Informacion y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Informacion y de Sistemas; Argentina

A unique feature extraction using MRDWT for automatic classification of abnormal heartbeat from ECG big data with Multilayered Probabilistic Neural Network classifier

This paper employs a novel adaptive feature extraction techniques of electrocardiogram (ECG) signal for detection of cardiac arrhythmias using multiresolution discrete wavelet transform from ECG big data. In this paper, five types ECG arrhythmias including normal beats have been classified. The MIT-BIH database of 48 patient records is utilized for detection and analysis of cardiac arrhythmias. Proposed feature extraction utilizes Daubechies as wavelet function and extracts 21 feature points which include the QRS complex of ECG signal. The Multilayered Probabilistic Neural Network (MPNN) classifier is proposed as the best-suited classifier for the proposed feature. Total 1700 ECG betas were tested using MPNN classifier and compared with other three classifiers Back Propagation (BPNN), Multilayered Perceptron (MLP) and Support Vector Machine (SVM). The system efficiency and performance have been evaluated using seven types of evaluation criteria: precision (PR), F-Score, positive predictivity (PP), sensitivity (SE), classification error rate (CER) and specificity (SP). The overall system accuracy, using MPNN technique utilizing the proposed feature, obtained is 99.53% whereas using BPNN, MLP and SVM provide 97.94%, 98.53%, and 99%. The processing time using MPNN classifier is only 3 s which show that the proposed techniques not only very accurate and efficient but also very quick.

Semi-Lagrangian particle methods for high-dimensional Vlasov–Poisson systems

This paper deals with the implementation of high order semi-Lagrangian particle methods to handle high dimensional Vlasov–Poisson systems. It is based on recent developments in the numerical analysis of particle methods and the paper focuses on specific algorithmic features to handle large dimensions. The methods are tested with uniform particle distributions in particular against a recent multi-resolution wavelet based method on a 4D plasma instability case and a 6D gravitational case. Conservation properties, accuracy and computational costs are monitored. The excellent accuracy/cost trade-off shown by the method opens new perspective for accurate simulations of high dimensional kinetic equations by particle methods.

Biorthogonal Bases of Spaces of an n-Separate Multiresolution Analysis and Multiwavelets

We construct biorthogonal bases of spaces of an n-separate multiresolution analysis and wavelets for n scaling functions. Fast algorithms are presented for finding the coefficients of expansions of functions in such bases.

An Online Energy Management Control for Hybrid Electric Vehicles Based on Neuro-Dynamic Programming

Hybrid electric vehicles are a compromise between traditional vehicles and pure electric vehicles and can be part of the solution to the energy shortage problem. Energy management strategies (EMSs) are highly related to energy utilization in HEVs’ fuel economy. In this research, we have employed a neuro-dynamic programming (NDP) method to simultaneously optimize fuel economy and battery state of charge (SOC). In this NDP method, the critic network is a multi-resolution wavelet neural network based on the Meyer wavelet function, and the action network is a conventional wavelet neural network based on the Morlet function. The weights and parameters of both networks are obtained by an algorithm of backpropagation type. The NDP-based EMS has been applied to a parallel HEV and compared with a previously reported NDP EMS and a stochastic dynamic programing-based method. Simulation results under ADVISOR2002 have shown that the proposed NDP approach achieves better performance than both the methods. These indicate that the proposed NDP EMS, and the CWNN and MRWNN, are effective in approximating a nonlinear system.

Denoising of image using bilateral filtering in multiresolution

One of the very efficient and resource conservative image processing methodology is with the help of bilateral filters. This technique filters the image without the help of edge smoothing but it does employs spatial averaging in a non-linear way. The filtering technique discussed above is very much dependent on the parameters of its filters. A very slight change in filter parameter values effects the outputs and results in a most drastic manner. In this paper, the author has worked on two contributions. In the applications concerning image denoising, the author has contributed in study of the parameter selection of bilateral filters which are optimal in nature. The contribution number two is about extending the present work i.e. extension of the filters which are bilateral in nature. In this process, the bilateral filtering of images is applied to the lower frequency sub-bands which is also known as approximation sub-band. This sub-band is obtained by using the wavelet transformations. Hence, a new framework for image denoising will be created which will be combination of multiresolution bilateral filtering and wavelets transformation techniques. As a matter of fact, this combination is efficient in contradicting noise from an image.

Spind: an R Package to Account for Spatial Autocorrelation in the Analysis of Lattice Data.

spind is an R package aiming to provide a useful toolkit to account for spatial dependence in the analysis of lattice data. Grid-based data sets in spatial modelling often exhibit spatial dependence, i.e. values sampled at nearby locations are more similar than those sampled further apart. spind methods, described here, take this kind of two-dimensional dependence into account and are sensitive to its variation across different spatial scales. Methods presented to account for spatial autocorrelation are based on the two fundamentally different approaches of generalised estimating equations as well as wavelet-revised methods. Both methods are extensions to generalised linear models. spind also provides functions for multi-model inference and scaling by wavelet multiresolution regression. Since model evaluation is essential for assessing prediction accuracy in species distribution modelling, spind additionally supplies users with spatial accuracy measures, i.e. measures that are sensitive to the spatial arrangement of the predictions.

SeeSway – A free web-based system for analysing and exploring standing balance data

Background and objectivesComputerised posturography can be used to assess standing balance, and can predict poor functional outcomes in many clinical populations. A key limitation is the disparate signal filtering and analysis techniques, with many methods requiring custom computer programs. This paper discusses the creation of a freely available web-based software program, SeeSway (www.rehabtools.org/seesway), which was designed to provide powerful tools for pre-processing, analysing and visualising standing balance data in an easy to use and platform independent website. MethodsSeeSway links an interactive web platform with file upload capability to software systems including LabVIEW, Matlab, Python and R to perform the data filtering, analysis and visualisation of standing balance data. Input data can consist of any signal that comprises an anterior-posterior and medial-lateral coordinate trace such as center of pressure or mass displacement. This allows it to be used with systems including criterion reference commercial force platforms and three dimensional motion analysis, smartphones, accelerometers and low-cost technology such as Nintendo Wii Balance Board and Microsoft Kinect. Filtering options include Butterworth, weighted and unweighted moving average, and discrete wavelet transforms. Analysis methods include standard techniques such as path length, amplitude, and root mean square in addition to less common but potentially promising methods such as sample entropy, detrended fluctuation analysis and multiresolution wavelet analysis. These data are visualised using scalograms, which chart the change in frequency content over time, scatterplots and standard line charts. This provides the user with a detailed understanding of their results, and how their different pre-processing and analysis method selections affect their findings. ResultsAn example of the data analysis techniques is provided in the paper, with graphical representation of how advanced analysis methods can better discriminate between someone with neurological impairment and a healthy control. ConclusionsThe goal of SeeSway is to provide a simple yet powerful educational and research tool to explore how standing balance is affected in aging and clinical populations.

A New Hybrid Active Noise Control System with Convex Combination of Time and Frequency Domain Filtered-X LMS Algorithms

A conventional single-channel feedforward active noise control (ANC) system encounters noise from multiple sources. In many cases, there is a reference signal that is independently available which is correlated with the ambient noise. However, in some situations, a portion of the noise generated is uncorrelated with the reference signal. In this paper, a new hybrid ANC (HANC) system is proposed using a convex combination of time and frequency domain Filtered-X LMS (FXLMS) algorithms. The feedforward part uses a time domain FXLMS (TDFXLMS) algorithm to control disturbances that are correlated with the reference noise and the feedback part uses a multiresolution analysis-based frequency domain wavelet packet FXLMS (WPFXLMS) algorithm to control the uncorrelated disturbances encountered during the operation of an ANC system. An extra adaptive filter is used to smoothen the error signal in the HANC system. An effort is made to exploit the benefits of the structural design and time–frequency domain signal processing techniques for active control of disturbances that are both correlated and uncorrelated with the reference noise. As a result of the convex combination of the TDFXLMS and WPFXLMS algorithms in a HANC system, the proposed method is successful in canceling the disturbances encountered by the ANC system from multiple noise sources.

Implementing Efficient Audio and Image Watermarking Using Multi-resolution Dual Wavelet and Fireflies Approach in Wireless Network

Watermarking is the process of embedding the particular information into the audio signal for managing the ownership copyrights through wireless network. During the watermarking process, the audio signal consumes high energy, conflicting problem of robustness and imperceptibility, signal to noise ratio, bit error rate and normalized correlation. To overcome these issues present in the audio watermarking process, novel wavelet decomposition and evolutionary algorithm is utilized. Initially the input information or message has been split into two and the spillted message is watermarked using the audio and the image in wireless network. The first half of the message is watermarked with the help of the image and the next half of the image is watermarked by audio. Initially the watermarked image is transferred into YIQ image, from the transferred image the scrambled image is generated with the help of the Hidden Markov tree counter let wavelet transform method for generating the watermarking image. Then the next part of information is watermarked by audio signal which is decomposed into various sub bands using the Multi-resolution complex dual tree wavelet method. From the decomposed audio signal, the message authentication code based watermarks have been embedded in the lower frequency coefficients. Then the embedded process is performed by using the Dead zone quantization process which is optimized with the help of the Fireflies algorithm for enhancing the quality of the watermarking process. Finally both watermarking process is embedded for improving the security to the information with efficient manner through wireless network. In addition the efficient watermarking process through wireless network reduces the various attacks while extracting the water marker. Thus the optimized wavelet and quantization process improves the image and audio watermarking through wireless network and efficiency of the proposed system is evaluated using the experimental results in terms of the Signal to noise ratio, normalized correlation and bit error rate.

Efficient Colorful Fourier Ptychographic Microscopy Reconstruction With Wavelet Fusion

Fourier ptychographic microscopy (FPM) is a recently developed microscope that offers wide field, high resolution, and quantitative phase imaging. Combining the concepts of ptychography, synthetic aperture, and phase retrieval, FPM overcomes the space-bandwidth-product (SBP) limit of the optical system. In FPM, a LED array is used as the illumination module and specimen images illuminated with angular varying illuminations are captured. These images are synthesized to recover the high SBP complex field of the specimen. There have been many improvements of FPM since its appearance and FPM is of great potential in the field of hematology and pathology. However, low efficiency in capturing data, especially in the situation of capturing all three color channels, limits the application of FPM. Although spectral multiplex strategy for FPM is developed, the reconstruction quality and speed is decreased for exchanging the capture efficiency. On the other hand, the reconstruction quality of FPM is significantly degraded by the imaging noise in the dark-field images because the high-frequency information in dark-field images is of low energy and contaminated by noise. In this paper, we propose an efficient colorful FPM reconstruction method using multi-resolution wavelet color fusion. We also propose an adaptive denoising method by analyzing the noise information of the dark-frame. Both simulation and experiment results are carried out to validate our methods. Results demonstrate that the imaging noise is suppressed and the colorful reconstruction is of high efficiency and quality.

Advanced Multiresolution Wavelet Based Wideband Spectrum Sensing Technique for Cognitive Radio

An advance multiresolution wavelet based approach for wideband spectrum sensing for cognitive radio system is proposed in this paper. Prime focus is made on the coarse detection part for interweaved system, in which unoccupied spectrum can be used efficiently by the cognitive users. Quick and immediate shifting over the sensed vacant channel is extremely vital and is a challenging task. To overcome this issue, fast and efficient spectrum sensing technique is proposed for cognitive radios by improvising the Discrete Wavelet Packet Transform (DWPT) for multiresolution interweaved systems. This proposed scheme not only increases the system speed but also reduces complexity. Simulation results are used to analyse the system performance and numerical analysis for computing system complexity.

Cognitive Privacy Middleware for Deep Learning Mashup in Environmental IoT

Data mashup is a Web technology that combines information from multiple sources into a single Web application. Mashup applications support new services, such as environmental monitoring. The different organizations utilize data mashup services to merge data sets from the different Internet of Multimedia Things (IoMT) context-based services in order to leverage the performance of their data analytics. However, mashup, different data sets from multiple sources, is a privacy hazard as it might reveal citizens specific behaviors in different regions. In this paper, we present our efforts to build a cognitive-based middleware for private data mashup (CMPM) to serve a centralized environmental monitoring service. The proposed middleware is equipped with concealment mechanisms to preserve the privacy of the merged data sets from multiple IoMT networks involved in the mashup application. In addition, we presented an IoT-enabled data mashup service, where the multimedia data are collected from the various IoMT platforms, and then fed into an environmental deep learning service in order to detect interesting patterns in hazardous areas. The viable features within each region were extracted using a multiresolution wavelet transform, and then fed into a discriminative classifier to extract various patterns. We also provide a scenario for IoMT-enabled data mashup service and experimentation results.