arXiv subjects

Zhengrui Huang

Publications and source records attributed to Zhengrui Huang.

5 recordsLinked to original sources

arXiv2023

Multilink and AUV-Assisted Energy-Efficient Underwater Emergency Communications

Recent development in wireless communications has provided many reliable solutions to emergency response issues, especially in scenarios with dysfunctional or congested base stations. Prior studies on underwater emergency communications, however, remain under-studied, which poses a need for combining the merits of different underwater communication links (UCLs) and the manipulability of unmanned vehicles. To realize energy-efficient underwater emergency communications, we develop a novel underwater emergency communication network (UECN) assisted by multiple links, including underwater light, acoustic, and radio frequency links, and autonomous underwater vehicles (AUVs) for collecting and transmitting underwater emergency data. First, we determine the optimal emergency response mode for an underwater sensor node (USN) using greedy search and reinforcement learning (RL), so that isolated USNs (I-USNs) can be identified. Second, according to the distribution of I-USNs, we dispatch AUVs to assist I-USNs in data transmission, i.e., jointly optimizing the locations and controls of AUVs to minimize the time for data collection and underwater movement. Finally, an adaptive clustering-based multi-objective evolutionary algorithm is proposed to jointly optimize the number of AUVs and the transmit power of I-USNs, subject to a given set of constraints on transmit power, signal-to-interference-plus-noise ratios (SINRs), outage probabilities, and energy, which achieves the best tradeoff between the maximum emergency response time (ERT) and the total energy consumption (EC). Simulation results indicate that our proposed approach outperforms benchmark schemes in terms of energy efficiency (EE), contributing to underwater emergency communications.

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arXiv2023

Hybrid Device-to-Device and Device-to-Vehicle Networks for Energy-Efficient Emergency Communications

Recovering postdisaster communications has become a major challenge for search and rescue. Device-to-device (D2D) and device-to-vehicle (D2V) networks have drawn attention. However, due to the limited D2D coverage and onboard energy, establishing a hybrid D2D and D2V network is promising. In this article, we jointly establish, optimize, and fuse D2D and D2V networks to support energy-efficient emergency communications. First, we establish a D2D network by optimally dividing ground devices (GDs) into multiple clusters and identifying temporary data caching centers (TDCCs) from GDs in clusters. Accordingly, emergency data returned from GDs is cached in TDCCs. Second, given the distribution of TDCCs, unmanned aerial vehicles (UAVs) are dispatched to fetch data from TDCCs. Therefore, we establish a UAV-assisted D2V network through path planning and network configuration optimization. Specifically, optimal path planning is implemented using cascaded waypoint and motion planning and optimal network configurations are determined by multiobjective optimization. Consequently, the best tradeoff between emergency response time and energy consumption is achieved, subject to a given set of constraints on signal-to-interference-plus-noise ratios, the number of UAVs, transmit power, and energy. Simulation results show that our proposed approach outperforms benchmark schemes in terms of energy efficiency, contributing to large-scale postdisaster emergency response.

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arXiv2021

CN-LBP: Complex Networks-based Local Binary Patterns for Texture Classification

To overcome the limitations of original local binary patterns (LBP), this article proposes a new texture descriptor aided by complex networks (CN) and LBP, named CN-LBP. Specifically, we first abstract a texture image (TI) as directed graphs over different bands with the help of pixel distance, intensity, and gradient (magnitude and angle). Second, several CN-based feature measurements (including clustering coefficient, in-degree centrality, out-degree centrality, and eigenvector centrality) are selected to further decipher the texture features, which generates four feature images that can retain the image information as much as possible. Third, given the original TIs, gradient images (GI), and generated feature images, we can obtain the discriminative representation of texture images based on uniform LBP (ULBP). Finally, the feature vector is obtained by jointly calculating and concatenating the spatial histograms. In contrast to original LBP, the proposed texture descriptor contains more detailed image information, and shows resistance to imaging and noise. Experiment results on four datasets demonstrate that the proposed texture descriptor can significantly improve the classification accuracies compared with the state-of-the-art LBP-based variants and deep learning-based methods.

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arXiv2021

Fusion of Complex Networks-based Global and Local Features for Texture Classification

To realize accurate texture classification, this article proposes a complex networks (CN)-based multi-feature fusion method to recognize texture images. Specifically, we propose two feature extractors to detect the global and local features of texture images respectively. To capture the global features, we first map a texture image as an undirected graph based on pixel location and intensity, and three feature measurements are designed to further decipher the image features, which retains the image information as much as possible. Then, given the original band images (BI) and the generated feature images, we encode them based on the local binary patterns (LBP). Therefore, the global feature vector is obtained by concatenating four spatial histograms. To decipher the local features, we jointly transfer and fine-tune the pre-trained VGGNet-16 model. Next, we fuse and connect the middle outputs of max-pooling layers (MP), and generate the local feature vector by a global average pooling layer (GAP). Finally, the global and local feature vectors are concatenated to form the final feature representation of texture images. Experiment results show that the proposed method outperforms the state-of-the-art statistical descriptors and the deep convolutional neural networks (CNN) models.

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arXiv2021

Multi-Feature Fusion-based Scene Classification Framework for HSR Images

To realize high-accuracy classification of high spatial resolution (HSR) images, this letter proposes a new multi-feature fusion-based scene classification framework (MF2SCF) by fusing local, global, and color features of HSR images. Specifically, we first extract the local features with the help of image slicing and densely connected convolutional networks (DenseNet), where the outputs of dense blocks in the fine-tuned DenseNet-121 model are jointly averaged and concatenated to describe local features. Second, from the perspective of complex networks (CN), we model a HSR image as an undirected graph based on pixel distance, intensity, and gradient, and obtain a gray-scale image (GSI), a gradient of image (GoI), and three CN-based feature images to delineate global features. To make the global feature descriptor resist to the impact of rotation and illumination, we apply uniform local binary patterns (LBP) on GSI, GoI, and feature images, respectively, and generate the final global feature representation by concatenating spatial histograms. Third, the color features are determined based on the normalized HSV histogram, where HSV stands for hue, saturation, and value, respectively. Finally, three feature vectors are jointly concatenated for scene classification. Experiment results show that MF2SCF significantly improves the classification accuracy compared with state-of-the-art LBP-based methods and deep learning-based methods.

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