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  • Open Access

    ARTICLE

    A Novel Method to Enhance the Inversion Speed and Precision of the NMR T2 Spectrum by the TSVD Based Linearized Bregman Iteration

    Yiguo Chen1,2,3,*, Congjun Feng1,2, Yonghong He3, Zhijun Chen3, Xiaowei Fan3, Chao Wang3, Xinmin Ge4

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2451-2463, 2023, DOI:10.32604/cmes.2023.021145 - 09 March 2023

    Abstract The low-field nuclear magnetic resonance (NMR) technique has been used to probe the pore size distribution and the fluid composition in geophysical prospecting and related fields. However, the speed and accuracy of the existing numerical inversion methods are still challenging due to the ill-posed nature of the first kind Fredholm integral equation and the contamination of the noises. This paper proposes a novel inversion algorithm to accelerate the convergence and enhance the precision using empirical truncated singular value decompositions (TSVD) and the linearized Bregman iteration. The L1 penalty term is applied to construct the objective More > Graphic Abstract

    A Novel Method to Enhance the Inversion Speed and Precision of the NMR T<sub>2</sub> Spectrum by the TSVD Based Linearized Bregman Iteration

  • Open Access

    ARTICLE

    Detection of Frost-Resistance Property of Large-Size Concrete Based on Impact-Echo Method

    Qi Feng1, Zhengyue Ren2, Dan Wang3,*

    Structural Durability & Health Monitoring, Vol.17, No.1, pp. 71-88, 2023, DOI:10.32604/sdhm.2023.024912 - 02 March 2023

    Abstract The dynamic elasticity modulus (Ed) is the most commonly used indexes for nondestructive testing to represent the internal damage of hydraulic concrete. Samples with a specific size is required when the transverse resonance method was used to detect the Ed, resulting in a limitation for field tests. The impact-echo method can make up defects of traditional detection methods for frost-resistance testing, such as the evaluation via the loss of mass or strength. The feasibility of the impact-echo method to obtain the relative Ed is explored to detect the frost-resistance property of large-volume hydraulic concretes on site. Results… More >

  • Open Access

    ARTICLE

    Application of Hankel Dynamic Mode Decomposition for Wide Area Monitoring of Subsynchronous Resonance

    Lei Wang1, Tiecheng Li1, Hui Fan2, Xuekai Hu1, Lin Yang3, Xiaomei Yang3,*

    Energy Engineering, Vol.120, No.4, pp. 851-867, 2023, DOI:10.32604/ee.2023.025383 - 13 February 2023

    Abstract In recent years, subsynchronous resonance (SSR) has frequently occurred in DFIG-connected series-compensated systems. For the analysis and prevention, it is of great importance to achieve wide area monitoring of the incident. This paper presents a Hankel dynamic mode decomposition (DMD) method to identify SSR parameters using synchrophasor data. The basic idea is to employ the DMD technique to explore the subspace of Hankel matrices constructed by synchrophasors. It is analytically demonstrated that the subspace of these Hankel matrices is a combination of fundamental and SSR modes. Therefore, the SSR parameters can be calculated once the More >

  • Open Access

    ARTICLE

    Liver Tumor Decision Support System on Human Magnetic Resonance Images: A Comparative Study

    Hiam Alquran1,2, Yazan Al-Issa3, Mohammed Alslatie4, Isam Abu-Qasmieh1, Amin Alqudah3, Wan Azani Mustafa5,7,*, Yasmin Mohd Yacob6,7

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1653-1671, 2023, DOI:10.32604/csse.2023.033861 - 09 February 2023

    Abstract Liver cancer is the second leading cause of cancer death worldwide. Early tumor detection may help identify suitable treatment and increase the survival rate. Medical imaging is a non-invasive tool that can help uncover abnormalities in human organs. Magnetic Resonance Imaging (MRI), in particular, uses magnetic fields and radio waves to differentiate internal human organs tissue. However, the interpretation of medical images requires the subjective expertise of a radiologist and oncologist. Thus, building an automated diagnosis computer-based system can help specialists reduce incorrect diagnoses. This paper proposes a hybrid automated system to compare the performance… More >

  • Open Access

    ARTICLE

    IRMIRS: Inception-ResNet-Based Network for MRI Image Super-Resolution

    Wazir Muhammad1, Zuhaibuddin Bhutto2,*, Salman Masroor3,4, Murtaza Hussain Shaikh5, Jalal Shah2, Ayaz Hussain1

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1121-1142, 2023, DOI:10.32604/cmes.2023.021438 - 06 February 2023

    Abstract Medical image super-resolution is a fundamental challenge due to absorption and scattering in tissues. These challenges are increasing the interest in the quality of medical images. Recent research has proven that the rapid progress in convolutional neural networks (CNNs) has achieved superior performance in the area of medical image super-resolution. However, the traditional CNN approaches use interpolation techniques as a preprocessing stage to enlarge low-resolution magnetic resonance (MR) images, adding extra noise in the models and more memory consumption. Furthermore, conventional deep CNN approaches used layers in series-wise connection to create the deeper mode, because More >

  • Open Access

    ARTICLE

    Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution Model

    Debnath Bhattacharyya1, Eali. Stephen Neal Joshua2, N. Thirupathi Rao2, Yung-cheol Byun3,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1447-1465, 2023, DOI:10.32604/cmc.2023.031878 - 06 February 2023

    Abstract Lung cancer is the leading cause of mortality in the world affecting both men and women equally. When a radiologist just focuses on the patient’s body, it increases the amount of strain on the radiologist and the likelihood of missing pathological information such as abnormalities are increased. One of the primary objectives of this research work is to develop computer-assisted diagnosis and detection of lung cancer. It also intends to make it easier for radiologists to identify and diagnose lung cancer accurately. The proposed strategy which was based on a unique image feature, took into… More >

  • Open Access

    ARTICLE

    Automated Brain Hemorrhage Classification and Volume Analysis

    Maryam Wardah1, Muhammad Mateen1,*, Tauqeer Safdar Malik2, Mohammad Eid Alzahrani3, Adil Fahad3, Abdulmohsen Almalawi4, Rizwan Ali Naqvi5

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2283-2299, 2023, DOI:10.32604/cmc.2023.030706 - 06 February 2023

    Abstract Brain hemorrhage is a serious and life-threatening condition. It can cause permanent and lifelong disability even when it is not fatal. The word hemorrhage denotes leakage of blood within the brain and this leakage of blood from capillaries causes stroke and adequate supply of oxygen to the brain is hindered. Modern imaging methods such as computed tomography (CT) and magnetic resonance imaging (MRI) are employed to get an idea regarding the extent of the damage. An early diagnosis and treatment can save lives and limit the adverse effects of a brain hemorrhage. In this case,… More >

  • Open Access

    ARTICLE

    Xception-Fractalnet: Hybrid Deep Learning Based Multi-Class Classification of Alzheimer’s Disease

    Mudiyala Aparna, Battula Srinivasa Rao*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6909-6932, 2023, DOI:10.32604/cmc.2023.034796 - 28 December 2022

    Abstract Neurological disorders such as Alzheimer’s disease (AD) are very challenging to treat due to their sensitivity, technical challenges during surgery, and high expenses. The complexity of the brain structures makes it difficult to distinguish between the various brain tissues and categorize AD using conventional classification methods. Furthermore, conventional approaches take a lot of time and might not always be precise. Hence, a suitable classification framework with brain imaging may produce more accurate findings for early diagnosis of AD. Therefore in this paper, an effective hybrid Xception and Fractalnet-based deep learning framework are implemented to classify… More >

  • Open Access

    ARTICLE

    Brain Tumor Segmentation in Multimodal MRI Using U-Net Layered Structure

    Muhammad Javaid Iqbal1, Muhammad Waseem Iqbal2, Muhammad Anwar3,*, Muhammad Murad Khan4, Abd Jabar Nazimi5, Mohammad Nazir Ahmad6

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5267-5281, 2023, DOI:10.32604/cmc.2023.033024 - 28 December 2022

    Abstract The brain tumour is the mass where some tissues become old or damaged, but they do not die or not leave their space. Mainly brain tumour masses occur due to malignant masses. These tissues must die so that new tissues are allowed to be born and take their place. Tumour segmentation is a complex and time-taking problem due to the tumour’s size, shape, and appearance variation. Manually finding such masses in the brain by analyzing Magnetic Resonance Images (MRI) is a crucial task for experts and radiologists. Radiologists could not work for large volume images… More >

  • Open Access

    ARTICLE

    Vibration of a Two-Layer “Metal+PZT” Plate Contacting with Viscous Fluid

    Zeynep Ekicioglu Kuzeci1,*, Surkay D. Akbarov2,3

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4337-4362, 2023, DOI:10.32604/cmc.2023.033446 - 31 October 2022

    Abstract The present work investigates the mechanically forced vibration of the hydro-elasto-piezoelectric system consisting of a two-layer plate “elastic+PZT”, a compressible viscous fluid, and a rigid wall. It is assumed that the PZT (piezoelectric) layer of the plate is in contact with the fluid and time-harmonic linear forces act on the free surface of the elastic-metallic layer. This study is valuable because it considers for the first time the mechanical vibration of the metal+piezoelectric bilayer plate in contact with a fluid. It is also the first time that the influence of the volumetric concentration of the… More >

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