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  • Open Access


    Facial Expression Recognition Based on Multi-Channel Attention Residual Network

    Tongping Shen1,2,*, Huanqing Xu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 539-560, 2023, DOI:10.32604/cmes.2022.022312

    Abstract For the problems of complex model structure and too many training parameters in facial expression recognition algorithms, we proposed a residual network structure with a multi-headed channel attention (MCA) module. The migration learning algorithm is used to pre-train the convolutional layer parameters and mitigate the overfitting caused by the insufficient number of training samples. The designed MCA module is integrated into the ResNet18 backbone network. The attention mechanism highlights important information and suppresses irrelevant information by assigning different coefficients or weights, and the multi-head structure focuses more on the local features of the pictures, which improves the efficiency of facial… More >

  • Open Access


    Automatic Detection of Outliers in Multi-Channel EMG Signals Using MFCC and SVM

    Muhammad Irfan1, Khalil Ullah2, Fazal Muhammad3,*, Salman Khan3, Faisal Althobiani4, Muhammad Usman5, Mohammed Alshareef4, Shadi Alghaffari4, Saifur Rahman1

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 169-181, 2023, DOI:10.32604/iasc.2023.032337

    Abstract The automatic detection of noisy channels in surface Electromyogram (sEMG) signals, at the time of recording, is very critical in making a noise-free EMG dataset. If an EMG signal contaminated by high-level noise is recorded, then it will be useless and can’t be used for any healthcare application. In this research work, a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals. A modified version of mel frequency cepstral coefficients (mMFCC) is proposed for the extraction of features from sEMG channels along with… More >

  • Open Access


    Opportunistic Routing with Multi-Channel Cooperative Neighbour Discovery

    S. Sathish Kumar1,*, G. Ravi2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2367-2382, 2023, DOI:10.32604/iasc.2023.030054

    Abstract Due to the scattered nature of the network, data transmission in a distributed Mobile Ad-hoc Network (MANET) consumes more energy resources (ER) than in a centralized network, resulting in a shorter network lifespan (NL). As a result, we build an Enhanced Opportunistic Routing (EORP) protocol architecture in order to address the issues raised before. This proposed routing protocol goal is to manage the routing cost by employing power, load, and delay to manage the routing energy consumption based on the flooding of control packets from the target node. According to the goal of the proposed protocol technique, it is possible… More >

  • Open Access


    Performance Analysis of Multi-Channel CR Enabled IoT Network with Better Energy Harvesting

    Afiya Kiran1, Ahmad Karim2,*, Yasser Obaid Alharbi3, Diaa Mohammed Uliyan3

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 183-197, 2022, DOI:10.32604/cmc.2022.021860

    Abstract Wireless Sensor Networks (WSNs) can be termed as an auto-configured and infrastructure-less wireless networks to monitor physical or environmental conditions, such as temperature, sound, vibration, pressure and motion etc. WSNs may comprise thousands of Internet of Things (IoT) devices to sense and collect data from its surrounding, process the data and take an automated and mechanized decision. On the other side the proliferation of these devices will soon cause radio spectrum shortage. So, to facilitate these networks, we integrate Cognitive Radio (CR) functionality in these networks. CR can sense the unutilized spectrum of licensed users and then use these empty… More >

  • Open Access


    A Hybrid Deep Learning Scheme for Multi-Channel Sleep Stage Classification

    Wei Pei1, Yan Li1, Siuly Siuly1,*, Peng Wen2

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 889-905, 2022, DOI:10.32604/cmc.2022.021830

    Abstract Sleep stage classification plays a significant role in the accurate diagnosis and treatment of sleep-related diseases. This study aims to develop an efficient deep learning based scheme for correctly identifying sleep stages using multi-biological signals such as electroencephalography (EEG), electrocardiogram (ECG), electromyogram (EMG), and electrooculogram (EOG). Most of the prior studies in sleep stage classification focus on hand-crafted feature extraction methods. Traditional hand-crafted feature extraction methods choose features manually from raw data, which is tedious, and these features are limited in their ability to balance efficiency and accuracy. Moreover, most of the existing works on sleep staging are either single… More >

  • Open Access


    An Emotion Analysis Method Using Multi-Channel Convolution Neural Network in Social Networks

    Xinxin Lu1,*, Hong Zhang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 281-297, 2020, DOI:10.32604/cmes.2020.010948

    Abstract As an interdisciplinary comprehensive subject involving multidisciplinary knowledge, emotional analysis has become a hot topic in psychology, health medicine and computer science. It has a high comprehensive and practical application value. Emotion research based on the social network is a relatively new topic in the field of psychology and medical health research. The text emotion analysis of college students also has an important research significance for the emotional state of students at a certain time or a certain period, so as to understand their normal state, abnormal state and the reason of state change from the information they wrote. In… More >

  • Open Access


    Measuring displacement of a bridge using multi-channel image processing techniques

    Jong-Jae Lee, Jong-Woong Park, Young-Soo Park, Hyung-Jo Jung

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.12, No.3, pp. 92-92, 2009, DOI:10.3970/icces.2009.012.092

    Abstract Measuring the displacement of a flexible bridge is difficult particularly on a long span bridge. In this study, real-time displacement measurement of a long span bridge was carried out using digital image processing techniques which require a target recognition algorithm, projection of the captured image, and calculation of the actual displacement using target geometry and the number of pixels moved. To measure the displacement of a bridge from a distant location which can be regarded as a fixed reference point, a novel image processing method has been devised. By measuring the same target with two independent cameras placed in a… More >

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