Abstract: Class-incremental learning (CIL) enables models to continuously learn new classes while addressing catastrophic forgetting. With the introduction of pre-trained models, new tuning paradigms ...

Abstract: Optimization theory assisted algorithms have received great attention for precoding design in multiuser multiple-input multiple-output (MU-MIMO) systems. Although the resultant optimization ...

Abstract: Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the ...

Abstract: 3D Gaussian Splatting (3DGS) has emerged as a prominent technique with the potential to become a mainstream method for 3D representations. It can effectively transform multi-view images into ...

Abstract: Blockchain-based vehicular edge computing (VEC) is regarded as a promising computing paradigm that can enhance the computing capabilities of mobile vehicles while ensuring security during ...

Abstract: In dynamic traffic flow conditions, lane-changing maneuvers hold significant potential for achieving energy and time efficiency. However, existing research often overlooks the influence of a ...

Abstract: Deep convolutional neural networks (CNNs) have proven their effectiveness and are widely acknowledged as the dominant method for image classification. However, their lack of explainability ...

Abstract: Satellite communication offers the prospect of service continuity over uncovered and under-covered areas, service ubiquity, and service scalability. However, several challenges must first be ...

Abstract: Flexible wearable sensors, characterized by their stretchability, flexibility, lightweight, high sensitivity, and the ability to dynamically and continuously measure multiple parameters, ...

Abstract: In order to effectively provide ultra reliable low latency communications and pervasive connectivity for Internet of Things (IoT) devices, next-generation wireless networks can leverage ...

Abstract: Channel prediction is an effective approach for reducing the feedback or estimation overhead in massive multi-input multi-output (m-MIMO) systems. However, existing channel prediction ...

Abstract: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced. The diffusion coefficient is chosen to vary spatially in such a way ...