Shahid Naseem¹, Tariq Mahmood^2,3, Amjad Rehman Khan², Umer Farooq¹, Samra Nawazish⁴, Faten S. Alamri^5,*, Tanzila Saba²

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 801-817, 2024, DOI:10.32604/cmc.2024.047623

Abstract Recently, there have been several uses for digital image processing. Image fusion has become a prominent application in the domain of imaging processing. To create one final image that proves more informative and helpful compared to the original input images, image fusion merges two or more initial images of the same item. Image fusion aims to produce, enhance, and transform significant elements of the source images into combined images for the sake of human visual perception. Image fusion is commonly employed for feature extraction in smart robots, clinical imaging, audiovisual camera integration, manufacturing process monitoring, electronic circuit design, advanced device… More >

Lanze Zhang, Yijun Gu^*, Jingjie Peng

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1701-1731, 2024, DOI:10.32604/cmes.2023.045129

Abstract Graph Neural Networks (GNNs) play a significant role in tasks related to homophilic graphs. Traditional GNNs, based on the assumption of homophily, employ low-pass filters for neighboring nodes to achieve information aggregation and embedding. However, in heterophilic graphs, nodes from different categories often establish connections, while nodes of the same category are located further apart in the graph topology. This characteristic poses challenges to traditional GNNs, leading to issues of “distant node modeling deficiency” and “failure of the homophily assumption”. In response, this paper introduces the Spatial-Frequency domain Adaptive Heterophilic Graph Neural Networks (SFA-HGNN), which integrates adaptive embedding mechanisms for… More >

Feiyan Zhou^1,*, Hui Yin¹, Chen Luo², Haixin Tong², Kun Yu², Zewen Li², Xiangjun Zeng²

Energy Engineering, Vol.120, No.9, pp. 1979-1990, 2023, DOI:10.32604/ee.2023.029480

Abstract The load types in low-voltage distribution systems are diverse. Some loads have current signals that are similar to series fault arcs, making it difficult to effectively detect fault arcs during their occurrence and sustained combustion, which can easily lead to serious electrical fire accidents. To address this issue, this paper establishes a fault arc prototype experimental platform, selects multiple commonly used loads for fault arc experiments, and collects data in both normal and fault states. By analyzing waveform characteristics and selecting fault discrimination feature indicators, corresponding feature values are extracted for qualitative analysis to explore changes in time-frequency characteristics of… More > Graphic Abstract

Zipu Yan^1,2, Honghua Dai^1,2,*, Qisi Wang^1,2, Satya N. Atluri³

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1419-1459, 2023, DOI:10.32604/cmes.2023.028198

Abstract The harmonic balance (HB) method is one of the most commonly used methods for solving periodic solutions of both weakly and strongly nonlinear dynamical systems. However, it is confined to low-order approximations due to complex symbolic operations. Many variants have been developed to improve the HB method, among which the time domain HB-like methods are regarded as crucial improvements because of their fast computation and simple derivation. So far, there are two problems remaining to be addressed. i) A dozen of different versions of HB-like methods, in frequency domain or time domain or in hybrid, have been developed; unfortunately, misclassification… More >

Fawad Nasim^1,2,*, Sohail Masood^1,2, Arfan Jaffar^1,2, Usman Ahmad¹, Muhammad Rashid³

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3175-3190, 2023, DOI:10.32604/csse.2023.034550

Abstract An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning. The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car. Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts. The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults. A possible fault is determined in the vehicle based on this processed sound. Binary classification is done at the first stage… More >

Woochan Lee¹, Woobin Park¹, Jaeyoung Park², Young-Joon Kim³, Moonseong Kim^4,*

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1585-1602, 2023, DOI:10.32604/iasc.2023.033112

Abstract The finite element method is a key player in computational electromagnetics for designing RF (Radio Frequency) components such as waveguides. The frequency-domain analysis is fundamental to identify the characteristics of the components. For the conventional frequency-domain electromagnetic analysis using FEM (Finite Element Method), the system matrix is complex-numbered as well as indefinite. The iterative solvers can be faster than the direct solver when the solver convergence is guaranteed and done in a few steps. However, such complex-numbered and indefinite systems are hard to exploit the merit of the iterative solver. It is also hard to benefit from matrix factorization techniques… More >

Md Zia Ur Rahman^1,*, S. Rooban¹, P. Rohini², M. V. S. Ramprasad³, Pradeep Vinaik Kodavanti³

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5713-5726, 2022, DOI:10.32604/cmc.2022.027672

Abstract The Thoracic Electrical Bioimpedance (TEB) helps to determine the stroke volume during cardiac arrest. While measuring cardiac signal it is contaminated with artifacts. The commonly encountered artifacts are Baseline wander (BW) and Muscle artifact (MA), these are physiological and non-stationary. As the nature of these artifacts is random, adaptive filtering is needed than conventional fixed coefficient filtering techniques. To address this, a new block based adaptive learning scheme is proposed to remove artifacts from TEB signals in clinical scenario. The proposed block least mean square (BLMS) algorithm is mathematically normalized with reference to data and error. This normalization leads, block… More >

Anisha Rebinth^1,*, S. Mohan Kumar²

Computer Systems Science and Engineering, Vol.42, No.3, pp. 867-880, 2022, DOI:10.32604/csse.2022.022955

Abstract In this study, a Discriminator Model for Glaucoma Diagnosis (DMGD) using soft computing techniques is presented. As the biomedical images such as fundus images are often acquired in high resolution, the Region of Interest (ROI) for glaucoma diagnosis must be selected at first to reduce the complexity of any system. The DMGD system uses a series of pre-processing; initial cropping by the green channel’s intensity, Spatially Weighted Fuzzy C Means (SWFCM), blood vessel detection and removal by Gaussian Derivative Filters (GDF) and inpainting algorithms. Once the ROI has been selected, the numerical features such as colour, spatial domain features from… More >

Yunzuo Zhang^1,*, Shasha Zhang¹, Jiayu Zhang¹, Kaina Guo¹, Zhaoquan Cai²

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 259-272, 2021, DOI:10.32604/iasc.2021.017200

Abstract Video key frame extraction, reputed as an essential step in video analysis and content-based video retrieval, and meanwhile, also serves as the basis and premise of generating video synopsis. Video key frame extraction means extracting the meaningful parts of the video by analyzing their content and structure to form a concise and semantically expressive summary. Up to now, people have achieved many research results in key frame extraction. Nevertheless, because the surveillance video has no specific structure, such as news, sports games, and other videos, it is not accurate enough to directly extract the key frame with the existing effective… More >

Oakyoung Han¹, Jaehyoun Kim^{2, *}

CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 553-566, 2020, DOI:10.32604/cmc.2020.09362

Abstract The processing of sound signals is significantly improved recently. Technique for sound signal processing focusing on music beyond speech area is getting attention due to the development of deep learning techniques. This study is for analysis and process of music signals to generate tow-dimensional tabular data and a new music. For analysis and process part, we represented normalized waveforms for each of input data via frequency domain signals. Then we looked into shorted segment to see the difference wave pattern for different singers. Fourier transform is applied to get spectrogram of the music signals. Filterbank is applied to represent the… More >