Improving the downlink throughput of multi-UAV air-to-ground mmWave systems under rapid motion and channel uncertainty is a critical challenge. In this letter, UAV states are estimated and the resulting position uncertainty is propagated to the channel statistics through closed-form Jacobians. We propose an alternating optimization mechanism, where a surrogate sum-rate metric is used for inner-layer precoder design and a Riemannian trust-region solver is employed for outer-layer BS reconfiguration. Simulation results demonstrate downlink sum-rate improvements over non-optimized and Euclidean optimization baselines in dynamic multi-UAV mmWave deployments.

To enhance the processing of complex multi-modal documents (e.g. e-books, long web pages, etc.), it is an efficient way for users to take digital screenshots of key parts and reorganize them into a new collage E-Note. Existing methods for assisting collage layout design primarily employ a semantic relevance-first strategy, with arranging related contents together. Though capable, it can not ensure the visual readability of screenshots and may conflict with human natural reading patterns. In this paper, we introduce CollageNoter for real-time collage layout design that adapts to various devices (e.g. laptop, tablet, phone, etc.), offering users with visually and cognitively well-organized screenshot-based E-Notes. Specifically, we construct a novel two-stage pipeline for collage design, including 1) readability-first layout generation and 2) cognitive-driven layout adjustment. In addition, to achieve real-time response and adaptive model training, we propose a cascade transformer-based layout generator named CollageFormer and a size-aware collage layout builder for automatic dataset construction. Extensive experimental results have confirmed the effectiveness of our CollageNoter.

Graphic designs are pervasive in our daily lives and widely used to communicate information hierarchically to humans. To achieve this, the layout plays an essential role in guiding readers to understand the importance of different elements and comprehend the content. To deal with the rapidly increasing demands of graphic designs, recent studies attempt to automatically generate layouts based on category information and spatial relations, often resulting in layouts with poor communication quality. In this article, we make the first attempt to explore element importance-aware layout generation under the guidance of a novel design graph, which attracts readers’ attention to a layout by formulating aesthetic relations implicitly involved in graphic designs between element pairs. The core of our approach is a learning-based framework with a new multimodality cascade transformer (MCT) in a coarse-to-fine manner. A hierarchical multimodality fusion (HMF) mechanism and two new losses are introduced to guide the training process progressively. We further collect a new fine-grained advertisement poster layout dataset containing more than 30 K layouts labeled with 91 element labels. Both qualitative and quantitative experiments demonstrate the effectiveness of our approach against existing works. We also conduct user studies and cognitive experiments to evaluate the direct adaptability and attractiveness of generated layouts.

UAV information dissemination or multicast is a critical use case for UAV communication systems. However, UAVs’ size and load limitations limit their onboard energy and endurance time. In this paper, we consider a multi-antenna unmanned aerial vehicle (UAV) enabled multicasting system, where a multi-antenna UAV is dispatched to transmit a shared file to a set of ground terminals (GTs). Our objective is to minimize mission completion time while satisfying the UAV speed and transmission power constraints, as well as guaranteeing the successful file recovery probability at the GTs. To address this issue, we introduce rank-two multicast beamforming and propose an online optimization scheme. The proposed scheme consists of two steps: 1) Before the UAV takes off, the open-loop rank-two beamforming without the channel state information (CSI) is used to obtain the initial planning, including the horizontal auxiliary distance parameter, UAV trajectory, UAV multicast trajectory segments (MTSs) on which the UAV will transmit multicast information, the number of multicasting packets to be transmitted at each MTS and mission completion time which is the upper bound. 2) Based on step 1, the UAV optimizes the rank-two multicast beamforming vectors using real-time CSI during flight to reduce the number of multicasting packets that need to be transmitted at each MTS, further decreasing mission completion time. Finally, numerical results indicate that the proposed scheme can significantly reduce mission completion time compared to the single antenna scheme.

The advantages of modular robot systems stem from their ability to change between different configurations, enabling them to adapt to complex and dynamic real-world environments. Then, how to perform the accurate and efficient change of the modular robot system, i.e., the self-reconfiguration problem, is essential. Existing reconfiguration algorithms are based on discrete motion primitives and are suitable for lattice-type modular robots. The modules of freeform modular robots are connected without alignment, and the motion space is continuous. It renders existing reconfiguration methods infeasible. In this paper, we design a parallel distributed self-reconfiguration algorithm for freeform modular robots based on multi-agent reinforcement learning to realize the automatic design of conflict-free reconfiguration controllers in continuous action spaces. To avoid conflicts, we incorporate a collaborative mechanism into reinforcement learning. Furthermore, we design the distributed termination criteria to achieve timely termination in the presence of limited communication and local observability. When compared to the baselines, simulations show that the proposed method improves efficiency and congruence, and module movement demonstrates altruism.

Layout generation is important in the field of graphic design and has attracted intensive research attention recently. To further prompt human-computer interactions, we construct the ALDA to assist users throughout the design process, which achieves adaptive and diverse content expansions upon only one element for beginners and generates high-quality posters subsequently. Specifically, to obtain diverse contents, we propose aesthetic-aware design graphs (AGs) for effective poster representation and propose a self-constrained blending strategy upon related examples. In addition, we build a novel layout generator to better arrange elements conditioned on our AGs. Finally, we implement ALDA as an online tool with a set of controllable factors to enhance its practicality.