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  • Open Access

    ARTICLE

    A Low-Power 12-Bit SAR ADC for Analog Convolutional Kernel of Mixed-Signal CNN Accelerator

    Jungyeon Lee1, Malik Summair Asghar1,2, HyungWon Kim1,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4357-4375, 2023, DOI:10.32604/cmc.2023.031372

    Abstract As deep learning techniques such as Convolutional Neural Networks (CNNs) are widely adopted, the complexity of CNNs is rapidly increasing due to the growing demand for CNN accelerator system-on-chip (SoC). Although conventional CNN accelerators can reduce the computational time of learning and inference tasks, they tend to occupy large chip areas due to many multiply-and-accumulate (MAC) operators when implemented in complex digital circuits, incurring excessive power consumption. To overcome these drawbacks, this work implements an analog convolutional filter consisting of an analog multiply-and-accumulate arithmetic circuit along with an analog-to-digital converter (ADC). This paper introduces the… More >

  • Open Access

    ARTICLE

    Multi-Generator Discriminator Network Using Texture-Edge Information

    Kyeongseok Jang1, Seongsoo Cho2, Kwang Chul Son3,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3537-3551, 2023, DOI:10.32604/cmc.2023.030557

    Abstract In the proposed paper, a parallel structure type Generative Adversarial Network (GAN) using edge and texture information is proposed. In the existing GAN-based model, many learning iterations had to be given to obtaining an output that was somewhat close to the original data, and noise and distortion occurred in the output image even when learning was performed. To solve this problem, the proposed model consists of two generators and three discriminators to propose a network in the form of a parallel structure. In the network, each edge information and texture information were received as inputs, More >

  • Open Access

    ARTICLE

    Grid Side Distributed Energy Storage Cloud Group End Region Hierarchical Time-Sharing Configuration Algorithm Based on Multi-Scale and Multi Feature Convolution Neural Network

    Wen Long*, Bin Zhu, Huaizheng Li, Yan Zhu, Zhiqiang Chen, Gang Cheng

    Energy Engineering, Vol.120, No.5, pp. 1253-1269, 2023, DOI:10.32604/ee.2023.026395

    Abstract There is instability in the distributed energy storage cloud group end region on the power grid side. In order to avoid large-scale fluctuating charging and discharging in the power grid environment and make the capacitor components show a continuous and stable charging and discharging state, a hierarchical time-sharing configuration algorithm of distributed energy storage cloud group end region on the power grid side based on multi-scale and multi feature convolution neural network is proposed. Firstly, a voltage stability analysis model based on multi-scale and multi feature convolution neural network is constructed, and the multi-scale and… More >

  • Open Access

    ARTICLE

    Identification of Key Links in Electric Power Operation Based-Spatiotemporal Mixing Convolution Neural Network

    Lei Feng1, Bo Wang1,*, Fuqi Ma1, Hengrui Ma2, Mohamed A. Mohamed3

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1487-1501, 2023, DOI:10.32604/csse.2023.035377

    Abstract As the scale of the power system continues to expand, the environment for power operations becomes more and more complex. Existing risk management and control methods for power operations can only set the same risk detection standard and conduct the risk detection for any scenario indiscriminately. Therefore, more reliable and accurate security control methods are urgently needed. In order to improve the accuracy and reliability of the operation risk management and control method, this paper proposes a method for identifying the key links in the whole process of electric power operation based on the spatiotemporal… More >

  • Open Access

    ARTICLE

    Deep Learning for Image Segmentation: A Focus on Medical Imaging

    Ali F. Khalifa1, Eman Badr1,2,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1995-2024, 2023, DOI:10.32604/cmc.2023.035888

    Abstract Image segmentation is crucial for various research areas. Many computer vision applications depend on segmenting images to understand the scene, such as autonomous driving, surveillance systems, robotics, and medical imaging. With the recent advances in deep learning (DL) and its confounding results in image segmentation, more attention has been drawn to its use in medical image segmentation. This article introduces a survey of the state-of-the-art deep convolution neural network (CNN) models and mechanisms utilized in image segmentation. First, segmentation models are categorized based on their model architecture and primary working principle. Then, CNN categories are More >

  • Open Access

    ARTICLE

    Faster Metallic Surface Defect Detection Using Deep Learning with Channel Shuffling

    Siddiqui Muhammad Yasir1, Hyunsik Ahn2,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1847-1861, 2023, DOI:10.32604/cmc.2023.035698

    Abstract Deep learning has been constantly improving in recent years, and a significant number of researchers have devoted themselves to the research of defect detection algorithms. Detection and recognition of small and complex targets is still a problem that needs to be solved. The authors of this research would like to present an improved defect detection model for detecting small and complex defect targets in steel surfaces. During steel strip production, mechanical forces and environmental factors cause surface defects of the steel strip. Therefore, the detection of such defects is key to the production of high-quality… More >

  • Open Access

    ARTICLE

    MDEV Model: A Novel Ensemble-Based Transfer Learning Approach for Pneumonia Classification Using CXR Images

    Mehwish Shaikh1, Isma Farah Siddiqui1, Qasim Arain1, Jahwan Koo2,*, Mukhtiar Ali Unar3, Nawab Muhammad Faseeh Qureshi4,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 287-302, 2023, DOI:10.32604/csse.2023.035311

    Abstract Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful; thus, catching it early is crucial. Medical physicians’ time is limited in outdoor situations due to many patients; therefore, automated systems can be a rescue. The input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’ experience. Therefore, radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest x-rays. In medical classifications, deep convolution neural networks are commonly used. This research aims to use deep pre-trained transfer learning models to… More >

  • Open Access

    ARTICLE

    Vessels Segmentation in Angiograms Using Convolutional Neural Network: A Deep Learning Based Approach

    Sanjiban Sekhar Roy1, Ching-Hsien Hsu2,3,4,*, Akash Samaran1, Ranjan Goyal1, Arindam Pande5, Valentina E. Balas6

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 241-255, 2023, DOI:10.32604/cmes.2023.019644

    Abstract Coronary artery disease (CAD) has become a significant cause of heart attack, especially among those 40 years old or younger. There is a need to develop new technologies and methods to deal with this disease. Many researchers have proposed image processing-based solutions for CAD diagnosis, but achieving highly accurate results for angiogram segmentation is still a challenge. Several different types of angiograms are adopted for CAD diagnosis. This paper proposes an approach for image segmentation using Convolution Neural Networks (CNN) for diagnosing coronary artery disease to achieve state-of-the-art results. We have collected the 2D X-ray… More >

  • Open Access

    ARTICLE

    Early Detection Glaucoma and Stargardt’s Disease Using Deep Learning Techniques

    Somasundaram Devaraj*, Senthil Kumar Arunachalam

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1283-1299, 2023, DOI:10.32604/iasc.2023.033200

    Abstract Retinal fundus images are used to discover many diseases. Several Machine learning algorithms are designed to identify the Glaucoma disease. But the accuracy and time consumption performance were not improved. To address this problem Max Pool Convolution Neural Kuan Filtered Tobit Regressive Segmentation based Radial Basis Image Classifier (MPCNKFTRS-RBIC) Model is used for detecting the Glaucoma and Stargardt’s disease by early period using higher accuracy and minimal time. In MPCNKFTRS-RBIC Model, the retinal fundus image is considered as an input which is preprocessed in hidden layer 1 using weighted adaptive Kuan filter. Then, preprocessed retinal… More >

  • Open Access

    ARTICLE

    Novel Multimodal Biometric Feature Extraction for Precise Human Identification

    J. Vasavi1, M. S. Abirami2,*

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1349-1363, 2023, DOI:10.32604/iasc.2023.032604

    Abstract In recent years, biometric sensors are applicable for identifying important individual information and accessing the control using various identifiers by including the characteristics like a fingerprint, palm print, iris recognition, and so on. However, the precise identification of human features is still physically challenging in humans during their lifetime resulting in a variance in their appearance or features. In response to these challenges, a novel Multimodal Biometric Feature Extraction (MBFE) model is proposed to extract the features from the noisy sensor data using a modified Ranking-based Deep Convolution Neural Network (RDCNN). The proposed MBFE model… More >

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