Visually impaired individuals face significant challenges in navigating safely and independently, particularly under adverse weather conditions such as fog. To address this issue, we propose YOLO-Extreme, an enhanced object detection framework based...
Despite the rapid advances in deep learning (DL) for object detection, existing techniques still face several challenges. In particular, object detection in adverse weather conditions (AWCs) requires complex and computationally costly models to achie...
Reliable Internet connectivity is essential for latency-sensitive services such as video conferencing, media streaming, and online gaming. Round-trip time (RTT) is a key indicator of network performance and is central to setting retransmission timeou...
Retention time drift caused by fluctuations in physical factors such as temperature ramping rate and carrier gas flow rate is ubiquitous in chromatographic measurements. Proper peak matching and identification across different chromatograms is critic...
Rett syndrome (RTT) is a rare disorder and one of the most abundant causes of intellectual disabilities in females. Single mutations in the gene coding for methyl-CpG-binding protein 2 (MeCP2) are responsible for the disorder. MeCP2 regulates gene ex...
Rett Syndrome (RTT) is a severe neurodevelopmental disorder predominately diagnosed in females and primarily caused by pathogenic variants in the X-linked gene Methyl-CpG Binding Protein 2 (MECP2). Most often, the disease causing the MECP2 allele res...
This article proposes a prediction model that combines network physical characteristics and deep learning methods to solve the problem of insufficient round-trip time (RTT) prediction accuracy caused by complex dynamic characteristics in civil aviati...
The introduction of noise during image acquisition and transmission is inevitable, leading to a significant reduction in the accuracy of image processing tasks, such as target classification, localization, and recognition. To address this issue, this...
Rett Syndrome (RTT) is an X linked neurodevelopmental disorder caused by mutations in the methyl-CpG-binding protein 2 (MECP2) gene, resulting in severe cognitive and physical disabilities. Despite an apparent normal prenatal and postnatal developmen...
Pedestrian detection technology refers to identifying pedestrians within the field of view and is widely used in smart cities, public safety surveillance, and other scenarios. However, in real-world complex scenes, challenges such as high pedestrian...
In complex real-world scenarios, object detection faces significant challenges due to severe noise interference and feature degradation. To overcome these limitations, this paper proposes DFA-YOLO, an enhanced YOLOv11 framework integrating three key...
Path-aware networking architectures, such as SCION, give endpoints explicit visibility into multiple inter-domain paths, opening new opportunities for data-driven path selection, reliability prediction, and automated diagnosis. However, the lack of s...
In foggy weather scenarios, the scattering and absorption of light by water droplets and particulate matter cause object features in images to become blurred or lost, presenting a significant challenge for target detection in autonomous driving vehic...
Foreign object detection during unmanned aerial vehicle (UAV) grid inspection suffers from severe visual degradation under adverse weather conditions, such as haze and heavy rain. Existing approaches often struggle to distinguish target textures from...
Rain, snow, fog, and dust can degrade road-scene images, blur fine details, and consequently reduce the reliability of perception systems for autonomous driving. To address this problem, this paper proposes PF-ConvNeXt, an adverse weather recognition...
5G networks require dynamic network monitoring and advanced security solutions. This work performs the essential steps to implement a basic 5G digital twin (DT) in a warehouse scenario. This study provides a paradigm of end-to-end connection and encr...
Object detection technology has been widely applied in fields such as autonomous driving and security surveillance, where it serves as a vital component of intelligent systems. However, under adverse hazy weather conditions, objects obscured by haze...
In autonomous driving systems, fog decreases visibility and contrast, blurs the boundary of objects, and increases scale variations, which results in missed detections. To solve these problems, we introduce Fog-YOLO, a lightweight real-time detector...
Object detection powered by deep learning is extensively utilized across diverse sectors, yielding substantial outcomes. However, adverse weather conditions such as rain, snow, and haze interfere with images, leading to a decline in quality and makin...
Robust perception in adverse meteorological conditions remains a critical challenge for autonomous driving systems. Generic object detectors often suffer from feature degradation and domain shifts in foggy environments due to the loss of high-frequen...
The increasing demand for Arctic route planning, climate change studies, and the growing volume of satellite sensor data have made automated sea ice mapping an essential task. In this study, we propose a multi-task sea ice mapping framework based on...
Despite deep learning-based object detection techniques showing promising results, identifying items from low-quality images under unfavorable weather settings remains challenging because of balancing demands and overlooking useful latent information...
Foggy weather degrades road images by reducing visibility, attenuating object contrast, and blurring boundaries, while autonomous ground vehicles require accurate and lightweight environment perception on resource-constrained onboard platforms. This...
In order to solve the problem of pedestrian positioning in the indoor environment, this paper proposes a high-precision indoor pedestrian positioning system (HPIPS) based on smart phones. First of all, in view of the non-line-of-sight and multipath p...
With the development of intelligent manufacturing and smart logistics, automated guided vehicles (AGVs) are gradually expanding from traditional indoor environments to factory roads, logistics parks, industrial parks, and other outdoor road environme...
Modern data center power distribution networks place greater demands on the stability and reliability of power supply. Growing network computing demands and complex network environments can cause network congestion, which in turn leads to network tra...
The widespread adoption of encrypted communication protocols has significantly enhanced network security and user privacy, simultaneously elevating the importance of encrypted traffic analysis across various domains, including network anomaly detecti...
Remote sensing (RS) object detection faces intrinsic challenges arising from the overhead imaging paradigm and the diversity of climatic conditions. In particular, atmospheric phenomena such as clouds and haze cause severe visual degradation, making...
Haze removal methods based on the estimation of scene depth ratio in the Atmospheric Scattering Model (ASM) have achieved satisfactory results. However, the ASM ignores the blur equivalent to a point spread function caused by forward scattering. This...
With the rapid development of technologies such as cloud computing, big data, and the Internet of Things (IoT), Software-Defined Networking (SDN) is emerging as a new network architecture for the modern Internet. SDN separates the control plane from...