New Evaluation Method on Gear Dynamics Using Continuous and Discrete Wavelet Transforms

The aim of this study is to propose a new evaluation method of gear dynamics using the continuous and discrete wavelet transforms. The wavelet transform (WT) is a method for the time-frequency analysis of signals. In order to evaluate the difference in the gear dynamics due to the gear materials, which are sintered and steel ones, the dynamic characteristics of gears were measured using a power circulating gear testing machine. The gear dynamics were analyzed in a time-frequency domain by the continuous and discrete WTs. The new evaluation method using the WTs proposed in this paper was more useful compared with the conventional one to investigate the damping characteristic and the dynamic condition of the gear equipment.

As an important excitation source of gear dynamic problems, the time-varying friction of tooth surface is closely related to its topography. Considering the friction characteristics of tooth surface is of great significance to improve the calculation accuracy of gear dynamic characteristics. In order to solve the problem that the variation of tooth friction coefficient was simplified too much in the previous gear dynamics research, the time-varying tooth friction coefficient is obtained by fitting the results of twin-disc test in this research. To investigate the influence of tooth surface topography on the friction and dynamic characteristics of spur gears, the relationship between surface topography and friction coefficient under line contact condition is studied using twin-disc tester and analyzed by 3D topography parameters in ISO 25178. The time-varying friction coefficients of spur gears with different tooth surface topographies during meshing are fitted with the experimental results. The influence of time-varying friction coefficient caused by the tooth surface topography on the dynamic characteristics of spur gears under different operating conditions is examined by substituting the fitting curves of time-varying friction coefficient into the multi-degree-of-freedom dynamic model of spur gears. The results show that this influence is mainly embodied in the off-line-of-action direction, which is the direction of friction force acting on the tooth surface. The dynamic characteristics of gears with different surface topography are obviously different under various working conditions. The method presents in this paper simplifies the application of tooth contact analysis in the study of time-varying tooth friction characteristics, which will provide a new way for the gear dynamics research considering the tooth surface topography.

For an effective communication system whether indoor or outdoor, the most important concern is minimum noise. In this paper, an efficient noise reduction technique is presented using various wavelet transform techniques for indoor optical wireless communication system (IOWC). In IOWC system, Fluorescent Light Interference (FLI) is main source of noise. Here, in this paper three methods are used to reduce the effect of noise from a digital signal. These are Discrete Wavelet Transform (DWT), Stationary Wavelet transform (SWT) and Discrete Wavelet transform-Stationary Wavelet Transform (DWT-SWT). Through sub band coding in DWT the signal is decomposed into lower sub bands of high and low frequency respectively of unequal size; while in SWT the decomposed signal have sub bands of equal size. In DWT-SWT the high frequency components of both DWT and SWT are added. Using Pulse Position Modulation, the comparison between these three techniques is described here to enhance the overall performance of the IOWC system.

This paper deals with health monitoring and evaluation of dynamic characteristics in gear sets. The wavelet transform (WT) is the method used for the time-frequency analysis. The dynamic characteristics of both steel and sintered gears were measured using a power circulating gear testing machine and were analysed in the time-frequency domain using continuous and discrete WTs. Furthermore, health monitoring of gear sets was carried out and tooth surface failure by pitting was diagnosed using WTs. The vibration acceleration characteristics of the gearbox could be divided into two different behaviours above and below the tooth mesh frequency. A failed tooth could be found by a change in WT intensity of the vibration acceleration, taking the tooth meshing into consideration. Since the discrete WT is able to provide the signal with the component of tooth mesh frequency, it was found to be superior to the continuous WT. In the case of failure at the dedendum surface of a driven gear, the amplitude of the vibration acceleration with the component of tooth mesh frequency became larger as after meshing of the failed tooth. Therefore, not only the vibration of the gear sets but also the condition of tooth meshing would have to be taken into consideration to diagnose failed teeth.

The purpose of the current study was to assess the effectiveness of rehabilitation in patients after anterior cruciate ligament reconstruction (ACLR) using a wavelet analysis of the torque-time curve patterns of the extensors of the affected knee. The analysis aimed at the quantitative evaluation of irregularities in these torque-time patterns. The study involved a group of 22 men who had had ACL reconstruction. The torque-time characteristics were recorded 3, 6 and 12 months after the surgery by an isokinetic dynamometer. They were then examined using the orthogonal Daubechies 4 (Db 4) and biorthogonal Bior 3.1 wavelets. A statistical analysis of the results revealed significant differences in values of the high-frequency energy stored in the details of the signal from the dynamometer between the first and last measurements, both for the Db 4 ( p ≤ 0.023) and Bior 3.1 ( p ≤ 0.01) wavelets. These differences were found in 73% of patients whose curve patterns were analysed using the Db 4 wavelet and in 82% of patients in the case of the Bior 3.1 wavelet. The wavelet transform proved to be an effective research tool in the qualitative evaluation of irregularities occurring in the curve patterns of the torque generated by the extensors of the ACL reconstructed knee. The findings of the study suggest that time-frequency analyses of these characteristics can be of practical importance, as they help assess the state of the patient's knee joint and his progress in rehabilitation after ACLR.

The proliferation of digital communication has necessitated robust methods for secure data transmission.Audio steganography, the art of concealing secret information within audio signals, has emerged as a critical field for copyright protection and covert communication.This paper presents a comprehensive literature review of a multilayer security scheme that combines Advanced Encryption Standard (AES) with Wavelet Transform (Discrete Wavelet Transform -DWT, Lifting Wavelet Transform -LWT, or Integer Wavelet Transform -IWT).The proposed review focuses on a specific methodology where secret text is first encrypted using AES to ensure confidentiality and subsequently embedded into the wavelet-transformed coefficients of a cover audio file.This multilayer approach-cryptographic scrambling followed by steganographic embedding-aims to achieve high imperceptibility, robustness against signal processing attacks, and enhanced security.We analyze 15 significant papers, highlighting their methodologies, advantages, and limitations, to provide a roadmap for future research in high-security audio steganography.

Objectives: Image Denoising refers to the recovery of an image that has been corrupted by noise due to poor quality of image acquisition and transmission. Accordingly, there is a need to reduce the noise present in the image as a consequence of the denoised image formed. Methods/Statistical Analysis: This paper presents Image denoising using Wavelet transforms, Contourlet transforms and Block Matching Transforms governed by bivariate shrinkage (Bi-shrink) filter techniques. The Wavelet transform uses up-sampling, down-sampling, low pass filter and high pass filter to perform denoising operation, the Contourlet transform uses up-sampling, down-sampling, low pass filter and high pass filter and directional filter banks to perform denoising operation, the Block Matching Transform uses Haar Transforms, Discrete cosine transforms and Karhunen Loeve transform to perform denoising operation. Findings: The performance of wavelet transforms, Contourlet transforms and Block Matching Transforms are evaluated for Reference images (such as towers, shades and ruler images) corrupted by gaussian noise and salt and pepper noise, by computing two error metrics Peak Signal to Noise Ratio (PSNR) and Execution Time (ET) with help of shrinkage function. Programming these using MATLAB R2014a by exploring its wavelet transform, Contourlet transform, image processing and signal processing toolboxes and the values are presented in tabular forms and discussed in the section 6. In this paper the block matching haar discrete cosine transform is proposed for denoising of images (especially for those images possessing detailed textures) that works through haar transform and discrete cosine transform outstrips the basic transform discrete wavelet transform and semi translation invariant contourlet transform. For the images corrupted by Gaussian noise and denoised by the proposed transform outstrips the basic transform “Discrete Wavelet Transform by PSNR = 6.71 dB, ET = 25.89 sec” and “Semi Translation Invariant Contourlet Transforms by PSNR = 5.49 dB, ET = 5.89 sec”. For the images corrupted by Salt and Pepper noise and denoised by the proposed transform outstrips the basic transform “Discrete Wavelet Transform by PSNR = 21.15 dB, ET = 0.27 sec” and “Semi Translation Invariant Contourlet Transforms by PSNR = 20.05 dB, ET = 5.80 sec”. Application/Improvements: In this paper Block Matching Haar Discrete cosine transform is proposed to overcome the limitations of wavelet transforms and Contourlet transforms, hence to attain the trade-off between high peak signal to noise ratio and less execution time. Results and Discussion section illustrates the efficacy of the proposed transform in terms of peak signal to noise ratio, execution time and visual quality of images.

Purpose:To design and implement a GPU‐based discrete wavelet transformation (DWT) to be used in medical image reconstruction, image processing, and data compression. DWTs are widely used in medical physics, but the computation of DWTs is time consuming for large volumetric data. An efficient parallel implementation of DWTs is essential for many time sensitive applications, such as 4DCT.Methods:We choose Daubechies wavelet transformations as a benchmark, implementing both DWT and inverse DWT (IDWT). The reference CPU code is from “Numerical Recipes in C”. We implemented GPU‐based codes using C++ with CUDA 7.5. A GPU is specialized processors with a highly parallel structure originally designed for manipulating computer graphics. GPU computation is highly memory‐bounded. To optimize GPU memory access pattern and achieve best performance for transformation along Y‐direction, the data are transposed before and after the DWT (IDWT), which can sharply reduce computing efforts. Both CPU and GPU codes are unit‐tested and the result differences between two implementations are verified to be within rounding error. The hardware platform was a desktop with Intel Xeon E5‐1607 3.00 GHz CPU and NVIDIA Quadro K2000 GPU.Results:With the GPU implemented code, we process a medical CT image size of 512×512×256 in 0.5 seconds, which is 60 times faster than the CPU implementation. A naive non‐optimized GPU implementation by direct parallelization approaches only 10 times speedup for 2‐D and 3‐D DWT and IDWT. In comparison, the optimization of GPU memory access pattern can obtain an extra 6x speedup.Conclusion:We developed an efficient implementation of GPU‐based DWT/IDWT for medical image processing. To achieve the best performance of any medical imaging processing algorithm, developers should pay attention to memory access pattern optimization when implementing on GPU architecture.This project is supported by CPRIT grant under RP150485.

In this paper comparison of Discrete Wavelet and Discrete Cosine Transform for contrast enhancement of gray image is discussed and implemented using proposed mask. The technique converts the image into DCT domain and the DCT coefficients are modified using proposed mask then the enhanced image is reconstructed using inverse DCT. After comparing enhancement based on DCT with enhancement based on wavelet transform I found that the discrete cosine transform outperforms with better image quality and with highest PSNR value.

<p>The problem of the research is to find medical images of purity, high quality and free of impurities, which contributes to enabling doctors to obtain the results of analyzing the health status of each patient according to his disease data. Therefore, it was necessary to use discrete first chebysheve wavelets transform (DFCWT) technique in order to remove the associated impurities that appear in the medical images, and then analyze the results for all of the above, the algorithm DFCWT has been combined with and linking it to a neural network based on convolutional neural network (CNN) and this contributes to obtaining the results of analyzing image data with high accuracy and speed. The new algorithm proposed in this paper is based on deep learning finding the identification of kidney stones using DFCWT and the same process can be repeated on skin cancer, bones and fractures, processing by discrete first chebyshev wavelet transformation convolution neural network (DFCWTCNN).</p>

Asymptomatic circulating emboli can be detected by Doppler ultrasound. Embolic Doppler ultrasound signals are short duration transient like signals. The wavelet transform is an ideal method for analysis and detection of such signals by optimizing time-frequency resolution. We propose a detection system based on the discrete wavelet transform (DWT) and study some parameters, which might be useful for describing embolic signals (ES). We used a fast DWT algorithm based on the Daubechies eighth-order wavelet filters with eight scales. In order to evaluate feasibility of the DWT of ES, two independent data sets, each comprising of short segments containing an ES (N = 100), artifact (N = 100) or Doppler speckle (DS) (N = 100), were used. After applying the DWT to the data, several parameters were evaluated. The threshold values used for both data sets were optimized using the first data set. While the DWT coefficients resulting from artifacts dominantly appear at the higher scales (five, six, seven, and eight), the DWT coefficients at the lower scales (one, two, three, and four) are mainly dominated by ES and DS. The DWT is able to filter out most of the artifacts inherently during the transform process. For the first data set, 98 out of 100 ES were detected as ES. For the second data set, 95 out of 100 ES were detected as ES when the same threshold values were used. The algorithm was also tested with a third data set comprising 202 normal ES; 198 signals were detected as ES.

Advanced signal processing approaches such time-frequency analysis are widely used for online evaluation, damage detection, and wear state classification. The idea of this paper is to introduce a new methodology for online examination of wear phenomena in metallic structure by means of acoustic emission (AE), Short-Time Fourier Transform (STFT) and Wavelet Transform (WT). The proposed novel low-cost system is developed for analyzing and monitoring specific signals indicating tribological effects with focus on field programmable gate array (FPGA) implementation of discrete WT (DWT). In addition, experimental results obtained from each approach are given showing the success of the introduced approach.

An Image Resolution Enhancement Technique based on Interpolation of the high frequency sub-band of colour images obtained by Discrete Wavelet Transform and the input colour image is proposed in this paper. Interpolation determines the intermediate values on the basis of observed values. One of the commonly used interpolation technique is Bicubic Interpolation. The edges are enhanced by introducing an intermediate stage by using Stationary Wavelet Transform. It is designed to overcome the lack of Translation-Invariance of Discrete Wavelet Transform. This is widely used in Signal Denoising and Pattern Recognition. Discrete Wavelet Transform is applied in order to decompose an input colour image into different sub-bands. Then the high frequency sub-bands as well as the input colour image are interpolated separately. The interpolated high frequency sub-bands and the Stationary Wavelet Transform high frequency sub-bands have the same size which means they can be added with each other. The new corrected high frequency sub-bands can be interpolated further for higher enlargement. Then all these sub-bands are combined with interpolated input image for new high resolution image by using Inverse Discrete Wavelet Transform. This has been done by MATLAB. The Peak Signal-Noise Ratio was obtained upto 5dB greater than the conventional and state-of-art image resolution enhancement techniques.

Abstract: In the current research to determine the mineralization pattern and discuss the mineralization components, the information of position - scale domain of geochemical data has been analyzed. A new method is proposed based on coupling discrete wavelet transforms (DWT) and principal component analysis (PCA) for mineralization elements forecasting applications. The results of this study indicate the potential of DWT – PCA method for geochemical data processing. Wavelet transform (WT), as a multi-spectral analysis method, can decompose the spatial and temporal signals into different frequencies. The features of mineralization can be identified using the position - scale domain of geochemical data that may not be achievable in spatial domain. The geochemical data from the Dalli region have been processed in the spatial domain using PCA. The surface geochemical data of 30 elements have been transformed to position – scale domain using two-dimensional discrete wavelet transform (2DDWT). Wavelet functions (WFs) of Haar, Coiflet2, Biorthogonal3.3 and Symlet7 have been applied separately to decompose the geochemical data to high and low frequencies in one level. To obtain more accurate and complete information of mineralization, a new index has been presented based on wavelet coefficients. Based on this new index, significant results have been obtained by using PCA of the index. The coefficients distribution map (CDM) as a new exploratory criterion has been generated based on 2DDWT to show the geochemical distribution map (GDM). Finally, the results of WT have been compared with the results of spatial domain and the best method of wavelet for interpretation of geochemical data has been introduced. The results of geochemical data analysis by DWT – PCA approach have been confirmed by the exploratory drillings in the study area.

This paper is concerned with the analysis of the first (S1) and second (S2) heart sound of the Phonocardiogram signal (PCG) using Discrete Wavelet Transform (DWT) and Continuous Wavelet Transform (CWT). The second heart sound S2 consists of two major components A2 and P2. The time delay between them plays very vital role in medical diagnosis. DWT is used for denoising and finding the best split of A2 and P2 components of S2, where as, CWT is used to determine the number of frequency components of S1 and S2. Experiments are performed on normal and pathological PCG signals. Denoising and finding split between A2 and P2 is carried out using DWT. The frequency components of S1 and S2 of PCG are determined using CWT. Also split between A2 and P2 have been measured using CWT.