ALI NOUR EL HAJJ | Engineering | Research Excellence Award

Published on by Academic Excellence

Research Excellence Award

Ali Nour El Hajj
American University of Beirut, Lebanon

Ali Nour El Hajj
Researcher Ali Nour El Hajj
Affiliation American University of Beirut
Country Lebanon
Scopus ID 7006586247
Documents 168
Citations 1,511
h-index 21
Subject area Engineering
Event International Academic Excellence Awards
ORCID 0000-0003-2865-6290

Ali Nour El Hajj is a researcher affiliated with the American University of Beirut in Lebanon whose reported scholarly profile includes work in engineering, electromagnetic measurement, dielectric characterization, and technology-oriented research. The Research Excellence Award profile recognizes research activity and publication contributions associated with this subject area. The bibliographic information supplied for this page highlights studies on estimating material properties through electromagnetic measurements and digitizing electrocardiogram records for potential computational analysis. [1]

Abstract

This profile summarizes selected publications and researcher metrics supplied for Ali Nour El Hajj in support of a Research Excellence Award article. The highlighted works address non-contact methods for determining dielectric properties, calibration-free material characterization, and digitization of paper electrocardiogram records. These topics demonstrate the potential connection between engineering measurement techniques and practical applications in materials analysis and health technology. The publication details and identifiers are provided through the linked scholarly records. [2]

Keywords

  • Engineering research
  • Dielectric characterization
  • Electromagnetic measurement
  • Horn antenna measurements
  • Biomedical signal digitization
  • Research excellence

Introduction

Engineering research often combines theoretical modeling, experimental measurement, and data processing to solve practical problems. In electromagnetic characterization, accurate estimates of relative permittivity and dielectric constant can support the study and evaluation of materials. In biomedical engineering, digitizing historical ECG records can make waveform information more accessible for analysis and future machine-learning workflows. The supplied publications connect these broad research directions through measurement methods and computationally supported applications. [3]

Research Profile

The profile information supplied for El Hajj lists 168 documents, 1,511 citations, and an h-index of 21. These bibliometric indicators describe publication and citation activity as reported in the provided data; they can vary as indexing services update their records. The Scopus author profile is the primary destination for checking the current indexed record, while the ORCID profile provides a persistent researcher identifier. [1]

Research Contributions

The selected engineering publications focus on approaches for estimating dielectric properties from reflected signals and near-field antenna measurements. Such methods are relevant to material evaluation where reliable characterization is needed. A separate publication on paper-based ECG digitization addresses the conversion of printed clinical waveforms into digital information, potentially enabling subsequent computational processing. These descriptions summarize the stated topics and do not imply that every method is universally applicable without validation.

Publications

  1. “Monostatic Free-Space Method for Relative Permittivity Determination Using Horn Antenna Near-Field Measurements.” IEEE Antennas and Wireless Propagation Letters, April 2024. [2]
  2. “A Calibration-Free Method for the Dielectric Constant Calculation of Low-Loss Materials.” IEEE Transactions on Instrumentation and Measurement, 2021. [3]
  3. “High Precision Digitization of Paper-Based ECG Records: A Step Toward Machine Learning.” IEEE Journal of Translational Engineering in Health and Medicine, 2019. [4]
  4. “Breaking Stress Criterion That Changes Everything We Know about Materials Failure.” Practice Periodical on Structural Design and Construction, 2023. Bibliographic details supplied; citation count was listed as zero in the provided record.
  5. “Dielectric Constant Estimation Using Calibration-Free Reflected Voltage Measurements.” Conference paper, 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, 2022. [5]

Research Impact

The selected work illustrates potential value in measurement reliability, material characterization, and the digitization of biomedical records. The reported citation and h-index figures offer one view of scholarly visibility, but they should be interpreted alongside research quality, reproducibility, practical relevance, and independent assessment. No single bibliometric measure fully represents the influence or significance of an individual research contribution.

Award Suitability

The Research Excellence Award profile places emphasis on the supplied publication record and engineering research themes. The listed papers provide concrete examples of scholarly activity spanning electromagnetic techniques and applied data processing. Final award decisions and eligibility should be determined by the organizing body’s published criteria and verification of the researcher’s bibliographic information.

Conclusion

Ali Nour El Hajj’s supplied profile presents a body of engineering-related research that includes dielectric measurement methods and biomedical record digitization. The listed publications and reported metrics provide a starting point for reviewing this work. Readers should consult the linked DOI records, Scopus author page, and ORCID profile for authoritative bibliographic details and the most current researcher information.

References

  1. Elsevier. (n.d.). Scopus author details: Ali Nour El Hajj, Author ID 7006586247. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=7006586247
  2. “Monostatic Free-Space Method for Relative Permittivity Determination Using Horn Antenna Near-Field Measurements.” (2024). IEEE Antennas and Wireless Propagation Letters.
    https://doi.org/10.1109/LAWP.2024.3355478
  3. “A Calibration-Free Method for the Dielectric Constant Calculation of Low-Loss Materials.” (2021). IEEE Transactions on Instrumentation and Measurement.
    https://doi.org/10.1109/TIM.2020.3011765
  4. “High Precision Digitization of Paper-Based ECG Records: A Step Toward Machine Learning.” (2019). IEEE Journal of Translational Engineering in Health and Medicine.
    https://doi.org/10.1109/JTEHM.2019.2949784
  5. “Dielectric Constant Estimation Using Calibration-Free Reflected Voltage Measurements.” (2022). 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting Proceedings. Bibliographic details as supplied.
  6. ORCID. (n.d.). Ali Nour El Hajj researcher identifier record.
    https://orcid.org/0000-0003-2865-6290

Yogesh Shankar | Engineering | Innovative Research Award

Published on by Academic Excellence

Innovative Research Award

Yogesh shankar
Infineon Technologies Asia Pacific, Singapore

Yogesh shankar
Affiliation Infineon Technologies Asia Pacific
Country Singapore
Scopus ID 57215433281
Documents 2
Citations 21
h-index 2
Subject Area Engineering
Event International Academic Excellence Awards

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.

References

  1. 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.
  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.
  3. IEEE. (2019). 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019). Conference publication record associated with the documented research.
  4. International Academic Excellence Awards. (n.d.). Academic Excellence Awards.
    https://academicexcellenceawards.com/