Sinha Namrata Ieee Access Link Site

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A baseline evaluation of whether the paper introduces novel data to the existing body of knowledge.

Despite promising results, several challenges remain. First, many deep-learning studies rely on laboratory datasets that do not fully represent industrial variability (load changes, sensor placement, environmental noise). Second, there is limited work on computationally efficient architectures suited for edge deployment in resource-constrained monitoring devices. Third, the impact of preprocessing choices (denoising, windowing, transform parameters) on model robustness is not well quantified in the literature.

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Designing subwavelength structures or metasurfaces for wireless energy harvesting and wireless power transfer.

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Utilizing neural networks for pattern recognition or classification in industrial applications. How to Find the Direct Link

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If you have seen a citation like: N. Sinha, A. Kumar, and P. Sharma, “Title of Paper,” IEEE Access, vol. 10, pp. 1000–1010, 2022, doi: 10.1109/ACCESS.2022.1234567. Then the direct link is: https://ieeexplore.ieee.org/document/1234567 (using the DOI suffix). Second, there is limited work on computationally efficient

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As communication systems continue to evolve, Sinha Namrata's research will likely focus on emerging areas such as 6G networks, Internet of Things (IoT), and artificial intelligence-based communication systems. Her future research directions may include:

Since IEEE Access is 100% open access, you can download the full PDF of any article found there without a subscription. IEEE Access - Decision on Manuscript ID Access-2020-31789