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  • Open Access

    ARTICLE

    ECG Classification Using Deep CNN Improved by Wavelet Transform

    Yunxiang Zhao1, Jinyong Cheng1, *, Ping Zhang1, Xueping Peng2

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1615-1628, 2020, DOI:10.32604/cmc.2020.09938 - 30 June 2020

    Abstract Atrial fibrillation is the most common persistent form of arrhythmia. A method based on wavelet transform combined with deep convolutional neural network is applied for automatic classification of electrocardiograms. Since the ECG signal is easily inferred, the ECG signal is decomposed into 9 kinds of subsignals with different frequency scales by wavelet function, and then wavelet reconstruction is carried out after segmented filtering to eliminate the influence of noise. A 24-layer convolution neural network is used to extract the hierarchical features by convolution kernels of different sizes, and finally the softmax classifier is used to More >

  • Open Access

    ARTICLE

    Sound Signal Based Fault Classification System in Motorcycles Using Hybrid Feature Sets and Extreme Learning Machine Classifiers

    T. Jayasree1,*, R. Prem Ananth2

    Sound & Vibration, Vol.54, No.1, pp. 57-74, 2020, DOI:10.32604/sv.2020.08573 - 01 March 2020

    Abstract Vehicles generate dissimilar sound patterns under different working environments. These generated sound patterns signify the condition of the engines, which in turn is used for diagnosing various faults. In this paper, the sound signals produced by motorcycles are analyzed to locate various faults. The important attributes are extracted from the generated sound signals based on time, frequency and wavelet domains which clearly describe the statistical behavior of the signals. Further, various types of faults are classified using the Extreme Learning Machine (ELM) classifier from the extracted features. Moreover, the improved classification performance is obtained by More >

  • Open Access

    ARTICLE

    Fusion of Medical Images in Wavelet Domain: A Hybrid Implementation

    Satya Prakash Yadav1, *, Sachin Yadav2

    CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.1, pp. 303-321, 2020, DOI:10.32604/cmes.2020.08459 - 01 January 2020

    Abstract This paper presents a low intricate, profoundly energy effective MRI Images combination intended for remote visual sensor frameworks which leads to improved understanding and implementation of treatment; especially for radiology. This is done by combining the original picture which leads to a significant reduction in the computation time and frequency. The proposed technique conquers the calculation and energy impediment of low power tools and is examined as far as picture quality and energy is concerned. Reenactments are performed utilizing MATLAB 2018a, to quantify the resultant vitality investment funds and the reproduction results show that the More >

  • Open Access

    ARTICLE

    Balanced GHM Mutiwavelet Transform Based Contrast Enhancement Technique for Dark Images Using Dynamic Stochastic Resonance

    S. Deivalakshmi*, P. Palanisamy1, X. Z. Gao2

    Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 459-471, 2019, DOI:10.31209/2018.100000001

    Abstract The main aim of this paper is to propose a new technique for enhancing the contrast of dark images using Dynamic Stochastic Resonance (DSR) and Multi Wavelet Transform (MWT), which is computationally more efficient than the conventional methods. In the work, for enhancing the contrast of dark images, the intrinsic noise (darkness) of dark images has been used. The proposed MWT-based DSR scheme (MWT-DSR) can yield better performances in terms of visual information and color preservation than already reported techniques. The desired output response is validated by the Relative Contrast Enhancement Factor (F), Perceptual Quality More >

  • Open Access

    ARTICLE

    Line Trace Effective Comparison Algorithm Based on Wavelet Domain DTW

    Nan Pan1, Yi Liu2, Dilin Pan2, Junbing Qian1, Gang Li3

    Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 359-366, 2019, DOI:10.31209/2019.100000097

    Abstract It will face a lot of problems when using existing image-processing and 3D scanning methods to do the similarity analysis of the line traces, therefore, an effective comparison algorithm is put forward for the purpose of making effective trace analysis and infer the criminal tools. The proposed algorithm applies wavelet decomposition to the line trace 1-D detection signals to partially reduce background noises. After that, the sequence comparison strategy based on wavelet domain DTW is employed to do trace feature similarity matching. Finally, using linear regression machine learning algorithm based on gradient descent method to More >

  • Open Access

    ARTICLE

    A Meaningful Image Encryption Algorithm Based on Prediction Error and Wavelet Transform

    Mengling Zou1, Zhengxuan Liu2, Xianyi Chen3, *

    Journal on Big Data, Vol.1, No.3, pp. 151-158, 2019, DOI:10.32604/jbd.2019.09057

    Abstract Image encryption (IE) is a very useful and popular technology to protect the privacy of users. Most algorithms usually encrypt the original image into an image similar to texture or noise, but texture and noise are an obvious visual indication that the image has been encrypted, which is more likely to cause the attacks of enemy. To overcome this shortcoming, many image encryption systems, which convert the original image into a carrier image with visual significance have been proposed. However, the generated cryptographic image still has texture features. In line with the idea of improving More >

  • Open Access

    ARTICLE

    Gear Fault Detection Analysis Method Based on Fractional Wavelet Transform and Back Propagation Neural Network

    Yanqiang Sun1, Hongfang Chen1,*, Liang Tang1, Shuang Zhang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.3, pp. 1011-1028, 2019, DOI:10.32604/cmes.2019.07950

    Abstract A gear fault detection analysis method based on Fractional Wavelet Transform (FRWT) and Back Propagation Neural Network (BPNN) is proposed. Taking the changing order as the variable, the optimal order of gear vibration signals is determined by discrete fractional Fourier transform. Under the optimal order, the fractional wavelet transform is applied to eliminate noise from gear vibration signals. In this way, useful components of vibration signals can be successfully separated from background noise. Then, a set of feature vectors obtained by calculating the characteristic parameters for the de-noised signals are used to characterize the gear More >

  • Open Access

    ARTICLE

    A Robust Image Watermarking Scheme Using Z-Transform, Discrete Wavelet Transform and Bidiagonal Singular Value Decomposition

    N. Jayashree1,*, R. S. Bhuvaneswaran1

    CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 263-285, 2019, DOI:10.32604/cmc.2019.03924

    Abstract Watermarking is a widely used solution to the problems of authentication and copyright protection of digital media especially for images, videos, and audio data. Chaos is one of the emerging techniques adopted in image watermarking schemes due to its intrinsic cryptographic properties. This paper proposes a new chaotic hybrid watermarking method combining Discrete Wavelet Transform (DWT), Z-transform (ZT) and Bidiagonal Singular Value Decomposition (BSVD). The original image is decomposed into 3-level DWT, and then, ZT is applied on the HH3 and HL3 sub-bands. The watermark image is encrypted using Arnold Cat Map. BSVD for the More >

  • Open Access

    ARTICLE

    Use of Discrete Wavelet Features and Support Vector Machine for Fault Diagnosis of Face Milling Tool

    C. K. Madhusudana1, N. Gangadhar1, Hemantha Kumar, Kumar,*,1, S. Narendranath1

    Structural Durability & Health Monitoring, Vol.12, No.2, pp. 111-127, 2018, DOI:10.3970/sdhm.2018.01262

    Abstract This paper presents the fault diagnosis of face milling tool based on machine learning approach. While machining, spindle vibration signals in feed direction under healthy and faulty conditions of the milling tool are acquired. A set of discrete wavelet features is extracted from the vibration signals using discrete wavelet transform (DWT) technique. The decision tree technique is used to select significant features out of all extracted wavelet features. C-support vector classification (C-SVC) and ν-support vector classification (ν-SVC) models with different kernel functions of support vector machine (SVM) are used to study and classify the tool More >

  • Open Access

    ARTICLE

    Robust Image Hashing via Random Gabor Filtering and DWT

    Zhenjun Tang1,*, Man Ling1, Heng Yao1, Zhenxing Qian2, Xianquan Zhang1, Jilian Zhang3, Shijie Xu1

    CMC-Computers, Materials & Continua, Vol.55, No.2, pp. 331-344, 2018, DOI:10.3970/cmc.2018.02222

    Abstract Image hashing is a useful multimedia technology for many applications, such as image authentication, image retrieval, image copy detection and image forensics. In this paper, we propose a robust image hashing based on random Gabor filtering and discrete wavelet transform (DWT). Specifically, robust and secure image features are first extracted from the normalized image by Gabor filtering and a chaotic map called Skew tent map, and then are compressed via a single-level 2-D DWT. Image hash is finally obtained by concatenating DWT coefficients in the LL sub-band. Many experiments with open image datasets are carried More >

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