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Enhanced Fourier Transform Using wavelet packet decomposition

Journal of Sensor Technology 2024年第1期14卷 1-15页

作者： Wouladje Cabrel Golden Tendekai Mumanikidzwa Jianguo Shen Yutong Yan Department of Physics and Electronic Information Engineering Optical Engineering Zhejiang Normal University Jinhua China Department of Computer Science and Technology Software Engineering Zhejiang Normal University Jinhua China Department of Physics and Electronic Information Engineering Electronic Engineering Zhejiang Normal University Jinhua China

Many domains, including communication, signal processing, and image processing, use the Fourier Transform as a mathematical tool for signal analysis. Although it can analyze signals with steady and transitory properties, it has limits. The wavelet packet decomposition (WPD) is a novel technique that we suggest in this study as a way to improve the Fourier Transform and get beyond these drawbacks. In this experiment, we specifically considered the utilization of Daubechies level 4 for the wavelet transformation. The choice of Daubechies level 4 was motivated by several reasons. Daubechies wavelets are known for their compact support, orthogonality, and good time-frequency localization. By choosing Daubechies level 4, we aimed to strike a balance between preserving important transient information and avoiding excessive noise or oversmoothing in the transformed signal. Then we compared the outcomes of our suggested approach to the conventional Fourier Transform using a non-stationary signal. The findings demonstrated that the suggested method offered a more accurate representation of non-stationary and transient signals in the frequency domain. Our method precisely showed a 12% reduction in MSE and a 3% rise in PSNR for the standard Fourier transform, as well as a 35% decrease in MSE and an 8% increase in PSNR for voice signals when compared to the traditional wavelet packet decomposition method.

关键词： Fourier Transform wavelet packet decomposition Time-Frequency Analysis Non-Stationary Signals

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Appropriate Sub-band Selection in wavelet packet decomposition for Automated Glaucoma Diagnoses

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International Journal of Automation and computing 2015年第4期12卷 393-401页

作者： Chandrasekaran Raja Narayanan Gangatharan Department of Electronics and Communication Engineering Anjalai Ammal Mahalingam Engineering College Department of Electronics and Communication Engineering R.M.K.College of Engineering and Technology

The most common reason for blindness among human beings is Glaucoma.The increase of fluid pressure damages the optic nerve which gradually leads to irreversible loss of vision.A technique for automated screening of Glaucoma from the fundal retinal images is presented in this paper.This paper intends to explore the significance of both the approximate and detail coefficients through wavelet packet decomposition(WPD).decomposition is done with "db3" wavelet function and the images are decomposed up to level-3producing 84 sub-bands.Two features,the energy and the entropy are calculated for each sub-band producing two feature matrices(158 images × 84 features).The above step is purely a statistical measure based on WPD.To enhance the diagnostic accuracy,the second phase considers the structural(biological) region of interest(ROI) in the image and then extracts the same features.It is worthy to note that direct biological features are not extracted to eliminate the drawbacks of segmentation whereas the biologically significant region is taken as biological-ROI.Interestingly,the detailed coefficient sub-bands(prominent edges) show more significance in the biological-ROI phase.Apart from enhancing the diagnostic accuracy by feature reduction,the paper intends to mark the significance indices,uniqueness and discrimination capability of the significant features(sub-bands) in both the phases.Then,the crisp inputs are fed to the classifier ANN.Finally,from the significant features of the biological-ROI feature matrices,the accuracy is raised to 85%which is notable than the accuracy of 79%achieved without considering the ROI.

关键词： Glaucoma wavelet packet decomposition feature reduction feature significance artificial neural networks.

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Separation of closely spaced modes by combining complex envelope displacement analysis with method of generating intrinsic mode functions through filtering algorithm based on wavelet packet decomposition

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Applied Mathematics and Mechanics(English Edition) 2013年第7期34卷 801-810页

作者： Y.S.KIM 陈立群 Shanghai Institute of Applied Mathematics and Mechanics Department of Mechanics Kim Ⅱ Sung University Department of Mechanics College of Sciences Shanghai University Shanghai Key Laboratory of Mechanics in Energy Engineering Shanghai University

One of the important issues in the system identification and the spectrum analysis is the frequency resolution, i.e., the capability of distinguishing between two or more closely spaced frequency components. In the modal identification by the empirical mode decomposition （EMD） method, because of the separating capability of the method, it is still a challenge to consistently and reliably identify the parameters of structures of which modes are not well separated. A new method is introduced to generate the intrin- sic mode functions （IMFs） through the filtering algorithm based on the wavelet packet decomposition （GIFWPD）. In this paper, it is demonstrated that the CIFWPD method alone has a good capability of separating close modes, even under the severe condition beyond the critical frequency ratio limit which makes it impossible to separate two closely spaced harmonics by the EMD method. However, the GIFWPD-only based method is impelled to use a very fine sampling frequency with consequent prohibitive computational costs. Therefore, in order to decrease the computational load by reducing the amount of samples and improve the effectiveness of separation by increasing the frequency ratio, the present paper uses a combination of the complex envelope displacement analysis （CEDA） and the GIFWPD method. For the validation, two examples from the previous works are taken to show the results obtained by the GIFWPD-only based method and by combining the CEDA with the GIFWPD method.

关键词： empirical mode decomposition （EMD） wavelet packet decomposition com- plex envelope displacement analysis （CEDA） closely spaced modes modal identification

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Transient Characteristic Extraction Based on wavelet packet decomposition and EMD

Transient Characteristic Extraction Based on Wavelet Packet ...

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第25届中国控制与决策会议

作者： ZHAO Ling HUANG Da-rong CHENG Fa-bin College of Information Science and Engineering Chongqing Jiaotong University China Academy of Engineering Physics General Engineering Institute

ISBN: (纸本)9781467355339

EMD has its own interpretation of combinations of pure tones, and mode mixing is one of the problems because of the noise and multi component. We give a solution involving wavelet packet decomposition with EMD. The improved signal method is based on wavelet packet decomposition, the noise of the signal is eliminated and we get the IMF without mode mixing. The method also allows EMD to be used to separate components that are similar in frequency that would be inseparable with standard EMD techniques. The experiment shows the method to improve EMD is practical and effective.

关键词： wavelet packet decomposition Empirical Mode decomposition Mode Mixing Fault Diagnosis

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Voiceprint Feature Extraction Based on wavelet packet decomposition

Voiceprint Feature Extraction Based on Wavelet Packet Decomp...

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第37届中国控制会议

作者： Huang Jinjie Lei Ming Lu Chao Yu Qingyuan Harbin University of Science and Technology

The main purpose of speech signal recognition system research is to improve the recognition rate of speech signal and reduce the recognition time. The feature extraction of speech signal in the process of recognition is one of the key aspects. In this paper, the speech feature extraction is to use the auditory characteristics of the human ear to decompose the wavelet packet into five levels and extract the dynamic features contained in the frame signal. After further processing, the speech feature parameters（DWPT parameters） are obtained. Simulation shows that the speaker recognition rate has been significantly improved compared with other traditional methods.

关键词： wavelet packet decomposition DWPT characteristic parameters voiceprint extraction speaker recognition

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Fault Characteristics Analysis for Rotor Systems with Misalignment Based on wavelet packet decomposition and Frequency-band Energy Ratio Analysis

Fault Characteristics Analysis for Rotor Systems with Misali...

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第三十届中国控制会议

作者： ZHANG Shuai~1,QU Yanhuai~1,HAN Qingkai~2 1.School of Information and Electronic Engineering,Shandong Institute of Business and Technology,Yantai 264005,P.R.China 2.School of Mechanical Engineering and Automation,Northeastern University,Shenyang 110819,P.R.China

＜正＞In this paper,combining with model experiment,fault characteristics of single-span and dual-disc rotor system with bearing misalignment are investigated by the combination of wavelet packet decomposition（WDP） and frequency -band energy ratio analysis method.Firstly,resampling is applied to ensure the accurate and efficient decomposition of signal frequency-band.And then with the mother wavelet function ’db44’,the displacement signals were decomposed into 4 levels.Moreover,through a comparison with the time-domain waveforms and amplitude spectrums of decomposed signals in different decomposing levels,the effects of different misalignments to system vibration are shown.And corresponding frequency-band energy ratio values can have an excellent corresponding relationship with the misalignment degree and actual speed which can be applied to fault feature extraction to implement effective diagnosis of misalignment fault.

关键词： Model Experiment Bearing Misalignment Fault Characteristics Single-span and Dual-disc Rotor System wavelet packet decomposition Frequency-band Energy Ratio Analysis

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Coupling framework for a wind speed forecasting model applied to wind energy

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Science China(Technological Sciences) 2022年第10期65卷 2462-2473页

作者： DENG Ying CHONG KaiLeong WANG BoFu ZHOU Quan LU ZhiMing Shanghai Institute of Applied Mathematics and Mechanics School of Mechanics and Engineering ScienceShanghai UniversityShanghai 200072China Shanghai Key Laboratory of Mechanics in Energy Engineering Shanghai 200072China

Wind energy is the burgeoning renewable energy. Accurate wind speed prediction is necessary to ensure the stability and reliability of the power grid for wind energy. This study focuses on developing a novel hybrid forecasting model to tackle adverse effects caused by strong variability and abrupt changes in wind speed. The hybrid model combines data decomposition and error correction strategy for a wind speed forecasting model applied to wind energy. First, wavelet packet decomposition is applied to wind speed series to obtain stationary subseries. Next, outlier robust extreme learning machine is implemented to predict subseries. Finally, an error correction strategy coupled with data decomposition is designed to repair preliminary prediction results. In addition, four measured datasets from China and USAwind farms with different time intervals are used to evaluate the performance of the proposed approach. Experimental analysis indicates that the proposed model outperforms the compared models. Results show that(1) the prediction accuracy of the proposed model is remarkably improved compared with other conventional models;(2) the proposed model can reduce the influence of the end effect in the decomposition-based forecasting model;(3) the coupling framework is successful for enhancing performance of hybrid forecasting model.

关键词： wind speed forecasting artificial intelligence hybrid model data preprocessing error correction wavelet packet decomposition

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Reduced information transmission in the internal segment of the globus pallidus of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-induced rhesus monkey models of Parkinson's disease

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Neural Regeneration Research 2012年第26期7卷 2028-2035页

作者： Yan He Jue Wang Guodong Gao Guangjun Zhang The Key Laboratory of Biomedical Information Engineering of Ministry of Education and Institute of Biomedical Engineering School of LifeScience and Technology Xi'an Jiaotong University National Engineering Research Center of Health Care and Medical Devices Xi'anJiaotong University Branch Xi'an 710049 Shaanxi Province China Department ofNeurosurgery Tangdu Hospital Affiliated to the Fourth Military Medical University Xi'an 710038 Shaanxi Province China

Rhesus monkey models of Parkinson＇s disease were induced by injection of N-methyl-4-phenyl- 1,2,3,6-tetrahydropyridine. Neural firings were recorded using microelectrodes placed in the interna segment of the globus pallidus. The wavelets and power spectra show gradual power reduction during the disease process along with increased firing rates in the Parkinson＇s disease state. Singular values of coefficients decreased considerably during tremor-related activity as well as in the Parkinson＇s disease state compared with normal signals, revealing that higher-frequency components weaken when Parkinson＇s disease occurs. We speculate that the death of neurons could be reflected by irregular frequency spike trains, and that wavelet packet decomposition can effectively detect the degradation of neurons and the loss of information transmission in the neural circuitry.

关键词： neuronal oscillation microelectrode Parkinson＇s disease wavelet packet decomposition singularvalue neural regeneration

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Impact Localization of CFRP Structure Based on FBG Sensor Network

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Photonic Sensors 2020年第1期10卷 88-96页

作者： Yaozhang SAI Xiuxia ZHAO Lili WANG Dianli HOU School of Information and Electrical Engineering Ludong UniversityYantai 264025China TIANRUN CRANKSHAFT CO. LTDWendeng 264400China

Low energy impact can induce invisible damage of carbon fiber reinforced polymer(CFRP).The damage can seriously affect the safety of the CFRP structure.Therefore,damage detection is crucial to the CFRP structure.Impact location information is the premise of damage detection.Hence,impact localization is the primary issue.In this paper,an impact localization system,based on the fiber Bragg grating(FBG)sensor network,is proposed for impact detection and localization.For the completed impact signal,the FBG sensor and narrow-band laser demodulation technology are applied.wavelet packet decomposition is introduced to extract available frequency band signals and attenuate noise.According to the energy of the available frequency band signal,an impact localization model,based on the extreme learning machine(ELM),is established with the faster training speed and less parameters.The above system is verified on the 500 mm×500 mm×2 mm CFRP plate.The maximum localization error and the minimum localization error are 30.4 mm and 6.7 mm,respectively.The average localization error is 14.7 mm,and training time is 0.7 s.Compared with the other machine learning methods,the localization system,proposed in this paper,has higher accuracy and faster training speed.This paper provides a practical system for impact localization of the CFRP structure.

关键词： Carbon fiber reinforced polymer fiber Bragg grating extreme learning machine impact localization wavelet packet decomposition

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ESO-KELM-based minor sensor fault identification

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The Journal of China Universities of Posts and Telecommunications 2021年第4期28卷 53-63页

作者： Zhao Kai Song Jia Wang Xinlong School of Astronautics Beihang UniversityBeijing 100191China

Aiming at the sensor faults of near-space hypersonic vehicles(NSHV), a fault identification method based on the extended state observer and kernel extreme learning machine(ESO-KELM) is proposed in this paper. The method is generated by a combination of the model-based method and the data-driven method. As the source of the fault diagnosis, the residual signals represent the difference between the ESO output and the result measured by the sensor in particular. The energy of the residual signals is distributed in both low frequency bands and high frequency bands. However, the energy of the sensor concentrates on the low-frequency bands. Combined with more different features detected by KELM, the proposed method devotes to improving the accuracy. Meanwhile, it is competent to calculate the magnitude of minor faults based on time-frequency analysis. Finally, the simulation is performed on the longitudinal channel of the Winged-Cone model published by the national aeronautics and space administration(NASA). Results show the validity and the accuracy in calculating the magnitude of the minor faults.

关键词： minor fault diagnosis near-space hypersonic vehicles extended state observer kernel extreme learning machine wavelet packet decomposition

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