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  • Open Access

    ARTICLE

    Research on Sensor Network Coverage Enhancement Based on Non-Cooperative Games

    Chaofan Duan1, Jing Feng1,*, Haotian Chang1, Jianping Pan2, Liming Duan1

    CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 989-1002, 2019, DOI:10.32604/cmc.2019.06033

    Abstract Coverage is an important issue for resources rational allocation, cognitive tasks completion in sensor networks. The mobility, communicability and learning ability of smart sensors have received much attention in the past decade. Based on the deep study of game theory, a mobile sensor non-cooperative game model is established for the sensor network deployment and a local information-based topology control (LITC) algorithm for coverage enhancement is proposed. We both consider revenue of the monitoring events and neighboring sensors to avoid nodes aggregation when formulating the utility function. We then prove that the non-cooperative game is an exact potential game in which… More >

  • Open Access

    ARTICLE

    Retinal Vessel Extraction Framework Using Modified Adaboost Extreme Learning Machine

    B. V. Santhosh Krishna1, *, T. Gnanasekaran2

    CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 855-869, 2019, DOI:10.32604/cmc.2019.07585

    Abstract An explicit extraction of the retinal vessel is a standout amongst the most significant errands in the field of medical imaging to analyze both the ophthalmological infections, for example, Glaucoma, Diabetic Retinopathy (DR), Retinopathy of Prematurity (ROP), Age-Related Macular Degeneration (AMD) as well as non retinal sickness such as stroke, hypertension and cardiovascular diseases. The state of the retinal vasculature is a significant indicative element in the field of ophthalmology. Retinal vessel extraction in fundus imaging is a difficult task because of varying size vessels, moderately low distinction, and presence of pathologies such as hemorrhages, microaneurysms etc. Manual vessel extraction… More >

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