Applications in airflow monitoring: aircraft monitoring and flow field mapping. (a) MAV equipped with two FCF sensors and an external IMU module, with an exploded view of the FCF sensor [82]. (b) Comparison of the flight velocity calculated by the EKF method with the relative velocity estimated by the FCF sensor when the MAV performs forward flight, backward flight, and hovering outdoors [82]. (c) Comparison of the spectra of signals from IMU and P1 of FCF sensor #2, illustrating the wing vibration with 10 and 20 Hz as the characteristic frequencies. IMU inertial measurement unit, EKF extended Kalman filter [82]. (d) Photo of a flexible 3 × 2 thermal flow sensor array attached onto a pipe with a 7.5 cm diameter, with a photo showing the flow sensor is comprised of eight temperature sensors surrounding a heater [16]. (e) Thermal mapping image under air flow corresponding to the flow direction monitoring [16]. (f) 2D cross-sectional FEM simulation of the air flow distribution affected by a circular obstruction [16]. (g) Schematic map of a device, with indication of the tube position (blue shading), and the temperatures at upstream (T_u) and downstream (T_d) locations. i, j, and k represent coordinates for sensor identification (j and k for T_u and T_d, respectively) [49]. (h) Thermographs from IR imaging (top) and epidermal sensing array (ESA)-generated temperature maps (bottom) in the absence (left) and presence (right) of flow (0.02 mL/min; flow from right to left) with actuation at 1.8 mW/mm². All data were collected on a skin phantom [49]. (i) Photo of the sensor system attached onto the robotic arm [50]. (j) Cubic convolution interpolation of the eight temperature sensors as the robotic arm runs for one cycle [50].