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Graph Fractional Fourier Transform: A Unified Theory

We are pleased to announce that our paper on generalizing the fractional Fourier transform to the graph domain titled “Graph Fractional Fourier Transform: A Unified Theory” published in IEEE Transactions on Signal Processing! Highlights of our contributions include: A rigorous extension of the fractional power-based definition of GFRFT to support any graph structure and transform […]

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Text-RGNNs: Relational Modeling for Heterogeneous Text Graphs

Happy to share our latest paper titled “Text-RGNNs: Relational Modeling for Heterogeneous Text Graphs” published in IEEE Signal Processing Letters! Building on the foundational Text-graph convolutional Network (TextGCN), which represents corpus with heterogeneous text graphs, we addressed a key limitation: GCNs are inherently designed to operate within homogeneous graphs, potentially limiting their performance. To overcome […]

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UMRAM presentations at ISMRM 2024

    UMRAM had broad participation in the 2024 ISMRM & ISMRT Annual Meeting & Exhibition held 04-09 May 2024 in Singapore. Two educational lectures as well as five oral, one power pitch and two digital poster presentations were delivered by UMRAM faculty and students.   Educational lecture: Dr. Ergin Atalar: “Gradient Coil Design” Dr. […]

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Prof. Tolga Çukur Named Fellow of ISMRM

Prof. Tolga Çukur of the Department of Electrical and Electronics Engineering and director of UMRAM has been named a fellow of International Society for Magnetic Resonance in Medicine (ISMRM). ISMRM is an international, nonprofit, scientific association whose purpose is to promote communication, research, development and applications in the field of magnetic resonance in medicine and […]

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Wiener Filtering in Joint Time-Vertex Fractional Fourier Domains

We are excited to share our latest work on joint time-vertex signal processing published in IEEE Signal Processing Letters! In this paper, we explore the complexities of time-varying graph signals and how they can be more efficiently processed using the joint time-vertex framework. Traditionally, separating signal from noise in these structures has posed significant challenges. […]

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