Tag: matlab

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Paper Published in APSIPA Transactions on Signal and Information Processing

The following paper has been published in APSIPA Transactions on Signal and Information Processing (Q1: Information Systems).

It is Open Access.

  • Shu Abe, Yuya Kodama, Hiroyoshi Yamada, Shogo Muramatsu, “RTL Evaluation of ℓ2-Norm Approximation with Rotated ℓ1-Norm for 2-Tuple Arrays,” APSIPA Transactions on Signal and Information Processing,Vol. 14: No. 1, e3. http://dx.doi.org/10.1561/116.20240068, 30 Jan 2025

SCImago Journal & Country Rank

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Released TanSacNet Pre-release20250105

We updated TanSacNet Project for developing tangent space adaptive control networks.

  • New PyTorch Implementation: Introduced a PyTorch-based 2-D LSUN framework, including batch processing in orthonormalTransform.py and a low-dimensional approximation sample. Optimized gradient computation and sequential matrix processing to improve efficiency in PyTorch and CUDA environments.
  • Enhanced MATLAB Support: Improved data type and device management to ensure stability and performance in MATLAB workflows.
  • Code Refactoring and Stability: Streamlined code structure for maintainability and resolved initialization and CPU processing issues across both frameworks.

This pre-release highlights the new PyTorch implementation, along with key updates to MATLAB support and overall code stability.

Paper Published in IAPSIPA Transactions on Signal and Information Processing

 

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Paper Published in IAPSIPA Transactions on Signal and Information Processing

The following paper has been published in APSIPA Transactions on Signal and Information Processing (Q1: Information Systems).

It is a result of Grant-in-Aid for Scientific Research (A) “(JP22H00512) Locally Structured Unitary Networks and Tangent Space Learning for Dynamic Systems Modeling”.

It is Open Access. The source code is available at Code Ocean.

SCImago Journal & Country Rank

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Released AuGFT

Augmented Graph Fourier Transform (AuGFT) – File Exchange – MATLAB Central (mathworks.com) has been released.

It includes functions for augmented GFT, which facilitates the realization of real-valued filters for directed graph signals, and scripts for their application.

Please try them out.

Acknowledgements: This research was supported by Grant-in-Aid for Scientific Research 21H04596 and Grant-in-Aid for Scientific Research 22H00512.

 

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Published SaivDr-Release20200903

We updated SaivDr (Sparsity-Aware Image and Volumetric Data Restoration) package for the first time in about six months.

New this time, we added custom layers and sample codes for use with MATLAB Deep Learning Toolbox. It allows for more flexible DAG configuration than before.

NSOLT enables you to realize Parseval tight, symmetric and multi-resolution convolutional layers, and you can place NSOLT as a convolutional layer in a corner of a convolutional neural network.

We hope you will give it a try.

Acknowledgments: This work was supported by KAKENHI JP19H04135.