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Robust Readout of Cross-Point Resistive Arrays via Time-Domain Optimization

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Overview

Event synthesis

This research proposes a time-domain optimization method to improve the robustness of reading cross-point resistive arrays used in high-density tactile sensing. Unlike conventional frequency-based methods that struggle with noise and long interconnects, this approach fits a time-domain observation model to measured waveforms. Simulations on a 16x16 array show a 20% reduction in mean readout error compared to the baseline. Validation over 1,000 trials indicates further improvements, reducing mean error by 24.6% and standard deviation by 77.6%, confirming enhanced estimation robustness under noisy observation conditions typical of robotic sensing environments.

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Reported quantity · trials conducted: 1000 other · reported trials
Supporting report

“20% compared with the baseline method. Furthermore, validation over 1,000 trials showed that the proposed method achieves more robust estimation than the conventional method, reducing the mean error and standard deviation by 24.6% and 77.6%, respectively. The improvement was also confirmed in cross-point array simulations with resistance distributions emulating tactile sensor responses.”

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Reported quantity · trials for validation: 1000 other · reported trials
Supporting report

“20% compared with the baseline method. Furthermore, validation over 1,000 trials showed that the proposed method achieves more robust estimation than the conventional method, reducing the mean error and standard deviation by 24.6% and 77.6%, respectively. The improvement was also confirmed in cross-point array simulations with resistance distributions emulating tactile sensor responses.”

Exact source · revision 1

Source owner not reported

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11/9
  1. arXiv Robotics — research abstractsSignal
    Robust Readout of Cross-Point Resistive Arrays via Time-Domain Optimization

    This research proposes a time-domain optimization method to improve the robustness of reading cross-point resistive arrays used in high-density tactile sensing. Unlike conventional frequency-based methods that struggle with noise and long interconnects, this approach fits a time-domain observation model to measured waveforms. Simulations on a 16x16 array show a 20% reduction in mean readout error compared to the baseline. Validation over 1,000 trials indicates further improvements, reducing mean error by 24.6% and standard deviation by 77.6%, confirming enhanced estimation robustness under noisy observation conditions typical of robotic sensing environments.

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