研究課題/領域番号 |
22K17883
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研究種目 |
若手研究
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配分区分 | 基金 |
審査区分 |
小区分60060:情報ネットワーク関連
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研究機関 | 大阪大学 |
研究代表者 |
エルデーイ ヴィクトル 大阪大学, 大学院情報科学研究科, 特任助教(常勤) (40850938)
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研究期間 (年度) |
2022-04-01 – 2026-03-31
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研究課題ステータス |
交付 (2022年度)
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配分額 *注記 |
4,680千円 (直接経費: 3,600千円、間接経費: 1,080千円)
2025年度: 650千円 (直接経費: 500千円、間接経費: 150千円)
2024年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
2023年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
2022年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
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キーワード | Object recognition / wireless sensing / localization / backscatter tags / object recognition / privacy / interaction tracking |
研究開始時の研究の概要 |
We aim to develop technological solutions to improve our awareness of our environment, with a focus on our interactions with objects. Such information has several applications including healthcare and well-being (lifestyle analysis, navigation assistance), asset tracking, and security screening. These applications require us to recognize, identify and track objects and their interactions. In this project, we will: (1) Build a contact-less object recognition system for plain, uninstrumented objects (2) Develop a scalable, low-power, privacy-preserving system for tracking object interactions
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研究実績の概要 |
Object recognition: We prepared and submitted a full paper to the IEEE IoT Journal. We developed RadioRec, a deep learning-based system that recognizes everyday objects based on their interactions with microwave signals in a contact-less manner. RadioRec is a significant upgrade compared to our earlier workshop paper, both in terms of the learning capabilities and in terms of evaluation. RadioRec works by transmitting a microwave signal through the object using a single antenna pair. RadioRec extracts features automatically using an autoencoder, and uses them to train an object classification model. Our evaluation shows that RadioRec can detect and recognize 26 everyday objects of various materials and shapes with an accuracy of over 97%. The paper was rejected. The reviewers requested additional evaluation in a different environment, and also several clarifications in the text. We are now working on addressing the reviewers’ comments and preparing an updated submission to the same journal, as suggested by the reviewers. So far, we have performed additional experiments in another environment, with various object orientations and object positions, and have obtained promising preliminary results. Object localization: We have prepared a preliminary implementation of SpotFi (DOI: 10.1145/2785956.2787487) for the USRP software-defined radio platform to enable the simultaneous estimation of angle of arrival and time of flight on backscatter tags. We are currently working on getting the estimation to work correctly in a controlled environment (for now, without backscatter tags).
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現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
Based on the details reported in the achievements section, we can say that the research itself has progressed more or less according to plan. However, the publication of our object recognition paper is being delayed due to our initial submission having been rejected.
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今後の研究の推進方策 |
Regarding object recognition, we will continue to address the reviewers’ comments related to our rejected journal paper and work on resubmitting the paper. Regarding localization, we plan to continue working on the USRP-based implementation of the SpotFi algorithm. We will try to achieve successful estimation of angle of arrival, and time of flight, first in very controlled environments, then gradually moving towards less controlled environments (e.g., ones that have multipath effects). Then, we plan to use the obtained estimates to determine the location of the target. Wa also hope to explore the idea of enabling privacy-enhanced encounter-based interaction tracking with backscatter tags as outlined in the original research proposal. However, we have also identified new application possibilities related to object recognition and localization. In the field of wood processing, there seems to be a need to identify the presence or absence of knots and their 3D location in unprocessed logs. With this information, woodworkers can determine the optimal cutting method. Knowing the location of knots inside the log, which is difficult to determine from the exterior, allows the sawyer to avoid knots and produce high-quality knot-free boards, increasing the value of the wood while reducing waste. We may experiment with various types of electromagnetic waves and wireless imaging techniques to perform this task. Since this application has significant potential for direct use in society, we might prioritize this direction in our future work (depending on discussions with stakeholders).
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