AI & ML
Multi-Threshold Sampling: Signal Space, Sampling Operators, and First Recorded Crossing-Time Distributions
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arXiv:2609.11610v3 Announce Type: replace Abstract: Multi-threshold (MT) sampling records crossing times for parameter estimation and waveform reconstruction. We develop signal and observation spaces and stochastic crossing, timing, and selection operators with associated Markov kernels. Finite point measures accommodate variable crossing counts and preserve multiplicity after quantization. For nonhomogeneous Poisson photon arrivals with shared threshold noise, shared timing jitter before quantization, and identity selection, we derive exact first recorded crossing-time distributions. A Poisson series covers general admissible paths with a time-uniform truncation bound. For continuous paths, interval extrema give the exact cumulative distribution function (CDF) and a Fourier representation with contact corrections. A biexponential arrival-intensity and response model admits a two-dimensional first-crossing integral equation on arbitrary finite observation windows, with a unique bounded measurable solution and a Poisson-tail approximation bound. For continuous causal responses with finitely many analytic pieces and bounded first and second branch derivatives, we prove a conditional standard-deviation (SD) limit at first mean crossings with positive slope under relative threshold-noise and fine timing scales. For the biexponential model, its coefficient increases strictly on a parameter-uniform interval and, under a shape condition, up to the first mean peak. Scintillation experiments compare the calculated CDF and conditional SDs with independent waveform simulations on a window extending beyond the single-photon response peak.
Source:https://arxiv.org/abs/2609.11610PreviousUntangling Dense Crowds with mmWave Radar: From Crowd Semantics to Individual Spatial BehaviorsNextSensor Response-Time Reduction using Long-Short Term Memory Network Forecasting
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