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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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Graph Receptive Transformer Encoder for Text Classification

Happy to share our latest paper titled “Graph Receptive Transformer Encoder (GRTE) for Text Classification” published in IEEE Transactions on Signal and Information Processing over Networks! Our novel approach combines graph neural networks (GNNs) with large-scale pre-trained models to address limitations in attention mechanisms of transformers for text classification. GRTE retrieves global and contextual information […]

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Trainable Fractional Fourier Transform

Please check our new article at the intersection of machine learning and signal processing published at IEEE Signal Processing Letters! We extend the theory of FrFT, a parametric signal transformation, by introducing it as a trainable layer in neural network architectures. We showed that the transformation parameters can be learned along with the remaining network […]

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