Joint multi-UAV deployments for air–ground integrated networks

Unmanned aerial vehicles (UAV) can perform high-speed and reliable transmissions with the properties in air superiority, high agility, and fast deployment. They have shown advantages in flexibility and reliability when the ground communication facilities cannot provide satisfactory services. In this article, a multi-UAV-based air–ground integrated network (AGIN) model is established with a novel joint optimal multi-UAV deployment framework, where UAVs serve as aerial base stations (BSs) or relays. To improve the communication performance of the AGIN, the following deployment schemes are proposed: 1) a static multi-UAV BS deployment to maximize the number of covered users by user clustering; 2) a mobile multi-UAV BS deployment to maximize the transmission performance of users, which is achieved by jointly optimizing UAV trajectories and user scheduling; 3) an optimized UAV multihop relay deployment to minimize the number of UAV relays and the communication outage probability. Moreover, UAV formation relay is deployed as a virtual multiantenna array to improve the relaying performance by beamforming and orthogonal space-time block coding, and some further open researches and challenges are also discussed

Adversarial erasing attention for person re-identification in camera networks under complex environments

Person re-identification (Re-ID) in camera networks under complex environments has achieved promising performance using deep feature representations. However, most approaches usually ignore to learn features from non-salient parts of pedestrian, which results in an incomplete pedestrian representation. In this paper, we propose a novel person Re-ID method named Adversarial Erasing Attention (AEA) to mine discriminative completed features using an adversarial way. Specifically, the proposed AEA consists of the basic network and the complementary network. On the one hand, original pedestrian images are used to train the basic network in order to extract global and local deep features. On the other hand, to learn features complementary to the basic network, we propose the adversarial erasing operation, that locates non-salient areas with the help of attention map, to generate erased pedestrian images. Then, we utilize them to train the complementary network and adopt the dynamic strategy to match the dynamic status of AEA in the learning process. Hence, the diversity of training samples is enriched and the complementary network could discover new clues when learning deep features. Finally, we combine the features learned from the basic and complementary networks to represent the pedestrian image. Experiments on three databases (Market1501, CUHK03 and DukeMTMC-reID) demonstrate the proposed AEA achieves great performances

Fuzzy multilayer clustering and fuzzy label regularization for unsupervised person reidentification

Unsupervised person reidentification has received more attention due to its wide real-world applications. In this paper, we propose a novel method named fuzzy multilayer clustering (FMC) for unsupervised person reidentification. The proposed FMC learns a new feature space using a multilayer perceptron for clustering in order to overcome the influence of complex pedestrian images. Meanwhile, the proposed FMC generates fuzzy labels for unlabeled pedestrian images, which simultaneously considers the membership degree and the similarity between the sample and each cluster. We further propose the fuzzy label regularization (FLR) to train the convolutional neural network (CNN) using pedestrian images with fuzzy labels in a supervised manner. The proposed FLR could regularize the CNN training process and reduce the risk of overfitting. The effectiveness of our method is validated on three large-scale person reidentification databases, i.e., Market-1501, DukeMTMC-reID, and CUHK03

Multi-evidence and multi-modal fusion network for ground-based cloud recognition

In recent times, deep neural networks have drawn much attention in ground-based cloud recognition. Yet such kind of approaches simply center upon learning global features from visual information, which causes incomplete representations for ground-based clouds. In this paper, we propose a novel method named multi-evidence and multi-modal fusion network (MMFN) for ground-based cloud recognition, which could learn extended cloud information by fusing heterogeneous features in a unified framework. Namely, MMFN exploits multiple pieces of evidence, i.e., global and local visual features, from ground-based cloud images using the main network and the attentive network. In the attentive network, local visual features are extracted from attentive maps which are obtained by refining salient patterns from convolutional activation maps. Meanwhile, the multi-modal network in MMFN learns multi-modal features for ground-based cloud. To fully fuse the multi-modal and multi-evidence visual features, we design two fusion layers in MMFN to incorporate multi-modal features with global and local visual features, respectively. Furthermore, we release the first multi-modal ground-based cloud dataset named MGCD which not only contains the ground-based cloud images but also contains the multi-modal information corresponding to each cloud image. The MMFN is evaluated on MGCD and achieves a classification accuracy of 88.63% comparative to the state-of-the-art methods, which validates its effectiveness for ground-based cloud recognition

Towards Arabic multi-modal sentiment analysis

In everyday life, people use internet to express and share opinions, facts, and sentiments about products and services. In addition, social media applications such as Facebook, Twitter, WhatsApp, Snapchat etc., have become important information sharing platforms. Apart from these, a collection of product reviews, facts, poll information, etc., is a need for every company or organization ranging from start-ups to big firms and governments. Clearly, it is very challenging to analyse such big data to improve products, services, and satisfy customer requirements. Therefore, it is necessary to automate the evaluation process using advanced sentiment analysis techniques. Most of previous works focused on uni-modal sentiment analysis mainly textual model. In this paper, a novel Arabic multimodal dataset is presented and validated using state-of-the-art support vector machine (SVM) based classification method

Lithium-ion battery prognostics through reinforcement learning based on entropy measures

Lithium-ion is a progressive battery technology that has vastly been used in different electrical systems. Failure in the battery can lead to failure in the entire system where the battery is embedded and cause irreversible damage. To avoid the probable damages, research is actively conducted, and data-driven methods are proposed based on prognostics and health management (PHM) systems. PHM can use multiple time-scale data and stored information from battery capacities over cycles to determine the battery state of health (SOH) and its remaining useful life (RUL). This results in battery safety, stability, reliability, and longer lifetime. In this paper, we propose different data-driven approaches to battery prognostics that rely on: Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA) and Reinforcement Learning (RL) based on the Permutation Entropy of battery voltage sequences at each cycle since they take into account the vital information from the past data and result in high accuracy

Energy-efficient resource allocation for simultaneous wireless information and power transfer in GFDM cooperative communications

Simultaneous wireless information and power transfer (SWIPT) for generalized frequency division multiplexing (GFDM) cooperative communications is proposed to save consumed energy of relay and destination. GFDM subsymbols are allocated for information decoding (ID) and energy harvesting (EH), respectively. Energy efficiency of SWIPT based GFDM system is maximized by jointly optimizing subsymbol and power allocations for ID and EH. A joint optimization algorithm is proposed to obtain the optimal solution to the energy-efficiency optimization problem. Simulation results show that energy efficiency of this scheme is 5bps/J/Hz higher than that of power-splitting SWIPT at total transmit power of 5mW

Throughput maximization for RIS-UAV relaying communications

In this paper, we consider a reconfigurable intelligent surface (RIS) assisted unmanned aerial vehicle (UAV) relaying communication system, where the RIS is mounted on the UAV and can move at a high speed. Compared with the conventional static RIS, better performance and more flexibility can be achieved with the assistance of the mobile UAV. We maximize the average downlink throughput by jointly optimizing the UAV trajectory, RIS passive beamforming and source power allocation for each time slot. The formulated non-convex optimization problem is decomposed into three subproblems: passive beamforming optimization, trajectory optimization and power allocation optimization. An alternating iterative optimization algorithm of the three subproblems is proposed to achieve the suboptimal solutions. The numerical results indicate that the RIS-UAV relaying communication system with trajectory optimization can get higher throughput

Integration transformer for ground-based cloud image segmentation

Recently, convolutional neural networks (CNNs) dominate the ground-based cloud image segmentation task, but disregard the learning of long-range dependencies due to the limited size of filters. Although Transformer-based methods could overcome this limitation, they only learn long-range dependencies at a single scale, hence failing to capture multiscale information of cloud images. The multiscale information is beneficial to ground-based cloud image segmentation, because the features from small scales tend to extract detailed information, while features from large scales have the ability to learn global information. In this article, we propose a novel deep network named Integration Transformer (InTransformer), which builds long-range dependencies from different scales. To this end, we propose the hybrid multihead transformer block (HMTB) to learn multiscale long-range dependencies and hybridize CNNs and HMTB as the encoder at different scales. The proposed InTransformer hybridizes CNNs and Transformer as the encoder to extract multiscale representations, which learns both local information and long-range dependencies with different scales. Meanwhile, in order to fuse the patch tokens with different scales, we propose a mutual cross-attention module (MCAM) for the decoder of InTransformer which could adequately interact multiscale patch tokens in a bidirectional way. We have conducted a series of experiments on the large ground-based cloud detection database TJNU Large Scale Cloud Detection Database (TLCDD) and Singapore Whole sky IMaging SEGmentation Database (SWIMSEG). The experimental results show that the performance of our method outperforms other methods, proving the effectiveness of the proposed InTransformer