arXiv subjects

Morteza Azmoudeh Afshar

Publications and source records attributed to Morteza Azmoudeh Afshar.

3 recordsLinked to original sources

arXiv2025

Privacy-Preserving and Simultaneous Authentication in High-Density V2X Networks

The rapid expansion of Vehicle-to-Everything (V2X) networks within the Internet of Vehicles (IoV) demands secure and efficient authentication to support high-speed, high-density and mobility-challenged environments. This paper presents a privacy-preserving authentication scheme that incorporates batch authentication, mutual authentication, and secure key establishment, enabling users to authenticate one another without a central authority. Our proposed scheme facilitates simultaneous multi-user authentication, significantly enhancing scalability, robustness and security in dynamic IoV networks. Results from realistic implementations show that our method achieves average authentication and verification times of 10.61 ms and 1.78 ms, respectively, for a fleet of 100 vehicles, outperforming existing methods. Scalability tests demonstrate efficient processing for larger groups of up to 500 vehicles, where average authentication times remain low, establishing our scheme as a robust solution for secure communication in IoV systems.

cs.CR↗

arXiv2025

Accuracy Enhancement in Refractive Index Sensing via Full-Spectrum Machine Learning Modeling

We present a full-spectrum machine learning framework for refractive index sensing using simulated absorption spectra from meta-grating structures composed of titanium or silicon nanorods under TE and TM polarizations. Linear regression was applied to 80 principal components extracted from each spectrum, and model performance was assessed using five-fold cross-validation, simulating real-world biosensing scenarios where unknown patient samples are predicted based on standard calibration data. Titanium-based structures, dominated by broadband intensity changes, yielded the lowest mean squared errors and the highest accuracy improvements: up to a 6065-fold reduction compared to the best single-feature model. In contrast, silicon-based structures, governed by narrow resonances, showed more modest gains due to spectral nonlinearity that limits the effectiveness of global linear models. We also show that even the best single-wavelength predictor is identified through data-driven analysis, not visual selection, highlighting the value of automated feature preselection. These findings demonstrate that spectral shape plays a key role in modeling performance and that full-spectrum linear approaches are especially effective for intensity-modulated index sensors.

physics.optics↗

arXiv2025

Machine Learning-Enabled Multidimensional Data Utilization Through Multi-Resonance Architecture: A Pathway to Enhanced Accuracy in Biosensing

A novel framework is proposed that combines multi-resonance biosensors with machine learning (ML) to significantly enhance the accuracy of parameter prediction in biosensing. Unlike traditional single-resonance systems, which are limited to one-dimensional datasets, this approach leverages multi-dimensional data generated by a custom-designed nanostructure, a periodic array of silicon nanorods with a triangular cross-section over an aluminum reflector. High bulk sensitivity values are achieved for this multi-resonant structure, with certain resonant peaks reaching up to 1706 nm/RIU. The field analysis reveals Mie resonances as the physical reason behind the peaks. The predictive power of multiple resonant peaks from transverse magnetic (TM) and transverse electric (TE) polarizations is evaluated using Ridge Regression modeling. Systematic analysis reveals that incorporating multiple resonances yields up to three orders of magnitude improvement in refractive index detection precision compared to single-peak analyses. This precision enhancement is achieved without modifications to the biosensor hardware, highlighting the potential of data-centric strategies in biosensing. The findings establish a new paradigm in biosensing, demonstrating that the synergy between multi-resonance data acquisition and ML-based analysis can significantly enhance detection accuracy. This study provides a scalable pathway for advancing high-precision biosensing technologies.

q-bio.QM↗