Roman Vinokur^*

Sound & Vibration, Vol.57, pp. 1-13, 2023, DOI:10.32604/sv.2023.041784

Abstract The loss factors and their effects on the magnitude and frequency of resonance peaks in various mechanical systems are reviewed for acoustic, vibration, and vibration fatigue applications. The main trends and relationships were obtained for linear mechanical models with hysteresis damping. The well-known features (complex module of elasticity, total loss factor, etc.) are clarified for practical engineers and students, and new results are presented (in particular, for 2-DOF in-series models with hysteresis friction). The results are of both educational and practical interest and may be applied for NVH analysis and testing, mechanical and aeromechanical design, and noise and vibration control… More >

Turki M. Alanazi^*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1553-1568, 2023, DOI:10.32604/iasc.2023.039686

Abstract Image processing is becoming more popular because images are being used increasingly in medical diagnosis, biometric monitoring, and character recognition. But these images are frequently contaminated with noise, which can corrupt subsequent image processing stages. Therefore, in this paper, we propose a novel nonlinear filter for removing “salt and pepper” impulsive noise from a complex color image. The new filter is called the Modified Vector Directional Filter (MVDF). The suggested method is based on the traditional Vector Directional Filter (VDF). However, before the candidate pixel is processed by the VDF, the MVDF employs a threshold and the neighboring pixels of… More >

Jing Geng^1,*, Huali Yang², Shengze Hu³

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1311-1324, 2023, DOI:10.32604/iasc.2023.038481

Abstract Educational data mining based on student cognitive diagnosis analysis can provide an important decision basis for personalized learning tutoring of students, which has attracted extensive attention from scholars at home and abroad and has made a series of important research progress. To this end, we propose a noise-filtering enhanced deep cognitive diagnosis method to improve the fitting ability of traditional models and obtain students’ skill mastery status by mining the interaction between students and problems nonlinearly through neural networks. First, modeling complex interactions between students and problems with multidimensional features based on cognitive processing theory can enhance the interpretability of… More >

Philippe Apparicio¹ , Jérémy Gelb¹, Paula Negron-Poblete², Mathieu Carrier¹, Stéphanie Potvin¹ , Élaine Lesage-Mann¹

Revue Internationale de Géomatique, Vol.30, No.2, pp. 155-179, 2020, DOI:10.3166/rig.2021.00110

Abstract Air pollution and road traffic noise are two important environmental nuisances that could be harmful to the health and well-being of urban populations. In Mexico City, as in many North American cities, there has been an upsurge in bicycle ridership. However, Mexico City is also well known for having high levels of noise and air pollution. The purpose of this study is threefold: 1) evaluate cyclists’ exposure to air pollution (nitrogen dioxide) and road traffic noise; 2) identify local factors that increase or reduce cyclists’ exposure, in paying particular attention to the type of road and bicycle path or lane… More >

Jiajia Yu^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 777-797, 2023, DOI:10.32604/iasc.2023.037354

Abstract This study aims to reduce the interference of ambient noise in mobile communication, improve the accuracy and authenticity of information transmitted by sound, and guarantee the accuracy of voice information delivered by mobile communication. First, the principles and techniques of speech enhancement are analyzed, and a fast lateral recursive least square method (FLRLS method) is adopted to process sound data. Then, the convolutional neural networks (CNNs)-based noise recognition CNN (NR-CNN) algorithm and speech enhancement model are proposed. Finally, related experiments are designed to verify the performance of the proposed algorithm and model. The experimental results show that the noise classification… More >

Saleh Naif Almuayqil¹, Reham Arnous^2,*, Noha Sakr³, Magdy M. Fadel³

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5177-5192, 2023, DOI:10.32604/cmc.2023.038625

Abstract Skin segmentation participates significantly in various biomedical applications, such as skin cancer identification and skin lesion detection. This paper presents a novel framework for segmenting the skin. The framework contains two main stages: The first stage is for removing different types of noises from the dermoscopic images, such as hair, speckle, and impulse noise, and the second stage is for segmentation of the dermoscopic images using an attention residual U-shaped Network (U-Net). The framework uses variational Autoencoders (VAEs) for removing the hair noises, the Generative Adversarial Denoising Network (DGAN-Net), the Denoising U-shaped U-Net (D-U-NET), and Batch Renormalization U-Net (Br-U-NET) for… More >

Zhiping Lu¹, Xinwei Yue^2,*, Shuo Chen², Weiguo Ma¹

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 789-812, 2023, DOI:10.32604/cmes.2023.027663

Abstract Active intelligent reflecting surface (IRS) is a novel and promising technology that is able to overcome the multiplicative fading introduced by passive IRS. In this paper, we consider the application of active IRS to non-orthogonal multiple access (NOMA) networks, where the incident signals are amplified actively through integrating amplifier to reflecting elements. More specifically, the performance of active/passive IRS-NOMA networks is investigated over large and small-scale fading channels. Aiming to characterize the performance of active IRS-NOMA networks, the exact and asymptotic expressions of outage probability for a couple of users, i.e., near-end user and far-end user are derived by exploiting… More >

Vidit Kumar^1,*, Hemant Petwal², Ajay Krishan Gairola¹, Pareshwar Prasad Barmola¹

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2711-2724, 2023, DOI:10.32604/csse.2023.032047

Abstract Fine-grained image search is one of the most challenging tasks in computer vision that aims to retrieve similar images at the fine-grained level for a given query image. The key objective is to learn discriminative fine-grained features by training deep models such that similar images are clustered, and dissimilar images are separated in the low embedding space. Previous works primarily focused on defining local structure loss functions like triplet loss, pairwise loss, etc. However, training via these approaches takes a long training time, and they have poor accuracy. Additionally, representations learned through it tend to tighten up in the embedded… More >

Ahmed Fahim^1,2,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3695-3712, 2023, DOI:10.32604/cmc.2023.036820

Abstract Finding clusters based on density represents a significant class of clustering algorithms. These methods can discover clusters of various shapes and sizes. The most studied algorithm in this class is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). It identifies clusters by grouping the densely connected objects into one group and discarding the noise objects. It requires two input parameters: epsilon (fixed neighborhood radius) and MinPts (the lowest number of objects in epsilon). However, it can’t handle clusters of various densities since it uses a global value for epsilon. This article proposes an adaptation of the DBSCAN method so… More >

Cong Lin¹, Chenghao Qiu¹, Can Wu¹, Siling Feng^1,*, Mengxing Huang^1,2,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4317-4340, 2023, DOI:10.32604/cmc.2023.036249

Abstract The random-value impulse noise (RVIN) detection approach in image denoising, which is dependent on manually defined detection thresholds or local window information, does not have strong generalization performance and cannot successfully cope with damaged pictures with high noise levels. The fusion of the K-means clustering approach in the noise detection stage is reviewed in this research, and the internal relationship between the flat region and the detail area of the damaged picture is thoroughly explored to suggest an unique two-stage method for gray image denoising. Based on the concept of pixel clustering and grouping, all pixels in the damaged picture… More >