A reciprocity-based cross-directional compensation framework estimates a shared propagation baseline from opposing ultrasonic backscatter profiles to reduce distance-dependent bias while preserving local scattering variations in heterogeneous media.
Ben Britton, Tea-Sung Jun, Weimin Gan, Michael Hofmann, Fionn P.E
4 Pith papers cite this work. Polarity classification is still indexing.
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Identifiability limits in ultrasonic microstructure characterization are governed by forward-map structure and intrinsic stochastic variability, with combined observables improving conditioning through complementary sensitivities.
An extended dual-solute framework predicts co-segregation bounds in multicomponent alloys by machine-learning pairwise segregation energies that include solute-solute interactions and is validated on magnesium systems.
TD-MARL uses shared topological states and invariants to coordinate soft robots and reduce entanglement risk, outperforming standard DRL in simulated convergence and anti-winding performance.
citing papers explorer
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A Reciprocity-Based Signal Compensation Framework for Ultrasonic Backscatter Measurements in Heterogeneous Scattering Media
A reciprocity-based cross-directional compensation framework estimates a shared propagation baseline from opposing ultrasonic backscatter profiles to reduce distance-dependent bias while preserving local scattering variations in heterogeneous media.
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Identifiability Limits in Ultrasonic Microstructure Characterisation: A Canonical and Stochastic Framework
Identifiability limits in ultrasonic microstructure characterization are governed by forward-map structure and intrinsic stochastic variability, with combined observables improving conditioning through complementary sensitivities.
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Predicting co-segregation in multicomponent alloys with solute-solute interactions
An extended dual-solute framework predicts co-segregation bounds in multicomponent alloys by machine-learning pairwise segregation energies that include solute-solute interactions and is validated on magnesium systems.
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Topology-Driven Anti-Entanglement Control for Soft Robots
TD-MARL uses shared topological states and invariants to coordinate soft robots and reduce entanglement risk, outperforming standard DRL in simulated convergence and anti-winding performance.