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  • Bentham Science Publishers Ltd.  (3)
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  • Bentham Science Publishers Ltd.  (3)
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  • 1
    Online Resource
    Online Resource
    Bentham Science Publishers Ltd. ; 2014
    In:  The Open Electrical & Electronic Engineering Journal Vol. 8, No. 1 ( 2014-9-16), p. 42-49
    In: The Open Electrical & Electronic Engineering Journal, Bentham Science Publishers Ltd., Vol. 8, No. 1 ( 2014-9-16), p. 42-49
    Abstract: Recent patents and progress on scan chain balance algorithms have been reviewed. With a significant increase of the SoC (System on Chip) integration and scale, the test time of SoC increase dramatically, and this makes the test cost of SoC grow rapidly. In order to reduce test cost and expense, the paper proposes an OBBO (Opposition-based learning and Biogeography Based Optimization) algorithm and designs wrapper scan chains for the IP(Intellectual Property) using OBBO algorithm, which can make wrapper scan chains equilibration so that we can make the test time of IP be minimum. The new method is a random optimization algorithm which combines BBO (Biogeography Based Optimization) algorithm with OBL (Opposition-based learning). By using migration operation, mutation operation and OBL operation, we achieve a balance between different wrapper chains so that we can shorten the wrapper scan chain which is longest. Experimental results show that OBBO can obtain shorter longest wrapper scan chain in most case and at the same time the convergence speed can be faster.
    Type of Medium: Online Resource
    ISSN: 1874-1290
    Language: English
    Publisher: Bentham Science Publishers Ltd.
    Publication Date: 2014
    detail.hit.zdb_id: 2396175-2
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  • 2
    Online Resource
    Online Resource
    Bentham Science Publishers Ltd. ; 2015
    In:  The Open Cybernetics & Systemics Journal Vol. 8, No. 1 ( 2015-1-21), p. 104-110
    In: The Open Cybernetics & Systemics Journal, Bentham Science Publishers Ltd., Vol. 8, No. 1 ( 2015-1-21), p. 104-110
    Type of Medium: Online Resource
    ISSN: 1874-110X
    Language: English
    Publisher: Bentham Science Publishers Ltd.
    Publication Date: 2015
    detail.hit.zdb_id: 2396183-1
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  • 3
    Online Resource
    Online Resource
    Bentham Science Publishers Ltd. ; 2021
    In:  Current Medical Imaging Formerly Current Medical Imaging Reviews Vol. 17, No. 3 ( 2021-04-22), p. 374-383
    In: Current Medical Imaging Formerly Current Medical Imaging Reviews, Bentham Science Publishers Ltd., Vol. 17, No. 3 ( 2021-04-22), p. 374-383
    Abstract: Both CT and PET radiomics is considered as a potential prognostic biomarker in head and neck cancer. This study investigates the value of fused pre-treatment functional imaging (18F-FDG PET/CT) radiomics for modeling of local recurrence of head and neck cancers. Material and Methods: Firstly, 298 patients have been divided into a training set (n = 192) and verification set (n = 106). Seco ndly, PETs and CTs are fused based on wavelet transform. Thirdly, radiomics features are extracted from the 3D tumor area from PETCT fusion. The training set is used to select the features reduction and predict local recurrence, and the random forest prediction models combining radiomics and clinical variables are constructed. Finally, the ROC curve and KM analysis are used to evaluate the prediction efficiency of the model on the validation set. Results: Two PET/CT fusion radiomics features and three clinic parameters are extracted to construct the radiomics model. AUC value in the verification set 0.70 is better than no fused sets 0.69. The accuracy of 0.66 is not the highest value (0.67). Either consistency index CI 0.70 (from 0.67 to 0.70) or the p-value 0.025 (from 0.03 to 0.025) get the best result in all four models. Conclusion: The radiomics model based on the fusion of PETCT is better than the model based on PET or CT alone in predicting local recurrence, the inclusion of clinical parameters may result in more accurate predictions, which has certain guiding significance for the development of personalized, precise treatment scheme.
    Type of Medium: Online Resource
    ISSN: 1573-4056
    Language: English
    Publisher: Bentham Science Publishers Ltd.
    Publication Date: 2021
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