Action-Conditioned World Model for Goal Plane Probe Guidance in Robotic Ultrasound

We present an action-conditioned world model framework for goal plane probe guidance in robotic ultrasound, with a focus on neck ultrasound scanning. Autonomous ultrasound tasks often require large numbers of probe-motion trajectories for training, but collecting high-quality demonstrations is labor-intensive and explicit simulators are difficult to build because ultrasound appearance depends on contact, tissue deformation, and view-dependent acoustic artifacts. We address this problem with a two-stage model-based learning pipeline. First, a latent conditional diffusion world model predicts future ultrasound observations from recent context frames, probe motions and temporal offset. Second, a goal-conditioned temporal transformer predicts ordered probe motions and is fine-tuned using rewards from the frozen world model. Experiments on the self-collected dataset show that the world model preserves action-dependent anatomical structure on target-directed scans. In real-world closed loop experiments, the framework achieves success rates of 70.0\% for carotid guidance and 65.0\% for thyroid guidance. These results demonstrate the potential of learned ultrasound dynamics for training goal-directed robotic probe navigation.

A Novel Grant Prediction Method for 5G NR Terminals

5G NR user equipment suffers from high power consumption due to continuous PDCCH monitoring. Predictive dynamic power management (DPM) can save energy by forecasting data grants, but accurate prediction is challenging due to unobservable scheduling states and bursty grant patterns. This paper proposes IOHMM-BO, a high-order input-output hidden Markov model with Bayesian optimization. Based on real 5G NR traces, we capture long-range dependencies via a compound state and jointly optimize model order and listening window using Bayesian optimization. Experiments on real traces show that IOHMM-BO achieves 45.3% accuracy, 5.0% false negative rate, and 43% energy saving with low computational overhead. The method provides a balanced trade-off between reliability and energy efficiency.