Sinha Namrata Ieee Access Link Jun 2026

This diversity highlights that a name alone is rarely enough; context is everything. For a researcher or student looking for a specific technical paper, the professional context provided by an IEEE Xplore profile (including affiliation and co-author network) is invaluable.

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Sinha Namrata is a researcher with a strong background in [insert field of expertise]. Her research interests include [insert specific areas of interest]. With a passion for innovation and a commitment to excellence, Namrata has been actively involved in various research projects, collaborating with esteemed institutions and researchers worldwide.

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: Designing circuits where finite element methods (FEM) validate inductance ( ) and capacitance ( ) configurations. This diversity highlights that a name alone is

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: Achieving slant polarization natively through via-hole shifting rather than physical antenna rotation. Performance Evaluation Metrics in IEEE Access Sinha Namrata is a researcher with a strong

Recent advances in deep learning have demonstrated significant potential for automated feature extraction and robust classification in fault diagnosis tasks. Convolutional neural networks (CNNs) can learn hierarchical representations from raw signals or their time–frequency transforms (e.g., spectrograms, scalograms) and have achieved state-of-the-art results in bearing and rotor fault detection. Combining multiple sensor modalities, such as vibration and stator current, further improves diagnostic performance by capturing complementary information: vibration sensors are sensitive to mechanical defects while current signals reflect electromagnetic irregularities caused by faults.

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The remainder of the paper details related work (Section II), experimental setup and datasets (III), preprocessing and feature extraction (IV), the proposed model (V), training and evaluation (VI–VII), discussion (VIII), and conclusions (IX).