Jin Ning

Ping Huang, Bin Duo, Ziedor Godfred et al.

Natural disasters often damage ground infrastructure, making unmanned aerial vehicles (UAVs) essential for emergency supply delivery. Yet safe operation in complex post-disaster environments requires reliable command-and-control (C2) links; link instability can cause loss of control, delay rescue, and trigger severe secondary harm. To provide continuous three-dimensional (3D) C2 coverage during dynamic missions, we propose a Heterogeneous Dual-Network Framework (HDNF) for safe and reliable emergency delivery. HDNF tightly couples an Emergency Communication Support Network (ECSN), formed by hovering UAV base stations, with a Delivery Path Network (DPN), formed by fast-moving delivery UAVs. The ECSN dynamically safeguards mission-critical flight corridors, while the DPN aligns trajectories with reliable coverage regions. We formulate a joint optimization problem over task assignment, 3D UAV-BS deployment, and DPN path planning to maximize end-to-end C2 reliability while minimizing UAV flight energy consumption and base-station deployment cost. To solve this computationally intractable NP-hard problem, we develop a layered strategy with three components: (i) a multi-layer C2 service model that overcomes 2D-metric limitations and aligns UAV-BS deployment with mission-critical 3D phases; (ii) a 3D coverage-aware multi-agent reinforcement learning algorithm that addresses the high-dimensional search space and improves both training efficiency and topology resilience; and (iii) a 3D communication-aware A* planner that jointly optimizes C2 quality and flight energy, mitigating trajectory--coverage mismatch and improving routing safety. Extensive simulations show that HDNF markedly improves C2 reliability, eliminates outages in critical phases, and sustains high task success rates while reducing hardware deployment cost.

Di Xu, Hengjie Liu, Yang Yang et al.

Accelerated dynamic volumetric magnetic resonance imaging (4DMRI) is essential for applications relying on motion resolution. Existing 4DMRI produces acceptable artifacts of averaged breathing phases, which can blur and misrepresent instantaneous dynamic information. Recovery of such information requires a new paradigm to reconstruct extremely undersampled non-Cartesian k-space data. We propose B-FIRE, a binning-free diffusion implicit neural representation framework for hyper-accelerated MR reconstruction capable of reflecting instantaneous 3D abdominal anatomy. B-FIRE employs a CNN-INR encoder-decoder backbone optimized using diffusion with a comprehensive loss that enforces image-domain fidelity and frequency-aware constraints. Motion binned image pairs were used as training references, while inference was performed on binning-free undersampled data. Experiments were conducted on a T1-weighted StarVIBE liver MRI cohort, with accelerations ranging from 8 spokes per frame (RV8) to RV1. B-FIRE was compared against direct NuFFT, GRASP-CS, and an unrolled CNN method. Reconstruction fidelity, motion trajectory consistency, and inference latency were evaluated.