Unmanned aerial vehicle (UAV) enabled Internet of Things (IoT) can keep network connectivity when the ground infrastructures are paralyzed. However, its transmission perform will be restricted due to ...the limited energy of the UAV. In this paper, a multi-UAV enabled IoT is proposed, where the UAVs as base stations send information to the ground IoT nodes via downlink within the flight time. And a fair energy-efficient resource optimization scheme for the IoT is studied to ensure fair energy consumption of multiple UAVs. The optimization problem seeks to maximize the minimum energy efficiency of each UAV by jointly optimizing communication scheduling, power allocations and trajectories of the UAVs. We decompose the non-convex optimization problem into three sub-optimization problems and solve them by Dlinkelbach method and successive convex approximation (SCA). Then a joint optimization algorithm is put forward to obtain the global optimal solutions by iteratively optimizing the three sub-optimization problems. The simulations results show that the multi-UAV enabled IoT can achieve significant performance improvement, and the energy efficiency between UAVs can achieve relative fairness by the fair resource optimization.
Person re-identification (Re-ID) is a challenging task due to variations in pedestrian images, especially in cross-domain scenarios. The existing cross-domain person Re-ID approaches extract the ...feature from single pedestrian image, but they ignore the correlations among pedestrian images. In this paper, we propose Heterogeneous Convolutional Network (HCN) for cross-domain person Re-ID, which learns the appearance information of pedestrian images and the correlations among pedestrian images simultaneously. To this end, we first utilize Convolutional Neural Network (CNN) to extract the appearance features for pedestrian images. Then we construct a graph in the target dataset where the appearance features are treated as the nodes and the similarity represents the linkage between the nodes. Afterwards, we propose Dual Graph Convolution (DGConv) to explicitly learn the correlation information from the similar and dissimilar samples, which could avoid the over-smoothing caused by the fully connected graph. Furthermore, we design HCN as a multi-branch structure to mine the structural information of pedestrians. We conduct extensive evaluations for HCN on three datasets, i.e. Market-1501, DukeMTMC-reID and MSMT17, and the results demonstrate that HCN is superior to the state-of-the-art methods.
Most ground-based remote sensing cloud classification methods focus on learning representation features for cloud images while ignoring the correlations among cloud images. Recently, graph ...convolutional network (GCN) is applied to provide the correlations for ground-based remote sensing cloud classification, in which the graph convolutional layer aggregates information from the connected nodes of graph in a weighted way. However, the weights assigned by GCN cannot reflect the importance of connected nodes precisely, which declines the discrimination of the aggregated features (AFs). To overcome the limitation, in this article, we propose the context graph attention network (CGAT) for ground-based remote sensing cloud classification. Specifically, the context graph attention layer (CGA layer) of CGAT is proposed to learn the context attention coefficients (CACs) and obtain the AFs of nodes based on the CACs. We compute the CACs not only considering the two connected nodes but also their neighborhood nodes in order to stabilize the aggregation process. In addition, we propose to utilize two different transformation matrices to transform the node and its connected nodes into new feature spaces, which could enhance the discrimination of AFs. We concatenate the AFs with the deep features (DFs) as final representations for cloud classification. Since existing ground-based cloud data sets (GCDs) have limited cloud images, we release a new data set named GCD that is the largest one for ground-based cloud classification. We conduct a series of experiments on GCD, and the experimental results verify the effectiveness of CGAT.
Cross-modality person re-identification (ReID) aims at searching a pedestrian image of RGB modality from infrared (IR) pedestrian images and vice versa. Recently, some approaches have constructed a ...graph to learn the relevance of pedestrian images of distinct modalities to narrow the gap between IR modality and RGB modality, but they omit the correlation between IR image and RGB image pairs. In this paper, we propose a novel graph model called Local Paired Graph Attention Network (LPGAT). It uses the paired local features of pedestrian images from different modalities to build the nodes of the graph. For accurate propagation of information among the nodes of the graph, we propose a contextual attention coefficient that leverages distance information to regulate the process of updating the nodes of the graph. Furthermore, we put forward Cross-Center Contrastive Learning (C3L) to constrain how far local features are from their heterogeneous centers, which is beneficial for learning the completed distance metric. We conduct experiments on the RegDB and SYSU-MM01 datasets to validate the feasibility of the proposed approach.
Unmanned ariel vehicle (UAV) can be used in cognitive radio (CR) due to its high mobility and line-of-sight (LoS) transmission. However, the throughput of secondary user (SU) may decrease because of ...interference arising from spectrum sharing. Reconfigurable intelligent surface (RIS) may overcome the interference by reconstructing the propagation links. Our aim is to maximize the throughput of SU subject to the interference constraint of primary user (PU) through the joint optimization of the UAV's trajectory, RIS's passive beamforming and UAV's power allocation. We divide the formulated non-convex optimization problem into three subproblems: passive beamforming optimization, power allocation optimization and trajectory design, and then propose an alternating iterative optimization algorithm of the subproblems to get the suboptimal solutions. Numerical results show the proposed algorithm can achieve remarkable throughput gain.
•We propose a novel deep graph model to learn the inter-local relationship of the corresponding parts among pedestrian images and the intra-local relationship between adjacent parts to obtain ...discriminative features for person Re-ID.•We propose the fractional dynamic mechanism to optimize the adjacency matrix of intra-local graph for accurately describing the correlation between adjacent parts.•Extensive experiments verify that the proposed PGCN exceeds the state-of-the-art methods.
Recently, part-based deep models have achieved promising performance in person re-identification (Re-ID), yet these models ignore the inter-local relationship of the corresponding parts among pedestrian images and the intra-local relationship between adjacent parts in one pedestrian image. As a result, the feature representations are hard to learn the information from the same parts of other pedestrian images and are lack of the contextual information of pedestrian. In this paper, we propose a novel deep graph model named Part-Guided Graph Convolution Network (PGCN) for person Re-ID, which could simultaneously learn the inter-local relationship and the intra-local relationship for feature representations. Specifically, we construct the inter-local graph using the local features extracted from the same parts of pedestrian images and build the adjacency matrix using the similarity so as to mine the inter-local relationship. Meanwhile, we construct the intra-local graph using the local features extracted from different body parts in one pedestrian image, and propose the fractional dynamic mechanism (FDM) to accurately describe the correlations between adjacent parts in the optimization process. Finally, after the graph convolutional operation, the inter-local relationship and the intra-local relationship are injected into the feature representations of pedestrian images. Extensive experiments are conducted on Market-1501, CUHK03, DukeMTMC-reID and MSMT17, and the experimental results show the proposed PGCN exceeds state-of-the-art methods by an overwhelming margin.
Unmaned aerial vehicle (UAV) can be deployed as an aerial base station (BS) of Internet of Things (IoT) due to its high mobility and low cost, while the ground BSs are absent or overload. We propose ...a rate splitting multiple access (RSMA)-UAV assisted IoT communication model, where the UAV flies over the target area and provide communication services for multiple ground users in pairs. In the RSMA-UAV scheme, the information of each ground user is split into common information and private information. In a user pair, the common information is decoded by all the users and the private information is only decoded by a specific user. The RSMA-UAV can control the inter-user interference by adjusting the split of common and private information, thus improving the rate of ground users. We formulate an optimization problem to maximize the system throughput by jointly optimizing communication schedule, common rate, transmit power and UAV flight trajectory. By leveraging block coordinate descent technique, we decompose the non-convex optimization problem into some solvable subproblems, and propose an alternating iterative optimization algorithm to achieve the suboptimal solutions. The simulations have shown that the RSMA-UAV scheme can achieve higher throughout than the orthogonal multiple access (OMA)-UAV scheme, and improve the minimum average achievable rate of users compared with the non-orthogonal multiple access (NOMA)-UAV scheme.
Many methods for ground-based remote sensing cloud detection learn representation features using the encoder-decoder structure. However, they only consider the information from single scale, which ...leads to incomplete feature extraction. In this article, we propose a novel deep network named dual pyramid network (DPNet) for ground-based remote sensing cloud detection, which possesses an encoder-decoder structure with dual pyramid pooling module (DPPM). Specifically, we process the feature maps of different scales in the encoder through dual pyramid pooling. Then, we fuse the outputs of the dual pyramid pooling in the same pyramid level using the attention fusion. Furthermore, we propose the encoder-decoder constraint (EDC) to relieve information loss in the process of encoding and decoding. It constrains the values and the gradients of probability maps from the encoder and the decoder to be consistent. Since the number of cloud images in the publicly available databases for ground-based remote sensing cloud detection is limited, we release the TJNU Large-scale Cloud Detection Database (TLCDD) that is the largest database in this field. We conduct a series of experiments on TLCDD, and the experimental results verify the effectiveness of the proposed method.
This research method contributes to the literature by measuring commensurably ‘the usage of information for (or web traffic on) web-based research studies’. The introduced method deepens the ...understanding the functionality of online media by focusing on specific web metrics to make the usage of this type of media efficient for disclosure function. The inputs of new method are originally based on publicly available data, and it can be applied through 3 consecutive steps. Accordingly, this method is applicable to assess stakeholders’ engagement for any web-based research study. In sum, this method presents:•The inputs of the method are publicly available data.•This method is applicable for variety web-based research studies regardless of the applied methodological approach (e.g., qualitative, quantitative).
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