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  • Open Access


    CNN Accelerator Using Proposed Diagonal Cyclic Array for Minimizing Memory Accesses

    Hyun-Wook Son1, Ali A. Al-Hamid1,2, Yong-Seok Na1, Dong-Yeong Lee1, Hyung-Won Kim1,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1665-1687, 2023, DOI:10.32604/cmc.2023.038760

    Abstract This paper presents the architecture of a Convolution Neural Network (CNN) accelerator based on a new processing element (PE) array called a diagonal cyclic array (DCA). As demonstrated, it can significantly reduce the burden of repeated memory accesses for feature data and weight parameters of the CNN models, which maximizes the data reuse rate and improve the computation speed. Furthermore, an integrated computation architecture has been implemented for the activation function, max-pooling, and activation function after convolution calculation, reducing the hardware resource. To evaluate the effectiveness of the proposed architecture, a CNN accelerator has been… More >

  • Open Access


    Object Detection for Cargo Unloading System Based on Fuzzy C Means

    Sunwoo Hwang1, Jaemin Park1, Jongun Won2, Yongjang Kwon3, Youngmin Kim1,*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 4167-4181, 2022, DOI:10.32604/cmc.2022.023295

    Abstract With the recent increase in the utilization of logistics and courier services, it is time for research on logistics systems fused with the fourth industry sector. Algorithm studies related to object recognition have been actively conducted in convergence with the emerging artificial intelligence field, but so far, algorithms suitable for automatic unloading devices that need to identify a number of unstructured cargoes require further development. In this study, the object recognition algorithm of the automatic loading device for cargo was selected as the subject of the study, and a cargo object recognition algorithm applicable to More >

  • Open Access


    Fruits and Vegetable Diseases Recognition Using Convolutional Neural Networks

    Javaria Amin1, Muhammad Almas Anjum2, Muhammad Sharif3, Seifedine Kadry4, Yunyoung Nam5,*

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 619-635, 2022, DOI:10.32604/cmc.2022.018562

    Abstract As they have nutritional, therapeutic, so values, plants were regarded as important and they’re the main source of humankind’s energy supply. Plant pathogens will affect its leaves at a certain time during crop cultivation, leading to substantial harm to crop productivity & economic selling price. In the agriculture industry, the identification of fungal diseases plays a vital role. However, it requires immense labor, greater planning time, and extensive knowledge of plant pathogens. Computerized approaches are developed and tested by different researchers to classify plant disease identification, and that in many cases they have also had… More >

  • Open Access


    YOLOv2PD: An Efficient Pedestrian Detection Algorithm Using Improved YOLOv2 Model

    Chintakindi Balaram Murthy1, Mohammad Farukh Hashmi1, Ghulam Muhammad2,3,*, Salman A. AlQahtani2,3

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3015-3031, 2021, DOI:10.32604/cmc.2021.018781

    Abstract Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance. The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases. Therefore, the proposed algorithm YOLOv2 (“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection (referred to as YOLOv2PD) would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes. The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion (MLFF) strategy, which helps to improve the model’s feature… More >

  • Open Access


    3D Semantic Deep Learning Networks for Leukemia Detection

    Javaria Amin1, Muhammad Sharif2, Muhammad Almas Anjum3, Ayesha Siddiqa1, Seifedine Kadry4, Yunyoung Nam5,*, Mudassar Raza2

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 785-799, 2021, DOI:10.32604/cmc.2021.015249

    Abstract White blood cells (WBCs) are a vital part of the immune system that protect the body from different types of bacteria and viruses. Abnormal cell growth destroys the body’s immune system, and computerized methods play a vital role in detecting abnormalities at the initial stage. In this research, a deep learning technique is proposed for the detection of leukemia. The proposed methodology consists of three phases. Phase I uses an open neural network exchange (ONNX) and YOLOv2 to localize WBCs. The localized images are passed to Phase II, in which 3D-segmentation is performed using deeplabv3 More >

  • Open Access


    Convolutional Bi-LSTM Based Human Gait Recognition Using Video Sequences

    Javaria Amin1, Muhammad Almas Anjum2, Muhammad Sharif3, Seifedine Kadry4, Yunyoung Nam5,*, ShuiHua Wang6

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2693-2709, 2021, DOI:10.32604/cmc.2021.016871

    Abstract Recognition of human gait is a difficult assignment, particularly for unobtrusive surveillance in a video and human identification from a large distance. Therefore, a method is proposed for the classification and recognition of different types of human gait. The proposed approach is consisting of two phases. In phase I, the new model is proposed named convolutional bidirectional long short-term memory (Conv-BiLSTM) to classify the video frames of human gait. In this model, features are derived through convolutional neural network (CNN) named ResNet-18 and supplied as an input to the LSTM model that provided more distinguishable More >

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