People Recognition by RGB and NIR Analysis from Digital Image Database Using Cross-Correlation and Wavelets

This document presents a framework for recognizing people by palm vein distribution analysis using cross-correlation based signatures to obtain descriptors. Haar wavelets are useful in reducing the number of features while maintaining high recognition rates. This experiment achieved 97.5% of individuals classified correctly with two levels of Haar wavelets. This study used twelve-version of RGB and NIR (near infrared) wavelength images per individual. One hundred people were studied;therefore 4,800 instances compose the complete database. A Multilayer Perceptron (MLP) was trained to improve the recognition rate in a k-fold cross-validation test with k = 10. Classification results using MLP neural network were obtained using Weka (open source machine learning software).

An Improved r-Adaptive Galerkin Boundary Element Method Based on Unbalanced Haar Wavelets

An r-adaptive boundary element method（BEM） based on unbalanced Haar wavelets（UBHWs） is developed for solving 2D Laplace equations in which the Galerkin method is used to discretize boundary integral equations.To accelerate the convergence of the adaptive process,the grading function and optimization iteration methods are successively employed.Numerical results of two representative examples clearly show that,first,the combined iteration method can accelerate the convergence;moreover,by using UBHWs,the memory usage for storing the system matrix of the r-adaptive BEM can be reduced by a factor of about 100 for problems with more than 15 thousand unknowns,while the error and convergence property of the original BEM can be retained.

Least square method based on Haar wavelet to solve multi-dimensional stochastic Ito-Volterra integral equations

This paper proposes a method combining blue the Haar wavelet and the least square to solve the multi-dimensional stochastic Ito-Volterra integral equation.This approach is to transform stochastic integral equations into a system of algebraic equations.Meanwhile,the error analysis is proven.Finally,the effectiveness of the approach is verified by two numerical examples.

BLIND SYMBOL RATE ESTIMATION OF SATELLITE COMMUNICATION SIGNAL BY HAAR WAVELET TRANSFORM

As the raised cosine shaping filter is often employed in practical satellite communication system,the envelope fluctuation at the symbol transition point is decreased which leads to the failure of the common wavelet algorithm under low SNR.Accordingly,a method of blind symbol rate estimation using signal preprocessing and Haar wavelet is proposed in this paper.Firstly,the effect of filter shaping can be reduced by the signal preprocessing.Then,the optimal scale factor is searched and the signal is processed and analyzed by the Haar wavelet transform.Finally,the symbol rate line is extracted and a nonlinear filter method is inducted for improving the estimation performance.Theoretical analysis and computer simulation show the efficiency of the proposed algorithm under low SNR and small roll-off factor.

THREE-DIMENSIONAL ANALYSIS OF FUNCTIONALLY GRADED PLATE BASED ON THE HAAR WAVELET METHOD

A three-dimensional analysis of a simply-supported functionally graded rectangular plate with an arbitrary distribution of material properties is made using a simple and effective method based on the Haar wavelet. With good features in treating singularities, Haar series solution converges rapidly for arbitrary distributions, especially for the case where the material properties change rapidly in some regions. Through numerical examples the influences of the ratio of material constants on the top and bottom surfaces and different material gradient distributions on the structural response of the plate to mechanical stimuli are studied.

Medical Image Compression Based on Wavelets with Particle Swarm Optimization

This paper presents a novel method utilizing wavelets with particle swarm optimization(PSO)for medical image compression.Our method utilizes PSO to overcome the wavelets discontinuity which occurs when compressing images using thresholding.It transfers images into subband details and approximations using a modified Haar wavelet(MHW),and then applies a threshold.PSO is applied for selecting a particle assigned to the threshold values for the subbands.Nine positions assigned to particles values are used to represent population.Every particle updates its position depending on the global best position(gbest)(for all details subband)and local best position(pbest)(for a subband).The fitness value is developed to terminate PSO when the difference between two local best(pbest)successors is smaller than a prescribe value.The experiments are applied on five different medical image types,i.e.,MRI,CT,and X-ray.Results show that the proposed algorithm can be more preferably to compress medical images than other existing wavelets techniques from peak signal to noise ratio(PSNR)and compression ratio(CR)points of views.

Wireless Transmission Based Image Quality Analysis Using Uni-Level Haar Wavelet Transform

This article deals with picture excellence examination by different parameters utilizing uni-level Haar wavelet transmission in excess of remote channel. The quality is analyzed based on power. The goal is towards reducing absolute power assigned in favour of picture compression and communication, while power in favour of every bit is reserved at prearranged value. Two Power Algorithms were presented. The greatest iterative power control calculation and Minimum Power Adaptation Algorithm (MPAA) are proposed. Those algorithms methodology was utilized for improving the aggregate power dispensed for multimedia such as picture because of input compression and transmission focus towards a settled bit source mutilation. Simulations were performed utilizing Haar wavelet than Additive White Gaussian Ration (AWGN) channel. Different picture excellence parameters, for example, Peak Signal to Noise Ratio (PSNR), M-Normalized Cross-Correla- tion, Average Difference;Structural Content parameters, for example, Maximum Difference, Normalized Absolute Error, Elapsed Time, CPU time, demonstrate a improved presentation with MPAA, Maximum Power Adaptation Algorithms (MAPAA) instead of Conventional Power Adaptation Algorithm (CPAA).

Phase-domain blind estimation of symbol duration based on Haar wavelet transform

A phase-domain blind estimator of symbol duration based on Haar wavelet transform（HWT） is proposed.It can estimate the symbol duration of phase modulated signals,such as M-ary phase-shift keying（MPSK） signals and polyphase coded signals.The closed form of the spectrum of HWT is derived.Theoretical analysis shows the frequency of the first spectral peak is equal to the symbol rate,which is the reciprocal of symbol duration.Thus the symbol duration can be extracted from the spectrum.Subsequently,the optimum wavelet scale is determined according to the maximum output signal to noise ratio（OSNR） criterion.MAT-LAB simulations show that this algorithm can blindly estimate the symbol duration without any prior knowledge.This estimator need not estimate the carrier frequency and has the characteristics of low computation complexity and high accuracy.

Efficient Numerical Scheme for the Solution of HIV Infection CD4^(+)T-Cells Using Haar Wavelet Technique

In this paper,Haar collocation algorithmis developed for the solution of first-order ofHIV infection CD4^(+)T-Cells model.In this technique,the derivative in the nonlinear model is approximated by utilizing Haar functions.The value of the unknown function is obtained by the process of integration.Error estimation is also discussed,which aims to reduce the error of numerical solutions.The numerical results show that the method is simply applicable.The results are compared with Runge-Kutta technique,Bessel collocation technique,LADM-Pade and Galerkin technique available in the literature.The results show that the Haar technique is easy,precise and effective.

A novel Haar wavelet-based precoded BPSK-OFDM system with reduced PAPR

The performance of OFDM systems may be degraded when intersymbol interference （ISI） channels have spectral nulls. Recently, the precoded OFDM was proposed to combat this problem. However, due to inserting （M- K） zeros between each two sets of K consecutive information symbols, the average transmitting power of the precoded OFDM system reduces by 10log10（M/K） dB compared with the conventional OFDM system. Under the same points inverse fast Fourier transformation （IFFF）, the precoded OFDM system has a higher peak-to-average power ratio （PAPR） compared with the conventional OFDM system. This paper proposes a novel precoded BPSK-OFDM system based on Haar wavelet transformation. The Haar wavelet transformation operating decomposition over the vector information symbols produced by a precoder shows that half of the information symbols are zeros and the rest are either √2- or √2. Then, we have the peak power and PAPR reduced by 10log1002=3dB at most compared with the precoded OFDM system. Finally, we compare PAPR of the proposed OFDM system with the precoded OFDM and the conventional OFDM system.

Spectral Analysis and Validation of Parietal Signals for Different Arm Movements

Brain signal analysis plays a significant role in attaining data related to motor activities.The parietal region of the brain plays a vital role in muscular movements.This approach aims to demonstrate a unique technique to identify an ideal region of the human brain that generates signals responsible for muscular movements;perform statistical analysis to provide an absolute characterization of the signal and validate the obtained results using a prototype arm.This can enhance the practical implementation of these frequency extractions for future neuro-prosthetic applications and the characterization of neurological diseases like Parkinson’s disease(PD).To play out this handling method,electroencepha-logram(EEG)signals are gained while the subject is performing different wrist and elbow movements.Then,the frontal brain signals and just the parietal signals are separated from the obtained EEG signal by utilizing a band pass filter.Then,feature extraction is carried out using Fast Fourier Transform(FFT).Subse-quently,the extraction process is done by Daubechies(db4)and Haar wavelet(db1)in MATLAB and classified using the Levenberg-Marquardt Algorithm.The results of the frequency changes that occurred during various wrist move-ments in the parietal region are compared with the frequency changes that occurred in frontal EEG signals.This proposed algorithm also uses the deep learn-ing pattern analysis network to evaluate the matching sequence for each action that takes place.Maximum accuracy of 97.2%and maximum error range of 0.6684%are achieved during the analysis.Results of this research confirm that the Levenberg-Marquardt algorithm,along with the newly developed deep learn-ing hybrid PatternNet,provides a more accurate range of frequency changes than any other classifier used in previous works of literature.Based on the analysis,the peak-to-peak value is used to define the threshold for the prototype arm,which performs all the intended degrees of freedom(DOF),verifying the results.These results would aid the specialists in their decision-making by facilitating the ana-lysis and interpretation of brain signals in the field of neuroscience,specifically in tremor analysis in PD.

Outlier detection algorithm for satellite gravity gradiometry data using wavelet shrinkage de-noising

On the： basis of wavelet theory, we propose an outlier-detection algorithm for satellite gravity ometry by applying a wavelet-shrinkage-de-noising method to some simulation data with white noise and ers. The result Shows that this novel algorithm has a 97% success rate in outlier identification and that be efficiently used for pre-processing real satellite gravity gradiometry data.

WAVELET ANALYSIS OF MODULATED SIGNALS

The relationship between Haar wavelet decomposition coefficients and modulated signal parame-ters is discussed. A new modulation classification method is presented. The new method uses the amplitude, frequency and phase information derived from Haar wavelet decomposition as feature vectors to distinguish the modulation types of M-ary Frequency-Shift Keying (MFSK), M-ary Phase-Shift Keying (MPSK) and Quadrature Amplitude Modulation (QAM) modulation types. A parallel combined classifier is designed based on these feature vectors. The overall successful recognition rate of 92.4% can be achieved even at a low Sig-nal-to-Noise Ratio (SNR) of 5dB.

AN APPLICABLE APPROXIMATION METHOD AND ITS APPLICATION

In this work, by choosing an orthonormal basis for the Hilbert space L^2[0, 1], an approximation method for finding approximate solutions of the equation （I ＋ K）x = y is proposed, called Haar wavelet approximation method （HWAM）. To prove the applicabifity of the HWAM, a more general applicability theorem on an approximation method （AM） for an operator equation Ax = y is proved first. As an application, applicability of the HWAM is obtained. Fhrthermore, four steps to use the HWAM are listed and three numerical examples are given in order to illustrate the effectiveness of the method.

Reversible Data Hiding for Medical Images in Cloud Computing Environments Based on Chaotic Hénon Map

Reversible data hiding techniques are capable of reconstructing the original cover image from stego-images. Recently, many researchers have focused on reversible data hiding to protect intellectual property rights. In this paper, we combine reversible data hiding with the chaotic Henon map as an encryption technique to achieve an acceptable level of confidentiality in cloud computing environments. And, Haar digital wavelet transformation （HDWT） is also applied to convert an image from a spatial domain into a frequency domain. And then the decimal of coefficients and integer of high frequency band are modified for hiding secret bits. Finally, the modified coefficients are inversely transformed to stego-images.

Haar wavelet discretization method for free vibration study of laminated composite beam under generalized boundary conditions

Investigation on vibration of laminated composite beam(LCB)is an important issue owing to its wide use as fundamental component.In the present work,we study the free vibration of arbitrarily LCB with generalized elastic boundary condition(BC)by using Haar wavelet discretization method(HWDM).Timoshenko beam theory is utilized to model the free vibration of LCB.The LCB is first split into several segments,and then the displacement for each segment is obtained from the Haar wavelet series and their integral.Hamilton’s principle is applied to construct governing equations and the artificial spring boundary technique is adopted to obtain the general elastic boundary and continuity conditions at two ends of LCB.The vibration characteristics of beam with different fiber orientations and lamina numbers is investigated and its displacements are compared with those in previous works.Numerical results are shown graphically and demonstrate the validation of our method.

Analysis of Linear Time-varying Systems via Haar Wavelet

In this paper Haar wavelet integral operational matrices are introduced and then applied to analyse linear time varying systems. The method converts the original problem to solving linear algebraic equations. Hence, computational difficulties are considerably reduced. Based on the property of time frequency localization of Haar wavelet bases, the solution of a system includes both the frequency information and the time information. Other orthogonal functions do not have this property. An example is given, and the results are shown to be very accurate.

Haar Wavelet and Its Application in Optimal Control of Linear Time-invariant Systems

This paper uses Haar wavelet integral operational matrices to approximate the solution of the optimal control problem with quadratic performance measures. The method reduces the original problem to the solution of linear algebraic equations. Hence, the computational difficulties are considerably reduced. Since Haar wavelet bases cooperate time frequency localization, the system solution includes both frequency information and time information. Other orthogonal functions do not have this property. An example shows that the results are very accurate.

Analysis of Vibrational Properties of Horn-Shaped Magneto-Elastic Single-Walled Carbon Nanotube Mass Sensor Conveying Pulsating Viscous Fluid Using Haar Wavelet Technique

This research explores the dynamic behaviour of horn-shaped single-walled carbon nanotubes(HS-SWCNTs)conveying viscous nanofluid with pulsating the influence of a longitudinal magnetic field.The analysis utilizes Euler-Bernoulli beam model,considering the variable cross section,and incorporating Eringen’s nonlocal theory to formulate the governing partial differential equation of motion.The instability domain of HS-SWCNTs is estimated using Galerkin’s approach.Numerical analysis is performed using the Haar wavelet method.The critical buckling load obtained in this study is compared with previous research to validate the proposed model.The results highlight the effectiveness of the proposed model in assessing the vibrational characteristics of a complex multi-physics system involving HS-SWCNTs.Dispersion graphs and tables are presented to visualize the numerical findings pertaining to various system parameters,including the nonlocal parameter,magnetic flux,Knudsen number,and viscous factor.

Dyadic Bivariate Wavelet Multipliers in L^2（R@2）

The single 2 dilation wavelet multipliers in one-dimensional case and single A-dilation （where A is any expansive matrix with integer entries and [detA[ = 2） wavelet multipliers in twodimen- sional case were completely characterized by Wutam Consortium （1998） and Li Z., et al. （2010）. But there exist no results on multivariate wavelet multipliers corresponding to integer expansive dilation matrix with the absolute value of determinant not 2 in L^2（R^2）. In this paper, we choose 2I2 = （02 20 ） as the dilation matrix and consider the 212-dilation multivariate wavelet ψ = {ψ1, ψ2, ψ3 } （which is called a dyadic bivariate wavelet） multipliers. Here we call a measurable function family f ={fl, f2, f3} a dyadic bivariate wavelet multiplier if ψ1 = （F^-1（f1ψ1）,F^-1（f2ψ2）, F-l（f3ψ3）} is a dyadic bivariate wavelet for any dyadic bivariate wavelet ψ = {ψ1, ψ2, ψ3}, where f and F^- 1 denote the Fourier transform and the inverse transform of function f respectively. We study dyadic bivariate wavelet multipliers, and give some conditions for dyadic bivariate wavelet multipliers. We also give concrete forms of linear phases of dyadic MRA bivariate wavelets.