arXiv subjects

Gevindu Ganganath

Publications and source records attributed to Gevindu Ganganath.

2 recordsLinked to original sources

arXiv2026

Radar and Acoustic Sensor Fusion using a Transformer Encoder for Robust Drone Detection and Classification

The use of drones in a wide range of applications is steadily increasing. However, this has also raised critical security concerns such as unauthorized drone intrusions into restricted zones. Therefore, robust and accurate drone detection and classification mechanisms are required despite significant challenges due to small size of drones, low-altitude flight, and environmental noise. In this letter, we propose a multi-modal approach combining radar and acoustic sensing for detecting and classifying drones. We employ radar due to its long-range capabilities, and robustness to different weather conditions. We utilize raw acoustic signals without converting them to other domains such as spectrograms or Mel-frequency cepstral coefficients. This enables us to use fewer number of parameters compared to the stateof-the-art approaches. Furthermore, we explore the effectiveness of the transformer encoder architecture in fusing these sensors. Experimental results obtained in outdoor settings verify the superior performance of the proposed approach compared to the state-of-the-art methods.

eess.SP↗

arXiv2026

Curiosity as Linguistic Intervention: Using LLM Tutoring Dialogues to Influence Exploratory Learning Behavior

Large Language Models (LLMs) provide a new opportunity to study how language shapes exploratory cognition because conversational strategies can be systematically manipulated at inference time. We introduce CURIOBOT, a framework that operationalizes Berlyne's collative variables, novelty, complexity, conflict, and uncertainty, as adaptive linguistic interventions for conversational tutoring. Across 270 tutoring conversations spanning multiple model families, domains, and topic complexity levels, curiosity-oriented interventions consistently increased exploratory learner behaviors, producing up to 2.4x more conversational turns under fixed time budgets. To measure these effects, we further introduce a learner-centered evaluation framework capturing exploratory questioning, conversational agency, productive struggle, and observable curiosity. Learner-side gains persisted even when tutor-side instructional quality remained unchanged, suggesting that curiosity functions as a partially independent interaction-level mechanism. More broadly, our results demonstrate that LLM-mediated dialogue can serve as a scalable experimental framework for studying how language shapes exploratory learning behavior.

cs.CL↗