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#4963 Dual-Band RF Energy Harvesting (RFEH) Architecture

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#4963  Dual-Band RF Energy Harvesting (RFEH) Architecture Google Antigravity: TRIZ Workflow App Synthesized Innovation Architectures Primary Architecture Selection : A decoupled dual-channel rectenna architecture utilizing a 3D folded dual-resonant PIFA, integrated low-loss LC diplexer, independent narrowband L-match networks, 3-stage SMS7630 zero-bias Schottky multipliers, passive magnetic cold-start step-up bootstrap, and an event-driven PMIC regulated at 0.48 * V_oc MPPT with sub-25 nA static bias current. Concept : Decoupled Diplexed Dual-Channel Rectenna with Adaptive Self-Biasing Dickson Multiplier First Principles Basis: Bode-Fano matching limits dictate that non-contiguous broadband impedance transformation to a reactive load suffers severe reflection; separating the 902-928 MHz and 2.4 GHz paths via an LC diplexer allows independent, narrowband, high-Q voltage-boosting matching stages (Q matching Chu-Harrington limits for compact ka < 0.5) that feed independent low-barr...
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#4962 AI Agent Team

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#4962 AI Agent Team #4962  I need an AI Agent Team with manager (orchestrator), finder, analyzer, recommender, implementer, next steps, as well as one or more critics To build an effective multi-agent system, you need clear role boundaries, structured handoffs (ideally in JSON or strict Markdown schemas), and a feedback loop so the Critic can reject subpar work before it reaches the final stage. Below is a production-ready architecture and system prompt blueprint for your 7-agent team. You can implement this in frameworks like LangGraph , CrewAI , AutoGen , or a custom Python loop. 1. System Architecture & Workflow Instead of a purely linear pipeline, use a Hub-and-Spoke model controlled by the Manager (Orchestrator) with an embedded Critic Loop : [User Goal] │ ▼ ┌──────────────────────────────────────────────────────────┐ │ 1. MANAGER (Orchestrator) │ │ Decomposes goal, delegates tasks, manages state/retries │ └────┬───────...
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