Unlike other deep learning (DL) models, Transformer has the ability to extract long-range dependency features from hyperspectral image (HSI) data. Masked autoencoder (MAE), which is based on ...

This paper studies the computational offloading of CNN inference in dynamic multi-access edge computing (MEC) networks. To address the uncertainties in communication time and edge servers’ available ...

The increasing automation in the design process of electrical machines for vehicles generates huge amounts of data, leading to a growing interest in using machine learning for faster predictions and ...

This article introduces a two-phase learning approach for hyperspectral image (HSI) classification using few-shot learning (FSL). For the first phase, we present a novel spatiospectral masked ...

We propose a novel autoencoder framework for FTN transmission to jointly optimize the ISI caused by FTN signaling and the impairments of the physical channel. The feasibility of the framework is ...

Video surveillance systems are essential for an intelligent and smart traffic monitoring system. Detecting and recognizing traffic anomalies is the foremost task for the safety and security of human ...

Missing values are a fundamental issue in many applications by constraining the application of different learning methods or by impairing the attained results. Many solutions have been proposed by ...

Potential transformers (PTs) are essential measurement equipment in power systems. Developing monitoring techniques for them is crucial to enable measurement-based applications, where the challenge ...