Papers by Keyword: Wavelet

Abstract: This paper shows the application of discrete wavelet transform in the analysis of ECG signals to detect R-peaks of the QRS complex. The proper analysis of the ECG signal is crucial to reveal the changes in the waveform to detect heart related diseases. The detection of the P-wave, T-wave, QRS complex is important and the wavelet transform offers a good possibility to recognize the abnormalities. Three different wavelets, Symlet, Coiflet and Daubechies are used for the detection and are compared to choose the most efficient one to identify the R-peaks. Multiresolution analysis (MRA) is used to determine the sufficient level of decomposition. MIT-BIH Arrhythmia Database is used as a dataset for the experiment.

Abstract: The detection of welding defects is becoming an important operation in the industry and the field of non-destructive testing. Among the most used techniques in the detection of weld defects, it is radiography. The radiographic images acquired are generally of low contrast, poor quality, and uneven lighting. Therefore, the detection of welding defects becomes a difficult task. In this work, a new hybrid approach based on the combination of several techniques is proposed. It consists of three stages: firstly, we define the region of interest (ROI). Secondly, a preprocessing operation based on an improved version of denoising by soft thresholding of wavelet coefficients and an optimized threshold is applied to improve the image quality (noise reduction, contrast enhancement). Thirdly, an enhanced Chan-Vese model is proposed to segment the denoised ROI region. This enhanced model is based on the choice of a cluster obtained by the Fuzzy C-Mean algorithm (FCM) as the initial contour. The proposed approach is applied to the various radiographic welding images from the GDxray database to extract the characteristics of the welding defects. The results obtained clearly show the effectiveness of the proposed approach compared to conventional techniques.

Abstract: Multi-sensor remote sensing data can significantly improve the interpretation and usage of large volume data sources. A combination of satellite Synthetic Aperture Radar (SAR) data and optical sensors enables the use of complementary features of the same image. In this paper, SAR data is injected into optical image using a combining fusion method based on the integration of wavelet Transform and IHS (Intensity, Hue, and Saturation) transform. Not only to preserve the spectral information of the original (MS) image, but also to maintain the spatial content of the high-resolution SAR image. Two data sets are used to evaluate the proposed fusion algorithm: one of them is Pleiades, Turkey and the other one is Boulder, Colorado, USA. The different fused outputs are compared using different image quality indices. Visual and statistical assessment of the fused outputs displays that the proposed approach has an effective translation from SAR to the optical image. Hence, enhances the SAR image interpretability.

Abstract: Based on the structure and working principle of DVC Drum, noise and vibration characteristics of mechanical noise, electromagnetic noise and radiated noise of DVC drum was analyzed comprehensively and systematically. FEA was done to DVC drum and four order natural vibration modes were got. Noise resource of DVC drum had been identified and recognized by the method of cepstrum analysis and it was consistent with the experimental result. And specific ways used wavelet analysis to identify noise resource and diagnose fault was got. The method of noise resource identification was expanded and noise resource of DVC drum was identified accurately. Targeted design improvements were done to inner diameter of bottom drum, process of hot extrusion, connect method of tray bracket in high temperature tester, operating method in production line and packing method. Then fraction defective of DVC drum noise was reduced from 2.5% to 0.6%.It had important practical significance to research on vibration and noise reduction of similar products.

Abstract: The concept of vibration based condition monitoring technology has been developing at a rapid stage in the recent years suiting to the maintenance of sophisticated and complicated machines. Nowadays, wavelet analysis based signal processing technique is applied as effective tool for condition monitoring. The experimental studies were conducted on the gear testing apparatus to obtain the vibration signal from a healthy gear and an induced faulty gear. In this paper, two different techniques using Laplace wavelet as base function are used to characterize the fault in the gear signals, specifically wavelet enveloped power spectrum and wavelet kurtosis. The wavelet parameters are optimized using genetic algorithm to select most fault related features. A comparative study detailing features of fault characterization is also given in order to understand the effectiveness of both the wavelet based signal processing methods and their fault diagnosis capability.

Abstract: It has been determined the best mother wavelet for stock prediction of SONY 2006 and BNI 2012 based on the Adaplet Method (The Adaptive Filter which is use wavelet as initial coefficients). The Mother Wavelets, which is used, are Coiflet 1-5, Daubechies 1-5, and Symlet 1-5. The prediction analysis includes overshoot, autocorrelation error and data pattern conformity, three days prediction, and segmentation. According to the overshoot analysis, it shown that for all the data, the overshoot at the beginning of data increased as its wavelet level increased. While using Daubechies 1 and Symlet 1 produced smallest overshoot among other wavelets (112.2%). The autocorrelation error of data pattern prediction indicates conformity with the original data. As its wavelet level increased, the autocorrelation error pattern also ramped (near zero). Coiflet 5 and Daubechies 1 produced the smallest mean square error (MSE), which is equal to 0.0147; meanwhile, Coiflet 1 shows the best result with an average error of 0.001 in next three days prediction analysis. On the other hands, Symlet 3 shows the best MSE of 1.213. Symlet offers the best result, according to best wavelet sequence assessment of each method.

Abstract: In this paper, we study long-range dependence of hydrological records with high frequent and massive data set. For detecting breakpoints, we apply the Evolutionary Wavelet Spectrum (EWS) to provide a segmentation of the original time series. And rescaled range analysis (R/S) for estimating the Hurst exponent that describe the long-range dependence phenomenon are used. The results affirm that the hydrological records have long-range dependent (LRD) behaviors.

Abstract: The paper presents a method of optical inspection of manufacturing processes in the visible band. The presented model of a test bench was based on a monochrome CCD camera that allows you to conduct research in the field of digital image analysis. The wavelet transform was proposed for the detection of objects and was compared with a Fast Fourier Transform (FFT). The article presents an analysis of the results of experiments for selected objects using the proposed method, allowing the evaluation of the correctness of classification. The paper also presents an analysis of the efficiency of the compared methods. In the article, the advantages of using the proposed method in the sample manufacturing process have been discussed.

Abstract: In this paper, a set of voltage disturbance detection device based on DSP2812 was designed and a method of transient voltage disturbance detection based on DSP and Wavelet Transform was studied. The device collects electrical signal through a Hall sensor and the parallel A/D converter and regards TMS320F2812, a kind of high-performance digital signal processor (DSP), as a core data processing unit. Discrete Wavelet Transform (DWT) algorithm on DSP board was carried out to detect transient voltage disturbance. Software modules including the main program module, A/D module, interrupt module, communication module and so on was designed. The DWT algorithm on DSP board which could detect transient voltage disturbance on line was carried out based on the strong operation ability of DSP and the high effectiveness of DWT algorithm. This device can simultaneously realize power acquisition and voltage disturbance analysis in real-time. Contrast tests show that the device is of high-precision, of high-data-processing-speed and has a capability of voltage disturbance detecting in real-time.

Abstract: With good repeatability and simple structure, transmission line pulse (TLP) has been used in immunity test of integrated circuit and printed circuit board. A TLP generator is first manufactured and its output waveform is presented. By using wavelet transform, the waveform is denoised and discriminated to components inherent to system function and parasitic parameters. Frequency spectrum changed with time is also obtained by continuous wavelet transform of complex morlet. Decomposed damping oscillation component and high frequency component in instant frequency spectrum show influence of inductance in circuit on the waveform. Improvement of rising time and overshoot is achieved by change of probe connection with shorter grounding line.