Multi-agent multi-objective systems (MAMOS) have emerged as powerful frameworks for modelling complex decision-making problems across various real-world domains, such as robotic exploration, autonomous traffic management, and sensor network optimisation. MAMOS enhances scalability and robustness through decentralised control and more accurately captures inherent trade-offs between conflicting objectives. In MAMOS, each agent uses utility functions that map return vectors to scalar values. Existing MAMOS optimisation methods face significant challenges in handling heterogeneous objective and utility function settings, where training non-stationarity is intensified due to private utility functions and the associated policies. In this paper, we first theoretically prove that direct access to, or structured modeling of, global utility functions is necessary to achieve the Bayesian Nash Equilibrium under decentralised execution constraints. To access the global utility functions while preserving the decentralised execution, we propose an Agent-Attention Multi-Agent Multi-Objective Reinforcement Learning (AA-MAMORL) framework. Our approach implicitly learns a joint belief over other agents’ utility functions and their associated policies during centralised training, effectively mapping global states and utilities to each agent's policy. During execution, each agent independently selects actions based on local observations and its private utility function to approximate a BNE, without relying on inter-agent communication. We evaluate our framework through extensive experiments in a custom-designed MAMO Particle environment and the standard MOMALand benchmark. The results demonstrate that accessibility to global preferences and our proposed AA-MAMORL significantly improves performance and consistently outperforms state-of-the-art methods.

Accurate beam prediction in millimeter-wave bands for 6G networks is challenged by complex channel dynamics, limiting the reliability of RF-only solutions. Recent advances in multimodal fusion leveraging non-RF cues have shown potential, yet existing approaches predominantly focus on fusion architectures while overlooking modality robustness under diverse conditions, thereby constraining their effectiveness. To overcome these limitations, we propose the Advanced Multimodal Representation Network (AMR-Net), a unified framework for robust cross-domain beam prediction. AMR-Net introduces probabilistic latent representations, modeling each sample as a learnable distribution to enhance generalization, and incorporates supervised contrastive learning on reparameterized embeddings to improve discriminability. Moreover, an uncertainty-aware composite fusion module is devised, integrating entropy, divergence, and confidence-based metrics to generate dynamic, sample-specific modality weights, thereby enabling reliable inference. Comprehensive evaluations on public vehicle-to-infrastructure (V2I) benchmarks, training under sunny conditions and testing across both matched and unseen scenarios such as varying weather conditions, different signal-to-noise ratio (SNR) levels, and partial modality loss, validate the effectiveness of AMR-Net. The results show that AMR-Net consistently surpasses state-of-the-art multimodal baselines in both accuracy and robustness, highlighting its superior generalization ability and real-world applicability.

Channel estimation in extremely large-scale multiple-input multiple-output (XL-MIMO) systems presents substantial challenges, especially under low pilot overhead. This paper proposes a novel tensor-based hybrid-field (TBHF) channel estimation scheme for XL-MIMO. We first develop a unified hybrid-field tensor decomposition framework that models received signals as a third-order low-rank tensor, from which channel parameters, including angle of arrival/departure (AoA/AoD), distance, time delay, and path gain, are jointly estimated via factor matrices. To distinguish far-field and near-field components, we propose a path classification mechanism that does not require prior knowledge of path component proportions. For near-field estimation, we introduce an angle-adaptive distance grid design and a two-step estimation strategy, enhancing accuracy and reducing complexity. We analyze the uniqueness of the Canonical Polyadic Decomposition, demonstrating the potential of TBHF to substantially reduce training overhead. The Cramér-Rao Bound for hybrid-field parameter estimation is derived, and simulations verify that TBHF outperforms existing methods in normalized mean square error.

Channel estimation in ultra-massive MIMO (UM-MIMO) systems with an array-of-subarrays (AoSA) architecture is challenged by high-dimensional channels and spherical-wave propagation. To address these issues, we propose a novel tensorbased joint SA (TB-JSA) channel estimation framework for UM-MIMO systems by representing the received signal of each subarray (SA) as a third-order low-rank tensor. The proposed TB-JSA framework enables joint channel estimation across multiple SAs while restricting the computationally intensive tensor decomposition to a carefully selected subset of SAs. Specifically, we first develop an SA selection strategy based on the energy-diversity criterion, which significantly reduces the overall computational complexity without sacrificing estimation performance. Subsequently, we propose a joint SA processing scheme that exploits the inherent spatial correlations among the selected SAs. The proposed method applies Canonical Polyadic Decomposition on the received SA signal tensors to extract path parameters, resolves permutation ambiguities through path matching, and jointly estimates the global parameters using a robust weighted least squares. This joint estimation enables accurate reconstruction of the full channel. Simulation results demonstrate that TB-JSA consistently outperforms all baseline methods in terms of accuracy, with estimation performance approaching the Cram´er–Rao Bound (CRB). Moreover, adjusting the number of selected SAs enables a controllable trade-off between estimation accuracy and computational efficiency.

This paper proposes a hierarchical task scheduling and adaptive resource management framework for intelligent unmanned aerial vehicle (UAV) systems to address the complex demands of oil tank monitoring tasks. A hybrid path planning strategy is adopted: at the global level, an improved Grey Wolf Optimization algorithm is used for task allocation and initial path generation; in local emergency scenarios, an enhanced Particle Swarm Optimization algorithm is employed to generate refined schedules. The system architecture supports dynamic task distribution and efficient resource utilization within UAV swarms, while the embedded implementation enables real-time execution in edge environments. Experimental evaluations demonstrate the effectiveness of the proposed method in enhancing scheduling flexibility, response efficiency, and robustness in complex inspection scenarios.

Accurate rebuild cost assessment (RCA) is crucial for insurance providers, customers, and government agencies involved in setting relevant building standards. RCA has relied heavily on the expertise and judgment of surveyors, which can be costly and may lead to inconsistent results, particularly in the case of less experienced surveyors new to the industry. The emergence of machine learning (ML) methods offers a promising solution to address these challenges. This paper is the first to explore the use of industry-scale data combined with explainable ML approaches to advance the field of ML-based RCA. We compiled a dataset of over 83,000 properties surveyed by experts and developed XGBoost and Random Forest models trained on this dataset. Extensive experiments were conducted to evaluate key performance metrics, including root mean square error, mean absolute error, coefficient of determination, and a business-focused measure, the Percentage Mean Absolute Error Ratio, along with ablation studies based on property types and surveyor expertise. The comprehensive results demonstrate that the performance of the developed ML models is comparable to that of human surveyors. As potentially the first study of its kind using realworld survey data for development and validation, this research provides unique insights for the insurance industry, property owners, and government agencies to enable more reliable and efficient insurance cost estimation and decision-making.

Multimodal learning often encounters the under-optimized problem and may have worse performance than unimodal learning. Existing methods attribute this problem to the imbalanced learning between modalities and rebalance them through gradient modulation. However, they fail to explain why the dominant modality in multimodal models also underperforms that in unimodal learning. In this work, we reveal the optimization conflict between the modality encoder and modality fusion module in multimodal models. Specifically, we prove that the cross-modal fusion in multimodal models decreases the gradient passed back to each modality encoder compared with unimodal models. Consequently, the performance of each modality in the multimodal model is inferior to that in the unimodal model. To this end, we propose a disentangled gradient learning (DGL) framework to decouple the optimization of the modality encoder and modality fusion module in the multimodal model. DGL truncates the gradient back-propagated from the multimodal loss to the modality encoder and replaces it with the gradient from unimodal loss. Besides, DGL removes the gradient back-propagated from the unimodal loss to the modality fusion module. This helps eliminate the gradient interference between the modality encoder and modality fusion module while ensuring their respective optimization processes. Finally, extensive experiments on multiple types of modalities, tasks, and frameworks with dense cross-modal interaction demonstrate the effectiveness and versatility of the proposed DGL. Code is available at \href{https://github.com/shicaiwei123/ICCV2025-GDL}{https://github.com/shicaiwei123/ICCV2025-GDL}

Earth observation foundation models have shown strong generalization across multiple Earth observation tasks, but their robustness under real-world perturbations remains underexplored. To bridge this gap, we introduce REOBench, the first comprehensive benchmark for evaluating the robustness of Earth observation foundation models across six tasks and twelve types of image corruptions, including both appearance-based and geometric perturbations. To ensure realistic and fine-grained evaluation, our benchmark focuses on high-resolution optical remote sensing images, which are widely used in critical applications such as urban planning and disaster response. We conduct a systematic evaluation of a broad range of models trained using masked image modeling, contrastive learning, and vision-language pre-training paradigms. Our results reveal that (1) existing Earth observation foundation models experience significant performance degradation when exposed to input corruptions. (2) The severity of degradation varies across tasks, model architectures, backbone sizes, and types of corruption, with performance drop varying from less than 1% to over 25%. (3) Vision-language models show enhanced robustness, particularly in multimodal tasks. REOBench underscores the vulnerability of current Earth observation foundation models to real-world corruptions and provides actionable insights for developing more robust and reliable models.

Autonomous aerial vehicles (AAVs) can be harnessed to provide temporary communication resources and act as a relay to assist users in transmitting their tasks to an external base station or cloud. How to allocate AAV resources to assist users has become an important topic in AAV-assisted communications. Although existing studies have considered terrain characteristics, communication throughput and user equality, task properties have often been overlooked, which would impact the overall quality of service. Due to the multiple complex attributes of tasks and dynamic complexity of the environment, optimization often involves high dimensionality and dynamism, making it difficult for traditional algorithms to distinguish primary tasks and effectively transmit important information. We thus propose a multiconstrained nonconvex joint optimization problem modeled as a partially observed Markov decision process, which brings the resource optimization challenges of AAV communication resources. This article further proposes a deep reinforcement learning (DRL)-based algorithm named weighted-K-means-DDPG (WK-Means-DDPG). The innovation of this algorithm lies in the combination of the traditional Weighted K-Means algorithm and DRL algorithm deep deterministic policy gradient (DDPG) to solve the deployment and resource allocation problems of AAVs and improve the performance of the reinforcement learning algorithm. Simulation results show that our algorithm outperforms state of the art and baseline algorithms in transmitting important information and can be generalized to different size of areas, number of users, and number of AAVs.

Semantic segmentation is commonly used for Oil Spill Detection (OSD) in remote sensing images. However, the limited availability of labelled oil spill samples and class imbalance present significant challenges that can reduce detection accuracy. Furthermore, most existing methods, which rely on convolutional neural networks (CNNs), struggle to detect small oil spill areas due to their limited receptive fields and inability to effectively capture global contextual information. This study explores the potential of State-Space Models (SSMs), particularly Mamba, to overcome these limitations, building on their recent success in vision applications. We propose OSDMamba, the first Mambabased architecture specifically designed for oil spill detection. OSDMamba leverages Mamba’s selective scanning mechanism to effectively expand the model’s receptive field while preserving critical details. Moreover, we designed an asymmetric decoder incorporating ConvSSM and deep supervision to strengthen multiscale feature fusion, thereby enhancing the model’s sensitivity to minority class samples. Experimental results show that the proposed OSDMamba achieves state-of-the-art performance, yielding improvements of 8.9% and 11.8% in OSD across two publicly available datasets. The source codes will be made publicly available at https://github.com/Chenshuaiyu1120/Oil-Spill-detection.

One of the main challenges facing hyperspectral image super-resolution is the complex high dimensional data processing. Mamba leverages its ability to model long-range dependencies of linear complexity to capture the global spatial and spectral information of high-dimensional data while maintaining linear complexity. However, its visual state space equation mainly focuses on the band dimension mapping of the image, while ignoring the modeling of the spatial dimension. To overcome this limitation, we develop a Mamba hyperspectral image super-resolution framework, which comprises three essential components. The first component, i.e., spatial Mamba sub-network, models the spatial dimensions of hyperspectral data. It captures long-range dependencies in the pixel space, thereby integrating global spatial information into the framework. The second component, i.e., spectral Mamba sub-network, serves to capture long-range spectral dependencies. The third component, i.e., reconstruction, generates hyperspectral images with rich spatial and spectral details through pixel interpolation. Our Mamba framework fully develops the potential of the Mamba model in hyperspectral image super-resolution, significantly enhancing the restoration quality and accuracy of hyperspectral images. Extensive experiments on the Houston and QUST-1 datasets show that our framework outperforms state-of-the-art methods in both quantitative metrics and visual quality across diverse scenarios. We release our source code at https://gitee.com/xu_yinghao/MambaHSISR for public evaluations.

UAV swarms have attracted much attention due to their high potential to execute complex missions more robustly and effectively. Essential technologies for swarms are the family of algorithms that allow the individual agents to undertake tasks intelligently, localize their relative positions, perceive surroundings, and plan and track collision-free and low-cost trajectories cooperatively so that the swarm’s overall objectives are efficiently achieved. There is still a lack of corresponding surveys that provide a systematic summary covering the control layer to task allocation and guide application-driven researchers in leveraging these capabilities for diverse UAV swarm applications. This survey debates the essential technologies of UAV swarms, including swarm trajectory planning, task assignment, control approaches, localization, perception, and communications. State-of-the-art algorithms and recent technical advancements have been investigated to expose the potential for developing highly autonomous and intelligent swarm systems. It further explores the use cases of UAV swarms in civil applications and critically analyzes existing technologies. The paper concludes by emphasizing the challenges for autonomous and intelligent UAV swarms and outlining potential future research directions. Overall, this paper provides a contemporary and comprehensive review of UAV swarm technologies and investigates their potential to transform civil application fields and support future technology advancement.

With the explosion of connected devices and Internet-of-Things (IoT) services in the smart city, the challenge to meet the demands of urban computing is increasingly prominent. Recent advances in vehicle-to-everything (V2X) communications promote urban Internet-connected vehicles to become excellent candidates for computing tasks. However, due to the limited computing capacity of vehicles, conducting computation in the vehicular networks themselves is insufficient to satisfy the demands of smart city applications. Edge computing, which delivers computing tasks to edge servers (e.g., base stations, BSs, or roadside units, RSUs) with plenty of computing resources, could be a possible solution. However, the static deployment of edge servers may cause severe load unbalance among servers in both real-time communication and computation, thereby decreasing the system performance. This paper explores a two-hop vehicle-assisted edge computing network framework in which vehicles are able to offload the tasks beyond their capabilities to underloaded edge servers relaying via neighbor vehicles. According to the state of the time-varying vehicular environment and the dynamic traffic loads among RSUs, we formulate the task offloading, relay node selection, and resources allocations problem as a Markov decision process (MDP) aiming at maximizing the performance of the computation offloading capacity with the considerations of load balancing and latency constraints. We propose a deep reinforcement learning (DRL) algorithm with a DNN as Q action-value function approximator to solve this problem. Extensive simulation results reveal that the proposed scheme can significantly improve the system performance compared to other state-of-the-art algorithms.

Logit knowledge distillation attracts increasing attention due to its practicality in recent studies. However, it of-ten suffers inferior performance compared to the feature knowledge distillation. In this paper, we argue that existing log it-based methods may be sub-optimal since they only leverage the global logit output that couples multiple se-mantic knowledge. This may transfer ambiguous knowl-edge to the student and mislead its learning. To this end, we propose a simple but effective method, i.e., Scale De-coupled Distillation (SDD), for logit knowledge distillation. SDD decouples the global logit output into multi-ple local logit outputs and establishes distillation pipelines for them. This helps the student to mine and inherit fine-grained and unambiguous logit knowledge. Moreover, the decoupled knowledge can be further divided into consis-tent and complementary logit knowledge that transfers the semantic information and sample ambiguity, respectively. By increasing the weight of complementary parts, SDD can guide the student to focus more on ambiguous samples, im-proving its discrimination ability. Extensive experiments on several benchmark datasets demonstrate the effective-ness of SDD for wide teacher-student pairs, especially in the fine-grained classification task. Code is available at: https://github.comishicaiwei123/SDD-CVPR2024

Deep learning (DL)-based methods have demonstrated remarkable achievements in addressing orthogonal frequency division multiplexing (OFDM) channel estimation challenges. However, existing DL-based methods mainly rely on separate real and imaginary inputs while ignoring the inherent correlation between the two streams, such as amplitude and phase information that are fundamental in communication signal processing. This paper proposes AE-DENet, a novel autoencoder(AE)-based data enhancement network to improve the performance of existing DL-based channel estimation methods. AE-DENet focuses on enriching the classic least square (LS) estimation input commonly used in DL-based methods by employing a learning-based data enhancement method, which extracts interaction features from the real and imaginary components and fuses them with the original real/imaginary streams to generate an enhanced input for better channel inference. Experimental findings in terms of the mean square error (MSE) results demonstrate that the proposed method enhances the performance of all state-of-the-art DL-based channel estimators with negligible added complexity. Furthermore, the proposed approach is shown to be robust to channel variations and high user mobility.

Real-time object detection in disasters facilitates efficient rescue operations and mitigates secondary disasters. Artificial intelligence techniques and remote sensing have recently been widely used in disaster management, especially for vision-related tasks. To counter the need for more annotated data in deep learning-based object detection, we introduce a disaster-specific dataset called DisasterScope containing high-resolution disaster-related imagery. The dataset encompasses a range of images depicting diverse disaster scenarios, enhancing the breadth of training data for improved model performance. Besides, we present an enhanced version of the Real-Time Models for Object Detection (RTMDet) and conduct training and validation on the mentioned dataset. Experimental results demonstrate that the proposed approach outperforms state-of-the-art methods.

In this paper, we introduce an efficient and lightweight hybrid Transformer architecture, ingeniously integrating convolutions within Transformer blocks for semantic segmentation of remote sensing Very High Resolution (VHR) imagery. To simultaneously avoid the high computational complexity in the shallow layers and capture the local representations of the VHR images, we propose the Group-Team Convolution Modulation (GTCM) module that uses convolutions to approximate the effect of attention mechanisms and modulates features in channel dimension. Additionally, to enlarge the effective receptive field (ERF) in the decoder, based on the grouping philosophy, we adopt dilated convolutions with multiple dilated rates to further enhance the performance. The superiority and efficiency of our proposed hybrid structure are demonstrated by outperforming state-of-the-art methods on the Vaihingen and Potsdam datasets with relatively lower complexity and fewer parameters.

This paper investigates future emergency wireless communication systems based on multiple unmanned vehicles cooperative deployment. A terrestrial carrier vehicle with wireless communication and management capabilities are deployed to release multiple unmanned aerial vehicles (UAVs) which will serve as aerial mobile stations (UAV-BSs) to cover a disaster affected area, forming an emergency Internet of Things (IoT) network. Under the proposed system architecture, we formulate a joint optimization challenge considering the UAV-BSs’ dynamic deployment positions and the association policy between user equipments (UEs) and BSs to maximize the throughput and coverage in dynamic scenarios as a time-varying mixed-integer non-convex sequential programming (MINSP) problem. To solve this problem, we first investigate the impact of decision delay caused by physical networking and computing environment on system performance to illustrate the urgent need for efficient algorithms. Then, a two-stage iterative training algorithm called centralized training multi-agent soft actor-critic with branch-and-cut (CT-MASAC-BAC) is proposed for computing globally optimal solutions. Numerical results show that CT-MASAC-BAC outperforms the heuristic algorithms and other benchmark deep reinforcement learning algorithms in terms of system utility. Furthermore, the experimental results show that the proposed algorithm is scalable with an increasing number of deployed UAV-BSs, contributing to potentially increased performance with more serving UAV-BSs.

Unmanned aerial vehicle (UAV) is becoming a crucial aerial platform to provide emergency or enhanced communication and sensing services benefited from its unique features, including agile mobility and high probability of Line-of-Sight coverage. In this article, we investigate the novel UAV trajectory design problem where the UAV communicates with multiple users and simultaneously senses the positions of multiple targets, via the integrated communication and sensing design. To evaluate the overall system utility, we first derive the communication throughput and localization error Cramér-Rao bound (CRB) model and highlight the coupled challenge and tradeoffs on UAV’s trajectory. We thus model the three typical integrated sensing and communication scenarios: 1) communication centric; 2) sensing centric; and 3) tradeoff scenarios. We further introduce path discretization to support in-flight communication and hovering sensing, which make it possible to jointly optimize the UAV trajectory, communication throughput and target localization estimation error CRB with limited complexity. Because this optimization problem involves integer programming caused by multiuser association, we propose a novel trajectory initialization framework based on traveling salesman problem to determine the service order for sensing targets and the initial UAV trajectory. To address the nonconvexity of this optimization problem, we propose an efficient iterative algorithm using the successive convexity approximation technique to obtain the approximate optimal solution, which is dynamically reconfigured and optimized with updated information in flight. Extensive numerical results demonstrate that the proposed algorithm achieves superior performance in the tradeoff between average achievable rate and CRB in all three scenarios.

As communication environments grow increasingly complex, efficient modulation scheme identification is critical. Automatic modulation classification (AMC) with deep learning has proven effective under diverse and noisy conditions, but its success hinges on the quality and diversity of training data. This article tackles the challenge of acquiring diverse training data sets through an innovative data augmentation method inspired by chromosomal mutations from genetic algorithms. Designed for I/Q modulation signals, the method introduces six radio augmentations: 1) interstitial deletion; 2) terminal deletion; 3) inversion; 4) breakage; 5) ring; and 6) translocation. These augmentations enrich and diversify training data, enhancing the adaptability of AMC models. Experiments show a ninefold expansion of the training sample space, significantly boosting the benchmark AMC models’ performance. Notably, our method achieves state-of-the-art results with CLDNN on RML2016.10A and RML2016.10B, with mean accuracies of 67.11% and 69.02%, respectively. It also improves the Transformer-based TRN model’s mean accuracy on RML2016.10B by 9.55%. Our approach effectively addresses data scarcity in deep learning-based AMC and offers promising avenues for future communication systems.

The joint use of multisource remote-sensing data for Earth observation has drawn much attention due to its robust performance. Although many methods have been proposed to fuse multimodal data, they tend to improve the interaction of different modality data while ignoring the optimization of each modality. Existing studies show that high-performance modalities will suppress the learning of weak ones, leading to under-optimized multimodal learning. To this end, we propose a general framework called gradient decoupled network (GDNet) to assist the multimodal remote sensing (RS) classification. GDNet guides each modality encoder in the multimodal model to learn probabilistic representations instead of deterministic ones. This helps decouple their gradient, reducing their influence on each other and encouraging them to learn the modality-specific information. Then, we further introduce the unimodal regularization for each modality encoder to align their logit output with the multimodal one and label distribution simultaneously. This helps introduce independent gradient paths for each morality encoder to accelerate their optimization when preserving the modality-share information. Finally, extensive experiments conducted on three benchmark datasets demonstrate that the proposed GDNet can effectively address the under-optimized problem in multimodal RS image classification. Code is available athttps://github.com/shicaiwei123/TGRS-GDNet.

Learning based on multimodal data has attracted increasing interest recently. While a variety of sensory modalities can be collected for training, not all of them are always available in practical scenarios, which raises the challenge to infer with incomplete modality. This article presents a general framework termed multimodal hallucination (MMH) to bridge the gap between ideal training scenarios and real-world deployment scenarios with incomplete modality data by transferring the complete multimodal knowledge to the hallucination network with incomplete modality input. Compared with the modality hallucination methods that restore privileged modalities information for late fusion, the proposed framework not only helps to preserve the crucial cross-modal cues but relates the study in complete modalities and in incomplete modalities. Then, we introduce two strategies called region-aware distillation and discrepancy-aware distillation to transfer the response-based and joint-representation-based knowledge of pre-trained multimodal networks, respectively. Region-aware distillation establishes and weights knowledge transferring pipelines between the response of multimodal and hallucination networks at multiple regions, which guides the hallucination network to focus on discriminative regions and avoid wasted gradients. Discrepancy-aware distillation guides the hallucination network to mimic the local inter-sample distance of multimodal representations, which enables the hallucination network to acquire the inter-class discrimination refined by multimodal cues. Extensive experiments on multimodal action recognition and face anti-spoofing demonstrate the proposed multimodal hallucination framework can overcome the problem of incomplete modality input in various scenes and achieve state-of-the-art performance.https://github.com/shicaiwei123/TMM-MMH

Dynamic time division duplex (D-TDD) is a promising technology for the future networks, enabling communication nodes to change uplink and downlink traffic opportunistically in the time domain to improve communication resource management. In this article, we propose an innovative system architecture where a full-duplex unmanned aerial vehicle (UAV) serves as a base station (BS) to enhance the performance of D-TDD networks composed of multiple full-duplex ground users and provides essential mobility and duplexing flexibility. We theoretically and numerically analyze two typical communication scenarios under this architecture, namely single transceiver pair (single-pair case) and multiple transceiver pairs (multi-pair case), at any time slot. In the single-pair case, the full-duplex UAV-BS communicates with at most one uplink user and one downlink user at the same time. In the multi-pair case, the UAV-BS supports multiple uplink and downlink users simultaneously. We formulate the throughput optimization problems for each case by jointly considering the uplink/downlink scheduling, power allocation, energy consumption and UAV-BS trajectory. Efficient algorithms are developed to deal with the non-convex problems and proved to be convergent. In addition, extensive simulation results show that our algorithms can cater to asymmetric uplink and downlink traffic and outperform other competitive benchmarks.

Multimodal learning has shown great potentials in numerous scenes and attracts increasing interest recently. However, it often encounters the problem of missing modality data and thus suffers severe performance degradation in practice. To this end, we propose a general framework called MMANet to assist incomplete multimodal learning. It consists of three components: the deployment network used for inference, the teacher network transferring comprehensive multimodal information to the deployment network, and the regularization network guiding the deployment network to balance weak modality combinations. Specifically, we propose a novel margin-aware distillation (MAD) to assist the information transfer by weighing the sample contribution with the classification uncertainty. This encourages the deployment network to focus on the samples near decision boundaries and acquire the refined inter-class margin. Besides, we design a modality-aware regularization (MAR) algorithm to mine the weak modality combinations and guide the regularization network to calculate prediction loss for them. This forces the deployment network to improve its representation ability for the weak modality combinations adaptively. Finally, extensive experiments on multimodal classification and segmentation tasks demonstrate that our MMANet outperforms the state-of-the-art significantly. Code is available at: https://github.com/shicaiwei123/MMANet

Flexible duplex is regarded as a promising technology for the fifth generation (5G) and beyond networks. In flexible duplex networks, communication nodes are capable of changing uplink and downlink traffic adaptively to improve communication resource management. In this article, we employ an in-band full-duplex (IBFD) unmanned aerial vehicle (UAV) serving as a base station (BS) to improve the performance of flexible duplex networks. In order to maximize the system throughput, we formulate an optimization problem by jointly designing the uplink/downlink scheduling, power allocation and UAV trajectory. Sequential parametric convex approximation (SPCA) and successive convex approximation (SCA) methods are utilized to provide an efficient solution for this highly non-convex problem. Furthermore, numerical simulation results show that our algorithm improves the system throughput compared to other designs.

In the real world, relationships between objects are often complex, involving multiple variables and modes. Hypergraph neural networks possess the capability to capture and represent such intricate relationships by deriving and inheriting their graph-based counterparts. Nevertheless, both graph and hypergraph neural networks suffer from the problem of over-smoothing when multiple graph convolution layers are stacked. To address this issue, this article introduces the Multi-modal Hypergraph Neural Network with Parametric Filtering and Feature Sampling (MHNet) to encode complex hypergraph features and mitigate over-smoothing. The proposed approach uses hypergraph structures to model high-order and multi-modal data correlations, a polynomial hypergraph filter to dynamically extract multi-scale node features through parametric polynomial fitting, and a feature sampling strategy to learn from sparse and labeled samples while avoiding overfitting. Experimental results on four hypergraph datasets and two multi-modal visual datasets demonstrate that the proposed MHNet outperforms state-of-the-art algorithms.

Multimodal learning has shown great potential in remote sensing image classification and attracted increasing interest in the community. Although it is preferable to collect multiple modalities for training, not all of them are available in practical scenarios. To this end, we propose a general diversity-guided distillation network (DGDNet) with modality-center regularization to facilitate accurate model inference when modalities are missing. Compared with existing modality reconstruction methods, DGDNet does not need prior knowledge of the missing modality and can handle various missing modalities via only one model. Specifically, DGDNet consists of two components: the deployment network extracting the modality-invariant representation for robust inference and the teacher network transferring comprehensive multimodal information to the deployment network. This enables the deployment network to learn the modality invariant and specific information simultaneously while maintaining robustness for incomplete modality input. In particular, we design a novel diversity-guided distillation method that transfers knowledge by matching the feature diversity. This helps overcome the representation heterogeneity when encouraging the deployment network to learn modality-specific information. Besides, a modality-center regularization strategy is proposed to address the unbalanced training of teacher and deployment networks by constraining the intra-class inter-modality variations. This helps alleviate the underfitting for weak modality, improving the model performance. Finally, extensive experiments demonstrate that the proposed DGDNet can address the problem of missing modalities effectively and achieves state-of-the-art performance. The code is available at https://github.com/shicaiwei123/TGRS-DGDNet.

Learning based multimodal data has attracted increasing interest in the remote sensing community owing to its robust performance. Although it is preferable to collect multiple modalities for training, not all of them are available in practical scenarios due to the restriction of imaging conditions. Therefore, how to assist the model inference with missing modalities is significant for multimodal remote sensing image processing. In this work, we propose a general framework called modality-shared hallucination network (MSH-Net) to address this issue by reconstructing complete modality-shared features from the incomplete inference modalities. Compared to conventional privilege modality hallucination methods, MSH-Net does not only help preserve the cross-modal interactions for model inference, but also scales well with the increasing number of missing modalities. We further develop a novel joint adaptation distillation (JAD) method that guides the hallucination model to learn the modality-shared knowledge from the multimodal model by matching the joint probability distributions between representation and groundtruth. This overcomes the representation heterogeneity caused by the discrepancy between inputs and structures of multimodal and hallucination model, while preserving the decision boundaries refined by multimodal cues. Finally, extensive experiments conducted on four common modality combinations demonstrate that the proposed MSH-Net can effectively address the problem of missing modalities and achieve state-of-the-art performance. Code is available at: https://github.com/shicaiwei123/MSHNet.

Considering that low-cost and resource-cons- trained sensors coupled inherently could be vulnerable to growing numbers of intrusion threats, industrial Internet-of-Things (IIoT) systems are faced with severe security concerns. Data sharing for building high-performance intrusion detection models is also prohibited due to the sensitivity, privacy, and high value of IIoT data. This article presents an anomaly-based intrusion detection system with federated learning for privacy-preserving machine learning in future IIoT networks. To tackle the urgent issue of training local models with non-independent and identically distributed (non-IID) data, we adopt instance-based transfer learning at local. Furthermore, to boost the performance of this system for IIoT intrusion detection, we propose a rank aggregation algorithm with a weighted voting approach. The proposed system achieves superior detection performance with 95.97% and 73.70% accuracy for AdaBoost and Random Forest, respectively, outperforming the baseline models by 12.72% and 14.8%.

Endoscopy has been routinely used to diagnose stomach diseases including intestinal metaplasia (IM) and gastritis atrophy (GA). Such routine examination usually demands highly skilled radiologists to focus on a single patient with substantial time, causing the following two key challenges: 1) the dependency on the radiologist's experience leading to inconsistent diagnosis results across different radiologists; 2) limited examination efficiency due to the demanding time and energy consumption to the radiologist. This paper proposes to address these two issues in endoscopy using novel machine learning method in three-folds. Firstly, we build a novel and relatively big endoscopy dataset of 21,420 images from the widely used White Light Imaging (WLI) endoscopy and more recent Linked Color Imaging (LCI) endoscopy, which were annotated by experienced radiologists and validated with biopsy results, presenting a benchmark dataset. Secondly, we propose a novel machine learning model inspired by the human visual system, named as local attention grouping, to effectively extract key visual features, which is further improved by learning from multiple randomly selected regional images via ensemble learning. Such a method avoids the significant problem in the deep learning methods that decrease the resolution of original images to reduce the size of input samples, which would remove smaller lesions in endoscopy images. Finally, we propose a dual transfer learning strategy to train the model with co-distributed features between WLI and LCI images to further improve the performance. The experiment results, measured by accuracy, specificity, sensitivity, positive detection rate and negative detection rate, on IM are 99.18 %, 98.90 %, 99.45 %, 99.45 %, 98.91 %, respectively, and on GA are 97.12 %, 95.34 %, 98.90 %, 98.86 %, 95.50 %, respectively, achieving state of the art performance that outperforms current mainstream deep learning models.

Greedy routing efficiently achieves routing solutions for vehicular networks due to its simplicity and reliability. However, the existing greedy routing algorithms have mainly considered simple routing metrics only, e.g., distance based on the local view of an individual vehicle. This consideration is insufficient for analysing dynamic and complicated vehicular communication scenarios which inevitably degrades the overall routing performance. Software-Defined Vehicular Network (SDVN) and Graph Convolutional Network (GCN) can overcome these limitations. Thus, this paper presents a novel GCN-based greedy routing algorithm (NGGRA) in the hybrid SDVN. The SDVN control plane trains the GCN decision model based on the globally collected data. Vehicles with transmission requirements can adopt this model for inferring and making the routing decisions. The proposed node-importance-based graph convolutional network (NiGCN) model analyses multiple correlated metrics to accurately evaluate the dynamic vehicular network is available at:https://github.com/a824899245/NiGCN. Meanwhile, the SDVN architecture offers a global view for model training and routing computation. Extensive simulation results demonstrate that NiGCN outperforms popular GCN models in training efficiency and accuracy. In addition, NGGRA can improve the packet delivery ratio and substantially reduce delay compared with its counterparts.

Pneumoconiosis staging has been a challenging task for deep neural networks due to the stage ambiguity in early pneumoconiosis. In this article, we propose a deep log-normal label distribution learning method named DLN-LDL for pneumo-coniosis staging by exploring the intrinsic stage distribution pat-terns of pneumoconiosis. DLN-LDL effectively prevents the deep network from overfitting features in ambiguous chest radiographs that are irrelevant to the stage to which they belong by replacing the one-hot labels with log-normally distributed vectors. The experiments on our collected pneumoconiosis dataset confirm that the proposed DLN-LDL algorithm outperforms other classical methods in terms of Accuracy, Precision, Sensitivity, Specificity, F1-score and Area Under the Curve.

The goal of this paper is to empower cameras to sense the wind from videos by capturing the motion information using optical flow and machine learning models, to potentially revolutionise the spatiotemporal resolution of existing professional wind records that are often at the city scale. To this end, we build a novel video dataset of over 6000 labeled video clips, covering eleven wind classes of the Beaufort scale. The videos are collected from social media, public cameras, and self-recording. Every video clip has a fixed 10 seconds length with varied frame rates, and contains scenes of various trees swaying in different scales of wind. We describe the key statistics of the dataset, how it was collected and annotated, and evaluate both one-stage and two-stage models trained and tested for wind scale estimation on this dataset to give some baseline performance figures. The dataset is publicly accessible11https://sme.uds.exeter.ac.uk/folders/48caf5102d6196b9645fab1f46e494ec. Please contact the authors to get the access key due to the server protection policy..

To provide various onboard entertainment services, the ever-increased Internet contents to be exchanged among remote data centers, roadside units (RSUs), and vehicles demand reliable and fast content dissemination in the vehicular networks. Edge precaching technology is expected to provide flexible and low-latency content dissemination by allowing edge nodes (i.e., RSUs and vehicles) to precache contents. However, the content dissemination process of edge precaching still suffers from high mobility and highly dynamic topology of vehicular networks. The recently proposed platoon-based vehicular network has potentials to mitigate the mobility challenges, but need to deal with multihop wireless content dissemination’s latency and reliability issues. Additionally, the network resources are limited in edge nodes, whereas various onboard Internet services with different Quality-of-Service (QoS) requirements share the same resource pool by the same network resource scheduling policy, thereby decaying the network performance. Based on the above observations, to cope with the challenging content precaching problem under diverse QoS requirements in a platoon-based edge vehicular network, we first abstract two isolated virtual content service slices with different QoS requirements based on network slicing technology to provide on-demand customized services. Then, we propose an intelligent deep reinforcement learning (DRL)-based content precaching scheme, which optimally matches the available communication resources and limited caching capacities in the edge vehicular network. The scheme jointly considers the impacts of content precaching policy and multihop wireless transmission on the content precaching performance. Simulation results show that our proposed DRL-based content precaching scheme achieves a competitive performance of reliability and latency comparing with other state-of-the-art algorithms.

This article proposes a controlling framework for multiple unmanned aerial vehicles (UAVs) to integrate the modes of formation flight and swarm deployment over fixed and switching topologies. Formation strategies enable UAVs to enjoy key collective benefits including reduced energy consumption, but the shape of the formation and each UAV's freedom are significantly restrained. Swarm strategies are thus proposed to maximize each UAV's freedom following simple yet powerful rules. This article investigates the integration and switch between these two strategies, considering the deployment environment factors, such as poor network conditions and unknown and often highly mobile obstacles. We design a distributed formation controller to guide multiple UAVs in orderless states to swiftly reach an intended formation. Inspired by starling birds and similar biological creatures, a distributed collision avoidance controller is proposed to avoid unknown and mobile obstacles. We further illustrated the stability of the controllers over both fixed and switching topologies. The experimental results confirm the effectiveness of the framework.

Pneumoconiosis staging has been a very challenging task, both for certified radiologists and computer-aided detection algorithms. Although deep learning has shown proven advantages in the detection of pneumoconiosis, it remains challenging in pneumoconiosis staging due to the stage ambiguity of pneumoconiosis and noisy samples caused by misdiagnosis when they are used in training deep learning models. In this article, we propose a fully deep learning pneumoconiosis staging paradigm that comprises a segmentation procedure and a staging procedure. The segmentation procedure extracts lung fields in chest radiographs through an Asymmetric Encoder-Decoder Network (AED-Net) that can mitigate the domain shift between multiple datasets. The staging procedure classifies the lung fields into four stages through our proposed deep log-normal label distribution learning and focal staging loss. The two cascaded procedures can effectively solve the problem of model overfitting caused by stage ambiguity and noisy labels of pneumoconiosis. Besides, we collect a clinical chest radiograph dataset of pneumoconiosis from the certified radiologist's diagnostic reports. The experimental results on this novel pneumoconiosis dataset confirm that the proposed deep pneumoconiosis staging paradigm achieves an Accuracy of 90.4%, a Precision of 84.8%, a Sensitivity of 78.4%, a Specificity of 95.6%, an F1-score of 80.9% and an Area Under the Curve (AUC) of 96%. In particular, we achieve 68.4% Precision, 76.5% Sensitivity, 95% Specificity, 72.2% F1-score and 89% AUC on the early pneumoconiosis 'stage-1'.

The ever-expanding scale of cloud datacenters necessitates automated resource provisioning to best meet the requirements of low latency and high energy-efficiency. However, due to the dynamic system states and various user demands, efficient resource allocation in cloud faces huge challenges. Most of the existing solutions for cloud resource allocation cannot effectively handle the dynamic cloud environments because they depend on the prior knowledge of a cloud system, which may lead to excessive energy consumption and degraded Quality-of-Service (QoS). To address this problem, we propose an adaptive and efficient cloud resource allocation scheme based on Actor-Critic Deep Reinforcement Learning (DRL). First, the actor parameterizes the policy (allocating resources) and chooses actions (scheduling jobs) based on the scores assessed by the critic (evaluating actions). Next, the resource allocation policy is updated by using gradient ascent while the variance of policy gradient is reduced with an advantage function, which improves the training efficiency of the proposed method. We conduct extensive simulation experiments using real-world data from Google cloud datacenters. The results show that our method can obtain the superior QoS in terms of latency and job dismissing rate with enhanced energy-efficiency, compared to two advanced DRL-based and five classic cloud resource allocation methods.

Change detection plays a vital role in monitoring and analyzing temporal changes in Earth observation tasks. This paper proposes a novel adaptive multi-scale and multi-level features fusion network for change detection in very-high-resolution bi-temporal remote sensing images. The proposed approach has three advantages. Firstly, it excels in abstracting high-level representations empowered by a highly effective feature extraction module. Secondly, an elaborate feature fusion module incorporated with the channel and spatial attention mechanism is proposed to provide efficient fusion strategies for multi-scale and multi-level features from bi-temporal images and multiple convolutional layers. Finally, a novel perceptual auxiliary component is designed to capture the perceptual loss of the global perceptual and structural differences and address the optimization problem caused by only using per-pixel loss function in change detection. Comprehensive experiments on two benchmark datasets confirm that our proposed framework outperforms state-of-the-art algorithms in both quantitative assessment and visual interpretation.

Cellular communications have been regarded as promising approaches to deliver high-broadband communication links for unmanned aerial vehicles (UAVs), which have been widely deployed to conduct various missions, e.g., precision agriculture, forest monitoring, and border patrol. However, the unique features of aerial UAVs, including high-altitude manipulation, 3-D mobility, and rapid velocity changes, pose challenging issues to realize reliable cellular-enabled UAV communications, especially with the severe intercell interference generated by UAVs. To deal with this issue, we propose a novel position-based robust beamforming algorithm through complementarily integrating the navigation information and wireless channel information to improve the performance of cellular-enabled UAV communications. Specifically, in order to achieve the optimal beam weight vector, the navigation information of the UAV system is innovatively exploited to predict the changes of the direction-of-Arrival (DoA) angle. To fight against the high mobility of UAV operations, an optimization problem is formed by considering the tapered surface of DoA angle and solved to correct the inherent position error. Comprehensive simulation experiments are conducted and the results show that the proposed robust beamforming algorithm could achieve over 90% DoA estimation error reduction and up to 14-dB SINR gain compared with five benchmark beamforming algorithms, including linearly constrained minimum variance (LCMV), position-based beamforming, diagonal loading (DL), robust capon beamforming (RCB), and robust LCMV algorithm.

Vehicular edge computing (VEC) is a new computing paradigm that has great potential to enhance the capability of vehicle terminals (VTs) to support resource-hungry in-vehicle applications with low latency and high energy efficiency. In this article, we investigate an important computation offloading scheduling problem in a typical VEC scenario, where a VT traveling along an expressway intends to schedule its tasks waiting in the queue to minimize the long-term cost in terms of a tradeoff between task latency and energy consumption. Due to diverse task characteristics, dynamic wireless environment, and frequent handover events caused by vehicle movements, an optimal solution should take into account both where to schedule (i.e., local computation or offloading) and when to schedule (i.e., the order and time for execution) each task. To solve such a complicated stochastic optimization problem, we model it by a carefully designed Markov decision process (MDP) and resort to deep reinforcement learning (DRL) to deal with the enormous state space. Our DRL implementation is designed based on the state-of-the-art proximal policy optimization (PPO) algorithm. A parameter-shared network architecture combined with a convolutional neural network (CNN) is utilized to approximate both policy and value function, which can effectively extract representative features. A series of adjustments to the state and reward representations are taken to further improve the training efficiency. Extensive simulation experiments and comprehensive comparisons with six known baseline algorithms and their heuristic combinations clearly demonstrate the advantages of the proposed DRL-based offloading scheduling method.

In this article, we computationally model the social behavior of beetles and apply it to the tracking control of manipulators. The beetles demonstrate excellent skills to forage food in a previously unknown environment by merely using their olfactory senses. The goal of the beetle is to search the region with the maximum smell. Therefore, the actions of the beetle can be characterized as an optimization algorithm. This article mathematically models this behavior in the form of a recurrent neural network (RNN) with a temporal-feedback connection. We apply the formulated RNN controller for the redundancy resolution and tracking control of the redundant manipulators with an unknown kinematic model. Most of the industrial robots have redundant manipulators, and kinematic trajectory tracking is a fundamental problem for any industrial task. The behavior of the beetle allows us to formulate a position-level controller without relying on the manipulation of the Jacobian matrix. It is in contrast with the conventional velocity-level controllers, which require an accurate kinematic model of the manipulator and calculation of pseudoinverse of Jacobian, a computationally expensive task. The proposed algorithm, called Beetle Antennae Olfactory Recurrent Neural Network (BAORNN) algorithm, is capable of driving the manipulator by only using the feedback from the position and orientation sensors. The stability and convergence of the proposed algorithm are theoretically proved, and the simulations results using a seven-degree-of-freedom (DOF) industrial robotic arm, KUKA LBR IIWA14, are presented to demonstrate the performance of the proposed algorithm.

During the last decades, concepts of Intelligent Transportation Systems (ITS) were continuously adapted and improved based on new insights into human travel behavior. Drivers for improvements are the quantity and quality of available mobility data, which increased significantly in recent years. Based on travel behavior, literature proposes a large number of different solutions for next step or future location prediction. However a holistic spatio-temporal prediction, which could further improve the quality of ITS, creates a more complex task. The prediction of medium-term mobility for one to seven days is challenging in particular for atypical travel behavior, since the weekdays' order delivers no reliable indication for the next day's travel behavior. With our contribution, we explore the benefits of various prediction approaches for medium-term mobility prediction and combine them dynamically to predict individual mobility behavior for a period of one week. The derived framework utilizes an exhaustive search approach to benefit from a machine learning based clustering method on location data. In conjunction with an Artificial Neural Network, the prediction framework is robust against prediction errors created by atypical behavior. With two data sets consisting of smartphone and vehicle data, we demonstrate the framework's real-world applicability. We show that clustering an individual's historical movement data can improve the prediction accuracy of different prediction methods that will be explained in detail and illustrate the interrelation of entropy and prediction accuracy.

Inspired by mobile edge computing (MEC), vehicular edge computing (VEC) enables vehicle terminals to support resource-hungry on-vehicle applications with significantly lower latency and less energy consumption. In this paper, we investigate the computation offloading problem in a typical VEC scenario, where a vehicle offloads its computation tasks to the VEC servers deployed in the road side unit (RSU) to minimize its long-term user cost. The mobility of the vehicle coupled with the high dynamics of the environment makes the problem particularly difficult. To tackle this challenge, a deep reinforcement learning (DRL) based offloading method is proposed, which approximates the offloading policy (OP) by a deep neural network (DNN) and trains the DNN with the proximal policy optimization (PPO) algorithm without a priori knowledge of the environment dynamics. Extensive simulation experiments and comprehensive comparison with six baseline algorithms demonstrate that it can achieve the lowest user cost in most cases.

In this paper, we propose a novel video coding scheme to significantly reduce the coding complexity and enhance overall coding efficiency in videos acquired by high mobility devices such as unmanned aerial vehicles (UAVs). In order to reduce the encoded data bits and encoding time to facilitate real-time data transmission, as well as minimize the image distortion caused by the jitter of onboard camera, a sensor-assisted global motion estimation (GMV) algorithm is designed to calculate perspective transformation model and global motion vectors, which are used in both the inter-frame coding to improve the coding efficiency and intra-frame coding to reduce block search complexity. We conducted comprehensive simulation experiments on official HM-16.10 codec and the performance results show the proposed method can achieve faster block search by 50% to 60% speedup and lower bitrate by 15% to 30% compared with standard HEVC coding software.

Unmanned Aerial Vehicles (UAVs) have demonstrated exceptional capabilities in many real-world applications such as remote sensing, emergency medicine delivery and precision agriculture. LTE networks are regarded as key candidates to offer high performance broadband wireless services to support UAV applications and safe deployment. However, the unique features of aerial UAVs including high-altitude manipulation, three-dimension (3D) mobility and rapid velocity changes, pose challenging issues for optimising LTE wireless communications to support UAVs, especially under the severe inter-cell interference generated by UAVs in the sky. To deal with this issue, we propose a novel position-based robust beamforming algorithm to improve the performance of LTE networks to serve UAVs. For obtaining optimal weight vectors that could tolerate Direction-of-Arrival (DoA) estimation errors, we propose a hybrid method to integrate the channel information and UAV flight information to accurately estimate the DoA angle range. In order to validate the performance of the proposed robust beamforming algorithm, we conduct comprehensive simulation experiments under practical configurations. The results show that the proposed robust beamforming algorithm outperforms benchmark Linearly Constrained Minimum Variance (LCMV) beamforming and GPS-based beamforming algorithms.

Network function virtualization (NFV) has been considered as a promising technology for future Internet to increase the network flexibility, accelerate the service innovation, and reduce the Capital Expenditures and Operational Expenditures costs through migrating network functions from dedicated network devices to commodity hardware. Recent studies reveal that although this migration of network function brings the network operation unprecedented flexibility and controllability, NFV-based architecture suffers from serious performance degradation compared with traditional service provisioning on dedicated devices. In order to achieve a comprehensive understanding of the service provisioning capability of NFV, this paper proposes a novel analytical model based on Stochastic Network Calculus (SNC) to quantitatively investigate the end-to-end performance bound of the NFV networks. To capture the dynamic and on-demand NFV features, both the non-bursty traffic, e.g., the Poisson process, and the bursty traffic, e.g., the Markov Modulated Poisson Process, are jointly considered in the developed model to characterize the arriving traffic. To address the challenges of resource competition and end-to-end NFV chaining, the property of convolution associativity and leftover service technologies of SNC are exploited to calculate the available resources of the Virtual Network Function nodes in the presence of multiple competing traffic and transfer the complex NFV chain into an equivalent system for performance derivation and analysis. Both the numerical analysis and extensive simulation experiments are conducted to validate the accuracy of the proposed analytical model. Results demonstrate that the analytical performance metrics match well with those obtained from the simulation experiments and numerical analysis. In addition, the developed model is used as a practical and cost-effective tool to investigate the strategies of the service chain design and resource allocations in the NFV networks.

Simultaneous wireless information and power transfer (SWIPT) offers novel designs that could enhance the sustainability and resilience of communication systems. Due to the very limited receiving power from radio frequency (RF) signals, optimal splitting strategies play an essential role for many SWIPT systems. This paper investigates optimal power splitting from the outage perspective by formulating the power, information and joint outage performance using a Markov chain, and studying the boundary conditions for achieving an energy- neutral state. Our results show that: 1)the power splitting factor is the key factor of intrinsic trade-off between power and information outage; and 2) the proposed polynomial method can obtain the optimal power splitting factor which leads to the minimum joint outage probability.

The prediction of an individual's future locations is a significant part of scientific researches. While a variety of solutions have been investigated for the prediction of future locations, predicting departure and arrival times at predicted locations is a task with higher complexity and less attention. While the challenges of combining spatial and temporal information have been stated in various works, the proposed solutions lack accuracy and robustness. This paper proposes a simple yet effective way to predict not only an individual's future location, but also most probable departure and arrival times as well as the most probable route from origin to destination.

This paper presents a new, practical infrared video based surveillance system, consisting of a resolution-enhanced, automatic target detection/recognition (ATD/R) system that is widely applicable in civilian and military applications. To deal with the issue of small numbers of pixel on target in the developed ATD/R system, as are encountered in long range imagery, a super-resolution method is employed to increase target signature resolution and optimise the baseline quality of inputs for object recognition. To tackle the challenge of detecting extremely low-resolution targets, we train a sophisticated and powerful convolutional neural network (CNN) based faster-RCNN using long wave infrared imagery datasets that were prepared and marked in-house. The system was tested under different weather conditions, using two datasets featuring target types comprising pedestrians and 6 different types of ground vehicles. The developed ATD/R system can detect extremely low-resolution targets with superior performance by effectively addressing the low small number of pixels on target, encountered in long range applications. A comparison with traditional methods confirms this superiority both qualitatively and quantitatively.

We develop an automatic oil spill segmentation method in terms of f-divergence minimization. We exploit f-divergence for measuring the disagreement between the distributions of ground-truth and generated oil spill segmentations. To render tractable optimization, we minimize the tight lower bound of the f-divergence by adversarial training a regressor and a generator, which are structured in different forms of deep neural networks separately. The generator aims at producing accurate oil spill segmentation, while the regressor characterizes discriminative distributions with respect to true and generated oil spill segmentations. It is the coplay between the generator net and the regressor net against each other that achieves a minimal of the maximum lower bound for the f-divergence. The adversarial strategy enhances the representational powers of both the generator and the regressor and avoids requesting large amounts of labeled data for training the deep network parameters. In addition, the trained generator net enables automatic oil spill detection that does not require manual initialization. Benefiting from the comprehensiveness of f-divergence for characterizing diversified distributions, our framework can accurately segment variously shaped oil spills in noisy synthetic aperture radar images. Experimental results validate the effectiveness of the proposed oil spill segmentation framework.

We present a novel marine oil spill segmentation method that characterizes two smoothing modules at the label level and the pixel level separately. At the label level, we exploit the rolling guidance filter for smoothing the label cost volumes. It enables scale-aware labeling and thus alleviates the ambiguous segmentation that blurs the detailed structures of oil spills. At the pixel level, we adapt a cooperative model for smoothing higher order pixel variations, which has the potential of preserving elongated strips that often arise in oil spills. We integrate the two smoothing modules operating at different levels into an energy minimization formulation, which is referred to as dual smoothing. The coupling of the two smoothing modules enables an effective complement to each other such that the specific structures of oil spills are accurately characterized. We compute the optimal labeling of the dual-smoothing framework based on graph cuts. The proposed dual-smoothing framework is especially effective in segmenting elongated and detailed oil spills, and the experimental results demonstrate its advantages over thresholding- and graph-cut-based segmentations.

Future 5G networks have stringent end-user requirements on data rate and error performance. In order to satisfy these requirements, innovative wireless networking technologies and models need be researched. One particular example is the two-way relaying channel, which can have as much as 100% higher theoretical data rate than current systems where transmissions are arranged in an orthogonal manner. However, benefits of this model cannot be achieved without the application of proper relaying protocols. This paper proposes a novel protocol that directly addresses the problems of existing protocols of two-way relaying models, e.g. analogy network coding and physical network coding, and has improved performance. By combining direct and differential demodulation-forward schemes based on wireless channel qualities and signal to noise ratio, a new hybrid protocol is created. Theoretical analysis and numerical experiments show that the proposed solution has lower error rate than the existing ones, and can thus be applied to support future 5G networks.

The application of small civilian unmanned aerial vehicles (UAVs) has attracted great interest for disaster sensing. However, the limited computational capability and low energy resource of UAVs present a significant challenge to real-time data processing, networking and policy making, which are of vital importance to many disaster related applications such as oil-spill detection and flooding. In order to address the challenges imposed by the sheer volume of captured data, particularly video data, the intermittent and limited network resources, and the limited resources on UAVs, a new cloud-supported UAV application framework has been proposed and a prototype system of such framework has been implemented in this paper. The framework integrates video acquisition, data scheduling, data offloading and processing, and network state measurement to deliver an efficient and scalable system. The prototype of the framework comprises of a client-side set of components hosted on the UAV which selectively offloads the captured data to a cloud-based server. The server provides real-time data processing and information feedback services to the incident control centre and client device/operator. Results of the prototype system are presented to demonstrate the feasibility of such framework.

Two-Way Relay Channel (TWRC) plays an important role in relay networks, and efficient relaying protocols are particularly important for this model. However, existing protocols may not be able to realize the potential of TWRC if the two independent fading channels are not carefully handled. In this paper, a Hybrid DeModulate-Forward (HDMF) protocol is proposed to address such a problem. We first introduce the two basic components of HDMF - direct and differential DMF, and then propose the key decision criterion for HDMF based on the corresponding log-likelihood ratios. We further enhance the protocol so that it can be applied independently from the modulation schemes. Through extensive mathematical analysis, theoretical performance of the proposed protocol is investigated. By comparing with existing protocols, the proposed HDMF has lower error rate. A novel scheduling scheme for the proposed protocol is introduced, which has lower length than the benchmark method. The results also reveal the protocol's potential to improve spectrum efficiency of relay channels with unbalanced bilateral traffic.

The essential broadcasting feature of radio propagation channels provides an opportunity for multiple nodes to exchange information and work cooperatively, where each node, for example, mobile sensor or robot, plays both the role of transmitter and receiver. Especially in such machine‐to‐machine communication scenarios, the network performance and redundancy of information from different providers highly affect the work efficiency of each individual node and the whole system. To address these problems, a relay assisted centralised network model with physical layer network coding implemented in the relay is proposed in this study. This structure has the advantage of flexible data exchange and the capability to reduce redundancy in information. Its theoretical performance is analysed by the diversity multiplexing tradeoff, which proves the proposed model is versatile in reliable and high spectral‐efficiency information exchange. Experiments of multiple nodes in a machine‐to‐machine scenario – unmanned aerial vehicles, further reveal its potential in improving efficiency of communication and cooperation.

This paper proposes a novel interference cancellation algorithm for the two-path succussive relay system using network coding. The two-path succussive relay scheme was proposed recently to achieve full date rate transmission with half-duplex relays. Due to the simultaneous data transmission at the relay and source nodes, the two-path relay suffers from the so-called inter-relay interference (IRI) which may significantly degrade the system performance. In this paper, we propose to use the network coding to remove the IRI such that the interference is first encoded with the network coding at the relay nodes and later removed at the destination. The network coding has low complexity and can well suppress the IRI. Numerical simulations show that the proposed algorithm has better performance than existing approaches.

This paper proposes a full interference cancellation (FIC) approach for two-path cooperative communications. Unlike the single relay schemes, the two-path cooperative scheme involves two relay nodes, so that the source can continuously transmit data to the two relays alternatively and the full bandwidth efficiency with respect to the direct transmission can be retained. The two-path relay scheme may however suffer from inter-relay interference which is caused by the simultaneous transmission of the source and one of the relays at any time. In this paper, first the inter-relay interference is expressed as a single recursive term in the received signal, and then the FIC approach is proposed to fully remove the inter-relay interference. The FIC has not only better performance but also less complexity than existing approaches. Numerical examples are also given to verify the proposed approach.