Innovative Research Award
Yogesh shankar
Infineon Technologies Asia Pacific, Singapore
Yogesh shankar is an engineering researcher associated with Infineon Technologies Asia Pacific in Singapore. The supplied indexed record identifies research involving radar-based sensing, non-intrusive motion recognition, and deformable convolutional neural networks. The available bibliometric information reports two documents, 21 citations, and an h-index of 2. The documented publication record provides a focused example of applying deep-learning methods to sensing and recognition problems. [1]
Abstract
The supplied research profile concerns engineering applications of intelligent sensing and machine learning. Its identified publication, presented at the 18th IEEE International Conference on Machine Learning and Applications in 2019, investigates radar-based non-intrusive fall motion recognition using a deformable convolutional neural network. Such work combines radar sensing with deep-learning-based recognition, addressing the computational interpretation of motion without relying on direct physical contact. [2]
Keywords
Engineering; radar sensing; fall recognition; motion recognition; deep learning; convolutional neural networks; deformable convolution; non-intrusive sensing; machine learning; intelligent systems.
Introduction
Radar-based sensing provides a means of observing movement without requiring conventional wearable or contact-based instrumentation. Within this context, machine learning can be used to extract patterns from radar-derived information and classify specific human activities. The documented work by yogesh shankar and collaborators applies a deformable convolutional neural network to fall-motion recognition, placing the research at the intersection of radar sensing, signal interpretation, and artificial intelligence. [2]
Research Profile
The stated subject area is Engineering, with the available publication evidence specifically connected to machine learning and sensing applications. The supplied profile reports two documents, 21 citations, and an h-index of 2. These bibliometric indicators represent the supplied database snapshot and may change as additional publications and citations are indexed. [1]
Research Contributions
- Investigation of radar-based non-intrusive sensing for human fall-motion recognition. [2]
- Application of deformable convolutional neural networks to motion-recognition tasks. [2]
- Integration of radar sensing and deep-learning-based computational recognition within an engineering research context.
Publications
Radar-based non-intrusive fall motion recognition using deformable convolutional neural network was published as a conference paper in the Proceedings of the 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019). The listed authors are Y. Shankar, Yogesh Shankar, Souvik Hazra, and Avik Santra. The supplied record reports 12 citations for this publication. [2]
Research Impact
The supplied Scopus information reports 21 citations across two documents and an h-index of 2. The identified conference paper accounts for 12 citations in the supplied results. Citation counts are dynamic bibliometric measures and can differ between indexing services; therefore, they are best interpreted as indicators of documented scholarly visibility rather than as a complete measure of research significance. [1] [2]
Award Suitability
The documented publication provides evidence of research activity relevant to an Innovative Research Award within the Engineering subject area. Its focus on radar-based non-intrusive sensing and deformable convolutional neural networks represents an application of contemporary computational methods to human-motion recognition. Consideration for an award should be made alongside the complete research record and the applicable criteria of the International Academic Excellence Awards.
Conclusion
yogesh shankar’s supplied academic record demonstrates focused research at the intersection of engineering, radar sensing, and machine learning. The documented work on non-intrusive fall-motion recognition illustrates the use of deformable convolutional neural networks in an applied sensing context. The available bibliometric indicators and publication evidence provide a concise basis for academic recognition within the International Academic Excellence Awards framework.
External Links
References
- Elsevier. (n.d.). Scopus author details: Yogesh Shankar, Author ID 57215433281. Scopus. The supplied Scopus record reports two documents, 21 citations, and an h-index of 2.
- Shankar, Y., Shankar, Y., Hazra, S., & Santra, A. (2019). Radar-based non-intrusive fall motion recognition using deformable convolutional neural network. Proceedings of the 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019). The supplied record reports 12 citations.
- IEEE. (2019). 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019). Conference publication record associated with the documented research.
- International Academic Excellence Awards. (n.d.). Academic Excellence Awards.
https://academicexcellenceawards.com/