Abstract: Previous deep learning-based event denoising methods mostly suffer from poor interpretability and difficulty in real-time processing due to their complex architecture designs. In this paper, ...

If you find new code for RIS(IRS) paper, please remind me here. Best reading paper in RIS(IRS) is here, although slightly outdated. Some authorizations by authors can be found here and here. Please ...

Network pruning has been the driving force for the acceleration of neural networks and the alleviation of model storage/transmission burden. With the advent of AutoML and neural architecture search ...

The shallow sea underwater acoustic channel exhibits a significant sparse multipath structure. The temporally multiple sparse Bayesian learning (TMSBL) algorithm can effectively estimate this sparse ...

Abstract: Recently, convolutional sparse coding (CSC) has been successfully applied to seismic data denoising. CSC differs from traditional dictionary learning methods based on patching schemes in ...

Many of our academics speak to the media as experts in their field of research. If you are a journalist, please contact the University’s Media and PR Team: Sparse representations have been the key ...

Sampling from a given probability distribution is fundamental across various disciplines, including physics, signal processing, and artificial intelligence. In recent years, the ascendancy of flow, ...

Deep learning algorithms' powerful capabilities for extracting useful latent information give them the potential to outperform traditional financial models in solving problems of the stock market ...

In this article an energy disaggregation architecture using elastic matching algorithms is presented. The architecture uses a database of reference energy consumption signatures and compares them with ...