Proprietary Technology
Technology & Standards Archive
Optics
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This Version 2.0 technical report marks a major milestone: the transition from theoretical framework to Empirical Validation of Volumetric Optical Decoherence (OID).
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Clean Energy
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Conventional digital power conversion architectures relying on software-based Digital Signal Processors (DSPs) are fundamentally bounded by sequential interrupt latencies (~5.000 µs / 5,000 ns). Under sudden load surges, this processing lag triggers phase-angle hunting, severe output voltage droop, and instantaneous collapse of Zero-Voltage Switching (ZVS), generating destructive thermal spikes and risking digital system brownouts.
This technical white paper formalizes and empirically validates a deterministic sub-cycle Phase-Shift Full-Bridge (PSFB) power conversion architecture executed on a proprietary FPGA silicon engine deployed on the AMD/Xilinx Zynq-7000 SoC (PYNQ-Z2 PL fabric). Version 1.5 extends the theoretical 20.0 V digital twin foundation by introducing physical Silicon-in-the-Loop (HIL) 100-cycle active closed-loop benchmark data alongside complete SPICE-verified electrical isolation modeling.
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Proton Exchange Membrane Fuel Cell (PEMFC) power generation in zero-emission heavy-duty mobility (e.g., Hyundai XCIENT, Nikola Tre, Toyota Mirai) and multi-megawatt water electrolysis is fundamentally bottlenecked by the control dynamics of the high-voltage Fuel Cell DC-DC Converter (FDC). Unlike chemical batteries, electrochemical fuel cell stacks possess severe non-linear polarization impedances, mechanical air-compressor transport delays (air starvation), and extreme vulnerability to high-frequency switching current ripples, which accelerate catastrophic platinum (Pt) catalyst dissolution and carbon-support corrosion.
In conventional automotive architectures, software-based Digital Signal Processors (DSPs) introduce multi-microsecond interrupt latencies and multi-cycle shadow-register delays, precipitating acute 650 V traction bus sags (>80 V droop), limit-cycle duty hunting (35% ↔ 70%), and massive switching heat loads (>5.5 kW) that severely overwhelm vehicle cooling radiators.
This technical white paper establishes a deterministic sub-cycle FPGA silicon-accelerated 95 kW FDC boost converter deployed on an AMD/Xilinx Zynq-7000 SoC (PYNQ-Z2 PL fabric) executing at an uninterrupted 25.0 ns base clock period. Benchmarked through real-time Silicon-in-the-Loop (HIL) co-simulation under violent 20 kW → 95 kW tip-in acceleration transients, the silicon engine establishes three decisive technological breakthroughs.
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High-voltage lithium-ion Battery Management Systems (BMS) in passenger electric vehicles (400V/800V) and utility-scale Battery Energy Storage Systems (BESS) are fundamentally bounded by legacy sensing architectures. Conventional automotive tier-1 controllers rely on slow, multiplexed analog front-ends executing empirical Coulomb counting and static Open-Circuit Voltage (OCV) lookup tables on 32-bit automotive microcontrollers (MCUs). These macroscopic sensing methods remain electrochemically blind to internal solid electrolyte interphase (SEI) decomposition, lithium plating, and microscopic dendritic short-circuits until surface temperature spikes occur (< 30 seconds prior to catastrophic thermal runaway venting). Consequently, automakers are compelled to lock away 10–15% of physical battery capacity as conservative safety buffers, imposing massive vehicle cost and weight penalties.
Building upon the deterministic sub-cycle silicon framework established in QuantNature prior art QN-PSFB-2026-V1.5 and QN-FCEV-2026-V1.0, this technical white paper establishes the architecture and empirical hardware validation of a real-time online Electrochemical Impedance Spectroscopy (EIS) BMS architecture accelerated directly on the Programmable Logic fabric of an AMD/Xilinx Zynq-7000 SoC. Executing parallel complex impedance state extraction at a deterministic 25.0 ns clock period (40.00 MHz), the silicon engine achieves four decisive operational breakthroughs.
ROBOT
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This white paper introduces the Resonance Plexus (RP), a novel non-symbolic physical intelligence framework designed to overcome the structural brittleness of classical symbolic logic and the computational latency of foundation models (LLMs/LVMs) in robotic control.
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Wave Technology
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Universal Physical Waveform Forging (UPWF) establishes an autonomous physical-layer manufacturing paradigm that shifts sequence synthesis from discrete symbolic arithmetic and closed-form algebraic polynomials (Barker, Gold, Zadoff-Chu) to continuous thermodynamic state relaxation within the 100,000-node physical continuum of the Resonance Processing Unit (RPU).
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Specification QN-SFS-2026 is the official industrial standard specification establishing mandatory physical property requirements, theoretical boundary-breaking performance metrics, quality acceptance thresholds, and standardized inspection test methods for physical waveforms manufactured via continuous thermodynamic state relaxation across high-dimensional Resonance Processing Unit (RPU) continua.
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In orbital mechanics, deep-space telemetry, and Space Domain Awareness (SDA), the severe two-way geometric radar power collapse (∝ 1/R⁴) and extreme ambient thermal noise floor (SNR ≤ -18 dB) present an insurmountable barrier for conventional linear signal processing. In exoatmospheric vacuum, the Keplerian Equivalence Trap prevents kinematic trajectory tracking from distinguishing high-mass satellite payloads from lightweight tumbling fragmentation debris.
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This white paper introduces Stratified Waveguide Acoustic Probing (SWAP), a novel physical computing paradigm designed to overcome the fundamental limitations of oceanic acoustic shadow zones. Conventional linear sonar architectures fail in stratified maritime environments where steep thermal gradients (thermoclines) refract acoustic energy, blinding sensors to sub-surface anomalies at extreme signal-to-noise ratios (SNR ≤ -18.0 dB).
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In traditional signal processing, capturing sub-threshold signals buried deep beneath the noise floor (SNR < 0 dB) represents an insurmountable wall for linear filters and digital Fourier estimators. While classical Stochastic Resonance (SR) has long promised a noise-enabled sensing path, its industrial adoption has been paralyzed by the "Tuning Bottleneck"—the requirement for precise manual noise calibration and strict prior knowledge of target frequencies.
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For decades, synchronization across telecommunications, wave engineering, and biophysical systems has relied exclusively on linear closed-form mathematical sequences (e.g., Barker, Gold, and Zadoff-Chu codes). Under real-world non-linear propagation conditions—characterized by high-entropy noise, fractional frequency offsets, and multi-path fading—these analytical formulations experience severe aperiodic correlation degradation and timing uncertainty.
Computer
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This document introduces "The Resonant Computer" and the "Resonance Processing Unit (RPU)" as a foundational post-digital computing paradigm (Version 1.0). As traditional silicon architectures face the physical limits of Moore’s Law and Von Neumann memory bottlenecks, this paper proposes a third computing path: Substrate-Embedded Physical Wave Resonance.
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