让传感器自主决定何时采样，大幅节能且不丢信息。
Probabilistic Sensing: Intelligence in Data Sampling
- 用概率神经元模拟自主神经系统，动态决定是否采样。
- 实测误差仅0.41%，采样次数和运行时间减少93%。
- 适合需要实时低功耗数据采集的物联网与地震探测场景。
将传感器的智能延伸至数据采集过程——决定是否采样——可带来革命性的能效提升。然而，以确定性方式做出此类决策存在丢失信息的风险。本文提出一种概率感知范式，通过受自主神经系统启发的模拟特征提取电路驱动的概率神经元（p-neuron），实现采样决策的随机化。该系统响应时间在微秒级，突破了传统子采样率的响应限制，支持实时、智能、自主的数据采样激活。在主动地震勘探数据上的验证实验表明，实现了无损的概率数据采集，归一化均方误差仅为0.41%，系统主动运行时间与生成样本数均减少93%。
原文摘要 · Abstract (English)
Extending the intelligence of sensors to the data-acquisition process - deciding whether to sample or not - can result in transformative energy-efficiency gains. However, making such a decision in a deterministic manner involves risk of losing information. Here we present a sensing paradigm that enables making such a decision in a probabilistic manner. The paradigm takes inspiration from the autonomous nervous system and employs a probabilistic neuron (p-neuron) driven by an analog feature extraction circuit. The response time of the system is on the order of microseconds, over-coming the sub-sampling-rate response time limit and enabling real-time intelligent autonomous activation of data-sampling. Validation experiments on active seismic survey data demonstrate lossless probabilistic data acquisition, with a normalized mean squared error of 0.41%, and 93% saving in the active operation time of the system and the number of generated samples.
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