Oscilla Power (United States)

The synergy between powerful, cloud-hosted large models and efficient, on-device small models presents a new frontier for mobile artificial intelligence. However, enabling real-time, knowledge-intensive interactions, such as querying physical objects in the user’s environment, is severely hampered by the high latency of cloud-centric approaches and the limited reasoning capabilities of edge-only models. This paper introduces a novel synergistic cloud-edge intelligence framework designed to bridge this critical gap. Our framework implements a semantic partitioning of labor: a lightweight edge client performs real-time perception to generate compact multimodal features, while an intelligent trigger decides when to offload a structured, minimal-data payload to a powerful cloud-based reasoning engine for definitive entity linking and knowledge-grounded question answering. We validate our approach on SynergyQA, a new benchmark for this task. Experimental results show that our framework reduces end-to-end latency by over 80% and data transmission by over 98% compared to naive offloading strategies, while achieving question-answering performance that is competitive with state-of-the-art cloud-only models. Our work provides an efficient and robust blueprint for deploying the next generation of interactive, context-aware AI on mobile and edge devices.

Resonant power inverters are among the most efficient power conversion topologies. However, their widespread adoption is hindered by inefficient operation and control limitations under light-load conditions. This research focuses on facilitating the operation of a Series Resonant Inverter (SRI) by utilizing the recently introduced passive Linear Variable Capacitor (LVC) structure. LVC utilizes the nonlinear parasitic capacitance of two back-to-back reverse-biased diodes to establish a voltage- or current-dependent linear capacitor. This research analyzes combining LVC and constant linear capacitors to dynamically change SRI's resonant frequency for varying load and, that way, reduce the frequency regulation band of the SRI. The paper presents a complete analytical framework and describes a design procedure for an SRI with integrated LVC. The proposed framework is verified through simulations and experimentation. A control frequency band reduction of 39% and 37% is experimentally demonstrated for the two studied SRI designs. Additionally, the effects of temperature variation and device heterogeneity on LVC characteristics are analyzed, along with their impact on SRI performance.