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Calibration of a microwave system for measuring grain moisture content.

, , , , and . IEEE Trans. Instrumentation and Measurement, 48 (3): 778-783 (1999)

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Calibration of a microwave system for measuring grain moisture content., , , , and . IEEE Trans. Instrumentation and Measurement, 48 (3): 778-783 (1999)Review of MRI brain tumor segmentation and MGMT promoter classification methods on BraTs dataset based on Deep learning., , , and . ATSIP, page 249-254. IEEE, (2024)Computer Aided Diagnosis (CAD) tool for MS lesions exploration In multimodal brain MRI., , , , and . ATSIP, page 1-6. IEEE, (2022)Deep Transfer Learning (DTL) Based-Framework for an Accurate Multi-classification of MRI Brain Tumors., , , , and . CW, page 86-93. IEEE, (2023)Histogram equalization-based techniques for contrast enhancement of MRI brain Glioma tumor images: Comparative study., , , and . ATSIP, page 1-6. IEEE, (2018)Comparative study of supervised and unsupervised classification methods: Application to automatic MRI glioma brain tumors segmentation., , , , , , and . ATSIP, page 1-5. IEEE, (2018)A computer aided diagnosis 'CAD' for brain glioma exploration., , , , , and . ATSIP, page 243-248. IEEE, (2014)High-resolution imaging-guided electroencephalography source localization: temporal effect regularization incorporation in LORETA inverse solution., , , , and . J. Electronic Imaging, 24 (6): 061204 (2015)Deep Convolutional Encoder-Decoder algorithm for MRI brain reconstruction., , , , , and . Medical Biol. Eng. Comput., 59 (7-8): 85-106 (2021)Review of Computer Aided-Diagnosis (CAD) Systems for MRI Gliomas brain tumors explorations based on Machine Learning and Deep learning., , , and . ATSIP, page 1-6. IEEE, (2022)