WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/6

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Subject: search.filters.subject.Artificial IntelligenceWoS Q: search.filters.wosQuartile.Q4

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  • Article
    A Novel Acoustic Source Localization Technique for Edge AI Applications: A Lightweight Framework and Implementation for IoT and Smart Sensing Devices
    (Istanbul University, 2025) Yarkan, S.
    This paper presents a novel and computationally efficient three-point signal estimation method for acoustic direction finding, designed specifically for low-cost embedded platforms. The proposed approach offers a lightweight alternative to traditional cross-correlation techniques by minimizing computational complexity while preserving high angular resolution. The method was implemented and tested on an STM32F429 microcontroller using a pair of MAX4466 electret microphones arranged on a fixed baseline. The system architecture leverages bare-metal signal processing routines optimized with Acorn RISC Machine Cortex. Microcontroller Software Interface Standard (ARM CMSIS-DSP) libraries, enabling real-time performance on resource-constrained hardware. Extensive experiments were conducted to evaluate the angular estimation accuracy under varying signal-tonoise ratios and source orientations. Results show that the system maintains sub-degree mean square error for source angles up to 30°, with noticeable performance degradation observed at 40° due to the directional response characteristics of the microphone elements. A comprehensive explanation is provided linking this degradation to reduced microphone sensitivity at wider angles of incidence. The proposed solution is ideal for applications requiring embedded acoustic localization, including smart interfaces, vehicular monitoring, and surveillance systems. In addition, the paper discusses the implications of deploying such systems in artificial intelligence (AI)-enabled and security-critical environments, highlighting emerging threats such as adversarial acoustic interference and spoofing attacks. These challenges underscore the importance of resilient and efficient DF methods that can operate reliably within the constraints of embedded systems. The presented work lays the foundation for future research in secure, scalable, and AI-compatible acoustic sensing platforms. © 2025 Elsevier B.V., All rights reserved.
  • Article
    Implementation of an AI-Enhanced Motor and Cognitive Intervention: A Case Study in Developmental Delay
    (Routledge Journals, Taylor & Francis Ltd, 2025) Bektas, Selen Aydoner; Bumin, Gonca; Aydoner Bektas, Selen
    This study aimed to explore the implementation of an AI-enhanced motor and cognitive intervention for a 7-year-old child with developmental delay. A case study design was employed using an A-B framework (pre-test, intervention, post-test) over 12 weeks. The intervention incorporated AI-based tools such as Lumosity, Just Dance, and Cogmed for tailored motor and cognitive activities. The Bruininks-Oseretsky Test of Motor Proficiency-2 Brief Form (BOT-2 BF) and the Dynamic Occupational Therapy Cognitive Assessment for Children (DOTCA-Ch) were used to evaluate outcomes. Post-intervention, significant improvements were observed in BOT-2 BF and DOTCA-Ch scores, indicating enhanced motor coordination, and cognitive abilities. AI-enhanced interventions demonstrated the potential to address developmental delays by providing adaptive, engaging, and effective therapeutic activities. The findings highlight the feasibility of integrating AI tools into therapy, with implications for broader adoption in addressing developmental challenges. Further research is recommended to explore generalizability and long-term effects.