Tiny Machine Learning Zoo for Long-Term Compensation of Pressure Sensor Drifts

Pressure sensors embodied in very tiny packages are deployed in a wide range of advanced applications. Examples of applications range from industrial to altitude location services. They are also becoming increasingly pervasive in many other application fields, ranging from industrial to military to consumer. However, the inexpensive manufacturing technology of these sensors is strongly affected by environmental stresses, which ultimately affect their measurement accuracy in the form of variations in gain, hysteresis, and nonlinear responses. Thermal stresses are the main source of sensor behavior deviation. They are particularly insidious because even a few minutes of high temperature exposure can cause measurement drift for many days in the sensor responses. Therefore, conventional calibration techniques are challenged in their adequacy to achieve high accuracy and over the entire deployment life of the sensor. To manage this, several costly and time-consuming calibration procedures have to be performed. Machine learning (ML) techniques are known, supported by the universal approximation theorem, to provide effective data-driven solutions to the above problems. In this context, this paper addresses two case studies, corresponding to post-soldering thermal stresses and exposure to moderately high temperatures, for which two separate datasets have been built and 53 different tiny ML models (collected into a zoo) have been devised and compared. The ML zoo has been constructed with models such as artificial neural networks (ANN), random forest (RFR), and support vector regressors (SVR), able to predict the error introduced by the thermal drift and to compensate for the drift of the measurements. The models in the zoo also satisfy the memory, computational, and accuracy constraints associated with their deployment on resource-constrained embedded devices to be integrated at the edge. Quantitative results achieved by the zoo are reported and discussed, as well as their deployability on tiny micro-controllers. These results reveal the suitability of a tiny ML zoo for the long-term compensation of MEMS pressure sensors affected by drift in their measurements

Mapping Global Agricultural History: A Map and Gazetteer for Sub-Saharan Africa, c. 1800 AD

Recent global historical cropland modelling grossly underestimates the pre-colonial development of agriculture in the Americas and many parts of sub-Saharan Africa. Such models are usually developed by back casting from recent land cover, combined with environmentally deterministic algorithms. Historical geographers have been slow in responding to a new demand for a global synthesis. In this paper, a preliminary map of African agricultural systems dating to AD 1800 is presented. It forms a component of the project Mapping Global Agriculture and is based on the existing historical literature, observations by early travelers, archaeology and archaeobotany. It should be emphasized that the generated map should be considered preliminary.</p