In this paper, the problem of using several controlled unmanned aerial vehicles (UAVs) to localize a target UAV in real time is investigated. In the considered model, the controlled UAV consists of one active UAV and ...
In this paper, the problem of using several controlled unmanned aerial vehicles (UAVs) to localize a target UAV in real time is investigated. In the considered model, the controlled UAV consists of one active UAV and four passive UAVs. Each passive UAV receives signals transmitted from the active UAV and reflected by the target UAV, and then estimates the distance from the active UAV to the target UAV and then from the target UAV to the passive UAV. Each passive UAV then transmits this distance information to a base station (BS), which estimates the location of the target UAV. Since the target UAV will change its location according to its performed task, each controlled UAV must optimize its trajectory to continuously localize the target UAV. This trajectory design problem is formulated as an optimization problem whose goal is to jointly optimize the trajectories of active and passive UAVs so as to maximize the target UAV positioning accuracy. To solve this problem, a Z function decomposition based reinforcement learning (ZD-RL) method is proposed. Compared to value function decomposition based RL (VD-RL), the proposed method can find the probability distribution of the sum of future rewards to accurately estimate the expected value of the sum of future rewards, thus finding better trajectories for controlled UAVs and improving target UAV positioning accuracy. Simulation results show that the proposed ZD-RL method can reduce the positioning errors by up to 58.3% and 84.8%, compared to VD-RL and independent DRL methods, respectively.
With the rising demand for intelligent services and privacy protection in consumer artificial intelligence (AI), federated edge learning has emerged as a beacon for privacy-preserving distributed machine learning. Thi...
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SONICUMOS is an enhanced behavior-based face liveness detection system that combines ultrasonic and video signals to sense the 3D head gestures. As face authentication becomes increasingly prevalent, the need for a re...
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In this paper, the problem of joint sensing and communications is studied over terahertz (THz) vehicular networks. In the studied model, a set of service provider vehicles provide either communication service or sensi...
In this paper, the problem of joint sensing and communications is studied over terahertz (THz) vehicular networks. In the studied model, a set of service provider vehicles provide either communication service or sensing service to communication target vehicles or sensing target vehicles, respectively. Therefore, it is necessary to determine the service mode (i.e., providing sensing or communication service) for each service provider vehicle and the subset of target vehicles that each service provider vehicle will serve. The problem is formulated as an optimization problem aiming to maximize the sum of the data rates of all communication target vehicles while satisfying the sensing service requirements of all sensing target vehicles by determining the service mode and the user association for each service provider vehicle. To solve this problem, a graph neural network (GNN) based algorithm with a heterogeneous graph representation is proposed. The proposed algorithm enables the central controller to extract each vehicle's graph information related to its location, connection, and communication interference. Using the extracted graph information, the joint service mode selection and user association strategy will be determined. Simulation results show that the proposed GNN-based scheme can achieve 94% of the sum rate produced by the optimal solution, and yield up to 3.95% and 36.16% improvements in sum rate, respectively, compared to a homogeneous GNN-based algorithm and the conventional optimization algorithm without using GNNs.
The mission of cloth-changing person re-identification (CC-ReID) is to discover cloth-invariant and identity-related cues, while traditional person ReID methods rely on appearance features that are biased to cloth-rel...
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ISBN:
(数字)9798350359312
ISBN:
(纸本)9798350359329
The mission of cloth-changing person re-identification (CC-ReID) is to discover cloth-invariant and identity-related cues, while traditional person ReID methods rely on appearance features that are biased to cloth-related cues. To tackle this cloth-biased problem, many CC-ReID methods introduced auxiliary body shape information to extract cloth-invariant features, such as 2D sketch images or the 3D Skinned Multi-Person Linear (SMPL) model. However, 2D auxiliary information lacks 3D spatial features, while the 3D SMPL model encounters challenges in capturing features at a finer granularity due to manually defined parameters. To extract fine-grained 3D shape features, we estimate depth maps that contain richer shape information and propose a Fine-grained Depth feature Mining and Distillation (FDMD) framework. We introduce a depth branch and design a fine-grained local feature interaction module to mine fine-grained 3D body shape knowledge from estimated depth maps by exploring the context of semantic-aware local body-part features. To integrate cloth-invariant depth knowledge into the appearance features, the fine-grained 3D shape features are transferred to an appearance branch by feature-space-aligned distillation. Extensive experiments demonstrate that FDMD can achieve state-of-the-art performance on three widely used CC-ReID benchmarks PRCC, Celeb-reID and LaST.
We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data t...
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The use of IEEE 802.15.7 for Heterogeneous Visible Light Communication (VLC) has garnered significant attention due to the expanding application area of Optical Body Area Networks (OBAN). However, lowering the packet ...
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ISBN:
(数字)9798350354133
ISBN:
(纸本)9798350354140
The use of IEEE 802.15.7 for Heterogeneous Visible Light Communication (VLC) has garnered significant attention due to the expanding application area of Optical Body Area Networks (OBAN). However, lowering the packet delivery ratio has been shown in earlier research to be an effective way to cut end-device energy use. This paper presents an approach to enhance the packet delivery ratio in OBAN heterogeneous based on VLC systems. The suggested method considers store overflow when modifying the duty cycle within the IEEE 802.15.7 beacon-enabled mode. The outcomes of the simulation confirm this method's efficacy.
As an affordable and convenient eye scan, fundus photography holds the potential for preventing vision impairment, especially in resource-limited regions. However, fundus image degradation is common under intricate im...
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Motion planning in navigation systems is highly susceptible to upstream perceptual errors, particularly in human detection and tracking. To mitigate this issue, the concept of guidance points—a novel directional cue ...
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The visual commonsense reasoning (VCR) task is to choose an answer and provide a justifying rationale based on the given image and textural question. Representative works first recognize objects in images and then ass...
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