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In this paper, a reweighted sparse recovery algorithm based on the optimal weighted subspace fitting (WSF) for non-circular signals in direction-dependent mutual coupling (MC) is proposed. Firstly, a new augmented model is constructed by ...

In this paper, a novel reinforcement learning (RL)-based adaptive event-triggered control problem is studied for non-affine multi-agent systems (MASs) with time-varying dead-zone. The purpose is to design an efficient event-triggered mechanism to ...

• Inspired by the event-triggered mechanism in [25,26], an improved smooth event-triggered mechanism is proposed to better reduce the waste of communication resources. Specifically, the improved trigger signal is smooth and differentiable, ...

The geometric model-based hyperspectral anomaly detection techniques have garnered a lot of interest recently. These methods are predicated on the assumption that the background can be represented by a dictionary of spectral vectors, but the ...

Crowdsourced testing has gained prominence in the field of software testing due to its ability to effectively address the challenges posed by the fragmentation problem in mobile app testing. The inherent openness of crowdsourced testing brings diversity ...

New Wi-Fi systems have leveraged beamforming to manage a significant portion of traffic for achieving high throughput and reliability. Unfortunately, this has amplified certain security risks since beamforming critically relies on the clear-text ...

Semantic segmentation of remote sensing images (RSIs) is vital for geospatial intelligence. However, traditional methods face challenges with mixed pixels and complex land cover types. Convolutional neural networks and transformers have led the field of ...

With the rapid growth of geographic applications, automatable and intelligent models are essential to be designed to handle the large volume of information. However, few researchers focus on geographic natural language processing, and there has ...

This paper investigates the problem of semantic segmentation in high-resolution remote sensing images, aiming to predict semantic labels at a pixel-level granularity. Faced with the complexity and heterogeneity inherent in high-resolution remote ...

In this paper, we argue that the same rank setting of LoRA will inhibit its potential. In response, we proposed two novel PEFT strategies to improve the capability of LoRA in single-task and multi-task scenarios. We also conducted extensive ...

Progressive training has unfolded its superiority on a wide range of downstream tasks. However, it may fail in fine-grained recognition (FGR) due to special challenges with high intra-class and low inter-class variances. In this paper, we propose ...

• We are the first to exploit the sub–modularity for active sample selection. By our problem formulation, the optimal category subsets can be progressively selected for obtaining steady cumulative gain.
• We combine submodular optimization ...

Noise modeling is an important research field in computer vision; the design of an accurate model for imaging sensor noise depends on not only a comprehensive benchmark dataset of the real world, but also a precise design of the noise modeling ...

• Current realistic datasets could not describe diverse noise properties sufficiently.
• Parametric noise models cannot characterize the real-world noise exactly.
• Constructing a more comprehensive real-world benchmark dataset.
• ...

Graph Neural Networks (GNNs) gain remarkable success in various graph learning tasks under homophily graph assumption. This assumption is extremely fragile since real-world graphs with heterophily are ubiquitous. Under this circumstance, existing ...

• We discuss the weakness of the current spectral graph neural networks in expressive power.
• We present a novel simple but effective frequency adaptive filter learning method.
• By utilizing the adaptive filter at each layer, our model ...

The concept of ambient IoT was introduced by 3GPP to describe low-cost, self-powered, or battery-free sensor nodes, which may reach up to 10 trillion units in the future. By replacing chemical batteries in many standalone IoT devices, we can achieve ...

Micro-expressions (MEs) are unconscious, instant and slight facial movements, revealing people’s true emotions. Locating MEs is a prerequisite of classifying them, while only a few researches focus on this task. Among them, sliding window based ...

• We utilize multiple sliding windows of different scales and modes to generate multiple weak detectors, each accommodating MEs (Micro-Expressions) of certain durations and transition modes.
• We design a majority voting based aggregation ...

Recently, deep unfolding networks have become the mainstream approach for deep compressed sensing methods due to their good interpretability and high reconstruction performance. However, these existing deep unfolding networks, which usually rely ...

In this paper, by applying the supplementary variable method (SVM), some high-order, conformal structure-preserving, linearized algorithms are developed for the damped nonlinear Schrödinger equation. We derive the well-determined SVM systems with ...

Object detection in unmanned aerial vehicle (UAV) images presents challenges such as high altitudes, small object sizes, and complex backgrounds. Additionally, many deep learning object detection algorithms require substantial computational ...

Researching genes and their interactions is crucial for deciphering the fundamental laws of cellular activity, advancing disease treatment, drug discovery, and more. Large language Models (LLMs), with their profound text comprehension and generation ...

Crowd counting endeavors to estimate the numerical count of individuals present within an image depicting a gathering of people. In recent years, there has been notable and gradual advancement in the realm of crowd counting, driven by the integration of ...

With the popularity and advancement of the Internet and video-sharing platforms, video affective content analysis has greatly developed. Temporal information is crucial for this task. Nevertheless, existing methods often overlook the fact that there is ...