This paper presents a sub-milliwatt K$_u$-band cryogenic low-noise amplifier (LNA) implemented in GlobalFoundries (GF) 130CBIC SiGe BiCMOS, intended for a sub-Kelvin qubit readout chain integrating a JPA and SiGe LNA on the Still stage. The four-stage cascode LNA features balun-coupled interstage matching with a Q-enhanced resonance tank controlled by 2-bit switches tank for frequency tuning and a voltage-controlled cross-coupled negative-$g_m$ cell. The operating point of the Q-cell is optimized at each temperature stage to achieve the best in-band performance under a stable condition. Characterized at 2.5 K for the initial demonstration, the LNA achieves an average noise temperature of $\sim$14 K across 12-18 GHz and 28 dB peak gain around 16.5 GHz, while consuming only 0.74 mW. To the authors' knowledge, this is the lowest reported DC power for a K$_u$-band cryogenic SiGe LNA.
The rapid expansion of offshore wind energy is central to the European Union's climate-neutrality targets, with High Voltage Direct Current-connected offshore wind power plants (HVDC-OWPPs) becoming increasingly important for integrating gigawatt-scale renewable generation over l…
Motion planning algorithms compute control sequences that drive autonomous robots to goal regions while avoiding unsafe states. Existing methods, from sampling-based planning to deep reinforcement learning, typically provide task-completion guarantees only with respect to a nomin…
This letter proposes the Predictive Lightweight Multi-Agent Reinforcement Learning (PL-MARL) framework to ensure resilient coverage in bandwidth-constrained UAV swarms. To counter coordination collapse caused by sparse signaling and information aging, we introduce a Kinematic-Awa…
This paper investigates physics-informed neural networks for modeling the full dynamic behavior of droop-controlled grid-forming converters. The approach is trained on synthetic data generated via numerical solvers and benchmarked against both traditional integration methods and…
This paper introduces a novel multivariate Transformer \emph{StateFormer} that forecasts degradation dynamics of large-scale battery systems. The model learns across time scales, from short-term thermal fluctuations to long-term aging trajectories, enabling accurate prediction of…
We introduce trajectory-regularized stochastic optimal control (TRSOC), which augments standard stochastic optimal control (SOC) with a Kullback--Leibler (KL) divergence between controlled and reference trajectory distributions. Using Girsanov's theorem, the trajectory KL reduces…