Probabilistic Sensing: Intelligence in Data Sampling
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914285256441856 |
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| author | Albulushi, Ibrahim Bunaiyan, Saleh Cheema, Suraj S. ElSawy, Hesham Al-Dirini, Feras |
| author_facet | Albulushi, Ibrahim Bunaiyan, Saleh Cheema, Suraj S. ElSawy, Hesham Al-Dirini, Feras |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19953 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Probabilistic Sensing: Intelligence in Data Sampling Albulushi, Ibrahim Bunaiyan, Saleh Cheema, Suraj S. ElSawy, Hesham Al-Dirini, Feras Machine Learning Artificial Intelligence Hardware Architecture Emerging Technologies Systems and Control 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. |
| title | Probabilistic Sensing: Intelligence in Data Sampling |
| topic | Machine Learning Artificial Intelligence Hardware Architecture Emerging Technologies Systems and Control |
| url | https://arxiv.org/abs/2601.19953 |