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  • Open Access

    ARTICLE

    Reliability Analysis of Correlated Competitive and Dependent Components Considering Random Isolation Times

    Shuo Cai1, Tingyu Luo1, Fei Yu1,*, Pradip Kumar Sharma2, Weizheng Wang1, Lairong Yin3

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2763-2777, 2023, DOI:10.32604/cmc.2023.037825

    Abstract In the Internet of Things (IoT) system, relay communication is widely used to solve the problem of energy loss in long-distance transmission and improve transmission efficiency. In Body Sensor Network (BSN) systems, biosensors communicate with receiving devices through relay nodes to improve their limited energy efficiency. When the relay node fails, the biosensor can communicate directly with the receiving device by releasing more transmitting power. However, if the remaining battery power of the biosensor is insufficient to enable it to communicate directly with the receiving device, the biosensor will be isolated by the system. Therefore, a new combinatorial analysis method… More >

  • Open Access

    ARTICLE

    Secured ECG Signal Transmission Using Optimized EGC with Chaotic Neural Network in WBSN

    Ishani Mishra1,*, Sanjay Jain2, Vivek Maik3

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1109-1123, 2023, DOI:10.32604/csse.2023.025999

    Abstract In wireless body sensor network (WBSN), the set of electrocardiogram (ECG) data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient. While transmitting these collected data some adversaries may capture and misuse it due to the compromise of security. So, the major aim of this work is to enhance secure transmission of ECG signal in WBSN. To attain this goal, we present Pity Beetle Swarm Optimization Algorithm (PBOA) based Elliptic Galois Cryptography (EGC) with Chaotic Neural Network. To optimize the key generation process in Elliptic Curve Cryptography… More >

  • Open Access

    ARTICLE

    Detection and Classification of Diabetic Retinopathy Using DCNN and BSN Models

    S. Sudha*, A. Srinivasan, T. Gayathri Devi

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 597-609, 2022, DOI:10.32604/cmc.2022.024065

    Abstract Diabetes is associated with many complications that could lead to death. Diabetic retinopathy, a complication of diabetes, is difficult to diagnose and may lead to vision loss. Visual identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for clinicians. Because clinical testing involves complex procedures and is time-consuming, an automated system would help ophthalmologists to detect DR and administer treatment in a timely manner so that blindness can be avoided. Previous research works have focused on image processing algorithms, or neural networks, or signal processing techniques alone to detect diabetic retinopathy.… More >

  • Open Access

    ARTICLE

    Optimized Compressive Sensing Based ECG Signal Compression and Reconstruction

    Ishani Mishra1,*, Sanjay Jain2

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 415-428, 2022, DOI:10.32604/iasc.2022.022860

    Abstract In wireless body sensor network (WBSN), the set of electrocardiograms (ECG) data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient. However, due to the size of the ECG data, the performance of the signal compression and reconstruction is degraded. For efficient wireless transmission of ECG data, compressive sensing (CS) frame work plays significant role recently in WBSN. So, this work focuses to present CS for ECG signal compression and reconstruction. Although CS minimizes mean square error (MSE), compression rate and reconstruction probability of the CS is… More >

  • Open Access

    ARTICLE

    Classification for Glass Bottles Based on Improved Selective Search Algorithm

    Shuqiang Guo1, *, Baohai Yue1, Manyang Gao2, Xinxin Zhou1, Bo Wang3

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 233-251, 2020, DOI:10.32604/cmc.2020.010039

    Abstract The recycling of glass bottles can reduce the consumption of resources and contribute to environmental protection. At present, the classification of recycled glass bottles is difficult due to the many differences in specifications and models. This paper proposes a classification algorithm for glass bottles that is divided into two stages, namely the extraction of candidate regions and the classification of classifiers. In the candidate region extraction stage, aiming at the problem of the large time overhead caused by the use of the SIFT (scale-invariant feature transform) descriptor in SS (selective search), an improved feature of HLSN (Haar-like based on SPP-Net)… More >

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