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Universal Acoustic Resonance Imaging: Real-Time Multi-Target Soliton Discrimination and In-Situ Near-Field Ego-Noise Nulling across Sub-Wavelength Arrays

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)
Aerodynamics and Acoustics in Jet Flows

Abstract

QuantNature Technical White Paper (Document ID: QN-UARI-2026-V2.0) Title Universal Acoustic Resonance Imaging (UARI v2.0): Real-Time Multi-Target Soliton Discrimination and In-Situ Near-Field Ego-Noise Nulling across Sub-Wavelength Arrays Author: QuantNature Global (https://www.quantnature.com)Release Date: September 9, 2026Document Version: v2.0 (In-Situ Ego-Noise Nulling, 3-Zone Stratified Continuum & Real-Time 38.4 FPS Fire-Control Milestone) Executive Summary & Abstract Conventional acoustic phased arrays and industrial sonic cameras constrained by compact sub-wavelength apertures (D ≤ 71 mm, where D < λ) suffer from catastrophic diffraction beam broadening (θ ≥ 70°) and are rendered blind by overwhelming multi-rotor propulsion noise (SPL ≥ 88 dB at r ≤ 0.20 m). Consequently, closely spaced airborne threats and host platform self-propulsion noise coalesce into unresolved, saturating energy clouds under linear Delay-and-Sum (DAS) beamforming, while classical Active Noise Cancellation (FxLMS) and adaptive nulling (MVDR) fundamentally fail due to near-field spherical wavefront curvature singularities and ESC motor rotational flutter. This technical white paper formalizes Universal Acoustic Resonance Imaging v2.0 (UARI v2.0), a closed-loop physical-layer computing framework integrating: (1) near-field radial ego-noise annihilation (r ≤ 0.20 m 4th-order Super-Gaussian barrier, Wego), (2) a 3-zone stratified physical continuum across the 100,000-node Resonance Processing Unit (RPU), and (3) zero-disk direct memory DMA streaming (τDMA ≤ 0.05 ms). Governing declarative potential landscapes (Vbias) through non-linear substrate relaxation, broad acoustic wavefronts autonomously bifurcate into discrete, localized ground-state solitons (Emin) while transient acoustic chatter and multi-path reflections are naturally dissipated. Key Empirical Outcomes & Hardware Benchmarks (v2.0 Milestone) Evaluated via live benchtop characterizations on a 7-element MEMS phased array (D = 71 mm): In-Situ 20 cm Near-Field Ego-Noise Annihilation: Complete physical suppression of the host drone's 88 dB propulsion roar inside the r ≤ 0.20 m perimeter. Live knife-edge testing (r = 24.1 cm) confirms authentic crescent-shaped physical boundary diffraction rather than superficial rectangular display masking. Super-Resolution Multi-Well Soliton Bifurcation: Transformation of merged linear acoustic clouds into double-well potential manifolds via supercritical pitchfork bifurcation. Live dual-emitter testing successfully resolves uncoupled sources separated by 68.0 cm with 98.0% geometric fidelity (Δrresolved = 69.4 cm, spatial error < 28 mm) on a sub-wavelength 71 mm aperture. Deterministic 38.40 FPS Fire-Control Throughput: Complete physical-layer re-engineering—eliminating disk I/O via zero-disk direct DMA registers, halving hardware ring buffer latency (1,024 samples / 21.33 ms), and streamlining relaxation epochs—compressed end-to-end loop latency from 127.98 ms (v1.5) down to 25.80 ms (38.40 FPS) on a standard NVIDIA GeForce GTX 1070 GPU (4.96× speedup). Tactical Kinetic Kinematic Closure: At a 20 m/s closing intercept velocity, sensor-to-fire-control kinematic lag displacement is reduced from 2.56 m to merely 0.51 m, falling well within the physical blast radius of autonomous Counter-UAS hunter-drone interceptors. Temporal Persistence Resonance Sifting (Zone 3): Continuous dissipative leaky-integrator (αdecay = 0.68, βbuildup = 0.45) routing that eliminates non-periodic environmental shocks (speech, coughing, mechanical impacts) within ≤ 40–60 ms, preventing false-alarm reticle divergence. Zero-Data Semantic Acoustic DNA Sifting: 100.0% confidence automated threat classification (Kamikaze FPV vs. Heavy Strike UAV vs. Stealth Recon) derived purely from 1:2.00 Blade Passing Frequency (BPF) integer harmonic symmetry, without requiring offline neural network training. Published by QuantNature Global. All intellectual property, mathematical derivations, and RPU physical continuum frameworks formalized under Technical White Paper Document ID: QN-UARI-2026-V2.0.

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