Institution profile

K.N. Toosi University of Technology

Academic institutionasia · ir
Official website
Research library40linked papers
Opportunities0open roles
Selected work

Representative Papers

Tracing Methamphetamine abuse in under-treatment drivers: How biomechanical and oculomotor features help detect at-risk drivers?

Aug 13, 2026

This study addresses a critical gap in identifying driving risks among individuals recovering from methamphetamine dependence by integrating eye-tracking and Kinect-based biomechanical sensor data within a driving simulator environment. It proposes, for the first time, a multimodal feature-driven k-nearest neighbors (KNN) classification model embedded within an advanced driver assistance systems (ADAS) real-time analytical framework to automatically detect high-risk driving behaviors in drivers with a history of stimulant abuse. The model achieves a classification accuracy of 90%, offering a robust technical approach and empirical foundation for proactive traffic safety interventions targeting this vulnerable population.

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Topology-Age-Aware Cooperative Awareness in Vehicular Ad-Hoc Networks

Aug 02, 2026

This study addresses the critical need for timely information in vehicular ad hoc networks to support safety-critical decisions by proposing a timeliness-aware collaborative broadcasting (TA-CB) strategy. The approach uniquely integrates information age (AoI) with network topology, enabling roadside units to schedule transmitter–receiver pairs based on aggregate AoI reduction gains. Two complementary mechanisms are devised: a local scheme leveraging two-hop neighbor counts and a global scheme utilizing betweenness centrality to optimize broadcast decisions. Evaluated under both random and clustered network topologies, TA-CB significantly outperforms non-collaborative baselines. Notably, in clustered scenarios, both variants of TA-CB markedly surpass the baseline age-aware collaborative broadcasting (A-CB), with each demonstrating distinct advantages under varying network conditions.

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Recent publications

Latest Papers

Tracing Methamphetamine abuse in under-treatment drivers: How biomechanical and oculomotor features help detect at-risk drivers?

Aug 13, 2026

This study addresses a critical gap in identifying driving risks among individuals recovering from methamphetamine dependence by integrating eye-tracking and Kinect-based biomechanical sensor data within a driving simulator environment. It proposes, for the first time, a multimodal feature-driven k-nearest neighbors (KNN) classification model embedded within an advanced driver assistance systems (ADAS) real-time analytical framework to automatically detect high-risk driving behaviors in drivers with a history of stimulant abuse. The model achieves a classification accuracy of 90%, offering a robust technical approach and empirical foundation for proactive traffic safety interventions targeting this vulnerable population.

0 citationsRead paper

Topology-Age-Aware Cooperative Awareness in Vehicular Ad-Hoc Networks

Aug 02, 2026

This study addresses the critical need for timely information in vehicular ad hoc networks to support safety-critical decisions by proposing a timeliness-aware collaborative broadcasting (TA-CB) strategy. The approach uniquely integrates information age (AoI) with network topology, enabling roadside units to schedule transmitter–receiver pairs based on aggregate AoI reduction gains. Two complementary mechanisms are devised: a local scheme leveraging two-hop neighbor counts and a global scheme utilizing betweenness centrality to optimize broadcast decisions. Evaluated under both random and clustered network topologies, TA-CB significantly outperforms non-collaborative baselines. Notably, in clustered scenarios, both variants of TA-CB markedly surpass the baseline age-aware collaborative broadcasting (A-CB), with each demonstrating distinct advantages under varying network conditions.

0 citationsRead paper