User Equipment as a Virtual Base Station (UE-VBS) computing paradigm represents a significant advancement in wireless networking. It enables User Equipment (UE) to form: i) Virtual Base Stations (VBSs) by dynamically ...
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User Equipment as a Virtual Base Station (UE-VBS) computing paradigm represents a significant advancement in wireless networking. It enables User Equipment (UE) to form: i) Virtual Base Stations (VBSs) by dynamically integrating Cluster Heads (referred to as UE-VBSCH), or Virtual Relays (referred to as UE-VBSRL), in the far-edge domain. This research focuses on enhancing the Quality of Service (QoS) (and thereby improving user experience) in networks supported by UE-VBS computing through outage prediction, network optimization, and advanced wireless techniques. In addition, the paper presents a detailed outage probability analysis and explores the trade-off between efficiency and reliability (namely, spectral and energy efficiency and link-level reliability (outage probability)), which are core contributions of this work. For a representative urban density of 2 UEs per m2, a single-hop UE-VBS slice lowers the outage probability from 0.78 to 0.23, raises the peak area-spectral efficiency to 4.3 bits−1 Hz−1 ( ≈ 4.8x the baseline), and delivers an energy efficiency of 2.4 x 105 bit J−1 (≈ 4.6x improvement). These concrete figures substantiate the claimed gains and illustrate how UE-VBS computing simultaneously improves efficiency and reliability. Specifically, it provides a thorough examination of UE-VBS computing’s capacity to enhance service quality, reduce congestion, and promote energy efficiency. Also, it empirically confirms UE-VBS computing’s superior performance, including mitigating coverage gaps coverage gaps are localized areas inside a nominally covered cell where received SINR falls below the outage threshold because of shadowing or cell-edge distance), optimizing network traffic, and reducing battery consumption compared to traditional networks/non-UE-VBS computing-supported networks. Enhanced QoS aims to minimize the challenges associated with restricted network coverage, ensuring consistent data transmission rates and improving overall user satisfaction
Moving target detection is one of the most basic tasks in computer *** conventional wisdom,the problem is solved by iterative optimization under either Matrix Decomposition(MD)or Matrix Factorization(MF)*** utilizes f...
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Moving target detection is one of the most basic tasks in computer *** conventional wisdom,the problem is solved by iterative optimization under either Matrix Decomposition(MD)or Matrix Factorization(MF)*** utilizes foreground information to facilitate background *** uses noise-based weights to fine-tune the *** both noise and foreground information contribute to the recovery of the *** jointly exploit their advantages,inspired by two framework complementary characteristics,we propose to simultaneously exploit the advantages of these two optimizing approaches in a unified framework called Joint Matrix Decomposition and Factorization(JMDF).To improve background extraction,a fuzzy factorization is *** fuzzy membership of the background/foreground association is calculated during the factorization process to distinguish their contributions of both to background *** describe the spatio-temporal continuity of foreground more accurately,we propose to incorporate the first order temporal difference into the group sparsity constraint *** temporal constraint is adjusted *** foreground and the background are jointly estimated through an effective alternate optimization process,and the noise can be modeled with the specific probability *** experimental results of vast real videos illustrate the effectiveness of our *** with the current state-of-the-art technology,our method can usually form the clearer background and extract the more accurate ***-noise experiments show the noise robustness of our method.
Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object *** frameworks for oriented detection modules are constrained by i...
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Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object *** frameworks for oriented detection modules are constrained by intrinsic limitations,including excessive computational and memory overheads,discrepancies between predefined anchors and ground truth bounding boxes,intricate training processes,and feature alignment *** overcome these challenges,we present ASL-OOD(Angle-based SIOU Loss for Oriented Object Detection),a novel,efficient,and robust one-stage framework tailored for oriented object *** ASL-OOD framework comprises three core components:the Transformer-based Backbone(TB),the Transformer-based Neck(TN),and the Angle-SIOU(Scylla Intersection over Union)based Decoupled Head(ASDH).By leveraging the Swin Transformer,the TB and TN modules offer several key advantages,such as the capacity to model long-range dependencies,preserve high-resolution feature representations,seamlessly integrate multi-scale features,and enhance parameter *** improvements empower the model to accurately detect objects across varying *** ASDH module further enhances detection performance by incorporating angle-aware optimization based on SIOU,ensuring precise angular consistency and bounding box *** approach effectively harmonizes shape loss and distance loss during the optimization process,thereby significantly boosting detection *** evaluations and ablation studies on standard benchmark datasets such as DOTA with an mAP(mean Average Precision)of 80.16 percent,HRSC2016 with an mAP of 91.07 percent,MAR20 with an mAP of 85.45 percent,and UAVDT with an mAP of 39.7 percent demonstrate the clear superiority of ASL-OOD over state-of-the-art oriented object detection *** findings underscore the model’s efficacy as an advanced solution for challenging remote sensing object detection tasks.
Temperature sensing on a flexible platform is essential to many healthcare, e-skin, and robotic applications. In this regard, a polymer-based temperature sensor is fabricated where a conductive polymer material i.e. P...
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To expedite the characteristic mode analysis (CMA) of electromagnetic target structures, this paper combines frequency- and material-independent reactions (FMIR) with characteristic mode analysis using the volume-surf...
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The microgrid is a potential solution for implementing smart distributed systems. However, controlling a microgrid is still a complex issue, and many proposed solutions are only based on locally measured signals witho...
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Energy prices have increased by more than 62% globally on average, while power companies are trying to provide more affordable energy with different options: fixed-rate, slab-based tariffs, and Time-of-Use pricing. Th...
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Phasor Measurement Units (PMUs) enable high-speed and high-precision power quality measurements, but their vulnerability to cyber-attacks poses substantial risks to the stability and reliability of power systems. This...
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Skin cancer is a highly prevalent form of cancer worldwide. The clinical assessment of skin lesions is crucial for evaluating the disease's characteristics. However, this assessment is often hindered by the variab...
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作者：
Alsharif, MaramRawat, Danda B.
Department of Electrical Engineering & Computer Science WashingtonDC20059 United States
Machine learning based Intrusion detection (ML-IDS) has been long enforced for the protection of the IoT against malicious attacks. Researchers focused on improving ensemble intrusion detection methods in order to boo...
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