https://scholar.dgist.ac.kr/handle/20.500.11750/10139 2026-04-04T11:17:37Z 2026-04-04T11:17:37Z Piao, Shiquan https://scholar.dgist.ac.kr/handle/20.500.11750/59403 2026-01-21T18:01:28Z 2025-11-30T15:00:00Z

Title: Airborne Acoustic Communication using Inaudible Frequencies Supported by Smart Devices Author(s): Piao, Shiquan Abstract: Aerial acoustic communication enables low-rate data exchange using audible or inaudible acoustic waves and has the advantage of operating on virtually all smart devices without additional hardware, unlike NFC, whose adoption remains limited by hardware and platform constraints. Standard microphones and speakers can therefore be used for both transmission and reception, making the technology an appealing and practical complement to existing wireless methods.However, commodity devices primarily support the audible band, much of which overlaps with speech and ambient noise, leaving only a narrow portion suitable for reliable communication. To address this limitation, the proposed approach utilizes a frequency region that is broadly supported across devices yet minimally influenced by everyday acoustic environments, thereby enhancing overall stability and robustness.Furthermore, this paper introduces a Zoom-FFT-based narrow-band acoustic communication technique that improves robustness and frequency resolution within this constrained spectrum. By exploiting its high-resolution spectral analysis, the system can reliably extract communication signals even in noisy indoor settings, supporting practical short-range data exchange applications across diverse usage scenarios.

2025-11-30T15:00:00Z 서보건 김지선 손준우 박명옥 김기섭 https://scholar.dgist.ac.kr/handle/20.500.11750/59402 2026-01-21T18:01:28Z 2025-12-31T15:00:00Z

Title: VLM 시대의 목표 객체 탐색을 위한 지식 융합 전략 서베이 Author(s): 서보건; 김지선; 손준우; 박명옥; 김기섭 Abstract: The rapid advancement of robotics and deep learning has increasingly accelerated the use of Embodied AI, where robots autonomously explore and reason in complex real-world environments. With the growing demand for domestic service robots, efficient navigation in unfamiliar settings has become even more crucial. Object Goal Navigation (OGN) is a fundamental task for this capability, requiring a robot to find and reach a user-specified object in an unknown environment. Solving OGN demands advanced perception, contextual reasoning, and effective exploration strategies. Recent Vision-Language Models (VLMs) and Large Language Models (LLMs) provide agents with external common knowledge and reasoning capabilities. This paper poses the critical question: “Where should VLM/LLM knowledge be fused into Object Goal Navigation?” We categorize knowledge integration into the three stages adapted from the Perception-Prediction-Planning paradigm to offer a structured survey of Object Goal Navigation approaches shaped by the VLM era. We conclude by discussing current dataset limitations and future directions, including further studies on socially interactive navigation and operation in mixed indoor - outdoor environments.

2025-12-31T15:00:00Z Lyu, Hong-Kun Yun, Sanghun Park, Seung https://scholar.dgist.ac.kr/handle/20.500.11750/59388 2026-01-21T18:01:20Z 2025-08-31T15:00:00Z

Title: Comparative Analysis of Base-Width-Based Annotation Box Ratios for Vine Trunk and Support Post Detection Performance in Agricultural Autonomous Navigation Environments Author(s): Lyu, Hong-Kun; Yun, Sanghun; Park, Seung Abstract: AI-driven agricultural automation increasingly demands efficient data generation methods for training deep learning models in autonomous robotic systems. Traditional bounding box annotation methods for agricultural objects present significant challenges including subjective boundary determination, inconsistent labeling across annotators, and physical strain from extensive mouse movements required for elongated objects. This study proposes a novel base-width standardized annotation method that utilizes the base width of a vine trunk and a support post as a reference parameter for automated bounding box generation. The method requires annotators to specify only the left and right endpoints of object bases, from which the system automatically generates standardized bounding boxes with predefined aspect ratios. Performance assessment utilized Precision, Recall, F1-score, and Average Precision metrics across vine trunks and support posts. The study reveals that vertically elongated rectangular bounding boxes outperform square configurations for agricultural object detection. The proposed method is expected to reduce time consumption from subjective boundary determination and minimize physical strain during bounding box annotation for AI-based autonomous navigation models in agricultural environments. This will ultimately enhance dataset consistency and improve the efficiency of artificial intelligence learning.

2025-08-31T15:00:00Z Choi, Eun-Sook Kim, Jung-Hee Kim, Eunjoo https://scholar.dgist.ac.kr/handle/20.500.11750/59386 2026-01-21T08:40:15Z 2025-11-30T15:00:00Z

Title: Detection of airborne Der p 1 allergen for indoor air quality evaluation using a biosensor platform Author(s): Choi, Eun-Sook; Kim, Jung-Hee; Kim, Eunjoo Abstract: Airborne house dust mite allergens are a major global contributor to asthma and allergic rhinitis. Der p 1 is one of the major allergens of dust mites (Dermatophagoides pteronyssinus) and is a protein that belongs to the biochemical cysteine protease family. This protein is known to promote allergic reactions and generally has a molecular weight of approximately 25 kDa. In Korea, Der p 1 is the dominant allergen, responsible for sensitization in 70–80% of allergy patients. While the conventional ELISA offers high sensitivity, its complexity, long assay time (3–4 h), and dependence on expensive equipment render it unsuitable for rapid point-of-care (POC) diagnosis in domestic settings. Herein, we report the development of an electrochemical immunosensor designed to overcome these limitations. The sensor platform utilized a gold screen-printed electrode (SPE) surface, functionalized via an 11-mercaptoundecanoic acid (11-MUA) self-assembled monolayer (SAM) and EDC/NHS activation chemistry to efficiently immobilize DerP1-specific antibodies. The resulting DerP1 biosensor converts the specific antigen-antibody binding event into a quantifiable electrochemical signal, enabling rapid and convenient detection of Der p 1 in indoor dust samples. This innovative platform demonstrates significant potential as a portable POC tool for environmental monitoring, substantially contributing to the management of allergic respiratory diseases.

2025-11-30T15:00:00Z