Edgar Choueiri

A fundamental upper limit, $$\hat{\eta}$$ , for the thrust efficiency of self-field magnetoplasmadynamic thrusters (MPDTs) is derived from the generalized Ohm’s law and the minimization of the volume integral of the square of the current density, which controls dissipation in the MPDT. It is found that $$\hat{\eta}\simeq1/(1+4/R_{m_{ci}}\xi^4)$$ , where $$\xi$$ is the MPDT scaling number (the total current normalized by the current at which an equipartition of power occurs between thrust and ionization), and $$R_{m_{ci}}$$ is the magnetic Reynolds number evaluated at $$\xi=1$$ .

Coordinate-conditioned neural networks can generate head-tracked personal sound zone (PSZ) loudspeaker filters in real time, but they are sensitive to localization uncertainty. Small fluctuations in estimated listener coordinates, caused by optical distortion, temporary occlusions, or tracking jitter, may produce large filter changes even when listeners are physically stationary. This paper proposes neighbor-consistent neural filters that regularize the coordinate-to-filter mapping by penalizing filter differences at randomly perturbed neighboring coordinates during training. To evaluate robustness against tracking noise, we introduce a decoupled protocol that fixes the acoustic transfer functions at a physical anchor while perturbing only the coordinate inputs used for filter generation. Isolation quality and local stability are evaluated using neighborhood median and lower-tail statistics of inter-zone and inter-program isolation, together with spatial variation rates that quantify metric sensitivity within a coordinate neighborhood. In simulation with a split-band woofer-tweeter system and 25 randomly sampled anchor positions, neighbor consistency reduces the root-mean-square (RMS) variation rate by up to 55.9% in the woofer band and 30.3% in the tweeter band while largely preserving isolation quality and improving lower-tail robustness. In in-situ measurements using a 24-driver array and two stationary head-and-torso simulators, the proposed regularization improves worst-case neighborhood isolation by up to 16.9% and reduces spatial variation rates by up to 61.8%. These results demonstrate that neighbor-consistency regularization effectively stabilizes PSZ rendering under localization uncertainty.

Coordinate-conditioned neural networks can generate head-tracked personal sound zone (PSZ) loudspeaker filters in real time, but they are sensitive to localization uncertainty. Small fluctuations in estimated listener coordinates, caused by optical distortion, temporary occlusions, or tracking jitter, may produce large filter changes even when listeners are physically stationary. This paper proposes neighbor-consistent neural filters that regularize the coordinate-to-filter mapping by penalizing filter differences at randomly perturbed neighboring coordinates during training. To evaluate robustness against tracking noise, we introduce a decoupled protocol that fixes the acoustic transfer functions at a physical anchor while perturbing only the coordinate inputs used for filter generation. Isolation quality and local stability are evaluated using neighborhood median and lower-tail statistics of inter-zone and inter-program isolation, together with spatial variation rates that quantify metric sensitivity within a coordinate neighborhood. In simulation with a split-band woofer-tweeter system and 25 randomly sampled anchor positions, neighbor consistency reduces the root-mean-square (RMS) variation rate by up to 55.9% in the woofer band and 30.3% in the tweeter band while largely preserving isolation quality and improving lower-tail robustness. In in-situ measurements using a 24-driver array and two stationary head-and-torso simulators, the proposed regularization improves worst-case neighborhood isolation by up to 16.9% and reduces spatial variation rates by up to 61.8%. These results demonstrate that neighbor-consistency regularization effectively stabilizes PSZ rendering under localization uncertainty.

A stereo personal sound zone (PSZ) rendering framework employing a spatially adaptive neural network for two head-tracked listeners is proposed. The framework facilitates independent optimization of acoustic fields at each ear of the two listeners. Unlike traditional mono-PSZ approaches, separate filters are generated for the listeners' left and right ears, establishing two distinct bright zones and effectively minimizing acoustic energy in dark zones intended for another listener. To reflect realistic acoustic conditions, the framework relies on including effects of transducer directivity and listener HRTF as well as anechoically measured impulse response of the transducers. Simulation and experimental results demonstrate improved inter-zone isolation (IZI) and inter-program isolation (IPI), and enhanced spatial audio fidelity compared to previous mono implementation. The proposed approach offers precise stereo acoustic field manipulation in realistic scenarios while preserving real-time adaptability to listeners' movements, highlighting its potential for immersive spatial audio applications, and laying the groundwork for future integration with crosstalk cancellation to further enhance spatial realism.