Sinha Namrata Ieee Access Link ((free)) -

Sinha Namrata's research has had a significant impact on the field of electrical engineering and computer science. Her publications in IEEE Access have been widely cited and have received recognition from the research community. Her work on millimeter-wave beamforming and energy-efficient resource allocation has the potential to shape the future of wireless communication systems.

If you are citing a specific paper published in this journal, it is helpful to note that IEEE Access Highly Ranked : It is classified as a Q1 journal with a 2024 Impact Factor of Multidisciplinary

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For more specific details, you can view the author profile for Namrata Sinha on . Rapid Peer Review - IEEE Access

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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.

Binary feedback (accept or reject) shortens publication delays for fast-evolving tech. 100% Gold Open Access