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  • Open Access

    ARTICLE

    Comparative Analysis of Wavelet Transform for Time-Frequency Analysis and Transient Localization in Structural Health Monitoring

    Ahmed Silik1,2, Mohammad Noori3,*, Wael A. Altabey1,4, Ramin Ghiasi1, Zhishen Wu1

    Structural Durability & Health Monitoring, Vol.15, No.1, pp. 1-22, 2021, DOI:10.32604/sdhm.2021.012751 - 22 March 2021

    Abstract A critical problem facing data collection in structural health monitoring, for instance via sensor networks, is how to extract the main components and useful features for damage detection. A structural dynamic measurement is more often a complex time-varying process and therefore, is prone to dynamic changes in time-frequency contents. To extract the signal components and capture the useful features associated with damage from such non-stationary signals, a technique that combines the time and frequency analysis and shows the signal evolution in both time and frequency is required. Wavelet analyses have proven to be a viable… More >

  • Open Access

    ARTICLE

    A Triple-Channel Encrypted Hybrid Fusion Technique to Improve Security of Medical Images

    Ahmed S. Salama1,2,3, Mohamed Amr Mokhtar3, Mazhar B. Tayel3, Esraa Eldesouky4,6, Ahmed Ali5,6,*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 431-446, 2021, DOI:10.32604/cmc.2021.016165 - 22 March 2021

    Abstract Assuring medical images protection and robustness is a compulsory necessity nowadays. In this paper, a novel technique is proposed that fuses the wavelet-induced multi-resolution decomposition of the Discrete Wavelet Transform (DWT) with the energy compaction of the Discrete Wavelet Transform (DCT). The multi-level Encryption-based Hybrid Fusion Technique (EbhFT) aims to achieve great advances in terms of imperceptibility and security of medical images. A DWT disintegrated sub-band of a cover image is reformed simultaneously using the DCT transform. Afterwards, a 64-bit hex key is employed to encrypt the host image as well as participate in the… More >

  • Open Access

    ARTICLE

    Multimodal Medical Image Registration and Fusion for Quality Enhancement

    Muhammad Adeel Azam1, Khan Bahadar Khan2,*, Muhammad Ahmad3, Manuel Mazzara4

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 821-840, 2021, DOI:10.32604/cmc.2021.016131 - 22 March 2021

    Abstract For the last two decades, physicians and clinical experts have used a single imaging modality to identify the normal and abnormal structure of the human body. However, most of the time, medical experts are unable to accurately analyze and examine the information from a single imaging modality due to the limited information. To overcome this problem, a multimodal approach is adopted to increase the qualitative and quantitative medical information which helps the doctors to easily diagnose diseases in their early stages. In the proposed method, a Multi-resolution Rigid Registration (MRR) technique is used for multimodal… More >

  • Open Access

    ARTICLE

    Threshold Parameters Selection for Empirical Mode Decomposition-Based EMG Signal Denoising

    Hassan Ashraf1, Asim Waris1,*, Syed Omer Gilani1, Muhammad Umair Tariq1, Hani Alquhayz2

    Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 799-815, 2021, DOI:10.32604/iasc.2021.014765 - 01 March 2021

    Abstract Empirical Mode Decomposition (EMD) is a data-driven and fully adaptive signal decomposition technique to decompose a signal into its Intrinsic Mode Functions (IMF). EMD has attained great attention due to its capabilities to process a signal in the frequency-time domain without altering the signal into the frequency domain. EMD-based signal denoising techniques have shown great potential to denoise nonlinear and nonstationary signals without compromising the signal’s characteristics. The denoising procedure comprises three steps, i.e., signal decomposition, IMF thresholding, and signal reconstruction. Thresholding is performed to assess which IMFs contain noise. In this study, Interval Thresholding… More >

  • Open Access

    ARTICLE

    Improving Reconstructed Image Quality via Hybrid Compression Techniques

    Nancy Awadallah Awad1,*, Amena Mahmoud2

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3151-3160, 2021, DOI:10.32604/cmc.2021.014426 - 28 December 2020

    Abstract Data compression is one of the core fields of study for applications of image and video processing. The raw data to be transmitted consumes large bandwidth and requires huge storage space as a result, it is desirable to represent the information in the data with considerably fewer bits by the mean of data compression techniques, the data must be reconstituted very similarly to the initial form. In this paper, a hybrid compression based on Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) is used to enhance the quality of the reconstructed image. These techniques are… More >

  • Open Access

    ARTICLE

    Severity Recognition of Aloe vera Diseases Using AI in Tensor Flow Domain

    Nazeer Muhammad1, Rubab2, Nargis Bibi3, Oh-Young Song4, Muhammad Attique Khan5,*, Sajid Ali Khan6

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2199-2216, 2021, DOI:10.32604/cmc.2020.012257 - 26 November 2020

    Abstract Agriculture plays an important role in the economy of all countries. However, plant diseases may badly affect the quality of food, production, and ultimately the economy. For plant disease detection and management, agriculturalists spend a huge amount of money. However, the manual detection method of plant diseases is complicated and time-consuming. Consequently, automated systems for plant disease detection using machine learning (ML) approaches are proposed. However, most of the existing ML techniques of plants diseases recognition are based on handcrafted features and they rarely deal with huge amount of input data. To address the issue,… More >

  • Open Access

    ARTICLE

    Nonlinear Correction of Pressure Sensor Based on Depth Neural Network

    Yanming Wang1,2,3, Kebin Jia1,2,3,*, Pengyu Liu1,2,3

    Journal on Internet of Things, Vol.2, No.3, pp. 109-120, 2020, DOI:10.32604/jiot.2020.010138 - 16 September 2020

    Abstract With the global climate change, the high-altitude detection is more and more important in the climate prediction, and the input-output characteristic curve of the air pressure sensor is offset due to the interference of the tested object and the environment under test, and the nonlinear error is generated. Aiming at the difficulty of nonlinear correction of pressure sensor and the low accuracy of correction results, depth neural network model was established based on wavelet function, and Levenberg-Marquardt algorithm is used to update network parameters to realize the nonlinear correction of pressure sensor. The experimental results More >

  • Open Access

    ARTICLE

    Emotion Recognition Using WT-SVM in Human-Computer Interaction

    Zequn Wang, Rui Jiao, Huiping Jiang*

    Journal of New Media, Vol.2, No.3, pp. 121-130, 2020, DOI:10.32604/jnm.2020.010674 - 04 September 2020

    Abstract With the continuous development of the computer, people's requirements for computers are also getting more and more, so the brain-computer interface system (BCI) has become an essential part of computer research. Emotion recognition is an important task for the computer to understand social status in BCI. Affective computing (AC) aims to develop the model of emotions and advance the affective intelligence of computers. There are various emotion recognition approaches. The method based on electroencephalogram (EEG) is more reliable because it is higher in accuracy and more objective in evaluation than other external appearance clues such… More >

  • Open Access

    ARTICLE

    Wind Speed Prediction Modeling Based on the Wavelet Neural Network

    Zhenhua Guo1,2, Lixin Zhang1,*, Xue Hu1, Huanmei Chen2

    Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 625-630, 2020, DOI:10.32604/iasc.2020.013941

    Abstract Wind speed prediction is an important part of the wind farm management and wind power grid connection. Having accurate prediction of short-term wind speed is the basis for predicting wind power. This paper proposes a short-term wind speed prediction strategy based on the wavelet analysis and the multilayer perceptual neural network for the Dabancheng area, in China. Four wavelet neural network models using the Morlet function as the wavelet basis function were developed to forecast short-term wind speed in January, April, July, and October. Predicted wind speed was compared across the four models using the More >

  • Open Access

    ARTICLE

    Discrete Wavelet Transmission and Modified PSO with ACO Based Feed Forward Neural Network Model for Brain Tumour Detection

    Machiraju Jayalakshmi1, *, S. Nagaraja Rao2

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1081-1096, 2020, DOI:10.32604/cmc.2020.011710 - 20 August 2020

    Abstract In recent years, the development in the field of computer-aided diagnosis (CAD) has increased rapidly. Many traditional machine learning algorithms have been proposed for identifying the pathological brain using magnetic resonance images. The existing algorithms have drawbacks with respect to their accuracy, efficiency, and limited learning processes. To address these issues, we propose a pathological brain tumour detection method that utilizes the Weiner filter to improve the image contrast, 2D- discrete wavelet transformation (2D-DWT) to extract the features, probabilistic principal component analysis (PPCA) and linear discriminant analysis (LDA) to normalize and reduce the features, and More >

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