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2023 Fiscal Year Final Research Report

A study of signal processing algorithms toward advanced network tomography

Research Project

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Project/Area Number 18K19804
Research Category

Grant-in-Aid for Challenging Research (Exploratory)

Allocation TypeMulti-year Fund
Review Section Medium-sized Section 61:Human informatics and related fields
Research InstitutionTokyo Institute of Technology

Principal Investigator

Yamada Isao  東京工業大学, 工学院, 教授 (50230446)

Project Period (FY) 2018-06-29 – 2024-03-31
Keywordsネットワークトモグラフィ / 超複素テンソル補完 / 複数行列の近似同時対角化 / テンソルCP分解 / LiGMEのMoreau強化行列
Outline of Final Research Achievements

In order to advance the integrated signal processing technologies that certainly realize the evolution of network tomography, we studied various related signal processing problems that have not been sufficiently applied in conventional network tomography, such as "hypercomplex tensor completion", "Tensor CP decomposition algorithms based on approximate simultaneous diagonalization" and "LiGME-type signal estimation algorithms that utilize sparsity as a priori knowledge". Fortunately, we obtained many useful results that can contribute to the evolution of modern signal processing.

In particular, [AYY21] and [CYY23] have received the 2021 IEICE Best Paper Award, and the 2023 IEICE Best Paper Award, respectively.

Free Research Field

信号処理, 最適化, 逆問題

Academic Significance and Societal Importance of the Research Achievements

深刻なサイバー攻撃の脅威に日々晒され続けているネットワークを安全に保守するため,異常通信発生状況をトラヒックデータから検出できるネットワークトモグラフィへの期待は高まる一方であるが,これを支える信号処理技術の進化も重要である.実際に国際会議でネットワークトモグラフィのスペシャルセッションを企画し,信号処理に求められる技術的課題について情報収集した結果,旧来のネットワークトモグラフィを支えてきた特別な信号処理技術の他に,関連する多くの信号推定アルゴリズムの性能向上が求められていることが確認された.本プロジェクトでは,関連分野で2編の学会賞受賞論文を含む実りある多くの研究成果が生むことができた.

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Published: 2025-01-30  

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