Abstract: Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled data using a deep ...

Abstract: Formation control algorithms are a prominent area of study in the domain of drones and drone swarms. The goal of the formation control algorithms is to manage and manipulate individuals to ...

Abstract: Electroencephalography (EEG) has been a staple method for identifying certain health conditions in patients since its discovery. Due to the many different types of classifiers available to ...

Abstract: The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML ...

Abstract: Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more than two ...

Abstract: Federated learning obtains a central model on the server by aggregating models trained locally on clients. As a result, federated learning does not require clients to upload their data to ...

Abstract: Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks. As speech signal contains ...

Abstract: Enhancements to the forwarding process that support frame preemption are provided in this amendment to IEEE Std 802.1Q-2014. Scope: This amendment specifies procedures, managed objects, and ...

Abstract: In this article, we investigate intelligent anti-jamming communication method for wireless sensor networks. The stochastic game framework is introduced to model and analyze the multi-user ...