The most reliable method. Search ORCID database (orcid.org) for Namrata Sinha. If she has claimed her ID, all her IEEE Access papers will be listed.
Assuming Namrata Sinha’s IEEE Access paper deals with signal processing/AI, several open problems emerge:
A follow-up paper—perhaps a second IEEE Access publication—would likely address one of these gaps.
As of 2025, IEEE Access maintains a JIF around 3.9–4.5, placing it in Q1 (top 25%) of multidisciplinary engineering journals. A paper by Sinha thus carries weight.
Tables comparing Bit Error Rate (BER) vs. SNR for:
A figure showing convergence of training/validation loss over epochs.
Since a specific paper is not listed in this request, let us build a realistic profile of what a Sinha Namrata IEEE Access publication would likely contain, based on common research themes.
If you’d like, I can:
(Invoking related search suggestions.)
Searching for " Namrata Sinha " in relation to IEEE Access highlights her contribution as an author in the field of antenna design and wireless communication
The following write-up summarizes her research profile and primary work published in this prestigious multidisciplinary journal. Research Spotlight: Namrata Sinha Namrata Sinha is a researcher known for her work in high-performance antenna systems
. Her publications often focus on improving the technical parameters of wireless devices, such as polarization, bandwidth, and gain, which are critical for modern 5G and satellite communications. Key Publication in IEEE Access
A significant work associated with her involves the design of slant polarized antennas using inverted resonators. Core Innovation
: The research introduces a generic design procedure for achieving dual-slant polarization (
). This is achieved by strategically switching "via hole" positions at two resonators to control the direction of current flow. Technical Impact Polarization Diversity
: Enables better signal stability in complex environments by utilizing multiple polarization states. Validation
: The study provides a comprehensive theory analysis, simulation results, and experimental validation, marking it as a "promising" contribution to the field of inverted resonator antennas Context of the Journal IEEE Access sinha namrata ieee access
is a high-impact, open-access journal (Impact Factor 3.6 as of 2024) that undergoes rigorous peer review, typically providing an accept/reject decision within 4 to 6 weeks. About IEEE Access
As a contributor to this journal, Namrata Sinha joins a community of researchers publishing in a "Q1" (top-tier) publication known for its rapid dissemination of technically correct and original research across all IEEE fields of interest. specific paper or a summary of her other research interests? AI responses may include mistakes. Learn more IEEE Access - Decision on Manuscript ID Access-2020-31789
To develop a feature or address submission requirements for Namrata Sinha
(or similarly named authors such as Namrata Simha) regarding IEEE Access, you must follow specific editorial workflows for manuscript updates and feature extraction schemes typically found in their research domains (such as AI, ADAS, or network security). 1. Manuscript Development & Submission Features
If you are currently developing or revising a manuscript for IEEE Access, the platform requires specific "features" or documents to be included in your submission package:
Response to Reviewers: A document detailing each reviewer's concern, your response, and the specific action taken to remedy the issue.
Highlighted Manuscript: An updated copy of your manuscript with all individual and grammatical changes highlighted (ideally using a yellow highlight tool in the PDF).
Formatted Clean Copy: A final, clean version of the manuscript in the standard IEEE double-column format. 2. Feature Engineering in Research (e.g., ADAS or Security) The most reliable method
Research authored by individuals such as Namrata Simha (Microsoft/Azure) often focuses on "feature ownership" and "feature reduction" in advanced AI systems:
ADAS Feature Ownership: Development involves internal and external planning, delivery management, and the integration of AI software (like 2D object detection or semantic segmentation) into Advanced Driver Assistance Systems (ADAS) pipelines.
Feature Reduction Schemes: For network security or intrusion detection, researchers develop novel schemes (e.g., using Monarch Butterfly Optimization or Correlation-based Fitness Functions) to handle large feature sets and improve detection accuracy.
Feature Extraction Sub-networks: In deep learning architectures, development may include Squeeze-and-Excitation (SE) modules that scale channels to highlight important features and reduce computational complexity. 3. Relevant Collaborative Work
You may also be looking for specific publications to cite or build upon:
Object Detection: "Robustness and deployability of deep object detectors in autonomous driving" (2019 IEEE Intelligent Transportation Systems Conference).
IoMT Frameworks: "Miner Selection in an Internet of Medical Things Framework using Fuzzy Logic" (Applied Soft Computing, 2024), co-authored by Namrata Singh and Ditipriya Sinha.
A rigorous paper would include: