Xinwei Zhu, Jianxun Zhang^*, Huan Zeng

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-25, 2026, DOI:10.32604/cmc.2025.068578 - 09 December 2025

Abstract Underwater images often affect the effectiveness of underwater visual tasks due to problems such as light scattering, color distortion, and detail blurring, limiting their application performance. Existing underwater image enhancement methods, although they can improve the image quality to some extent, often lead to problems such as detail loss and edge blurring. To address these problems, we propose FENet, an efficient underwater image enhancement method. FENet first obtains three different scales of images by image downsampling and then transforms them into the frequency domain to extract the low-frequency and high-frequency spectra, respectively. Then, a distance… More >

Jiahui Song, Yuepeng Zhang, Wenhao Yuan^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.069918 - 10 November 2025

Abstract Synthetic speech detection is an essential task in the field of voice security, aimed at identifying deceptive voice attacks generated by text-to-speech (TTS) systems or voice conversion (VC) systems. In this paper, we propose a synthetic speech detection model called TFTransformer, which integrates both local and global features to enhance detection capabilities by effectively modeling local and global dependencies. Structurally, the model is divided into two main components: a front-end and a back-end. The front-end of the model uses a combination of SincLayer and two-dimensional (2D) convolution to extract high-level feature maps (HFM) containing local… More >

Qiang Zhang¹, Shuo Feng¹, Shanxiong Chen², Teng Wan¹, Ying Qi^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-22, 2026, DOI:10.32604/cmc.2025.068247 - 10 November 2025

Abstract Human motion modeling is a core technology in computer animation, game development, and human-computer interaction. In particular, generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge. Existing methods typically rely on dense keyframe inputs or complex prior structures, making it difficult to balance motion quality and plausibility under conditions such as sparse constraints, long-term dependencies, and diverse motion styles. To address this, we propose a motion generation framework based on a frequency-domain diffusion model, which aims to better model complex motion distributions and enhance generation… More >

Kangkang Zhao¹, Yaojie Chen^1,*, Jianbo Li²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 745-762, 2025, DOI:10.32604/cmc.2025.063906 - 09 June 2025

Abstract Traditional cameras inevitably suffer from motion blur when facing high-speed moving objects. Event cameras, as high temporal resolution bionic cameras, record intensity changes in an asynchronous manner, and their recorded high temporal resolution information can effectively solve the problem of time information loss in motion blur. Existing event-based deblurring methods still face challenges when facing high-speed moving objects. We conducted an in-depth study of the imaging principle of event cameras. We found that the event stream contains excessive noise. The valid information is sparse. Invalid event features hinder the expression of valid features due to… More >

Dawei Qiu¹, Peng Wu^1,*, Xiaoming Zhang^2,*, Renjie Xu³

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1753-1769, 2025, DOI:10.32604/cmc.2025.062599 - 16 April 2025

Abstract Recently, many Sequential Recommendation methods adopt self-attention mechanisms to model user preferences. However, these methods tend to focus more on low-frequency information while neglecting high-frequency information, which makes them ineffective in balancing users’ long- and short-term preferences. At the same time, many methods overlook the potential of frequency domain methods, ignoring their efficiency in processing frequency information. To overcome this limitation, we shift the focus to the combination of time and frequency domains and propose a novel Hybrid Time-Frequency Dual-Branch Transformer for Sequential Recommendation, namely HyTiFRec. Specifically, we design two hybrid filter modules: the learnable… More >

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 - 25 April 2024

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,… 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 - 29 January 2024

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… 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 - 03 August 2023

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… 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 - 26 June 2023

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,… 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 - 03 April 2023

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… More >