Yuewei Tian¹, Yang Su², Yujia Wang¹, Lisa Guo¹, Xuyang Wu^3,*, Lei Cao⁴, Fang Ren³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072081 - 12 January 2026

Abstract This study addresses the risk of privacy leakage during the transmission and sharing of multimodal data in smart grid substations by proposing a three-tier privacy-preserving architecture based on asynchronous federated learning. The framework integrates blockchain technology, the InterPlanetary File System (IPFS) for distributed storage, and a dynamic differential privacy mechanism to achieve collaborative security across the storage, service, and federated coordination layers. It accommodates both multimodal data classification and object detection tasks, enabling the identification and localization of key targets and abnormal behaviors in substation scenarios while ensuring privacy protection. This effectively mitigates the single-point… More >

Yan Kong¹, Xinpeng Guo², Chih-Hsien Hsia^3,4,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071269 - 12 January 2026

Abstract With the development of technology, diffusion model-based solvers have shown significant promise in solving Combinatorial Optimization (CO) problems, particularly in tackling Non-deterministic Polynomial-time hard (NP-hard) problems such as the Traveling Salesman Problem (TSP). However, existing diffusion model-based solvers typically employ a fixed, uniform noise schedule (e.g., linear or cosine annealing) across all training instances, failing to fully account for the unique characteristics of each problem instance. To address this challenge, we present Graph-Guided Diffusion Solvers (GGDS), an enhanced method for improving graph-based diffusion models. GGDS leverages Graph Neural Networks (GNNs) to capture graph structural information… More >

Musheng Chen^1,2, Qiang Wen¹, Xiaohong Qiu^1,2, Junhua Wu^1,*, Wenqing Fu¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071207 - 12 January 2026

Abstract In multi-modal emotion recognition, excessive reliance on historical context often impedes the detection of emotional shifts, while modality heterogeneity and unimodal noise limit recognition performance. Existing methods struggle to dynamically adjust cross-modal complementary strength to optimize fusion quality and lack effective mechanisms to model the dynamic evolution of emotions. To address these issues, we propose a multi-level dynamic gating and emotion transfer framework for multi-modal emotion recognition. A dynamic gating mechanism is applied across unimodal encoding, cross-modal alignment, and emotion transfer modeling, substantially improving noise robustness and feature alignment. First, we construct a unimodal encoder More >

Weichen Wang¹, Shaofeng Wang¹, Wenjing Liu^1,*, Luncai Zhou², Erqing Zhang¹, Ting Gao³, Grigory Petrishin⁴

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.071278 - 08 January 2026

Abstract In dry-coupled ultrasonic thickness measurement, thick rubber layers introduce high-amplitude parasitic echoes that obscure defect signals and degrade thickness accuracy. Existing methods struggle to resolve overlap-ping echoes under variable coupling conditions and non-stationary noise. This study proposes a novel dual-criterion framework integrating energy contribution and statistical impulsivity metrics to isolate specimen re-flections from coupling-layer interference. By decomposing A-scan signals into Intrinsic Mode Functions (IMFs), the framework employs energy contribution thresholds (>85%) and kurtosis indices (>3) to autonomously select IMFs containing valid specimen echoes. Hybrid time-frequency thresholding further suppresses interference through amplitude filtering and spectral focusing. More >

Fatima Khan¹, Amna Khan¹, Tariq Ali², Tariq Shahzad³, Tehseen Mazhar^4,*, Sunawar Khan⁵, Muhammad Adnan Khan^6,*, Habib Hamam^7,8,9,10

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-27, 2026, DOI:10.32604/cmc.2025.068228 - 09 December 2025

Abstract The rise in noise and air pollution poses severe risks to human health and the environment. Industrial and vehicular emissions release harmful pollutants such as CO₂, SO₂, CO, CH₄, and noise, leading to significant environmental degradation. Monitoring and analyzing pollutant concentrations in real-time is crucial for mitigating these risks. However, existing systems often lack the capacity to monitor both indoor and outdoor environments effectively.This study presents a low-cost, IoT-based pollution detection system that integrates gas sensors (MQ-135 and MQ-4), a noise sensor (LM393), and a humidity sensor (DHT-22), all connected to a Node MCU (ESP8266) microcontroller. The… More >

Jeongsu Park¹, Moohong Min^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3913-3948, 2025, DOI:10.32604/cmes.2025.072261 - 23 December 2025

Abstract Time series anomaly detection is critical in domains such as manufacturing, finance, and cybersecurity. Recent generative AI models, particularly Transformer- and Autoencoder-based architectures, show strong accuracy but their robustness under noisy conditions is less understood. This study evaluates three representative models—AnomalyTransformer, TranAD, and USAD—on the Server Machine Dataset (SMD) and cross-domain benchmarks including the Soil Moisture Active Passive (SMAP) dataset, the Mars Science Laboratory (MSL) dataset, and the Secure Water Treatment (SWaT) testbed. Seven noise settings (five canonical, two mixed) at multiple intensities are tested under fixed clean-data training, with variations in window, stride, and More > Graphic Abstract

Akbare Yaqub^1,2, Muhammad Sadiq Orakzai², Muhammad Farrukh Qureshi^3,4, Zohaib Mushtaq⁵, Imran Siddique^6,7, Taha Radwan^8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2503-2533, 2025, DOI:10.32604/cmes.2025.071571 - 26 November 2025

Abstract Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, necessitating efficient diagnostic tools. This study develops and validates a deep learning framework for phonocardiogram (PCG) classification, focusing on model generalizability and robustness. Initially, a ResNet-18 model was trained on the PhysioNet 2016 dataset, achieving high accuracy. To assess real-world viability, we conducted extensive external validation on the HLS-CMDS dataset. We performed four key experiments: (1) Fine-tuning the PhysioNet-trained model for binary (Normal/Abnormal) classification on HLS-CMDS, achieving 88% accuracy. (2) Fine-tuning the same model for multi-class classification (Normal, Murmur, Extra Sound, Rhythm Disorder), which yielded… More >

Geunil Lee¹, Daeil Kwon^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3329-3342, 2025, DOI:10.32604/cmes.2025.069402 - 30 September 2025

Abstract Nondestructive testing (NDT) methods such as visual inspection and ultrasonic testing are widely applied in manufacturing quality control, but they remain limited in their ability to detect defect characteristics. Visual inspection depends strongly on operator experience, while ultrasonic testing requires physical contact and stable coupling conditions that are difficult to maintain in production lines. These constraints become more pronounced when defect-related information is scarce or when background noise interferes with signal acquisition in manufacturing processes. This study presents a non-contact acoustic method for diagnosing defects in scroll compressors during the manufacturing process. The diagnostic approach… More >

Shih-Lin Lin^*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1365-1382, 2025, DOI:10.32604/cmc.2025.067764 - 29 August 2025

Abstract This paper proposes an integrated multi-stage framework to enhance frequency modulated continuous wave (FMCW) automotive radar performance under high noise and interference. The four-stage pipeline is applied consecutively: (i) an improved independent component analysis (ICA) blindly separates the two-channel echoes, isolating target and interference components; (ii) a recursive least-squares (RLS) filter compensates amplitude- and phase-mismatches, restoring signal fidelity; (iii) variational mode decomposition (VMD) followed by the Hilbert-Huang Transform (HHT) extracts noise-free intrinsic mode functions (IMFs) and sharpens their time-frequency signatures; and (iv) HHT-based beat-frequency estimation reconstructs a clean echo and delivers accurate range information. Finally, More >

Yuanqing Ding^1,2, Hanming Zhai¹, Qiming Ma¹, Liang Zhang¹, Lei Shao², Fanliang Bu^1,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 905-923, 2025, DOI:10.32604/cmc.2025.066307 - 29 August 2025

Abstract As the use of deepfake facial videos proliferate, the associated threats to social security and integrity cannot be overstated. Effective methods for detecting forged facial videos are thus urgently needed. While many deep learning-based facial forgery detection approaches show promise, they often fail to delve deeply into the complex relationships between image features and forgery indicators, limiting their effectiveness to specific forgery techniques. To address this challenge, we propose a dual-branch collaborative deepfake detection network. The network processes video frame images as input, where a specialized noise extraction module initially extracts the noise feature maps.… More >